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Fault Location Estimation on Transmission Lines

Using Wavelet Transform and Artificial Neural

Network

Las Vegas Nevada, USA, June 26-29, 2006

S. EKICI

Department of Electrical Education

Firat University

Elazig, Turkey

S. YILDIRIM

Department of Electrical Education

Firat University

Elazig, Turkey

Abstract - This paper presents a wavelet transform

(WT) and artificial neural network (ANN) basedalgorithm for estimating fault location on

transmission lines. The algorithm is developed as a

one-end frequency based technique and used both

voltage and current effect resulting from remote end

of the power system. Fault simulation is carry out in

Alternative Transient Program (ATP). One cycle of

waveform, covering pre-fault and post-fault

information is abstracted for analysis. The discrete

wavelet transform (DWT) is used for data

preprocessing and this data are used for training

and testing ANN. Five types of mother wavelet are

used for signal processing to identify a suitable

wavelet family that is more appropriate for use in

estimating fault location. It is found that the

proposed method gives satisfactory results and will

be useful for estimating fault location.

Keywords: Transmission lines, fault location,

wavelet transform, artificial neural network.

1.0 Introduction

Fault location estimation is very important issuein power system engineering in order to clear faultsquickly and restore power supply as soon as possible

with minimum interruption. This is necessary forhealth of power equipment and satisfaction ofcustomer. In the past, several methods have beenused for estimating fault location with differenttechniques such as line impedance based numericalmethod, traveling wave methods and fourier analysis

[1]. Nowadays, high frequency components in steadof traditional methods have been used [2]. Fouriertransform are used to abstract fundamentalfrequency component but it has been shown thatfourier transform based analysis sometimes are notexactly enough. Recently wavelet transform hasbeen used extensively for estimating fault location

accurately. The most important characteristic ofwavelet transform is to analyze the waveform ontime-scale rather than frequency. In this paper,discrete wavelet transform which has a change in the

analyzing scale by the factor of two has been used

[3].This paper is aimed to propose a WT and ANNbased fault locator algorithm. By this purpose, 380kV transmission line has been simulated using ATP[4]. The voltage and current waveforms which areobtained from receiving end of power system hasbeen abstracted for further analysis. These signalsare then used in DWT. Five types of motherwavelets, Daubechies (db5), Biorthogonal (bior5.5),Coiflets (coif5), Symlets (sym5) and ReverseBiorthogonal (rbio5.5) have been considered forsignal processing. The data sets which are obtainedfrom DWT, has been used for training and testing ofANN.

2.0 Wavelet Transform

Wavelet transform (WT) is a mathematical

technique used for many application of signal

processing [3], [5]. Wavelet is much more powerful

than conventional method in processing the

stochastic signals because of analyzing the

waveform time-scale region. In wavelet transform,

the band of analysis can be adjusted so that low

frequency and high frequency components can be

windowing by different scale factor. Recently WT iswidely used in signal processing applications, such

as denoising, filtering, and image compression [1].

Many pattern recognition algorithms have been

developed based on the wavelet transforms. It also

has been used widely by the power system

researchers. According to scale factor, wavelet

categorized different section. In this paper the

wavelet which is named Discrete Wavelet

Transform (DWT) by two scale factor was used. For

any function ( f ), DWT is written as,

( ) ( )∑

−ψ =

k m

m

m 2

2k n

k f 2

1

n,mDWT (1)

where, ψ is mother wavelet [3], [5]. The

decomposition for three levels is shown Figure 1.

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Signal

cD3cA3

cD2cA2

cD1cA1

Fig.1. Three level signal decomposition diagram.

3.0 Artificial Neural Network

Artificial neural network (ANN) is made up ofmany computational processing elements called

neurons or nodes [6], [7]. These nodes operate inparallel and are connected together in topologies thatare loosely modeled after biological neural systems[8]. The training of ANN is carried out to associatecorrect output responses to particular input pattern.Once trained properly, an ANN has the ability togeneralize when similar, but not identical patternsare introduced to the network [9].

A popular model for ANN is the multilayered one

shown in Figure 2. The multi-layer perceptron has

the ability of handling complex and non-linear

input-output relationship with hidden layer [10].

Application of ANN to pattern recognition [11], [12]

has been widely studied.

Multilayer feed forward networks [13] were

chosen to process the prepared input data which

were obtained from wavelet transform. For

designing WT and ANN based fault locator, 6 inputsand 1 output neuron were used for training ANN.

For testing ANN, 5 inputs and 1 output neuron were

chosen among 11 fault data which were obtained

from ATP simulation.

The number of neurons for the hidden layer is

chosen to be 12. Hyperbolic tangent function was

used as the activation function of the hidden layer

neurons. Saturated linear function was used for the

output layer.

V&I

V&I

x1

x6

Location

input layer

hidden layer

output layer

Fi

Fig. 2. The proposed ANN structure.

4.0 Case Studies and SimulationResults

In this paper, fault location estimation was

performed on a power system simulation as shown

in Figure 3. The simulation was performed usingATP. The detection of fault location using

conventional methods involves very complicate

operation and may be introduced error. The main

purpose of this paper is to detect distance from

sending end to fault point accurately and easily.

Three phase model of overhead power transmission

line has 360 km long and 380 kV voltage. The fault

location is assumed at 11 point L1-L11 every 36 km

distance. The simulation time is 110 ms with 10sµ

time step. Scale factor is 1:1000 for power system

characteristic parameter. Considering the scale

factor, line resistance 13 ohm, line inductance 290

mH, mutual capacitance 1 Fµ , earth resistance 5

ohm and earth capacitance 2 Fµ was selected.

L2 L3 L4 L5 L6 L7 L8 L9 L10 L11L1

Load

380 kV Line

360 km

Fig. 3. Sample power system.

The ANN’s were trained and tested for different

fault point and wavelet families when a phase to

ground fault occurred on the transmission line.

Actual location of the faults and the outputs of ANN

are shown in Table 1. For every ANN output, the

results were compared as shown

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Table 1. Actual and estimated fault location

Actual

location,kmdb5 bior5.5 coif5 sym5 rbio5.5

36 35.74 37.45 33.66 36.25 34.99

108 107.98 107.74 108.34 108.03 108.12180 180.03 180.12 180.04 180.12 180.15

252 251.93 252.07 252.05 252.10 252.02324 324.00 324.05 323.90 324.21 323.95

d b 5

b i o r 5 . 5

c o i f 5

s y m 5

r b i o 5 . 5 a

c t u a l

31

32

33

34

35

36

37

38

a c

t u a l

d b 5

b i o r 5 . 5

c o i f 5

s y m 5 r b

i o 5 . 5

107,4

107,5

107,6

107,7

107,8

107,9

108

108,1

108,2

108,3

108,4

a c t u a l d

b 5

b i o r 5 . 5

c o i f 5

s y m 5 r b i o

5 . 5

179,9

179,95

180

180,05

180,1

180,15

180,2

ti

a c t u a l

d b 5

b i o r 5 . 5

c o i f 5

s y m 5

r b i o 5 . 5

251,8

251,85

251,9

251,95

252

252,05

252,1

252,15

b i o r 5 . 5

c o i f 5

s y m 5

r b i o 5 . 5

a c t u a l

d b 5

323,7

323,75

323,8

323,85

323,9

323,95

324

324,05

324,1

324,15

324,2

324,25

Fig. 4. The comparison of actual and estimated fault

locations.

5.0 Conclusion

The application of the wavelet transform to detectfault location in transmission lines has beeninvestigated. The most suitable wavelet family hasbeen made to identify for use in detection of faultlocate. Five different types of wavelet have beenchosen as a mother wavelet for ANN’s input andoutput data set. It is found that better results can beproduced using Daubechies ‘db5’ wavelet. The useof ANN as a pattern classifier to simulate a faultlocator has been also investigated. The results ofANN are very satisfactory.

The simulation for 380 kV transmission line was

performed using ATP. Assuming the fault occur at

different point of transmission line, fault current and

voltage waveform abstracted for further analysis.

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