# Defect Prediction for Capacitive Equipment in Power System
## 2 Related Work  
The early online monitoring of capacitive equipment primarily relied on manual inspections and periodic offline testing [2]. This approach incurred significant manpower and time costs, hindering the prompt detection of equipment faults and resulting in equipment damage and downtime. With the increasing prevalence and application scope of capacitive equipment, researchers began investigating parameter-based monitoring methods to evaluate equipment status and performance. The primary indicators monitored include the dielectric loss tangent, leakage current value, and capacitance value [3]. These indicators can be effectively utilized to discern early stage defects in capacitive equipment [4; 5; 6; 7]. As sensor and computer technologies advanced, signal-processing-based monitoring methods emerged, analyzing signals from capacitive equipment such as current and voltage to evaluate equipment status and performance [8]. This method involves employing techniques such as spectral analysis and wavelet transform to extract features like amplitude, frequency, and partial discharge quantity for diagnosing faults in capacitive equipment. The application of frequency response analysis (FRA) technology for diagnosing faults in transformer bushings was proposed [9]. By integrating the magnitude and phase information of measured FRA features into a polar plot, more feature parameters were obtained compared to traditional amplitude plots, enabling the finer-level diagnosis of transformer bushing faults and the evaluation of insulation oil degradation status. A capacitor bushing fault diagnosis method based on high-frequency partial discharge measurement technology was proposed, addressing issues encountered in traditional diagnostic methods such as difficulties in detecting defects in the initial stage and determining fault types only when certain or multiple deteriorations have fully occurred [10]. With the continuous development of artificial intelligence (AI) technology, AI-based monitoring methods have garnered attention [11]. These methods utilize techniques such as machine learning and deep learning to analyze the characteristic parameters of capacitive equipment and diagnose them based on historical data and experience. The application of multilayer perceptron to capacitance tomography imaging sensor data exhibited promising performance in fluid classification [12]. Support vector regression (SVR) and artificial neural network (ANN) techniques were employed to identify and compensate for the effect of temperature on the output of capacitive differential pressure sensors [13]. The utilization of artificial neural networks (ANNs) has progressively enhanced the efficiency and effectiveness of power transformer fault diagnosis [14; 15; 16; 17; 18; 19]. Online monitoring systems for capacitive equipment have evolved into intelligent systems, integrating various emerging technologies such as data acquisition, processing, communication transmission, and intelligent diagnosis [20]. These systems facilitate comprehensive, accurate, and real-time monitoring and the diagnosis of capacitive equipment, offering relevant warnings and suggestions for fault handling. An online monitoring system for capacitive-type equipment insulation was developed, with hardware based on DSP+FPGA, showing promising results in a simulation environment in a high-voltage laboratory [21]. To enhance the accuracy and reliability of leakage current measurement, an online insulation monitoring system for high-voltage capacitive substation equipment based on the Zigbee wireless sensor network was introduced [22]. The real-time monitoring of various capacitive equipment operating statuses can be achieved through the establishment of an intelligent auxiliary monitoring terminal [23].