Wind Speed Forecasting for Pitch Control in Wind Turbines: A Comprehensive Review

Abstract

Wind energy is a pillar of the clean-energy transition, yet wind-speed variability remains a major challenge for wind turbine control. Blade pitch control is essential for safe operation and power regulation, but conventional strategies are mostly reactive and may struggle with gusts, nonlinear aerodynamics, and actuator limits. In above-rated conditions (wind speeds above the rated value, where pitching limits aerodynamic power), insufficient anticipation can increase structural loads, accelerate fatigue, and reduce power quality. Short-term wind speed forecasting offers a solution by providing feedforward information that complements feedback control and enables earlier disturbance rejection. However, existing reviews often examine wind forecasting and pitch control separately, limiting guidance for aligning forecasting design choices with pitch-control requirements. This review analyses how forecasting horizons, input data, latency constraints, and uncertainty characteristics can be aligned with pitch control objectives and real-time implementation requirements in turbines. Forecasting approaches are grouped into physical, statistical, AI-based, and hybrid methods, and pitch control strategies are discussed with emphasis on controllers integrating predictive signals. Across the reviewed studies, forecasting-informed pitch control is reported to deliver measurable gains, including up to 24% MSE reduction for enhanced PID schemes and around 40–45% reduction in power oscillations with forecast-driven MPC. Overall, integrating wind forecasts into pitch control can improve load mitigation and power regulation, provided that practical constraints and forecast uncertainty are explicitly addressed.