# A Deep Parallel Diagnostic Method for Transformer Dissolved Gas Analysis
## 4 Conclusions  
In this paper, a deep parallel diagnostic method was proposed into the field of transformer DGA fault diagnosis. ADASYN and transfer learning were introduced to deal with the problem of data imbalance and insufficiency. The conclusions are as follows:  
* In view of the situation that the actual fault samples of the transformer DGA are imbalanced and inadequate, which will cause the ineffectiveness of the fault diagnostic method, ADASYN and transfer learning technology were used to effectively improve the diagnostic ability, especially the ability to troubleshoot the minor latent faults. The accuracy of the deep parallel diagnostic method before and after data augmentation was improved by 3.64%.
* The complex non-linear mapping relationship between dissolved gas in transformer oil and transformer fault states was modeled with the help of two important deep learning methods, LSTM and CNN. The diagnostic accuracy of the LSTM network was 93.6%, while that of the CNN network was 92.1%.
* Using DPF, which fully considers the sensitivity differences of deep learning methods to different fault features, retains the independence of different deep learning diagnostic methods to a large extent. As a result, the final diagnostic result was 96.9%.
* The anti-interference ability of the proposed method is better than most of the methods compared in this paper. When the dataset was superimposed with 3% random noises, the diagnostic accuracy of the proposed method only decreased by 0.62%, which shows it has strong adaptability and generalization ability.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON] and [PERSON]; software, [PERSON] and [PERSON]; validation, [PERSON] and X.W.; formal analysis, [PERSON]; investigation, [PERSON] and [PERSON]; resources, [PERSON]; data curation, [PERSON] and X.W.; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], [PERSON] and Y.H.; visualization, [PERSON] and [PERSON]; supervision, [PERSON]; project administration, [PERSON] and [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant No. 51977153, 51977161, 51577046, the State Key Program of National Natural Science Foundation of China, grant No. 51637004, the National Key Research and Development Plan of China \"Important Scientific Instruments and Equipment Development\", grant No. 2016 YFF0102200, Equipment Research Project in Advance of China, grant No. 41402040301.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Diagnostic Methods** & \begin{tabular}{c} **Diagnostic Accuracy** \\ **without Noises** \\ \end{tabular} & \
\begin{tabular}{c} **Diagnostic Accuracy** \\ **with Noises** \\ \end{tabular} &
\begin{tabular}{c} **Decreased Percentage** \\ \end{tabular} \\ \hline SVM & 87.4\% & 85.9\% & 1.72\% \\ KNN & 91.5\% & 91.1\% & 0.44\% \\ GBDT & 86.9\% & 80.1\% & 7.83\% \\ NN & 85.1\% & 80.9\% & 4.94\% \\ FCM & 87.5\% & 85.9\% & 1.83\% \\ CNN & 92.1\% & 91.5\% & 0.65\% \\ LSTM & 93.6\% & 92.7\% & 0.96\% \\ Deep parallel diagnosis & 96.9\% & 96.3\% & 0.62\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Diagnostic effects of different diagnostic methods with or without noises.