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Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning
[article]
2022
arXiv
pre-print
As power systems are undergoing a significant transformation with more uncertainties, less inertia and closer to operation limits, there is increasing risk of large outages. Thus, there is an imperative need to enhance grid emergency control to maintain system reliability and security. Towards this end, great progress has been made in developing deep reinforcement learning (DRL) based grid control solutions in recent years. However, existing DRL-based solutions have two main limitations: 1)
arXiv:2101.05317v2
fatcat:55lmzubo5fhrfjsderg3csbfae