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Graph Signal Processing for Situational Awareness in Power Systems
Abstract- The availability of large volume of measurement data in power systems presents novel prospects for enhancing their situational awarene
ss andcritical functions. The energy data inherently exhibit structures emerged from the complex interactions among the system’s components, influenced by the physics of electricity, operational factors, and the cyber functionalities governing these systems. Incorporating these structures, often represented in graph models, into the analysis of power systems can improve the modeling and understanding of the power system’s state and dynamics. Graph Signal Processing (GSP), which extends the classical signal processing techniques and tools to irregular graph domain, offers a framework for analyzing such structured data and the dynamics of systems with interconnected components. By defining signals over the vertices of a graph, namely graph signals, the interactions and dynamics of measurements in power systems can be modeled, captured and analyzed through the lens of rich GSP tools and techniques. This presentation will cover GSP-based methods for tackling diverse challenges in situational awareness in power systems, including the detection and localization of cyber and physical stresses, as well as the recovery of power system state information. Additionally, an exploration of a GSP-based approach to analyze the impact of a single bus perturbation in power systems will be discussed. It will be demonstrated that the global and local smoothness properties of the power system graph signals reflect embedded patterns related to spreadability and stress in the system. The tools and techniques derived from GSP exhibit a broad range of applications both in power systems and other structured data and networked systems.
Bio- Dr. Mia Naeini is an Assistant Professor in the Department of Electrical Engineering at the University of South Florida. She received her Ph.D. degree in Electrical and Computer Engineering with a minor in Mathematics from University of New Mexico in 2014. Her research interests include leveraging data analytics, network science, stochastic processes, graph signal processing and graph-empowered machine learning to integrate security and reliability measures into the design and control of cyber physical systems with a focus on smart grids. Her research has received support from various funding agencies including National Science Foundation (NSF), Defense Threat Reduction Agency, and Florida Center for Cybersecurity. She also received the NSF CAREER award in 2023. She is a senior member of IEEE and has served as the associate editor of the IEEE Communication Letters and as the chair and technical program committee member of several workshops and conferences in the area of power and communication systems.
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