PIDGeuN: Graph Neural Network-Enabled Transient Dynamics Prediction of Networked Microgrids Through Full-Field Measurement

PIDGeuN: Graph Neural Network-Enabled Transient Dynamics Prediction of Networked Microgrids Through Full-Field Measurement

A Physics-Informed Dynamic Graph Neural Network (PIDGeuN) is presented to accurately, efficiently and robustly predict the nonlinear transient dynamics of microgrids in the presence of disturbances. The graph-based architecture of PIDGeuN provides a natural representation of the microgrid topology....

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Bibliographic Details
Main Authors: Yin Yu, Xinyuan Jiang, Daning Huang, Yan Li, Meng Yue, Tianqiao Zhao
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Graph neural network (GNN)
networked microgrids (NMs)
transient dynamics
prediction
distributed energy resources (DERs)
Online Access:https://ieeexplore.ieee.org/document/10489968/

Internet

https://ieeexplore.ieee.org/document/10489968/

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