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ARTIFICIAL NEURAL NETWORK

APPROACH FOR LOCATING FAULT INRADIAL DISTRIBUTION SYSTEM

A Major Project submitted to

Rajiv Gandhi Proudyogik i Vishwavidyalaya, Bhopal

in partial fulfillment of the for the award of

degree of requirements

Bachelor of Engineering

(Electrical And Electronic Engineering)

by

Aayush patidar

Anjali Dubey

Hemant Mukati

Rohit Maula

Under the guidance of

Prof. Vaishali Holkar

Department of Electrical And Electronic Engineering

Chameli Devi Group of Institutions, Indore

2014-15

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DECLARATION

We certify that the work contained in this report is original and has been done by us /me

under the guidance of my supervisors.

a. The work has not been submitted to any other Institute for any degree or diploma.

b. I have followed the guidelines provided by the Institute in preparing the report.

c. I have conformed to the norms and guidelines given in the Ethical Code of

Conduct of the Institute.

d.

Whenever I have used materials (data, theoretical analysis, figures, and text) from

other sources, I have given due credit to them by citing them in the text of the

report and giving their details in the references. Further, I have taken permission

from the copyright owners of the sources, whenever necessary.

Aayush patidar Hemant Mukati

0832ex111001 0832ex111021

Rohit Maula Anjali Dubey

0832ex111043 0832ex111008

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CDGIS

CHAMELI DEVI SCHOOL OF ENGINEERING (CDSE),

INDORE

CERTIFICATE

Certified that the project report entitled ANN Approach For Locating Fault In Radial

Distribution System is a bonafidework done under my guidance by Aayush

patidar(0832ex111001);Anjali Dubey(0832ex111008); Hemant Mukati(0832ex111021);

Rohit Maula(0832ex111043) in partial fulfillment of the requirements for the award of

degree of Bachelor of Engineering in Electrical And Electronics Engineering.

Date: Prof. Vaishali Holkar

Guide

Dr.Sanjay Warkad Dr. C.N.S. Murty

Head of the Department Dean

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Acknowledgement

We take the opportunity to express our cordial gratitude and deep sense of indebtedness

to our guide Prof. Vashali Holkar, for her valuable guidance and inspiration throughoutthe project duration. We are also thankful to Prof. Sourabh Kothari for the technical

guidance and assistance in project whenever we needed. We feel grateful to all these

faculties for their innovative ideas and inspiring words which led to accomplishment of

project.

We express our gratitude to Dr. Sanjay B. Warkad (Professor & Head, Electrical &

Electronics Engineering Department), Prof. Shweta Jain and Prof. Pushpendra Mishra for

his invaluable support and encouragement at every stage of project duration.

We owe our deep sense of gratitude towards Dr. C.N.S. Murty, Dean, Chameli Devi

School Of Engineering for support and guidance.

At the same time, we would like to thank all the other faculty members and non-teaching

staff in Electrical & Electronics Engineering Department, who cooperated with us in

direct and indirect way.

Aayush patidar

Anjali Dubey

Hemant Mukati

Rohit Maula

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ABSTRACT

In power distribution system it is essential to minimize transients, line voltage dips and

spikes which are present due to the occurrence of fault. As a fault occurs in the power

system, the necessary steps will be taken to remove the fault using relays & circuitbreakers conventionally. But, if this fault occurrence is predicted in advance, then we can

maintain a better voltage profile and quality power to consumers.

Then effective fault location technique is proposed. Using the current samples of the

distribution feeder measured at the substation, this proposed technique first determines

the type of fault. Furthermore, an artificial neural network (ANN) is trained for each type

of fault. The ANNs are trained to estimate the fault distance to the substation .The Inputs

of the ANNs are data of 3 phase voltages, currents and active powers of the feeder are

measured at the substation in pre-fault and fault stages. The proposed method does not

need data of loads of consumers. The proposed method is tested on IEEE 14-bus testfeeder. Each ANN is trained by operating patterns. In order for ANNs cover the total

operating space of the radial distribution network; fault location, fault resistance and

loads are changed in each pattern. The outputs of ANNs for the operating test patterns,

not presented in the training stage, are shown the accuracy of the ANNs. The trained

ANNs can estimate fault distance to the substation; even the structure of the distribution

network is changed. Proposed method is effective while the input data are contained

errors of measuring

.Neural network in the power system, which can learn and therefore be trained to find

solutions, recognize patterns, classify data and forecast future events. In general, we use acontrol methodology in Artificial Neural Network (A.N.N) which can classify & predict

the future events. It will be shown that it is possible to predict with good accuracy, the

magnitude of control variables based on previously acquired samples and use these

values to recognize the type of abnormal event that is about to occur on the network.

This type of computation performed by neural networks is termed as Neuro

Computing. It is one of fastest growing areas of research in the fields of Artificial

Intelligence and pattern recognition. A neural network design and simulation

environment for real time fault diagnosis and detection is presented. An analysis of the

learning & generalization characteristics of elements in power system is presented usingNeural Network toolbox in Matlab.

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TABLE CONTENT

Sl. No CONTENTSPage

No.

Title Page i

Declaration ii

Certificate iii

Acknowledgement iv

List of Figures vList of Tables vi

Abstract vi

1 Chapter 1-Introduction 16

1.1 Section (Introduction) 1-2

1.2 Section (Proposed Methodology) 3-4

1.3 Section (Literarture Review) 5-6

2 Chapter 2-Theoretical Study 7-30

2.2 Section (ARTIFICIAL NEURAL NETWORK) 7-22

2.3 Section (Fault Analysis) 23-30

3 Chapter 3-Conclusion and Future scope 31

References

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LIST OF FIGURE

Sl. No CONTENTSPage

No.

1 Flowchart of proposed fault location method 4

2 Neural network 12

3 Two layer neural network 19

4 Four common fault in power system 23

5 One line diagram of simple 3 bus power system 25

6 Impedence bus system 25

7 Thevenin equivalent network 26

8 Positive, negative and zero network 27

9 3 phase equivalent circuit for L-G fault 27

10 Single phase L-G fault sequence 28

11 3 phase equivalent circuit for L-L fault 29

12 Single phase L-L fault sequence 29

13 3 phase equivalent circuit for L-L-G fault 30

14 Double line to ground fault sequence 30

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List of table

Sl. No CONTENTSPage

No.

1 Determine type of fault 4