## 3 Results
### Study Characteristics
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Cites** & **Year** & **ML Algorithm** & **ML Dataset Source** & **Contribution** \\ \hline
[108] & 2 & 2022 & Isomap and IchOA—LSSVM & Not Specified & It presents a fault diagnosis method for improving diagnosis accuracy based on Isomap and IchOA-LSSVM. The model starts by reducing dimensionality and then optimizes the least square support vector machine algorithm to improve the overall model accuracy. The presented model achieved an accuracy of 90.83\%. \\ \hline
[109] & 5 & 2022 & ISSA—SVM & Shaanari Province & It proposes a fault diagnosis method based on the Improved Sparrow Search Algorithm and the optimized support vector machine. Various activation functions are used to enhance the accuracy of the model. The proposed model achieved an accuracy of 91.4\%. \\ \hline
[110] & 9 & 2021 & Refined Deep Residual & IEC TC10, State Grid Hubei & It proposes a fault identification model to improve the performance of transformers by coupling a high-dimensional feature map of gas concentrations and associated variables. The proposed model achieved an accuracy of 97.6\%. \\ \hline
[111] & 49 & 2014 & PSO-SVM & UK National Grid & It presents a feature prioritization and detection model that assesses fault types, specific gas ratios, and statistical characteristics of the DGA data. The presented model achieved an accuracy of 96.50\%. \\ \hline
[112] & 12 & 2021 & IVSVM & China Southern Power Grid and Literature & It introduces a combined fault diagnosis model based on improved information entropy and a vague support vector machine in which it uses improved information entropy to obtain gas weights from DGA datasets and the trained data to obtain classifiers using a vague support vector machine. The introduced model achieved an accuracy of 90.60\%. \\ \hline
[113] & 9 & 2020 & MLP & Electric Technology Research & It constructs a multilayer perceptron model for fault detection in transformers using the Asciens Library, The model is tested using two datasets and it achieved an accuracy of 96.70\%. \\ \hline
[114] & 17 & 2020 & MVO-PNN & China Electric Power Research Institute & It presents a hybrid model for fault diagnosis based on probability neural network optimized by Multi-Worse Optimizer algorithm which has a strong global convergence from improving fault diagnosis accuracy. The presented model achieved an accuracy of 97.49\%. \\ \hline
[115] & 0 & 2023 & Improved SVM & Not Specified & It introduces a fault location recognition model based on k-means and constructs a high-precision with fast convergence diagnosis. The proposed model achieved an accuracy of 98.40\%. \\ \hline
[116] & 0 & 2022 & SVM & IEC TC10 Database and Literature & It proposes an improved hybrid fault diagnosis model comprising the Slime Mold Optimized support vector machine and logarithmic entrapment transform with data transformation and optimization capabilities of this model equipped to improve the diagnosis accuracy. The proposed model achieved an accuracy of 90.10\%. \\ \hline
[117] & 32 & 2017 & Fuzzy inference system & Iran Power Grid & It studied a large dataset of more than 300 transformers and applied a fuzzy inference system to validate the state of suspicious transformers by validating the fault detection accuracy. Also, the proposed fuzzy inference system was able to detect the decomposition of the insulation paper. The proposed model achieved an accuracy of 91.20\%. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._