ORCID

https://orcid.org/0000-0003-1040-9769

Keywords

Cyber-physical power systems security; cyber-resilient distribution grid operation; hardware-in-the-loop simulation; grid edge artificial intelligence; large language models; resilient load restoration

Subject Categories

Electrical and Computer Engineering | Power and Energy | Systems Engineering

Abstract

Modern power distribution systems are rapidly evolving into cyber-physical, DER-rich, and data-driven networks that rely on extensive sensing, communication, automation, grid-edge intelligence, and operator decision support. While this transformation improves flexibility, observability and controllability, it also expands the cyber-attack surface and increases the risk that cyber intrusions can propagate into physical disturbances, compromised DER operation, degraded situational awareness, and interrupted service continuity. This dissertation advances the cyber-physical security and resilience of modern distribution systems by developing a high-fidelity real-time cyber-physical hardware-in-the-loop testbed using OPAL-RT, EXata CPS, industrial relays, SCADA/RTAC, HMI, and grid-edge devices to emulate realistic DER-integrated distribution grid operation. The developed testbed enables vulnerability-driven assessment of hardware, software, communication, and access-control weaknesses, as well as real-time quantification of the physical impacts caused by DER set-point manipulation, inverter voltage-reference tampering, topology attacks, and coordinated multiwave cyber intrusions. Building on this experimental foundation, an edge-AI-enabled monitoring framework is implemented through a custom DNP3 data-streaming pipeline and industrial edge IED deployment for online cyberattack detection. To further enhance trustworthy situational awareness, a calibrated ensemble framework combining XGBoost, Transformer Encoder modeling, and explainable artificial intelligence is developed to detect and localize unobservable false-data injection attacks at the bus, phase, and measurement-channel levels. The dissertation also investigates a domain-specific large-language-model-based cybersecurity framework for vulnerability assessment, anomaly interpretation, system-log reasoning, threat reporting, and human-in-the-loop operational decision-making. Finally, resilient recovery is addressed through a rotational load restoration framework that models time-dependent cold load pickup, prioritizes critical loads, and optimizes equitable service restoration under limited power availability. Together, these contributions provide an integrated pathway from realistic cyber-physical experimentation and secure operation to intelligent monitoring and resilient recovery for modern active distribution grids.

Completion Date

2026

Semester

Summer

Committee Chair

Sun, Wei

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Electrical and Computer Engineering

Format

PDF

Document Type

Dissertation

Language

English

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