Intelligent substation communication network fault location method based on dynamic spatiotemporal graph association perception Article Swipe

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Ligang Ye , Wei Li , J. Zhao , Yuanyuan Liu ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.4108/ew.10423

INTRODUCTION: Accurately locating faults in intelligent substation communication networks is crucial for power grid safety. Existing methods fail to fully capture dynamic fault characteristic evolution and complex dependencies within network topologiesOBJECTIVES: This paper aims to (1) model spatiotemporal fault features in communication networks, (2) enhance fault pattern capture through multi-view learning, and (3) improve fault location accuracy.METHODS: We propose a multi-view spatiotemporal dynamic graph network. First, a multi-view graph neural network models spatial dependencies via cross-view comparative learning using topological and attribute data. Second, a gated recurrent unit with dynamic time windows extracts temporal evolution trends, focusing on local fault patterns and short-term dependencies.RESULTS: Evaluations on a 220kV substation communication network show our method achieves higher fault location accuracy versus baselines, effectively capturing spatiotemporal fault characteristics.CONCLUSION: The proposed framework addresses dynamic fault evolution and topological dependencies, providing a robust solution for intelligent substation fault diagnosis.

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Type: article
Language: en
Landing Page: https://doi.org/10.4108/ew.10423
PDF: https://publications.eai.eu/index.php/ew/article/download/10423/3728
OA Status: diamond
References: 8
OpenAlex ID: https://openalex.org/W4415503305

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DOI: https://doi.org/10.4108/ew.10423

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Title: Intelligent substation communication network fault location method based on dynamic spatiotemporal graph association perception

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Language: en

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Publication year: 2025

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Publication date: 2025-10-10

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Authors: Ligang Ye, Wei Li, J. Zhao, Yuanyuan Liu

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Landing page: https://doi.org/10.4108/ew.10423

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