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

2026/07/24 by Hussein Jaffal, Arianna Fois, Sarra Bouchkati +2
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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.

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