摘要
Accurate fault detection and diagnosis is important for secure and profitable operation of modern power systems.In this paper,an ensemble of conflict-resolving Fuzzy ARTMAP classifiers,known as Probabilistic Multiple Fuzzy ARTMAP with Dynamic Decay Adjustment(PMFAMDDA),for accurate discrimination between normal and faulty operating conditions of a Circulating Water(CW)system in a power generation plant is proposed.The decisions of PMFAMDDA are reached through a probabilistic plurality voting strategy that is in agreement with the Bayesian theorem.The results of the proposed PMFAMDDA model are compared with those from an ensemble of Probabilistic Multiple Fuzzy ARTMAP(PMFAM)classifiers.The outcomes reveal that PMFAMDDA,in general,outperforms PMFAM in discriminating operating conditions of the CW system.
Accurate fault detection and diagnosis is important for secure and profitable operation of modern power systems. In this paper, an ensemble of conflict-resolving Fuzzy ARTMAP classifiers, known as Probabilistic Multiple Fuzzy ARTMAP with Dynamic Decay Adjustment （PMFAMDDA）, for accurate discrimination between normal and faulty operating conditions of a Circulating Water （CW） system in a power generation plant is proposed. The decisions of PMFAMDDA are reached through a probabilistic plurality voting strategy that is in agree- ment with the Bayesian theorem. The results of the proposed PMFAMDDA model are compared with those from an ensemble of Probabilistic Multiple Fuzzy ARTMAP （PMFAM） classi- fiers. The outcomes reveal that PMFAMDDA, in general, out- performs PMFAM in discriminating operating conditions of the CW system,
基金
supported by the Fundamental Research Grant Scheme of Ministry of Higher Education,Malaysia(No.6711195)
Multi media University and University of Science Malaysia
关键词
故障检测
神经网络
模糊ARTMAP
诊断
艺术
应用
电力系统
电厂循环水
fault detection and diagnosis
fuzzy ARTMAP
dynamic decay adjustment algorithm
pluralityvoting
circulating water system
