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

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dc.contributor.authorCaobianco, Luiz Gustavo
dc.contributor.authorGuido, Rodrigo Capobianco [UNESP]
dc.contributor.authorSilva, Ivan Nunes da
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2021-06-25T11:07:26Z
dc.date.available2021-06-25T11:07:26Z
dc.date.issued2021-01-01
dc.description.abstractEnergy 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.en
dc.description.affiliationDepartamento de Engenharia Elétrica Escola de Engenharia de São Carlos Universidade de São Paulo (USP), Av Trabalhador SãoCarlense 400
dc.description.affiliationInstituto de Biociências Letras e Ciências Exatas Unesp - Univ Estadual Paulista (São Paulo State University), Rua Cristóvão Colombo 2265
dc.description.affiliationUnespInstituto de Biociências Letras e Ciências Exatas Unesp - Univ Estadual Paulista (São Paulo State University), Rua Cristóvão Colombo 2265
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdCNPq: 2019/04475-0
dc.description.sponsorshipIdCNPq: 306808/2018-8
dc.identifierhttp://dx.doi.org/10.1016/j.measurement.2020.108711
dc.identifier.citationMeasurement: Journal of the International Measurement Confederation, v. 170.
dc.identifier.doi10.1016/j.measurement.2020.108711
dc.identifier.issn0263-2241
dc.identifier.scopus2-s2.0-85096379846
dc.identifier.urihttp://hdl.handle.net/11449/208160
dc.language.isoeng
dc.relation.ispartofMeasurement: Journal of the International Measurement Confederation
dc.sourceScopus
dc.subjectEvent classification
dc.subjectLow-voltage grids
dc.subjectParaconsistent Feature Engineering (PFE)
dc.subjectWavelets
dc.titleWavelet-based features selected with Paraconsistent Feature Engineering successfully classify events in low-voltage gridsen
dc.typeArtigo

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Artigos - Ciência da Computação e Estatística - IBILCE