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Research on Ageing Diagnosis Technology for Oil-paper Insulation Based on Probabilistic Neural Network
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Changhai SUNa, Tianming LIa, Baitong CHENb, Jiabin GUOa, Shuang JUa
Insulating Materials | 2021, 54(6) : 107 - 115
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Insulating Materials | 2021, 54(6): 107-115
• Insulation Technology •
Research on Ageing Diagnosis Technology for Oil-paper Insulation Based on Probabilistic Neural Network
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Changhai SUNa, Tianming LIa, Baitong CHENb, Jiabin GUOa, Shuang JUa
Affiliations
  • aSchool of Electrical Engineering, Dalian University of Technology, Dalian 116024, China
  • bSchool of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
Published: 2021-06-20 doi: 10.16790/j.cnki.1009-9239.im.2021.06.017
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An oil-paper sample was conducted accelerate thermal ageing treatment, and its ageing process was divided into five ageing stages according to the variation of polymerization degree. Partial discharge tests were conducted on the air gap discharge model, and the PRPD patterns of the oil-paper sample were collected at different ageing stages. The feature quantities were extracted by using statistical operator, the dimension of the original feature data was reduced by factor analysis method, and the clustering characteristics of the feature data before and after dimension reduction were compared. A probabilistic neural network model (PNN) was established to identify the ageing stages of oil-paper insulation, and a back propagation (BP) neural network model and a support vector machine (SVM) model were built as comparison. The three models were trained by the same data, and their recognition results were compared. The results show that ageing will cause pores in the pressboard, which promotes the occurrence of partial discharge. Compared with other models, the FAM-PNN model has obvious advantages in recognition accuracy and operation efficiency. The ageing state of transformer oil-paper insulation can be evaluated accurately and efficiently using the FAM-PNN model.

oil-paper insulation  /  air gap discharge  /  ageing stage identification  /  factor analysis method  /  probabilistic neural network
Changhai SUN, Tianming LI, Baitong CHEN, Jiabin GUO, Shuang JU. Research on Ageing Diagnosis Technology for Oil-paper Insulation Based on Probabilistic Neural Network[J]. Insulating Materials, 2021 , 54 (6) : 107 -115 . DOI: 10.16790/j.cnki.1009-9239.im.2021.06.017
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Article Info
doi: 10.16790/j.cnki.1009-9239.im.2021.06.017
  • Receive Date：2020-07-28
  • Online Date：2026-03-03
  • Published：2021-06-20
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  • Received：2020-07-28
  • Revised：2020-09-17
Affiliations
    aSchool of Electrical Engineering, Dalian University of Technology, Dalian 116024, China
    bSchool of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
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https://castjournals.cast.org.cn/joweb/jycl/EN/10.16790/j.cnki.1009-9239.im.2021.06.017
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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