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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  3/2017 - 7

A Differential Particle Swarm Optimization-based Support Vector Machine Classifier for Fault Diagnosis in Power Distribution Systems

CHO, M. Y. See more information about CHO, M. Y. on SCOPUS See more information about CHO, M. Y. on IEEExplore See more information about CHO, M. Y. on Web of Science, HOANG, T. T. See more information about HOANG, T. T. on SCOPUS See more information about HOANG, T. T. on SCOPUS See more information about HOANG, T. T. on Web of Science
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Download PDF pdficon (1,322 KB) | Citation | Downloads: 361 | Views: 947

Author keywords
fault diagnosis, particle swarm optimization, power distribution lines, reflectometry, support vector machines

References keywords
power(16), fault(15), systems(13), location(9), distribution(9), system(7), networks(7), artificial(6), swarm(5), neural(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-08-31
Volume 17, Issue 3, Year 2017, On page(s): 51 - 60
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.03007
Web of Science Accession Number: 000410369500007
SCOPUS ID: 85028567448

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This paper proposes a new differential particle swarm optimization (DPSO) method for obtaining optimum support vector machine (SVM) parameters used for electrical fault diagnosis in radial distribution systems. Further, a multiple-stage DPSO-SVM classifier is developed to enhance classification accuracy in the fault diagnosis. Also, time-domain reflectometry (TDR) method with pseudo-random binary sequence (PRBS) excitation is utilized for generating the dataset required for validating this proposed approach. According to the characteristic of echo responses found in different types of faults, 12 features are extracted as input vectors for purposes of classification. The proposed fault diagnosis approach is tested on a typical radial distribution system to classify ten types of short-circuit faults accurately. Further, to demonstrate the superiority of the proposed DPSO algorithm, comparative studies of fault diagnosis are performed using SVM having parameters selected using cross-validation, GA and PSO. The overall classification accuracy obtained for fault diagnosis is 98.5%, which shows the effectiveness of the proposed approach.

References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 6,552 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 211 ACR
SCOPUS® Average Citations per reference: 0

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2019-06-18 00:34 in 172 seconds.

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Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

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