Dynamic state estimation for power networks using distributed MAP technique

Autores
Sun, Yibing; Fu, Minyue; Wang, Bingchang; Zhang, Huanshui; Marelli, Damian Edgardo
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper studies a distributed state estimation problem for a network of linear dynamic systems (called nodes), which evolve autonomously, but their measurements are coupled through neighborhood interactions. Power networks are typical networked systems obeying such features, with other examples including traffic networks, sensor networks and many multi-agent systems. We develop a new distributed state estimation approach, for each node to update its local state. The core of this distributed approach is a distributed maximum a posteriori (MAP) estimation technique, which delivers a globally optimal estimate under certain assumptions. We apply the distributed approach to an IEEE 118-bus system, and compare it with a centralized approach, which provides the optimal state estimate using all the measurements, and with a local state estimation approach, which uses only local measurements to estimate local states. Simulation results show that under different scenarios including normal operation, bad measurements and sudden load change, the distributed approach is clearly more accurate than the local state estimation approach and distributed static state estimation approach. Although the result is a bit less accurate than that by a centralized algorithm, the distributed algorithm enjoys low computational complexity and communication load, and is scalable to large power networks.
Fil: Sun, Yibing. Shandong University; China
Fil: Fu, Minyue. Universidad de Newcastle; Australia. Guangdong University of Technology; China
Fil: Wang, Bingchang. Shandong University; China
Fil: Zhang, Huanshui. Shandong University; China
Fil: Marelli, Damian Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina. Guangdong University of Technology; China
Materia
Distributed Map Estimation
Distributed State Estimation
Kalman Filter
Power Systems
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:11336/52845

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network_name_str CONICET Digital (CONICET)
spelling Dynamic state estimation for power networks using distributed MAP techniqueSun, YibingFu, MinyueWang, BingchangZhang, HuanshuiMarelli, Damian EdgardoDistributed Map EstimationDistributed State EstimationKalman FilterPower Systemshttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2This paper studies a distributed state estimation problem for a network of linear dynamic systems (called nodes), which evolve autonomously, but their measurements are coupled through neighborhood interactions. Power networks are typical networked systems obeying such features, with other examples including traffic networks, sensor networks and many multi-agent systems. We develop a new distributed state estimation approach, for each node to update its local state. The core of this distributed approach is a distributed maximum a posteriori (MAP) estimation technique, which delivers a globally optimal estimate under certain assumptions. We apply the distributed approach to an IEEE 118-bus system, and compare it with a centralized approach, which provides the optimal state estimate using all the measurements, and with a local state estimation approach, which uses only local measurements to estimate local states. Simulation results show that under different scenarios including normal operation, bad measurements and sudden load change, the distributed approach is clearly more accurate than the local state estimation approach and distributed static state estimation approach. Although the result is a bit less accurate than that by a centralized algorithm, the distributed algorithm enjoys low computational complexity and communication load, and is scalable to large power networks.Fil: Sun, Yibing. Shandong University; ChinaFil: Fu, Minyue. Universidad de Newcastle; Australia. Guangdong University of Technology; ChinaFil: Wang, Bingchang. Shandong University; ChinaFil: Zhang, Huanshui. Shandong University; ChinaFil: Marelli, Damian Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina. Guangdong University of Technology; ChinaPergamon-Elsevier Science Ltd2016-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/52845Sun, Yibing; Fu, Minyue; Wang, Bingchang; Zhang, Huanshui; Marelli, Damian Edgardo; Dynamic state estimation for power networks using distributed MAP technique; Pergamon-Elsevier Science Ltd; Automatica; 73; 11-2016; 27-370005-1098CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.automatica.2016.06.015info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0005109816302424info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:53:19Zoai:ri.conicet.gov.ar:11336/52845instacron: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 09:53:19.51CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Dynamic state estimation for power networks using distributed MAP technique
title Dynamic state estimation for power networks using distributed MAP technique
spellingShingle Dynamic state estimation for power networks using distributed MAP technique
Sun, Yibing
Distributed Map Estimation
Distributed State Estimation
Kalman Filter
Power Systems
title_short Dynamic state estimation for power networks using distributed MAP technique
title_full Dynamic state estimation for power networks using distributed MAP technique
title_fullStr Dynamic state estimation for power networks using distributed MAP technique
title_full_unstemmed Dynamic state estimation for power networks using distributed MAP technique
title_sort Dynamic state estimation for power networks using distributed MAP technique
dc.creator.none.fl_str_mv Sun, Yibing
Fu, Minyue
Wang, Bingchang
Zhang, Huanshui
Marelli, Damian Edgardo
author Sun, Yibing
author_facet Sun, Yibing
Fu, Minyue
Wang, Bingchang
Zhang, Huanshui
Marelli, Damian Edgardo
author_role author
author2 Fu, Minyue
Wang, Bingchang
Zhang, Huanshui
Marelli, Damian Edgardo
author2_role author
author
author
author
dc.subject.none.fl_str_mv Distributed Map Estimation
Distributed State Estimation
Kalman Filter
Power Systems
topic Distributed Map Estimation
Distributed State Estimation
Kalman Filter
Power Systems
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv This paper studies a distributed state estimation problem for a network of linear dynamic systems (called nodes), which evolve autonomously, but their measurements are coupled through neighborhood interactions. Power networks are typical networked systems obeying such features, with other examples including traffic networks, sensor networks and many multi-agent systems. We develop a new distributed state estimation approach, for each node to update its local state. The core of this distributed approach is a distributed maximum a posteriori (MAP) estimation technique, which delivers a globally optimal estimate under certain assumptions. We apply the distributed approach to an IEEE 118-bus system, and compare it with a centralized approach, which provides the optimal state estimate using all the measurements, and with a local state estimation approach, which uses only local measurements to estimate local states. Simulation results show that under different scenarios including normal operation, bad measurements and sudden load change, the distributed approach is clearly more accurate than the local state estimation approach and distributed static state estimation approach. Although the result is a bit less accurate than that by a centralized algorithm, the distributed algorithm enjoys low computational complexity and communication load, and is scalable to large power networks.
Fil: Sun, Yibing. Shandong University; China
Fil: Fu, Minyue. Universidad de Newcastle; Australia. Guangdong University of Technology; China
Fil: Wang, Bingchang. Shandong University; China
Fil: Zhang, Huanshui. Shandong University; China
Fil: Marelli, Damian Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina. Guangdong University of Technology; China
description This paper studies a distributed state estimation problem for a network of linear dynamic systems (called nodes), which evolve autonomously, but their measurements are coupled through neighborhood interactions. Power networks are typical networked systems obeying such features, with other examples including traffic networks, sensor networks and many multi-agent systems. We develop a new distributed state estimation approach, for each node to update its local state. The core of this distributed approach is a distributed maximum a posteriori (MAP) estimation technique, which delivers a globally optimal estimate under certain assumptions. We apply the distributed approach to an IEEE 118-bus system, and compare it with a centralized approach, which provides the optimal state estimate using all the measurements, and with a local state estimation approach, which uses only local measurements to estimate local states. Simulation results show that under different scenarios including normal operation, bad measurements and sudden load change, the distributed approach is clearly more accurate than the local state estimation approach and distributed static state estimation approach. Although the result is a bit less accurate than that by a centralized algorithm, the distributed algorithm enjoys low computational complexity and communication load, and is scalable to large power networks.
publishDate 2016
dc.date.none.fl_str_mv 2016-11
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/11336/52845
Sun, Yibing; Fu, Minyue; Wang, Bingchang; Zhang, Huanshui; Marelli, Damian Edgardo; Dynamic state estimation for power networks using distributed MAP technique; Pergamon-Elsevier Science Ltd; Automatica; 73; 11-2016; 27-37
0005-1098
CONICET Digital
CONICET
url http://hdl.handle.net/11336/52845
identifier_str_mv Sun, Yibing; Fu, Minyue; Wang, Bingchang; Zhang, Huanshui; Marelli, Damian Edgardo; Dynamic state estimation for power networks using distributed MAP technique; Pergamon-Elsevier Science Ltd; Automatica; 73; 11-2016; 27-37
0005-1098
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.automatica.2016.06.015
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0005109816302424
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 Pergamon-Elsevier Science Ltd
publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
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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