30 citations · 65 across the 13 of their papers we have counts for
16 papers
Learning-Based Augmentation and Adaptation for Grid Sim-to-Real Model Discrepancy
Sayak Mukherjee, Kyung-Bin Kwon, Ramij R. Hossain +1
Modern power systems can encounter increased discrepancy between the operators' simulation model and the actual true dynamics of the grid, driven by uncertainties caused by integra…
Policy Gradient-Based EMT-in-the-Loop Learning to Mitigate Sub-Synchronous Control Interactions
Sayak Mukherjee, Ramij R. Hossain, Kaustav Chatterjee +2
This paper explores the development of learning-based tunable control gains using EMT-in-the-loop simulation framework (e.g., PSCAD interfaced with Python-based learning modules) t…
On the System Theoretic Offline Learning of Continuous-Time LQR with Exogenous Disturbances
Sayak Mukherjee, Ramij R. Hossain, Mahantesh Halappanavar
We analyze offline designs of linear quadratic regulator (LQR) strategies with uncertain disturbances. First, we consider the scenario where the exogenous variable can be estimated…
Identification of Sub/Super-Synchronous Control Interaction Paths Using Dissipative Energy Flow
Kaustav Chatterjee, Sameer Nekkalapu, Sayak Mukherjee +2
Sub- and super-synchronous control interactions (SSCIs) are oscillations arising from adverse interactions between inverter-based resource (IBR) controls and the power network. SSC…
Managing Risks from Large Digital Loads Using Coordinated Grid-Forming Storage Network
Soumya Kundu, Kaustav Chatterjee, Ramij R. Hossain +2
Anticipated rapid growth of large digital load, driven by artificial intelligence (AI) data centers, is poised to increase uncertainty and large fluctuations in consumption, threat…
Physics-Informed Learning of Proprietary Inverter Models for Grid Dynamic Studies
Kyung-Bin Kwon, Sayak Mukherjee, Ramij R. Hossain +1
This letter develops a novel physics-informed neural ordinary differential equations-based framework to emulate the proprietary dynamics of the inverters -- essential for improved…
Frequency Control and Disturbance Containment Using Grid-Forming Embedded Storage Networks
Kaustav Chatterjee, Ramij Raja Hossain, Sai Pushpak Nandanoori +3
The paper discusses fast frequency control in bulk power systems using embedded networks of grid-forming energy storage resources. Differing from their traditional roles of regulat…
Coordinated Frequency Regulation in Grid-Forming Storage Network via Safety-Consensus
Ramij Raja Hossain, Kaustav Chatterjee, Sai Pushpak Nandanoori +4
Inverter-based storages are poised to play a prominent role in future power grids with massive renewable generation. Grid-forming inverters (GFMs) are emerging as a dominant techno…
Grid-Forming Storage Networks: Analytical Characterization of Damping and Design Insights
Kaustav Chatterjee, Ramij Raja Hossain, Sai Pushpak Nandanoori +4
The paper presents a theoretical study on small-signal stability and damping in bulk power systems with multiple grid-forming inverter-based storage resources. A detailed analysis…
Structural Impact of Grid-Forming Inverters on Power System Coherency
Sayak Mukherjee, Ramij Raja Hossain, Kaustav Chatterjee +4
This paper addresses the following fundamental research question: how does the integration of grid-forming inverters (GFMs) replacing conventional synchronous generators (SGs) impa…
Resilient Control of Networked Microgrids using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations
Sayak Mukherjee, Ramij R. Hossain, Sheik M. Mohiuddin +7
Improving system-level resiliency of networked microgrids is an important aspect with increased population of inverter-based resources (IBRs). This paper (1) presents resilient con…
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…
Data-Driven Linear Koopman Embedding for Networked Systems: Model-Predictive Grid Control
Ramij R. Hossain, Rahmat Adesunkanmi, Ratnesh Kumar
This paper presents a data-learned linear Koopman embedding of nonlinear networked dynamics and uses it to enable real-time model predictive emergency voltage control in a power ne…
Distributed-MPC with Data-Driven Estimation of Bus Admittance Matrix in Voltage Control
Ramij R. Hossain, Ratnesh Kumar
This article presents a distributed model-predictive control (MPC) design for real-time voltage control in power systems, including an online method to estimate the bus admittance…
MPC-based Realtime Power System Control with DNN-based Prediction/Sensitivity-Estimation
Ramij Raja Hossain, Ratnesh Kumar
This paper presents a model predictive control (MPC)-based online real-time adaptive control scheme for emergency voltage control in power systems. Despite tremendous success in va…