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

Ning, Keqing; Ye, Lin; Song, Wei; Guo, Wei; Li, Guanyuan; Yin, Xiang; Zhang, Mingze

doi:10.3390/math13020225

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Open AccessArticle

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

by

Keqing Ning

¹,

Lin Ye

¹,

Wei Song

¹

,

Wei Guo

^2,*,

Guanyuan Li

³,

Xiang Yin

² and

Mingze Zhang

⁴

¹

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

²

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

³

Beijing Institute of Metrology, Beijing 100020, China

⁴

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

^*

Author to whom correspondence should be addressed.

Mathematics 2025, 13(2), 225; https://doi.org/10.3390/math13020225

Submission received: 12 December 2024 / Revised: 7 January 2025 / Accepted: 9 January 2025 / Published: 10 January 2025

(This article belongs to the Special Issue Complex Process Modeling and Control Based on AI Technology)

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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.

Keywords: high-impedance fault; Gramian angular field; parallel network; Crested Porcupine Optimizer

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MDPI and ACS Style

Ning, K.; Ye, L.; Song, W.; Guo, W.; Li, G.; Yin, X.; Zhang, M. A Dual-Path Neural Network for High-Impedance Fault Detection. Mathematics 2025, 13, 225. https://doi.org/10.3390/math13020225

AMA Style

Ning K, Ye L, Song W, Guo W, Li G, Yin X, Zhang M. A Dual-Path Neural Network for High-Impedance Fault Detection. Mathematics. 2025; 13(2):225. https://doi.org/10.3390/math13020225

Chicago/Turabian Style

Ning, Keqing, Lin Ye, Wei Song, Wei Guo, Guanyuan Li, Xiang Yin, and Mingze Zhang. 2025. "A Dual-Path Neural Network for High-Impedance Fault Detection" Mathematics 13, no. 2: 225. https://doi.org/10.3390/math13020225

APA Style

Ning, K., Ye, L., Song, W., Guo, W., Li, G., Yin, X., & Zhang, M. (2025). A Dual-Path Neural Network for High-Impedance Fault Detection. Mathematics, 13(2), 225. https://doi.org/10.3390/math13020225

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

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