Identification Method for Series Arc Faults Based on Wavelet Transform and Deep Neural Network

Qiongfang Yu, Yaqian Hu and Yi Yang
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Qiongfang Yu: School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China
Yaqian Hu: School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China
Yi Yang: School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China

Energies, 2019, vol. 13, issue 1, 1-12

Abstract: The power supply quality and power supply safety of a low-voltage residential power distribution system is seriously affected by the occurrence of series arc faults. It is difficult to detect and extinguish them due to the characteristics of small current, high stochasticity, and strong concealment. In order to improve the overall safety of residential distribution systems, a novel method based on discrete wavelet transform (DWT) and deep neural network (DNN) is proposed to detect series arc faults in this paper. An experimental bed is built to obtain current signals under two states, normal and arcing. The collected signals are discomposed in different scales applying the DWT. The wavelet coefficient sequences are used for forming training set and test set. The deep neural network trained by training set under 4 different loads adaptively learn the feature of arc faults. The accuracy of arc faults recognition is sent through feeding test set into the model, about 97.75%. The experimental result shows that this method has good accuracy and generality under different types of loading.

Keywords: series arc faults; wavelet transform; deep neural network; low-voltage system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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