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Sung In Cho
On-Line PD (Partial Discharge)
Monitoring of Power System Components
School of Electrical Engineering
Thesis submitted for examination for the degree of Master of
Science in Technology.
Espoo 09.09. 2011
Thesis supervisor:
Prof. Matti Lehtonen
Thesis instructor:
D.Sc. (Tech.) Petri Hyvönen
i
Abstract
AALTO UNIVERSITY ABSTRACT OF THE
SCHOOL OF ELECTRICAL ENGINEERING MASTER‘S THESIS
Author: Sung In Cho
Title: On-Line PD (Partial Discharge) Monitoring of Power system Components
Date: 09.09.2011 Language: English Number of pages: 13+135
Department of Electrical Engineering
Professorship: Power systems and High voltage Engineering Code: S-18
Supervisor: Prof. Matti Lehtonen
Instructor: D.Sc. (Tech.) Petri Hyvönen
Condition based maintenance has emerged as a priority issue in modern power
systems, and has reminded so for last several decades. Appropriate monitoring
and diagnosis before severe faults occur make it possible to control and operate
power systems in a more reliable, effective, and sustainable way. Compared other
monitoring techniques, Partial Discharge (PD) monitoring seems the most
promising methodology for detecting possible dielectric breakdown, aging and
ultimately faults in power system components. In order to maximize the benefits
of PD monitoring, proper sensing, de-noising and interpretation of its PD signal is
of importance. On-line PD monitoring of power system apparatus is a very
promising technique that has arisen in the field of condition based maintenance to
assist in robust monitoring system which reduces outage time of the power
system. For that reason, it is a priority to organize and make a holistic review of
current on-line PD monitoring techniques of power system components in order to
understand recent developments and trends in theory and in practice. Therefore
this thesis is an intensive literature review of current on-line PD monitoring
technology.
Keywords: Partial Discharge, On-line PD monitoring, IEC 60270, IEC 62478,
Pattern recognition, Feature extraction, Conventional method, Unconventional
method
ii
Acknowledgements
This thesis was done in the department of Electrical Engineering in Aalto University
School of Electrical Engineering in Espoo, Finland in collaboration with Doble
Lemke in Dresden, Germany. To begin with, I truly appreciate to my supervisor, Prof.
Matti Lehtonen, with his guide and supports during this thesis work. In addition, I
also would like to express my gratitude to D.Sc. (Tech.) Petri Hyvönen, instructor, for
his guide, advice and encourage. Thanks to Dr. Stefan Kornhuber, engineering
manager in Doble Lemke, I can finalize my thesis work in a more fruitful, valuable,
and reliable way with his precise comments and critical advice. Moreover it is very
important to express my appreciation to the Service team in Doble Lemke and other
kind staffs especially for the one who took me to the city centre when I lost my last
bus at the first day in the Kesselsdorf.
I certainly appreciate my friends so-called ―Otaniemi Family” in Finland who has the
same family name, ByungJin, KyungHyun, and EunAh. I am deeply thankful to all
brothers, sisters and KOSAFI members who support me all the time and I strongly
believe they will be a great designer, engineer, and CEO in the very near future.
Lovely Seyoung, without your supports and encourages, this thesis even cannot come
into existence! Lovely Thanks to you!
Lastly thanks to universal absolute unlimited encouragement from my family during
my life in Finland!
Espoo, Finland
09.09.2011
Sung In Cho
iii
List of Abbreviations
PD Partial Discharge
IEC International Electrotechnical Commission
CBM Condition Based Maintenance
HF High Frequency
VHF Very High Frequency
UHF Ultra High Frequency
AE Acoustic Emission
UPS Uninterruptible Power Supply
HVE High Voltage Equipment
GIS Gas Insulated System
TEAM Thermal, Electrical Ambient, and Mechanical
SNR Signal to Noise Ratio
UV Ultra Violet
OHTL Over Head Transmission Line
LED Light Emitting Diode
DGA Dissolved Gas Analysis
SF6 Sulfur Hexafluoride
SVM Support Vector Machine
CCD Charge Coupled Device
HFCT High Frequency Current Transformer
CNT Carbon Nano Tube
PRPD Phase Resolved Partial Discharge
3-PARD 3 Phase Amplitude Relation Diagram
iv
AM/FM Amplitude Modulation/ Frequency Modulation
k-NN K Nearest Neighbour
NN Neural Network
BNN Back propagation Neural Network
PNN Probabilistic Neural Network
PSA Pulse Sequence Analysis
DP Degree of Polymerization
FDS Frequency Domain Spectrum
PDC Polarization/Depolarization Current analysis
FRA Frequency Response Analysis
C&PF Capacitance and Power Factor
C&DF Capacitance and Dissipation Factor
IRA Impulse Response Analysis
SRA Step Response Analysis
FRA Frequency Response Analysis
OLTC On Load Tap Changer
SCADA Supervisory Control And Data Acquisition
RFCT Radio Frequency Current Transformer
DC Direct Current
AC Alternating Current
DAC Damped Alternating Current
VLF Very Low Frequency
EHV Extra High Voltage
HV High Voltage
MV Medium Voltage
LV Low Voltage
v
XLPE Cross-linked Polyethylene
PVC Poly Vinyl Chloride
EPR Ethylene Propylene Rubber
TDR Time Domain Reflectometry
FTRC Frequency Turned Resonant Circuit
ITRC Inductively Turned Resonant Circuit
HVDC High Voltage Direct Current
PDIV Partial Discharge Inception Voltage
GPS Global Positioning System
TCP/IP Transmission Control Protocol/Internet Protocol
RTU Ring Main Unit
PILC Paper Insulated Lead Cable
MIND Mass-Impregnated Non-Draining paper insulated cable
EPR Ethylene Propylene Rubber
CLX Continuously Metal Clad Armored
FMC Flexible Magnetic Coupler
TEV Transient Earth Voltage
RM Rotating Machine
UMP Unbalanced Magnetic Pull
MCSA Motor Current Spectral Analysis
CT Current Transformer
SSC Stator Slot Coupler
WAN Wide Area Network
VT Voltage Transformer
SA Surge Arrestors
TEM Transverse Electromagnetic Wave
vi
TE Transverse Electric Wave
TM Transverse Magnetic Wave
UI User Interface
PC Personal Computer
RF Radio Frequency
TF map Time/Frequency map
vii
List of symbol
aC Capacitance of the test object
bC
Stray capacitance of the PD source
cC
Internal capacitance of PD source
kC
Measuring Capacitor
1U Applied test voltage
2U Voltage drop across the PD source
3U Voltage drop across the mR
mC Measuring capacitor
mR Measuring resistor
sG Grounding switch
1f Lower frequency limit
2f Upper frequency limit
f Frequency band width
mf Mid-band frequency which can be continuously tuned
Z Noise filter
U High voltage supply
miZ Input impedance of the coupling device
CD Coupling Device
MI Measuring Instrument
0C
Series capacitance of the calibrator
k Calibration factor
viii
0R
Reading of the PD instrument
0q
Known calibrating charge
iP The probability of appearance for that value ix in the i-th phase
u The mean value
2 The variance
1
1
i
i
dy
dx The differential coefficient before and after the peak of the distribution
ix The average discharge magnitude of the positive half cycle
iy The average discharge magnitude of the negative half cycle
sQ The sum value of discharges of the mean pulse height distribution in
the negative cycle
sQ The sum value of discharges of the mean pulse height distribution in
the positive cycle
N The number of discharges of the mean pulse height distribution in the
negative voltage cycle
N The number of discharges of the mean pulse height distribution in the
positive voltage cycle
inc The inception phase in the positive or negative voltage cycle
u The voltage difference between two consecutive pulses
The phase difference between two consecutive pulses
T Temperature in Celsius
R The gas constant (= 8.314 J/mole/°K)
E The activation energy (= 113kJ/mole)
finalDP Final DP
ix
initialDP Initial DP
e Euler‘s number
1C The HV capacitance of the bushing
2C The LV capacitance of the bushing
L The test inductance (or external inductor)
C The cable capacitance
c The cut-off wave length
a The outer radius of the conductor
b The inner radius of the conductor
0c The propagation velocity of the signal (30cm/ns)
x
Table of Contents
Abstract........................................................................................................................... i
Acknowledgements ....................................................................................................... ii
List of Abbreviations ................................................................................................... iii
List of symbol ............................................................................................................... vii
Table of Contents .......................................................................................................... x
CHAPTER 1 ................................................................................................................. 1
1 Introduction .......................................................................................................... 1
1.1 Motivation................................................................................................. 1
1.2 Condition Based Maintenance on Power System ..................................... 3
1.3 PD monitoring in power system ............................................................... 5
1.4 Thesis Overview ....................................................................................... 6
1.5 The aim of the Thesis ............................................................................... 7
CHAPTER 2 ................................................................................................................. 8
2 PD measurement System ..................................................................................... 8
2.1 PD monitoring system configuration ........................................................ 8
2.1.1 Conventional PD monitoring system (IEC 60270) ................................... 9
2.1.2 Unconventional PD monitoring system .................................................. 18
2.2 Correlation of conventional and unconventional method ....................... 22
2.3 On-line VS Off-line PD measurement system........................................ 25
2.4 PD monitoring system in power system ................................................. 26
CHAPTER 3 ............................................................................................................... 28
3 Sensing and Processing ...................................................................................... 28
3.1 Detectable PD signals ............................................................................. 29
3.1.1 Electrical signal ...................................................................................... 29
3.1.2 Acoustic signal........................................................................................ 30
3.1.3 Chemical signal ...................................................................................... 30
3.1.4 Optical signal .......................................................................................... 30
3.2 Sensors .................................................................................................... 31
3.2.1 Electric sensors ....................................................................................... 31
3.2.2 Non-electric sensors................................................................................ 34
3.3 PD monitoring visualization ................................................................... 35
3.3.1 Phase-Resolved Partial Discharge (PRPD) ............................................ 35
xi
3.3.2 Time resolved method ............................................................................ 37
3.3.3 3-Phase Amplitude Relation Diagram (3 PARD) ................................... 38
3.4 PD feature extraction and de-noising ..................................................... 38
3.4.1 Noises in PD ........................................................................................... 39
3.4.2 Gating and Windowing ........................................................................... 39
3.4.3 Pulse arrival time difference ................................................................... 40
3.4.4 Digital filter method ............................................................................... 41
3.4.5 Signal processing method ....................................................................... 41
3.4.6 Statistical method.................................................................................... 42
3.4.7 PD pulse shape method ........................................................................... 44
3.5 PD pattern classification ......................................................................... 44
3.5.1 Distance classifier ................................................................................... 44
3.5.2 Neural Network (NN) ............................................................................. 45
3.5.3 Support Vector Machine (SVM) ............................................................ 46
3.5.4 Pulse Sequence Analysis (PSA) ............................................................. 47
3.6 Signal processing of PD signal ............................................................... 48
CHAPTER 4 ............................................................................................................... 49
4 PD Monitoring on Power System Components ............................................... 49
4.1 Transformer ............................................................................................ 49
4.1.1 Transformer in power system ................................................................. 50
4.1.2 PD types in Transformer ......................................................................... 51
4.1.3 Different diagnosis and monitoring techniques on transformer ............. 52
4.1.4 On-line PD monitoring on transformer................................................... 55
4.1.5 Available products for on-line PD monitoring of transformer ............... 58
4.1.6 Summary and Conclusion ....................................................................... 60
4.2 Cable ....................................................................................................... 60
4.2.1 Cable system in power system ................................................................ 61
4.2.2 PD types in cable system ........................................................................ 63
4.2.3 Different diagnosis and monitoring techniques on cables ...................... 64
4.2.4 On-line PD monitoring on cable ............................................................. 68
4.2.5 Available products for on-line PD monitoring of cable ......................... 69
4.2.6 Summary and Conclusion ....................................................................... 71
4.3 Rotating Machine.................................................................................... 72
4.3.1 Rotating machine in power system ......................................................... 72
4.3.2 PD types in rotating machines ................................................................ 74
xii
4.3.3 Different diagnosis and monitoring techniques on rotating machines ... 75
4.3.4 On-line PD monitoring on rotating machines ......................................... 77
4.3.5 Available products for on-line PD monitoring of RM ........................... 79
4.3.6 Summary and Conclusion ....................................................................... 80
4.4 GIS (Gas Insulated System).................................................................... 81
4.4.1 GIS in power system ............................................................................... 81
4.4.2 PD types in GIS ...................................................................................... 83
4.4.3 Different diagnosis and monitoring techniques on GIS ......................... 84
4.4.4 On-line PD monitoring on GIS ............................................................... 85
4.4.5 Available products on-line PD monitoring of GIS ................................. 87
4.4.6 Summary and Conclusion ....................................................................... 89
4.5 On-line PD monitoring on power system components ........................... 90
CHAPTER 5 ............................................................................................................... 91
5 Conclusion and Future work ............................................................................ 91
References ................................................................................................................... 93
Appendix 1: CASE STUDY 1 ....................................................................................... 116
Appendix 2: CASE STUDY 2 ....................................................................................... 120
Appendix 3: CASE STUDY 3 ....................................................................................... 125
Appendix 4: Comparison of on-line PD monitoring products for Transformer ...... 129
Appendix 5: Comparison of on-line PD monitoring products Cable ....................... 130
Appendix 6: Comparison of on-line PD monitoring products for RM ..................... 131
Appendix 7: Comparison of on-line PD monitoring products for GIS ..................... 132
Appendix 8: Commercial Sensors .......................................................................... 133
1
CHAPTER 1
1 Introduction
1.1 Motivation
In modern society, electricity is regarded as the most important energy source
enabling many electric facilities to operate correctly. In order to maintain and sustain
those facilities, the quality of power from the grid should be as stable as possible to
meet the requirements of electric equipment. Especially nowadays there are many
factories and buildings that need a constant power supply to function, and the costs
when the electricity fails can be great. In this sense, the appropriate monitoring and
protection of power system is one of the significant issues in power system
development and monitoring. Even though there has been growing concern about this
issue, power systems have remained fairly similar for the last several decades. This
has led to catastrophic cascading blackouts occurring several times all over the world
in the recent years. These events illustrate the importance of protecting and
2
monitoring power systems which are the most intricate system humans have ever
made in history.
Compared to many protection methods in power system, Partial Discharge (PD) is
considered as one of the most promising solutions for monitoring and detecting
possible faults in the system before they occur. Thanks to the development of other
engineering areas such as radio communication, computer science and signal
processing, protection systems are becoming cheaper and more robust, also high
sensitivity. PD is able to find possible symptoms of faults in the system in the most
fundamental and simplest way.
With IEC 60270 and other standards regarding PD monitoring, PD measurement
techniques and calibration had been established with detailed explanations for
monitoring purposes. Since direct detection of PD is not possible, conventionally
technicians have been using so-called ―apparent change‖ detection. Whilst traditional
methods are detected after failure or discrete periodic interval monitoring, modem
techniques are largely dependent on the relative changes of important parameters in
time or frequency domain. As a result, Condition Based Maintenance (CBM) has been
considered a powerful tool for real-time monitoring on power system components. In
order to conduct on-line PD monitoring, the noise to signal ratio is the key variable to
determine whether there is PD activity or not. That is the reason unconventional
methods for detecting electromagnet PD phenomena using High Frequency (HF),
Very high Frequency (VHF), or Ultra High Frequency (UHF) detection and Acoustic
Emission (AE) detection have been developed for on-site and on-line PD monitoring
being supported by IEC 62478 in near future.
Nevertheless, whilst the theory behind of PD monitoring system is the same for
different components, the application on power system apparatus such as transformer,
switch gear, cable, or rotating machines differ from each other. Therefore, in order to
understand PD monitoring from the theoretical to practical, well-organized survey
reference will be required. Thus, intensive literature survey of PD monitoring of
power system components will be presented as a big picture in the field of on-line
monitoring.
3
1.2 Condition Based Maintenance on Power System
The most significant issue for industrial utilities is the protection of possible faults
which usually incur tremendous repair cost and inconvenience to the customer. Even
though Uninterruptible Power Supply (UPS) makes it possible to operate electrical
equipment in hospital or factories that require a stable and continuous power supply,
unexpected power interruption increases the possibility of large scale disaster and
cascade blackout. Therefore utilities have been developing proper monitoring system
for power system in order to predict and prevent electrical faults before they occur.
Largely, there are two considerable reasons for CBM.
1. Maintenance of good operating condition has become a priority for preventing
penalty cost and protecting expensive electric High Voltage Equipment (HVE).
2. With technological progress in computer science, signal processing, and radio
communication, CBM operating with reasonable price and reliable accuracy has
arisen [2].
In order to operate CBM in the power system, the monitoring equipment with proper
features should provide adequate information for scheduling of repairs or replacement
of HVE. Moreover, this information can be used for life prediction of power system
components. In this sense, PD monitoring has gained its reputation as continuous
monitoring for condition assessment purpose [3]. In particular, PD monitoring has
been widely used in monitoring and the protection of HVE with the following
purpose.
1. Design Test: for evaluating and checking a PD-free (or lower than some specific
level) system of new insulation system
2. Quality Assurance Test: for confirming that there are no voids or cracks created
during manufacturing and processing of the insulation system
3. Diagnostic Test: To determine if the high voltage electrical insulation such as
rotating machines, transformers, Gas Insulated System (GIS) and cables have
weakened because of any kind of electrical, mechanical or environmental stresses
during operation.
4
Figure. 1.1 TEAM stress on Power System Components [7, 8]
Regarding the monitoring insulation system of power system components, there are
four main influencing factors affecting the lifetime of the insulation system, known as
the TEAM approach; Thermal, Electrical, Ambient, and Mechanical. Indeed, all
different types of power system components are influenced by these factors
intensively. Therefore for CBM based insulation monitoring, all factors, shown in
Figure 1.1, should be taken into account.
For example, a rotating machine would be monitored by vibration monitoring,
temperature monitoring, electrical monitoring (e.g. partial discharge, dissipation
factor, motor current spectrum analysis etc) and chemical monitoring. This kind of
approach, of course, is possible for other power system components as well such as
transformer, cable, and GIS. Since PD monitoring on power system components has
already proven its efficiency compared to other monitoring techniques, continuous PD
monitoring will ensure a safer power system with CBM based operation for the
following reasons
1. On-line PD monitoring can be used on almost all HVE such as transformer, cable,
rotating machine, and GIS with a very similar procedure for each. PD pattern is
the only one and universal characteristic parameter in order to evaluate all HVE [4]
5
2. Appropriate signal processing makes it possible for on-line PD monitoring
without noises around the monitored equipment.
3. Sensors and tools for on-line PD monitoring are widely available at a relatively
reasonable price.
4. Continuous PD information with analysis facilitates possible life prediction
modelling of HVE [5].
5. On-line PD monitoring is possible while the system components are in operation
otherwise they need to be disconnected and tested in the laboratory, entailing
expensive costs for conducting off-line tests [6].
Since measuring electromagnetic field change is effective even in a noisy
environmental and while power components are in operation, on-line PD monitoring
on power system components will provide enough information for CBM, operating
the power system in a safe, reliable and, predictable way.
1.3 PD monitoring in power system
The application of PD monitoring on different power system components differs in
terms of other regarding sensor coupling, detecting method, and so forth. However, in
general the PD monitoring behind application is as simple as in Figure 1.2.
Figure. 1.2 General PD monitoring scheme
If there are any PD similar activities on certain HVE, sensing of PD is performed in
order to determine the existence or non-existence of PD activity by using sensor
6
placement internal or external of HVE. During sensing, back ground noise signal from
different system components can be mixed with PD signals from the examined
component. Therefore, noise deduction obtained from the sensor‘s signal generates
important PD features in order for a more precise diagnosis. These features have its
distinct characteristics so that it is possible to classify them by comparing with prior
data from the laboratory or on-site. This process is known as ―pattern recognition‖ or
―pattern classification‖. By doing so, the PD monitoring system finally estimates the
possible fault type. Finally, all of this process can be used for life prediction
modelling of the HVE. Based on all of the information from PD sensing to life
prediction, a more precise PD monitoring system is possible. Moreover on-line PD
monitoring based on the above diagram makes it possible for real-time monitoring
data analysis, resulting in a robust CBM operation.
1.4 Thesis Overview
The thesis consists of 5 chapters. The first chapter will explain motivations and a
general overview of the thesis. Chapter 1 also describes the PD scheme from a wide
perspective with a brief explanation about what PD monitoring is. Chapter 2 covers
PD monitoring configuration, categorizing the conventional and unconventional
methods which have been widely used by experts. In addition, on-line and off-line
monitoring scheme with different techniques are covered. Chapter 3 deals with signal
sensing and processing. Due to high Signal to Noise Ratio (SNR), promising or
currently used feature extraction techniques and classification methods will be
introduced. In chapter 4, the application of on-line the PD monitoring system on
power system components such as transformer, cable, rotating machine, and GIS is
examined. According to each system components, on-line PD monitoring
configuration can be different. Lastly, chapter 5 conclude the whole thesis and
illustrates the usefulness of on-line PD monitoring systems for life prediction
modelling.
7
1.5 The aim of the Thesis
The objective of the thesis is be a holistic review of existent PD measurement,
interpretation algorithms and applications for on-line monitoring of high voltage
power system components from a theoretical and practical perspective. The aim is to
not only collect the data related to PD monitoring system, but also categorization and
discuss of each application and PD monitoring system is presented. In addition, the
thesis clarifies the following questions regarding PD monitoring systems:
1. What kinds of methods are currently used to monitor a PD signal in power system
components?
2. What kinds of sensors are currently used on different power system components to
detect PD and its location?
3. How can the PD signal extracted from a noisy environment for on-line PD
monitoring?
4. Based on different sensors, how can fault situation be defined according to the PD
signal pattern?
5. In order to make more advanced on-line monitoring of PD, what kind of
algorithms and de-nosing techniques are used to the interpret PD signal?
6. What kind of commercially available solutions exist for on-line PD monitoring on
different power system components?
8
CHAPTER 2
2 PD measurement System
2.1 PD monitoring system configuration
In this chapter, the general PD detection system will be covered. The very beginning
of PD detection traces back to nineteenth century by G. Ch Lichtenberg [9]. However
in the 1970s with IEC 60270 standard, a lot of practical information was generated
and implemented on different power system components such as cables, transformers,
and switchgears. Especially the third version of 60270 describes the precise
equivalent circuit and calibration method in order to apply PD detection system in a
real case. On top of those, many papers have suggested knowledge rule on PD
detection due to the fact that only expert engineers can interpret the meaning of PD
signal in the right way at the moment.
9
Along with the growth of condition based monitoring system on power systems,
effects have been made to apply PD detection systems in-real time while power
system components are in operation. In this sense, it was pointed out that biggest
problem of the conventional method with IEC 60270 is the high ratio of noise level
per PD signal. Therefore recently different PD detection schemes such as ultra high
frequency method, acoustic, optical and chemical detection have been developed to
overcome the high level of noise without intricate signal processing. Moreover a new
standard of PD detection using electromagnetic and acoustical methods have arisen
named IEC 62478 in the near future. For this reason, this chapter will describe general
system configuration from conventional PD detection system for apparent charge
measurement and unconventional PD monitoring systems.
2.1.1 Conventional PD monitoring system (IEC 60270)
Conventional PD monitoring refers to PD measurement method according to the IEC
60270 standard [10], measuring induced apparent charge in the detection circuit.
Since direct detection of partial discharge is impossible physically, this detection
method uses recommended test circuits. Even though apparent charge measured by
measuring impedance is hard to have a strict constant relationship with real discharge
inside of the test object, the linear increase of apparent charge means higher partial
discharge taking place. In [10], specific test measuring circuit, quantities, and
calibration procedure are covered. This method has been widely used for on-site,
laboratory, and commissioning PD measurement due to its accumulated knowledge in
terms of measuring configuration and interpretation. In order to apply this method for
on-line cases, noises from background or power system grid make it difficult
especially on cable and rotating machine. In this section, overall system configuration,
theoretical background, and calibration will be presented.
System layout [11-13]
In order to comprehend the measuring mechanism according to the IEC 60270, the
basic physical rationale behind the measuring system should be understood
beforehand. As mentioned already, direct measurement of PD charge value is
10
impossible owing to inaccessibility to the PD spot inside of the test object. The simple
equivalent capacitor arrangement of system layout so-called a-b-c model and
measuring system is shown in Figure2.1.
Figure. 2.1 Simple capacitive a-b-c model and measuring mechanism [9]
aC = Capacitance of the test object which is not affected by any PD
bC = Stray capacitance of the PD source
cC = Internal capacitance of PD source
As we can see, three capacitance values represent capacitance of the insulation
system, capacitance in series of PD occurrence, and capacitance of PD respectively.
Usually the condition of the capacitances is bC << cC << aC . For calculating indirect
charge induced at kC (measuring capacitor), a-b-c capacitive model with measuring
capacitance can be used. This a-b-c model implies the fact that the induced or
measurable apparent charge is a small fraction of the real discharge at the spot of PD
occurrence by the ratio of the capacitance characteristic, bC / aC . Since usually aC is
much higher than bC , bC / aC is much less than 1. Therefore measured discharge with
measuring impedance is less than the actual discharge due to attenuation along to the
unknown propagation path depending upon the test object itself and insulation
structure. This lumped capacitors model, however, can be much more complicated in
the case of GIS or a high voltage cable due to the fact that electromagnetic waves
from PD propagate through the test object which should be regarded as a transmission
line. A more detailed mathematical frame work and explanation regarding the
relationship between induced and real discharge is in [14], and [15].
11
Apparent charge [9, 10]
Figure. 2.2 Apparent charge measurement equivalent circuit [9]
1U = Applied test voltage
2U =Voltage drop across the PD source
3U =Voltage drop across the mR
aC =Virtual test object capacitance
bC =Stray capacitance of the PD source
cC =Internal capacitance of the PD source
mC =Measuring capacitor
mR =Measuring resistor
sG =Grounding switch
The apparent charge measurement can be achieved by connecting measuring
impedance on the test object according to IEC 60270. The equivalent circuit of
apparent charge measuring is shown in Figure 2.2. The simple mathematical frame
work is below in order to calculate cq (the charge created by PD at internal
capacitance ( cC )). Firstly the transient voltage 3U across measuring device can be
obtained
12
3 1( )
a
a m
CU U
C C (2.1)
Simplification of the equation with the consideration that mC is much higher than aC
3 1m a aU C U C q (2.2)
Taking into account that bC ≪ aC , the equation can also be expressed as
1 2a a bq U C U C (2.3)
By multiply aC / aC , the final equation will be
2 a b ba c
a a
U C C Cq q
C C (2.4)
In other words, above equation describes that the discharge occurred at cC will causes
a voltage drop as 1U which will be transmitted through bC to the capacitance aC by the
ratio as bC / aC .Therefore the measureable charge ( aq ) is a certain portion of actual
charge ( cq ) at the PD site due to the fact that bC / aC ≪1. We should note that the
measureable charge is proportional to the virtual test object capacitance. Thus, the
apparent charge measured by the test coupling device cannot be a direct measure of
true PD magnitude, rather it can provide one piece of the important information with
regard to the condition assessment of the test object.
Quasi-integration of PD pulse and recommended frequency band [9]
The assumption of measuring apparent charge in frequency domain is the linear
integration with in measuring frequency band shown in Figure 2.3.
Figure. 2.3 Quasi-integration of PD measurement in frequency domain [9]
13
According to the IEC 60270, the relationship of frequency spectrum of PD and
measuring frequency band was covered. Firstly, the integrated part of PD should be
assumed as constant within measured frequency band width. Secondly, the upper and
lower frequency band cut-off (1f and
2f ) should be lower than measured constant
part of PD value. Lastly the recommended gain gap between frequency spectrum of
PD and measuring frequency band should be less than 6dB. Recommended frequency
band widths in IEC 60270 standard can be categorized wide and narrow band
measurement shown below.
Wide band measurement
Lower limit frequency: 30 kHz< 1f <100 kHz
Upper limit frequency: 2f <500 kHz
Frequency band-width: 100 kHz < 2 1f f f <400 kHz
Narrow band measurement
Frequency band-width: 9 kHz < f <30 kHz
Mid-band frequency: 50 kHz < mf < 1 MHz
1f =Lower frequency limit
2f = Upper frequency limit
f = Frequency band width
mf = Mid-band frequency which can be continuously tuned
Coupling mode and device [9- 11]
According to the IEC standard, there are two basic coupling modes depending on
measuring impedance connection condition. In Figure 2.4, the coupling mode with
measuring impedance is connected in series with the coupling capacitor. The Z, noise
filter, is used in order to prevent noise coming from the HV side of the test
transformer.
14
Figure. 2.4 Basic coupling mode in series with the coupling capacitor [11]
Figure. 2.5 Basic coupling mode in series with the test object capacitor [11]
Z= noise filter
U= High voltage supply
kC = Coupling Capacitance
aC = Test object capacitance
miZ = Input impedance of the coupling device
CD= Coupling Device
MI= Measuring Instrument
In Figure 2.5, a similar coupling mode is shown. However this method is slightly
different to that of Figure 2.4 in the sense that it can increase the sensitivity of PD
detection connected in series with the grounding of the test object which entails the
risk for the damage of measuring impedance due to possible high current flow.
15
Figure. 2.6 Polarity discrimination coupling mode [11]
Figure. 2.7 Balanced coupling mode [10]
Additionally, this coupling requires interrupting the grounding connection of the test
object that can be done in a special case from a practical point of view. Therefore
mostly the measuring impedance connected in series with coupling capacitor has been
widely used. IEC 60270 also suggests slightly different connection configuration in
order to resist background noise and other purposes shown in Figure 2.6 and 2.7.
Polarity discrimination coupling circuit was proposed in order to identify polarity of
PD. The logic system performs a comparison of the pulses from two coupling devices
(CD, CD1), and gate those signals for polarity correction of the pulses. The balanced
coupling mode, in Figure 2.7, can eliminate external electromagnetic noises by
adjusting impedance of miZ , and 1( )miZ with an amplifier. Even though the balanced
circuit can reduce certain amounts of noise, practically the coupling mode in Figure
2.4 is the most popular in which the measuring impedance and coupling capacitance
are connected in series.
16
Figure. 2.8 Coupling device described by IEC 60270 [9]
kC = Coupling Capacitance
aC = Test object virtual capacitance
mR = Measuring resistor
mL = Shunt inductor
mC = Measuring capacitor
Conventional coupling device, according to the IEC 60270, consists of measuring
impedance, signal filtering, high voltage protection part and so forth. Measuring
impedance is the main components of a coupling device to deliver output voltage
pulse converted from input PD signal. The signal filter can screen interferences
caused by test voltage. The high voltage protection part is for suppressing damage
from over voltage which can possibly occur in the case of a breakdown of the test
object. A possible detailed circuit configuration is presented in Figure 2.8.
Calibration [9, 10]
The calibration procedure circuit recommended at IEC 60270 is shown in Figure 2.9.
The basic idea for calibration of a PD measurement system is the injection of a known
pulse which can be detected by a coupling device, then a scaling of the measurement
system for estimating real PD magnitude and finally a calculation of the calibration
factor (k) which is the ratio between measurable apparent charge ( )aq and the reading
17
of the PD instrument 0( )R . The relationship of the series capacitance of the calibrator
( 0C ), test object ( aC ), and coupling capacitor ( kC ) can be expressed according to the
IEC 60270 as shown in below.
0 0.1 ( )a kC C C (2.5)
Commercially available calibrators inject a known pulse (0 0 0q C U ) with certain
time intervals connected near the coupling device shown in Figure 2.10. This can also
ensure the connection of the whole measurement system. The following equation can
simply explain how to calculate the calibration factor.
0
0
a
kq q
R (2.6)
Figure. 2.9 Calibration circuit recommended by IEC 60270 [9]
Figure. 2.10 Calibration graph with 2000pC calibrator (LDJ-5)
Conventional PD measurement system
IEC 60270 is regarded as a proven technique for performing PD measurements by
many experts, utilities, and in the academic field. Since conventional PD monitoring
with IEC standard 60270 has its strength by cumulated knowledge, references and
knowhow to detect PD in power system apparatus, it can be applied to all kinds of
18
power system components. The third version of IEC 60270 presents detailed
information from a coupling device to the calibration method as seen above. Even
though this method is vulnerable to noise and other interferences, the biggest
advantage over unconventional PD measurement system is the availability of the
estimated magnitude of PD.
A recent paper [16] pointed out some fundamental limitations of the conventional
method with three points; integration error in case of non-linear, possible
superposition error, calibration limits, and unknown attenuation of PD signal from PD
spots to sensors. Those challenges tackle the advantages of the conventional method
in terms of accuracy of the measurement system. Nevertheless, IEC 60270 has been
widely used as an application for new power system components testing and
commissioning, on-site measurement, and laboratory tests for periodic examination.
For on-line application, calibration procedure and high signal to noise ratio makes it
difficult to apply the IEC 60270 method. However transformer application such as
multi-terminal measurements and GIS application for sensitivity verification have
sometimes been combined with the unconventional method which will be covered in
the upcoming section.
2.1.2 Unconventional PD monitoring system
Unconventional PD measurement was developed for GIS application several decades
ago as a form of UHF PD detection system [17]. Since then, several other PD
measurement techniques have been introduced beside the conventional PD monitoring
method by using other indirect indicators of PD occurrence, which includes electrical
(HF/VHF/UHF), acoustic, optical, and, chemical measurements [18, 19]. The
unconventional method, in particular, has better characteristics regarding signal to
noise ratio for on-site or on-line measurement of power system apparatus. Some of
those techniques (Electrical/ Acoustic) will be standardized in the near future by IEC
62478. In this section, so-called unconventional PD measurement technique will be
covered.
19
Unconventional PD detection methods [20]
Electrical detection [21]: Electromagnetic measurement of PD consists of coupling
devices and data acquisition unit. The most suitable frequency band for application
regarding each power system components are shown in Table 2.1.
Cable Transformer GIS Rotating
Machine
HF (3 - 30MHz) O - - O
VHF (30 – 300MHz) Δ O O O
UHF (300M – 3GHz) Δ O O -
Table 2.1 Suitable frequency band according to system components (O=Good, Δ=OK, -=NO)
Appropriate sensors and its placement on test object detect electromagnetic signal.
Detection of electromagnetic transient signal from PD occurrence is usually
performed by capacitive or inductive sensors. More detailed information regarding
system configuration, sensor type, and placement according to each system
components is covered in chapter 4. The main advantage of this method is its
accuracy and accessibility of the information about intensity, source, and possible
fault type. However electrical interference during measurement is the main
disadvantage.
Acoustic detection [22, 23]: Some measurement installations of PD are affected by
severe electrical interferences that are difficult to control. However acoustic signal
from a PD source is immune from electromagnetic noise. An acoustic signal from
mechanical vibration of PD can be detected by piezoelectric transducers, fibre optic
acoustic sensors, accelerometers, condenser microphones and sound-resonance
sensors usual using frequency band as between 10 kHz and 300 kHz. AE detection
has been successfully used in order to localize the PD source inside of the test object
due to the fact that acoustic signal is strongly dependent upon the geometry of the test
object. Combined with electrical measurement techniques, acoustic measurement can
enhance its strength. Detailed acoustic wave propagation characteristics are shown in
[24]. This method is very efficient for localizing PD source because of its immunity
against electromagnetic noise.
20
Optical detection [25, 26]: Optical emission from PD can be detected by optical
sensors. Unlike electrical signals from PD, optical signals largely depend on different
factors such as insulation material, temperature, PD intensity and pressure. The
spectrum of hydrogen or nitrogen depending on the surrounding material is the most
dominant concerning the spectrum of PD. There are roughly two kind of optical PD
detection techniques as a result of different kind of ionization, excitation and
recombination processes during the discharge; direct detection of optical PD signal
and detect of change of an optical beam. Detection of optical signal includes surface
detection and the detection inside of the test object such as GIS and transformer. For
cable application, corona emits the spectrum range around 280nm to 410nm at high
voltage transmission line which can be detected by a UV-visible camera during the
daytime. The rationale behind this is the ultra violet radiation ranging from 240nm to
280nm tends to be absorbed by the ozone layer. The optical sensors transferring signal
to the outside at photomultiplier, also can be placed inside the test object which is
efficient for a light-tight GIS impulse voltage test. This impulse voltage test is not
suitable for an electrical PD detection system. Another method called opto-acoustic
measurement catches sonic or ultrasonic range acoustic emission caused by PD which
results in deformation of the optical fibre. One recent paper [27] describes optical PD
detection on Over Head Transmission Line (OHTL) using fibre optic sensors with
Light Emitting Diodes (LEDs), resulting in meaningful PD detection capability. The
main advantages of this method are the immunity from electromagnetic interferences
and high sensitivity compared to conventional electrical techniques.
Chemical detection [28-30]: Chemical PD detection on HVE is one of the most
popular and simplest methods. In particular, PD activities in oil or gas insulated object
can react chemically, emitting a by-product of the chemical reaction. For
transformers, the most relatively used method is Dissolved Gas Analysis (DGA) with
Duval Triangle diagnosis. For GIS, SF6 gas analysis with detecting its compound has
been used. Even though this method indicates an abnormal condition of HVE, it
provides only a rough condition assessment without any specific data regarding its
intensity, source or location.
21
Electrical Acoustical Optical Chemical
Advantage
• Applicative for
all kinds of
HVE
• Intensity,
source, type,
location of PD
is assessable
• The most
suitable for
continuous on-
line PD
monitoring
• High sensitivity
• Immunity
against
electrical noise
• Very efficient
for localization
of PD
• Relatively low
cost
• Immunity
against
electrical noise
• High sensitivity
• Location of PD
is assessable(in
some case)
• Test is possible
for impulse
voltage
condition
• Immunity
against
electrical noise
• Easy to
measure
• Provide critical
information for
Go/No Go
decision
Disadvantage
• High
electromagnetic
interference
• Relative
expensive cost
• Low signal
intensity
• Not good for
continuous PD
measurement
• No information
about
magnitude of
PD
• No information
about location,
source,
intensity, and
type of PD
Possible
Sensors
Capacitive
Inductive
Piezo-electric
transducers
Condenser
microphones
Optical fibre
UV detector
photomultiplier
tube
DGA Sensors
SF6 Sensors
Main
applicative
area
All HVE Transformer
GIS
Cable, GIS
Transformer
Transformer
GIS
Cable
Table 2.2 Different unconventional PD detection methods
Sensitivity and performance check
Unconventional PD detection systems cannot verify ‗apparent charge‘ by calibration
procedure. However there are sensitivity checks and performance checks in order to
ensure validity of nonconventional PD measurement. By doing the sensitivity check,
the measurement system obtains required sensitivity in a worst-case way with IEC
60270. Meanwhile performance check evaluates the function of whole measuring path
including sensors and the acquisition system which can also be used to find the
appropriate frequency band in on-site measurements [31]. Theoretical approach of
sensitivity check on cable and transformer is covered in [32] and [31, 33, 34]
respectively. In the GIS case, the sensitivity check is possible to have enough
sensitivity that is equivalent as that of IEC 60270 shown in [35, 36].
22
Unconventional PD measurement system
Unconventional PD measurement is much more suitable for on-site and on-line PD
measurement in which the external interferences largely influence the measured
signal. Especially electromagnetic wave and acoustic detection has been widely used
in the field since these two methods simply provide sufficient information concerning
the existence of PD and its possible location covering almost all kinds of power
systems components. As seen below in the Table 2.3, possible on-line application of
different system components can be realized by nonconventional PD measurement
systems.
Cable Transformer GIS Rotating
Machine
Acoustic Δ O O O
Electromagnetic O O O O
Optical - - - -
Chemical - O - -
Table 2.3 Possible on-line PD detection techniques on power system components [37]
Since the most interference for on-site or on-line PD monitoring is in the lower
frequency band, higher frequency monitoring within HF/VHF/UHF band has a good
signal to noise ratio. Supported by IEC 62478 in the near future, nonconventional PD
measurement will be used widely within a better frame work. The main disadvantage
of the unconventional method is that the measuring method depending upon test
object differs from each other. Because of that, the monitoring system covering all
HVE will be expensive compared to the conventional method. Moreover most
unconventional methods are not possible for calibration providing magnitude of PD
which might introduce mistakes in terms of decision making.
2.2 Correlation of conventional and unconventional method
Even though the conventional and unconventional method measure different physical
quantities, there has been some research regarding comparison and correlation of their
measurement results. Those studies include the PD pattern, linearity of measuring
quantity [38, 39, 40]. However so far, finding solid correlation between the two
methods seems to be very difficult due to the fact that the results from both methods
largely depend on the condition, sensor type, sensor location, manufacturer of test
23
object, test engineer and so on. In particular the nonconventional methods have not
been supported by standard, resulting many different test set ups regarding higher
frequency and other energy detection from PD occurrence. Standardization will
bolster the analysis of the correlation of the measured quantities from both methods.
On the other hand efforts have been made to combine the two techniques in order to
overcome each drawback. In particular a combined solution is effectively applicative
on transformer and GIS. This kind of integrated approach can detect PD occurrence
with accuracy and scalable quantity in a low noise environment. In this section,
correlation of the two measuring systems and its combining approach will be covered.
Conventional versus nonconventional PD monitoring
Fundamentally, PD measurement systems according to IEC 60270 and
nonconventional methods are measuring different quantities, apparent charge and
electromagnetic waves or others, even if it comes from the same source. Some
questions have arisen regarding the correlation between the two different methods and
interpretation of results [21]. The general comparison is shown below in Table2. 4.
Conventional Unconventional
Main Standard IEC 60270 IEC 62478 (standard draft)
Sensor type
Measuring impedance
(the sensor for conventional method
can be capacitive, inductive-HFCT
or Rogowski coil)
Electric sensors
Acoustic sensors
Optical Sensors
Chemical Sensors
Frequency band
Wide (30-500kHz)/ f=100-400kHz
Narrow(50kHz- 1MHz)/
f=9-30kHz
HF (3MHz-30MHz)*
VHF(30MHz-300MHz)**
UHF(300MHz-3GHz)***
AE (20 kHz to 250 kHz, and 100 Hz
to 3 kHz)
Calibration Must be calibrated Sensitivity check
Performance check
Measuring unit Usually pC, Amps, mV, V/mm or dB
24
Measuring quantity Apparent charge
Transient earth voltage or current
pulse ( Electromagnetic wave)
Acoustic, Chemical by products,
Optical spectrum
Measuring system Coupling device, transmission
system, measuring instrument
Sensing components, transmission
path, data acquisition unit
Noise Level Relatively high Relatively low
Application type
Mostly Off-line (Laboratory, On-
site)
On-line (Transformer)
Off-line and on-line
On-line (Electrical, Chemical)
Table 2.4 Comparison of conventional and nonconventional method
*typical narrow band width for HF/VHF is 2MHz
**Typical wide band range is 50MHz or higher
***Zero span mode for individual frequencies or for specific frequency range
between 4 and 6MHz or higher
Combining of conventional and nonconventional method
Several recent papers such as [41] have described the possibility of combining the two
PD measuring methods whilst taking advantages from each. Successful applications
have been conducted on transformer and GIS. In Figure 2.12, combined measurement
configuration on a transformer is presented. On the one hand, injected UHF sensors at
the oil valve can detect electromagnetic waves inside the transformer. On the other
hand, measuring impedance on three bushings measures apparent charge. If there is
no detection on UHF with high level of apparent charge from the IEC 60270 method,
the PD source can be considered to be external. On the contrary, meaningful
electromagnetic detection with high level of apparent charge measurement result can
imply the fact that PD occurs inside the transformer [42]. On top of that, this multi-
terminal measurement on each bushing ensures possible PD location by analyzing
each bushing‘s apparent charge magnitude [43]. For GIS, the conventional IEC 60270
method can be used for sensitivity verification of the UHF/AE method in order to
have enough sensitivity for instance 5 pC.
25
Figure. 2.11 Example of combining PD measurement methods on Transformer [29]
2.3 On-line VS Off-line PD measurement system
In this thesis, on-line PD monitoring means the system with following requirements
• PD measurement while the test object is in normal operation
• Continuous PD monitoring (Trendable)
• Permanent installation of PD coupling device
• Without any other voltage sources except for operating power from the grid
• Under the same circumstance as the normal operating condition such as
temperature, pressure, humidity and so on
On the other hand off-line measurement has the characteristic:
• PD measurement while the test object is out of connection from power grid
• Installation assessment and new high voltage equipment test
• Test voltage should be applied
• Inception and extinction voltage can be found
Seemingly both on-line and off-line PD measurement can be quite similar each other.
However it is fundamentally different from its configuration to measurement results.
The main disadvantage of off-line PD measurement is that problematic PD occurrence
26
sometimes cannot be detected using the off-line method because it is carried out in
different circumstance to that of real cases such as load condition, vibration,
temperature, humidity and so on. That means the test object which passes for off-line
PD test can have potential failure in the power grid. This method, moreover, is
expensive due to outage during PD measurement.
However off-line PD measurement usually has high sensitivity and accuracy because
of relatively low back ground noise and is very suitable for new equipment quality
control. For on-line PD measurement, on the contrary, the measurement is very
realistic because it performed under the real circumstances. The cost is relatively less
expensive and it is possible to have trendable data for the test object, meaning that the
life cycle management can be possible with on-line PD monitoring. The main
research ongoing in the on-line PD monitoring field concerns signal processing due to
high noise combined with a true PD signal. However recent papers and commercially
available on-line PD measurement systems ensures effective on-line PD measurement
with appropriate signal processing techniques.
2.4 PD monitoring system in power system
PD detection system has proven its efficiency as the most promising monitoring tool.
Commercially available PD measurement systems either conventional or
unconventional can provide appropriate information according to its application on
different test objects. Even though different PD measurement systems concern their
specific signal generated by PD, they surely indicate any abnormal condition of power
system components. The IEC 60270 method is already a proven technique in
laboratory or on-site measurement. On the other hand, the recent trend of PD
measurement systems is towards on-line monitoring which should provide PD
occurrence in real time while the machine is in operation. Therefore unconventional
PD monitoring techniques has been introduced and widely used.
Since the conventional method is the only one measuring the magnitude of PD
relatively accurately, correlation between unconventional methods with IEC 60270
can ensure linearity of PD measurement with true PD occurrence in the test object.
Thus, solid correlation which can possibly be achieved by sensitivity verification for
27
UHF/AE technique can determine the concrete status of the test object. However
different measuring configurations of UHF/AE make it difficult to have strict linearity
and correlation. In this sense, draft IEC 62478 can assist to clarify the promising
UHF/AE detection configuration and make it more robust in the near future.
28
CHAPTER 3
3 Sensing and Processing
In this chapter, signal processing analysis will be covered. The raw signal from the
sensor needs to be processed in order to erase back ground noise and take meaningful
features. This process can be called ―Feature extraction‖. Possible methods such as
statistical, pulse shape and digital signal processing technique will be presented in this
chapter. After being extracted, those features will be used for classification to find out
the PD type based on prior knowledge from expert data analysis. Among
classification techniques the most promising solutions such as distance classifier,
neural network (NN), Support Vector Machine (SVM) and Pulse Sequence Analysis
(PSA) will be shown.
29
3.1 Detectable PD signals
Partial discharge is detectable in a different way due to the fact that it generates
certain reactions according to the insulation materials in the system components.
Generated signals from PD are usually detectable in an electric, acoustic, chemical,
and optical way [44]. Electrical and chemical signals are referred to for finding out
PD occurrence in high voltage equipment, and acoustic signals are used to localize the
spot where PD takes place. Depending on the characteristic of the power system
components, appropriate signal detecting can differ. Nowadays, combining of the
methods guarantees more accurate PD detection. In [45], a more physical approach to
the PD mechanism is presented.
3.1.1 Electrical signal
PD occurrence in the power system equipment makes the electrical signal. That is
because partial discharge brings about electron transfer in a short current impulse
within nanoseconds [1]. As described in the Chapter 2, in order to detect electrical
signal, there are two different types of measurement setup required. The first one is
so-called apparent charge measurement that detects induced charge in the test circuit.
The second one is to detect electromagnetic radiation using radio a frequency antenna
or probe [1]. The biggest drawback of electric signal is the high noise to signal ratio
due to electrical radiations from other equipment. Because of this, the electrical signal
detection method needs more complicated signal processing techniques compared to
other methods. On top of that, owing to the complicated structure of power system
components, the signal can be attenuated or modified. However the electrical signal
method is popular for PD detection thanks to highly sensitive sensor, analyzer and
digital oscilloscope. Especially in the laboratory where there is relatively low noise
compared to on-site, electrical signal detection is more advantageous for PD
monitoring. Therefore this method is widely used for qualifying new power system
components before installing, and with signal processing tools after installed on-site.
For on-line PD monitoring, the electromagnetic wave (HF to UHF range) detection is
a promising method compared to apparent charge measurement because of the
possible immunity characteristic against background noise.
30
3.1.2 Acoustic signal
Even though electrical signals are the obvious evidence of PD occurrence, acoustic
signal generated from the mechanical wave of a small explosion around the spot
where the PD takes place is widely used for PD monitoring [46]. The biggest benefit
of acoustic signal is the immunity from electromagnetic interferences [47, 18].
Moreover acoustic detection is not an intrusive method compared to other
measurement types [46]. In addition, the acoustic signal detection method is favoured
for localizing PD in the test object. By using several acoustic sensors on the object
which have PD occurrences inside, the computation of travelling time difference from
each sensor provide geometric information of PD location [48]. However even though
acoustic signals represent is against electrical interferences, acoustic noise or
mechanical vibration from other high voltage equipment can affect acoustic signal
strength.
3.1.3 Chemical signal
Partial discharge also creates a chemical reaction with the insulation material. One
common usage of chemical signal detection is on the oil-insulated transformer. PD in
oil-insulated equipment caused chemical reactions releases, for example, carbon oxide,
hydrogen, or methane. DGA has been widely used for detection of possible faults
initiated by partial discharge. According to the gas generated by chemical reaction
with PD, information about the type of PD or involving insulation material is
available [49]. Therefore, this method is not appropriate for on-line or on-site PD
monitoring. This method thus has been performed in the laboratory periodically.
Chemical signal detection, moreover, does not determine the location of PD [47].
3.1.4 Optical signal
PD activities emit radiation in ultraviolet, visible, and infrared optical signals. The
spectrum of light emission depends on the surrounding insulation material such as oil
or gas. In particular, optical spectral diagnostics of the electromagnetic radiation for
wavelengths between 10 nm to 30 mm is of interest. Photomultiplier or Charge-
31
Coupled Device (CCD) Cameras can detect optical signals with relatively higher
sensitivity in air tight test objects such as GIS.
3.2 Sensors
In this section, the sensors used regarding PD detection are covered. Currently there
are many sensors which have been used depending upon the measuring method and
test object. Since the sensor plays an essential role in PD measuring configuration,
appropriate selection and its location can affect the measurement result significantly.
The basic requirements of PD sensors are below [52]
• Be able to sense and record measuring quantities from PD source for a set of
defined frequency bands
• Can differentiate between PD signal and background noises
• Small enough in order to be attached to the test object
The sensors traditionally detect PD below 500kHz due to technical limits and lack of
standardization. However for over ten years, higher frequency detection using a
variety of sensors which can be internal or external according to the application has
become attractive and applicative for all kinds of power system equipment [53].
Detailed applications on power system components of each sensor will be presented
in chapter 4. Here a general specification of widely used sensors will be included.
3.2.1 Electric sensors
HFCT (High Frequency Current Transformer): This sensor is one of the most
popular inductive sensors for all kind of applications on power system equipment due
to its portable, cost effective, non-intruding characteristic and the independency of the
frequency of the measured signal [54]. Using a ring type of ferrite core, the basic
structure of HFCT consists of six or seven turns of copper wire over the ring core.
Ferrites being ferromagnetic Ceramics with very high resistivity and permeability are
most attractive materials for high frequency applications [55]. HFCT has especially
been used in order to couple for ground rod or cable. The closed core or split core
32
version of HFCT is commercially available as shown in Figure 3.1. The HFCT detect
PD up to several hundred MHz.
Figure. 3.1 Commercially available core closed and split type of HFCTs
Rogowski coil [56, 57]: The Rogowski coil is a proper sensor for PD working on the
inductive principle with frequency bandwidth between 1 to 4 MHz. The Rogowski
coil has a structure of a circular plastic mold with a winding mounted for a uniformly
distributed density of turn with frequency dependant characteristic. By mounting
around conductor, Rogowski coil generates induced voltage signal as an output [58].
The followings advantages of Rogowski coil are:
• Very high band width.
• Capability of measuring large current
• Non-saturation due to air cored structure
• Ease of use due to possible thin and flexible clipped around a measured
conductor-Non-intrusive
• Very good linearity due to absence of magnetic materials.
Epoxy-mica encapsulated couplers [59-61]: This type of coupler is the most popular
sensor especially for transformer and rotating machines. The epoxy-mica
encapsulated coupler contains the capacity against the conductor and a stray capacity.
Commercially 80pF up to 2nF epoxy-mica coupler has been widely used. The
desirable frequency band can be achieved by the PD and noise ratio and winding
frequency characteristic for rotating machine case. The main short coming is that the
33
capacitors has to be designed in order to withstand 60 Hz high-voltages, and it should
be manufactured to have low inductance in order to have good high-frequency
response. These two considerations are the reason for the relatively high price
compared to for example radio frequency current transformer (RFCT) type detector.
On the other hand, the advantage is that the pulse signals are usually much larger
because they can be placed closer to PD spots. The PD activity in each phase,
moreover, can be determined.
Figure. 3.2 Commercially available Epoxy-mica encapsulated couplers
Antenna type coupler: For unconventional PD detection in the higher frequency
range (HF/VHF/UHF), antenna type sensors are widely used in a different shape
shown in Figure 3.3. Since there are many practical constraints for sensor installation,
practical antenna design can differ depending on the application. The external
mounted and internal oil-valve type, for instance, on transformer has proven to be
proper UHF PD detector. In particular, internal oil valve type sensors with as conical
shape have the most sensitivity [62].
Figure. 3.3 Commercially available UHF type sensors
34
Currently Doble lemke (DN 50/80), and Omicron (UVS 610) uses this kind of sensors
on their UHF PD measurement for power transformer. Externally mounted UHF
sensors first developed as a GIS application and it has been widely used as
transformer UHF detection as well [63]. Detailed information about various UHF
antennas such as horn, loop, and, dipole type for GIS applications is described in [54].
Directional coupler [21, 60, 64-65]: The directional coupler is a combination of a
capacitive with an inductive sensor. It is possible to use two directional in a cable
joint. By doing so, it is possible to distinguish PD impulses coming from outside (left
or right side) or inside the joint. In other cases, depending upon the direction of pulse,
energy can be coupled to a different output port in case of special sensors with two
outputs. The main application of this type of sensor is using a cable joint. For cable
joint application, a directional coupler can achieve high sensitivity. Typical operating
frequencies are usually from several MHz up to GHz.
3.2.2 Non-electric sensors
Fibre optic sensor: Detection of acoustic signal from a PD source can also be done
using a fibre optic sensor. In other words, ultrasonic signal generated by PD in high
voltage equipment can be successfully identified by fibre optic sensors which have
been proven by some papers [66]. This also detects optical signal covered in chapter 2
in detail. Ultra or ultrasonic signal produces the pressure on the optical fibre which
can be sent as light in the fibre [67].
Dissolved Gas Analysis (DGA) sensor [37, 68-69]: Usually the DGA technique is
used for periodic sampling of oil from a transformer. However recent DGA sensors
detecting chemical by-products such as hydrogen, carbon monoxide and so on have
been used as an indicator of PD in the transformer. The gas sensors can be applicative
as on-line monitoring of oil based insulated high voltage equipment as shown in some
papers. However the sensors only provide information regarding Go/No go decisions.
SF6 carbon nanotube sensor [70, 71]: Carbon nanotube (CNT) is a new material
which has unique physical and chemical characteristic. Since the conductivity of CNT
depends on the atomic structure and chemical absorption, unidentified oxidative
decomposition products generated by PD can change electrical conductance of CNT
35
by increasing positive hole density in a p-type semiconductor. This sensor is currently
only used in an academic field but its use has been shown in some papers. However
based on this sensor, off-line PD detection in GIS is possible.
Piezoelectric transducers [72]: The sensor is typically operating in the frequency
band in the 120–160 kHz range. In order to minimize the varying response according
to the electromagnetic fields, the transducer can be either a differential type utilizing
two crystals or a shielded single crystal transducer with an integral pre-amplifier
circuit. Usually an integral pre-amplifier circuit type is the more common
configuration due to high amplitude and low impedance output. Since the acoustic
impedance of a sensing crystal differs from as that of the steel transformer wall, an
efficient hard-epoxy resin material is used with thermal and electrical isolation
characteristic. Commercially available acoustic detection for PD localization which is
applicative for transformer has been successfully used.
3.3 PD monitoring visualization
In order to analyze the PD signal, visualization of the signal is of importance due to
the fact that appropriate display pattern visualization of PD signal has great
advantages. The trend of PD analysis is based on computer aided solutions [73]. In
this section, the base of PD pattern visualization is covered
3.3.1 Phase-Resolved Partial Discharge (PRPD)
PRPD proposed in the late 1970s. This method is the most popular among almost all
commercial PD measurement systems and has proven to be one of the most powerful
tools to interpret PD signal [73]. As the name implies, PD signal is shown with
respect to the test voltage as its phase resolved spot in Figure 3.4
36
.
Figure. 3.3 PRPD patterns as pulses and pattern (PD-Smart)
The most relevant information shown in PRPD is the measured PD signal with pulse
magnitude, the phase angle at which PD occur, and the number density [74]. Because
PRPD simply shows the most relevant quantities of PD, PRPD analysis of each
measurement has played an important role to identify possible fault types on specific
measured test objects [75]. The most commonly used distributions are below [41]:
• Number of PD pulses detected in each window plotted with respect to the
phase position
• Average discharge magnitude in each window plotted with respect to the
phase position
• Peak discharge in each window plotted with respect to the phase position
• Average discharge current in each window plotted with respect to the phase
position
Therefore the distributions and relationship of peak and average PD magnitude, phase
angle and the number of repeated rate enhance simple PD pattern recognition.
37
However, this PRPD pattern of each measurement cannot entail complete
identification of fault type because it depends on the PD measurement unit, sensor,
frequency band, test object and multiple causes of overlapping faults. Because of that,
there are some cases the typical patterns of PRPD do not match the true cause of PD
[76]. In order to increase accuracy of PRPD match with true fault causes, the same
measuring configuration and reference of each test object are required. A more
sophisticated display of PRPD in 3D in terms of PD amplitude, cycle number, and
phase position is shown in [77]. Pattern analysis and recognition based on PRPD will
be introduced in the on feature extraction and classification section.
3.3.2 Time resolved method
PD display based on measuring time can be called time resolved PD data shown in
Figure 3.5. Since this visualization focuses on more on the timing of PD occurrence,
time resolved data can provide information on the location of PD with several sensors
placed at different spots rather than PD magnitude. In [78], time of flight calculation
based on time resolved PD pattern at GIS is presented in detail in chapter 4. Other
applications of time resolved data is a Q-T diagram which uses the time between two
consecutive discharges shown in [79]. Time versus frequency analysis (TF map)
conducted in [80] is the analysis methods on time based PD measurement clustered by
a fuzzy logic classifier which has been realized by Techimp.
Figure. 3.4 On-line Time resolved PD pattern with terminal voltage of generator (PD-Smart)
38
3.3.3 3-Phase Amplitude Relation Diagram (3 PARD)
3-PARD, or a star diagram, is cross talk between more than one phase on each
measurement [43, 81-83]. So called multi-terminal measurement, measuring 3 phases
with three couplers, can acquire synchronous PD data for all three phases of the test
object such as three phase transformer or GIS. This method make it possible to
compare the magnitude of PD occurrence on each phases, helping locate PD source
occurring in perhaps one of the three phases and eliminating external noise shown in
the display. The 3-PARD is a plot with a 120° phase shift of the three phase axis
shown in Figure 3.6. This method has been developed by the Technical University of
Berlin.
Figure. 3.5 3-PARD comparing PD magnitude on each phase [29]
3.4 PD feature extraction and de-noising
The biggest problem for PD measurement is noise. In the case of on-line PD
monitoring especially, there is a lot of different noise which can cover a true PD
signal by a high noise signal level. Therefore, features of true PD from the measured
signal with noise is very critical for identifying PD occurrence and further
classification of the fault type. Several noise types can be successfully caught and
reduced by signal processing or other methods. Thus, PD feature extraction is the
process which detects true PD data in order to obtain the characteristic of PD
activities possibly classified as different faults type by a classification process. In
other words, the purpose of feature extraction is to reduce dimensionality of true PD
pattern with calculation of certain features or properties of the pattern [84]. In this
section, de-noising methods which provide a feature vector without considerable
noise will be covered.
39
3.4.1 Noises in PD
Detecting of a true PD signal from measured results is a matter of in-depth knowledge
and incremented experience on measured signal and noise characteristic in different
situations and test objects. Since PD activities in power equipment occur within less
than a few hundred nanoseconds as fast rising time which is low level pulse
depending on faults type of the test objects, the de-noising process can be achieved by
understanding the noise characteristic and eliminating them from the true PD signal.
Typical noise during PD measurement can be categorized [85, 86].
Sinusoidal noise: This type of noise is the narrow band noise signal such as
communication carrier signal from AM/FM modulation which can be removed by
applying, for instance, a digital filter.
Pulse type (repetitive or random) noise: This type of noise possibly comes from
power electronics, other switching operations or, Radio Frequency (RF) emissions
from power equipment. Even though repetitive noise can be rejected by a gating
circuit and other method which can detect periodic noise against PD signal, random
pulse type noise is hardly eliminated.
White noise: white noise can be referred to some random signal with flat spectrum
density. This type of noise can be detected and removed by several signal processing
techniques which will be covered in this section.
3.4.2 Gating and Windowing
Those de-noising techniques filter suspicious signals using gate antenna or manual
windowing control. The basic principal of the gating method is detecting noise
directly using gate antenna and those signals can be reduced by a gating signal shown
in Figure 3.7. The most advantage of this method is its simplicity of installation for
on-site PD measurement using gate antenna which are available from some
commercial PD measurement products. Windowing is the simplest method for noise
elimination. By applying phase window on the repetitive or suspicious PRPD
patterns, the signal located on the designated phase window will be removed as shown
in Figure 3.8 [87].
40
Figure. 3.6 Principal of gating method for noise reduction
Figure. 3.7 Windowing applied for PRPD pattern (PD-smart)
3.4.3 Pulse arrival time difference
This method applicative for on-line PD measurement uses two sensors for one
measurement located a certain distance at least 2 meters away.
Figure. 3.8 Configuration of two coupler installation for noise elimination
41
Those two couplers detect signals at different spots with time difference for the same
PD signal which can be from the test object side not from a grid. Thus, by comparison
of pulse arrival time on two couplers, one can distinguish noise from the grid side.
The basic scheme is shown in Figure 3.9 [1, 88].
3.4.4 Digital filter method
When PD is corrupted by noise caused by radio communication, a matched filter is a
very well-suited as a solution. First of all, a matched filter can make it possible to
maximize SNR of PD by suppressing noise. In addition, it can make accurate
estimation on the time of arrival and magnitude of maximum PD pulse. The time of
arrival of PD pulse and SNR are deeply related as those two variables are inversely
proportional. Simply a matched filter uses a template which is a prediction of the
shape and amplitude of a PD pulse. The coefficients should be determined in order to
construct a specific matched filter for a specific measurement. One solution for this,
for example for the cable case, is the injection of a known pulse and measuring its
pulse propagation characteristic as impedance. By calculating time-of-arrival, the PD
localization in cable can also be achieved which is realized by KEMA for cable on-
line PD monitoring [89-92].
3.4.5 Signal processing method
Even though the simplest signal processing method is Fourier transform of PD signal,
it has the main drawback that it loses time resolved information. Therefore the
wavelet transform which might be the most popular signal processing method applied
for PD measurement has been successfully used [73, 85, 93-95]. The wavelet
transformer, moreover, allows one to obtain the information regarding time domain
and frequency domain with its amplitude at the same time. Lots of research and
papers have described wavelet technique as continuous or discrete with multi
resolution . For on-line application of wave transform which shows non-stationary
characteristic of the data, an adaptive wave transform can be applied. Despite the fact
that on-line wavelet application is challenging because of difficulties in terms of
selection of mother wavelet and resolution, threshold level, it can be used for on-line
42
PD measurement, reducing noises and extracting a very small amount of data from
actual measurement [96]. The basic steps of wavelet transform applied for noise
reduction are described below.
Decomposition: set a mother wavelet and a maximum decomposition level,
computing the wavelet decomposition coefficients at each level from 1 to N.
Thresholding: Compute threshold coefficient for each and apply threshold to the
coefficients at each level
Reconstruction: Reconstruct the signal with the modified coefficients from 1 to N
3.4.6 Statistical method
Statistical methods for extracting PD features are based on PRPD pattern [73, 97-98].
By applying statistical computation on PRPD patterns, different distributions can be
characterized as statistical parameters. The following distribution functions are used.
Skewness: shows the asymmetry or degree of tilt of the data of the distribution
compared to a normal distribution.
3
3
( )i ix u PSK (3.1)
where ix is the measured value, 1
( ( ) / ( ))n
i i i
i
P f x f x is the probability of appearance
for that value ix in the i-th phase window, ( )i iu x P is the mean value, and
2 2( ( ) )i ix u P is the variance.
If the measured PRPD pattern is symmetrical skewness will be close to zero. For the
asymmetrical distribution to the left, skewess will be higher than zero, otherwise it
will be less than zero.
Kurtosis: shows sharpness of the distribution compared to a normal one.
4
4
( )3
i ix u PKU (3.2)
If the measured PRPD pattern is shaper than the normal distribution, kurtosis will be
higher than zero, in a flatter case, it will be less than zero.
43
Number of peaks: represents the distribution with single peak or more. The peak of
the distribution can be defined as:
1 1
1 1
0, 0 i i
i i
dy dy
dx dx (3.3)
Where 1
1
i
i
dy
dxis the differential coefficient before and after the peak of the distribution.
Cross-correlation factor: shows correlation of the distribution shape between
positive and negative cycles of the distribution.
2 2 2 2
/
[ ( ) / ] [ ( ) / ]
i i i i
i i i i
x y x y ncc
x x n y y n (3.4)
where ix is the average discharge magnitude of positive half cycle and iy is the that
of negative cycle. When cc is close to zero, it means the shape of positive and
negative cycles are the same, otherwise it will be asymmetrical.
Asymmetry: shows the comparison of the mean level of the positive and negative
half of the voltage cycle.
/
/
s
s
Q N
Q N (3.5)
Where sQ and sQ are the sum value of discharges of the mean pulse height
distribution in the negative and positive voltage cycle, and N and N indicate the
number of discharges of the mean pulse height distribution in each cycle.
If the asymmetry is very close to zero, it means the mean level of each distribution is
the same size. When the mean level of distribution on positive cycle is more than that
of negative cycle, the asymmetry is close to -1, otherwise it will be close to 1.
Phase factor: defines the difference in the inception voltage in the negative and
positive half of the voltage cycle which can be expressed as:
inc
inc
(3.6)
Where inc indicates the inception phase in the positive or negative voltage cycle.
44
3.4.7 PD pulse shape method
This method is based on time resolved PD data for instance, apparent charge and
voltage magnitude within a certain time interval due to the fact that different PD
source can generate different PD pulse shape [73, 99]. The features extracted on an
one to one basis using single discharge source. The Following parameters can be used
Pulse rise time: time required to rise from 10% to 90% levels of the peak value.
Pulse decay time: time required to decay from 90% to 10% levels of the peak value.
Pulse width: time interval between 50% levels on both sides of the peak value.
Area under pulse: area enclosed by the q-t curve in the time interval for 10% levels
in the rising and falling segments.
3.5 PD pattern classification
Many researchers and theses have studied the pattern classification of PD. Therefore
many different methods have been introduced in order to understand and trace of
certain PD pattern such as artificial neural network, fuzzy logic, genetic algorism, and
support vector machine [100]. Most of them require prior knowledge with respect to
feature vectors of PD measurement. Based on analysis of reference and history, new
PD patterns can be classified into one of the typical PD types which might suggest the
source and reason of the PD signal. This classification can help decision makers of the
system in order to determine ―go‖ or ―no go‖ for certain power system components
[97]. In [101], promising techniques and algorithms of computer science, neural
computation, information theory and statistics which can be used as classifier for PD
patter is introduced. Among many, some proven classifier here is presented
3.5.1 Distance classifier (k-NN)
A distance classifier is an efficient and simple method for classification [73, 102]. The
basic idea of the distance classification is based on the fact that similarity between the
measurement features presented as points in the Euclidean space is determination of
their closeness. k-NN (k-Nearest Neighbour) based on minimum distance is one of the
most promising classification technique by determining the number of neighbours (k).
45
The optimal number of neighbours depends on the data. Thus if there is new data
coming to the feature space, it is classified by ―major voting‖ of k-number closest
neighbours of the new data spot. This also can be a drawback because certain types of
classes with the more frequent examples tend to dominate and are highly possible to
be selected. In order to overcome this problem, the class should be weighted by
experts or based on experience. The mathematical explanation is in [103]. The
advantage of this classification is easy to update new data to reference and, it is
simple to implement because it do not require training. However if redundant features
concerning the classification are included, possible errors can occur [104]. Therefore
careful selection of the feature is of importance.
3.5.2 Neural Network (NN)
Artificial neural network has been applied for PD classification [73, 97, 105-108].
The basic idea of NN is based on biological neural functions taken from brain-like
problem solving. The basic structure of NN consists of three mutually connected
different types of layer, an input layer, hidden layers, and output layer shown in
Figure 3.10.
.
Figure. 3.90 Structure of Neural Network [108]
The input layer has several input neurons fed by different values of features extracted
from PD patterns such as statistical features of PRPD or time resolved PD pulse
pattern. The hidden layer is to extract classification information from the data and the
output later is defined according to user expectation showing final classification of a
PD pattern. Among different NN types, back propagation neural network (BNN) and
probabilistic neural network (PNN) seem to have good characteristics for PD
46
classification. Details of both methods are presented in the above references.
Although NN is a very efficient tool for PD pattern classification especially due to the
fact that it does not requires any assumption the PD data structure, it has several
drawbacks including; dependence of convergence criteria upon learning coefficient
such as the number of layers, learning time; and it is also difficult to include new
features which requires retraining.
3.5.3 Support Vector Machine (SVM)
Support vector machine is one of the most promising techniques works by using
outstanding learning algorithms especially in power systems such as load forecasting,
power stability, and fault location detection [100, 109-110]. The main idea of SVM is
to calculate the optimal hyperplane separating two classes. SVM uses the so-called
non-linear kernel trick. SVM can find the solution of non-linearly separable condition
using an implicit mapping technique into a high dimensional dot-product space called
the feature space through the use of the kernel trick. A detailed explanation of the
kernel method is shown in the above references. Despite the sophisticated procedure
for calculation of kernel function, the advantage of this SVM is its proven efficiency,
accuracy, and acceptable processing speed as classification. Some recent theses
consider SVM as the best tool for classification of PRPD pattern at the moment [81,
109].
Figure. 3.101 Basic idea of SVM describing optimal kernel function to separate class and
mapping of input to high dimensional feature space
47
3.5.4 Pulse Sequence Analysis (PSA)
Pulse Sequence Analysis (PSA) proposed by Martin Hoof and Rainer Patsch in 1990s
is one of the most popular techniques for visualization of PD pattern classification
[111-113]. The idea of this method is that two consecutive pulses caused by PD
activities have a strong relationship. This means that the previous PD pulse has an
impact on the condition of next pulse. Therefore analysis of the relationship of
continuous pulses of voltage change due to the corresponding change of the local
electric field at the PD spot is an important factor which can investigate correlations
between consecutive pulses as shown in Figure 3.12.
Figure. 3.112 Basic principle of PSA
u = The voltage difference between two consecutive pulses
= The phase difference between two consecutive pulses
The following quantities are interesting in PSA
• Discharge amplitude
• Pulse position (related to the phase of the sine wave)
• Absolute cycle number (related to measurement activation)
• Instantaneous voltage at each pulse
48
Figure. 3.123 Example of PSA in GIS; surface and corona discharge [113]
The advantage of PSA is its clear differences between certain PD patterns due to the
physical characteristics of PD activities according to the source of PD. However if the
voltage differences of continuous PD activities cannot be defined from measurement,
PSA is hard to apply
3.6 Signal processing of PD signal
Since measured PD signal has a very low magnitude happening with nano-second
duration, it needs appropriate signal processing which can reveal the true PD pulse
and its characteristics that can be used in order to interpret PD measurement in the
right way. Technically speaking, de-noising and extraction of a meaningful feature
vector from measured data is of importance for further classification. For the sake of a
hidden indication of PD, applied signal processing should yield diagnostic
information describing PD intensity, source and location for deciding optimal
operation and repair schedule of power system components.
49
CHAPTER 4
4 PD Monitoring on Power System Components
4.1 Transformer
This chapter is to provide all relevant information regarding on-line PD monitoring on
power transformer including other methods used for diagnosis and monitoring of
transformer briefly. The first section covers transformer insulation characteristics,
different PD types and detection methods, containing other possible off-line
protection and monitoring techniques such as DGA. The second part will show PD
detection systems which have been used for the last decades from conventional
method to the latest detection systems concerning sensors‘ specifications as well as
recommended location, coupling methods, possible calibration techniques and data
signal processing for each system. Finally some commercially available on-line PD
detection systems and up-to-date trends will be covered
50
4.1.1 Transformer in power system
The transformer is one of the most complicated structured components in the power
system. Normally most transformers operate efficiently for between 20-35 years,
which can be extended with proper maintenance [114]. Moreover, even though the
failure rate is quite low about 0.2-2% a year [115], it usually causes cascading faults
on different system components. Therefore, appropriate maintenance based
monitoring while in operation is the key point for preventing transformer failure.
Transformer insulations and its characteristics are also a bit complicated compared to
that of other components. The most common insulation material in transformer is
mineral oil which is being replaced by environmentally friendly oil and cellulose
[116]. In [33], failure rates according to the transformer parts are tap changer (41%),
windings (19%), tank and oil (13%), terminal (12%) and so forth. Another statistical
survey for transformer rate is shown in [117]
Transformer structure and failure rate
Components Description
Core Path for magnetic permeability between primary and secondary
winding.
Tank Case of the transformer including the dielectric material, the core,
and the windings or casing
Dielectric material Fluid oils, gases, or dry solids which have poor conductibility and
good characteristics for electrostatic fields
The expansion tank Container for dry air or dry inert gas to maintain the fluid level.
Bushing
an insulating structure to insulate unexpected electric path from the
grids or other electric transmission devices from the tank of the
transformer
Pressboard paper barriers insulator between the coils and between the coils and core
The tap changer connection point along a transformer winding allowing voltage
regulation by selecting desired the number of turn ratio
The radiator and fan dissipation device for the internal heat generated in the transformer
The pressure relief a protection device for the tank against excessive pressure release
inside a transformer tank.
Table 4.1 Main components of the Transformer
51
Continuous PD monitoring on a transformer
According to the CIGRE data [117, 118], the tap changer has the highest possibility of
failure and then leakage, winding etc. That means appropriate healthy monitoring of
transformer can prevent possible failure beforehand. One recent paper demonstrates
on load tap changer monitoring using a continuous DGA method [119]. When it
comes to PD monitoring on transformer, it can be categorized as electrical, acoustical,
and chemical detection [44]. Regarding the electrical signal detection method, both
IEC 60270 and UHF detection is widely used. Due to the complexity in transformer
and its bulky volume, electrical sensors can be mounted outside on a bushing (IEC
60270) or in side of the transformer using the oil drain valve (UHF). For locating PD
course inside of the transformer, the acoustic emission method is used to calculate the
time difference between different sensor placements [120, 121]. Chemical detection
has been widely used in a periodical way with techniques such as DGA or Furan
analysis.
4.1.2 PD types in Transformer
In some papers [4, 23, 122], there are different types of PD in the transformer which
are distinguishable and largely categorized as void, floating part, surface and corona
discharge.
Void: If there are any an air bubbles within any hardware equipment or crack in the
solid part of the transformer such as between windings or insulation paper and oil,
avoid type defect can occur.
Surface discharge: The surface discharge is the discharge between two parallel
dielectric surfaces. In the transformer, this kind of discharge can happen because of
the bubbles on the insulation surface or delaminating layers of the pressboard.
Corona discharge: Corona discharge is the discharge between a sharp point and
plane surface. Any particles from the manufacturing stage can generate corona
discharge. Also, the sensor coupling part on the transformer bushing can generate
corona type discharge which in this case can be classified as noise
Floating part: Basically in the transformer, two different conducting parts that have
different potential can generate a floating part type discharge due to capacitive
52
coupling. The main reason behind this discharge is a bad earth connection in a
transformer
4.1.3 Different diagnosis and monitoring techniques on transformer
Among many monitoring techniques, this section only includes the on-line applicative
monitoring method and compatible with continuous PD monitoring on power
transformer. In this section, monitoring techniques are categorized as oil testing,
electrical, mechanical, and thermal monitoring of transformer. In [123-125], more
detailed transformer diagnosis and monitoring techniques are covered
Oil testing
Oil is one of the widely used insulation materials for transformer. An Oil test is
carried out by analyzing gases produced by local thermal stress or partial discharge
taking place in the insulation liquid during abnormal operation. Therefore, gas
analysis as part of the oil method is widely used for detecting electrical thermal
insulation problems in transformer.
Dissolved Gas Analysis (DGA)
DGA has been proven to be the most powerful and reliable tool to find out any
incipient faults in oil-immersed transformer by detecting concentrations of gases that
tend to be dissolved in oil. Although there are different gases depending upon
different fault and insulation liquid, roughly Nitrogen (N2), Oxygen (O2), Hydrogen
(H2), Carbon dioxide (CO2), Carbon monoxide (CO), Methane (CH4), are the main
gases of faults [49, 126]. Moreover, those gases can indicate the source of a fault such
as corona, overheating, and arcing in the oil [114]. Duval‘s triangle for DGA analysis
is an efficient tool, which works by comparing the ratio of key gases and confirming
the source [127]. Usually this procedure has been widely used periodically in the
laboratory by sampling oil from the transformer with an occasional time interval.
Recently utilities have started to use on-line monitoring of DGA using sensors [126,
119]. According to [116], on-line DGA is too expensive to use except for very high
53
MVA rating transformers and a portable detector is not so precise compared to that of
one in a laboratory. However there have been many studies on this method including
combining artificial neural network and expert knowledge [126].
Furan Analysis and Degree of Polymerization (DP)
When the paper insulation in transformer lose the insulation strength, furanic
compounds that are by-products from paper insulation material appear in the oil,
which can be analyzed and used for paper aging prediction and DP. 2-furaldehyde is
considered the main product of aging, initiated by 5- furaldehyde in the early stages
[128]. Furan analysis is applied in the case of high level of thermal stress,
overloading, detection of high levels Carboxide, or sudden changes in oil color and
moisture content rates in the oil [114]. Life estimation of transformer according to the
DP is shown in [129]. The Constant K is defined as
( 237)
E
R Tk Ae (4.1)
T = temperature in Celsius,
R = the gas constant = 8.314 J/mole/°K
E = the activation energy = 113kJ/mole
A=the coefficient is obtained depending on operating conditions
With constant K, life of transformer can be calculated as
1 1
final initial
k lifeDP DP
(4.2)
finalDP = Final DP
initialDP = Initial DP
finalDP , initialDP can be substituted as 200 and 1000 respectively [130]. Therefore, the
life of transformer can be obtained by combining (4.1) and (4.2)
54
13600
( 237)0.004
=Estimated life of the transformer Te
A (4.3)
Thermal monitoring
Thermal monitoring is a widely used method and has possible on-line applications.
High temperature means abnormal condition in any parts of a transformer losing
electrical dielectric strength if the thermal continues without any maintenance or
appropriate remedy actions. Usually thermal spots indicate possible faults and
insulation failures caused by overloading or local overheating which can accelerate
insulation aging rapidly. Because the transformer is complex equipment which has
non-linear characteristics with different components such as winding, load tap
changer, and core, thermal monitoring are not so precise to pinpoint the exact failure
spots which may be inaccessible to an external probe [116]. Infrared scanning check
of the external temperature on the transformer is now available [114]. One of the
disadvantages is that this method costs a lot in order to sense temperature directly
using fibre optic [116, 2]
Electrical Monitoring
Electrical monitoring techniques of transformer have been widely used such as
Frequency Domain Spectrum (FDS)&Polarization/Depolarization Current Analysis
(PDC), loss factor, resistance of winding or insulation, FRA, Transfer function, Partial
Discharge, Response analysis, Leakage Reactance, Capacitance and Power Factor
(C&PF) and so on. The techniques inspect the dielectric characteristic of the
insulation material which is usually oil and cellulose in the case of transformer. C&PF
which is known also as C&DF (Capacitance & Dissipation Factor) have been used for
measuring capacitance distribution in the transformer which can be a barometer of
dielectric constant in the transformer [131]. FDS/PDC determines insulation humidity,
tangent delta, and the polarization index. Response analysis use different excitation
function such as Impulse Response Analysis (IRA), Step Response Analysis (SRA),
and Frequency Response Analysis (FRA). This method monitors transformer
behaviour depending on the input signal which it is possible to use for on-site
measurement. PD monitoring of transformer is also popular monitoring method.
55
Mechanical Monitoring
Mechanically, On Load Tap Changer (OLTC) is the part where many faults occur.
Moreover, winding and core vibration can be detected by vibration sensors on the
transformer wall. This vibration signature can be analyzed by Fourier or Wavelet
transform. For visual inspection, checking of the pump isolation valve and oil flowing
indicator should be performed in order to confirm oil circulation. Plus, the conservator
breather also should be checked for the correct oil level. Fan and radiators should be
kept clean in order to cool the transformer down.
4.1.4 On-line PD monitoring on transformer
On-line PD monitoring on transformer mostly uses the electrical detection method to
decide PD occurrence, and the acoustic detection method to locate the PD source
inside the transformer. Especially before installation, PD monitoring can be used for
new transformers in order to find any possible manufacturing problems [132]. In this
section, promising PD monitoring techniques using different methods, sensors,
recommended sensor coupling methods and signal processing will be covered.
Conventional method [4, 9, 43, 133]
PD monitoring using IEC 60270 is an already proven method and has been used
widely for several decades. The sensors used in this method are capacitive coupling
devices attached on bushing like the below Figure 4.1. Wide or narrow band pass
coupling devices can be installed on a all accessible terminals such as HV or LV
bushing, grounded neutral, grounded core clamps. However, bushing with capacitive
couplers on the HV bushing or HFCT on a ground lead are the most common methods.
Especially multi terminal measurement method can generate good results regarding
each phase of PD activities. The results from the multi terminal method can also be
used for pattern recognition of PD on transformer by using a 3-PARD diagram, then
reducing external noises and finally comparing three terminal PD measurement results
with each other. Since the signal from this method usually includes a high level of
56
noise, appropriate signal processing techniques will be required for continuous on-site
PD monitoring.
Figure. 4.1 IEC 60270 recommendation for PD monitoring system on bushing
aC =The test object capacitance
1C =The HV capacitance of the bushing
2C = The LV capacitance of the bushing
MI=Measuring Instrument
Unconventional method [21, 37, 43, 133, 135]
One obvious advantage of UHF PD monitoring on transformer is its strength despite
noise. There are two different coupling methods widely used for UHF PD monitoring
on transformer. The first method is to use inner sensing type sensors in the oil drain
valve which is a non-destructive coupling accessing the PD location more closely
seen in Figure 4.2. However this sensor type is not applicable for transformer which
does not have a straight-through oil valve. The second method is to install external
sensors against dielectric windows at different places on the transformer surface as
shown in Figure 4.3. Both UHF detection techniques are currently used and have
proven the efficiency as highlighted in research. In some papers, multi terminal IEC
60270 with UHF seems to be the most promising techniques in order to have a
sufficient PD signal above noise and to Figure out PD activities at each phase which
can assist in the localization of PD.
57
Figure. 4.2 Drain valve type UHF sensor [43, 136]
Figure. 4.3 UHF dielectric window type [137]
AE Method [121, 138-139]
Locating the PD source in the transformer is possible using acoustic emission
detection on the transformer. There are two different methods; using an
electromagnetic PD signal analyzed as a PD pulse shape and amplitude, and; acoustic
detection using known as triangulation. Acoustic detection for localization by using
piezoelectric sensors on the transformer wall and calculates the arrival time from
different sensor placements.
Figure. 4.4 Acoustic detection for localization of PD in Transformer [121]
58
In Figure 4.4, possible sensor place using Cartesian coordinates is shown. The biggest
problem of the AE detection method for localizing the PD source in the transformer is
its signal sensitivity. This method should measure acoustic signal at the same time
with at least 3 or 4 different sensors in different positions. In [121], detailed
mathematical explanations and possible signal processing techniques are covered.
4.1.5 Available products for on-line PD monitoring of transformer
Doble Lemke
Doble Lemke GmbH uses a conventional and unconventional method for on-line PD
monitoring of the transformer. In conventional PD monitoring, tap bushing coupling
with a low voltage capacitor is used as a sensor. This method is also applicative for
multi terminal measurement analyzed by a 3-PARD diagram eliminating noises and
comparing each phase. For the noise reduction, gating from a gate sensor and the
winding technique for phase-locked noise is used. In unconventional PD monitoring,
oil drain valve type sensor in the UHF (300 MHz-1 GHz) band or a UHF tap hatch
sensor is used. Furthermore, they use acoustic emission detector to localize the PD
source inside of the transformer by using piezoelectric, acoustic sensors attached on
the transformer wall which provide up to 8 sensors
Dynamic Ratings
Dynamic Ratings provides a combined solution for transformer monitoring such as
on-line DGA, and temperature monitoring. Regarding on-line PD monitoring, they
use external sensors such as Radio Frequency Current Transformer, bushing sensors
and a Rogowski coil. An AE sensor is compatible if it is necessary.
IPEC Limited
IPEC Limited provides on-line PD monitoring equipment applicable to transformers.
Sensors are HFCT, Capacitive coupler, and Airborne Acoustic Transducer. Total
monitoring solutions can be combined with temperature and humidity detection.
59
Power diagnostix System GmbH
Power diagnostix System GmbH uses the conventional method using capacitive tap
on the bushing and the PRPD visualization method.
PowerPD, Inc.
PowerPD, Inc uses electrical sensors (clamp-on type HFCT) and acoustical sensors on
the transformer wall for on-line PD transformer monitoring. The sensitivity of the
sensors is 5pC and 20pC respectively. This system is fully compatible with SCADA
and remote accessibility.
Qualitrol Company LLC
Qualitrol Company LLC uses an unconventional PD monitoring method by attaching
rod type, window type, or drain valve type and hatch installation UHF sensors from
three to six around the transformer orthogonally on the side and top wall. This method
provides digital and analog output for web based or SCADA (supervisory control and
data acquisition) monitoring applications. This method can be applied in many
transformers at the same time with a Master/Slave connection for each signal
collection box.
Techimp Energy Srl
Techimp Energy Srl uses a HFCT, inductive sensor, usually clamped to a ground
connection of transformer. In order to make it a non-invasive way of coupling, they
use TEV (Transient Earth Voltage) and Horn antenna as sensors. This company uses
fuzzy logic based PD identification technique and Time-Frequency map for noise
reduction and identification of PD type.
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4.1.6 Summary and Conclusion
Since transformer is the most intricate power system component, there are many
different ways or monitoring techniques for preventing possible faults. As some
companies have already provided on-line PD monitoring system on power
transformer for a couple of years, one can infer the fact that on-line PD monitoring of
transformer will be widely used in the very near future. Especially transformer
application PD monitoring techniques can be combined with other chemical,
mechanical or thermal monitoring with the other methods mentioned in this section.
On-line PD monitoring on the transformer focuses preliminary on PD magnitude
(peak value) and source location. Regardless of the apparent charge or UHF
measurement, changing or increasing of PD magnitude inside the transformer means
the fact that the transformer needs a more specific inspection or to be repaired.
However, for the purpose of on-line monitoring, the UHF method is more reliable due
to the strong resistance to back ground noise. For the localizing of the PD source,
acoustic emission detection technique has the key solution of locating PD source
inside the transformer as highlighted in many papers.
From a practical point of view, the on-line monitoring method uses capacitive sensors,
UHF sensors (oil drain valve type or dielectric window), or HFCT as the correct
sensor type. Capacitive sensor application is compatible with multi terminal sensing
which makes it possible to compare PD signals generated from each of the three
phases. It can also be used for further signal processing and, reducing phased-locked
noise. Nevertheless IEC 60270 has played an important role in guiding PD monitoring
on Transformer. The upcoming standard for UHF/AE, IEC 62478 will be the most
important standard especially for on-line PD monitoring of power transformer.
4.2 Cable
This chapter presents On-line PD monitoring for cable applications. There are
different reasons for aging of cable including thermal, electrical, mechanical, and
environmental. Based on these reasons, there are many different techniques used to
monitor, and diagnose the faults [140]. Regarding electrical aging monitoring, PD has
61
been widely used in the laboratory, on-site as the form of on-line or off-line
monitoring. Especially after installation of the cable system in the power system,
detecting faulty connection by different PD monitoring methods such as Damped AC
(DAC), Very Low Frequency (VLF) for example have been gaining its reputation.
Therefore, in this section, all kinds of PD monitoring techniques in cable will be
covered with detailed information regarding on-line PD monitoring in the cable
system as well as its available products in the market
4.2.1 Cable system in power system
Cable network systems in the power system are one of the most important part but
also the part most vulnerable to failure. Cable network can be categorized as Extra
High Voltage (EHV), High voltage (HV), Medium Voltage (MV) and Low Voltage
(LV) networks. The failure rate of the cable system is more frequent for lower voltage
networks, meaning LV networks have the greatest outage time of all network. More
than half of cable failure stems from electrical reason and the rest of them are due to
external non-electrical inference [132]. In particular, in MV networks, the causes of
outage time are the cable (81.1%), Switchgear (6.8%), transformer (3.8%) and others
(8.3%).
Cable network structure and insulation characteristics
The cable system in the power system consisted of cable joint, termination, and line.
For insulation purposes, Cross-linked polyethylene (XLPE) is commonly used for HV
and MV cables. Poly Vinyl Chloride (PVC) and ethylene propylene rubber (EPR) are
suitable for LV cables [141]. XLPE, in particular, is popular due to its low dielectric
losses. XLPE nowadays has been replacing oil-immerged insulated cables. Regardless
of operation voltage or frequency, the cable usually has the same structure as
described below in Table 4.2.In Figure 4.5, the typical XLPE cable structure is shown.
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Figure. 4.5 XLPE cable structure [142]
Components Description
Conductor Transfer current at lowest loss, usually made by Cupper (Cu) or
Aluminium (Al)
The inner and outer
semi-conductive screens
Contribute smooth and homogeneous boundary surface, and for
preventing any gaps or voids occurrence
Insulation layer Endure electrical stress, single layer construction
Earthed metallic screen Electrical shielding, creates return path for capacitive charging
current, provides mechanical stress when the cable is bending
Protection sheath Provide mechanical strength, and low moisture penetration
Table 4.2 Cable structure and its function [143]
Cable accessories are one of the main reasons for possible faults in the network
system. According to [132], cable joint and terminations are the biggest reason for
cable network failure. Because of this most tests are focused on these parts. In [144],
more detailed defect types and causes are demonstrated.
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Continuous PD monitoring on cable network
Traditionally, PD monitoring on cables has been widely used due to its effectiveness
in CBM based monitoring and in localizing of the faults area. Especially for PD
monitoring of cables, standard procedures such as Very Low Frequency (VLF),
Damped AC (DAC), Alternative Current (AC) or Direct Current (DC) testing are
popular due to the fact that they can verify possible faults areas of joint and
termination after assembling by detecting and localizing PD in the cable. However,
for the purpose of on-line monitoring, high attenuation of the PD signal along the long
cable line makes it difficult to pick the exact PD and its location. Nowadays on-line
PD monitoring of the cable has been used by sensing the PD signal with HFCT,
capacitive coupling sensors, and so on. More detail will be covered up in the coming
section.
4.2.2 PD types in cable system
PD occurrence in the cable system can be divided into an internal, surface, and
electrical tree [143, 145].
Internal PD: PD occurs in the air gaps or voids surrounded by solid material it
depends on the size and the location. This PD occurrence in cavity can pit and erode
the cable surface [146].
Surface PD: This type of PD can occur on the surface of solid-solid and solid-liquid
material parallel with the surface of insulation. This can be happen as consequence of
field enhancement on the area of missing outer semi-conductive screen or an
incompletely removed outer semi conductive screen in the cable
Electrical Tree PD: This PD type represents the PD generating tree-like shape on the
insulation or dielectric body where the PD occurs. The growth of electrical tree can
ultimately bridge undesired electrical paths potentially leading to complete
breakdown. Moreover, if there is a high level of moisture, a water tree can be
generated resulting in an electric tree at the same place. Usually electrical tree have
many branches, and produce a higher PD magnitude than in the cavities which can
grow until final breakdown occurs.
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Corona: PD occurs in open air around the cable.
4.2.3 Different diagnosis and monitoring techniques on cables
There are many ways to monitor cables in a laboratory, on-site, or with on/off-line
methods. After a brief explanation of different monitoring techniques used on cable
networks, this section focuses on electrical, especially partial discharge method. As
well as methods presented here, there are also destructive methods such as Cable
sampling, lead sheath analysis, and paper analysis [147].
Tangent Delta (Loss angle, or Dissipation Factor testing) Measurement
This method provides information regarding the aging of a cable by determining the
loss factor due to the tangent delta value which is related to the composition of the
connection, the trajectory, and the actual cable temperature. In perfect conditions, a
cable has capacitive characteristics maintaining the phase difference between voltage
and current at 90 degree. However, if there are defects in cable, the angle between
voltage and current is no longer 90 degrees; rather it will be less than usual. However,
this method is not used for XLPE cables owing to their low Tangent Delta value [147]
Leakage current monitoring
This monitoring method measures leakage current from a high voltage cable to the
ground by the surface of the insulator. The measured value is able to demonstrate
pollution issues of the cable and its accessories. For example, washing cable
accessories on the cable tower can influence leakage current which can be measured
with sensors and optical fibre. This method has also been commercially available in a
form of on-line [148, 149]
Temperature monitoring [150-152]
Temperature monitoring on the power cable is an efficient tool for detecting an
abnormal condition which is also applicative as an on-line monitoring tool by using
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appropriate temperature sensors. Semiconductor type sensor or optical fibre is popular
for continuous temperature monitoring of cables. The advantage of this monitoring is
to have real-time thermal behaviour of the cable that it is possible to use for thermal
rating re-assessment. However almost all cable systems are in operation practically at
low load condition for most of their service time, making it impossible to calculate
effective thermal resistivity of the cable. Therefore, temperature monitoring on a
cable should focus on a particular time and section of the cable.
Partial discharge monitoring
Even though thermal stress has a significant impact on the aging mechanism of the
cable, electrical stress is prominent cause of aging. PD monitoring of the cable is the
most effective method that is able to monitor electrical aging [153]. For localization
of PD, Time Domain Reflectometry (TDR) which uses the reflection of pulse signal at
the cable termination [154, 155] has been used. PD monitoring of the cable system
can be clearly categorized into the off-line and on-line method. Regarding the off-line
method, it has been widely used with an extensive voltage withstand test in order to
validate the acceptance test for cables from the factory. It can also be used for on-site
PD measurement. Before describing those techniques, a comparison of those the on
and off –line method are described.
a. On-line versus off-line cable monitoring
Because of the structure characteristic of cables, on-line monitoring of the cable
network is demanding due to signal attenuation depending on the cable length.
Therefore, offline PD monitoring on-site or in the laboratory has been widely used.
Especially multiple PD source in long cable line with relatively high background
noise from different power system components makes it extremely difficult for
carrying out on-line PD monitoring in cables. In many practical fields, both on-line
and off-line is widely used for on-site or laboratory tests [156]. The Table below
provides general comparison about on-line and off-line PD monitoring for the cable.
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On-line monitoring Off-line monitoring
Advantage
• Can be performed while cable is in
operation
• Economical
• Real operation condition can be taken
into account.
• Proven technology for on-site,
laboratory test, and commissioning
• High sensitivity
• Calibration possible
Disadvantage
• Low sensitivity
• Complicated data analysis is required
• Insulated earthing ground is required
• Out of connection is required
• Relatively bulky equipment required
• Outage cost
• PD occurrence can be differ compared
to its operation at service voltage
• Overall condition during testing
(Temperature, humidity, vibration)
can differ from operation condition
Table 4.3 On-line versus Off-line PD monitoring on Cable [157, 158]
b. On-line PD monitoring
An on-line cable PD monitoring technique has proven its efficiency recently. As
mentioned above, on-line PD monitoring on the cable network has many advantages
over the off-line method. However monitoring long cable lines while they are in
operation has too much noise and attenuation compared to other application cases.
Usually HFCT around cables or the earth connection, inductive couplers, capacitive
coupling sensors, and acoustic emissions have been used [159]. Due to the fact that
high frequency from a PD signal is significantly attenuated in the cable, the sensors
measure the HF/VHF range rather than UHF.
c. Off-line PD monitoring [160-162]
The off-line method usually energizes the cable network and monitors PD occurrence
by using a different voltage input. Roughly five methods have been used shown as
below according to the applicative situations.
Turned resonant test voltage method: There are two ways of carrying out this test
using different test circuits for resonant testing; Frequency Turned Resonant Circuit
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(FTRC) and, Inductively Turned Resonant Circuit (ITRC) with the following resonant
equation (4.4) in cable.
1
(2 )F
LC (4.4)
L= test inductance (or external inductor)
C= cable capacitance
The FTRC method uses power electronics converter generating harmonics and noises
in the test system. Therefore appropriate signal processing techniques are required.
However, there is no moving part included in this method. On the other hand, because
ITRC usually use auto transformer, there are no such electronic pulse noises.
Moreover voltage can increase smoothly which makes it easier to reach the PD
inception voltage. The drawback of ITRC is its moving components which should be
maintained periodically.
Damped AC (DAC) voltage method: This method consists of a direct voltage source,
switch, and inductance. After charging the direct voltage source enough for required
peak value, the switch connects inductance with the test cable so that the capacitance
of the cable and external inductance are able to oscillate. The frequency range of this
method can differ depending on the cable length and PD inception voltage and
occurrence at the cable. Advantages of Damped AC methods over others are
relatively less power demand, lighter, and they is also applicable for all types of MV
and HV cables.
Very Low Frequency (VLF) voltage method: Since capacitive power of the cable is
proportional to the frequency, if frequency decreases, the demand of capacitive power
also decreases. Therefore VLF method turns down the frequency as from 0.01Hz to
0.1Hz for extruded-dielectric cable. Advantages of this method are also its light
weight and low power demand.
DC voltage method: Even though the DC voltage method cannot represent ac voltage
related insulation stresses, this method has been used for thermal, conductivity
problems. HVDC can be applied for cable acceptance test for recommended duration.
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Advantages of this method are its simplicity, lightweight and cost effectiveness with
low power required
Impulse voltage method: Impulse voltage with a very fast rate of rise and decay rate
similar to power frequency can be applied for on-site tests. This method has its
strength owing to lightweight equipment. Disadvantages of this method are hard to
determine the inception voltage of PD, high attenuation along the long cable length,
distance dependent test results, and difficulty to find correlation between routine
factory and on-site test regarding partial discharge values.
4.2.4 On-line PD monitoring on cable
IEC 60270 method is not appropriately applicative for on-line PD monitoring on
cables. Usually HF or UHF detection for gaining high signal to noise ratio (SNR) is
an attractive method for this purpose [163-169]. Since cable terminal and joint is the
part of cable most vulnerable to failure, on-line PD monitoring on cable accessories is
important for cable monitoring.
Figure. 4.6 Capacitive coupling method near cable joint and terminal [166]
Regarding sensor type, capacitive coupler, inductive sensor (e.g. HFCT, Rogowski
coil), and directional coupler sensor near cable joint, terminal or cable earth have been
widely applied. In Figure 4.6, a possible capacitive coupling method is shown. Since
capacitive sensors have good sensitivity for nearby PD occurrence usually near
terminal and joint, capacitive sensors are located near cable accessories.
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Figure. 4.7 HFCT coupling application at the cable termination [168]
In Figure 4.7 HFCT in a slightly different location at the cable terminal is shown.
According to the availability, the coupling spot can be adjustable. In order to localize
the PD source in the cable system, dual sensor techniques (installing two sensors at
each end of cable or cable joint) are required. Because of strong attenuation along the
cable, PD localization requires more engineering techniques such as the pulse
injection method, GPS application, or TDR. The pulse injection method injects
periodic pulses from one side and the sensor located in other side detects the pulse
which synchronizes two sensors at each end of the cable system. Therefore the
propagation time and transfer impedance can be calculated. Another technique uses
TDR due to the symmetrical characteristic of the cable system the pulse can propagate
toward both ends of the cable with different magnitude and time. Therefore the direct
pulse and reflected pulse can be detected by sensors which can be synchronized with
GPS signals
4.2.5 Available products for on-line PD monitoring of cable
Doble Lemke
Doble Lemke monitors cable terminations, and joint using UHF sensor directly
attached to the sensing place such as GIS- cable termination. This data shown as
PRPD can be analyzed and transferred through a TCP/IP network.
70
Emerson
Their approach for online cable PD monitoring combines Tangent Delta testing, off-
line method with VLF PD monitoring, and the ultrasonic method. RF embedded noise
reduction can eliminate noise from PD with RFCT as a sensor. Regarding localization
of the PD source, they can make it possible to have about 1 % accuracy in up to 3
miles of cable length, which is an application for XLPE, EPR, PILC and CLX
Armored cable types
HVPD
HVPD uses HFCT attached around the earth connections and TEV attached
magnetically to the outside of metal-clad switchgear sensor which is applicable for
Polymeric (XLPE, PVC), Paper (PILC, MIND), Rubber (EPR), both 3-Core and
Single-Core Cables, and 'Mixed' cables with transition joints. Two cable ends attached
sensors are monitored for PD localization using a pulse injection method which is
successfully performed for up to 5 km on MV cable
IPEC
IPEC‘s method monitors two ends of the cable terminal which provide PD source
location with TEV sensors. Basically they use HFCT, capacitive coupling, and
airborne acoustic sensors. This is applicative for MV, HV, and EHV cable networks.
KEMA
KEMA uses inductive sensors at two ends of cable termination, avoiding significant
signal attenuation at RTU (Ring Main Unit) or substation and monitoring only for the
cable itself. This method also localizes the source of PD by injecting periodic pulse
and measuring propagation time. By doing so, two different sensors located at each
end of cable can communicate and get time synchronization with each other. All the
information from each sensor is transferred to a control centre. Maximum cable length
of this application method is 8 km (for XLPE), 4km (for PILC, MIND), and 2km (for
71
EPR). 3D visualization of PD in the cable moreover makes it possible to check PD
occurrence according to the cable length, time and intensity.
Power PD
Power PD uses HFCT as a sensor on shield ground cables which can be shown as a
PRPD or 3D graph.
Techimp
Techimp uses HFCT sensors, and FMC (Flexible Magnetic Coupler) sensors directly
at the two terminations of the cable. In long cables, the installations can be performed
at the middle of cable. For localization of a PD source, they analyze Amplitude/
Frequency characteristics of PD, TDM method, and Arrival Time Analysis with GPS
(Global Positioning System). Moreover this can be connected to a Ethernet network,
and controlled from a remote location.
4.2.6 Summary and Conclusion
In particular, cable PD monitoring with an on-site (off-line) method with different
voltage levels and frequencies has been used. The after laying test is a proper example
of on-site PD monitoring. Even though there are different methods of monitoring on
cables, PD monitoring seems the most promising technique providing the possible
faults including their location.
For on-line PD monitoring on cables, IEC 60270 is not an appropriate detecting
method because of its low frequency cover range in which high noise level makes it
hard to achieve accurate PD measurement. Plus, long cables will attenuate
propagating signal, making impossible to calibrate according to the IEC 60270 [164].
Recent research shows that for on-line monitoring on cables, the monitoring
frequency range should be up to 100 MHz because of lower noise compared to low
frequency band measurement. On the other hand, the cable acts as a low-pass filter,
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thus the higher frequency pulses related to PD activity is only detected near the PD
source.
The most appropriate sensor selection for cable case is capacitive coupling and HFCT
according to the application. Since cable accessories, joint and terminal, are the
biggest cause of possible faults, on-line PD monitoring near joint or terminal of cable
has been widely used. However, using two HFCT at each end of cable with PD
localizing techniques by TDR or pulse injection method has been proven its efficiency
on on-line PD monitoring for long length cable.
4.3 Rotating Machine
Rotating machine such as synchronous generator, induction motors and DC or AC
machines is one of the most important parts of the power system. The main reasons of
faults in rotating machines are thermal, electrical and, mechanical stress. Continuous
PD monitoring of rotating machine has been considered as efficient diagnostic tool for
several decades [170, 171]. In this section, on-line PD monitoring of rotating machine
will be covered, including their insulation structure, different monitoring techniques
and ultimately its configuration with specific Figures.
4.3.1 Rotating machine in power system
Rotating machine structure and material
Rotating Machines (RM) have the most intricate structure among power system
components similar to transformer. On top of that, they vary according to the types
such as synchronous, induction, or permanent magnetic machines. In Table 4.4,
typical electrical machine‘s materials are shown.
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Subassembly Component Materials
Enclosure
• Enclosure
• Heat exchanger
electrical
connections
• Bushings
• Bearings
• Fabricated structural steel
• Steel, copper or brass tube
• Cast epoxy resin
• Steel Babbitt, high tensile steel rolling elements or soft
bearing alloy on bearing shells
Stator body
• Frame
• Core
• Core clamp
• Structural steel
• Electrical steel laminations
• Structural steel or non-magnetic, low-conductivity alloy
Stator winding
• Conductors
insulation
• End Winding
support
• Hard drawn copper or copper wire
• Mica-paper, glass or film impregnated with resin
• Glass fibre structural materials and impregnated insulation
felt, ropes and board
Rotor winding
• Conductors
• Insulation
• End winding
support
• Hard drawn copper or copper wire
• Mica-paper, glass, or film impregnated with resin
• Impregnated glass fibre rope
Rotor body
• Shaft
• Core
• Core clamp slip
rings
• Brushgear
• Structural steel or forging
• Electrical steel laminations or steel
• Forging integral with shaft
• Structural steel or non-magnetic, low conductivity alloy steel,
brass or copper
• Carbon or copper brushes in brass brush holders
Table 4.4 Materials used on general electrical machine in power system [172]
As we can see in the above Table, materials used in different part of rotating machine
consist of a wide range of different components and its common structure is laminated
or impregnated. Because of that PD attenuation and distortion occurs all over the
rotating machine [172, 173].
Faults rates and Continuous PD monitoring on RM
PD monitoring with appropriate sensors in North American utilities has been adapted
on more than 50% of large generators [174]. In [172], there is detailed information for
possible faults in rotating machines. The biggest cause of faults in rotating machines
is mechanical stress. Electrical failure on RM which is one-third of the total failure
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rate comes from persistent overloading (4.2%), and normal deterioration (26.4%). The
main failed components on RM are stator ground insulation (23%), turn insulation
(4%), and others (8%) [175,176]. More detailed information regarding RM failure is
in [177]. Therefore PD monitoring stator windings has normally been performed in
many industries and utilities. Continuous PD monitoring provide several advantages
for rotating machines; (i) provides warning for personnel, and (ii) solves the problem
of difficulty for RM testing under the same condition by supplying continuous
trendable data [174]. Moreover, other stress such as thermal or mechanical vibration
on RM can create a void or cracks which are detectable in the form of PD, expressed
as a symptom of stator winding failure [178].
4.3.2 PD types in rotating machines
The most popular sensing place for PD monitoring on RM is at the machine terminal.
However PD can occur inside of RM usually from stator winding which can be
attenuated or distorted during propagation from the PD source to the measuring place.
Therefore analysis of the magnitude and wave form of PD sometime provides
inaccurate information regarding the location and type of PD [179]. Moreover, PD
measurement can differ according to the loading, temperature, manufacturer, size and
so on. Because of that PD monitoring and appropriate pattern recognition of RM is
difficult compared to other part of the system. Here typical PD types and locations is
shown below
Slot-Discharge: The most harmful PD, slot-discharge happens between a magnetic
core and bar or coil of the stator winding. In detail, so-called slot discharge takes
place between the iron core and bar coil inside the slot [180, 181]. Slot-discharge
erodes gradually semiconductor coating of coil and bar if it occurs continuously. It is
load dependent and usually has a much larger magnitude in a negative cycle [182].
Internal Discharge (Voids or Delaminations): The voids in the ground wall
insulation or delaminations inside of the coil can lead to internal discharge. This stems
from bad quality of impregnation processes which are durable compared to
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delaminated at the copper conductor due to the thermal overstressing [181]. This
depends on the thermal condition of RM [182]
Endwinding Discharge: This usually occurs in the overhang region when a
contamination of the conductor takes place owing to mechanical corrosion or for
particular RM, where bar coils belonging to different phases locate in the same slot
[183]. Therefore the reason of this discharge results from phase to phase voltage with
not enough room between coils of different phases or partly conductive contamination
[76]. According to [182], this type of PD usually has a high magnitude in negative
cycle and it is temperature dependant.
4.3.3 Different diagnosis and monitoring techniques on rotating machines
In [184], an intensive review of almost all possible monitoring techniques with regard
to RM is covered in detail. Largely, there are thermal, chemical, mechanical and,
electrical monitoring techniques have been widely used.
Mechanical monitoring
Due to the high mechanical stress of RM compared to other power system
components, mechanical monitoring on RM is of high importance. These include
vibration monitoring, shock pulse method and examinations of Unbalanced Magnetic
Pull (UMP) in the air gap. Regarding vibration monitoring, precise selection of sensor
placement is of importance. The shock pulse method provides rotor bearing wear
level and UMP calculation in air gaps deliver the information regarding the static
eccentricity of the rotor with respect to the stator.
Thermal monitoring
According to [184], there are three different approaches for temperature monitoring
on RM as shown below.
• Estimate the local point temperature with an embedded temperature detector,
or resistance temperature detector. The placement of the detector is of
importance.
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• Use thermal imaging, to find a hot spot in the RM, which has been used on a
lot of other HVE.
• Evaluate distributed temperatures of RM or bulk temperatures of the coolant
fluid.
Chemical monitoring
High thermal stresses in RM generate chemical reactions in the insulation material,
usually starting from 120 Celsius by emitting hydrocarbons and ethylene. However,
this method tends to be expensive to perform and is limited by its accuracy.
Electrical monitoring [185]
With regard to electrical monitoring on RM, methods include the insulation resistance
and polarization index, partial discharge, Capacitance and Dissipation Factor, Motor
Current Spectral Analysis (MCSA), High Voltage DC Ramp and Power Monitoring
shown below.
Insulation resistance and polarization index uses moderate PD voltage (500V-
10000V) as an input across the ground wall insulation of the stator or rotor winding,
and they measure resultant current.
Capacitance and Dissipation Factor investigates the present of a void inside the
stator insulation. Also known as tangent delta the measurements are performed by
using bridges usually based on the transformer ratio-arm or Schering principle.
High Voltage DC Ramp supplies ramped input and measure the current as a function
of voltage. The results curve can indicate any abnormal condition or poor insulation
status.
Power Monitoring keeps track of power output at the terminal with the equation in
[184]. This method ensures electrical health monitoring of RM by measuring
instantaneous power and calculating the sum of each phase, which should be close to
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DC due to the cancellation of AC components from different phases if the flux and
torque is in a normal condition [186].
Motor Current Spectrum Analysis (MCSA) monitors stator current and its
spectrum. This can be easily implemented with Current Transformer (CT) around
supply cables. Because its accurate analysis and easy installation, this method has
been widely used.
Partial Discharge can be applied in two different ways, on-line and off-line. In the
case of off-line, just like off-line PD monitoring after laying the cable case, high AC
test voltage is fed into the cable and PD occurrences are recorded. Off- line PD
monitoring on RM which are not in operation are analysed without any operating
stress such as thermal or mechanical vibration, and other possible stresses while the
machine is in the grid. This information can mislead or failure to notice possible faults
in RM during operational condition. However, on-line PD monitoring on RM can
provide realistic data under the same circumstances of real conditions and situations
of load variation. In particular, on-line PD monitoring on RM largely depends on
operation temperature and load condition. One limitation of PD monitoring on RM is
that this cannot provide any information regarding the pulse-less discharge
phenomenon [181].The reason for this discharge results from phase to phase voltage
with limited room between coils of different phases or partly conductive
contamination [76]. According to [182], this type of PD usually has a high magnitude
in negative cycles and is temperature dependant.
4.3.4 On-line PD monitoring on rotating machines
VHF method [187-189]
For on-line PD monitoring on rotating machines, capacitive sensors, Rogowski coils,
and HFCT at the end of the voltage terminal have been widely used. In particular, 80
pF capacitive coupling installed at the generator bus bar or stator winding on each
phase has mostly been used for permanent on-line PD monitoring as shown in Figure
4.8. Since PD occurrence on RM depends on the load and temperature condition,
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measurement sometimes accompanies its temperature and load record, for example, a
full load at a moderate temperature.
Figure. 4.8 Capacitive coupling method on RM [110]
For noise reduction, two sensors installed at different spots in one phase terminal can
be used. The basis of this method is the arrival time difference between two sensors.
By doing so, sensors can recognize the PD signal source from an external or internal
spot.
Stator Slot coupler method (SSC) [190-191]
The SSC coupler method uses special sensors located on stator winding wedges as
name implied. The sensor in this application is a directional electromagnetic coupler,
which can be permanently installed at the stator slot. Nevertheless the exact shape can
vary according to the slot size of the generator.
Figure. 4.9 Stator Slot Coupler [220]
79
SSC is about 50 cm long and 1.7mm thick as shown in Figure 4.9. SSC has two
coaxial cable outputs at one end which can be connected to a signal collector located
on the outside of the generator. Typically six to nine SSC can be installed on one
generator. The advantages of this type of connection are noise immunity from stator
winding ends or other external sources, and a PD pulse detection ability from 1 to 5
nanosecond in stator winding.
4.3.5 Available products for on-line PD monitoring of RM
Doble Lemke
Doble Lemke installs capacitive coupling at the generator‘s bus bar of each phase.
The sensors cover the frequency range according to IEC 60270 and VHF. In order to
eliminate noises, gating antenna detecting noise signals are attached, for instance,
grounding of the machine enclosure is used. By using PRPD analysis, the signal is
interpreted and identified in terms of each phase
HVPD
HVPD utilizes HFCT (capacitive coupling sensor in case of above 1000 amps on the
supply cable) as a sensor attached around the supply cable, which should be capable
of high amps conducting through the supply cable. The software program will
automatically identify the PD types categorizing the end-winding and slot type.
IrisPower (Qualitrol Company LLC)
Qualitrol-Iris power uses an epoxy mica capacitive coupler for hydro generator stator
winding monitoring and a small turbo generator bus output rated 6kV and above. Plus,
regarding turbo generator application, they use SSC installed under the line end stator
winding wedges (in existing machines), or between the top and bottom bars (in new
or rewound machines). Noise separation techniques use 40MHz high pass filters,
time-of-arrival, and pulse shape characteristics. With Wide Area Networks (WAN),
data collection, change configuration and any kind of control activities are possible
from a remote location.
80
Power Diagnostix GmbH
Power Diagnostix GmbH installs a capacitive coupler close to windings such as a bus
bar at each phase if necessary especially for large machines. Global intranet access
and visualization of the monitoring data can be connected to this installation.
PDtech (Qualitrol Company LLC)
PDtech uses capacitive couplings near the generator terminal and HFCT around the
cable which is available for all HV-machines rated current and voltage. This
application provides an alarm and is compatible with SCADA systems.
PowerPD
They capture PD signals from generators and motors by coupling in each phase using
a Capacitor Coupler. This application has an early warning system and scans between
200 KHz-300 MHz.
Techimp
Techimp uses capacitive coupling (1000 pF high voltage dry-type (mica/epoxy)
capacitors) at each phase around machine terminal, but if it is not appropriate to
install capacitive coupler, HFCT can be a substitute around ground connections. For
noise reduction and classification of the PD signal, they use a TF map and fuzzy logic
based classifier. After connecting to the monitoring device, the data can be transferred
and interconnected with SCADA systems, which are also compatible with Ethernet
networks with a static IP address.
4.3.6 Summary and Conclusion
PD occurrence on rotating machine such as generators are typically at a higher level
even in normal state due to mechanical dynamic and rapid electrical field changes
inside for RM. Therefore compared to other parts of the power system components,
there is always a certain level of PD on RM while it is in operation. A large variety of
electrical monitoring techniques can also provide adequate information in terms of
abnormal operation conditions. Even though stator winding, in particular, has s higher
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vulnerability to faults, the PD signal from stator winding can be distorted or
attenuated at the sensing spot.
For online PD applications, capacitive sensors at the generator terminal, Rogowski
coils and HFCT at the end cable connection spot, or directional electromagnetic
coupler at stator winding slot wedge has proven efficient in research and available
products in the market. VHF is the most appropriate monitoring frequency range. In
order to reduce noise and locate the PD source, two sensors at one phase different
spots, pulse shape analysis with 3 PARD diagram can be used.
4.4 GIS (Gas Insulated System)
GIS has been widely used for HV insulation since 1960. Due to its high insulation
characteristic and break down voltage with injected gas- usually SF_6 compared to air,
GIS makes it possible to construct the substation in a more compact and reliable way
[192]. PD detection techniques in GIS are conventional, unconventional or combined
both covered in a recent paper [193]. Usually the sensor should be located within an
appropriate distance so that the sensor can detect a PD signal from the GIS. UHF
method in GIS was used for the first time in the 1980s [194]. In this section, on-line
PD monitoring applications for GIS will be presented with their basic structure,
failure type, and other possible monitoring methods.
4.4.1 GIS in power system
Structure and insulation components of GIS
GIS has a different design, type, and size depending on the rated voltage,
manufacturer, and so on. However the basic components and their materials are in
Table 4.5.
Components Description
GIS Enclosure
• Electrically integrated, grounded casing
• Single phase and Three phase types according to the application.
• Aluminum and steel is commonly used
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Gas
• Usually Use as gas insulation material or Mixture with [195]
• High pressure (4 bar) and low pressure (1.2bar)
• High pressure has better dielectric characteristics
Conductor system
• Aluminum tubes according to the rated voltage and current, its thickness and
diameter can be specified
• Silver plated contact surface
Solid Spacer
• Physical support of high-voltage conductors and mechanical operation of
switchgear
• Cause electrical field distortion within GIS
Table 4.5 GIS's insulation and enclosure components and material [141]
The inside components of the GIS usually vary between types, such as the circuit
breaker, disconnection switch, current transformer, voltage transformer, bus bar and
so forth [141]. Therefore, different components can cause different failures inside the
GIS. In [196], different analysis of GIS failures was conducted based on thirty years
failure history from five German utilities and 7 companies. Depending on the location
of GIS, the most common failures occurred in the switching compartment (40.4%),
Voltage Transformer (VT), Surge Arrestors (SA) and bushing compartment (17.3%)
and other compartment (42.2%). Failure in terms of the type of defects were particles
and foreign bodies (20%), shields & bad electrostatic contacts (18%), load current
flowing through poor contact (11%), poor dielectric withstand during capacitive
switching operation (10%), spacer defects (10%) and so on. When it comes to the
voltage state of failure, failure occurred at nominal voltage state (61%) and
overvoltage state during switching operation (39%). Lastly the reasons in terms of
failure origin were on-site installation or transportation (35%), poor design (32%),
manufacturing defects (24%) and unknown (9%).
PD propagation in GIS [197-199]
The PD occurrence in GIS can make electromagnetic transient up to 2 GHz, which
can propagate inside of the GIS among its coaxial and symmetrical structure. The
wave propagation at lower frequencies compared to the diameter of the structure has
the characteristic that the electric and magnetic field of the waves are totally
transverse to the direction of wave propagation, known as a Transverse
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Electromagnetic Wave (TEM). This wave cannot pass through the opened contact
switching compartment. At higher power frequencies, however, electric and magnetic
fields of the transmitting waves are not entirely transverse to the direction of wave
propagation, known as Transverse Electric (TE), Transverse Magnetic (TM). The
waves have short wave lengths compared to TEM, and either electric or magnetic
fields can have the components transmitting at the same direction toward wave
propagation direction, which can pass though the opened contact in GIS. The cut-off
frequency between TEM and TE, TM can be defined by the following equation
( )c a b (4.5)
Where c = cut-off wave length, a, b= outer and inner radius of the conductor and
chamber respectively. For example in the case of 420kV, c is about 1.2m so that the
cut-off frequency can be approximately 250MHz. This TEM and TE, TM can affect
the detectable frequency range for on-line PD monitoring on GIS. Appendix A in
[200], detail mathematical frame work of TEM, TE, and TM is covered.
4.4.2 PD types in GIS
In GIS, there are four distinguishable PD patterns according to the faults types such as
fixed protrusions, floating electrodes, free moving particle, and particles fixed on the
spacer or insulation surface. According to the situation, identifying all of the faults
can be difficult [197, 200-202].
Fixed protrusions: This can occur during a poor manufacturing process or possible
contacts of any part inside GIS during operation time. Protrusions in GIS can be
dangerous due to the fact that it can distort the electrical distribution field strength
which might cause breakdown under abnormal conditions such as lightening, and
switching impulses.
Floating electrodes: Floating components is relatively large discharge between a
floating and an adjacent electrode which is decided by the relationship between its
capacitance to the conductor and as to the ground. This type of PD in GIS can be
easily detected by acoustic sensor because it produces acoustic pressure waves which
contain much greater energy than corona discharge.
84
Free moving particle: This is a particle freely moving inside the GIS. Poor
manufacture processes and contact between different electrodes inside GIS can cause
free moving particles. Even though free moving particles have been found to be
harmless compared, for example, to floating electrodes, continuous PD occurrence
from free moving particle can result in SF_6 decomposition, and eventually
breakdown in the GIS [203]. This was pointed out as the main cause of failure in a
GIS [204]
Particles fixed on the spacer or insulation surface: This case indicates that certain
free moving particles can be fixed on the resin spacer or other insulation surface
inside the GIS. Fixed particles can generate corona type PD or breakdown in the gas-
solid interface by accelerating electric field distribution strength at that location [205].
4.4.3 Different diagnosis and monitoring techniques on GIS
According to [206], indicative methods to find possible faults are PD diagnostic
(22.4%), Visual Inspection of switchgear enclosure and surrounding area (18.1%),
and Thermo-graphic Inspection (12%). Therefore PD monitoring and visual
inspection will be reliable methods for continuous monitoring on GIS.
SF6 Gas leakage detection [207, 208]: Since keeping the SF6 pressure in GIS is
critical in order to maintain proper insulation characteristics, gas leakage detection is
of importance. The tightness of insulation gas in the GIS is critical. Thus there is
usually a gas density transmitter compensated temperature that analyzes gas density
providing information regarding gas pressure and internal arc possibility. The SF6 gas
is also a potent green house gas which should not leak in an inappropriate way.
Widely used techniques to monitor gas leakage in GIS use either electric detectors
which are hand held type alarming to detect gas leakage, and simple snoop detection
where suspicious spot of leakage. Recently, the SF6 laser image system has become
available with a video display pointing out the source of the leakage.
Partial discharge [209]: Partial discharge is a proven technique for condition
monitoring and commissioning of GIS. A PD-free test given the applied voltage over
normal operating voltage is a very efficient tool before new GIS installation. PD
detection, moreover, can be applicative for on-line monitoring enhancing safe
85
operation of GIS. The UHF/AE PD detection method has been regarded as the most
promising PD monitoring technique for GIS. Detailed techniques in terms of on-line
monitoring are covered in this chapter.
High frequency current detection [209]: Current pulses caused by discharge in GIS
can be detected using by capacitor or charged conductor and intermediary insulator
equipped with a receiver electrode immersed in resin. This method has been used and
is a possible application for permanent monitoring.
SF6 quality assessment [209]: Impurities of GIS cause a significant impact on
insulation failure. Therefore, appropriate monitoring for SF6 quality has been used
periodically in order to decide dielectric strength. Gas analysis performed by sampling
for gas-chromatography or infer-red spectrograph is a proven method. Air contents
measurement with a portable oxygen detector can give an immediate indication of air
contents in the GIS. Lastly continuous or periodic moisture measurement is efficient
as well. Because this method is relatively expensive and redundant, periodic detection
for the first month of operation is sufficient in order to ensure SF6 filling condition in
the GIS.
4.4.4 On-line PD monitoring on GIS
Conventional method for sensitivity verification [36, 210-211]
Conventional method on GIS PD detection is not appropriate for on-line application.
However synchronous measurement of UHF/AE methods and IEC 60270 enables
desired sensitivity in order to estimate apparent charge which is usually less than 5 pC
for the optimum case. Usually there are two steps for sensitivity verification proposed
by CIGRE. The first step is a laboratory test in order to Figure out the magnitude of
the artificial low voltage pulse related to real PD by using IEC 60270 and UHF
method at the same time which can be used later during an on-site test. The second
step is to inject artificial pulses corresponding to the values established during the
laboratory test. Finally the on-site sensitivity verification can be done by detecting
those signals using by UHF sensors.
86
Unconventional method [196, 210]
Unconventional on-line PD monitoring on GIS uses VHF/UHF detection and the AE
detection method. As mentioned above, since TEM is highly attenuated at higher
frequencies, TEM mode in GIS is strong in VHF (40 to 300 MHz). Even though the
VHF method has many similarities with UHF in the sense that they can provide the
location of PD, the most significant disadvantage of VHF is interference similar to
IEC method. However, VHF method sometimes makes it possible to be calibrated by
injecting a known pulse. On the other hand, the UHF (300 to 3 GHz) method can
detect TE and TM which are generated by a very fast rising time PD current within
ten picoseconds. In addition UHF has a similar sensitivity to the IEC method due to
its good immunity to noise. The rule of thumb regarding distance between UHF
sensors should be within 20m. The most widely used sensor types are in Figure 4.10.
Figure. 4.10 different types of VHF/UHF sensors for GIS application
Acoustic method [196, 210]
By mounting AE sensors externally that usually cover the frequency band between 20
to 100 kHz on GIS, acoustic PD signal can be detected with high sensitivity in the
case of, for instance, moving particles. This method can also be calibrated by IEC
60270 as the same procedure for the UHF PD detection method described above. AE
detection concerns amplitude and flight time in order to identify the defect type and
carry out a risk assessment. Since the intensity of an acoustic signal is lower than an
electrical one, the preferred distance between sensors is several meters. Detailed
characteristic of acoustic emission are covered in [212].
87
PD localization [135]
PD source localization inside the GIS simply uses time-of-flight measurement with
two different sensors. Electric PD signal inside the GIS can propagate two UHF PD
sensors at different time intervals according to the sensor placement.
Figure. 4.11 Time of flight method for PD localization in GIS [135]
In Figure 4.11, the simple scheme of PD localization is shown. If there are two
different UHF sensors and the PD occurs between them, the time domain PD location
can be calculated as below in a situation when the time of flight at two different UHF
sensors is known.
02 11
( )
2 2
x c tx x xx (4.6)
0c =the propagation velocity of the signal (30cm/ns)
which is proportional to insulator permittivity.
4.4.5 Available products on-line PD monitoring of GIS
Doble Lemke
Doble Lemke uses unconventional UHF techniques with an inductive sensor near the
GIS termination connected to the cable or GIS grounding bar. Possible noise can be
eliminated by gating and windowing technique. The PD signal can be analyzed using
PRPD.
Doble TransiNor AIA
88
Doble TransiNor AIA provide acoustic solution for GIS during normal service
operation using piezoelectric type monitoring the acoustic signal from the PD source
such as bouncing particles, protrusions and loose shields. The sensitivity of PD
measurement will be higher than conventional method (IEC 60270).
DMS (Qualitrol company Ltd)
DMS uses internal UHF coupler on the inside of the hatch cover plates. The signals
from up to 3 UHF sensors are collected using optical convertor unit in which
appropriate noise elimination takes place. Filtered signals from optical the convertor
unit are sent to equipment cabinet usually in a control room as an optical data stream.
HVPD
HVPD uses the unconventional way with Transient Earth Voltage (TEV) and external
capacitive coupler sensors attached to the outer casing of the switchgear in the correct
position. In addition, a HFCT clipped around the cable earth strap at the bottom of the
switchgear is also used. The noise can be reduced by using frequency analysis for
switch noise and wave shape analysis for RF and sinusoidal noise. Two or more TEV
sensors with the time-of-flight method enable PD localization. For detecting PD from
corona or the surface of the cable termination, sealing end, and air-insulated
switchgear, Airborne AE probes can be used, which also can be combined with
TEV/HFCT in order to localize PD source.
Power Diagnostix systems
They use internal flange type sensors at a spare flange, shielded ring antenna type
UHF sensors at isolated spacers, and external window sensors similar to internal
flange type sensors. Especially external window sensor size can vary according to the
GIS design. Ethernet cable connects the sensors to an acquisition unit. Power
Diagnostix system uses a particular calibrator injecting very steep voltage output into
the GIS which can cover up to 1.5GHz. By doing so, matrix of the attenuation
between sensors at each bar or bus bar section can be obtained. Power Doagnostix
systems also provides acoustic solution with the AIA compact which detect acoustic
signal by using piezo-electric acoustic sensors. In addition, the sensitivity of acoustic
89
method is mostly comparable to the conventional detection according to IEC 60270.
This method is superior in some defect type detection such as hopping or bouncing
particles.
PSD Tech
PSD Tech uses external or internal open barrier or metal-closed barrier type UHF
sensors with noise sensors. The signal is first collected to a data acquisition unit
which is connected to the main unit for signal analysis using neural network.
PowerPD
PowerPD uses 4 acoustic detection sensors for GIS ductwork, HV GIS circuit
breakers and cubicle type GIS (CGIS). Remote monitoring is possible via a PC or the
internet.
Techimp
Techimp uses VHF/UHF sensors such as a window coupler on the dielectric
inspection window, tem antenna near GIS bushing, spacer coupler at dielectric spacer,
and bushing coupler around a metal ring below the GIS bushing. Most of the sensors
have a sensitivity of 5pC. They use TF analysis and fuzzy logic based pattern
recognition as well as noise rejection.
4.4.6 Summary and Conclusion
Due to the different parts inside of the GIS and its complex structure, PD occurrence
in GIS occurs in a variety of types and possible locations. By the early 1990s, the
UHF method on GIS had developed which was the first unconventional electrical PD
detection application on power system components. PD characteristic in GIS is a bit
different compared to other power system components in the sense that there is an
open contact space inside the GIS. Therefore TEM or TE, TM waves can propagate
through GIS which can be detected using the VHF/UHF detection method.
90
For on-line PD monitoring, the VHF/UHF method has usually been widely used with
internal or external sensors located on the GIS surface. Localization of PD source can
be realized by using time of flight with two or more sensors in different places. From
a practical point of view, many companies use similar internal or external sensors in
different spots. On-line PD monitoring on GIS by companies using the UHF method
is very common world-wide.
4.5 On-line PD monitoring on power system components
As describeed above, on-line PD monitoring on power system components has been
widely used. Slightly different solutions are currently available depending on a variety
of commercial products. In order to apply appropriate on-line PD monitoring
techniques, a proper understanding of insulation characteristics and distinctive
operation mechanisms on each power system apparatus should to be a priority.
Combining with other monitoring techniques compensating for each method‘s
drawbacks can also be taken into account. UHF/AE shows very promising potential
for on-line PD monitoring.
91
CHAPTER 5
5 Conclusion and Future work
PD measurement and analysis can be considered very powerful tool to assess
insulation condition monitoring of high voltage apparatuses in power systems as
highlighted in this thesis. In the same was as PD has been widely used for
commissioning and new equipment installation testing for several decades, on-line
monitoring on power system components by means of PD measurement will enhance
condition based effective high voltage equipment monitoring. The most significant
benefits provided by continuous on-line PD monitoring are;
• Trend of insulation condition in real time
• HV equipment monitoring while the system components are in operation
• The monitoring can be done in the real operating condition
• Location specific information regarding the insulation condition and possible
92
fault types
• PD monitoring can be applied to all kinds of HVE
On-line PD monitoring is still a developing area which needs more research and
experience. That is why PD measurements are still being developed by experts despite
the fact that PD measurements have been performed for several decades. The author is
sure that on-line PD monitoring is the most promising techniques for condition
assessments of high voltage equipment.
Life prediction modelling and life cycle management: Since on-line PD monitoring
can provide trendable data for testing, based on continuous PD monitoring, the power
system operator can simulate possible life prediction and cycle management of power
system equipment. In order to complete this task, the following studies are required:
• Appropriate feature extraction: Since on-line PD monitoring generates huge
amount of data, appropriate feature selection and storage is essential for data
storage and analysis. However at the moment there is no dominant technique
for this matter.
• Built-in PD monitoring system: Power system components from different
manufactures have different structures, insulation materials, and life cycles
even for the same purpose. Therefore PD monitoring for important system
components from initial use is significant in order to make the right decisions
from trend data. The most preferred solution for this is a built-in PD
monitoring system, for example GIS or transformer in the manufacturing
process. Some manufacturers actually have embedded their own PD
monitoring systems including Japan AE, ABB, Mitsubishi Electric, Toshiba
and so on.
Integrating as part of a smart grid [213]: Smart grids are considered as the hottest
issue in modern power systems. By using all possible infrastructure and recent
techniques, optimum, reliable, economical, and efficient operation of power system is
the goal of smart grid. Therefore possible integration of on-line high voltage
equipment monitoring with smart grid infrastructure has high potential to make a leap
forward in the provision of robust and reliable power systems operation.
93
References
THESIS & BOOK
[1] Stone, G.C, ―Partial Discharge Diagnostics and Electrical Equipment Insulation
Condition Assessment,‖ dielectrics and Electrical Insulation, IEEE Transactions on
Volume: 12, Issue: 5. Page(s): 891 – 904, 2005
[2] Han, Y.; Song, Y. H, ―Condition monitoring techniques for electrical equipment: a
literature survey,‖ Power Engineering Review, IEEE Volume: 22, Issue: 9, Page(s):
59, 2002
[3] Montanari, G.C., Cavallini, A, ―Insulation condition assessment of power
equipments in electrical assets based on on-line monitoring of partial discharges,‖
Condition Monitoring and Diagnosis, 2008. CMD 2008. International Conference on,
Page(s): 7 – 12. 2008
[4] Jitka Fuhr, ―Procedure for identification and localization of dangerous PD sources
in power transformers,‖ Dielectrics and Electrical Insulation, IEEE Transactions on
Volume: 12, Issue: 5, Page(s): 1005 – 1014, 2005
[5] Blokhintsev, I.; Patterson, C.L.; Cassidy, B.J.; Loesch, A.H, ―Advantage of On-
Line Partial Discharge continuous monitoring of medium voltage substation,‖
Electrical Insulation Conference, 2009. EIC 2009. IEEE, Page(s): 153 – 158, 2009
[6] Ortega, D.F.; Castelli-Dezza, F, ―On line partial discharges test on rotating
machines supplied by IFDs,‖ Electrical Machines (ICEM), 2010 XIX International
Conference on, Page(s): 1 – 4, 2010
[7] C. Sumereder, M. Muhr, ―Estimation of Residual Lifetime –Theory and Practical
Problems,‖ 8th Höfler´s Days Portoroz, 6. – 8, November 2005
[8] Senn F., Muhr M, Ladstätter W., Grubelnik W., ―Complexity of Determining
factors for the thermal evaluation of high voltage insulation systems on the example
of rotating machines,‖ DISEE 2008 Demänovská Dolina, Slovakia, 2008
[9] WG D1.33 ―Guide for Electrical Partial Discharge Measurements in compliance to
IEC 60270,‖ Technical Brochure, Electra No.241, Page(s): 61-67, DEC 2008
94
[10] IEC 60270 High-voltage test techniques- Partial discharge measurements Edition
3, 2000
[11] E. Kuffel, W. S. Zaengl, J. Kuffel, ―High voltage engineering: fundamentals,‖
Book, Newnes, 2000
[12] Xin Liu, ―Partial Discharge Detection and Analysis in Low Pressure
Environments,‖ Doctoral Thesis, The ohio state university, USA, 2006
[13] Pemen, A.J.M.; van der Laan, P.C.T.; Wout de Leeuw, ―Propagation of partial
discharge signals in stator windings of turbine generators,‖ Energy Conversion, IEEE
Transactions on Volume: 21 , Issue: 1, Page(s): 155 – 161, 2006
[14] Pedersen, A.; Crichton, G.C.; McAllister, I.W., ―The theory and measurement of
partial discharge transients,‖ Electrical Insulation, IEEE Transactions on Volume: 26 ,
Issue: 3, Page(s): 487 – 497, 1991
[15] Fenger, M., ―Test procedure and acceptance criteria for PD commissioning
testing of transmission class cables,‖ Electrical Insulation (ISEI), Conference Record
of the 2010 IEEE International Symposium on, Page(s): 1 – 5, 2010
[16] Cavallini, A.; Montanari, G.C.; Tozzi, M., ―PD apparent charge estimation and
calibration: A critical review,‖ Dielectrics and Electrical Insulation, IEEE
Transactions on Volume: 17 , Issue: 1, Page(s): 198 – 205, 2010
[17] Hampton, B.F.; Meats, R.J., ―Diagnostic measurements at UHF in gas insulated
substations,‖ Generation, Transmission and Distribution, IEE Proceedings, Page(s):
137 – 145, 1988
[18] M. Muhr; R. Schwarz, ―Partial discharge measurement as a Diagnostic Tool for
HV-Equipments,‖ Properties and applications of Dielectric Materials, 2006. 8th
International Conference on, Page(s): 195 – 198, 2006
[19] Muhr, M.; Schwarz, R.; Pack, S.; Koerbler, B., ―Unconventional partial
discharge measurement [electrical insulation evaluation],‖ Electrical Insulation and
Dielectric Phenomena, 2004. CEIDP '04. 2004 Annual Report Conference on,
Page(s): 430 – 433, 2004
[20] Kemp, I.J., ―Developments in partial discharge plant-monitoring technology,‖
Partial Discharge, 1993., International Conference on, Page(s): 52 – 55, 1993
95
[21] CIGRE Working group D1.33 ―Guidelines for Unconventional Partial Discharge
Measurements,‖ Cigre Technical Brochure, DEC, 2010
[22] Lundgaard, L.E, ―Partial discharge. XIV. Acoustic partial discharge detection-
practical application,‖
Electrical Insulation Magazine, IEEE, Volume: 8, Issue: 5, Page(s): 34 – 43, 1992
[23] Lundgaard, L.E., ―Partial discharge. XIII. Acoustic partial discharge detection-
fundamental considerations,‖ Electrical Insulation Magazine, IEEE, Volume: 8, Issue:
4, Page(s): 25 – 31, 1992
[24] Kozako, M.; Yamada, K.; Morita, A.; Ohtsuka, S.; Hikita, M.; Kashine, K.;
Nakamura, I.; Koide, H., ―Fundamental study on partial discharge induced acoustic
wave propagation in simulated transformer composite insulation system,‖ Properties
and Applications of Dielectric Materials, 2009. ICPADM 2009. IEEE 9th
International Conference on the, Page(s): 477 – 480, 2009
[25] Schwarz, R.; Muhr, M. ―Modern technologies in optical partial discharge
detection,‖ Electrical Insulation and Dielectric Phenomena, 2007. CEIDP 2007.
Annual Report - Conference on, Page(s): 163 – 166, 2007
[26] R. Schwarz, M. Muhr, S. Pack, ―Evaluation of partial discharge impulses with
optical and conventional detection systems,‖ Proceedings of the XIVth International
Symposium on High Voltage Engineering, Tsinghua University, Beijing, China,
August 25-29, 2005
[27] Oliveira, S.C.; Fontana, E., ―Optical Detection of Partial Discharges on Insulator
Strings of High-Voltage Transmission Lines,‖ Instrumentation and Measurement,
IEEE Transactions on Volume: 58, Issue: 7, Page(s): 2328 – 2334, 2009
[28] Tominaga, S.; Kuwahara, H.; Hirooka, K.; Yoshioka, T., ―SF6 Gas Analysis
Technique and its Application for Evaluation of Internal Conditions in SF6 Gas
Equipment,‖ Power Apparatus and Systems, IEEE Transactions on Volume: PAS-
100, Issue: 9, Page(s): 4196 – 4206, 1981
[29] Muhamad, N.A.; Phung, B.T.; Blackburn, T.R., ―Dissolved gas analysis (DGA)
of partial discharge fault in bio-degradable transformer insulation oil,‖ Power
Engineering Conference, 2007. AUPEC 2007. Australasian Universities, Page(s): 1 –
6, 2007
96
[30] Kaufhold, M.; Bamji, S.S.; Bulinski, A.T., ―Optical detection of partial
discharges in gas-insulated systems,‖ Electrical Insulation and Dielectric Phenomena,
1996. IEEE 1996 Annual Report of the Conference on Volume: 2, Page(s): 618 - 623
vol.2, 1996
[31] Coenen, S.; Tenbohlen, S.; Markalous, S.M.; Strehl, T., ―Sensitivity of UHF PD
measurements in power transformers,‖ Dielectrics and Electrical Insulation, IEEE
Transactions on, Volume: 15 , Issue: 6 , Page(s): 1553 – 1558, 2008
[32] P.D. Agoris; S. Meijer; E. Gulski; J.J. Smit; T.J.W. Hermans; L. Lamballais,
―Sensitivity check for on-line VHF/UHF PD detection on Transmission cables,‖
Properties and applications of Dielectric Materials, 2006. 8th International
Conference on, Page(s): 204 – 207, 2006
[33] Meijer, S.; Judd, M.D.; Tenbohlen, S., ―Sensitivity check for radio frequency
partial discharge detection for power transformers,‖ Condition Monitoring and
Diagnosis, 2008. CMD 2008. International Conference on, Page(s): 1031 – 1035,
2008
[34] Agoris, P.; Meijer, S.; Smit, J.J., ―Sensitivity Check of an Internal VHF/UHF
Sensor for Transformer Partial Discharge Measurements,‖ Power Tech, 2007 IEEE
Lausanne, Page(s): 2065 – 2069, 2007
[35] Hoshino, T.; Maruyama, S.; Nojima, K.; Hanai, M., ―A unique sensitivity
verification combined with real-time partial-discharge identification method,‖ Power
Delivery, IEEE Transactions on Volume: 20, Issue: 3, Page(s): 1890 – 1896, 2005
[36] Knapp, M.; Feger, R.; Feser, K.; Breuer, A., ―Application of the CIGRE-
sensitivity verification for UHF PD detection in three-phase GIS,‖ High Voltage
Engineering, 1999. Eleventh International Symposium on (Conf. Publ. No. 467)
Volume: 5, Page(s): 82 - 85 vol.5, 1999
[37] CIGRE Working Group D1.02 ―Sensors and Sensing Used For Non-conventional
PD Detection,‖ CIGRE 2006
[38] Petchphung, P.; Leelajindakrairerk, M.; Pattanadech, N.; Yutthagowith, P.;
Aunchaleevarapan, K., ―The comparison study of PD measurement with conventional
method and unconventional method,‖ Condition Monitoring and Diagnosis, 2008.
CMD 2008. International Conference on, Page(s): 32 – 36, 2008
97
[39] Reid, A.; Judd, M.; Stewart, B.; Fouracre, R., ―Comparing IEC60270 and RF
partial discharge patterns,‖ Condition Monitoring and Diagnosis, 2008. CMD 2008.
International Conference on, Page(s): 89 – 92, 2008
[40] Reid, A.J.; Judd, M.D.; Fouracre, R.A.; Stewart, B.G.; Hepburn, D.M.,
―Simultaneous measurement of partial discharges using IEC60270 and radio-
frequency techniques,‖ Dielectrics and Electrical Insulation, IEEE Transactions on
Volume: 18 , Issue: 2, Page(s): 444 – 455, 2011
[41] Stewart, B.G.; Judd, M.D.; Reid, A.J.; Fouracre, R.A., ―Suggestions to augment
the IEC60270 partial discharge standard in relation to radiated electromagnetic
energy,‖ Electrical Insulation Conference and Electrical Manufacturing Expo, 2007,
Page(s): 175 – 178, 2007
ger, M.; Winter, P., ―Advanced
possibilities of synchronous conventional and UHF PD measurements for effective
noise suppression,‖ Electrical Insulation (ISEI), Conference Record of the 2010 IEEE
International Symposium on, Page(s): 1 – 4, 2010
[43] Tenbohlen, S.; Pfeffer, A.; Coenen, S, ―On-site experiences with multi-terminal
IEC PD measurements, UHF PD measurements and acoustic PD localization,‖
Electrical Insulation (ISEI), Conference Record of the 2010 IEEE International
Symposium on , Page(s): 1 – 5, 2010
[44] Xiaodong Wang; Baoqing Li; H.T. Roman; O.L. Russo; K. Chin; K.R. Farmer,
―Acousto-optical PD detection for transformers,‖ Power Delivery, IEEE Transactions
on Volume: 21, Issue: 3, Page(s): 1068 – 1073, 2006
[45] Morshuis, P.H.F, ―Partial discharge mechanism leading to breakdown, analyzed
by fast electrical and optical measurements,‖ Master thesis, Delft University of
Technology, 1993
[46] Jouseau, E.; Vivien, G.; Odic, E.; Maroni, C.-S, ―Exhibition of an ageing
criterion based on partial detection for Medium Voltage equipment,‖ Diagnostics for
Electric Machines, Power Electronics and Drives, 2005. SDEMPED 2005. 5th IEEE
International Symposium on, Page(s): 1 – 5, 2005
[47] Alison K. Lazarevich, ―Partial Discharge Detection and Localization in High
Voltage Transformers Using an Optical Acoustic Sensor‖ Master thesis, Virginia
Polytechnic Institute and State University, May 2003
98
[48] S.M. Markalous and K. Feser, ―All- Acoustic PD Measurements Of Oil/Paper-
insulated Transformers For PD-Location‖, Proc. of the Second International
Conference on Advances in Processing, Testing and Application of Dielectric
Materials (APTADM),September 2004.
[49] Imad-U-Khan; Zhongdong Wang; Cotton, I.; Northcote, S, ―Dissolved gas
analysis of alternative fluids for power transformers,‖ Electrical Insulation Magazine,
IEEE Volume: 23, Issue: 5, Page(s): 5 – 14, 2007
[50] Boczar, T.; Zmarzly, D., ―Optical spectra of surface discharges in oil,‖
Dielectrics and Electrical Insulation, IEEE Transactions on Volume: 13 , Issue: 3,
Page(s): 632 – 639, 2006
[51] Schwarz, R.; Muhr, M.; Pack, S., ―Partial discharge detection in oil with optical
methods,‖ Dielectric Liquids, 2005. ICDL 2005. 2005 IEEE International Conference
on, Page(s): 245 – 248, 2005
[52] Baker, P.C.; Judd, M.D.; Mcarthur, S.D.J, ―A frequency-based RF partial
discharge detector for low-power wireless sensing,‖ Dielectrics and Electrical
Insulation, IEEE Transactions on Volume: 17 , Issue: 1, Page(s): 133 – 140, 2010
[53] V.R Garcia-Colon, J. A. Estrada García, E. Betancourt Ramirez ―Development of
ultra wide band partial discharge sensors for power transformer winding insulation‖,
Contribution D1-211, Cigré. Session 2008
[54] Kaneko, S.; Okabe, S.; Yoshimura, M.; Muto, H.; Nishida, C.; Kamei, M.,
―Detecting characteristics of various type antennas on partial discharge
electromagnetic wave radiating through insulating spacer in gas insulated
switchgear,‖ Dielectrics and Electrical Insulation, IEEE Transactions on Volume: 16 ,
Issue: 5, Page(s): 1462 – 1472, 2009
[55] Mallikarjunappa, K.; Ratra, M.C., ―Detection of partial discharges in power
capacitors using high frequency current transformers,‖ Electrical Insulation and
Dielectric Phenomena, 1990. Annual Report., Conference on, Page(s): 379 – 384,
1990
[56] Ray, W.F.; Hewson, C.R., ―High performance Rogowski current transducers,‖
Industry Applications Conference, 2000. Conference Record of the 2000 IEEE
Volume: 5, Page(s): 3083 - 3090 vol.5, 2000
99
[57] Argueso, Marta; Robles, Guillermo; Sanz, Javier, ―Implementation of a
Rogowski coil for the measurement of partial discharges,‖ Review of Scientific
Instruments Volume: 76, Issue: 6, Page(s): 065107 - 065107-7, 2005
[58] Veloso, G.F.C.; da Silva, L.E.B.; Noronha, I.; Torres, G.L.; Haddad, J.; Pereira,
R.R.; Se Un Ahn, ―Detection of partial discharge in power transformers using
Rogowski coil and multi resolution analysis,‖ Power Electronics Conference, 2009.
COBEP '09. Brazilian, Page(s): 1006 – 1010, 2009
[59] Zhu, H.; Green, V.; Sasic, M.; Halliburton, S., ―Increased sensitivity of
capacitive couplers for in-service PD measurement in rotating machines,‖ Energy
Conversion, IEEE Transactions on Volume: 14 , Issue: 4, Page(s): 1184 – 1192, 1999
[60] Muhr, M.; Woschitz, R., ―Partial discharge diagnostic,‖ Properties and
Applications of Dielectric Materials, 2000. Proceedings of the 6th International
Conference on Volume: 1, Page(s): 223 - 226 vol.1, 2000
[61] Goodeve, T.E.; Stone, G.C.; Macomber, L., ―Experience with compact epoxy-
mica capacitors for rotating machine partial discharge detection,‖ Electrical
Electronics Insulation Conference, 1995, and Electrical Manufacturing & Coil
Winding Conference. Proceedings, Page(s): 685 – 689, 1995
[62] Lopez-Roldan, J.; Tang, T.; Gaskin, M., ―Optimisation of a sensor for onsite
detection of partial discharges in power transformers by the UHF method,‖ Dielectrics
and Electrical Insulation, IEEE Transactions on Volume: 15, Issue: 6, Page(s): 1634 –
1639, 2008
[63] Judd, M.D.; Li Yang; Hunter, I.B.B., ―Partial discharge monitoring of power
transformers using UHF sensors. Part I: sensors and signal interpretation,‖ Electrical
Insulation Magazine, IEEE Volume: 21, Issue: 2, Page(s): 5 – 14, 2005
[64] Pommerenke, D.; Strehl, T.; Heinrich, R.; Kalkner, W.; Schmidt, F.;
Weissenberg, W., ―Discrimination between internal PD and other pulses using
directional coupling sensors on HV cable systems,‖ Dielectrics and Electrical
Insulation, IEEE Transactions on Volume: 6 , Issue: 6, Page(s): 814 – 824, 1999
[65] Craatz, P.; Plath, R.; Heinrich, R.; Kalkner, W., ―Sensitive on-site PD
measurement and location using directional coupler sensors in 110 kV prefabricated
joints,‖ High Voltage Engineering, 1999. Eleventh International Symposium on
(Conf. Publ. No. 467) Volume: 5, Page(s): 317 - 321 vol.5, 1999
100
[66] Lima, S.E.U.; Frazao, O.; Farias, R.G.; Araujo, F.M.; Ferreira, L.A.; Santos, J.L.;
Miranda, V., ―Mandrel-Based Fiber-Optic Sensors for Acoustic Detection of Partial
Discharges—a Proof of Concept,‖ Power Delivery, IEEE Transactions on Volume: 25
, Issue: 4, Page(s): 2526 – 2534, 2010
[67] Zargari, A.; Blackburn, T.R., ―Application of optical fibre sensor for partial
discharge detection in high-voltage power equipment,‖ Electrical Insulation and
Dielectric Phenomena, 1996. IEEE 1996 Annual Report of the Conference on
Volume: 2, Page(s): 541 - 544 vol.2, 1996
[68] Varl, A., ―On-line diagnostics of oil-filled transformers,‖ Dielectric Liquids,
2002. ICDL 2002. Proceedings of 2002 IEEE 14th International Conference on,
Page(s): 253 – 257, 2002
[69] Xuezeng Zhao; Yangliu Li, ―An On-Line Monitoring System for Gases
Dissolved in Transformer Oil Using Wireless Data Acquisition,‖ Power and Energy
Engineering Conference, 2009. APPEEC 2009. Asia-Pacific, Page(s): 1 – 4, 2009
[70] Xiao-xing Zhang; Wang-ting Liu; Ju Tang; Peng Xiao, ―Study on PD detection
in SF6 using multi-wall carbon nanotube films sensor,‖ Dielectrics and Electrical
Insulation, IEEE Transactions on Volume: 17 , Issue: 3, Page(s): 833 – 838, 2010
[71] Ding, W.; Hayashi, R.; Suehiro, J.; Imasaka, K.; Hara, M., ―Observation of
dynamic behavior of PD-generated SF6 decompositions using carbon nanotube gas
sensor,‖ Electrical Insulation and Dielectric Phenomena, 2005. CEIDP '05. 2005
Annual Report Conference on, Page(s): 561 – 564, 2005
[72] IEEE Std C57.127-2000, ―IEEE Trial-Use Guide for the Detection of Acoustic
Emissions From Partial Discharges in Oil-Immersed Power Transformers,‖, 2000
[73] Sahoo, N.C.; Salama, M.M.A.; Bartnikas, R., ―Trends in partial discharge pattern
classification: a survey,‖ Dielectrics and Electrical Insulation, IEEE Transactions on
Volume: 12, Issue: 2, Page(s): 248 – 264, 2005
[74] Barkat, B.; Al-Marzouqi, H., ―A correlation based approach to interference
suppression from phase resolved partial discharge patterns,‖ Solid Dielectrics (ICSD),
2010 10th IEEE International Conference on, Page(s): 1 – 4, 2010
[75] Hudon, C.; Belec, M., ―Partial discharge signal interpretation for generator
diagnostics,‖ Dielectrics and Electrical Insulation, IEEE Transactions on Volume: 12 ,
Issue: 2, Page(s): 297 – 319, 2005
101
[76] Stone, G.C., ―Relevance of phase resolved PD analysis to insulation diagnosis in
industrial equipment,‖ Solid Dielectrics (ICSD), 2010 10th IEEE International
Conference on, Page(s): 1 – 5, 2010
[77] Strachan, S.M.; Rudd, S.; McArthur, S.D.J.; Judd, M.D.; Meijer, S.; Gulski, E.,
―Knowledge-based diagnosis of partial discharges in power transformers,‖ Dielectrics
and Electrical Insulation, IEEE Transactions on Volume: 15 , Issue: 1, Page(s): 259 –
268, 2008
[78] Hu, X.; Judd, M.D.; Siew, W.H., ―A study of PD location issues in GIS using
FDTD simulation,‖ Universities Power Engineering Conference (UPEC), 2010 45th
International, Page(s): 1 – 5, 2010
[79] Hoogenraad, G.; Beyer, J., ―Digital HVDC partial discharge testing,‖ Electrical
Insulation, 2000. Conference Record of the 2000 IEEE International Symposium on,
Page(s): 448 – 451, 2000
[80] Cavallini, A.; Montanari, G.C.; Puletti, F.; Contin, A., ―A new methodology for
the identification of PD in electrical apparatus: properties and applications,‖
Dielectrics and Electrical Insulation, IEEE Transactions on Volume: 12, Issue: 2,
Page(s): 203 – 215, 2005
[81] Belkov, A.; Koltunowicz, W.; Obralic, A.; Plath, R., ―Advanced approach for
automatic PRPD pattern recognition in monitoring of HV assets,‖ Electrical
Insulation (ISEI), Conference Record of the 2010 IEEE International Symposium on,
Page(s): 1 – 5, 2010
[82] Rethmeier, K.; Kruger, M.; Kraetge, A.; Plath, R.; Koltunowicz, W.; Obralic, A.;
Kalkner, W., ―Experiences in on-site partial discharge measurements and prospects
for PD monitoring,‖ Condition Monitoring and Diagnosis, 2008. CMD 2008.
International Conference on, Page(s): 1279 – 1283, 2008
[83] Koltunowicz, W.; Plath, R., ―Synchronous multi-channel PD measurements,‖
Dielectrics and Electrical Insulation, IEEE Transactions on Volume: 15, Issue: 6,
Page(s): 1715 – 1723, 2008
[84] Krivda, A., ―Automated recognition of partial discharges,‖ Dielectrics and
Electrical Insulation, IEEE Transactions on Volume: 2 , Issue: 5, Page(s): 796 – 821,
1995
[85] Hao Zhang; T.R. Blackburn; B.T. Phung; D. Sen, ―A novel wavelet transform
technique for on-line partial discharge measurements Part 1: WT de-noising
102
algorithm,‖ Dielectrics and Electrical Insulation, IEEE Transactions on Volume: 14 ,
Issue: 1, Page(s): 3 – 14, 2007
[86] Nagesh, V.; Gururaj, B.I., ―Evaluation of digital filters for rejecting discrete
spectral interference in on-site PD measurements,‖ Electrical Insulation, IEEE
Transactions on Volume: 28 , Issue: 1, Page(s): 73 – 85, 1993
[87] Q. Su, ―Partial Discharge Measurements on Generators Using a Noise Gating
System‖, Australasian Universities Power Engineering Conference, September, 26-29
1999, Darwin, Australia, 1999
[88] Stone, G.C.; Sedding, H.G., ―In-service evaluation of motor and generator stator
windings using partial discharge tests,‖ Industry Applications, IEEE Transactions on
Volume: 31 , Issue: 2, Page(s): 299 – 303, 1995
[89] Veen, J.; van der Wiellen, P.C.J.M., ―The application of matched filters to PD
detection and localization,‖ Electrical Insulation Magazine, IEEE Volume: 19 , Issue:
5, Page(s): 20 – 26, 2003
[90] Sher Zaman Khan; Zhu Deheng; Jin Xianhe; Tan Kexiong, ―A new adaptive
technique for on-line partial discharge monitoring,‖ Dielectrics and Electrical
Insulation, IEEE Transactions on Volume: 2 , Issue: 4, Page(s): 700 – 707, 1995
[91] Wagenaars, P.; Wouters, P.A.A.F.; van der Wielen, P.C.J.M.; Steennis, E.F.,
―Adaptive matched filter bank for noise reduction in online partial discharge
monitoring,‖ Electrical Insulation and Dielectric Phenomena, 2009. CEIDP '09. IEEE
Conference on, Page(s): 356 – 359, 2009
[92] Kopf, U.; Feser, K., ―Rejection of narrow-band noise and repetitive pulses in on-
site PD measurements,‖ Dielectrics and Electrical Insulation, IEEE Transactions on
Volume: 2 , Issue: 6, Page(s): 1180 – 1191, 1995
[93] Shuzhen Xu; Middleton, R.; Xiaodong liu; Chenchen, ―A new method based on
wavelet packet transformation to reduce narrow band noise in on-site PD
measurement,‖ Power Engineering Society Summer Meeting, 2002 IEEE Volume: 2,
Page(s): 898 - 903 vol.2, 2002
[94] Mortazavi, S.H.; Shahrtash, S.M., ―Comparing denoising performance of
DWT,WPT, SWT and DT-CWT for Partial Discharge signals,‖ Universities Power
Engineering Conference, 2008. UPEC 2008. 43rd International, Page(s): 1 – 6, 2008
103
[95] Ma, X.; Zhou, C.; Kemp, I.J., ―Interpretation of wavelet analysis and its
application in partial discharge detection,‖ Dielectrics and Electrical Insulation, IEEE
Transactions on Volume: 9, Issue: 3, Page(s): 446 – 457, 2002
[96] Gaouda, A.M.; El-Hag, A.; Abdel-Galil, T.K.; Salama, M.M.A.; Bartnikas, R.,
―On-line detection and measurement of partial discharge signals in a noisy
environment,‖ Dielectrics and Electrical Insulation, IEEE Transactions on Volume:
15, Issue: 4, Page(s): 1162 – 1173, 2008
[97] Andrej Krivda, ―Recognition of discharges Discrimination and classification,‖
Doctoral Thesis, Delft University of Technology, 1995
[98] James, R.E.; Phung, B.T., ―Development of computer-based measurements and
their application to PD pattern analysis,‖ Dielectrics and Electrical Insulation, IEEE
Transactions on Volume: 2, Issue: 5, Page(s): 838 – 856, 1995
[99] Mazroua, A.A.; Bartnikas, R.; Salama, M.M.A., ―Discrimination between PD
pulse shapes using different neural network paradigms,‖ Dielectrics and Electrical
Insulation, IEEE Transactions on Volume: 1 , Issue: 6, Page(s): 1119 – 1131, 1994
[100] Ab Aziz, N.F.; Hao, L.; Lewin, P.L., ―Analysis of Partial Discharge
Measurement Data Using a Support Vector Machine,‖ Research and Development,
2007. SCOReD 2007. 5th Student Conference on, Page(s): 1 – 6, 2007
[101] Ethem ALPAYDIN, ―Introduction to Machine Learning,‖ The MIT Press,
October 2004, ISBN 0-262-01211-1, 2004
[102] Kranz, H.-G., ―Diagnosis of partial discharge signals using neural networks and
minimum distance classification,‖ Electrical Insulation, IEEE Transactions on
Volume: 28 , Issue: 6, Page(s): 1016 – 1024, 1993
[103] Cover, T.; Hart, P., ―Nearest neighbor pattern classification,‖ Information
Theory, IEEE Transactions on Volume: 13, Issue: 1, Page(s): 21 – 27, 1967
[104] Poyhonen, S.; Conti, M.; Cavallini, A.; Montanari, G.C.; Filippetti, F.,
―Insulation defect localization through partial discharge measurements and numerical
classification,‖ Industrial Electronics, 2004 IEEE International Symposium on
Volume: 1, Page(s): 417 - 422 vol. 1, 2004
[105] Balasundaram Karthikeyan, Srinivasan Gopal, Srinivasan Venkatesh,
Subramanian Saravanan, ―Pnn and Its Adeptive Version – an ingenious Approach To
104
PD Pattern Classification Compared with BPA Network,‖, Journal of ELECTRICAL
ENGINEERING, VOL. 57, NO. 3, 2006, 138–145, 2006
[106] Hamilton-Wright, A. and Stashuk, D.W., ―Comparing "pattern discovery" and
back-propagation classifiers,‖ Neural Networks, 2005. IJCNN '05. Proceedings. 2005
IEEE International Joint Conference on Volume: 2, Page(s): 1286 - 1291 vol. 2, 2005
[107] Salama, M.M.A.; Bartnikas, R., ―Determination of neural-network topology for
partial discharge pulse pattern recognition,‖ Neural Networks, IEEE Transactions on
Volume: 13 , Issue: 2, Page(s): 446 – 456, 2002
[108] Po-Hung Chen; Hung-Cheng Chen; An Liu; Li-Ming Chen, ―Pattern
recognition for partial discharge diagnosis of power transformer,‖ Machine Learning
and Cybernetics (ICMLC), 2010 International Conference on Volume: 6, Page(s):
2996 – 3001, 2010
[109] Lai, K.X.; Phung, B.T.; Blackburn, T.R., ―Application of data mining on partial
discharge part I: predictive modelling classification,‖ Dielectrics and Electrical
Insulation, IEEE Transactions on Volume: 17, Issue: 3, Page(s): 846 – 854, 2010
[110] Hao, L.; Lewin, P.L., ―Partial discharge source discrimination using a support
vector machine,‖ Dielectrics and Electrical Insulation, IEEE Transactions on Volume:
17 , Issue: 1, Page(s): 189 – 197, 2010
[111] Hoof, M.; Patsch, R., ―Analyzing partial discharge pulse sequences-a new
approach to investigate degradation phenomena,‖ Electrical Insulation, 1994.,
Conference Record of the 1994 IEEE International Symposium on Digital Object
Identifier: 10.1109/ELINSL.1994.401501, Page(s): 327 – 331, 1994
[112] Hoof, M.; Patsch, R., ―Pulse-sequence analysis: a new method for investigating
the physics of PD-induced ageing,‖ Science, Measurement and Technology, IEE
Proceedings - Volume: 142 , Issue: 1, Page(s): 95 – 101, 1995
[113] H. R. Mirzaei, A. Akbari and M. Allahbakhshi, ―Partial Discharge Identification
Using Pulse Sequence Analysis and Neural Network,‖ Proceedings of the 16th
International Symposium on High Voltage Engineering SAIEE, Innes House,
Johannesburg, 2009
[114] Wang, M., Vandermaar, A.J., Srivastava, K.D., ―Review of condition
assessment of power transformers in service,‖ Electrical Insulation Magazine, IEEE,
Volume: 18 Issue:6, page(s): 12 – 25, Jan 2003
105
[115] Bengtsson, C, ―Status and trends in transformer monitoring,‖ Power Delivery,
IEEE Transactions on
Volume: 11 , Issue: 3, Page(s): 1379 – 1384, 1996
[116] Stewart R. Hardie, B.E. (Hons), ―A Prototype Transformer Partial Discharge
Detection System,‖, Ph.D Thesis, University of Canterbury, Christchurch, New
Zealand, January 2006
[117] Jongen R et al,, ―A Statistical Approach To Processing Power Transformer
Failure Data,‖ 19th. CIRED (International Conference on Electricity Distribution)
Vienna, Paper 0546, 21-24 May, 2007
[118] A. Bossi, J.E. Dind, J.M. Frisson, U. Khoudiakov, H.F. Light, et. al., ―An
International Survey of Failures in Large Power Transformers in Service‖, Final
Report of the CIGRE Working Group 12.05, Electra, no. 88, pp. 21-48, 1983
[119] Mackenzie, E.A.; Crossey, J.; de Pablo, A.; Ferguson, W, ―On-line monitoring
and diagnostics for power transformers,‖ Electrical Insulation (ISEI), Conference
Record of the 2010 IEEE International Symposium on, Page(s): 1 – 5, 2010
[120] Sebastian Coenen, Stefan Tenbohlen, Falk-Rüdiger Werner, Sacha Markalous,
―Localization of PD Sources inside Transformers by Acoustic Sensor Array and UHF
Measurements,‖ Conference Proceedings of CMD2010, 2010
[121] Markalous, S.; Tenbohlen, S.; Feser, K., ―Detection and location of partial
discharges in power transformers using acoustic and electromagnetic signals,‖
Dielectrics and Electrical Insulation, IEEE Transactions on, Volume: 15, Issue: 6,
Page(s): 1576 – 1583, 2009
[122] Jongen, R.A.; Morshuis, P.; Meijer, S.; Smit, J.J., ―Identification of partial
discharge defects in transformer oil,‖ Electrical Insulation and Dielectric Phenomena,
2005. CEIDP '05. 2005 Annual Report Conference on, Page(s): 565 – 568, 2005
[123] Agoris, Pantelis D.; Meijer, Sander; Gulski, Edward; Smit, Johan J.; Kanters, A.
Jos L. M, ―On-line partial discharge detection on transformers,‖ Electricity
Distribution, 2005. CIRED 2005. 18th International Conference and Exhibition on,
Page(s): 1 – 5, 2005
[124] V.V. Smekalov et al., ―Condition assessment and life time extension of power
transformer,‖ CIGRE Session 2002, pp. 12–102. 2002
106
[125] S. Tenbohlen, D. Uhde, J. Poittevin, H. Borsi, P. Werle, U. Sundermann, H.
Matthes, ―Enhanced Diagnosis of Power Transformers using On- and Off-line
Methods: Results, Examples and Future Trends,‖ CIGRE Session, paper 12-204. 2000
[126] Yishan Liang; Zhenyuan Wang; Yilu Liu, ―Power transformer DGA data
processing and alarming tool for on-line monitors,‖ Power Systems Conference and
Exposition, 2009. PSCE '09. IEEE/PES, Page(s): 1 – 8, 2009
[127] Duval, M., ―Dissolved gas analysis: It can save your transformer,‖ Electrical
Insulation Magazine, IEEE, Volume:5, Issue:6, page(s): 22, Nov.-Dec. 1989
[128] Emsley, A.M.; Xiao, X.; Heywood, R.J.; Ali, M, ―Degradation of cellulosic
insulation in power transformers. Part 2: formation of furan products in insulating
oil,‖ Science, Measurement and Technology, IEE Proceedings Volume: 147, Issue: 3,
Page(s): 110 – 114, 2000
[129] Aminian, M.; Kadir, M.; Ahmad, W.; Mahmood, S., ―Investigation of paper
aging assessment by furan concentration monitoring‖ Research and Development
(SCOReD), 2009 IEEE Student Conference on, Page(s): 360 – 362, 2009
[130] Emsley, A.M., Stevens, G.C., ―A Reassessment of the Low Temperature
Thermal Degradation of Cellulose,‖ IEEE 6th International Conference on, pp: 229-
232, 1992
[131] Rojas, A., ―Power Factor Testing in Transformer Condition Assessment -Is
There a Better Way?,‖ Transmission & Distribution Conference and Exposition: Latin
America, 2006. TDC '06. IEEE/PES, Page(s): 1 – 4, 2006
[132] CIGRE Working Group D1. ―Knowledge Rules for Partial Discharge Diagnosis
In Service‖, TECHNICAL BROCHURE NO. 226, Electra , Pages: 62-22, 2003
[133] A. Pfeffer, S. Coenen, S. Tenbohlen, S. M. Markalous, T. Strehl. ―Onsite
experiences with multi-terminal IEC PD measurements and UHF PD measurements‖,
Proceedings 17th ISH, No: C-51, Cape Town, South Africa, 2009
[134] Reid, A.; Judd, M.; Johnstone, C, ―Development of UHF transformer probe
sensors for on-line partial discharge measurement,‖ Universities Power Engineering
Conference (UPEC), 2009 Proceedings of the 44th International, Page(s): 1 – 4, 2009
[135] Tenbohlen, S.; Denissov, D.; Hoek, S.; Markalous, S.M., ―Partial discharge
measurement in the ultra high frequency (UHF) range,‖ Dielectrics and Electrical
Insulation, IEEE Transactions on Volume: 15 , Issue: 6, Page(s): 1544 – 1552, 2008
107
[136] S. Coenen, S. Tenbohlen, S. M. Markalous, T. Strehl: ―Fundamental
characteristics of UHF PD probes and the radiation behaviour of PD sources in power
transformer‖, Proceedings 17th ISH, Cape Town, South Africa, 2009
[137] M. D. Judd, L. Yang, C. J. Bennoch, and I. B. B. Hunter, ―UHF diagnostic
monitoring techniques for power transformers,‖ in EPRI Substation Equipment
Diagnostics Conf. XII, New Orleans, LA, February15–18, 2004
[138] S. Tenbohlen, S.M. Hoek, D. Denissov, R. Huber, U. Riechert, S.M. Markalous,
T. Strehl, T. Klein ―Electromagnetic (UHF) PD Diagnosis of GIS, Cable Accessories
and Oil-paper Insulated Power Transformers for Improved PD Detection and
Localization,‖ CIGRE 2006
[139] S.Coenen,M. Reuter, S. Tenbohlen, S. Markalous ―Influence of PD Location in
Transformer windings on IEC60270- and UHF-Measurements,‖ Conference
Proceedings of CMD2010, Tokio, 2010
[140] Densley, J., ―Ageing mechanisms and diagnostics for power cables - an
overview,‖ Electrical Insulation Magazine, IEEE Volume: 17, Issue: 1, Page(s): 14 –
22, 2001
[141] Hussein Anis, Roshdy Radwan, Mazen Abdel-Salam, Ahdab El Morshedy,
―High-Voltage Engineering: Theory and Practice, Second Edition, Revised and
Expanded,‖, Book, Marcel Dekker, 2000
[142] Luton M-H., Anders G.J., Braun J.M., Fujimoto N., Rizzetto S., Downes J.A.,
―Real Time Monitoring of Power Cables by Fibre Optic Technologies Tests,
Applications and Outlook,‖ JICABLE ' 03 - International Conference on Insulated
Power Cables, paper A.1.6, Paris 2003
[143] Jarot Setyawan, ―Investigation of Partial Discharge Occurrence and Detect
ability in High Voltage Power Cable Accessories,‖ Master thesis, Delft University of
technology, Nov 2009
[144] F.J. Wester, ―Condition Assessment of Power Cables Using PD Diagnosis at
Damped AC Voltages,‖ ISBN 90-8559-019-1, Ph.D. thesis TU Delft, 2004.
[145] Montanari, G.C.; Cavallini, A.; Puletti, F., ―A new approach to partial discharge
testing of HV cable systems,‖ Electrical Insulation Magazine, IEEE Volume: 22,
Issue: 1, Page(s): 14 – 23, 2006
108
[146] Boggs, S.; Densley, J., ―Fundamentals of partial discharge in the context of field
cable testing,‖
Electrical Insulation Magazine IEEE Volume: 16, Issue: 5, Page(s): 13 – 18, 2000
[147] Smit, J.J.; Gulski, E., ―Advanced condition assessment of high voltage power
cables,‖ Electrical Insulating Materials, 2005. (ISEIM 2005). Proceedings of 2005
International Symposium on, Volume: 3, Page(s): 869 - 872 Vol. 3, 2005
[148] Kheng Jern KHOR, ―Partial Discharge Propagation and Sensing in Overhead
Power Distribution Lines,‖ Master Thesis, School of Electrical and Computer
Engineering College of Science, Engineering & Health RMIT University, March 2010
[149] Oliveira, S.C.; Fontana, E.; Cavalcanti, F.J.M., ―Real time monitoring of the
leakage current of 230 kV insulator strings under washing‖ Transmission and
Distribution Conference and Exposition, 2008. T&D. IEEE/PES, Page(s): 1 – 4, 2008
[150] Bradley, R., ―Cable temperature monitoring,‖ Distribution and Transmission
Systems (Digest No. 1997/050), IEE Colloquium on Operational Monitoring of,
Page(s): 3/1 - 3/2, 1997
[151] Nitz, S.; Iwainsky, A., ―Design and utilization of fiber networks for temperature
monitoring along cable lines,‖ Industrial Electronics Society, 1998. IECON '98.
Proceedings of the 24th Annual Conference of the IEEE, Volume: 3, Page(s): 1824 -
1828 vol.3, 1998
[152] Kortschinski, J.; Leslie, J.R., ―A Power-Cable Temperature Monitoring
System,‖ Power Apparatus and Systems, IEEE Transactions on Volume: PAS-89 ,
Issue: 7, Page(s): 1429 – 1433, 1997
[153] Densley, J., ―An overview of aging mechanisms and diagnostics for extruded
power cables‖ Power Engineering Society Winter Meeting, 2000. IEEE Volume: 3,
Page(s): 1587 - 1592 vol.3, 2000
[154] Puletti, F.; Olivieri, M.; Cavallini, A.; Montanari, G.C., ―Localization of partial
discharge sources along HV and MV cable routes,‖ Power Engineering Conference,
2005. IPEC 2005. The 7th International, Page(s): 1 – 199, 2005
[155] Mashikian, M.S.; Bansal, R.; Northrop, R.B., ―Location and characterization of
partial discharge sites in shielded power cables‖ Power Delivery, IEEE Transactions
on Volume: 5 , Issue: 2, Page(s): 833 – 839, 1990
109
[156] Chengke Zhou; Hepburn, D.M.; Michel, M.; Xiaodi Song; Guobin Zhang,
―Partial discharge monitoring in medium voltage cables,‖ Condition Monitoring and
Diagnosis, 2008. CMD 2008. International Conference on, Page(s): 1021 – 1024,
2008
[157] Michel, Matthieu, ―Comparison of off-line and on-line partial discharge MV
cable mapping techniques,‖ Electricity Distribution, 2005. CIRED 18th International
Conference and Exhibition on, Page(s): 1 – 6, 2005
[158] Cavallini, A.; Montanari, G.C.; Puletti, F., ―Partial Discharge Analysis and
Asset Management: Experiences on Monitoring of Power Apparatus,‖ Transmission
& Distribution Conference and Exposition: Latin America, 2006. TDC '06.
IEEE/PES, Page(s): 1 – 6, 2006
[159] Tian, Y.; Lewin, P.L.; Wilkinson, J.S.; Sutton, S.J.; Swingler, S.G.,
―Continuous on-line monitoring of partial discharges in high voltage cables,‖
Electrical Insulation, 2004. Conference Record of the 2004 IEEE International
Symposium on, Page(s): 454 – 457, 2004
[160] Gulski, E.; Cichecki, P.; Wester, F.; Smit, J.; Bodega, R.; Hermans, T.; Seitz,
P.; Quak, B.; Vries, F., ―On-site testing and PD diagnosis of high voltage power
cables‖ Dielectrics and Electrical Insulation, IEEE Transactions on Volume: 15 ,
Issue: 6, Page(s): 1691 – 1700, 2008
[161] Working Group D1.33 Task Force 05, ―Experiences In Partial Discharge
Detection,‖ Technical brochure, ELECTRA, No. 208, pp: 34-43, Jun 2003
[162] IEEE Guide for Partial Discharge Testing of Shielded Power Cable Systems in
a Field Environment, IEEE Std 400.3-2006, Page(s): c1 – 36, 2007
[163] Phung, B.T.; Liu, Z.; Blackburn, T.R.; McMullan, P.; Burgess, G, ―Practical
experience in on-line partial discharge monitoring of power cables,‖ Power
Engineering Conference, 2008. AUPEC '08. Australasian Universities, Page(s): 1 – 5,
2008
[164] B.T. Phung, Z. Liu, T.R. Blackburn, H. Zhang, P., McMullan, G. Burgess, and
A. Zargari, ―On-line monitoring of MV and HV distribution cables using VHF partial
discharge detection‖, paper D1-202, CIGRÉ, Paris, 2008.
[165] L Renforth, R Mackinlay, M Seltzer-Grant, R Shuttleworth, ―On-line Partial
Discharge (PD) Spot Testing and Monitoring of High Voltage Cable Sealing Ends,‖
Session 2008: CIGRE 2008; PARIS. Cigre; 2008.
110
[166] Tian, Y.; Lewin, P.L.; Wilkinson, J.S.; Schroeder, G.; Sutton, S.J.; Swingler,
S.G., ―An improved optically based PD detection system for continuous on-line
monitoring of HV cables,‖ Dielectrics and Electrical Insulation, IEEE Transactions on
Volume: 12 , Issue: 6, Page(s): 1222 – 1234, 2005
[167] Wu, R.-N.; Chang, C.-K., ―The Use of Partial Discharges as an Online
Monitoring System for Underground Cable Joints‖ Power Delivery, IEEE
Transactions on Volume: PP , Issue: 99, Page(s): 1, 2011
[168] Van der Wielen, P.C.J.M.; Steennis, E.F., ―On-line PD monitoring system for
MV cable connections with weak spot location,‖ Power and Energy Society General
Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, 2008
IEEE, Page(s): 1 – 8, 2008
[169] Van der Wielen, P.C.J.M.; Veen, J.; Wouters, P.A.A.F.; Steennis, E.F.,
―Sensors for on-line PD detection in MV power cables and their locations in
substations‖ Properties and Applications of Dielectric Materials, 2003. Proceedings of
the 7th International Conference on Volume: 1, Page(s): 215 - 219 vol.1, 2003
[170] Stone, G.C., ―Partial discharge measurements to assess rotating machine
insulation condition: a survey‖ Electrical Insulation, 1996, Conference Record of the
1996 IEEE International Symposium on, Page(s): 19 - 23 vol.1, 1996
[171] Fenger, M.; Stone, G.C.; Lloyd, B.A., ―Experience with continuous partial
discharge monitoring of stator windings,‖ Electrical Insulation Conference and
Electrical Manufacturing & Coil Winding Conference, 2001. Proceedings, Page(s):
417 – 421, 2001
[172] P. Tavner, Peter. T, Li Ran, Jim Penman and Howard Sedding, ―Condition
Monitoring of Rotating Electrical Machines,‖ book, The Institution of Engineering
and Technology, London, UK 2008
[173] Smeeton, P.; Bousbaine, A., ―Fault diagnostic testing using Partial Discharge
measurements on High Voltage rotating machines,‖ Universities Power Engineering
Conference (UPEC), 2009 Proceedings of the 44th International, Page(s): 1 – 5, 2009
[174] Stone, G.C.; Lloyd, B.; Sasic, M., ―Experience with continuous on-line partial
discharge monitoring of generators and motors,‖ Condition Monitoring and
Diagnosis, 2008. CMD 2008. International Conference on, Page(s): 212 – 216, 2008
[175] Paolitti, G.; Golubev, A., ―Partial discharge theory and technologies related to
traditional testing methods of large rotating apparatus,‖ Industry Applications
111
Conference, 1999. Thirty-Fourth IAS Annual Meeting. Conference Record of the
1999 IEEE, Volume: 2, Page(s): 967 - 981 vol.2, 1999
[176] Paoletti, G.J.; Rose, A., ―Improving existing motor protection for medium
voltage motors,‖ Industry Applications, IEEE Transactions on Volume: 25 , Issue: 3,
Page(s): 456 – 464,1989
[177] IEEE Committee Report, ―Report of Large Motor Reliability Survey of
Industrial and Commercial Installations, Part I,‖ Industry Applications, IEEE
Transactions on Volume: IA-21, Issue: 4, Page(s): 853 – 864, 1985
[178] Tetrault, S.M.; Stone, G.C.; Sedding, H.G., ―Monitoring partial discharges on 4-
kV motor windings,‖ Industry Applications, IEEE Transactions on Volume: 35, Issue:
3, Page(s): 682 – 688, 1999
[179] Zhu, H.; Green, V.; Sasic, M., ―Identification of stator insulation deterioration
using on-line partial discharge testing,‖ Electrical Insulation Magazine, IEEE,
Volume: 17, Issue: 6, Page(s): 21 – 26, 2001
[180] Farahani, M.; Gockenbach, E.; Borsi, H.; Kaufhold, M., ―A method for the
evaluation of insulation systems for high voltage rotating machines,‖ Properties and
Applications of Dielectric Materials, 2003. Proceedings of the 7th International
Conference on Volume: 3, Page(s): 1108 - 1111 vol.3, 2003
[181] Hoof, M.; Lanz, S., ―PD diagnostics on rotating machines. Possibilities and
limitations,‖ Electrical Insulation Conference and Electrical Manufacturing & Coil
Winding Conference, 1999. Proceedings, Page(s): 195 – 200, 1999
[182] August Johannes Marie Pemen, ―Detection of Partial Discharge in Stator
Windings of Turbine Generators,‖ Doctoral Thesis, Eindhoven University of
Technology, 2000
[183] Cavallini, A.; Montanari, G.C.; Ciani, F.; Folesani, M., ―Artificial intelligence
algorithms for the recognition of PD-generating defects in rotating machines.‖
Electrical Insulating Materials, 2005. (ISEIM 2005). Proceedings of 2005
International Symposium on Volume: 2, Page(s): 451 - 454 Vol. 2, 2005
[184] Tavner, P.J., ―Review of condition monitoring of rotating electrical machines,‖
Electric Power Applications, IET Volume: 2, Issue: 4, Page(s): 215 – 247, 2008
112
[185] Sedding, H.; Brown, A., ―Review of effective diagnostic and condition
monitoring methods for rotating machines,‖ Power and Energy Society General
Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, 2008
IEEE, Page(s): 1 – 3, 2008
[186] Trzynadlowski, A.M.; Ghassemzadeh, M.; Legowski, S.F., ―Diagnostics of
mechanical abnormalities in induction motors using instantaneous electric power,‖
Energy Conversion, IEEE Transactions on Volume: 14, Issue: 4, Page(s): 1417 –
1423, 1999
[187] Bong-Keun Oh; Hyun-Il Kim; Dong-Sik Kang; Kee-Joe Lim, ―Comparison of
on-line and off-line PD measuring system for hydro generator stator winding,‖
Condition Monitoring and Diagnosis, 2008. CMD 2008. International Conference on,
Page(s): 204 – 207, 2008
[188] Green, V.; Sasic, M.; Wright, B., ―Summary of industry practices, guides and
standards related to on-line partial discharge monitoring of stator insulation
condition‖ Petroleum and Chemical Industry Conference, 2005. Industry Applications
Society 52nd Annual, Page(s): 359 – 365, 2005
[189] IEEE Trial-Use Guide to the Measurement of Partial Discharges in Rotating
Machinery, IEEE Std 1434-2000, 2000
[190] Sedding, H.G.; Campbell, S.R.; Stone, G.C.; Klempner, G.S., ―A new sensor for
detecting partial discharges in operating turbine generators,‖ Energy Conversion,
IEEE Transactions on, Volume: 6 , Issue: 4, Page(s): 700 – 706, 1991
[191] Stone, G.C.; Lloyd, B.A.; Campbell, S.R., ―On-line monitoring for condition
assessment of motor and generator stator windings,‖ Pulp and Paper Industry
Technical Conference, 1994., Conference Record of 1994 Annual, Page(s): 94 – 103,
1994
[192] Okubo, H., ―Recent activity and future trend on ageing characteristics of
electrical insulation in GIS from manufacturer's view point,‖ Properties and
Applications of Dielectric Materials, 1994., Proceedings of the 4th International
Conference on Volume: 2, Page(s): 837 - 840 vol.2, 1994
[193] Reid, A.J.; Judd, M.D.; Fouracre, R.A.; Stewart, B.G.; Hepburn, D.M.,
―Simultaneous measurement of partial discharges using IEC60270 and radio-
frequency techniques,‖ Dielectrics and Electrical Insulation, IEEE Transactions on
Volume: 18 , Issue: 2, Page(s): 444 – 455, 2011
113
[194] Hampton, B., ―UHF diagnostics for gas insulated substations,‖ High Voltage
Engineering, 1999. Eleventh International Symposium on (Conf. Publ. No. 467)
Volume: 5, Page(s): 6 - 16 vol.5, 1999
[195] Singha, S.; Thomas, M.J., ―Toepler's spark law in a GIS with compressed SF6-
N2 mixture,‖ Dielectrics and Electrical Insulation, IEEE Transactions on Volume: 10,
Issue: 3, Page(s): 498 – 505, 2003
[196] Joint Working Group 33/23.12 ―Insulation co-ordination of GIS: Return of
experience, on site tests and diagnostic techniques,‖ Electra No. 176, Page(s):67 – 97,
Feb 1998
[197] Meijer, S.,‖Partial Discharge Diagnosis of High-Voltage Gas-Insulated
Systems,‖ Doctoral Thesis, Delft University of Technology, 2001
[198] Judd, M.D.; Farish, O.; Hampton, B.F., ―Broadband couplers for UHF detection
of partial discharge in gas-insulated substations,‖ Science, Measurement and
Technology, IEE Proceedings - , Volume: 142 , Issue: 3, Page(s): 237 – 243, 1995
[199] Hampton, B.F.; Meats, R.J., ―Diagnostic measurements at UHF in gas insulated
substations,‖ Generation, Transmission and Distribution, IEE Proceedings C, Volume:
135 , Issue: 2, Page(s): 137 – 145, 1988
[200] Baumgartner, R.; Fruth, B.; Lanz, W.; Pettersson, K., ―Partial discharge. IX. PD
in gas-insulated substations-fundamental considerations‖ Electrical Insulation
Magazine, IEEE Volume: 7, Issue: 6, Page(s): 5 – 13, 1991
[201] Kurrer, R.; Klunzinger, K.; Feser, K.; de Kock, N.; Sologuren, D., ―Sensitivity
of the UHF-method for defects in GIS with regard to on-line partial discharge
detection,‖ Electrical Insulation, 1996., Conference Record of the 1996 IEEE
International Symposium on Volume: 1, Page(s): 95 - 98 vol.1, 1996
[202] Meijer, S.; Smit, J.J.; Gulski, E.; Girodet, A., ―Partial discharge early warning
system in gas-insulated switchgear‖ Transmission and Distribution Conference and
Exhibition 2002: Asia Pacific. IEEE/PES Volume: 2, Page(s): 931 - 936 vol.2, 2002
[203] Baumgartner, R.; Fruth, B.; Lanz, W.; Pettersson, K., ―Partial discharge. X. PD
in gas-insulated substations-measurement and practical considerations‖ Electrical
Insulation Magazine, IEEE Volume: 8 , Issue: 1, Page(s): 16 – 27,1992
114
[204] Hampton, B.F., ―Diagnostics for gas insulated substations,‖ Advances in Power
System Control, Operation and Management, 1993. APSCOM-93., 2nd International
Conference on, Page(s): 17 - 23 vol.1, 1993
[205] Meijer, S.; Smit, J.J.; Kanters, A.J.L.M., ―Experience with PD measurements in
GIS maintenance concept,‖ Condition Monitoring and Diagnosis, 2008. CMD 2008.
International Conference on, Page(s): 551 – 554, 2008
[206] Golubev, A.; Blokhintsev, I.; Paoletti, G.; Modrowski, J., ―On-line partial
discharge applications to MV electrical switchgear‖ Electrical Insulation Conference
and Electrical Manufacturing & Coil Winding Conference, 2001. Proceedings,
Page(s): 225 – 232, 2001
[207] Fung, K.Y.; Sit, K.L.; Chan, L.K.; So, K.L., ―Condition monitoring on HV
equipment in CLP Power,‖ Condition Monitoring and Diagnosis, 2008. CMD 2008.
International Conference on, Page(s): 679 – 683, 2008
[208] Kim, J.B.; Kim, M.S.; Kim, T.W.; Song, W.P.; Kim, D.S.; Park, W.Z.,
―Development of smart diagnosis system for 800 kV gas -insulated switchgear,‖
Transmission and Distribution Conference and Exposition, 2003 IEEE PES Volume:
2, Page(s): 486 - 490 vol.2, 2003
[209] J. Vigreux (on behalf of WG 23.03), ―Application of Condition Monitoring
Technoques in Gas Insulated Substations,‖ CIGRE ELECTRA, Feb, 1991
[210] Working group 15.03, ―Partial discharge detection system for GIS: sensitivity
verification for the UHF method and the acoustic method,‖ Electra No. 183, Page(s):
75-87, APRIL 1999
[211] Hoshino, T.; Koyama, H.; Maruyama, S.; Hanai, M., ―Comparison of sensitivity
between UHF method and IEC 60270 for onsite calibration in various GIS,‖ Power
Delivery, IEEE Transactions on, Volume: 21, Issue: 4, Page(s): 1948 – 1953, 2006
[212] Lundgaard, L.E.; Runde, M.; Skyberg, B., ―Acoustic diagnosis of gas insulated
substations: a theoretical and experimental basis,‖ Power Delivery, IEEE Transactions
on Volume: 5, Issue: 4, Page(s): 1751 – 1759, 1990
[213] Menno M.P. de Haas, ―Online Diagnostics in Smart Grids- An approach to the
Future Power Grid-,‖ Master Thesis, Delft University of Technology, June 2011.
115
WEBSITE
[214] www.doble-lemke.eu
[215] www.omicron.at
[216] www.techimp.com
[217] www.ipec.co.uk
[218] www.pd-systems.com
[219] www.kema.com
[220]www.irispower.com
[221] www.qualitrolcorp.com
[222] www.psdtech.com
[223] www.nexigmaint.com
[224] www.dynamicratings.com
[225] www.emersonnetworkpower.com
[226] www.hvpd.co.uk
116
Appendix 1: CASE STUDY 1
On-line PD monitoring on Rotating Machine
On-line periodic PD measurement was carried out at Leuna Saalekreis, Saxony-
Anhalt, Germany.
The generator specification is shown below
• Product name and manufacturer: GEC Alstorm T600C
• Rated power: 56471 MVA
• Rated terminal voltage: 10.5kV
System Configuration
An on-line PD monitoring system roughly consists of three capacitive sensors, data
acquisition unit, and personal computer shown below in Figure 1 and 2
Figure 1 On-line PD monitoring system configuration
System description
1. Capacitive coupler at U (Bandwidth: 20 MHz, Rated capacitance: 2 nF)
2. Capacitive coupler at V (Bandwidth: 20 MHz, Rated capacitance: 2 nF)
117
3. Capacitive coupler at W (Bandwidth: 20 MHz, Rated capacitance: 2 nF)
4. Generator terminal
5. Data acquisition unit
6. Sensor signal collector
7. Power supply (50Hz, 230V)
8. Optic fibre (Yellow cable) to personal computer
Three capacitive couplers are permanently installed at each generator terminal phase
(U, V, W) which are connected to a sensor signal collector installed outside the
generator. The data acquisition unit can be connected to a sensor directly via a sensor
signal collector. The data acquisition unit also has a voltage reference input for PRPD
analysis and possible gate noise reduction input if it is needed. Optical fibre (yellow
cable) directly connects the data acquisition unit and signal convertor in order to send
a signal using an Ethernet connection so that it can be connected to a personal
computer in which analysis and recording of the signal takes place using a User
Interface (UI).
Calibration
The calibration procedure was performed
according to IEC 60270. The Calibrator supplies
a pulse periodically to the sensors monitored by
PC on each phase. This can check also
connectivity between all equipment by detecting
exact pulse signal from the sensors. Plus, it
contains the signal attenuation characteristics
from sensor to PD. In this case, 2000nC pulses
for each measuring frequency band (100-
500kHz, 560-3000kHz, and 9500- 10500kHz)
were injected at each phase to the sensor. The
calibration system inside the generator is shown Figure 2 Calibration procedure
118
in Figure 2, highlighting the pulse injector. The arrows indicate connection spots
between the pulse injector and sensor. The recommended calibration acceptable error
is within ±5% of the reference calibration pulse value.
Measurement
The measurement was carried out while the generator was in operation in different
frequency ranges; 100 – 500 kHz (IEC recommended), 560 – 3000 kHz and 9500-
10500 kHz based on previous measurement record. In this thesis, 100-500 kHz
measuring graphs are covered. In PRPD pattern graph, a reddish color indicates
higher repetition rates compared to a blue or dark color which means low a repetition
rate.
119
Figure 3 PD occurrence on each phase visualized as PRPD pattern and pulse
The graph shows a clear higher peak value of PD on the first phase compared to other
phases. One noticeable thing here is the noise signal. Especially at the third phase,
there are phase-lock noises, periodic red dot pattern from the voltage reference. If the
noise is severe, then the gating technique can eliminate those noises. The pattern in
this measurement should be compared to a PD pattern reference library for more
accurate decision making.
120
Appendix 2: CASE STUDY 2
AE detection on transformer
The acoustic emission detection was performed on the transformer at Siemens
Transformer Factory, Dresden, Germany
The transformer specification is shown below
• Product name and manufacturer: Dewa D417371
• Rated voltage: 145kV/12kV
System Configuration
An AE detection system roughly consists of acoustic sensors, data acquisition unit,
and personal computer shown in Figure 4.
Figure 4 AE detection system on power transformer
System description
1. Acoustic sensor (total 8 sensors used)
2. Data acquisition unit (amplifier, 8 sensor signal input)
121
3. Personal Computer (Data analysis)
The transformer was being inspected due to a high level of PD. In the measuring
system, there are 8 acoustic sensors on the transformer applied test voltage, data
acquisition unit, and PC which was specially designed for the purpose of AE
measurement. There are amplifiers on each input in the data acquisition unit which
improve the measurement signal.
Measurement
The end user of the transformer reported abnormal conditions based on a high level of
PD. However, there was no indication of the location of the PD source inside the
transformer. Based on their experience and similar cases, the most likely parts of the
transformer for fault are the cable connection box and transformer terminal for the
cable shown in the white box in Figure 5. Especially the cable connection box inside
the red rectangle is the most likely. Therefore the measurement started by placing
sensors around the red box. Even though there was no significant signal detected by
the sensor in this area, the signal detected by lower sensors was stronger than the
upper sensors.
Figure 5 Sensor placements in the suspicious part of the transformer
122
While moving the sensors downward to find more AE signals, the strongest signals
were emitted inside the blue box shown in the Figure 2. The signal from each sensor
is shown in Figure 4. The graph shows eight different sensors‘ signals simultaneously
in two different frames (1 to 4 in the upper graph and 5 to 8 in the lower graph). The
strong signals come from the first and fifth sensor. As indicated on the x-axis, there
are clear time differences between sensors according to their placement on the
transformer cable connection box. Nevertheless the magnitude can vary according to
the propagation path and insulation material structure inside of the test object, the
pulse arrival time clearly depends on the sensor placement.
Figure 6 Signals from 8 sensors located different spot around suspicious PD source
PD location and Signal time frame
After gaining enough signal strength from each sensor, the coordinate of each sensor
was recorded and analysed regarding the location of the PD signal according to the
time difference of pulse propagation. In order to calculate the PD source, the signal
can be fixed at the PD occurrence time.
Sensor 1gfedcbSensor 2gfedcbSensor 3gfedcbSensor 4gfedcb
Time [ms]
109.89.69.49.298.88.68.48.287.87.67.47.276.86.66.46.265.85.65.45.254.84.64.44.243.83.63.43.232.82.62.42.221.81.61.41.210.80.60.40.20
Volta
ge [V
]
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
-1.2
Sensor 5gfedcbSensor 6gfedcbSensor 7gfedcbSensor 8gfedcb
Time [ms]
109.89.69.49.298.88.68.48.287.87.67.47.276.86.66.46.265.85.65.45.254.84.64.44.243.83.63.43.232.82.62.42.221.81.61.41.210.80.60.40.20
Volta
ge [V
]
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
-1.2
123
Figure 7 Time frame of the first sensor signal
Figure 8 Time frame of the fifth sensor signal
In Figure 7 and 8, time frame work was when the PD magnitude and pulse shape are
the most likely right time. The same process was done for all eight sensors for setting
the exact propagation time from possible PD sources to each sensor placement.
124
Figure 9 Coordinate in the cable connection box
As shown in Figure 9, it was assumed the 3 axis runs along to the contour of the
cable and connection box and pick the coordinates of each sensor placed inside of the
scope (e.g. (x, y, z)). The software in the computer analyzes possible fault locations
inside the rectangular space. As we can see in Figure 10, possible PD source can be
calculated according to the AE detection techniques in the transformer, which is in
detail at ―4.1.4. On-line transformer PD monitoring on transformer‖. The options for
this calculation are acoustic speed in oil dependent on temperature and the selectivity
of sensors.
Figure 10 localization PD source according to the pulse propagation time difference from 8
sensors
125
Appendix 3: CASE STUDY 3
On-site switchgear PD monitoring
On-site PD monitoring according to the IEC 60270 on switchgear in Altenberg
substation, Germany. Especially the voltage transformer in switchgear is the
measurement target.
The switchgear specification is shown below
• Product name: GMB 24 16 06/ SF6
• Rated voltage and current: 24 kV 630A (50 Hz)
• Test object: 2 block 14 Cubicles
System Configuration
An on-site PD monitoring system on switchgear roughly consists of Measuring
impedance, voltage supplier and auto transformer, data acquisition unit, and personal
computer shown below in Figure 11
Figure 11 On- site PD monitoring system on switchgear
126
Figure 12 Measuring impedance and calibrator
System description
1. Module reactor
2. Inductance (40mH)
3. Capacitance (0.3nF)
4. Switchgear terminal
5, 6. Low pass filter
7. Data acquisition unit
8. Autotransformer and controller
9. Personal Computer
10. Calibrator
11. Measuring Impedance
The on-site measurement was performed by applying AC test voltage to the
switchgear. The transformer and module reactor supply voltage from the tested object
to the switchgear terminal. If there is high noise, a low pass filter which is adjustable
in different frequency bands can be applied. A data acquisition unit collects data by
measuring the impedance which is connected to a the personal computer for recording
and analyzing. In order to avoid possible noise discharge at the other terminal of the
127
switchgear, there are an anti PD caps on other terminals which are grounded
appropriately to make sure PD such as corona does not occur at other terminals shown
in Figure12 in the white box.
Measurement
Before the measurement, there was a calibration according to the IEC 60270 which
injects a known pulse magnitude to the measuring impedance. The two different
measurements performed for switchgear were; High voltage withstanding test and a
PD test. According to the standard HV, testing was at a 40kV voltage level, which is
80% of the HV test voltage of 24kV, for 60 seconds according to the HV standard test.
This test was only for the purpose of withstanding.
128
Figure 13 Measurement results
The upper Figure shows a PRPD graph of the PD and the lower Figure shows the
applied voltage level. Since the rated voltage of the switch gear terminal is 24 kV, the
test voltage was about 26kV which is 1.1 X U (nominal) voltage. The measurement
result clearly shows that there are certain amounts of PD occurrence at a and b. Since
the PD magnitude in the first and second Figure above is within the dangerous
threshold (50pC), the switchgears needed further action to fix, for example, to localize
PD source. In the last Figure, periodic noises are shown at regular intervals.
129
Appendix 4: Comparison of on-line PD monitoring products for Transformer
Features\
products
Specific
features
PD-guard
Electrical/ UHF
(Doble Lemke)
PDcheck
(Techimp Energy
Srl)
Icmmonitor
(Powerdiagnostix
System)
DMS PDMT
(Qualitrol LLC) PD-TM series
(PowerPD, Inc.) PD-EYE
(IPEC Ltd) DTM
(Dynamic Ratings)
General
Feature
System type Conventional/
Unconventional
Conventional/
Unconventional
Conventional/
Unconventional Unconventional Unconventional
Conventional/
Unconventional
Conventional/
Unconventional
Sensor Capacitive,
UHF sensor
Capacitive,
HFCT,
TEV, Horn sensor
Capacitive,
UHF Sensor UHF sensor
AE sensor,
HFCT
HFCT,
AA transducer,
Capacitive
HFCT,
Bushing sensor,
AE (optinal)
Compatible
Standard IEC 60270 IEC 60270 IEC 60270 -
ANSI/TIA/EIA-422-B
(for communication) -
IEC 61850
(for communication)
Connection
to PC Optical Fibre Optical Fibre Optical Fibre - Optical Fibre - Optical Fibre
Other issues
SCADA, AE
detection
compatible
DGA, tan, VIB,
DTS integrations
option
- Master/Slave
connection SCADA compatible -
SCADA compatible
can be combined with
bushing monitoring
Electrical
Feature
PD acquisition
Frequency
Range
10kHz -2000khz
(Capacitive)
100MHz-
1000MHz (UHF)
16kHz -30MHz
Up to 2 GHz(with
Frequency shifter)
2-20MHz
(Standard)
40-800kHz
(optional)
300-2000MHz
(UHF)
100-3000MHz
(UHF)
80KHz~300KHz (AE)
100KHz~30MHz
(HFCT)
50kHz-20MHz
(HFCT)
20MHz-800MHz
(Capacitive)
40kHz±1kHz(AE)
-
Input impedance 50 ohm 50 ohm 50 ohm - - Roc. 50 ohm -
PD detection
Range
Up to100,000 pC
Up to 700mV Up to 4000mVpp - - - - -
Phase Accuracy <0.3 degree <1 degree - - - - -
PD signal
resolution 12 bit bipolar 10 bits - - - 12 bit -
Input channel
4 inputs for PD&
synchronization
signal
3 channels for PD
& synchronization
channel
8 inputs for PD
3-6 channel (up to
250 channel with
Master/Slave
connection)
5 channel
(AE-4ch,HFCT-1ch)
10 channel
(AE-8ch, HFCT-2ch)
8 channels 14 channels for PD
4 channels for AE
Software
Feature
De-noising
Techniques
Gating
Windowing
Multi terminal
Measurement
TF map (Time/
Frequency
Map), Fuzzy logic
based noise
elimination
-
Neural Network
Genetic
Algorithm
Fuzzy logic
- - -
Other issues Auto alarming
PRPD analysis - -
SMS/E-mail
alarm -
Data integrated web,
E-mail/SMS alarm -
130
Appendix 5: Comparison of on-line PD monitoring products Cable
Features\
products Specific features
PD-guard
Electrical/ UHF
(Doble Lemke)
Smart Cable
Guard
(KEMA) Emerson
PDcheck
(Techimp Energy Srl) HVPD Mini
(HVPD)
PD-EYE and
PrecisePD
(IPEC Ltd)
PD-MCC&G400A
(PowerPD)
General
Feature
System type Unconventional Unconventional Unconventional Unconventional Unconventional Unconventional Unconventional
Sensor Capacitive,
UHF sensor HFCT HFCT
HFCT,
Flexible Magnetic
Coupler
HFCT, TEV
HFCT,
AA transducer,
Capacitive
4 HFCTs
Locating PD
source -
Two HFCTs and
Pulse injection
Time of flight
analysis
TDR with GPS,
Arrival time
Analysis, amplitude&
frequency analysis
Two HFCTs and
Pulse injection
method
Distinguish arrival
time of TEV signal
from two ends
-
Maximum PD
localizing length -
~4km (PILC,
MIND)
~8km (XLPE)
~2km (EPR)
~4.8km - ~2km - -
Electrical
Feature
PD acquisition
Frequency Range
10kHz -2000khz
(Capacitive)
100MHz-
1000MHz (UHF)
200 kHz - 20MHz
(HFCT)
10khz - 300Mhz
(HFCT)
2 MHz - 100 MHz
(HFCT)
100KHz~10, 12,
20MHz
(HFCT) according
to the type
50kHz-20MHz
(HFCT)
20MHz-800MHz
(Capacitive)
40kHz±1kHz(AE)
-
Input impedance 50 ohm - - 50 ohm -50 ohm Roc. 50 ohm -
PD detection
Range
Up to100,000 pC
Up to 700mV - - Up to 4000mVpp - - -
Voltage class - 6kV - 36kV 4kV - 345kV. - 3.3 kV to 45 kV - -
PD signal
resolution 12 bit bipolar - - 10 bits - 12 bit -
Input channel
4 inputs for PD&
synchronization
signal
3 input for sensors
(control unit) -
3 channels for PD
& synchronization
channel
4 channel 8 channels 4 channels for HFCT
Two ends method - O - - O O -
Software
Feature
De-noising
Techniques
Gating
Windowing
Multi terminal
Measurement
Matched filter bank Built-in RF
noise reduction
TF map (Time/
Frequency
Map), Fuzzy logic
based noise
elimination
PD pulse shape
analysis based on
previous record
- -
Other issues Auto alarming
PRPD analysis
Early warning and
Alerts - Web MSG service
Web based
application
Data integrated
iSM web,
E-mail/SMS alarm
PRPD analysis
131
Appendix 6: Comparison of on-line PD monitoring products for RM
Features\
products
Specific
features
PD-guard
Electrical/ UHF
(Doble Lemke) Iris Power
Longshot
(HVPD) PDcheck
(Techimp Energy Srl)
Icmmonitor
(Powerdiagnostix
System)
MICAMAXX®pda
(PDtech) PD-MCC&G400A
(PowerPD)
General
Feature
System type Unconventional Unconventional Unconventional Unconventional Conventional/
Unconventional Unconventional Unconventional
Sensor Capacitive
Capacitive (80pF)
SSC (6 to 9 as a set,
6kV or higher)
Capacitive
HFCT
TEV
Capacitive coupler
HFCT
Capacitive
Capacitive
HFCT 4 HFCTs
Combined
measurement -
Load, Temperature
Active/Reactive
power
- -
Load or
Temperature
possible
- -
Connection
to PC Optical Fibre Coaxial Cable - - Optical Fibre - -
Electrical
Feature
PD acquisition
Frequency
Range
10kHz -2000khz
(Capacitive)
100MHz-
1000MHz (UHF)
10-1000Mhz
(SSC)
40-350Mhz
(Capacitive)
0 - 400Mhz
(HFCT)
4MHz-100MHz
(TEV)
2 MHz - 100 MHz
2-20MHz
(Capacitive)
- -
Input impedance 50 ohm 50ohm (SSC) - 50 ohm 50 ohm Roc. 50 ohm -
PD detection
Range
Up to100,000 pC
Up to 700mV - - Up to 4000mVpp - - -
PD signal
resolution 12 bit bipolar - - 10 bits - 12 bit -
Input channel
4 inputs for PD&
synchronization
signal
- 4 channel
3 channels for PD
& synchronization
channel
8 inputs for PD 3 channels 4 channels for HFCT
Software
Feature
De-noising
Techniques
Gating
Windowing
Multi terminal
Measurement
Pulse shape analysis
Time of arrival
High pass filter
Wavelet
denoising
Time of arrival
TF map (Time/
Frequency
Map), Fuzzy logic
based noise
elimination
- - -
Other issues Auto alarming
PRPD analysis
Alarm
PRPD PRPD
SCADA compatible
PRPD -
SCADA compatible
PRPD
Warning
PRPD analysis
132
Appendix 7: Comparison of on-line PD monitoring products for GIS
Features\
products
Specific
features
PD-guard
Electrical/ UHF
(Doble Lemke)
Longshot
(HVPD)
Icmmonitor
(Powerdiagnostix
System)
PDMG-R
(DMS) Amos 3.0
(PSD tech) AIA
(Doble TransNor ) PD-MAT400A
(PowerPD)
General
Feature
System type Unconventional Unconventional Unconventional Unconventional Unconventional Unconventional Unconventional
Sensor Inductive
HFCT, TEV,
Airborne AE,
external capacitive
Flange type,
shielded ring
antenna, external
window type
Internal or
external window
sensor
Internal or external of
oper barrier, metal-
closed barrier type
Acoustic
sensors(piezoelectric) 4 AE sonsors
Localization
Method - Time-of-flight - - -
Searching along the
GIS -
Connection
to PC Optical Fibre - Ethernet cable Optical fibre - Data cable -
Electrical
Feature
PD acquisition
Frequency
Range
100MHz to 1GHz
4MHz to 100MHz
(TEV)
100KHz to 50MHz
(HFCT)
300- 2000MHz
(window type)
500-1500MHz
(window type)
0.5-1.5GHz (for all
sensors)
10-100kHz
(recommended
setting)
100-300kHz
Input impedance 50 ohm - 50 ohm - - - -
PD detection
Range
Up to100,000 pC
Up to 700mV - - - - 2-50pC(sensitivity) -
Voltage class - 3.3-36kV - - - 145-800kV -
PD signal
resolution 12 bit bipolar - - - - - -
Input channel
4 inputs for PD&
synchronization
signal
-
8 inputs for PD
Coving one or
two bay
3 channel for
OCU & Max. 300
Channel for EC
16 channel for PD
2 channel for noise
1 BNC input and 1
preamplifier for
sensor
1 input for external
sync. Signal
4 channels for AE
Software
Feature
De-noising
Techniques
Gating
Windowing
Multi terminal
Measurement
Pulse shape
analysis
High pass filter
- ANN, fuzzy,
genetic algorithm
Gating, Neural
Network, Filtering Filtering -
Other issues Auto alarming
PRPD analysis
Alarm
PRPD -
SCADA/SCS
Alarm as E-mail
and SMS
Web-based
PRPD, PRPS
Threshold & Envelop
Alarms
133
Appendix 8: Commercial Sensors
Electrical sensor
Name
(Company)
PDDC-17/24
(Doble lemke)
1 nF
Coupler
(Techimp)
Type A
(Doble lemke)
UHF Sensor
drain valve
(DN50/80)
(doble lemke)
HFCT-140
(Techimp)
Flexible
Magnetic
Coupler
(Techimp)
TEV
(Techimp)
Horn Antenna
(Techimp)
TEM
(Techimp)
Rated
voltage
17.5kV/24kV 20kV - - - - - - -
Frequency
bandwidth
20MHz - 100MHz~
1GHz
200MHz~
1GHz
2MHz~
100MHz
30kHz~
200MHz
0.1MHz~
300MHz
500MHz~
3GHz
100MHz~ 3GHz
Sensor
type
Capacitive Capacitive inductive UHF Inductive
(HFCT)
Inductive Capacitive Electromagnetic Electromagnetic
Main
application
area
Rotating Machine Rotating
Machine
Cable
Termination
Transformer Cable
Grounding rod
bar
Cable joint
/terminal
Switchgear
GIS
GIS/GIL
Transformer
Switchgear
Induction motor
Physical
Figure
2,5kg/7.5kg
139(H*)140(D**)
350(H)180(D)
295 (H)
187(D)
85 (H)
103(D)
3kg 310x320x40
mm/6kg
120 x 480 x 9
mm/330g
130x 70 x 25
mm/80g
70 x 100 x50
mm/260g
80 x 150 x 50 mm/
250g
Load
impedance
- - - 50 ohm 50 ohm - 50 ohm 50 ohm
Other
issues
2nF (Capacitance) 1nF 2nF (With
integrated
capacitance)
- 10mV/mA
(Sensitivity)
- - N-type N-type
134
Electrical sensor
Name
(Company)
MCC112/124/
205/210 (Omicron)
MCT100/110
(Omicron)
UVS 610
(DN50/80)
(Omicron)
HFCT 100/50
(IPEC Ltd)
Capacitive
Coupled TEV
Sensor
(IPEC Ltd)
Sensor/
Injector Unit
–SUI405
(KEMA)
DMS PDMT
UHF
(Qualitrol)
DMS
PDMG-R
UHF
(Qualitrol)
Epoxy-mica
PDA coupler
(Irispower)
Rated voltage 12/24/50/100kV - - - - 6-36kV - 25/16/6.9kV
Frequency
bandwidth
- 80 ~ 5 MHz 150~1 GHz 50kHz~
20MHz
20~ 800MHz 200kHz-
20MHz
100 ~
3000 MHz
500~
1500MHz
-
Sensor type Capacitive Inductive
(HFCT)
UHF Inductive
(HFCT)
Capacitive
TEV
Inductive
(HFCT)
UHF UHF Capacitive
Main
application
area
Generator
MV network
Ground
connection
Transformer Earth
conductor
Switchgear Cable Transformer
Reactor
GIS Hydro generator
stator windings
Physical
Figure
140x200x140mm/
150x250x150mm/
450x575x450mm/
450x755x450mm/
3/3.5/8.2/10.5kg
115 x 120 x 65
mm/
110 x 120 x 55
mm/
3.1kg 50(internal
D)110
(external
D)mm/
0.4kg
60 (D) mm ×
30mm/80g
1.4kg
Dimensions
250x115x50
mm (L, W, D)
- 2.3/1.6/1.1kg
Load
impedance
- - - 50 ohm 50 ohm - - -
Other issues About 1nF
(Capacitance)
Split/
one piece
- Split core Magnetic
coupling
Maximum
cable circuit
length (PILC,
MIND=
4km
XLPE=8km
EPR=2km)
Hatch cover
Drain valve
type
Internal or
external
80pF±3pF
135
Electrical sensor Acoustic sensor
Name
(Company)
Stator Slot
Couplers
(Irispower)
UHF/internal
window type
(psdtech)
UHF/external
window type
(psdtech)
Epoxy-mica
capacitive
coupling DR-
EMC(dynamic
ratings)
HFCT(100/50)
(HVPD),
portable
HFCT(75/35)
(HVPD),
permanent
HVPD TEV Sensor
(HVPD)
PAC D9241
AE sensor
(Doble
TransNor)
Airborne
Acoustic
Transducer
(IPEC Ltd)
Rated
voltage
- - - 8/16/28kV Max current:
300A
Max current:
300A
- - -
Frequency
bandwidth
10-1000 MHz 0.5~1.5 GHz 0.5~1.5 GHz 0.5 ~
500 MHz
100kHz -
20MHz
100kHz -25MHz 1MHz - 50MHz 20-60 kHz
40KHz ±
1KHz
Sensor type Inductive UHF UHF Capacitive Inductive
(HFCT)
Inductive (HFCT) Capacitive TEV
sensor
Acoustic Acoustic
Main
application
area
gas or steam
turbine
generators.
GIS GIS Motors ,gener
ators and
switch gear
HV cable HV cable Switchgear GIS Switchgear
Physical
Figure
2.0 mm thick
length trimmable
to 53 cm
- - 86/126/185(H)
0.95/1.4/2.1
(kg)
0.4kg
45mm(Internal)
107mm(Externa
l diameter)
0.5kg
40x25mm(Internal)
90x120mm(Extern
al dimension)
60 x 50 x 25mm,
0.2kg
24x20mm/56
mg
120x 40mm
/75g
Load
impedance
50 ohm - - - 50 ohm 50 ohm 50 ohm - -
Other issues Max. sensitivity
< 2pC
Max.
sensitivity <
5pC
80 pF+ 3 pF
(Capacitance)
Sensitivity:1p
C
Different size of
split-core type
is available
(140/100,220/1
50)
Different size of
split-core type is
available (100/50)
Time of flight is
possible by using
more than 2 TEV
sensors
Resonance
frequency:
30kHz
10pC
(Sensitivity)
*H= Height
**D=Diameter
