Data-driven Modeling of Grid-following Control in Grid-connected Converters

TL;DR

This paper explores data-driven modeling techniques, like sparse identification and deep symbolic regression, to accurately capture the complex dynamics of modern grid-connected converters in renewable energy systems.

Contribution

It applies advanced data-driven methods to model grid-following converters, demonstrating their effectiveness in representing complex power system dynamics.

Findings

Successful synthetic data generation for converter dynamics

Effective identification of system behavior using data-driven methods

Potential for improved modeling of renewable energy integration

Abstract

As power systems evolve with the integration of renewable energy sources and the implementation of smart grid technologies, there is an increasing need for flexible and scalable modeling approaches capable of accurately capturing the complex dynamics of modern grids. To meet this need, various methods, such as the sparse identification of nonlinear dynamics and deep symbolic regression, have been developed to identify dynamical systems directly from data. In this study, we examine the application of a converter-based resource as a replacement for a traditional generator within a lossless transmission line linked to an infinite bus system. This setup is used to generate synthetic data in grid-following control mode, enabling the evaluation of these methods in effectively capturing system dynamics.