Master of Science
Electrical & Computer Engineering
As wind turbine generator systems become more common in the modern power grid, the question of how to adequately protect them from cyber criminals has become a major theme in the development of new control systems. As such, Artificial Intelligence (AI) and Machine Learning (ML) algorithms have become major contributors to preventing, detecting, and mitigating cyber-attacks in the power system. In their current state, wind turbine generator systems are woefully unprepared for a coordinated and sophisticated cyber-attack. With the implementation of the internet-of-things (IoT) devices in the power control network, cyber risks have increased exponentially. In the current literature, prevention and detection of cyber-attacks have been prioritized. This includes event trigger control schemes to detect communication disruption attacks, or AI and ML algorithms to weed out manipulated data. Mitigation has largely been left to power factor correction or fault mitigation devices. Due to the importance of keeping the power system safe and dependable, especially with respect to distributed energy resources, this thesis proposes implementing a cyber secure zero-trust architecture with an AI based, wind turbine generator controller that can prevent cyber attackers from gaining access to vital control functions and mitigate any effects that communication delays or bad data could have on a grid connected wind turbine generator or wind farm. The proposed techniques have been simulated and validated utilizing the MATLAB/Simulink software to demonstrate the effectiveness of the proposed methods.
Dissertation or thesis originally submitted to ProQuest.
Farrar, Nathan Oaks, "Cyber Resilient Wind Turbine Generator Control System" (2023). Electronic Theses and Dissertations. 3323.