Graph neural networks for the localization of faults in a partially observed regional transmission system
Year: 2025
Type: article
Abstract: Localization of faults in a large power system is one of the most important and difficult tasks of power systems monitoring. A fault, typically a shorted line, can be seen almost instantaneously by all measurement devices throughout the system, but determining its location in a geographically vast and topologically complex system is difficult. The task becomes even more difficult if measurements devices are placed only at some network nodes. We show that regression graph neural networks we construct, combined with a suitable statistical methodology, can solve this task very well. A chief advance of our methods is that we construct networks that produce localization without having being trained on data that contain fault localization information. We show that a synergy of statistics and deep learning can produce results that none of these approaches applied separately can achieve.
DOI: 10.1111/sjos.12763
Authors: Mantautas Rimkus, Piotr Kokoszka, Dongliang Duan, Xuao Wang, Haonan Wang
Institutions: Colorado State University, University of Wyoming
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,500
OpenAlex ID: W4406006345