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Computational Intelligence and Neuroscience
Volume 2017, Article ID 4135465, 9 pages
Research Article

Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems

Department of Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan

Correspondence should be addressed to Thi Thom Hoang; nv.ude.utn@thmoht

Received 25 March 2017; Accepted 28 May 2017; Published 11 July 2017

Academic Editor: Fabio La Foresta

Copyright © 2017 Ming-Yuan Cho and Thi Thom Hoang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.