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

被引：10

作者：

Yi LIU

机构：

来源：

Journal of Zhejiang University(Science A:An International Applied Physics & Engineering Journal) | 2008年 / 10期

基金：

中国国家自然科学基金;

关键词：

Forward and backward models; Fault diagnosis; Global single-population master-slave genetic algorithms (GPGAs); Parallel computation;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 sin-gle-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.

引用

页码：1420 / 1425

页数：6

共 6 条

[1] Data fusion for fault diagnosis using multi-class Support Vector Machines
Hu Z.-H.
Cai Y.-Z.
Li Y.-G.
Xu X.-M.
[J]. Journal of Zhejiang University-SCIENCE A, 2005, 6 (10): : 1030 - 1039
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