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Neuro-physical dynamic load modeling using differentiable parametric optimization

arXiv 2203.10582 ↗First posted 20 Mar 2022eess.SY cs.LG cs.NEcited by 0

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Abstract (arXiv)

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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References · 14

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2017OptNet: Differentiable Optimization as a Layer in Neural Networkscited by 599
  3. 2019Differentiable Convex Optimization Layerscited by 465
  4. 2018Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimizationcited by 227
  5. 2021DC3: A learning method for optimization with hard constraintscited by 184
  6. 2015Constrained Convolutional Neural Networks for Weakly Supervised Segmentationcited by 100
  7. 2017Constrained Deep Weak Supervision for Histopathology Image Segmentationcited by 51
  8. 2017Imposing Hard Constraints on Deep Networks: Promises and Limitationscited by 43
  9. 2019Constrained Deep Networks: Lagrangian Optimization via Log-Barrier Extensionscited by 30
  10. 2020Linearly Constrained Neural Networkscited by 18
  11. 2021Deep Learning Explicit Differentiable Predictive Control Laws for Buildingscited by 11
  12. 2018Constrained Deep Learning using Conditional Gradient and Applications in Computer Visioncited by 7
2 more
  1. 2017Task-based End-to-end Model Learning in Stochastic Optimizationcited by 4
  2. 2017Adaptive Nonlinear Model Reduction for Fast Power System Simulationcited by 1

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