2k citations · 3.7k across the 51 of their papers we have counts for
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Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learning
Ramij R. Hossain, Tianzhixi Yin, Yan Du +5
This article proposes a model-based deep reinforcement learning (DRL) method to design emergency control strategies for short-term voltage stability problems in power systems. Rece…
Physics-informed Evolutionary Strategy based Control for Mitigating Delayed Voltage Recovery
Yan Du, Qiuhua Huang, Renke Huang +4
In this work we propose a novel data-driven, real-time power system voltage control method based on the physics-informed guided meta evolutionary strategy (ES). The main objective…
Safe Reinforcement Learning for Emergency LoadShedding of Power Systems
Thanh Long Vu, Sayak Mukherjee, Tim Yin +3
The paradigm shift in the electric power grid necessitates a revisit of existing control methods to ensure the grid's security and resilience. In particular, the increased uncertai…
Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control
Renke Huang, Yujiao Chen, Tianzhixi Yin +6
Load shedding has been one of the most widely used and effective emergency control approaches against voltage instability. With increased uncertainties and rapidly changing operati…