Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Load Curtailment Estimation in Response to Extreme Events   A machine learning model is proposed in this paper to help estimate potential
nodal load curtailment in response to an extreme event. This is performed
through identifying which grid components will fail as a result of an extreme
event, and consequently, which parts of the power system will encounter a
supply interruption. The proposed model to predict component outages is based
on a Support Vector Machine (SVM) model. This model considers the category and
the path of historical hurricanes, as the selected extreme event in this paper,
and accordingly trains the SVM. Once trained, the model is capable of
classifying the grid components into two categories of outage and operational
in response to imminent hurricanes. The obtained component outages are then
integrated into a load curtailment minimization model to estimate the nodal
load curtailments. The merits and the effectiveness of the proposed models are
demonstrated using the standard IEEE 30-bus system based on various hurricane
path/intensity scenarios.

36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci