基于自适应分解层数和阈值的小波去噪算法

摘要

为了更好地降低电能质量扰动信号中的噪声,提出了一种基于自适应分解层数和阈值的小波去噪算法.通过计算小波细节系数的峰值比,自适应地确定最佳小波分解层数,根据各层细节系数中有用信息和噪声信息的分布特性以及细节系数的正、负峰值比,动态调整各层细节系数的上、下阈值.应用Matlab对暂态振荡和脉冲信号进行去噪处理,并与传统硬、软阈值算法和一种改进小波阈值算法相比.结果表明:本文提出的自适应分解层数和阈值的小波去噪算法得到的信噪比和均方根误差均优于以上3种方法,重构后信号更接近原始信号,并且较好地保留了扰动期间信号的特征信息.%In order to reduce noise in electric energy quality disturbance signals,a wavelet de-noising algorithm based on adaptive decomposition level and threshold is proposed.The algorithm adaptively determine number of optimal wavelet decomposition levels by calculating peak-to-sum ratio of the wavelet detail coefficients and according to distribution characteristic of useful signals and the noise signals in detail coefficients of each levels and the ratio of peak value of the negative and positive of the detail coefficients,dynamically adjust upper and lower thresholds of the detail coefficients of each levels.The transient oscillation and pulse signals are de-noised by using Matlab,and compared with conventional hard,soft threshold algorithm and an improved wavelet threshold algorithm.The results show that number of adaptive decomposition level and the proposed threshold wavelet denoising algorithm is superior to the other three methods in terms of signal-to-noise ratio (SNR) and root mean square error (RMSE) and the reconstructed signal is closer to the original signal,and better preserves the characteristic information of the signal during the disturbance period.