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Based on the random matrix model, we can build statistical models usingmassive datasets across the power grid, and employ hypothesis testing foranomaly detection. First, the aim of this paper is to make the first attempt toapply the recent free probability result in extracting big data analytics, inparticular data fusion. The nature of this work is basic in that new algorithmsand 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 relatedto anomaly detection that is very difficult for other approaches. To our bestknowledge, there is no similar report in the literature. Third, both linear andnonlinear polynomials of large random matrices can be handled in this newframework. Simulations demonstrate the following: Compared with the linearity,nonlinearity is more flexible in problem modeling and closer to the nature ofthe reality. In some sense, some other nonlinear matrix polynomials may be moreeffective for the power grid

A Novel Approach for Big Data Analytics in Future Grids Based on Free Probability