  The analogous deployment of phase measurement units (PMUs), the increase of
data quantum and the deregulation of energy market, all call for the robust
state evaluation in large scale power systems. Implementing model based
estimators is impractical because of the complexity scale of solving the high
dimension power flow equations. In this paper, we first represent massive
streaming PMU data as big random matrix flow. By exploiting the variations in
the covariance matrix of the massive streaming PMU data, a novel power state
evaluation algorithm is then developed based on the multiple high dimensional
covariance matrix tests. The proposed test statistic is flexible and
nonparametric, which assumes no specific parameter distribution or dimension
structure for the PMU data. Besides, it can jointly reveal the relative
magnitude, duration and location of a system event. For the sake of practical
application, we reduce the computation of the proposed test statistic from
$O(\varepsilon n_g^4)$ to $O(\eta n_g^2)$ by principal component calculation
and redundant computation elimination. The novel algorithm is numerically
evaluated utilizing the IEEE 30-, 118-bus system, a Polish 2383-bus system, and
a real 34-PMU system. The case studies illustrate and verify the superiority of
proposed state evaluation indicator.
