基于时域信号超分辨率的串联电弧检测方法

doi:10.19595/j.cnki.1000-6753.tces.251100

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Abstract

Series arc faults are a leading cause of electrical fires in low-voltage power distribution systems. Accurate detection of these faults is essential to ensure electrical safety, as arc faults can lead to hazardous conditions. Traditional detection methods generally rely on high-frequency fault signal features; however, these methods face challenges in practical applications due to limitations in the sensors used for fault signal acquisition. Open-type current transformers, commonly employed in real-world scenarios to ensure power supply reliability, struggle to capture high-frequency components due to their structural constraints, resulting in insufficient detection accuracy for series arc faults.
This paper proposes a novel time-domain super-resolution method for series arc fault detection, which aims to overcome the time-frequency limitations of traditional detection methods. By applying super-resolution techniques to reconstruct low-resolution data, the proposed method enhances the recovery of high-frequency components that are typically lost or obscured in traditional fault detection systems. The approach begins by setting up an arc fault simulation platform in compliance with UL1699 standards, where datasets are collected from various load types at three different sampling rates (25 kHz, 50 kHz, and 100 kHz). This enables comprehensive testing of the proposed method on data collected at varying resolutions.
A significant issue with current time-domain super-resolution algorithms is their inability to effectively restore high-frequency spectral information, which is essential for accurate arc fault detection. To address this challenge, the paper introduces a dual-domain collaborative generative time-frequency enhancement method based on TimeGAN, referred to as DCTF-GAN. This model integrates both time-domain and frequency-domain features, using a frequency-domain loss function and discriminator to improve high-frequency feature reconstruction. DCTF-GAN outperforms traditional methods and other deep learning models in terms of high-frequency detail recovery, providing a significant improvement in fault detection accuracy.
Furthermore, a lightweight RNN-Transformer hybrid detection model is developed to perform fault detection on the super-resolved data. By combining time-domain and frequency-domain features, this model achieves high detection accuracy while maintaining computational efficiency. Experimental results show that the proposed method achieves up to 99.7% detection accuracy, validating its effectiveness in various test scenarios. The results also indicate that the method significantly enhances detection performance by restoring lost high-frequency components in the signal, making it more reliable for detecting arc faults in real-world systems.
The proposed method addresses the challenges of low-frequency signal acquisition and limited high-frequency information, which have been long-standing obstacles in series arc fault detection. By leveraging the DCTF-GAN model, the system can restore high-frequency information and improve the detection capabilities of the fault detection system. Additionally, the RNN-Transformer network contributes to efficient processing, enabling real-time detection in industrial applications. This approach provides a more robust, accurate, and computationally efficient solution to series arc fault detection.
In conclusion, this paper presents a significant advancement in the detection of series arc faults. The integration of DCTF-GAN for high-frequency signal restoration and the RNN-Transformer hybrid network for fault detection represents a promising solution to the challenges of detecting arc faults in low-voltage power distribution systems. The proposed method improves detection accuracy by addressing signal acquisition limitations and enhances the overall performance of fault detection systems, making it suitable for practical deployment in electrical safety systems.

Key words： Series arc detection sampling time domain super-resolution DCTF-GAN RNN-transformer electrical fires

Received: 24 June 2025

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TM501+.2

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	Yang Tianpeng
	Long Kui
	Li Qiuhui
	Mou Weiqi
	Li Bin

Cite this article:

Yang Tianpeng,Long Kui,Li Qiuhui等. Series Arc Fault Detection Method Based on Time-Domain Signal Super-Resolution[J]. Transactions of China Electrotechnical Society, 0, (): 258112-258112.

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