肖勇, 李博, 曹敏. 基于小波阈值的电力谐波信号去噪研究[J]. 电测与仪表, 2024, 61(8): 78-83. DOI: 10.19753/j.issn1001-1390.2024.08.010
引用本文: 肖勇, 李博, 曹敏. 基于小波阈值的电力谐波信号去噪研究[J]. 电测与仪表, 2024, 61(8): 78-83. DOI: 10.19753/j.issn1001-1390.2024.08.010
XIAO Yong, LI Bo, CAO Min. Research on de-noising of power harmonic signal based on wavelet threshold[J]. Electrical Measurement & Instrumentation, 2024, 61(8): 78-83. DOI: 10.19753/j.issn1001-1390.2024.08.010
Citation: XIAO Yong, LI Bo, CAO Min. Research on de-noising of power harmonic signal based on wavelet threshold[J]. Electrical Measurement & Instrumentation, 2024, 61(8): 78-83. DOI: 10.19753/j.issn1001-1390.2024.08.010

基于小波阈值的电力谐波信号去噪研究

Research on de-noising of power harmonic signal based on wavelet threshold

  • 摘要: 电力系统不同程度的噪声污染会影响谐波检测的准确性,传统小波阈值去噪方法在工程实际应用上有诸多缺陷,例如硬阈值函数在阈值处不连续、软阈值函数存在固定偏差,量化规则缺乏尺度自适应性等。鉴于此,我们从阈值函数和阈值量化规则两方面改进小波阈值去噪。文中使用改进的软硬阈值折衷函数结合分层阈值选取规则对模拟谐波电力信号去噪。在MATLAB环境下仿真模拟染噪电力谐波信号使用传统和改进阈值方法去噪,从三个去噪效果衡量指标定量分析。结果表明,改进阈值去噪方法提高了重构信号的输出信噪比,降低了均方根误差和平滑度,在不牺牲计算复杂度的条件下可灵活有效去噪。

     

    Abstract: Different degrees of noise pollution in power system will affect the accuracy of harmonic detection. Traditional wavelet threshold de-noising methods have many defects in practical engineering applications, such as the hard threshold function is discontinuous at the threshold, the soft threshold function has fixed deviations, and the quantization rules lack of scale adaptability, etc. In view of this, we improve the wavelet threshold de-noising from two aspects of the threshold function and the threshold quantization rule. In this paper, the improved soft and hard threshold trade-off function combined with the layered threshold selection rule is used to de-noise the analog harmonic power signal. In the MATLAB environment, the simulated noise-affected power harmonic signal is de-noised using traditional and improved threshold methods. The quantitative indicators analysis is conducted from three de-noising effect. The results show that the improved threshold de-noising method improves the output signal-to-noise ratio of the reconstructed signal, which reduces the root mean square error and smoothness, and can flexibly and effectively de-noise without sacrificing computational complexity.

     

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