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Artificial neural network and support vector machine approach for locating faults in radial distribution systems

Artificial neural network and support vector machine approach for locating faults in radial distribution systems
Artificial neural network and support vector machine approach for locating faults in radial distribution systems
This paper presents an artificial neural network (ANN) and support vector machine (SVM) approach for locating faults in radial distribution systems. Different from the traditional Fault Section Estimation methods, the proposed approach uses measurements available at the substation, circuit breaker and relay statuses. The data is analyzed using the principal component analysis (PCA) technique and the faults are classified according to the reactances of their path using a combination of support vector classifiers (SVCs) and feedforward neural networks (FFNNs). A practical 52 bus distribution system with loads is considered for studies, and the results presented show that the proposed approach of fault location gives accurate results in terms of the estimated fault location. Practical situations in distribution systems, such as protective devices placed only at the substation, all types of faults, and a wide range of varying short circuit levels, are considered for studies. The results demonstrate the feasibility of applying the proposed method in practical distribution system fault diagnosis.
Power Distribution Systems Artificial Neural Networks Fault Location Support Vector Machines
0885-8977
710-721
Thukaram, D
f99462d8-5e91-4227-9a5d-8e6584eb0d1b
Khincha, H P
e560b162-72f5-4b26-907f-7e5e81904902
Pakka, V H
06b6d9a8-9645-4292-9b42-4fc2a31b04a4
Thukaram, D
f99462d8-5e91-4227-9a5d-8e6584eb0d1b
Khincha, H P
e560b162-72f5-4b26-907f-7e5e81904902
Pakka, V H
06b6d9a8-9645-4292-9b42-4fc2a31b04a4

Thukaram, D, Khincha, H P and Pakka, V H (2005) Artificial neural network and support vector machine approach for locating faults in radial distribution systems. IEEE Transactions on Power Delivery, 20 (2), 710-721.

Record type: Article

Abstract

This paper presents an artificial neural network (ANN) and support vector machine (SVM) approach for locating faults in radial distribution systems. Different from the traditional Fault Section Estimation methods, the proposed approach uses measurements available at the substation, circuit breaker and relay statuses. The data is analyzed using the principal component analysis (PCA) technique and the faults are classified according to the reactances of their path using a combination of support vector classifiers (SVCs) and feedforward neural networks (FFNNs). A practical 52 bus distribution system with loads is considered for studies, and the results presented show that the proposed approach of fault location gives accurate results in terms of the estimated fault location. Practical situations in distribution systems, such as protective devices placed only at the substation, all types of faults, and a wide range of varying short circuit levels, are considered for studies. The results demonstrate the feasibility of applying the proposed method in practical distribution system fault diagnosis.

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More information

Published date: April 2005
Keywords: Power Distribution Systems Artificial Neural Networks Fault Location Support Vector Machines
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 263109
URI: http://eprints.soton.ac.uk/id/eprint/263109
ISSN: 0885-8977
PURE UUID: 813d01e2-c94a-4233-bbfe-9ef69928b600

Catalogue record

Date deposited: 14 Oct 2006
Last modified: 26 Apr 2022 17:58

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Contributors

Author: D Thukaram
Author: H P Khincha
Author: V H Pakka

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