A Power Regulation Strategy for Grid-Forming Wind Power Clusters Based on Deep Reinforcement Learning

Renewable-rich power systems rely on converter-interfaced resources, reducing synchronous inertia and complicating frequency regulation. Grid-forming wind turbines can provide active-power support, but cluster-level commands must respect rotor-speed and DC-link voltage limits. This study develops a deep-reinforcement-learning power regulation strategy for grid-forming wind power clusters using a safety-constraint-integrated dual-layer Soft Actor–Critic algorithm. A coupled dynamic model of the regional grid, synchronous generator, wind-turbine grid-side converter, drivetrain, and DC link is established to characterize the active power–frequency response and unit operating constraints. The upper layer determines the cluster regulation demand, whereas the lower layer allocates commands among turbines and maps preliminary actions to componentwise operating bounds. A MATLAB/Simulink case study containing one 60 MW synchronous generator and twenty 2 MW grid-forming wind turbines compares the proposed strategy with traditional automatic generation control over 50 disturbance realizations. The proposed strategy reduced frequency standard deviation from 17.5 to 14.2 mHz, improved cumulative reward from −4.76×107 to −3.84×107, and increased safety-constraint satisfaction from 82.1% to 99.2%. The results support hierarchical reinforcement learning for coordinating fast frequency support while improving adherence to turbine operating limits.