Adaptive Control of VSG Inertia Damping Based on MADDPG

As renewable energy sources become more integrated into the power grid, traditional virtual synchronous generator (VSG) control strategies have become inadequate for the current low-damping, low-inertia power systems. Therefore, this paper proposes a VSG inertia and damping adaptive control method based on multi-agent deep deterministic policy gradient (MADDPG). The paper first introduces the working principles of virtual synchronous generators and establishes a corresponding VSG model. Based on this model, the influence of variations in virtual inertia (J) and damping (D) coefficients on fluctuations in active power output is examined, defining the action space for J and D. The proposed method is mainly divided into two phases: “centralized training and decentralized execution”. In the centralized training phase, each agent’s critic network shares global observation and action information to guide the actor network in policy optimization. In the decentralized execution phase, agents observe frequency deviations and the rate at which angular frequency changes, using reinforcement learning algorithms to adjust the virtual inertia J and damping coefficient D in real time. Finally, the effectiveness of the proposed MADDPG control strategy is validated through comparison with adaptive control and DDPG control methods.