a dwt approach for detection and classification of transmission line faults

IJIRST –International Journal for Innovative Research in Science & Technology| Volume 3 | Issue 02 | July 2016 ISSN (online): 2349-6010

All rights reserved by www.ijirst.org 102

A DWT Approach for Detection and

Classification of Transmission Line Faults

Prasad P. Kawale Prof. C. Veeresh

PG student Assistant Professor

Department of Electrical Engineering Department of Electrical Engineering

SND College of Engg. & RC, Yeola, (M.S.) India SND College of Engg. & RC, Yeola, (M.S.) India

Abstract

The rapid growth of electric power systems has resulted in a large increase of the number of lines in operation and their total

length. These lines are exposed to faults because of many reasons such as a result of lightning, short circuits, faulty equipments,

miss-operation, human errors, overload, and aging etc Due to these faults .long term power outages for customers and may lead

to significant losses. Therefore fast detection and classification of transmission line faults is important in maintaining a reliable

power system operation and to ensure quality performance of the power system. This paper aims at detecting and classifying the

transmission line faults by using Discrete Wavelet Transform (DWT) and Artificial neural network (ANN). Various types of

fault conditions such as Single line-to-ground faults (L-G), Line-to-Line faults (L-L) and Double Line-to-ground faults (L-L-G)

are simulated in Power System Computer Added Design (PSCAD) software. An extremely large data set of current and voltage

signals is generated by simulating various types of fault conditions by varying the system parameters. Then an advanced signal

processing tools such as discrete wavelet transform (DWT) is used for calculating detail coefficients energy of the fault signals.

Depending upon the detail coefficients energy the fault will be detected. A properly configured Artificial Neural Network (ANN)

can be utilized for classification of the faults based on the DWT signal.

Keywords: Power System Computer Added Design, Discrete Wavelet Transform, Artificial Neural Network,

Transmission line fault detection, fault type classification

_______________________________________________________________________________________________________

I. INTRODUCTION

Now-a-days for the fault detection, a high frequency components technique is used. This new technique is also known as

transient based techniques. In this technique, it is essential that the fault signal has to be analyzed accurately. Wavelet transform

has been used extensively for signal processing in recent years. It has been found that the wavelet transform is capable of

investigating the transient signals generated in a power system.

Wavelet theory is the mathematics, which deals with building a model for non-stationary signals, using a set of components

that look like small waves, called wavelets. The wavelet transformation is a tool which helps the signal to analyze in time as well

as frequency domain effectively. It uses short windows at high frequencies, long windows at low frequencies.

Wavelet transform has the advantage of fast response and increased accuracy as compared to conventional techniques. Using

multiresolution analysis, a particular band of frequencies present in the signal can be analyzed. The detection of fault is carried

out by the analysis of the wavelets coefficients energy related to currents and voltages. On the other hand, properly configured

Artificial Neural Network (ANN) can be utilized for classification of the faults based on the DWT signal. The neural networks

have the ability to learn, generalize and parallel processing, have made their applications for many systems ideal. The use of

neural network as pattern classifiers is among their most common and powerful applications.

II. DISCRETE WAVELET TRANSFORM

In DWT a time- scale representation of a discrete signal is obtained using digital filtering technique. The desired signal which to

be analyzed is passed through different filters having different cut off frequencies at different scales. In discrete wavelet

transform the scale is changed by up sampling and down sampling. Normally half band high pass and low pass filters are used.

The DWT is computed by successive lowpass and high pass filtering of the discrete time-domain signal as shown in figure1.

This is called the Mallat algorithm or Mallat-tree decomposition. Its significance is in the manner it connects the continuous-time

multiresolution to discrete-time filters. In the figure-1, the signal is denoted by the sequence x[n], where n is an integer. The low

pass filter is denoted by G0 while the high pass filter is denoted by H0. At each level, the high pass filter produces detail

information[n], while the low pass filter associated with scaling function produces approximations, a[n].

A DWT Approach for Detection and Classification of Transmission Line Faults (IJIRST/ Volume 3 / Issue 02/ 020)


Fig. 1: Wavelet Decomposition up to 7th level containing different frequency bands.

For the system taken here, we used 10kHz as sampling frequencies.

The typical wavelets used to detect power disturbance are mainly Haar, Daubechies, Coiflets, Bi-orthogonal, Morlet and

Symlets. In wavelet analysis, a MOTHER WAVELET is chosen as the prototype for generating other basis window functions.

i.e., all the window functions are obtained by translating and scaling the mother wavelet.

III. METHODOLOGY

In this work of detection and classification of EHV transmission line faults is carried out by using following steps.

A 765kV EHV transmission system between UNNAO - ANPARA shown in Fig.3 is simulated using PSCAD software.

Different types of faults are created at different locations with different inception angle.

Voltage and current signals are captured with 10 KHz sampling frequency.

Construction of modal signal.

Modal signal is decomposed upto 7th level using DWT.

The energy is computed from the detail coefficients of the modal signal which is used for the detection of the faults.

Preparation of data sheet of 7th level energies and importing it to ANN.

Training of ANN and validation of the trained ANN using test patterns to check its correctness and generalization.

Combination of different fault conditions are to be considered and training patterns are required to be generated by

simulating different kinds of faults on the power system.

Classification of faults is done by using ANN.



Fig. 2: Algorithm for classification of faults.

IV. SIMULATED SYSTEM

Fig-3 shows the 765kv single line diagram of 765kV transmission system between Unnao and Anpara (U.P.). The length of

transmission line is extended upto 430km.

Fig. 3: Single line diagram of 765kV Transmission system between Unnao and Anpara

Fig.-4 shows the same transmission system is simulated in PSCAD software with detailed specification.



Fig. 4: PSCAD simulated network of 765kV transmission line.

V. FAULT ANALYSIS

In this Paper 765KV network is simulated in PSCAD software with a sampling frequency of 10 KHZ. Different types of fault

LG, LL and LLG are created at different locations on the 430 km long transmission line at an interval of 50 km including the

different inception angle 0, 90, and 180 degrees. So for simplicity 5 different locations are considered here for the analysis of

fault i.e. 50km, 100km, 150km, 200km and 215 km from the sending end. Hence, 5 fault locations, three inception fault angles

and three different types of faults (5*3*3=45) constitute 45 cases. For the analysis of faults the three-phase line currents, the

three-phase sending end and receiving end voltages are recorded from the simulation. The data generated from the simulation is

very large. Therefore to avoid the complexity of handling of such huge data, a modal signal of current and sending end and

receiving end voltages is used.

The three phase voltages and three phase current signals for different fault conditions for different faults are taken into

account. In this paper for the fault classification different conditions are used. Fig. 5 shows the simulation waveforms of line

current (I), sending end voltage (V4) and receiving end voltage (V5) of 765kv transmission line for LG fault at a distance of

215km from the source and at 900 instant of blue phase and Fig. 6 shows the modal signal of LG fault. Fig. 7 to 9 shows the

wavelet decomposition for modal current signal, sending end & receiving end modal voltage signal upto 7th level using db4 as

mother wavelet. Similarly fig. 10 to 19 shows for the LL and LLG fault resp.

Fig. 5: Simulation waveforms of V & I of LG Fault at 215km with 900 inception angle.



Fig. 6: Modal signal of LG fault.

Fig.7: Wavelet decomposition for modal current signal (IM) of LG fault.



Fig. 8: Wavelet decomposition for modal voltage signal of sending end (VS) of LG fault.

Fig. 9: Wavelet decomposition for modal voltage signal of receiving end (VR) of LG fault.



Fig. 10: Simulation waveforms of V & I of LL Fault.

Fig. 11: Modal signal of LL fault.



Fig. 12: Wavelet decomposition for modal current signal of LL fault

Fig. 13: Wavelet decomposition for modal voltage signal of sending end (VS) of LL fault.



Fig. 14: Wavelet decomposition for modal voltage signal of receiving end (VR) of LL fault.

Fig.15: Simulation waveforms of V & I of LLG Fault.



Fig. 16: Modal signal of LLG fault.

Fig. 17: Wavelet decomposition for modal current signal(IM) of LLG fault.



Fig. 18: Wavelet decomposition for modal voltage signal of sending end (VS) of LLG fault.

Fig. 19: Wavelet decomposition for modal voltage signal of receiving end (VR) of LLG fault.



VI. ANN USED AS FAULT CLASSIFIER

Artificial neural networks are composed of simple elements which operate in parallel with interconnection between them. The

weights of connection determine the network function. It is considered as the simplest kind offered forward network. An

Artificial neural network when created, has to be configured which is done using training function. The elements of the network

are adjusted automatically to get a particular target output for specific input. A network can have several layers. Each layer has a

weight matrix, a bias vector and an output vector. Each neuron in one layer has direct connections to the neurons of the

subsequent layer. The second class of feed forward neural network distinguishes itself by the presence of one or more hidden

layers, whose computation nodes are called hidden neurons or hidden units. By increasing the number of layers and neurons the

network is enabled to extract higher order. In this work, Principal component analysis (PCA) network of ANN is used as fault

classifier.

VII. RESULTS AND DISCUSSION

After training, the artificial neural network based fault classifier is extensively tested using independent data set consisting of

different types of faults, different fault locations with different inception angles. The result of classification for a given system

are as shown in table-1. To inquire into the accuracy of the proposed method in these cases, 100% accurate results are found for

LG, LL and LLG type of faults. Hence principal component analysis, modal based fault classifier classifies the types of faults

with an accuracy of 100% in a very fast and effective manner.

The upper part of Table-1 shows the number of readings taken by ANN for classification of respective fault for 4th processing

element. From the table it is observed that the sum of the readings taken for three faults is 11 which is 25% of total readings

input to ANN i.e. 45 readings and the lower part of table shows the various errors and percentage of fault classification accuracy

for 4th processing element. Table – 1

Output / Desired LG LL LLG

LG 6 0 0

LL 0 2 0

LLG 0 0 3

Performance LG LL LLG

MSE 0.038605979 0.006681175 0.152392245

NMSE 0.155710783 0.044912343 0.768310901

MAE 0.169711048 0.055192952 0.322101867

Min Abs Error 0.023260345 0.00295256 0.010735036

Max Abs Error 0.343454236 0.241157773 0.621906662

R 0.980436039 0.97734324 0.663213496

Percent Correct 100 100 100

VIII. CONCLUSION

The work presented in this paper provides a new technique for EHV transmission line of 765kV and fault classification is done

by using db4 as mother wavelet. Decomposition of modaling end voltage at level 7th differentiate clearly the types of fault by

observing the decomposition waveform diagram. The time required for classification of the faults after constructing modal signal

is less so number of inputs is reduced and it required less memory space because they user of modal signal complexity for taking

adecision for ANN is reduced.

The proposed strategy with the use of DWT-ANN based algorithm is promising and suggests that this approach could lead to

useful application in an actual power system.

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a dwt approach for detection and classification of transmission line faults

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