Parameter-varying modeling and nonlinear model predictive control with disturbance prediction for spar-type floating offshore wind turbines

Abstract

This paper proposes novel methods for the modeling and control of spar-type floating offshore wind turbines (FOWTs) by focusing on the dependency of the equilibrium and perturbed dynamics on the rotor azimuth angle. In addition, three new reduced models for controller design are derived using trajectory linearization by accounting for the dependency of the equilibrium on the azimuth angle. A thorough simulation study shows that the proposed models reproduce the important dynamic characteristics of FOWTs more accurately than the conventional models. Then, nonlinear model predictive controllers (NMPCs) minimizing the nonquadratic cost functions are developed for the proposed models, which include nonlinear terms for the rotor azimuth angle. These NMPCs suppress the variation in the forces applied to the blades better than the conventional linear MPCs while maintaining a low computational cost. The best NMPC for the models is one that accounts for the dependency of both the equilibrium and perturbed dynamics on the rotor azimuth angle. This NMPC suppresses the platform yaw and forces added on the blades. The performance of such an NMPC can be further improved using the inflow wind disturbance data predicted using a light detection and ranging wind sensor.

Sink or Swim: A Tutorial on the Control of Floating Wind Turbines

“…To understand the importance of various decisions made in the MPC design process, competing MPCs must be compared side-by-side, and there appear to be only a few such studies. In [93], comparisons are made across approaches for obtaining the linearized model as well as between preview-enabled and non-preview-enabled MPC. MPC performance under different choices of cost function weights is considered in [19].…”