Adaptive predictive control for wind energy efficiency in real-time in smart grids

Abstract

With the increasing global interest in renewable energy, advanced control strategies for wind energy conversion systems (WECSs) are critical for achieving not only maximum efficiency but also grid compatibility. This paper introduces a new adaptive, forecast-based predictive control algorithm to maximize the real-time energy efficiency of wind turbines integrated with smart grid architectures. The method couples a short-term wind-speed prediction module with a model-predictive control algorithm that uses the predictions to anticipate and adjust turbine operating conditions, such as pitch angle and rotor speed, to counteract the mechanical load from wind shear while maximizing power production. The analysis employs a dataset of 487 high-resolution cases of wind speed and wind turbine performance metrics to train and validate the adaptive model. The simulations were conducted in MATLAB/Simulink, using a model that captures the turbine’s nonlinear behavior and the electrical grid connection. The results indicate that the proposed controller increases the cycle-averaged power coefficient by approximately 8.2% and reduces tip-speed-ratio tracking error by roughly 70% relative to a conventional proportional-integral-derivative (PID) controller, while also reducing pitch and torque actuation, thereby lowering structural fatigue loads. This study is expected to provide a reliable solution to wind energy intermittency and facilitate the more stable and efficient integration of wind farms into modern smart grid systems.