RII Track-4: Robust Matrix Completion State Estimation in Low-Observability Distribution Systems under False Data Injection Attacks

Kansas State University, Manhattan KS

Investigators

Abstract

The operational landscape at electric distribution grids is undergoing a radical transformation. Notably, the impact of distributed renewable energy sources and the impetus to improve cybersecurity are challenging the status quo and calling for innovative techniques to enhance situational awareness in the distribution grid. With the support of an EPSCoR Research Fellowship, the PI and a Ph.D. student will receive training on new techniques, including a novel state estimation approach and a next-generation cyber-physical system simulation platform, at the National Renewable Energy Laboratory (NREL). The PI and the student will closely collaborate with NREL researchers by focusing on how to acquire better state estimation in low-observability distribution grids under cyber data attacks. This fellowship will provide an excellent opportunity for a Ph.D. student and an underrepresented undergraduate to gain valuable experience and develop new skillsets. The PI will bring the new techniques back to the home institution, i.e., Kansas State University (KSU), and introduce them to other investigators in related fields. This fellowship will foster a strong partnership between KSU and NREL, and help the state of Kansas better meet its renewable energy goals. Legacy distribution systems traditionally have very low observability due to a limited amount of sensors. Obtaining required situational awareness is currently not feasible due to the immense scale of the network and limited availability of measurements. While the use of information from advanced metering infrastructure and phasor measurement units can improve the observability of distribution systems, another growing concern with the use of information is its susceptibility to cyber data attacks. The overarching goal of this fellowship is to support the PI?s training and collaborative research at the National Renewable Energy Laboratory (NREL). The training and research will focus on the development and validation of a novel state estimation approach in the low-observability distribution system under cyber data attacks. Specific training and research objectives include: i) developing a bad data detection approach based on matrix completion state estimation; ii) designing a fully distributed solution for scalability; iii) modeling false data injection attacks in distribution networks; iv) receiving training on Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS) platform; and v) performing proof-of-concept validation. This project will expand the PI?s research capacity and transform his career path towards a promising direction in enhancing cybersecurity of the power grid. This project will result in novel tools that can be used by system operators to enhance the situational awareness of distribution systems. The research outcome of this project will be integrated into the PI's courses to aid the retention of current STEM students. 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.