Series AC Arc Fault Diagnosis Based on Data Enhancement and Adaptive Asymmetric Convolutional Neural Network

Abstract

Arc fault diagnosis methods based on traditional machine learning rely heavily on expert-designed characteristics and are difficult to train models with strong generalization. Deep learning arc fault detection method requires a large number of experimental data to meet the diagnostic performance, and part of the experimental data is difficult to obtain, which increases the difficulty of model training. This paper presents an adaptive arc fault diagnosis model which provides data enhancement. In the data processing stage, the continuous time series can be discretely divided according to the half-period length, and the adaptive matrix transformation of the discrete sequence is carried out according to the convolution operation. At the same time, the generative adversarial network is used to enhance the original coding data of the carbonized path arc fault. In the training stage, an adaptive asymmetric convolution kernel is designed according to the data distribution characteristics of the coding matrix, which can effectively mine the continuous time information and correlation information in the time domain. Compared with the traditional convolutional neural network, the generative adversarial network can effectively enhance the data of carbonized path arc fault and improve the robustness of the diagnosis model. The adaptive asymmetric convolutional neural network improves the feature extraction of arc faults by convolutional layer and the ability of arc fault diagnosis.