# State Evaluation of Electrical Equipment in Substations Based on Data Mining
## 2 Methodology
### Data Analysis  
The research analyzed three sources of electrical equipment status monitoring data: experiment tests, online monitoring, and human inspections. This section introduces the dataset model. Due to current technological constraints, numerous electrical equipment tests must be conducted during power outages. The resulting data reflects the inherent properties of the equipment. The status information includes test date, test items, measured values, ambient conditions (temperature and humidity), and potentially analysis results from test personnel [25]. These attributes are categorized into three types: comparison, verification, and destructive testing. For instance, comparing resistance values with factory or previous test values belongs to the comparison category. Polarity and phase checks fall under verification, while various withstand voltage tests are classified as destructive testing. Online monitoring devices utilize various sensors to collect real-time data during power grid operation. This data represents the operational information of electrical equipment. It encompasses basic parameters (voltage, current, frequency) and advanced metrics (winding and oil temperatures, dissolved gases, circuit breaker operations, contact resistance, operating time, switchgear characteristics, and partial discharge conditions). These attributes are classified into comparison and validation categories. Unlike experimental data, real-time monitoring yields a more extensive dataset. The dataset selectively incorporates data points that significantly deviate from mean values or exhibit anomalous characteristics. Human inspection data, unlike experimental and online monitoring data, often poses digitization challenges. Quantifiable data is predominantly confined to instrument and meter measurements. Sensory observations (visual, olfactory, tactile) introduce subjectivity in assessments, including evaluations of equipment condition, environmental factors, and potential faunal threats. Despite their inherent subjectivity, these observations often identify issues that may escape detection by experimental or online monitoring methods. A tripartite logical coding system standardizes this subjective data: 1 (affirmative/good), 0 (negative/bad), and 2 (uncertain). This numerical conversion enhances subsequent data processing and analysis. The data feature mining processes categorize raw data into discrete and continuous values across multiple scales. This feature entry heterogeneity may influence rule establishment. A mixture Gaussian algorithm is employed to discretize values and achieve quantitative similarity [26]. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Tests** & **Online Monitoring** & **Human Inspection** \\ \hline \multirow{3}{*}{Voltage and current transformers} & \multirow{2}{*}{Insulation resistance} & Primary and secondary voltage and current values & \\ \cline{2-4}  & & Dielectric loss factor: & Insulation \\ \hline \multirow{2}{*}{Cables and bus} & \multirow{2}{*}{Insulation resistance} & Partial discharge & \\ \cline{2-4}  & & DC withstand voltage test & Joint or busbar temperature \\ \hline \multirow{2}{*}{GIS equipment} & \multirow{2}{*}{} & SF\({}_{8}\) gas concentration & \\ \cline{2-4}  & & Partial discharge monitoring & \\ \hline \multirow{3}{*}{Lightning arrester} & \multirow{2}{*}{Insulation resistance} & AC leakage current under operating voltage & Operation status of discharge counter \\ \cline{2-4}  & & leakage current at DC 1 ma voltage & Operation status of discharge counter \\ \cline{2-4}  & & u1 ma and 0.75 ulma & \\ \hline \multirow{3}{*}{Capacitor bank} & Capacitors insulation resistance & Shell intact & \\ \cline{2-4}  & & capacitance & \\ \cline{2-4}  & & Inductor insulation resistance & Secure wiring \\ \cline{2-4}  & & DC resistance & \\ \cline{2-4}  & & Discharge coil insulation resistance & \\ \cline{2-4}  & & winding DC resistance & \\ \hline \hline \end{tabular}
\end{table}