Smart Grid Security Against Massive Blackouts

Author

Jun Yan, University of Rhode IslandFollow

Date of Award

2017

Degree Type

Dissertation

Degree Name

Doctor of Philosophy in Electrical Engineering

Department

Electrical, Computer, and Biomedical Engineering

First Advisor

Haibo He

Abstract

The security of the smart grid is a grand challenge across cyber and physical domains. As an emerging critical infrastructure that integrates information and communication technologies (ICT) into power and energy systems (PES) with improved efficiency, reliability, and sustainability, the smart grid encompasses a transcontinental network of interdependent and interoperating cyber-physical systems (CPS) through the computerization, interconnection, and communication of systems and devices. Across the closely interwoven cyber-physical spaces, the vulnerability and exposure of critical systems and processes have been on the rise: malicious attackers may penetrate through access points in the cyberspace and exploit vulnerabilities in the physical systems, posing major threats to disrupt the delivery of electricity through massive cascading blackouts. Risks and impacts of such attacks have been demonstrated by intensive research efforts as well as real-world incidences recently, drawing increasing concerns from the government, the industry, and the public.

This dissertation will investigate the cyber-physical security of smart grid against potential massive blackouts. The work is composed of three synergistic tasks: 1) understand the mechanisms behind major cascading blackouts; 2) identify critical attack vectors that could initiate the cascading process; 3) develop effective strategies to enhance the resilience of the grid. The dissertation will first assess operational and structural vulnerabilities in massive cascading blackouts through steady state and complex network models, respectively. It will then examine malicious attacks that exploit the vulnerabilities through compromised control and measurements, where advanced machine learning algorithms are employed to identify critical attack vectors that would trigger massive cascades. Simulations results on IEEE standard benchmarks are evaluated and revealed the impact of sophisticated attacks. The dissertation aims to facilitate our awareness and preparedness toward an attack-resilient smart grid of the future.

Recommended Citation

Yan, Jun, "Smart Grid Security Against Massive Blackouts" (2017). Open Access Dissertations. Paper 608.
https://digitalcommons.uri.edu/oa_diss/608