Maximum wind energy extraction of floating offshore wind turbine using model predictive control with data-driven linear predictors

Under the influence of complex wind-wave-current disturbance and multiphysics coupling, the floating offshore wind turbine (FOWT) controller design is faced with a difficult trades-off between model accuracy and computational burden. In response to this problem, a data-driven model predictive control framework based on the Koopman operator is proposed for maximum wind energy extraction of floating offshore wind turbine. Based on coupled dynamics model, the Extended Dynamic Mode Decomposition (EDMD) method is applied to estimate the finite dimensional Koopman operator, obtaining the linear controlled form of the dynamic system. Considering the nonlinearity and coupling characteristics of the state variables in the coupled dynamics model, a lifting function suitable for FOWT is designed to improve model accuracy in nonlinear transformation process. Simulation results under three load cases demonstrate the superiority of the proposed controller in improving energy extraction and reducing torque fluctuations, and the real-time control time cost is close to that of linear model predictive controller. Additionally, the impact of the data collection and lifting function design on the prediction error of predictive model is analyzed. It provides a reference for designing controller that effectively trades-off the accuracy of predictive model and computational complexity for floating offshore wind turbines.