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Removing the influence of rotor harmonics for improved monitoring of offshore wind turbines
Manzato, S.; Devriendt, C.; Weijtjens, W.; Di Lorenzo, E.; Peeters, B.; Guillaume, P. (2014). Removing the influence of rotor harmonics for improved monitoring of offshore wind turbines, in: Catbas, F.N. (Ed.) Dynamics of Civil Structures, Volume 4. Proceedings of the 32nd IMAC, a conference and exposition on structural dynamics, 2014. pp. 299-312. hdl.handle.net/10.1007/978-3-319-04546-7_34
In: Catbas, F.N. (Ed.) (2014). Dynamics of Civil Structures, Volume 4. Proceedings of the 32nd IMAC, a conference and exposition on structural dynamics, 2014. Springer International Publishing: [s.l.]. ISBN 978-3-319-04545-0. hdl.handle.net/10.1007/978-3-319-04546-7, more

Available in  Authors 
Document type: Conference paper

Keyword
    Marine
Author keywords
    Wind turbine; Monitoring; Offshore; Automated modal analysis; Harmonic removal

Authors  Top 
  • Manzato, S.
  • Devriendt, C., more
  • Weijtjens, W., more
  • Di Lorenzo, E.
  • Peeters, B.
  • Guillaume, P., more

Abstract
    The ability to identify the dynamic properties of offshore wind turbines allows validating and updating numerical tools, which can be used to enhance the design. At the same time, these dynamic parameters can serve as a basis to continuously monitor the integrity of the machine. However, modal identification of turbines in operating conditions still poses some major issues, in particular in removing the rotor harmonic components, which are polluting the measured signals.This paper will present and discuss some recent developments for removing harmonic components from operational wind turbine data. The possibility to track the evolution of specific modes is compared against classical techniques such as Time Synchronous Averaging and Cepstrum, which show limitations due to rotational speed fluctuations, amplitude modulation of the harmonic components and the interaction between the harmonics and the aerodynamic loads. The methodologies are firstly presented and then applied to real data of an offshore wind turbine installed in the Belgian North Sea. The ability to identify more accurately the modal parameters will allow improving the correlation with the varying environmental conditions and provide additional input data to validate numerical models.

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