A maximum power point tracking control for wind energy conversion systems using regularized data-enabled predictive control
Chau T.T. Nguyen T.N. Nguyen L. Alhassan A.B. Do T.D.
15 April 2026Elsevier Ltd
Engineering Applications of Artificial Intelligence
2026#170
This paper applies Artificial Intelligence (AI) in the sense of data-driven learning and optimization to wind energy control. Specifically, the implemented AI method uses Data-Enabled Predictive Control (DeePC) combined with quadratic regularization for maximum power point tracking (MPPT) of a permanent magnet synchronous generator (PMSG)-based wind energy conversion system (WECS). The contribution is the use of regularized DeePC to construct a predictive controller directly from measured input/output data without explicit model identification, while improving robustness to noise and nonlinearity. The method is applied to MPPT via rotor-speed tracking and direct-axis current regulation under wind variations, disturbances, and parameter uncertainty. The quadratic regularization penalizes initial-condition mismatch and limits the trajectory parameter, mitigating prediction errors and yielding smoother control actions. The method is evaluated in a simulation with a linear quadratic regulator (LQR) and sliding mode control (SMC). DeePC achieves a settling time of 0.03 s (s) with minimal overshoot under step changes, compared with approximately 0.2 s for LQR and 0.5 s for SMC. In addition, DeePC reduces rotor-speed root mean square error (RMSE) to 0.15/0.23 radians per second (rad/s) (nominal/distorted parameters) under a realistic wind profile, compared to 0.42/0.53 rad/s for LQR and 0.76/0.81 rad/s for SMC. These results indicate that regularized DeePC is an effective data-driven alternative to model-based MPPT control within the validated operating regimes.
Data-enabled predictive control , Maximum power point tracking , Quadratic regularization , Robust data-driven control , Wind energy conversion system
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Department of Robotics, School of Engineering and Digital Sciences (SEDS), Nazarbayev University, Astana, 010000, Kazakhstan
Faculty of Information Technology, University of Economics Ho Chi Minh City, Vinh Long Campus, Vinh Long, 85000, Viet Nam
Institute of Innovation, Science and Sustainability, Federation University Australia, Mount Helen, VIC 3350, Australia
Department of Robotics
Faculty of Information Technology
Institute of Innovation
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Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026