# Partial Discharge Source Classification in Power Transformers: A Systematic Literature Review
## 4 Recent and Related Research Works
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref. Year** & **Year** & **Data Source** & **ML Algorithm** & **PD Source** & **Summary** \\ \hline
[90] & 2023 & Sensors & Convolutional neural network & Power Transformer & Analyses partial discharges occurring within bubbles in transformer insulation mineral oil with the use of CMOS image sensor. Developing a classification method which achieves a classification accuracy of 95\% for the validation set and 82\% for the test set. \\ \hline
[91] & 2020 & IEEE Conference on Electrical Insulation and Dielectric Phenomena & Support Vector Machine & Artificial PD Defect Model & Proposes the classification of different partial discharges occurring in outdoor electric insulators with the use of different machine learning algorithms and achieves a 93\% recognition rate for five PD defects using SVM with RBF kernels consistently achieving the highest recognition rates. \\ \hline
[92] & 2010 & IEEE Transactions on Dielectrics and Electrical Insulation & Support Vector Machine & Artificial PD Defect Model & Studies the use of a wide bandwidth PD measurement system which includes a radio frequency current transducer sensor to perform online automatic PD source identification. \\ \hline
[93] & 2021 & International Conference on Artificial Intelligence and Smart Systems & Support Vector Machine & Artificial PD Defect Model & Demonstrates a new technique for partial discharge prediction with the use of Deep Learning algorithm and tests in von vold, corona and surface discharges occurring in transformer insulation systems. \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._Table 2 possesses a collection of the recent literature in the research of automatic transformer PD classification using machine learning algorithms. The table provides a background of each of the collected literatures together with a summary of the research contents and the primary focus placed by the researchers. However, the literature of Table 2 provided much more knowledge, data and findings into available gaps in the literature from the research, employing different classes of machine learning algorithms. While great strides have been made regarding the use of ANNs for PD classification, ref. [18] expresses the lack of research on the utilization of ANNs for discerning the source positioning of PD. This introduces a literature gap, as the characteristics of PD sources may vary based on their position and whether it is a single- or multiple-source PD [18]. Most of the investigated literature utilizes PD data from experimental setups performed over a limited amount of time. This results in a lack of generality as PD patterns change over time. This therefore provides an opportunity for researchers to classify and compare the development of PD over much longer periods of time. Further to this, there is a greater need for use of onsite PD data taken under normal conditions in the presence of noise and electromagnetic disturbances in order to further upgrade the developed models. A common shortcoming of deep neural networks is that they are time-consuming due to their dependence on the back propogation algorithm. There is a necessity for improved and easier approaches to be developed which will result in reduced training times [18; 20]. The literature indicates research to further improve the performance of traditional machine learning algorithms. These include combining multiple ML algorithms as indicated by [83], where kNN is combined with SVM to input missing values. Ref. [50] constructs a convolutional neural network and a long-short memory hybrid method which is tested on UHF PD signals, while [32] combines local binary pattern and histogram of oriented gradient to extract features form PD images. These are proving to be promising methods, for which the literature proposes further research.