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Kalman filter based strain estimation for fatigue assessment of an offshore monopile wind turbine
Maes, K.; De Roeck, G.; Iliopoulos, A.; Weijtjens, W.; Devriendt, C.; Lombaert, G. (2016). Kalman filter based strain estimation for fatigue assessment of an offshore monopile wind turbine, in: Sas, P. et al. (Ed.) Proceedings of ISMA 2016 International Conference on Noise and Vibration Engineering and USD2016 International Conference on Uncertainty in Structural Dynamics. pp. 1649-1661
In: Sas, P. et al. (Ed.) (2016). Proceedings of ISMA 2016 International Conference on Noise and Vibration Engineering and USD2016 International Conference on Uncertainty in Structural Dynamics. KU Leuven, Departement Werktuigkunde: Leuven. ISBN 9789073802940. , meer
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| Beschikbaar in | Auteurs |
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Documenttype: Congresbijdrage
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| Abstract |
This paper presents an application of a Kalman filtering algorithm for strain estimation in the tower of an offshore monopile wind turbine in the Belgian North Sea. Real measured data obtained from in situ measurements are used in the validation study. The system model used in the strain estimation is constructed based on the mode shapes and natural frequencies obtained from a finite element model of the structure and the damping characteristics that are obtained from a prior output-only operational modal analysis (OMA) of the tower. A recently developed approach for quantification of the estimation uncertainty introduced by sensor noise and unknown excitation acting on the structure is applied. The approach is used to determine the noise statistics for Kalman filtering that minimize the estimation uncertainty. Finally, the quality of the strain estimates is assessed by comparison with the actual, measured strains. Very accurate strain estimates are obtained. |
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