二维离散小波变换在电能质量检测数据压缩中的应用

提出了用二维离散小波变换和能量阈值相结合的方法来解决电能质量扰动信号的压缩问题.利用二维db小波变换对矩阵数据分别进行行卷积和列卷积,把检测数据的高频信号和噪声信号分解在3个不同的方向上,且信号的能量集中在很少的小波系数上.再通过改进的能量阈值法,利用能量均值修正系数设置阈值使得压缩后的能量保留在99%以上,从而保证了重构信号的失真度很小且自适应地消除了加在扰动信号上的噪声.对6种扰动信号进行仿真并与小波包的压缩结果进行比较,结果表明该方法极大地提高了压缩率,并对噪声干扰有很好的去噪能力.

Data Compression Method Using 2 Dimensional Discrete Wavelet Transform for Power Quality Detection

This paper introduces a method combining a 2-dimensional discrete wavelet transform(2-DWT)with energy threshold method to deal with the data compression in power quality detection.The high frequency signals and noise signals are first decomposed into three different directions by making use of the 2-dimensional Daubechies wavelet transform to process matrix data with row convolution and line convolution.The energy of signals is then concentrated on a few wavelet coefficients. The energy thresholds are set with the helps of mean-energy correction coefficients,which guarantee the left energy after compression over 99%.Thus,the distortion ratio of reconstructed signals is small enough and the noise of disturbing signals could be adaptively eliminated.Finally,results of simulation with six kinds of disturbing signals indicate this method has a higher compression ratio and a better ability to eliminate noise compared with those of wavelet packet transform.