Safe Reinforcement Learning for Grid-forming Inverter Based Frequency Regulation with Stability Guarantee  

作　　者：Hang Shuai Buxin She Jinning Wang Fangxing Li 

机构地区：[1]Department of Electrical Engineering and Computer Science,University of Tennessee,Knoxville,TN,37996,USA

出　　处：《Journal of Modern Power Systems and Clean Energy》2025年第1期79-86,共8页现代电力系统与清洁能源学报(英文)

基　　金：funded in part by the CURENT Research Center and in part by the National Science Foundation(NSF)(No.ECCS-2033910)。

摘　　要：This study investigates a safe reinforcement learning algorithm for grid-forming(GFM)inverter based frequency regulation.To guarantee the stability of the inverter-based resource(IBR)system under the learned control policy,a modelbased 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 dynamic 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 process(GP)model is adopted by the proposed algorithm to effectively learn system dynamics from measurements.Numerical simulations validate the effectiveness of the proposed algorithm.

关 键 词：Inverter-based resource(IBR) virtual synchronous generator(VSG) safe reinforcement learning Lyapunov function frequency regulation grid-forming inverter 

分 类 号：TM464[电气工程—电器]