no code implementations • 21 Nov 2023 • Sayak Mukherjee, Ramij R. Hossain, Sheik M. Mohiuddin, YuAn Liu, Wei Du, Veronica Adetola, Rohit A. Jinsiwale, Qiuhua Huang, Tianzhixi Yin, Ankit Singhal
Improving system-level resiliency of networked microgrids is an important aspect with increased population of inverter-based resources (IBRs).
no code implementations • 17 Dec 2022 • Sayak Mukherjee, Ramij R. Hossain, YuAn Liu, Wei Du, Veronica Adetola, Sheik M. Mohiuddin, Qiuhua Huang, Tianzhixi Yin, Ankit Singhal
This paper presents a novel federated reinforcement learning (Fed-RL) methodology to enhance the cyber resiliency of networked microgrids.
no code implementations • 6 Dec 2022 • Ramij R. Hossain, Tianzhixi Yin, Yan Du, Renke Huang, Jie Tan, Wenhao Yu, YuAn Liu, Qiuhua Huang
We propose a novel model-based-DRL framework where a deep neural network (DNN)-based dynamic surrogate model, instead of a real-world power-grid or physics-based simulation, is utilized with the policy learning framework, making the process faster and sample efficient.
no code implementations • 2 Jun 2022 • 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 network.
no code implementations • 28 Feb 2022 • 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 matrix $\mathbf{Y}$ to let it be time-varying and unknown a priori.
no code implementations • 12 Nov 2021 • Sayak Mukherjee, Ramij R. Hossain
This paper proposes a robust learning methodology to place the closed-loop poles in desired convex regions in the complex plane.