Data-Driven Model Updating of an Offshore Wind Jacket Substructure

The present paper provides a model updating application study concerning the jacket substructure of an o?shore wind turbine. Theupdating is resolved in a sensitivity-based parameter estimation setting, where a cost function expressing the discrepancy betweenexperimentally obtained modal parameters and model-predicted ones is minimized. The modal parameters of the physical systemare estimated through stochastic subspace identification (SSI) applied to vibration data captured for idling and operational states ofthe turbine. From a theoretical outset, the identification approach relies on the system being linear and time-invariant (LTI) and theinput white noise random processes; criteria which are violated in this application due to sources such as operational variability, theturbine controller, and non-linear damping. Consequently, particular attention is given to assess the feasibility of extracting modalparameters through SSI under the prevailing conditions and subsequently using these parameters for model updating. On this basis,it is deemed necessary to disregard the operational turbine states—which severely promote non-linear and time-variant structuralbehaviour and, as such, imprecise parameter estimation results—and conduct the model updating based on modal parametersextracted solely from the idling state. The uncertainties associated with the modal parameter estimates and the model parameters tobe updated are outlined and included in the updating procedure using weighting matrices in the sensitivity-based formulation. Byconducting the model updating based on in-situ data harvested from the jacket substructure during idling conditions, the maximumeigenfrequency deviation between the experimental estimates and the model-predicted ones is reduced from 30% to 1%.