# Power Transformer Fault Diagnosis Based on Dissolved Gas Analysis by Correlation Coefficient-DBSCAN
## 4 Example Analysis and Results
### CCDBSCAN Method Classification Result Analysis  
The processed DGA data were used in CCDBSCAN analysis, and compared with the original DBSCAN method. The comparative analysis indexes were accuracy, precision, and recall, and were characterized by confusion matrix. (1) DBSCAN analysis  
After 60 groups of DGA data of fault transformers are normalized, DBSCAN method is directly used for cluster analysis. The cluster graph and confusion matrix are shown in Figure 6 and Table 3.  
(2) CCDBSCAN analysis  
After 60 groups of DGA data of fault transformers are normalized, CCDBSCAN method is used for cluster analysis. The cluster graph and confusion matrix are shown in Figure 7 and Table 4.  
By comparing the confusion matrix of the two methods, we can see that the accuracy of CCDBSCAN method is 90%, which is 31% higher than that of original DBSCAN method. The accuracy of original DBSCAN method in clustering PD, D1 and D2 is very low, and the accuracy is only 33%, which is caused by the less characteristic gas content of these three faults. \[\text{Accuracy}= \frac{10+9+10+9+8+8}{60}=90\%\]  
Through comparative observation of Figures 6 and 7, it is found that the most significant difference between Figures 6 and 7 is the clustering result in  
collected and sorted out under the actual operation conditions of the transformer, and illustrated with field drawings, so it is quite convincing to select the cases for diagnosis and analysis. In this paper, 30 DGA data including six fault modes are obtained by selecting the fault transformers with clear fault types through field inspection. See Table 5 for DGA data and fault types. The diagnosis effect is compared and analyzed by using the fault modes clustering in this paper and IEC 60,599 method. The above 30 data to be diagnosed are initialized with Equation (15), that is, after the absolute content of five gases is calculated as the percentage content of gases, the correlation coefficients between them and the six fault sets that have been clustered are calculated, and CCDBSCAN is used for clustering. Select T3, PD, D1 and D2 faults (corresponding to the 4 th, 9 th, 18 th and 25 th data in the table) to illustrate the clustering process and the improvement effect of CCDBSCAN. See Figure 8 for the line chart of correlation coefficient. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & **H2(ul/l)** & **CH4(ul/l)** & **C2H6(ul/l)** & **C2H4(ul/l)** & **C2H2(ul/l)** & **ICE60599** & **Fault Category** \\ \hline
1 & 54.54 & 71.93 & 9.72 & 93.37 & 6.58 & T3 & T3 \\
2\#1 & 5760 & 540 & 40.5 & 1000 & 2760 & D1 & D2 \\
3 & 20 & 80.2 & 24.6 & 68.6 & 0 & T2 & T2 \\
4 & 15.9 & 55.98 & 22.33 & 137.25 & 0.21 & T3 & T3 \\
5\# & 40 & 102.6 & 32.3 & 183.3 & 0.2 & T3 & D2 \\
6 & 2.369 & 119.69 & 21.891 & 20.15 & 0 & T1 & T1 \\
7\# & 87.17 & 17.26 & 3.94 & 12.87 & 32.81 & D1 & D1 \\
8\# & 605 & 1586 & 655 & 1901 & 2.3 & T2 & T3 \\  
& 32.5 & 80.8 & 0 & T1 & T1 \\ \hline \hline \end{tabular} \({}^{1}\) #indicates incorrect diagnosis.  
\end{table} 
Table 5: DGA data fault type and IEC60599 diagnosis results. The data in Figure 8a is T3 data, and the correlation coefficients of the data with six clusters are calculated. It is found that the correlation coefficients of cluster 2 and cluster 3 of the original data are high, and the maximum correlation coefficient with cluster 2 reaches 0.956, which will interfere with the density clustering. The correlation coefficient between the characteristic gas of the T3 data and six clusters is calculated after being amplified. The maximum correlation coefficient between the characteristic gas of the T3 data and the cluster 2 is reduced to 0.901, which improves the accuracy of the density clustering algorithm. Figure 8b also shows the same characteristics as 8a, especially Figure 8c,d. Between the low- (8c) and high-energy discharge faults (8d), the difference of the original data correlation coefficient line is not obvious.