Maximum Power Extraction for PMVG-Based WECS Using Q-Learning MPPT Algorithm With Finite-Time Control Scheme

This study is concerned with the problem of maximum power extraction for a permanent magnet vernier generator (PMVG)-based wind energy conversion system (WECS) using the Q-learning maximum power point tracking (MPPT) algorithm with the finite-time control (FTC) scheme. To do this, the model-free sensorless reinforcement Q-learning algorithm-based MPPT method is firstly proposed. At this time, the reward and learning rate are updated by the Q-values for each state-action pair. Next, the proposed learning algorithm is used to construct an optimal speed–power curve for achieving the fast and steady MPPT operation for the WECS using the learned action values. Besides, the wind-speed change detection algorithm is added to the proposed method so that the PMVG-based WECS can work in various wind speed conditions. And then, the FTC method is proposed to track the reference speed of PMVG which support to achieving the maximum power extraction, and Lyapunov stability theory is derived to ensure the overall system's stability. Finally, the simulation and experimental results from the 5 kW PMVG-based WECS are presented to demonstrate the applicability and superiority of the proposed control method.