Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Learning-to-Infer Method for Real-Time Power Grid Multi-Line Outage
  Identification   Identifying a potentially large number of simultaneous line outages in power
transmission networks in real time is a computationally hard problem. This is
because the number of hypotheses grows exponentially with the network size. A
new "Learning-to-Infer" method is developed for efficient inference of every
line status in the network. Optimizing the line outage detector is transformed
to and solved as a discriminative learning problem based on Monte Carlo samples
generated with power flow simulations. A major advantage of the developed
Learning-to-Infer method is that the labeled data used for training can be
generated in an arbitrarily large amount rapidly and at very little cost. As a
result, the power of offline training is fully exploited to learn very complex
classifiers for effective real-time multi-line outage identification. The
proposed methods are evaluated in the IEEE 30, 118 and 300 bus systems.
Excellent performance in identifying multi-line outages in real time is
achieved with a reasonably small amount of data.

24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ