Probabilistic clustering of wind energy conversion systems using classification models
Autor(a) principal: | |
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Data de Publicação: | 2015 |
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/10198/16158 |
Resumo: | This research intends to give insights on the pattern aggregation of wind energy conversion systems technologies through identification of homogeneous groups within a set of wind farms installed in Portugal. Pattern aggregation is performed using Hierarchical Cluster Analysis followed by Discriminant Analysis, in order to validate the results produced by the first one. The clustering support matrix uses three independent variables: installed capacity, net production and capacity factor, in a per year basis. Cluster labelling allows the identification of two homogenous groups of wind farms, whose main attributes are based on the technological conversion system trend: (1) asynchronous generator based technology and (2) direct driven synchronous generator based technology, with higher capacity factors. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Probabilistic clustering of wind energy conversion systems using classification modelsCluster analysisDiscriminant analysisWind farmsWind turbine generatorsThis research intends to give insights on the pattern aggregation of wind energy conversion systems technologies through identification of homogeneous groups within a set of wind farms installed in Portugal. Pattern aggregation is performed using Hierarchical Cluster Analysis followed by Discriminant Analysis, in order to validate the results produced by the first one. The clustering support matrix uses three independent variables: installed capacity, net production and capacity factor, in a per year basis. Cluster labelling allows the identification of two homogenous groups of wind farms, whose main attributes are based on the technological conversion system trend: (1) asynchronous generator based technology and (2) direct driven synchronous generator based technology, with higher capacity factors.Biblioteca Digital do IPBFernandes, Paula OdeteFerreira, Ângela P.2018-01-19T10:00:00Z20152015-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10198/16158engFernandes, Paula Odete; Ferreira, Ângela Paula (2015). Probabilistic clustering of wind energy conversion systems using classification models. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics. ISSN 0302-9743. 9156,p. 549-5600302-974310.1007/978-3-319-21407-8_39info: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:RCAAP2023-11-21T10:37:14Zoai:bibliotecadigital.ipb.pt:10198/16158Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:05:25.802541Repositório 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 |
Probabilistic clustering of wind energy conversion systems using classification models |
title |
Probabilistic clustering of wind energy conversion systems using classification models |
spellingShingle |
Probabilistic clustering of wind energy conversion systems using classification models Fernandes, Paula Odete Cluster analysis Discriminant analysis Wind farms Wind turbine generators |
title_short |
Probabilistic clustering of wind energy conversion systems using classification models |
title_full |
Probabilistic clustering of wind energy conversion systems using classification models |
title_fullStr |
Probabilistic clustering of wind energy conversion systems using classification models |
title_full_unstemmed |
Probabilistic clustering of wind energy conversion systems using classification models |
title_sort |
Probabilistic clustering of wind energy conversion systems using classification models |
author |
Fernandes, Paula Odete |
author_facet |
Fernandes, Paula Odete Ferreira, Ângela P. |
author_role |
author |
author2 |
Ferreira, Ângela P. |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Biblioteca Digital do IPB |
dc.contributor.author.fl_str_mv |
Fernandes, Paula Odete Ferreira, Ângela P. |
dc.subject.por.fl_str_mv |
Cluster analysis Discriminant analysis Wind farms Wind turbine generators |
topic |
Cluster analysis Discriminant analysis Wind farms Wind turbine generators |
description |
This research intends to give insights on the pattern aggregation of wind energy conversion systems technologies through identification of homogeneous groups within a set of wind farms installed in Portugal. Pattern aggregation is performed using Hierarchical Cluster Analysis followed by Discriminant Analysis, in order to validate the results produced by the first one. The clustering support matrix uses three independent variables: installed capacity, net production and capacity factor, in a per year basis. Cluster labelling allows the identification of two homogenous groups of wind farms, whose main attributes are based on the technological conversion system trend: (1) asynchronous generator based technology and (2) direct driven synchronous generator based technology, with higher capacity factors. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015 2015-01-01T00:00:00Z 2018-01-19T10:00:00Z |
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/10198/16158 |
url |
http://hdl.handle.net/10198/16158 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Fernandes, Paula Odete; Ferreira, Ângela Paula (2015). Probabilistic clustering of wind energy conversion systems using classification models. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics. ISSN 0302-9743. 9156,p. 549-560 0302-9743 10.1007/978-3-319-21407-8_39 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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 |
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1799135304457650176 |