  Data-driven approaches, when tasked with situation awareness, are suitable
for complex grids with massive datasets. It is a challenge, however, to
efficiently turn these massive datasets into useful big data analytics. To
address such a challenge, this paper, based on random matrix theory (RMT),
proposes a datadriven approach. The approach models massive datasets as large
random matrices; it is model-free and requiring no knowledge about physical
model parameters. In particular, the large data dimension N and the large time
span T, from the spatial aspect and the temporal aspect respectively, lead to
favorable results. The beautiful thing lies in that these linear eigenvalue
statistics (LESs) built from data matrices follow Gaussian distributions for
very general conditions, due to the latest breakthroughs in probability on the
central limit theorems of those LESs. Numerous case studies, with both
simulated data and field data, are given to validate the proposed new
algorithms.
