A prior knowledge-embedded reinforcement learning method for real-time active power corrective control in complex power systems

A prior knowledge-embedded reinforcement learning method for real-time active power corrective control in complex power systems

With the increasing uncertainty and complexity of modern power grids, the real-time active power corrective control problem becomes intractable, bringing significant challenges to the stable operation of future power systems. To promote effective and efficient active power corrective control, a prio...

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Bibliographic Details
Main Authors: Peidong Xu, Jun Zhang, Jixiang Lu, Haoran Zhang, Tianlu Gao, Siyuan Chen
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-09-01
Series:Frontiers in Energy Research
Subjects:
active power corrective control
deep Q-learning from demonstrations
graph pooling
power system
prior knowledge
Online Access:https://www.frontiersin.org/articles/10.3389/fenrg.2022.1009545/full

Internet

https://www.frontiersin.org/articles/10.3389/fenrg.2022.1009545/full

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