Detection and Locating Cyber and Physical Stresses in Smart Grids using Graph Signal Processing

TL;DR

This paper explores the use of graph signal processing to detect and locate cyber and physical stresses in smart grids, enhancing real-time monitoring and system security.

Contribution

It introduces GSP-based techniques for analyzing smart grid data to identify and locate stresses, demonstrating their effectiveness in a complex cyber-physical system.

Findings

GSP enables analysis of power grid data in vertex and frequency domains.

Proposed methods effectively detect and locate stresses in smart grids.

GSP shows promise for real-time monitoring and security enhancement.

Abstract

Smart grids are large and complex cyber physical infrastructures that require real-time monitoring for ensuring the security and reliability of the system. 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, graph signal processing (GSP) has been used to represent and analyze the power grid measurement data. It is shown that GSP can enable various analyses for the power grid's structured data and dynamics of its interconnected components. Particularly, the effects of various cyber and physical stresses in the power grid are evaluated and discussed both in the vertex and the graph-frequency domains of the signals. Several techniques for detecting and locating cyber and physical stresses based on GSP techniques have been presented and their performances have been evaluated and compared. The presented study shows that GSP can be a promising approach for analyzing the power grid's data.