电厂开关柜局部放电振动信号降噪方法研究
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摘要
针对电厂开关柜运行环境复杂,振动干扰较强,又需要保留振动干扰、分析振动干扰特征的问题,将基于相空间重构的ICA降噪方法应用到电厂开关柜局放振动信号中,进行信噪分离。首先依据相空间重构,在不破坏信号本身动力学特征的情况下构造高维相空间矩阵。然后利用独立分量分析方法进行信噪分离,提取有用振动信号。仿真信号和实际检测数据的应用表明,所提方法与小波法降噪能力相当,甚至更优;且不需要考虑最优小波基和最佳分解层数,自适应强,易于实现;且适用于电厂开关柜局放振动信号降噪。
In view of the problems of power switch cabinet such as complex operating environment, strong vibration disturbance, and needing to retain the vibration disturbance and analyze the characteristics of vibration disturbance, the Independent Component Analysis(ICA) de-noise method based on phase space reconstruction is applied to the partial discharge vibration signal of the switch cabinet of the power plant, and the signal separation is carried out. First, according to phase space reconstruction, this paper constructs the high dimension phase space matrix without destroying the dynamical characteristics of the signal itself. Then the independent component analysis method is used to separate the signal from the noise and extract the useful vibration signal. The application of the simulation signal and the actual data shows that the method is equivalent to or better than the wavelet method in de-noise. Besides, the optimal wavelet base and the optimal decomposition layer are not considered, and the self adaptation is strong and easy to implement. It is also suitable for de-noise of partial discharge vibration signals of switch cabinet of power plant.
In view of the problems of power switch cabinet such as complex operating environment, strong vibration disturbance, and needing to retain the vibration disturbance and analyze the characteristics of vibration disturbance, the Independent Component Analysis(ICA) de-noise method based on phase space reconstruction is applied to the partial discharge vibration signal of the switch cabinet of the power plant, and the signal separation is carried out. First, according to phase space reconstruction, this paper constructs the high dimension phase space matrix without destroying the dynamical characteristics of the signal itself. Then the independent component analysis method is used to separate the signal from the noise and extract the useful vibration signal. The application of the simulation signal and the actual data shows that the method is equivalent to or better than the wavelet method in de-noise. Besides, the optimal wavelet base and the optimal decomposition layer are not considered, and the self adaptation is strong and easy to implement. It is also suitable for de-noise of partial discharge vibration signals of switch cabinet of power plant.
引文
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[19]肖文龙,王维博,刘勇,等.基于小波分析理论的特高压输电线路故障选相研究[J].电力系统保护与控制,2016,44(17):90-96.XIAO Wenlong,WANG Weibo,LIU Yong,et al.Research of fault phase selection on UHV transmission lines based on wavelet analysis[J].Power System Protection and Control,2016,44(17):90-96.
[20]贾嵘,徐其惠,田录林,等.基于经验模态分解和固有模态函数重构的局部放电去噪方法[J].电工技术学报,2008,23(1):13-18.JIA Rong,XU Qihui,TIAN Lulin,et al.De-noising of partial discharge based on empirical mode decomposition and intrinsic mode function reconstruction[J].Transactions of China Electrotechnical Society,2008,23(1):13-18.
[21]王军涛,郑群珍,苏展,等.具有漏泄时滞和时变区间时滞的递归神经网络新的稳定性准则[J].河南师范大学学报(自然科学版),2016,44(3):166-171,177.WANG Juntao,ZHENG Qunzhen,SU Zhan,et al.New stability criteria for recurrent neural networks with leakage delay and time-varying interval delay[J].Journal of Henan Normal University(Natural Science Edition),2016,44(3):166-171,177.
[22]杨毅,高庆水,张楚,等.CPR1000技术核电机组高频振动故障诊断与处理[J].广东电力,2016,29(1):22-26.YANG Yi,GAO Qingshui,ZHANG Chu,et al.Fault Diagnosis and Processing on high frequency vibration of CPR1000 nuclear power unit[J].Guangdong Electric Power,2016,29(1):22-26.
[2]RAHUL,KAPOOR R,TRIPATHI M M.Detection and classification of multiple power signal patterns with Volterra series and interval type-2 fuzzy logic system[J].Protection and Control of Modern Power Systems,2017,2(2):92-101.DOI:10.1186/s41601-017-0039-z.
[3]陈曦,潘成,乌江,等.绝缘材料内部气隙中局部放电特性的影响因素[J].广东电力,2016,29(3):86-92.CHEN Xi,PAN Cheng,WU Jiang,et al.Influencing factors on characteristics of partial discharge in void of insulating materials[J].Guangdong Electric Power,2016,29(3):86-92.
[4]汪金刚,林伟,王志,等.基于紫外检测的开关柜电弧在线检测装置[J].电力系统保护与控制,2011,39(5):128-133,152.WANG Jingang,LIN Wei,WANG Zhi,et al.Online detecting device for switchgear arc based on ultraviolet detection[J].Power System Protection and Control,2011,39(5):128-133,152.
[5]王有元,刘强,陆国俊,等.开关柜结构对超高频电磁波传播特性的影响分析[J].电工技术学报,2013,28(11):293-300.WANG Youyuan,LIU Qiang,LU Guojun,et al.Propagation characteristics of ultra-high frequency electromagnetic wave influenced by the switchgear structure[J].Transactions of China Electrotechnical Society,2013,28(11):293-300.
[6]郑建康,郑雷,孙浩杰,等.内置式开关柜绝缘状态监测智能装置的研究[J].陕西电力,2016,44(4):46-50.ZHENG Jiankang,ZHENG Lei,SUN Haojie,et al.Study on build-in smart device for insulation-condition monitoring of metal-clad switchgear[J].Shaanxi Electric Power,2016,44(4):46-50.
[7]艾永乐,张王飞,闫玲玲,等.基于引入松弛因子的改进式单通道Fast ICA的间谐波检测方法[J].电力系统保护与控制,2017,45(16):90-94.AI Yongle,ZHANG Wangfei,YAN Lingling,et al.Interharmonic detection method based on improved single channel FastICA with relaxation factor[J].Power System Protection and Control,2017,45(16):90-94.
[8]张文民,徐樊浩,张慧娟,等.基于EMD的单通道变压器振动信号盲分离[J].陕西电力,2016,44(11):42-46.ZHANG Wenmin,XU Fanhao,ZHANG Huijuan,et al.Blind separation of single channel transformer vibration signal based on EMD[J].Shaanxi Electric Power,2016,44(11):42-46.
[9]顾熹,廖志伟.基于相空间重构和高斯过程回归的短期负荷预测[J].电力系统保护与控制,2017,45(5):73-79.GU Xi,LIAO Zhiwei.Short-term load forecasting based on phase space reconstruction and Gaussian process regression[J].Power System Protection and Control,2017,45(5):73-79.
[10]余先川,胡丹.盲源分离理论与应用[M].北京:科学出版社,2011.
[11]HYVARINEN A.Survey on independent component analysis[J].Neural Computing Surveys,1999,2(4):94-128.
[12]万敏.独立分量分析的神经网络方法[D].成都:电子科技大学,2011.WAN Min.Independent component analysis by neural networks approach[D].Chengdu:University of Electronic Science and Technology of China,2011.
[13]PHAM D,GARRAT P.Blind separation of mixture of independent sources through a quasi-maximum likelihood approach[J].IEEE Transactions on Signal Processing,1997,9(1):1457-1482.
[14]HYVARINEN A,OJA E.A fast fixed-point algorithm for independent component analysis[J].Neural Computation,1997,9(7):1483-1492.
[15]杨凯,张认成,杨建红,等.基于频率约束独立分量分析的开关柜局部放电故障识别[J].高电压技术,2014,40(11):3452-3460.YANG Kai,ZHANG Rencheng,YANG Jianhong,et al.Partial discharge fault identification of switchgear based on frequency constrained independent component analysis[J].High Voltage Engineering,2014,40(11):3452-3460.
[16]张文超,杨鼎才.语音信号相空间重构中时间延迟的选择方法[J].电子测量技术,2007,30(5):35-37,63.ZHANG Wenchao,YANG Dingcai.Method to determine delay time of speech signal in reconstructed phase space[J].Electronic Measurement Technology,2007,30(5):35-37,63.
[17]白焕莉.基于独立分量分析的煤矿机械缺陷超声信号处理技术研究[D].西安:西安科技大学,2012.BAI Huanli.Study on ultrasonic signal technology in defects of coal mine machinery based on independent component analysis[D].Xi'an:Xi'an University of Science and Technology,2012.
[18]许岩.含噪混沌时间序列相空间重构参数估计[D].重庆:重庆大学,2013.XU Yan.Estimating parameters in phase space reconstruction of noisy chaotic time series[D].Chongqing:Chongqing University,2013.
[19]肖文龙,王维博,刘勇,等.基于小波分析理论的特高压输电线路故障选相研究[J].电力系统保护与控制,2016,44(17):90-96.XIAO Wenlong,WANG Weibo,LIU Yong,et al.Research of fault phase selection on UHV transmission lines based on wavelet analysis[J].Power System Protection and Control,2016,44(17):90-96.
[20]贾嵘,徐其惠,田录林,等.基于经验模态分解和固有模态函数重构的局部放电去噪方法[J].电工技术学报,2008,23(1):13-18.JIA Rong,XU Qihui,TIAN Lulin,et al.De-noising of partial discharge based on empirical mode decomposition and intrinsic mode function reconstruction[J].Transactions of China Electrotechnical Society,2008,23(1):13-18.
[21]王军涛,郑群珍,苏展,等.具有漏泄时滞和时变区间时滞的递归神经网络新的稳定性准则[J].河南师范大学学报(自然科学版),2016,44(3):166-171,177.WANG Juntao,ZHENG Qunzhen,SU Zhan,et al.New stability criteria for recurrent neural networks with leakage delay and time-varying interval delay[J].Journal of Henan Normal University(Natural Science Edition),2016,44(3):166-171,177.
[22]杨毅,高庆水,张楚,等.CPR1000技术核电机组高频振动故障诊断与处理[J].广东电力,2016,29(1):22-26.YANG Yi,GAO Qingshui,ZHANG Chu,et al.Fault Diagnosis and Processing on high frequency vibration of CPR1000 nuclear power unit[J].Guangdong Electric Power,2016,29(1):22-26.