CLC number: TM734
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
Cited: 5
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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[J]. Journal of Zhejiang University Science A, 2008, 9(10): 1420-1425.
@article{title="Forward and backward models for fault diagnosis based on parallel genetic algorithms",
author="Yi LIU, Ying LI, Yi-jia CAO, Chuang-xin GUO",
journal="Journal of Zhejiang University Science A",
volume="9",
number="10",
pages="1420-1425",
year="2008",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0720087"
}
%0 Journal Article
%T Forward and backward models for fault diagnosis based on parallel genetic algorithms
%A Yi LIU
%A Ying LI
%A Yi-jia CAO
%A Chuang-xin GUO
%J Journal of Zhejiang University SCIENCE A
%V 9
%N 10
%P 1420-1425
%@ 1673-565X
%D 2008
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0720087
TY - JOUR
T1 - Forward and backward models for fault diagnosis based on parallel genetic algorithms
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A1 - Ying LI
A1 - Yi-jia CAO
A1 - Chuang-xin GUO
J0 - Journal of Zhejiang University Science A
VL - 9
IS - 10
SP - 1420
EP - 1425
%@ 1673-565X
Y1 - 2008
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A0720087
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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