Comparison of Bayesian and fuzzy ARTmap networks in HV transmission lines fault diagnosis
Abstract
Fault diagnosis is a vital discussion in power systems restoration. Recently, much research endeavors have been done for fault section diagnosis of power systems by using several techniques, such as rule-based expert system, logic-based expert system, fuzzy relation based expert system, neural network, optimization techniques based approach, etc. They diagnose the fault from different ways. However, each approach has its limitations. In this paper, a Bayesian approach by RBF learning using a simulation technique, the Markov chain Monte Carlo (MCMC) and Fuzzy ARTmap network are proposed to predict the fault in a typical power transmission line and the results are compared.
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- Comparison of Bayesian and fuzzy ARTmap networks in HV transmission lines fault diagnosis
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- Romanian Academy: Romanian Academy
- PUT: "Politehnica" University of Timisoara
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World Scientific and Engineering Academy and Society (WSEAS)
Stevens Point, Wisconsin, United States
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Published: 21 October 2010
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