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arXiv · 1908.08314

LEAP nets for power grid perturbations

Benjamin Donnot·Balthazar Donon·Isabelle Guyon·Zhengying Liu·Antoine Marot·Patrick Panciatici·Marc Schoenauer
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Abstract

We propose a novel neural network embedding approach to model power transmission grids, in which high voltage lines are disconnected and reconnected with one-another from time to time, either accidentally or willfully. We call our architeture LEAP net, for Latent Encoding of Atypical Perturbation. Our method implements a form of transfer learning, permitting to train on a few source domains, then generalize to new target domains, without learning on any example of that domain. We evaluate the viability of this technique to rapidly assess cu-rative actions that human operators take in emergency situations, using real historical data, from the French high voltage power grid.

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Benjamin Donnot, Balthazar Donon, Isabelle Guyon, Zhengying Liu, Antoine Marot, Patrick Panciatici, Marc Schoenauer. 2019-08-22. LEAP nets for power grid perturbations. https://arxiv.org/abs/1908.08314

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