  Emergency control, typically such as under-voltage load shedding (UVLS), is
broadly used to grapple with low voltage and voltage instability issues in
practical power systems under contingencies. However, existing emergency
control schemes are rule-based and cannot be adaptively applied to uncertain
and floating operating conditions. This paper proposes an adaptive UVLS
algorithm for emergency control via deep reinforcement learning (DRL) and
expert systems. We first construct dynamic components for picturing the power
system operation as the environment. The transient voltage recovery criteria,
which poses time-varying requirements to UVLS, is integrated into the states
and reward function to advise the learning of deep neural networks. The
proposed approach has no tuning issue of coefficients in reward functions, and
this issue was regarded as a deficiency in the existing DRL-based algorithms.
Extensive case studies illustrate that the proposed method outperforms the
traditional UVLS relay in both the timeliness and efficacy for emergency
control.
