Optical Monitoring of Pollution on Porcelain MV ...

Optical Monitoring of Pollution on

Porcelain MV Transformer

Bushings

Filipe A.T. Fernandes (604623)

Supervisors: Professor John Van Coller and Mr Nishal Mahatho (Eskom)

A dissertation submitted to the Faculty of Engineering and the Built Environment,

University of Witwatersrand, Johannesburg, in fulfilment of the requirements for the

degree of Master of Science in Engineering.

Johannesburg, 2019

i

Declaration

I declare that this dissertation is my own unaided work. It is being submitted to the degree

of Master of Science in Engineering to the University of the Witwatersrand,

Johannesburg. It has not been submitted before for any degree or examination to any other

University.

....................................................................................................................................

(Signature of Candidate)

………19th……. day of ……………May…………… year ………2020………….

ii

Publications

2021 – Optical Monitoring of Pollution on MV Transformer Bushings – South African

Institute of Electrical Engineers (SAIEE) – African Research Journal (ARJ): Special

Issue 1.

2020 January – Optical Monitoring of Pollution on MV Bushings – South African

Universities Power Engineering Conference (SAUPEC), Cape Town.

2019 August – Optical Pollution Monitoring of Bushings – Fifth Eskom Power Plant

Engineering Institute (EPPEI) Student Workshop, Eskom Academy of Learning.

2019 January – A Case for Optical Pollution Monitoring of Bushings - South African

Universities Power Engineering Conference (SAUPEC), Bloemfontein.

2018 August – Optical Pollution Monitoring of Bushings – Fourth Eskom Power Plant

Engineering Institute (EPPEI) Student Workshop, Eskom Academy of Learning.

iii

Abstract

This research aimed to optically monitor the dry pollution level on porcelain MV

transformer bushings and determine the possible leakage current should the dry polluted

surface be critically wetted. The research involved the implementation of an image

capturing system with appropriate image processing. Preliminary image capture of four

artificial levels of salt deposit pollution: clean, light, medium and heavy was successfully

achieved. The percentage level of surface pollution was found using image binary

thresholding. A Reflectance Transformation Imaging (RTI) array was designed and

implemented. It facilitated the virtual reconstruction of the imaged surface, yielding 26

different processed images. Twenty trials were conducted, each with a measured leakage

current and Equivalent Salt Deposit Density (ESDD) measurement. A loose exponential

relation was found between ESDD and leakage current. Each trial had a minimum of 250

dry surface images associated with it. A regression model, transfer learning convolutional

neural network (CNN) was implemented based upon the AlexNet image classification

CNN. The regression model was trained using 70% of the image data acquired in the

trials and validated on the remainder. Several iterations of the CNN were tested with

varying data organisation in order to ascertain the highest level of accuracy. The final

CNN had a relative RMSE of 0.3 mA for a predictive range of 0.1 mA to 10 mA. The

standard methods used to classify pollution types and severity are presented. The

dynamics governing bushing flashover under polluted conditions is discussed. The actual

pollution level and type is quantified using ESDD and Non-Soluble Deposit Density

(NSDD). Image segmentation and border extraction are described as a preliminary proof-

of-concept. RTI is described as a more robust method of image processing. With a

saliency mapping resolution of approximately 100 μm, the feature recognition between

salt deposits and bushing surface is more readily attained using RTI.

iv

Dedication

For Mom and Dad – Thanks for always being there for me through the years of my

university education. Your love and support have brought me through many tough times

and I would not be where I am today without you two. All my love.

v

Acknowledgements

I would like to thank Professor John Van Coller for all his guidance and patience with

me. He has made my Honours and Masters years a great and memorable experience.

I would like to extend my gratitude to Eskom and the EPPEI programme for their

financial and academic peer support.

vi

Table of Contents

Declaration ............................................................................................ i

Publications ..........................................................................................ii

Abstract .............................................................................................. iii

Dedication............................................................................................ iv

Acknowledgements .............................................................................. v

Table of Contents ..............................................................................vii

List of Figures ....................................................................................vii

List of Tables ....................................................................................... ix

Glossary of Terms and Abbreviations .............................................. x

Introduction ............................................................................... 1

Problem Specification ............................................................... 4

Literature Review ..................................................................... 9

Methodology ............................................................................ 22

Results ...................................................................................... 46

Conclusion ............................................................................... 62

References .......................................................................................... 63

Appendix A – Arduino Code ............................................................ 66

Appendix B – RTI Calibration......................................................... 77

Appendix C – CNN MATLAB Code ............................................... 80

Appendix D – Laboratory Trials Data ............................................ 83

Appendix E – Regression CNN Training ........................................ 85

vii

List of Figures

3.1 Basic Principal of RTI Image Capture Array (elevation φ) . . . . . . . 17

3.2 CNN basic architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.1 Method block diagram illustrating overall implementation of image

capture and reconstruction, real pollution level dynamics, and their

interactions in their neural network . . . . . . . . . . . . . . . . . . . . . . . .

22

4.2 Schematic diagram of AC leakage current test circuit . . . . . . . . . . 26

4.3 Laboratory test setup for leakage current measurement . . . . . . . . . 28

4.4 μCAM-III demo program used for preliminary image capture . . . . 30

4.5 AutoCAD RTI lighting array solid wire frame . . . . . . . . . . . . . . . . 31

4.6 Image Capture System Schematic . . . . . . . . . . . . . . . . . . . . . . . . . . 32

4.7 RTI LED Array Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.8 RTI Array (foreground) and Controller (background) . . . . . . . . . . 34

4.9 RTI Builder Selection of Calibration Sphere Area . . . . . . . . . . . . . 37

4.10 RTI Builder Prediction of Calibration Sphere Boundaries (Edge

Detection) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

37

4.11 Specular Reflection Highlight Detection (red cross) . . . . . . . . . . . 38

4.12 Blended Output of all the Detected Specular Reflections . . . . . . . . 38

4.13 Positioning of RTI array on the top rib of the test insulator . . . . . . 40

4.14 AlexNET CNN architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

5.1 Leakage Current vs. ESDD with loose trend line . . . . . . . . . . . . . . 48

5.2 Example Set of RTI Images for a Single Trial . . . . . . . . . . . . . . . . 49

asd

viii

5.3 Training Progress of Modified AlexNet CNN for SPS

Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

51

5.4 Regression Model AlexNet CNN Architecture . . . . . . . . . . . . . . 53

5.5 Training Progress of Modified AlexNet Regression CNN for

Leakage Current Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . .

54


Leakage Current Prediction in the Ranges of 0.1 mA to 1 mA . .

56


Leakage Current Prediction in the Ranges of 1 mA to 10 mA . .

57

5.8 Validation Sample of Smaller Order Leakage Current Prediction

Illustrating Input Image, Associated Leakage Current and

Predicted Leakage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

58

5.9 Validation Sample of Larger Order Leakage Current Prediction

Illustrating Input Image, Associated Leakage Current and

Predicted Leakage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

58

B.1 Visualisation of Calibration Position Vectors . . . . . . . . . . . . . . . 79

E.1 Training Progress of Modified AlexNet Regression CNN on all

Image Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

86

E.2 Training Progress of Modified AlexNet Regression CNN on

Image Data Larger than 10 kB . . . . . . . . . . . . . . . . . . . . . . . . . .

87

ix

List of Tables

4.1 Preliminary Images with Binary Thresholding . . . . . . . . . . . . . . . . 36

4.2 Image Rendering Options for PTM and RTI Fitters . . . . . . . . . . . . . 41

4.3 Type A pollution severity ESDD rating for long rod insulator . . . . . 44

5.1 Training data extracted from measurement data showing leakage

current and associated ESDD with SPS classification . . . . . . . . . . .

47

5.2 Example Set of Processed RTI Images for a Single Trial . . . . . . . . . 50

B.1 Calibration Data for RTI Array LED Position Vectors . . . . . . . . . . 77

D.1 : Measured and Calculated Laboratory Data . . . . . . . . . . . . . . . . . . 84

x

Glossary of Terms and Abbreviations

Abbreviation/Term Description

AI Artificial Intelligence

BPNN Back-propagated Neural Network

BRDF Bidirectional Reflectance Distribution Function

CNN Abstract Convolutional Neural Network

DMD Discrete Modal Decomposition

ESDD Electrolytic Soluble Deposit Density

HV High Voltage

NN Neural Network

NSDD Non-soluble Deposit Density

RTI Reflectance Transformation Imaging

RMSE Root Mean Square Error

SPS Site Pollution Severity

1

Chapter 1

Introduction

Effective insulation of HV equipment and transmission lines is vital for the un-

interrupted supply of energy to the customer. Prevention of bushing flashovers can avert

circuit breaker operations and hence interruption of supply.

The majority of monitoring methods regarding transformer bushings are reactive-

based, meaning action is taken only once the bushing has flashed over [1]. This method

relies upon trial and error, requiring bushings to be dimensioned specifically based upon

environmental data, if available. If a transformer bushing suffers a flashover this could

imply inadequate bushing clearances, inadequate specific creepage distance (form factor)

or a poor choice of bushing material [2]. The mean-time-to-flashover correlates closely

with pollution build up. Maintenance of expensive equipment should not be reactive-

based.

By following a more proactive approach to pollution monitoring, potential

bushing flashover can be forewarned. This is beneficial to utilities in that maintenance

and potential countermeasures can be effectively and efficiently made before a flashover

occurs. Instead of routinely shutting down transformers and spending unnecessary

resources on inspecting substation equipment, an automated method of inspection linked

to a central control station can be implemented. This ensures that call-outs or supply

interruptions are made only under critical maintenance circumstances.

Chapter 1. Introduction 2

Presented is a method of optically detecting the type and level of pollution on the

surface of porcelain MV transformer bushings. The determining of pollution type and

level alone is insufficient to evaluate the likelihood of bushing flashover. Flashover is

more probable under wetted surface conditions. As such, the detection algorithm should

infer the leakage current under wetted conditions, based upon readings of dry or partially

wetted (due to the ambient humidity) pollution levels. Ideally, site leakage current

measurements should be evaluated against the imaged pollution levels to evaluate the

need for bushing cleaning. Thus, any optical readings made in terms of dry pollution type

and level must be made relative to measured leakage current values under critically

wetted conditions. A more complicated algorithm could be implemented for pollution

that is already wetted so as to predict whether further wetting would result in leakage

currents increasing to a point of flashover. Measuring humidity would increase the input

information to the algorithm, thus allowing better prediction as to how much more

moisture would be required for flashover.

Chapter 2 provides an overview to what the objective of the research is,

highlighting the hypothesis, research question, requirements and scoping associated with

the implementation of the imaging system. Chapter 3 contains the literature survey. The

methods of insulator dimensioning are presented in Section 3.1, to understand how

bushings are selected for a particular site. Section 3.2 provides background into the two

types of pollution that occur. Section 3.3 details the three main environments concerned

with site pollution severity, providing reasoning for the specific pollution monitoring

requirements of the project. Thereafter, the mechanisms governing flashover across

polluted bushings are presented, to better understand the aim of the research. Section 3.5

illustrates the standard method used to measure bushing surface pollution numerically in

terms of conductivity and mass, important in controlling test samples. Sections 3.6 and

Section 3.7 detail the two imaging techniques used for surface reconstruction and feature

extraction. Section 3.8 presents a brief background into convolutional neural networks,

the main tool for leakage current prediction.

Chapter 4 describes the research methodology in detail pertaining to the steps

required in obtaining training data and the details regarding the design of the monitoring

Chapter 1. Introduction 3

system. The method and test circuit used for leakage current measurement is presented in

Section 4.1. The following section describes the design and implementation of the image

capture system, where the specifications for the RTI array are presented along with the

associated control circuitry. Section 4.3 describes the imaging methods used for binary

segmentation, as a preliminary proof-of-concept. The imaging techniques implemented

for the monitoring system are illustrated in Section 4.4, where details regarding the

methods for RTI reconstruction are presented. Section 4.5 describes the neural network

architecture implemented, offering insight into how transfer learning is used to benefit

the rapid implementation of the monitoring system.

Chapter 5 presents the performance results of the monitoring system. In this

chapter, a consolidated table of the trials conducted is presented. A relationship between

ESDD and leakage current is drawn and its validity discussed. Several iterations

pertaining to different CNN architectures are presented and the systematic improvement

of predictive accuracy discussed. A conclusion is drawn on the success of the monitoring

system in Chapter 6 along with recommendations for improvement of the design and

potential future work.

4

Chapter 2

Problem Specification

2.1 Hypothesis

An optical method of monitoring pollution levels on porcelain MV transformer

bushings can determine the type and level of surface pollution and predict the leakage

current if the surface pollution were to be wetted, thereby indicating how close the

bushing is to flashover. Furthermore, this method of optical monitoring could reduce

the need for testing of MV transformer bushings that are in service.

2.2 Research Question

How effective would a method of optical detection be in remotely monitoring

the pollution levels on the surface of transformer bushings relative to measured

leakage current and how would this method compare with existing methods of

pollution monitoring?

Chapter 2. Problem Specification 5

2.3 Rationale

The local utility has a long track record in dealing with industrial pollution, in

particular for a coal-fired power station HV yard. However, over the past few years,

renewables have started to appear as a significant portion of South Africa’s power

production [3]. They could be located close to the sea and hence are subject to marine

pollution. One such example is the Sere Wind Farm, located on the coast of the

Western Province, South Africa. Owing to its geographical location and proximity to

the Tormin mineral sands mine, this site is prone to bushing flashover. This site in

particular has suffered flashover in the past due to marine and industrial pollutants

deposited on the surface being critically wetted.

It would be ideal if the monitoring of transformer bushings, particularly those

prone to high pollution levels, could be done without the need for inspection call-outs.

The research forewarns of possible flashover under wetted conditions by monitoring

the dry pollution on porcelain MV transformer bushings.

2.4 Requirements and Success Criteria

The aim of this research was to develop a method of optically imaging

pollution levels on the surface of porcelain MV transformer bushings. The pollution

types were not restricted to only dry pollution types. The type (marine or desert) and

level of dry pollution had to be measured and related to a leakage current value

correlating to the same pollution type and level under wetted conditions. The leakage

current was the relative gauge used to assess the severity of the surface pollution, the

methods of which are described in Section 8 of SANS 60815-1 – Evaluating Site

Pollution Severity (SPS) [2]. Humidity levels at dawn are a major threat to porcelain

bushings. The ambient humidity together with high dry pollution levels are the main

contributor to high leakage currents experienced on polluted bushings. The leakage


current can be thought of as the probability of flashover. The imaging system thus

inferred how close the bushing was to flashover.

The success of this research depended on the effectiveness of the implemented

method and design, in that the following criteria had to be met:

An optical method must be safe.

Desert and marine pollution must be identified.

The levels of surface pollution must be measured by the imaging method.

By analysing the dry pollution level, the imaging system must estimate the

leakage current that would flow if the surface pollution were to be wetted to

the extent that flashover could occur (critically wetted).

Measurement of the SPS must allow warning of probable flashover, notifying

maintenance staff that cleaning is required.

Secondary success criteria would include the expansion of the optical methods

to transmission line insulators. If the insulators are polymeric this would require the

benchmarking and comparison of flashovers occurring on insulators reaching their

end of life (at approximately 40 years of service). This would be a precautionary

action, emphasising the monitoring of the insulator until it were to be replaced. This

would determine how effective the insulator could possibly be without its

hydrophobic properties.

2.5 Constraints

The research did not have strict constraints governing the complexity or cost

of providing a solution. It however had scoping that focused on the intended solution.

Firstly, only outdoor porcelain type bushings were considered as these are the types

installed at the Sere Wind Farm, (further detailed in Chapter 3.3 – Site Pollution

Severity). Secondly, again relating to the location, only marine and desert pollution


were considered, needing to be identified and differentiated. Thirdly, only medium

voltage levels were considered.

In order to ensure repeatability of the experimental design, various factors had

to be considered. Due to the varying lighting conditions during daytime, the bushing

surface would only be imaged at night under well-defined artificial lighting using a

dome-shaped LED array, implemented for the RTI. The image resolution was fixed at

640x480 which coincided with the reported resolution of the camera implemented in

this monitoring system. The resolution was fixed to avoid mismatched data sets and

potential errors that could arise therefrom. The images used for the monitoring system

were that of an artificially polluted bushing, to better control what types of pollutants

were present on the bushing surface and their levels. The methods of artificially

polluting bushings are outlined in SANS 60815-1 [2]. The rib dimensions of the

bushing used in this investigation were fixed to that of a typical 33 kV porcelain

bushing for a wind farm step-up transformer. All measurements were made on the

same bushing. The measurements of leakage current were conducted in a lab under

controlled conditions (within a dark-room to emulate night-time conditions).

With regard to the AI aspect of the imaging system, the aim was to effectively

estimate the leakage current under critically wetted conditions within an acceptable

range of certainty. The AI method should effectively produce this result with relative

simplicity, without having to assess and compare more complicated and advanced

methods of AI. In this regard, an Abstract Convolutional Neural Network (CNN) was

used to extract certain feature maps from the input images and ultimately estimate the

leakage current therefrom. The combination of image feature extraction and a working

neural network structure ensured the highest chances of prediction accuracy. The

network used would not be built from the start, but rather transfer learning was used

(based on a prebuilt, pre-trained CNN) owing to the limited amount of data initially

available. This form of AI was the chosen technique to output the quantitative measure

of the ESDD, along with the associated leakage current, for the general case (whilst

remaining within the constraints illustrated above).


In order to fully realise the effectiveness of the implemented CNN as much

data characterising the captured images had to be extracted. Maximising the image

input data ensured that the output predictions were as accurate as possible. Image data

extraction had to include parameters of coverage (surface area of the bushing that

contains pollution), area ratio (the ratio between polluted and clean bushing surface

area), eccentricity (the relative circularity of pollutant particles), shape factor (the

shape and grouping of pollutants on the bushing surface), reflectance (relative

specularity of pollutants) and saliency (the surface topology of the bushing).

9

Chapter 3

Literature Review

3.1 Insulator Selection and Dimensioning

In practice, bushings dimensioned for use in areas with predicted high levels of

surface pollution follow three main approaches as described in SANS 60815-1 [2]: past

experience; measure and test; and measure and design. The approach based on past

experience is usually the primary method used in bushing dimensioning. Here,

experience from existing sites is applied to nearby sites or sites with similar conditions.

The other two approaches entailing measurement, testing and design require time and

resources [4]. When site conditions are different these methods have to be considered.

Alternatively, if the first approach is to be followed (in which experience need be

gathered), an increase in flashover rates can be expected.

Measurement and testing requires first that the SPS be either measured or

accurately estimated. A variety of suitable bushings are then selected and tested in a

laboratory under various criteria, described in IEC 60507 [5], so as to verify their

viability at field sites. As mentioned earlier, this approach requires further resources and

expenditure than the first approach as testing requires several bushings to be considered

on a trial and error basis. Finally, similar to the second approach, measurement and

design also requires SPS evaluation. However, instead of running trials on existing

bushings, a new bushing is designed to cater for the field site pollution requirements [4].

This method, while surpassing the other two in dimensioning bushings for new field

Chapter 3. Literature Review 10

sites, requires the most resources in both design and manufacturing of a completely new

bushing [4].

3.2 Pollution Types

The environmental conditions in which bushings have to operate will change their

performance characteristics. As explained by CIGRE Taskforce 3.04.01 [6], flashover

related to pollution across insulators may arise as a result of two main types of pollution:

Type A and Type B.

Type A pollution refers to any solid pollution with soluble or non-soluble

components. This is the dry component of surface pollution. Soluble pollution describes

both high and low solubility salts deposited on the bushing surface that can form a

conductive layer when wetted. These arise most commonly in coastal environments,

where the vast majority of soluble pollution is fast dissolving salts deposited on the

surface by sea spray-wind and fog. The severity of this form of pollution is quantified

using the Equivalent Salt Deposit Density (ESDD), described further in Chapter 3.5 –

Conventional Measurement of ESDD and NSDD. The non-soluble components of

Type A pollution that are of importance include sand and dust. These non-soluble

components can serve as a binding layer for soluble pollution and can also contain

conductive particles. The severity of this form of pollution is quantified using the Non-

Soluble Deposit Density (NSDD), described further in Chapter 3.5 – Conventional

Measurement of ESDD and NSDD.

Type B pollution refers to liquid electrolytic pollution with sparse non-soluble

components. This type of pollution is most commonly found in coastal areas where salt

fog is deposited on the bushing surface. Furthermore, the ambient humidity leads to

surface wetting of Type A pollution described previously, yielding a combination of

Type A and Type B pollution.


3.3 Site Pollution Severity

It is important to consider the environment that characterises the type of pollution

in an area. The various measurements of SPS depend on the type of pollution present in

the region.

3.3.1 Desert Type Environments

This type of environment is generally described as having areas of sandy soil and

periods of dry conditions. In these regions, the dominant pollution type is airborne low

solubility salts present in the surrounding sandy soil, combined with high NSDD Type A

pollution. Often, wetting by dew will increase the likelihood of bushing flashover.

3.3.2 Coastal Type Environments

These areas near the coast are affected by rapid build-up of Type B pollution

from wind, fog and ocean spray. The extent of the environment depends on the natural

topography of the area, extending up to 50 km inland in some cases. Over extended

periods a Type A soluble pollution layer can be formed from the evaporation of Type B

pollution.

3.3.3 Industrial Type Environments

Industrial areas are prone to both Type A and Type B pollution. Normally, this

form of pollution arises from heavy particles forming a deposit on the bushing surface.

Type A pollution contains low solubility components such as gypsum. Type B pollution

includes high conductivity particulates such as coal or dissolved nitrogenous and

sulphurous gases.


The three environments described above apply to the Sere Wind Farm, located in

the north-western fynbos biome in the Western Province, South Africa. The area has

small shrubs growing in sandy types of soil and is not subject to much rain, thus

categorising the area as a desert type environment. The site extends about 10 km inland,

to a maximum elevation of approximately 120 m, thus susceptible to both the Type A

and Type B pollution of a coastal type environment. The Tormin coal mine is located

almost 2 km from the closest turbine at the Sere Wind Farm. Tormin is a coastal

mineral-sands mine harvesting coal from mineral rich coastal sand deposits. The area is

thus prone to Type B industrial pollution, pertaining to high conductivity particulates.

As illustrated above, the Sere Wind Farm is prone to a mixture of Type A and

Type B pollution from the desert, coastal and industrial environments. The interactions

between each of the environmental pollution types increases the dynamics of the surface

pollution. For example, the ocean spray of Type B coastal pollution could critically wet

already deposited desert and industrial Type A pollution. Furthermore, dew, occurring

in a desert environment could in turn critically wet coastal and industrial Type A

pollution.

3.4 Pollution Flashover Mechanisms

As described in Annex B of SANS 60815 [2], the flashover mechanisms across

insulators can be divided into six parts. These parts describe flashover for a hydrophilic

surface with Type A surface pollution deposits. A hydrophilic surface when wetted

forms a complete electrolytic film along the surface of the bushing, compared to

hydrophobic surfaces that tend to form an incomplete layer of droplets. Considering MV

for this research, the arc propagation may complete several cycles of extinction and re-

ignition before complete flashover occurs. Furthermore, the rib dimensions of the

bushing itself may also worsen the flashover performance of the bushing by potentially

shorting some sections of the bushing. The six phases are described as follows:


1. A bushing is coated first with a layer of Type A pollution. Under dry

conditions the layer has very low conductivity, thus requiring the bushing

to thereafter be critically wetted before flashover may occur.

2. Critical wetting occurs from various sources. Firstly, moisture absorption

occurs as a result of high relative atmospheric humidity (>75%), coupled

with similar ambient and bushing surface temperatures. Condensation

forms on the bushing surface when its temperature is less than the dew

point, usually occurring at dawn. Finally, rain may either wash the surface

or assist in breakdown by shorting sections across the ribs of the bushing.

3. Once the surface is polluted and wetted, leakage currents begin to flow. The

heating effect of the leakage current forms dry bands at the narrowest point

along the bushing.

4. The formation of dry bands breaks the conduction path of the leakage

current.

5. A surge in leakage current occurs when the line-to-earth voltage across the

dry bands is substantial enough to cause air breakdown, where the bridged

arcs are in series with the wetted portions of the bushing.

6. Flashover will occur if the bridged arcs are sustained long enough and the

resistance of the wetted surface is low enough such that more and more of

the surface is bridged by arcs. The subsequent decrease in series resistance

will eventually bridge the entire bushing, causing a phase-to-earth fault.

The flashover process is dependent upon the interaction between the insulator

material properties, pollution layer, wetting conditions and applied voltage. Flashover is

more probable at higher leakage current levels and as such is dependent upon the surface

resistivity. The surface resistivity of a uniformly polluted distribution is found using the

form factor (Ff) described as a surface integral in (1).


𝐹𝑓 = ∫𝑑𝑙

𝑝(𝑙)

𝐿

0

l is the length of the partial creepage distance and p(l) is a function that describes

the insulator width as 2πr(l).

3.5 Conventional Measurement of ESDD and NSDD

Annex C of SANS 60815 describes the methods required to determine both the

ESDD and NSDD on bushing surfaces [2, 7]. The ESDD measurement is made based

upon a saline solution whose salt component is the soluble pollution on the bushing

surface. It is described as the relation between salinity (Sa) volume per bushing surface

area of collected pollutants (A), as illustrated below in (2).

𝐸𝑆𝐷𝐷 = 𝑆𝑎𝑣𝑜𝑙

𝐴

Sa is the salinity of the solution (kg/m3), V is the volume of distilled water used

to prepare the solution (cm3) and A is the bushing surface area from which pollutants

have been collected (cm2). Sa is described as a function of volume conductivity at 20 °C

(σ20), illustrated below in (3).

𝑆𝑎 = (5.7𝜎20)1.03

𝜎20 = 𝜎𝜃[1 − 𝑏(𝜃 − 20)]

The volume conductivity at 20°C (S/m) is described in (4), where: θ is the

solution temperature (°C); σθ is the volume conductivity at θ°C (S/m); and b is a factor

dependant on θ, the calculation of b is illustrated in Clause 7 of IEC 60507 [5].

(3)

(4)

(1)

(2)


In determining NSDD, non-soluble pollutants are filtered out of solution, dried

and weighed. NSDD (mg/cm2) is determined using (5) below.

𝑁𝑆𝐷𝐷 = 1000(𝑊𝑓 −𝑊𝑖)

Wf is the combined weight of filter paper and pollutants (g), Wi is the initial

weight of filter paper without pollutants (g) and A is the bushing surface area from

which pollutants have been collected (cm2).

3.6 Image Segmentation and Border Extraction

With the pollution types, discussed in Section 3.3 – Site Pollution Severity, and

the methods used to quantify the type and level of pollution on bushings, discussed in

the previous section, certain methods of image data capture are considered. The first,

described in this section, relates to 2D image data used for the purpose of defining a

preliminary measure of pollutant coverage on the bushing surface, illustrated further

in Section 4.3 – Image segmentation. The implemented method of image capture,

Reflectance Transformation Imaging (RTI), considers the bushing surface and the

pollutants present as a 3D data-source, discussed further in the following section.

As described by Wang et al. [8], it is possible to determine the level of

hydrophobicity on the surface of non-ceramic transformer bushings using imaging

techniques. Wang et al. describe a method of binary thresholding images and the use

of the Canny operator for edge detection. From this image processing, four parameters

are used to describe the hydrophobic grade of each image: water area ratio (average

ratio between water droplet area and dry surface), water coverage (overall area of

water on the surface), shape factor (the shape and grouping of water droplets on the

surface) and eccentricity (relative circularity of the water droplets). These parameters

form part of the input layer of a four-layer probabilistic neural network. The four

imaging parameters of 140 samples are passed through the trained neural network,

upon which each image is assigned one of seven hydrophobic grades, along with a

percentage of certainty.

(5)


The technique of parametrising captured images may allow for estimation of

the quantity of pollution and to an extent the pollution type. To the latter end, the

eccentricity may be used to determine the shape of the pollutants, thereby assessing

what type of pollution is present. However, in order to truly determine the shape of

pollutants, a higher order of magnification is required, not just a macroscopic view of

the surface. The overall quantity of surface pollution may be determined by the area

ratio and coverage.

3.7 Reflectance Transformation Imaging

As previously mentioned, RTI is a method of representing raw image data as three

dimensional image data. Although mostly focused primarily for use in the evaluation of

artwork and the examination of archaeological specimens [9, 10], this imaging method

has shown promise in the engineering field, as illustrated by Coules et al. where RTI was

used in the visual analysis of mechanical components that had failed (fatigue cracks and

ductile tearing) [11]. RTI is used to represent the surface saliency of objects. An RTI

image set has to be obtained of the surface in question. The RTI image set consists of

images of the surface under varying illumination from different light sources, but imaged

from a fixed viewpoint. Thus, each image in the set (normally 40 images per set)

represents the surface imaged from a particular lighting source/position vector relative to

a fixed viewpoint. Figure 3.1 represents the arrangement of an RTI array, using the actual

illumination position vectors used in this research. Further detail pertaining to the

implementation of the RTI array is discussed in Section 4.2 – Image Capture System and

in Section 4.4 – Image RTI Processing.

It is noted that the viewpoint must be orthogonal to the surface in order to achieve

the highest accuracy of surface saliency reconstruction. If the surface is slanted, shadow

artefacts arising from the varying illumination would accentuate certain angles (those in

the opposite orientation to the plane) and diminish angles orientated in the same direction

to the plane.


On a pixel level, each image in the RTI image set represents the subject pixel of

the surface under a certain illumination condition. The incident light direction vector of

the particular illumination condition is used to derive a continuous function for each of

the RGB values of the pixel in question. Thus, in mapping each of the subject pixels

(consisting of the same relative pixel in each image), the saliency of the imaged surface

is captured.

Various techniques exist for the saliency mapping of imaged surfaces, such as

the Bidirectional Reflectance Distribution Function (BRDF) [12]. However, as

Pitard et al. explain [13], these techniques are complex and time-consuming to

implement. As such, Pitard et al. propose a novel method of saliency mapping used

for RTI. In order to reconstruct the saliency map, each pixel is imaged orthogonal to

the surface at varying lighting positions as required. As such each pixel to be

represented by an nth dimensional set of values, with each corresponding to a set of

illumination values. The angular reflectance of each pixel is interpolated using three

methods, with the most recent, Discrete Modal Decomposition (DMD), proving to be

the most resolved [14].

FIGURE 3.1: Basic Principal of RTI Image Capture Array (elevation φ) [11]


Pitard et al. are able to reconstruct saliency to a resolution of 1.25 μm in width

and 58.32 μm in depth, with visibility of 65% [13]. As such, the shape and

arrangement of pollutants on the surface are more accurately represented. This is of

importance in differentiating between clumps of coal, desert dust, and crystalline salt

formations. The shape and eccentricity, described in the previous section, is resolved

to approximately 100 μm. This increased resolution facilitates a more accurate data

set representation and a better probabilistic outcome from the neural network in

determining pollution type.

Another technique, developed by Malzbender et al., is Polynoimal Texture

Mapping (PTM) [15]. Following the same requirements of each pixel being

represented by an nth dimensional set of values corresponding to a set of illumination

values, PTM models the luminance of each pixel as a biquadratic function of the

projection of the position vector of the illumination source in question. Finally,

Hemispherical Harmonics (HSH) differs only in the method of representing the pixel’s

luminance relative to the lighting vector. As Gautron et al. explain, HSH maps the

functions of each pixel to a 2nd or 3rd order Legendre polynomial, hemispherical in

basis [16]. These techniques, pertaining to the software-based solvers used to realise

the surface reconstructions, are further discussed in Section 4.4 – Image RTI

Processing.

3.8 Convolutional Neural Networks

Neural networks are a powerful tool used in Artificial Intelligence (AI) consisting

of three distinct layers: input, hidden and output. Neural networks are trained in

probabilistic prediction or supervision. Various methods exist when considering

implementation and learning of the neural network. As described by Oukrich et al. [17],

one such technique is the Back-Propagation Neural Network (BPNN). This method

functions in linking the various hidden layers to the input and output layers by means of

weighted set-values. The gradient of a loss function is used to update error values which


in turn are used to update the weighted set values within the network. A predefined

threshold is set for the number of iterations the network is back propagated, dependent

on the accuracy required. Once complete, the neural network is considered trained, with

respect to that data set.

When dealing with image input data, traditional fully-connected neural networks

loose predictive accuracy when complex images with several spatial and temporal

dependencies are considered. It would seem logical that one only need flatten the image

matrix to a Nx1 vector and use that as the input to a BPNN. However, as Cireşan et al.

explain [18], doing so would not only overlook spatial pixel dependencies within the

image, but also increase the hidden units in the BPNN to extremely large quantities (for

a 640x480 image – 307,2k hidden units, resulting in 95’000 million parameters). Thus,

in order to make better use of computational power, areas of the image are addressed on

a per-turn basis. This is known as image convolution. A kernel or filter, of a certain matrix

dimension is passed over the image matrix, much the same as graphical convolution of

functions. The output of the convolution of the kernel and image matrix are feature maps.

The feature maps extract high-level features, those spatial and temporal dependencies.

The extent of feature extraction, and type, is dependent on the size, weighting and stride

pattern (how the kernel is multiplied along the image matrix) of the kernel. In order to

reduce the spatial size further, pooling is done on the various feature maps obtained from

the convolution. Pooling of the feature maps is done either as a mean of values of a region

or as a maxima of values of a region. The process of convolution (obtaining feature maps)

and pooling (selecting certain features from the feature maps) is repeated two to three

times, depending on how the neural network is engineered. The output from the entire

convolution process is then flattened (i.e. Nx1 matrix where N is the total number of

output elements, for example a 3x3 matrix is expressed as a 9x1) such that the values are

fed to the traditional fully-connected back-propagated neural network described earlier.

The overall topology of a CNN is expressed in a simplified diagram in Figure 3.2.


as

As image based deep-learning problems often require vast amounts of data,

transfer learning is used as a preliminary approach. Transfer learning can be described as

a method of re-purposing and existing neural network architecture that has been

previously trained and shown to have a high level of accuracy in its predictive ability,

relative to what the neural network is targeting as the output (e.g. classifying certain

images such as architecture or animals, or fitting a regression line to predict a certain

floating-point value such as angle of slant in images of handwritten numbers). The pre-

trained neural networks used for transfer learning thus already have predetermined set-

weights in the various nodes that offer the greatest predictive accuracy for the relevant

task. It is especially useful in the initial stages of a project, where limited data is

available [19]. Large volumes of image data are required to not only train the neural

network, but to also engineer weights and topologies of kernel convolution layers [18].

By implementing transfer learning the overall time required to design a new CNN

topology is reduced. It can also be beneficial to use an existing CNN topology and adapt

part of its structure to meet the particular requirements of a different deep-learning

problem [19]. Furthermore, as transfer learning networks are pertained networks, some

of the potential feature maps already identify key spatial dependencies that may be useful

in a new deep-learning problem (edge detection, clustering). This pertains to the pre-

processing images require in a CNN topology. In order to adapt these networks to new

deep-learning problems, changes to the last few layers of the network, including the

FIGURE 3.2: CNN basic architecture [13]


output layers, are made. The changes range from structural topology changes, to weight

bias changes. Once the network has been modified to meet the new requirements, it is re-

trained (using back-propagation and an error loss-function) on the data pertaining to the

new problem. The re-training slightly adjusting weights within the CNN to suit the

predictive requirements of the new deep-learning problem.

22

Chapter 4

Methodology

Figure 4.1 illustrates the methodology for the research. The various

components of the system are divided into three main sub-components. Each of the

various components was implemented separately following an iterative process.

Iterations were implemented to ensure expected functioning of each separate

component.

FIGURE 4.1: Method block diagram illustrating overall implementation of

image capture and reconstruction, real pollution level dynamics, and their

interactions in their neural network

Pollute Bushing

Leakage current measurement

ESDD & NSDD

Pollution Level

Leakage Current Value & Certainty

Pollution Level Detection

Neural Network

RTI Data Acquisition

Visual Saliency Estimation

Image Acquisition

Image Segmentation

Image Border Extraction

Feature Extraction

Imaging & Reconstruction

Ground Truth

Chapter 4. Methodology 23

The implementation of the optical monitoring is highlighted in further detail:

1. Initially, ESDD type pollution was considered (NaCl).

2. A dome-shaped LED array was built around the camera, with the camera

being oriented vertically and located at the peak of the dome (required

for the RTI to function properly). The LED array functioned as a well-

defined artificial light source, improving future repeatability.

3. RTI and greyscale images were obtained of a polluted bushing on the top

shed, the reasoning of which is explained in Section 4.1 – Leakage

Current Measurement and Section 4.5 – Neural Network Architecture.

The bushing itself and the camera positioning however did not change.

The images of the polluted surface were needed to test whether the

relevant image capture methods were working correctly.

4. Once the image data was acquired, the relevant image feature extraction

techniques were tested: coverage, area ratio, eccentricity, shape factor

and reflectance. With the imaging system operational, it was then

correlated with measured pollution levels.

5. With a functional image capture and feature extraction system,

laboratory measurements began with varying levels of NaCl. Five

different salinity ranges were considered along the ESDD scale as

illustrated in SANS 60815-1 [2]. A total of 20 trails across the entire

range were made.

6. The bushing was first cleaned as per SANS 60815-1 [2].

7. A saline solution of known concentration (0.2 kg/m3) was prepared and

applied (via spray) to the bushing.

8. Whilst the bushing was still wetted, a leakage current measurement was

made (described in more detail in Section 4.1 – Leakage Current

Measurement).

9. The bushing was left to dry and was then imaged using the RTI array

(described in more detail in Section 4.5 – Image RTI Processing).


10. The pollutants were then washed off into a solution and the ESDD value

obtained. It was noted that SANS 60815-1 [2] measures the ESDD before

application of the pollutant to the bushing. As it is a destructive

measurement, a post-critical-wetting measurement is required for higher

accuracy. It is this post-polluting measurement that was used as the true

ESDD.

11. The particular NaCl pollution level was now characterised by two

measurements of ESDD (prior and post application), two dry pollution

image sets (RTI and greyscale totalling a minimum of 41 images per trial)

and a leakage current under critically wetted conditions.

12. The NaCl solution concentration was increased and Steps 7 through 11

were repeated until no more NaCl dissolved (22.5 kg/m3). At this point,

instead of measuring leakage current straight after the application of the

solution, the surface was left to dry before another application of the

saline spray.

13. Step 12 was repeated until the ESDD scale in SANS 60815-1 [2] was

adequately sampled (~20 points along the axis). This was to ensure full

representation of the scale and the effect of ESDD level on leakage

current. It also ensured that the neural network had sufficient data sets to

be trained and sufficiently varying data sets to be tested (i.e. training data

different from testing data.)

14. With the actually measured values obtained in Step 11, AI was used to

predict ESDD and leakage current from the image features alone. As each

set of images had an actual ESDD and leakage current measurement,

those served as the actual values used for training the CNN. The neural

network was trained on 70% of the acquired data set, and tested for

classification accuracy with the remaining 30%.

Further work could include:

15. The process will revert to Step 5, with a combination of ESDD and

NSDD.


16. Each level of ESDD acquired in Steps 7 through 11 will be paired with

five fixed levels of NSDD (fixed non-soluble mass of desert sand).

NSDD is limited to 5 levels to reduce the overall dimensionality of the

samples acquired (150).

17. Optimisation of the system to increase the certainty of the CNN and the

series of NSDD values.

18. Once the monitoring system is proven and making predictions with a

suitable level of certainty, the hardware aspects related to field

implementation are considered (i.e. remote implementation over a

wireless network).

19. Two image capture devices are set up on two separate bushings (camera

and LED array), where the image data is sent to a central computer where

each data set of each device is passed through the locally trained and

optimised NN.

20. The output data is then presented in a GUI that will also monitor the

operation of the devices and control the scheduling of image capture for

each device.

4.1 Leakage Current Measurement

In order to relate the detected pollution (from the imaging system) to the

measured pollution (from the artificially polluted ESDD and NSDD levels), a leakage

current measurement under critically wetted surface conditions was required.

Zhao et al. describe a method for flashover voltage prediction based on leakage

current [20], similar to what is required for this research. Zhao et al. artificially

polluted a bushing with ESDD type pollution and measured the leakage current using

a test setup similar to that illustrated in Figure 4.2 [20]. The value of leakage current

is calculated from the voltage of the two series connected non-inductive resistors: a

50 Ω resistor for smaller leakage current measurement and a 2 Ω resistor for larger

leakage current measurement.


In the Zhao et al. setup, the important parameter of critical wetting is

controlled for 100% humidity at 10°C in an artificial fog chamber [20]. However,

Shiny and Prakash provide an alternative to the fog chamber to control critical wetting

by constantly spraying the surface with fine mist between U50 tests [21]. This method

simplifies the overall experimental setup and was deemed sufficient in capturing the

leakage currents relevant to the scope of this research. The method of prediction

served the purpose of supplying the data required for training the neural network,

where a known level of ESDD was paired with a measured leakage current.

Figure 4.2 depicts the test circuit implemented, based upon the setup by

Zhao et al. [20]. The circuit was slightly modified from the original, and its

application altered. The voltage applied to the 240V/33kV transformer was controlled

using an auto transformer. This was done to ensure that all leakage current tests were

conducted at the same phase rated voltage of the bushing, for a single phase-to-earth

of 33𝑘𝑉

√3. In the setup by Zhao et al., increasing voltage is applied until breakdown was

reached [20]. Even though the HV transformer made available had an atypical rating

of 40kV open load, the decision to conduct the leakage current measurements at the

phase voltage was three-part in nature. Firstly, the insulator itself was rated at

33kVphase, thus using the equivalent line voltage would yield a potential of √3 × 33𝑘𝑉

across the bushing. Hence, to get the equivalent line rating of the bushing (phase-to-

FIGURE 4.2: Schematic diagram of AC leakage current test circuit


earth), 33𝑘𝑉

√3 was used as the maximum applied voltage during tests. Secondly, in order

to ensure stability of the voltage applied, the transformer was not operated under full

load conditions (i.e. at full rated voltage), to ensure there was still enough headroom

for current supply at the desired phase-to-earth voltage (33𝑘𝑉

√3). The use of the auto

transformer ensured that the voltage supply to the HV transformer was adequate to

both maintain the voltage potential across the bushing, and maintain the leakage

current while the bushing was under polluted and wetted conditions. (It is noted that

initial tests were done at 33kV phase-to-earth and they yielded excessively high

leakage currents for a bushing under non-polluted conditions. Once polluted with

ESDD rated more severe than light, the measured leakage current plateaued, reaching

the current limiting value of the current limiting resistor.) Finally, the aim of the

research was to forewarn the possibility of flashover (i.e. at what level of ESDD, and

by extension the leakage current, did the measured leakage current exceed the

acceptable standards in SANS 60815 [2]). Thus, conducting tests above rated voltages

would prove unnecessary for the scope of the research. Furthermore, the

implementation of the neural network was such that it required a narrow range of

leakage current values (within the same order of magnitude) to provide accurate

predictions based on dry ESDD images of the bushing surface. This phenomenon is

explained in Section 4.5 – Neural Network Architecture and illustrated in

Section 5 – Results.

The current limiting resistor was measured using a Fluke 107 multimeter and

had a value of 780kΩ ±0.4%. Current limiting was necessary in order to firstly, ensure

leakage current values were within a useable range as described above and illustrated

in Figure 5.1: Leakage Current vs. ESDD with loose trend line, and secondly, to

ensure the HV transformer was not operated under excessively high currents, should

the bushing have flashed-over. The leakage current measurement resistance was

measured in the same way with a value of 63.7Ω ±0.1%, slightly higher than that in

Figure 4.2. This equivalent resistance was achieved by placing several carbon

composition resistors in parallel. The type of resistors used and the configuration

thereof was chosen to decrease the parasitic inductance and increase the power rating,

respectively. It was assumed that the overall parasitic inductance was too small to


affect voltage measurements across the leakage current measurement resistance.

Furthermore, the bushing was placed in the centre of a cut out metal plate that served

the role of the tank of a transformer. The base of the bushing was coupled to the

ground plate. The ground plate was connected in series with the leakage measurement

resistance before being earthed. This was considered enough parasitic capacitance to

counteract any parasitic inductance present within the leakage current measurement

resistance.

The device used to measure the voltage applied to the bushing was a

MajorTECH MT-883 multimeter (V1 in Figure 4.2). The multimeter was connected

to a Fluke 80K-40 HV probe (1000:1) which was hooked to the top copper coupling

of the insulator in the laboratory setup illustrated in Figure 4.3. The figure however

shows the probe hooked, along with the earth-stick, to the HV output of the

transformer (this was a safety measure during non-operation). During testing, the auto

transformer was increased gradually from 0V until the reading on V1 was 33𝑘𝑉

√3. This

voltage was maintained for five seconds to observe the maximum peak in leakage

current, measured as a voltage across the leakage current resistance using a Fluke 107

multimeter (V2 in Figure 4.2). A period of 5 seconds was chosen to be long enough

to observe the maximum (i.e. worst possible) leakage current that could flow under

FIGURE 4.3: Laboratory test setup for leakage current measurement


the particular polluted conditions before surface heating would dry the wetted

pollution. The ammeter (A in Figure 4.2) was an analogue Taylor Model 50. This was

an initial validation precaution to ensure the sampling rate of the digital multimeter

(V2 in Figure 4.2) was high enough to detect any peaks in leakage current. The

ammeter served as a visual aid rather than a measurement device and in reality was

connected after the leakage current measurement resistance. It was however omitted

from the circuit in the tests used as part of the data-set for the neural network.

4.2 Image Capture System

A preliminary image capture system was implemented using 4D Systems’

μCAM-III. In order to capture images for preliminary analysis of the various imaging

techniques, the pre-programmed on-board capture system of the camera and a serial

data cable were used. An illustration of the image capture interface (supplied as a

demo program by 4D Systems [22]) and the respective settings used for preliminary

image capture is shown in Figure 4.4.

Various images were captured to represent four sets of different pollution

levels (clean, light, medium and heavy) at three different locations on the bushing

(top, middle and bottom). The images were used as the basis for a proof-of-concept

of image segmentation, as illustrated in Table 4.1 and discussed further in

Chapter 4.4 – Image Segmentation. Finally, in order to avoid shadow and reflective

artefacts in the captured images, but still ensure sufficient image saturation, the

camera was used in mid-to-late afternoon lighting. Ultimately, implementation of an

integrated lighting source was required for the RTI saliency mapping described in

Chapter 3.7 – Reflectance Transformation Imaging in order to realise the full scope

of the project.


The implemented image capture system for bushing surface analysis was

comprised of 4D Systems’ μCAM-III mounted in an RTI array. The AutoCAD design

of the array is illustrated in Figure 4.5, which was used to then 3D-print the structure.

The RTI array was designed based on an arched dome with a diameter of 100 mm.

Arches were used instead of a solid dome so as to prevent shielding the imaging area

from environmental pollution. Normally, RTI arrays are designed as solid domes to

eliminate interference from external light sources. However, with an open-arched

dome, external light sources would affect the saliency reconstruction. To minimise

this effect, imaging would only occur at night.

FIGURE 4.4: μCAM-III demo program used for preliminary image capture [17]


The array had eight arches with five white surface-mount LEDs

(Bivar SM0805) on each arch, thus translating to 40 image samples per surface

reconstruction. The LEDs selected were small enough to fit on the arches of the array

and each had a sufficiently low power rating (maximum 78 mW) such that a minimum

of 5 LEDs could run simultaneously off the microcontroller without loss of average

luminosity. Five LEDs were required for illuminating the surface evenly for images

used with binary thresholding techniques, discussed further in the following section.

The reduced power consumption per LED avoided the need for additional power

circuitry to supply the LEDs thereby reducing the size and complexity of the

implemented image monitoring system. At full intensity each LED had a high

luminosity (55 mcd) and wide viewing angle (130°). This ensured even lighting of the

bushing surface, relative to the LED in the array that had been turned on during the

RTI image capture sequence. Details pertaining to the RTI image capture and

processing are discussed further in Section 4.5 – Image RTI Processing.

The controller used for the monitoring system was the ATMega2560. This

particular microchip was selected owing to the number of PWM ports available for

the LED array control aspect of the monitoring system, and the large number of digital

I/O ports required for image data communication. Figure 4.6 illustrates the

configuration of the controller, camera and LED array. It is noted that while only 9

digital I/O ports were utilised for the implemented monitoring system, a more robust

FIGURE 4.5: AutoCAD RTI lighting array solid wire frame


version of the system would require wireless communication so the need for extra I/O

ports was warranted.

During laboratory testing, commands were relayed to the controller via the

SoftwareSerial protocol from a computer. The code used to program the

microcontroller can be found in Appendix A. Seven commands exist that were input

by the user. These commands are listed as follows:

0. Wake Command – Sends a message to activate the camera if it is

unresponsive (camera module sleep mode activated after 10 minutes of

idling)

FIGURE 4.6: Image Capture System Schematic


1. Sync Command – Synchronises the camera for communication with the

microcontroller (set to minimum of 9600 baud to mitigate data loss).

2. Initialise Command – Sets default initialisation parameters for the

camera to return 640x480 jpeg images.

3. Packet Command – Set camera to transfer 512 bytes per packet for jpeg

image transfer to microcontroller.

4. Snapshot Command – Order camera module to store 640x480 jpeg image

in buffer.

5. Request Command – Request sizing parameters of buffer stored image

(how many packets to expect) and proceed to transfer image data to

microcontroller.

6. Auto Command – Have the microcontroller automatically execute

commands 2 through 5 for each of the LED light sources in the RTI array

(the microcontroller will sequentially turn on each of the 40 LEDs in the

array, capturing and storing an image of the bushing surface for each).

7. Lights On Command – Turns on the 5 LEDs surrounding the closest to

the camera for an overall illumination of the bushing surface (legacy

option for the capture of images for use with binary thresholding

techniques).

8. Lights Off Command – Turns off the aforementioned 5 LEDs.

For the RTI image set, the controller sequentially turned on the LEDs in the

array and communicated with the μCAM-III to capture and store each image on a

memory card, as illustrated in the list above by Command 6 (Auto Command). The

LED array was controlled with PWM in order to vary lighting intensity so as to

minimise unwanted surface reflection and specularity. The LED array is configured

as shown in Figure 4.7. Trials with several smaller sample sets at different lighting

intensities showed that a PWM of 50% yielded the least number of visible reflectance

artefacts in the reconstructed surface map of a clean surface. For the binary

segmentation image, each LED closest to the camera on each arch (5 in total) was

turned on with a PWM of 30%, again chosen to have the least reflectance artefacts on


a clean bushing surface. The constructed RTI array and controller are illustrated in

Figure 4.8.

FIGURE 4.7: RTI LED Array Configuration

FIGURE 4.8: RTI Array (foreground) and Controller (background)


4.3 Image Segmentation

The means of attaining a preliminary evaluation of pollution severity was

obtained following the parameterisation techniques briefly described in Chapter 3.6 –

Image Segmentation and Border Extraction. The difference in grayscale intensity

between the background bushing surface and the foreground pollutants under

observation lends itself well to a binary converted image based upon a predetermined

threshold. From this, a pixel count between the background (0) and the foreground (1)

of the binary image, would indicate the percentage of pollution coverage on the

surface of the bushing. From the saturation of the captured images in Table 4.1, a

binary greyscale threshold of 0.8 yielded the most useful results.

From the single image captured (using 5 LEDs as lighting the lighting source),

described in Chapter 4.2 – Image Capture System, MATLAB’s im2bw function was

used. The four captured images in Table 4.1 were processed in which the resultant

image, pixel count, and relative percentage covering was illustrated. The binary image

threshold method was a crude estimation of the pollution level. However, it did offer

an overall evaluation of the severity, as seen by the sizeable percentage difference

between the sample images in Table 4.1. Thus, the technique served as a proof-of-

concept, in terms of overall pollution severity estimation.

The image segmentation technique had to be accompanied by a more accurate

evaluation. Chapter 3.7 – Reflectance Transformation Imaging, describes surface

saliency reconstruction that can be used to define the shape of pollutants and thus the

type and surface covering percentage of each pollutant. This increases the amount of

input data to the convolutional neural network, in order to ensure a higher AI

predictive accuracy.


TABLE 4.1 – Preliminary Images with Binary Thresholding

4.4 Image RTI Processing

A requirement for the RTI imaging technique, described in Section 3.7 –

Reflectance Transformation Imaging, is knowing the vector locations of the light

sources relative to the camera. Cultural Heritage Imaging (CHI) provide open-source

software called RTI Builder that allows the lighting positions to be calculated from

the reflected light on a sphere [23].

The process began by placing a shiny black marble in the centre field-of-view

of the RTI array. Commands 1 and 6, described in Section 4.2 – Image Capture

System, were sent to the controller to obtain the set of 40 images representing each of

the lighting positions. The 40 images of the sphere were then imported as a new

project in the RTI Builder software. On one of the images, a section of area was user

defined to contain the calibration sphere, as illustrated in Figure 4.9. The program was

then given the command to Detect Spheres, where the centre and radius of the

calibration sphere in each image was found through edge detection using the Hough

Transform. As illustrated in Figure 4.10, the program did its best to predict the centre

of the calibration sphere and highlight its boundaries.


sdfsd

FIGURE 4.9: RTI Builder Selection of Calibration Sphere Area [18]

FIGURE 4.10: RTI Builder Prediction of Calibration Sphere Boundaries

(Edge Detection) [18]


Once ensuring the calibration sphere was correctly identified and centred in

each image, the program was then given the command to do Highlight Detection,

where the specular reflection of the LED on the calibration sphere was automatically

identified for each image. The program placed a little dot on the point it predicted was

the centre of the specular reflection detected. However, owing to the importance of

having a true and accurate calibration file pertaining to this RTI array, each image had

to be checked to ensure the Highlight Detection dot was as centred on the specular

reflection as possible. This is illustrated in Figure 4.11. The program outputed a

combined image file with all the specular reflections it detected, placed on a single

calibration sphere, shown in Figure 4.12.

Finally, once all the aforementioned steps and precautions were taken in

identifying all the required parameters on the calibration sphere, the program could

then execute the HSH Fitter [23] to generate a .lp calibration file. The calibration data

was interpolated from the position vectors of the reflected light on the imaged sphere

to the position vectors of the light sources on the RTI array. This yielded a calibration

file specific for the RTI array used to capture the image set. Within this file are the

FIGURE 4.11: Specular Reflection

Highlight Detection (red cross)

FIGURE 4.12: Blended Output of all the

Detected Specular Reflections


normalised position vector co-ordinates for each of the LEDs in the designed RTI

array. This calibration file was set as the default when running the RTI Processor,

which was used for saliency mapping the imaged bushing surface, provided the

camera’s position and orientation remained the same relative to the arches of the

array [24]. The position values obtained for the calibration of the implemented RTI

array are found in Appendix B.

The RTI Processor program, designed by Pawlowicz [24], functions as a front-

end to command line plugins that used the 40 images captured of the bushing surface,

along with the lighting positions calibration data, to create RTI and PTM files. The

RTI files were created by the HSH Fitter software, provided as open-source by the

Cultural Heritage Imaging Organisation [23]. The PTM files were created by the PTM

Fitter software that is not open source but still made freely available by HP Labs [25].

As explained by Pawlowicz [24], both pieces of software fit a certain polynomial to

the lighting curve at each pixel. RTI files (having the best fit) use Legendre

polynomials to fit the curve, while PTM files (having more image processing

functionality with the RTI Viewer software [23]) use binomial quadratics to fit the

curve.

As described by the steps outlined in the beginning of the methodology, the

ESDD and leakage current were obtained for each trial. Likewise, each trial required

a set of images corresponding to the ESDD and leakage current measured for that

particular trial. Step 9 of the methodology, pertaining to RTI image capture is further

explained. Once the bushing had been artificially polluted and the leakage current

measured under wetted conditions, the bushing was left to dry for at least 24 hours.

This is the time required to elapse for salt crystals to fully form. Following drying,

the RTI array was carefully placed such that the top surface of the first shed of the

insulator was imaged.

The positioning of the array is depicted in Figure 4.13. The lens of the camera

in the array was focused to ensure that the central area of the image was in greatest

focus. This was due to the surface being imaged at an angle. The image data set


*Photo could not be obtained due to COVID-19 lockdown. Refer to Figure 4.3, the array was placed on the top of the

bushing, where the red wire from the current limiting resistor would be connected.

(comprising of 40 RTI images and one thresholding image) was acquired using the

commands described in Chapter 4.2 – Image Capture System. The images that the

controller had stored on the SD card were transferred to a computer for the image

processing herein discussed to be done. For each of the trials at least two sets of RTI

images were taken with slightly different focal points on the image. This was in an

attempt to increase data quantity and variation for training of the neural network.

For each set of RTI images, the reconstruction of the saliency image map from

the 40 varying lighting images, using both RTI Fitter and PTM Fitter programs, was

visualised using RTIViewer, as supplied by the Cultural Heritage Imaging

Organisation [23]. The RTIViewer application served as part of the image processing

tools used in the monitoring system. A total of 26 processed images were created from

the RTI image reconstruction. These post-processed images, highlighting certain

features within the RTI image reconstruction, were used as part of the training and

validation data for the neural network.

The predictive ability of the monitoring system was highly dependent on the

specific variation and accurate feature representation of the image data it used. The

various types of filtered images obtained from the RTI Viewer are indicated in

*FIGURE 4.13: Positioning of RTI array on the top rib of the test insulator


Table 4.2. It is noted that the PTM Fitter has two options, the first, LRGB fits the

polynomials only to the luminosity values of the pixels, while RGB fits polynomials

to the red, green and blue channels of each pixel to account for any possible colour

changes with lighting angle. Furthermore, the RTI Fitter has two options, 2nd and 3rd

order Legendre polynomial fitting.

4.5 Neural Network Architecture

Section 8 of SANS 60815 [2], illustrates the non-linear relationship between

the NSDD and ESDD pollution severity levels. This relationship is important to note

as the interaction between electrolytic soluble materials and non-solubles yields a

unique characteristic. Excessively high levels of non-soluble pollution curtail the

conductive effects of the soluble electrolytic pollutants, thus decreasing the net

pollution severity. This relationship must be considered so as to avoid over-fitting for

the optical detection of pollution and subsequent erroneous prediction of leakage

current.

Initially, it was decided that the image processing and the neural network based

prediction would be addressed as two individual sub-systems. However, following

TABLE 4.2: Image Rendering Options for PTM and RTI Fitters


further background research relating to imaging based deep-learning problems, the

option of integrating part of the image processing with the neural network was

implemented. As discussed in Chapter 3.8 – Convolutional Neural Networks, a CNN

is most often used for image based deep-learning problems.

A CNN was implemented in MATLAB using transfer learning and the Deep

Network Designer. The implemented CNN was based off the AlexNet which was

designed by Krizhevsky et al. as a classification network [26]. Its basic structure is

highlighted in Figure 4.14. It was comprised of 8 learned layers, the first five being

convolutional layers and the following three fully-connected. As Krizhevsky et al.

explain [26], AlexNet was trained on a subset of the ImageNet database. The subset

used to train AlexNet comprised 1.2 million training images with 1000 different

categories.

As the sample size of ESDD polluted bushing surface images and leakage

currents was limited, using transfer learning was beneficial in terms of training time

and accuracy, as explained previously in Chapter 3.8 – Convolutional Neural

Networks. In order to adapt AlexNet for the purposes of leakage current prediction for

the optical monitoring system, several aspects pertaining to the original neural

network had to be considered. Firstly, the input to the network was constrained to a

fixed image resolution of 256x256. Thus, all the images captured by the RTI array,

and any subsequent processed images had to be augmented to meet the input

parameters of the neural network. The augmentedImageDatastore function

was used to rescale the 640x480 images to the required dimensions. The data store

contained all the images captured, with a label indicative of the measured leakage

current associated to each image.

The transfer learning of the AlexNet took place in two parts. Initially, the

network remained a classification type CNN, with captured images from trials being

categorised in ranges of pollution severity (E1 – E7). Once the network worked as a

classifier of discrete values of ESDD range, the network was then modified to a

regression type and retrained to predict a continuous value of leakage current. Thus,


the images of the dry pollutants on the first shed were linked to the reality of a leakage

current flowing for an entirely wetted bushing of a particular ESDD. As the neural

network was making a prediction based on ESDD (yielding a leakage current) the

image of the first shed was sufficient representation of the visual appearance of a

particular ESDD, and by extension, a leakage current (point 3 in the methodology of

Chapter 4 – Methodology).

As per Section 8 of SANS 60815 [2], seven distinct regions of pollution

severity are outlined. The scale depicting the ranges of ESDD severity, extracted from

SANS 60815, is illustrated in Table 4.3. The pollution rating ranges from very light

(E1) to very heavy (E7). As a first iteration to the transferred learning CNN, each of

these levels served as an output prediction of the neural network. The last fully-

FIGURE 4.14: AlexNET CNN architecture


connected layer of the CNN was changed to seven categorical outputs. The CNN still

behaved as a classification type model, similar to its original architecture. The

reasoning in initially maintaining similar architecture with minimal alteration of the

original model, was to verify the CNN’s viability in transfer learning for the intended

application of optical monitoring, as described in Chapter 2 – Problem Specification.

For the classification solution, each of the images was placed into one of the seven

pollution severity categories, depending on the measured amount of ESDD. Due to

the non-linear relation of NSDD and ESDD, two sub-regions within medium and

heavy pollution levels account for the seven total levels, including light pollution.

The regression model CNN predicted a continuous value of leakage current.

For this architecture, the final fully-connected layer was replaced with a singular

output layer, such that the value it predicted was a continuous number. The input data

used for training was also different from the classification CNN. The data store

mentioned earlier contained all the images captured, with a numerical label indicative

of the measured leakage current associated to each image. The regression model CNN

was used as part of the monitoring system and the code associated with its

implementation is found in Appendix C.

The augmented data store, with the resized images, was split into three subsets.

From the main dataset, 70% of all images and labels were randomly selected as part

of the training subset. A further 10% were selected for validation, with the remaining

20% used in testing. The model was trained using adaptive moment estimation in mini

batches of 10 data samples each, for a maximum of 5 epochs. These learning

parameters were used owing to the nature of the problem. As Kingma and Lei Ba

explain [27], adaptive moment estimation is well suited to learning problems whose

TABLE 4.3: Type A pollution severity ESDD rating for long rod insulator [2]


parameters are large and have sparse gradients. In this case, leakage currents ranged

from ~0.2 mA to ~6 mA. This is important to note as only 20 samples along the ESDD

scale were related to a leakage current. Increased sampling would be required to

increase predictive accuracy and offer a truer representation of the relationship

between leakage current and ESDD. Furthermore, owing to the stochastic nature of

flashover itself, it was prudent to use a solver that accounted for less tightly related

variables.

46

Chapter 5

Results

The success of the monitoring system was dependent on its ability to predict

the leakage current from the imaged dry pollution on the surface of a porcelain type

insulator. As outlined in the previous chapter, certain steps had to be followed in order

to implement the monitoring system. The first to be discussed are the results obtained

from the artificial polluting of the bushing. This phase of the implementation required

the measurement of the leakage current of the polluted bushing under critically wetted

conditions. Each leakage current measurement was accompanied with a measurement

of ESDD that was conducted once imaging of the dry surface was complete. The

measurement of ESDD followed the standards set out in SANS 60815, discussed in

Chapter 3.5 – Conventional Measurement of ESDD and NSDD. When measuring the

leakage current, the methods described in Chapter 4.1 – Leakage Current

Measurement were followed.

Table 5.1 illustrates the measured leakage current under critically wetted

conditions and the related ESDD measurement. The full table of results, illustrating

measured salinity readings of the solution and measured voltages is illustrated in

Appendix D. The results in Table 5.1 are part of the input learning data to the neural

network. As such, in order to better translate the relation between ESDD and leakage

current, the results are plotted graphically in Figure 5.1.

Chapter 5. Results 47

In Figure 5.1 there is evidence of an exponential relation between the level of

ESDD and the leakage current flow under critically wetted conditions. However, when

plotting the data in Figure 5.1, certain anomalies were considered. A large outlier

corresponding to Trial 3 had to be removed. Other outliers, however not as severe,

corresponded to Trial 5 and Trail 6. The reason the outliers began to occur after

~0.3 mg/cm2 is due to the stochastic nature of flashover. The leakage current values

recorded were not average values of current flowing over time, but rather were

indicative of the maximum peak current recorded during a particular trial. The trials

that were considered outliers had increased rates of minor arcing, thus yielding an

increased leakage current. This phenomenon was important to note as it began to occur

TABLE 5.1: Training data extracted from measurement data showing leakage

current and associated ESDD with SPS classification


around and above 0.3 mg/cm2. According to SANS 60815, this value corresponds to

a Heavy SPS rating of ESDD. It is at this point that the threshold was set for

unacceptable levels of ESDD pollution as past this point chances of arcing increase

greatly.

For each trial, after polluting the bushing and measuring leakage current under

critically wetted conditions the bushing was left to dry for a minimum of 24 hours

before imaging took place. The methods pertaining to image capture are highlighted

in Chapter 4.2 – Image Capture System. For each trial, a minimum of two sets of RTI

images were taken. On average, each trial had approximately 270 images, inclusive

of processed images. An example image set obtained by the RTI array is illustrated in

Figure 5.2. The image set was processed, following the methods described in

Chapter 4.4 – Image RTI Processing, in order to obtain the saliency map of the

surface and other feature image renderings. An extract of the processed images is

illustrated in Table 5.2.

As previously explained, several image renders were output from both the RTI

and PTM based reconstructions. These images served to increase the quantity of

feature data that was used to train the CNN. As these images contained the largest

amount of feature data, only the processed images were used to test the fully trained

FIGURE 5.1: Leakage Current vs. ESDD with loose trend line


CNN. The most important image renders were the Default – featuring a 2D

reconstruction of the surface under varying lighting; Specular Enhancement –

featuring exaggeration of light reflected off the facets of the salt crystals on the

bushing surface; and Normals Visualisation – featuring the surface normals of the

reconstructed saliency map encoded in false colour.

With the captured and processed images, leakage current and ESDD, all the

data necessary for the neural network to be trained was available. The first iteration

of the CNN remained a classification model, using the SPS levels from Table 4.3 as

the categories. The image data was sorted into folders corresponding to the Class in

Table 5.1 each trial belonged to. The imageDatastore function labeled each

image E1 – E7, depending on which folder it was in. Figure 5.3 illustrates the training

progress of the preliminary CNN configured as a classification model which is

described in Chapter 4.5 – Neural Network Architecture.

The classification CNN worked subjectively well with minimal tuning of

weights and biases. The modification to the original AlexNet came with the

replacement of the final fully-connected layer, where it was altered to have seven

outputs. Furthermore, the learning rate bias was set higher in the last few layers (fully-

connected layers) in order to facilitate more rapid training of the new CNN, while

preserving the pre-trained feature recognition benefits of transfer learning.

FIGURE 5.2: Example Set of RTI Images for a Single Trial


sdf

TABLE 5.2: Example Set of Processed RTI Images for a Single Trial


df

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The validation accuracy of the trained CNN was 97.62%. Even though this is

considered a high level of accuracy, it is important to note that during the training,

there were several iterations that deviated largely from the predicted values. This was

due to the presence of very dark images in the training batch. These images formed

part of the RTI sets, but due to the positioning and angle relative to the bushing

surface, they appeared very dark. An example of this is seen in the last two rows of

the third and fourth columns in the example RTI image set in Figure 5.2. In the

regression model CNN, only a single value was being predicted per image that was

passed through the network, in comparison to simply classifying it into a categorical

bin. Thus, in terms of data preparation, it was important to remove the images that

skewed the prediction data in order to increase its predictive accuracy. Finally,

changing the length of the training period did little to improve overall accuracy of the

model. At times, it even resulted in a greater deviation from the loss function. Thus,

remaining with 10 samples per iteration over 5 epochs was both the standard duration

for most transfer learning models [19], and the best option for this particular use case.

To configure the CNN to a regression model, the final fully-connected layer

had to be replaced. It had to be configured such that it only outputted a single

continuous value. The new regression model CNN is illustrated in Figure 5.4. While

it is similar to that of Figure 4.14, the output block was changed to be of a Regression

type. This was important for training and validation purposes, so that the solver

understood what type of ground truth accompanied each image. In this case, the error

was reported as a relative RMSE value. The images for the regression CNN were

placed in folders labelled with the associated measured leakage current from

Table 5.1. The imageDatastore function stored each image with a numerical

value corresponding to the leakage current measured for the trial the image belonged

to. The image data used in the classification CNN and the regression CNN was the

same. The difference arose from what type of label (categorical or numerical) each

image was given. Figure 5.5 illustrates the training progress of the final CNN

configured as a regression model, described in Chapter 4.5 – Neural Network

Architecture.


The validation RMSE of the regression CNN was 0.3822 mA. The low RMSE

obtained was due to the data selection that was made before the CNN was trained. As

before, any images that were too dark to discern any surface characteristics were

omitted. This corresponded to any images being smaller than ~10 kB in size. This

brought the total number of training images down from 4960 to 4580. The resultant

improvement in RMSE was from 0.7930 mA for the full image set, down to

0.5861 mA for the data set containing no images less than 10 kB in size. The training

progress reports for the two aforementioned iterations are found in Appendix E.

Reducing the size of data set by 8 % improved the RMSE by 26 % on a single training

session. This illustrates how important data organisation is for deep-learning.

FIGURE 5.4: Regression Model AlexNet CNN Architecture


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The training progress in Figure 5.5 was the second cycle of training of the

CNN from a previous RMSE of 0.5861 mA, yielding the 0.3822 mA RMSE. This

illustrated the importance of maintaining the adjusted weights from previous learning

sessions. How accurately the CNN was able to predict leakage current was dependent

on how diverse and on how much data was available to train on. In order to decrease

the RMSE further, additional data organisation was done. The trials were separated

by the order of magnitude of the leakage current value.

Two separate CNNs were trained, illustrated in Figure 5.6 and Figure 5.7. In

Figure 5.6, the CNN was trained on 2700 images that were within the leakage current

range of 0.1 mA to 1 mA. In this iteration of the regression CNN, the gradients

between ground truth values used to train the CNN were not as sparse when compared

to training the CNN on the full range data set. By narrowing the predictive focus to a

certain range of leakage current the predictive accuracy was increased, resulting in a

relative RMSE of 0.09308 mA.

In Figure 5.7, the CNN was trained on 1880 images. The leakage current range

considered was between 1 mA and 10 mA. The reported RMSE was 0.5449 mA. It is

interesting to note that this value for RMSE is close to that of Figure 5.5, where the

entire range of leakage current was considered. This illustrates that the CNN was

trained to predict larger values of leakage current with more relative accuracy than

smaller values of leakage current. Figure 5.8 illustrates the predictive accuracy of the

smaller order of magnitude CNN (Figure 5.6 – 0.1 mA to 1 mA). Figure 5.9 illustrates

the predictive accuracy of the larger order of magnitude CNN (Figure 5.7 – 1 mA to

10 mA).


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FIGURE 5.8: Validation Sample of Smaller Order Leakage Current Prediction

Illustrating Input Image, Associated Leakage Current and Predicted Leakage

FIGURE 5.9: Validation Sample of Larger Order Leakage Current Prediction

Illustrating Input Image, Associated Leakage Current and Predicted Leakage


5.1 Discussion

As illustrated previously in Figure 5.1, an exponential trend line was centred

at 0.3 mg/cm2 ESDD and 1 mA leakage current. The value of ESDD corresponded to

the beginning of the Heavy SPS rating. As such, this was considered the point of

criticality, where any ESDD measurement above the threshold was considered too

high, translating to the need for bushing cleaning. ESDD readings above 0.3 mg/cm2

would most likely yield leakage currents in excess of 1 mA. Thus, the predictive

accuracy of the full range regression CNN is sufficient in estimating the leakage

current. Any prediction above 1 mA is considered critical in terms of ESDD pollution

severity.

The measurement data acquired to show the relationship between ESDD and

leakage current was sufficient enough to develop preliminary models. However, in

order to improve the overall resilience of the monitoring system to a wide variety of

ESDD pollution severities, further trials must be conducted. It is noted that Trial 13

illustrates the leakage current under no bushing pollution conditions and no critical

wetting. Should a relative measurement of leakage current be necessary, this trial

would serve as that reference. Furthermore, with respect to the 1 mA threshold

explained earlier – while the majority of high level ESDD did have leakage currents

in excess of 1 mA, there were still trials that showed high levels of leakage current

for lower levels of ESDD. Whether this phenomenon would imply causality for

flashover is unknown.

Possible reasoning for the discrepancy in leakage current measurement is due

to two anomalies. Firstly, the stochastic nature of flashover, and the mechanisms

leading to the event of flashover could account for spikes in leakage current

measurements, where parts of the bushing surface were shorted by arcs. Secondly,

when considering a steady-state leakage current that is higher for similar readings of

ESDD, the reasoning is systematic in nature. In this case, the method used to critically

wet the bushing surface is under question. It is previously stated in Chapter 4.1 –

Leakage Current Measurement, that the most reliable way to ensure uniform critical


wetting is to conduct measurements with the polluted bushing placed in a well

regulated humidity chamber. This would ensure that minor discrepancies of leakage

current measurements between similar ESDD conditioned trials are minimised.

Spraying the surface with saline solution is a suitable option for initial relations to be

identified. However, over a protracted range of measurements it would be prudent to

maintain as many variables as constant as possible.

The monitoring system worked well in predicting the leakage current for a

bushing polluted with light to heavy levels of ESDD pollution. It is possible to expand

the system to include NSDD pollution however it is predicted that the estimation of

leakage current by the CNN would be within similar ranges, as if only ESDD were

concerned. This is alluded to by the fact that limiting the image source data by orders

of magnitude (Figure E.1 – full data range of 0.1 mA to 10 mA and Figure 5.7 –

ranging from 1 mA to 10 mA) had little effect in improving prediction resolution, with

both having a relative RMSE of ~0.5 mA.

It would be possible to expand the monitoring system to be used with other

porcelain MV bushings. As 11 kV and 22 kV bushings have similar circumferential

dimensions (differing only in clearance height), the system could be translated for use

with these bushings with no changes to the AI component of the system. The leakage

current values obtained for various pollution levels on the 33 kV bushing would most

likely be similar for the same levels of pollution on the lower voltage rated bushings

(lower voltage but also smaller surface resistance). As the CNN used the ESDD as the

predictive indicator for leakage current, provided the bushing material is the same,

the system can be used on other porcelain MV bushings. Were the system to be used

on other bushings, for example polymeric type bushings, bushings with substantially

different dimensions or disk insulators, then new images would have to be captured

and related leakage currents be measured in order to increase the predictive ability of

the CNN. Several tailored CNN could be trained on data pertaining to a particular type

of insulator, and selected depending on which type of insulator the system is

monitoring. Finally, in order to fully realise the extended scope of the research, each

monitoring system could be modularised and connected remotely over a wireless


network to a central computer where computation and translation of the data could be

done and presented to the technician.

62

Chapter 6

Conclusion

By implementing a method of optically monitoring pollution severity of

bushings, this research aimed to avoid the current methods of reactive based

maintenance related to the management of bushing flashover. There were three main

components that constituted the implementation of the monitoring method:

measurement of artificial pollution levels and related leakage currents; imaging of

surface pollutants; and data consolidation in the form of image feature extraction

understandable by the neural network. The presented method required imaging of the

bushing surface using an RTI array in order to determine the severity of the pollution

present. As leakage current is the most important indicator of probable flashover, a

correlation between the imaged surface of dry pollution was made with measured

leakage current values for critically wetted conditions. The implemented monitoring

system was successful in predicting the leakage current from images of dry ESDD

pollution on the surface of an insulator. The RTI array captured 40 images per set

which were combined and processed to form a saliency reconstruction of the surface.

Twenty-six additional image renders were yielded from the image processing

technique. The implemented regression model CNN used transfer learning for the

majority of the image feature extraction. The CNN was trained on trial data

comprising of RTI images and measured leakage currents. The CNN had a predictive

accuracy described by a relative RMSE of 0.5449 mA for a leakage current range of

1 mA to 10 mA.

63

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on the Performance of the Bushing,” International Conference on Energy Efficient

Technologies for Sustainability (ICEETS), pp. 315–319, 2016.

[22] 4D Systems, “Serial Camera Module μCAM-III demo,” vers. 1.1, March 2019.

[23] Cultural Heritage Imaging, 2002 – 2019, http://culturalheritageimaging.org.

[24] L. Pawlowicz, “Affordable Reflectance Transformation Imaging Dome,”

hackaday.io, May 2016.

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Packard Laboratories.

[26] A. Krizhevsky, I. Sutskever, and G.E. Hinton, “ImageNet Classification with

Deep Convolutional Neural Networks,” Advances in neural information

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International Conference on Learning Representations, 2015.

66

Appendix A

Arduino Code

The code used to program the Arduino ATMega2560 controller is presented.

The user may input commands to choose what functions the controller must execute.

The controller is responsible for PWM switching control of the RTI array, serial

communication with the μCAM-III module, and data storage.

1 #include <SoftwareSerial.h>

2 #include <SD.h>

3 #include <SPI.h>

4

5 SoftwareSerial uCAMSerial(15, 14); //Rx3, Tx3

6

7 int value = 0;

8 int leg_GRN_BLK = 12; // the PWM pin the LED is attached to

9 int leg_WHT_BLK = 11; // the PWM pin the LED is attached to

10 int leg_BLU_BLK = 9; // the PWM pin the LED is attached to

11 int leg_BLK_WHT = 8; // the PWM pin the LED is attached to

12 int leg_RED_GRN = 7; // the PWM pin the LED is attached to

13 int leg_RED_ORN = 6; // the PWM pin the LED is attached to

14 int leg_BLU_RED = 5; // the PWM pin the LED is attached to

15 int leg_WHT_RED_BLK = 3; // the PWM pin the LED is attached to

16

17 int gnd_BLU_WHT = 25;

18 int gnd_RED_BLK = 27;

19 int gnd_ORN_GRN = 29;

20 int gnd_GRN = 31;

21 int gnd_WHT_RED = 33;

22

23 int i = 0;

24 int j = 1;

25

26 int packetNum;

27

28 byte SYNC[6] = {0xAA, 0x0D, 0x00, 0x00, 0x00, 0x00};

29 byte uCAMRx[6];

30 byte uCAMRx2[6];

31 byte uCAMRx3[512];

32 byte SDBuff[506];

33 byte ACK_SYNC[6] = {0xAA, 0x0E, 0x0D, 0x00, 0x00, 0x00};

34 byte INITIAL[6] = {0xAA, 0x01, 0x00, 0x07, 0x07, 0x07}; //Set image capture

to JPEG 640x480

35 byte ACK_INITIAL[6] = {0xAA, 0x0E, 0x01, 0x00, 0x00, 0x00};

36 byte PACKET[6] = {0xAA, 0x06, 0x08, 0x00, 0x02, 0x00}; //Set package size to

512 bytes

37 byte ACK_PACKET[6] = {0xAA, 0x0E, 0x06, 0x00, 0x00, 0x00};

Appendix A. Arduino Code 67

38 byte SNAPSHOT[6] = {0xAA, 0x05, 0x00, 0x00, 0x00, 0x00}; //Order camera to

hold JPEG in buffer

39 byte ACK_SNAPSHOT[6] = {0xAA, 0x0E, 0x05, 0x00, 0x00, 0x00};

40 byte GETPIC[6] = {0xAA, 0x04, 0x01, 0x00, 0x00, 0x00}; //Request snapshot

picture

41 byte ACK_GETPIC[6] = {0xAA, 0x0E, 0x04, 0x00, 0x00, 0x00};

42 byte DATA_GETPIC[6] = {0xAA, 0x0A, 0x01, 0x00, 0x00, 0x00};

43 byte ACK_ID[6] = {0xAA, 0x0E, 0x00, 0x00, 0x00, 0x00}; //First Package ID

44 byte ACK_ID2[6] = {0xAA, 0x0E, 0x00, 0x00, 0x01, 0x00}; //First Package ID



47

48 int CSpin = 53; //Chip Select pin.

49 File myFile;

50

51 //byte byte0;

52 //byte byte1;

53 //byte byte2;

54 //word imgSize;

55 //int numPack;

56

57 // the setup routine runs once when you press reset:

58 void setup()

59 {

60

61 // Open serial communications and wait for port to open:

62 Serial.begin(9600);

63 Serial3.begin(9600);

64

65 pinMode(leg_GRN_BLK, OUTPUT);

66 pinMode(leg_WHT_BLK, OUTPUT);

67 pinMode(leg_BLU_BLK, OUTPUT);

68 pinMode(leg_BLK_WHT, OUTPUT);

69 pinMode(leg_RED_GRN, OUTPUT);

70 pinMode(leg_RED_ORN, OUTPUT);

71 pinMode(leg_BLU_RED, OUTPUT);

72 pinMode(leg_WHT_RED_BLK, OUTPUT);

73

74 Serial.println("Goodnight moon!");

75

76 // set the data rate for the SoftwareSerial port

77 uCAMSerial.begin(9600);

78

79 pinMode(CSpin,OUTPUT);//Required by the SD library?

80 Serial.print("Initializing SD card...");

81

82 if (!SD.begin(4))

83 {

84 Serial.println("initialization failed!");

85 while (1);

86 }

87 Serial.println("initialization done.");

88 }

89

90 void callGround()

91 {

92 for(j = 1; j < 6; j++)

93 {

94 if(j == 1)

95 {

96 pinMode(gnd_BLU_WHT, OUTPUT);

97 digitalWrite(gnd_BLU_WHT, LOW);

98 Serial.println("j=1");

99 delay(1000);

100

101 callSNAPSHOT();

102 delay(1000);

103

104 packetNum = callGETPIC(); //NOTE: Camera will wait here till data is sent

105 callDATA(packetNum);

106 delay(1000);

107

108 pinMode(gnd_BLU_WHT, INPUT); //Turn off the light

109 }


110 if(j == 2)

111 {

112 pinMode(gnd_RED_BLK, OUTPUT);

113 digitalWrite(gnd_RED_BLK, LOW);


115 delay(1000);

116

117 callSNAPSHOT();

118 delay(1000);

119



122 delay(1000);

123

124 pinMode(gnd_RED_BLK, INPUT); //Turn off the light

125 }

126 if(j == 3)

127 {

128 pinMode(gnd_ORN_GRN, OUTPUT);

129 digitalWrite(gnd_ORN_GRN, LOW);


131 delay(1000);

132

133 callSNAPSHOT();

134 delay(1000);

135



138 delay(1000);

139

140 pinMode(gnd_ORN_GRN, INPUT); //Turn off the light

141 }

142 if(j == 4)

143 {

144 pinMode(gnd_GRN, OUTPUT);

145 digitalWrite(gnd_GRN, LOW);


147 delay(1000);

148

149 callSNAPSHOT();

150 delay(1000);

151



154 delay(1000);

155

156 pinMode(gnd_GRN, INPUT); //Turn off the light

157 }

158 if(j == 5)

159 {

160 pinMode(gnd_WHT_RED, OUTPUT);

161 digitalWrite(gnd_WHT_RED, LOW);


163 delay(1000);

164

165 callSNAPSHOT();

166 delay(1000);

167



170 delay(1000);

171

172 pinMode(gnd_WHT_RED, INPUT); //Turn off the light

173 }

174 }

175 }

176

177 void printLOG(byte *a)

178 {

179 for(int i = 0; i < 6; i++)

180 {

181 Serial.print(a[i], HEX);

182 Serial.print(" ");

183 }


184 }

185

186 void printLOG2(byte *a)

187 {

188 for(int i = 0; i < 512; i++)

189 {



192 }

193 }

194

195 void fillSDBuff(byte *a)

196 {

197 for(int i = 0; i < 506; i++)

198 {

199 SDBuff[i]= a[i+4];

200 }

201 }

202

203 void clearSDBuff(byte *a)

204 {

205 for(int i = 0; i < 506; i++)

206 {

207 SDBuff[i]= 0;

208 }

209 }

210

211 void clearuCAMRx3(byte *a)

212 {

213 for(int i = 0; i < 512; i++)

214 {

215 uCAMRx3[i]= 0;

216 }

217 }

218

219 void printSDBuff(byte *a)

220 {

221 for(int i = 0; i < 506; i++)

222 {



225 }

226 }

227

228 String generateFiles()

229 {

230 //derived from code found at http://forum.arduino.cc/index.php?topic=57460.0

231 String fileName = String();

232 String message = String();

233 unsigned int filenumber = 01;

234 while(!filenumber==0)

235 {

236 fileName = "file_";

237 fileName += filenumber;

238 fileName += ".txt";

239 message = fileName;

240 char charFileName[fileName.length() + 1];

241 fileName.toCharArray(charFileName, sizeof(charFileName));

242

243 if (SD.exists(charFileName))

244 {

245 message += " exists.";

246 filenumber++;

247 }

248 else

249 {

250 File dataFile = SD.open(charFileName, FILE_WRITE);

251 message += " created.";

252 dataFile.close();

253 filenumber = 0;

254 }

255 Serial.println(message);

256 }

257


258 return fileName;

259 }

260

261 void saveSDBuff(byte *a, String fileName)

262 {

263 myFile = SD.open(fileName, FILE_WRITE);

264

265 // if the file opened okay, write to it:

266 if (myFile)

267 {

268 Serial.print("Writing to ");

269 Serial.print(fileName);

270 Serial.print("...");

271

272 for(int i = 0; i < 506; i++)

273 {

274 myFile.write(a[i]);

275 }

276 // close the file:

277 myFile.close();

278 Serial.println("done.");

279 } else

280 {

281 // if the file didn't open, print an error:

282 Serial.print("error opening ");

283 Serial.print(fileName);

284 Serial.println(".");

285 }

286 }

287

288 boolean array_cmp(byte *a, byte *b)

289 {

290 int n;

291

292 // test each element to be the same. if not, return false

293 for (n=0;n<6;n++)

294 {

295 if (a[n]!=b[n])

296 {

297 return false;

298 }

299 }

300

301 //ok, if we have not returned yet, they are equal :)

302 return true;

303 }

304

305 int callSYNC(int maxTry)//maxTry = 30 Begin synchronisation with camera Command 1

306 {

307

308 int tries;

309

310 for (tries = 1; tries < maxTry; tries++)

311 {

312 Serial.print("Sync atempt ");

313 Serial.print(tries);

314 Serial.print(".\n");

315 Serial.print("Ping with SYNC: ");

316 printLOG(SYNC);

317 Serial.print("\n");

318

319 Serial.print("Waiting for response ACK:");

320 printLOG(ACK_SYNC);

321 Serial.print("...\n");

322

323 Serial3.write(SYNC, 6);

324 Serial3.readBytes(uCAMRx, 6);

325

326 printLOG(uCAMRx);


328

329 if(array_cmp(uCAMRx, ACK_SYNC)) //are the values equal

330 {



332 if(array_cmp(uCAMRx, SYNC)) //are the values equal

333 {

334 Serial3.write(ACK_SYNC, 6);

335

336 Serial.print("Success!\n");


338 Serial.print("recieved.\n");


340 Serial.print("recieved in ");

341 Serial.print(tries + 5);

342 Serial.print("ms.\n");

343

344 Serial.print("Ping with ACK: ");



347

348 Serial.print("Camera Ready!\n");

349

350 return tries; //Synchronisation Successful

351 }

352 }

353 else

354 {

355 Serial.print("Sync atempt failed. ");


357 Serial.print("not recieved.\n");

358

359 delay(5 + tries); //pause between tries

360 }

361 }

362

363 return 0; //Synchronisation falied

364 }

365

366 int callWAKE() //Wake camera Command 0

367 {

368 Serial.print("Ping with SYNC: ");

369 printLOG(SYNC);


371




375

376 Serial3.write(SYNC, 6);







383

384 Serial3.write(ACK_SYNC, 6);

385 Serial.print("Reply ACK:");



388

389 Serial.print("Ready!");


391

392 return 0;

393 }

394

395 int callINITIAL() //Set image capture to JPEG 640x480 Command 2

396 {

397 Serial.print("Ping with INITIAL: ");

398 printLOG(INITIAL);


400


402 printLOG(ACK_INITIAL);


404

405 Serial3.write(INITIAL, 6);





409

410 Serial.print("Received ");

411 printLOG(ACK_INITIAL);


413

414 Serial.print("Initialisation 640x480 JPEG Successful!");


416

417 return 0;

418 }

419

420 int callPACKET() //Set package size to 512 bytes Command 3

421 {

422 Serial.print("Ping with PACKET: ");

423 printLOG(PACKET);


425


427 printLOG(ACK_PACKET);


429

430 Serial3.write(PACKET, 6);




434


436 printLOG(ACK_PACKET);


438

439 Serial.print("Package size set to 512 Bytes Successful!");


441

442 return 0;

443 }

444

445 int callSNAPSHOT() //Order camera to hold JPEG in buffer Command 4

446 {

447 Serial.print("Ping with SNAPSHOT: ");

448 printLOG(SNAPSHOT);


450


452 printLOG(ACK_SNAPSHOT);


454

455 Serial3.write(SNAPSHOT, 6);




459


461 printLOG(ACK_SNAPSHOT);


463

464 Serial.print("JPEG held in buffer Successful!");


466

467 return 0;

468 }

469

470 int callGETPIC() //Request snapshot picture Command 4

471 {

472 unsigned long imgSize = 0;

473 unsigned long numPack = 0;

474 unsigned long byte0 = 0;



477

478 Serial.print("Ping with GETPIC: ");

479 printLOG(GETPIC);



481


483 printLOG(ACK_GETPIC);


485

486 Serial3.write(GETPIC, 6);





491


493 printLOG(ACK_GETPIC);


495

496 Serial.print("Snapshot requested Successful! Data to follow.");


498

499 Serial.print("DATA: ");



502

503 byte0 = uCAMRx[3]; //The last packet will have this many bytes

504 byte1 = uCAMRx[4];

505 byte2 = uCAMRx[5];

506

507 Serial.println(byte0);



510

511 if(byte2 == 1)

512 {

513 imgSize = byte0 + 256*byte1 + 65536;

514 }

515 else

516 {

517 imgSize = byte0 + 256*byte1;

518 }

519 Serial.println(imgSize);

520 numPack = imgSize/506;

521 Serial.println(numPack);

522

523 //if(byte0 > 0) //Rounding up

524 //{

525 numPack++;

526 Serial.println(numPack);

527 //}

528

529 return numPack;

530 }

531

532 int callDATA(int a) //Request snapshot picture Command 4

533 {

534

535 //myFile = SD.open("test.jpeg", FILE_WRITE);

536

537 String fileName = generateFiles();

538

539 int finVal = 0;

540

541 for(int i = 0; i < a; i++)

542 {

543 ACK_ID[4] = i;

544

545 Serial.print("Request packet ");

546 Serial.print(i);

547 Serial.print(" : ");

548 printLOG(ACK_ID);


550

551 Serial3.write(ACK_ID, 6); //Send first package ID

552 Serial3.readBytes(uCAMRx3, 512);

553 //printLOG2(uCAMRx3);



555 fillSDBuff(uCAMRx3);

556 //printSDBuff(SDBuff); //Data to be written (packet without ID, Data

Size and Verify code)

557

558 //myFile.write(SDBuff, 506);

559 saveSDBuff(SDBuff, fileName);

560


562 clearSDBuff(SDBuff);

563 clearuCAMRx3(uCAMRx3);

564

565 finVal++; //Increment counter for final packet number

566 }

567

568 ACK_ID[4] = finVal;

569

570 Serial.print("Final marker ");

571 Serial.print(finVal);

572 Serial.print(" : ");

573 printLOG(ACK_ID);


575 Serial3.write(ACK_ID, 6); //Send terminating package ID

576

577 return 0;

578 }

579

580 void loop()

581 {

582 if(Serial.available()>0)

583 {

584 value = Serial.read();

585 delay(50);

586

587 if(value == '6')

588 {

589 for(i = 1; i < 9; i++)

590 {

591 if(i == 1)

592 {

593 analogWrite(leg_GRN_BLK, 50);

594 Serial.println("i=1");

595 callGround();

596 delay(1000);

597 digitalWrite(leg_GRN_BLK, LOW);

598 }

599 if(i == 2)

600 {

601 analogWrite(leg_WHT_BLK, 50);


603 callGround();

604 delay(1000);

605 digitalWrite(leg_WHT_BLK, LOW);

606 }

607 if(i == 3)

608 {

609 analogWrite(leg_BLU_BLK, 50);


611 callGround();

612 delay(1000);

613 digitalWrite(leg_BLU_BLK, LOW);

614 }

615 if(i == 4)

616 {

617 analogWrite(leg_BLK_WHT, 50);


619 callGround();

620 delay(1000);

621 digitalWrite(leg_BLK_WHT, LOW);

622 }

623 if(i == 5)

624 {

625 analogWrite(leg_RED_GRN, 50);



627 callGround();

628 delay(1000);

629 digitalWrite(leg_RED_GRN, LOW);

630 }

631 if(i == 6)

632 {

633 analogWrite(leg_RED_ORN, 50);


635 callGround();

636 delay(1000);

637 digitalWrite(leg_RED_ORN, LOW);

638 }

639 if(i == 7)

640 {

641 analogWrite(leg_BLU_RED, 50);


643 callGround();

644 delay(1000);

645 digitalWrite(leg_BLU_RED, LOW);

646 }

647 if(i == 8)

648 {

649 analogWrite(leg_WHT_RED_BLK, 50);


651 callGround();

652 delay(1000);

653 digitalWrite(leg_WHT_RED_BLK, LOW);

654 }

655 }

656 }

657 if (value == '1') //maxTry = 30 Begin synchronisation with camera Command 1

658 {

659 callSYNC(30);

660 }

661 if (value == '0') //Wake camera Command 0

662 {

663 callWAKE();

664 }

665 if (value == '2') //Set image capture to JPEG 640x480 Command 2

666 {

667 callINITIAL();

668 }

669 if (value == '3') //Set package size to 512 bytes Command 3

670 {

671 callPACKET();

672 }

673 if (value == '4') //Order camera to hold JPEG in buffer Command 4

674 {

675 callSNAPSHOT();

676 }

677 if (value == '5') //Request Snapshot picture Command 5

678 {



681 }

682 if(value == '7') //Turn top circumf of lights on

683 {

684 analogWrite(leg_GRN_BLK, 30);

685 analogWrite(leg_WHT_BLK, 30);

686 analogWrite(leg_BLU_BLK, 30);

687 analogWrite(leg_BLK_WHT, 30);

688 analogWrite(leg_RED_GRN, 30);

689 analogWrite(leg_RED_ORN, 30);

690 analogWrite(leg_BLU_RED, 30);

691 analogWrite(leg_WHT_RED_BLK, 30);

692

693 pinMode(gnd_BLU_WHT, OUTPUT);

694 digitalWrite(gnd_BLU_WHT, LOW); //Turn on the lights

695

696 }

697 if(value == '8') //Turn top circumf of light off.

698 {

699 digitalWrite(leg_GRN_BLK, LOW);


700 digitalWrite(leg_WHT_BLK, LOW);

701 digitalWrite(leg_BLU_BLK, LOW);

702 digitalWrite(leg_BLK_WHT, LOW);

703 digitalWrite(leg_RED_GRN, LOW);

704 digitalWrite(leg_RED_ORN, LOW);

705 digitalWrite(leg_BLU_RED, LOW);

706 digitalWrite(leg_WHT_RED_BLK, LOW);

707

708 pinMode(gnd_BLU_WHT, INPUT); //Turn off the lights

709 }

710 }

711 }

77

Appendix B

RTI Calibration

The position vectors of the light sources in the RTI array are necessary in order

to calculate the orthogonal normal for each pixel of the saliency reconstruction. The

calibration position vectors are unique to this particular RTI and is dependent on the

camera orientation, relative to the LED lighting sequence. The lighting sequence is

described in the Arduino code of Appendix A (lines 587 – 655). A visualisation of the

position vectors listed in Table B.1 is illustrated in Figure B.1. Each pivot point on

the surface illustrated in Figure B.1 represents the position of an LED in the RTI array.

Number of Images 40

x y z

-0.19269659 0.38683176 0.9017922

-0.27608934 0.66554254 0.6934175

-0.30739304 0.8486838 0.4304014

-0.3387415 0.92352325 0.17988603

-0.34606388 0.9314726 0.11224358

0.14804314 0.37764886 0.9140375

0.32460698 0.62399626 0.7108157

0.35484293 0.8035059 0.47797987

0.3928411 0.8924984 0.22163592

0.40281844 0.91474766 0.031209946

TABLE B.1: Calibration Data for RTI Array LED Position Vectors

Appendix B. RTI Calibration 78

0.37939978 0.13432392 0.9154304

0.65144646 0.23029669 0.7228976

0.8309564 0.3062681 0.46444735

0.92073506 0.3169897 0.22751807

0.94246477 0.32331076 0.085031

0.41595057 -0.25330895 0.8733955

0.62655354 -0.32982713 0.70614785

0.76346916 -0.39916432 0.5077231

0.8618827 -0.46884564 0.19324085

0.8707832 -0.47709772 0.11880401

0.07792439 -0.38232 0.9207384

0.178718 -0.61564785 0.7674878

0.2520881 -0.848508 0.46528032

0.28214908 -0.9287489 0.24045236

0.27755782 -0.95947915 0.04859466

-0.22412229 -0.3843872 0.8955533

-0.36014935 -0.6608621 0.658448

-0.429824 -0.82922983 0.35725236

-0.45232946 -0.8746341 0.1743939

-0.45364493 -0.8894815 0.05503568

-0.39949727 -0.087511204 0.91254795

-0.6879534 -0.2016659 0.69717354

-0.87947816 -0.28155154 0.3837277

-0.938435 -0.32217053 0.12468287

-0.9481228 -0.31253535 0.05817903

-0.44718313 0.18938163 0.8741635

-0.65133876 0.30712116 0.69385475

-0.81964505 0.37016344 0.43721962

-0.87370497 0.37434143 0.31065753

-0.9063811 0.38801134 0.16709456

Asdasd

Appendix B. RTI Calibration 79

asdasd

FIGURE B.1: Visualisation of Calibration Position Vectors

80

Appendix C

CNN MATLAB Code

The code used to implement the regression model CNN is presented. The code

is based off the MATLAB tutorial Convert Classification Network into Regression

Network. The implementation of the CNN uses transfer learning with the AlexNet as

the model neural network. The original CNN is modified from being a classification

model to a regression model by altering the last layers of the architecture. Image data

is stored and organised in preparation for training of the CNN. The image data is

augmented (resized) to match the input of the CNN. The training options for the new

CNN are configured (10 samples per iteration for 5 Epochs using adaptive moment

estimation solver) and the model is trained.

1 % %%%%%%%%%%%%%%%%%%%%%%%%%%%Create the CNN

2 % layers = [

3 % imageInputLayer([227 227 3],"Name","data")

4 % convolution2dLayer([11 11],96,"Name","conv1","BiasLearnRateFactor",2, ...

5 % "Stride",[4 4])

6 % reluLayer("Name","relu1")

7 % crossChannelNormalizationLayer(5,"Name","norm1","K",1)

8 % maxPooling2dLayer([3 3],"Name","pool1","Stride",[2 2])

9 % groupedConvolution2dLayer([5 5],128,2,"Name","conv2", ...

10 % "BiasLearnRateFactor",2,"Padding",[2 2 2 2])


12 % crossChannelNormalizationLayer(5,"Name","norm2","K",1)


14 % convolution2dLayer([3 3],384,"Name","conv3","BiasLearnRateFactor",2, ...

15 % "Padding",[1 1 1 1])









24 % fullyConnectedLayer(4096,"Name","fc6","BiasLearnRateFactor",2)


26 % dropoutLayer(0.5,"Name","drop6")

27 % fullyConnectedLayer(4096,"Name","fc7","BiasLearnRateFactor",2)

Appendix C. CNN MATLAB Code 81


29 % dropoutLayer(0.5,"Name","drop7")

30 % fullyConnectedLayer(1,"Name","fc","BiasLearnRateFactor",10, ...

31 % "WeightLearnRateFactor",10)

32 % regressionLayer("Name","regressionoutput")];

33

34 %%%%%%%%%%%%%%%%%%%%%%%%%%Freeze wieghts of initial layers

35 %layers(1:22) = freezeWeights(layers(1:22));

36

37 %%%%%%%%%%%%%%%%%%%%%%%%%%Plot the layers

38 %plot(layerGraph(layers));

39

40 %%%%%%%%%%%%%%%%%%%%%%%%%%%Load pre-trained network

41 net = alexnet;

42

43 %%%%%%%%%%%%%%%%%%%%%%%%%%%Read in the data

44 imds = imageDatastore('Data_set_regression_no_black_big_range', ...

45 'IncludeSubfolders',true,'LabelSource','foldernames');

46

47 %%%%%%%%%%%%%%%%%%%%%%%%%%%Convert categorical labels to double

48 [g,gN] = grp2idx(imds.Labels);

49 gN(g);

50 ydouble = str2num(cell2mat(gN(g)));

51 imds.Labels = ydouble/10000;

52

53 %Splits data randomly into two subset image data stores

54 n = numpartitions(imds);

55 indices = randperm(n);

56 st = round(0.7 * n);

57 imdsTrain = subset(imds, indices(1:st));

58 imdsValidation = subset (imds, indices(st+1:n));

59

60 %%%%%%%%%%%%%%%%%%%%%%%%%%%Remake Training Data to Table of aumented images

61 % Define the image files as cell arrays

62 filename = [imdsTrain.Files];

63 % Define the target values

64 value = [imdsTrain.Labels];

65 % Create a table data

66 tbl = table(filename, value);

67 % Data Augmentation (Resize Images)

68 augimdsTrain = augmentedImageDatastore([227,227, 3],tbl);

69

70 %%%%%%%%%%%%%%%%%%%%%%%%%%%Remake Validation Data to Table of aumented images

71 % Define the image files as cell arrays

72 filename = [imdsValidation.Files];

73 % Define the target values

74 value = [imdsValidation.Labels];

75 % Create a table data

76 tbl = table(filename, value);

77 % Data Augmentation (Resize Images)

78 augimdsValidation = augmentedImageDatastore([227,227, 3],tbl);

79

80 %%%%%%%%%%%%%%%%%%%%%%%%%%%Display 4 sample training images with ground

81 %%%%%%%%%%%%%%%%%%%%%%%%%%%truth values

82 idx = randperm(numel(imdsTrain.Files),4);

83 figure

84 for i = 1:numel(idx)

85 subplot(2,2,i)

86 %I = readimage(imdsTrain,idx(i));

87 [I, info] = readimage(imdsTrain,idx(i));

88 imshow(I)

89 %label = imfinfo(imdsTrain,idx(i));

90 %title(string(label) + ", " + num2str(100*max(probs(idx(i),:)),3) + "%");

91 title("Measured Leakage Current: " + string(info.Label) + " mA");

92 end

93

94 %%%%%%%%%%%%%%%%%%%%%%%%%%Specify training options

95 miniBatchSize = 10;

96 valFrequency = floor(numel(imdsTrain.Files)/miniBatchSize);

97 options = trainingOptions('adam', ...

98 'MiniBatchSize',miniBatchSize, ...

99 'MaxEpochs',5, ...

100 'InitialLearnRate',3e-4, ...

101 'Shuffle','every-epoch', ...

Appendix C. CNN MATLAB Code 82

102 'ValidationData', augimdsValidation, ...

103 'ValidationFrequency',valFrequency, ...

104 'Verbose',false, ...

105 'Plots','training-progress');

106

107 % options = trainingOptions('adam',...

108 % 'InitialLearnRate',0.001, ...

109 % 'ValidationData', augimdsValidation,...

110 % 'Plots','training-progress',...

111 % 'Verbose',false);

112

113

114

115

116 %%%%%%%%%%%%%%%%%%%%%%%%%%%Train network

117 trainedNet = trainNetwork(augimdsTrain, layers_1, options);

118

119 %%%%%%%%%%%%%%%%%%%%%%%%%%%Test performance of network by evaluating

120 %%%%%%%%%%%%%%%%%%%%%%%%%%%accuracy on validation data

121 YPred = predict(trainedNet, augimdsValidation);

122 %accuracy = mean(YPred == imdsValidation.Labels)

123

124 %%%%%%%%%%%%%%%%%%%%%%%%%%%Calculate prediction error between prdeicted

125 %%%%%%%%%%%%%%%%%%%%%%%%%%%leakage current and actual leakage current

126 predictionError = imdsValidation.Labels - YPred;

127

128 %%%%%%%%%%%%%%%%%%%%%%%%%%%Set threshold to 0.5mA and calculate number of

129 %%%%%%%%%%%%%%%%%%%%%%%%%%%predictions within threshold

130 thr = 0.5;

131 numCorrect = sum(abs(predictionError) < thr);

132 numImagesValidation = numel(imdsValidation.Labels);

133

134 accuracy = numCorrect/numImagesValidation

135

136 %%%%%%%%%%%%%%%%%%%%%%%%%%%Use RMSE to measure difference between predicted

137 %%%%%%%%%%%%%%%%%%%%%%%%%%%and actual leakage current

138 rmse = sqrt(mean(predictionError.^2))

139

140 %%%%%%%%%%%%%%%%%%%%%%%%%%%Display four sample validation images with predicted

141 %%%%%%%%%%%%%%%%%%%%%%%%%%%labels and predicted probabilities.

142 idx = randperm(numel(imdsValidation.Files),4);

143 figure

144 for i = 1:4

145 subplot(2,2,i)

146 I = readimage(imdsValidation,idx(i));

147 [I, info] = readimage(imdsValidation,idx(i));

148 imshow(I)

149 label = YPred(idx(i));

150 title("Measured Leakage Current: " + string(info.Label) + " mA" + ...

151 newline + "Predicted Leakage Current: " + string(label) + " mA");

152 end

83

Appendix D

Laboratory Trials Data

The results obtained in the laboratory trials, where ESDD and leakage current

are measured, are presented. The value of leakage current is derived from a

measurement of voltage over a known resistance. ESDD is calculated from the

conductivity, volume, and temperature of the resultant solution used to wash the

artificially polluted bushing. Values in Table D.1 that were obtained through

measurement are shaded in blue. All other values arise from calculation (or are

constants in calculations). The equations and constants used to calculate the ESDD

are described in Chapter 3.5 – Conventional Measurement of ESDD and NSDD.

Values that are used in Table 5.1 are double-bordered in Table D.1.

Appendix D. Laboratory Trials Data 84

asd TABLE D.1: Measured and Calculated Laboratory Data

85

Appendix E

Regression CNN Training

The training progress graphs for two iterations of the implemented regression

CNN are presented. It is important to organise the training data for neural networks

to learn on, such that it does not cause any skew in the prediction model. Figure E.1

illustrates the training progress on all the image data, including black images.

Figure E.2 illustrates the training progress on image data excluding images with a file

size smaller than 10 kB.

Appendix E. Regression CNN Training 86

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Appendix E. Regression CNN Training 87

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