Wind Equivalent Model Parameter Estimation Through Mean-Variance Mapping Optimization and Trajectory Sensitivity Method

Detalhes bibliográficos
Autor(a) principal: Vargas, Paul Junior Zapana
Data de Publicação: 2023
Outros Autores: Santos, Gustavo Henrique de Paula, Cari, Elmer Pablo Tito
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Manancial - Repositório Digital da UFSM
dARK ID: ark:/26339/001300000ptmc
Texto Completo: http://repositorio.ufsm.br/handle/1/30777
Resumo: Wind energy is a renewable source of vital importance and is constantly growing. In this scenario, it is imperative to carry out studies through mathematical models. However, the mathematical representation becomes excessively complex when considering the number of wind turbines present in a wind farm. This paper proposes the estimation of parameters in a wind farm equivalent model using two different approaches. The first method is responsible for performing a global search over a wide interval, thus providing an intelligent initial parameter. The values estimated by this first method are then used in the second method, which will be responsible for performing the final estimation. Simulation results show that the combination of the two methods was adequate to obtain the parameters of the wind farm equivalent model. https://doi.org/10.53316/cbgd2023.007
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spelling Wind Equivalent Model Parameter Estimation Through Mean-Variance Mapping Optimization and Trajectory Sensitivity MethodEstimação dos Parâmetros de um Modelo Equivalente Eólico por meio da Otimização do Mapeamento da Variância e Média e do Método de Sensibilidade da TrajetóriaWind energyMathematical modelParameter estimationCNPQ::ENGENHARIASCNPQ::ENGENHARIAS::ENGENHARIA ELETRICAWind energy is a renewable source of vital importance and is constantly growing. In this scenario, it is imperative to carry out studies through mathematical models. However, the mathematical representation becomes excessively complex when considering the number of wind turbines present in a wind farm. This paper proposes the estimation of parameters in a wind farm equivalent model using two different approaches. The first method is responsible for performing a global search over a wide interval, thus providing an intelligent initial parameter. The values estimated by this first method are then used in the second method, which will be responsible for performing the final estimation. Simulation results show that the combination of the two methods was adequate to obtain the parameters of the wind farm equivalent model. https://doi.org/10.53316/cbgd2023.007Brasil2023-12-04T14:19:52Z2023-12-04T14:19:52Z2023-11-17info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectapplication/pdfhttp://repositorio.ufsm.br/handle/1/30777ark:/26339/001300000ptmceng8° Congresso Brasileiro de Geração Distribuída (CBGD 2023)Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessVargas, Paul Junior ZapanaSantos, Gustavo Henrique de PaulaCari, Elmer Pablo Titoreponame:Manancial - Repositório Digital da UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM2023-12-04T14:19:52Zoai:repositorio.ufsm.br:1/30777Biblioteca Digital de Teses e Dissertaçõeshttps://repositorio.ufsm.br/ONGhttps://repositorio.ufsm.br/oai/requestatendimento.sib@ufsm.br||tedebc@gmail.comopendoar:2023-12-04T14:19:52Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)false
dc.title.none.fl_str_mv Wind Equivalent Model Parameter Estimation Through Mean-Variance Mapping Optimization and Trajectory Sensitivity Method
Estimação dos Parâmetros de um Modelo Equivalente Eólico por meio da Otimização do Mapeamento da Variância e Média e do Método de Sensibilidade da Trajetória
title Wind Equivalent Model Parameter Estimation Through Mean-Variance Mapping Optimization and Trajectory Sensitivity Method
spellingShingle Wind Equivalent Model Parameter Estimation Through Mean-Variance Mapping Optimization and Trajectory Sensitivity Method
Vargas, Paul Junior Zapana
Wind energy
Mathematical model
Parameter estimation
CNPQ::ENGENHARIAS
CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
title_short Wind Equivalent Model Parameter Estimation Through Mean-Variance Mapping Optimization and Trajectory Sensitivity Method
title_full Wind Equivalent Model Parameter Estimation Through Mean-Variance Mapping Optimization and Trajectory Sensitivity Method
title_fullStr Wind Equivalent Model Parameter Estimation Through Mean-Variance Mapping Optimization and Trajectory Sensitivity Method
title_full_unstemmed Wind Equivalent Model Parameter Estimation Through Mean-Variance Mapping Optimization and Trajectory Sensitivity Method
title_sort Wind Equivalent Model Parameter Estimation Through Mean-Variance Mapping Optimization and Trajectory Sensitivity Method
author Vargas, Paul Junior Zapana
author_facet Vargas, Paul Junior Zapana
Santos, Gustavo Henrique de Paula
Cari, Elmer Pablo Tito
author_role author
author2 Santos, Gustavo Henrique de Paula
Cari, Elmer Pablo Tito
author2_role author
author
dc.contributor.author.fl_str_mv Vargas, Paul Junior Zapana
Santos, Gustavo Henrique de Paula
Cari, Elmer Pablo Tito
dc.subject.por.fl_str_mv Wind energy
Mathematical model
Parameter estimation
CNPQ::ENGENHARIAS
CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
topic Wind energy
Mathematical model
Parameter estimation
CNPQ::ENGENHARIAS
CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
description Wind energy is a renewable source of vital importance and is constantly growing. In this scenario, it is imperative to carry out studies through mathematical models. However, the mathematical representation becomes excessively complex when considering the number of wind turbines present in a wind farm. This paper proposes the estimation of parameters in a wind farm equivalent model using two different approaches. The first method is responsible for performing a global search over a wide interval, thus providing an intelligent initial parameter. The values estimated by this first method are then used in the second method, which will be responsible for performing the final estimation. Simulation results show that the combination of the two methods was adequate to obtain the parameters of the wind farm equivalent model. https://doi.org/10.53316/cbgd2023.007
publishDate 2023
dc.date.none.fl_str_mv 2023-12-04T14:19:52Z
2023-12-04T14:19:52Z
2023-11-17
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://repositorio.ufsm.br/handle/1/30777
dc.identifier.dark.fl_str_mv ark:/26339/001300000ptmc
url http://repositorio.ufsm.br/handle/1/30777
identifier_str_mv ark:/26339/001300000ptmc
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 8° Congresso Brasileiro de Geração Distribuída (CBGD 2023)
dc.rights.driver.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Brasil
publisher.none.fl_str_mv Brasil
dc.source.none.fl_str_mv reponame:Manancial - Repositório Digital da UFSM
instname:Universidade Federal de Santa Maria (UFSM)
instacron:UFSM
instname_str Universidade Federal de Santa Maria (UFSM)
instacron_str UFSM
institution UFSM
reponame_str Manancial - Repositório Digital da UFSM
collection Manancial - Repositório Digital da UFSM
repository.name.fl_str_mv Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)
repository.mail.fl_str_mv atendimento.sib@ufsm.br||tedebc@gmail.com
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