SUPER-RESOLUTION RECONSTRUCTION OF SERIES ARC CURRENT UTILIZING TIME-GAN

Long Kui; Mou Weiqi; Li Qiuhui; Liu Jiawei; Chen Xukai; Su Sheng

doi:10.19912/j.0254-0096.tynxb.2024-1199

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (11) : 260-271. DOI: 10.19912/j.0254-0096.tynxb.2024-1199

SUPER-RESOLUTION RECONSTRUCTION OF SERIES ARC CURRENT UTILIZING TIME-GAN

Long Kui¹, Mou Weiqi¹, Li Qiuhui¹, Liu Jiawei², Chen Xukai², Su Sheng²

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Abstract

Extracting high-frequency data related to series arc faults presents considerable challenges.To solve this problem, we propose a super-resolution reconstruction method for series arc current based on Time-GAN （Time-Generative Adversarial Network）. This method aims to perform super-resolution processing on time-domain sampled current signals to obtain higher frequency time-domain signals, thereby achieving higher frequency domain signals. Firstly, a true-scale experimental platform for low-voltage arc faults in photovoltaic systems was established, and relevant data were collected at different sampling rates. Secondly, we analyzed the impact of different sampling rates on time-frequency domain data collection. Then, we applied the super-resolution algorithm to the low sampling rate time series data to generate high sampling rate time-frequency domain data, and compared it with the original high sampling rate time-frequency domain data. Experimental results demonstrate that the Time-GAN-based time super-resolution algorithm significantly enhances the sampling frequency of current signals.

Key words

series arc / electrical fire / signal sampling / Time-GAN algorithm / time-frequency domain signal reconstruction / photovoltaic system

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Long Kui, Mou Weiqi, Li Qiuhui, Liu Jiawei, Chen Xukai, Su Sheng. SUPER-RESOLUTION RECONSTRUCTION OF SERIES ARC CURRENT UTILIZING TIME-GAN[J]. Acta Energiae Solaris Sinica. 2025, 46(11): 260-271 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1199

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