Experimental evaluation of an advanced predictive control technique for variable-speed wind turbine systems

• Efficient Wind Energy Use – Highlights the importance of controlling wind energy for sustainability and efficiency. • Advanced Control Strategy – Emphasizes the role of Model Predictive Control (MPC) in optimizing wind turbine performance. • Real-Time Implementation – Stresses the necessity of rapid adjustments for system stability and effectiveness. • Experimental Validation – Demonstrates practical testing using a DFIG system with a Dspace DS1104 board. • Performance Enhancement – Confirms improved tracking accuracy, response speed, and reliability of DFIG-based wind turbines. Wind energy control plays a crucial role in optimizing the performance of Doubly Fed Induction Generators (DFIGs) by maximizing power extraction while ensuring stable grid integration. To achieve this, a Maximum Power Point Tracking (MPPT) strategy is employed to determine the optimal mechanical speed and reference power, enabling efficient wind energy conversion. However, maintaining precise control over the active and reactive power exchange remains a challenge, especially under varying operating conditions. This paper presents an experimental study on the application of deadbeat predictive control to a DFIG-based wind energy system, integrating MPPT for optimal power tracking. The study, conducted using a DSPACE DS1104 test bench, includes the development of a comprehensive mathematical model, an analysis of the deadbeat control strategy, and its implementation in MATLAB/Simulink. Experimental validation demonstrates that the proposed control method achieves faster response time (0.0504 s), reduced Total Harmonic Distortion (THD) to 0.5 %, and enhanced robustness against parameter variations, ensuring both maximum power extraction and high-quality power injection into the grid. These results confirm the superiority of the MPPT-integrated deadbeat predictive control over conventional methods in terms of efficiency, power quality, and system stability. However, while this method shows promising results, its implementation in real-world, large-scale systems requires further investigation to address challenges such as grid stability under fluctuating conditions and the scalability of the control strategy. In terms of practical implications, the proposed control method offers potential for improving the performance and efficiency of DFIG-based wind energy systems, contributing to more sustainable and reliable energy production. The research also holds social implications by advancing renewable energy technologies, which are essential for reducing dependency on fossil fuels and mitigating the effects of climate change.