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20232025
most citedPhysics-informed Graphical Neural Network for Power System State Estimation

9 citations · 9 across the 5 of their papers we have counts for

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5 papers

eess.SY2025

A Survey on IBR Penetrated Power System Stability Analysis Using Frequency Scanning

Shuvangkar Chandra Das, Lokesh Saravana, Le Minh Vu +4

The rapid rise in inverter-based renewable resources has heightened concerns over subsynchronous resonance and oscillations, thereby challenging grid stability. This paper reviews…

eess.SY2024

Preventive Energy Management for Distribution Systems Under Uncertain Events: A Deep Reinforcement Learning Approach

Md Isfakul Anam, Tuyen Vu, Jianhua Zhang

As power systems become more complex with the continuous integration of intelligent distributed energy resources (DERs), new risks and uncertainties arise. Consequently, to enhance…

eess.SY2024

Deep Reinforcement Learning for Optimizing Inverter Control: Fixed and Adaptive Gain Tuning Strategies for Power System Stability

Shuvangkar Chandra Das, Tuyen Vu, Deepak Ramasubramanian +3

This paper presents novel methods for tuning inverter controller gains using deep reinforcement learning (DRL). A Simulink-developed inverter model is converted into a dynamic link…

eess.SY2024

Recurrent Graph Transformer Network for Multiple Fault Localization in Naval Shipboard Systems

Quang-Ha Ngo, Isabel Barnola, Tuyen Vu +4

The integration of power electronics building blocks in modern MVDC 12kV Naval ship systems enhances energy management and functionality but also introduces complex fault detection…

eess.SY2023★ 9 cited

Physics-informed Graphical Neural Network for Power System State Estimation

Quang-Ha Ngo, Bang L. H. Nguyen, Tuyen V. Vu +2

State estimation is highly critical for accurately observing the dynamic behavior of the power grids and minimizing risks from cyber threats. However, existing state estimation met…