电能质量扰动信号的自适应去噪方法
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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.
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.
引文
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[15]钱勇,黄成军,陈陈.基于经验模态分解的局部放电去噪方法[J].电力系统自动化,2005,29(12):53-56.QIAN Yong,HUANG Chengjun,CHEN Chen.Denoising of partial discharge based on empirical mode decomposition[J].Automation of Electric Power Systems,2005,29(12):53-56.
[16]LI Peng,GAO Jing,XU Duo,et al.Hilbert-Huang transform with adaptive waveform matching extension and its application in power quality disturbance detection for microgrid[J].Journal of Modern Power Systems and Clean Energy,2016,4(1):19-27.
[17]焦尚彬,黄璜,张青.基于双曲S变换的电能质量信号降噪新方法[J].电网技术,2011,35(9):105-110.JIAO Shangbin,HUANG Huang,ZHANG Qing.A new hyperbolic S-transform based denoising method of power quality signals[J].Power System Technology,2011,35(9):105-110.
[18]DABOV K,FOI A,KATKOVNIK V,et al.Image denoising by sparse 3D transform-domain collaborative filtering[J].IEEE Trans on Image Processing,2007,16(8):2080-2095.
[19]刘太智,刘文江,戎蒙恬.BM3D算法中块匹配模块的架构设计[J].信息技术,2012(3):24-26.LIU Taizhi,LIU Wenjiang,RONG Mengtian.Architecture design of block-matching module in BM3D algorithm[J].Information Technology,2012(3):24-26.
[20]李政,刘文江,戎蒙恬,等.BM3D视频去噪算法实现与评估[J].信息技术,2012(4):30-32.LI Zheng,LIU Wenjiang,RONG Mengtian,et al.Implementation and evaluation of BM3D video denoising algorithms[J].Information Technology,2012(4):30-32.
[21]黄牧,黄文清,李俊柏,等.基于BM3D图像去噪算法的参数研究[J].工业控制计算机,2014,27(10):99-101.HUANG Mu,HUANG Wenqing,LI Junbo,et al.Parameters study based on BM3Dimage denoising algorithm[J].Industrial Control Computer,2014,27(10):99-101.
[22]刘向乐,冯象初.小波域三维块匹配图像去噪[J].计算机工程与应用,2010,46(16):185-187.LIU Xiangle,FENG Xiangchu.Image denoising by mixing wavelet transformation with sparse 3D collaborative filtering[J].Computer Engineering and Applications,2010,46(16):185-187.
[23]MANIMALA K,SELVI K,AHILA R.Hybrid soft computing techniques for feature selection and parameter optimization in power quality data mining[J].Applied Soft Computing,2011(11):5485-5497.
[24]LEE C Y,SHEN Y X.Optimal feature selection for powerquality disturbances classification[J].IEEE Trans on Power Delivery,2011,26(4):2342-2351.
[2]DWIVEDI U D,SINGH S N.Denoising techniques with change-point approach for wavelet-based power-quality monitoring[J].IEEE Trans on Power Delivery,2009,24(3):1719-1727.
[3]MANIKANDAN M S,SAMANTARAY S R,KAMWA I.Detection and classification of power quality disturbances using sparse signal decomposition on hybrid dictionaries[J].IEEE Trans on Instrumentation and Measurement,2015,64(1):27-38.
[4]LIAO C C,YANG H T,CHANG H H.Denoising techniques with a spatial noise-suppression method for wavelet-based power quality monitoring[J].IEEE Trans on Instrumentation and Measurement,2011,60(6):1986-1996.
[5]唐良瑞,尚飞,祁兵.基于似然比判决准则的电能质量去噪算法[J].中国电机工程学报,2006,26(23):49-54.TANG Liangrui,SHANG Fei,QI Bing.A de-nosing algorithm of power quality signals based on decision criterion of likelihood ratio[J].Proceedings of the CSEE,2006,26(23):49-54.
[6]欧阳森,宋政湘,陈德桂,等.小波软阈值去噪技术在电能质量检测中的应用[J].电力系统自动化,2002,26(19):56-60.OUYANG Sen,SONG Zhengxiang,CHEN Degui,et al.Application of wavelet soft-threshold de-noising technique to power quality detection[J].Automation of Electric Power Systems,2002,26(19):56-60.
[7]柯慧,顾洁.电能质量信号的小波阈值去噪[J].电力系统及其自动化学报,2010,22(2):103-108.KE Hui,GU Jie.Wavelet threshold de-noising of power system signals[J].Proceedings of the CSU-EPSA,2010,22(2):103-108.
[8]李天云,陈昌雷,程汪刘,等.交叉验证方法在电能质量信号白噪声抑制中的应用[J].电力系统自动化,2007,31(16):75-78.LI Tianyun,CHEN Changlei,CHENG Wangliu,et al.Application of cross validation in power quality de-noising[J].Automation of Electric Power Systems,2007,31(16):75-78.
[9]张明,李开成,胡益胜.基于小波邻域阈值分类的电能质量信号去噪算法[J].电力系统自动化,2010,34(10):84-89.ZHANG Ming,LI Kaicheng,HU Yisheng.A power quality signal de-noising algorithm based on wavelet domain neighbouring thresholding classification[J].Automation of Electric Power Systems,2010,34(10):84-89.
[10]贾毅婷,张东来,张斌.基于小波尺度相关性的暂态数据降噪压缩方法[J].电力系统自动化,2013,37(5):68-73.JIA Yiting,ZHANG Donglai,ZHANG Bin.Power quality disturbance data denoising and compression using signal’s scale-dependencies of wavelet coefficients[J].Automation of Electric Power Systems,2013,37(5):68-73.
[11]秦代春,刘强,周林,等.电能质量信号去噪方法研究[J].华东电力,2009,37(5):767-771.QIN Daichun,LIU Qiang,ZHOU Lin,et al.De-noising methods for power quality signals[J].East China Electric Power,2009,37(5):767-771.
[12]岳蔚,刘沛.基于数学形态学消噪的电能质量扰动检测方法[J].电力系统自动化,2002,26(7):13-17.YUE Wei,LIU Pei.Detection of power quality disturbances based on mathematical morphology filter[J].Automation of Electric Power Systems,2002,26(7):13-17.
[13]赵静,何正友,钱清泉.利用广义形态滤波与差分熵的电能质量扰动检测[J].中国电机工程学报,2009,29(7):121-127.ZHAO Jing,HE Zhengyou,QIAN Qingquan.Detection of power quality disturbances utilizing generalized morphological filter and difference-entropy[J].Proceedings of the CSEE,2009,29(7):121-127.
[14]胡卫红,舒泓,栾宇光.基于奇异值分解的电能质量信号去噪[J].电力系统保护与控制,2010,38(2):30-33.HU Weihong,SHU Hong,LUAN Yuguang.Power quality signals’de-noising method based on singular value decomposition(SVD)[J].Power System Protection and Control,2010,38(2):30-33.
[15]钱勇,黄成军,陈陈.基于经验模态分解的局部放电去噪方法[J].电力系统自动化,2005,29(12):53-56.QIAN Yong,HUANG Chengjun,CHEN Chen.Denoising of partial discharge based on empirical mode decomposition[J].Automation of Electric Power Systems,2005,29(12):53-56.
[16]LI Peng,GAO Jing,XU Duo,et al.Hilbert-Huang transform with adaptive waveform matching extension and its application in power quality disturbance detection for microgrid[J].Journal of Modern Power Systems and Clean Energy,2016,4(1):19-27.
[17]焦尚彬,黄璜,张青.基于双曲S变换的电能质量信号降噪新方法[J].电网技术,2011,35(9):105-110.JIAO Shangbin,HUANG Huang,ZHANG Qing.A new hyperbolic S-transform based denoising method of power quality signals[J].Power System Technology,2011,35(9):105-110.
[18]DABOV K,FOI A,KATKOVNIK V,et al.Image denoising by sparse 3D transform-domain collaborative filtering[J].IEEE Trans on Image Processing,2007,16(8):2080-2095.
[19]刘太智,刘文江,戎蒙恬.BM3D算法中块匹配模块的架构设计[J].信息技术,2012(3):24-26.LIU Taizhi,LIU Wenjiang,RONG Mengtian.Architecture design of block-matching module in BM3D algorithm[J].Information Technology,2012(3):24-26.
[20]李政,刘文江,戎蒙恬,等.BM3D视频去噪算法实现与评估[J].信息技术,2012(4):30-32.LI Zheng,LIU Wenjiang,RONG Mengtian,et al.Implementation and evaluation of BM3D video denoising algorithms[J].Information Technology,2012(4):30-32.
[21]黄牧,黄文清,李俊柏,等.基于BM3D图像去噪算法的参数研究[J].工业控制计算机,2014,27(10):99-101.HUANG Mu,HUANG Wenqing,LI Junbo,et al.Parameters study based on BM3Dimage denoising algorithm[J].Industrial Control Computer,2014,27(10):99-101.
[22]刘向乐,冯象初.小波域三维块匹配图像去噪[J].计算机工程与应用,2010,46(16):185-187.LIU Xiangle,FENG Xiangchu.Image denoising by mixing wavelet transformation with sparse 3D collaborative filtering[J].Computer Engineering and Applications,2010,46(16):185-187.
[23]MANIMALA K,SELVI K,AHILA R.Hybrid soft computing techniques for feature selection and parameter optimization in power quality data mining[J].Applied Soft Computing,2011(11):5485-5497.
[24]LEE C Y,SHEN Y X.Optimal feature selection for powerquality disturbances classification[J].IEEE Trans on Power Delivery,2011,26(4):2342-2351.