parallel real-time nonlinear model predictive control of dfig-based wind turbines over all operating regions

Nonlinear model predictive control (nmpc) is a viable solution for control problems in the industry. in this paper, a real-time nmpc approach is proposed for the control of wind turbine (wt) over operating regions. using wind speed predictions, the nmpc achieves the right compromise between maximizing power and reducing wt fatigue loads while limiting the generator torque activity and the blade pitch angle and smoothing out the electrical power. the control scheme is tested in a simulation environment with a set of standard high turbulence wind profiles and coherent gusts, utilizing complete aeroelastic modeling of the wt and an all-nonlinear model of the doubly fed induction generator (dfig) over the whole operation region. besides, the nmpc has been implemented in a parallel newton-type approach to make it more efficient and implementable. a wide range of simulation scenarios, as well as statistical analysis, were also performed to demonstrate the performance and robustness of the proposed controller against model parameter uncertainties. in addition, finite-time convergence of the controller is guaranteed by employing terminal constraints. the results show 1.7% increase in power extraction, 11% decrease in shaft load, and 12% decrease in tower load while reducing the activity of control inputs and smoothing the generator power.