Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Data-driven Approach to Multi-event Analytics in Large-scale Power
  Systems Using Factor Model   Multi-event detection and recognition in real time is of challenge for a
modern grid as its feature is usually non-identifiable. Based on factor model,
this paper porposes a data-driven method as an alternative solution under the
framework of random matrix theory. This method maps the raw data into a
high-dimensional space with two parts: 1) the principal components (factors,
mapping event signals); and 2) time series residuals (bulk, mapping
white/non-Gaussian noises). The spatial information is extracted form factors,
and the termporal infromation from residuals. Taking both spatial-tempral
correlation into account, this method is able to reveal the multi-event: its
components and their respective details, e.g., occurring time. Case studies
based on the standard IEEE 118-bus system validate the proposed method.

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The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
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The categories of this paper are: econ
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
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The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom