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Application of Artificial Intelligence in Partial Discharge Detection Part Ⅰ: Denoising and Fault Location
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Yuncai LU1, Lu FAN2, Fengbo TAO1, Yi YIN2
Insulating Materials | 2021, 54(5) : 10 - 20
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Insulating Materials | 2021, 54(5): 10-20
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Application of Artificial Intelligence in Partial Discharge Detection Part Ⅰ: Denoising and Fault Location
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Yuncai LU1, Lu FAN2, Fengbo TAO1, Yi YIN2
Affiliations
  • 1Electric Power Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211102, China
  • 2School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Published: 2021-05-20 doi: 10.16790/j.cnki.1009-9239.im.2021.05.002
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Partial discharge would accelerate the ageing of power equipment insulation, which is an important monitoring indicator for the condition assessment of power equipment. The research related to partial discharge mainly include denoising of discharge signals, pattern recognition of defect types, equipment status assessment, and fault location of discharge sources. The artificial intelligence can effectively solve the problems of non-linear fitting and optimal solution in partial discharge detection. This paper introduces the detection methods of partial discharge, summarizes the application of artificial intelligence in both denoising of discharge signals and fault location of discharge sources, and points out the shortcomings and solutions in the current research.

partial discharge  /  optimization  /  denoising  /  fault location  /  neural network
Yuncai LU, Lu FAN, Fengbo TAO, Yi YIN. Application of Artificial Intelligence in Partial Discharge Detection Part Ⅰ: Denoising and Fault Location[J]. Insulating Materials, 2021 , 54 (5) : 10 -20 . DOI: 10.16790/j.cnki.1009-9239.im.2021.05.002
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Article Info
doi: 10.16790/j.cnki.1009-9239.im.2021.05.002
  • Receive Date：2020-06-08
  • Online Date：2026-03-03
  • Published：2021-05-20
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History
  • Received：2020-06-08
  • Revised：2020-06-15
Affiliations
    1Electric Power Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211102, China
    2School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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