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Supervised assisted deep reinforcement learning for emergency voltage control of power systems

文献类型：期刊论文

作者Li, Xiaoshuang5,6; Wang, Xiao4,6; Zheng, Xinhu3; Dai, Yuxin2; Yu, Zhihong1; Zhang, Jun Jason2; Bu, Guangquan1; Wang, Fei-Yue6
刊名NEUROCOMPUTING
出版日期2022-02-28
卷号475页码:69-79
关键词Deep reinforcement learning Behavioral cloning Dynamic demonstration Emergency control
ISSN号0925-2312
DOI10.1016/j.neucom.2021.12.043
通讯作者Wang, Fei-Yue(feiyue.wang@ia.ac.cn)
英文摘要The increasing complexity of power systems makes existing deep reinforcement learning-based emergency voltage control methods face challenges in learning speed and data utilization efficiency. Meanwhile, the accumulated data containing expert experience and domain knowledge has not been fully utilized to improve the performance of the deep reinforcement learning methods. To address the above issues, a novel hybrid emergency voltage control method that combines expert experience and machine intelligence is proposed in this paper. Specifically, the expert experience in the off-line demonstration is extracted through a behavioral cloning model and the deep reinforcement learning method is applied to discover and learn new knowledge autonomously. A special supervised expert loss is designed to utilize the pre-trained behavioral cloning model to assist the self-learning process. The demonstration is dynamically updated during the training process such that the behavioral cloning model and the deep reinforcement learning model can facilitate each other continuously. Experiments are conducted on the open-source RLGC platform to validate the performance and the experimental results show that the proposed method can effectively improve the learning speed and the applicability of the model to different test situations. (c) 2021 Elsevier B.V. All rights reserved.
WOS关键词GAME ; GO
资助项目National Key R&D Program of China[2018AAA0101500] ; National Key R&D Program of China[2018AAA0101502]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000761797700006
出版者ELSEVIER
资助机构National Key R&D Program of China
源URL[http://ir.ia.ac.cn/handle/173211/48047]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Wang, Fei-Yue
作者单位1.China Elect Power Res Inst, State Key Lab Power Grid Safety & Energy Conserva, Beijing 100192, Peoples R China
2.Wuhan Univ, Sch Elect Engn & Automat, Wuhan 430072, Peoples R China
3.Univ Minnesota, Dept Elect & Comp Engn, Minneapolis, MN 55455 USA
4.Qingdao Acad Intelligent Ind, Parallel Intelligence Res Ctr, Qingdao 266109, Peoples R China
5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
6.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Li, Xiaoshuang,Wang, Xiao,Zheng, Xinhu,et al. Supervised assisted deep reinforcement learning for emergency voltage control of power systems[J]. NEUROCOMPUTING,2022,475:69-79.
APA Li, Xiaoshuang.,Wang, Xiao.,Zheng, Xinhu.,Dai, Yuxin.,Yu, Zhihong.,...&Wang, Fei-Yue.(2022).Supervised assisted deep reinforcement learning for emergency voltage control of power systems.NEUROCOMPUTING,475,69-79.
MLA Li, Xiaoshuang,et al."Supervised assisted deep reinforcement learning for emergency voltage control of power systems".NEUROCOMPUTING 475(2022):69-79.

入库方式： OAI收割

来源：自动化研究所

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