Anticipatory Pitch Control for Small Wind Turbines Using Short-Term Rotor-Speed Prediction with Machine Learning

Small wind turbines operating at low heights frequently experience rapidly fluctuating and highly turbulent wind conditions that challenge conventional reactive pitch-control strategies. Under these non-stationary regimes, sudden gusts produce overspeed events that increase mechanical stress, reduce energy capture, and compromise operational safety. Addressing this limitation requires a control scheme capable of anticipating aerodynamic disturbances rather than responding after they occur. This work proposes a hybrid anticipatory pitch-control approach that integrates a conventional PI regulator with a data-driven rotor-speed prediction model. The main novelty is that short-term rotor-speed forecasting is embedded into a standard PI loop to provide anticipatory action without requiring additional sensing infrastructure or changing the baseline control structure. Using six years of real wind and turbine-operation data, an optimized Random Forest model is trained to forecast rotor speed 20 s ahead based on a 60 s historical window, achieving a prediction accuracy of RMSE = 0.34 rpm and R2 = 0.73 on unseen test data. The predicted uses a sliding-window representation of recent wind–rotor dynamics to estimate the rotor speed at a fixed horizon (t + Δt), and the predicted signal is used as the feedback variable in the PI loop. The method is validated through a high-fidelity MATLAB/Simulink model of 14 kW small horizontal-axis wind turbine, evaluated under four wind scenarios, including two previously unseen conditions characterized by steep gust gradients and quasi-stationary high winds. The simulation results show a reduction in overspeed peaks by up to 35–45%, a decrease in the integral absolute error (IAE) of rotor speed by approximately 30%, and a reduction in pitch-actuator RMS activity of about 25% compared with the conventional PI controller. These findings demonstrate that short-term AI-based rotor-speed prediction can significantly enhance safety, dynamic stability, and control performance in small wind turbines exposed to highly variable atmospheric conditions.