# Partial Discharge Source Classification in Power Transformers: A Systematic Literature Review
## 5 Results
_RQ6: Which PD measuring methods are used in the sampled literature?_  
Partial discharge is a complex physical process with random distribution properties [10]. The discharges produce phenomena such as sound, light and electromagnetic waves and release electric charges. These phenomena are used in the detection and measurement of PD within a transformer. Within the literature reviewed for transformer partial discharge classification using machine learning algorithms in the period between 2010 and 2023, the most used detection methods were Ultra-High Frequency (UHF) and Electric with 30% and 27%, respectively as shown in Figure 10. The UHF method is mostly preferred due to its high resistance to external Disturbances such as electromagnetic interference, which can be expected in industrial areas where PD testing takes place [96]. It further has a high signal-to-noise ratio as well as good signal detection sensitivity [97]. The electrical  
Figure 8: Artificial PD defect models. Figure 9: Sources of Partial Discharge. method also has its advantages, which include excellent PD signal recording in laboratory environments, high sensitivity, low noise levels, low signal attenuation and high precision measurements [98]. Acoustic emission came third with 17% of the publications, while optical emission had 9%. Acoustic emission benefits from noise resistance, which makes it an asset for online measurements, and since sensors can be placed at different locations of the test device, it provides better discharge position information [99]. In 15% of the reviewed literature, the researchers did not specify the technique they utilized to measure the PD. This is a gap in the literature on which researchers can further improve. The PD measuring techniques noted in Figure 10 are not without their drawbacks. While the UHF method has exceptional sensitivity, it cannot be calibrated. The electrical method is mainly used in laboratory environments and is not for onsite or online use. It is also highly sensitive to electromagnetic interference. Acoustic emission has low sensitivity, adds complexity to data handling and processing and requires additional costs due to the requirement of multiple sensors [98]. _RQ7: Which measuring equipment is used in the measuring of PD in the literature?_  
The analysis of measuring equipment used in the collection of PD activity for the research into transformer partial discharge classification using machine learning techniques indicates a lack of variety. Figure 11 indicates the oscilloscope as the preferred measurement device with 29 research papers citing its use. This high preference for using the oscilloscope can be attributed to its availability in research laboratories. This is also the case with the data received in Figure 9 of RQ5 where it was determined that 60% of the sources of PD data in this literature were collected from artificial PD defect models and experimental setups. Since most of the research has been done in laboratories, it is more cost-effective to utilize the available equipment where it can achieve the results. Of the 81 scholarly works reviewed, 27 publications did not specify the equipment used to measure the data which was utilized. This is because the primary focus of this research is developing machine  
Figure 10: Partial Discharge Measuring Techniques. algorithms which can classify transformer partial discharge at a very high accuracy. The works on experimental setups and data collection are already well-developed and the literature is well-publicized. The use of various types of PD detectors was the third most utilized means of measuring PD in this collected literature. The PD detectors were cited in 17 publications in the period between 2010 and 2023. The PD detectors are generally used by companies who specialize in performing PD tests on actual power transformers and on tests which occur outside the laboratories.