There are many types of power quality disturbance signals, the components are complex, and the disturbance characteristics are easily removed as noise.For these problems, an improved wavelet threshold de-noising algorithm was proposed in this paper.This algorithm could determine the noise by calculating the peak-to-sum ratio for each layer of wavelet coefficients, so that the correction factor Fj could adaptively adjust the general threshold according to the noise distribution characteristics of different disturbance signals.Meanwhile, an improved threshold function was proposed, which could adjust the soft and hard characteristics by changing the value of the parameter ato determine the appropriate threshold function.The improved algorithm was used to de-noise the seven kinds of power quality signals.The simulation results show that the improved algorithm achieves better signal-to-noise ratios, stable de-noising effects and better waveforms of reconstructed signals for various types of disturbance signals in different noise interferences.Moreover, it retains the disturbance characteristics in the de-noising process, which can provide accurate and effective information for the subsequent power quality analysis.%针对电能质量扰动类型多, 成分复杂, 扰动特征易被当作噪声去除等问题, 提出一种改进小波阈值去噪算法.该算法通过计算每层小波系数的峰和比来确定该层噪声含量, 使修正因子Fj可根据不同扰动信号的噪声分布特点自适应调整通用阈值.同时, 提出了改进的阈值函数, 可变参数a能调节自身软、硬特性从而确定合适的阈值函数.采用该算法对七种常见电能质量扰动信号去噪, 仿真结果表明, 改进小波阈值去噪算法在不同噪声干扰下, 对各类扰动信号都能达到较好的信噪比, 去噪效果稳定, 重构信号波形恢复较好, 且在去噪过程中保留了扰动特征, 能为后续电能质量分析提供准确有效的信息.
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