  This paper presents a new framework of identifying a series of cyber data
attacks on power system synchrophasor measurements. We focus on detecting
"unobservable" cyber data attacks that cannot be detected by any existing
method that purely relies on measurements received at one time instant.
Leveraging the approximate low-rank property of phasor measurement unit (PMU)
data, we formulate the identification problem of successive unobservable cyber
attacks as a matrix decomposition problem of a low-rank matrix plus a
transformed column-sparse matrix. We propose a convex-optimization-based method
and provide its theoretical guarantee in the data identification. Numerical
experiments on actual PMU data from the Central New York power system and
synthetic data are conducted to verify the effectiveness of the proposed
method.
