JZUS - Journal of Zhejiang University SCIENCE

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

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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.

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