Composite Reliability Assessment Based on Monte Carlo Simulation and Artificial Neural Networks
Autor(a) principal: | |
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Data de Publicação: | 2007 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://repositorio.inesctec.pt/handle/123456789/3063 |
Resumo: | This paper presents a new methodology for reliability evaluation of composite generation and transmission systems, based on non-sequential Monte-Carlo simulation and artificial neural network concepts. Artificial neural network (ANN) techniques are used to classify the operating states during the Monte Carlo sampling. A polynomial network, named Group Method Data Handling (GMDH), is used and the states analyzed during the beginning of the simulation process are adequately selected as input data for training and test sets. Based on this procedure, a great number of success states are classified by a simple polynomial function, given by the ANN model, providing significant reductions in the computational cost. Moreover, all types of composite reliability indices (i.e. loss of load probability, frequency, duration and energy/power not supplied) can be assessed not only for the overall system but also for areas and buses. The proposed methodology is applied to the IEEE Reliability Test System (IEEE-RTS), to the IEEE-RTS 96 and to a configuration of the Brazilian South-Southeastern System. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Composite Reliability Assessment Based on Monte Carlo Simulation and Artificial Neural NetworksThis paper presents a new methodology for reliability evaluation of composite generation and transmission systems, based on non-sequential Monte-Carlo simulation and artificial neural network concepts. Artificial neural network (ANN) techniques are used to classify the operating states during the Monte Carlo sampling. A polynomial network, named Group Method Data Handling (GMDH), is used and the states analyzed during the beginning of the simulation process are adequately selected as input data for training and test sets. Based on this procedure, a great number of success states are classified by a simple polynomial function, given by the ANN model, providing significant reductions in the computational cost. Moreover, all types of composite reliability indices (i.e. loss of load probability, frequency, duration and energy/power not supplied) can be assessed not only for the overall system but also for areas and buses. The proposed methodology is applied to the IEEE Reliability Test System (IEEE-RTS), to the IEEE-RTS 96 and to a configuration of the Brazilian South-Southeastern System.2017-11-17T10:06:57Z2007-01-01T00:00:00Z2007info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/3063engArmando M. Leite da SilvaVladimiro MirandaLeónidas ResendeLuiz Antônio Mansoinfo: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:RCAAP2023-05-15T10:20:57ZPortal AgregadorONG |
dc.title.none.fl_str_mv |
Composite Reliability Assessment Based on Monte Carlo Simulation and Artificial Neural Networks |
title |
Composite Reliability Assessment Based on Monte Carlo Simulation and Artificial Neural Networks |
spellingShingle |
Composite Reliability Assessment Based on Monte Carlo Simulation and Artificial Neural Networks Armando M. Leite da Silva |
title_short |
Composite Reliability Assessment Based on Monte Carlo Simulation and Artificial Neural Networks |
title_full |
Composite Reliability Assessment Based on Monte Carlo Simulation and Artificial Neural Networks |
title_fullStr |
Composite Reliability Assessment Based on Monte Carlo Simulation and Artificial Neural Networks |
title_full_unstemmed |
Composite Reliability Assessment Based on Monte Carlo Simulation and Artificial Neural Networks |
title_sort |
Composite Reliability Assessment Based on Monte Carlo Simulation and Artificial Neural Networks |
author |
Armando M. Leite da Silva |
author_facet |
Armando M. Leite da Silva Vladimiro Miranda Leónidas Resende Luiz Antônio Manso |
author_role |
author |
author2 |
Vladimiro Miranda Leónidas Resende Luiz Antônio Manso |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Armando M. Leite da Silva Vladimiro Miranda Leónidas Resende Luiz Antônio Manso |
description |
This paper presents a new methodology for reliability evaluation of composite generation and transmission systems, based on non-sequential Monte-Carlo simulation and artificial neural network concepts. Artificial neural network (ANN) techniques are used to classify the operating states during the Monte Carlo sampling. A polynomial network, named Group Method Data Handling (GMDH), is used and the states analyzed during the beginning of the simulation process are adequately selected as input data for training and test sets. Based on this procedure, a great number of success states are classified by a simple polynomial function, given by the ANN model, providing significant reductions in the computational cost. Moreover, all types of composite reliability indices (i.e. loss of load probability, frequency, duration and energy/power not supplied) can be assessed not only for the overall system but also for areas and buses. The proposed methodology is applied to the IEEE Reliability Test System (IEEE-RTS), to the IEEE-RTS 96 and to a configuration of the Brazilian South-Southeastern System. |
publishDate |
2007 |
dc.date.none.fl_str_mv |
2007-01-01T00:00:00Z 2007 2017-11-17T10:06:57Z |
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://repositorio.inesctec.pt/handle/123456789/3063 |
url |
http://repositorio.inesctec.pt/handle/123456789/3063 |
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 |
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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 |
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1777302473348743168 |