## 4 Discussion  
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 considering transformer fault interpretation methods based on DGA data. **Q1. **: _What are the primary objectives and research aims that current research addresses on DGA-based transformer fault classification?_  
The systematic review 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. **Q2**: _Which research repositories and publication types are commonly used to study DGA-based transformer fault classification?_  
The study found that among the 10 repositories used to extract papers, MDPI has the highest outputs 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 is 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.  
**Q3. **: _Which global countries and regions are at the forefront of DGA-based transformer fault classification research?_  
From the study, there is 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 and work conducted on machine learning algorithms and transformer fault monitoring using DGA reflects 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%. Figure 7 shows the distribution of countries publishing works on transformer DGA and machine learning. **Q4. **: _How has the research in the area of ML application in interpreting transformer faults using DGA evolved over the period 2014-2024?_  
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 the 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 this graph (April 2024), only had seven reported outputs. The trends exhibited by this graph suggest that the application of artificial intelligence in electrical transformers has been attracting researchers to explore the potential it possesses in transformer monitoring. With the growing interest in artificial intelligence, there will likely be linear or exponential growth in the research outputs in this study area. **Q5. **: _Which machine learning techniques are employed to investigate DGA-based transformer faults?_The study shows that various machine learning algorithms were used for transformer monitoring using DGA data, the support vector machine (SVM), artificial neural networks (ANN), and k-Nearest Neighbor (kNN) stand out as the most commonly used and adapted algorithms with percentages of 32%, 17%, and 12%, respectively (excluding hybrid algorithms of the aforementioned).