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Thesis defences

PhD Oral Exam - Hang Du, Information Systems Engineering

Advancing Cybersecurity in Power Grids with High Penetration of Wind Energy: From Modeling to Mitigation of Cyberattacks against Wind Farms


Date & time
Tuesday, February 25, 2025
2 p.m. – 5 p.m.
Cost

This event is free

Organization

School of Graduate Studies

Contact

Dolly Grewal

Wheel chair accessible

Yes

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

The increasing integration of wind energy and the reliance on digital communication systems for the remote operation of wind farms (WFs) have significantly expanded the grid’s vulnerability to cyberattacks. These risks are particularly concerning for power grids with degrading system strength (SS) and high penetration of converter-based resources, where cyber-induced instabilities threaten grid stability. This thesis investigates critical aspects of cyber risks in wind-integrated power grids, focusing on attack modeling and prevention, risk management, and mitigation strategies.

The first part introduces a cyberattack model targeting SS provision to permanent magnet synchronous generator (PMSG)-based WFs. To address this, an anomalous command verification (ACV) module is proposed to detect and prevent cyber-induced converter-driven instabilities by estimating SS buffer capacity. The second part develops a cyber-informed risk management framework using Bayesian attack graphs and distributionally robust optimization (DRO) to quantify and mitigate cyber risks. This framework incorporates converter-driven stability support (CDSS) from WFs and is validated on the IEEE 118-bus system. The third part focuses on mitigating attack-induced instabilities in offshore wind farms (OWFs) connected via High Voltage Direct Current (HVDC) systems. It introduces a physics-informed iterative control (PIC) strategy based on neural networks to reduce instability magnitudes progressively.

These contributions provide an integrated approach to addressing cyber risks in wind-integrated power grids, enhancing their security and stability in the face of evolving threats.

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