Research on intelligent analysis and prediction of low-voltage causes in rural distribution networks based on deep learning

Wei Wang, Shuman Sun, Pengxuan Liu, Xiaomeng Yan, Jiadong Zhao and Wei Jiang

International Journal of Low-Carbon Technologies, 2025, vol. 20, 791-797

Abstract: The article introduces an advanced diagnostic approach for identifying the causes of low voltage in power distribution networks. This method integrates empirical analysis of low-voltage causes and employs both the particle swarm optimization-enhanced support vector machine algorithm and the self-organizing map algorithm to forecast low-voltage events within the distribution network. By merging low-voltage cause analysis with machine learning algorithms, it achieves precise diagnostics of low-voltage issues. This methodology has demonstrated remarkable performance in real-world applications across multiple regions, effectively pioneering an automated diagnostic technology for detecting low-voltage problems in power distribution networks.

Keywords: distribution network; low-voltage incident; deep learning approach (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:20:y:2025:i::p:791-797.

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