TY - JOUR
T1 - Data-driven Model Updating of an Offshore Wind Jacket Substructure
AU - Augustyn, Dawid Jakub
AU - Smolka, Ursula
AU - Tygesen, Ulf T.
AU - Ulriksen, Martin Dalgaard
AU - Sørensen, John Dalsgaard
PY - 2020
Y1 - 2020
N2 - The present paper provides a model updating application study concerning the jacket substructure of an offshore wind turbine. The updating is resolved in a sensitivity-based parameter estimation setting, where a cost function expressing the discrepancy between experimentally obtained modal parameters and model-predicted ones is minimized. The modal parameters of the physical system are estimated through stochastic subspace identification (SSI) applied to vibration data captured for idling and operational states of the turbine. From a theoretical outset, the identification approach relies on the system being linear and time-invariant (LTI) and the input white noise random processes; criteria which are violated in this application due to sources such as operational variability, the turbine controller, and non-linear damping. Consequently, particular attention is given to assess the feasibility of extracting modal parameters 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 structural behaviour and, as such, imprecise parameter estimation results---and conduct the model updating based on modal parameters extracted solely from the idling state. The uncertainties associated with the modal parameter estimates and the model parameters to be updated are outlined and included in the updating procedure using weighting matrices in the sensitivity-based formulation. By conducting the model updating based on in-situ data harvested from the jacket substructure during idling conditions, the maximum eigenfrequency deviation between the experimental estimates and the model-predicted ones is reduced from 30 % to 1 %.
AB - The present paper provides a model updating application study concerning the jacket substructure of an offshore wind turbine. The updating is resolved in a sensitivity-based parameter estimation setting, where a cost function expressing the discrepancy between experimentally obtained modal parameters and model-predicted ones is minimized. The modal parameters of the physical system are estimated through stochastic subspace identification (SSI) applied to vibration data captured for idling and operational states of the turbine. From a theoretical outset, the identification approach relies on the system being linear and time-invariant (LTI) and the input white noise random processes; criteria which are violated in this application due to sources such as operational variability, the turbine controller, and non-linear damping. Consequently, particular attention is given to assess the feasibility of extracting modal parameters 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 structural behaviour and, as such, imprecise parameter estimation results---and conduct the model updating based on modal parameters extracted solely from the idling state. The uncertainties associated with the modal parameter estimates and the model parameters to be updated are outlined and included in the updating procedure using weighting matrices in the sensitivity-based formulation. By conducting the model updating based on in-situ data harvested from the jacket substructure during idling conditions, the maximum eigenfrequency deviation between the experimental estimates and the model-predicted ones is reduced from 30 % to 1 %.
KW - Condition monitoring
KW - Digital twin
KW - Jacket substructure
KW - Model updating
KW - Operational modal analysis
KW - Wind turbines
UR - http://www.scopus.com/inward/record.url?scp=85092102821&partnerID=8YFLogxK
U2 - 10.1016/j.apor.2020.102366
DO - 10.1016/j.apor.2020.102366
M3 - Journal article
SN - 0141-1187
VL - 104
JO - Applied Ocean Research
JF - Applied Ocean Research
M1 - 102366
ER -