Application of nonlinear Kalman filters to the identification of customer phase connection in distribution grids

This paper presents a state estimation approach to address the problem of identifying the phase to which single-phase customers are connected in three-phase distribution grids. The proposed method performs Kalman filtering on the information provided simultaneously by the smart meter of every custom...

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Detalles Bibliográficos
Autores: González Cagigal, Miguel Ángel, Rosendo Macías, José Antonio, Gómez Expósito, Antonio
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2021
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/154812
Acceso en línea:https://hdl.handle.net/11441/154812
https://doi.org/10.1016/j.ijepes.2020.106410
Access Level:acceso abierto
Palabra clave:Ensemble Kalman filter
Cubature Kalman Filter
Unscented Kalman Filter
Phase identification
State estimation
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spelling Application of nonlinear Kalman filters to the identification of customer phase connection in distribution gridsGonzález Cagigal, Miguel ÁngelRosendo Macías, José AntonioGómez Expósito, AntonioEnsemble Kalman filterCubature Kalman FilterUnscented Kalman FilterPhase identificationState estimationThis paper presents a state estimation approach to address the problem of identifying the phase to which single-phase customers are connected in three-phase distribution grids. The proposed method performs Kalman filtering on the information provided simultaneously by the smart meter of every customer and the aggregated energy consumption measured at each phase of the secondary substation feeding the set of customers. Different nonlinear formulations of the Kalman filter are tested and their performance compared, showing that the ensemble Kalman filter provides better estimation results when the system size increases. The accuracy, robustness and limitations of the estimator are also tested when measurement errors are considered.ElsevierIngeniería EléctricaTEP196: Sistemas de Energía EléctricaMinisterio de Educación y Formación Profesional. España2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/154812https://doi.org/10.1016/j.ijepes.2020.106410reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésInternationa Journal of Electrical Power and Energy Systems, 125, 106410.FPU17/06380PI1897/12/2019https://www.sciencedirect.com/science/article/pii/S0142061519344138info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1548122026-06-17T12:51:07Z
dc.title.none.fl_str_mv Application of nonlinear Kalman filters to the identification of customer phase connection in distribution grids
title Application of nonlinear Kalman filters to the identification of customer phase connection in distribution grids
spellingShingle Application of nonlinear Kalman filters to the identification of customer phase connection in distribution grids
González Cagigal, Miguel Ángel
Ensemble Kalman filter
Cubature Kalman Filter
Unscented Kalman Filter
Phase identification
State estimation
title_short Application of nonlinear Kalman filters to the identification of customer phase connection in distribution grids
title_full Application of nonlinear Kalman filters to the identification of customer phase connection in distribution grids
title_fullStr Application of nonlinear Kalman filters to the identification of customer phase connection in distribution grids
title_full_unstemmed Application of nonlinear Kalman filters to the identification of customer phase connection in distribution grids
title_sort Application of nonlinear Kalman filters to the identification of customer phase connection in distribution grids
dc.creator.none.fl_str_mv González Cagigal, Miguel Ángel
Rosendo Macías, José Antonio
Gómez Expósito, Antonio
author González Cagigal, Miguel Ángel
author_facet González Cagigal, Miguel Ángel
Rosendo Macías, José Antonio
Gómez Expósito, Antonio
author_role author
author2 Rosendo Macías, José Antonio
Gómez Expósito, Antonio
author2_role author
author
dc.contributor.none.fl_str_mv Ingeniería Eléctrica
TEP196: Sistemas de Energía Eléctrica
Ministerio de Educación y Formación Profesional. España
dc.subject.none.fl_str_mv Ensemble Kalman filter
Cubature Kalman Filter
Unscented Kalman Filter
Phase identification
State estimation
topic Ensemble Kalman filter
Cubature Kalman Filter
Unscented Kalman Filter
Phase identification
State estimation
description This paper presents a state estimation approach to address the problem of identifying the phase to which single-phase customers are connected in three-phase distribution grids. The proposed method performs Kalman filtering on the information provided simultaneously by the smart meter of every customer and the aggregated energy consumption measured at each phase of the secondary substation feeding the set of customers. Different nonlinear formulations of the Kalman filter are tested and their performance compared, showing that the ensemble Kalman filter provides better estimation results when the system size increases. The accuracy, robustness and limitations of the estimator are also tested when measurement errors are considered.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/154812
https://doi.org/10.1016/j.ijepes.2020.106410
url https://hdl.handle.net/11441/154812
https://doi.org/10.1016/j.ijepes.2020.106410
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Internationa Journal of Electrical Power and Energy Systems, 125, 106410.
FPU17/06380
PI1897/12/2019
https://www.sciencedirect.com/science/article/pii/S0142061519344138
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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