电工技术学报2016，Vol.31Issue(24)：164-172,9.
基于小波近似熵的串联电弧故障识别方法
Series Arc Fault Identification Method Based on Wavelet Approximate Entropy
摘要
Abstract
A series arc fault generator was built according to UL1699. Experiments were carried out under different load conditions. Loop current waveforms with and without series arc fault were obtained. Firstly, the current signal was decomposed and reconstructed by wavelet transform. Then the irregular degrees of signals in each frequency band were quantified with approximate entropy algorithm, and the feature vectors of current signals were obtained. Finally, all the feature vectors were used as input variables of support vector machine (SVM). The series arc fault can be recognized by classifying those feature vectors with SVM. It is shown that the feature vectors obtained by wavelet approximate entropy algorithm can diagnose series arc fault.关键词
电弧故障/近似熵/特征向量/小波分解/支持向量机Key words
Arc fault/approximate entropy/feature vector/wavelet decomposition/support vector machine分类
信息技术与安全科学引用本文复制引用
郭凤仪,李坤,陈昌垦,刘艳丽,王喜利,王智勇..基于小波近似熵的串联电弧故障识别方法[J].电工技术学报,2016,31(24):164-172,9.基金项目
国家自然科学基金资助项目（51277090、51674136）。 （）
