2 citations · 3 across the 3 of their papers we have counts for
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eess.SY2024
Physics-Informed AI Inverter
Qing Shen, Yifan Zhou, Peng Zhang +3
This letter devises an AI-Inverter that pilots the use of a physics-informed neural network (PINN) to enable AI-based electromagnetic transient simulations (EMT) of grid-forming in…
eess.SY2023★ 2 cited
Physics-Aware Neural Dynamic Equivalence of Power Systems
Qing Shen, Yifan Zhou, Qiang Zhang +3
This letter devises Neural Dynamic Equivalence (NeuDyE), which explores physics-aware machine learning and neural-ordinary-differential-equations (ODE-Net) to discover a dynamic eq…
eess.SY2023
Scalable Neural Dynamic Equivalence for Power Systems
Qing Shen, Yifan Zhou, Huanfeng Zhao +4
Traditional grid analytics are model-based, relying strongly on accurate models of power systems, especially the dynamic models of generators, controllers, loads and other dynamic…