STARDUST II - Spatial and Temporal Assessment
of high Resolution Depth profiles Using novel Sampling Technologies The fate of pollutants in fluvial and marine sediments in cross-border zones
Influence of the harmonics on the modal behavior of wind turbine drivetrains
Gioia, N.; Daems, P.J.; Peeters, C.; El-Kafafy, M.; Guillaume, P.; Helsen, J. (2019). Influence of the harmonics on the modal behavior of wind turbine drivetrains, in: Di Maio, D. (Ed.) Rotating machinery, vibro-acoustics & laser vibrometry, volume 7. pp. 231-238. https://hdl.handle.net/10.1007/978-3-319-74693-7_22
In: Di Maio, D. (Ed.) (2019). Rotating machinery, vibro-acoustics & laser vibrometry, volume 7. Springer: Cham. ISBN 978-3-319-74692-0; e-ISBN 978-3-319-74693-7. VIII, 244 pp. https://hdl.handle.net/10.1007/978-3-319-74693-7
In the last decades, noise, vibration and harshness (NVH) problems became critical issues to be tackled by the wind industry. They have been caused by the upscaling trend that has imposed bigger (not quasi-static) loads on turbine subcomponents: the dynamic loads are significantly influencing the fatigue life of the wind turbine components and the tonalities generated. To validate complex simulation models, it is of high interest to continuously track the modal parameters of the fundamental modes of a wind turbine during operating conditions. At this purpose, operational modal analysis (OMA) represents a powerful tool.The work investigates and implements a completely automated OMA technique for continuously tracking the modes of a wind turbine drive train under normal operating conditions. The methodologies implemented are illustrated using data acquired during a long-term monitoring campaign of an offshore wind turbine. Modal estimation is based on the state-of-the-art pLSCF algorithm. To make it suitable for continuous analysis, the algorithm is improved by eliminating all the human interactions required. The procedure is then coupled with a method that automatically tracks the modal parameters along different data sets. Since this work focuses on the application of OMA on rotating machines, harmonics need to be dealt with. At this purpose, the use of a cepstrum lifter is analyzed and implemented. Modal estimates obtained from an automated analysis on stand still data and rotating turbine data are compared. The data coming from the rotating machine are pre-processed by means of a cepstrum-based procedure. It is shown that the automatic procedure is able to detect modes close to narrow harmonic components, while it still fails in case of broader harmonics. The cepstrum lifter is able to properly filter out also the harmonics influencing broader frequency bands, making OMA possible on the complete frequency band of interest.
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STARDUST II is a project funded by the INTERREG III A programme (France/Walloon Region/Flanders
French-Flemish subprogramme) of the European Community's Regional Development Fund.
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