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CLC number: TM734

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Received: 2007-11-26

Revision Accepted: 2008-05-04

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Journal of Zhejiang University SCIENCE A 2008 Vol.9 No.10 P.1420~1425

http://doi.org/10.1631/jzus.A0720087


Forward and backward models for fault diagnosis based on parallel genetic algorithms


Author(s):  Yi LIU, Ying LI, Yi-jia CAO, Chuang-xin GUO

Affiliation(s):  School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   powersystemee@163.com

Key Words:  Forward and backward models, Fault diagnosis, Global single-population master-slave genetic algorithms (GPGAs), Parallel computation


Yi LIU, Ying LI, Yi-jia CAO, Chuang-xin GUO. Forward and backward models for fault diagnosis based on parallel genetic algorithms[J]. Journal of Zhejiang University Science A, 2008, 9(10): 1420~1425.

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Abstract: 
In this paper, a mathematical model consisting of forward and backward models is built on parallel genetic algorithms (PGAs) for fault diagnosis in a transmission power system. A new method to reduce the scale of fault sections is developed in the forward model and the message passing interface (MPI) approach is chosen to parallel the genetic algorithms by global single-population master-slave method (GPGAs). The proposed approach is applied to a sample system consisting of 28 sections, 84 protective relays and 40 circuit breakers. Simulation results show that the new model based on GPGAs can achieve very fast computation in online applications of large-scale power systems.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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