电力系统保护与控制2011，Vol.39Issue(22)：52-56,5.
基于GA与PSO混合优化FCM聚类的变压器故障诊断
Transformer fault diagnosis based on optimized FCM clustering by hybrid GA and PSO
雷浩辖 1刘念 1崔东君 1马铁军 1徐海霸1
作者信息
- 1. 四川大学电气信息学院,四川成都610065
- 折叠
摘要
Abstract
Optimized FCM clustering by hybrid GA and PSO (GAPSO-FCM) is introduced to diagnose the fault of transformer in order to conquer the shortages of FCM clustering, GA-FCM clustering and PSO-FCM clustering in transformer fault diagnosis. GAPSO-FCM clustering carries out global search, conquering the problem of FCM clustering easily falling into local minimum. According to the best global individual, GAPSO-FCM clustering makes GA algorithm and PSO algorithm organically link together, GA and PSO share a best individual, and the iterative process includes GA operation and PSO operation. It enlarges search area by the randomicity of GA, then searches more carefully according to PSO round the founded individual, conquering the premature problem of optimized FCM clustering based only on single GA or PSO. Simulation and case analysis indicate that GAPSO-FCM clustering for fault diagnosis is of higher accuracy than the other three kinds of clustering.关键词
变压器/故障诊断/遗传算法/粒子群优化/模糊C均值聚类Key words
transformer/ fault diagnosis/ GA/ PSO/ FCM clustering分类
信息技术与安全科学引用本文复制引用
雷浩辖,刘念,崔东君,马铁军,徐海霸..基于GA与PSO混合优化FCM聚类的变压器故障诊断[J].电力系统保护与控制,2011,39(22):52-56,5.
