# Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence
## 1 Introduction  
With the continuous development of the electric power industry, cables occupy an increasingly important role in the construction and transformation of power grids [1], and the reliability of cables has a significant impact on the operation of the entire power system. The main insulating material of power cables is cross-linked polyethylene, which is widely used in the power grid system [2; 3]. There are many main factors that cause cable insulation failures, such as the following: the deterioration of the insulation layer due to the residue from the tips and burrs in the cut that are caused by the exploitation of the insulation-shielding layer during the construction; the insulation material being exposed to the radiation of a strong electric field for a long time; or the impact caused by the on-site installation. The deterioration of the performance of power cables is a slow and gradual process, and these different faults can lead to the failure of the insulation layer after a long period of cable usage [4; 5], resulting in the partial discharge (PD) phenomenon, which may even lead to an explosion or burnout [6; 7; 8; 9]. The ability to recognize discharge patterns can serve as a diagnostic basis for cable maintenance and repair, reducing downtime and enhancing overhaul efficiency. PD is a discharge phenomenon that occurs within a localized area of an insulator and is mainly found in HV electrical equipment. Typically, such discharges are triggered by an excessive concentration of localized electric fields within or on the surface of the insulator. These discharges are characterized by being brief and transient, usually manifesting as pulses with a duration of less than 1 \(\upmu\)s [10]. According to different cable fault mechanisms, PD can be divided into different types. Different PDs have different characteristics in frequency, amplitude, and discharge phase [11]. In response to these characteristics, while the occurrence of PD does not immediately lead to complete failure of the insulation, its long-term presence will gradually erode the insulation medium, resulting in irreversible deterioration of the insulation performance. This process eventually leads to the destruction of the equipment's insulation layer [12; 13]. Currently, cable PD-detection methods can be mainly categorized into electrical and non-electrical detection methods [14]. Electrical detection methods primarily encompass the ultrasonic method [15], the pulsed current method [16], and the UHF method [17; 18]. The pulsed current method operates by generating a high-frequency pulse current within the cable when a PD occurs. This pulse current propagates along the wire and ultimately grounds through the equipment's grounding wire into the earth. A high-frequency current transformer (HFCT) is commonly employed to sense local discharge signals. Its operation is based on electromagnetic induction, with the HFCT being typically placed on the grounding cable or accessory metal shielding wire to induce local discharge signals [19]. For local discharge feature extraction, two main methods are employed: the characteristic parameter method and the distribution mapping method. The characteristic parameter method includes various aspects such as discharge amount, mean values, average phase, or skewness of discharge, among others [20; 21; 22]. Meanwhile, the distribution mapping method encompasses a pulse sequence phase distribution map (PRPS), a discharge phase profile (PRPD), W-N mapping, and others. Currently, most detection equipment outputs detect PD signals in the form of phase-resolved partial discharge (PRPD) mapping [23; 24; 25]. When making practical judgments about the type of discharge, professional technicians are required to further analyze the PRPD mapping's characteristics. However, this method necessitates extensive training, and skilled professionals may incur high costs while its accuracy can be influenced by subjective factors [26; 27].