Fault Arc Detection Based on Channel Attention Mechanism and Lightweight Residual Network

Xiang Gao, Gan Zhou (), Jian Zhang, Ying Zeng, Yanjun Feng and Yuyuan Liu
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Xiang Gao: School of Software Engineering, Southeast University, Nanjing 211189, China
Gan Zhou: School of Electrical Engineering, Southeast University, Nanjing 211189, China
Jian Zhang: State Grid Guangdong Electric Power Company, Guangzhou 510600, China
Ying Zeng: State Grid Guangdong Electric Power Company, Guangzhou 510600, China
Yanjun Feng: School of Electrical Engineering, Southeast University, Nanjing 211189, China
Yuyuan Liu: School of Electrical Engineering, Southeast University, Nanjing 211189, China

Energies, 2023, vol. 16, issue 13, 1-16

Abstract: An arc fault is the leading cause of electrical fire. Aiming at the problems of difficulty in manually extracting features, poor generalization ability of models and low prediction accuracy in traditional arc fault detection algorithms, this paper proposes a fault arc detection method based on the fusion of channel attention mechanism and residual network model. This method is based on the channel attention mechanism to perform global average pooling of information from each channel of the feature map assigned by the residual block while ignoring the local spatial data to enhance the detection and recognition rate of the fault arc. This paper introduces a one-dimensional depth separable convolution (1D-DS) module to reduce the network model parameters and shorten the time of single prediction samples. The experimental results show that the F1 score of the network model for arc fault detection under mixed load conditions is 98.07%, and the parameter amount is reduced by 46.06%. The method proposed in this paper dramatically reduces the parameter quantity, floating-point number and time complexity of the network structure while ensuring a high recognition rate, which improves the real-time response ability to detect arc fault. It has a guiding significance for applying arc fault on the edge side.

Keywords: arc fault detection; convolutional neural network; residual network; channel attention mechanism; one-dimensional depth separable convolution (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: 2023
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