The increasing presence of renewable generations and distributed energy resources in transmission systems heightens the need for fast-timescale situational awareness for system reliability, resiliency, and both the operational and cyber security. Despite the invention of phasor measurement units that promised close-to-real-time monitoring of the system states, the limited deployment of phasor measurement units had hampered the ability of the system operator to uncover trends of instability, react to system contingencies, and detect malicious attacks on the power grid.
This research develops new hardware and software solutions for high-fidelity, high-resolution, and secure monitoring and control of the future grid. By harnessing and exploiting the increasingly abundant and diverse data sources and through novel applications of machine learning and artificial intelligence, this research advances the state-of-the-art monitoring of cyber-physical systems in three fronts. First, this research develops machine learning approaches to high-resolution state estimation for power systems that are unobservable by existing phasor measurement units. Second, this research offers new solutions to detecting and mitigating data anomaly caused by malfunctions of sensors, communications systems, and cyber attacks by adversarial agents. Third, this research develops a new hardware architecture and prototypes for future digital substations that provide hardware-based security.
This research has broader impacts on enhancing national security in critical infrastructures, promoting economic competitiveness through accelerated adoption of phasor measurement technology, and broadening participation of women and under-represented minority groups in science, technology, engineering, and mathematics.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
