Transportation and Power Grid in Smart Cities

23 Spatiotemporal Big Data Analysis for Smart Grids Based on Random Matrix Theory

Robert Qiu^1,2,3 Lei Chu^2,3 Xing He^2,3 Zenan Ling^2,3 and Haichun Liu^2,3

¹ Tennessee Technological University, Cookeville, TN, 38505, USA

² Department of Electrical Engineering, Shanghai Jiaotong University, Shanghai, 200240, China

³ Research Center for Big Data Engineering and Technology, State Energy Smart Grid Research and Development Center, Shanghai, China

A cornerstone of the smart grid is the advanced monitorability on its assets and operations. Increasingly pervasive installation of phasor measurement units (PMUs) allows the so‐called synchrophasor measurements to be taken roughly 100 times faster than the legacy supervisory control and data acquisition (SCADA) measurements, time‐stamped using the Global Positioning System (GPS) signals to capture the grid dynamics. On the other hand, the availability of low‐latency two‐way communication networks will pave the way to high‐precision real‐time grid state estimation and detection, remedial actions upon network instability, and accurate risk analysis and post‐event assessment for failure prevention.

In this chapter, we firstly model spatiotemporal PMU data in large‐scale grids as random matrix sequences. Secondly, some basic principles of random matrix theory (RMT), such as asymptotic spectrum laws, transforms, convergence rate, and free probability, are introduced briefly in order to improve the understanding and application of RMT ...