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Fast Power system security analysis with Guided Dropout

Abstract : We propose a new method to efficiently compute load-flows (the steady-state of the power-grid for given productions, consumptions and grid topology), substituting conventional simulators based on differential equation solvers. We use a deep feed-forward neural network trained with load-flows precomputed by simulation. Our architecture permits to train a network on so-called " n-1 " problems, in which load flows are evaluated for every possible line disconnection, then generalize to " n-2 " problems without retraining (a clear advantage because of the combinatorial nature of the problem). To that end, we developed a technique bearing similarity with " dropout " , which we named " guided dropout ".
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Contributor : Benjamin Donnot <>
Submitted on : Monday, January 29, 2018 - 5:16:36 PM
Last modification on : Tuesday, April 21, 2020 - 1:08:28 AM
Document(s) archivé(s) le : Friday, May 25, 2018 - 2:32:51 PM


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  • HAL Id : hal-01695793, version 1
  • ARXIV : 1801.09870


Benjamin Donnot, Isabelle Guyon, Marc Schoenauer, Antoine Marot, Patrick Panciatici. Fast Power system security analysis with Guided Dropout. 26th European Symposium on Artificial Neural Networks, Apr 2018, Bruges, Belgium. ⟨hal-01695793⟩



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