Harnessing distributed GPU computing for generalizable graph convolutional networks in power grid reliability assessments

Harnessing distributed GPU computing for generalizable graph convolutional networks in power grid reliability assessments

Although machine learning (ML) has emerged as a powerful tool for rapidly assessing grid contingencies, prior studies have largely considered a static grid topology in their analyses. This limits their application, since they need to be re-trained for every new topology. This paper explores the deve...

Full description

Saved in:
Bibliographic Details
Main Authors: Somayajulu L.N. Dhulipala, Nicholas Casaprima, Audrey Olivier, Bjorn C. Vaagensmith, Timothy R. McJunkin, Ryan C. Hruska
Format: Article
Language:English
Published: Elsevier 2025-01-01
Series:Energy and AI
Subjects:
Complex systems
Graph neural networks
Power grids
Grid reliability
Generalizable models
Online Access:http://www.sciencedirect.com/science/article/pii/S2666546825000035
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items