## 3 Results
### Study Characteristics
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref.** & **Cites** & **Year** & **ML Algorithm** & **ML Dataset Source** & **Contribution** \\ \hline
[77] & 9 & 2023 & AdaBoost-WBLS & State Grid Corporation of China and Literature & It proposes a novel transformer fault diagnosis model that utilizes a Filter-Wrapper Combined Feature Selection method and an AdaBoost integrated weighted broad learning system (AdBoost-WBLS) to select significant features and eliminate redundant ones. The proposed model achieved an accuracy of 95.05%. \\ \hline
[78] & 13 & 2021 & kNN, RT, RF & Not Specified & It investigates the application of random tree, random forest, and k-Nearest Neighbors algorithms for classifying cross-country faults from transformer disturbances. The investigated model achieved an accuracy of 98.68%. \\ \hline
[79] & 4 & 2024 & RF & IEC TC10 and Literature & It presents a multi-method technique that uses Scoring Index and random forest principles to combine conventional DCA interpretation methods into a multi-method technique. The presented model achieved an accuracy of 92.57%. \\ \hline
[80] & 43 & 2016 & PNN & Not Specified & It proposes a fault prediction method using a probabilistic neural network algorithm which applies its non-linear processing capabilities to predict internal faults. The proposed model achieved an accuracy of 90%. \\ \hline
[81] & 7 & 2023 &
\begin{tabular}{c} Res-Variational-Auto-Encoder and \\ Encoder and \\ ensemble learning \\ \end{tabular} & Guangzhou Substation & It proposes a hybrid fault diagnosis method comprising Re-Variational-Auto-Encoder and ensemble learning algorithms. This method extracts fault characteristics using a residual convolutional neural network. The proposed model achieved an accuracy of 98.68%. \\ \hline
[82] & 27 & 2023 & RF & Not Specified & It develops a fault identification method based on the combination of Random Forest and SMOTE algorithms to reduce the limitations and complexities associated with implementing graphical DPM. The developed model achieved an accuracy of 96.5%. \\ \hline
[83] & 3 & 2022 & BPMF and GRU-NN & Industrial Transformer & It proposes a transformer oil dissolved gas concentration for data loss scenarios using Bayesian probabilistic matrix factorization (BPMF) and gated recurrent unit (GRU) neural network. \\ \hline
[84] & 13 & 2022 & Saterima model & Industrial Transformer & It presents a time series forecasting model to forecast the dissolved gas concentration based on the Seasonal Autoregressive Integrated Moving Average (SARIMA). \\ \hline
[85] & 10 & 2023 & \
\begin{tabular}{c} Deep belief network \\ and BPNN \\ \end{tabular} &
\begin{tabular}{c} Electric Power Research \\ Institute of Yunnan Power \\ Grid Co., Ltd. \\ \end{tabular} & It introduces a deep belief in network-based fault diagnosis methods based on the characteristics of the precipitated gases. The introduced model achieved an accuracy of 90\%. \\ \hline
[86] & 17 & 2022 & Deep Coupled Dense CNN & IEC TC10 & It reconstructs the transformer oil characteristic gas to improve its features and compare fault type against various gases using the Adaptive Synthetic Oversampling (ADASVN) algorithm. The proposed model achieved an accuracy of 94.05%. \\ \hline
[87] & 2 & 2024 & \begin{tabular}{c} Bagged Trees, kNN, \\ and QSVM \\ \end{tabular} & \
\begin{tabular}{c} Various South \\ African Substations \\ \end{tabular} &
\begin{tabular}{c} It proposes a fault diagnosis model used to analyze oil samples that showed stray gassing characteristics by combining four different classifiers to enhance fault diagnosis accuracy. The proposed model achieved an accuracy of 87.7\%. \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 6: _Cnt._