Data-driven modeling of power networks

Abstract

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 snapshot data obtained from the original nonlinear swing equations describing the underlying power network such that the lifted-data corresponds to quadratic nonlinearity. The lifted data is then projected onto a lower dimensional basis and the reduced quadratic matrices are fit to this reduced lifted data using a least-squares measure. The effectiveness of the proposed approach is investigated by two power network models.

References in corpus (4)

• Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems
• Comparative analysis of existing models for power-grid synchronization
• Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms
• Balanced Truncation Model Order Reduction For Quadratic-Bilinear Control Systems