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Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-11
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Cho, Ming-Yuan& Hoang, Thi Thom. 2017. Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1140929
Modern Language Association (MLA)
Cho, Ming-Yuan& Hoang, Thi Thom. Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1140929
American Medical Association (AMA)
Cho, Ming-Yuan& Hoang, Thi Thom. Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1140929
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1140929