# Power Transformer Fault Diagnosis Based on Dissolved Gas Analysis by Correlation Coefficient-DBSCAN
## 5 Conclusions  
DBSCAN is an important method of clustering algorithm, but in transformer fault diagnosis, due to the difference of characteristic gas content used to distinguish each fault and the vagueness of their Euclidean distance distinction, the effect of DBSCAN method directly applied to fault diagnosis is not satisfying. In this paper, according to the correlation characteristics of the same kind of fault data of the transformer, the aggregation coefficient is designed to represent the similarity degree and the \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Number** & **Cluster1** & **Cluster2** & **Cluster3** & **Cluster4** & **Cluster5** & **Cluster6** & **Cluster Type** \\ \hline
1 & 0.66 & 0.82 & 0.95 & 0.02 & \(-\)0.23 & 0.13 & T3 \\
2 & \(-\)0.06 & \(-\)0.60 & \(-\)0.28 & 0.76 & 0.87 & 0.94 & D2 \\
3 & 0.62 & 0.95 & 0.89 & \(-\)0.30 & -0.61 & \(-\)0.33 & T2 \\
4 & 0.47 & 0.92 & 0.98 & \(-\)0.25 & \(-\)0.39 & \(-\)0.11 & T3 \\
5 & 0.54 & 0.93 & 0.99 & \(-\)0.21 & -0.40 & \(-\)0.10 & T3 \\
6 & 0.88 & 0.85 & 0.13 & 0.39 & \(-\)0.62 & \(-\)0.61 & T1 \\
7 & 0.07 & \(-\)0.59 & \(-\)0.30 & 0.86 & 0.80 & 0.93 & D2 \\
8 & 0.60 & 0.97 & 0.96 & \(-\)0.24 & \(-\)0.55 & \(-\)0.24 & T2 \\
9 & 0.50 & \(-\)0.36 & \(-\)0.27 & 0.91 & 0.35 & 0.61 & PD \\
10 & 0.71 & 0.79 & 0.93 & 0.16 & \(-\)0.19 & 0.22 & T3 \\
11 & 0.15 & \(-\)0.57 & \(-\)0.31 & 0.93 & 0.91 & 0.90 & D1 \\
12 & 0.64 & 0.92 & 0.97 & \(-\)0.13 & \(-\)0.42 & \(-\)0.08 & T3 \\
13 & 0.53 & 0.86 & 0.98 & \(-\)0.10 & \(-\)0.26 & 0.06 & T3 \\
14 & \(-\)0.32 & \(-\)0.43 & \(-\)0.08 & 0.23 & 0.82 & 0.72 & D1 \\
15 & 0.62 & 0.91 & 0.98 & \(-\)0.10 & \(-\)0.36 & \(-\)0.01 & T3 \\
16 & 0.62 & 0.97 & 0.88 & \(-\)0.31 & \(-\)0.69 & \(-\)0.41 & T2 \\
17 & 0.53 & \(-\)0.21 & 0.02 & 0.94 & 0.50 & 0.84 & PD \\
18 & 0.25 & \(-\)0.25 & 0.08 & 0.83 & 0.70 & 0.95 & D2 \\
19 & 0.92 & 0.38 & 0.59 & 0.69 & 0.14 & 0.61 & T1 \\
20 & 0.66 & 0.90 & 0.98 & \(-\)0.03 & \(-\)0.35 & 0.02 & T3 \\
21 & 0.61 & 0.87 & 0.97 & \(-\)0.10 & \(-\)0.31 & 0.02 & T3 \\
22 & 0.37 & 0.89 & 0.96 & \(-\)0.28 & \(-\)0.37 & \(-\)0.12 & T3 \\
23 & \(-\)0.29 & \(-\)0.74 & \(-\)0.54 & 0.33 & 0.75 & 0.58 & 1 \\
24 & 0.52 & 0.86 & 0.96 & \(-\)0.19 & \(-\)0.29 & \(-\)0.01 & T3 \\
25 & \(-\)0.24 & \(-\)0.73 & \(-\)0.43 & 0.65 & 0.90 & 0.87 & D1 \\
26 & 0.52 & 0.92 & 0.99 & \(-\)0.17 & \(-\)0.38 & \(-\)0.07 & T3 \\
27 & 0.62 & 0.69 & 0.89 & 0.27 & \(-\)0.04 & 0.37 & T3 \\
28 & 0.17 & \(-\)0.52 & \(-\)0.25 & 0.92 & 0.92 & 0.93 & D1 \\
29 & 0.67 & 0.96 & 0.72 & \(-\)0.34 & \(-\)0.45 & \(-\)0.37 & T2 \\
30 & 0.91 & 0.58 & 0.67 & 0.51 & 0.31 & 0.49 & T1 \\ \hline \end{tabular}
\end{table} 
Table 6: DGA data diagnosis results using CCDBSCAN. cluster diagnosis of CCDBSCAN is completed by optimizing and amplifying the fault characteristics. Through calculation, the following conclusions are obtained:  
1. The method proposed in this paper is different from the traditional Dissolved Gas Analysis in oil; we introduce the concept of correlation coefficient into cluster analysis, and the aggregation coefficient is constructed to represent the similarity degree of the data. Through the optimized amplification coefficient, some gas which is important but less in content gets amplified, successfully making the correlation coefficient of dissolved gas in oil with the same fault higher than before. 2. By introducing the correlation coefficient into the DBSCAN method, the accuracy of clustering is improved by 31%, which successfully solved the problem of low accuracy of DBSCAN method in clustering. When used in fault diagnosis, the similarity between test set and each fault can be represented by the correlation coefficient instead of a simple diagnosis result, which is more in line with the engineering practice. 3. Using the correlation coefficient to represent the similarity degree of data, and the CCDBSCAN method for clustering, the accuracy of fault diagnosis is significantly improved compared with the iec60599-2015 method, providing a better prospect for application. Conceptualization and supervision, B.S.