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The application study of deep learning technology in intelligent power supervision systems

Author

Listed:
  • Wang Ye
  • Jiesheng Yuan
  • Huayi Ye
  • Yufeng Chen
  • Ling Xie
  • Dailing Cai

Abstract

This study explores the application of a combined model incorporating bidirectional long short-term memory networks and gated recurrent units within intelligent power supervision systems, demonstrating its efficacy in recognizing anomalies within power grids. Based on historical operational data of two critical indicators—grid sectional security margin and generation-consumption balance margin—this paper constructs an anomaly detection model. This model effectively processes time series data, promptly identifying equipment malfunctions and abnormal load fluctuations, thereby enhancing the safety and stability of power grids.

Suggested Citation

  • Wang Ye & Jiesheng Yuan & Huayi Ye & Yufeng Chen & Ling Xie & Dailing Cai, 2025. "The application study of deep learning technology in intelligent power supervision systems," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 112-118.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:112-118.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctae273
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