Evaluation of Pattern Recognition Algorithms for Applications on Power Factor Compensation
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s40313-017-0352-9 http://hdl.handle.net/11449/179511 |
Resumo: | This paper assesses different applied pattern recognition algorithms to decide the most appropriate power factor compensator for a particular point of common coupling. Power factor, current unbalance factor, total demand distortion, voltage harmonic distortion and reactive power daily variation, as well as human expertise, are the key parameters used to set each recognition algorithm. These algorithms are then trained with a series of both simulation and experimental data. Numerical results consistently indicate the decision-tree algorithm with depth 20 as the best classifier for power factor improvement in terms of all metrics considered in this work. |
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Repositório Institucional da UNESP |
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Evaluation of Pattern Recognition Algorithms for Applications on Power Factor CompensationActive compensatorsPassive compensatorsPattern recognitionPower factorReactive and harmonic compensationThis paper assesses different applied pattern recognition algorithms to decide the most appropriate power factor compensator for a particular point of common coupling. Power factor, current unbalance factor, total demand distortion, voltage harmonic distortion and reactive power daily variation, as well as human expertise, are the key parameters used to set each recognition algorithm. These algorithms are then trained with a series of both simulation and experimental data. Numerical results consistently indicate the decision-tree algorithm with depth 20 as the best classifier for power factor improvement in terms of all metrics considered in this work.Telecommunications and Mechatronic Engineering Department (DETEM) Federal University of São João del-Rei (UFSJ), Rodovia MG 443, KM 7Institute of Science and Technology São Paulo State University (Unesp), Av. Três de Março, 511Department of Energy and Systems (DSE) School of Electrical and Computer Engineering (FEEC) University of Campinas (UNICAMP), Av. Albert Einstein, 400Centre for Wireless Communications (CWC) University of Oulu, Erkki Koiso-Kanttilan katu 3Institute of Science and Technology São Paulo State University (Unesp), Av. Três de Março, 511Universidade Federal de Sergipe (UFS)Universidade Estadual Paulista (Unesp)Universidade Estadual de Campinas (UNICAMP)University of OuluMoreira, Alexandre C.Paredes, Helmo K. M. [UNESP]de Souza, Wesley A.Nardelli, Pedro H. J.Marafão, Fernando P. [UNESP]da Silva, Luiz C. P.2018-12-11T17:35:28Z2018-12-11T17:35:28Z2018-02-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article75-90application/pdfhttp://dx.doi.org/10.1007/s40313-017-0352-9Journal of Control, Automation and Electrical Systems, v. 29, n. 1, p. 75-90, 2018.2195-38992195-3880http://hdl.handle.net/11449/17951110.1007/s40313-017-0352-92-s2.0-850406514562-s2.0-85040651456.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of Control, Automation and Electrical Systems0,2740,274info:eu-repo/semantics/openAccess2023-12-11T06:11:52Zoai:repositorio.unesp.br:11449/179511Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:59:45.307500Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Evaluation of Pattern Recognition Algorithms for Applications on Power Factor Compensation |
title |
Evaluation of Pattern Recognition Algorithms for Applications on Power Factor Compensation |
spellingShingle |
Evaluation of Pattern Recognition Algorithms for Applications on Power Factor Compensation Moreira, Alexandre C. Active compensators Passive compensators Pattern recognition Power factor Reactive and harmonic compensation |
title_short |
Evaluation of Pattern Recognition Algorithms for Applications on Power Factor Compensation |
title_full |
Evaluation of Pattern Recognition Algorithms for Applications on Power Factor Compensation |
title_fullStr |
Evaluation of Pattern Recognition Algorithms for Applications on Power Factor Compensation |
title_full_unstemmed |
Evaluation of Pattern Recognition Algorithms for Applications on Power Factor Compensation |
title_sort |
Evaluation of Pattern Recognition Algorithms for Applications on Power Factor Compensation |
author |
Moreira, Alexandre C. |
author_facet |
Moreira, Alexandre C. Paredes, Helmo K. M. [UNESP] de Souza, Wesley A. Nardelli, Pedro H. J. Marafão, Fernando P. [UNESP] da Silva, Luiz C. P. |
author_role |
author |
author2 |
Paredes, Helmo K. M. [UNESP] de Souza, Wesley A. Nardelli, Pedro H. J. Marafão, Fernando P. [UNESP] da Silva, Luiz C. P. |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de Sergipe (UFS) Universidade Estadual Paulista (Unesp) Universidade Estadual de Campinas (UNICAMP) University of Oulu |
dc.contributor.author.fl_str_mv |
Moreira, Alexandre C. Paredes, Helmo K. M. [UNESP] de Souza, Wesley A. Nardelli, Pedro H. J. Marafão, Fernando P. [UNESP] da Silva, Luiz C. P. |
dc.subject.por.fl_str_mv |
Active compensators Passive compensators Pattern recognition Power factor Reactive and harmonic compensation |
topic |
Active compensators Passive compensators Pattern recognition Power factor Reactive and harmonic compensation |
description |
This paper assesses different applied pattern recognition algorithms to decide the most appropriate power factor compensator for a particular point of common coupling. Power factor, current unbalance factor, total demand distortion, voltage harmonic distortion and reactive power daily variation, as well as human expertise, are the key parameters used to set each recognition algorithm. These algorithms are then trained with a series of both simulation and experimental data. Numerical results consistently indicate the decision-tree algorithm with depth 20 as the best classifier for power factor improvement in terms of all metrics considered in this work. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-12-11T17:35:28Z 2018-12-11T17:35:28Z 2018-02-01 |
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/10.1007/s40313-017-0352-9 Journal of Control, Automation and Electrical Systems, v. 29, n. 1, p. 75-90, 2018. 2195-3899 2195-3880 http://hdl.handle.net/11449/179511 10.1007/s40313-017-0352-9 2-s2.0-85040651456 2-s2.0-85040651456.pdf |
url |
http://dx.doi.org/10.1007/s40313-017-0352-9 http://hdl.handle.net/11449/179511 |
identifier_str_mv |
Journal of Control, Automation and Electrical Systems, v. 29, n. 1, p. 75-90, 2018. 2195-3899 2195-3880 10.1007/s40313-017-0352-9 2-s2.0-85040651456 2-s2.0-85040651456.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal of Control, Automation and Electrical Systems 0,274 0,274 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
75-90 application/pdf |
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_ |
1808129146845396992 |