Real-time system for automatic detection and classification of single and multiple power quality disturbances

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorRibeiro, Eduardo G.-
Autor(es): dc.creatorMendes, Thais M.-
Autor(es): dc.creatorDias, Guilherme L.-
Autor(es): dc.creatorFaria, Euler R. S.-
Autor(es): dc.creatorViana, Felipe M.-
Autor(es): dc.creatorBarbosa, Bruno H. G.-
Autor(es): dc.creatorFerreira, Danton D.-
Data de aceite: dc.date.accessioned2026-02-09T11:08:58Z-
Data de disponibilização: dc.date.available2026-02-09T11:08:58Z-
Data de envio: dc.date.issued2019-04-16-
Data de envio: dc.date.issued2019-04-16-
Data de envio: dc.date.issued2018-10-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/33591-
Fonte completa do material: dc.identifierhttps://www.sciencedirect.com/science/article/pii/S0263224118305852-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1134813-
Descrição: dc.descriptionIt is known that the quality of power has been the subject of several researches aiming to provide relevant information to users of electrical systems that are becoming increasingly smart. This study presents an approach for single and multiple power quality disturbance detection and classification using multidimensional analysis, higher-order statistics and a neuro-tree based classifier. The system was implemented in an FPGA (Field Programmable Gate Array), a real-time processor and a remote computer, with LabVIEW interface. This implementation enables real-time execution and its application to monitor smart grids. It is able to detect deviations in the measured voltage waveform from the nominal one and classify 20 classes of single and multiple disturbances with a global efficiency upper to 97%.-
Idioma: dc.languageen-
Publicador: dc.publisherElsevier-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceMeasurement-
Palavras-chave: dc.subjectHigher-order statistics-
Palavras-chave: dc.subjectNeuro-tree-
Palavras-chave: dc.subjectReal-time-
Palavras-chave: dc.subjectLabVIEW software-
Título: dc.titleReal-time system for automatic detection and classification of single and multiple power quality disturbances-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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