Identification of Power Systems with Droop-Controlled Units Using Neural Ordinary Differential Equations

TL;DR

This paper investigates neural ordinary differential equations (NODEs) for modeling power system dynamics with droop-controlled units, offering a data-driven approach that balances accuracy and model flexibility in complex, partially unknown systems.

Contribution

It introduces the application of NODEs to identify power system dynamics, comparing their performance with SINDy, and demonstrates their effectiveness without prior system knowledge.

Findings

NODEs achieve good prediction accuracy in power system modeling.

SINDy provides more accurate models when system nonlinearities are known.

NODEs do not require prior knowledge of system nonlinearities.

Abstract

In future power systems, the detailed structure and dynamics may not always be fully known. This is due to an increasing number of distributed energy resources, such as photovoltaic generators, battery storage systems, heat pumps and electric vehicles, as well as a shift towards active distribution grids. Obtaining physically-based models for simulation and control synthesis can therefore become challenging. Differential equations, where the right-hand side is represented by a neural network, i.e., neural ordinary differential equations (NODEs), have a great potential to serve as a data-driven black-box model to overcome this challenge. This paper explores their use in identifying the dynamics of droop-controlled grid-forming units based on inputs and state measurements. In numerical studies, various NODE structures used with different numerical solvers are trained and evaluated. Moreover, they are compared to the sparse identification of nonlinear dynamics (SINDy) method. The results demonstrate that even though SINDy yields more accurate models, NODEs achieve good prediction performance without prior knowledge about the system's nonlinearities which SINDy requires to work best.