Comprehensive Performance-Orianted Control for Grid-forming Converters

Grid-forming control demonstrates higher stability compared to grid-following control in expanding inverter-dominated power grids. However, parameters are not harmoniously adjusted for large-scale inverter-dominated grids. For instance, while the damping factor can enhance the stability of local inverters, it may lead to instability in other inverters. Furthermore, existing literature fails to consider other performance metrics such as response speed and overshoot. Therefore, this letter proposes an online adaptive control based on reinforcement learning. Leveraging the flexible computing paradigm of cloud-edge-terminal systems, the proposed approach comprehensively assesses the performance of grid-forming inverters.