Graph attention convolution network for power flow calculation considering grid uncertainty
With the increasing penetration of renewable energy sources and the growing complexity of power system structures, the grid is increasingly impacted by both external and internal uncertainties. In this context, probabilistic power flow models based on artificial intelligence need to possess stronger...
Saved in:
Main Authors: | Haochen Li, Liqun Liu, Shaojuan Yu, Qiusheng He, Qingfeng Wu, Jianfeng Zhang, Qinxiong Lu |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2025-04-01
|
Series: | International Journal of Electrical Power & Energy Systems |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S014206152500064X |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Network Traffic Prediction Model Based on Layered Training Graph Convolutional Network
by: Yulian Li, et al.
Published: (2025-01-01) -
Integrating fault detection and classification in microgrids using supervised machine learning considering fault resistance uncertainty
by: Morteza Barkhi, et al.
Published: (2024-11-01) -
A power load forecasting method using cosine similarity and a graph convolutional network
by: JI Shan, et al.
Published: (2025-01-01) -
Resonant suppression strategy of impedance remodeling for multi-inverter grid-connected system in weak grid
by: ZHANG Shicong, et al.
Published: (2025-01-01) -
CONDITIONS FOR GRAPHS ON n VERTICES WITH THE SUM OF DEGREES OF ANY TWO NONADJACENT VERTICES EQUAL TO n-2 TO BE A HAMILTONIAN GRAPH
by: Nhu An Do, et al.
Published: (2024-02-01)