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Condition monitoring and fault diagnosis of a wind turbine synchronous generator drive train

Yang, W.; Tavner, P.J.; Wilkinson, M.R.

Authors

W. Yang

P.J. Tavner

M.R. Wilkinson



Abstract

Some large grid connected wind turbines use a low-speed synchronous generator, directly coupled to the turbine, and a fully rated converter to transform power from the turbine to mains electricity. The condition monitoring and diagnosis of mechanical and electrical faults in such a machine are considered, bearing in mind that it has a slow variable speed and is subject to the stochastic, aerodynamic effects of the wind. The application of wavelet transforms is investigated in the light of the disadvantages of spectral analysis in processing signals subject to such stochastic effects. The technique can be used to monitor generator electrical and drive train mechanical faults. It is validated experimentally on a wind turbine condition monitoring test rig using a three-phase, permanent-magnet, slow-speed, synchronous generator, driven by a motor controlled by a model representing the aerodynamic forces from a wind turbine. The possibility of detecting mechanical and electrical faults in wind turbines by electrical signal and particularly power analysis is heralded.

Citation

Yang, W., Tavner, P., & Wilkinson, M. (2009). Condition monitoring and fault diagnosis of a wind turbine synchronous generator drive train. IET Renewable Power Generation, 3(1), 1-11. https://doi.org/10.1049/iet-rpg%3A20080006

Journal Article Type Article
Publication Date Mar 1, 2009
Deposit Date Apr 6, 2010
Journal IET Renewable Power Generation
Print ISSN 1752-1416
Electronic ISSN 1752-1424
Publisher Institution of Engineering and Technology (IET)
Peer Reviewed Peer Reviewed
Volume 3
Issue 1
Pages 1-11
DOI https://doi.org/10.1049/iet-rpg%3A20080006
Keywords Condition monitoring, Fault diagnosis, Synchronous generator drive train, Low-speed synchronous generator, Wind turbines, Electrical faults, Mechanical faults, Aerodynamic effects, Wavelet transforms, Spectral analysis, Stochastic effects.