基于特征提取的有源电力滤波器故障诊断方法  

Fault diagnosis method for active power filter based on feature extraction

作　　者：马立新[1] 吴兴锋 穆清伦[1] 

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《电测与仪表》2015年第15期124-128,共5页Electrical Measurement & Instrumentation

基　　金：国家自然科学基金资助项目(61205076);国家科技部政府间科技合作项目(2009014)

摘　　要：针对有源电力滤波器APF(Active Power Filter)的IGBT功率管易发生故障的问题,提出了基于故障特征提取的有源电力滤波器故障诊断方法。构建了APF故障仿真模型和基于小波包分析的故障特征提取方法,仿真分析了APF网侧电流波形,并运用小波包分析对IGBT故障时的网侧电流波形进行处理,提取了IGBT故障特征向量,最后运用神经网络对特征向量的分类来实现对APF的故障诊断。在APF故障诊断系统上进行测试,验证了该诊断方法的有效性和可行性。For the sake of easily damaged characteristic of IGBT, a based on fault feature extraction. The model of APF fault simulation method to diagnose the fault of APF was proposed and the method of the feature extraction based on the wavelet packet analysis were established, and the waveforms of grid side current were simulated, processed the waveforms of grid side current of IGBT open circuit fault by wavelet packet analysis, and extracted the fault features vector. Finally, the classification function of the neural network was applied for the APF fault diagnosis. The effec- tiveness and feasibility of the diagnosis method are validated by test results of APF fault diagnosis system.

关 键 词：有源电力滤波器 特征提取 故障诊断 小波包 神经网络 

分 类 号：TM714[电气工程—电力系统及自动化]