This paper proposes a hierarchical Bayesian model updating approach to quantify variability of aerodynamic stiffness and damping of an offshore wind turbine (OWT) under different environmental and operational conditions (EOCs) using in-situ vibration data and SCADA (supervisory control and data acquisition) over two months of continuous monitoring. The considered OWT is a Haliade 150, 6 MW GE turbine on a jacket substructure located at the Block Island Wind Farm in Rhode Island, USA. The OWT has been instrumented with a continuous monitoring system including an array of accelerometers and strain gauges. The modal parameters of the OWT are extracted using an automated system identification approach. These parameters exhibit significant variations under varying EOCs. This variation is more significant in natural frequencies and damping ratios of the first fore-aft bending mode due to the aerodynamic effects. In this paper, a modeling approach is proposed by introducing a spring and a damper at the nacelle level in the fore-aft direction to account for the observed aerodynamic effects. A hierarchical Bayesian model updating is formulated and implemented to update parameters representing the effects of aerodynamic stiffness and damping, as well as their statistical properties such as mean and covariance matrix which are updated as hyperparameters. To account for the correlation between aerodynamic effects and EOCs, the updating parameters are assumed to be functions of EOCs such as rpm and wind speed. Two levels of hierarchical Bayesian model updating are performed and compared. In level 1, only modal parameters are used in model updating, while in level 2, the assumed correlation between EOCs and modal parameters are accounted for to reduce the uncertainty of aerodynamic effects in the model. In addition to hyperparameters, the proposed hierarchical Bayesian approach provides statistical properties of error function by collecting the residual uncertainties that have not been accounted for, e.g., modeling errors, measurement noise and random disturbances. The predicted modal parameters using two levels of hierarchical Bayesian approach are compared with their identified counterparts, and the results indicate that level 2 approach significantly reduces estimation uncertainty in aerodynamic effects and produces model predictions consistent with observed values of natural frequencies and damping ratios.
Read full abstract