Research on Optimization Control Strategy Using Model Predictive Control of Wind Turbine Generators

Efficient control strategies are essential for maximizing wind energy conversion in turbine systems. This study focuses on enhancing energy capture efficiency through the application of Model Predictive Control (MPC) algorithms while considering operational constraints and environmental variables.The MPC approach iteratively predicts the turbine system’s behavior, optimizing control variables to minimize a predefined cost function. A dynamic model of the wind turbine system is developed for accurate predictions, allowing real-time implementation of optimized control actions.Through simulations and case studies, the effectiveness of the proposed optimization framework is demonstrated. Results show that the MPC-based strategy significantly improves energy conversion efficiency, reducing power fluctuations and ensuring compliance with grid codes and operational limits. The strategy also proves robust against disturbances.In conclusion, this research contributes to wind energy optimization by introducing an advanced MPC-based control strategy. The study underscores the importance of predictive control in achieving efficient energy conversion and offers insights for developing next-generation wind turbine control systems.