# State Evaluation of Electrical Equipment in Substations Based on Data Mining
## 1 Introduction  
Smart substations, essential to modern power transmission and distribution systems, face complex challenges in equipment status assessment. Rapid artificial intelligence (AI) advancements have improved substation equipment monitoring and evaluation, enhancing system reliability and efficiency. AI technology robustly supports the safe and economic operation of modern power systems. Smart substations generate vast amounts multidimensional operational data, including temperature, mechanical parameters, and power load. Traditional manual analysis methods are time-consuming and error prone. AI algorithms efficiently process data through data mining and machine learning, transforming raw data into valuable insights. Ref. [1] proposes that recurrent neural networks (RNNs) can capture dynamic equipment status changes in substation operational time-series data. This improves data processing speed and state assessment accuracy, aiding fault diagnosis. Ref. [2] employs a self-organizing map (SOM) to analyze the comprehensive value of transformer fault gas. AI can pre-emptively identify fault risks and issue early warnings. AI and data fusion techniques enable multisource data analysis [3]. For instance, combining temperature anomaly detection and vibration data analysis enables more accurate differentiation between internal faults and environmental factors. AI models autonomously update parameters based on operational data, adapting to new environments and fault modes. This enables intelligent substations to effectively manage operational uncertainties [4]. Artificial intelligence algorithms significantly reduce unplanned downtime through real-time monitoring and precise evaluation, outperforming traditional manual inspection and maintenance [5]. State evaluation is crucial for smart substation operations, ensuring grid safety, reliability, and efficiency. Continuous optimization of data mining, big data analysis andAI technology can further enhance intelligent substations performance and robustness. Ref. [6] proposed a fault assessment method for measurement and control devices based on the Markov model, using spectral clustering to evaluate substation equipment reliability. The model incorporates hardware, software, auxiliary equipment, and human-related faults. Spectral clustering analysis learns key point similarities, generates charts, and performs data clustering. A multidimensional fuzzy comprehensive evaluation method was proposed for online substation equipment monitoring, based on comparisons of data quality methods [7; 8]. The method establishes a two-layer data evaluation set, fuzzy relationship matrices and data weights to quantitatively score online monitoring data, demonstrating effectiveness. Ref. [9] points out that substation operation and maintenance inspections generate substantial data while ensuring normal equipment operation. The study elaborates on data mining applications in daily inspections, defect management, and fault analysis. Future equipment status and monitoring data are predicted using trend extrapolation, predictive modeling, and least squares regression. A Lyapunov prediction model for equipment defects and malfunctions is developed using historical data and case studies. Inspection datasets serve as test sets for defect or malfunction prediction, enabling continuous model refinement. Analyzing equipment status using image data and deep convolutional neural networks (CNNs) is viable for intelligent substation equipment evaluation [10]. Multisource monitoring using edge computing and CNNs enhances efficiency in comprehensive substation equipment health evaluation. YOLOv4 incorporates CSPNet, enhancing relevant feature channels while suppressing less important ones. Additionally, PANet utilizes depth-wise separable convolution, enhancing efficiency and evaluation performance. Ref. [11] proposed an electrical equipment defect detection method based on clustering analysis and a priori algorithm.