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

doi:10.19595/j.cnki.1000-6753.tces.201706

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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%.

Key words： Aviation arc fault empirical wavelet transform multi-feature fusion extreme learning machine empirical mode decomposition time-frequency analysis

Received: 29 December 2020

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TM501

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	Cui Ruihua
	Zhang Zhen
	Tong Deshuan
	Cui Jianping

Cite this article:

Cui Ruihua,Zhang Zhen,Tong Deshuan等. Aviation AC Series Arc Fault Detection Based on Improve Empirical Wavelet Transform Multi-Feature Fusion[J]. Transactions of China Electrotechnical Society, 2022, 37(12): 3148-3161.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.201706 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/I12/3148

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