Data-Driven MPC for Variable-Speed Variable-Pitch Wind Turbines

This paper investigates the effectiveness of a datadriven model predictive control (DD-MPC) technique for variable-speed variable-pitch wind energy conversion systems (WECS), comparing its performance with multiple model predictive control under different wind speed ranges. Simulation results indicate that DD-MPC significantly enhances power coefficient stability and reduces torsional torque fluctuations in low wind speed conditions, resulting in improved energy generation and increased system reliability. In cases of high wind speeds, the proposed technique efficiently reduces power fluctuations while maintaining rotor speed stability, hence providing reliable power generation, improved power quality, and reducing mechanical stress. These improvements facilitate improved grid integration, reduce mechanical wear, and extend turbine lifespan. The results demonstrate the effectiveness of DDMPC in optimizing WECS performance, presenting it as an attractive option for improving energy economy, dependability, and sustainability in wind power generation.