# Dissolved Gas Analysis for Fault Prediction in Power Transformers Using Machine Learning Techniques
## 5 Conclusions  
In this paper, fault diagnosis for a power transformer was performed using six machine learning classification methods and three dataset transformation techniques, namely DTM, DRM, and RRM. The six classification techniques used were RF, BPNN, KNN, SVM, DT, and NB. The three transformation methods were implemented to enhance the accuracy of fault prediction, as well as other criteria such as the precision, recall, F1 score, specificity, and Matthews correlation coefficient (MCC) of the suggested methods. The dataset transformation methods involved gas percentages and gas ratios.  
While the results obtained from the work have been compared, it was noticed that a better diagnostic accuracy, precision, recall, F1 score, and Matthews correlation coefficient (MCC) for all cases were observed with the four gas ratios used in [PERSON]'s method. The utilization of different dataset inputs does not always positively affect the classification performance; for example, it was noticed that the classification performance decreased in case C, which used the Rogers ratio method.  
This illustrates that a certain dataset used does not hold the essential information for an algorithm to produce an exact diagnosis. The results show that the dataset content in this work for classification algorithms for training and testing had a great effect on the performance.  
The results of RF were the best for the three types of datasets because this method relies on generating a good number of solutions and selects the best among them, which distinguishes it from other methods of machine learning.  
Methodology, S.R.A.-S. and G.A.B.; investigation S.R.A.-S., G.A.B. and A.T.S.; writing--original draft preparation, S.R.A.-S., G.A.B. and A.T.S.; writing--review and editing, S.R.A.-S., A.T.S. and W.A.K.A.-M.; supervision, W.A.K.A.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are available at the following: [[https://github.com/ahmedtariq71/Dataset1](https://github.com/ahmedtariq71/Dataset1)]([https://github.com/ahmedtariq71/Dataset1](https://github.com/ahmedtariq71/Dataset1)) (accessed on 13 December 2024) and [[https://github.com/ahmedtariq71/PowerTransformerCode](https://github.com/ahmedtariq71/PowerTransformerCode)]([https://github.com/ahmedtariq71/PowerTransformerCode](https://github.com/ahmedtariq71/PowerTransformerCode)) (accessed on 13 December 2024).  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Best**} & \multicolumn{5}{c}{**Metrics**} \\ \cline{2-9}  & **Algorithm** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F1 Score** & **MCC** \\ \hline Case A: Three gas percentages (Duval triangle method) & RF & 95.2\% & 95.8\% & 95.2\% & 99.3\% & 94.9\% & 90.1\% \\ \hline Case B: Four ratios (Doernenburg’s method) & RF and DT & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline Case C: Three ratios (Rogers ratio method) & RF and DT & 99.2\% & 99.2\% & 99.2\% & 98.8\% & 99.2\% & 98.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: Best diagnostic performance results for different dataset cases.