# Partial Discharge Source Classification in Power Transformers: A Systematic Literature Review
## 4 Recent and Related Research Works
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[27] & 2020 & IEEE Transactions on Dielectric and Electrical Insulation & Probabilistic Neural Network, Support Vector Machine & & & & \\  & & & & & \\  & & & & & \\ \begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref. Year** & **Year** & **Data Source** & **ML Algorithm** & **PD Source** & **Summary** \\ \hline
[33] & 2015 &
\begin{tabular}{c} IEEE Transactions on \\ Dielectrics and Electrical \\ Insulation \\ \end{tabular} & Support Vector Machine & Experimental setup & Addresses the issue of classifying varying PD types collected via acoustic emission under differing measurement conditions such as PD source location, oil temperatures and barrier insertion. \\ \hline
[34] & 2017 & Energies Journal & Artificial Neural Network, \
\begin{tabular}{c} Artificial PD Defect Model \\ \end{tabular} &
\begin{tabular}{c} Computers the accuracy of artificial neural networks and Fuzzy \\ logic in recognizing different partial discharge sources. \\ \end{tabular} \\ \hline
[35] & 2013 & \
\begin{tabular}{c} Electrical Power and \\ Energy Systems \\ \end{tabular} & Support Vector Machine & Experimental setup &
\begin{tabular}{c} Utilizes multiple optical sensors inside a sheet tank in a laboratory setup to develop a method of identifying single and multiple partial discharge sources. \\ \end{tabular} \\ \hline
[36] & 2011 & Energies & \
\begin{tabular}{c} Improved Bagging \\ Algorithm, Support Vector \\ Machine, Back Propagation \\ Neural Network \\ \end{tabular} & Experimental setup &
\begin{tabular}{c} Introduces an improved Bagging Algorithm (Bka) which enhances the generalization capability and improves the accuracy of the Backpropagation neural network. \\ \end{tabular} \\ \hline
[37] & 2013 & \begin{tabular}{c} IEEE Transactions on \\ Dielectrics and Electrical \\ Insulation \\ \end{tabular} & \
\begin{tabular}{c} Bayesian networks, k-nearest \\ Neighbors, Multi-layer \\ perceptron, Fuzzy Support \\ Vector Machine \\ \end{tabular} & Artificial PD Defect Model &
\begin{tabular}{c} Investigates three challenging issues linked to the automatic classification of artificial PD sources, namely acquiring symbolic \\ characteristics using feature extraction, identifying different types of PD using pattern recognition algorithms and identifying multiple PD sources. \\ \end{tabular} \\ \hline
[38] & 2018 & \begin{tabular}{c} IET Science, Measurement \\ and Technology \\ \end{tabular} & \
\begin{tabular}{c} Kernel partial least squares \\ regression (RPLS) \\ \end{tabular} & Artificial PD Defect Model &
\begin{tabular}{c} Introduces the variable predictive model-based class \\ discrimination (PVMED) method which is based on kernel \\ partial least squares (RPLS) regression to exploit the inter-relations of extracted features of PD signals. \\ \end{tabular} \\ \hline
[39] & 2018 & Energies & \
\begin{tabular}{c} One Class Support Vector \\ Machine \\ \end{tabular} & Power Transformer &
\begin{tabular}{c} Researchers the use of a One-Class Support Vector Machine \\ (CCSVN) as an alternative to the binary QM for PD assessment, indicating the benefit of noise-eliminating capabilities for PD assessment. \\ \end{tabular} \\ \hline
[40] & 2018 & \
\begin{tabular}{c} IET Science, Measurement \\ and Technology \\ \end{tabular} & Support Vector Machine & Test transformer &
\begin{tabular}{c} Develop a method of identifying unknown partial discharge \\ patterns utilizing an improved Support Vector Data Description \\ (SVDD). This method produces higher recognition accuracy, is more efficient and can be used to recognize unknown PD types where regular supervised algorithms fall short. \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
\begin{table}
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**Ref.