Deep Reinforcement Learning Adaptive Variable Frequency Control Algorithm for Large-Scale Wind Power Grid Integration: Simulation Verification and Real-time Optimization

Addressing the power grid frequency stability issues caused by large-scale wind power integration, this paper proposes a deep reinforcement learning (DRL) adaptive variable frequency control algorithm for doubly-fed induction generator (DFIG)–electrically excited synchronous machine (EESM) systems. The algorithm constructs a Markov decision process model incorporating electromechanical coupling dynamic characteristics and employs a soft actor-critic (SAC) algorithm to design a hierarchical control architecture with differentiated time scales (1000/100/10/5 ms), achieving online optimization of operating parameters in low-frequency variable frequency transmission systems. Steady-state performance evaluation over 20 Monte Carlo runs demonstrates that the proposed algorithm controls frequency deviation to 8.2 ± 1.3 mHz (root-mean-square, referenced to 20 Hz nominal), reduces power fluctuation to 2.1 ± 0.4%, and achieves 96.8% system efficiency, with all improvements over baseline methods statistically significant (p < 0.001). Transient testing shows that low-voltage ride-through recovery time is shortened to 150 ms, representing a 45% improvement over conventional PI control. Annual extrapolation yields a 5.9% increase in power generation. The online learning mechanism enables continuous system optimization during long-term operation, with frequency control accuracy improving by 25.7% and energy consumption decreasing by 15.9% after one year. The research results provide theoretical foundation and technical support for deep reinforcement learning applications in power system control.