首页|期刊导航|中国电机工程学会电力与能源系统学报（英文版）|Active Power Correction Strategies Based on Deep Reinforcement Learning—Part Ⅰ:A Simulation-driven Solution for Robustness
中国电机工程学会电力与能源系统学报（英文版）2022，Vol.8Issue(4)：1122-1133,12.DOI:10.17775/CSEEJPES.2020.07090
Active Power Correction Strategies Based on Deep Reinforcement Learning—Part Ⅰ:A Simulation-driven Solution for Robustness
Active Power Correction Strategies Based on Deep Reinforcement Learning—Part Ⅰ:A Simulation-driven Solution for Robustness
摘要
关键词
Active power corrective control/deep reinforcement learning/graph attention networks/simulation-drivenKey words
Active power corrective control/deep reinforcement learning/graph attention networks/simulation-driven引用本文复制引用
Peidong Xu,Jiajun Duan,Jun Zhang,Yangzhou Pei,Di Shi,Zhiwei Wang,Xuzhu Dong,Yuanzhang Sun..Active Power Correction Strategies Based on Deep Reinforcement Learning—Part Ⅰ:A Simulation-driven Solution for Robustness[J].中国电机工程学会电力与能源系统学报（英文版）,2022,8(4):1122-1133,12.基金项目
The work is supported by the National Key R&D Program of China under Grant 2018AAA0101504 and the Science and technology project of SGCC(State Grid Corporation of China):fundamental theory of human-in-the-loop hybrid-augmented intelligence for power grid dispatch and control. （State Grid Corporation of China）
