High-impedance fault modeling and classification in power distribution networks

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorCarvalho, José Genilson Sousa-
Autor(es): dc.creatorAlmeida, Aryfrance Rocha-
Autor(es): dc.creatorFerreira, Danton Diego-
Autor(es): dc.creatorSantos Junior, Bartolomeu Ferreira dos-
Autor(es): dc.creatorVasconcelos, Luis Henrique Pereira-
Autor(es): dc.creatorSobreira, Danilo de Oliveira-
Data de aceite: dc.date.accessioned2026-02-09T12:22:02Z-
Data de disponibilização: dc.date.available2026-02-09T12:22:02Z-
Data de envio: dc.date.issued2022-04-26-
Data de envio: dc.date.issued2022-04-26-
Data de envio: dc.date.issued2022-03-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/49801-
Fonte completa do material: dc.identifierhttps://doi.org/10.1016/j.epsr.2021.107676-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1159849-
Descrição: dc.descriptionThis work presents a novel method for the classification of high-impedance faults (HIFs) in power distribution networks based on the association of higher-order statistics (HOS) and a multilayer perceptron (MLP) artificial neural network (ANN). An alternative model is developed to represent the HIF phenomenon considering five different contact surfaces with the ground. A broad analysis comprising six types of typical events that occur in distribution networks is performed in the Alternative Transient Program (ATP) software, including several conditions such as: normal operation; single-phase faults; two-phase faults; three-phase faults; energization of transformers and capacitor banks; switching of inductive loads; as well as faults involving five modeled surfaces. HOS is combined with Fisher’s discriminant ratio (FDR) to extract the best characteristics. At the end, the MLP-type ANN is used to recognize the specific patterns of each event aiming to identify each event accurately, especially HIFs. The obtained results demonstrate that the proposed technique proves to be a reliable and accurate tool, achieving classification hits above 98%.-
Idioma: dc.languageen-
Publicador: dc.publisherElsevier-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceElectric Power Systems Research-
Palavras-chave: dc.subjectDistribution networks-
Palavras-chave: dc.subjectHigh-impedance faults-
Palavras-chave: dc.subjectHigher-order statistics-
Palavras-chave: dc.subjectFisher’s discriminant ratio-
Palavras-chave: dc.subjectArtificial neural networks-
Palavras-chave: dc.subjectRedes de distribuição-
Palavras-chave: dc.subjectFaltas de alta impedância-
Palavras-chave: dc.subjectEstatísticas de ordem superior-
Palavras-chave: dc.subjectFunção discriminante de Fisher-
Palavras-chave: dc.subjectRedes neurais artificiais-
Título: dc.titleHigh-impedance fault modeling and classification in power distribution networks-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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