Name
Title | ||
|---|---|---|
Power Cable Fault Recognition Based On An Annealed Chaotic Competitive Learning Network |
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.3390/a7040492 | ALGORITHMS |
Keywords | Field | DocType |
power cable, cable faults, SVM, recognition, competitive learning network, annealed chaotic | Particle swarm optimization,Competitive learning,Power cable,Support vector machine,Normal conditions,Electric power system,Artificial intelligence,Fault recognition,Chaotic,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
7 | 4 | 1999-4893 |
Citations | PageRank | References |
1 | 0.39 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuebin Qin | 1 | 32 | 7.95 |
| Mei Wang | 2 | 5 | 3.83 |
| Jzau-Sheng Lin | 3 | 106 | 13.48 |
| Xiaowei Li | 4 | 1 | 0.39 |