On-line self-learning fault diagnosis for circuit breakers based on artificial immune network

被引：0

作者：

Lü, Chao ^{[1
]}

Yu, Hong-Hai ^{[2
]}

Wang, Li-Xin ^{[1
]}

机构：

[1] School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, Heilongjiang Province, China

[2] Harbin Ultra High Voltage Bureau, Harbin 150090, Heilongjiang Province, China

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2009年 / 29卷 / 34期

关键词：

Timing circuits - Vibrations (mechanical) - Failure (mechanical) - Fault detection - Electric circuit breakers - E-learning - Learning systems;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

The existing diagnostic methods for high voltage circuit breakers lack in the ability to pursue the change of mechanical state. An on-line self-learning classifier, C-aiNet, for identifying mechanical failures of HVCBs based on artificial immune network aiNet is presented. The training and self-learning process were introduced in details, and principles for selecting network parameters were discussed as well. This method can trace the new regions of clusters, discard old ones, and recognize new patterns. The results of simulation based on measured vibration characteristic data of HVCBs show that self-learning method can achieve more precise judgment of the mechanical state of HVCBs over neural network method. © 2009 Chin. Soc. for Elec. Eng.

引用

页码：128 / 134