Distributed condition monitoring of renewable microgrids using adaptive-then-combine algorithm

Rana, MM; Li, L; Su, SW

Distributed condition monitoring of renewable microgrids using adaptive-then-combine algorithm

Rana, MM

Li, L

Su, SW

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Publication Type:: Conference Proceeding
Citation:: IEEE Power and Energy Society General Meeting, 2016, 2016-November
Issue Date:: 2016-11-10

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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.available	2016-02-18	en_US
dc.date.issued	2016-11-10	en_US
dc.identifier.citation	IEEE Power and Energy Society General Meeting, 2016, 2016-November	en_US
dc.identifier.isbn	9781509041688	en_US
dc.identifier.issn	1944-9925	en_US
dc.identifier.uri	http://hdl.handle.net/10453/96768
dc.description.abstract	© 2016 IEEE. This paper explores the problem of distributed state estimation including packet losses for the environment-friendly renewable microgrid incorporating electricity generating circuits. The problem is becoming critical due to the global warming, increasing green house gas emissions, and practical infeasibility with computational burden of the large-scale centralized power system monitoring. To address the impending problem, a novel distributed microgrid state estimation algorithm is derived in the context of microgrids. Specifically, after modelling the microgrid, this paper proposes a local microgrid state estimation algorithm considering packet losses. Then a novel optimal weighting factor calculation method for the global state estimation is proposed. Particularly, it can automatically adjust the optimal weighting factors for different sensor measurements based on the observation quality, improving the estimation accuracy of the global estimation. Simulations show that the desired state estimation accuracy is achievable.	en_US
dc.relation.ispartof	IEEE Power and Energy Society General Meeting	en_US
dc.relation.isbasedon	10.1109/PESGM.2016.7741544	en_US
dc.title	Distributed condition monitoring of renewable microgrids using adaptive-then-combine algorithm	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2016-November	en_US
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
pubs.volume	2016-November	en_US

Abstract:

© 2016 IEEE. This paper explores the problem of distributed state estimation including packet losses for the environment-friendly renewable microgrid incorporating electricity generating circuits. The problem is becoming critical due to the global warming, increasing green house gas emissions, and practical infeasibility with computational burden of the large-scale centralized power system monitoring. To address the impending problem, a novel distributed microgrid state estimation algorithm is derived in the context of microgrids. Specifically, after modelling the microgrid, this paper proposes a local microgrid state estimation algorithm considering packet losses. Then a novel optimal weighting factor calculation method for the global state estimation is proposed. Particularly, it can automatically adjust the optimal weighting factors for different sensor measurements based on the observation quality, improving the estimation accuracy of the global estimation. Simulations show that the desired state estimation accuracy is achievable.

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http://hdl.handle.net/10453/96768