{"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/adaptive-power-system-emergency-control-using","title":"Adaptive Power System Emergency Control using Deep Reinforcement Learning","arxiv_id":"1903.03712","date":"2019-03-09","proceeding":null,"authors":["Qiuhua Huang","Renke Huang","Weituo Hao","Jie Tan","Rui Fan","Zhenyu Huang"],"abstract":"Power system emergency control is generally regarded as the last safety net\nfor grid security and resiliency. Existing emergency control schemes are\nusually designed off-line based on either the conceived \"worst\" case scenario\nor a few typical operation scenarios. These schemes are facing significant\nadaptiveness and robustness issues as increasing uncertainties and variations\noccur in modern electrical grids. To address these challenges, for the first\ntime, this paper developed novel adaptive emergency control schemes using deep\nreinforcement learning (DRL), by leveraging the high-dimensional feature\nextraction and non-linear generalization capabilities of DRL for complex power\nsystems. Furthermore, an open-source platform named RLGC has been designed for\nthe first time to assist the development and benchmarking of DRL algorithms for\npower system control. Details of the platform and DRL-based emergency control\nschemes for generator dynamic braking and under-voltage load shedding are\npresented. Extensive case studies performed in both two-area four-machine\nsystem and IEEE 39-Bus system have demonstrated the excellent performance and\nrobustness of the proposed schemes.","url_abs":"http://arxiv.org/abs/1903.03712v2","url_pdf":"http://arxiv.org/pdf/1903.03712v2.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":"adaptive-power-system-emergency-control-using","repo_url":"https://github.com/RLGC-Project/RLGC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.03712","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.03712"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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