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

• Main Authors: Xing He, Lei Chu, Robert Caiming Qiu, Qian Ai, Zenan Ling
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: Big data analytics; linear eigenvalue statistics; random matrix theory; situation awareness; statistical indicator
• Online Access: https://ieeexplore.ieee.org/document/8319485/

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, proposes a data-driven approach. The approach models massive datasets as large random matrices; it is model-free and requires 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) are built from data matrices to 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.