## 3 Results
### Study Characteristics  
To obtain insights into the research questions defined in Section 1.4, the 124 resulting papers from the screening conducted using the PRISMA flow diagram were examined concerning transformer fault interpretation methods based on DGA data. The studied literature shows that the existing literature seeks to improve on the existing traditional fault interpretation methods which include SVM, ANN, kNN, etc. The systematic literature review further shows that even though research has been conducted in this research area, there are still shortcomings and limitations. Out of the 10 repositories which were consulted \begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{1}{c}{**Research Repositories**} & \multicolumn{1}{c}{**Number of Results Obtained from the**} \\  & **Initial Search** \\ \hline Association for Computing Machinery Digital & 852 \\ Library & 786 \\ Emerald Insight & 3531 \\ MDPI & 5324 \\ ResearchGate & 1493 \\ Science Direct & 4120 \\ Scientific Reports & 1265 \\ Springer Link & 4836 \\ The Institute of Engineering and Technology & 2353 \\ Wiley Online Library & 2013 \\ Total & 26,573 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Search results for the number of publications per repository. for the literature, MDPI had the highest output at 28 publications, closely followed by Science Direct, Springer Link, and IEEE Xplore at 22, 20, and 19, respectively. The rest of the papers were published by the other six repositories. Figure 5 shows the distribution of publishers. Furthermore, from these repositories, it was investigated which type of research output was reported and it was found that journal articles are the most published papers at 105 outputs compared to the 18 outputs of conference proceedings shown in Figure 6.  
Figure 5: The distribution of publishers. Figure 6: Type of research outputs. The study also reveals compelling evidence that China is at the forefront of this research with a dominant 53% of the total publications on electrical transformers monitoring based on DGA using machine learning algorithms. China's interest in and work on machine learning algorithms and transformer fault monitoring using DGA reflect their involvement in the adaptation of artificial intelligence in electrical power systems. From China, the other countries following from a distance are India at 5%, Saudi Arabia at 4%, and the UK, Algeria, Australia, and Indonesia all at 3%. The rest of the publications spread over 20 other countries including South Africa at 2%. The significant gap between China and the rest of the world in terms of participation in this research area could result from the educational and industrial investments China have made in advancing their infrastructures and being intentional in leading the fourth industrial revolution compared to the other countries which are following the trend. Figure 7 shows the distribution of countries publishing works on transformer DGA and machine learning. Furthermore, with the growing development of machine learning integration into electrical power systems, there has been a substantial growth in research publications of machine learning algorithms application in electrical transformers monitoring techniques to interpret faults using DGA data. Figure 8 shows research activities on machine learning and transformers with the presented data ranging from January 2014 to April 2024. From the figure, it is noticeable that there has been a steady incline in the publications, with years 2021 and 2023 topping the charts in research outputs, while 2024, at the time of generating  
Figure 7: Countries of presented publication sources. this graph (April 2024), only had seven reported outputs. The trends exhibited by this graph suggest that artificial intelligence in electrical transformers is attracting researchers to explore its potential in monitoring transformers. With the growing interest in artificial intelligence, there will likely be linear or exponential growth in the research outputs in this study area.