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Wavelet Threshold De-noising of Power Quality Signals

机译:电能质量信号的小波阈值去噪

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It is an important application of wavelet analysis in power system to de-noise power quality (PQ) signals so as to detect and locate the disturbing points. At present soft threshold and hard threshold de-noising methods are widely used. To improve the de-noising effect of PQ signals, an improved algorithm based on soft threshold and hard threshold de-noising methods is put forward. The key factor of wavelet thresholding de-noising is how to construct thresholding function and select the threshold. The method could combine the advantages of hard and soft threshold methods, and then achieve better access to estimate the threshold of wavelet coefficients by adjusting the parameters properly, making the improved threshold function between the soft and hard threshold functions. It could not only overcome the shortcomings of poor de-noising effect while using hard threshold method,but also effectively solve the difficult problem that leads to signal distortion within soft threshold method beacause of too smooth. The simulation results show that this method can reduce the loss of information while de-noising. The enhancement of SNR and the reduction of the RMSE indicate that the performance of our method is better than soft threshold and hard threshold de-noising methods.
机译:小波分析在电力系统中的重要应用是对电能质量(PQ)信号进行去噪,从而检测和定位干扰点。目前,软阈值和硬阈值去噪方法被广泛使用。为了提高PQ信号的去噪效果,提出了一种基于软阈值和硬阈值去噪方法的改进算法。小波阈值去噪的关键因素是如何构造阈值函数和选择阈值。该方法可以结合硬阈值和软阈值方法的优点,然后通过适当地调整参数,使软阈值函数和硬阈值函数之间的阈值函数得到改进,从而更好地访问估计小波系数的阈值。它不仅可以克服使用硬阈值方法时去噪效果差的缺点,而且可以解决由于软阈值方法过于平滑而导致信号失真的难题。仿真结果表明,该方法可以减少去噪时的信息丢失。 SNR的提高和RMSE的降低表明,我们的方法的性能优于软阈值和硬阈值去噪方法。

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