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

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.

Multi-Branch Line Fault Arc Detection Method Based on the Improved Northern Goshawk Optimization Adaptive Base Class LogitBoost Algorithm

“…Reference [10] used the maximum mutual information coefficient for mining highly recognizable features and implemented series arc fault detection based on a support vector machine (SVM). Reference [11] applied raw data as the input to the lightweight residual network (LRN) to detect arc faults. Reference [12] utilized a discrete wavelet transform and color map indexing to obtain the image features of arc signals, and then a deep residual network (DRN) was used to identify arc faults.…”