TechRxiv

AI Assisted Electrical Distribution System Design

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posted on 2023-11-22, 04:51 authored by Thomas MaharThomas Mahar

We find that traditional GNNs are not well suited to learning on utility-scale power distribution graphs due to typical attributes of power distribution systems such as: their large size and low density, their heterophilic nature, and the long paths between nodes along which information must travel. Herein we outline a novel inductive GNN architecture which has been optimized for learning on power distribution graphs and which can pass information efficiently across the graph. We also demonstrate the performance of this algorithm by applying it to ComEd's data as part of a practical use-case, and we benchmark our algorithm's performance on this data versus other well-known GNN architectures.

Submitted to IEEE PES General Meeting 2024

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Email Address of Submitting Author

thomas.mahar@comed.com

ORCID of Submitting Author

0009-0001-9925-8321

Submitting Author's Institution

ComEd

Submitting Author's Country

  • United States of America

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