Wavelet-based features selected with Paraconsistent Feature Engineering successfully classify events in low-voltage grids
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.measurement.2020.108711 http://hdl.handle.net/11449/208160 |
Resumo: | Energy 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. |
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Repositório Institucional da UNESP |
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Wavelet-based features selected with Paraconsistent Feature Engineering successfully classify events in low-voltage gridsEvent classificationLow-voltage gridsParaconsistent Feature Engineering (PFE)WaveletsEnergy 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.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Departamento de Engenharia Elétrica Escola de Engenharia de São Carlos Universidade de São Paulo (USP), Av Trabalhador SãoCarlense 400Instituto de Biociências Letras e Ciências Exatas Unesp - Univ Estadual Paulista (São Paulo State University), Rua Cristóvão Colombo 2265Instituto de Biociências Letras e Ciências Exatas Unesp - Univ Estadual Paulista (São Paulo State University), Rua Cristóvão Colombo 2265CNPq: 2019/04475-0CNPq: 306808/2018-8Universidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Caobianco, Luiz GustavoGuido, Rodrigo Capobianco [UNESP]Silva, Ivan Nunes da2021-06-25T11:07:26Z2021-06-25T11:07:26Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.measurement.2020.108711Measurement: Journal of the International Measurement Confederation, v. 170.0263-2241http://hdl.handle.net/11449/20816010.1016/j.measurement.2020.1087112-s2.0-85096379846Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengMeasurement: Journal of the International Measurement Confederationinfo:eu-repo/semantics/openAccess2021-10-23T18:56:46Zoai:repositorio.unesp.br:11449/208160Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T22:02:42.729328Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Wavelet-based features selected with Paraconsistent Feature Engineering successfully classify events in low-voltage grids |
title |
Wavelet-based features selected with Paraconsistent Feature Engineering successfully classify events in low-voltage grids |
spellingShingle |
Wavelet-based features selected with Paraconsistent Feature Engineering successfully classify events in low-voltage grids Caobianco, Luiz Gustavo Event classification Low-voltage grids Paraconsistent Feature Engineering (PFE) Wavelets |
title_short |
Wavelet-based features selected with Paraconsistent Feature Engineering successfully classify events in low-voltage grids |
title_full |
Wavelet-based features selected with Paraconsistent Feature Engineering successfully classify events in low-voltage grids |
title_fullStr |
Wavelet-based features selected with Paraconsistent Feature Engineering successfully classify events in low-voltage grids |
title_full_unstemmed |
Wavelet-based features selected with Paraconsistent Feature Engineering successfully classify events in low-voltage grids |
title_sort |
Wavelet-based features selected with Paraconsistent Feature Engineering successfully classify events in low-voltage grids |
author |
Caobianco, Luiz Gustavo |
author_facet |
Caobianco, Luiz Gustavo Guido, Rodrigo Capobianco [UNESP] Silva, Ivan Nunes da |
author_role |
author |
author2 |
Guido, Rodrigo Capobianco [UNESP] Silva, Ivan Nunes da |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade de São Paulo (USP) Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Caobianco, Luiz Gustavo Guido, Rodrigo Capobianco [UNESP] Silva, Ivan Nunes da |
dc.subject.por.fl_str_mv |
Event classification Low-voltage grids Paraconsistent Feature Engineering (PFE) Wavelets |
topic |
Event classification Low-voltage grids Paraconsistent Feature Engineering (PFE) Wavelets |
description |
Energy 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. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06-25T11:07:26Z 2021-06-25T11:07:26Z 2021-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.measurement.2020.108711 Measurement: Journal of the International Measurement Confederation, v. 170. 0263-2241 http://hdl.handle.net/11449/208160 10.1016/j.measurement.2020.108711 2-s2.0-85096379846 |
url |
http://dx.doi.org/10.1016/j.measurement.2020.108711 http://hdl.handle.net/11449/208160 |
identifier_str_mv |
Measurement: Journal of the International Measurement Confederation, v. 170. 0263-2241 10.1016/j.measurement.2020.108711 2-s2.0-85096379846 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Measurement: Journal of the International Measurement Confederation |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129386454450176 |