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Supraharmonic analysis by filter bank and compressive sensing

机译：超谐波分析通过滤波器组和压缩感测

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摘要

Smart grids encourage the use of new technologies such as electric vehicles, smart metering, as well as the use of renewable energy sources. One of the crucial challenges in designing smart grids is to improve the quality of the power delivered. Besides harmonic current generation in the frequency range below 2 kHz, these new technologies are also responsible for current emission in the range of 2-150 kHz, commonly known as supra harmonic emission. The supraharmonic analysis has not been explored for years, due to the lack of standards and equipment that are capable of reaching this frequency range. In recent years, the frequency range above 2 kHz has become the object of study by power quality researchers. This work proposes a technique to access the supraharmonics with reduced sampling rate and resolution enhancement. The technique is based on analog filter bank and compressive sensing. The use of the filter bank allows the reduction of the number of samples processed by the Fast Fourier Transform (FPT). In addition, it is proposed a method to improve frequency resolution of supraharmonic estimation, based on the technique of compressive sensing (CS). The use of the CS algorithm allows to achieve enhanced frequency accuracy without significantly extending the total observation time.
机译：智能电网鼓励使用电动汽车，智能计量等新技术以及可再生能源的使用。设计智能电网的关键挑战之一是提高所输送电能的质量。除了在低于2 kHz的频率范围内产生谐波电流外，这些新技术还负责产生2-150 kHz范围内的电流，通常称为超谐波发射。由于缺乏能够达到该频率范围的标准和设备，因此超谐波分析已经进行了多年没有探索。近年来，2 kHz以上的频率范围已成为电能质量研究人员的研究对象。这项工作提出了一种以降低的采样率和分辨率增强来访问超谐波的技术。该技术基于模拟滤波器组和压缩感测。使用滤波器组可以减少通过快速傅立叶变换（FPT）处理的样本数量。另外，提出了一种基于压缩感测（CS）技术的提高超谐波估计频率分辨率的方法。 CS算法的使用可以在不显着延长总观测时间的情况下提高频率精度。

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