CAREER: Physics-informed Graph Learning for Anomaly Detection in Power Systems

This NSF CAREER project aims to improve state estimation and anomaly detection problems for power system operators. The project will bring transformative changes in how operators detect the system states and abnormal events, including cyber-attacks. This will be achieved by using adaptive graph-based and physics-based methods.

The intellectual merits of the project include 1) Development of spatial-temporal graph neural networks (ST-GNN) for state estimation and anomaly detection, 2) Integration of physics into the ST-GNN, 3) Correlation of IT/OT events, and 4) Development of state estimation and anomaly detection at scale. The broader impacts of the project include spurring the interests of future female engineers in sustainable energy and cyber-physical security through Clarkson's Horizons Program, supporting underrepresented undergraduates in research, enhancing collaboration between national labs and universities, and enhancing awareness of utilities through annual workshops for utility students.

Timely identification and mitigation of emerging cyber-physical system (CPS) risks necessitate more sophisticated and resilient tools for state estimation and anomaly detection employed by grid operators. The primary objective of this CAREER project is to improve the state estimation and anomaly detection capabilities of power system operators through adaptive graph-based and physics-based methods.

This objective will be realized through a meticulously crafted work plan aimed at (1) improving the precision and robustness of state estimation using physics-informed learning on graphs and (2) enhancing anomaly detection while achieving scalability through hybrid and distributed approaches. These goals will be pursued through four key research activities: 1) Developing spatial-temporal graph neural networks for enhanced state estimation, 2) Establishing a physics-informed framework to handle imbalanced data, 3) Integrating information technology (IT) data with operational technology (OT) data to bolster anomaly detection, and 4) Creating distributed methods for achieving scalable state estimation and anomaly detection.

On the educational front, the aim is to broaden and strengthen the power engineering workforce by encouraging women to participate in the field, training the next generation of power engineers, and elevating the skills and awareness of current power engineers to effectively navigate emerging CPS threats in power system operations.

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.