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Neuro-physical dynamic load modeling using differentiable parametric optimization
[article]
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
arXiv:2203.10582v1
fatcat:ca46n6anebd63kjix6vtya7u3e