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We propose a new method to efficiently compute load-flows (the steady-stateof the power-grid for given productions, consumptions and grid topology),substituting conventional simulators based on differential equation solvers. Weuse a deep feed-forward neural network trained with load-flows precomputed bysimulation. Our architecture permits to train a network on so-called "n-1"problems, in which load flows are evaluated for every possible linedisconnection, then generalize to "n-2" problems without retraining (a clearadvantage because of the combinatorial nature of the problem). To that end, wedeveloped a technique bearing similarity with "dropout", which we named "guideddropout".

Fast Power system security analysis with Guided Dropout