An hybrid approach based on neural networks and regression Tree Models for fast dynamic security assessment
Autor(a) principal: | |
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Data de Publicação: | 2003 |
Outros Autores: | |
Tipo de documento: | Livro |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/19473 |
Resumo: | This paper presents a new hybrid automatic learning approach, which combines artificial neural networks (ANN) and regression trees (RT), to perform on-line dynamic security assessment of power systems. In the proposed method, the RT is firstly used to split the vast amount of knowledge data that describes a security problem into several less spread and disjoint problems. Then, an ANN is trained for each of these new smaller problems, resulting in a tree structure with an ANN predicting function associated to each leaf. Moreover, the capability of the RT to perform feature subset selection before ANN training is also tested. With this new method, the advantages of the two techniques are exploited in order to obtained a more accurate model without compromising prediction time. The quality of the approach is illustrated through its application to a major security problem of the power system of Madeira Island (Portugal). |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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An hybrid approach based on neural networks and regression Tree Models for fast dynamic security assessmentEngenhariaEngineeringThis paper presents a new hybrid automatic learning approach, which combines artificial neural networks (ANN) and regression trees (RT), to perform on-line dynamic security assessment of power systems. In the proposed method, the RT is firstly used to split the vast amount of knowledge data that describes a security problem into several less spread and disjoint problems. Then, an ANN is trained for each of these new smaller problems, resulting in a tree structure with an ANN predicting function associated to each leaf. Moreover, the capability of the RT to perform feature subset selection before ANN training is also tested. With this new method, the advantages of the two techniques are exploited in order to obtained a more accurate model without compromising prediction time. The quality of the approach is illustrated through its application to a major security problem of the power system of Madeira Island (Portugal).20032003-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttps://hdl.handle.net/10216/19473engMaria Helena VasconcelosJoão Peças Lopesinfo: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:RCAAP2023-11-29T15:13:41Zoai:repositorio-aberto.up.pt:10216/19473Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:18:30.436834Repositório 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 |
An hybrid approach based on neural networks and regression Tree Models for fast dynamic security assessment |
title |
An hybrid approach based on neural networks and regression Tree Models for fast dynamic security assessment |
spellingShingle |
An hybrid approach based on neural networks and regression Tree Models for fast dynamic security assessment Maria Helena Vasconcelos Engenharia Engineering |
title_short |
An hybrid approach based on neural networks and regression Tree Models for fast dynamic security assessment |
title_full |
An hybrid approach based on neural networks and regression Tree Models for fast dynamic security assessment |
title_fullStr |
An hybrid approach based on neural networks and regression Tree Models for fast dynamic security assessment |
title_full_unstemmed |
An hybrid approach based on neural networks and regression Tree Models for fast dynamic security assessment |
title_sort |
An hybrid approach based on neural networks and regression Tree Models for fast dynamic security assessment |
author |
Maria Helena Vasconcelos |
author_facet |
Maria Helena Vasconcelos João Peças Lopes |
author_role |
author |
author2 |
João Peças Lopes |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Maria Helena Vasconcelos João Peças Lopes |
dc.subject.por.fl_str_mv |
Engenharia Engineering |
topic |
Engenharia Engineering |
description |
This paper presents a new hybrid automatic learning approach, which combines artificial neural networks (ANN) and regression trees (RT), to perform on-line dynamic security assessment of power systems. In the proposed method, the RT is firstly used to split the vast amount of knowledge data that describes a security problem into several less spread and disjoint problems. Then, an ANN is trained for each of these new smaller problems, resulting in a tree structure with an ANN predicting function associated to each leaf. Moreover, the capability of the RT to perform feature subset selection before ANN training is also tested. With this new method, the advantages of the two techniques are exploited in order to obtained a more accurate model without compromising prediction time. The quality of the approach is illustrated through its application to a major security problem of the power system of Madeira Island (Portugal). |
publishDate |
2003 |
dc.date.none.fl_str_mv |
2003 2003-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/book |
format |
book |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10216/19473 |
url |
https://hdl.handle.net/10216/19473 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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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1799136103651868672 |