IMIS | Flanders Marine Institute
 

Flanders Marine Institute

Platform for marine research

IMIS

Publications | Institutes | Persons | Datasets | Projects | Maps
[ report an error in this record ]basket (0): add | show Printer-friendly version

Evaluating different automated Operational Modal Analysis techniques for the continuous monitoring of offshore wind turbines
El-Kafafy, M.; Devriendt, C.; Weijtjens, W.; De Sitter, G.; Guillaume, P. (2014). Evaluating different automated Operational Modal Analysis techniques for the continuous 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. 313-329. hdl.handle.net/10.1007/978-3-319-04546-7_35
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
    Monitoring; Offshore wind turbine; Operational modal analysis; Model parameters estimators; Automated

Authors  Top 
  • El-Kafafy, M., more
  • Devriendt, C., more
  • Weijtjens, W., more
  • De Sitter, G., more
  • Guillaume, P., more

Abstract
    This paper will evaluate different automated operational modal analysis techniques for the continuous monitoring of offshore wind turbines. The experimental data has been obtained during a long-term monitoring campaign on an offshore wind turbine in the Belgian North Sea. State-of-the art operational modal analysis techniques and the use of appropriate vibration measurement equipment can provide accurate estimates of natural frequencies, damping ratios and mode shapes of offshore wind turbines. To allow a proper continuous monitoring the methods have been automated and their reliability improved. The advanced modal analysis tools, which will be used, include the poly-reference Least Squares Complex Frequency-domain estimator (pLSCF), commercially known as PolyMAX, the polyreference maximum likelihood estimator (pMLE), and the frequency-domain subspace identification (FSSI) technique. The robustness of these estimators with respect to a possible change in the implementation options that could be defined by the user (e.g. type of polynomial coefficients used, parameter constraint used…) will be investigated. In order to improve the automation of the techniques, an alternative representation for the stabilization charts as well as robust cluster algorithms will be presented.

All data in IMIS is subject to the VLIZ privacy policy Top | Authors