# Partial Discharge Source Classification in Power Transformers: A Systematic Literature Review
## 1 Introduction  
Insulation systems play a pivotal role in the proper functioning and long-term operation of high-voltage electrical equipment such as transformers, cables, switchgear, motors and generators [1, 2]. Insulation systems in transformers are divided into liquid and solid insulation [3]. Oil makes up the liquid insulation [3, 4, 5], while solid insulation in transformers includes kraft paper [6, 7], Nomex [8, 9] and pressboard [9]. PD develops within the insulation systems of HV electrical equipment. These are localized electric discharges that can degrade the dielectric capability and lead to premature failure of the transformer [10, 11]. While PD is a phenomenon that impacts all HV electrical equipment, such as gas-insulated switchgear, cables, motors, generators, etc., this systematic literature review places focus on electrical transformers. Transformers make up an essential part of an electrical reticulation or network, as they allow for efficient long-range interconnection of transmission lines while also ensuring that the voltage is transformed to the levels required by end users. The failure of a transformer results in the loss of power supply to portions of the electrical network, with potential costly damage as well as a loss of productionor income for customers. Losses may also lead to long downtime as the manufacturing process of a large transformer can take more than 24 months. Monitoring PD in electrical transformers forms a great basis for planning essential preventative maintenance, which can prevent costly failures before they occur and allow the transformer owner to plan adequately and execute the most ideal maintenance during planned outages. The emergence of Artificial Intelligence (AI) has allowed scholars to research possible means of improving the condition monitoring of equipment. The use of AI has been shown to greatly improve the time taken to analyze and classify PD in the monitored equipment, while also improving the accuracy of detection compared to manually performed analysis. Studies in the use of Machine Learning (ML) techniques for PD classification in transformers have been around for some time; however, this Systematic Literature Review (SLR) will be limited to the literature published between the years 2010 and 2023. Further restrictions were applied by discounting the literature about any HV electrical equipment other than electrical transformers, as well as disregarding other faults occurring in transformers but considering only partial discharge faults. The SLR presented a series of Research Questions (RQ) with the aim of identifying possible literature gaps within the study of automatic transformer partial discharge classification using machine learning algorithms, as well as to offer context to be utilized by the reader to develop future studies. The RQ are as follows:  
1. RQ1: Which resource publication sources have been predominantly utilized in the literature to study the use of machine learning in monitoring electrical transformer partial discharge? 2. RQ2: From which countries do the publications on transformer partial discharge monitoring with the use of machine learning originate? 3. RQ3: How has the research on the automatic classification of transformer partial discharge using machine learning evolved over the period under review? 4. RQ4: Which partial discharge types does the collected literature investigate? 5. RQ5: From which sources were the partial discharge data used in the literature collected? 6. RQ6: Which PD measuring methods are used in the sampled literature? 7. RQ7: Which measuring equipment is used in the measuring of PD in the literature? 8. RQ8: Which national and international standards are referenced in the literature? 9. RQ9: Which feature extraction methods are mostly utilized in the collected literature? 10. RQ10: Which machine learning algorithms are the most utilized for monitoring partial discharge in electric transformers? 11.