Classification and regression analysis using support vector machine for classifying and locating faults in a distribution system

Gururajapathy, Sophi Shilpa and Mokhlis, Hazlie and Illias, Hazlee Azil (2018) Classification and regression analysis using support vector machine for classifying and locating faults in a distribution system. Turkish Journal of Electrical Engineering and Computer Sciences, 26 (6). pp. 3044-3056. ISSN 1300-0632

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Official URL: https://doi.org/10.3906/elk-1711-194

Abstract

Various fault location methods have been developed in the past to identify the faulty phase, fault type, faulty section, and distance. However, this identification is commonly conducted in a separate manner. An effective fault location should be able to identify all of these at the same time. Therefore, in this work, a method using a support vector machine (SVM) to identify the fault type, faulty section, and distance considering the faulty phase is proposed. The proposed method uses voltage sag magnitude of the distribution system as the main feature for the SVM to identify faults. The fault type is classified using a directed acyclic graph SVM. The possible faulty sections are identified by estimating the fault resistance using support vector regression and matching the voltage sag data in the database with the actual voltage sag data. The most possible faulty sections are ranked using ranking analysis. The fault distance for the possible faulty sections is then identified using support vector regression analysis and its overfitting or underfitting issues are addressed by the proper selection of a regularization parameter. The feasibility of the proposed method was tested on an actual Malaysian distribution system. The results of faulty phase, fault type, faulty section, and fault distance are analyzed. The performance of the proposed method is compared with various other intelligent methods such as the artificial neural network, deep neural network, extreme learning machine, and kriging method. The test results indicate that the faulty phase and fault type yield 100% accurate results. All the faulty sections are identified and the proposed method obtains reliable fault location.

Item Type: Article
Uncontrolled Keywords: Artificial neural network; Deep neural network; Electrical power; Extreme learning machine; Fault location; Kriging; Power distribution; Support vector machine
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 20 Aug 2019 07:49
Last Modified: 20 Aug 2019 07:49
URI: http://eprints.um.edu.my/id/eprint/21991

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