Comparison of Bayesian and fuzzy ARTmap networks in HV transmission lines fault diagnosis

Authors:
Hamed Nafisi;Babak Abdi;Amirhossein Aghakhani
Affiliations:
Department of Electrical Engineering, Islamic Azad University, Damavand, Iran;Department of Electrical Engineering, Islamic Azad University, Damavand, Iran;Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
Venue:
MMACTEE'10 Proceedings of the 12th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering
Year:
2010

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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.