Wavelet-based features selected with Paraconsistent Feature Engineering successfully classify events in low-voltage grids

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorCaobianco, Luiz Gustavo-
Autor(es): dc.creatorGuido, Rodrigo Capobianco [UNESP]-
Autor(es): dc.creatorSilva, Ivan Nunes da-
Data de aceite: dc.date.accessioned2022-02-22T00:52:34Z-
Data de disponibilização: dc.date.available2022-02-22T00:52:34Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2020-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.measurement.2020.108711-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/208160-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/208160-
Descrição: dc.descriptionEnergy quality, either in centralized or distributed generation, is directly affected by events in electrical lines. Consequently, the precise identification of those issues is of paramount importance, where the features extracted from domestic or industrial low-voltage sources should be able to properly represent the events for a subsequent classification. Nevertheless, current algorithms for event diagnosis suffer from a number of drawbacks such as the lack of real data to model the problem, since the majority of strategies is supported by simulated signals, and the uncertainty on the best features to conveniently address the occurrences. Thus, our contribution in this paper is twofold: we describe our own database, which is freely available under request, and innovatively apply Paraconsistent Feature Engineering (PFE) to analyze and select favorite wavelet-based features to classify events in low-voltage grids. Lastly, an example application where a set of features was capable of distinguishing specific events from normal signals with a value of accuracy of 96% using just an Euclidean distance classifier is shown, reassuring the efficacy of the proposed approach. Notably, the association of wavelets with PFE to handle energy quality issues had never been reported in literature.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionDepartamento de Engenharia Elétrica Escola de Engenharia de São Carlos Universidade de São Paulo (USP), Av Trabalhador SãoCarlense 400-
Descrição: dc.descriptionInstituto de Biociências Letras e Ciências Exatas Unesp - Univ Estadual Paulista (São Paulo State University), Rua Cristóvão Colombo 2265-
Descrição: dc.descriptionInstituto de Biociências Letras e Ciências Exatas Unesp - Univ Estadual Paulista (São Paulo State University), Rua Cristóvão Colombo 2265-
Descrição: dc.descriptionCNPq: 2019/04475-0-
Descrição: dc.descriptionCNPq: 306808/2018-8-
Idioma: dc.languageen-
Relação: dc.relationMeasurement: Journal of the International Measurement Confederation-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectEvent classification-
Palavras-chave: dc.subjectLow-voltage grids-
Palavras-chave: dc.subjectParaconsistent Feature Engineering (PFE)-
Palavras-chave: dc.subjectWavelets-
Título: dc.titleWavelet-based features selected with Paraconsistent Feature Engineering successfully classify events in low-voltage grids-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.