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arXiv · 2203.10582

Neuro-physical dynamic load modeling using differentiable parametric optimization

Shrirang Abhyankar·Jan Drgona·Andrew August·Elliot Skomski·Aaron Tuor
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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.

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Shrirang Abhyankar, Jan Drgona, Andrew August, Elliot Skomski, Aaron Tuor. 2022-03-20. Neuro-physical dynamic load modeling using differentiable parametric optimization. https://arxiv.org/abs/2203.10582

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