A Big Data Architecture Design for Smart Grids Based on Random Matrix Theory
X. He, Q. Ai, C. Qiu, W. Huang, L. Piao, H. Liu
Model-based analysis tools, built on assumptions and simplifications, are
difficult to handle smart grids with data characterized by 4Vs data. This
paper, using random matrix theory (RMT), motivates data-driven tools to
perceive the complex grids in highdimension; meanwhile, an architecture with
detailed procedures is proposed. In algorithm perspective, the architecture
performs a high-dimensional analysis, and compares the findings with RMT
predictions to conduct anomaly detections. Mean Spectral Radius (MSR), as a
statistical indicator, is defined to reflect the correlations of system data in
different dimensions. In management mode perspective, a group-work mode is
discussed for smart grids operation. This mode breaks through regional
limitations for energy flows and data flows, and makes advanced big data
analyses possible. For a specific large-scale zone-dividing system with
multiple connected utilities, each site, operating under the group-work mode,
is able to work out the regional MSR only with its own measured/simulated data.
The large-scale interconnected system, in this way, is naturally decoupled from
statistical parameters perspective, rather than from engineering models
perspective. Furthermore, a comparative analysis of these distributed MSRs,
even with imperceptible different raw data, will produce a contour line to
detect the event and locate the source. It demonstrates that the architecture
is compatible with the block calculation only using the regional small
database; beyond that, this architecture, as a data-driven solution, is
sensitive to system situation awareness, and practical for real large-scale
interconnected systems. Five case studies and their visualizations validate the
designed architecture in various fields of power systems. To our best
knowledge, this study is the first attempt to apply big data technology into
smart grids.