Distributed dynamic state estimation over a lossy communication network with an application to smart grids

Rana, MM; Li, L; Su, SW

Distributed dynamic state estimation over a lossy communication network with an application to smart grids

Rana, MM

Li, L

Su, SW

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Publication Type:: Conference Proceeding
Citation:: 2016 IEEE 55th Conference on Decision and Control, CDC 2016, 2016, pp. 6657 - 6662
Issue Date:: 2016-12-27

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Field	Value	Language
dc.contributor.author	Rana, MM https://orcid.org/0000-0003-2317-6327	en_US
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216	en_US
dc.contributor.author	Su, SW https://orcid.org/0000-0002-5720-8852	en_US
dc.date.issued	2016-12-27	en_US
dc.identifier.citation	2016 IEEE 55th Conference on Decision and Control, CDC 2016, 2016, pp. 6657 - 6662	en_US
dc.identifier.isbn	9781509018376	en_US
dc.identifier.uri	http://hdl.handle.net/10453/79288
dc.description.abstract	© 2016 IEEE. In contrast to the traditional centralised power system state estimation methods, this paper investigates the interconnected optimal filtering problem for distributed dynamic state estimation considering packet losses. Specifically, the power system incorporating microgrids is modelled as a state-space linear equation where sensors are deployed to obtain measurements. Basically, the sensing information is transmitted to the energy management system through a lossy communication network where measurements are lost. As the system states are unavailable, so the estimation is essential to know the overall operating conditions of the electricity network. The proposed estimator is based on the mean squared error between the actual state and its estimate. To obtain the distributed estimation, the optimal local and neighbouring gains are computed to reach a consensus estimation after exchanging their information with the neighbouring estimators. Then the convergence of the developed algorithm is theoretically proved. Afterwards, a distributed controller is designed based on the semidefinite programming approach. Simulation results demonstrate the accuracy of the developed approaches under the condition of missing measurements.	en_US
dc.relation.ispartof	2016 IEEE 55th Conference on Decision and Control, CDC 2016	en_US
dc.relation.isbasedon	10.1109/CDC.2016.7799294	en_US
dc.title	Distributed dynamic state estimation over a lossy communication network with an application to smart grids	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. In contrast to the traditional centralised power system state estimation methods, this paper investigates the interconnected optimal filtering problem for distributed dynamic state estimation considering packet losses. Specifically, the power system incorporating microgrids is modelled as a state-space linear equation where sensors are deployed to obtain measurements. Basically, the sensing information is transmitted to the energy management system through a lossy communication network where measurements are lost. As the system states are unavailable, so the estimation is essential to know the overall operating conditions of the electricity network. The proposed estimator is based on the mean squared error between the actual state and its estimate. To obtain the distributed estimation, the optimal local and neighbouring gains are computed to reach a consensus estimation after exchanging their information with the neighbouring estimators. Then the convergence of the developed algorithm is theoretically proved. Afterwards, a distributed controller is designed based on the semidefinite programming approach. Simulation results demonstrate the accuracy of the developed approaches under the condition of missing measurements.

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http://hdl.handle.net/10453/79288