Penghe Zhang, Yang Xue, Runan Song, Xiaochen Ma, Dejie Sheng
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引用次数: 0
Abstract
Distributed photovoltaic systems have encountered unprecedented opportunities for development given their environmentally friendly nature and flexible power generation characteristics. However, numerous connecting lines and taps within the distributed photovoltaic system can be subject to insulation issues, which will consequently cause direct current (DC) arc faults and severe electrical fire hazards. Moreover, the power semiconductor devices in the photovoltaic inverter can introduce common-mode noises to the DC current, resulting in unwanted tripping of the DC arc fault detector. The study proposes an arc fault detection method utilizing a deep residual shrinkage network (DRSN) to address this issue, thereby precisely detecting DC arc faults. A test platform for series arc faults in photovoltaic systems is built. The arc current data are collected for characteristic analysis in time and frequency domains to determine which bandwidth is preferred for the algorithm. The model’s depth is increased by introducing residual connections, enhancing its feature extraction, and improving noise reduction capabilities. The residual shrinkage network has been enhanced to prevent a computation increase from increased network depth. Introducing a convolutional auto-encoder for data dimension reduction has decreased neural network parameters, thereby improving training speed. A prototype for detecting photovoltaic DC arc faults was constructed using Raspberry Pi 4B, validating the practical application value of the proposed method. Experimental results demonstrate that the prototype for detecting photovoltaic DC arc faults successfully fulfills the real-time detection standard of the conduction test.
分布式光伏系统因其环保和灵活的发电特性,迎来了前所未有的发展机遇。然而,分布式光伏系统中的众多连接线和分路器可能存在绝缘问题,从而导致直流(DC)电弧故障和严重的电气火灾隐患。此外,光伏逆变器中的功率半导体器件会给直流电流带来共模噪声,导致直流电弧故障检测器意外跳闸。针对这一问题,研究提出了一种利用深度残余收缩网络(DRSN)的故障电弧检测方法,从而精确检测直流故障电弧。建立了光伏系统串联电弧故障测试平台。收集电弧电流数据进行时域和频域特性分析,以确定算法首选的带宽。通过引入残差连接、加强特征提取和提高降噪能力,增加了模型的深度。残差收缩网络已得到增强,以防止网络深度增加导致计算量增加。通过引入卷积自动编码器来降低数据维度,减少了神经网络参数,从而提高了训练速度。利用 Raspberry Pi 4B 构建了一个用于检测光伏直流电弧故障的原型,验证了所提方法的实际应用价值。实验结果表明,光伏直流电弧故障检测原型成功达到了传导测试的实时检测标准。
期刊介绍:
The scope of Journal of Power Electronics includes all issues in the field of Power Electronics. Included are techniques for power converters, adjustable speed drives, renewable energy, power quality and utility applications, analysis, modeling and control, power devices and components, power electronics education, and other application.