A Novel Data-Driven Situation Awareness Approach for Future Grids--Using Large Random Matrices for Big Data Modeling

TL;DR

This paper introduces a data-driven, model-free approach for situation awareness in complex power grids using large random matrices, leveraging random matrix theory and eigenvalue statistics for effective big data analysis.

Contribution

It proposes a novel approach based on random matrix theory that models massive datasets as large matrices, enabling efficient analysis without physical model knowledge.

Findings

Eigenvalue statistics follow Gaussian distributions under general conditions.

The approach is validated with simulated and real field data.

Results demonstrate improved big data analytics for grid situation awareness.

Abstract

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.