## 1 Introduction  
The safe operation of cables is always critical in low-voltage distribution networks [1]. Cables offer advantages such as a small footprint and high reliability; however, low-voltage cables are often installed in complex environments near areas of daily living and work, making their insulation more vulnerable to mechanical damage from excavation, compression, and other external forces [2, 3]. Insulation defects can lead to arc discharges and leakage currents, which may ignite cable insulation materials or nearby combustible substances, thus posing a significant fire hazard [4]. The statistics indicate that from 2011 to 2016, a total of 524,000 electrical fires occurred in China, with those caused by low-voltage distribution lines accounting for over 50% of all electrical fires annually. Among these, insulation degradation was a major contributing factor [5]. Unlike medium-voltage and high-voltage cables, low-voltage cables operate at lower voltages. Insulation defects in low-voltage cables are particularly prone to intermittent arcs in wet environments, such as exposure to rainwater [1, 6, 7]. In the incipient stages of a fault, these intermittent arcs typically last for a very short duration and often fail to trigger overcurrent protection devices [8]. For Residual Current Devices (RCDs) used to detect leakage currents, their high sensitivity may lead to nuisance tripping when natural leakage currents are excessively high [9]. RCDs are also not applicable for the TN-C (Terre Neutral Combined) and TN-C-S (Terre Neutral Combined-Separated) systems with multiple grounding points [10; 11]. Therefore, studying the characteristics of incipient faults in low-voltage cables is crucial for the early detection of defective cables, thereby reducing the risk of electrical fires caused by insulation defects and enhancing the safety of low-voltage distribution networks [12; 13]. Researchers have conducted studies on incipient faults in cables. [PERSON] et al. [14; 15; 16] investigated the evolution of incipient faults in cable joints under humid conditions through fault simulation experiments, dividing the fault process into three sub-stages. They also analyzed the non-electrical characteristics of incipient faults, including insulation erosion, liquid electrode displacement, and deformation. Furthermore, based on their experimental research on incipient faults in medium-voltage cable joints, they examined the characteristics of fault resistance and the influence of external parameters, such as transmission line properties, on fault resistance. They proposed a fault identification device structure along with corresponding detection logic. [PERSON] et al. [17] obtained typical voltage and current waveforms due to incipient fault through experiments, extracted disturbance signals using time-domain analysis, and proposed a voltage disturbance model. [PERSON] et al. [18] experimentally observed intermittent internal arcs in cables, classified incipient fault signals into three primary forms, and developed corresponding models. [PERSON] et al. [19] introduced an incipient fault detection method based on cumulative summation and neural networks, enabling high-speed detection and effectively distinguishing incipient faults from other signals in distribution systems. Studies [20; 21; 22] applied deep learning methods, using convolutional neural networks and deep generative models to extract features of incipient fault signals, followed by various classifiers for fault identification. However, all the above research focuses on MV cables. The limited literature regarding LV-cable incipient faults and their study results is summarized as follows. [PERSON] et al. [1] conducted corrosion experiments by immersing damaged aluminum-core cables in liquid and observed short-duration intermittent currents. Similar current peaks were also recorded during long-term on-site monitoring. [PERSON] [23] experimentally identified the occurrence of self-recoverable internal arc discharges in low-voltage defective cables.