## 3 Results
### Study Characteristics
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Cites** & **Year** & **ML Algorithm** & **ML Dataset Source** & & **Contribution** \\ \hline
[39] & 35 & 2020 & SVM & Electrical Utilities & It proposes a support vector machine-based Daval pentagon Method to determine the severity of transformer faults by assessing the graphical Daval pentagon method using incorporating gas level, gas rate, and DGA interpretation results into one interpretation method. The proposed model achieved a diagnosis accuracy of 88%. \\ \hline
[40] & 1 & 2024 & TimesNet & Literature & It proposes a multi-model fusion transformer fault diagnosis method based on TimesNet and Informer with the adoption of an MCA module to reduce complexity and computational burden. The proposed model achieved a diagnosis accuracy of 88.46\%. \\ \hline
[41] & 19 & 2021 & KosaNet & Kenya Power Ltd. & It proposes a multinomial KosaNet classification model which is based on the decision tree algorithm, and investigates the application of various machine learning techniques to detect incipient faults and interpret DGA results. The proposed model achieved a diagnosis accuracy of 99.98\%. \\ \hline
[42] & 33 & 2020 & Spatial Clustering &
\begin{tabular}{c} Huzhou Power Supply \\ Company and IEC ICU \\ \end{tabular} & It proposes a fault diagnosis method based on the correlation coefficient density clustering which extrapolates the correlation coefficient of DGA data using density clustering. The proposed model achieved a diagnosis accuracy of 90\%. \\ \hline
[43] & 4 & 2023 & TPE-XGBoost & Literature & It proposes an Extreme Gradient Boosting algorithm for fault diagnosis it utilizes a Random Forest Regression algorithm to determine missing DGA data and hyperparameter optimization of data using a Tree-structured probability density estimator algorithm. The proposed model achieved a diagnosis accuracy of 98.9\%. \\ \hline
[44] & 9 & 2022 & BCSVM &
\begin{tabular}{c} South African \\ transformer manufacturer \\ \end{tabular} & It proposes a binary classification support vector machine with the application of the kernel function for transformer fault prediction; the model accuracy is then compared against the IEC gas ratio and ANN methods and yields an accuracy of 92\%. \\ \hline
[45] & 16 & 2021 & Logistic Regression & Not Specified & It presents the application of a regularized Logistic Regression Algorithm for improving DGA interpretation accuracy. The model accuracy is then compared against the conventional and intelligent methods and yields an accuracy of 85.6\%. \\ \hline
[46] & 18 & 2023 & ISCSO & Not Specified & It proposes a power transformer fault diagnosis method based on landmark isometric mapping (L-Isomap) and Improved Sand Cat Swarm Optimization (ICSO) to optimize the BGGU (SSCSO-BCRGU). The proposed model achieved an accuracy of 94.8\%. \\ \hline
[47] & 11 & 2023 & Improved BP neural network & Not Specified & It proposes an improved transformer fault diagnosis method based on a residual BP neural network which uses mapping to extract gas feature information with more significant feature differences after fusion expansion. \\ \hline
[48] & 2 & 2023 & Deep believe network & Literature & It applies a non-coding ratio method to generate 35 fault gas ratios based on the 5 fault gases, subsequently refined through correlation analysis to eliminate redundant feature variables, resulting in 15 significantly representative fault gas ratios. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Ref. ** & **Cites** & **Year** & **ML Algorithm** & **ML Dataset Source** & **Continuution** \\ \hline
[49] & 35 & 2018 & GRNN & Shandong Substation & It proposes a kernel principal component analysis and a generalized regression neural network (GRNN) using an improved fruit fly optimization algorithm (FFOA) to select the smooth factor.