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.
Guo Fengyi,Li Kun,Chen Changken等. Series Arc Fault Identification Method Based on Wavelet Approximate Entropy[J]. Transactions of China Electrotechnical Society, 2016, 31(24): 164-172.
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