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ZJUS-A
Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering)  2008, Vol. 9 Issue (10): 1420-1425    DOI: 10.1631/jzus.A0720087
Electrical & Electronic Engineering     
Forward and backward models for fault diagnosis based on parallel genetic algorithms
Yi LIU, Ying LI, Yi-jia CAO, Chuang-xin GUO
School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
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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.

Key words： Forward and backward models      Fault diagnosis      Global single-population master-slave genetic algorithms (GPGAs)      Parallel computation     
Received: 26 November 2007     
CLC:  TM734  
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Yi LIU
Ying LI
Yi-jia CAO
Chuang-xin GUO
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Yi LIU, Ying LI, Yi-jia CAO, Chuang-xin GUO. Forward and backward models for fault diagnosis based on parallel genetic algorithms. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2008, 9(10): 1420-1425.

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http://www.zjujournals.com/xueshu/zjus-a/10.1631/jzus.A0720087     OR     http://www.zjujournals.com/xueshu/zjus-a/Y2008/V9/I10/1420

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