A Q-learning based model-free wide-area damping control under GPS spoofing attacks

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Ensuring the cybersecurity of modern electric power grids is critical to the social function and national security. The potential consequences of a successful large-scale cyberattack on a power grid cannot be overstated, since grid failures are not only costly but also profoundly disrupt people's daily lives and the delivery of essential services (water, food, health care, etc.). The proposed research project intends to study the impacts of one specific cyberattack, namely the global position system (GPS) spoofing attacks towards phasor measurement units (PMUs), on the wide-area damping control (WADC) of power systems and design a resilient WADC against the GPS spoofing attacks using machine learning techniques. The research outcomes will pave the way for enhancing the overall security and stability of Canadian power grids resilient to the GPS spoofing attacks. The results will also shed light on how we can defend against cyberattacks on electric power grids and minimize potential catastrophic consequences, ensuring the security and integrity of Canadian electric power grids.