Abstract
The most common type of Floating Offshore Wind Turbine (FOWT) installed in Norway, is the spar FOWT in which a delta mooring system (DMS) is used. The mooring system of a FOWT is an essential part for its station-keeping, whose loss can lead to the collapse of the FOWT and the endangerment of the human safety. Thus, early detection of damages in the mooring system is vital. In this study, damage detection in the DMS of a spar FOWT under varying environmental conditions (ECs) is investigated through a comparison of the Multiple Model-AutoRegressive (MM-AR) method, the Multiple Model-Power Spectral Density (MM-PSD) method and the Functional Model Based Method (FMBM). The MM-AR and the MM-PSD methods are based on multiple PSD based or AR models and the FMBM on a single Functional Model (FM) for the description of the healthy FOWT’s dynamics under varying ECs. The results show that successful and precise damage detection in a spar FOWT’s DMS can be achieved through the employed statistical methods as the MM-AR method and the FMBM detect all the examined 7 healthy and 16 damage cases whereas the MM-PSD method misses only one damage case. The results also show that the parametric AR models and FM describe more precisely the FOWT dynamics under varying ECs in comparison to the non-parametric PSDs.
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Acknowledgements
This research work has been funded by the Research Council of Norway through the projects: (i) Analytics for asset Integrity Management of Windfarms (AIMWind), a collaboration between University of Agder, Norwegian Research Center AS and Technical University Delft, grant no. 312486. Origo Solutions is included as advisory partner. (ii) Design Optimisation of Power Cable, Shared Electrical Line and Mooring configurations for Floating Offshore Wind Turbines, grant no. 320902.
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Sakaris, C.S., Schnepf, A., Schlanbusch, R., Ong, M.C. (2023). A Comparative Study on Damage Detection in the Delta Mooring System of Spar Floating Offshore Wind Turbines. In: Theilliol, D., Korbicz, J., Kacprzyk, J. (eds) Recent Developments in Model-Based and Data-Driven Methods for Advanced Control and Diagnosis. ACD 2022. Studies in Systems, Decision and Control, vol 467. Springer, Cham. https://doi.org/10.1007/978-3-031-27540-1_25
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