136 citations · 221 across the 23 of their papers we have counts for
23 papers
Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning
Sayak Mukherjee, Ramij R. Hossain, Yuan Liu +6
This paper presents a novel federated reinforcement learning (Fed-RL) methodology to enhance the cyber resiliency of networked microgrids. We formulate a resilient reinforcement le…
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…
Safe Reinforcement Learning for Grid Voltage Control
Thanh Long Vu, Sayak Mukherjee, Renke Huang +1
Under voltage load shedding has been considered as a standard approach to recover the voltage stability of the electric power grid under emergency conditions, yet this scheme usual…
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…
Learning to run a power network with trust
Antoine Marot, Benjamin Donnot, Karim Chaouache +4
Artificial agents are promising for real-time power network operations, particularly, to compute remedial actions for congestion management. However, due to high reliability requir…
Improving Safety in Deep Reinforcement Learning using Unsupervised Action Planning
Hao-Lun Hsu, Qiuhua Huang, Sehoon Ha
One of the key challenges to deep reinforcement learning (deep RL) is to ensure safety at both training and testing phases. In this work, we propose a novel technique of unsupervis…
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…
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…
Component Importance and Interdependence Analysis for Transmission, Distribution and Communication Systems
Tao Fu, Dexin Wang, Xiaoyuan Fan +1
For critical infrastructure restoration planning, the real-time scheduling and coordination of system restoration efforts, the key in decision-making is to prioritize those critica…
Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning
Renke Huang, Yujiao Chen, Tianzhixi Yin +6
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,…
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…
Achieving Disaster-Resilient Distribution Systems via Emergency Response Resources: A Practical Approach
Santosh Sharma, Qifeng Li, Qiuhua Huang +1
This paper presents a practical approach to utilizing emergency response resources (ERRs) and post-disaster available distributed energy resources (PDA-DERs) to improve the resilie…
Flexibility Management in Economic Dispatch with Dynamic Automatic Generation Control
Lei Fan, Chaoyue Zhao, Guangyuan Zhang +1
As the installation of electronically interconnected renewable energy resources grows rapidly in power systems, system frequency maintenance and control become challenging problems…
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…
Hybrid EMT-TS Simulation Strategies to Study High Bandwidth MMC-Based HVdc Systems
Yuan Liu, Marcelo A. Elizondo, Suman Debnath +7
Modular multilevel converters (MMCs) are widely used in the design of modern high-voltage direct current (HVdc) transmission system. High-fidelity dynamic models of MMCs-based HVdc…
A Nonlinear Regression Method for Composite Protection Modeling of Induction Motor Loads
Soumya Kundu, Zhigang Chu, Yuan Liu +6
Protection equipment is used to prevent damage to induction motor loads by isolating them from power systems in the event of severe faults. Modeling the response of induction motor…
Adaptive Power System Emergency Control using Deep Reinforcement Learning
Qiuhua Huang, Renke Huang, Weituo Hao +3
Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based…
A Learning-based Power Management for Networked Microgrids Under Incomplete Information
Qianzhi Zhang, Kaveh Dehghanpour, Zhaoyu Wang +1
This paper presents an approximate Reinforcement Learning (RL) methodology for bi-level power management of networked Microgrids (MG) in electric distribution systems. In practice,…
Impact of Building-Level Motor Protection on Power System Transient Behaviors
Y. Liu, Y. Zhang, Q. Huang +6
Protection strategies for transmission and distribution systems have been extensively investigated to facilitate better coordination of physical protection devices. A diverse range…
Advanced EMT and Phasor-Domain Hybrid Simulation with Simulation Mode Switching Capability for Transmission and Distribution Systems
Qiuhua Huang, Vijay Vittal
Conventional electromagnetic transient (EMT) and phasor-domain hybrid simulation approaches presently exist for trans-mission system level studies. Their simulation efficiency is g…
InterPSS: A New Generation Power System Simulation Engine
Mike Zhou, Qiuhua Huang
This paper presents the design of InterPSS simulation engine, including its object model, open software architecture, and software development process. Several advanced application…
A Comparative Study of Interface Techniques for Transmission and Distribution Dynamic Co-Simulation
Qiuhua Huang, Renke Huang, Rui Fan +5
Transmission and distribution dynamic co-simulation is a practical and effective approach to leverage existing simulation tools for transmission and distribution systems to simulat…
A Reference Implementation of WECC Composite Load Model in Matlab and GridPACK
Qiuhua Huang, Renke Huang, Bruce J. Palmer +5
The composite load model (CLM) proposed by the Western Electricity Coordinating Council (WECC) is gaining increasing traction in industry, particularly in North America. At the sam…