7. Electrical - IJEEER - a Novel Method for Detection of - Naveen Gaur

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International Journal of Electrical andElectronics Engineering Research (IJEEER)ISSN(P): 2250-155X; ISSN(E): 2278-943XVol. 4, Issue 5, Oct 2014, 63-74

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A NOVEL METHOD FOR DETECTION OF ELECTRIC TRANSMISSION LINE

FAULTS USING DISCRETE WAVELET TRANSFORM

NAVEEN GAUR1, RAM NIWASH MAHIA

2& OM PRAKAS H MAHELA

3

1Principal, Aryan Polytechnic College, Ajmer, Rajasthan, India

2Research Scholar, Indian Ins titute of Technology, Jodhpur, Rajasthan, India

3Research Scholar, IIT Jodhpur, Rajasthan, India

3Assistant Engineer, RRVPNL, Jodhpur, Rajasthan , India

ABSTRACT

The power utility companies have been trying to identify and locate three-phase transmission line faults in the

minimum possible time in order to prevent economic losses. In the last few decades technology used for power system

protection has evolved and shifted from electromechanical devices to solid state and micro-processor based intelligent

devices, which require fast and accurate detection of faults in the transmission lines. This paper presents a novel discrete

wavelet transform (DWT) based multi-resolution analysis technique for detection of transmiss ion line faults including and

without including ground. A comparative study of all types of faults is presented. A test system having generation, load

and transmission line in two parts is modeled in MATLAB/ Simulink environment. The MATLAB programming is used

for DWT analysis of the faults.

KEYWORDS:

Fault Detection, Discrete Wavelet Transform, Power System, Transmission Line, Transmission LineFault

1. INTRODUCTION

Transmission and distribution lines are important parts of the electrical power system and provide the path to

transfer electrical power from central generating stations to load centers. These lines are susceptible to faults due to

continuous environment e xpos ition and switching operations [1]. The performance of power system is affected by faults on

transmission lines, which results in interruption by power flow [2]. The fast and accurate detection, classification and

location of the transmission line fault must be done to de-energize the faulted line, protecting the power system network

from the harmful effects of the fault [3].

The continuous expansion of power networks in both scale and complexity has been imposing a requirement for

fast fault clearance to improve system stability and reliability [4]. For the same, efficient and reliable protection techniques

have been developed. In [5], authors proposed a digital distance protection scheme for transmission lines based on

analyzing the measured voltage and current signals at the relay location using wavelet transform (WT) with

multi-resolution analysis (MRA). In [6], authors presented a scheme for the protection of parallel transmission lines in

which WT with its magnificent characteristics is employed to detect the disturbances in the current signals during faulty

conditions and to estimate the phasors of all the signals as well as to achieve high-speed relaying. The wavelet-based ultra

high speed directional transmiss ion line protection has been proposed in [7].

A prototype Kerr Cell has been constructed by the authors in [8] and tested for detecting and identifying faults by
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64 Naveen Gaur, Ram Niwash Mahia & Om Prakash Mahela

Impact Factor (JCC): 5.9638 Index Copernicus Value (ICV): 3.0

monitoring high voltages in power system. An approach to automated transmission line fault analysis using synchronized

sampling at two ends of the line is presented in [9]. In [10], authors presented a new high-impedance fault detection

method based on the WT for feature extraction, and principal component analysis for the selection of features, and a fuzzy

inference system for decision making. A novel hybrid framework that is able to detect rapidly and locate a fault on power

transmiss ion lines is presented in [11].

This paper presents, a discrete wavelet transform based approach for detection of transmission line faults which

involves the capturing current signals generated in a transmission line under faulty conditions. The detection process is

performed through s ignal decomposition using db4 as mother wavelet. In this paper, we have used the moving window of

size 32 samples and average is calculated. The window is moved by one sample in each step and iterated successively to

cover all the samples available in wavelet coefficients. In each step, average of samples is calculated and plotted.

The results of this proposed technique are compared with the absolute values of the wavelet coefficients .

This paper is divided into six sections . Starting with an introduction in section 1, the section 2 covers the discretewavelet transform analysis for detection of transmission line faults and section 3 describes the test power system model

used for the detection of transmiss ion line faults. Section 4 includes proposed algorithm and the s imulation results and their

discussion are presented in section 5. Finally the concluding remark is included in the section 6.

2. WAVELET TRANSFORM ANALYSIS FOR FAULT DETECTION

The digital signal processing techniques are widely us ed for processing the s ignals associated with power system.

These techniques have been classified into two categories, the frequency-based and time-frequency techniques.

The frequency based techniques, such as Fourier transform, are used for stationary signal analysis. The time-frequency

based techniques, such as short- time Fourier t ransform (STFT), wavelet t ransform (WT), ambiguity function (AF),

and wigner-ville distribution (WVD) are usually used for extracting transient features from the non-stationary signals [12].

The wavelet transform is a mathematical tool, much like a Fourier transform in analyzing a stationary signal that

decomposes a signal into different scales with different levels of resolution by dilating a single prototype function.

The decomposition into scales is made possible by the fact that the WT is based on a square-integrable funct ion and group

theory representation. The wavelet transform provides a local representation, in both time and frequency, of a given signal.

Therefore, it is suitable for analyzing a signal where time-frequency resolution is needed such as disturbance transition

events during faulty conditions in the transmission lines [13]-[14].

The discrete wavelet transform (DWT) is the bas ic tool for the feature extraction. DWT is the discrete counter part

of the continuous wavelet transform (CWT). The CWT of a continuous time signal is defined as [15]

(1)

(2)

where (t) is the mother wavelet, the asterisks denote complex conjugates, a and bare scaling and translating

parameters respectively.


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A Novel Method for Detection of Electric Transmission Line Faults Using Discrete Wavelet Transform 65


For detection of transmission line faults, the DWT is used instead of the CWT. This is implemented by using

discrete values and for the scaling parameter and translation parameter, respectively. Then, the mother

wavelet is given as

(3)

Where m and n indicate the frequency localization and the time localization, respectively. When a0=2 and b0=1

are used, then the WT is known as dyadic-orthonormal wavelet transform and bas is for multi-resolution analysis (MRA).

In MRA, signal is passed through a series of high pass filters (HPF) to analyze the high frequencies, and it is also

passed through a s eries of low pass filters (LPF) to analyze the low frequencies. The signal (S) is decomposed into two

types of components: approximation (A) and detail (D). The approximat ion is h igh scale, low frequency component of the

signal. The detail is low scale, high frequency component. The decomposition process can be iterated, with successiveapproximations being decomposed in turn, so that one signal into many lower resolution components which is called the

wavelet decomposition tree as shown in Figure 1 [16]. The LPF and HPF filters form a family of scaling (t) and wavelet

(t)functions as given below.

(4)

(5)

Where , his low pass filter andgis high pas s filter.

Figure 1: W avelet Decomposition Tree

The choice of filters h and g with four coefficients is known as daubechies wavelet with four filter coefficients

(or Daub4). Daub4 wavelet and Daub4 scaling functions are shown in Figure 2.

0 1 2 3 4 5 6 7

-0.2

0

0.2

0.4

0.6

0.8

1

Scaling function phi

(a)0 1 2 3 4 5 6 7

-0.5

0

0.5

1

Wavelet function psi

(b)

Figure 2: (a) Daub4 Scaling Function and (b) Daub4 Wavelet Function


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3. PROPOSED POWER SYSTEM MODEL

For detections of transmission line faults, s ingle line d iagram of the experimental s et up used is cons isting of three

buses, one source, and one load as shown in Figure 3. The tes t system trans mission line parameters are given in Tab le 1.

The transmission lines with two sections of 100 km length each are used. The type of transmission line is -section.

In transmission lines, the positive and negative sequence parameters are same; therefore, only positive sequence parameter

values are given in the table.

Table 1: Test System Transmiss ion Line Parameters

S. No. Attributes Value

1 Positive s equence resistance R1 (/km) 0.01273

2 Zero sequence resistance R0 (/km) 0.3864

3 Positive sequence inductance L1 (H/km) 0.9337e-3

4 Zero sequence inductance L0 (H/km) 4.1264e-3

5 Positive sequence capacitance C1 (F/km) 12.74e-9

6 Zero sequence capacitance C0 (F/km) 7.751e-9

The details of transformers used at the generating end (XG-1) for step-up of the voltage and load end (XL-2) for

step-down of the voltage are given in Table 2. The X/R ratio of the source generator is 7 and 12 MVA load is used.

The supply frequency used is 60 Hz.

Table 2: Transformer Parameters

Transformer MVA Kv-High Kv-LowHV Winding LV Windi ng

R ( ) X ( ) R ( ) X ( )

XG-1 25 220 33 29.095 211.60 2.1100 4.8312

XL-2 25 220 11 29.095 211.60 0.1142 0.8306

The current signals for feature extraction of faults are captured at bus 1 near the generator. The fault detection

point in the system may be taken depending on the type and locat ion of protection scheme provided in the s ystem. In the

proposed study the fault is located at bus no. 3. Three types of faults v iz. line to ground (LG), double line to ground (LLG),

double line (LL) and three-phase faults are created at bus no. 3 one by one and analyzed WT with db4 as mother wavelet.

Figure 3: Propose d Model of Power System for Detection of Trans mission Line Faults

4. PROPOSED TRANSMISSION LINE FAULT DETECTION ALGORITHM

In the power system, faults are abnormal events which are not part of normal operation and unwanted by the

network operator. A fault detector must detect the fault inception and to issue an output signal indicating this condition.

During normal operating conditions the currents and voltage of the power system are sinusoidal signals. Load variation

with t ime may produce low amplitude changes in current signals and, in lesser extent, in voltage signals. The inception of

fault introduces abrupt changes of amplitude and phase in current and voltage signals [17]. After fault occurs in the power

system, a non-linear signal of transient travelling wave is generated and runs along faulted transmiss ion line to both ends of


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the line. Those travelling waves contain information about fault nature. The fault initial travelling wave has a wide

frequency spectrum from DC co mponent to high frequencies. When such fault travelling wave arrives at the substat ion bus

bar, it will change incisively, i.e . travelling wave head will present the sudden change in the time-frequency diagram.

In that way, travelling wave arr ival to the measuring point (usually the busbar voltage transformers) is e xactly a moment of

sudden change recorded on measuring substation [18]. The system is simulated in MATLAB/simulink environment.

The fault is created at 10th

cycle from the start of the simulation and cleared at 20th

cycle from start of the simulation.

The detected three phase current signals at bus no. 3 are passed through DWT with db4 as mother wavelet and different

details up to level 4 and approximation at level 4 are obtained. The sampling frequency of 1920 Hz is used for DWT

decomposition. If the value of h igh frequency detail (HFD) coefficients is greater than the threshold value (Td) the fault is

detected, otherwise no fault is detected. The flow chart of proposed algorithm is shown in Figure 4. The absolute values of

wavelet coefficients are plotted in each case. In this paper, we have used the moving window of size 32 samples and

average is calculated. The window is moved by one sample in each step and iterated successively to cover all the samples

available in wavelet coefficients. In each step, average of samples is calculated and plotted. The results of this proposed

technique are compared with the abs olute values of the wavelet coefficients.

Figure 4: Flow Chart of DWT Based Fault Detection Algorithm

5. SIMULATION RESULTS AND DISCUSSIONS

The power system model shown in Figure 3 is simulated in MATLAB/Simulink environment with fault created

on bus no. 3 on phase-A at 10th

cycle from start of the simulation and cleared at 20th

cycle from the start of the simulation

in each case. The current signal of phase-A, is passed through DWT with db4 as mother wavelet. The absolute values of

the wavelet coefficients as well as average values of wavelet coefficients as calculated by the proposed method mentioned

in section 4 are p lotted in each case and compared.

5.1 LG Fault on Power System

The current signal of phase-A at bus 1 with LG fault on bus 3 and the absolute value of the detail coefficients up

to level 4 and approximation coefficient at level 4 are shown in Figure 5. The current signal and detail coefficient upto

level 4 and appro ximation coefficient at level 4, as calculated by the proposed method are shown in Figure 6.


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0 500 1000 1500 2000-5000

0

5000

Original Signal

20 40 60 80 100 1200

2000

4000

6000

Absolute value of approximation Coefficient cA4

0 20 40 60 80 100 120 1400

5000

10000

Absolute value of detail Coefficient cD4

50 100 150 200 2500

500

1000

Absolute coefficient of detail Coefficient cD3

50 100 150 200 250 300 350 400 450 5000

50

100

150

200


0 200 400 600 800 10000

50

100


Figure 5: Absol ute Wavelet Coefficients Using Db4 wi th LG Faul t at Bus-3

0 500 1000 1500 2000-5000

0

5000

Original Signal

0 20 40 60 80 100 1200

5

10x 10

4 Approximation coefficient Coefficient cD1

0 20 40 60 80 100 1200

1

2

3x 10

5 Detail Coefficient cD4

0 50 100 150 200 2500

1

2

3x 10


0 100 200 300 400 5000

500

1000

1500

Detail Coefficient cD2

0 200 400 600 800 10000

1000

2000


Figure 6: Absolute Wavelet Coefficients Using Db4 wi th LG Faul t at Bus-3 with Proposed Algorithm

5.2 LL Faul t on Power System

The current s ignal of phase-A at bus 1 with LL fault on bus 3 and the absolute value of the detail coefficients up

to level 4 and approximation coefficient at level 4 are shown in Figure 7. The current signal and detail coefficient upto


0 500 1000 1500 2000-5000

0

5000

10000

Original Signal

20 40 60 80 100 1200

5000

10000

15000


0 20 40 60 80 100 120 1400

5000

10000


50 100 150 200 2500

1000

2000

3000


0 100 200 300 400 5000

500

1000

1500


0 200 400 600 800 10000

500

1000

1500

2000


Figure 7: Absolute Wavelet Coefficients Using Db4 wi th LL Fault at Bus-3

0 500 1000 1500 2000-5000

0

5000

10000

Original Signal

0 20 40 60 80 100 1200

5

10

15x 10


0 20 40 60 80 100 1200

1

2

3x 10


0 50 100 150 200 2500

1

2

3x 10


0 100 200 300 400 5000

0.5

1

1.5

2x 10


0 200 400 600 800 10000

1

2

3

4x 10


Figure 8: Absol ute Wavelet Coefficients Using Db4 wi th LL Fault at Bus-3 wi th Propose d Algorithm


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5. 3 LLG Fault on Power System

The current signal of phase-A at bus 1 with LLG fault on bus 3 and the absolute value of the detail coefficients up

to level 4 and approximation coefficient at level 4 are shown in Figure 9. The current signal and detail coefficient up to


0 500 1000 1500 2000-5000

0

5000

10000

Original Signal

20 40 60 80 100 1200

5000

10000

15000


0 20 40 60 80 100 120 1400

5000

10000

15000


50 100 150 200 2500

1000

2000

3000


0 100 200 300 400 5000

500

1000

1500


0 200 400 600 800 10000

1000

2000

3000


Figure 9: Absolute Wavelet Coefficients Using Db4 wi th LLG Faul t at Bus-3

0 500 1000 1500 2000-5000

0

5000

10000

Original Signal

0 20 40 60 80 100 1200

5

10

15x 10


0 20 40 60 80 100 1200

1

2

3x 10


0 50 100 150 200 2500

1

2

3x 10


0 100 200 300 400 5000

0.5

1

1.5

2x 10


0 200 400 600 800 10000

1

2

3

4x 10


Figure 10: Absolute Wavelet Coefficients Using Db4 wi th LLG Fault at Bus-3 wi th Propose d Algorithm

5.4 Three-Phase Fault on Power System

The current signal of phase-A at bus 1 with three-phase fault on bus 3 and the absolute value of the detail

coefficients up to level 4 and approximation coefficient at level 4 are shown in Figure 11. The current signal and detail

coefficient upto level 4 and approximation coefficient at level 4, as calculated by the proposed method are shown in

Figure 12.

0 500 1000 1500 2000-5000

0

5000

10000

Original Signal

20 40 60 80 100 1200

5000

10000

15000


0 20 40 60 80 100 120 1400

5000

10000

15000


50 100 150 200 2500

500

1000

1500


0 100 200 300 400 5000

500

1000

1500


0 200 400 600 800 10000

500

1000

1500

2000


Figure 11: Absolute Wavelet Coefficients Using Db4 wi th Three-Phase Fault at Bus-3


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0 500 1000 1500 2000-5000

0

5000

10000

Original Signal

0 20 40 60 80 100 1200

5

10

15x 10


0 20 40 60 80 100 1200

2

4x 10


0 50 100 150 200 2500

1

2

3x 10


0 100 200 300 400 5000

5000

10000

15000


0 200 400 600 800 10000

1

2

3x 10


Figure 12: Absolute Wavelet Coefficients Using Db4 wi th Three-Phase Fault at Bus-3 with Propose d Algorithm

5.5 Discussions of Simulation Results

The maximum values of absolute wavelet coefficients and average wavelet coefficients in proposed method for

each type of transmission line faults are provided in Table 3. The current of phase-A captured at bus-1 of the proposed

power system model is used for analysis. Comparing the results of Figure 5 and 6 for LG fault, Figure 7 and 8 for LL fault,

Figure 9 and 10 for LLG fault and Figure 11 and 12 for three-phase fault, it is concluded that proposed algorithm is more

effective for detection of all types of transmission line faults. The magnitude of wavelet coeff icients, as provided in table 3,

is high in the proposed algorithm as compared to the original coefficients which clearly detects the faults. In LG fault, the

magnitude of absolute coefficients is very low, which is not efficient to detect the faults as such magnitude of coefficients

may also be obtained due to other type of power system transients. The presence of fault is detected by the high values of

wavelet coefficients as compared to normal conditions. Double line to ground fault is most severe leading to maximum

unbalancing in the system, which is indicated by the highest values of wavelet coefficients. The three-phase fault, actually

results in same changes in all the phases.

Table 3: Maximum Values of Wavelet Coefficients

Type of Fault

Absolute Maximum Value of Wavelet

Coefficient

Maximum Value of Wavelet Coefficient with

Proposed Algorithm

Cd1 Cd2 Cd3 Cd4 Ca4 Cd1 Cd2 Cd3 Cd4 Ca4

LG 95 140 800 8000 5500 1200 1300 2*104 2.6*10

4 8*10

4

LL 2000 1100 2000 10000 10000 3.8*104 1.6*10

4 2.8*10

4 2.8*10

5 10*10

4

LLG 2000 1400 2000 10500 10000 4*104 1.7*10

4 2.8*10

4 2.9*10

5 11*10

4

Three-phase 1600 1200 1400 12500 10000 2.8*10 12000 2.9*10 3*10 15*10

6. CONCLUSIONS

In this paper, a new DWT based technique using moving window of size 32 samples is proposed for detection of

transmission line faults. The proposed model of the power system is simulated in the MATLAB/Simulink environment.

The results show the relative severity of transmission line faults on the power system. The LL and LLG faults are more

severe and produce maximum unbalancing in the system. The proposed method effectively detects the transmission line

faults, which further can be used for the on-line protection system in the power system. The accuracy of the proposed

method has been found to be high and the consistency of the results demonstrates the effectiveness of the method.

REFERENCES

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AUTHORS DETAILS

Naveen Gaur received Engineering Diploma, from Govt. Polytechnic College, Ajmer, India in 2000. He received

B. E. (Electrical) fro m Rajasthan Institute of Engg. and Tech. Jaipur, India in 2004 and M. Tech. (Power System) from

Bhagwant University, Ajmer, India, in 2014.

Presently he is working as a Principal at Aryan Polytechnic College, Ajmer, India. He also worked as a Principal

at Santosh Adarsh Pvt. ITI, Riya Badi, Nagaur, since July-2013 to Aug-14 and also worked as a Lecturer at Aryan

Polytechnic College, Ajmer from Oct-2011 to July-2013. His research interest includes the power system and power

electronics.

Ram Niwash Mahiareceived his B.E. degree in Electronics instrumentation and Control Engineering from Govt.

Engineering College Bikaner, Bikaner, India and his M.E. degree in Control and Instrumentation under Electrical

Department from Delhi College of Engineering, Delhi, India in 2007 and 2009, respectively. He is pursuing Ph. D. degree

in Information Communication and Technology from Indian Institute of Technology Jodhpur, Rajasthan, India, since

August-2011. From March 2010 to july-2011, he was an Assistant Professor with the Department of Electronics

Instrumentation and Control Engineering, Global Institute of Technology, Jaipur, Rajasthan, India. His research interests

include control of multi-agent systems, nonlinear control, robust control and its applications for uncertain s ystems.


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Om Prakash Mahelawas born in Sabalpura (Kuchaman City) in the Rajasthan state of India, on April 11, 1977.

He studied at Govt. College of Engineering and Technology (CTAE), Udaipur, and received the electrical engineering

degree from Maharana Pratap University of Agriculture and Technology (MPUAT), Udaipur, India in 2002. He received

M. Tech. in 2013. He is currently pursuing PhD from Indian Institute of Technology, Jodhpur, India.

From 2002 to 2004, he was Assistant Professor with the RIET, Jaipur. From 2004 to 2013, he has been Junior

Engineer-I with the Rajasthan Rajya Vidhyut Prasaran Nigam Ltd. (RRVPNL), India.. Presently he has been Assistant

Engineer with RRVPNL. His special fields of interest are Transmission and Distribution (T&D) grid operations, Power

Electronics, Power Quality, Renewable energy s ources and Load Forecasting. He is an author of 34 International Journals

and Conference papers. He is a Member of IEEE. He is Member of IEEE Power & Energy Society. Mr. Mahela is recipient

of University Rank certificate from MPUAT, Udaipur, India, in 2002 and Gold Medal in 2013.


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