## 1 Introduction  
The distribution network's structure and operating conditions grow increasingly complex as the power system grows in size. It is also more vulnerable to different kinds of line faults, with single-phase grounding faults accounting for 80% of all faults [1], which can have a major impact on the network's ability to operate safely and steadily. The safety of people and equipment is greatly threatened by high-impedance-grounding faults (HIFs), which are particularly weak and difficult to detect reliably if not detected and removed in a timely manner. HIFs are frequently triggered at the point of fault arcing and cause high-temperature fires [2]. Therefore, research into a trustworthy technique for detecting high-resistance grounding faults in distribution networks is urgently needed. The following are the current techniques for detecting grounding faults in distribution network systems: currently, impedance, time-frequency, and traveling wave approaches are used in transient analysis investigations for neutral ungrounded systems. Short-time Fourier transform [3, 4, 5] (STFT), wavelet transform [6, 7] (WT), and S-transform [8] (ST) are all included in the time-frequency technique. Among them, STFT is a reliable, fast, and computationally small method, but its available time-frequency resolution is limited by its fixed window length. In contrast, WT provides better time-frequency resolution due to the automatic change in its window size. With a predefined mother wavelet and a selected scale, WT acts like a filter to highlight target frequency features. However, the process becomes more difficult due to the iterative computation and selection of the mother wavelet in WT. The ST method's benefits include low computing effort, quick reaction, and great accuracy. But compared to STFT, it is less resilient to fault circumstances. The traveling wave approach is a novel research area and development path that works well in the field of distribution network fault detection since it is independent of the system operation mode [9; 10]. The maximum value of the zero-mode current mode after binary wavelet transform is used to realize fault routing in the literature [11], which employs cross-wavelet transform to achieve adaptive adjustment of the time-frequency window width. The literature [12] builds the fault-routing criterion based on the relationship between the magnitudes of directional traveling wave energy. The literature [13] suggests the characteristic positive and reverse traveling wave integration method to realize fault routing using binary wavelet transform. In order to achieve fault routing, all of the aforementioned techniques rely on the fault instantaneous initial voltage, electric current wave head parameters, and other single-feature quantities. However, when resistance constraints are high, it becomes challenging to detect the initial wave-head attenuation, and the detection sensitivity and reliability are significantly diminished. In order to accomplish high-resistance fault detection for resonant grounding systems, current domestic and international experts primarily rely on injection methods and artificial neural networks. In order to identify fault lines, the injection method primarily uses an active injection signal generator [14]. In addition to the commonly used \"S\" injection method [15; 16], the neutral point online injection signal identification of single-phase grounding fault method has also been more widely used [17], but the size of the injected current depends on the detection accuracy. High-resistance fault identification is a promising area for artificial neural network technology, which can achieve precise fault diagnosis by combining several parameters to differentiate from typical electrical volume. To be accurate, neural network techniques, however, need extensive training [18]. This approach is not commonly utilized in engineering applications since it is challenging to train neural networks with a high number of samples in actual engineering.