Control‐Oriented Modelling and Adaptive Parameter Estimation for Hybrid Wind‐Wave Energy Systems

ABSTRACT Hybrid wind‐wave energy systems, integrating floating offshore wind turbine (FOWT) and wave energy converters (WECs), have received much attention in recent years due to its potential benefits in increasing the power harvesting density and reducing the levelized cost of electricity (LCOE). Recent studies show that advanced model‐based control strategies have the great potential to significantly improve their overall control performance. However, the performance of these advanced control strategies relies on the computationally efficient control‐oriented models with sufficient fidelity, which are normally difficult to derive due to the complexity of the hydro‐, aero‐dynamic effects and the couplings. In most available results, the hybrid wind‐wave energy system models are established by using the boundary element method (BEM), devoting to understanding the hydrodynamic responses and performance analysis. However, such models are complex and involved in relatively heavy computational burden, which cannot be directly used for the advanced model‐based control methods in practice. To overcome this issue, this paper proposes a control‐oriented model of the hybrid wind‐wave energy system with six degrees of freedom (DOFs). First, the Newton's second law and fluid mechanics are employed to characterize the motion behavior of the hybrid wind‐wave energy system with the coupled aero‐hydro‐mooring dynamics. Then, a novel adaptive parameter estimation algorithm with simple low‐pass filter approach is developed to estimate the system unknown coefficients. Different from the conventional parameter estimation methods, such as gradient descent method and recursive least‐squares (RLS) method, the estimated parameters can be driven to their true values with guaranteed convergence. Finally, numerical analysis using the AQWA and MATLAB are applied to validate the fidelity of the control‐oriented model under different wind and wave conditions. The results indicate that the control‐oriented model predicts the motion response accurately in comparison to the BEM‐based model. Overall, the results pave the way for designing advanced hybrid wind‐wave energy system control method.