A multi-objective Economic Nonlinear Model Predictive Controller for Power and Platform Motion on Floating Offshore Wind Turbines

Abstract. A main target of the wind energy industry is the reduction of the Levelized Cost of Energy, especially for the emerging sector of floating turbines. An Economic Nonlinear Model Predictive Controller is developed to maximise power and reduce longitudinal motion, increasing revenues, and reducing capital and operating expenses. A novel comprehensive nonlinear Reduced Order Model of floating turbines is developed to predict platform motion, rotor thrust, aerodynamic power, and generator temperature. A grey-box approach and a black-box approach to platform modelling have been successfully vali dated and compared, identifying pros and cons. Then, the model is used in a constrained optimisation problem that computes the control action. The objectives are maximising aerodynamic power and reducing longitudinal nacelle velocity under realistic constraints (including bounds on rotor thrust, generator temperature, and platform velocities). The controller performance and robustness are assessed using a wide set of realistic wind and sea state load cases. Significant higher power production and a lower longitudinal platform motion concerning the standard NREL reference controller are achieved by adopting the multi-objective ENMPC. Finally, considering the difficulty in predicting the sea diffraction forces and the incoming wind, the performances are positively verified in the absence of that information.