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Fault Classification in Power Distribution Systems Using Multiresolution Analysis and a Fuzzy-ARTMAP Neural NetworkAnalysis and a Fuzzy-ARTMAP Neural Network

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Detalhes bibliográficos
Resumo:This paper presents a tool for the detection and classification of short-circuits in electric power distribution systems, which is based on the combined use of multi-resolution analysis and Fuzzy-ARTMAP neural network. The multiresolution analysis allows the identification of singularities in the waveforms and the ART family network guarantees to the classifier the ability to continuously learn without losing the previously acquired knowledge. The entire diagnosis procedure is performed in a single step, reducing the computational effort. The efficiency of the system is verified by a direct accuracy analysis, and by a comparison between the Fuzzy-ARTMAP and the EuclideanARTMAP. Results show that the system is efficient and robust, being able to detect and classify 100% of the electrical faults considering the test that were performed.
Autores principais:Bernardes, Haislan
Outros Autores:Tonelli-Neto, Mauro; Minussi, Carlos Roberto
Assunto:Electric Distribution System Fault Classification Fault Detection Fuzzy-ARTMAP Intelligent Systems Multiresolution Analysis
Ano:2022
País:Brasil
Tipo de documento:artigo
Tipo de acesso:acesso aberto
Instituição associada:IBICT
Idioma:português
Origem:Oasisbr
Descrição
Resumo:This paper presents a tool for the detection and classification of short-circuits in electric power distribution systems, which is based on the combined use of multi-resolution analysis and Fuzzy-ARTMAP neural network. The multiresolution analysis allows the identification of singularities in the waveforms and the ART family network guarantees to the classifier the ability to continuously learn without losing the previously acquired knowledge. The entire diagnosis procedure is performed in a single step, reducing the computational effort. The efficiency of the system is verified by a direct accuracy analysis, and by a comparison between the Fuzzy-ARTMAP and the EuclideanARTMAP. Results show that the system is efficient and robust, being able to detect and classify 100% of the electrical faults considering the test that were performed.

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