# Partial Discharge Source Classification in Power Transformers: A Systematic Literature Review
## 5 Results
The huge increase in 2018 can be related to an increased understanding attained in the field from the research of the years thus far. The results of years post-2018 can be attributed to further development and building on the successes and research attained thus far. _RQ4: Which partial discharge types does the collected literature investigate?_  
A total of 231 partial discharges were analyzed in the 81 publications under review, as can be seen in Figure 7. Corona and surface discharge are the two predominantly tested types of PD in the research in transformer partial discharge classification using machine learning algorithms. Corona discharge was investigated in 55 research papers, while surface discharge was investigated in 50 of the research papers and 75.3% of the research papers under review investigated multiple partial discharge types in one publication. The benefit for researchers to investigate multiple PD types is that transformers are constructed with solid and liquid insulation systems. These insulation systems are susceptible to a variety of degradation mechanisms due to their chemical compositions. Investigating multiple PD allows the researchers to develop classification models for different types of PD while also comparing the accuracy of the model for each PD type. Figure 6: Timeline of literature publications. Corona discharge is the most common type of discharge occurring in HV electrical equipment, while surface discharge is considered to be the most destructive type of PD [95]; therefore, their prevalence in the literature indicates the investment provided to understand and possibly mitigate or reduce their long-term effect. The rather low number of publications relating to discharges in oil brings forth some prominent observations. It indicates a research gap in the literature, considering the various developments in transformer insulating oils which include mineral oil, vegetable oil and synthetic oil. The development of PD in various insulating oils and their propagation introduce an available opportunity for further research and development of automatic classification models as well as providing input to enhancing the compositions of the oils. Secondly, the low publications are possibly a result of reliance on transformer dissolved gas analysis, which has proven to be a very effective tool for detecting degradation of solid insulation via the gases trapped in the transformer oil. _RQ5: From which sources was the partial discharge data used in the literature collected?_  
The study of the sources used to collect partial discharge data to be used in the context of transformer partial discharge classification using machine learning techniques opens some notable information. The leading source of PD data is collected from artificial PD defect models. This makes up 35% of the results from the literature reviewed. Artificial defect models are laboratory-constructed models of different designs and sizes. These models are specifically set up to simulate a particular PD defect type. Figure 8 illustrates an example of four PD defect models used in the classification of multiple PD defects using machine learning algorithms. In Figure 8, defect model (a) is an air gap defect model, (b) is a floating defect, (c) is a surface defect and (d) is a tip defect model. Artificial defect models are preferred by researchers as they can model the specific PD defect that they are looking for. In a machine learning algorithm, this data allows for better training of the algorithm and permits fewer outliers as the data collection is specific to the defect. The testing phase of the algorithm is also improved, as the algorithm is trained with specific data and can deal better with outlying data points. Figure 7: Types of partial discharges. PD experimental setups came second with 25% of the publications as can be seen in Figure 9, while data collected from actual transformers came third with 16% of the publications in the period between 2010 and 2023.