# An Improved Arc Fault Location Method of DC Distribution System Based on EMD-SVD Decomposition
## 1 Introduction  
The vigorous development of new energy makes the traditional AC distribution network face challenges [1; 2; 3]. In contrast, the DC distribution form has better economy and security, and it can simultaneously meet the direct access of DC power sources and DC loads [4; 5]. Currently, the DC power distribution system has been widely used in industrial, commercial, military, and other energy systems, and it will be the future trend of urban power distribution [6; 7; 8]. Affected by the natural environment and human factors, faults will inevitably occur on distribution lines, so accurate detection and localization of faults is an important prerequisite to achieving fault isolation and power supply restoration to ensure the safe and stable operation of power systems [9]. The occurrence of electric arc faults is accompanied by phenomena such as arc light and exothermic reactions, which will seriously threaten people's lives and property if not handled in time [10; 11]. At present, the research on protection methods for DC distribution networks is still in the exploratory stage. Fault localization methods are mainly divided into two main categories: passive fault localization methods and active fault localization methods [12]. Passive fault location methods can be categorized into the traveling wave, impedance, natural frequency, and artificial intelligence methods. The traveling wave method is more widely used in DC transmission systems. Researchers process the traveling wave signals using different signal processing methods, such as EMD, CEEMD, and VMD. Study [13] proposes a fault localization method based on the combination of improved variational modal decomposition (VMD) and a generalized S-transform, where the improved VMDalgorithm is used to decompose the traveling wave signals in the time-frequency domain, and then the S-transform is used to locate the mutation points. Similarly, other scholars have made attempts at fault localization by the traveling wave method [14; 15; 16]. In addition, artificial intelligence algorithms have been applied to fault localization in recent years. Study [17] proposes an algorithm for an artificial neural network. Different fault points are set on the line, and the voltage difference between mode 1 and mode 0 voltages on the line at the corresponding fault distance is calculated and plotted as a sample for training. Such methods rely on a large amount of training data, and the richer the data, the more accurate the results. The fault location calculation method based on the line RL model is relatively simple and easy to implement, but the accuracy of the line parameters is required to be higher. Study [18] proposes to construct a release loop for the inductive energy of the line after the tripping of a DC circuit breaker. Then the loop equations are written to derive the ranging formula. Active fault localization methods calculate the fault distance by attaching additional circuits to inject signals into the faulted line. Studies [19; 20] all propose a non-iterative fault localization technique using power probes. Power components such as capacitors, reactors, and power supplies are utilized to form a circuit with the faulted line, and the RLC circuit equations are solved to obtain the location of the fault occurrence. In contrast to such methods of attaching additional circuits, study [21] utilizes a converter on the line as a probe. When a fault occurs on the line, the control strategy of the dual active bridge converter is changed so that it acts as a harmonic source, injecting harmonic currents into the faulted line. This method uses the converter on the line directly as a fault probe source, which saves hardware costs and has good economics but relies on the accuracy of the model. This paper proposes a traveling wave-ranging scheme for arc faults in DC distribution networks. It is based on the combination of empirical modal decomposition (EMD) [22] and singular value decomposition (SVD) [23].