## 1 Introduction  
Due to the huge demand for clean energy, the application of photovoltaic (PV) systems has been increasing rapidly [1, 2, 3, 4]. It is expected that solar photovoltaic installations will reach 800 GW by 2021 [5]. Unfortunately, PV systems are prone to suffer electrical fire hazards because of arc faults on the DC side. There is no natural zero-crossing in the DC current, which results in the relevant fault arcing being difficult to extinguish by itself [6, 7]. According to the relationship between arcs and PV inverters, arc faults can be categorized as series and parallel ones. Compared with the parallel fault arc, due to the series connection with the photovoltaic inverter, the current of the PV inverters will decrease rather than increase, which is more challenging for the fault diagnosis [8]. The noise of the PV inverters is generally below 10 kHz. At the same time, the high-frequency characteristic signals caused by the arc are typically concentrated in the 10-80 kHz frequency band, the high-frequency characteristic of a series arc covers the noise of the photovoltaic inverter [9]. However, conventional protection devices used for overcurrent or leakage current protection cannot function well in the event of a series arc fault [10]. Different methods have been proposed to solve this issue. The arc current signals are most commonly used for fault detection due to their independence of where the arc occurs. The arcs usually show transient and random changes in the current, which can be described by indexes in time, frequency, or time-frequency domains [11]. [PERSON] [12] proposed an arc fault detection method for DC series arcs, which uses three windows (scanning windows) running over the electrical current waveform. [PERSON] [13] uses the unstable fluctuation in the time domain and extra arc noise in the time-frequency domain as identification features for arc fault detection. [PERSON] [14] proposed a time and time-frequency domain analysis method combining the loop current and the PV-side voltage for detecting the series DC arc fault. [PERSON] [15] presented a novel approach to detect arcs in DC microgrids via their high-frequency spectral pattern using ideas from compressed sensing. [PERSON] [16] proposed an arc fault detection algorithm based on the relative comparison of current variability according to the frequency spectrum and time series. [PERSON] [17] investigated the difference in the integrated Fast Fourier Transform (FFT) of the current signal by a Rogowski coil correlates with the series faults. [PERSON] [18] presented a novel approach based on voltage differential protection to detect the series arc faults. A commercial arc fault detection evaluation board for PV systems has been released by Texas Instruments Inc., which is using the Fast Fourier Transform algorithm [19]. This method can easily be implemented by hardware and can be applied by most commercial arc fault detection products. However, interference noise in PV systems sometimes exhibits similar features to the arc signal in the time or frequency domain, resulting in nuisance tripping. Artificial Intelligence (AI) based methods provide new ideas for DC arc fault detection. Unlike traditional methods, the AI-based method is robust to the influence of Maximum Power Point Tracking (MPPT) controls or switch operations on the current. [PERSON] [20] proposed domain adaptation combined with a deep convolutional generative adversarial network (DA-DCGAN)-based methodology for DC series arc fault diagnosis. [PERSON] [21] employed Back Propagation Neural Network for the detection of DC fault arcs. [PERSON] [22] adopted the radial basis networks to identify the DC arc faults. [PERSON] [23] proposed a method based on ensemble machine learning algorithms to detect series DC arc faults. [PERSON] [24] uses the Fourier transformation to analyze the DC arc ratio fault signals, and design an artificial neural network to detect an arcing fault.