Robust Adaptive Control of Maximum Power Point Tracking for Wind Power System

• Main Authors: Peng Chen, Dezhi Han, Kuan-Ching Li
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Adaptive dynamic programming; maximum power point tracking; neural network; wind power system
• Online Access: https://ieeexplore.ieee.org/document/9262840/

A novel data-driven robust approximate optimal Maximum Power Point Tracking (MPPT) control method is proposed for the wind power generation system by using the adaptive dynamic programming (ADP) algorithm. First, a data-driven model is established by a recurrent neural network (NN) to reconstruct the wind power system dynamics using available input-output data. Then, in the design of the controller, based on the obtained data-driven model, the ADP algorithm is utilized to design the approximate optimal tracking controller, which consists of the steady-state controller and the optimal feedback controller. Ulteriorly, developing a robustifying term to compensate for the NN approximation errors introduced by implementing the ADP method. Based on the Lyapunov approach, it proves the stability of the designed model and controller to show that the proposed controller guarantees the system power asymptotically tracking the maximum power. Finally, the simulation results demonstrate that the control method stabilizes the tip speed ratio near the optimal value when the wind speed is lower than the rated wind speed. Moreover, the tracking response speed of the proposed method is fast, which enhances the stability and robustness of the system.