基于VMD和脉冲因子的水轮机摆度信号特征提取分析
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摘要
作为水电机组运行状况的基本判据,水轮机的水导摆度信号是水电站运行研究中的重点课题。为研究更好的提取水轮机摆度信号特征,针对现场采集的水轮机水导摆度信号中出现的异常尖峰成分,提出一种基于变分模态分解和脉冲因子的水轮机摆度信号特征提取方法,并介绍了其基本原理及求解步骤。首先,使用VMD对原始信号进行分解,分离出不同成分的变分模态分量(简称模态分量)。然后,计算每个模态分量的脉冲因子值并设定脉冲因子的标准值。最后,将超出该值的模态分量去除,并将剩余的分量进行重构,获得除去异常尖峰成分后的重组信号。通过对仿真信号以及实际信号的分析,该方法可以将水导摆度信号中的异常尖峰成分去除,并对相关物理特征进行有效地提取。最后得出结论:(1)变分模态分解可以将水导摆度信号中的异常尖峰成分分解在各个模态中;(2)以脉冲因子作为脉冲能量指标,能够将含有少量噪声的模态提取出来。研究成果可为水轮机的设计和数值模拟提供参考。
As the basic criteria for the operation condition of hydropower unit, the water guiding system swing signal of hydro-turbine is the key issue in the study made on the operation of hydropower station. In order to study how to better extract the characteristics of hydro-turbine swing signal, a variational modal decomposition and pulse factor-based method for extracting the swing signal of hydro-turbine is proposed herein for the abnormal peak components occurred in water guiding system swing signal of hydro-turbine collected in situ, and then its basic principle and solving procedure are described as well. At first, the initial signal is decomposed with VMD for separating out the variational modal components of different components(abbreviated as modal components). Afterwards, the values of the pulse factors of all the modal components are calculated, while the standard values of the pulse factors are set. Finally, the modal components beyond the values are removed, while the remaining components are reconstructed to obtain the reconstructed signals after removing the abnormal peak components. Through the analysis on both the simulated signal and the actual signal, the abnormal peak components in the water guiding system swing signal can be removed, and then the relevant physical characteristics can be effectively extracted. At last, it is concluded that(1) the abnormal peak components in the water guiding system swing signal can be decomposed into all the modes by the variational modal decomposition;(2) the mode with a small amount of noise can be extracted by taking the pulse factor as the pulse energy index. The study result can provide a reference for the design of hydro-turbine as well as its numerical simulation.
As the basic criteria for the operation condition of hydropower unit, the water guiding system swing signal of hydro-turbine is the key issue in the study made on the operation of hydropower station. In order to study how to better extract the characteristics of hydro-turbine swing signal, a variational modal decomposition and pulse factor-based method for extracting the swing signal of hydro-turbine is proposed herein for the abnormal peak components occurred in water guiding system swing signal of hydro-turbine collected in situ, and then its basic principle and solving procedure are described as well. At first, the initial signal is decomposed with VMD for separating out the variational modal components of different components(abbreviated as modal components). Afterwards, the values of the pulse factors of all the modal components are calculated, while the standard values of the pulse factors are set. Finally, the modal components beyond the values are removed, while the remaining components are reconstructed to obtain the reconstructed signals after removing the abnormal peak components. Through the analysis on both the simulated signal and the actual signal, the abnormal peak components in the water guiding system swing signal can be removed, and then the relevant physical characteristics can be effectively extracted. At last, it is concluded that(1) the abnormal peak components in the water guiding system swing signal can be decomposed into all the modes by the variational modal decomposition;(2) the mode with a small amount of noise can be extracted by taking the pulse factor as the pulse energy index. The study result can provide a reference for the design of hydro-turbine as well as its numerical simulation.
引文
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[2] JENA D P,PANIGRAHI S N,KUMAR R.Gear fault identification and localization using analytic wavelet transform of vibration signal[J].Measurement,2013,46(3):1115- 1124.
[3] KHAZAEE M,AHMADI H,OMID M,et al.Feature-level fusion based on wavelet transform and artificial neural network for fault diagnosis of planetary gearbox using acoustic and vibration signals[J].Insight:non-destructive testing and condition monitoring,2013,55(6):323- 329.
[4] HUANG N E,SHEN Z,LONG S R,et al.The Empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings of the royal society amathematical physical & engineering sciences,1998,454(11):903- 995.
[5] WU Z,HUANG N E.A study of the characteristics of white noise using the empirical mode decomposition method[J].Proceedings of the royal society of London,2004,460(2046):1597- 1611.
[6] WU Z,HUANG N E.Ensemble empirical mode decomposition:a noise-assisted data analysis method[J].Advances in adaptive data analysis,2009,1(1):1- 41.
[7] 王振威.基于变分模态分解的故障诊断方法研究[D].秦皇岛:燕山大学,2015.
[8] 刘江,吕勇.基于变模式分解降噪的滚动轴承故障诊断研究[J].机械设计与制造,2015(10):21- 25.
[9] AN X,ZENG H.Pressure fluctuation signal analysis of a hydraulic turbine based on variational mode decomposition[J].Proceedings of the institution of mechanical engineers part ajournal of power & energy,2015,229(8):23- 28.
[10] 赵佳佳,贾嵘,武桦,等.基于FastICA-EEMD的振动信号特征提取[J].水力发电学报,2017,36(3):63- 70.
[11] 于晓东,潘罗平,安学利.基于VMD和排列熵的水轮机压力脉动信号去噪算法[J].水力发电学报,2017,36(8):78- 85.
[12] 付文龙,李雄,邹祖冰,等.基于增强VMD相关分析的水电机组摆度信号降噪[J].水力发电学报,2018,37(12):112- 120.
[13] 汪泉,李德忠.基于EEMD的水轮机摆度信号特征提取分析[J].人民长江,2017,48(5):96- 100.
[14] DRAGOMIRETSKIY K,ZOSSO D.Variational mode decomposition[J].IEEE transactions on signal processing,2014,62(3):531- 544.
[15] 刘长良,武英杰,甄成刚.基于变分模态分解和模糊C均值聚类的滚动轴承故障诊断[J].中国电机工程学报,2015,35(13):3358- 3365.