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

作     者：Yi LIU Ying LI Yi-jia CAO Chuang-xin GUO 

作者机构：School of Electrical Engineering Zhejiang University Hangzhou 310027 China 

出 版 物：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 (浙江大学学报（英文版）A辑（应用物理与工程）)

年 卷 期：2008年第9卷第10期

页      面：1420-1425页

核心收录：

学科分类：080802[工学-电力系统及其自动化] 0808[工学-电气工程] 08[工学] 

基　　金：the National Natural Science Foundation of China (No. 50677062) the New Century Excellent Talents in Uni-versity of China (No. NCET-07-0745) the Natural Science Foundation of Zhejiang Province, China (No. R107062) 

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

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.