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.
@article{arxiv.1908.08314,
title = {LEAP nets for power grid perturbations},
author = {Benjamin Donnot and Balthazar Donon and Isabelle Guyon and Zhengying Liu and Antoine Marot and Patrick Panciatici and Marc Schoenauer},
journal= {arXiv preprint arXiv:1908.08314},
year = {2019}
}