Journal
IEEE ACCESS
Volume 9, Issue -, Pages 133258-133273Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3115519
Keywords
Partial discharges; Power cables; Discharges (electric); Insulation; Power cable insulation; Fault location; Corona; Partial discharge (PD); cross-linked polyethylene (XLPE) cable; solid insulator; pattern recognition; feature extraction; artificial neural network (ANN)
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Funding
- Universiti Sains Malaysia (USM) under the Research Universiti Grant (RUI) [1001/PELECT/8014050 (UO1620/2018/0320)]
- Ministry of Higher Education, Malaysia, under the Fundamental Research Grant Scheme [FRGS/1/2019/TK04/UNIMAP/03/8]
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This paper reviews the research on PD patterns and classifiers for XLPE cables, discusses the differences in sensor development based on PD detection in the past 27 years, and concludes that using artificial neural network (ANN) for PD signal pattern recognition performs better in terms of accuracy and repeatability.
Partial discharge (PD) signal classification analysis on cross-linked polyethylene (XLPE) cables is complex, requiring a comprehensive understanding of the characteristics of PD patterns. In the realm of high-voltage electrical insulation, PD pattern characteristics, such as PD charge and inception voltage, are essential as assessment criteria in diagnostics systems using PD classifiers. This paper provides a review of the various PD patterns and classifiers used by previous researchers, specifically for XLPE cables. In addition, the differences of the studies on various sensor developments based on PD detection in the past 27 years are also discussed. The repeatability, recognition accuracy, recognition speed, and effect of feature sizes on each PD classification method are reviewed and explained. This review indicates that the pattern recognition for PD signal using artificial neural network (ANN) exhibits better performance than the other methods in terms of accuracy and repeatability, and the reduction of feature size does not affect the accuracy of ANN.
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