chapter 3 intelligent fault diagnosis of power...

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CHAPTER 3

INTELLIGENT FAULT DIAGNOSIS OF POWER

TRANSMISSION LINE

3.1 INTRODUCTION

The reliable operation of a large power system is highly dependent

on control systems and protection devices. Transmission line protection is a

fundamentally important aspect of guaranteeing the correct operation of

power systems. Protection devices are responsible for detecting fault

occurrences and isolating only the faulted portion of the system. It is also very

important that the system restoration takes place as soon as possible. Then, it

is essential that faults be detected quickly and accurately.

The different faults in transmission line are Line-Ground (LG),

Double Line (LL), Double Line-Ground (LLG), 3 phase fault (LLL/LLLG).

For each fault condition, the fault signal is different and the protection system

should be able to isolate the fault under all conditions. The protection system

should also be capable of isolating only the part of the system that is faulty.

This allows other parts of the transmission system to operate without any

interruption. The conventional relays can be used to trip the faulty part. The

disadvantage of using conventional relays is that they operate on fixed

settings and have to be reset for changes in the network configuration.

Changes in network condition can also affect the operation of the relays. This

affects the performance of the relays to a large extent. Artificial Intelligence

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has evolved to be a remarkable tool for adapting to the changing network conditions and configurations and for providing an excellent performance. Girgis et al (1989) applied expert system for fault section identification, classification and location in transmission line.

For transmission line protection, it is desirable to have a system of relays to generate a trip signal whenever a fault is detected within its protection zone. Girgis et al (1992) reported that the relay system will be modular where fault area estimation is very much dependent upon accurate fault type classification, in which fault type is verified before verifying fault location.

Accurate fault location on power transmission line is important for both protection and maintenance purposes. Conventional fault location methods use the fault steady state components of voltage and current measured at one or more points along the transmission line. The fault distance can be estimated from the measured impedance of the transmission line at the power system frequency. The impedance is assumed to be proportional to the fault distance. The impedance measurement used in distance protection schemes is inaccurate for precise fault location as the error in the estimated fault location can be as high as 10% of line length.

Recent reformation in the power industry such as open access and regulation may have an impact on the reliability and security of power systems. New methodologies for various protection and control schemes are a must to maintain system reliability and security within an acceptable level. Artificial intelligence techniques are among the top candidates to realize this new methodology.

Since the majority of power system protection techniques are involved in defining the system state through identifying the patterns of the associated voltage and current, the development of a normal fault classification technique and fault location can be essentially treated as a

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problem of pattern recognition. Recently, different attempts have been made using pattern recognition techniques for fault classification and location. Some of the recent papers have used fuzzy logic, artificial neural network (ANN), and support vector machine for this purpose. Dash et al (2001), Mahanty et al (2004), Gracia et al (2005) and Samantary et al (2006) have applied ANN for fault classification and location in transmission line.

Although the neural-network based approaches have been quite

successful, the main disadvantage of ANN is that it requires a considerable

amount of training effort for good performance, especially under a wide

variation of operating conditions. Support vector machine is a recently used

popular method for classification and regression problems because of its

generalization capability. Extreme learning machine is another recent popular

method for classification and regression problems because of its universal

approximation capability.

In Chapter 3.2, faulty phase detection, classification and location in

power transmission line using WT-SVM and WT-ELM is discussed. In

Chapter 3.3, performance evaluation of multi-category classifications

methods like SVM-OVO, SVM-OVR, binary decision tree based SVM

classifier and MCELM are discussed. In Chapter 3.4, multi-category

classification using combined wavelet transform-Extreme learning machine

for faulted line identification and classification for practical transmission line

is discussed.

3.2 INTELLIGENT APPROACHES USING WT-SVM AND WT-

ELM FOR TRANSMISSION LINE PROTECTION

3.2.1 Introduction

Transmission line protection is the most elaborate and challenging

function in power system protection. Very often, fault classification is an

overall protection scheme. This is particularly so in techniques based on a

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modular approach whereby correct fault discrimination is very much dependent upon accurate fault type classification.

Fault location estimation is another desirable feature in any

protection scheme. Locating the fault on the transmission line accelerates line

restoration and reduces the power disruption to consumers.

In this work, an attempt is made to protect transmission line using

WT-SVM and WT-ELM. The proposed system is tested on a 240-kV, 225-km

transmission line for ten types of short circuit faults (e.g., a-g, b-g, c-g, a-b, b-

c, c-a, a-b-g, b-c-g, c-a-g, a-b-c / a-b-c-g). The performance of the WT-SVM

and WT-ELM has been tested over a large data set (9600 test cases)

considering wide variation in system operating conditions. The results of WT-

SVM based approach is compared in terms of classification accuracy and

fault location error with WT-ELM.

3.2.2 Transmission Line Protection using WT-SVM and WT-ELM

The framework of the entire protection scheme using WT-SVM

and WT-ELM technique is shown in Figure 3.1.

Figure 3.1 Framework for transmission line protection using WT-SVM

and WT-ELM

Discrete wavelet transform

SVM/ ELM for faulty phase detection

SVM /ELM for fault classification

SVR/ELM for fault location

Fault current samples

Fault classification

Fault location

Faulty phase detection

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Sampled current values at the relaying point when the fault is

persisting are used in this work as proposed by Dash et al (2007) and Urmil

B.Parikh (2008). Initially, the features of the line current are extracted by first

level decomposition of the current samples using DWT. Subsequently, the

extracted features are applied as input to SVMs and ELMs.

3.2.2.1 Power System Model

The single line diagram of a sample system is shown in Figure 3.2. It

consists of two areas connected by a 225-km, 240-kV, 50 Hz transmission

line. The transmission line is modeled as a distributed parameter line. The

parameters of the line are: the positive sequence impedance of, ZL(1) =

8.05+j110.66 Ω and the zero sequence impedance of ZL(0) =

79.19+j302.77 Ω. The positive sequence capacitance is 13nF/km and the

zero sequence capacitance is 8.5nF/km. The Thevenin impedance of area

A is ZA = 5 + j27.7 Ω and the area B is ZB = 0.6 + j9.3 Ω. The sample

system is modeled and simulated using MATLAB Simulink and simulink

block diagram is shown in Appendix 1.

Figure 3.2 Single line diagram of the system

ZA ZB

Area A

Relay

Area B 240 kV, 225 km

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Current samples

Feature extraction using DWT

Determination of the best values of SVM parameters C and σ

Calculating the support vectors and optimal hyper plane

Training accomplished

Normalization

3.2.2.2 Training Process

The training process of SVM is shown in Figure 3.3. Input data are

the half cycle current samples from the three phases during the occurrence of

the fault.

Figure 3.3 Flow diagram of the training process

Input samples are pre-processed using wavelet transform and

normalized between [-1 1]. For the normal scaling method, if the maximum

and minimum values of the ith attributes are Mi and mi respectively, then

scaling to [-1 1] means x1= 1)()(2

imiMimx

.

To study the effectiveness of the proposed methods under different

system conditions, different combination of source impedances Zs (75%,

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100%, 125%) are considered. The fault simulation studies have been carried

out under variation of load angle (10º, 30º), fault inception angle (0º, 90º),

fault resistance (10Ω, 20Ω), and fault locations (40%, 80%) for each of this

ten system fault conditions. Thus 10×3×2×2×2×2=480 test cases are

simulated for training.

3.2.2.3 Feature Extraction

For faulty phase detection, fault classification and location half

cycle fault current signal is pre-processed through wavelet transform to find

out the features. Through a series of studies the sampling rate employed is 1.6

kHz (32 samples per cycle at 50 Hz). Four different types of Daubechies

mother wavelets (db1-db4) are analyzed. The wavelet coefficients after the

first level decomposition of the fault current of each phase are used as input to

SVMs and ELMs for training and testing.

3.2.2.4 Parameter Selection

The parameter to be selected in SVM is regularization constant C

and kernel width σ. After several simulation studies, the best combination of

C and σ is 0.1 and 2 respectively for faulty phase detection and fault

classification. Similarly, for fault location best combination of C and σ is

1000 and 2 respectively. In ELM, the parameter to be selected is the number

of hidden neurons. For ELM, the numbers of hidden neurons are gradually

increased by 1 and optimal number of hidden neurons for ELM is then

selected based on trial and error method. For faulty phase detection and fault

classification, three ELMs, one for each phase, are used. After several

simulation studies, the number of hidden neurons selected for a, b and c

phases are 15, 67 and 26 respectively. Similarly, for fault location, the number

of hidden neurons is selected as 8, 9, 11, and 10 for LG, LL, LLG and LLL

faults respectively.

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Studies have also been carried out regarding suitability of different

types of db mother wavelets and the classification accuracies obtained with

different db mother wavelets are shown in Table.3.1. From this Table, it is

observed that among different mother wavelets, db2 gives the best accuracy,

and therefore, it has been used for feature extraction.

Table.3.1 Classification accuracy for different types of Daubechies

mother wavelet

Sl.No Type of mother wavelet

Classification accuracy (%)

SVM ELM

1 db1 93.87 89.92

2 db2 99.11 91.89

3 db3 94.03 89.34

4 db4 95.17 91.43

3.2.2.5 Faulty Phase Detection

The wavelet coefficients obtained for the half-cycle fault current

samples after the fault inception are taken as input to SVM and ELM. The

structure of SVM/ELM for faulty phase detection is shown in Figure 3.4.

Three SVMs (SVM-a, SVM-b, SVM-c) are used to detect the faulty phase(s).

Similarly, three ELMs (ELM-a, ELM-b, ELM-c) are designed for three

phases. The training and test patterns are normalized to [-1 1]. The output of

each SVM/ ELM is either “+1” or “-1” indicating if there is a fault on that

phase or not.

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Figure 3.4 Faulty phase detection using WT-SVM and WT-ELM

For example, if the outputs of SVM/ELM are: ya=1, yb= -1, yc= -1;

Then fault is detected in “a” phase.

Further to test the validity of the approach for high impedance fault

at 80% transmission line length, 0º fault inception angle and 10º load angle

for a-b-c fault, fault resistance of 150 Ω and 200 Ω is initiated.

The outputs of SVM/ELM are: ya=1, yb=1, yc=1;

Thus fault is detected correctly.

3.2.2.6 Fault Classification

The half cycle fault current samples after pre-processing using

DWT are taken as input to the SVM. The training and test patterns are

normalized to [-1 1]. For fault type classification, three SVMs are designed

for three phases. Similarly, three ELMs are designed for three phases. Fault

classification using WT-SVM and WT-ELM are depicted in Figure 3.5.


SVM-a/ ELM-a

SVM-b/ ELM-b

SVM-c/ ELM-c

yc


ya

yb

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Figure 3.5 Flow chart for fault classification using WT-SVM and WT-

ELM



Detail coefficients

SVM/ELM

Yes

Fault detected

Calculation of Index value

Ground involved

Ground not involved

a-g,b-g, c-g, ab-g, bc-g, ca-g, abc-g

fault

ab,bc,ca, abc fault

Yes

No

No

No fault

Start

If a =1,b=1,c=1

(either combination)

If Index >=

0.05

Ground detector

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The output of the network consisting of states of a, b, c and g reflects

the involvement of the phase in the particular fault situation. During

training any phase involved in the fault is assigned “1” else “-1”. To detect

the involvement of ground during fault, a zero sequence current based

indicator of the type proposed by Akke et al (1998) is used in this work. In

ground detector, Index value is calculated using

),,( cba

cba

IIImedianIII

Index

(3.1)

When the index value exceeds the threshold value of 0.05, it

indicates the involvement of fault with ground. The ground detection is carried out in parallel with the SVM and ELM calculations.

3.2.2.7 Fault Location using WT-SVM and WT-ELM Regression

The training patterns are generated by simulating the four types of fault at different locations (starting from 10 km with increment of 10 km) on the transmission lines. Overall, the training patterns are generated for each type of fault on the transmission line over 19 locations with 2 fault resistances, 2 source impedance, 2 load angle and 2 fault inception angles. For each type of fault, the number of training pattern is 19×2×2×2×2=304 patterns.

The testing patterns are generated for each type of fault on the transmission line over 19 locations with 10 fault resistances, 2 source impedance, 2 fault inception angle and 2 load angle. For each type of fault, the number of testing pattern for fault location is 19×10×2×2×2=1520.

The structure of SVR/ELM for fault location consists of four regression blocks as shown in Figure 3.6. The pre-processed fault current signals are used to train SVR and ELM. The training and test patterns are normalized to [-1 1] and given as input to the SVR modules and ELM

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DWT Fault current samples

SVR2/ELM2 LL

SVR3/ELM3 LLG

SVR4/ELM4 LLL

SVR1/ELM1 LG

Fault distance from relay location

modules. In this case, the target value of each pattern is the distance from relay locations.

Figure 3.6 Block description of the WT – SVR and WT-ELM for estimation of fault distance from relay location

The criterion for evaluating the performance of the fault locator is defined as

linetheoflengthlocationFaultoutputSVM

error

% *100 (3.2)

3.2.3 Results and Discussions

To test the robustness of the developed algorithm, the fault

simulation studies have been carried out with varying fault resistance, fault

inception angle, pre-fault power level, source impedance and fault distance.

An extensive study is carried out in order to ascertain the overall performance

of WT-SVM and WT-ELM for faulty phase detection and fault classification

with five different combination of source impedance. The test sets are

composed of over 9600 cases including different fault resistance

(1Ω,50Ω,150Ω,200Ω), fault inception angle (36º,72º,108º,126º), load angle

(10º,30º,60º) and fault distance (45%, 55%, 65%,75%).

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Test results for faulty phase detection with WT-SVM and WT-

ELM are given in Table. 3.2. As observed from Table. 3.2, the proposed

system using WT-SVM and WT-ELM detects high impedance fault correctly.

As seen from the Table, for the c-a fault at 75% transmission line length,

Rf=10Ω, FIA=126º, =60º using WT-SVM the output of “a” phase is “-1”

instead of “1” and using WT-ELM the output of “c” phase is “-1” instead of

“1” . Similarly with WT-ELM for b-c fault at 55% transmission line length,

Rf = 20Ω, FIA=126º, =30º, output of “c” phase is “-1” instead of “1”.

Table.3.2 Faulty phase detection using WT-SVM and WT-ELM

Fault Type WT-SVM WT-ELM

ya yb yc ya yb yc a-g fault at 55% , Rf=1Ω, FIA=36º, =10º.

1 -1 -1 1 -1 -1

b-g fault at 40% , Rf=10Ω, FIA=90º,=60º

-1 1 -1 -1 1 -1

c-g fault at 75% , Rf=50Ω, FIA=72º,=10º.

-1 -1 1 -1 -1 1

a-b fault at 45% , Rf=150Ω , FIA=0º,=30º.

1 1 -1 1 1 -1

b-c fault at 55%, Rf=20Ω, FIA=126º,=30º.

-1 1 1 -1 1 -1

c-a fault at 75%, Rf=10Ω, FIA=126º,=60º.

-1 -1 1 1 -1 -1

a-b-g fault at 65% Rf=200Ω , FIA=90º,=10º

1 1 -1 1 1 -1

b-c-g fault at 45% Rf=150Ω , FIA=0º, =30º.

-1 1 1 -1 1 1

c-a-g fault at 80% Rf=200Ω , FIA=36º,=10º

1 -1 1 1 -1 1

a-b-c fault at 40% Rf=200Ω , FIA=0º,=10º.

1 1 1 1 1 1

1-fault; -1- no fault

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Test results for fault classification with WT-SVM and WT-ELM

are given in Table. 3.3. As observed from Table. 3.3, the overall accuracy for

fault classification with WT-SVM is 99.11% and the overall classification

accuracy with WT-ELM for fault classification is 91.89% for variation in

parameters. Most of the misclassification occurs in WT-ELM with LLG fault.

Table.3.3 Comparison of fault classification accuracy of WT-SVM

and WT-ELM

Type of

fault

Samples

tested

WT-SVM WT-ELM

True classification

% Accuracy

True

Classification

% Accuracy

LG 2880 2880 100 2808 97.5

LL 2880 2867 99.54 2672 92.78

LLG 2880 2808 97.5 2473 85.87

LLL 960 960 100 869 90.52

Total 9600 9515 99.11 8822 91.89

% testing error for various faults with WT-SVR is shown in Figure 3.7. As observed from Figure 3.7, the maximum error in all four types fault is less than 1% for testing with 1520 samples for each type of fault. Similarly, %testing error with WT-ELM is shown in Figure 3.8 and the maximum error is less than 1%. The minimum, the maximum and the mean error with WT-SVR and WT-ELM for variation in fault resistance is given in Table.3.4. As observed from Table.3.4, the mean error with WT-ELM is slightly lower than WT-SVM. The training and testing parameters are given in Table 3.5. As shown in Table 3.5, the system is trained with a fault resistance of 10Ω and 20Ω only and tested with fault resistance up to 200Ω. Similarly, the proposed method is also tested with parameters which are not included as part of training. Hence, it is observed that the proposed method is robust to parameter variations.

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0 200 400 600 800 1000 1200 1400 16000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Test samples

% e

rror

(a) % error for LG fault with WT-SVR

0 200 400 600 800 1000 1200 1400 16000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Test samples

% e

rror

(b) % error for LL fault with WT-SVR

Figure 3.7 (Continued )

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0 200 400 600 800 1000 1200 1400 16000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Test samples

% e

rror

(c) % error for LLG fault with WT-SVR.

0 200 400 600 800 1000 1200 1400 16000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Test samples

% e

rror

(d) % error for LLL fault with WT-SVR.

Figure 3.7 % error for different types of fault with WT-SVR

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0 200 400 600 800 1000 1200 1400 16000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Test samples

% e

rror

(a) % error for LG fault with WT-ELM.

0 200 400 600 800 1000 1200 1400 16000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Test samples

% e

rror

(b) % error for LL fault with WT-ELM

Figure 3.8 (Continued)

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0 200 400 600 800 1000 1200 1400 16000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Test samples

% e

rror

(c) % error for LLG fault with WT-ELM.

0 200 400 600 800 1000 1200 1400 16000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Test samples

% e

rror

(d) % error for LLL fault with WT-ELM.

Figure 3.8 % error for different types of fault with WT-ELM.

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Table.3.4 Comparison of fault location error of WT-SVR and WT-ELM

Type of fault

Min Error Max Error Mean Error

WT-ELM WT-SVR WT-ELM WT-SVR WT-ELM WT-SVR

LG 2.3564e-006 3.6330e-004 0.9848 0.7132 0.1082 0.1942

LL 1.0962e-009 4.3248e-004 0.9909 0.7828 0.0793 0.2007

LLG 1.3975e-007 2.8159e-004 0.9975 0.8451 0.1279 0.1963

LLL 3.6541e-009 3.2633e-004 0.9916 0.8360 0.0850 0.2029

Table.3.5 Training and testing patterns for WT-SVM and WT-ELM

for faulty phase detection and classification

Parameters Training Testing

Fault resistance 10Ω, 20Ω 1Ω,50Ω,150Ω, 200Ω

Fault Inception angle

0º,90º 36º,72º,108º,126º

Fault distance 40%,80% 45%,55%,65%,75%

Load angle 10º,30º 10º,30º, 60º

In order to validate the effect of wavelet transform on the

classification, fault classification is conducted with SVM and ELM without

DWT pre-processing. The overall classification accuracy obtained for fault

classification is 92.36% and 90.27% with SVM and ELM respectively. This

experiment clearly indicates the effect of pre-processing with DWT for

classification. As the techniques presented gives better accuracy, WT-SVM

and WT-ELM technique with half cycle current data are recommended for

faulty phase detection, fault classification and location in transmission line.

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3.3 FAULT CLASSIFICATION IN TRANSMISSION LINE

USING MULTI-CATEGORY CLASSIFICATION METHODS

3.3.1 Introduction

Protecting transmission line is one important task to safeguard

electric power systems. Power transmission lines are spread over wide regions

and are exposed to various types of faults. The faults on transmission line are

Line - Ground fault

Line - Line fault

Double line - Ground fault

Three phase fault

For each fault condition, the fault signal is different and the

protection system should be able to classify different faults accurately.

Classification of fault in transmission line is identified as problem of multi-

category classification problem, well known SVM can be used, which uses

some combination of binary classifiers on a One- Versus- One (OVO), One-

Versus-Rest (OVR) and binary decision tree based SVM classifier. Multi-

class SVM and their performance comparison are discussed in HSU et al

(2002). Statnikov et al (2005) have used the multi-class SVM for

classification problem in the cancer diagnosis area and Peter G.V Axelberg et

al (2007) have applied decision tree based SVM classifier for classification of

voltage disturbances. ELM is another popular method for solving multi-

category classification methods. Nan-Ying Liang et al (2006) have applied

ELM for classification of mental tasks and Runxuan Zhang et al (2007)

applied ELM for directing multi-category classification problems in the

cancer diagnosis area. In this work, an attempt is made to classify faults in

transmission line using Multi-Class SVM (MCSVM) and Multi-Class ELM

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(MCELM). The aim of the work is to investigate the performance of MCSVM

such as SVM-OVO, SVM-OVR, Binary Decision Tree (BDT) based SVM

classifier and MCELM as fault classifier when training data and testing data

are different.

3.3.2 Fault Classification using Multi-Class Classifiers

The framework of the fault classification scheme for the sample

system explained in section 3.2.2.1 is shown in Figure 3.9. Sampled current

values at the relaying point during faults are used for classification of fault

types. MATLAB 7 software is used to simulate fault data for different power

system conditions. The wavelet multi-resolution analysis is used for

decomposing each signal into high frequency details. The information is used

for extracting the features and forming the patterns for ELM and SVM.

Figure 3.9 Block diagram of the proposed method for fault classification

3.3.2.1 Feature extraction

For fault classification, fault current signal is pre-processed through

wavelet transform to find out the features. Pre-processing is a useful method

to reduce the dimensionality of the input data set. The pre-processing stage

can significantly reduce the size, which in turn improves the performance and

speed of the training process. The sampling rate employed is 1.6 kHz (32

samples per cycle at 50Hz). The wavelet transform technique is first applied

in order to decompose the different current signals into a series of wavelet

Feature extraction using

DWT

Multi class ELM/SVM



46

components, each of which is a time domain signal that covers a specific

frequency band. In this work, Daubechies wavelet (db2) is used as mother

wavelet for feature extraction.

3.3.2.2 Parameter selection

The parameter to be selected in ELM is hidden neurons. The

numbers of hidden neurons are gradually increased by 1 and the nearly

optimal number of nodes for ELM is then selected. In this work, number of

neurons selected for fault classification in transmission line is 10.

For SVMs using RBF kernel, the regularization constants C and

kernel parameter are to be selected. The generalized accuracy is estimated

for different combination of C and . Average results of 25 trials of

simulation with each combination of (C, ) are obtained and best

performance obtained is used for fault classification. In this work, the optimal

combination (100, 1) produces better generalization performance. Time taken

to select the parameters with ELM is much less compared to SVM for the same data set.

3.3.2.3 Training and testing

The half cycle fault current samples after the fault inception are

taken as input to the SVM. In this work, training patterns are generated by

creating all five types of fault at fault inception angle of 0º with different fault

resistance (1Ω, 20Ω). The fault locations selected are 5%, 25%, and 50% of

the total transmission line length. This work presents an approach to classify

the various faults after detecting the fault in the network. 240 fault cases are

generated for each LG, LL, LLG, LLL and LLLG fault respectively. The

network so obtained is tested by data which are not used during training. The

data include all shunt faults at different fault resistance (10 Ω, 100 Ω),

inception angle (36º, 90º). The proposed classifier is trained with a fault

47

distance of 5%, 25% and 50% of line length and tested with 40% and 75% of line length.

3.3.2.4 Fault classification with SVM-OVO

Different types of fault can occur in transmission line including phase faults and ground faults. The faults in transmission line are Line-Ground (LG), Line-Line (LL), Double Line – Ground (LLG), 3 phase fault (LLL), 3phase – ground (LLLG). For the above five types of fault (k=5) ten binary classifiers [k (k-1)/2] are constructed and trained. After training phase is over, each classifier is tested for the test data and the classification is made based on voting. The SVM1 is trained and tested to classify class 1 (LG) and class 2 (LL). In the testing phase, vote of “+1” is assigned for the class for which it belongs to. No vote is assigned for the test value not belonging to class 1 or class 2. The decision function assigns an instance to a class that has the largest number of votes, so-called Max wins strategy. The optimal hyper plane with RBF kernel for different faults is shown in Figure. 3.10.

(a) Optimum hyper plane for LG Vs LLL fault

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(b) Optimum hyper plane for LL Vs LLG fault

(c) Optimum hyper plane for LLG Vs LLLG fault

Figure 3.10 Optimal hyper plane for SVM-OVO classification with RBF

kernel

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LG ?

Input features SVM1 trained to classify LG fault

Yes

LG LL ?

SVM2 trained to classify LL fault

Yes

No

LL

No

LLG ? Yes No

SVM3 trained to classify LLG fault

LLL ? LLG

LLL

Yes No

LLLG ?

SVM4 trained to classify LLL fault

Classification failed

Yes No

LLLG

SVM5 trained to classify LLLG fault

3.3.2.5 Fault Classification with SVM-OVR

To classify faults in transmission line five SVMs are used. SVM1 is trained to classify LG fault, SVM2 is trained to classify LL, SVM3 is trained to classify LLG fault, SVM4 is trained to classify LLL fault, SVM5 is trained to classify LLLG fault. During training of SVM1, value of “+1” is assigned for LG fault and “-1” is assigned for all the remaining four faults. Similarly, rest of the SVMs is trained. The classification is made by taking the maximum of the real valued output among the five decision functions.

3.3.2.6 Binary Decision Tree (BDT) based SVM Classifier for Fault classification

To be able to classify faults, five binary SVMs are used SVMk (k=1,…,5) and each SVM is trained to classify one of the fault. The binary decision tree based SVM classifier used for fault classification is shown in Figure 3.11.

Figure 3.11 Binary decision tree based on individual binary SVMk

classifiers for fault classification

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To classify faults in transmission line five SVMs are used. SVM1 is trained to classify LG fault, SVM2 is trained with the remaining four types of fault (excluding LG fault) to classify LL fault, SVM3 is trained with the remaining three types of fault to classify LLG fault, SVM4 is trained with the remaining two types of fault to classify LLL fault and SVM5 is trained to classify LLLG fault.

3.3.2.7 Fault classification with MCELM

Classification of fault in power transmission line is identified as a problem of multi-category classification method. Extreme learning machine is used for directing multi-category classification problems in power transmission line. One ELM is designed to classify five types of fault in transmission line. To classify faults in transmission line, five neurons will be automatically set as it is a five-class problem. The classification is made by taking the maximum of the real valued output among the five decision functions.

3.3.3 Performance Evaluation of Multi-Class Classifiers for Fault Classification

Performance evaluation and comparison are carried out in terms of classification accuracy and training time. The pre-estimated optimal classifier parameters are applied for each classifier. The classification accuracy for each fault type with SVM-OVO, SVM-OVR, binary decision tree based SVM classifier and MCELM is given in Table.3.6. It can be seen from the Table.3.6 that classification accuracy of MCELM is more or less similar to MCSVM for fault classification in transmission line. The training time of SVM-OVO, SVM-OVR, binary decision tree based SVM classifiers and MCELM are given in Table.3.7. It can be seen from the Table.3.7 that ELM learning algorithm runs around 109 times faster than SVM-OVO, 425 times faster than SVM-OVR and 1476 times faster than binary decision tree based SVM classifier.

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Table.3.6 Comparison of classification accuracy of MCSVM and

MCELM for fault classification in transmission line

Fault

type

Samples

tested


SVM-OVO SVM-OVR BDT SVM classifier

MCELM

LG 100 98 98 98 97

LL 100 99 98 97 97

LLG 100 98 97 97 96

LLL 100 99 99 98 98

LLLG 100 98 98 98 97

Table.3.7 Comparison of training time of MCSVM and MCELM for

fault classification in transmission line

Training time (s)

SVM-OVO SVM-OVR BDT SVM MCELM

5.1023 19.9837 69.0772 0.0468

3.4 MULTI-CATEGORY CLASSIFICATION USING WT-ELM

FOR FAULTED LINE IDENTIFICATION AND

CLASSIFICATION IN TRANSMISSION LINE

3.4.1 Introduction

Faulted line identification and fault classification in multi bus

system is identified as a problem of multi-category classification method.

Instead of directly dealing with multi-category problems, well-known SVM

can be used, which uses some combination of binary classifiers on a One-

Versus-One (OVO) and One-Versus-Rest (OVR). However, this way of

52

implementation will result in combining many binary classifiers and thus

increase system complexities. It also causes a greater computational burden

and larger training time.

ANN based approach provides an attractive alternative to the above

approach for directing multi-category classification problems. However,

conventional neural networks usually produce lower classification accuracy

than SVM. Neural network schemes usually adopt gradient based learning

methods which are susceptible to local minima and long training time. To

overcome these difficulties, in this work, Extreme learning machine is used

for multi-category classification problem in power transmission line. When

the number of categories for the classification task is large, ELM achieves its

task with less training time, better generalization and a smaller network

structure.

In this work, combined wavelet transform-Extreme learning

machine is used for directing multi-category classification problems in

transmission line. The proposed system is tested on a practical 230-kV Tamil

Nadu Electricity Board (TNEB) transmission line under variety of fault

conditions.

3.4.2 Power System Model

The single line diagram of a part of 230-kV TNEB system is shown

in Figure 3.12. In this work, the substation bus number 2 is selected for

monitoring. The bus 2 is connected by 4 transmission lines to other substation

numbered 1,3 and 4 namely 1-2(1), 1-2(2), 2-3 and 2-4. The transmission

lines 1-2(1), 1-2(2), 2-3 and 2-4 are labeled as class-1,2,3 and 4 respectively.

53

Figure 3.12 Part of TNEB network

3.4.3 WT-ELM Approach for Multi-Category Classification

Sampled current values at the relaying point during faults are used

for faulted line identification and fault classification. The framework of the

proposed system for faulted line identification using WT-ELM is given in

Figure 3.13. Initially, the features of the line currents are extracted by first

level decomposition of the current samples using discrete wavelet transform.

Subsequently, the extracted features are applied as input to ELM for faulted

line identification. The framework of the proposed system for fault

classification using WT-ELM is given in Figure 3.14. Four ELMs are used to

classify faults in four transmission lines.

4

2

3

1

5 7

6

1-2(1)

1-2(2)

SR Pudur

TTPS

Kayathar

Pasumalai Checkanurani

Paramakudi

Anadhapur

54



ELM for faulted line identification

Line1/Line2/ Line3//Line4

DWT


ELM2

ELM3

ELM4

ELM1 LG/LL/LLG/LLL

LG/LL/LLG/LLL

LG/LL/LLG/LLL

LG/LL/LLG/LLL

Figure 3.13 Frame work for faulted line identification using WT-ELM

Figure 3.14 Frame work for fault classification using WT-ELM

3.4.3.1 Feature extraction

For faulted line identification and classification half cycle fault

current signal is pre-processed through wavelet transform to find out the

features. Through a series of studies, the sampling rate of 5 kHz (100 samples

per cycle at 50 Hz) is selected in this work. Four different types of

Daubechies mother wavelets (db1-db4) are analyzed in this work. The

wavelet coefficients after the first level decomposition of the fault current of

each phase are used as input to ELMs for training and testing.

3.4.3,2 ELM training

The fault simulation studies have been carried out for each line

under variation of fault inception angle (0º, 90º), fault resistance (1Ω, 50Ω),

load angle (10º, 60º ) and fault locations (40%, 80%) for each of the ten fault

conditions. Thus for 4 lines 4×10×2×2×2×2=640 cases are simulated for

training.

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3.4.3.3 Parameter selection

MCELM is designed for faulted line identification and

classification of fault in multi bus transmission line. For faulted line

identification one ELM is used and after several simulation studies, the

number of hidden neurons selected for faulted line identification is 24.

Similarly, for fault classification four ELMs are used for four lines. The

number of hidden neurons for lines 1, 2, 3 and 4 are 19, 23, 35 and 54

respectively.

The number of hidden neurons for faulted line identification with

LMBPNN is 11. Similarly, for fault classification the number of hidden

neurons for lines 1, 2, 3 and 4 are 7, 17, 5 and 9 respectively. Studies have

also been carried out regarding suitability of different types of Daubechies

mother wavelets and the classification accuracies obtained with different

Daubechies mother wavelets are shown in Table.3.8. From this Table, it is

observed that among different mother wavelets, db2 gives the best accuracy,

and therefore, it has been used for feature extraction.

LMBPNN with single hidden layer architecture is used for comparing

the results of ELM for the same data set. The parameter to be selected for

LMBPNN is number of hidden neurons. Initially, the number of hidden

neuron is set as 1 and is gradually increased by 1 and the optimal number of

hidden neurons for LMBPNN is then selected based on training accuracy.

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Table.3.8 Testing accuracy obtained with different mother wavelet for

faulted line identification and classification

Sl.No Type of mother wavelet


Faulted line identification


1 db1 90.87 91.87

2 db2 93.37 94.10

3 db3 89.34 91.03

4 db4 91.43 93.17

3.4.3.4 Identification of faulted line


DWT are taken as input to the ELM. The training and test patterns are

normalized to [-1 1]. One ELM is used for identification of faulted line

connected to bus number 2. In multi class classification using ELM, number

of output neurons will be automatically set equal to number of classes.

Identification of faulted line is considered as four-class problem as four lines

are connected to bus number 2. In this case, four output neurons will be

automatically set as it is a four-class problem. The classification is made by

taking the maximum of the real valued output among the four decision

functions i.e, neuron three has the highest output means input belongs to fault

in line 3.

3.4.3.5 Fault classification


DWT are taken as input to the ELM. The training and test patterns are

normalized to [-1 1]. To classify fault in four line, four ELM are trained.

ELM1 is trained to classify fault in line labeled 1, ELM2 is trained to classify

57

fault in line labeled 2, ELM3 is trained to classify fault in line labeled 3, and

ELM4 is trained to classify fault in line labeled 4. ELM1 is designed to

classify four types of fault in transmission line labeled 1. In ELM1, four

neurons will be automatically set as it is a four-class problem. Similarly, all

other ELMs are also designed to classify faults in respective transmission line.

The classification is made by taking the maximum of the real valued output among the four decision functions.

3.4.4 Results and Discussions

To test the robustness of the developed algorithm, the fault simulation studies have been carried out with varying fault resistance, fault inception angle, pre-fault power level and fault distance. An extensive study is carried out in order to ascertain the overall performance of WT-ELM for faulted line identification and classification. The test sets are composed of over 7680 cases for faulted line identification and 13824 cases for fault classification including different fault resistance (10Ω,20Ω,100Ω,200Ω), fault inception angle (36º,72º,108º,126º), load angle (20º,30º,60º) and fault distance (45%, 55%, 65%,75%).

Test results for faulted line identification are given in Table 3.9. As observed from Table 3.9, the accuracy of the proposed method is quite satisfactory for all types of faults. The performance of WT-ELM is also compared with WT-LMBPNN and results are given in Table.3.10. As observed from Table 3.10, the overall accuracy for faulted line identification obtained by WT-ELM is 93.37% while the overall accuracy obtained by WT-LMBPNN is 77.77%. The test results for fault classification are given in Table.3.11. As observed from Table.3.11, the overall classification accuracy for fault classification with WT-ELM is 94.10% and accuracy with WT-LMBPNN is 80.38% for variation in parameters. Thus ELM has better generalization than LMBPNN. Comparison results of ELM and LMBPNN for fault classification are given in Table.3.12. As observed from Table 3.10 and 3.12, training time of ELM is less than LMBPNN.

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Table 3.9 Comparison of testing accuracy of WT-ELM and WT-LMBPNN for faulted line identification

Line Samples tested

WT-ELM WT-LMBPNN % Accuracy Correct

classification Mis-

classification Correct

classification Mis-

classification WT-ELM

WT-LMBPNN

Line1 1920 1845 75 1448 472 96.09 75.41

Line2 1920 1819 101 1549 371 94.73 80.67

Line3 1920 1767 153 1567 353 92.03 81.61

Line4 1920 1740 180 1409 511 90.62 73.38

Total 7680 7171 509 5973 1707 93.37 77.77

Table.3.10 Performance comparison of WT-ELM and WT-LMBPNN classifiers for faulted line identification

Classifiers Training time (sec)

Testing accuracy (%) Number of neurons

WT-ELM 0.1256 93.37 24

WT-LMBPNN 32 77.77 11

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Table.3.11 Comparison of testing accuracy of WT-ELM and WT-LMBPNN for fault classification

Fault type

Samples tested

WT-ELM WT-LMBPNN % Accuracy Correct

classification Mis-

classification Correct

classification Mis-

classification WT-ELM

WT-LMBPNN

LG 2304 2202 102 1893 411 95.57 82.16 LL 4608 4335 273 3626 982 94.07 78.68

LLG 4608 4346 262 3787 821 94.31 82.18 LLL 2304 2126 178 1806 498 92.27 78.38 Total 13824 13009 815 11112 2712 94.10 80.38

Table.3.12 Comparison of ELM and LMBPNN classifiers for fault classification

Performance ELM1 ELM2 ELM3 ELM4 LMBPNN (line1)

LMBPNN (line2)

LMBPNN (line3)

LMBPNN (line4)

Training time (sec)

0.1553 0.1667 0.1723 0.1821 39 37 41 38

Number of neurons

19 23 35 54 7 17 5 9

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The training and testing parameters are given in Table 3.13. As

shown in Table 3.13, the system is trained with a fault resistance of 1 Ω and

50 Ω only and tested with fault resistance up to 200 Ω. Similarly, the

proposed method is also tested with parameters which are not included during

training. Hence, it is observed that the proposed method is robust to parameter

variations.

Table.3.13 Training and testing patterns for WT-ELM and WT-

LMBPNN

Parameters Training Testing

Fault resistance 1Ω, 50Ω 10Ω,20Ω,100Ω, 200Ω

Fault Inception angle 0º,90º 36º,72º,108º,126º

Fault distance 40%,80% 45%,55%,65%,75%

Load angle 10º,60º 20º,30º, 60º

Studies also carried out for faulted line identification and

classification using the current samples directly as input to ELM without pre-

processing by DWT. The overall classification accuracy obtained from this

study for faulted line identification and classification is 89.07% and 90.63%

respectively. As the technique presented in this work gives better accuracy,

the combined WT-ELM technique with half cycle current data can be

recommended for faulted line identification and classification.

3.5 CONCLUSION

The following conclusions are arrived from the work on fault

diagnosis of transmission line.

The performance of SVM and ELM can be improved by

extracting fault current samples using discrete wavelet

61

transform. The studies show that db2 mother wavelet performs well for fault classification and location.

The protection scheme designed for single circuit

transmission line using combined wavelet transform support

vector machine provides accurate and robust fault classifier

for faulty phase detection and classification. On testing 9600

fault cases for wide variation in operating conditions, the

average fault classification accuracy of WT-SVM is 99.11%

and WT-ELM is 91.89%. Moreover, the performance of

SVM is better than ELM for unseen data.

WT-SVR and WT-ELM based fault locator performs well

for varying fault resistance as maximum fault location error

for both locator is less than 1%.

The performance of multi-category classification methods

like SVM and ELM are compared for fault classification in

transmission line. SVM for multi-category classifications is

done by modifying the binary classification method of SVM

to a one-versus-one, one-versus-rest and binary decision tree

based SVM classifiers. This inevitably involves more

classifiers, greater system complexities and computational

burden and a longer training time. ELM can perform the

multi-category classification directly, without any

modification.

For fault classification in transmission line using multi-

category classification methods, classification accuracy of

MCELM is more or less similar to MCSVM. It can also be

seen that the ELM learning algorithm runs around 109 times

62

faster than SVM-OVO, 425 times faster than SVM-OVR and

1476 times faster than binary decision tree based SVM

classifier.

A new approach for faulted line identification and fault

classification in a practical system using WT-ELM is

presented. Faulted line identification and classification in a

multi bus system is identified as a problem of multi-category

classification and so MCELM is applied which reduces the

complexity, network structure and training time.

The performance of WT-ELM for multi-category

classification problem in transmission line is compared with

LMBPNN. The results show that ELM needs less training

time compared to LMBPNN classifiers for faulted line

identification and fault classification. Also, the classification

accuracy of WT-ELM is much better than WT-LMBPNN.

Also, the WT-SVM and WT-ELM approaches are robust to

parameter variations such as fault distance, fault inception

angle, pre-fault power level, source impedance and fault

resistance.

The intelligent approaches using WT-SVM and WT-ELM

are capable of protecting transmission line under wide

variations in operating conditions in about half-a-cycle

period of fundamental frequency, and hence, are quite

suitable for integration in digital distance protection scheme.

chapter 3 intelligent fault diagnosis of power...

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