In a household AC power supply and distribution system,arc faults may occur due to reasons such as aging of lines and loose contacts.Arc faults are highly dangerous and can cause serious electrical fires and property damage.In this paper,various types of loads were used in series arc fault experiments based on the actual usage of household loads,and samples were obtained at different sampling frequencies.In order to quickly and accurately detect arc faults,this paper used short-time Fourier transform(STFT)to consider both the time-domain and frequency-domain characteristics of the current signal,and converted the analysis results into RGB color images as the input information of the network.A light-weight convolutional neural network(LCNN)was proposed,and both the detection performance and scale of the network during the network construction process were considered,gradually building up the optimal network structure.This detec-tion method had good adaptability,could maintain high accuracy at sampling frequencies of 5 kHz and above,and in com-parison with other methods,was proved to have superior performance.
series arc faultshort-time Fourier transformconvolutional neural networklightweight type
万方数据
维普
