# Partial Discharge Source Classification in Power Transformers: A Systematic Literature Review
## 5 Results
#### RQ8: Which national and international standards are referenced in the literature? The analysis of the international standards used in the research on transformer partial discharge classification using a machine learning algorithm is as expected, with the IEC 60270 dominating the literature with 35 publications citing this standard as seen in Figure 12. The IEC 60270 is the high-voltage test technique for partial discharge measurements. It defines the requirements for PD measurements, the measuring systems, measurement techniques, types of tests, circuits, etc. Therefore, it is expected that researchers will utilize this standard for guidance. Other international standards that feature in the literature include IEC 60076.3 [100], IEC 60034-27 [101], IEC 60112 [102], IEC 60296 [103], IEC 62478 [104] and IEC 60060-1 [105]. IEC 60076.3 concerns power transformers (insulation levels, dielectric tests and external clearances in air), IEC 60034-27 concerns rotating electric machines (measurement of insulation resistance and polarization index of winding insulation of rotating electric machines), IEC 60112 concerns the method for the determination of the proof and the comparative tracking indices of solid insulating materials, IEC 60296 concerns the fluids for electrotechnical applications (unused mineral insulating oils for transformers and switchgear) and IEC 60060-1 concerns high-voltage test techniques (general definitions and test requirements). As can be seen, the above standards all refer to high-voltage electrical tests or insulation tests. These standards are all valid for the research on transformer partial discharge as PD is a high-voltage discharge that occurs in the insulation systems of transformers. Figure 11: Partial discharge data collection equipment. _RQ9: Which feature extraction methods are mostly utilized in the collected literature?_  
Feature extraction is a process of extracting relevant features from the raw data used in machine learning. It reduces the amount of data without losing vital information required for developing the algorithm. Feature extraction therefore reduces the memory required, reduces computation time and improves the efficiency and the accuracy of the developed model. The analysis of the feature extraction techniques used in the literature for transformer partial discharge classification using machine learning algorithms indicates that researchers are open to utilizing a large variety of techniques; however, most of the scholarly works published between 2010 and 2023 indicated a preference for utilizing statistical features. These far outweighed the rest of the techniques, with 35% of publications indicating their use. Figure 13 shows the breakdown of the feature extraction techniques in the collected literature. Principal Component Analysis (PCA) came in second with only 19% of the publications. PCA is a linear technique with the advantage of reducing dimensionality and identifying key variables; this makes its use very attractive to researchers. A total of 18 feature extraction techniques were found in this study, which indicates a wide variety of choices and willingness for scholars; however, 21% of publications did not specify the technique utilized. The possible reasons for not specifying are that for that data the researchers did not use a feature extraction technique, or that the authors chose to place a major focus on the primary objective of the research. This indicates a likely gap in documenting from the perspective of those research papers. _RQ10: Which machine learning algorithms are the most utilized for monitoring partial discharge in electric transformers?_  
A remarkably noticeable variety of artificial intelligence (AI) algorithms can be identified in the literature for transformer partial discharge classification using machine learning algorithms (Figure 14). Support vector machine (SVM) stands out as the most abundantly utilized algorithm with 26% of researchers in the literature opting for it.