Neuro-physical dynamic load modeling using differentiable parametric optimization [article]

Shrirang Abhyankar, Jan Drgona, Andrew August, Elliot Skomski, Aaron Tuor
2022 arXiv   pre-print
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
more » ... ogram. The performance and accuracy of this neurophysical ZIP load model is presented on a medium-scale 350-bus transmission-distribution network.
arXiv:2203.10582v1 fatcat:ca46n6anebd63kjix6vtya7u3e