Please wait a minute...
浙江大学学报(工学版)
浙江大学学报(工学版)  2017, Vol. 51 Issue (9): 1834-1843    DOI: 10.3785/j.issn.1008-973X.2017.09.018
电气工程     
采用改进互补集总经验模态分解的电能质量扰动检测方法
吴新忠, 邢强, 陈明, 成江洋, 杨春雨
中国矿业大学 信息与电气工程学院, 江苏 徐州 221008
Power quality disturbance detection method using improved complementary ensemble empirical mode decomposition
WU Xin-zhong, XING Qiang, CHEN Ming, CHENG Jiang-yang, YANG Chun-yu
School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221008, China
 全文: PDF(2064 KB)   HTML
摘要：

针对集总经验模态分解（EEMD）方法加噪参数（噪声幅值、集总次数）需人为确定、分解残余噪声大以及计算耗时长的缺点，提出一种自适应快速互补集总经验模态分解（AFCEEMD）方法.该方法分析不同频率形式噪声对极值点分布的影响，确定加噪频率采用高频辅助分解的优势，并以极值点分布特性作为评价指标自适应选择最优加噪频率.通过对EEMD加噪准则的研究，推导出加噪幅值和分解次数采取固定值：0.01 SD和2次，且以正负成对的形式加入到原始信号中.通过仿真实验和搭建的电能质量扰动平台的实测数据验证了所提方法的自适应性和计算性能，而且适用于电能质量扰动检测与分析.
Abstract:

An adaptively fast complementary EEMD (AFCEEMD) was proposed aiming at the shortcomings of the ensemble empirical mode decomposition (EEMD) method, where the two critical parameters (the amplitude of the added white noise and the number of ensemble trials) were obtained artificially, the contamination of residue noise in the signal decomposition and the computational complexity. In the proposed method, the influence of white noise with different frequency forms on the distribution uniformity of signal extreme points was analyzed, the advantage of employing high-frequency white noise auxiliary decomposition was determined, and the distribution characteristic of signal extreme points was taken as an evaluation index to adaptively select the optimal frequency of additive white-noise. Furthermore, the two key parameters respectively fixed as 0.01 times standard deviation of the original signal and two ensemble trials were deduced by investigating the principle of added white noise in EEMD method, in which the white noise was added in pairs with plus and minus signs to the targeted signal. Both simulation tests and experiments data measured from the built power quality disturbance platform demonstrate the adaptability and computational efficiency of the proposed method, which is suitable for detection and analysis of power quality disturbance.
收稿日期: 2016-06-17 出版日期: 2017-08-25
CLC:  TU111  
基金资助:

国家自然科学基金资助项目（61374043）；教育部中央高校基本科研业务费专项资金资助项目（2013QNA50）.
作者简介: 吴新忠(1976-),男,副教授,从事电力系统远程监控研究.orcid.org/0000-0003-4876-7166.E-mail:zxwcumt@163.com
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章　　

引用本文:

吴新忠, 邢强, 陈明, 成江洋, 杨春雨. 采用改进互补集总经验模态分解的电能质量扰动检测方法[J]. 浙江大学学报(工学版), 2017, 51(9): 1834-1843.

WU Xin-zhong, XING Qiang, CHEN Ming, CHENG Jiang-yang, YANG Chun-yu. Power quality disturbance detection method using improved complementary ensemble empirical mode decomposition. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(9): 1834-1843.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2017.09.018    或    http://www.zjujournals.com/eng/CN/Y2017/V51/I9/1834

[1] 徐永海,赵燕.基于短时傅里叶变换的电能质量扰动识别与采用奇异值分解的扰动时间定位[J].电网技术,2011,35(8):174-180. XU Yong-hai, ZHAO Yan. Identification of power quality disturbance based on short-term fourier transform and disturbance time orientation by singular valuedecomposition[J]. Power System Technology, 2011,35(8):174-180.
[2] 赵凤展,杨仁刚.基于短时傅里叶变换的电压暂降扰动检测[J].中国电机工程学报,2007,27(10):28-34. ZHAO Feng-zhan, YANG Ren-gang. Voltage sag disturbance detection based on short time fourier transform[J]. Proceedings of the CSEE,2007,7(10):28-34.
[3] MAHELA O P, SHAIK A G, GUPTA N. A critical review of detection and classification of power qualityevents[J]. Renewable and Sustainable Energy Reviews, 2015, 41(C):495-505.
[4] THIRUMALA K, UMARIKAR A C, JAIN T. Estimation of single-phase and three-phase power-quality indices using empirical wavelet transform[J]. IEEE Transactions on Power Delivery, 2015, 30(1):445-454.
[5] DE YONG D, BHOWMIK S, MAGNAGO F. An effective power quality classifier using wavelet transform and support vector machines[J]. Expert Systems with Applications, 2015, 42(15):6075-6081.
[6] 李涛,夏浪,张宇,等.基于提升复小波的暂态电能质量扰动的检测与定位[J].中国电机工程学报,2011,31(25):66-72. LI Tao, XIA Lang, ZHANG Yu, et al. Detection and localization of power quality transient disturbances based on lifting complex wavelet[J]. Proceedings of the CSEE, 2011, 31(25):66-72.
[7] 张志禹,满蔚仕,郗垒,等.快速S变换在电能质量分析中的应用[J].电网技术,2013,37(5):1285-1290. ZHANG Zhi-yu, MAN Wei-shi, XI Lei, et al. Application of fast S-transform in power quality analysis[J]. Power System Technology, 2013, 37(5):1285-1290.
[8] 徐方维,杨洪耕,叶茂清,等.基于改进S变换的电能质量扰动分类[J].中国电机工程学报,2012, 32(4):77-84. XU Fang-wei, YANG Hong-geng, YE Mao-qing, et al. Classification for power quality short duration disturbances based on generalized S-transform[J]. Proceedings of the CSEE, 2012, 32(4):77-84.
[9] XIAO X, XU F, YANG H. Short duration disturbance classifying based on s-transform maximum similarity[J]. International Journal of Electrical Power and Energy Systems, 2009, 31(7/8):374-378.
[10] MANDIC D P, REHMAN N U, WU Z, et al. Empirical mode decomposition-based time-frequency analysis of multivariate signals:the power of adaptive data analysis[J]. IEEE Signal Processing Magazine, 2013,30(6):74-86.
[11] NORDEN E H, THOMAS Y H. Adaptive data analysis:theory and applications[J]. Philosophical Transactions of the Royal Society A-Mathematical Physical and Engineering Sciences, 2016, 20(12):141-151.
[12] 汤宝平,董绍江,马靖华.基于独立分量分析的EMD模态混叠消除方法研究[J].仪器仪表学报,2012, 33(7):1477-1482. TANG Bao-ping, DONG Shao-jiang, MA Jing-hua. Study on the method for eliminating mode mixing of empirical mode decomposition based on independent component analysis[J]. Chinese Journal of Scientific Instrument, 2012, 33(7):1477-1482.
[13] WU Z, HUANG N E. Ensemble empirical mode decomposition:a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2011, 1(1):1-41.
[14] YEH J R, SHIEH J S, HUANG N E. Complementary ensemble empirical mode decomposition:a novel noise enhanced data analysis method[J]. Advances in Adaptive Data Analysis, 2010, 2(2):135-156.
[15] TORRES M E, COLOMINAS M A, SCHLOTTHAUER G, et al. A complete ensemble empirical mode decomposition with adaptive noise[J]. Brain Research Bulletin, 2011, 125(3):4144-4147.
[16] LEI Y, LI N, LIN J, et al. Fault diagnosis of rotating machinery based on an adaptive ensemble empirical mode decomposition[J]. Sensors, 2013, 13(12):16950-16964.
[17] 孔德同,刘庆超,雷亚国,等.一种改进的EEMD方法及其应用研究[J].振动工程学报,2015,28(6):1015-1021. KONG De-tong, LIU Qing-chao, LEI Ya-guo, et al. The improved EEMD method and its application[J]. Journal of Vibration Engineering, 2015, 28(6):1015-1021.
[18] HUANG W, CAI N, XIE W, et al. ECG baseline wander correction based on ensemble empirical mode decomposition with complementary adaptive noise[J]. Journal of Medical Imaging and Health Informatics, 2015, 5(8):1796-1799.
[19] 王学伟,王琳,苗桂君,等.暂态和短时电能质量扰动信号压缩采样与重构方法[J].电网技术,2012,36(3):191-196. WANG Xue-wei, WANG Lin, MIAO Gui-jun, et al. An approach for compressible sampling and reconstruction of transient and short-time power quality disturbance signals[J] Power System Technology, 2012,36(3):191-196.
[20] ZHENG J, CHENG J, YANG Y. Partly ensemble empirical mode decomposition:an improved noise-assisted method for eliminating mode mixing[J]. Signal Processing, 2014, 9(6):362-374.
[21] 李天云,程思勇,杨梅.基于希尔伯特-黄变换的电力系统谐波分析[J].中国电机工程学报,2008,28(4):109-113. LI Tian-yun, CHENG Si-yong, YANG Mei. Power system harmonic analysis based on hilbert-huang transform[J]. Proceedings of the CSEE, 2008, 28(4):109-113.
[1] 叶居东, 杨贞军, 刘国华, 姚勇. 超高性能混凝土-螺旋钢纤维拉拔力的解析解及实验验证[J]. 浙江大学学报(工学版), 2018, 52(10): 1911-1918.
[2] 唐苇羽, 陈景祥, 韩锦程, 何燕, 李蔚, 刘志春. 不同强化换热管内流动沸腾换热特性对比[J]. 浙江大学学报(工学版), 2018, 52(6): 1216-1222.
[3] 周佳立, 陈以军, 武敏. 基于FPGA监听的图像采集与预处理方法[J]. 浙江大学学报(工学版), 2018, 52(2): 398-405.
[4] 丁智, 洪其浩, 魏新江, 张孟雅, 郑勇. 地铁列车荷载下人工冻融软土微观试验研究[J]. 浙江大学学报(工学版), 2017, 51(7): 1291-1299.