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

Massive Streaming PMU Data Modeling and Analytics in Smart Grid State Evaluation Based on Multiple High-Dimensional Covariance Tests