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Application of Artificial Intelligence in Partial Discharge Detection Part Ⅱ: Pattern Recognition and Condition Assessment
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Lu FAN1, Yuncai LU2, Fengbo TAO2, Yi YIN1
Insulating Materials | 2021, 54(7) : 10 - 24
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Insulating Materials | 2021, 54(7): 10-24
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Application of Artificial Intelligence in Partial Discharge Detection Part Ⅱ: Pattern Recognition and Condition Assessment
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Lu FAN1, Yuncai LU2, Fengbo TAO2, Yi YIN1
Affiliations
  • 1School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
  • 2Electric Power Research Institute of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211102, China
Published: 2021-07-20 doi: 10.16790/j.cnki.1009-9239.im.2021.07.002
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The fault type recognition and condition assessment play the crucial roles in fault diagnosis and maintenance. Different defects will produce partial discharge signals with difference, and the partial discharge signal also changes with the defect severity and the evolution of partial discharge. This situation can be regarded as pattern recognition of different severity levels and evolution stages, and the pattern recognition is a typical classification problem. In this paper, classification problems such as pattern recognition and state assessment were reviewed. Compared to classification results based on mathematical statistics, artificial intelligence has achieved nearly 100% of recognition accuracy. However, there are still some shortcomings in current research, this paper gives some solution strategies and prospects future research direction.

partial discharge  /  classification  /  feature extraction  /  pattern recognition  /  condition assessment  /  artificial intelligence
Lu FAN, Yuncai LU, Fengbo TAO, Yi YIN. Application of Artificial Intelligence in Partial Discharge Detection Part Ⅱ: Pattern Recognition and Condition Assessment[J]. Insulating Materials, 2021 , 54 (7) : 10 -24 . DOI: 10.16790/j.cnki.1009-9239.im.2021.07.002
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Article Info
doi: 10.16790/j.cnki.1009-9239.im.2021.07.002
  • Receive Date：2020-06-08
  • Online Date：2026-03-20
  • Published：2021-07-20
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History
  • Received：2020-06-08
  • Revised：2020-07-14
Affiliations
    1School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    2Electric Power Research Institute of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211102, China
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