PhD Oral Exam - Masoud Babaei Vavdareh, Information and Systems Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

Modern power systems are increasingly evolving into cyber-physical infrastructures due to the integration of wide-area monitoring and control technologies and inverter-based renewable resources. While these advancements enhance system observability and operational efficiency, they also introduce new vulnerabilities to cyber threats that can compromise stability and reliability. This thesis generally investigates and addresses the impact of cyberattacks on wide-area damping controllers (WADCs) and inverter-based resources (IBRs), with a particular focus on false data injection attacks (FDIAs) and resonance-based disturbances. Regarding damping controllers, a systematic framework is developed to design realistic and stealthy FDIAs by considering system dynamics, operational constraints, and attack timing. To address these threats, a data-driven detection approach based on a modified conditional generative adversarial network (MCGAN) is proposed, enabling improved performance under limited and imbalanced datasets. In addition, an alternative model-based defense strategy based on a cooperative control is introduced to maintain system stability without relying on redundant measurements. Regarding the wide-area support functions received by IBRs, a class of resonance-based attacks targeting IBRs is investigated, demonstrating how high-frequency resonance can be exploited to induce instability. To detect such attacks, a frequency- aware framework and a neural network trained by stochastic gradient descent with momentum (SGDM) is developed, and different mitigation approaches are investigated. Similar to the previous study, but focused on lower-frequency oscillations, an event-triggered data-driven control strategy is also proposed to enhance oscillation damping while reducing unnecessary control actions. The effectiveness of the proposed methods is validated on benchmark power systems, demonstrating improved resilience against threats.