feature selection and parameters optimization of svm using particle swarm optimization for fault classification in power distribution systems

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ID: 168798
2017
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Abstract
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.
Reference Key
cho2017computationalfeature Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Ming-Yuan Cho;Thi Thom Hoang
Journal Organic Chemistry Frontiers
Year 2017
DOI
10.1155/2017/4135465
URL
http://dx.doi.org/10.1155/2017/4135465
Keywords
computer applications to medicine medical informaticsneurosciences neuropsychiatry biological psychiatry

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