As photovoltaic systems grow in size, there has been an increasing desire to automate the detection of arc faults. Any automated system for arc detection must be as fast and accurate as possible: delayed detection of electrical arcs can lead to fire and considerable system damage, while false positives that cause preventative system shutdown are associated with a significant financial cost. In this article, we present a novel approach to detect arcs in dc microgrids via their high-frequency (HF) spectral pattern using ideas from compressed sensing. The acquisition and analysis of HF signals using analog-to-digital converter technology typically requires costly hardware and is not feasible for on-site installation at power plants. However, sparsifying the signal by filtering everything but a narrow HF band enables the use of a modulated wideband converter to sample the arc signature at sub-Nyquist frequencies. We then calculate a characteristic band power within the selected spectrum slice over time and show that it can be used to reliably detect arc events via simple thresholding. We have evaluated our methods on both simulated and experimentally generated arc signals. Finally, we perform statistical analysis of power distributions using linear discriminant analysis in order to identify the frequency range best suited for arc detection.

中文翻译：

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使用压缩传感检测光伏系统中的电弧故障

随着光伏系统尺寸的增长，人们越来越希望自动检测电弧故障。任何用于电弧检测的自动化系统都必须尽可能快速和准确：电弧的延迟检测会导致火灾和相当大的系统损坏，而导致预防性系统关闭的误报会带来巨大的财务成本。在本文中，我们提出了一种利用压缩传感思想通过高频 (HF) 频谱模式检测直流微电网中电弧的新方法。使用模数转换器技术采集和分析 HF 信号通常需要昂贵的硬件，并且不适用于发电厂的现场安装。然而，通过过滤除窄 HF 频带之外的所有内容来稀疏信号，可以使用调制宽带转换器在亚奈奎斯特频率下对电弧特征进行采样。然后，我们随时间计算所选频谱切片内的特征带功率，并表明它可用于通过简单的阈值可靠地检测电弧事件。我们已经在模拟和实验生成的电弧信号上评估了我们的方法。最后，我们使用线性判别分析对功率分布进行统计分析，以确定最适合电弧检测的频率范围。我们已经在模拟和实验生成的电弧信号上评估了我们的方法。最后，我们使用线性判别分析对功率分布进行统计分析，以确定最适合电弧检测的频率范围。我们已经在模拟和实验生成的电弧信号上评估了我们的方法。最后，我们使用线性判别分析对功率分布进行统计分析，以确定最适合电弧检测的频率范围。