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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/52845
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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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1842269216357482496 |
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13.13397 |