Critical Contingencies Ranking for Dynamic Security Assessment Using Neural Networks

Autores
Schweickardt, Gustavo Alejandro; Gimenez Alvarez, Juan Manuel
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A number of contingencies simulated during dynamic security assessment do not generate unacceptable values of power system state variables, due to their small influence on system operation. Their exclusion from the set of contingencies to be simulated in the security assessment would achieve a significant reduction in computation time. This paper defines a critical contingencies selection method for on-line dynamic security assessment. The selection method results from an off-line dynamical analysis, which covers typical scenarios and also covers various related aspects like frequency, voltage, and angle analyses among others. Indexes measured over these typical scenarios are used to train neural networks, capable of performing on-line estimation of a critical contingencies list according to the system state.
Fil: Schweickardt, Gustavo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Fundación Bariloche. Instituto de Economía Energetica; Argentina
Fil: Gimenez Alvarez, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería; Argentina
Materia
CRITICAL CONTINGENCIES
DYNAMIC SECURITY ASSESSMENT
NEURAL NETWORKS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/216609
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Critical Contingencies Ranking for Dynamic Security Assessment Using Neural NetworksSchweickardt, Gustavo AlejandroGimenez Alvarez, Juan ManuelCRITICAL CONTINGENCIESDYNAMIC SECURITY ASSESSMENTNEURAL NETWORKShttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2https://purl.org/becyt/ford/5.2https://purl.org/becyt/ford/5A number of contingencies simulated during dynamic security assessment do not generate unacceptable values of power system state variables, due to their small influence on system operation. Their exclusion from the set of contingencies to be simulated in the security assessment would achieve a significant reduction in computation time. This paper defines a critical contingencies selection method for on-line dynamic security assessment. The selection method results from an off-line dynamical analysis, which covers typical scenarios and also covers various related aspects like frequency, voltage, and angle analyses among others. Indexes measured over these typical scenarios are used to train neural networks, capable of performing on-line estimation of a critical contingencies list according to the system state.Fil: Schweickardt, Gustavo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Fundación Bariloche. Instituto de Economía Energetica; ArgentinaFil: Gimenez Alvarez, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería; ArgentinaDavid Publishing Company2012-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/216609Schweickardt, Gustavo Alejandro; Gimenez Alvarez, Juan Manuel; Critical Contingencies Ranking for Dynamic Security Assessment Using Neural Networks; David Publishing Company; Journal of Energy and Power Engineering; 6; 10; 10-2012; 1663-16721934-8975CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.davidpublisher.com/index.php/Home/Article/index?id=19666.htmlinfo:eu-repo/semantics/altIdentifier/doi/10.17265/1934-8975/2012.10.016info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:50:52Zoai:ri.conicet.gov.ar:11336/216609instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:50:52.564CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Critical Contingencies Ranking for Dynamic Security Assessment Using Neural Networks
title Critical Contingencies Ranking for Dynamic Security Assessment Using Neural Networks
spellingShingle Critical Contingencies Ranking for Dynamic Security Assessment Using Neural Networks
Schweickardt, Gustavo Alejandro
CRITICAL CONTINGENCIES
DYNAMIC SECURITY ASSESSMENT
NEURAL NETWORKS
title_short Critical Contingencies Ranking for Dynamic Security Assessment Using Neural Networks
title_full Critical Contingencies Ranking for Dynamic Security Assessment Using Neural Networks
title_fullStr Critical Contingencies Ranking for Dynamic Security Assessment Using Neural Networks
title_full_unstemmed Critical Contingencies Ranking for Dynamic Security Assessment Using Neural Networks
title_sort Critical Contingencies Ranking for Dynamic Security Assessment Using Neural Networks
dc.creator.none.fl_str_mv Schweickardt, Gustavo Alejandro
Gimenez Alvarez, Juan Manuel
author Schweickardt, Gustavo Alejandro
author_facet Schweickardt, Gustavo Alejandro
Gimenez Alvarez, Juan Manuel
author_role author
author2 Gimenez Alvarez, Juan Manuel
author2_role author
dc.subject.none.fl_str_mv CRITICAL CONTINGENCIES
DYNAMIC SECURITY ASSESSMENT
NEURAL NETWORKS
topic CRITICAL CONTINGENCIES
DYNAMIC SECURITY ASSESSMENT
NEURAL NETWORKS
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
https://purl.org/becyt/ford/5.2
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv A number of contingencies simulated during dynamic security assessment do not generate unacceptable values of power system state variables, due to their small influence on system operation. Their exclusion from the set of contingencies to be simulated in the security assessment would achieve a significant reduction in computation time. This paper defines a critical contingencies selection method for on-line dynamic security assessment. The selection method results from an off-line dynamical analysis, which covers typical scenarios and also covers various related aspects like frequency, voltage, and angle analyses among others. Indexes measured over these typical scenarios are used to train neural networks, capable of performing on-line estimation of a critical contingencies list according to the system state.
Fil: Schweickardt, Gustavo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Fundación Bariloche. Instituto de Economía Energetica; Argentina
Fil: Gimenez Alvarez, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería; Argentina
description A number of contingencies simulated during dynamic security assessment do not generate unacceptable values of power system state variables, due to their small influence on system operation. Their exclusion from the set of contingencies to be simulated in the security assessment would achieve a significant reduction in computation time. This paper defines a critical contingencies selection method for on-line dynamic security assessment. The selection method results from an off-line dynamical analysis, which covers typical scenarios and also covers various related aspects like frequency, voltage, and angle analyses among others. Indexes measured over these typical scenarios are used to train neural networks, capable of performing on-line estimation of a critical contingencies list according to the system state.
publishDate 2012
dc.date.none.fl_str_mv 2012-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/216609
Schweickardt, Gustavo Alejandro; Gimenez Alvarez, Juan Manuel; Critical Contingencies Ranking for Dynamic Security Assessment Using Neural Networks; David Publishing Company; Journal of Energy and Power Engineering; 6; 10; 10-2012; 1663-1672
1934-8975
CONICET Digital
CONICET
url http://hdl.handle.net/11336/216609
identifier_str_mv Schweickardt, Gustavo Alejandro; Gimenez Alvarez, Juan Manuel; Critical Contingencies Ranking for Dynamic Security Assessment Using Neural Networks; David Publishing Company; Journal of Energy and Power Engineering; 6; 10; 10-2012; 1663-1672
1934-8975
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.davidpublisher.com/index.php/Home/Article/index?id=19666.html
info:eu-repo/semantics/altIdentifier/doi/10.17265/1934-8975/2012.10.016
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv David Publishing Company
publisher.none.fl_str_mv David Publishing Company
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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