基于改进阈值和阈值函数的电能质量小波去噪方法
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摘要
针对电能质量扰动类型多,成分复杂,扰动特征易被当作噪声去除等问题,提出一种改进小波阈值去噪算法。该算法通过计算每层小波系数的峰和比来确定该层噪声含量,使修正因子Fj可根据不同扰动信号的噪声分布特点自适应调整通用阈值。同时,提出了改进的阈值函数,可变参数a能调节自身软、硬特性从而确定合适的阈值函数。采用该算法对七种常见电能质量扰动信号去噪,仿真结果表明,改进小波阈值去噪算法在不同噪声干扰下,对各类扰动信号都能达到较好的信噪比,去噪效果稳定,重构信号波形恢复较好,且在去噪过程中保留了扰动特征,能为后续电能质量分析提供准确有效的信息。
There are many types of power quality disturbance signals,the components are complex,and the disturbance characteristics are easily removed as noise.For these problems,an improved wavelet threshold de-noising algorithm was proposed in this paper.This algorithm could determine the noise by calculating the peak-to-sum ratio for each layer of wavelet coefficients,so that the correction factor Fj could adaptively adjust the general threshold according to the noise distribution characteristics of different disturbance signals.Meanwhile,an improved threshold function was proposed,which could adjust the soft and hard characteristics by changing the value of the parameter ato determine the appropriate threshold function.The improved algorithm was used to de-noise the seven kinds of power quality signals.The simulation results show that the improved algorithm achieves better signal-to-noise ratios,stable de-noising effects and better waveforms of reconstructed signals for various types of disturbance signals in different noise interferences.Moreover,it retains the disturbance characteristics in the de-noising process,which can provide accurate and effective information for the subsequent power quality analysis.
There are many types of power quality disturbance signals,the components are complex,and the disturbance characteristics are easily removed as noise.For these problems,an improved wavelet threshold de-noising algorithm was proposed in this paper.This algorithm could determine the noise by calculating the peak-to-sum ratio for each layer of wavelet coefficients,so that the correction factor Fj could adaptively adjust the general threshold according to the noise distribution characteristics of different disturbance signals.Meanwhile,an improved threshold function was proposed,which could adjust the soft and hard characteristics by changing the value of the parameter ato determine the appropriate threshold function.The improved algorithm was used to de-noise the seven kinds of power quality signals.The simulation results show that the improved algorithm achieves better signal-to-noise ratios,stable de-noising effects and better waveforms of reconstructed signals for various types of disturbance signals in different noise interferences.Moreover,it retains the disturbance characteristics in the de-noising process,which can provide accurate and effective information for the subsequent power quality analysis.
引文
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[17]Donoho D L.De-noising by soft thresholding[J].IEEE Transactions on Information Theory,1995,41(3):613-627.
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[19]孔玲军.MATLAB小波分析超级学习手册[M].北京:人民邮电出版社,2014.
[20]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&Clean Energy,2016,4(1):19-27.
[21]李发亮.电能质量扰动信号对节能减损的影响及研究[D].哈尔滨:哈尔滨理工大学,2016.
[22]Parameswariah C,Cox M.Frequency characteristics of Wavelets[J].IEEE Transactions on Power Delivery,2002,17(3):800-803.
[23]Santoso S,Powers E J,Grady W M.Power quality disturbance data compression using wavelet transform method[J].IEEE Transactions on Power Delivery,1997,12(3):1250-1257.
[24]张明,李开成,胡益胜.基于Bayes估计的双小波维纳滤波电能质量信号去噪算法[J].电力系统保护与控制,2011,39(4):52-56.Zhang Ming,Li Kaicheng,Hu Yisheng.Power quality signals denoising via double-wavelet Wiener filtering based on Bayes estimation[J].Power System Protection and Control,2011,39(4):52-56.
[2]Srivastava M,Anderson C L,Freed J H.A new wavelet denoising method for selecting decomposition levels and noise thresholds[J].IEEE Access,2016(4):3862-3877.
[3]Shukla S,Mishra S,Singh B.Power quality event classification under noisy conditions using EMD-based de-noising techniques[J].IEEE Transactions on Industrial Informatics,2014,10(2):1044-1054.
[4]刘宇舜,周文俊,李鹏飞,等.基于广义S变换模时频矩阵的局部放电特高频信号去噪方法[J].电工技术学报,2017,32(9):211-220.Liu Yushun,Zhou Wenjun,Li Pengfei,et al.Partial discharge ultrahigh frequency signal denoising method based on generalized S-transform modular time-frequency matrix[J].Transactions of China Electrotechnical Society,2017,32(9):211-220.
[5]胡卫红,舒泓,栾宇光.基于奇异值分解的电能量信号去噪[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.
[6]王蓓,张根耀,李智.阈值去噪联合数学形态学的胃部图像边缘检测[J].计算机仿真,2015,32(3):375-377,446.Wang Bei,Zhang Genyao,Li Zhi.Image edge detection of stomach based on wavelet thresholding de-noising and mathematical morphology[J].Computer Simulation,2015,32(3):375-377,446.
[7]Puneet K J,Anil K T.An adaptive thresholding method for the wavelet based denoising of phonocardiogram signal[J].Biomedical Signal Processing and Control,2017,38:388-399.
[8]马丹丹,王晓茹.基于小波模极大值的单端行波故障测距[J].电力系统保护与控制,2009,37(3):55-59.Ma Dandan,Wang Xiaoru.Single terminal methods of traveling wave fault location based on wavelet modulus maxima[J].Power System Protection and Control,2009,37(3):55-59.
[9]江天炎,李剑,杜林,等.粒子群优化小波自适应阈值法用于局部放电去噪[J].电工技术学报,2012,27(5):77-83.Jiang Tianyan,Li Jian,Du Lin,et al.De-noising for partial discharge signals using PSO adaptive wavelet threshold estimation[J].Transactions of China Electrotechnical Society,2012,27(5):77-83.
[10]叶瑞丽,郭志忠,刘瑞叶,等.基于小波包分解和改进Elman神经网络的风电场风速和风电功率预测[J].电工技术学报,2017,32(21):103-111.Ye Ruili,Guo Zhizhong,Liu Ruiye,et al.Wind speed and wind power forecasting method based on wavelet packet decomposition and improved elman neural network[J].Transactions of China Electrotechnical Society,2017,32(21):103-111.
[11]黄建招,谢建,高钦和,等.基于改进阈值法的平移不变小波压力脉动去噪研究[J].机床与液压,2012,40(15):161-164,144.Huang Jianzhao,Xie Jian,Gao Qinhe,et al.Research of pressure fluctuation signal denoising based on translation invariance wavelet and improved threshold method[J].Machine Tool&Hydraulics,2012,40(15):161-164,144.
[12]邬春明,谢妮娜.改进的小波阈值在电能质量信号去噪中的应用[J].计算机工程与应用,2012,48(3):114-116.Wu Chunming,Xie Nina.Application of improved wavelet threshold in power quality signal denoising[J].Computer Engineering and Applications,2012,48(3):114-116.
[13]刘卫东,刘尚合,胡小锋,等.小波阈值去噪函数的改进方法分析[J].高电压技术,2007,33(10):59-63.Liu Weidong,Liu Shanghe,Hu Xiaofeng,et al.Analysis of modified methods of wavelet threshold de-noising function[J].High Voltage Engineering,2007,33(10):59-63.
[14]袁开明,舒乃秋,孙云莲,等.基于阈值寻优法的小波去噪分析[J].武汉大学学报(工学版),2015,48(1):74-80.Yuan Kaiming,Shu Naiqiu,Sun Yunlian,et al.Wavelet denoising based on threshold optimization method[J].Engineering Journal of Wuhan University,2015,48(1):74-80.
[15]Davoudabadi M J,Aminghafari M.A fuzzy-wavelet denoising technique with applications to noise reduction in audio signals[J].Journal of Intelligent&Fuzzy Systems,2017,33(4):2159-2169.
[16]龙虹毓,张晓勇,胡晓锐,等.蚁群优化小波阈值算法用于变电设备状态信号提取[J].电工技术学报,2015,30(12):422-428.Long Hongyu,Zhang Xiaoyong,Hu Xiaorui,et al.Extraction of condition signals of electrical plants by ACO wavelet threshold estimation[J].Transactions of China Electrotechnical Society,2015,30(12):422-428.
[17]Donoho D L.De-noising by soft thresholding[J].IEEE Transactions on Information Theory,1995,41(3):613-627.
[18]Srivastava M,Georgieva E R,Freed J H.A new wavelet denoising method for experimental timedomain signals:pulsed dipolar electron spin resonace[J].The Journal of Physical Chemistry A,2017,121:2452-2465.
[19]孔玲军.MATLAB小波分析超级学习手册[M].北京:人民邮电出版社,2014.
[20]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&Clean Energy,2016,4(1):19-27.
[21]李发亮.电能质量扰动信号对节能减损的影响及研究[D].哈尔滨:哈尔滨理工大学,2016.
[22]Parameswariah C,Cox M.Frequency characteristics of Wavelets[J].IEEE Transactions on Power Delivery,2002,17(3):800-803.
[23]Santoso S,Powers E J,Grady W M.Power quality disturbance data compression using wavelet transform method[J].IEEE Transactions on Power Delivery,1997,12(3):1250-1257.
[24]张明,李开成,胡益胜.基于Bayes估计的双小波维纳滤波电能质量信号去噪算法[J].电力系统保护与控制,2011,39(4):52-56.Zhang Ming,Li Kaicheng,Hu Yisheng.Power quality signals denoising via double-wavelet Wiener filtering based on Bayes estimation[J].Power System Protection and Control,2011,39(4):52-56.