基于因子分析和支持向量机算法的高压断路器机械故障诊断方法

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摘要高压断路器操作过程中的振动信号能够反映断路器的机械状态。以高压断路器机械振动信号中振动事件的起始点作为特征参量,使用因子分析对特征量进行降维优化、支持向量机（Support Vector Machine, SVM）经粒子群参数寻优（Particle Swarm Optimization, PSO）后可对断路器的不同状态进行分类。本文对断路器机械故障进行了模拟试验,结果表明,因子分析和支持向量机算法适于诊断高压断路器的机械状态。

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关键词 ：高压断路器, 振动信号, 故障诊断, 因子分析, 支持向量机

Abstract：The mechanical state of high voltage circuit breaker can be reflected by its vibration signals during operation. The starting moment of vibration events extracted from the vibration signal can be taken as the characteristic parameter. The factor analysis can optimize and reduce the dimension of the characteristic parameters. The support vector machine(SVM) optimized by particle swarm optimization(PSO) can classify the different states of the circuit breaker. Several mechanical failures of circuit breaker are simulated and the results show that the factor analysis and SVM are suitable for mechanical state diagnosis of high voltage circuit breaker.

Key words： High voltage circuit breaker vibration signal fault diagnosis factor analysis SVM

收稿日期: 2013-05-06 出版日期: 2014-08-05

PACS:	TM561
	TM507

基金资助:国家高技术研究发展计划(863计划) (2011AA05A121)和国家电网公司资助项目

作者简介: 程序男,1981年生,硕士,高级工程师,研究方向为电力设备状态检测与故障诊断技术。关永刚男,1974年生,副教授,博士生导师,研究方向为高压电气设备及智能化技术。

引用本文:

程序, 关永刚, 张文鹏, 唐诚. 基于因子分析和支持向量机算法的高压断路器机械故障诊断方法[J]. 电工技术学报, 2014, 29(7): 209-215. Cheng Xu, Guan Yonggang, Zhang Wenpeng, Tang Cheng. Diagnosis Method on the Mechanical Failure of High Voltage Circuit Breakers Based on Factor Analysis and SVM. Transactions of China Electrotechnical Society, 2014, 29(7): 209-215.

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http://dgjsxb.ces-transaction.com/CN/Y2014/V29/I7/209

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