Coordinated control strategy of grid-forming converter based on passive control and deep reinforcement learning

For frequency and voltage stability control of grid-forming converters in high-power electronic scenarios, this paper proposes a grid-forming converter grid-connection stability control strategy based on passive control and deep reinforcement learning. Firstly, the virtual synchronous generator (VSG) is written in the port-Hamiltonian form to clarify the interconnection and dissipative structure, and the achievable passive control law is obtained by energy shaping and damping injection. Then, DDPG is introduced to adjust the damping parameters online, so that the control has adaptive ability under multiple working conditions, and the closed-loop system is proved to be asymptotically stable based on Lyapunov function. Finally, the simulation example analysis is carried out. In the simulation of power mutation, voltage imbalance, short-circuit fault and load change, this method significantly reduces the overshoot and adjustment time compared with VSG-PI and fixed parameter PBC, and improves the steady-state error and energy dissipation rate. The simulation results verify the effectiveness of the combination of physical consistency and strategy adaptation.