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arXiv · 1801.09870

Fast Power system security analysis with Guided Dropout

Benjamin Donnot·Isabelle Guyon·Marc Schoenauer·Antoine Marot·Patrick Panciatici
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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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Benjamin Donnot, Isabelle Guyon, Marc Schoenauer, Antoine Marot, Patrick Panciatici. 2018-01-30. Fast Power system security analysis with Guided Dropout. https://arxiv.org/abs/1801.09870

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