Health condition assessment of wind turbine generators based on supervisory control and data acquisition data

Li, J; Li, Q; Zhu, J

Health condition assessment of wind turbine generators based on supervisory control and data acquisition data

Li, J Li, Q Zhu, J

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Publication Type:: Journal Article
Citation:: IET Renewable Power Generation, 2019, 13 (8), pp. 1343 - 1350
Issue Date:: 2019-06-10

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Field	Value	Language
dc.contributor.author	Li, J	en_US
dc.contributor.author	Li, Q	en_US
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047	en_US
dc.date.accessioned	2020-04-04T20:25:07Z
dc.date.available	2020-04-04T20:25:07Z
dc.date.issued	2019-06-10	en_US
dc.identifier.citation	IET Renewable Power Generation, 2019, 13 (8), pp. 1343 - 1350	en_US
dc.identifier.issn	1752-1416	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139799
dc.description.abstract	© 2019 The Institution of Engineering and Technology. As the running time of a wind turbine generator unit (WTGU) increases, the ageing and wear of its components will be aggravated gradually, which leads to deterioration of its operation condition. In order to ensure the safe and stable operation of WTGU and prolong its service life, it is of great significance to know the health condition of the WTGU and reasonably arrange maintenance. Based on the structure of the WTGU and the operation principle of the wind turbine generator (WTG), the condition assessment indexes of WTG were built, and the factors influencing the assessment index were determined. The relationship function between the assessment indexes and their influencing factors was established by using the least square support vector machine, which was used to determine the dynamic limitation of condition assessment index of WTG. The dynamic weight of each assessment index was used to characterise the influence of deterioration degree of different components on WTG conditions. Then the health conditions of WTG were evaluated using a similar cloud and fuzzy comprehensive assessment method separately. Finally, the proposed methods were verified by two examples including a directdrive permanent magnet wind generator and a doubly-fed wind generator.	en_US
dc.relation.ispartof	IET Renewable Power Generation	en_US
dc.relation.isbasedon	10.1049/iet-rpg.2018.5504	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Energy	en_US
dc.title	Health condition assessment of wind turbine generators based on supervisory control and data acquisition data	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.citation.volume	13	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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CCET - Centre for Clean Energy Technology
utslib.copyright.status	closed_access	*
pubs.issue	8	en_US
pubs.publication-status	Published	en_US
pubs.volume	13	en_US

Abstract:

© 2019 The Institution of Engineering and Technology. As the running time of a wind turbine generator unit (WTGU) increases, the ageing and wear of its components will be aggravated gradually, which leads to deterioration of its operation condition. In order to ensure the safe and stable operation of WTGU and prolong its service life, it is of great significance to know the health condition of the WTGU and reasonably arrange maintenance. Based on the structure of the WTGU and the operation principle of the wind turbine generator (WTG), the condition assessment indexes of WTG were built, and the factors influencing the assessment index were determined. The relationship function between the assessment indexes and their influencing factors was established by using the least square support vector machine, which was used to determine the dynamic limitation of condition assessment index of WTG. The dynamic weight of each assessment index was used to characterise the influence of deterioration degree of different components on WTG conditions. Then the health conditions of WTG were evaluated using a similar cloud and fuzzy comprehensive assessment method separately. Finally, the proposed methods were verified by two examples including a directdrive permanent magnet wind generator and a doubly-fed wind generator.

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http://hdl.handle.net/10453/139799