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.
作者
余本富
王维博
郑永康
董蕊莹
YU Ben-fu;WANG Wei-bo;ZHENG Yong-kang;DONG Rui-ying(School of Electrical and Electronic Information,Xihua University,Chengdu 610039,China;State Grid Sichuan Electric Power Institute,Chengdu 610072,China)
出处
《传感器与微系统》
CSCD
2017年第12期126-129,133,共5页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(61571371)
教育部"春晖计划"(Z2012026)
四川省高校重点实验室开放基金资助项目(SZJJ2017-046)
四川省电力公司课题(17209226)
西华大学研究生创新基金资助项目(YCJJ2017165)
关键词
小波去噪
自适应
峰值比
电能质量
wavelet de-noising
adaptive
ratio of peak values
electric energy quality
