Integrated data-driven topology reconstruction and risk-aware reconfiguration for resilient power distribution systems under incomplete observability

Integrated data-driven topology reconstruction and risk-aware reconfiguration for resilient power distribution systems under incomplete observability

Abstract This paper proposes a unified data-driven framework for topology identification, risk quantification, and reconfiguration optimization in power distribution networks under incomplete and fragmented observability. Motivated by real-world challenges where asset metadata, SCADA records, GIS la...

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Bibliographic Details
Main Authors: Sipei Sun, Ning Li, Liang Zhang, Dongpo Zhao, Di Lun, Liang Feng
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
Subjects:
Graph convolutional network (GCN)
Topology identification
Distribution system reconfiguration
Risk-aware optimization
Bilevel modeling
Data fusion
Online Access:https://doi.org/10.1038/s41598-025-15440-8
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https://doi.org/10.1038/s41598-025-15440-8

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