activity
20172021
most citedScalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning

8 citations · 9 across the 3 of their papers we have counts for

collaborators
Showing eess.SYShow all

5 papers · 1 filter

eess.SY2021

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…

eess.SY20218 cited

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…

eess.SY2020

Coordinated Frequency and Voltage Regulation of Grid-Following and Grid-Forming Inverters

Ankit Singhal, Thanh Long Vu, Wei Du

In a purely inverter-based microgrid, both grid-forming (GFM) and grid-following (GFL) inverters will have a crucial role to play in frequency/voltage regulation and maintaining po…

eess.SY2020

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…

eess.SY20201 cited

Imposing Robust Structured Control Constraint on Reinforcement Learning of Linear Quadratic Regulator

Sayak Mukherjee, Thanh Long Vu

This paper discusses learning a structured feedback control to obtain sufficient robustness to exogenous inputs for linear dynamic systems with unknown state matrix. The structural…