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AI Assisted Electrical Distribution System Design
  • Thomas Mahar
Thomas Mahar
ComEd

Corresponding Author:[email protected]

Author Profile

Abstract

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