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Algorithms 2014, 7(4), 492-509; doi:10.3390/a7040492

Power Cable Fault Recognition Based on an Annealed Chaotic Competitive Learning Network

1
College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an 710054, China
2
Department of Computer Science & Information Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Chen-Chung Liu
Received: 13 June 2014 / Revised: 3 September 2014 / Accepted: 15 September 2014 / Published: 26 September 2014
(This article belongs to the Special Issue Advanced Data Processing Algorithms in Engineering)
View Full-Text   |   Download PDF [474 KB, uploaded 26 September 2014]   |  

Abstract

In electric power systems, power cable operation under normal conditions is very important. Various cable faults will happen in practical applications. Recognizing the cable faults correctly and in a timely manner is crucial. In this paper we propose a method that an annealed chaotic competitive learning network recognizes power cable types. The result shows a good performance using the support vector machine (SVM) and improved Particle Swarm Optimization (IPSO)-SVM method. The experimental result shows that the fault recognition accuracy reached was 96.2%, using 54 data samples. The network training time is about 0.032 second. The method can achieve cable fault classification effectively. View Full-Text
Keywords: power cable; cable faults; SVM; recognition; competitive learning network; annealed chaotic power cable; cable faults; SVM; recognition; competitive learning network; annealed chaotic
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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. (CC BY 4.0).

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Qin, X.; Wang, M.; Lin, J.-S.; Li, X. Power Cable Fault Recognition Based on an Annealed Chaotic Competitive Learning Network. Algorithms 2014, 7, 492-509.

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