{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fast-power-system-security-analysis-with","title":"Fast Power system security analysis with Guided Dropout","arxiv_id":"1801.09870","date":"2018-01-30","proceeding":null,"authors":["Benjamin Donnot","Isabelle Guyon","Marc Schoenauer","Antoine Marot","Patrick Panciatici"],"abstract":"We propose a new method to efficiently compute load-flows (the steady-state\nof the power-grid for given productions, consumptions and grid topology),\nsubstituting conventional simulators based on differential equation solvers. We\nuse a deep feed-forward neural network trained with load-flows precomputed by\nsimulation. Our architecture permits to train a network on so-called \"n-1\"\nproblems, in which load flows are evaluated for every possible line\ndisconnection, then generalize to \"n-2\" problems without retraining (a clear\nadvantage because of the combinatorial nature of the problem). To that end, we\ndeveloped a technique bearing similarity with \"dropout\", which we named \"guided\ndropout\".","url_abs":"http://arxiv.org/abs/1801.09870v1","url_pdf":"http://arxiv.org/pdf/1801.09870v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fast-power-system-security-analysis-with","repo_url":"https://github.com/BDonnot/FPSSA-GuidedDropout","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}