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author:

Lin, Zhi-Yong (Lin, Zhi-Yong.) [1] | Cai, Jin-Ding (Cai, Jin-Ding.) [2]

Indexed by:

EI Scopus PKU CSCD

Abstract:

Aiming at the problem of the particle swarm algorithm easily into the local optimum in the process of solving oil-paper insulation equivalent circuit model parameters, a new method of calculating the oil-paper insulation equivalent circuit parameters was proposed combined with the chaos theory. According to the chaos algorithm characteristics of ergodicity and no repetitiveness, combining the chaos disturbance with the particle swam algorithm, which does not fall into local optimal solution, oil-paper insulation equivalent circuit parameters was identified. The calculation results show that the return voltage polarization spectrum obtained by improved particle swarm optimization algorithm can be matched better with the return voltage polarization spectrum obtained by field test. The oil-paper insulation equivalent circuit model based on the new algorithm can reflect the insulation condition of oil-paper insulation transformer accurately, which laid an important foundation for the subsequent diagnosising oil-paper insulation aging.

Keyword:

Chaos theory Circuit simulation Electric network parameters Equivalent circuits Identification (control systems) Insulation Oil filled transformers Paper Parameter estimation Particle swarm optimization (PSO) Polarization Timing circuits

Community:

  • [ 1 ] [Lin, Zhi-Yong]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 2 ] [Cai, Jin-Ding]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China

Reprint 's Address:

  • 蔡金锭

    [cai, jin-ding]college of electrical engineering and automation, fuzhou university, fuzhou; 350108, china

Email:

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Source :

Electric Machines and Control

ISSN: 1007-449X

CN: 23-1408/TM

Year: 2014

Issue: 8

Volume: 18

Page: 62-66

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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