gas analysis technique for gas insulated...

GAS ANALYSIS TECHNIQUE FOR GAS INSULATED SWITCHGEAR

CONDITION MONITORING AND DIAGNOSIS

VISA MUSA IBRAHIM

UNIVERSITI TEKNOLOGI MALAYSIA

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GAS ANALYSIS TEC HNIQUE FOR GAS INSULATED SWITCHGEAR

CONDITION MONITORING AND DIAGNOSIS

VISA MUSA IBRAHIM

A thesis submitted in fulfilment of the

Requirements for the award of the degree of

Doctor of Philosophy

Faculty of Electrical Engineering

Universiti Teknologi Malaysia

APRIL, 2018

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DEDICATION

To the Almighty God the creator of the whole universe.

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ACKNOWLEDGEMENT

First of all, I want to thank the Almighty God for giving me the privilege to

embark and complete my PhD programme successfully.

My profound gratitude goes to my able supervisor Prof. Dr. Zulkurnain Abdul-

Malek for his training, support, encouragement, patient and fatherly care during my

research programme. Also, my appreciation goes to Dr. Nor Asiah Muhamad and Ir

Dr. Lau Kwan Yiew for their supervisory role in my study.

I wish to thank the Nigerian Government for TETFUND, Modibbo Adama

University of Technology Yola, Nigeria for the study leave, Ministry of Education

Malaysia for the financial aid, and TNB Research Sdn Bhd for providing the FTIR

spectrometer.

My special thanks to all who in one way or another contributed toward the

success of my study.

This is the LORD'S doing; it is marvellous in our eyes (Psalm 118:23).

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ABSTRACT

Sulphur hexafluoride gas insulated switchgear (GIS) is widely used in electrical

power supply system and therefore needs regular preventive maintenance. Usual diagnosis

methods used are based on acoustic, optical, electrical and ultra high frequency techniques.

A new method with great potential is using gas by-products analysis. Previous gas by-

products research is confined to a plane-plane electrode instead of typical coaxial GIS

configuration, a limited number of defect types and the by-products analysis using gas

chromatography. In this thesis, partial discharge experiments using a purposely designed

coaxial GIS chamber were carried out to expand the diagnosis database for a new set of

simulated defects represented by three categories, namely sole defect, hybrid defect, and

material dependent defect. A total of eight defects namely, free conducting particle,

electrode to dielectric void, electrode protrusion, fixed particle aluminium on spacer, fixed

copper particle on spacer, electrode protrusion-fixed copper particle hybrid, electrode

protrusion-free copper particle hybrid, and electrode to dielectric void-free copper particle

hybrid were simulated. In each experiment lasting up to 50 hours, continually applied

voltage at 0.2 MPa pressure, samples of gas by-products were taken at 10 hour intervals

for an off-line Fourier transform infrared spectrometer gas analyses. A total of 12 gas by-

products due to partial discharge activity in all defects were detected. Arranged according

to significance, these are hexafluoroethane, sulphur dioxide, sulfuryl fluoride,

octafluoropropane, silicon tetrafluoride, thionyl fluoride, carbon monoxide, disulfur

decafluoride, hydrogen fluoride, tetrafluoromethane, carbonyl sulphide and tetrafluoride.

Arranged according to significance, the most harmful gases are produced by the defects

such as electrode protrusion-fixed copper particle hybrid, fixed copper particle, electrode

protrusion-free copper particle hybrid and electrode protrusion. The type, number,

concentration and chemical stability of by-product gases are found to be closely correlated

to the type of defect. Further analyses using pattern recognition with eight algorithms

based on the presence and concentration of the gas by-products were carried out. The

random forest algorithm successfully recognises a given defect with an accuracy of 87.5%.

The performance of the random forest algorithm is 1.5 times better than the next best

algorithm. This research illustrates the feasibility and applicability of an effective GIS

diagnostic using gas by-products analyses, in particular, using the random forest pattern

recognition.

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ABSTRAK

Gas penebat perkakas suis (GIS) sulfur hexafluorida digunakan secara meluas

dalam sistem bekalan kuasa elektrik dan oleh yang demikian ia memerlukan

penyelenggaraan pencegahan yang kerap. Kaedah diagnosis yang biasa digunakan adalah

berasaskan teknik-teknik akustik, optik, elektrik dan frekuensi ultra tinggi. Kaedah baru

yang berpotensi besar adalah dengan menggunakan analisis gas produk sampingan.

Penyelidikan gas produk sampingan sebelum ini terhad kepada elektrod satah-satah dan

bukannya konfigurasi kabel sepaksi untuk GIS, bilangan jenis kecacatan yang terhad, dan

analisis produk sampingan menggunakan kromatografi gas. Dalam tesis ini, ujikaji discas

separa menggunakan GIS sepaksi koaksial direka untuk memperluaskan lagi pangkalan

data diagnosis untuk satu set kecacatan baru yang diwakili oleh tiga kategori, iaitu

kecacatan tunggal, kecacatan hibrid dan kecacatan yang bergantung kepada jenis bahan.

Lapan kecacatan yang digunakan adalah zarah bebas, rongga dielektrik ke elektrod,

penonjolan elektrod, zarah tetap aluminium pada penjarak, zarah tembaga tetap pada

penjarak, hibrid zarah tembaga tetap-penonjolan elektrod, hibrid zarah tembaga bebas-

penonjolan elektrod dan hibrid zarah tembaga bebas-rongga dielektrik ke elektrod. Dalam

setiap eksperimen yang berlanjutan sehingga 50 jam, voltan berterusan dikenakan pada

tekanan 0.2 MPa, sampel gas diambil selang 10 jam bagi analisis gas spektrometer jelmaan

Fourier inframerah secara luar-talian. Sejumlah dua belas gas produk sampingan

disebabkan oleh aktiviti discas separa untuk semua kecacatan telah dikesan. Diatur

mengikut kepentingannya, produk sampingan terhasil adalah heksafluoretana, sulfur

dioksida, sulfuril fluorida, oktafloropropana, silikon tetrafluorida, tionil fluorida, karbon

monoksida, disulfur dekafluorida, hidrogen fluorida, tetrafluorometan, karbonil sulfida

dan tetrafluorida. Dirumuskan mengikut kepentingannya, gas yang paling berbahaya

dihasilkan oleh kecacatan seperti hibrid penonjolan elektrod-zarah tembaga tetap, zarah

tembaga tetap, hibrid penonjolan elektrod-zarah tembaga bebas dan penonjolan elektrod.

Jenis, bilangan, ketumpatan dan kestabilan kimia gas produk sampingan didapati berkait

dengan jenis kecacatan. Analisis lanjut menggunakan pengenalan corak dengan tujuh

algoritma berdasarkan kehadiran dan ketumpatan gas produk sampingan dijalankan.

Algoritma hutan rawak berjaya mengenal pasti kecacatan yang dianalisis dengan

ketepatan 87.5%. Prestasi algoritma hutan rawak adalah 1.5 kali lebih baik daripada

algoritma terbaik seterusnya. Kajian ini menggambarkan kebolehlaksanaan dan

kebolehgunaan diagnostik GIS yang berkesan menggunakan analisis gas produk

sampingan, khususnya menggunakan pengenalan corak hutan rawak.

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TABLE OF CONTENT

CHAPTER TITLE PAGE

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENT vii

LIST OF TABLES xiv

LIST OF FIGURES xvi

LIST OF ABBREVIATIONS xxiii

LIST OF SYMBOLS xxiv

1 INTRODUCTION 1

1.1 Research Background 1

1.2 Research Motivation 3

1.3 Problem Statement 4

1.4 Objectives 6

1.5 Scope of Work 6

1.6 Research Contributions 6

1.7 Thesis Outline 8

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2 DIAGNOSTIC TECHNIQUES FOR GAS

INSULATED SWITCHGEAR 10

2.1 Introduction 10

2.2 Sulphur Hexafluoride Gas 10

2.2.1 Basic Properties of Sulphur Hexafluoride

Gas 11

2.2.2 Ionisation Phenomena in SF6 12

2.2.3 Decomposition Mechanism of Sulphur

Hexafluoride Gas under Partial Discharge 13

Sulphur dioxide (SO2) 15

Hydrogen Fluoride (HF) 16

Thionyl Fluoride (SOF2) 16

Sulphur Tetrafluoride (SF4) 16

Thionyl Tetrafluoride (SOF4) 16

Sulfuryl Fluoride (SO2F2) 17

Disulfur Decafluoride (S2F10) 17

Silicon Tetrafluoride (SiF4) 17

Carbon Monoxide (CO) 18

Carbonyl Sulphide (COS) 18

Octafluoropropane (C3F8) 18

Hexafluoroethane (C2F6) 18

Tetrafluoromethane (CF4) 19

2.3 Genesis of Partial Discharge 19

2.4 Diagnostic Techniques for Partial Discharge

Detection 21

2.4.1 Optical Technique 21

2.4.2 Acoustic Emission Technique 21

2.4.3 Conventional Electrical Discharge

Technique 22

2.4.4 Ultra-High Frequency Technique 22

2.4.5 Chemical By-Product Technique 22

Detector Tube 23

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Gas Analyser 24

Gas Chromatograph 25

Fourier Transform Infrared

Spectrometer 26

A Recap of Chemical By-Product

Diagnostic Technique for PD

Detection 27

2.4.6 A Recap of Diagnostic Technique for PD

Detection 28

2.5 By-Product Gas Analysis 30

2.5.1 Tree-Based Model Learning Algorithm 31

Decision Tree Algorithm 31

Random Forest 32

2.5.2 A Recap of the Methods for Fault

Classification Based on Gas Analysis 33

2.6 Summary 34

3 RESEARCH METHODOLOGY 36

3.1 Overall Research Procedure 36

3.2 SF6 Gas Decomposition Chamber Prototype 37

3.2.1 High Voltage Electrode 40

3.2.2 Spacers 40

3.3 Artificial Defects 41

3.3.1 Sole Defects 42

Free Conducting Particle Defect 42

Electrode Protrusion Defect 42

Electrode-Dielectric Void Defect 43

Fixed Conducting Particle Defect 44

3.3.2 Hybrid Defects 45

Electrode-Dielectric Void with


x

Electrode Protrusion-Fixed

Conducting Particle Defect 46

Electrode Protrusion-Free


3.3.3 Material Dependent Defect 48

Fixed Aluminium Particle Defect 48

Fixed Copper Particle Defect 49

3.4 High Voltage Test System 49

3.5 Partial Discharge Detector System 50

3.6 Sulphur Hexafluoride Gas Sampler 51

3.7 SF6 By-product Detection System 52

3.8 Experimental Setup 54

3.8.1 Safety Procedure 55

3.8.2 Test Procedure 55

3.8.3 Test Procedure for FTIR Spectrometer 59

3.9 Procedure for the Analysis of Results 61

3.10 Pattern Recognition Using Waikato Environment

for Knowledge Analysis (WEKA) 62

3.10.1 Ten-Fold Cross-Validation 63

3.10.2 Confusion Matrix 64

3.10.3 Accuracy and Error Rate 64

3.10.4 Precision 65

3.10.5 Recall or Sensitivity 66

3.10.6 Specificity 66

3.10.7 F-measure 66

3.10.8 Threshold Receiver Operating

Characteristic Curve 67

3.10.9 Matthew Correlation Coefficient 67

3.10.10 Precision-Recall Curve 68

3.11 Procedure of Pattern Recognition using WEKA

Workbench-Random Forest Algorithm 68

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3.12 Summary 72

4 RESULTS AND DISCUSSIONS 74

4.1 Introduction 74

4.2 Effects of Sole Defects on SF6 By-Products 75

4.2.1 Free Conducting Particle Defect 75

4.2.2 Electrode Protrusion Defect 77

4.2.3 Electrode-Dielectric Void Defect 79

4.2.4 Fixed Conducting Particle Defect 81

4.3 Effects of Hybrid Defects on SF6 By-Products 83

4.3.1 Electrode-Dielectric Void with Free


4.3.2 Electrode Protrusion-Fixed Conducting

Particle Defect 85

4.3.3 Electrode Protrusion-Free Conducting

Particle Defect 87

4.4 Effects of Material Dependent Defects on SF6 By-

Products 89

4.4.1 Fixed Conducting Particle Defect

(Aluminium) 89

4.4.2 Fixed Conducting Particle Defect (Copper) 91

4.5 Discussions 91

4.5.1 Results of Sole Defects 94

4.5.2 Results of Hybrid Defects 96

4.5.3 Results of Material Dependent Defects 98

4.5.4 Results of All Defects 100

4.5.5 Comparison of the SF6 By-Products 101

4.6 Comparison with Previous Work 104

4.7 Application of SF6 By-Product Technique to Gas

Insulated Switchgear 106

4.8 Defect Classification Using Pattern Recognition 107

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4.9 Pattern Recognition Pre-Process 108

4.9.1 Pre-Process on Sole Defect 108

Number of By-Product Gases 108

Identity of By-Product Gases 109

Concentration of By-Product

Gases 110

4.9.2 Pre-Process on Hybrid Defect 114




Gases 116

4.9.3 Pre-Process on Material Dependent Defect 119




Gases 121

4.9.4 Pre-Process on the Overall Defects 122


Identification of By-Product

Gases 123

Concentration of By-Products

Gases 124

4.10 Classification of Defects using Random Forest

Algorithm 128

4.10.1 Classification of Sole Defect 129

4.10.2 Classification of Hybrid Defect 131

4.10.3 Classification of Material Dependent

Defect 133

4.10.4 Classification of All Defects 134

4.11 Evaluation of Classification Model 137

4.11.1 Evaluation of Sole Defect Classification 137


Electrode Protrusion Defect 138

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Electrode to Dielectric Void

Defect 139

Fixed Copper Particle Defect 140

4.11.2 Evaluation of Hybrid Defect Classification 140

4.11.3 Evaluation of Material Dependent Defect

Classification 142

4.11.4 Evaluation of All Defects Classification 143

4.12 Comparison of Classification Accuracy with Other

Algorithms 146

4.13 Summary 148

5 CONCLUSIONS AND RECOMMENDATIONS 151

5.1 Conclusions 151

5.2 Recommendations for Future Work 153

REFERENCES 155

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LIST OF TABLES

TABLE NO. TITLE PAGE

Important physical properties of SF6 gas [3] 12

Summary of ionisation conditions of SF6 13

Summary of methods of chemical by-product diagnostic

technique for PD detection 28

Summary of diagnostic techniques for PD detection 29

Methods for fault classification based on gas analysis 34

Applied voltage 59

Confusion matrix [96] 64

A template of the result of detailed accuracy by class 71

A template of confusion matrix of classification result 71

Summary of results 93

Comparison with previous work 106

Classification accuracy of random forest algorithm for the

sole defect 129

Confusion matrix table for sole defect 130

Summary for the classification result of sole defect 131

The detailed classification result for the hybrid defect 131

Confusion matrix for the hybrid defect 132

Summary of the classification result of hybrid defect 132

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The detailed classification results for the material

dependent defects 133

Confusion matrix of the classification result of material


Summary of the classification result of fixed material defect

134

The detailed classification results of all defects 135

Confusion matrix of the classification result of all the

experimental defects 136

Summary of the classification result for all defects 137

Comparison of the classification accuracy of various

algorithms for sole defect classification 146


algorithms for hybrid defect classification 147


algorithms for material dependent defect 147


algorithms for all defects 148

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LIST OF FIGURES

FIGURE NO. TITLE PAGE

2.1 Decomposition mechanism of sulphur hexafluoride gas

under partial discharge zone model [34] 14

2.2 Typical defects in practical GIS [34] 20

2.3 Detector tube and sampling arrangement [44] 24

2.4 SF6 gas analyser 25

2.5 Functional components of a gas chromatography [78] 26

2.6 Block diagram of basic components of an FTIR

spectrometer 27

2.7 Outlook of decision tree structure [99] 32

2.8 Model of random forest [99] 33

3.1 Flowchart for the research 37

3.2 Coaxial SF6 gas decomposition chamber, (a) schematic

diagram, (b) constructed chamber 39

3.3 High voltage electrode assembly 40

3.4 Two spacers 41

3.5 Free conducting particle defect 42

3.6 Electrode protrusion defect on HV electrode 43

3.7 Electrode-dielectric void defect (1 mm spacing between

spacers and HV electrode) 44

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3.8 Fixed conducting particle (Cu) defect on the surface of the

spacer 45

3.9 Electrode-dielectric void with free conducting particle

defect 46

3.10 Electrode protrusion-fixed conducting particle (Cu) defect 47

3.11 Electrode protrusion-free conducting particle defect 48

3.12 Fixed conducting particle (Al) defect on the surface of the

spacers 49

3.13 High voltage test equipment 50

3.14 Partial discharge detector display system type TE 571

model 51

3.15 TEDLAR-PVF sampling bag 52

3.16 FTIR spectrometer 53

3.17 Schematic diagram of SF6 decomposition and detection

system 54

3.18 SF6 decomposition system 55

3.19 Flowchart for the test procedure 56

3.20 Gas chamber subjected to vacuum pump 57

3.21 Gas chamber being filled with SF6 gas 57

3.22 Connection of KAL 451 calibrator 58

3.23 Test procedure SF6 by-products using FTIR spectrometer 60

3.24 Flowchart for the procedure of the analysis of the result 61

3.25 Ten-fold cross-validation process [96] 63

3.26 Threshold curve for class 1, x and y axis are efficiency in

per unit plot. (Area under ROC=0.9958) 67

3.28 A sample of confusion matrix of classification result 72

4.1 Variation of SF6 by-products concentration with stress

duration for the case of free conducting particle defect 76

xviii


duration for the case of electrode protrusion defect 78


duration for the case of electrode-dielectric void defect 80


duration for the case of fixed conducting particle defect 82


duration for the case of electrode- dielectric void together

with free conducting particle defect 85


duration for the case of electrode protrusion together with

fixed conducting particle (Cu) defect 86


duration for the case of electrode protrusion together with

free conducting particle defect 88


duration for the case of fixed conducting particle (Al) defect

90

4.9 A summary plot of the SF6 by-products of the four types of

sole defects 94

4.10 The stable SF6 by-products of sole defects 95

4.11 The unstable SF6 by-products of sole defects 95

4.12 A summary plot of the SF6 by-products of the three hybrid

defects 96

4.13 The stable SF6 by-products of hybrid defects 97

4.14 The unstable SF6 by-products of hybrid defects 98

4.15 A summary plot of the SF6 by-products of the material

dependent defect 99

4.16 The stable SF6 by-products of material dependent defects 99

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4.17 The unstable SF6 by-products of material dependent defects

100

4.18 A summary plot of the SF6 by-products of all the defects 101

4.19 Comparison of the SF6 by-products of all the defects 102

4.20 The stable SF6 by-products of the eight experimental

defects 103

4.21 The unstable SF6 by-products of the eight experimental

defects 104

4.22 Number of by-product gases for each type of sole defect 109

4.23 Types of by-product gases produced by all sole defects

plotted as a stacked histogram 110

4.24 The frequency of appearance of the gas concentrations at

10-hour stress duration for all sole defects 111









4.29 The number of by-product gases as a function of hybrid

defect type 115

4.30 Types of by-product gases for the hybrid defects 116


10-hour stress duration for all hybrid defects 116



xx







4.36 The number of by-product gases for both material


4.37 Type of SF6 by-products of two material dependent defects 121


10-hour stress duration for both material dependent defects

122

4.39 Number of by-product gases for all defects 123

4.40 Types of partial discharge by-products for all tested defects

124


10-hour stress duration for all defects 125









4.46 Confusion matrix plot for the sole defect 130

4.47 Confusion matrix plot for the hybrid defect 132

4.48 Confusion matrix graph for the material dependent defects

134

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4.49 Confusion matrix graph for all defects 136

4.50 Threshold curve for free conducting particle, x and y-axis

are efficiency in per unit plot (Area under ROC=1) 138

4.51 Threshold curve for electrode protrusion, x and y-axis are

efficiency in per unit plot (Area under ROC=0.9512) 139

4.52 Threshold curve for void, x and y-axis are efficiency in per

unit plot (Area under ROC=1) 139

4.53 Threshold curve for fixed Cu, x and y-axis are efficiency in

per unit plot (Area under ROC= 1) 140

4.54 Threshold curve for protrusion - fixed Cu, x and y-axis are


4.55 Threshold curve for protrusion–free conducting particle, x

and y-axis are efficiency in per unit plot (Area under

ROC=0.9427) 141

4.56 Threshold curve for void–free conducting particle, x and y-

axis are efficiency in per unit plot (Area under

ROC=0.9931) 141

4.57 Threshold curve for fixed Al, x and y-axis are efficiency in

per unit plot (Area under ROC=1) 142



4.59 Threshold curve for free conducting particle x and y-axis

are efficiency in per unit plot (Area under ROC=1) 143

4.60 Threshold curve for protrusion, x and y-axis are efficiency

in per unit plot (Area under ROC=0.9941) 143

4.61 Threshold curve for void, x and y-axis are efficiency in per

unit plot (Area under ROC=1) 144

4.62 Threshold curve for fixed Al, x and y-axis are efficiency in


xxii

4.63 Threshold curve for protrusion-fixed Cu, x and y-axis are


4.64 Threshold curve for protrusion-free conducting particle, x

and y-axis are efficiency in per unit plot (Area under

ROC=0.7898) 145

4.65 Threshold curve for void-free conducting particle, x and y-

axis are efficiency in per unit plot (Area under

ROC=0.9973) 145


per unit plot (Area under ROC=0.9658) 145

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LIST OF ABBREVIATIONS

BIL - Basic lighting impulse withstand level

FTIR - Fourier transform infrared

GC - Gas chromatography

GIS - Gas insulated switchgear

MPa - Mega pascal

PD - Partial discharge

ppmv - Part per million volume

UHF - Ultra-high frequency

UV - Ultraviolet

µl - Micro litre

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LIST OF SYMBOLS

CF4 - Carbon tetrafluoride

C2F6 - Hexafluoroethane

C3F8 - Octafluoropropane

CO - Carbon monoxide

CO2 - Carbon dioxide

COS - Carbonyl sulphide

HF - Hydrogen fluoride

HO2 - Water

O2 - Oxygen

SF - Sulphur fluoride

SF2 - Sulphur difluoride

SF3 - Sulphur trifluoride

SF4 - Sulphur tetrafluoride

SF5 - Sulphur pentafluoride

SF6 - Sulphur hexafluoride

S2F10 - Disulfur decafluoride

SiF4 - Silicon tetrafluoride

SO2 - Sulphur dioxide

SOF2 - Thionyl fluoride

SOF4 - Thionyl tetrafluoride

SO2F2 - Sulphuryl fluoride

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LIST OF APPENDICES

APPENDIX TITLE PAGE

A List of Publications 166

B

C

D

E

FTIR Results of Sole Defects

FTIR Results of Hybrid Defects

FTIR Results of Material Dependent Defects

Picture of real life gas insulated switchgear

167

168

169

170

1

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1 INTRODUCTION

1.1 Research Background

In any modern society, the social welfare and economic development depend

exclusively on the availability of reliable and cheap supply of functional electrical

energy. Extensive electrical power system installation network at high voltage in

industrialized countries have been built and in developing countries, they are being

constructed at an ever-increasing rate for the purpose of transporting electrical energy

or power to consumers (industries, research laboratories, homes, and etcetera) for the

sustenance of modern civilization [1]. A large amount of electrical power is generated,

transmitted and distributed by the power system network over a long distance is best

accomplished using high voltage for achieving efficiency, reliability, and economy,

thus high voltage equipment (including gas insulated switchgear) are required. In

short, high voltage equipment serve as the backbone of a modern power system [2, 3].

Gas insulated switchgear (GIS) is an electromechanical device that comprises

the combination of electrical switches, fuses, circuit breakers, current and capacitive

voltage transformers, and etcetera, that is used to control, protect and isolate various

other high voltage equipment. A switchgear is also used to de-energize high voltage

equipment in a power system network to enable fault of all types to be rectified [4, 5].

2

Gas insulated switchgear is one of the main devices of the electricity

transmission and distribution infrastructure that is used to transfer power from power

stations to consumers because of its high reliability and performance, compact in

dimension, non-explosive, long lifespan (about 40-50 years), low maintenance

requirements during its whole lifetime, outstanding compatibility with the

environment, and ability to interrupt fault current in a power system network.

Furthermore, its operation is noiseless and well insulated against external

interferences, such as changes in weather or electromagnetic environment [6-10]. The

increase in demand for electricity and the growing energy density in the metropolitan

areas have made it necessary to extend the high voltage network right up to the

consumer unit in an economical manner while ensuring a high degree of quality and

reliability of supply. Gas insulated switchgear in gas insulated substation provides the

best solution to this challenge [11].

A gas insulated switchgear uses sulphur hexafluoride (SF6) gas as an insulant

and coolant in view of the fact that it has superior dielectric properties with excellent

arc quenching properties compared to air and vacuum [12-16]. SF6 gas is inert in

nature, odourless, colourless, tasteless, chemically stable, non-toxic, non-inflammable

and has high vapour pressure (about 21 bar at ambient temperature) [7, 17-20]. It can

be used down to -35 C without liquefaction occurring at pressures typical to its

application (about 5 bar) [21]. In addition to its high dielectric strength, it also has

good thermal transfer characteristics. SF6 gas has high (three times that of air) and

reasonably constant dielectric strength over a wide range of frequencies. At about 6

bar pressure, its dielectric strength is approximately equal to that of the transformer oil

[21].

Although SF6 has high and constant dielectric strength, it is a brittle gas. This

means ionization will build up very rapidly if the critical field strength of SF6, which

is at 89 kV/cm bar, is exceeded during a GIS operation [21]. In practice, this can

happen in the vicinity of any small defect, such as due to a contamination in the form

of a free conducting particle or a fixed conducting particle on the surface of the GIS

spacer, a protrusion or a sharp point on the high voltage or ground electrodes, and a

3

gap or void at the electrode or dielectric interface [11, 21]. These defects will cause

partial discharge to occur and its characteristics is dependent on the nature of a

particular defect. The partial discharge which occurs due to the local field

enhancement may eventually result in the lowering of the insulation maximum

operating stress to about 20-80 % of the designed value, and hence, premature failure

of GIS [22]. Such failures are sometimes sudden, catastrophic and almost include

irreversible internal damage of the system resulting in power outages in the system

network that in turn paralyze economic and other activities, incur personal and

environmental hazards, and incur high cost of equipment replacement. Therefore,

being one of the critical assets, the GIS equipment should be monitored closely and

continuously using a reliable and effective technique to assess its operating condition

and to diagnose fault early so as to ensure its maximum uptime [23].

1.2 Research Motivation

About 85% of GIS disruptive failure is caused by partial discharge [1, 15, 24].

The failure of live assets is often sudden and catastrophic, with the release of large

amounts of energy, leading to explosion and fire resulting in an unrepairable damage

to substation equipment, injury or death of personnel working in the substation, and a

power outage that will paralyze economic, social, educational, military, security and

medical activities. When a dielectric failure occurs in the GIS, the arc will not be

extinguished by the insulant gas; this will lead to an internal build up pressure that will

drill a hole in the metal wall of the GIS due to the concentration of the arcing thereby

causing SF6 gas that is a highly potent greenhouse gas to leak into the atmosphere,

then causing global warming.

Sulphur hexafluoride (SF6) is a highly potent greenhouse gas with a global

warming potential of about 24,000 times greater than carbon dioxide (CO2) [17, 25].

SF6 gas also has an atmospheric lifespan of about 3,200 years, so it will contribute to

global warming for a very long time. One pound of SF6 gas has the global warming

equivalent of 11 tonnes of CO2 [13, 17, 25-27].

4

Under high-temperature conditions, SF6 gas decomposes into by-products that

are toxic and corrosive. The decomposition by-products can exist when SF6 gas is

exposed to spark discharge, partial discharge, and switching arc. These by-products

are in the form of gases or powders. It can affect human health and cause the following

ill health in humans: irritation to the eyes, nose, and throat, pulmonary oedema and

other lungs damage, skin and eye burns, nasal congestion, bronchitis and body rashes

[13, 28-33].

In order to avert the occurrence of the above-stated problems, researchers in

the world employed techniques to monitor and diagnose partial discharge in GIS.

These techniques are photo diagnostic technique, acoustic diagnostic technique,

electrical diagnostic technique, ultra-high frequency (UHF) diagnostic technique, and

chemical by-product diagnostic technique [1, 15].

Photo, acoustic, electrical and UHF diagnostic techniques are based on the

measurement of energy released by the PD activities. Among the released energy are

in the form of electromagnetic and acoustic emissions. The magnitude of the energy

released can be correlated with the level of SF6 deterioration. Even though these

methods perform effectively to some extent, the bottleneck of these methods is the

ingress of external interferences, such as noise and electromagnetic interference. The

interferences directly affect the sensitivity and reliability of the acquired PD data [34,

35]. Furthermore, these methods can be likened to as symptoms diagnostic techniques

since the measurements are based on only the released PD energy. Hence, there is a

need for an effective and more reliable technique for condition monitoring and

diagnosis of GIS.

1.3 Problem Statement

The causes of defect occurrence inside a GIS could be due to many factors,

such as poor machining during GIS manufacturing, vibration during transportation or

assembly of GIS, undetected scratches on electrodes, poor electrical contacts, and

5

mechanical abrasion movement of the conductor during load cycling [1, 5, 6, 54, 55].

The presence of defects results in the nuisance occurrence of partial discharges during

GIS operation. There are several existing techniques used to detect the partial

discharge occurrence in a GIS. A technique based on the detection of chemical by-

products in a GIS as a result of partial discharge occurrence is still being studied by

many researchers. In the studies, a chosen defect is purposely introduced inside the

GIS so as to determine the resultant by-product gases. All of the introduced defects

can be categorised as sole defect, that is, only one type of defect occurs at a given time.

Examples of sole defects are a void in a solid dielectric, free conducting particles in

the chamber, an electrode protrusion, and fixed conducting particles on a spacer. The

effects of two defects occurring simultaneously are yet to be studied.

Apart from the limitation of using only a sole defect, previous studies are also

limited in terms of experimental configuration, whereby only a plane-plane electrode

configuration was used instead of a coaxial configuration which is more typical of a

real GIS chamber. In terms of results, previous studies reported only a limited number

of by-product gases, namely, thionyl fluoride (SOF2), sulfuryl fluoride (SO2F2),

tetrafluoromethane (CF4), and carbon dioxide (CO2). This could be due to the

inferiority of the gas chromatography technique used for by-product gas detection [34-

36].

A reliable partial discharge detection technique in a GIS using the by-product

gas detection requires more practical results and analyses based on actual GIS

configuration and all possible occurrences of defects. In view of the above-stated

limitations, there is a need for a new study using an improved and more effective

methodology to give the desired results.

6

1.4 Objectives

The main objective of this research is to develop an improved, effective, and

more reliable method of gas analysis technique for condition monitoring and diagnosis

of gas insulated switchgear. The specific objectives of this research are;

i. To formulate an experimental setup for partial discharge studies consisting

of a prototype coaxial gas chamber typical to real life GIS, PD artificial

defects, PD detector systems, and Fourier transform infrared spectrometer.

ii. To perform PD gas by-product experiments on three categories of defects,

namely, sole, hybrid, and material dependent.

iii. To determine the correlation between PD by-product gases produced and

the type of defect causing the PD.

iv. To propose and implement an accurate PD causing defect classification

using a suitable pattern recognition algorithm.

1.5 Scope of Work

The scope of this research covers the staging of an experimental setup for

partial discharge studies using a coaxial gas-insulated switchgear apparatus prototype

and designed artificial defects. The defects used are limited to three categories, as

mentioned above, to give a total of eight types of PD artificial defects. The gas

detection only utilises the FTIR spectrometer technique. Defect classification is carried

out using one technique, namely, the pattern recognition (random forest algorithm).

However, eight different algorithms are investigated to determine the best among

them.

1.6 Research Contributions

The main contributions of this thesis work are outlined as follows:

7

i. GIS Chamber Prototype for PD Studies

This study has successfully formulated an experimental setup using a GIS

coaxial chamber prototype typical to real life GIS with three categories of

purposely introduced defects, namely, sole, hybrid, and material dependent

defects. The chamber is capable of being energised up to 70 kV and

pressurised up to 10 bars. A total of eight simulated defects are free

conducting particle, electrode to dielectric void, electrode protrusion, fixed

particle aluminium on the spacer, fixed copper on spacer, electrode

protrusion-fixed copper particle hybrid, electrode protrusion-free copper

particle hybrid, and electrode to dielectric void-free copper particle hybrid.

ii. Newly detected PD by-product gases

The use of FTIR for gas analysis has enabled more by-product gases to be

detected. A total of twelve gas by-products due to partial discharge activity

in all defects were detected. Arranged according to significance, these are

hexafluoroethane (C2F6), sulphur dioxide (SO2), sulfuryl fluoride (SO2F2),

octafluoropropane (C3F8), silicon tetrafluoride (SiF4), thionyl fluoride

(SOF2), carbon monoxide (CO), disulfur decafluoride (S2F10), hydrogen

fluoride (HF), tetrafluoromethane (CF4), carbonyl sulphide (COS) and

tetrafluoride (SOF4).

iii. Detected harmful PD by-product gases

The presence of CO, COS, SiF4 and HF gases can be harmful to the GIS

system due to their flammable and corrosive nature. Arranged according to

significance, the most harmful gases are produced by the following defects:

electrode protrusion-fixed copper particle hybrid, fixed copper particle,

electrode protrusion-free copper particle hybrid and electrode protrusion.

iv. Defect classification using by-product gases pattern recognition

The type, number, concentration, and chemical stability of by-product

gases are found to be closely correlated to the type of defect. Generally the

number and concentration of the by-product gases increases with electrical

stress duration and the presence of the by-product gas and its concentration

can be said to be an indication of a fault in GIS and the fault is harmful to

the GIS. Further analyses using pattern recognition with eight algorithms

8

based on the presence and concentration of the gas by-products were

carried out. The random forest algorithm successfully recognises a given

defect with an accuracy of 87.5%.

From the analyses using Waikato Environment for Knowledge Analysis

(WEKA) workbench machine learning and data mining, in particular, the

random forest algorithm of pattern recognition, the defect classification of

sole, hybrid, and material dependent were successfully obtained with

classification accuracies of 93.8%, 80%, and 96.4%, respectively.

Therefore, the random forest algorithm can be applied as a very good tool

for pattern recognition and prediction of multi-fault in a gas insulated

system.

v. Random forest algorithm performance

Seven other algorithms of pattern recognition were investigated. The

performance of the random forest algorithm is 1.5 times better than the next

best algorithm. This research illustrates the feasibility and applicability of

an effective GIS diagnostic using gas by-products analyses, in particular,

using the random forest pattern recognition.

1.7 Thesis Outline

The outline of the thesis is described below.

Chapter 2 covers the literature review on diagnostic techniques of gas

insulated switchgear, SF6 basic properties, ionization phenomena and decomposition

mechanism of SF6 in gas insulated switchgear, genesis and diagnostic techniques for

partial discharge detection, and an overview of pattern recognition classification using

the model tree based algorithm, or random forest algorithm, in WEKA workbench.

9

Chapter 3 presents the research methodology which begins with the research

design, procedure and experimental works, followed by the design of partial discharge

artificial defects in three categories, namely, sole, hybrid, and material dependent

defects. A total of eight different defects were designed. Detailed experimental

procedures and procedures for pattern recognition of by-product gases for defect

classification are described.

Chapter 4 reports all by-product gases detected for all defects. The types of

gases and their concentrations are given in detail in the form of tables and graphical

plots. Discussion is made on the newly found gases and their significance for each

category of defect. Also, the chapter reports the results of intelligent defect

classification using by-product gases pattern recognition. The WEKA software written

in Java was used. The software contains tools for supervised machine learning and

data mining. Several algorithms were used to classify the eight types of defects. The

performance of random forest algorithm against seven other algorithms is reported.

Chapter 5 presents the conclusions obtained from this work and

recommendation for future research work.

2

2

155

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APPENDIX A

LIST OF PUBLICATIONS

1. Ibrahim, V. M., Abdul-Malek, Z., and Muhamad, N. A. Status Review on Gas

Insulated Switchgear Partial Discharge Diagnostic Technique for Preventive

Maintenance. Indonesian Journal of Electrical Engineering and Computer

Science, 2017. 7(1): 9-17.

2. Ibrahim, V. M., Abdul-Malek, Z., and Muhamad, N. A. Chemical by-Product

Diagnostic Technique for Gas Insulated Switchgear Condition

Monitoring. Indonesian Journal of Electrical Engineering and Computer

Science, 2017. 7(1): 18-28.

3. Ibrahim, V. M., Abdul-Malek, Z., Muhamad, N. A., Mousa, M.I., Nawawi, Z.,

Sidik, M. A. B. and Jambak, M. I. Comparison of the Effect of Fixed Metallic

Defects in Coaxial Gas Insulated Switchgear Condition Monitoring.

International Conference on Electrical Engineering and Computer Science

(ICECOS). Sriwijaya, Indonesia. 2017.


Sidik, M. A. B. and Jambak, M. I. Sulphur Hexafluoride Gas Decomposition

Products of Fixed Metallic Defect in Coaxial Gas Insulated Switchgear.

International Conference on Electrical Engineering and Computer Science

(ICECOS) Sriwijaya, Indonesia. 2017.


Sidik, M. A. B. and Jambak, M. I. The upshot of hybrid defects in coaxial gas

insulated switchgear. International Conference on High Voltage and Power

System, ICHVEPS. Bali, Indonesia, 2017.

gas analysis technique for gas insulated...

Documents