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

Author

Listed:
  • Wei Wang
  • Shuman Sun
  • Pengxuan Liu
  • Xiaomeng Yan
  • Jiadong Zhao
  • Wei Jiang

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.

Suggested Citation

  • Wei Wang & Shuman Sun & Pengxuan Liu & Xiaomeng Yan & Jiadong Zhao & Wei Jiang, 2025. "Research on intelligent analysis and prediction of low-voltage causes in rural distribution networks based on deep learning," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 791-797.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:791-797.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctaf038
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