Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Performance Analysis of Sparse Recovery Models for Bad Data Detection
  and State Esti-mation in Electric Power Networks   This paper investigates the sparse recovery models for bad data detection and
state estimation in power networks. Two sparse models, the sparse L1-relaxation
model (L1-R) and the multi-stage convex relaxation model (Capped-L1), are
compared with the weighted least absolute value (WLAV) in the aspects of the
bad data processing capacity and the computational efficiency. Numerical tests
are conducted on power systems with linear and nonlinear measurements. Based on
numerical tests, the paper evaluates the performance of these robust state
estimation mod-els. Furthermore, suggestion on how to select parameter of
sparse recovery models is also given when they are used in elec-tric power
networks.

14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
1
The categories of this paper are: cs
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph