INTERPRETATION TECHNIQUE OF PARTIAL DISCHARGE …eprints.utm.my/id/eprint/77864/1/AuliaPFKE2016.pdf · Penebatan voltan tinggi mesti direkabentuk supaya mempunyai ketahanan yang tinggi

INTERPRETATION TECHNIQUE OF PARTIAL DISCHARGE PULSE COUNT

AND SURFACE DEFECT ANALYSIS FOR EVALUATION OF COMPOSITE

MATERIALS

AULIA

UNIVERSITI TEKNOLOGI MALAYSIA

INTERPRETATION TECHNIQUE OF PARTIAL DISCHARGE PULSE COUNT

AND SURFACE DEFECT ANALYSIS FOR EVALUATION OF COMPOSITE

MATERIALS

AULIA

A thesis submitted in partial fulfilment of the

requirements for the award of degree of

Doctor of Philosophy (Electrical Engineering)

Faculty of Electrical Engineering

Universiti Teknologi Malaysia

JUNE 2016

iii

DEDICATION

To my lovely parents who passed away, whose introduced me to Allah and Rasulullah and

Al-Quran and gave me the endless love, trust, constant encouragement over the years during

their lives, and for their prayers.

To my family members, my wife Yessi Anggraini, and my children Jundullah Ilhaq

Aulia, Mujahid Salafi Aulia, and Widad Elqudsi Aulia, for their patience, support,

love, and for enduring the ups and downs during the completion of this PhD thesis.

This thesis is dedicated to them.

iv

ACKNOWLEDGEMENT

I wish to express my deepest appreciation to all those who helped me, in one

way or another, to complete this PhD thesis. First and foremost I thank Allah who

provided me with strength, direction and purpose throughout my PhD thesis. Special

thanks to my supervisor, Professor Dr. Zulkurnain Abdul-Malek, and my co-

supervisor Dr. Yanuar Zulardiansyah Arief, for all their patience, guidance and support

throughout my PhD work duration. Through their expert guidance, I was able to

overcome all obstacles that I encountered in these enduring years. In fact, they always

gave me immense hope every time I consulted them over problems relating to my PhD

study.

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ABSTRACT

High voltage insulation must be designed in such a way that it is very resistant

to ageing including that from partial discharge (PD). Many studies were previously

carried out on composites based on low density polyethylene (LDPE). However, the

use of natural rubber (NR) and nanosilica (SiO2 nanoparticle) in the LDPE-based

composites are relatively new. Furthermore, the PD resistant performance of the

composites is yet to be extensively researched. It is desired to know the weight

percentage of each component in the LDPE-NR-SiO2 composite, especially the

nanosilica filler weight percentage, for an optimum PD resistant performance. Due to

specific research requirements, a customized laboratory scale PD data acquisition

system (PD-DAQS) is desired to be developed. With the availability of several

parameters obtained from the PD experiment, there is another need to devise an

interpretation tool that can correctly correlate all parameters to the PD resistance. This

work aims to develop a new PD system for measuring, analysing and interpreting PD

signals, which can then be used to determine a new nanocomposite material with high

PD resistance. A new PD-DAQS comprising CIGRE Method II test cell, Picoscope

interfacing device, and LabVIEW based program was successfully developed.

Scanning electron as well as normal microscopes were used for composite sample’s

image capturing and morphological analyses. A new PD interpretation technique

based on PD pulse count and its surface image analysis was obtained and successfully

categorised the PD performance of a given dielectric sample on scores of 1 to 5

corresponding to very bad to very good. PD improvement index and a scoring system

were also introduced and utilised for the new dielectric work. Three groups of new

composites with varying compositions and nanosilica content were made and tested

by applying high voltage stress using the CIGRE Method II test cell for 60 minutes.

Results have shown that the addition of nanosilica filler into LDPE and LDPE-NR

base polymers have increased the PD resistance of the new composites. The highest

PD resistance score of 5 was achieved by the best nanocomposite sample of LDPE-

SiO2 with 4.5 weight percent of nanosilica. Even though the addition of natural rubber

to LDPE matrix has caused a decrease in PD resistance, the addition of 6 weight

percent of nanosilica as fillers in the LDPE-NR (80:20) composite has tremendously

improved its PD resistant performance. The LDPE as well as LDPE-NR based

nanocomposites can be potentially developed as a high voltage insulating material.

The developed PD measuring and interpretation system can also be utilised for future

PD and nanodielectric studies.

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ABSTRAK

Penebatan voltan tinggi mesti direkabentuk supaya mempunyai ketahanan

yang tinggi terhadap penuaan termasuk yang disebabkan oleh nyahcas separa. Banyak

kajian ke atas komposit berasaskan polyethylene berketumpatan rendah (LDPE) telah

dilakukan. Walau bagaimanapun, penggunaan getah asli (NR) dan nanosilika (SiO2)

di dalam komposit berasaskan LDPE masih baru. Tambahan pula, prestasi rintangan

PD komposit masih perlu kajian lanjut. Ingin diketahui adalah peratus berat setiap

komponen komposit LDPE-NR-SiO2, terutama peratus pengisi nanosilika bagi

prestasi PD optimum. Disebabkan oleh keperluan penyelidikan khusus, satu sistem

perolehan data (PD-DAQS) berskala makmal perlu dibangunkan. Dengan adanya

beberapa parameter yang diperolehi dari ujikaji PD, terdapat keperluan untuk

menyediakan satu alat penafsiran yang dapat mengaitkan semua parameter dengan

rintangan PD. Tujuan penyelidikan ini adalah untuk membangunkan satu sistem PD

baru untuk mengukur, menganalisis dan mentafsir isyarat PD yang kemudiannya boleh

digunakan untuk mendapatkan satu bahan nanokomposit baru dengan rintangan PD

yang tinggi. Sebuah PD-DAQS baru yang terdiri daripada sel ujian kaedah CIGRE II,

peranti antara muka Picoscope, dan program berasaskan LabVIEW berjaya

dibangunkan. Mikroskop pengimbas elektron dan mikroskop biasa digunakan untuk

mengambil gambar sampel komposit dan untuk analisis morfologi. Satu penafsiran

PD yang baru berdasarkan hitungan denyut PD dan analisis imej permukaan telah

diperolehi dan ianya berjaya mengklasifikasikan prestasi PD sampel dielektrik dengan

skor 1 hingga 5, atau sangat buruk hingga sangat baik. Indeks penambahbaikan PD

dan satu sistem penilaian juga diperkenal dan digunakan dalam penyelidikan dielektrik

baru. Tiga kumpulan komposit baru dengan pelbagai komposisi dan kandungan

nanosilika telah dibuat dan diuji menggunakan voltan tinggi dan sel ujian kaedah

CIGRE II selama 60 minit. Hasil menunjukkan bahawa penambahan pengisi

nanosilika ke dalam polimer asas LDPE dan LDPE-NR telah meningkatkan rintangan

PD komposit baru tersebut. Skor rintangan PD tertinggi 5 dicapai oleh sampel

nanokomposit terbaik iaitu LDPE-SiO2 yang mangandungi 4.5 peratus berat

nanosilika. Walaupun penambahan getah asli ke dalam LDPE telah menyebabkan

penurunan rintangan PD, namun penambahan 6 peratus berat nanosilika sebagai

pengisi dalam komposit LDPE-NR (80:20) telah menambahbaik rintangan PD secara

mendadak. Nanokomposit berasaskan LDPE serta LDPE-NR berpotensi dibangunkan

sebagai bahan penebat voltan tinggi. Sistem pengukuran PD dan sistem tafsiran yang

dibangunkan juga boleh dimanfaatkan untuk kajian PD dan nanodielektrik pada masa

depan.

vii

TABLE OF CONTENTS

CHAPTER TITLE PAGE

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES xii

LIST OF FIGURES xiv

LIST OF ABBREVIATIONS xxiii

LIST OF SYMBOLS xxiv

1 INTRODUCTION 1

1.1 Preliminaries 1

1.2 Research Problem Statement 4

1.3 Research Objectives 5

1.4 Research Scopes 6

1.5 Research Contribution 6

1.6 Research Significance 9

1.7 Thesis Organization 9

2 LITERATURE REVIEW 11

2.1 Introduction 11

2.2 Partial Discharge Detection and Data Acquisition System 11

2.3 Partial Discharge Interpretation Techniques 16

2.3.1 Partial Discharge Pulse Count 16

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2.3.2 Surface Imaging 19

2.3.2.1 Image Histogram Method 20

2.3.2.2 Image Quantifying Method 21

2.3.2.3 Machine Vision 22

2.3.3 PD Improvement Index 23

2.3.4 Scoring System 23

2.4 Polymer Insulator 26

2.4.1 Role of Nanofiller 27

2.4.2 Polymeric Materials with Nanosilica 28

2.4.3 Partial Discharge Resistance of

Nanocomposite 31

2.5 Summary 32

3 RESEARCH METHODOLOGY 34

3.1 Introduction 34

3.2 Partial Discharge Measurement and Data Acquisition

System 35

3.2.1 Partial Discharge Measurement Procedure and

Data-Acquisition System Set-up 36

3.2.2 Partial Discharge Experimental Set-up 38

3.2.3 Hardware Specification of PD-DAQS 39

3.2.4 Schematic Diagram of the DAQS for PD

Measurement 40

3.2.5 PD Waveform 42

3.2.6 Noises and Power Supply Signals 43

3.2.7 Charge Calibration 44

3.2.8 Filtering the Partial Discharge Signals 44

3.2.9 PD Data Recording 45

3.2.10 Sub VI to Detect the PD Pulses 46

3.3 PD Interpretation Using PD Pulse Count and Surface

Image 48

3.3.1 Partial Discharge Pulse Count Analysis 48

3.3.2 Statistical Analysis 49

3.3.2.1 Mean 50

ix

3.3.2.2 Standard Deviation 50

3.3.2.3 Skewness 51

3.3.2.4 Kurtosis 51

3.3.3 Surface Defect Analysis 52

3.3.3.1 NI Vision Assistant 54

3.3.3.2 Image Histogram Analysis 56

3.3.3.3 Image Quantifying Analysis 58


3.3.5 The Scoring System 59

3.4 Design of a New Composite 63

3.4.1 Sample Composition 63

3.4.2 Sample Blending Process 64

3.4.3 Sample Moulding 67

3.4.4 CIGRE Method II 68

3.4.5 New Dielectric Sample Analyses 69

3.5 Summary 71

4 RESULTS AND DISCUSSION 72

4.1 Introduction 72

4.2 Development of New Partial Discharge and Data

Acquisition System 73

4.2.1 Noises Elimination 74

4.2.2 Charge Calibration 76

4.2.3 Sampling Rate Effect 78

4.3 A New PD Data Interpretation Technique Using PD

Pulse Count and Surface Image 81

4.3.1 PD Pulse Count Analysis 81

4.3.2 Surface Defect Analysis 83

4.3.3 PD Improvement Index Analysis 84

4.3.4 Scoring Analysis 85

4.4 New Composite Material 85

4.4.1 PD Pulse Count Characteristics of LDPE-NR

Composites 86

4.4.1.1 Cumulative PD Pulse Count 86

x

4.4.1.2 Average Positive and Negative PD

Pulse Count 88

4.4.1.3 Positive and Negative PD Pulse

Count per Cycle 89

4.4.1.4 Total PD Pulse Count 90

4.4.2 PD Pulse Count Characteristics of LDPE-SiO2

Composites 92



Pulse Count 94

4.4.2.3 Positive and Negative PD Pulse Count

Pattern 95


4.4.3 PD Pulse Count Characteristics of LDPE-NR-SiO2

Composites 97



Pulse Count 99

4.4.3.3 Positive and Negative PD Pulse Count

per Cycle 100


4.4.3.5 Overall Pulse Count Analysis 102

4.4.4 Surface Image Analysis 105

4.4.4.1 Scanning Electron Macroscopic

Analyses 105

4.4.4.2 Surface Morphology Analysis Using

NI Vision Assistant 110

4.4.4.3 Surfaces Images Histogram Analysis 122

4.4.4.4 Percentage of Eroded Area Using the

Histogram Method 127

4.4.4.5 The Percentage of Eroded Area Using

Quantifying Method 130


4.4.6 Scoring System for Composites Material

xi

Assessment 137

4.4.7 Comparison of Histogram and Quantifying Method 143

4.5 Summary 145

5 CONCLUSION AND FUTURE WORK 147

5.1 Conclusion 147

1. Partial Discharge Data Acquisition System 147

2. A New Partial Discharge Interpretation Tool 148

3. New Composite Material 149

5.2 Future Work 150

REFFERENCES 151

APPENDIX A 168

xii

LIST OF TABLES

TABLE NO. TITLE PAGE

2.1 Partial discharge testing related to the insulation condition 18

2.2 Various evaluation techniques and their respective

objectives 24

2.3 The evaluated criteria and its related result of different

composite 25

2.4 The summary of the base polymer and the filler

percentage of the nanosilica 29

3.1 PC and DAQS specification 39

3.2 The score rule for PD count analysis and PoEA analysis

using histogram and quantifying methods 61

3.3 The score rule of the item in each category 62

3.4 Samples prepared for three different categories 63

3.5 The picture of new dielectric samples prepared for three

different groups 70

4.1 Memory used and numbers of files could be saved every

minute for different sampling rates 80

4.2 Pulse waveform in different sampling rates 80

4.3 Template for image histogram analysis of sample N before

and after the high voltage stress 83

4.4 Percentage of eroded area and corresponding histogram

for sample N after the high voltage stress 84

4.5 The blank score distribution of LDPE-NR-SiO2 composite

samples 85

4.6 LDPE-NR composites intensity histogram of sample A1

(100:0) and sample A3 (80:20) before and after 60

minutes high voltage stress 123

xiii

4.7 LDPE-SiO2 composites histogram of samples B1 (100:0),

B2, B3 and B4 (80:20) before and after 60 minutes of high

voltage stress 124

4.8 Histogram of samples C1, C2, C3 and C4 before and after

60 minutes of high voltage stress 126

4.9 Percentage of eroded area and related histogram of LDPE-

NR composites of sample A1 (100:0) and A3 (80:20) after

60 minutes high voltage stress 131

4.10 Percentage of defect area and histogram for samples B1

(100:1.5), B2 (100:3), B3 (100:4.5) and B4 (100:6) after

60 minutes of high voltage stress 132

4.11 Percentage of defect area and histogram of samples C1,

C2, C3 and C4 after 60 minutes of high voltage stress 134

4.12 The score distribution of LDPE-NR-SiO2 composite

samples after 60 minutes of high voltage stress; A1

(100:0:0), A3 (80:20:0), B1 (100:0:1.5), B2 (100:0:3), B3

(100:0:4.5), B4 (100:0:6), C1 (80:20:1.5), C2 (80:20:3),

C3 (80:20:4.5) and C4 (80:20:6) 139

xiv

LIST OF FIGURES

FIGURE NO. TITLE PAGE

3.1 PD experimental procedure using PD-DAQS 37

3.2 Test set-up for the partial discharge test 38

3.3 Picoscope 5203 series [163] 40

3.4 (a) Schematic of PD-DAQS, (b) graphical user interface

(GUI) unit of PD-DAQS using LabVIEW 41

3.5 Typical parameters describing the shape of an idealized

PD pulse [164] 42

3.6 High pass filter Sub VI used in the program to digitally

filter the input signals to extract the PD pulses 45

3.7 The file formats and conditions to create a new file 46

3.8 (a) Peak detector SubVI and (b) Statistics SubVI 47

3.9 The PD pulse count analysis flow chart 49

3.10 Positive and negative skewness 51

3.11 An illustration of kurtosis; (a) negative kurtosis and (b)

positive kurtosis 52

3.12 The flow chart for the image analysis 53

3.13 (a) The general steps in designing an NI Vision

application, (b) Inspection steps for building a Vision

application 56

3.14 The steps to calculate PoEA using QM 58

3.15 The flow chart for the scoring system 60

3.16 Schematic flow diagram of the mixing process in

preparing the LDPE-NR composite, nanosilica LDPE

composite, and nanosilica LDPE-NR composite 65

3.17 (a) Conceptual view of rubber mill rolls, (b) The actual

picture of two roll mill 66

xv

3.18 Hot press machine for molding process 67

3.19 The mould for the ClGRE Method II electrode cell. (a) A

detailed dimension of the mould, and (b) the actual mould. 68

3.20 Structure of the ClGRE method II test cell 69

4.1 (a) PD and 50 Hz power supply signals, and (b) zoom out

view of corresponding PD signal waveform. 75

4.2 (a) PD signal [176] and input voltage (kV), (b) PD signal

waveform 76

4.3 Charge calibration for Picoscope reading. (a) Positive

charge, and (b) negative charge 77

4.4 Effect of sampling rate to PD pulse waveform; (a) 1 MS/s,

(b) 42 MS/s, (c) 125 MS/s, (d) 250 MS/s and (e) 500 MS/s 79

4.5 Four templates for PD pulse count analysis; (a) cumulative

PD pulse count, (b) average PD pulse count, (c) PD pulse

count pattern, and (d) total PD pulse count. 82

4.6 PD improvement index (A-PD Index) 84

4.7 Cumulative positive and negative PD pulse counts for

different LDPE-NR compositions over 60 minutes stress

duration; (a) sample A1 (100:0), (b) sample A2 (90:10),

(c) sample A3 (80:20), and (d) sample A4 (70:30) 87

4.8 Average positive and negative PD pulse count over 10

minutes stress duration for six consecutive cycles (60

minutes total stress duration) for different LDPE-NR

composites; (a) sample A1 (100:0), (b) sample A2 (90:10),

(c) sample A3 (80:20), (d) sample A4 (70:30) 88

4.9 Comparison of average PD pulse count over 10 minutes

stress duration for six consecutive cycles (60 minutes total

stress duration) of different LDPE-NR composites; sample

A1 (100:0), sample A2 (90:10), sample A3 (80:20),

sample A4 (70:30). (a) positive PD and (b) negative PD 89

4.10 The comparison of average PD pulse count for every 10

minutes stress duration for six consecutive cycles (60

minutes total stress duration) of different LDPE-NR

composites; sample A1 (100:0), sample A2 (90:10),

xvi

sample A3 (80:20), sample A4 (70:30). (a) positive PD

and (b) negative PD 90

4.11 Positive and negative PD pulse counts of different LDPE-

NR compositions over 60 minutes stress duration; sample

A1 (100:0), sample A2 (90:10), sample A3 (80:20),

sample A4 (70:30) 91

4.12: The difference of positve and negative PD pulse counts of

different LDPE-NR composition over 60 minutes stress

duration; sample A1 (100:0), sample A2 (90:10), sample

A3 (80:20), sample A4 (70:30) 91

4.13 Total PD pulse counts of different LDPE-NR composition

over 60 minutes stress duration; sample A1 (100:0),

sample A2 (90:10), sample A3 (80:20), sample A4 (70:30) 92

4.14 Cumulative PD pulse count for various samples of LDPE-

SiO2 composites over 60 minutes stress duration; (a)

sample B1 (100:1.5), (b) sample B2 (100:3), (c) for

sample B3 (100:4.5), and (d) for sample B4 (100:6) 93


minutes stress duration for six consecutive cycles for

various samples of LDPE-SiO2 composites; (a) sample B1

(100:1.5), (b) sample B2 (100:3), (c) for sample B3

(100:4.5) and (d) for sample B4 (100:6) 94

4.16 The comparison of average PD pulse count for every

sample over 10 minutes stress duration for six consecutive

cycles of various samples of LDPE-SiO2; sample B1

(100:1.5), sample B2 (100:3), sample B3 (100:4.5) and

sample B4 (100:6). (a) Positive PD and (b) Negative PD 95

4.17 Comparison of PD pulse count over 10 minutes stress

duration for six consecutive cycles for various samples of

LDPE-SiO2; sample B1 (100:1.5), sample B2 (100:3),

sample B3 (100:4.5) and sample B4 (100:6), (a) positive

PD, and (b) negative PD 96

4.18: The difference between positive PD and negative PD pulse

counts of various samples of LDPE-SiO2 composite,

xvii

sample B1 (100:1.5), sample B2 (100:3), sample B3

(100:4.5) and sample B4 (100:6) 96

4.19 The total PD pulse counts for various samples of LDPE-

SiO2 composite, sample B1 (100:1.5), sample B2 (100:3),

sample B3 (100:4.5) and sample B4 (100:6) 97

4.20 The cumulative PD pulse count for various samples of

LDPE-NR-SiO2 composite; (a) sample C1 (80:20:1.5), (b)

sample C2 (80:20:3), (c) sample C3 (80:20:4.5) and (d)

sample C4 (80:20:6) 98


minutes stress duration for six consecutive cycles of

various samples of LDPE-NR-SiO2; (a) sample C1

(80:20:1.5), (b) sample C2 (80:20:3), (c) sample C3

(80:20:4.5) and (d) sample C4 (80:20:6) 99

4.22 Comparison of average PD pulse count for every sample

over 10 minutes high voltage stress duration for six

consecutive cycles of various samples of LDPE-NR-SiO2;

sample C1 (80:20:1.5), sample C2 (80:20:3), sample C3

(80:20:4.5) and sample C4 (80:20:6). (a) positive PD and

(b) negative PD 100

4.23 The average PD pulse count for every 10 minutes high

voltage stress duration for six consecutive cycles of

various samples of LDPE-NR-SiO2; sample C1,

(80:20:1.5), sample C2 (80:20:3), sample C3 (80:20:4.5)

and sample C4 (80:20:6). (a) positive PD and (b) negative

PD 101

4.24 The difference of positive PD pulse count and negative PD

pulse count of different samples of LDPE-NR-SiO2

composite; sample C1 (80:20:1.5) ,sample C2 (80:20:3),

sample C3 (80:20:4.5) and (e) sample C4 (80:20:6) 101

4.25 The total PD pulse count of different samples of LDPE-

NR-SiO2 composites; sample C1 (80:20:1.5), sample C2

(80:20:3), sample C3 (80:20:4.5) and (e) sample C4

(80:20:6) 102

xviii

4.26 The summary of the positive and negative PD pulse counts

of LDPE-NR-SiO2 composites for all samples after 60

minutes high voltage stress applied to them; A1 (100:0:0),

A2 (90:10:0), A3 (80:20:0), A4 (70:30:), B1 (100:0:1.5),

B2 (100:0:3), B3 (100:0:4.5), B4 (100:0:6), C1

(80:20:1.5), C2 (80:20:3), C3 (80:20:4.5) and C4

(80:20:6). (a) positive PD pulse count and, (b) negative

PD pulse count 103

4.27 Total PD pulse counts of LDPE-NR-SiO2 composites for

all samples after 60 minutes high voltage stress; A1

(100:0:0), A2 (90:10:0), A3 (80:20:0), A4 (70:30:), B1

(100:0:1.5), B2 (100:0:3), B3 (100:0:4.5), B4 (100:0:6),

C1 (80:20:1.5), C2 (80:20:3), C3 (80:20:4.5) and C4

(80:20:6) 104

4.28 SEM images of LDPE-SiO2 in different compositions

prior to being stressed with the 500 and 20,000

magnification respectively, (a1) and (a2) for sample B1

(100:1.5), (b1) and (b2) for sample B2 (100:3), (c1) and

(c2) for sample B3 (100:4.5) and (d1) and (d2) for sample

B4 (100:6) 106

4.29 SEM cross sectional images of LDPE-SiO2 in different

compositions prior to being stressed; (a) sample B1

(100:1.5), (b) sample B2 (100:3), (c) for sample B3 107

4.30 SEM images of LDPE-NR-SiO2 in different compositions

prior to being stressed with the precision of 500 Mag and

20,000 Mag respectively, (a1) and (a2) for sample C1

(80:20:1.5) , (b1) and (b2) for sample C2 (80:20:3), (c1)

and (c2) for sample C3 (80:20:4.5) and (d1) and (d2) for

sample C4 (80:20:6) 109

4.31 SEM Cross section images of LDPE-NR-SiO2 in different

compositions prior to being stressed, (a) sample C1

(80:20:1.5) , (b) sample C2 (80:20:3), (c) sample C3

(80:20:4.5) and (d) sample C4 (80:20:6) 110

xix

4.32 The surface images of LDPE-NR composite sample A1

(100:0) before and after 60 minutes high voltage stress; (a)

and (b) are the original image, (c) and (d) are a texture

image two-dimensional (2D), (e) and (f) are a texture

image three-dimension (3D) 112

4.33 The surface images of LDPE-NR composite sample A3





4.34 The surface images of LDPE-SiO2 composite sample B1

(100:1.5) before and after 60 minutes high voltage stress;

(a) and (b) are the original image, (c) and (d) are a texture



4.35 The surface images of LDPE-SiO2 composite sample B2





4.36 The surface images of LDPE-SiO2 composites sample B3

(100:4.5) before and after 60 minutes high voltage stress;

(a) and (b) are the original image, (c) and (d) are a texture



4.37 The surface images of LDPE-SiO2 composites sample B4





4.38 The surface images of LDPE-NR-SiO2 composite sample

C1 (80:20:1.5) before and after 60 minutes high voltage

stress; (a) and (b) are the original image, (c) and (d) are a

xx

texture image two-dimensional (2D), (e) and (f) are a

texture image three-dimension (3D) 119

4.39 The surface images of LDPE-NR-SiO2 composites

sample C2 (80:20:3) before and after 60 minutes high

voltage stress; (a) and (b) are the original image, (c) and

(d) are a two-dimensional texture image (2D), (e) and (f)

are a three-dimension of texture image (3D) 120

4.40 The surface images of LDPE-NR-SiO2 composites sample

C3 (80:20:4.5) before and after 60 minutes high voltage

stress; (a) and (b) are the original image, (c) and (d) are a

texture image two-dimensional (2D), (e) and (f) are a


4.41 The surface images of LDPE-NR-SiO2 composites sample

C4 (80:20:6) before and after 60 minutes of high voltage

stress; (a) and (b) are the original image, (c) and (d) are

the texture image two-dimensional (2D), (e) and (f) are the


4.42 Percentage change of key parameters for LDPE-NR-SiO2

samples after 60 minutes of high voltage stress; A1

(100:0:0), A3 (80:20:0), B1 (100:0:1.5), B2 (100:0:3), B3

(100:0:4.5), B4 (100:0:6), C1 (80:20:1.5), C2 (80:20:3),

C3 (80:20:4.5) and C4 (80:20:6) 128

4.43 The percentage of eroded area using histogram approach

of LDPE-NR-SiO2 composite after 60 minutes high

voltage stress; A1 (100:0:0), A3 (80:20:0), B1 (100:0:1.5),

B2 (100:0:3), B3 (100:0:4.5), B4 (100:0:6), C1

(80:20:1.5), C2 (80:20:3), C3 (80:20:4.5) and C4 (80:20:6) 129

4.44 (a) Changes of eroded area of samples B1 (100:0:1.5), B2

(100:0:3), B3 (100:0:4.5), B4 (100:0:6) with respect to the

reference sample A1 (100:0:0), and (b) Changes of eroded

area of samples C1 (80:20:1.5), C2 (80:20:3), C3

(80:20:4.5) and C4 (80:20:6) with respect to the reference

sample A3 (80:20). The histogram method was used for

the computation. 130

xxi

4.45 The percentage of eroded area using quantifying method

of LDPE-NR-SiO2 composite after 60 minutes high


B2 (100:0:3), B3 (100:0:4.5), B4 (100:0:6), C1

(80:20:1.5), C2 (80:20:3), C3 (80:20:4.5) and C4 (80:20:6) 135

4.46 (a) Changes of eroded area for samples B1 (100:0:1.5), B2

(100:0:3), B3 (100:0:4.5), B4 (100:0:6) with respect to the

reference sample A1 (100:0:0), and (b) Changes of eroded

area for samples C1 (80:20:1.5), C2 (80:20:3), C3

(80:20:4.5) and C4 (80:20:6) with respect to the reference

sample A3 (80:20). The quantifying method was used for

the computation. 136

4.47 PD improvement index of LDPE-SiO2 composites samples

refer to the sample A1 as reference and PD improvement

index of LDPE-NR-SiO2 refer to sample A3 after 60

minutes high voltage stress; A1 (100:0:0), A3 (80:20:0),

B1 (100:0:1.5), B2 (100:0:3), B3 (100:0:4.5), B4

(100:0:6), C1 (80:20:1.5), C2 (80:20:3), C3 (80:20:4.5)

and C4 (80:20:6) 137

4.48 The score distribution for 20 items from 6 categories of

LDPE-NR-SiO2 composite sample after 60 minutes high


B2 (100:0:3), B3 (100:0:4.5), B4 (100:0:6), C1

(80:20:1.5), C2 (80:20:3), C3 (80:20:4.5) and C4 (80:20:6) 140

4.49 The number of score for each sample of LDPE-NR-SiO2

composite after 60 minutes high voltage stress; A1

(100:0:0), A3 (80:20:0), B1 (100:0:1.5), B2 (100:0:3), B3

(100:0:4.5), B4 (100:0:6), C1 (80:20:1.5), C2 (80:20:3),

C3 (80:20:4.5) and C4 (80:20:6) 141

4.50 The total score of LDPE-NR-SiO2 composite after 60


B1 (100:0:1.5), B2 (100:0:3), B3 (100:0:4.5), B4

(100:0:6), C1 (80:20:1.5), C2 (80:20:3), C3 (80:20:4.5)

and C4 (80:20:6) 141

xxii

4.51 The qualitative score of LDPE-NR-SiO2 composite after

60 minutes high voltage stress; A1 (100:0:0), A3

(80:20:0), B1 (100:0:1.5), B2 (100:0:3), B3 (100:0:4.5),

B4 (100:0:6), C1 (80:20:1.5), C2 (80:20:3), C3 (80:20:4.5)

and C4 (80:20:6) 142

4.52 The comparison between the results of histogram method

and quantifying method calculating the percentage of

eroded area (PoEA) of LDPE-NR-SiO2 composite after 60


B1 (100:0:1.5), B2 (100:0:3), B3 (100:0:4.5), B4

(100:0:6), C1 (80:20:1.5), C2 (80:20:3), C3 (80:20:4.5)

and C4 (80:20:6) 143

4.53 The difference between the results of histogram method

and quantifying method in calculating the percentage of

eroded area of LDPE-NR-SiO2 composite after 60 minutes

high voltage stress; A1 (100:0:0), A3 (80:20:0), B1

(100:0:1.5), B2 (100:0:3), B3 (100:0:4.5), B4 (100:0:6),

C1 (80:20:1.5), C2 (80:20:3), C3 (80:20:4.5) and C4

(80:20:6) 144

xxiii

LIST OF ABBREVIATIONS

ADC - analog-digital converter

CAD - computer aided design

CNR - contrast-to-noise ratio

DAQS - data acquisition system

LDPE - low density polyethylene

GS/s - giga sampling per second

IEC - International Electrotechnical Commission

IHM - image histogram method

IQM - image quantifying method

NR - natural rubber

PC - personal computer

PD - partial discharge

PD-DQAS - partial discharge and data acquisition system

PE - polyethylene

PoEA - percentage of eroded area

PP - polypropylene

RGB - red, green and blue

SEM - scanning electron microscope

SiO2 - nanosilica

XLPE - cross-linked polyethylene

xxiv

LIST OF SYMBOLS

S - standard deviation

V - voltage

q - charge

i - current

I - average discharge current

n - repetition rate

P - discharge power

y - filtered sequence

h - FIR filter coefficient

CHAPTER 1

INTRODUCTION

1.1 Preliminaries

A partial discharge (PD) is a dielectric phenomenon that occurs in solid or

liquid insulating materials. It is said to have occurred when an electrical pulse or

discharge partially bridges a gas void (whether they are adjacent or not). A PD is

usually initiated inside voids, cracks, or inclusions within a solid dielectric or at

conductor-dielectric interfaces within solid or liquid dielectrics or in bubbles within

liquid dielectrics [1]. PD can also occur along the surface of solid insulating materials

if the surface tangential electric field is high enough to cause a breakdown along the

insulator surface. This dielectric phenomenon only partially bridges the gap between

phase insulation and ground or between phase-to-phase insulation. Thus the PD

phenomenon could be used as an effective assessment technique in maintenance and

monitoring of electrical distribution equipment conditions [2-5].

However, despite possessing good electrical properties, polymer insulation

materials have been observed to have low PD resistance. Furthermore, the voids and

defects in the structure of polymer, which are usually because of imperfections in the

production process, contribute to accelerating the ageing of the material. The

dimension of voids could be in nanometer or micrometer size in which the gases are

trapped. It is in these voids in which ionizations are most likely to start under any high

electric field stress.

http://en.wikipedia.org/wiki/Conductor_%28material%29

http://en.wikipedia.org/wiki/Dielectric

2

Partial discharge measurement is a well-known and promising method for

condition assessment of insulation systems in high voltage equipment. It is used for

preventive maintenance for assessing the medium voltage motors and cable joints [6-

8]. The partial discharge measurements represent a universally accepted technique

which gives the indication or status of the insulation. Partial discharge analysis is a

noninvasive or nondestructive technique which monitors the integrity of the insulation.

The discharge analysis is an efficient means used by stake holders to avoid insulation

failures in electrical equipment and to reduce their maintenance costs. The analysis is

used to identify deterioration mechanisms in insulation systems such as slot discharge,

internal voids, wedge and winding looseness. These problems need to be monitored

and corrected because their individual failures can lead to catastrophic failures of the

system.

Under high voltage stress, the insulation material suffers from the electron

bombardment that causes defect to the insulation surface gradually. The level of the

defect depends on internal and external factors. The internal factors are such as the

insulation quality and the behavior of the insulation to the high voltage stress. The

external factors are like the level of the high voltage stress and the electrode shape.

The size of surface damage varies in size from micrometer, centimeter, or the complete

damage of the surface. By looking at the picture of the defected area, the affected area

can be analyzed with image histogram analysis.

Surface defect is a common problem facing the industrial process. The defect

is identified by the abnormality of texture and colour. The surface defect detection is

a common process in an industrial engineering to detect the inferior product. The

defect detection process can be done automatically using a high-resolution camera

supported by a suitable software, but the basic defect detection is done by human visual

inspection supported by image-processing software [9]. This technique was used to

detect the texture defect of the surface like metal, wood, steel, wafer and even for non-

flat object like fruits and aircraft surfaces [10].

Nowadays the nanofillers are gaining interest from researchers to get a better

polymeric insulator due to its promising properties [11-13]. The improvement of

3

nanofiller filled polymeric insulator is due to very small size of the filler and hence

very large interfacial surface between the polymer and the filler [14-16], and the

behavior of the nanocomposite molecule inside the polymer [17, 18].

Undeniably, the addition of nanofillers in polymer matrices has resulted in

enhanced partial discharge resistance of the polymeric nanocomposite. It was found

that the addition of silica nanofiller to the epoxy resin improved the PD resistance of

the composite material [19]. It is also highlighted that epoxy/clay composite is

superior in partial discharge resistance compared to base epoxy resins. The conclusion

was drawn based on the evaluation on the depth of erosion. At the same time, the

research on the surface discharge degradation of metal oxide nano-filled epoxy showed

that nanoalumina and nanotitania filled epoxy had considerably improved its

resistance to surface discharges [15]. These facts show that there are potential

applications of nanocomposites in electrical insulation [20].

Although there are many experimental techniques that were used to determine

partial discharge characteristics of composites, a comprehensive method to evaluate

the PD resistance is still a big challenge. It is true that the PD phenomenon in

composites was extensively studied and different PD models were also introduced by

many researchers to understand the PD phenomenon. However, in order to choose the

best performance of the composite samples, there is no evaluation technique yet

proposed to rank the composite samples based on the PD data. Due to the complicated

nature of composite materials, there is no single characterization method that can

reveal the complete characteristics of composite materials. It is true that the

breakdown test can be taken as one characteristic of the composite materials, but the

information from the test is very general and cannot explain the physical

characteristics or the internal structures of materials [21].

A complete and expensive partial discharge data acquisition system (PD-

DAQS) is mostly used by the electrical power companies [6-8]. In contrary, only a

limited number of PD-DAQS are owned by other institutions, for example, a testing

laboratory. The limited market for PD-DAQS makes the price of the PD-DAQS to be

relatively on the high side for laboratories that intend to purchase. A comprehensive

4

PD-DAQS is a basic necessity for any study on partial discharge characteristics in a

laboratory environment.

Equally important for PD studies, especially when trying to determine the

performance of a new composite or dielectric is the interpretation technique of the

massive collected PD data. The interpretation should not be based on just PD

magnitude but rather on other PD characteristics and PD related changes on the studied

materials such as the morphological profiles of the materials.

1.2 Research Problem Statement

Experimental work is vital in PD studies, and hence the availability of complete

experimental setups is a must for a successful research. One key setup for PD studies

is the partial discharge data acquisition system (PD-DAQS). Because of specific

research requirements, a customized laboratory scale PD-DAQS may incur a high

procurement cost, especially when the facility itself is usually sold in a limited market.

It is therefore desired to develop a custom-made PD-DAQS that suits the objectives of

this research.

The partial discharge pulse count is one of PD characteristics normally

measured in PD experiments and then used to reflect how resistant a material is to a

PD activity, based on the correlation between the PD pulse count and the PD resistance

of a given material. However, the use of just the PD pulse count to decide on the PD

resistant property of a material is not sufficient. There is a need to also consider other

PD characteristics apart from the PD pulse count, such as images of material surface

defects caused by PD. In fact, because of the massive amount of data captured in

typical PD experiments, the PD pulse count itself need to be statistically analysed to

give several representative parameters before it can be used to determine the PD

resistance. With the availability of several parameters obtained from the PD

experiment, there is another need to devise an interpretation tool that can correctly

correlate all parameters to the PD resistance. Furthermore, the tool also need to be

5

validated. The interpretation tool is vital when designing a new dielectric material with

an improved PD resistance.

Studies had been carried out to obtain new materials with high PD resistance.

These materials include low density polyethylene (LDPE), natural rubber (NR), and

their composites. Attempts had also been made to also include micro and nano fillers,

such as nanosilica. Even though some other studies were previously carried out using

LDPE-NR-SiO2 composites, the PD resistant performance of the composites is yet to

be extensively researched. In particular, it is desired to know the weight percentage

of each component in the LDPE-NR-SiO2 composite, especially the nanosilica filler

weight percentage, for an optimum PD resistant performance.

1.3 Research Objectives

This research work was undertaken to address the above problem statements.

It aims to provide a comprehensive PD measuring and assessment system so that a

new and improved LDPE based composite material can be found. To achieve this aim

the following objectives of the research are listed.

1. To develop a partial discharge data acquisition system (PD-DAQS) to

acquire, store and analyze the PD data.

2. To develop and validate new partial discharge interpretation techniques

based on PD pulse count and percentage of eroded area.

3. To determine a new low density polyethylene (LDPE) and natural rubber

(NR) based nanocomposites with superior PD resistance performance.

6

1.4 Research Scopes

The study carried out in this work was limited to several scopes described

below.

The partial discharge measurements were carried out using CIGRE Method II

set-up which was suitable for small samples of dielectrics. Therefore all measured PD

data such as PD pulse counts were based on this set-up with specified PD inception

voltage levels. All other requirements of the set-up including noise elimination and

equipment calibration were observed.

The morphological analyses carried out in this work are limited by the capacity

of available microscopes including the SEM. The materials used for the determination

of the new dielectric are limited to LDPE and natural rubber. Only nanosilica was

used as the filler material. Sample preparation and finishing techniques are as

described in this thesis.

1.5 Research Contribution

Major contributions of this study are as listed below.

1. Development of a partial discharge data acquisition system (PD-

DAQS)

Partial discharge data acquisition (PD-DAQ) systems are available in

different scales and for different purposes. However, most of them suffer

from many disadvantages such as the lack of memory capacity for long

data storage. In this study, a mobile, economical and customizable partial

discharge data acquisition system (PD-DAQS) suitable for laboratory scale

work had been successfully developed. The developed PD-DAQS

successfully handled huge PD data in a much shorter time compared to the

7

alternative MS Office Excel based system and with no memory crash

problem.

2. PD data interpreting techniques by using different methods as listed

below.

a. An improved PD pulse count analysis

PD pulse count is one important property of PD characteristics and was

used for a long time to justify the PD resistance of an insulation. PD pulse

count represents the frequency of PD occurrence during a period of time.

In the usual statistical method, the PD pulse count was mostly presented

together with PD phase and PD amplitude to form the well-known PD

pattern graph. In this research work, the PD pulse count was successfully

evaluated using a statistical approach by taking the advantage of histogram

parameters, namely, mean, standard deviation, kurtosis, and skewness. It

was clearly seen that the PD pulse count for each sample was different in

each interval of time reflecting the random behavior of the sample response

to the electric field stress.

b. The surface defect analysis

The surface image analysis using two main processing techniques called

the image histogram and image quantifying methods to calculate the

percentage of eroded area (PoEA) was successfully evaluated. Both

methods were successfully applied to identify the percentage of the eroded

area (PoEA) of the samples.

c. Correlation between PD pulse count and surface defects

PD phenomenon causes defect on the insulation material including its

surface. Until now, the correlation between PD events and related surface

defects are still not widely discussed in depth. In this study, a correlation

analysis between the PD pulse count and the corresponding surface defect

had been presented. It was found that there is a strong correlation between

8

the frequencies of the pulse count or frequency of PD, which reflects the

higher value of PoEA.

d. An Improved PD resistance index (APD index)

The PD improvement (APD Index) of the composite can help the

researchers to identify the contribution of nanofillers whether positive or

negative compared to the base of the polymer. In this study, an improved

PD resistance index (APD index) to analyze the PD resistance had been

developed and proposed.

e. A Scoring System

To evaluate the performance of groups of composite samples, a

comprehensive technique is needed. A scoring system was developed by

considering the result of PD pulse count analysis, and combining with the

results of PD pulse count and image analyses. The PD pulse count, the

image histogram, and PoEA were considered to give the score for each

parameter, which ranges from 1 to 5 representing qualitative values from

VERY BAD to VERY GOOD. The evaluation system identified that

addition of nanosilica to LDPE improved the quality of LDPE from GOOD

to VERY GOOD. Also, the addition of nanosilica to LDPE-NR (80:20)

composite material improved the composite quality from BAD to GOOD.

3. New composite material compositions with improved PD resistance

Additions of nanosilica filler to LDPE and to LDPE-NR composites had

improved the PD resistance performance. Using the developed evaluation

system, it was successfully identified that LDPE-SiO2 composite with 4.5

wt% of nanosilica filler loading has the highest score, followed by LDPE-

NR-SiO2 composite with 6 wt% nanosilica filler.

9

1.6 Research Significance

The surface defect of the composite sample has a strong correlation with the

PD pulse count. Smaller PD pulse count is in line with the smaller PoEA. This

correlation can make the PoEA analysis as an alternative to PD pulse count analysis,

or as a complement to PD pulse count analysis. In the absence of PD-DAQS, the

surface defect analysis can be a good technique to classify the PD performance based

on the PoEA of samples.

The PD improvement (APD Index) of the composite help the researchers to

understand the contribution of nanofillers whether positive or negative compared to

the base polymer. By adopting the PD resistance index to analyze the PD resistance,

a new set of dielectric materials can be better understood not only for experts in this

research area but also for the management team and those in common engineering.

The new compositions of composite materials have a higher PD resistance

compared to their base polymers. The current proposed compositions can be a starting

point for further research by doing different aspects of study in order to get a more

comprehensive knowledge of these composites while looking at the opportunity to be

applied as a high voltage insulating material.

1.7 Thesis Organization

Chapter 2 presents a literature review with respect to the background of

composite materials, partial discharge measurements, PD data acquisitions, PD data

interpretations, image processing methods to quantify the percentage of eroded area

(PoEA), the evaluation system in general, and techniques used in the composite

diagnosis. Chapter 3 presents the research methodology that explains the sample

preparations, the experimental procedure of PD and data acquisition system, image

processing method to compute the PoEA, and the evaluation procedure to select the

best composite sample.

10

Chapter 4 presents the results and discussions of the SEM observations, the

experimental results of PD pulse count, and the image processing used to count the

PoEA of tested composites. The morphological characteristics of each sample are also

described. The evaluation of result is also presented. Chapter 5 concludes the thesis

and also presents recommendations for future work.

151

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