# Partial Discharge Source Classification in Power Transformers: A Systematic Literature Review
## 8 Conclusions  
This paper provides an in-depth analysis and review of the existing research and literature in the field of transformer partial discharge classification with the use of machine learning algorithms. The foundation of this paper introduces the electrical transformer, with particular emphasis on the solid and liquid insulation systems. This is then accompanied by a focus on the explanation of partial discharge, its development within transformer dielectric and its effects on the transformer, as well as how it can be used to monitor the health of the transformers. The use of artificial intelligence in monitoring and classifying PD activity is then introduced, together with the advantages it provides. This systematic review included a robust methodology developed using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) flowchart. Using the PRISMA flowchart as well as a well-developed inclusion and exclusion criteria, a total of 81 research papers published on reputable research repositories between 2010 and 2023 were examined as part of this review. The study develops a series of pertinent research questions which need to be answered to provide insight into the critical elements surrounding the development in the field of utilizing AI to classify PD in electric transformers. The review commences with the identification of the prevalent publication sources in the collected literature, indicating a substantial inclination towards journals over conferencepapers and research articles. After that, it studies which countries have contributed to the literature in this field. This revealed a significant range of involvement with 20 countries from diverse continents participating in this field showing worldwide interest in the development. However, China dominated the number of publications in the period under review, which is indicative of their continued investment in technology and the field of artificial intelligence as well as its practical use on electric transformers. The paper further reviewed PD's existence in transformers in detail, starting with examining the types of PD that have been most widely researched. This highlighted the propensity for scholars to investigate multiple types of PD, training their algorithm on each type and comparing the accuracy. This reveals that researchers are moving more towards multisource PD classification algorithms rather than single-source ones which were more prevalent in the past. Secondly, it identified the types of sources used to generate the PD data, artificial defect models surpassed transformer models and actual transformers, respectively, in the top three, thus indicating that most PD data used in the literature has been collected in laboratory environments; furthermore, it indicates that this may be the most reliable form of data for training of the machine learning algorithms. The third PD assessment was that of the methods used to measure the PD activity, Ultra-high frequency, Electric and Acoustic emissions were among the most utilized methods. This assessment further indicated a wide variety of testing methods which are disposable for researchers, scholars and PD testing companies, and which can be utilized in laboratory settings, onsite, offline and online. Oscilloscopes were identified as the preferred equipment for recording PD activity. This further expounds on most of the research occurring in laboratory environments, as oscilloscopes are readily available in most electrical Engineering laboratories. The SLR concluded by identifying the AI aspect of this research, finding that statistical features were predominant in the feature extraction space. However, a large portion of the literature did not specify the feature extraction methods used, thus leaving a sizeable gap in this space. Support Vector Machine dominated the machine learning algorithms in the literature, due to its simple nature for classifying linear data.