Low Voltage Fault Arc Detection Method Based on Wavelet Threshold and Residual Neural Network

In order to improve the accuracy and efficiency of fault arc detection, this paper adopts the characteristic quantity of arc current wavelet decomposition coefficient to pre-judge arc signals, and adopts the wavelet Mallet algorithm to perform multi-scale decomposition of arc signals. The decomposed wavelet coefficient of arc current wave is taken as the characteristic quantity, and an adaptive threshold algorithm is proposed for detection. According to the results of the threshold detection algorithm, the current signal is selectively input into the residual neural network, which effectively improves the detection accuracy. Finally, the feasibility of detecting fault arc with residual neural network is verified, and its accuracy rate reaches 98.73%.