  Based on the random matrix model, we can build statistical models using
massive datasets across the power grid, and employ hypothesis testing for
anomaly detection. First, the aim of this paper is to make the first attempt to
apply the recent free probability result in extracting big data analytics, in
particular data fusion. The nature of this work is basic in that new algorithms
and analytics tools are proposed to pave the way for the future's research.
Second, using the new analytic tool, we are able to make some discovery related
to anomaly detection that is very difficult for other approaches. To our best
knowledge, there is no similar report in the literature. Third, both linear and
nonlinear polynomials of large random matrices can be handled in this new
framework. Simulations demonstrate the following: Compared with the linearity,
nonlinearity is more flexible in problem modeling and closer to the nature of
the reality. In some sense, some other nonlinear matrix polynomials may be more
effective for the power grid
