A multi-objective economic nonlinear model predictive controller for power and platform motion on floating offshore wind turbines

Abstract

An Economic Nonlinear Model Predictive Controller is developed to maximize power and reduce fore-aft motion of floating wind turbines compared to a standard power controller. A 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 modeling are validated and compared. The model is used for the synthesis of ENMPC that determines the optimal generator torque and pitch angle over a future time horizon. The objective of this optimization is a combination of aerodynamic power and fore-aft nacelle velocity under realistic constraints. The controller’s performance and robustness are assessed using a wide set of realistic wind and sea conditions. Significantly higher power production and lower fore-aft platform motion 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. The main drawback of the multi-objective controller is the increase of fatigue loads when it is requested to minimize the platform fore-aft motion due to the use of thrust to control it.