Series Arc Fault Detection Based on Wavelet Compression Reconstruction Data Enhancement and Deep Residual Network

Abstract

Series arc fault is the main cause of electrical fire. Because of the complex load types and the randomness of arc fault in low-voltage distribution system, it is difficult to obtain fault data for data-driven machine learning algorithm to achieve higher accuracy. Therefore, an arc fault detection model based on residual network (ResNet) is proposed from the perspective of computer vision, and an appropriate data enhancement method is given. We first analyze the time-domain current signals of different load arc faults by multilayer discrete wavelet analysis. The obtained five-layer discrete wavelet detail values are fused into a matrix, and the coefficient matrix is then converted into a phase space image (RGB color space) using a colormap index. The phase space feature map is made into a classification dataset according to the load and fault type, which is input into ResNet in the form of three channels (RGB) for convolution and classification recognition. Then, aiming at the over-fitting phenomenon of neural network caused by small sample, a data enhancement method based on wavelet compression reconstruction is proposed. Finally, we set up different datasets to compare our method with typical neural network regularization methods. The results show that our method effectively solves the over-fitting phenomenon of deep network ResNet152 and improves the accuracy of ResNet 50/101/152 to 97.91%, 96.30%, and 97.69%, respectively.