  The paradigm shift in the electric power grid necessitates a revisit of
existing control methods to ensure the grid's security and resilience. In
particular, the increased uncertainties and rapidly changing operational
conditions in power systems have revealed outstanding issues in terms of either
speed, adaptiveness, or scalability of the existing control methods for power
systems. On the other hand, the availability of massive real-time data can
provide a clearer picture of what is happening in the grid. Recently, deep
reinforcement learning(RL) has been regarded and adopted as a promising
approach leveraging massive data for fast and adaptive grid control. However,
like most existing machine learning (ML)-basedcontrol techniques, RL control
usually cannot guarantee the safety of the systems under control. In this
paper, we introduce a novel method for safe RL-based load shedding of power
systems that can enhance the safe voltage recovery of the electric power grid
after experiencing faults. Numerical simulations on the 39-bus IEEE benchmark
is performed to demonstrate the effectiveness of the proposed safe RL emergency
control, as well as its adaptive capability to faults not seen in the training.
