Enhanced physics-informed neural networks (PINNs) for high-order power grid dynamics
Machine Learning
2024-10-11 v1 Systems and Control
Systems and Control
Abstract
We develop improved physics-informed neural networks (PINNs) for high-order and high-dimensional power system models described by nonlinear ordinary differential equations. We propose some novel enhancements to improve PINN training and accuracy and also implement several other recently proposed ideas from the literature. We successfully apply these to study the transient dynamics of synchronous generators. We also make progress towards applying PINNs to advanced inverter models. Such enhanced PINNs can allow us to accelerate high-fidelity simulations needed to ensure a stable and reliable renewables-rich future grid.
Cite
@article{arxiv.2410.07527,
title = {Enhanced physics-informed neural networks (PINNs) for high-order power grid dynamics},
author = {Vineet Jagadeesan Nair},
journal= {arXiv preprint arXiv:2410.07527},
year = {2024}
}
Comments
Accepted to the Tackling Climate Change with Machine Learning workshop at NeurIPS 2024