September 21, 2022
Conference Paper

Data-driven Resilience Characterization of Control Dynamical Systems


In this paper, we define and quantify resiliency of a power network and propose data-driven algorithms for computing the same for the power grid. To do this, we use the Koopman operator framework to lift the controlled dynamical system to an abstract (possibly higher) dimensional space, where the evolution is linear. The linear system representation allows us to relate small time local controllability and observability of a general nonlinear control system to the controllability and observability of the lifted linear system. Finally, we define the resiliency of the underlying power grid in terms of the controllability and observability gramians of the lifted linear system. We illustrate the proposed approach to compute the resiliency metrics on time-series data obtained from a microgrid.

Published: September 21, 2022


Sinha S., S. Nandanoori, T. Ramachandran, C. Bakker, and A. Singhal. 2022. Data-driven Resilience Characterization of Control Dynamical Systems. In American Control Conference (ACC 2022), June 8-10, 2022, Atlanta, GA, 2186-2193. Piscataway, New Jersey:IEEE. PNNL-SA-166887. doi:10.23919/ACC53348.2022.9867785