Neuro-physical dynamic load modeling using differentiable parametric optimization
Systems and Control
2022-04-12 v1 Machine Learning
Neural and Evolutionary Computing
Systems and Control
Abstract
In this work, we investigate a data-driven approach for obtaining a reduced equivalent load model of distribution systems for electromechanical transient stability analysis. The proposed reduced equivalent is a neuro-physical model comprising of a traditional ZIP load model augmented with a neural network. This neuro-physical model is trained through differentiable programming. We discuss the formulation, modeling details, and training of the proposed model set up as a differential parametric program. The performance and accuracy of this neurophysical ZIP load model is presented on a medium-scale 350-bus transmission-distribution network.
Cite
@article{arxiv.2203.10582,
title = {Neuro-physical dynamic load modeling using differentiable parametric optimization},
author = {Shrirang Abhyankar and Jan Drgona and Andrew August and Elliot Skomski and Aaron Tuor},
journal= {arXiv preprint arXiv:2203.10582},
year = {2022}
}
Comments
7 pages, 9 figures