## 1 Introduction  
With the development of power systems and the increasing diversification of electrification equipment, the low-voltage power load is increasing year by year, and the frequent occurrence of electrical fire has caused huge losses to the national economic security. As one of the main causes of electrical fire, fault arc is often accompanied by high temperature and arc light as well as other physical characteristics. Taking a copper electrode as an example, when the arc is freely ignited in air, the arc length is 5-20 mm, and the current is about 2-20 A. The average temperature of the fault arc is about 2500 K, and the arc column area can even reach about 6500 K. There is a relatively complete protection system for the short circuit and overload faults. Meanwhile, the arc grounding fault and parallel electric arc fault also had a more reliable protection. However, a series of arc fault detection is required for more common usage scenarios. Its concealment of failure can affect fire prevention, and protecting user security is a difficult problem in equipment maintenance and repair work. There are three main types of research on low-voltage series fault arc. (1) The method based on an arc mathematical model is difficult to be used in practice due to the large number of parameters required and many constraints. (2) The fault arc is detected by various sensors at the arc location to detect the physical characteristics when the fault occurs, such as arc light and sound. Because the actual circuit is long and the arc occurrence time and location are random, it is difficult to realize series fault arc detection. (3) At present, the mainstream trend of research on low-voltage series fault arc is to use a deep learning algorithm to extract category features for fault arc detection after the current data are processed by signal or mathematical operations such as wavelet transform [1] and Fourier transform [2]. Among them, [PERSON] et al. [3] normalized current waveforms of different loads and transformed gray data to generate two-dimensional images, and they used self-normalized convolutional neural network to identify features. [PERSON] used the generative adversarial network to enhance the original data, and they design an adaptive asymmetric convolution kernel according to the data distribution characteristics to improve the accuracy of fault detection [4]. [PERSON] et al. [5] used chaotic fractal theory to study the spatial characteristics of AC fault arc current and established a fault arc diagnosis model, making up for the deficiency of time-frequency characteristics in fault arc detection. [PERSON] [6] proposes an arc fault detection method of channel threshold (DRSN-CW) based on continuous wavelet transform and a deep residual shrinkage network to effectively reduce the impact of a less fault arc public dataset in the deep learning model of arc fault diagnosis. In the research of low-voltage series three-phase fault arc, fault phase classification is a difficult problem to be solved because its characteristics are not easy to extract. [PERSON] et al. [7] used the first-order difference to enhance the mutation component and constructed the rectangular fault arc region through time-frequency domain analysis. This method can effectively detect fault arc in a low-voltage three-phase system. [PERSON] et al. [8] improved the fault arc characteristics in the singular value vector obtained by an attractor trajectory matrix on the basis of first-order difference, and they proved that when three-phase series fault arc was generated, any electrical signal of one phase contained the fault arc and fault phase information of each phase. At present, the research on three-phase fault arc phase selection mainly relies on signal and mathematical transformation to extract fault features, and the construction of neural networks is relatively simple, ignoring the mining of deep information by complex neural networks in deep learning.