Fault Line Selection in Distribution Networks Based on Dual-Channel Time-Frequency Fusion Network

Yuyi Ma, Wei Guo (), Yuntao Shi, Jianing Guan, Yushuai Qi, Xiang Yin and Gang Liu
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Yuyi Ma: School of Electrical and Control Engineering, North China University of Technology, Shijingshan District, Beijing 100144, China
Wei Guo: School of Electrical and Control Engineering, North China University of Technology, Shijingshan District, Beijing 100144, China
Yuntao Shi: School of Electrical and Control Engineering, North China University of Technology, Shijingshan District, Beijing 100144, China
Jianing Guan: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Yushuai Qi: School of Electrical and Control Engineering, North China University of Technology, Shijingshan District, Beijing 100144, China
Xiang Yin: School of Electrical and Control Engineering, North China University of Technology, Shijingshan District, Beijing 100144, China
Gang Liu: State Grid Zibo Power Supply Company, Zibo 255000, China

Mathematics, 2025, vol. 13, issue 16, 1-18

Abstract: In distribution networks, single-phase ground faults often lead to abnormal changes in voltage and current signals. Traditional single-modal fault diagnosis methods usually struggle to accurately identify the fault line under such conditions. To address this issue, this paper proposes a fault line identification method based on a multimodal feature fusion model. The approach combines time-frequency images—generated using a Short-Time Fourier Transform (STFT) and Wigner–Ville Distribution (WVD) fusion algorithm with one-dimensional time-series signals for classification. The time-frequency images visualize both temporal and spectral features of the signal and are processed using the RepLKNet model for deep feature extraction. Meanwhile, the raw one-dimensional time-series signals preserve the original temporal dependencies and are analyzed using a BiGRU network enhanced with a global attention mechanism to improve feature representation. Finally, features from both modalities are extracted in parallel and fused to achieve accurate fault line identification. Experimental results demonstrate that the proposed method effectively leverages the complementary nature of multimodal data and shows strong robustness in the presence of noise interference.

Keywords: STFT-WVD; multi-modal feature fusion network; fault line selection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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