Long-term modal behaviour assessment of wind turbine drivetrains
Daems, P.-J.; Gioia, N.; Peeters, C.; Helsen, T.; Guillaume, P. (2019). Long-term modal behaviour assessment of wind turbine drivetrains, in: Amador, S.D.R. et al. 8th International Operational Modal Analysis Conference (IOMAC 2019). pp. 749-755
In: Amador, S.D.R. et al. (2019). 8th International Operational Modal Analysis Conference (IOMAC 2019). International Operational Modal Analysis Conference ( IOMAC ): Gijón. ISBN 9781510888333. 778 pp., meer
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| Beschikbaar in | Auteurs |
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Documenttype: Congresbijdrage
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| Author keywords |
Automatic Operational Modal Analysis; Wind turbine drivetrain; Harmonics; Machine learning; Noise Vibration and Harshness |
| Auteurs | | Top |
- Daems, P.-J., meer
- Gioia, N.
- Peeters, C.
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- Helsen, T.
- Guillaume, P., meer
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| Abstract |
Over the last years Noise, Vibration and Harshness (NVH) problems have become important drivers in the design of wind turbine drivetrains. The knowledge of an accurate modal model is critical to tackle these issues. In this context, Operational Modal Analysis (OMA) is a commonly used method, as it allows to characterize the dynamic response behaviour of machines for their most important operating points. One of the most stringent limitations at present day is that processing vibration data classically requires user interaction. This paper investigates an automated OMA methodology. Long-term data of an offshore wind turbine will be processed to illustrate the developed automated algorithms. As vibration data of rotating machines is processed, there needs to be dealt with harmonic content. To this end, cepstral liftering and Order Based Modal Analysis (OBMA) will be used. After this pre-processing step, a p-LSCF algorithm will be used to perform the parameter estimation. Finally, the modal estimates will automatically be tracked to monitor their evolution in order to be able to deduce correlations between the operating regime of the turbine and the dynamic behaviour. |
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