Grid-forming Control of Converter Infinite Bus System: Modeling by Data-driven Methods

Abstract

This study explores data-driven modeling techniques to capture the dynamics of a grid-forming converter-based infinite bus system, critical for renewable-integrated power grids. Using sparse identification of nonlinear dynamics and deep symbolic regression, models were generated from synthetic data simulating key disturbances in active power, reactive power, and voltage references. Deep symbolic regression demonstrated more accuracy in capturing complex system dynamics, though it required substantially more computational time than sparse identification of nonlinear dynamics. These findings suggest that while deep symbolic regression offers high fidelity, sparse identification of nonlinear dynamics provides a more computationally efficient approach, balancing accuracy and runtime for real-time grid applications.