# Partial Discharge Source Classification in Power Transformers: A Systematic Literature Review
## 4 Recent and Related Research Works  
Employing machine learning techniques has proven to greatly improve the classification and recognition of different types of partial discharge mechanisms occurring in different parts of power transformers, both inside and outside, and in oil or air. Table 2 shows a collection of literature analyzing the use of machine learning algorithms to classify PD in power transformers. \begin{table}
\begin{tabular}{c c c} \hline \hline
\begin{tabular}{c} Articles that do not relate to machine learning, partial discharge or electric transformers, and do not include machine learning. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Inclusion and Exclusion Criteria. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref. Year** & **Year** & **Data Source** & **ML Algorithm** & **PD Source** & **Summary** \\ \hline
[18] & 2016 & Energies Journal & Artificial Neural Network & Artificial PD Defect Model & Focus on reviewing the published literature on the use of ANN for partial discharge pattern recognition and proposes improvements such as establishing the optimal training weights, use of extensive PD data for training, recognition of different levels of PD degradation as well as techniques to shorten training time. \\ \hline
[19] & 2022 & Energies Journal & Support Vector Machine, K-nearest Neighbors, Naive Bayes, Classification, Random Forest, Probabilistic Neural Network & Not specified & Analyses eight partial discharge classes occurring in transformer solid and liquid insulation systems in a laboratory setup measured with the use of acoustic emission techniques. PD data is analyzed with few different classification algorithms and the results are compared against one another. \\ \hline
[20] & 2015 & IEEE Electrical Insulation Conference & Deep Neural Network & Not specified & Illustrates the improvements in partial discharge diagnostic accuracy which can be attained by the application of deep neural networks. \\ \hline
[21] & 2020 & Energies Journal & Convolutional Neural Network & PD images & Presents the application of a neural network based on the CNN architecture to partial discharge images to diagnose the aging of electrical insulation. \\ \hline
[22] & 2010 & International Symposium & Support Vector Machine, Probabilistic Neural Network & Experimental setup & Compares the performance of two partial discharge classification methods, namely SVM and RNN, on four PD sources to determine the classification accuracy. \\ \hline
[23] & 2019 & IEEE Transactions on Power Delivery & Deep learning & Artificial PD Defect Model & Explores a novel approach of using PD current as input signal from four kinds of PD defects, while implementing two dimensionality reduction techniques, PCA and TSNE, the data to improve classification accuracy. \\ \hline
[24] & 2019 & IEEE Transactions on electrical and electronic engineering & Support Vector Machine & Solid insulation & Utilizes a Weibull distribution-based method on the PD of a power transformer off-upper insulation to determine the remaining service life of the transformer. \\ \hline
[25] & 2019 & Energies & Deep neural network, Convolutional Neural Network & Scholarly works & Reviews the progress made on the use of deep learning artificial intelligence methods for the automatic identification of partial discharge in transformers over the period between 2015 to 2023. \\ \hline \end{tabular}
\end{table} 
Table 2: Transformer PD Literature. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref. Year** & **Year** & **Data Source** & **ML Algorithm** & **PD Source** & **Summary** \\ \hline
[26] & 2014 & IEEE Transactions on Power Delivery & Neuro-Fuzzy Technique & Power transformer & Introduces a neuro-fuzzy PD recognition technique on a medium voltage transformer to enhance the recognition accuracy compared to orthogonal transforms and calibration line methods for the main types of PD. & for the main types of PD.