基于稳定性保证的构网型逆变器频率调节安全强化学习

This study investigates a safe reinforcement learn-ing algorithm for grid-forming(GFM)inverter based frequency regulation.To guarantee the stability of the inverter-based re-source(IBR)system under the learned control policy,a model-based reinforcement learning(MBRL)algorithm is combined with Lyapunov approach,which determines the safe region of states and actions.To obtain near optimal control policy,the control performance is safely improved by approximate dynam-ic programming(ADP)using data sampled from the region of attraction(ROA).Moreover,to enhance the control robustness against parameter uncertainty in the inverter,a Gaussian pro-cess(GP)model is adopted by the proposed algorithm to effec-tively learn system dynamics from measurements.Numerical simulations validate the effectiveness of the proposed algorithm.