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Cited by 192

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

Open access
Yang, W., Tavner, P.J., Wilkinson, M.R.·2009·IET Renewable Power Generation

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.

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Crossref · 10.1049/iet-rpg:20080006

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Metadata

Authors
Yang, W., Tavner, P. J., & Wilkinson, M. R.
Venue
IET Renewable Power Generation
Published
2009-03-09
Volume
3
Issue
1
Pages
1-11
ISSN
  • 1752-1416 (ISSN-L)
  • 1752-1424
OpenAlex
W2015969773
Cited by
192
References
1
FWCI
8.64
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Condition monitoring and fault diagnosis of a wind turbine synchronous generator drive train. https://app.cochat.ai/papers/10.1049/iet-rpg:20080006
The paper
Yang, W., Tavner, P. J., & Wilkinson, M. R. (2009). Condition monitoring and fault diagnosis of a wind turbine synchronous generator drive train. IET Renewable Power Generation. https://doi.org/10.1049/iet-rpg%3A20080006

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.