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Arc fault signal detection - Fourier transformation vs. wavelet decomposition techniques using synthesized data

机译：电弧故障信号检测-使用合成数据的傅里叶变换与小波分解技术

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Arc faults are a significant reliability and safety concern for photovoltaic (PV) systems and can cause intermittent operation, system failure, electrical shock hazard, and even fire. Further, arc faults in deployed systems are seemingly random and challenging to faithfully create experimentally in the laboratory, which makes the study of arc fault signature detection difficult. While it may seem trivial to simply record arcing signatures from real-world system, an obstacle in capturing these arc signals is that arc faults in the PV systems do not happen predictably, and depending on the location of the sensors relative to the arc location, may contribute a negligible portion to the magnitude of the sensed current or voltage waveform. The high-frequency content of the arc requires fast sampling, long memory, and fast processing to acquire, store, and analyze the waveforms; this adds substantial balance-of-system cost when considering widespread deployment of arc fault detectors in PV applications. In this paper, we study the performance of the fast Fourier transform arc detection method compared to the wavelet decomposition method by using synthetic waveforms. These waveforms are created by combining measured waveforms of normal background noise from inverters in DC PV arrays along with waveforms of arcing events. Using this technique allows the ratio of amplitudes are varied. Combining these separate waveforms in various amplitude proportions enables creation of test signals for the study of detection algorithm efficacy. It will be shown that the wavelet transformation technique produce more easily recognized detection results and can perform this detection using a much lower sampling rate than what is required for the fast Fourier transform
机译：电弧故障是光伏（PV）系统的重要可靠性和安全问题，可能导致间歇性运行，系统故障，电击危险，甚至引起火灾。此外，部署的系统中的电弧故障似乎是随机的，并且难以如实地在实验室中通过实验创建，这使得对电弧故障特征检测的研究变得困难。仅从实际系统中记录电弧信号似乎很琐碎，但是捕获这些电弧信号的障碍在于，PV系统中的电弧故障不会可预测地发生，并且取决于传感器相对于电弧位置的位置，可以对感测到的电流或电压波形的幅度贡献可忽略的部分。电弧的高频成分需要快速采样，较长的存储空间和快速处理以获取，存储和分析波形。当考虑在光伏应用中广泛部署电弧故障检测器时，这会增加可观的系统平衡成本。与合成的小波分解方法相比，本文研究了快速傅里叶变换电弧检测方法的性能。这些波形是通过将来自DC PV阵列中逆变器的正常背景噪声的测量波形与电弧事件的波形进行组合而创建的。使用这种技术可以改变振幅的比率。将这些单独的波形以各种幅度比例组合在一起，就可以创建测试信号，以研究检测算法的功效。结果表明，小波变换技术可以产生更容易识别的检测结果，并且可以以比快速傅立叶变换所需的采样率低得多的采样率执行此检测。

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