## 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
[127] & 3 & 2022 &
\begin{tabular}{c} Boosted Trees, FUS Boosted \\ Trees, and Subspace kNN \\ \end{tabular} & Local Power Utility & It proposes a fault detection model that is capable of handling imbalanced DGA data while improving the accuracy of predictions using machine learning algorithms Boosted Trees, RUS Boosted Trees, and Subspace k-Nearest Neighbors. The Boosted Trees algorithm was the best-performing algorithm and achieved an accuracy of 82.6\%. \\ \hline
[128] & 12 & 2019 & ANFIS &
\begin{tabular}{c} Iran-Tansfo Company \\ Chemical Laboratory and \\ Damavand \\ Power Substations \\ \end{tabular} & It presents a hybrid incipient faults detection method using the DGA information and Adaptive Neuro-Fuzzy Inference System algorithm. It further uses the Association Rules Mining technique to select features from the DGA dataset. The proposed model achieved an accuracy of 99.6\%. \\ \hline
[129] & 2 & 2023 &
\begin{tabular}{c} Fuzzy logic and \\ deep learning \\ \end{tabular} & Electrical Utility & It proposes a fuzzy logic DGA interpretation technique that combines the use of gas level, gas rate, and the Dual pantom approach with the aim of increasing the reliability of fault detection and performance accuracy. The proposed model achieved an accuracy of 89.80\%. \\ \hline
[130] & 8 & 2017 &
\begin{tabular}{c} PNN \\ \end{tabular} & Literature & It proposes a transformer fault diagnosis technique employing & Multi-input and Multi-Output Polynomial Neural Network. This technique is divided into two parts, the first part single output classification method is trained and the transformer state is divided into binary states normal and abnormal, the results are then used to build a fault diagnosis model. \\ \hline
[131] & 5 & 2023 &
\begin{tabular}{c} RF, Tree Ensemble, Gradient \\ Boosted Tree, and Extreme \\ Gradient Tree Algorithms \\ \end{tabular} & Literature & It discusses the impact that the feature input vector of traditional diagnostic methods has on the performance of DGA-based artificial intelligent fault diagnosis methods. The algorithms used to assess the performance were the random forest, Tree Ensemble, Gradient Boosted Tree, and Extreme Gradient Tree. The random forest algorithm was the best-performing algorithm and achieved an accuracy of 98.37\%. \\ \hline
[132] & 16 & 2022 &
\begin{tabular}{c} Decision tree, support vector \\ machine, random forest, \\ neural network, Naive Bayes, \\ and AdaBoost \\ \end{tabular} & Not Specified & It examines machine learning algorithms composed used for transformer fault identification using DGA data namely decision trees, support vector machine, random forest, neural networks, Naive Bayes, and AdaBoost & Not Specified & It examines machine learning algorithms commonly used for transformer fault identification using DGA data namely decision trees, support vector machine, random forest, neural networks, Naive Bayes, and AdaBoost & Not Specified & It examines machine learning algorithms commonly used for transformer fault identification using DGA data namely decision trees, support vector machine with a performance accuracy of 99.80\%. \\ \hline
[133] & 0 &
\begin{tabular}{c} CVA-SVM \\ \end{tabular} & Not Specified & It examines machine learning algorithms composed used for transformer fault identification using DGA data namely decision trees, support vector machine, random forest, neural networks, Naive Bayes, and AdaBoost as well the traditional DGA fault identification methods method. DTI, DTI, and DTMAS. Of all these algorithms and methods, the random forest algorithm was the best-performing method with a performance accuracy of 99.80\%. \\ \hline
[134] & 5 & 2021 & Fuzzy logic & IEC TC10 & It proposes a fault diagnosis method for multiple transformer incipient faults using fuzzy logic based on the concentration of gases obtained from the transformer oil. The proposed model achieved an accuracy of 98.30\%. \\ \hline \hline \end{tabular}
\end{table}