Physics-Informed Neural Network for Modeling the Dynamic Behavior of Grid-Forming Converters

Abstract

This paper investigates physics-informed neural networks for modeling the full dynamic behavior of droop-controlled grid-forming converters. The approach is trained on synthetic data generated via numerical solvers and benchmarked against both traditional integration methods and a vanilla neural network. Results show higher predictive accuracy than the vanilla network using the same training data and substantially reduced runtime compared with numerical solvers.

This work has been accepted by IFAC for publication under a Creative Commons license CC-BY-NC-ND