  In this work we propose a novel data-driven, real-time power system voltage
control method based on the physics-informed guided meta evolutionary strategy
(ES). The main objective is to quickly provide an adaptive control strategy to
mitigate the fault-induced delayed voltage recovery (FIDVR) problem.
Reinforcement learning methods have been developed for the same or similar
challenging control problems, but they suffer from training inefficiency and
lack of robustness for "corner or unseen" scenarios. On the other hand,
extensive physical knowledge has been developed in power systems but little has
been leveraged in learning-based approaches. To address these challenges, we
introduce the trainable action mask technique for flexibly embedding physical
knowledge into RL models to rule out unnecessary or unfavorable actions, and
achieve notable improvements in sample efficiency, control performance and
robustness. Furthermore, our method leverages past learning experience to
derive surrogate gradient to guide and accelerate the exploration process in
training. Case studies on the IEEE 300-bus system and comparisons with other
state-of-the-art benchmark methods demonstrate effectiveness and advantages of
our method.
