Mechanics‐based model updating for identification and virtual sensing of an offshore wind turbine using sparse measurements

Abstract

Offshore wind turbines are complex systems operating in harsh environment. The dynamic demands in these systems often differ from values used in design, leading to unexpected mechanical and structural failures. This signifies the importance of remote monitoring technologies for damage diagnosis and prognosis in offshore wind turbines. This study is focused on developing mechanics-based digital twins for offshore wind turbine monitoring through a model-updating process using sparse measurement data. Digital twins can be used to estimate the system unmeasured response (i.e., virtual sensing) and to predict the remaining useful fatigue life and failure point of different structural components. A time-domain sequential Bayesian finite element model updating is proposed for mechanics-based digital twinning. This approach is formulated for application to offshore wind turbine and jointly estimates the updating model parameters and the time history of unknown input forces. A classical modal-based model updating followed by modal expansion method is also implemented for comparison. In this approach, updating model parameters are estimated to minimize the discrepancies between the identified and model-predicted modal parameters of the turbine. The performance of these two approaches are studied on a 2-MW offshore wind turbine at the Blyth wind farm in the United Kingdom. Strain response time history at mudline is estimated through both approaches and compared with actual measurements for validation. It is observed that both approaches are capable of accurate response prediction while the Bayesian approach leads to slightly better results. Furthermore, the Bayesian approach allows for identification of input loads and uncertainty quantification.