Probabilistic clustering of wind energy conversion systems using classification models

Detalhes bibliográficos
Autor(a) principal: Fernandes, Paula Odete
Data de Publicação: 2015
Outros Autores: Ferreira, Ângela P.
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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spelling 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:RCAAP2023-08-09T01:23:42ZPortal AgregadorONG
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
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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)
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repository.mail.fl_str_mv
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