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author:

Cai, Jinding (Cai, Jinding.) [1] | Ye, Rong (Ye, Rong.) [2] | Chen, Hancheng (Chen, Hancheng.) [3]

Indexed by:

EI Scopus PKU CSCD

Abstract:

An oil-paper insulation state diagnosis method based on multiple parameter regression of recovery voltage was proposed, which is easy to be one-sided and inaccurate in the assessment of oil-paper insulation. Firstly, according to the field measured data of different aging states collected in recent 10 years, bringing in the regression analysis method, used the furfural content in oil for the y axis, with the time-domain characteristic parameters for the x axis, to establish a five dimensional regression system. Secondly, the verification results of this model satisfied the regression check, which manifest that the calculated value of the dependent variable has a certain correlation with the measured value, and five parameters all have significant contribution. Finally, according to actual running state of transformers, the state classification strategy was formulated, and the circular classification diagram of insulation state was constructed, accuracy and credibility of the evaluation system were verified by the measured data of transformers without regression analysis. This method can provide new ideas for aging diagnosis of oil-paper insulation transformers. © 2018, Electrical Technology Press Co. Ltd. All right reserved.

Keyword:

Aldehydes Furfural Insulation Oil filled transformers Paper Regression analysis Time domain analysis

Community:

  • [ 1 ] [Cai, Jinding]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 2 ] [Ye, Rong]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 3 ] [Chen, Hancheng]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China

Reprint 's Address:

  • 叶荣

    [ye, rong]college of electrical engineering and automation, fuzhou university, fuzhou; 350108, china

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Source :

Transactions of China Electrotechnical Society

ISSN: 1000-6753

CN: 11-2188/TM

Year: 2018

Issue: 21

Volume: 33

Page: 5080-5089

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 15

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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