  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.
