Neural-network-based approach applied to harmonic component estimation in microgrids
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/COBEP53665.2021.9684083 http://hdl.handle.net/11449/234231 |
Resumo: | Power quality in smart microgrids must be carefully analyzed, whereas adverse consequences may harm the electrical systems without power management and appropriate measures. The main goal of this paper is to develop a 5th, 7th, 11th, and 13th voltage harmonic components identification method based on artificial neural network (ANN). This tool could provide information to the smart microgrid management and control system or be an alternative solution to the harmonic identification process of a harmonic compensator embededs into power converters. The trained algorithm can identify harmonic components amplitude and phase angle in the interfacing point between microgrid and power converters. it was possible to generate a voltage waveform with a maximum difference of 0.04 p.u. between the expected waveform and the one built with the parameters identified by ANN. The ANN method validation was performed through computer simulations. |
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Repositório Institucional da UNESP |
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Neural-network-based approach applied to harmonic component estimation in microgridsArtificial neural networksharmonic component identificationmicrogridspower qualityPower quality in smart microgrids must be carefully analyzed, whereas adverse consequences may harm the electrical systems without power management and appropriate measures. The main goal of this paper is to develop a 5th, 7th, 11th, and 13th voltage harmonic components identification method based on artificial neural network (ANN). This tool could provide information to the smart microgrid management and control system or be an alternative solution to the harmonic identification process of a harmonic compensator embededs into power converters. The trained algorithm can identify harmonic components amplitude and phase angle in the interfacing point between microgrid and power converters. it was possible to generate a voltage waveform with a maximum difference of 0.04 p.u. between the expected waveform and the one built with the parameters identified by ANN. The ANN method validation was performed through computer simulations.Federal University of São Carlos Dept. of Electrical EngineeringFederal University of Technology - Paraná Dept. of Electrical EngineeringSão Paulo State University Dept. of Control and Automation EngineeringDept. of Production Engineering Federal University of S ao CarlosSão Paulo State University Dept. of Control and Automation EngineeringUniversidade Federal de São Carlos (UFSCar)Dept. of Electrical EngineeringUniversidade Estadual Paulista (UNESP)Federal University of S ao CarlosReis Bernardino, Luiz GustavoDo Nascimento, Claudionor FranciscoTavares Neto, Roberto FernandesDe Souza, Wesley AngelinoMarafao, Fernando Pinhabel [UNESP]2022-05-01T15:13:34Z2022-05-01T15:13:34Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/COBEP53665.2021.96840832021 Brazilian Power Electronics Conference, COBEP 2021.http://hdl.handle.net/11449/23423110.1109/COBEP53665.2021.96840832-s2.0-85125741026Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2021 Brazilian Power Electronics Conference, COBEP 2021info:eu-repo/semantics/openAccess2022-05-01T15:13:34Zoai:repositorio.unesp.br:11449/234231Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T22:45:37.733900Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Neural-network-based approach applied to harmonic component estimation in microgrids |
title |
Neural-network-based approach applied to harmonic component estimation in microgrids |
spellingShingle |
Neural-network-based approach applied to harmonic component estimation in microgrids Reis Bernardino, Luiz Gustavo Artificial neural networks harmonic component identification microgrids power quality |
title_short |
Neural-network-based approach applied to harmonic component estimation in microgrids |
title_full |
Neural-network-based approach applied to harmonic component estimation in microgrids |
title_fullStr |
Neural-network-based approach applied to harmonic component estimation in microgrids |
title_full_unstemmed |
Neural-network-based approach applied to harmonic component estimation in microgrids |
title_sort |
Neural-network-based approach applied to harmonic component estimation in microgrids |
author |
Reis Bernardino, Luiz Gustavo |
author_facet |
Reis Bernardino, Luiz Gustavo Do Nascimento, Claudionor Francisco Tavares Neto, Roberto Fernandes De Souza, Wesley Angelino Marafao, Fernando Pinhabel [UNESP] |
author_role |
author |
author2 |
Do Nascimento, Claudionor Francisco Tavares Neto, Roberto Fernandes De Souza, Wesley Angelino Marafao, Fernando Pinhabel [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de São Carlos (UFSCar) Dept. of Electrical Engineering Universidade Estadual Paulista (UNESP) Federal University of S ao Carlos |
dc.contributor.author.fl_str_mv |
Reis Bernardino, Luiz Gustavo Do Nascimento, Claudionor Francisco Tavares Neto, Roberto Fernandes De Souza, Wesley Angelino Marafao, Fernando Pinhabel [UNESP] |
dc.subject.por.fl_str_mv |
Artificial neural networks harmonic component identification microgrids power quality |
topic |
Artificial neural networks harmonic component identification microgrids power quality |
description |
Power quality in smart microgrids must be carefully analyzed, whereas adverse consequences may harm the electrical systems without power management and appropriate measures. The main goal of this paper is to develop a 5th, 7th, 11th, and 13th voltage harmonic components identification method based on artificial neural network (ANN). This tool could provide information to the smart microgrid management and control system or be an alternative solution to the harmonic identification process of a harmonic compensator embededs into power converters. The trained algorithm can identify harmonic components amplitude and phase angle in the interfacing point between microgrid and power converters. it was possible to generate a voltage waveform with a maximum difference of 0.04 p.u. between the expected waveform and the one built with the parameters identified by ANN. The ANN method validation was performed through computer simulations. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 2022-05-01T15:13:34Z 2022-05-01T15:13:34Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/COBEP53665.2021.9684083 2021 Brazilian Power Electronics Conference, COBEP 2021. http://hdl.handle.net/11449/234231 10.1109/COBEP53665.2021.9684083 2-s2.0-85125741026 |
url |
http://dx.doi.org/10.1109/COBEP53665.2021.9684083 http://hdl.handle.net/11449/234231 |
identifier_str_mv |
2021 Brazilian Power Electronics Conference, COBEP 2021. 10.1109/COBEP53665.2021.9684083 2-s2.0-85125741026 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2021 Brazilian Power Electronics Conference, COBEP 2021 |
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_ |
1808129460157808640 |