# State Evaluation of Electrical Equipment in Substations Based on Data Mining
## 2 Methodology
### Case Study
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rule** & _Conf_(\%) & **Reasons** & **Result** \\ \hline
1 & 91.556 & L1 = 1A and L7 = 7A and L8 = 8A & A \\
2 & 89.784 & L1 = 1B and L7 = 7A and L8 = 8A & B \\
3 & 81.846 & L1 = 1B and L7 = 7C and L8 = 8B & C \\
4 & 100 & L1 = 1C & C \\
5 & 92.485 & L2 = 2A and L7 = 7A and L8 = 8A & A \\
6 & 82.31 & L2 = 2B and L7 = 7B and L8 = 8A & A \\
7 & 85.661 & L2 = 2B and L8 = 8A & B \\
8 & 100 & L2 = 2C & C \\
9 & 92.766 & L3 = 3A and L7 = 7A and L8 = 8A & A \\
10 & 81.523 & L3 = 3A and L8 = 8B & B \\
11 & 100 & L3 = 3C & C \\
12 & 100 & L4 = 4C and L5 = 5C & C \\
13 & 100 & L4 = 4A and L5 = 5A & A \\
14 & 100 & L4 = 4B and L5 = 5B & B \\
15 & 95.647 & L6 = 6A and L7 = 7A & A \\
16 & 90.112 & L6 = 6B and L7 = 7C and L8 = 8A & B \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Association rules extracted from Table 4 (excerpt). Figure 6: Comparison between predicted results and actual results. proved electrical equipment status prediction. Furthermore, there is significantly enhanced accuracy of the proposed method compared to the a priori algorithm alone. Comparison and analysis of effectiveness  
To compare the effectiveness of the proposed electrical equipment condition assessment method with other classifier algorithms, we used data from various sources related to the main transformer, including online monitoring, testing, operation and maintenance, and environmental factors. Next followed the process described in (1), using 70% of the dataset as a training set and 30% as a test set for quality assessment of the electrical equipment. Then, we compared our method's effectiveness with other classifier algorithms using various performance metrics to predict the main transformer results: accuracy, recall, precision, F1-score, receiver operating characteristic (ROC) curve, and area under the curve (AUC). Table 6 shows the distribution of data processed by the Gaussian mixture model. Table 7 presents association rules mined using the method described in Section 2.4. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline  & **Label** & **Description** & **Parameter** & **Classification** \\ \hline L1 & Absorptance (Test) & Insulation resistance ratio: measured using a 2500 V megger, comparing winding resistance at 60 s and 15 s of pressurization. & \(>\)1.8 & Normal (1A) \\ \hline L2 & DC resistance (Test) & Phase winding DC resistance calculated as the deviation of each phase from the three-phase average value. & \(<\)1\% & Attention (1B) \\ \hline L3 & Ratio test (Test) & Tap changer voltage ratio: expressed as the deviation from the rated tapping voltage ratio for each tap position. & \(<\)0.5\%, \(\times\)1\% & Attention (1B) \\ \hline L4 & Gas content of oil (Online monitoring) & Volume fraction of gases in transformer oil, resulting from thermal and moisture effects. & \(<\)2\% & Normal (4A) \\ \hline L5 & Oil temperature (Inspection) & Measured at the upper oil level during transformer operation. & \(<\)85 & Normal (5A) \\ \hline L6 & Ground current (Online monitoring) & Measured on the iron core lead wire at the single grounding point of the transformer core. & \(<\)55 & Normal (6A) \\ \hline L7 & External contact temperature (Inspection) & Infrared thermometer measurements: quantities temperature at contact or poor contact. & \(<\)20 & Moderate temperature (7A) \\ \hline L8 & Oil indicator (Inspection) & Readings from the transformer’s oil level gauge and oil thermometer. & Oil temperature \(<\) 85, oil level [35\%, 75\%] & High temperature (7B) \\ \hline L9 & Ambient temperature (Inspection) & Thermometer measurements. & \(>\)30 & \(<\)10 \\ \hline L10 & Ambient humidity (Inspection) & \(\begin{array}{c}\text{Hygrometer measurements.}\end{array}\) & \(\begin{array}{c}\text{30,80}\end{array}\) & Moderate (10A) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Distribution of processed data. The method used these association rules to form probability parameters, then inputs the test dataset into a trained Naive Bayes classifier.