Wave forecast and its application to the optimal control of offshore floating wind turbine for load mitigation

Control algorithms play an important role in energy capture and load mitigation for offshore floating wind turbines (OFWTs). One of the advanced and effective control techniques is the feedforward or model predictive control approach, which requires the forecast of incoming environment conditions. For OFWTs, wave loading is one of the dominant sources to excite structural responses. This study is thus motivated to develop forecasting algorithms for wave elevations and wave excitation forces with the purpose of applying feedforward controllers on OFWTs. Two forecasting algorithms, the approximate Prony Method based on ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) and SVM (Support Vector Machine) regression, are developed and validated using wave records from tank tests. Utilizing the forecasted wave elevations and wave excitation forces, a feedforward LQR controller is designed to mitigate structural loads of an OFWT system. •Two forecast algorithms for wave elevations and wave forces are developed and validated using real-time measurements.•On the basis of the ability to forecast wave force, a deterministic finite-horizon LQR controller is designed for OFWT.•The optimal controller reduces the fore-aft tower base bending moment, especially components excited by the wave force.•The performance of the optimal control is effective and robust for the load reduction comparing to the baseline control.