Please wait a minute...
浙江大学学报(工学版)  2018, Vol. 52 Issue (5): 988-995    DOI: 10.3785/j.issn.1008-973X.2018.05.020
机械与能源工程     
采用经验小波变换的风力发电机振动信号消噪
陈学军1,2, 杨永明2
1. 莆田学院 机电工程学院, 福建 莆田 351100;
2. 重庆大学 输配电装备及系统安全与新技术国家重点实验室, 重庆 400044
De-noising for vibration signals of wind power generator using empirical wavelet transform
CHEN Xue-jun1,2, YANG Yong-ming2
1. School of Mechanical and Electrical Engineering, Putian University, Putian 351100, China;
2. State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China
 全文: PDF(1701 KB)   HTML
摘要:

针对风力发电机振动信号非线性特征及恶劣监测环境,分析经验小波变换理论(EWT)及自适应分解特性,提出基于经验小波变换的振动信号消噪方法.采用带噪声leleccum和轴承故障仿真信号对该方法进行消噪效果检验;在同信号源下,与基于db1强制消噪方法、db1软阈值消噪方法和sym5消噪方法分析比较消除噪声效果.针对真实的风力发电机振动信号,验证了基于经验小波变换方法的消噪效果,对同样信号采用其他3种方法进行消噪分析和比较.仿真和实验分析结果表明,基于EWT小波消噪方法,与基于db1强制消噪方法、db1软阈值消噪方法和sym5消噪方法能够达到同样的消噪效果和目的,甚至更优;不损耗原振动信号能量,在自适应模态分解层数方面甚至优于经验模态分解,并且具有较强的鲁棒性.

Abstract:

The empirical wavelet transform theory (EWT) and its adaptive characteristics were analyzed according to the nonlinear characteristics of wind power generator vibration signal and the poor monitoring environment. Then a de-noising method was proposed based on EWT. The proposed method was tested by using the leleccum and bearing fault simulation signal with noises, compared with de-noising method based on db1 wavelet with compulsion, db1 wavelet with soft threshold, and sym5 wavelet. The de-noising effect based on EWT was verified for the actual vibration signals of wind power generator. The other three methods were used to eliminate noises analysis and compared with the same signals. The simulation and experimental results show that the de-noising method based on EWT can achieve the same de-noising effect, or even better than the methods based on db1 wavelet with compulsion, db1 wavelet with soft threshold, and sym5 wavelet. The proposed method does not loss the original vibration signals energy, and it is better than the empirical mode decomposition in the adaptive mode decomposition with strong robustness.

收稿日期: 2017-04-07 出版日期: 2018-11-07
CLC:  TM315  
基金资助:

国家自然科学基金资助项目(51477015);福建省高校杰出青年科研人才培育计划资助项目(2015054);输配电装备及系统安全与新技术国家重点实验室访问学者资助项目(2007DA10512714406);莆田市科技资助项目(2016G2021).

作者简介: 陈学军(1980-),男,副教授,从事电气设备状态监测与故障诊断等研究.orcid:org/0000-0002-0554-7342.E-mail:cxjnet@126.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:

陈学军, 杨永明. 采用经验小波变换的风力发电机振动信号消噪[J]. 浙江大学学报(工学版), 2018, 52(5): 988-995.

CHEN Xue-jun, YANG Yong-ming. De-noising for vibration signals of wind power generator using empirical wavelet transform. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2018, 52(5): 988-995.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2018.05.020        http://www.zjujournals.com/eng/CN/Y2018/V52/I5/988

[1] MOON D S, KIM S K, KIM S H. A fault detection system for wind power generator based on intelligent clustering method[J]. Journal of Institute of Control, Robotics and Systems, 2013, 19(1):27-33.
[2] FATTAHI S J, ZABIHOLLAH A, ZAREIE S. Vibration monitoring of wind turbine blade using fiber bragg grating[J]. Wind Engineering, 2010, 34(6):721-731.
[3] MAY A, MCMILLAN D, TH NS S. Economic analysis of condition monitoring systems for offshore wind turbine sub-systems[J]. IET Renewable Power Generation, 2015, 9(8):900-907.
[4] 黄玲玲,曹家麟,张开华,等.海上风电机组运行维护现状研究与展望[J].中国电机工程学报,2016,36(3):729-738. HUANG Ling-ling, CAO Jia-lin, ZHANG Kai-hua, et al. Status and prospects on operation and maintenance of offshore wind turbines[J]. Proceedings of the CSEE, 2016, 36(3):729-738.
[5] 李辉,胡姚刚,唐显虎,等.并网风电机组在线运行状态评价方法[J].中国电机工程学报,2010,30(33):103-109. LI Hui, HU Yao-gang, TANG Xian-hu, et al. Method for online operating conditions assessment for a grid-connected wind turbine generator system[J]. Proceedings of the CSEE, 2010, 30(33):103-109.
[6] 盛迎新,周继威.风电机组在线振动监测系统及现场应用[J].振动、测试与诊断,2010,30(6):703-705. SHENG Ying-xin, ZHOU Ji-wei. Online wind turbine vibration monitoring system and its application[J]. Journal of Vibration, Measurement and Diagnosis, 2010, 30(6):703-705.
[7] MADSEN H A, YE M. Low frequency noise from wind turbines mechanisms of generation and its modelling[J]. Journal of Low Frequency Noise Vibration and Active Control, 2010, 29(4):239-251.
[8] WATSON S J, XIANG B, YANG W, et al. Condition monitoring of the power output of wind turbine generators using wavelets[J]. IEEE Transactions on Energy Conversion, 2010, 25(3):715-721.
[9] 林勇,周晓军,张文斌,等.基于形态小波理论和双谱分析的滚动轴承故障诊断[J].浙江大学学报:工学版,2010,44(3):432-439. LIN Yong, ZHOU Xiao-jun, ZHANG Wen-bin, et al. Rolling bearing fault diagnosis based on morphological wavelet theory and bi-spectrum analysis[J]. Journal of Zhejiang University:Engineering Science, 2010, 44(3):432-439.
[10] CHEN Q, YE M. Analysis of the fault diagnosis method for wind turbine generator bearing based on improved wavelet Packet-BP neural network[J]. Communications in Computer and Information Science, 2014, 463:13-20.
[11] 许同乐,郎学政,张新义,等.基于EMD相关方法的电动机信号降噪的研究[J].船舶力学,2014,18(5):599-603. XU Tong-le, LANG Xue-zheng, ZHANG Xin-yi, et al. Study on the electric motor vibration signal de-noising using EMD correlation de-noising algorithm[J]. Journal of Ship Mechanics, 2014, 18(5):599-603.
[12] 向东阳,吴正国,侯新国,等.改进的多小波变换系数相关去噪算法[J].高电压技术,2011,37(7):1728-1733. XIANG Dong-yang, WU Zheng-guo, HOU Xin-guo, et al. Improved denoising method using the correlation of multiwavelet coefficient[J]. High Voltage Engineering, 2011, 37(7):1728-1733.
[13] TANG B, LIU W, SONG T. Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution[J]. Renewable Energy, 2010, 35:2862-2866.
[14] ZHENG H, LI Z, CHEN X, et al. Gear fault diagnosis based on continuous wavelet transform[J]. Mechanical systems and Signal Processing, 2002, 16(2/3):447-457.
[15] 胡爱军,唐贵基,安连锁.基于数学形态学的旋转机械振动信号降噪方法[J].机械工程学报,2006,42(4):127-130. HU Ai-jun, TANG Gui-ji, AN Lian-suo. De-noising technique for vibration signals of rotating machinery based on mathematical morphology filter[J]. Chinese Journal of Mechanical Engineering, 2006, 42(4):127-130.
[16] HUANG N E. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[C]//Proceedings of the Royal Society of London A:mathematical, physical and engineering sciences. London:The Royal Society, 1998, 454(A):903-995.
[17] HUANG N E, WU Z. A review on Hilbert-huang transform:method and its applications to geophysical studies[J]. Reviews of Geophysics, 2008, 46(2):1-23.
[18] YAN R, GAO R. Hilbert-Huang Transform-Based vibration signal analysis for machine health monitoring[J]. IEEE Transactions on Instrumentation and Measurement, 2006, 55(6):2320-2329.
[19] Gilles J. Empirical wavelet transform[J]. IEEE Transactions on Signal Processing, 2013, 61(16):3999-4010.
[20] KARTHIK T, AMOD C U, TRAPTI J. 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.
[21] LI Y S, XUE B, HONG H, et al. Instantaneous pitch estimation based on empirical wavelet transform[C]//Proceedings Of The 19th International Conference On Digital Signal Processing. Hong Kong:IEEE, 2014:250-253.
[22] 李志农,朱明,褚福磊,等.基于经验小波变换的机械故障诊断方法研究[J]. 仪器仪表学报,2014,35(11):2423-2432. LI Zhi-nong, ZHU Ming, CHU Fu-lei. Mechanical fault diagnosis method based on empirical wavelet transform[J]. Chinese Journal of Scientific Instrument, 2014, 35(11):2423-2432.
[23] 陈浩,郭军海,齐巍.基于经验小波变换的目标加速度估计算法[J].北京航空航天大学学报,2015,41(1):154-159. CHEN Hao, GUO Jun-hai, QI Wei. Estimation of target's acceleration based on empirical wavelet transform[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(1):154-159.
[24] OMKAR S, RAMESH K S. Onset detection in arterial blood pressure pulses using empirical wavelet transform[C]//2nd International Conference on Computing for Sustainable Global Development. New Delhi:IEEE, 2015:1612-1615.
[25] PATRICK F, GABRRIEL R, PAULO G. Empirical mode decomposition as a filter bank[J]. IEEE Signal Processing Letters, 2004, 11(2):112-114.
[26] 艾延廷,冯研研,周海仑.小波变换和EEMD-马氏距离的轴承故障诊断[J].噪声与振动控制,2015,35(1):235-239. AI Yan-ting, FENG Yan-yan, ZHOU Hai-lun. Fault diagnosis of roller bearings using wavelet transform and EEMD-Mahalanobis distance[J]. Noise and Vibration Control, 2015, 35(1):235-239.

No related articles found!