A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimization
Autor(a) principal: | |
---|---|
Data de Publicação: | 2018 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.6/7056 |
Resumo: | Determining the mathematical model parameters of photovoltaic (PV) cells and modules represents a great challenge. In the last few years, several analytical, numerical and hybrid methods have been proposed for extracting the PV model parameters from datasheets provided by the manufacturers or from experimental data, although it is difficult to determine highly reliable solutions quickly and accurately. In this paper, we propose a new method for determining the PV parameters of both the single-diode and the double-diode models, based on the guaranteed convergence particle swarm optimization (GCPSO), using experimental data under different operating conditions. The main advantage of this method is its ability to avoid premature convergence in the optimization of complex and multimodal objective functions, such as the function that determines PV parameters. To validate performance, the GCPSO method was compared with several analytical, numerical and hybrid methods found in the literature. This validation considered three different case studies. The first two are important reference case studies in the literature and have been widely used by researchers. The third was performed in an experimental environment, in order to test the proposed method under a real implementation. The proposed methodology can find highly accurate solutions while demanding a reduced computational cost. Comparisons with other published methods demonstrate that the proposed method produces very good results in the extraction of the PV model parameters. |
id |
RCAP_587cec54c9b01fa5c1a1c8e2f37c21ea |
---|---|
oai_identifier_str |
oai:ubibliorum.ubi.pt:10400.6/7056 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimizationDouble-diode modelExperimental dataGuaranteed convergence particle swarm optimizationParameter extractionSingle-diode modelDetermining the mathematical model parameters of photovoltaic (PV) cells and modules represents a great challenge. In the last few years, several analytical, numerical and hybrid methods have been proposed for extracting the PV model parameters from datasheets provided by the manufacturers or from experimental data, although it is difficult to determine highly reliable solutions quickly and accurately. In this paper, we propose a new method for determining the PV parameters of both the single-diode and the double-diode models, based on the guaranteed convergence particle swarm optimization (GCPSO), using experimental data under different operating conditions. The main advantage of this method is its ability to avoid premature convergence in the optimization of complex and multimodal objective functions, such as the function that determines PV parameters. To validate performance, the GCPSO method was compared with several analytical, numerical and hybrid methods found in the literature. This validation considered three different case studies. The first two are important reference case studies in the literature and have been widely used by researchers. The third was performed in an experimental environment, in order to test the proposed method under a real implementation. The proposed methodology can find highly accurate solutions while demanding a reduced computational cost. Comparisons with other published methods demonstrate that the proposed method produces very good results in the extraction of the PV model parameters.uBibliorumNunes, H.G.G.Pombo, José Álvaro NunesMariano, S.Calado, M. do RosárioFelippe de Souza, J.A.M.2019-05-02T13:13:25Z2018-022018-02-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.6/7056eng0306261910.1016/j.apenergy.2017.11.078metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-15T09:46:07Zoai:ubibliorum.ubi.pt:10400.6/7056Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:47:39.413684Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimization |
title |
A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimization |
spellingShingle |
A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimization Nunes, H.G.G. Double-diode model Experimental data Guaranteed convergence particle swarm optimization Parameter extraction Single-diode model |
title_short |
A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimization |
title_full |
A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimization |
title_fullStr |
A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimization |
title_full_unstemmed |
A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimization |
title_sort |
A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimization |
author |
Nunes, H.G.G. |
author_facet |
Nunes, H.G.G. Pombo, José Álvaro Nunes Mariano, S. Calado, M. do Rosário Felippe de Souza, J.A.M. |
author_role |
author |
author2 |
Pombo, José Álvaro Nunes Mariano, S. Calado, M. do Rosário Felippe de Souza, J.A.M. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
uBibliorum |
dc.contributor.author.fl_str_mv |
Nunes, H.G.G. Pombo, José Álvaro Nunes Mariano, S. Calado, M. do Rosário Felippe de Souza, J.A.M. |
dc.subject.por.fl_str_mv |
Double-diode model Experimental data Guaranteed convergence particle swarm optimization Parameter extraction Single-diode model |
topic |
Double-diode model Experimental data Guaranteed convergence particle swarm optimization Parameter extraction Single-diode model |
description |
Determining the mathematical model parameters of photovoltaic (PV) cells and modules represents a great challenge. In the last few years, several analytical, numerical and hybrid methods have been proposed for extracting the PV model parameters from datasheets provided by the manufacturers or from experimental data, although it is difficult to determine highly reliable solutions quickly and accurately. In this paper, we propose a new method for determining the PV parameters of both the single-diode and the double-diode models, based on the guaranteed convergence particle swarm optimization (GCPSO), using experimental data under different operating conditions. The main advantage of this method is its ability to avoid premature convergence in the optimization of complex and multimodal objective functions, such as the function that determines PV parameters. To validate performance, the GCPSO method was compared with several analytical, numerical and hybrid methods found in the literature. This validation considered three different case studies. The first two are important reference case studies in the literature and have been widely used by researchers. The third was performed in an experimental environment, in order to test the proposed method under a real implementation. The proposed methodology can find highly accurate solutions while demanding a reduced computational cost. Comparisons with other published methods demonstrate that the proposed method produces very good results in the extraction of the PV model parameters. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-02 2018-02-01T00:00:00Z 2019-05-02T13:13:25Z |
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://hdl.handle.net/10400.6/7056 |
url |
http://hdl.handle.net/10400.6/7056 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
03062619 10.1016/j.apenergy.2017.11.078 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799136371982467072 |