8 citations · 17 across the 7 of their papers we have counts for
10 papers
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…
Barrier Function-based Safe Reinforcement Learning for Emergency Control of Power Systems
Thanh Long Vu, Sayak Mukherjee, Renke Huang +1
Under voltage load shedding has been considered as a standard and effective measure to recover the voltage stability of the electric power grid under emergency and severe condition…
Quantifying Bounds of Model Gap for Synchronous Generators
Peng Wang, Shaobu Wang, Renke Huang +1
In practice, uncertainties in parameters and model structures always cause a gap between a model and the corresponding physical entity. Hence, to evaluate the performance of a mode…
Scalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning
Sayak Mukherjee, Renke Huang, Qiuhua Huang +2
This paper presents a novel hierarchical deep reinforcement learning (DRL) based design for the voltage control of power grids. DRL agents are trained for fast, and adaptive select…
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…