Fault identification in an unbalanced distribution system using support vector machine

Gururajapathy, S.S. and Mokhlis, Hazlie and Illias, Hazlee Azil and Bakar, Ab Halim Abu and Awalin, L.J. (2016) Fault identification in an unbalanced distribution system using support vector machine. Journal of Electrical Systems, 12 (4). pp. 786-800. ISSN 1112-5209

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Official URL: http://journal.esrgroups.org/jes/papers/12_4_11.pd...

Abstract

Fast and effective fault location in distribution system is important to improve the power system reliability. Most of the researches rarely mention about effective fault location consisting of faulted phase, fault type, faulty section and fault distance identification. This work presents a method using support vector machine to identify the faulted phase, fault type, faulty section and distance at the same time. Support vector classification and regression analysis are performed to locate fault. The method uses the voltage sag data during fault condition measured at the primary substation. The faulted phase and the fault type are identified using three-dimensional support vector classification. The possible faulty sections are identified by matching voltage sag at fault condition to the voltage sag in database and the possible sections are ranked using shortest distance principle. The fault distance for the possible faulty sections isthen identified using support vector regression analysis. The performance of the proposed method was tested on an unbalanced distribution system from SaskPower, Canada. The results show that the accuracy of the proposed method is satisfactory.

Item Type: Article
Uncontrolled Keywords: Support Vector Machine; Faulted Phase; Fault type; Faulty section; Fault distance
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 05 Oct 2017 02:05
Last Modified: 10 Oct 2019 02:36
URI: http://eprints.um.edu.my/id/eprint/17883

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