摘要
有效地降低电能质量信号中的噪声,是做好电能质量信号检测、识别等工作的基础。为了克服一维电能质量信号降噪的难点问题,即有效地去除噪声并完整地保留奇异点的特征,对目前图像处理领域中针对高斯等噪声降噪性能最好的基于块匹配的三维变换域联合滤波(BM3D)算法进行了改进,提出一种电能质量扰动信号的自适应去噪新方法。该方法参数较少,无需估计噪声方差,也无需人为设定滤波阈值,而是通过自适应估算较为准确的阈值实现离散余弦变换(DCT)域的滤波。通过对电压中断、电压暂降、电压暂升、脉冲暂态、振荡暂态和谐波这6种常见的电能质量信号进行降噪仿真实验,并与应用较为广泛的小波阈值去噪法进行对比分析,最后应用于实际电能质量扰动数据的降噪,验证了所述算法的有效性。
Effective noise reduction of power quality（PQ）signals is the basis of detection and recognition of PQ signals.The block-matching and 3Dcollaborative filtering（BM3D）algorithm is at present the most efficient de-noising algorithm for Gauss noise and other noise models in the field of image processing.To overcome the difficulty of one-dimensional PQ signals denoising,that is,effectively removing noise and well keeping singularities intact,the BM3 Dalgorithm is improved and a novel adaptive noise reduction method for PQ disturbances is proposed.For the proposed method,as less parameters are used,there is no need to estimate noise variance,nor is it necessary to artificially set the filter thresholds.Rather,accurate thresholds are calculated adaptively for the discrete cosine transform（DCT）domain filtering to obtain the de-noising results of PQ disturbances.The simulation experiments of six common kinds of PQ disturbances,including voltage interruption,voltage sag,voltage swell,impulsive transient,oscillation transient and harmonic,are performed and a comparison with the widely used wavelet threshold de-noising method is analyzed.Finally,the actual PQ data is employed.The experimental results indicate that the proposed method is effective.
出处
《电力系统自动化》
EI
CSCD
北大核心
2016年第23期109-117,共9页
Automation of Electric Power Systems
基金
中央高校基本科研业务费专项资金资助项目(2682014RC07)~~
关键词
电能质量
基于块匹配的三维变换域联合滤波
离散余弦变换
自适应去噪
信号突变点
power quality
block-matching and 3D collaborative filtering（BM3D）
discrete cosine transform（DCT）
adaptive de-noising
signal singularity
