On-line dynamic security assessment based on kernel regression trees
Autor(a) principal: | |
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Data de Publicação: | 2000 |
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/10216/19480 |
Resumo: | This paper presents a new approach to perform online dynamic security assessment and monitoring of electric power systems exploiting a statistical hybrid learning technique-kernel regression trees. This technique, besides producing fast security classification, can still quantify, in real-time, the security degree of the system, by emulating continuous security indices that translate the power system dynamic behavior. Moreover it can provide interpretable security structures. The feasibility of this approach was demonstrated in the dynamic security assessment of isolated systems with large amounts of wind power production, like in the Crete island electric network (Greece). Comparative results regarding performances of decision trees and neural networks are also presented and discussed. From the obtained results, the proposed approach showed to provide good predicting structures whose performance stands up to the performance of the two other existing methods. (c) 2000 IEEE. |
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On-line dynamic security assessment based on kernel regression treesEngenharia electrotécnica, Engenharia electrotécnica, electrónica e informáticaElectrical engineering, Electrical engineering, Electronic engineering, Information engineeringThis paper presents a new approach to perform online dynamic security assessment and monitoring of electric power systems exploiting a statistical hybrid learning technique-kernel regression trees. This technique, besides producing fast security classification, can still quantify, in real-time, the security degree of the system, by emulating continuous security indices that translate the power system dynamic behavior. Moreover it can provide interpretable security structures. The feasibility of this approach was demonstrated in the dynamic security assessment of isolated systems with large amounts of wind power production, like in the Crete island electric network (Greece). Comparative results regarding performances of decision trees and neural networks are also presented and discussed. From the obtained results, the proposed approach showed to provide good predicting structures whose performance stands up to the performance of the two other existing methods. (c) 2000 IEEE.20002000-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttps://hdl.handle.net/10216/19480eng10.1109/PESW.2000.850089João A. Peças LopesMaria Helena Vasconcelosinfo: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:RCAAP2023-11-29T15:23:07Zoai:repositorio-aberto.up.pt:10216/19480Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:22:18.487277Repositório 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 |
On-line dynamic security assessment based on kernel regression trees |
title |
On-line dynamic security assessment based on kernel regression trees |
spellingShingle |
On-line dynamic security assessment based on kernel regression trees João A. Peças Lopes Engenharia electrotécnica, Engenharia electrotécnica, electrónica e informática Electrical engineering, Electrical engineering, Electronic engineering, Information engineering |
title_short |
On-line dynamic security assessment based on kernel regression trees |
title_full |
On-line dynamic security assessment based on kernel regression trees |
title_fullStr |
On-line dynamic security assessment based on kernel regression trees |
title_full_unstemmed |
On-line dynamic security assessment based on kernel regression trees |
title_sort |
On-line dynamic security assessment based on kernel regression trees |
author |
João A. Peças Lopes |
author_facet |
João A. Peças Lopes Maria Helena Vasconcelos |
author_role |
author |
author2 |
Maria Helena Vasconcelos |
author2_role |
author |
dc.contributor.author.fl_str_mv |
João A. Peças Lopes Maria Helena Vasconcelos |
dc.subject.por.fl_str_mv |
Engenharia electrotécnica, Engenharia electrotécnica, electrónica e informática Electrical engineering, Electrical engineering, Electronic engineering, Information engineering |
topic |
Engenharia electrotécnica, Engenharia electrotécnica, electrónica e informática Electrical engineering, Electrical engineering, Electronic engineering, Information engineering |
description |
This paper presents a new approach to perform online dynamic security assessment and monitoring of electric power systems exploiting a statistical hybrid learning technique-kernel regression trees. This technique, besides producing fast security classification, can still quantify, in real-time, the security degree of the system, by emulating continuous security indices that translate the power system dynamic behavior. Moreover it can provide interpretable security structures. The feasibility of this approach was demonstrated in the dynamic security assessment of isolated systems with large amounts of wind power production, like in the Crete island electric network (Greece). Comparative results regarding performances of decision trees and neural networks are also presented and discussed. From the obtained results, the proposed approach showed to provide good predicting structures whose performance stands up to the performance of the two other existing methods. (c) 2000 IEEE. |
publishDate |
2000 |
dc.date.none.fl_str_mv |
2000 2000-01-01T00:00:00Z |
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/10216/19480 |
url |
https://hdl.handle.net/10216/19480 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1109/PESW.2000.850089 |
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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1799136139026628608 |