# Transformer Fault Diagnosis Method Based on Incomplete Data and TPE-XGBoost
## 5 Conclusions  
A transformer fault diagnosis model based on RFR and TPE-XGBoost algorithms which is able to handle incomplete datasets, is proposed in this paper. First, the RFR algorithm was used to fill in the missing values in the sample data and compare them with statistical methods and other data-filling methods. Then, the TPE method was used to optimize the XGBoost model and improve the accuracy of the fault diagnosis model. Finally, the accuracy of TPE-XGBoost was compared with other artificial intelligence algorithms to verify its effectiveness. The conclusions of this paper are as follows.  
1. The RFR algorithm outperformed traditional statistical methods and CART, kNN, and Ridge for the missing data problem of dissolved gas in transformer oil. The accuracy of the RFR model reached 80% when the missing rate of DGA data was 20%, indicating that the RFR-filled values could restore the information of the dissolved gas in the transformer oil to a greater extent.  
Figure 11: The influence of data missing rate on diagnostic result.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Sample Size** & **Minimum** & **Maximum** & **Mean** & **Standard Deviation** & \(t\)**Value** & \(p\)**Value** \\ \hline kNN & 10 & 0.661 & 0.857 & 0.772 & 0.062 & \(-\)4.495 & 0.001 \\ CART & 10 & 0.694 & 0.861 & 0.779 & 0.057 & \(-\)4.46 & 0.002 \\ DNN & 10 & 0.639 & 0.917 & 0.792 & 0.097 & \(-\)2.231 & 0.053 \\ RF & 10 & 0.75 & 0.895 & 0.834 & 0.049 & \(-\)1.68 & 0.127 \\ XGBoost & 10 & 0.767 & 0.943 & 0.861 & 0.056 & 0.048 & 0.963 \\ NGBoost & 10 & 0.768 & 0.956 & 0.877 & 0.063 & 0.852 & 0.417 \\ TPE-XGBoost & 10 & 0.837 & 0.955 & 0.9 & 0.037 & 3.374 & 0.008 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical test results of different methods.  
Based on the DGA sample database, after the RFR model fills in the missing data, the accuracy of the XGBoost model was improved from 80% to 89.5% after feature derivation and hyper-parameter optimization of the TPE algorithm. The evaluation metric F1-score of the TPE-XGBoost model was 87%, indicating the effectiveness of the TPE-XGBoost diagnostic model.
3. The TPE-XGBoost algorithm was compared with kNN, CART, RF and DNN. Based on the DGA sample dataset with 20% missing values, the average accuracy of the TPE-XGBoost model was 89.5% and was much higher than the other algorithms. The superiority of the TPE-XGBoost algorithm in dealing with transformer fault diagnosis problems was demonstrated. The diagnosis results obtained in the case of partially missing data were still credible.  
Conceptualization, T.W. and Q.L.; methodology, T.W. and J.Y.; software, T.W., T.X. and P.W.; writing--original draft preparation, T.W.; validation, P.W.; writing--review and editing, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Project of Jiangsu Electric Power Test and Research Institute Co., Ltd., grant number DSY202202.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.