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.
| Original language | English |
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| Title of host publication | IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, Proceedings |
| Publisher | IEEE Computer Society |
| ISBN (Electronic) | 9781665464543 |
| DOIs | |
| State | Published - 2024 |
| Event | 50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024 - Chicago, United States Duration: Nov 3 2024 → Nov 6 2024 |
Publication series
| Name | IECON Proceedings (Industrial Electronics Conference) |
|---|---|
| ISSN (Print) | 2162-4704 |
| ISSN (Electronic) | 2577-1647 |
Conference
| Conference | 50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024 |
|---|---|
| Country/Territory | United States |
| City | Chicago |
| Period | 11/3/24 → 11/6/24 |
Funding
This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (https://www.energy.gov/doepublic-access-plan). This work has been supported in part by US DOE’s Office of Electricity, Advanced Grid Modeling (AGM) program, in part by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory (ORNL). This research used birthright cloud resources of the Compute and Data Environment for Science (CADES) at ORNL, which is supported by the Office of Science of the DOE.
Keywords
- graph neural networks
- network reconfiguration
- power systems
- resilience