基于SVM与合分闸线圈电流参数的高压断路器机械故障诊断  

Mechanical Failure Diagnosis of High Voltage Circuit Breaker Based on SVM and Opening/Closing Coil Current Parameters

作　　者：袁金丽[1] 李奎[1] 郭志涛[1] 岳大为[1] 王尧[1] 

机构地区：[1]河北工业大学,天津300130

出　　处：《高压电器》2011年第3期26-30,共5页High Voltage Apparatus

基　　金：河北省自然科学基金资助项目杰出青年基金(E2009001584)~~

摘　　要：高压断路器故障的早期诊断是有效提高电力系统运行可靠性的重要手段,笔者提出应用高性能的支持向量机(SVM)进行高压断路器的机械故障诊断。支持向量机核函数参数的选择直接影响分类结果的好坏,该诊断方法采用群智能算法PSO确定支持向量机中核函数的最优参数以提升分类器性能,将从高压断路器的机械参数信号中提取的动作特性特征量作为支持向量机训练和识别的样本。试验结果表明,该方法确实达到了较高的故障诊断正确率。Early diagnosis of high-voltage circuit breaker failure is an important way of improving the reliability of power system.In this paper,the application of high-performance support vector machine（SVM） to fault diagnosis of high voltage breaker was suggested.For conventional traditional SVM,the selection of parameters in kernel function directly affected diagnosis result.In the present method,for improving the performance of the classification,a novel intelligent particle swarm optimization（PSO）algorithm was adopted to search the optimal parameters.The operating characteristics parameters extracted from the mechanical parameters signals of the high voltage circuit breakers were taken as training and identification samples of support vector machine.The test results show that the present diagnosis method achieves high accuracy of failure diagnosis.

关 键 词：支持向量机 粒子群优化算法 高压断路器 故障诊断 径向基核函数 

分 类 号：TM561[电气工程—电器]