APPLICATION OENHANCED POWER SYSTEM PROTECTION IN THE

DEPARTMENT OF ELECTRICAL ENGINEERING

Ebere Omeje

KALU OBINNA OBUMA

PG/M.ENG/14/68136

APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR ENHANCED POWER SYSTEM PROTECTION IN THE

NIGERIAN 330kV NETWORK

DEPARTMENT OF ELECTRICAL ENGINEERING

FACULTY OF ENGINEERING

Ebere Omeje Digitally Signed by: Content manager’s Name

DN : CN = Webmaster’s name

O= University of Nigeria, Nsukka

OU = Innovation Centre

i

F ARTIFICIAL NEURAL NETWORK FOR ENHANCED POWER SYSTEM PROTECTION IN THE

DEPARTMENT OF ELECTRICAL ENGINEERING

ENGINEERING

Digitally Signed by: Content manager’s Name

DN : CN = Webmaster’s name

O= University of Nigeria, Nsukka

ii

UNIVERSITY OF NIGERIA, NSUKKA

FACULTY OF ENGINEERING

DEPARTMENT OF ELECTRICAL ENGINEERING

APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR ENHANCED POWER SYSTEM PROTECTION IN THE NIGERIAN

330kV NETWORK

A THESIS SUBMITTED IN PARTIAL FULFILMENT FOR THE REQUIREMENT OF THE AWARD OF M.ENG

(POWER SYSTEMS ENGINEERING)

BY

KALU OBINNA OBUMA

PG/M.ENG/14/68136

DECEMBER, 2015

iii

Title Page

APPLICATION OF ARTIFICAIL NEURAL NETWORK FOR ENHANCED POWER SYSTEMS PROTECTION ON THE NIGERIAN

330kV NETWORK

A THESIS SUBMITTED IN PARTIAL FULFILMENT FOR THE REQUIREMENT OF THE AWARD OF M.ENG

(POWER SYSTEMS ENGINEERING)

BY

KALU OBINNA OBUMA

PG/M.ENG/14/68136

SUPERVISOR: PROF. T.C. MADUEME

DECEMBER 2015

iv

APPROVAL PAGE

APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR ENHANCED POWER SYSTEM PROTECTION IN THE NIGERIAN 330kV NETWORK

BY

KALU OBINNA OBUMA

(PG/M.EMG/14/68136)

A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS

FOR THE AWARD OF MASTER OF ENGINEERING DEGREE(M.ENG) IN ELECTRICAL ENGINEERING

DECEMBER, 2015

Kalu Obinna ObumaSignature: Date:

(Student)

Engr. Prof. T.C. Madueme Signature: Date:

Engr. Prof. A.O. IbeSignature: Date:

(External Supervisor)

Engr. Prof. E.C EjioguSignature: Date:

(Head of Department)

Engr. Prof. E.S. ObeSignature: Date:

(Fac. Of Engineering Rep. SPGS)

(Supervisor)

v

Certification

Kalu Obinna Obuma, a master’s degree student in the department of electrical engineering with registration number, PG/M.ENG/14/68136 has satisfactorily completed the requirements for the award of the degree of Masters of Engineering (M.Eng) in Electrical Engineering.

The work embodied in this project is original and has not been submitted in part or full for any other diploma or degree of this or any other university to the best of my knowledge.

Engr. Prof. T.C. Madueme

(Supervisor)

Engr.Prof.E.C Ejiogu

(Head of Department)

Engr. Prof. E.S Obe

(Fac.of Engineering Rep. SPGS)

vi

Dedication

I dedicate this work to all students of 2014 post graduate set of the department of Electrical Engineering Department.

vii

Acknowledgement

This work would not have been possible without the contribution of others, truly the saying ‘no man is an island’ adequately applies.

Many thanks to my supervisor, Prof T.C Madueme, whose consistency in lending assistance, being there when you need him and giving needed assistance where necessary, truly a father.

Prof.Obe, your assistance with procuring much needed research materials is greatly appreciated. Truly this thesis would not have taken much longer if not for your assistance in procuring needed materials.

Prof A.U.Ekwue, many thanks to you as well, even from a distance your assistance be it electronically, your tutelage as my lecturer made understanding this thesis easy. I will not fail to mention Prof.Anih who answered important questions relating to my work. Dr Ogbuka, your input with my Matlab Simulation saved me a lot of blunders, many thanks.

To the staff of the Protection, Control and Metering department of the New Haven &Onistha Transmission Station, I can’t thank you enough for obliging me with needed data to make my simulation more realistic, the head of department of both Departments for answering my questions and granting me supervised access to the control room.

To my friends who supported me during my project work I will never forget; Onwaokangba Anthony, Odoh Benjamin, UzoetoIfeanyi, NwaoguChijioke; am really grateful. To my family, always a constant in my life, your prayers and support really kept me going. To my siblings, thanks for looking up to me, truly responsibility, duty, and discipline. All these traits I’ve developed thanks to you guys.

I thank almighty God, during the stressful moments trying to figure out my project, my travels to transmission companies up to the moment it finally made sense. To you I give all the glory.

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Contents

Approval Page I

Certification II

Dedication III

Acknowledgement IV

Abstract V

List of Figures VIII

List of Tables X1

Chapter 1: INTRODUCTION

1.1 Introduction 1

1.2 Statement of problem 1

1.3 Aim/objective of the study

1.4 Significance of the study 3

1.5 Scope of the work 3

Chapter 2: LITERATURE REVIEW

2.1 State of the art power system protection 4

2.2 Faults in Power System 4

2.3 Symmetrical Faults 4

2.3.1 Transient on a Transmission Line 4

2.3.2 Symmetrical Components 6

2.3.3. Symmetrical Component Transformation 6

2.4. Unsymmetrical Fault Analysis 8

2.4.1 Single Line to Ground Fault 8

2.4.2 Line to Line Fault 10

2.4.3 Double Line to Ground Fault 12

2.5. Types of Protection 14

2.5.1 Distance Relays 14

2.5.2 Pilot protection 16

ix

2.6. Single Auto Reclosure Technique 17

2.7. System Configuration 17

2.8 Transmission Line Protection 17

2.8.1 Fault Detection & Location 18

2.8.2 Fault Classification 19

2.8.3 Enhanced Power System Protection 19

2.9 Artificial Neural Network 20

2.9.1 Multi-Layer Perceptron 21

2.9.2 Feed Forward Artificial Neural Network & Back Propagation Algorithm 21

2.9.3 Unsupervised Learning Algorithm 22

2.9.3.1 Self Organized Map Function 24

2.9.4. Clustering 26

Chapter 3: METHODOLOGY 27

3.1 Power System under Consideration 27

3.2 Data Pre-processing using fast Fourier transform 28

3.3 Overview of the Training Process 29

3.4 Overview of the Testing Process 31

3.5 Performance Evaluation 33

3.6 Clustering with Self-Organized Neural Network Algorithm 34

3.7 Neural Network Methodology for Adaptive Reclosure 35

3.8 Arc Modelling in Adaptive Reclosure Scheme 37

Chapter 4: SIMULATION RESULTS

4.1 Structure & training of neural fault detector 38

4.2 Discussion of Figures for A.N.N. Fault Detector 42

4.3 Structure & Training of A.N.N. fault location Algorithm 42

4.3.1 Discussion of plots from A.N.N. Fault Location Algorithm 48

4.4 Simulation Results for Fault Classification via Self Organising Map Function 49

x

4.4.1 Discussion of Results of Fault Classification via Self Organising Map Function 50

4.5 Simulation Results for Adaptive Auto Reclosure Scheme 50

4.5.1 Discussion of Results for Adaptive Fault Classifier Plots 54

4.6 Testing the Neural Network Fault Detection Algorithm 54

4.6.1 Discussion of Test Results of A.N.N. Fault Detector Algorithm 59

4.7 Test Results for Neural Network Fault Location Algorithm 59

4.7.1 Discussion of Simulation Results from Testing Fault Location Algorithm 62

4.8 Test Results for Neural Network Fault Classification Algorithm 62

Chapter 5: CONCLUSION

5.1 Conclusion 79

5.2 Recommendations 79

xi

List of Abbreviations

O.H.L Overhead Line

A.A Adaptive Autoreclosure

C.B Circuit Breaker

A.N.N Artificial Neural Network

F.F.N.N Feed Forward Neural Network

S.O.M Self Organized Map

R.O.C Receiver Operating Characteristics

C.V.T Current VoltageTransformer

E.H.V Extra High Voltage

S.P.A.R Single Pole Auto Reclosure

A.D.S.P.A.R Adaptive Single Pole Auto Reclosure

C.E Cross Entropy

%E Percentage Entropy

M.S.E Mean Square Error

R Regression

xii

List of Figures

Fig 2.1 RL model circuit 5

Fig 2.2 Single line to ground fault 8

Fig 2.3 Connection of sequence network for a single line to ground fault 9

Fig 2.4 Line to line fault 10

Fig 2.5 Sequence network for L-L fault 11

Fig 2.6 Double line to ground fault

Fig 2.7 Connection of Sequence Networks for LLG Fault 13

Fig 2.8 Three zone step distance relaying to protect 100% of a line and back up neighbouring line 16

Fig 2.9 Three layer network 22

Fig 2.10 Self organising map neighbourhoods 25

Fig 3.1 Fault current graph of A-G fault 27

Fig 3.2 Voltage signal of A-G fault 28

Fig 3.3 F.F.T. analysis of voltage waveform 28

Fig 3.4 F.F.T analysis of current waveform 29

Fig 3.5 Flow Chart of ANN Fault Diagnostic Algorithm 32

Fig 3.6 Block diagram of neural network training algorithm 34

Fig 3.7 Flow Chart of Adaptive Auto Reclosure Scheme 36

Fig 4.1 Current signal of B-C fault 38

Fig 4.2 Voltage signal of A-B-C fault 38

Fig 4.3 Current signal of A-B-C fault 39

Fig 4.4 Receiver Operating characteristics of fault detector using current values 40

Fig 4.4b Confusion matrix of fault detector 40

Fig 4.5a Receiver operating characteristics of fault detector using voltage & current values 41

Fig 4.5b Confusion Matrix for Fault detector using voltage & current values 41

Fig 4.6 Plot of regression fit for fault location on zone 1 42

Fig 4.7 Error histogram of fault locator using current values 43

xiii

Fig 4.8 Regression plot for fault using current values on zone 2 43

Fig 4.9 Error histogram for fault locator using current values on zone 2 44

Fig 4.10 Regression fit for fault locator using current values on zone 3 44

Fig 4.11 Regression fit for fault locator using voltage & current values on zone 1 45

Fig 4.12 Error histogram for fault locator using voltage & current values on zone 1 45

Fig 4.12b Output Plot for Fault Locator using voltage & current values in zone 1 46

Fig 4.13 Regression fit for fault locator using voltage & current values on zone 2 46

Fig 4.14 Error histogram for fault locator using voltage & current values on zone 2 47

Fig 4.15 Regression fit for fault locator using voltage & current values on zone 3 47

Fig 4.16 Error histogram for fault locator using voltage and current values on zone 3 48

Fig 4.17 Plot of S.O.M sample hits 49

Fig 4.18 Plot of S.O.M neighbour weight distances 49

Fig 4.19 Plot of S.O.M input planes 50

Fig 4.20 Transient Fault waveform for A-G fault 51

Fig 4.21 Permanent fault waveform of A-G fault 52

Fig 4.22 Confusion matrix for fault classifier using voltage and current values 52

Fig 4.23 Receiver operating characteristics for fault classifier using voltage & current values 53

Fig 4.24 Regression plot of adaptive reclosure scheme using voltage & current values 53

Fig 4.25 Receiver operating characteristics plot for adaptive reclosure scheme using voltages &

current values 54

Fig 4.26 Confusion matrix plot for testing fault detector using current values 55

Fig 4.27 Plot of training state for testing fault detector using current values 55

Fig 4.28 Error histogram for testing fault detector using current values 56

Fig 4.29 Receiver operating characteristics for testing fault detector using current values 56

Fig 4.30 Plot of training state for testing fault detector using voltage & current values 57

Fig 4.31 Error histogram for testing fault detector using voltage & current values 57

Fig 4.32 Confusion matrix for testing fault detector using voltage and current values 58

xiv

Fig 4.33 Confusion matrix for testing fault detector using voltage & current values 58

Fig 4.34 Training state results for testing fault locator using current & voltage values 59

Fig 4.35 Error histogram for testing fault detector using current values 59

Fig 4.36 Regression plot for testing for testing fault locator using current values 60

Fig 4.37 Training state plot for testing fault locator using voltage & current values 60

Fig 4.38 Error histogram for testing fault locator using voltage & current values 61

Fig 4.39 Regression plot after testing fault locator using voltage & current values 61

Fig 4.40 S.O.M neighbour weight distances for testing fault locator 62

Fig 4.41 S.O.M input planes for fault classifier 63

Fig 4.42 Sample hits plots for testing fault classifier 63

Fig 4.43 Confusion matrix for test on adaptive reclosure 64

Fig 4.44 Receiver operating characteristics for test results on adaptive reclosure scheme. 65

xv

List of Tables

Table 4.1 Performance table for fault detector network 65

Table 4.2 Performance table for adaptive fault classifier neural network 66

Table 4.3 Performance table for adaptive reclosure scheme network 66

Table 4.4 Performance table for fault locator using neural network 67

Table 4.5 Output of trained faulted phase detector network using current values only for zone 1 68

Table 4.6 Output of trained faulted phase detector network using current & voltage values 69

Table 4.7 Output of trained faulted phase detector network using current values for zone 2 70

Table 4.8 Output of trained faulted phase detector using voltage & current values for zone 2 71

Table 4.9 output of trained fault phase detector using current values for all zones 72

Table 4.10 output of trained faulted phase detector using voltage and current values for all zones 73

Table 4.11 Comparison of estimated and target output for fault locator zone 2 74

Table 4.12 Comparison of estimated and target output of fault locator zone 1 75

Table 4.13 Comparison of estimated and target output for fault locator zone 3 76

Table 4.14 Adaptive fault classifier for transient or permanent fault 78

xvi

Abstract

This work investigates an improved protection solution based on the use of artificial neural network on the 330kV Nigerian Network modelled using Matlab R2014a. Measured fault voltages and currents signals decomposed using the discrete Fourier transform implemented via fast Fourier transform are fed as inputs to the neural network. The output plots of the neural network shows its successful application to fault diagnosis (fault detection, fault classification and fault location). The neural networks application to fault location shows a mean square error of 3.5331 and regression value of 0.99976 which shows a very close relationship between the output and target values fed to the neural network. Unlike conventional protection schemes, the neural network can be adapted to distances which can cover the entire length of the protected line. Numerical assessment carried out on the neural network fault locator shows a reduced time of operation of 5.15miliseconds as compared to the 0.350seconds with the use of ordinary numerical relays. This work also investigates the adaptive auto reclosure scheme implemented using artificial neural network. The adaptive reclosure scheme has been adapted for use in the Nigerian Network successfully to distinguish transient and permanent faults. Simulation results prove that the adaptive reclosure scheme was able to detect a line-to-ground transient fault and clear this fault in 0.1s while the line-to-ground permanent fault is cleared after 0.14s. The auto reclosure scheme is designed using two separate neural networks, one nework to distinguish the faults either as transient or permanent fault, and using this fault distinguishing network as input to the second network to classify decision, either as ‘safe to reclose’ represented by logic ‘1’ or ‘do not reclose’ represented as logic ‘0’. The Fault diagnostic algorithm designed using artificial neural network (A.N.N.) for the 330kV network was tested on a 132kV network. Results show and prove that the algorithm is flexible and can be adopted to other networks.

1

CHAPTER ONE

INTRODUCTION

1.1 Background of the study

The demand for constant power supply in Nigeria is ever increasing; however the demand is met with lots of constraint. One of them being system faults. Faults on transmission line in particular is of great interest to the power holding company of Nigeria as more investment is put into restructuring the current infrastructure and also expanding existing ones.

The power sector of Nigeria is subdivided into policy, regulations, customers, operations. The operations division brings to light the activities of the transmission company of Nigeria that controls the high voltage delivery of power from generating plants to the substations for transmission to distribution stations. T.C.N handles a 330kv system capacity of 6870MW over a total distance of 5650Km[1], their focus is to maintain power system stability, reliability and sustainability.

The major protection schemes currently employed are distance protection, over current protection, differential protection e.t.c. distance protection being the predominant suffers from inaccuracy due to restraints of relays on protection schemes i.e. reach settings. The relay cannot fully adapt to fluctuations in power system conditions especially in parallel lines as well as distinguish between transient and permanent fault following a short circuit.

This work brings to view the application of artificial neural network for enhanced power system protection in regards to fault detection, fault location, and application of the adaptive auto reclosure schemes as opposed to conventional approach; travelling wave approach[2][3], synchronous compensators[4] to name a few.

1.2 Statement of the Problem

Among several power system components, transmission line is one of the most important components of the power system network and is mostly affected by several types of faults. Generally, 80%-90% of the fault occurs on the transmission line and the rest of substation equipment and bus bar combined[5]. The necessary requirement of all the power system is to maintain reliability of operation which may be done by detecting, classifying and isolating various faults occurring in the system. It is required that a corrective decision should be made by the protective device to minimize the period of trouble and limit outage time, damage and related problems. If any fault or disturbances occurred in the transmission is not detected, located, and eliminated quickly, it may cause instability in the power system and causes significant changes in system quantities like over-current, under or over voltage, power factor, impedance, frequency and power. The appropriate percentage of occurrence of single line to ground fault is about 70-80%, line to line to ground faults is 10-17%, line to line fault is 8-10% and three phase is 3%[6]. The three faults occur rarely but if it exists in a system it is quite expensive.

2

1.3 Significance of the Study

Distance protection is considered covering various effects like high fault impedance, non-linear arc resistance and variable source impedance. Distance relaying principle, due to their high speed fault clearance compared with over current relays is widely used protective scheme for high voltage transmission lines in Nigeria. A distance relay estimates the electrical distance to the fault and compares the result with a given threshold, which determines the protection zone. There is need for measuring algorithms that have the ability to adapt dynamically to the system operating conditions such as changes in the configuration. Numerical relays acquire sequential samples of A.C. quantities in numeric (digital) data form through the data acquisition system, process the data using the algorithm to calculate fault discriminate and make trip decision. The reach accurateness of an electromechanical, static or a microprocessor based distance relay is affected by different fault conditions and network configuration settings. Artificial neural network makes use of samples of currents and voltages directly as inputs without calculation of phasor and related symmetrical components. The algorithm makes available automatic determination of fault direction and fault location after one cycle from the initiation of fault. For protection of transmission line using artificial neural network, it doesn’t necessitate any communication link to recover remote end data from local end only i.e. voltages and currents are captured from the bus bar. Then, pre processing of obtained signal can be done to pass it into A.N.N level making it the best tool to solve under reach and overstretch problems which are very regular with conventional distance relay design.

1.4 Aim/Objective of the study

The aim of this project is to demonstrate the application of artificial neural network to fault diagnostic as well as implementation of adaptive single pole auto reclosure scheme in power system protection.This work presents the outcomes of both the feed forward A.N.N and self-organized neural network application to;

Fault detection of all types of faults

Fault classification (line to ground, line to line, line to line to ground, three phase faults, three phase to ground faults)

Fault location on three zones presented in this work

Application to adaptive single pole auto-reclosure scheme

The objectives of the study are;

Fault pattern generation from transmission network modelled on Matlab Simulink Environment.

Pre-processing of voltage and current signals using Fast Fourier transform. Normalization of the extracted features in order to match A.N.N input level of ± 1.

3

Selection of appropriate neural network architecture for various protection problems; fault detection, location e.t.c.

Training of appropriate neural network.

1.5 Scope of the Work

This work centres on the application of artificial neural networks on the Nigerian 330kv network. For the purpose of this project, three transmission networks are considered; Onistha, Benin and new-haven. Needed data’s like single line diagram of each network as well as line and bus data of this networks are collated for the purpose of this work. The author did his best to create a Simulink model of these networks taking Onistha network as the reference to other networks represented as one entity. The fault breaker block is placed on each line representing the three different protection zones to induce different fault types on each line. Certain assumptions were made in the modelling of these networks; the generator as well as step-up transformer data of a different generating plant but giving the same output voltage as desired for this work, fault data also retrieved from Onistha T/S was used to compare simulated to real time data

4

CHAPTER TWO

LITERATURE REVIEW

2.1State of the Art Power System Protection

Power system protection is a branch of electrical power engineering that deals with the protection of power systems from faults through the isolation of faulted parts from the rest of the network. Protection systems in electricity delivery networks have a major role to play in increasing of systems, and a broad understanding of their current and future application can aid in better taking them into account for achieving future energy networks that adapt for the incorporation of renewable energy generation sources. This chapter provides a survey of faults generally, state of art of some protection techniques as well as protection schemes. The unifying theme of this work is to highlight the potentials of artificial intelligence namely artificial neural networks in overcoming the restraints of traditional protection techniques[7] thus the enhanced protection scheme is introduced .

2.2 Faults in Power System

Fault is an unwanted short circuit condition that occurs either between two phases of wires or between a phase of wire and ground. Short circuit is the most risky type of fault as flow of heavy currents can cause overheating or create mechanical forces which may damage equipment and other elements of power system. Faults can be classified into three types, which are symmetrical faults, unsymmetrical faults, and open faults.

2.3 Symmetrical Faults

The fault that results in symmetrical fault current (equal currents with 120 displacement) is known as symmetrical fault. Three phase faults is an example of symmetrical fault where all three phases are short circuited with or without involving the ground.

2.3.1 Transient on a Transmission Line

To consider the short circuit transient on a transmission line, certain simplifying assumptions made at this stage

The line is fed from a constant voltage source

Short circuit takes place when the line is unloaded

Line capacitance is negligible and the line is represented by a lumped RL series circuit

With the above assumptions the line can be represented by the circuit model of fig 1 below. The short circuit is assumed to take place at t=0. The parameter α controls the instant on the voltage wave when the short circuit occurs. It is known from the circuit theory that the current after short circuit is comprised of two parts i.e.

5

i = i + i (2.1)

where i = steady state current = √( )|| sin(ωt + α − ɵ)

Z = (R + ωL) < ɵ = tan! ω"# $ (2.2)

Where i = transient current'it is such that i(0) = i(0) + i(0) = 0) Being an inductive circuit, it decays correspondingly to the time constant L R . i = −i(0)e!*+$ = √|| sin(θ − α) e,*+$- (2.3)

This short circuit is given by

i = √ || sin(ωt + α − θ) + √ || sin(θ − α) e!.# " / (2.4)

In power system terminology, the sinusoidal steady state current is called the symmetrical short circuit current and the unidirectional transient component is called the DC offset current, which causes the total circuit current to be unsymmetrical till the transient decays[17]. The maximum momentary short circuit current i012 corresponds to the first peak. If the decay of transient current in this short time is neglected,

i012 = √|| sin(θ − α) + √|| (2.5)

Since transmission line reactance is small, θ≅90

Symmetrical short circuit

current

DC offset

R

L

Fig 2.1 RL model circuit

6

i012 = √|| cosα + √|| (2.6)

This has the maximum possible value for α=0, that is short circuit occurring when the

voltage wave is going through zero. Thus i012 = √|| is twice the maximum symmetrical

short circuit current (doubling effect).

2.3.2 Symmetrical Components

Under such operations the system impedances in each phase are identical and the three phase voltages and currents throughout the system are completely balanced that is they have equal magnitudes in each phase and are progressively displaced in time phase by 120 (phase ‘a’ leads/lags phase ‘b’ by 120 and phase ‘b’ leads/lags phase ‘c’ by 120 ). In a balanced system, analysis can proceed on a single-phase basis. The knowledge of voltage and current in one phase is sufficient to completely determine voltages and currents in other two phases. Real and reactive powers are simply three times the corresponding per phase values. A method of analyzing unbalanced operation is through symmetrical components where the three phase voltages (and currents) which may be unbalanced are transformed into three sets of balanced voltages and currents called symmetrical components.

2.3.3 Symmetrical Component Transformation

A set of three balanced voltages (phasors) 67, 69, 6: is characterized by equal magnitudes and interphase difference of 120 . The three phasors can be expressed in terms of the reference phasor67 as

V1 = V1, V< =∝ V1,V> = αV1 (2.7)

Where the complex number operator α is defined as α=e@A, it has the following parameters

α = e@BA° = e!@A° =∝∗

(∝)∗ =∝

∝E= 1

1+ ∝ + ∝= 0

If the phase sequence is acb (negative sequence), then

V1 = V1,V< =∝ V1,V> =∝ V1 (2.8)

Thus a set of balanced phasorsi is fully characterized by its reference phasor (sayV1 ) and its phase sequence (positive or negative).

Suffix 1 is normally used to indicate positive sequence. A set of balanced negative sequence phasors is written as

7

Va,Vb =∝ Va,Vc =∝ Va (2.9)

Similarly, suffix 2 is used to indicate negative sequence, a set of balance negative sequence phasors is written as

Va,Vb =∝ Va,Vc =∝ Va (2.10)

A set of three voltages (phasors) equal in magnitude and having the same phase is said to have zero sequence. Thus a set of zero phase sequence phasors is written as

VaA = Vb = VcA = 0 (2.11)

Consider now a set of three voltages (phasors) 67, 69, 6:which in general may be unbalanced. According to fortesque’s theorem the three phasors can be expressed as the sum of positive, negative, and zero phasors defined. Thus

V1 = Va + Va + VaA (2.12)

V< = Vb + Vb + VbA (2.13)

V> = Vc + Vc + VcA (2.14)

The three phase sequences (positive, negative and zero) are called the symmetrical components of the original phasorVa, Vb, Vc.

Equations (12),(13),(14) can be expressed in terms of reference phasorsVa,Va and VaA. thus

V1 = Va + Va + VaA (2.15)

V< =∝ Va +∝ VaA + VaA (2.16)

V> =∝ Va +∝ Va + VaA (2.17)

Construction of current phasors from their symmetrical components:

Ia = E (I1 +∝ I< +∝ I>) (2.18)

Ia = E (I1 +∝ I< +∝ I>) (2.19)

IaA = E (I1 + I< + I>) (2.20)

8

2.4 Unsymmetrical Fault Analysis

Various types of unsymmetrical faults that occur in power systems are:

Single line-to-ground (LG) fault

Line-to-line (LL) fault

Double line-to-ground (LLG) fault

2.4.1 Single line-to-ground (LG) fault

Figure 2.2 shows a line-to-ground fault at F in a power system through a fault impedance Zf. The phases are so labelled that the fault occurs on phase a.

At the fault point F, the current out of the power system and the line to ground voltage are constrained as follows:

I< = 0 (2.21)

I> = 0 (2.22)

V1 = ZJI1 (2.23)

The symmetrical components of the fault currents are

KIaIaIaEL = 13 K1 ∝ ∝1 ∝ ∝1 1 1 L KI100L

Zf

Ib=0

Ic=0

Fig 2.2 Single Line to Ground fault at F

a

b

c

9

For which it is easy to see that

Ia = Ia = IaA = E I1 (2.24)

Expressing (23) in terms of symmetrical components, we have

Va + Va + VaA = ZJI1 = 3ZJI1 (2.25)

As per (3.24) and (3.25) all sequence currents are equal and the sum of sequence

voltages3ZJIa.. Therefore, these equations suggests a series connection of sequence

networks through an impedance 3ZJ as shown in figs.3

NO = PQ(RSTRUTRV)TERW (2.26)

Fault current N7 is given by

N7 = 3NO = EPQ(RSTRUTRV)TERW (2.27)

F

F

6O

6O

6OA

NOA = NO

NO

NO = NO

3XY N7XY

NO = NO = NOA = 13 N7

Fig 2.3: connection of sequence network for a single line to ground fault (LG) fault

F

10

The above results can also be obtained directly from (24) to (25) by using 6O, 6O OZ[ 6OA and from the equation below

K6O6O6OAL = K\700 L − KX 0 00 X 00 0 XA

L KNONONOAL (2.28)

Thus,

(\7 − NOX) + (−NOX) + (−NOXA) = 3XYN 7

Or

NO = \7(X + X + XA) + 3XY

The voltage of line b to ground under fault condition is

69 =∝ 6O+∝ 6O + 6OA

=∝ ]\7 − X N73 ^ +∝ ]−X N73 ^ + ]−XA N73 ^

Substituting for N7 from (27) and reorganizing we get,

69 = \7 E∝URWTRU.∝U!∝/TRV(∝U!)(RSTRUTRV)TERW (2.29)

2.4.2 Line to Line Fault

Fig 4 shows a line to line fault at F in a power system on phases ‘b’ and ‘c’ through fault impedance XY .

The currents and voltages at the fault can be expressed as

N7

a

b

c

F

XY N9 N:

Fig 2.4: Line to Line (L-L) fault through impedance XY

11

N_ = K N7 = 0N9N: = −N9L ; 69 − 6: = N9XY (2.30)

The symmetric components of the faults currents are

KNONONOAL = 13 K1 ∝ ∝1 ∝ ∝1 1 1 L K 0N9−N9

L From which we get

NO = −NO (2.31)

NOA = 0 (2.32)

The symmetrical components of voltage at F under fault are

K6O6O6OAL = E K1 ∝ ∝1 ∝ ∝1 1 1 L a 676969 − XYN9

b (2.33)

The first two equations

36O = 67 + (∝ +∝)69 −∝ XYN9

36O = 67 + (∝ +∝)69 −∝ XYN9

From which we get

3(6O − 6O) = (∝ −∝)XYN9 = c√3XYN9 (2.34)

Now,

N9 = (∝ −∝)NO (NO = −NO; NOA = 0)

= −d√3NO (2.35)

Substitute N9from (3.31) and (3.35) parallel connection of positive and negative sequence

networks through a series impedance XY as shown in fig 5 since NOA = 0, the zero sequence network is unconnected

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In terms of the thevenin equivalents, we get

NO = \7X + X + XY

From (2.35) we get

N9 = −N: = −c√3\7X + X + XY

2.4.3DOUBLE LINE TO GROUND (LLG) FAULT

Fig 6 shows a double line to ground fault at F in a power system. The fault may be in general having impedance XY as shown.

XY

NO NO

6O 6O

Fig 2. 5 sequence network for L-L fault

a

b

c

F

XY N9 N: 3NOA

NOA = 0

Fig 2.6: double line to ground (LLG) fault through impedance XY

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The current and voltage (to ground) conditions at the fault are expressed as

N7 = 0

NO + NO + NOA = 0 (2.36)

69 = 6: = XY(N9 + N:) = 3XYNOA (2.37)

The symmetrical components of voltages are given by

K6O6O6OAL = E K1 ∝ ∝1 ∝ ∝1 1 1 L K67696:

L (2.38)

From which it follows that

6O = 6O = E '67 + (∝ +∝)69) (2.39a)

6OA = E (67 + 269)(2.39b)

From (2.39a) and (2.39b)

6OA − 6O = 13 (2−∝ −∝)69 = 69 = 3XYNOA

Or

6OA = 6O + 3XYNOA

The sequence connections is shown in fig 2.7

3XY

F F F 6O 6O

6OA

Fig2. 7 Connection of sequence networks for a double line to ground (LLG) fault

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In terms of the thevenin equivalents, the new equation translates from

NO = \7X + X (XA + 3XY)

= PQRSTRU.RVTERW/.RUTRVTERW/ (2.40)

2.5 Types of Protection

Protection of transmission or distribution network serves to protect the plant as well as the personnel by disconnecting equipment which experiences an overload or a short to the earth. Some forms of protection are;

Overload and backup for distance (over-current): overload protection requires a current transformer which simply measures the current in a circuit. There are two types of overload protection; instantaneous over current and time over current (T.O.C). Instantaneous over current requires that the current exceeds a pre-determined level for the circuit breakers to operate.

Earth-fault: earth fault protection again requires current transformers and series an imbalance in a three-phase circuit. Normally the three phase currents are in balance, which is roughly in magnitude. If one or two phases become connected to earth via long impedance fault, their magnitudes will increase dramatically and cause imbalance. If this imbalance exceeds a pre-determined value, a circuit breaker should operate.

Distance (impedance relay): distance protection detects both the voltage and current. A fault on a circuit will generally create a sag in the voltage level. If the ratio of voltage to current measured at the relay terminals, which equates to impedance, leads within a pre-determined level the circuit breaker will operate. This is useful for reasonable length lines, lines longer than 10 miles because its operating characteristics are based on the line characteristics. This means that when a fault appears on the line the impedance setting relay is compared to the apparent impedance of the line from the relay terminals to the fault. If the relay setting is determined to be below the apparent impedance it is determined that the fault is within the zone of protection[8].

Back up: the objective of protection is to remove only the affected position of a plant and nothing else. A circuit breaker or protection relay may fail to operate. In important systems, a failure of primary protection will usually result in the operation of back-up protection[9]. Remote back up protection will generally remove the affected and unaffected items of plant to clear the fault. Local back-up protection will remove the affected items of plant to clear the fault. Local back-up protection will remove the affected items of the plant to clear the fault.

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2.5.1 Distance Relays

The distance protection scheme is the dominant scheme used in the Nigerian 330Kv networks thus a further review of this scheme and its implication as regards this work is paramount. The distance protection is implemented in a transmission network by the protection equipment known as distance relays. Distance relays respond to the voltage and current i.e. impedance at the relay location. The impedance per mile is fairly constant so these relays respond to the distance between the relay location and a fault location. As the power systems becomes more complex and the fault current varies with changes in generation and system configuration, directional over current relays are more difficult to apply and to set for all contingencies, whereas the distance relay setting is constant for a wide variety of changes external to the protection line. There are three general types; impedance relay, admittance relay, reactance relay each is distinguished by its application and its operating characteristics.

In a three phase power system, 11 types of fault are possible; three single phase to ground, three phase –phase to ground, three double phase to ground, and two three phase faults. It is essential that the relays provided have the same setting regardless of the type of fault. This is possible if the relays are connected to respond to delta voltages and currents. The delta quantities are defined as the difference between any two phase currents, for example, \7 − \9 is the delta quantity between phases ‘a & b’. In general, for multiphase-fault between phases x and y,

PQ!PefQ!fe = X (2.41)

Where X is the positive sequence impedance between the relay location and the fault. For ground distance relays, the faulted phase voltage, and a compensated faulted phase must be used.

PQfQTgfV = X (2.42)

Where m is a constant depending on the line impedance, and NA is zero sequence current in the transmission line. A full complement of relays consisting of three phase distance relays and three ground relays. This is the preferred protective scheme for high and extra high voltage systems[6].

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2.5.2 Pilot Protection

Step distance protection does not offer instantaneous clearing of faults over 100% of line segment. To cover the 10-20% of the line not covered by zone 1, the information regarding the location of the fault is transmitted from each terminal to the other terminals. A communication channel is used for this transmission. Pilot channels can be over power line carrier, microwave, fibre optic, or wire pilot. Power line carrier uses the protected line itself as a channel, superimposing a high frequency signal on-top of the 50Hz power frequency. Since the line being protected is also the medium used to actuate the protective devices, a blocking signal is used. This means a trip will occur at both ends of the line unless a signal is received from the remote end. Pilot protection is not in use in the south-eastern Nigerian transmission network due to some stations yet to be connected to the grid.The issues associated with the distance relay, problems of under reach and over reach introduce a high error in distance relaying. In the case of pilot protection, cost of implementing communication channel presents a setback to its use regardless of its efficiency. The constraints presented, informs the decision to research on a cost effective and robust protection scheme; many research on improved protection, enhanced protection can be characterized as ADAPTIVE PROTECTION; Adaptive protection is a protection philosophy

Fig 2.8 Three zone step distance relaying to protect 100% of a line and backup neighbouring line. (From

S.Horowitz,Transmission Line Protection, 2nd

ed..,2007.CRC Press, Taylor % Francis Group)

17

which permits and seeks to make adjustments to various protection functions automatically in order to make them more attuned to prevailing power system conditions. The ADAPTIVE DISTANCE PROTECTION[10] is one of such research areas in power system protection; this scheme seeks to keep the protected zone constant at a predetermined boundary by adapting the tripping impedance under varying power system conditions. Another research area is BOUNDARY PROTECTION[11][12], which happens to be a step beyond the adaptive distance protection. A fast growing research interest is the single pole auto reclosure technique further developed to the ADAPTIVE SINGLE POLE AUTO RECLOSURE SCHEME[13][14][15].

2.6 Single Pole Auto Reclosure Technique

The most common faults on EHV transmission lines are single phase to ground types and for such faults SPAR provides an improvement in the overall protection of transmission system[13]. S.P.A.R is imperative in applications construction of additional circuits may not be possible due to environmental pressure/costs, a practical example is the Nigerian Transmission Network which does not utilize the scheme in the protection of its transmission lines. The conventional S.P.A.R has cases of unsuccessful reclosures due to a fixed dead time in the case of transient fault, or reclosure onto a permanent fault may aggravate the potential damage to the system and equipment. Notwithstanding, the method of autoreclosure is economical and effective technique for high capacity electric power systems to improve reliability and stability if autoreclosure is successfully executed, it usually restores the stability of the system and maintains the continuity of electric power transmission. In auto reclosure techniques, it is very important to distinguish permanent fault from temporary faults and to apply an adaptive algorithm in each case. In this respect, adaptive S.P.A.R offers many advantages such as increased rate of successful reclosure, improved system stability and reduction in system and equipment shock under a permanent fault.

2.6.1 Auto Reclosure Relaying System

A research work on auto reclosure scheme[16] proposes the utilization of adjustable dead times by accurately identifying arc extinction times. It is inferred that if the dead time is too short, is possible to reignite the arc, which leads to re-striking arc faults, so we must ensure a long enough dead time to ensure improvement in power system stability and reduction in system shock can be achieved easily by applying adaptive reclosing. In this study, the adaptive S.PA.R is implemented using artificial neural network.

2.7System Configuration

Although the fundamentals of transmission line protection apply in almost all system configurations, there are different applications that are more or less dependent upon specific situations.

Operational Voltages- transmission lines will be those lines operating at 138kV and above, sub transmission lines are 34.5kV to 138kV, and distribution lines are below

18

34.5kV. These are not rigid definitions are only used to generically identify a transmission system and connote the type of protection usually provided. The higher voltage systems would normally be expected to have more complex, hence more expensive, relay systems. This is so because higher voltages have more expensive equipment associated with them and one would expect that this voltage class is more important to the security of the power system. The higher relay costs, therefore, are more easily justified.

Multi-Terminal Lines - occasionally, transmission lines may be tapped to provide intermediate connections to additional sources without the expense of a circuit breaker or other switching device. Such a configuration is known as a multi-terminal line and, although it is an inexpensive, measure for strengthening the power system, it presents special problems for the protection engineer. The difficulty arises from the fact that a relay receives its input from the local transducers, i.e., the current and voltage at the relay location. The total fault current is the sum of the local current plus the contribution from the intermediate source, and voltage at the relay location is the sum of the two voltage drops, one of which is the product of the unmonitored current and the associated line impedance.

Line Length- the length of a line has a direct effect on the type of protection, the relays applied, and the settings. It is helpful to categorize the line length as “short,” “medium,” or “long” as this helps establish the general relaying applications although the definition of “short,” “medium,” and “long” is not precise. A short line is one in which the ratio of the source to the line impedance of a line varies more with the nominal voltage of the line than with its physical length or impedance. So a “short” line at one voltage level may be a “medium” or “long” line at another.

2.8Transmission line protection

The study of transmission line protection presents many fundamental relaying considerations that apply, in one degree or another, to the protection of other types of power system protection. Each electrical element of course will have problems unique to itself, but the concepts of reliability, selectivity, local and remote backup, zones of protection, coordination and speed which may be present in the protection of one or more other electrical apparatus are all present in the considerations surrounding transmission line protection.

Since transmission lines are also the links to adjacent lines or connected equipment, transmission line protection must be compatible with the protection of all of these other elements. This requires coordination of settings, operating times and characteristics[17]. The purpose of power system protection is to detect faults or abnormal operating conditions and to initiate corrective action. Relays must be able to evaluate a wide variety of parameters to establish that corrective action is required. Obviously, a relay cannot prevent the fault. Its primary purpose is to detect the fault and take the necessary action to minimize the damage to the equipment or to the system. The most common parameters which reflect the presence of a

19

fault are the voltages and currents at the terminals of the protected apparatus or at the appropriate zone boundaries. The fundamental problem in power system protection is to define the quantities that can differentiate between normal and abnormal conditions. In this study, Transmission line protection is carried out in the following categories; fault detection, fault classification, fault location and adaptive auto reclosure technique

2.8.1 Fault Detection&Location

When a fault occurs on a transmission line, it is very important to quickly detect and locate it in order to make necessary repairs and to restore power as soon as possible, the time needed to determine the fault point along the line will affect the quality of power delivery. Fault location has been a subject of interest for many years. Many fault locating algorithms have been developed; the power frequency based approach[18], transient signals based approach[19] and super imposed component based approach[20]. Currently the most widely used method of overhead line fault location is to determine apparent reactance of the line during the time the fault is flowing and to convert the Ohmic result into distance based on the parameter of the line, however this method is subject to errors when the fault resistance is varied and the line is fed from both ends.

Many successful applications of artificial neural networks to power systems have demonstrated the use of artificial neural networks for direction estimation[21], faulted phase selection[8], fault location under CT saturation. However these applications merely use the A.N.N ability of classification i.e. the ANNs output of 1 or 0 and mainly work on singly fed source without consideration to different zones of protection. In this study, three fault detectors carried out on all zones of protection as well as fault locators are implemented using A.N.N.

2.8.2 Fault Classification

Overhead transmission lines are vulnerable to faults since they extend over long distances and are often exposed to severe climate conditions. Symmetrical faults and unsymmetrical faults can easily be classified into transient and permanent faults. The fault classification problem is mostly treated as a pattern recognition problem[22][23] which is implemented using the back propagation learning algorithm, the usual case in many research works, however this algorithm has generalization and convergence problems associated with it, an algorithm with convergence issues has a high percentage error thus low effectiveness[24].

The paper[3] also utilizes feed forward ANN to fault classification current signals only as input signals due to high economical cost of such devices. The use of only current signals gives room for errors in fault detection and location for faulted phases to ground takes into account zero sequence and voltage of faulted phases thus in this study, a different approach that employs both voltage and current signals as well as zero sequence of both quantities is used as input to the neural network designed. The approach implemented in most research work treats fault classification as a pattern recognition problem carried out using the feed forward back propagation algorithm[3][25]; however this algorithm is beset with

20

generalization problems if the input data set is not large enough which cause intolerable percentage error. A different approach is treated in this work, the use of unsupervised competitive layer self-organised map algorithm implemented as a clustering problem. Results shown in chapter four proves the problems inherent in the previous algorithm are taken care of with this approach.

2.8.3Enhanced Power System Protection

The traditional line protection scheme based on fundamental frequency components of the fault generated transient voltage and current signals can be classified into two categories; non-unit protection and unit protection. The non-unit protection schemes use one end transmission line data whilst the unit protection schemes use data from the two ends. The non-unit protection such as distance relay cannot protect the entire length of the primary line because it cannot differentiate the internal faults from external occurring around multi zone boundaries. Back up protection may be introduced as a trade-off for protecting the entire length of the transmission line. For unit protection such as pilot protection, it usually requires a communication link to transmit the blocking or transfer tripping signals therefore the reliability of the protection scheme highly relies on the reliability of the communication link[5][6][11]. The cost of communication link also needs to be taken into account. Recently, new techniques using high frequency components of the faulted generated signals were studied and some useful solutions were obtained[11][12][15]. An approach known as “adaptive single pole auto reclosure scheme” for solving the disadvantages of conventional non-unit protection scheme was proposed. This approach introduces the possibility of differentiating the permanent and transient fault using data from one end only; other proposed solutions are boundary protection and adaptive distance protection[10].

Regarding the fault selection or classification, the traditional method is based on the fundamental frequency phasors. The feature formed by a non-linear ratio between voltage and current phasors is compared to the threshold to find out the faulted phase. This kind of method is affected by different conditions such as fault resistance, mutual coupling of parallel lines e.t.c. This study proves an alternative solution in the use of neural network based algorithm based on fast Fourier transform and self-organized neural network and back propagation neural network to realise fault classification as well as adaptive reclosure scheme. A similar research[12][26] work utilized same self-organized neural network for fault classification as well as adaptive reclosure for fault classification and boundary protection. Although different, this study proves that both algorithms can be combined to solve the generalization problems associated with fault classification problems associated with only back propagation algorithm. To further illustrate the functionality of neural network and its various algorithms, a summary of neural network description, back propagation algorithm and self-organised neural network is presented in the next section.

21

2.9Artificial Neural Network

A neural network is amassively parallel distributed processor made up of simple processing unit that has a natural propensity for storing experimental knowledge and making it available for use. Artificial neural network is inspired by biological neural network and is composed of a number of interconnected units known as artificial neurons. Artificial neurons are used to transmit signal from one layer to the other, its complex network of interconnected neurons is analogous to firing of electrical pulses via its connections that leads to information propagation. A.N.N. consists of three layers i.e. input layer, hidden layer and output layer having number of neurons present in it[8].

Neural networks are primarilyof three basic learning algorithms such as supervised learning, unsupervised and reinforced learning. For the sake of this work only supervised and unsupervised training algorithm is utilized. The supervised learning algorithm is the popular error back propagation for diagnosis of faults in power systems. However due to slow training speeds and generalization issues, the unsupervised was also adapted in this work. A review of the multilayer perceptron, error back propagation as well as unsupervised training algorithm is carried out for the purpose of better understanding of its functionality as applied to this research.

2.9.1 Multilayer Perceptron

The cascaded layer perceptron is an example notation of the multilayer perceptron as illustrated in fig 9 below. The output of the first network is the input to the second network, and the output of the second network is the input to the third network. Each layer may have a different number of neurons, and even a different transfer function. The weight matrix for the first layer is written as hand the weight matrix for the second layer is h. to identify the structure of a multilayer network, the following shorthand notations, where the number of inputs is followed by the number of neurons in each layer[27].

i − j − j − jE (2.43)

22

2.9.2Feed forward Artificial Neural Network & Back Propagation Learning Algorithm

The feed forward multilayer network was utilized and training done using the back propagation algorithm. The F.F.N.N consists of an input layer, hidden layer and output layer representing the response of the network after training[3]. The log sigmoid transfer function is the transfer function implemented with this algorithm[27]; this transfer function takes in the input and squashes it into this expression

O = Tk,l (2.44)

Back propagation is an approximate steepest descent algorithm,in which the performance index is the mean square error.

OgT = mgT(ngTOg + ogT)mpqr = 0,1, … r − 1 (2.45)

M is the number of layers in the network. The neurons in the first layer receive external inputs OA = t which provides the starting point for (1). The output of the neurons in the last layer are considered the network outputs

O = Og (2.46)

The steepest descent algorithm for approximate mean square error is

ngu,v(w + 1) = ngc(w)−∝ xYxyz,| (2.48)

Where α is the learning rate

Fig 2.9: three layer network (from M. T. Hagan and M. H. Beale, “Neural Network Design.”,2nd edition,ebook

23

oug(w + 1) = og(w) − ~Y~9z (2.49)

xYxy,|z = xYxz ∗ xzxyz,| (2.50)

xYx9z = xYxz ∗ xzx9z (2.51)

jg = xYxz (2.52)

Zug = ∑ hu,vgOvg! + ougz,Sv (2.53)

xzxy,|z = Ovg!, xz

x9z = 1 (2.54)

Approximate steepest algorithm is

hu,vg(w + 1) = hu,vg(w)−∝ jugOv g! (2.56)

oug(w + 1) = oug(w) − jug (2.57)

In matrix form, this becomes

og(T) = og()−∝ jg (2.58)

Afterwards comes computing the sensitivities Sm, which requires application of chain rule and gives us the term back propagation, because it describes a recurrence relationship in which the selectivity at layer m is computed from the sensitivity at layer m+1

ZgTZv g =

ZgT

Zg ZgTZg ZgT

ZgjgZgTZg ZgT

Zg ZgTZgjrZzS gT

Zg ZzS gTZg ZzSgT

Zz g

2.9.3Unsupervised Learning Algorithm

In unsupervised learning, the weights and biases are modified in response to network inputs only. There are no target outputs available. At first glance this might seem to be impractical. How can you train a network if you don’t know what it is supposed to do? Most of these algorithms perform some kind of clustering operation. They learn to categorize the input patterns into a finite number of classes. This is especially useful in such applications as vector quantization. A good example of unsupervised learning algorithm is the self-organized map function[27].

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2.9.3.1 Self Organized Map Function

In order to emulate the activity bubbles of biological systems, without having to implement the nonlinear on-centre/off-surround feedback connections, Kohonen designed the following simplification. His self-organizing feature map (SOFM) network first determines the winning neuron using the same procedure as the competitive layer. Next, the weight vectors for all neurons within a certain neighbourhood of the winning neuron are updated using the Kohonen rule,

h() = h( − 1)+∝ .() − h( − 1)/

= (1−∝)uh( − 1)+∝ () ∈ u∗([), (2.59)

Where the neighbourhood contains the indices for all of the neurons that lie within a radius of the winning neuron ∗:

([) = .c, [uv [/. When a vector is presented, the weights of the winning neuron and its neighbours will move towards. The result is that, after many presentations, neighbouring neurons will have learned vectors similar to each other.To demonstrate the concept of a neighbourhood, consider the two diagrams shown in Figure 10. The left diagram illustrates a two-dimensional neighbourhood of radius around neuron. The right diagram showsa neighbourhood of radius .The definition of these neighbourhoods would be

We should mention that the neurons in an SOFM do not have to be arranged in a two-dimensional pattern. It is possible to use a one-dimensional arrangement, or even three or more dimensions. For a one-dimensional SOFM, a neuron will only have two neighbours

Fig 2.10: Self Organising Map Neighbourhoods

25

within a radius of 1 (or a single neighbour if the neuron is at the end of the line). It is also possible to define distance in different ways. For instance, Kohonen has suggested rectangular and hexagonal neighbourhoods for efficient implementation. The performance of the network is not sensitive to the exact shape of the neighbourhoods.

The diagram in the left margin shows the initial weight vectors for the feature map. Each three-element weight vector is represented by a dot on the sphere. (The weights are

normalized therefore they will fall on the surface of a sphere.) Dots of neighbouring neurons are connected by lines so you can see how the physical topology of the network is arranged in the input space. The diagram to the left shows a square region on the surface of the sphere. We will randomly pick vectors in this region and present them to the feature map. Each time a vector is presented, the neuron with the closest weight vector will win the competition. The winning neuron and its neighbours move their weight vectors closer to the input vector (and therefore to each other). For this example we are using a neighbourhood with a radius of 1.The weight vectors have two tendencies: first, they spread out over the input space as more vectors are presented; second, they move toward the weight vectors of neighbouring neurons. These two tendencies work together to rearrange the neurons in the layer so that they evenly classify the input space.

2.9.4 Clustering

In clustering problems, you want a neural network to group data by similarity. For example, market segmentation can be done by grouping people according to their buying patterns, data

Fig 2.11: Self-Organizing Feature Map (M. T. Hagan and M. H. Beale, “Neural Network Design.”,2nd edition,ebook

26

mining can be done by partitioning data into related subsets, and bioinformatics analysis can be done by grouping genes with related expression patterns. In clustering problems, we generally don’t have a set of network targets available, so clustering networks are trained by unsupervised training algorithms. Instead of training a network to produce a desired response, we want to analyze a data set to look for hidden patterns. There are many application areas for clustering. It is widely used in data mining, in which we analyze large data sets to identify similarities within subsets of the data. It is used in city planning, when town councils apportion regions of the city into areas of similar home type and land usage. It is used in image compression, in which a small set of prototype sub-images are identified and combined to represent a large collection of images[27]. It is used in speech recognition systems, in which speakers are clustered into categories in order to simplify the problem of speaker-independent recognition. Clustering is used by marketers to identify distinct groups in their customer bases. It has also been used to organize large bibliographic data bases so that related material can be quickly accessed. The neural network that we will use in this application is the self-organizing feature map (SOFM). This clustering network has a unique attribute that enables us to visualize large data sets in many dimensions.

CHAPTER THREE

METHODOLOGY FOR RESEARCH

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The supervised and unsupervised learning algorithm of neural networks discussed in previous chapter is used in the implementation of the fault detection and fault location as well as adaptive auto reclosure scheme. The methodology of this research describes in details the neural network based approach for fault detection, fault location, fault classification and adaptive auto reclosure.

3.1 Power System under Consideration

The transmission line network considered in this research is the Onistha, Benin, and New Haven 330kV transmission network. A model of this work is simulated using Matlab Simulink R2014a. The transmission lines are represented by four distributed parameters, two sources, one at the sending end and another at the receiving end. The sending end source consists of a synchronous generator and a three phase step-up transformer. The synchronous generator gives an output of 13.8kV, the step up transformer steps it up to a peak value of 346kV for transmission to Onistha transmission station, which is used as the reference station for this model supplying Benin T/S and New Haven T/S respectively. Each T/S is assigned a particular load according to protocols by the electricity regulatory body of Nigeria. This load parameter used in the simulation is ascertained by calculating the average MW hourly reading taken from the bus data log book at the Onistha T/S. A 3-ϕ VI measurement block from the Simulink power library is used to represent the several buses used in the simulated network. A 3-ϕ circuit breaker block also used but modelled with parameters gotten from the New Haven T/S. A Fault block is used to induce different faults at varied fault resistance however the same fault inception time was used for the entire simulation. The summary of collated data used in the simulation are listed below, with figures represented as appendix A, B, C, D;

Line data Bus data Generator data Single line diagram of each network considered Transformer data Fault data

3.2Data Pre-processing using Fast Fourier Transform

The Discrete Fourier transform of a sequence or it’s inverse. Fourier analysis converts a signal from its original domain (often time or space) to a representation in the frequency domain and vice versa. An F.F.T rapidly computes such transformations by factorizing the D.F.T matrix into a product of sparse (mostly zero) factors. As a result, it manages to reduce its complexity of computing the D.F.T from Ѻ (n2) which arises if one simply applies the definition of D.F.T, to Ѻ (ZpZ), where n is the data size. Many F.F.T algorithms are also much more accurate than evaluating the D.F.T definition directly as discussed below.

Let A, … . . , ! be the complex numbers. The D.F.T formula is given by

28

= !l!A

By far the most commonly used F.F.T is the Cooley-Turkey algorithm[28]. This is a divide and conquer algorithm that recursively breaks down a D.F.T of any composite size N=N1N2 into many smaller D.F.T of sizes N1 and N2 along with Ѻ (N) multiplications by complex roots of unity. The known use of Cooley-Turkey algorithm is to divide the transform into two pieces of size N/2 at each step, and is therefore limited to power of two sizes, but any factorization can also be used in general. The F.F T algorithm is used to obtain the voltage and current magnitudes of each phase; the output of the F.F.T is scaled accordingly and used as input to the neural network. Fig 12 shows the waveform obtained for voltage and current when a line to ground fault is introduced on the line. Fig 13 shows the F.F.T window displaying the magnitudes with respect to fundamental and harmonic frequencies.

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Time (seconds)

data

Time Series Plot:

Fig 3.1 Fault Current Graph of A-G fault at 50Km, Fault Resistance 30ohm Zone 1

29

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Time (seconds)

data

Time Series Plot:

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

-0.2

0

0.2

Selected signal: 10 cycles. FFT window (in red): 3 cycles

Time (s)

Fig 3.2 voltage signal of A-G fault at 50Km, fault resistance 30ohm zone 1

Fig 3.3 F.F.T analysis for voltage waveform after A-G fault

30

3.3 Overview of the Training Process

Two important steps in the application of neural networks for any purpose are training and testing. Training is the process by which the neural network learns from the inputs and updates its weight accordingly. In order to train the neural network, we need a set of data called training data which is a set of input output pairs fed into the network. Thereby, we teach the neural network what the output should be, when that particular input is fed into it. The ANN slowly learns the training set and slowly develops an ability to generalize upon this data and will eventually be able to produce an output when a new data is fed into it. During the training process, the neural network’s weights are updated with the prime goal of minimizing the performance function. This performance function can be user defined, but usually feed-forward neural networks employ Mean Square Error as the performance function and the same is adopted throughout this work. The outputs, depending upon the purpose of the neural network might be fault detection, the type of fault or the location of the fault on the transmission line.

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

0

0.2

0.4

0.6

0.8

Selected signal: 10 cycles. FFT window (in red): 3 cycles

Time (s)

Fig 3.4 F.F.T analysis of current waveform after fault

31

For the task of training the neural networks for different stages, sequential feeding of input and output pair has been adopted. In order to obtain the training set, the fault distance and fault resistance are varied simultaneously for each type of fault. A total of 426 fault samples are used in this implementation throughout this work.

3.4 Overview of Testing Process

The next important step to be performed before the application of artificial neural networks is to test the trained network. Testing the neural network is very important in order to make sure the trained network can generalize well and produce desired outputs when new data is presented to it.

There are several techniques used to test the performance of a trained network, one of such technique is to plot the best linear regression fit between the actual neural networks and desired targets. Analysing the slope of this line gives us an idea on the training process. Ideally the slope should be 1. Also, the correlation coefficient(r), of the outputs and the targets measures how well the ANN’s outputs track the desired targets. The closer the value of ‘r’ is, to 1, the better the performance of the neural network. Another technique employed to test the neural network is to plot the confusion matrix and look at the actual number of cases that have been classified positively by the neural network. Ideally this percentage is 100 which means there has been no confusion in the classification process hence if the confusion matrix indicates very low positive classification rates, it indicates that the neural network might not perform well. The last and very obvious means of testing the neural network is to present new data different from the dataset used for the training process. If the average percentage error in the A.N.N’s output is acceptable, the neural network has passed the test and can be readily used for future use. The flow chart in fig 3.5 below illustrates the algorithm adopted for fault diagnostic using neural network for this work.

32

Receive Voltage &

current Values

Data pre-processing using FFT

Fault on Phase C Fault on Phase B Fault on Phase C

ANN Fault Classifier

Single Phase Fault

Phase-Phase Fault

Line-Line

Ground Fault

Three Phase Fault

No Fault

Three Phase

Fault Location

Line-Line-

Ground ANN

Fault Location

Phase-Phase ANN Fault

Location

Single Phase ANN Fault

Location

Fault Location

Fig 3.5 flowchart of ANN Fault Diagnostic Algorithm

33

3.5 Performance Evaluation

Neural networks represent a technology that is at the mercy of the data. The training data must span the full range of the input space for which the network will be used. The amount of data required depends on the complexity of the underlying problem case we are trying to implement. For the model in this research, the fault cases has many inflexion points thus the need for large amount of data. The performance function for pattern recognition is cross entropy given by the expression

() = − ∑ ∑ u,Z 7,, f¡ (3.1)

Fig 5-6 represents the confusion matrix and receiver operating characteristics plot realised after the training of the network Minimizing cross entropy error results in good classification, lower values are better zero means no error. Confusion matrix is a table whose columns represent the target class and whose rows represent the output class. The diagonal columns represent good classification as shown in fig 7. The values for C.E show the neural network for the fault detector (pattern recognition) is adequate.

In the case of the fault locator, a useful tool used to analyse the neural network is a regression between the trained network outputs and the corresponding targets, this is expressed as

O = r + ¢ + £ (3.2)

M=slope, C=offset, is the target value, Ois a trained network output, £ is the residual

error of the regression. In addition to computing the regression coefficients, the correlation coefficient between the andO which is known as R value.

i = ∑ . − /(O −¡ O¥) (3.3)

Where j = ¦ ¡! ∑ ( − ) §¡! (3.4)

and j7 = ¦ ¡! ∑ (O − O¥¡ ) (3.5)

The square of the correlation can also be used.

34

3.6Clustering with Self Organized Neural Network Algorithm

The major advantage of neural network is that it can take into account several features of the input signals simultaneously and compare the patterns according to their mutual similarity instead of the ‘hard’ thresholds.

There are several types of neural networks used for power system protection the (M.L.P) neural network with back propagation algorithm is one that was dominantly used in the power system studies since it can be easily realized. Dealing with large input set, selecting the number of hidden neurons, and facing convergence problems are inherent issues when applying M.L.P neural networks.

Clustering is the process of training a neural network on patterns so that the network comes up with its own classifications according to pattern similarity and relative topology. The

Collect/Pre Process

Data

Select Network

Type/Architecture

Select Training

Algorithm

Initialize Weights &

Train Network Analyze Network

Performance

Analyze Network

Performance

Fig 3.6 Block Diagram of Neural Network Training Procedure

35

scaled fault data used for the fault detector and fault locator is used as input to the network. The self-organised learning algorithm is utilized; no target is used for this algorithm. Several plots are used to test the generalization properties of the trained network such as S.O.M topology, S.O.M neighbour distances, S.O.M input planes, S.O.M sample hits, S.O.M weight positions.

3.7Neural Network Methodology for Adaptive Reclosure Scheme

Adaptive auto reclosing on overhead lines is the ability to distinguish between a transient or permanent fault following a short circuit and then issue a reclose signal if and only if the transient fault no longer exists. Permanent fault has a constant resistance since it usually involve a physical short circuit due to vegetarian, downed line, or broken conductor. In contrast, a transient fault involves arcing across the arcing horns and is usually caused by lightning or adverse weather conditions.

The behaviour of the arc occurs in two stages, before and after the circuit breaker opens, the primary arc is a heavy current, high energy arc, fed by the short circuit on the associated phase conductor. The secondary arc initiates after the CB opens, following the single phase breaking opening, the healthy phases( those that remain energized) mutually couple and drive a highly non-linear lower current secondary arc on the faulted phase. When this arc finally extinguishes, it is safe to reclose the CB and brings the line back into normal service.

The notable difference between second arcing and a permanent fault gives rise to the possibility of robust diagnosis between the two cases. However, there is acomplex interplay of parameters that determine voltage signatures someof which cannot be known pre-fault. Researchers have addressed this problem a number of ways, including signal processing and neural networks[15], fuzzy logic[29] and wavelet transforms[12]and straightforward numerical techniques[15][30]. However only the A.N.N have been deployed on a real system and documented in the literature. The flow chart displayed in fig 3.7 shows the algorithm carried out to achieve the neural network adaptive auto reclosure.

36

Voltage & Current Input

Pre-Process Input Samples

Using FFT

Single Phase

Fault

Phase to phase

Fault

Line-Line Ground

Fault

Three Phase

Fault

No Fault

Adaptive Fault Classifier

If Fault

Time >0.14s Do Not Reclose Reclose Yes No

Yes

Yes

Yes

Yes

No

No

No

No

Figure 3.7 Flow Chart of Adaptive Auto Reclosure Scheme

37

3.8Arc Modelling in Adaptive Auto Reclosing Schemes

In AA schemes,it is necessary to determine whether a fault is transient or permanent. Transient faults exhibit arcing behaviour with high frequency signatures due to dynamic resistance.

Arcing behaviour can be described by the primary and secondary stages. The primary arc is in the period before CB opens and is due to fault current flowing from energised phase to ground. The lower current secondary is saturated by the mutual coupling between the faulted and healthy phases and only present when one or more of the phases remain energised.

The behaviour of both arcs are governed by time varying conductance, and can be described by the dynamic equation for unconstrained arcs in the air.

¨©¨ = ª (« − ) (3.6)

G is stationary arc conductance; g is the time dependent arc conductance and ¬is the time constant. During a single phase to ground, the current and voltage waveforms were measured at the O.H.L terminating bus bars in front of the breakers. Reduced current is due to much less real power transfer on both circuits.

38

CHAPTER FOUR

EXPERIMENTAL RESULTS AND DISCUSSION

4.1Structure and Training of Neural Fault Detector

The fault detection task can be formulated as a pattern classification problem. The feed forward multilayer network used to classify the input dataset into fault/no-fault response using the back propagation algorithm. The datasets for the input are six consecutive samples sampled at 2KHz corresponding to different types of faults (a-g ,b-g, c-g, a-b-g, a-c-g, b-c-g, a-b-c, a-b-c-g) where a, b, c are related to the different phases and g corresponds to ground at various locations and fault resistance on the three zones .the distance of the lines on the three zones are; line 1 spans a length of 95km,line2 137km,and line 3 95km. For this simulation, the reach settings of the relays at the different zones are; zone 1(91% of line 1), zone 2(99.5% of line2), zone 3(100% of line 3). Fault resistance (3Ω-62Ω).

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-3

-2

-1

0

1

2

3

Time (seconds)

Cur

rent

Time Series Plot:

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Time (seconds)

Vol

tage

Time Series Plot:

Fig 4.1 current signal of B-C fault, 30Km, fault resistance 48ohms zone 2

Fig 4.2 Voltage signal A-B-C Fault at 88Km, fault resistance 60 ohms zone 3

39

Figs 4.1-4.3 shows the time plot waveform of the voltage and current post fault condition for varied fault types at different protection zones in the transmission network modelled in the Matlab Simulink Environment. It is easily observed, the sag and spikes of faulted phase in relation to other phases. The effect of the faulted phase on other phases is as a result of mutual coupling between healthy and faulted phase. This signal is imported to the Matlab workspace for use in the F.F.T window to get the magnitudes of individual phases for current and voltage signals at fundamental frequency as well as other harmonic frequencies. The maximum frequency set for total harmonic distortion calculations is set at Nyquist frequency. The Nyquist theorem states that the frequency for T.H.D calculations should be at least twice the maximum frequency of the signal in question.

The next step is to divide the total dataset to be used for the training process as well as testing data. The data samples were divided as such; 70% of the input dataset to be used for training, 15% to be used for validation and 15% to be used for testing. The output of the neural network is just a yes or a no (1 or 0) depending on whether or not a fault has been detected[31].

The Levenberg-Marquardt optimization technique was employed for this classification problem[3]. Three fault detectors are designed using current values alone, voltage values alone and voltage and current values combination. The figures 4.4-4.5b show the performance plots for the fault detector neural network.

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Time (seconds)

Cur

rent

Time Series Plot:

Fig 4.3 Current Signal A-B-C fault at 88Km, fault resistance 60ohms zone 3

40

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

False Positive Rate

True Pos

itive

Rate

Training ROC

Class 1

Class 2Class 3

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

False Positive Rate

True Pos

itive

Rate

Validation ROC

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

False Positive Rate

True Pos

itive

Rate

Test ROC

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

False Positive Rate

True Pos

itive

Rate

All ROC

1 2 3

1

2

3

1434.1%

00.0%

00.0%

100%0.0%

00.0%

1229.3%

00.0%

100%0.0%

00.0%

00.0%

1536.6%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

Target Class

Outp

ut C

lass

Training Confusion Matrix

1 2 3

1

2

3

333.3%

00.0%

00.0%

100%0.0%

00.0%

333.3%

00.0%

100%0.0%

00.0%

00.0%

333.3%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

Target Class

Outp

ut C

lass

Validation Confusion Matrix

1 2 3

1

2

3

333.3%

00.0%

00.0%

100%0.0%

00.0%

555.6%

00.0%

100%0.0%

00.0%

00.0%

111.1%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

Target Class

Outp

ut C

lass

Test Confusion Matrix

1 2 3

1

2

3

2033.9%

00.0%

00.0%

100%0.0%

00.0%

2033.9%

00.0%

100%0.0%

00.0%

00.0%

1932.2%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

Target Class

Outp

ut C

lass

All Confusion Matrix

Fig 4.4 Receiver Operating Characteristics of fault detector network using current values

Fig 4.4b Confusion Matrix of Fault Detector Network zone 1 current values

41

4.2 Discussion of Figures for A.N.N. Fault Detector

The figures 4.4-4.5b show receiver operating characteristics and confusion matrix for A.N.N. fault detector using pre-processed current values alone as well as combining current and voltage values as input data. Confusion matrix is a table whose columns represent the target class and whose rows represent the output class. The green squares show the level of accurate responses and red squares show the level of incorrect responses. A high number in green squares shows a high accuracy of classification and the zero value signifies no

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

False Positive Rate

True Positive Rate

Training ROC

Class 1

Class 2Class 3

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

False Positive Rate

True Positive Rate

Validation ROC

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

False Positive Rate

True Positive Rate

Test ROC

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

False Positive Rate

True Positive Rate

All ROC

1 2 3

1

2

3

1126.8%

00.0%

00.0%

100%0.0%

00.0%

1639.0%

00.0%

100%0.0%

00.0%

00.0%

1434.1%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

Target Class

Outp

ut C

lass

Training Confusion Matrix

1 2 3

1

2

3

666.7%

00.0%

00.0%

100%0.0%

00.0%

222.2%

00.0%

100%0.0%

00.0%

00.0%

111.1%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

Target Class

Outp

ut C

lass

Validation Confusion Matrix

1 2 3

1

2

3

333.3%

00.0%

00.0%

100%0.0%

00.0%

222.2%

00.0%

100%0.0%

00.0%

00.0%

444.4%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

Target Class

Outp

ut C

lass

Test Confusion Matrix

1 2 3

1

2

3

2033.9%

00.0%

00.0%

100%0.0%

00.0%

2033.9%

00.0%

100%0.0%

00.0%

00.0%

1932.2%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

Target Class

Outp

ut C

lass

All Confusion Matrix

Fig 4.5b: Confusion Matrix for fault detector using voltage and current values

Fig 4.5 Receiver Operating Characteristics of Fault Detector using Current and Voltage values

42

misclassification occur. Figure 4.4.b and 4.5b shows 100% in the green squares showing maximum classification and zero value in the red squares showing no misclassification of the A.N.N. fault detector on each phase. The R.O.C. factor is a plot of receiver operating characteristics. The coloured line in each axis is a deviation between the output and target class. The figure 4.4a and 4.5a shows no deviation thus the output corresponds perfectly to the target output fed to it.

4.3Structure and Training of Fault Locator

The fault location task can be formulated as an approximation function problem[31][32]. The back propagation algorithm was utilized in training the network; the inputs are the magnitudes of voltage and current phasors corresponding to fundamental frequency of 50Hz. The number of inputs to the network as well as neurons in the input and hidden layers are selected empirically through trial and error procedure on various network configurations in order to obtain appropriate network with satisfactory performance. The network for fault location was trained using Levenberg-Marquardt and Scaled Conjugate Gradient algorithm as optimization techniques. Three fault locators­7, ­9, ­: were designed by using scaled current values, voltage values, and voltage and current combination. The output consists of one neuron to estimate fault location. The simulated fault samples used for fault detector forms the input data sets for fault locator network. The performance graph of the trained network are shown in figures 4.6-4.16.

0.2 0.4 0.6 0.8

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Target

Output ~

= 1*Target + -0

.005

7

Training: R=0.9998

Data

Fit

Y = T

0.4 0.5 0.6 0.70.35

0.4

0.45

0.5

0.55

0.6

0.65

0.7

0.75

Target

Output ~

= 1.1*Target + -0

.028

Validation: R=1

Data

Fit

Y = T

0.2 0.4 0.60.1

0.2

0.3

0.4

0.5

0.6

0.7

Target

Output ~

= 1*Target + -0

.033

Test: R=0.99999

Data

FitY = T

0.2 0.4 0.6 0.8

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Target

Output ~

= 1*Target + -0

.013

All: R=0.99879

Data

FitY = T

Fig 4.6 Plot of regression fit for fault location on zone 1

43

0

0.5

1

1.5

2

2.5

3

3.5

4

Error Histogram with 20 Bins

Inst

ance

s

Errors = Targets - Outputs

-0.0

1449

-0.0

12

-0.0

095

-0.0

0701

-0.0

0452

-0.0

0202

0.00

0472

0.00

2966

0.00

5461

0.00

7955

0.01

045

0.01

294

0.01

544

0.01

793

0.02

043

0.02

292

0.02

541

0.02

791

0.03

04

0.03

29

Training

ValidationTest

Zero Error

0.1 0.2 0.3 0.4 0.5 0.6

0.1

0.2

0.3

0.4

0.5

0.6

Target

Outp

ut ~

= 1*

Tar

get

+ -6

.2e-

05

Training: R=1

Data

Fit

Y = T

0.44 0.46 0.48 0.5 0.52 0.54

0.44

0.46

0.48

0.5

0.52

0.54

Target

Outp

ut ~

= 1.

2*Tar

get

+ -0

.12

Validation: R=0.99811

Data

Fit

Y = T

0.3 0.4 0.5 0.6

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0.65

Target

Outp

ut ~

= 1*

Tar

get

+ 0

.009

5

Test: R=0.99985

Data

FitY = T

0.1 0.2 0.3 0.4 0.5 0.6

0.1

0.2

0.3

0.4

0.5

0.6

Target

Outp

ut ~

= 1*

Tar

get

+ -0

.000

53

All: R=0.99952

Data

FitY = T

Fig 4.7 Error Histogram of fault locator using current values on zone 1

Fig 4.8 Regression plot for fault locator using current values on zone 2

44

0

1

2

3

4

5

6

7

8

9

10

11Error Histogram with 20 Bins

Inst

ance

s

Errors = Targets - Outputs

-0.0

1467

-0.0

1305

-0.0

1143

-0.0

0982

-0.0

082

-0.0

0658

-0.0

0497

-0.0

0335

-0.0

0173

-0.0

0011

0.00

1502

0.00

3119

0.00

4736

0.00

6353

0.00

7969

0.00

9586

0.01

12

0.01

282

0.01

444

0.01

605

Training

ValidationTest

Zero Error

0.2 0.4 0.6 0.8

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Target

Outp

ut ~

= 1*

Tar

get

+ -4

.1e-

05

Training: R=1

Data

Fit

Y = T

0.2 0.25 0.3 0.35 0.4 0.450.2

0.25

0.3

0.35

0.4

0.45

Target

Outp

ut ~

= 1*

Tar

get

+ 0

.012

Validation: R=0.99946

Data

Fit

Y = T

0.2 0.3 0.4 0.5

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Target

Outp

ut ~

= 1.

1*Tar

get

+ -0

.025

Test: R=0.9996

Data

FitY = T

0.2 0.4 0.6 0.8

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Target

Outp

ut ~

= 1*

Tar

get

+ 0

.001

8

All: R=0.99949

Data

FitY = T

Fig 4.9 Error Histogram for fault location using current values on zone 2

Fig 4.10 Regression fit for fault locator using current values on zone 3

45

0.2 0.4 0.6 0.8

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Target

Outp

ut ~

= 1*

Tar

get

+ -0

.000

62

Training: R=0.99998

Data

Fit

Y = T

0.05 0.1 0.15 0.20.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

Target

Outp

ut ~

= 0.

78*T

arget

+ 0

.047

Validation: R=0.99815

Data

Fit

Y = T

0.25 0.3 0.35 0.40.24

0.26

0.28

0.3

0.32

0.34

0.36

0.38

0.4

Target

Outp

ut ~

= 1.

1*Tar

get

+ -0

.035

Test: R=0.99928

DataFitY = T

0.2 0.4 0.6 0.8

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Target

Outp

ut ~

= 0.

99*T

arget

+ 0

.009

6

All: R=0.99916

DataFitY = T

0

1

2

3

4

5

6

7

8

9

Error Histogram with 20 Bins

Inst

ance

s

Errors = Targets - Outputs

-0.0

3457

-0.0

3207

-0.0

2958

-0.0

2709

-0.0

2459

-0.0

221

-0.0

196

-0.0

1711

-0.0

1462

-0.0

1212

-0.0

0963

-0.0

0713

-0.0

0464

-0.0

0215

0.00

0347

0.00

2841

0.00

5335

0.00

7829

0.01

032

0.01

282

Training

ValidationTest

Zero Error

Fig 4.11 Regressions fit for fault locator using voltage and current values on zone 1

Fig 4.12: Error histogram for fault locator using voltage and current values on zone 1

46

0

2

4

6

8

10

12

14

16

18

20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

FAULT LOCATOR PLOT

INPUT VALUES DISTANCE

0.1 0.2 0.3 0.4 0.5 0.6

0.1

0.2

0.3

0.4

0.5

0.6

Target

Outp

ut ~

= 0.97

*Tar

get

+ 0.004

7

Training: R=0.99976

Data

Fit

Y = T

0.1 0.2 0.3 0.4 0.5

0.1

0.2

0.3

0.4

0.5

Target

Outp

ut ~

= 0.93

*Tar

get

+ 0.019

Validation: R=0.99992

Data

Fit

Y = T

0.2 0.3 0.4 0.50.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Target

Outp

ut ~

= 0.73

*Tar

get

+ 0.12

Test: R=0.99707

Data

FitY = T

0.1 0.2 0.3 0.4 0.5 0.6

0.1

0.2

0.3

0.4

0.5

0.6

Target

Outp

ut ~

= 0.93

*Tar

get

+ 0.027

All: R=0.99529

Data

FitY = T

Fig 4.13 Regression fit for fault locator using voltage and current values in zone 2

Fig 4.12b Output Plot for Fault Locator using current and voltage values Zone 1

47

0

1

2

3

4

5

6

Error Histogram with 20 Bins

Instan

ces

Errors = Targets - Outputs

-0.083

36

-0.078

15

-0.072

94

-0.067

73

-0.062

52

-0.057

32

-0.052

11

-0.046

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-0.041

69

-0.036

48

-0.031

27

-0.026

07

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86

-0.015

65

-0.010

44

-0.005

23

-2.4e-05

0.00

5184

0.01

039

0.01

56

Training

ValidationTest

Zero Error

0.2 0.4 0.6 0.8

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Target

Outp

ut ~

= 1*

Tar

get

+ -0

.000

65

Training: R=1

Data

Fit

Y = T

0.45 0.5 0.55 0.6 0.65 0.70.45

0.5

0.55

0.6

0.65

0.7

Target

Outp

ut ~

= 0.

93*T

arget

+ 0

.054

Validation: R=0.99906

Data

Fit

Y = T

0.1 0.2 0.3 0.4 0.5 0.6

0.1

0.2

0.3

0.4

0.5

0.6

Target

Outp

ut ~

= 1.

1*Tar

get

+ -0

.045

Test: R=0.99923

Data

FitY = T

0.2 0.4 0.6 0.8

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Target

Outp

ut ~

= 1*

Tar

get

+ -0

.006

3

All: R=0.99914

Data

FitY = T

Fig 4.14: Error histogram for fault locator using voltage and current values in zone 2

Fig 4.15: Regression fit for Fault Locator using Voltage and Current Values on Zone 3

48

4.3.1 Discussion of Plots from A.N.N. Fault Locator

Fig 4.7-4.16 are plots of error histogram and regression plots result from the algorithm carried out using A.N.N. to locate faults.Regression plot is a plot used to validate the network after training is done. It shows the relationship between outputs of the A.N.N. and target output fed to the network. The solid line represents the best linear regression fit between output and targets. The R value of 1 shows a perfect linear relationship. Fig 4.8, 4.10, 4.11, 4.13, 4.15 shows R values within 0.995-0.999 which means an excellent result is obtained from validating the trained network. Fig 4.7, 4.9, 4.12, 4.14 and 4.16 are plots of error histogram. This graph plots instances versus errors. The error for each training, validation and test data sample are all below 0.002, the zero errors occurs for the highest amongst other errors showing lots of inputs are validated. Fig 4.12b is a plot of voltage and current input signals versus estimated output derived from the A.N.N. fault locator algorithm, the estimated output are locations function of distances in Km.

0

5

10

15

Error Histogram with 20 Bins

Inst

ance

s

Errors = Targets - Outputs

-0.0

2413

-0.0

2064

-0.0

1714

-0.0

1365

-0.0

1015

-0.0

0666

-0.0

0316

0.00

0331

0.00

3825

0.00

732

0.01

081

0.01

431

0.01

78

0.02

13

0.02

479

0.02

829

0.03

178

0.03

528

0.03

877

0.04

227

Training

ValidationTest

Zero Error

Fig 4.16: Error histogram for fault locator using voltage and current values on zone 3

49

4.4Simulation Results for Fault Classification via Self Organizing Map Function

0 2 4 6 8 10-1

0

1

2

3

4

5

6

7

8

9

1 0 1 2 2 2 2 1 2 2 2

0 1 1 1 2 2 3 1 1 1 6

1 2 0 0 3 2 4 3 1 1 3

0 1 2 0 2 2 2 2 1 2 1

1 3 0 1 1 0 2 1 2 1 2

3 0 1 0 0 1 5 4 1 1 1

1 1 0 2 2 4 4 1 2 2 1

1 2 1 5 3 5 0 1 3 2 4

4 1 1 1 3 1 2 0 5 2 2

1 1 2 3 1 1 2 1 0 3 2

3 5 2 3 4 4 4 1 4 0 1

Hits

0 2 4 6 8 10-1

0

1

2

3

4

5

6

7

8

9

SOM Neighbor Weight Distances

Fig 4.17: plot of S.O.M Sample Hits

Fig 4.18: plot of S.O.M Neighbour weight distances

50

4.4.1 Discussion of Results of Fault Classification via Self Organising Map Function

Fig 4.17 shows a sample hit plot. This plot shows how many samples fall into each clusters which represents different fault classes. It can be viewed that there are four major clusters from the plot. Each cluster represent four fault types (L-G, L-L, LLL, LLLG) Faults. Fig 4.18 shows the neighbour weight distance plot consisting of 100 neurons with bright colours between the neurons whose clusters are similar and dark clusters which are farther apart. The neighbour weight distance in fig 4.18 shows major bright colours showing similarity between A-G, B-G, C-G faults since this clusters have little difference in their fault values after simulation. This is also extended to A-B-G, A-C-G, B-C-G faults as their clusters are closely related since they fall within double line to ground (LLG) Faults. The dark features shows few input samples that are not properly classified. Fig 4.19 is a plot of the self-organising map input planes confirming the observation made in the neighbour weight planes; demonstrating input samples dependent on another.

0 5 10

0

2

4

6

8

Weights from Input 1

0 5 10

0

2

4

6

8

Weights from Input 2

0 5 10

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6

8

Weights from Input 3

0 5 10

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8

Weights from Input 4

0 5 10

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6

8

Weights from Input 5

0 5 10

0

2

4

6

8

Weights from Input 6

Fig 4.19: plot of S.O.M input planes

51

4.5Simulation Results for Adaptive Auto-Reclosure Scheme

The example fault waveforms are generated via the Matlab Simulink to model the behaviour of transmission line. In the simulation,transient and permanent faults are simulated. Transient faults are simulated using a realistic arc model, in particular the secondary arc model which develops once the faulted phase line breakers have opened. In simulation of permanent faults, fault arc resistances between (200-250) ohms is used. In any fault study,voltages and currents seen at the end of a faulted line depends on a number of different system parameters as discussed in the previous chapter. Fig 4.20 and 4.21 shows a transient and permanent fault time series for the 330kV system generated using Matlab Simulink. At point 1 on fig 4.20, the transient arcing fault occurs. The circuit breaker closes at point 2 and the secondary arc begins, which eventually extinguishes leaving a plain permanent sinusoid. In fig 4.21, the same sequence occurs except the resistance of the fault is fixed. After point 2, the CB operates and after a short period of transients, a bare sinusoid occurs. From the figures, the post arcing period are both plain sinusoids but belong to different classes in the problem space. To distinguish these classes, a feed forward network is designed as a pattern classifier to distinguish the classes; the output of this network is fed into another A.N.N pattern classifier network to implement reclosing. Reclosing is done if the A.N.N deems the fault to be transient.

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

-5000

0

5000

Ub: Three-Phase Fault/Fault A

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

-5000

0

5000

Ub: Three-Phase Fault/Fault B

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

-5000

0

5000

Ub: Three-Phase Fault/Fault C

Fig 4.20 Transient fault waveform, A-G fault, fault resistance 30ohm, Distance

1 2 Secondary Arc

52

The results presented here are results of the neural network pattern classifier. First the network is trained to act as a fault phase detector/ selector then the second neural network works as a fault classifier, in the sense that it classifies the fault into two classes; transient or permanent fault which informs the decision on whether or not to reclose.

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

-5000

0

5000

Ub: Three-Phase Fault/Fault A

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

-5000

0

5000

Ub: Three-Phase Fault/Fault B

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

-5000

0

5000

Ub: Three-Phase Fault/Fault C

1 2

1

2

1346.4%

00.0%

100%0.0%

00.0%

1553.6%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

Target Class

Output C

lass

Training Confusion Matrix

1 2

1

2

466.7%

00.0%

100%0.0%

00.0%

233.3%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

Target Class

Output C

lass

Validation Confusion Matrix

1 2

1

2

350.0%

00.0%

100%0.0%

00.0%

350.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

Target Class

Output C

lass

Test Confusion Matrix

1 2

1

2

2050.0%

00.0%

100%0.0%

00.0%

2050.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

Target Class

Output C

lass

All Confusion Matrix

Fig 4.22 confusion matrix for fault classifier using voltage and current values

Fig 4.21 Permanent Fault Waveform of A-G Fault Resistance 200ohm, Distance 88Km

1 2

53

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

False Positive Rate

True Pos

itive

Rate

Training ROC

Class 1

Class 2

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

False Positive Rate

True Pos

itive

Rate

Validation ROC

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

False Positive Rate

True Pos

itive

Rate

Test ROC

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

False Positive Rate

True Pos

itive

Rate

All ROC

1 2

1

2

1346.4%

00.0%

100%0.0%

00.0%

1553.6%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

Target Class

Outp

ut C

lass

Training Confusion Matrix

1 2

1

2

350.0%

00.0%

100%0.0%

00.0%

350.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

Target Class

Outp

ut C

lass

Validation Confusion Matrix

1 2

1

2

466.7%

00.0%

100%0.0%

00.0%

233.3%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

Target Class

Outp

ut C

lass

Test Confusion Matrix

1 2

1

2

2050.0%

00.0%

100%0.0%

00.0%

2050.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

Target Class

Outp

ut C

lass

All Confusion Matrix

Fig 4.23: Receiver operating characteristics for fault classifier using voltage and current values

Fig 4.24: Confusion Matrix for fault classifier using voltage and current values

54

4.5.1 Discussion of Results for Adaptive Fault Classifier Plots

Fig 4.22-4.25 are plots of neural network fault detector and adaptive fault classifier (transient and permanent faults). The figures are plots of confusion matrix and receiver operating characteristics. As explained in section 4.2, the confusion matrix in this plot of fig 4.22 and 4.24 shows green squares all with 100% values thus the algorithm created works well as a fault detector. The receiver operating characteristics plot of fig 4.23 and 4.25 shows a perfect line, no deviation between the A.N.N. output and target outputs fed to the network. The straight line is also an indication 100% specificity and 100% sensitivity of the adaptive fault classifier.

4.6 Testing the Neural Network Fault Detection Algorithm

Once the neural network has been trained, its performance is tested by taking three factors into consideration. The first is the plot of confusion matrix. The confusion matrices for training, testing, and validation, and the three kinds of data combined[27]. The network outputs are very accurate, as you can see by the high numbers of correct responses in the green squares and the low numbers of incorrect responses in the red squares as shown in fig 4.26.

The second factor is the plot of the receiver operating characteristics. The coloured lines in each axis represent the ROC curves. The ROC curve is a plot of the true positive rate

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

False Positive Rate

Tru

e Pos

itive

Rat

e

Training ROC

Class 1

Class 2

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

False Positive Rate

Tru

e Pos

itive

Rat

e

Validation ROC

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

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1

False Positive Rate

Tru

e Pos

itive

Rat

e

Test ROC

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

False Positive Rate

Tru

e Pos

itive

Rat

e

All ROC

Fig 4.25: Receiver operating characteristics plot for adaptive reclosure scheme using voltage and current values

55

(sensitivity) versus the false positive rate (1 - specificity) as the threshold is varied. A perfect test from fig 4.29 show points in the upper-left corner, with 100% sensitivity and 100% specificity.

The third test would be to use new data set different from the ones used in the training of the fault detection network. The new data set was gotten from a different model; 132kV with different parameters to input fresh set of values to the 330kV neural network algorithm.

1 2 3

1

2

3

1434.1%

00.0%

00.0%

100%0.0%

00.0%

1434.1%

00.0%

100%0.0%

00.0%

12.4%

1229.3%

92.3%7.7%

100%0.0%

93.3%6.7%

100%0.0%

97.6%2.4%

Target Class

Outp

ut C

lass

Training Confusion Matrix

1 2 3

1

2

3

444.4%

00.0%

00.0%

100%0.0%

00.0%

333.3%

00.0%

100%0.0%

00.0%

00.0%

222.2%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

Target Class

Outp

ut C

lass

Validation Confusion Matrix

1 2 3

1

2

3

222.2%

00.0%

00.0%

100%0.0%

00.0%

333.3%

00.0%

100%0.0%

00.0%

00.0%

444.4%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

Target Class

Outp

ut C

lass

Test Confusion Matrix

1 2 3

1

2

3

2033.9%

00.0%

00.0%

100%0.0%

00.0%

2033.9%

00.0%

100%0.0%

00.0%

11.7%

1830.5%

94.7%5.3%

100%0.0%

95.2%4.8%

100%0.0%

98.3%1.7%

Target Class

Outp

ut C

lass

All Confusion Matrix

Fig 4.26 Confusion matrix plot for testing fault detector using current values

56

10-2

10-1

100

grad

ient

Gradient = 0.022603, at epoch 28

0 5 10 15 20 250

2

4

6

val f

ail

28 Epochs

Validation Checks = 6, at epoch 28

0

20

40

60

80

100

120

140

160Error Histogram with 20 Bins

Inst

ance

s

Errors = Targets - Outputs

-0.7

26

-0.6

432

-0.5

604

-0.4

776

-0.3

948

-0.3

12

-0.2

292

-0.1

464

-0.0

6359

0.01

921

0.10

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0.18

48

0.26

76

0.35

04

0.43

32

0.51

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0.59

88

0.68

16

0.76

44

0.84

72

Training

ValidationTest

Zero Error

Fig 4.27 Plot of training state for testing fault detector using current values

Fig 4.28 Error histogram for testing fault detector using current values

57

0 0.2 0.4 0.6 0.8 10

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0.6

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1

False Positive Rate

Tru

e Pos

itive

Rat

e

Training ROC

Class 1

Class 2Class 3

0 0.2 0.4 0.6 0.8 10

0.2

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1

False Positive Rate

Tru

e Pos

itive

Rat

e

Validation ROC

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

False Positive Rate

Tru

e Pos

itive

Rat

e

Test ROC

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

False Positive Rate

Tru

e Pos

itive

Rat

e

All ROC

10-10

10-5

100

grad

ient

Gradient = 7.5004e-07, at epoch 39

0 5 10 15 20 25 30 350

0.5

1

val f

ail

39 Epochs

Validation Checks = 0, at epoch 39

Fig 4.29 Receiver operating characteristics for testing fault detector using current values

Fig 4.30 Plot of training state for testing fault detector using voltage and current values

58

0

20

40

60

80

100

120

140

160

180

Error Histogram with 20 Bins

Inst

ance

s

Errors = Targets - Outputs

-0.0

099

-0.0

0886

-0.0

0782

-0.0

0678

-0.0

0573

-0.0

0469

-0.0

0365

-0.0

0261

-0.0

0156

-0.0

0052

0.00

0522

0.00

1565

0.00

2607

0.00

365

0.00

4692

0.00

5735

0.00

6777

0.00

782

0.00

8862

0.00

9905

Training

ValidationTest

Zero Error

1 2 3

1

2

3

1741.5%

00.0%

00.0%

100%0.0%

00.0%

1434.1%

00.0%

100%0.0%

00.0%

00.0%

1024.4%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

Target Class

Outp

ut C

lass

Training Confusion Matrix

1 2 3

1

2

3

111.1%

00.0%

00.0%

100%0.0%

00.0%

111.1%

00.0%

100%0.0%

00.0%

00.0%

777.8%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

Target Class

Outp

ut C

lass

Validation Confusion Matrix

1 2 3

1

2

3

222.2%

00.0%

00.0%

100%0.0%

00.0%

555.6%

00.0%

100%0.0%

00.0%

00.0%

222.2%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

Target Class

Outp

ut C

lass

Test Confusion Matrix

1 2 3

1

2

3

2033.9%

00.0%

00.0%

100%0.0%

00.0%

2033.9%

00.0%

100%0.0%

00.0%

00.0%

1932.2%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

Target Class

Outp

ut C

lass

All Confusion Matrix

Fig 4.31 Error histogram for testing fault detector using voltage and current values

Fig 4.32 Confusion matrix for testing fault detector using voltage and current values

59

4.6.1Discussion of Test Results of A.N.N. Fault Detector Algorithm

Figures 4.26-4.32 show plots of simulation results for tests carried out on a 132kV Network model using the A.N.N. fault detection algorithm designed for 330kV network. The inputs of the pre-processed fault voltages and currents are now the new samples generated from the 132kV simulation also carried out on Matlab Simulink environment. The green boxes of fig 4.26 and 4.32 are all in the region of 90%-100% value showing good classification. Fig 4.27 and 4.30 shows training state plot generated after running the algorithm on the 132kV network. Fig 4.27 shows several failed values for different iterations, at iterations 11-16 errors from 1-5 is observed. Iterations 21-30 show error in the range of 1-6. Fig 4.30 is different because the input samples are of current and voltage values. The only error observed occur at iteration 6.

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

False Positive Rate

True Positive Rate

Training ROC

Class 1

Class 2Class 3

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

False Positive Rate

True Positive Rate

Validation ROC

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

False Positive Rate

True Positive Rate

Test ROC

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

False Positive Rate

True Positive Rate

All ROC

Fig 4.33 Confusion matrix for testing fault detector using voltage and current values

60

4.7Test Results for Neural NetworkFault Location Algorithm

10-20

100

1020

grad

ient

Gradient = 1.6281e-11, at epoch 43

10-10

10-5

100

mu

Mu = 1e-10, at epoch 43

0 5 10 15 20 25 30 35 400

2

4

val f

ail

43 Epochs

Validation Checks = 0, at epoch 43

0

2

4

6

8

10

12

14

16

Error Histogram with 20 Bins

Instan

ces

Errors = Targets - Outputs

-0.019

52

-0.014

03

-0.008

55

-0.003

06

0.00

2431

0.00

792

0.01

341

0.01

89

0.02

438

0.02

987

0.03

536

0.04

085

0.04

634

0.05

183

0.05

731

0.06

28

0.06

829

0.07

378

0.07

927

0.08

476

Training

ValidationTest

Zero Error

Fig 4.34 Training state results for testing fault locator using current values

Fig 4.35 Error Histogram for testing fault detector using current values

61

0.1 0.2 0.3 0.4 0.5 0.6

0.1

0.2

0.3

0.4

0.5

0.6

Target

Outp

ut ~

= 1*

Tar

get + 4.1e-13

Training: R=1

Data

FitY = T

0.1 0.2 0.3 0.4 0.5 0.6

0.1

0.2

0.3

0.4

0.5

0.6

Target

Outp

ut ~

= 1*

Tar

get + -0

.003

9

Validation: R=0.99998

Data

FitY = T

0.1 0.2 0.3 0.4 0.5 0.6

0.1

0.2

0.3

0.4

0.5

0.6

Target

Outp

ut ~

= 0.75

*Tar

get + 0.086

Test: R=0.99983

Data

FitY = T

0.1 0.2 0.3 0.4 0.5 0.6

0.1

0.2

0.3

0.4

0.5

0.6

TargetOutp

ut ~

= 0.97

*Tar

get + 0.012

All: R=0.9948

Data

FitY = T

10-10

10-5

100

grad

ient

Gradient = 5.9533e-09, at epoch 5

10-10

10-5

100

mu

Mu = 1e-08, at epoch 5

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

1

2

val f

ail

5 Epochs

Validation Checks = 2, at epoch 5

Fig 4.36 Regression plot for testing fault locator using current values

Fig 4.37 Training state plot for testing fault locator using voltage and current values

62

0

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= 1.1*Target + -0

.015

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FitY = T

0.2 0.4 0.6

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FitY = T

Fig 4.38: Error histogram for testing fault locator using voltage and current values

Fig 4.39 Regression plot after testing fault locator using voltage and current values

63

4.7.1 Discussion of Simulation Results from Testing Fault Location Algorithm

Fig 4.34-4.37 are training state plots for testing the fault location algorithm designed for the 330kV network model on a different 132kV model. In fig 4.37, the error values are present only at numbers 4 & 5 iterations. The number of iterations shows how many iterations before convergence is obtained. The speed of convergence of the neural network training depends on the accuracy of the input values as well as the adherence of this values to normalization constraint of neural network architecture. The error histogram of fig 4.38 shows extremely low error value for all instances of training, validation and testing.

4.8 Tests Results for Neural Network Fault Classification Algorithm

The fault classification neural network is a clustering problem carried out through the self-organized map, a competitive layer algorithm. 11 cases of different fault types are used as input to the network. As discussed in the previous chapter, self-organized map function classifies the input data unsupervised i.e. without target data by recognizing similarities in input patterns. The training results show the S.O.M network classified the entire fault data into four different classes (LG, LL, LLG, LLL, and LLLG). Although five varying fault classes, it can be deduced from the results that the S.O.M network classified LLL and LLLG fault into the same class. To test the trained S.O.M network, new sets of data from a different zone2 of a 132kV network is utilized. The results are presented in figures below.

0 2 4 6 8 10

-1

0

1

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SOM Neighbor Weight Distances

Fig 4.40 SOM Neighbour Weight Distances for testing fault classifier

64

0 5 10

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8

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0 5 10

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1 1 1 0 5 2 0 5 5 1 2

4 2 0 0 0 0 0 4 1 1 4

1 0 5 2 2 4 2 2 2 2 4

0 0 0 0 2 1 2 0 0 1 0

1 2 2 1 0 2 0 4 2 2 4

4 1 0 0 3 5 4 6 2 2 1

5 3 2 1 1 1 0 0 1 2 2

1 1 0 2 1 0 2 5 2 2 1

0 2 2 0 2 2 0 0 2 1 2

3 5 2 0 2 1 3 2 2 4 3

Hits

Fig 4.41 SOM Input Planes for Fault Classifier

Fig 4.42 Sample hits plots for testing fault classifier

65

Fig 4.41 shows the self-organised map planes with combination of dark and bright colour pigments showing interdependence of different fault types on each other. A-G, B-G, C-G faults for example have fault voltage and current values that do not vary over a wide range. Fig4.42 shows the sample hit plot showing distribution of fault data samples into 6 clusters. It is observed that 1-4 clusters occur frequently. This implies that most of the input samples fall within this clusters consequently proving the algorithm is able to classify faults for other networks successfully. Fig 4.43 shows the confusion matrix for test carried out on adaptive reclosure scheme. All green features possess 100% values. The receiver operating characteristics of fig 4.44 shows a perfect line connecting the output and target class, no deviation whatsoever is observed. This proves that the algorithm is able to determine reclosing decision at 100% specificity and 100% sensitivity.

1 2

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All Confusion Matrix

Fig 4.43 Confusion matrix for test on adaptive reclosure scheme

66

0 0.2 0.4 0.6 0.8 10

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Class 1

Class 2

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Zones Current Values Voltage and current Values

Hidden Layers

CE %E CE %E

Zone 1 10 1.81358e-0 0 4.41113e-0 0 5.46648e-0 0 12.56130e-0 0 5.37691e-0 0 12.78173e-0 0

Zone 2 15 1.25726e-0 0 3.92247e-0 0 3.01199e-0 0 10.62411e-0 0 3.07638e-0 0 13.93316e-0 0

Zone 3 31 1.32389e-0 4.76e-0 2.19115e-0 2.38e-0

3.53742e-0 0 5.35857e-0 0 3.70216e-0 11.1e-0 5.31707e-0 0

Fig 4.44 Receiver operating characteristics for test on adaptive reclosure scheme

Table 4.1 performance table for fault detector neural network

67

Zones Current Values Voltage and current Values

Data Divisions

Hidden Layers

CE %E CE %E

Zone 1 10 1.73761e-0 0 2.38956e-0 0 Training 2.44491e-0 0 6.75411e-0 0 Validating 2.56877e-0 0 6.68094e-0 0 Testing

Figure 4.1 shows a table of cross entropy C.E and percentage error %E of A.N.N. fault detector scheme for zone 1-3. The number of neurons in the hidden layers were randomly chosen based on trial and error methods during training procedures. These layers work like a conduit between the input and output layer of the neural network architectural design. Each zone has corresponding C.E. and %E values. The C.E column have three separate rows; training, validating, and testing columns respectively. Training column covers dataset presented to the network during training and how well the network is adjusted to its error. Minimizing cross entropy error results in good classification, lower values are better, zero means no error. Percentage error represents fractions of samples misclassified. The values presented for each zone in the C.E column are all low values. It is observed that cross entropy values for zone 1 and 2 using voltage and current values are a bit higher compared to current values alone. For all zones 0% error is mostly obtained except the training data column for zone 3 where 2.83% is observed. Tables 4.2 and 4.3 are performance tables for adaptive fault classifier and adaptive reclosing; the cross entropy values and %E values show low values of 0%. Zone 2 records C.E of 10.77542 in the testing column under voltage and current values used as input samples still well within acceptable limit for cross entropy values

Zones Current Values Voltage and current Values

Data Divisions

Hidden Layers

CE %E CE %E

Zone 1 20 1.49311e-0 0 2.16318e-0 0 Training 5.14771e-0 0 6.96687e-0 0 Validating 5.23641e-0 0 7.17856e-0 0 Testing

Zone 2 25 2.94365e-0 0 3.62898e-0 0 Training 7.760704e-0

0 10.72542e-0 0 Validating

8.60164e-0 0 11.37871e-0 0 Testing Zone 3 30 2.57941e-0 4.76e-0 2.91388e-0 2.38e-0 Training

7.52102e-0 0 8.36907e-0 0 Validating 7.48334e-0 11.1e-0 8.29864e-0 0 Testing

Table 4.2 Performance table for adaptive fault classifier neural network

Table 4.3: Performance Table for Adaptive Reclosure Scheme Network

68

Fig 4.4 is the performance table for the fault locator algorithm simulated. The columns show mean squared error and regression values. Mean square error is the average squared difference between outputs and targets if the mean square error is zero then no error occurs. R represents regression values measuring correlation between output and target. An R value of 1 means a close linear relationship. Zero means random relationship. These columns have rows for training, validation and testing. The regression values for all the three zones are well close to 1. Low values for M.S.E in the range from 1-7 is observed. The highest values are observed in zone 2.

.

Zones Current Values Voltage and current Values

Data Divisions

Hidden Layers

M.S.E R M.S.E R

Zone 1 10 3.53331e-0 0.999796 4.02650e-6 0.99977 Training 1.07993e-0 0.999999 7.20332e-4 0.98151 Validating 1.10549e-0 0.9994 6.754441e-5 0.999281 Testing

Zone 2 14 7.54288e-0 0.993357 2.40135e-6 0.999978 Training 3.24661e-0 0.999172 6.94280e-4 0.99949 Validating 7.24130e-0 0.99406 1.64933e-2 0.994971 Testing

Zone 3 22 2.47634e-8 0.99999 2.09400e-6 0.99984 Training 3.80027e-4 0.99456 6.36174e-4 0.99685 Validating 1.42481e-4 0.999603 2.29223e-3 0.994046 Testing

Table 4.4: Performance Table for Fault Locator Neural Network

69

Actual Output Network Output

A B C ®7 ®9 ®:

1 0 0 0.99797 0.0021983 3.4255e-07

1 0 0 0.99862 0.00031272 5.7421e-07

1 0 0 0.99892 6.6702e-05 1.4445e-06

1 0 0 0.99905 1.3048e-05 3.1301e-06

0 1 0 0.004136 0.97556 4.848e-05

0 1 0 0.0020006 0.98146 7.6016e-06

0 1 0 0.0013137 0.98091 5.0511e-07

0 1 0 0.00093317 0.97866 4.4615e-08

0 0 1 7.9193e-05 0.0013349 1

0 0 1 3.1191e-05 0.0014288 1

0 0 1 1.929e-05 0.00066873 1

0 0 1 6.851e-06 0.00034928 1

Table 4.5 Output of Trained Faulted Phase Detector Network using Current Values only for Zone 1

70

Actual Output Network Output

A B C ®7 ®9 ®:

1 0 0 1 3.187e-09 2.9406e-11

1 0 0 1 5.2048e-09 7.296e-18

1 0 0 1 4.6853e-09 4.96e-16

1 0 0 1 2.9435e-09 4.7864e-15

0 1 0 2.2993e-14 1 1.0785e-15

0 1 0 5.2404e-10 1 2.3859e-13

0 1 0 1.3103e-09 1 4.7025e-12

0 1 0 3.9816e-09 1 2.4605e-11

0 0 1 1.0566e-08 4.5532e-7 1

0 0 1 2.7311e-19 4.0572e-7 1

0 0 1 8.6396e-19 8.6091e-8 1

0 0 1 3.4763e-18 9.1578e-09 1

Table 4.6 Output of Trained Faulted Phase Detector Network using Current & Voltage Values for Zone

1onfor zone 1

71

Actual Output Network Output

A B C ®7 ®9 ®:

1 0 0 1 1.3992e-08 9.0419e-7

1 0 0 1 1.4023e-08 7.1044e-8

1 0 0 1 1.4487e-08 2.9234e-8

1 0 0 1 1.4017e-08 2.0501e-8

0 1 0 2.2033e-10 1 7.5956e-11

0 1 0 6.6657e-11 1 3.756e-9

0 1 0 9.2096e-11 1 3.6956e-6

0 1 0 6.4726e-08 1 5.5874e-9

0 0 1 4.6128e-11 5.2934e-9 1

0 0 1 1.5756e-10 4.7686e-7 1

0 0 1 7.3582e-10 1.8523e-6 1

0 0 1 4.2926e-9 1.6599e-07 1

Table 4.7 Output of Trained Fault Phase Detector Network Using Current Values only for Zone 2

72

Actual Output Network Output

A B C ®7 ®9 ®:

1 0 0 1 4.9113e-13 7.671e-7

1 0 0 1 3.1197e-13 1.502e-7

1 0 0 1 8.5553e-14 8.8122e-8

1 0 0 1 4.5722e-14 5.0125e-7

0 1 0 4.0768e-13 1 2.4829e-9

0 1 0 1.711e-13 1 7.2075e-9

0 1 0 2.1219e-12 1 5.6725e-6

0 1 0 1.5122e-08 1 1.7028e-7

0 0 1 1.6831e-7 4.7491e-9 1

0 0 1 8.8786e-8 4.1184e-7 1

0 0 1 1.5212e-8 3.4071e-6 1

0 0 1 3.5697e-8 4.8571e-07 1

Table 4.8 Output of Trained Fault Phase Detector Network using Voltage & Current values for Zone 2

222222

73

Actual Output Network Output

A B C ®7 ®9 ®:

1 0 0 1 5.0387e-8 3.0117e-9

1 0 0 1 1.2102e-8 5.2331e-9

1 0 0 1 9.7796e-9 4.7442e-9

1 0 0 1 6.0627e-9 1.0761e-8

0 1 0 6.4083e-8 1 5.1838e-11

0 1 0 3.0927e-8 1 3.2455e-9

0 1 0 5.3297e-8 1 7.2582e-7

0 1 0 6.9285e-07 1 1.9416e-8

0 0 1 9.1997e-9 2.1645e-8 1

0 0 1 7.6195e-9 6.0341e-9 0.99997

0 0 1 5.4824e-9 4.3582e-9 1

0 0 1 3.5571e-9 1.1723e-08 0.99999

Table 4.9 Output of Trained Fault Phase Detector Network Using Current values for Zones 3

74

Actual Output Network Output

A B C ®7 ®9 ®:

1 0 0 1 7.2344e-13 6.2369e-10

1 0 0 1 4.7142e-13 3.8236e-9

1 0 0 1 4.2928e-13 1.3729e-8

1 0 0 1 1.6309e-12 7.234e-8

0 1 0 9.6231e-8 1 2.0559e-7

0 1 0 2.277e-10 1 4.9453e-8

0 1 0 4.9739e-10 1 3.2428e-8

0 1 0 4.7967e-08 1 9.0871e-9

0 0 1 6.3113e-11 9.0798e-8 1

0 0 1 3.9469e-11 1.9304e-8 1

0 0 1 5.2518e-11 2.9428e-9 1

0 0 1 1.1796e-10 2.2532e-09 1

Table 4.5 is a comparison of target values against A.N.N. output values using just pre-processed fault current values generated from the 330kV network model used for this research as input samples. The value ‘1’ represents presence of fault while ‘0’ represents no fault. The first three columns represents phase A, B, and C while m[¯, m[° , m[± which are the A.N.N fault detector on phase A, B, C is represented in the next three columns. table 4.5 and 4.6 are comparisons made for zone 1. Although the results are accurate, an improved accuracy by 13.8% is recorded when voltage and current values is used to train the neural network for fault detection. Tables 4.7 and 4.8 represent comparisons for zone 2. For this cases expressed in tables 4.7 and 4.8, an improvement of 11% is observed when voltage and current values is used as input samples. Fault detection comparison in fig 4.9 and 4.10 records an improvement of 12% using voltage and current values as input samples to train the network. A particular threshold will be used when programming the numerical relays using this A.N.N. algorithm generated for fault detection. 0.95-1 to represent presence of fault and 1!A − 1!B to represent no fault present.

Table 4.10 Output of Trained Faulted Phase Detector network using Voltage & Current Values only for Zones 3

75

Actual Output*1000(Km)

²³´(current and voltage)*1000(Km)

²³µ(¶·¸¸¹º» ¼´½·¹¾)∗ ¿ÀÀÀ(ÁÂ)

0.05 0.051309 0.050606

0.07 0.085325 0.070551

0.14 0.22596 0.14016

0.24 0.23915 0.23999

0.28 0.27906 0.29259

0.32 0.32012 0.31994

0.36 0.359 0.35965

0.4 0.39664 0.39937

0.44 0.42937 0.42314

0.48 0.46694 0.47963

0.5 0.48241 0.50312

0.52 0.50179 0.52813

0.54 0.52526 0.54537

0.57 0.55805 0.55805

0.59 0.57383 0.57383

0.62 0.60576 0.60576

0.63 0.61758 0.61758

0.64 0.63387 0.63381

0.66 0.65076 0.65076

0.68 0.66801 0.66801

Table 4.11 Comparison of Estimated and Target Output of Fault Locator Zone 2

76

Actual Output*1000(Km)

²³´(current and voltage)*1000(Km)

²³µ(¶·¸¸¹º» ¼´½·¹¾)*1000(Km)

0.04 0.039793 0.040474

0.1 0.10177 0.10039

0.12 0.08832 0.12038

0.15 0.14779 0.15274

0.18 0.17507 0.22142

0.2 0.16586 0.1848

0.25 0.23392 0.31858

0.35 0.34142 0.34378

0.38 0.37241 0.36593

0.4 0.39245 0.44087

0.42 0.41428 0.48579

0.5 0.50611 0.51579

0.52 0.52147 0.53286

0.58 0.58264 0.49158

0.62 0.61711 0.62025

0.68 0.68339 0.7066

0.72 0.72072 0.72939

0.75 0.71613 0.75077

0.78 0.79574 0.80137

0.82 0.82133 0.82958

Table 4.12: Comparison of Estimated and Target Output of Fault Locator Zone 1

77

Actual Output*1000(Km)

²³´(current and voltage)*1000(Km)

²³µ(¶·¸¸¹º» ¼´½·¹¾)*1000(Km)

0.04 0.040034 0.03989

0.10 0.099974 0.099728

0.12 0.11998 0.075985

0.15 0.13064 0.14958

0.18 0.17999 0.17955

0.20 0.21801 0.19958

0.25 0.24782 0.24924

0.30 0.29998 0.29212

0.35 0.36455 0.34928

0.40 0.39985 0.39948

0.45 0.47458 0.47588

0.50 0.50692 0.51179

0.55 0.54991 0.54946

0.60 0.59955 0.60012

0.65 0.65017 0.65287

0.70 0.6998 0.70414

0.75 0.75001 0.74979

0.80 0.80008 0.801

0.85 0.85014 0.85039

0.95 0.9499 0.95038

Table 4.11 and tables 4.12-4.13 are tables of comparisons of target output in kilometres

separated by 10km interval until the maximum distance for each zone is reached i.e. zone 1

68km, zone 2 68km and zone 3 95km as well as A.N.N. output using either current or current

Table 4.13 Comparison of Estimated and Target Output of Fault Locator Zone 3

78

and voltage input samples. The values obtained with current and voltage values are closer to

the target output compared to using just current values. Tables 4.11-4.13 are output tables

highlighting the fault location algorithm scheme. Tables 4.11-4.13 further elucidates the

mean square error and regression values in fig 4.4 which proves the fault location algorithm

works effectively. Numerical calculation is done based on the output plot of fig 4.12b to

determine the speed of response of the A.N.N. fault locator;

To test the speed of operation of the neural network fault locator, the following assessment is

carried out; fig 3.9 shows a plot of A-G fault at 50Km with fault resistance of 30ohm, it is

observed that the fault inception time is 0.00875s. Another plot showing the output (distance

in Km against instances of data input) shown in fig 4.12b reveals that at 50Km, the fault

locator output estimates 50.611Km at sample no.12 with sampling time 5e-5s. It is important

to note that this calculation is carried out based on the neural network designed with both

voltage and current signals on phases A, B, C making six samples in all by re-ordering of

data in matrix. The above deductions are used to perform the following calculations:

Fault inception time = 0.00875s

Fault located at sample no.12= 12*6(consecutive samples)*5e-5s=3.6e-3

Time of operation = 0.00875 – 0.0036 = 0.00515s

79

Target Values Estimated Values 1 0 0.99998 0.0040611 1 0 0.99978 0.0016642 1 0 0.99957 0.0093235 1 0 0.99898 0.054378 1 0 0.99773 0.078368 1 0 0.99598 0.091319 1 0 0.98456 0.002456 1 0 0.93917 0.000065489 1 0 0.93967 0.0021379 1 0 0.93976 0.011087 1 0 0.95069 0.050172 0 1 1.8854e-05 0.99594 0 1 0.00022104 0.99834 0 1 0.00043285 0.99068 0 1 0.0010199 0.94562 0 1 0.0022676 0.92163 0 1 0.0040226 0.90868 0 1 0.015444 0.99754 0 1 0.060829 0.99993 0 1 0.060333 0.99786 0 1 0.060237 0.98891

The table 4.14 shows the adaptive fault classifier where ‘1’ represents transient fault ‘0’

represents permanent fault. The output of the adaptive classifier in fig 4.43 and 4.44 as well

as table 4.3 is used to determine the necessary reclose command that is whether or not to

reclose. The table is grouped with the first ten rows having the transient and permanent fault

classifier while the next ten rows the classifier is reversed having permanent fault first before

transient fault. The estimated output for each shows the A.N.N. adaptive classifier is able to

track the target output fed to it. For the first ten cases, if a transient fault occurs, the reclose

operation is issued, if the pattern classifier senses another fault peradventure a permanent

fault is sensed, the command to break the circuit is issued. The next ten rows is used if the

A.N.N. classifier senses a permanent fault, the do not reclose command is issued until the

fault dissipates then the circuit is reconnected. According to the time plot of the transient and

permanent fault simulated in fig 4.20 and 4.21, the time it takes for a transient fault to

dissipate is 0.1s while for a permanent fault it takes 0.14s. After 0.14s, the reclose command

is then issued to reconnect the break circuit

Table 4.14: Adaptive Fault Classifier for Transient or permanent Fault

80

CHAPTER FIVE

CONCLUSION AND RECOMMENDATION

Conclusion

This research work describes the application of artificial neural networks to fault detection, fault location, fault classification. The design process and test results proves the successful adaptation of neural network to varied fault conditions as well as system parameters on the Benin, Onistha, and New Haven transmission network modelled on MATLAB SIMULINK. It also shows the A.N.N’s immunity to noise, D.C offset or harmonics in the voltage/current waveform hence the possibility of relay non-reach/over-reach is minimized. The adaptive reclosure scheme has proven to be a good example for system protection, although not yet existing on the Nigerian power system thus the minimum field data available. The input utilized for these networks are generally simulated notwithstanding the test proves real time implementation will be successful. The results from this work show for the fault location algorithm, on average an improved accuracy of 12% is obtained when voltage and current values are used as inputs to the neural network compared to using only current values as input samples. Also, successful combination of two neural networks, one to classify the fault type (transient or permanent); the time evaluated to clear a transient fault is 0.1s while the time to clear permanent fault is 0.14s and using this output determine or adapt the reclosing time to ensure successful reclosure. The speed of operation of the fault locator is evaluated as 3.6ms when A.N.N. is applied compared to the distance relay time of responseof the existing protection which is 0.350s as recorded in the relay data obtained from the New Haven Transmission station shown in appendix B. This proves the neural fault locator locates faults faster.

Recommendations

Some notable recommendations from this work to power system engineers and relevant authorities are as follows:

The self-organised neural network can be used inconjunction with K-means clustering as well as fuzzy K-nearest neighbour (K-NN) algorithm can be used to substitute the error back propagated pattern classifier due to convergence issues.

The adaptive auto reclosing scheme applied in this research is based on the theory of single pole auto reclosure technique. The three pole technique although not popular in many power systems establishment can also be adapted using artificial intelligence tools.

Another enhanced protection scheme that I recommend to be investigated using artificial neural network is the adaptive distance protection.

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REFERENCES

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