3 papers
eess.SY2022
Structure-Preserving Model Reduction for Nonlinear Power Grid Network
Bita Safaee, Serkan Gugercin
We develop a structure-preserving system-theoretic model reduction framework for nonlinear power grid networks. First, via a lifting transformation, we convert the original nonline…
eess.SY2021
Data-driven modeling of power networks
Bita Safaee, Serkan Gugercin
We develop a non-intrusive data-driven modeling framework for power network dynamics using the Lift and Learn approach of \cite{QianWillcox2020}. A lifting map is applied to the sn…
eess.SY2021
Structure-preserving Model Reduction of Parametric Power Networks
Bita Safaee, Serkan Gugercin
We develop a structure-preserving parametric model reduction approach for linearized swing equations where parametrization corresponds to variations in operating conditions. We emp…