# Partial Discharge Source Classification in Power Transformers: A Systematic Literature Review
## 5 Results
#### RQ8: Which national and international standards are referenced in the literature?
SVM is a powerful and very popular supervised learning algorithm that is highly effective for use in a two-dimensional space as it determines an ideal hyperplane for classifying data. The preference of researchers to use SVM indicates the likelihood of mostly linear data being prevalent in transformer PD samples. Another rationale is on the basis of most researchers opting for  
Figure 12: Partial discharge employed standards. data collected from artificial defect models as elaborated in RQ5, resulting in data that is clearly defined and has clear separation. Linear SVM is limited by the data size as well as the presence of noise. This limitation is generally overcome with the use of kernels. Artificial neural networks (ANN) and convolutional neural networks (CNN) came in second and third place, with 12% and 11% of the publications, respectively. ANN and CNN are neural networks which lean towards deep learning algorithms. Neural networks have gained increased interest in the field of artificial intelligence as a viable alternative to traditional machine learning. They are capable of performing automatic feature learning, thus eliminating the step of manual feature extraction; they can also handle much larger datasets compared to traditional algorithms. These benefits make neural networks very pleasing for researching a variety of different PD types occurring in transformers. _RQ11: What are the challenges that are experienced when utilizing machine learning in classifying transformer partial discharge as documented in the literature?_  
Some challenges and constraints on transformer partial discharge classification with machine learning algorithms which have been recorded in the reviewed literature are as follows:  
* PD mechanisms of multiple defects have not yet been fully understood; the development of machine learning techniques has. * Determining the effect of frequency on degradation induced by PD. * The effect of nanostructured insulators on PD resistance and how coarse a surface is. * PD mechanisms in the presence of space charge resulting from cavity discharge. * The correlation between PD breakdown under DC voltage. _RQ12: What are the possible future research opportunities highlighted in the literature?_  
The possible future research opportunities that have been highlighted in the literature include the following:  
* Understanding of the propagation of PD signals inside transformers. Current machine learning models and the research has primarily placed focus on detecting the present state of PD, as well as improving the accuracy, speed and reducing memory. Developing models which will allow for understanding the propagation of PD within a transformer can provide further insight to grow the knowledge base within the research space. Figure 13: Feature Extraction Techniques. * The optimization of the design of PD sensors. The use of optical and acoustic sensors for multiple PD source detection and localization is currently limited by the cost of multiple sensors required. Optimizing on the design of these can reduce cost and size, therefore allowing for further research and use in a variety of locations. * Proper calibration techniques for PD charge. The current PD detection techniques and application thereof in multiple locations are limited as some techniques cannot be calibrated as prescribed in IEC 60270. The development of accredited calibratable sensors and calibration techniques can aid in the uptake of newer techniques. * Research into surface tracking and flashover due to static electrification in the interface of oil and pressboard. The research into surface discharge and tracking occurring on pressboard and oil together with the degradation mechanisms and the results thereof can be highly beneficial in understanding the long-term effect of PD on transformer insulation systems. * Further research into the interpretation of automated classification of PD formed under the presence of conducting particles, moisture, temperature, etc.