Aviation AC Series Arc Fault Detection Based on Improve Empirical Wavelet Transform Multi-Feature Fusion
Cui Ruihua1,2, Zhang Zhen1,2, Tong Deshuan1,2, Cui Jianping3
1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin 300130 China; 2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province Hebei University of Technology Tianjin 300130 China; 3. Avic Tianjin Aviation Electro-Mechanical Co. Ltd Tianjin 300308 China
Abstract：An arc fault detection method based on improved empirical wavelet transform (IEWT) multi-feature fusion and extreme learning machine (ELM) was proposed to deal with the mode mixing phenomenon of the time-frequency domain analysis method (EMD). Firstly, the arc current signal was decomposed into five empirical mode components (EMFs) by IEWT, and the weight energy entropy of EMFs, sample entropy of EMF4, and root mean square value of EMF1 were extracted as characteristic variables. After data standardization, the three arc fault features were fused to form a multi-dimensional feature matrix, and finally the fault was identified by ELM. Comparing the IEWT and EMD decomposition, the results show that the IEWT method is superior to the signal processing of EMD, and it also avoids the misjudgment caused by a single feature under the multi-feature extraction. Combined with ELM, arc faults can be accurately identified, and the average accuracy is 97.85%.
崔芮华, 张振, 佟德栓, 崔建平. 基于改进经验小波变换多特征融合的航空交流串联电弧故障检测[J]. 电工技术学报, 2022, 37(12): 3148-3161.
Cui Ruihua, Zhang Zhen, Tong Deshuan, Cui Jianping. Aviation AC Series Arc Fault Detection Based on Improve Empirical Wavelet Transform Multi-Feature Fusion. Transactions of China Electrotechnical Society, 2022, 37(12): 3148-3161.
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2022, Vol. 37 
