Distributed dynamic state estimation considering renewable generation and packet losses

Rana, MM; Li, L; Su, SW

Distributed dynamic state estimation considering renewable generation and packet losses

Rana, MM

Li, L

Su, SW

Permalink

Publication Type:: Conference Proceeding
Citation:: 2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016, 2017
Issue Date:: 2017-01-31

Closed Access

	Filename	Description	Size
	07838784.pdf	Published version	143.12 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

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	2017-01-31	en_US
dc.identifier.citation	2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016, 2017	en_US
dc.identifier.isbn	9781509035496	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126598
dc.description.abstract	© 2016 IEEE. The penetration of renewable distributed energy resources such as wind turbine has been dramatically integrated in distribution networks due to clean, sustainable, and economical green energy. Due to intermittent wind speed, the power generation patterns vary which can risk the distribution network operation. So, it is intrinsically required to monitor the wind turbines in a distributed way. This paper presents an adaptive-then-combine distributed dynamic approach for monitoring the grid under lossy communication link between the wind turbines and energy management system. Firstly, the wind turbine is represented by a state-space linear equation, with sensors deployed to obtain system state information. Based on the mean squared error principle, an adaptive approach is proposed to estimate the local state information. The global estimation is designed by combining estimation results with weighting factors which are calculated by minimizing estimation error covariance based on semi-definite programming. Finally, the convergence analysis indicates that estimation error is gradually decreased, so the estimated state converges to the actual state. The efficacy of the developed approach is verified using the turbine model. The research is valuable for green energy technologies, households and interconnected industrial information societies, and also provides the knowledge towards a smart and secure green energy future.	en_US
dc.relation.ispartof	2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016	en_US
dc.relation.isbasedon	10.1109/ICARCV.2016.7838784	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Distributed dynamic state estimation considering renewable generation and packet losses	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0907 Environmental 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	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. The penetration of renewable distributed energy resources such as wind turbine has been dramatically integrated in distribution networks due to clean, sustainable, and economical green energy. Due to intermittent wind speed, the power generation patterns vary which can risk the distribution network operation. So, it is intrinsically required to monitor the wind turbines in a distributed way. This paper presents an adaptive-then-combine distributed dynamic approach for monitoring the grid under lossy communication link between the wind turbines and energy management system. Firstly, the wind turbine is represented by a state-space linear equation, with sensors deployed to obtain system state information. Based on the mean squared error principle, an adaptive approach is proposed to estimate the local state information. The global estimation is designed by combining estimation results with weighting factors which are calculated by minimizing estimation error covariance based on semi-definite programming. Finally, the convergence analysis indicates that estimation error is gradually decreased, so the estimated state converges to the actual state. The efficacy of the developed approach is verified using the turbine model. The research is valuable for green energy technologies, households and interconnected industrial information societies, and also provides the knowledge towards a smart and secure green energy future.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/126598