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

Keqing Ning, Lin Ye, Wei Song, Wei Guo (), Guanyuan Li, Xiang Yin and Mingze Zhang
Additional contact information
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

Mathematics, 2025, vol. 13, issue 2, 1-19

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 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/2/225/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/2/225/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:2:p:225-:d:1564598

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().