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A Dual-Path Neural Network for High-Impedance Fault Detection

Author

Listed:
  • Keqing Ning

    (College of Information Science and Technology, North China University of Technology, Beijing 100144, China)

  • Lin Ye

    (College of Information Science and Technology, North China University of Technology, Beijing 100144, China)

  • Wei Song

    (College of Information Science and Technology, North China University of Technology, Beijing 100144, China)

  • Wei Guo

    (School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China)

  • Guanyuan Li

    (Beijing Institute of Metrology, Beijing 100020, China)

  • Xiang Yin

    (School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China)

  • Mingze Zhang

    (State Grid Jilin Electric Power Research Institute, Changchun 130015, China)

Abstract

High-impedance fault detection poses significant challenges for distribution network maintenance and operation. We propose a dual-path neural network for high-impedance fault detection. To enhance feature extraction, we use a Gramian Angular Field algorithm to transform 1D zero-sequence voltage signals into 2D images. Our dual-branch network simultaneously processes both representations: the CNN extracts spatial features from the transformed images, while the GRU captures temporal features from the raw signals. To optimize model performance, we integrate the Crested Porcupine Optimizer (CPO) algorithm for the adaptive optimization of key network hyperparameters. The experimental results demonstrate that our method achieves a 99.70% recognition accuracy on a dataset comprising high-impedance faults, capacitor switching, and load connections. Furthermore, it maintains robust performance under various test conditions, including different noise levels and network topology changes.

Suggested Citation

  • Keqing Ning & Lin Ye & Wei Song & Wei Guo & Guanyuan Li & Xiang Yin & Mingze Zhang, 2025. "A Dual-Path Neural Network for High-Impedance Fault Detection," Mathematics, MDPI, vol. 13(2), pages 1-19, January.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:2:p:225-:d:1564598
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