Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adaptive Load Shedding for Grid Emergency Control via Deep Reinforcement
  Learning   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.

35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con