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

被引：3
|
作者：
Qin, Xuebin [1 ]
Wang, Mei [1 ]
Lin, Jzau-Sheng [2 ]
Li, Xiaowei [1 ]
机构：
[1] Xian Univ Sci & Technol, Coll Elect & Control Engn, Xian 710054, Peoples R China
[2] Natl Chin Yi Univ Technol, Dept Comp Sci & Informat Engn, Taichung 41170, Taiwan
来源：
ALGORITHMS | 2014年 / 7卷 / 04期
关键词：
power cable; cable faults; SVM; recognition; competitive learning network; annealed chaotic;
D O I：
10.3390/a7040492
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：492 / 509
页数：18
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