A Graph Signal Processing Framework for Detecting and Locating Cyber and Physical Stresses in Smart Grids

Monitoring the smart grid involves analyzing continuous data-stream from various measurement devices deployed throughout the system, which are topologically distributed and structurally interrelated. In this paper, a graph signal processing (GSP) framework is used to represent and analyze the inter-related smart grid measurement data for security and reliability analyses. The effects of various cyber and physical stresses in the system are evaluated in different GSP domains including vertex domain, graph-frequency domain, and the joint vertex-frequency domain. Two novel techniques based on vertex-frequency energy distribution, and the local smoothness of graph signals are proposed and their performance have been evaluated for detecting and locating various cyber and physical stresses. Based on the presented analyses, the proposed techniques show promising performance for detecting sophisticated stresses with no sharp changes at the onset, for detecting abrupt load changes, and also for locating stresses.