{"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/guided-machine-learning-for-power-grid","title":"Guided Machine Learning for power grid segmentation","arxiv_id":"1711.09715","date":"2017-11-13","proceeding":null,"authors":["Antoine Marot","Sami Tazi","Benjamin Donnot","Patrick Panciatici"],"abstract":"The segmentation of large scale power grids into zones is crucial for control\nroom operators when managing the grid complexity near real time. In this paper\nwe propose a new method in two steps which is able to automatically do this\nsegmentation, while taking into account the real time context, in order to help\nthem handle shifting dynamics. Our method relies on a \"guided\" machine learning\napproach. As a first step, we define and compute a task specific \"Influence\nGraph\" in a guided manner. We indeed simulate on a grid state chosen\ninterventions, representative of our task of interest (managing active power\nflows in our case). For visualization and interpretation, we then build a\nhigher representation of the grid relevant to this task by applying the graph\ncommunity detection algorithm \\textit{Infomap} on this Influence Graph. To\nillustrate our method and demonstrate its practical interest, we apply it on\ncommonly used systems, the IEEE-14 and IEEE-118. We show promising and original\ninterpretable results, especially on the previously well studied RTS-96 system\nfor grid segmentation. We eventually share initial investigation and results on\na large-scale system, the French power grid, whose segmentation had a\nsurprising resemblance with RTE's historical partitioning.","url_abs":"http://arxiv.org/abs/1711.09715v3","url_pdf":"http://arxiv.org/pdf/1711.09715v3.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":"guided-machine-learning-for-power-grid","repo_url":"https://github.com/rte-france/grid2op-milp-agent","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"community-detection","task_name":"Community Detection"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}