  Under voltage load shedding has been considered as a standard and effective
measure to recover the voltage stability of the electric power grid under
emergency and severe conditions. However, this scheme usually trips a massive
amount of load which can be unnecessary and harmful to customers. Recently,
deep reinforcement learning (RL) has been regarded and adopted as a promising
approach that can significantly reduce the amount of load shedding. However,
like most existing machine learning (ML)-based control techniques, RL control
usually cannot guarantee the safety of the systems under control. In this
paper, we introduce a novel safe RL method for emergency load shedding of power
systems, that can enhance the safe voltage recovery of the electric power grid
after experiencing faults. Unlike the standard RL method, the safe RL method
has a reward function consisting of a Barrier function that goes to minus
infinity when the system state goes to the safety bounds. Consequently, the
optimal control policy can render the power system to avoid the safety bounds.
This method is general and can be applied to other safety-critical control
problems. 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.
