Hybrid method for power system state estimation

Autores
Risso, Mariano Angel; Rubiales, Aldo Jose; Lotito, Pablo Andres
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
State estimation in power systems is classically based on the weighted least squares method. Recently, different extensions of Kalman filters have been proposed. Among them, the 'unscented' Kalman filter (UKF) improves the results of weighted least squares methods, when there are small changes in the system, as it considers the history of the state. The novel algorithm presented in this work combines the best of both approaches. To perform this task a new index is defined to allow the algorithm to choose in real time, and for each iteration, between a static or a dynamic estimator. This combination allows overcoming the anomalies observed when the UKF faces abrupt variations of the system state and also the lack of observability that weighted least squares could present. The proposed methodology was tested with three test cases outperforming the previously mentioned algorithms.
Fil: Risso, Mariano Angel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina
Fil: Rubiales, Aldo Jose. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Lotito, Pablo Andres. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
STATE
ESTIMATION
KALMAN
FILTERING
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:11336/180221
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Hybrid method for power system state estimationRisso, Mariano AngelRubiales, Aldo JoseLotito, Pablo AndresSTATEESTIMATIONKALMANFILTERINGhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2State estimation in power systems is classically based on the weighted least squares method. Recently, different extensions of Kalman filters have been proposed. Among them, the 'unscented' Kalman filter (UKF) improves the results of weighted least squares methods, when there are small changes in the system, as it considers the history of the state. The novel algorithm presented in this work combines the best of both approaches. To perform this task a new index is defined to allow the algorithm to choose in real time, and for each iteration, between a static or a dynamic estimator. This combination allows overcoming the anomalies observed when the UKF faces abrupt variations of the system state and also the lack of observability that weighted least squares could present. The proposed methodology was tested with three test cases outperforming the previously mentioned algorithms.Fil: Risso, Mariano Angel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; ArgentinaFil: Rubiales, Aldo Jose. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Lotito, Pablo Andres. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaInstitution of Engineering and Technology2015-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/180221Risso, Mariano Angel; Rubiales, Aldo Jose; Lotito, Pablo Andres; Hybrid method for power system state estimation; Institution of Engineering and Technology; Iet Generation Transmission & Distribution; 9; 7; 4-2015; 636-6431751-8687CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2014.0836info:eu-repo/semantics/altIdentifier/doi/10.1049/iet-gtd.2014.0836info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:46:16Zoai:ri.conicet.gov.ar:11336/180221instacron: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:34982025-09-29 09:46:17.067CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Hybrid method for power system state estimation
title Hybrid method for power system state estimation
spellingShingle Hybrid method for power system state estimation
Risso, Mariano Angel
STATE
ESTIMATION
KALMAN
FILTERING
title_short Hybrid method for power system state estimation
title_full Hybrid method for power system state estimation
title_fullStr Hybrid method for power system state estimation
title_full_unstemmed Hybrid method for power system state estimation
title_sort Hybrid method for power system state estimation
dc.creator.none.fl_str_mv Risso, Mariano Angel
Rubiales, Aldo Jose
Lotito, Pablo Andres
author Risso, Mariano Angel
author_facet Risso, Mariano Angel
Rubiales, Aldo Jose
Lotito, Pablo Andres
author_role author
author2 Rubiales, Aldo Jose
Lotito, Pablo Andres
author2_role author
author
dc.subject.none.fl_str_mv STATE
ESTIMATION
KALMAN
FILTERING
topic STATE
ESTIMATION
KALMAN
FILTERING
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv State estimation in power systems is classically based on the weighted least squares method. Recently, different extensions of Kalman filters have been proposed. Among them, the 'unscented' Kalman filter (UKF) improves the results of weighted least squares methods, when there are small changes in the system, as it considers the history of the state. The novel algorithm presented in this work combines the best of both approaches. To perform this task a new index is defined to allow the algorithm to choose in real time, and for each iteration, between a static or a dynamic estimator. This combination allows overcoming the anomalies observed when the UKF faces abrupt variations of the system state and also the lack of observability that weighted least squares could present. The proposed methodology was tested with three test cases outperforming the previously mentioned algorithms.
Fil: Risso, Mariano Angel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina
Fil: Rubiales, Aldo Jose. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Lotito, Pablo Andres. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description State estimation in power systems is classically based on the weighted least squares method. Recently, different extensions of Kalman filters have been proposed. Among them, the 'unscented' Kalman filter (UKF) improves the results of weighted least squares methods, when there are small changes in the system, as it considers the history of the state. The novel algorithm presented in this work combines the best of both approaches. To perform this task a new index is defined to allow the algorithm to choose in real time, and for each iteration, between a static or a dynamic estimator. This combination allows overcoming the anomalies observed when the UKF faces abrupt variations of the system state and also the lack of observability that weighted least squares could present. The proposed methodology was tested with three test cases outperforming the previously mentioned algorithms.
publishDate 2015
dc.date.none.fl_str_mv 2015-04
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/11336/180221
Risso, Mariano Angel; Rubiales, Aldo Jose; Lotito, Pablo Andres; Hybrid method for power system state estimation; Institution of Engineering and Technology; Iet Generation Transmission & Distribution; 9; 7; 4-2015; 636-643
1751-8687
CONICET Digital
CONICET
url http://hdl.handle.net/11336/180221
identifier_str_mv Risso, Mariano Angel; Rubiales, Aldo Jose; Lotito, Pablo Andres; Hybrid method for power system state estimation; Institution of Engineering and Technology; Iet Generation Transmission & Distribution; 9; 7; 4-2015; 636-643
1751-8687
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2014.0836
info:eu-repo/semantics/altIdentifier/doi/10.1049/iet-gtd.2014.0836
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
application/pdf
dc.publisher.none.fl_str_mv Institution of Engineering and Technology
publisher.none.fl_str_mv Institution of Engineering and Technology
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
_version_ 1844613445693997056
score 13.070432

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