Reinforcement Learning-Based Current Compensation for Brushless Doubly Fed Induction Generators Under Transient- and Low-Voltage Ride-Through Faults

Wind and solar energy are increasingly vital for meeting clean renewable energy needs, with Brushless Doubly Fed Induction Generators gaining popularity due to their cost efficiency and reliability. A key challenge in integrating wind energy into the grid is ensuring low-voltage ride-through capability during faults and mitigating voltage fluctuations at the point of common coupling. Existing techniques, such as analytical models and evolutionary algorithms, aim to optimize reactive current compensation but suffer from low accuracy and high response times, respectively. This paper introduces a novel reinforcement learning-based current compensation technique for brushless doubly fed induction generators to address these limitations. The proposed reinforcement learning agent dynamically adjusts the reactive power to minimize voltage dips and stabilize the voltage profile during transient- and low-voltage ride-through faults, leveraging a reward function that penalizes deviations in voltage magnitude and increases in total harmonic distortion beyond 3%. By integrating reinforcement learning with traditional methods, the approach achieves faster and more adaptive compensation. Simulation results show that the reinforcement learning-based technique improves voltage recovery time by up to 50%, reduces total harmonic distortion by up to 44%, and minimizes current overshoot by up to 90% compared to state-of-the-art methods, enhancing the reliability and efficiency of wind energy systems.