- By:
- Liu, Boming ; Zhang, Chen ; Dong, Jin ; Balaprakash, Prasanna ; Liu, Yan ; Eiffert, Brett C
- Page Number:
- 1-4
- Book Title:
- IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society
- Publication Date:
- March 12, 2026
- Publisher Location:
- IEEE, New Jersey, United States of America
- Conference Name:
- The 2024 Annual Conference of the IEEE Industrial Electronics Society (IECON)
- Conference Location:
- Chicago, Illinois, United States of America
- Conference Sponsor:
- IEEE
- View DOI Listing:
- https://doi.org/10.1109/IECON55916.2024.10905187
Abstract
This paper explores the applications of Fusion Graph Neural Network (FuGNN) on power distribution systems. FuGNN effectively models dynamic networks with evolving topology and features. Applied to power system network reconfiguration, FuGNN demonstrates its feasibility in optimizing switch configurations to minimize unserved loads and operational costs during extreme events. Additionally, FuGNN supports various downstream tasks, such as node feature prediction, further enhancing its versatility and applicability in power system resilience.
