# Partial Discharge Source Classification in Power Transformers: A Systematic Literature Review
## 5 Results  
The literature collected after the PRISMA flowchart is used to formulate the responses to the twelve research questions developed in Section 1 concerning transformer partial discharge classification using machine learning algorithms. _RQ1: Which resource publication sources have been predominantly utilized in the literature to study the use of machine learning in monitoring partial discharge in electrical transformers?_  
Figure 4 shows the literature published in journals to greatly dominate the publications of scholarly works on using machine learning algorithms for the classification of partial discharge in electric transformers. From analysis of the collected literature, 56 of the 81 literatures under review were journal papers; this makes up more than double the number of conference papers, which account for only 25 publications in the period under review. This indicates a vast preference for scholars to publish their works in journals. _RQ2: From which countries do the publications on transformer partial discharge monitoring with the use of machine learning originate_? The representation of countries in the study of using machine learning techniques for classification of partial discharge in transformers was found to be wide, with 20 countries being represented in the collected literature published between 2010 and 2023. This indicates that this is a field of study that has gained interest worldwide, but also indicates the importance that scholars and users have placed on monitoring and understanding the health of their transformers. The motivation for these countries to invest in research can be related to the need to reduce costly transformer failures, extend the life of their assets,improve condition monitoring, adequately improve planned maintenance and eliminate grid failures which can have massive economic impacts. From the 20 countries represented in the literature, it can be seen in Figure 5 that China has contributed most of the literature at 32%. The superiority of China in this research indicates a continued sign of China's active investment and contribution to the advancement of technology in the field of artificial intelligence. When it comes to the field of power transformers and partial discharge, it can be attributed to the large landscape of China, the economy which has been one of the fastest growing in recent years and the economy's reliance on a reliable electrical grid. China is currently the largest producer of electricity with 8534 TerraWatt-hours of electricity produced in 2021 according to [94]. This made up 30% of the entire world's electricity production in 2021. The research places India in second position with 10% of the publications, Spain in third with 7% and closely followed by Indonesia with 6%. Although India only produced 6% of the total electricity in 2021, the electricity production grew by 10% in that year, which was equal to China's growth. Figure 4: Research publication sources. Figure 5: Countries represented in the literature. _RQ3: How has the research on the automatic classification of transformer partial discharge using machine learning evolved over the period under review?_  
Publications of partial discharge source classification using machine learning algorithms have shown very steady and consistent growth over the period from 2010 to 2023, as shown in Figure 6. While four papers were published in the year 2010, the following year saw a decline to the lowest publications in the period under review, with only one documented paper published in 2011. The years from 2012 to 2017 consisted of publications of three or five papers per year. The greatest documented studies came in the year 2018, which peaked at 13 publications in that year. The interest in this study has remained post-2018, with publications remaining consistently higher than the preceding years. The number of documented papers in the years up to 2017 indicates persistent interest in developing the knowledge base and research in the field.