基于改进Hilbert-Huang变换和支持向量机的高压断路器触头超程状态识别
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摘要
引入集合经验模态分解(EEMD)对Hilbert-Huang变换(HHT)方法进行改进,并将改进的HHT方法结合支持向量机(SVM)应用于高压断路器振动信号特征提取和触头超程状态识别中。采用EEMD提取反映振动信号局部特性的本征模态函数(IMF)分量,并计算IMF分量的Hilbert边际谱能量值,由此构造高压断路器触头超程状态特征量,利用得到的特征向量对SVM进行训练,实现高压断路器触头超程状态的自动识别。试验提取了高压断路器在不同触头超程下的振动信号并进行分析,结果表明所提方法能够有效识别高压断路器触头超程状态。
The HHT( Hilbert-Huang Transform) is improved by EEMD( Ensemble Empirical Mode Decomposition).The improved HHT method combined with SVM( Support Vector Machine) is applied to feature extraction of vibration signal of HVCB( High-Voltage Circuit Breaker) and over-travel state recognition of HVCB's electrical contacts. The IMF( Intrinsic Mode Function) components reflecting local characteristics of HVCB's vibration signal are extracted by EEMD,then their Hilbert marginal spectrum energy is calculated to build characteristic vector of electrical contact over-travel of HVCB. The SVM is trained by the characteristic vector to realize the automatic detection of electrical contact over-travel. The vibration signals of HVCB under different over-travel states are extracted and analyzed,and the results show that the proposed method can effectively detect the electrical contact over-travel of HVCB.
The HHT( Hilbert-Huang Transform) is improved by EEMD( Ensemble Empirical Mode Decomposition).The improved HHT method combined with SVM( Support Vector Machine) is applied to feature extraction of vibration signal of HVCB( High-Voltage Circuit Breaker) and over-travel state recognition of HVCB's electrical contacts. The IMF( Intrinsic Mode Function) components reflecting local characteristics of HVCB's vibration signal are extracted by EEMD,then their Hilbert marginal spectrum energy is calculated to build characteristic vector of electrical contact over-travel of HVCB. The SVM is trained by the characteristic vector to realize the automatic detection of electrical contact over-travel. The vibration signals of HVCB under different over-travel states are extracted and analyzed,and the results show that the proposed method can effectively detect the electrical contact over-travel of HVCB.
引文
[1]段传宗,鄢志平,鄢志辉.高压断路器故障检测与诊断技术[M].北京:中国电力出版社,2014:9-12.
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[9]赵洪山,李浪,王颖.一种基于盲源分离和流形学习的风电机组轴承故障特征提取方法[J].太阳能学报,2016,37(2):269-275.ZHAO Hongshan,LI Lang,WANG Ying. Fault feature extraction method of wind turbine bearing based on blind source separation and manifold learning[J]. Acta Energiae Solaris Sinica,2016,37(2):269-275.
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[16]WU Z H,HUANG N E. A study of the characteristics of white noise using the empirical mode decomposition method[J]. Proceedings of the Royal Society A:Mathematical,Physical and Engineering Sciences,2003,460(2046):1597-1611.
[17]LIU Z G,CUI Y,LI W H. A classification method for complex power quality disturbances using EEMD and rank wavelet SVM[J]. IEEE Transactions on Smart Grid,2015,6(4):1678-1685.
[18]孙一航,武建文,廉世军.结合经验模态分解能量总量法的断路器振动信号特征向量提取[J].电工技术学报,2014,29(3):228-236.SUN Yihang,WU Jianwen,LIAN Shijun. Extraction of vibration signal feature vector of circuit breaker based on empirical mode decomposition amount of energy[J]. Transactions of China Electrotechnical Society,2014,29(3):228-236.
[19]陈攀,姚陈果,廖瑞金.分频段能量谱及马氏聚类算法在开关柜局部放电模式识别中的应用[J].高电压技术,2015,41(10):3332-3341.CHEN Pan,YAO Chenguo,LIAO Ruijin. Application of signals separated band energy spectrum and mahalanobis clustering algorithm for switchgear partial discharge pattern recognition[J]. High Voltage Engineering,2015,41(10):3332-3341.
[20]胡伟,李勇,曹一家,等.基于LOF和SVM的智能配电网故障辨识方法[J].电力自动化设备,2016,36(6):7-12.HU Wei,LI Yong,CAO Yiji,et al. Fault identification based on LOF and SVM for smart distribution network[J]. Electric Power Automation Equipment,2016,36(6):7-12.
[21]程序,关永刚,张文鹏.基于因子分析和支持向量机算法的高压断路器机械故障诊断方法[J].电工技术学报,2017,29(7):209-215.CHENG Xu,GUAN Yonggang,ZHANG Wenpeng. Diagnosis method on the mechanical failure of high voltage circuit breakers based on factor analysis and SVM[J]. Transactions of China Electrotechnical Society,2017,29(7):209-215.
[22]REN L K,LV W M,JIANG S W,et al. Fault diagnosis using a joint model based on sparse representation and SVM[J]. IEEE Transactions on Instrumentations and Measurement,2016,65(10):2313-2320.
[2]颜君凯,马宏忠,李凯,等.基于振动信号的变压器绕组松动故障诊断方法[J].电力系统自动化,2017,41(3):122-128.YAN Junkai,MA Hongzhong,LI Kai,et al. Vibration signal based diagnosis method for looseness fault of transformer winding[J]. Automation of Electric Power Systems,2017,41(3):122-128.
[3]江东,周卫宏,孔德善.磁悬浮振动测试系统信号分析及振动信号的提取[J].电机与控制学报,2017,21(10):102-107.JIANG Dong,ZHOU Weihong,KONG Deshan. Maglev vibration test system signals analyses and vibration signals extraction[J]. Electric Machines and Control,2017,21(10):102-107.
[4]王建国,吴林峰,秦绪华.基于自相关分析和LMD的滚动轴承振动信号故障特征提取[J].中国机械工程,2014,25(2):186-191.WANG Jianguo,WU Linfeng,QIN Xuhua. Rolling bearing vibration signal fault feature extraction based on auto-correlation analysis and LMD[J]. China Mechanical Engineering,2014,25(2):186-191.
[5]童基均,李琳,林勤光,等.采用平滑伪Wigner-Ville分布的SSVEP脑机接口系统[J].浙江大学学报(工学版),2017,51(3):598-604.TONG Jijun,LI Lin,LIN Qinguang,et al. SSVEP Brain-Computer Interface(BCI)system using smoothed pseudo Wigner-Ville distribution[J]. Journal of Zhejiang University(Engineering Science),2017,51(3):598-604.
[6]郭谋发,游林旭,洪翠,等.基于LCD-Hilbert谱奇异值和多级支持向量机的配电网故障识别方法[J].高电压技术,2017,43(4):1239-1247.GUO Moufa,YOU Linxu,HONG Cui,et al. Identification method of distribution network faults based on singular value of LCD-Hilbert spectrums and multilevel SVM[J]. High Voltage Engineering,2017,43(4):1239-1247.
[7]丁锋,秦峰伟.小波降噪及Hilbert变换在电机轴承故障诊断中的应用[J].电机与控制学报,2017,21(6):89-95.DING Feng,QIN Fengwei. Application of wavelet denoising and Hilbert transform in fault diagnosis of motor bearing[J]. Electric Machines and Control,2017,21(6):89-95.
[8]鲁军,李侠,王重马.基于小波分析的MSMA振动传感器信号处理与故障检测[J].电工技术学报,2015,30(10):354-360.LU Jun,LI Xia,WANG Chongma. Signal process and fault detection of MSMA vibration sensor based on wavelet analysis[J]. Transactions of China Electrotechnical Society,2015,30(10):354-360.
[9]赵洪山,李浪,王颖.一种基于盲源分离和流形学习的风电机组轴承故障特征提取方法[J].太阳能学报,2016,37(2):269-275.ZHAO Hongshan,LI Lang,WANG Ying. Fault feature extraction method of wind turbine bearing based on blind source separation and manifold learning[J]. Acta Energiae Solaris Sinica,2016,37(2):269-275.
[10]郭俊,汲胜昌,沈琪.盲源分离技术在振动法检测变压器故障中的应用[J].电工技术学报,2012,27(10):68-78.GUO Jun,JI Shengchang,SHEN Qi. Blind source separation technology for the detection of transformer fault based on vibration method[J]. Transactions of China Electrotechnical Society,2012,27(10):68-78.
[11]段若晨,王丰华,周荔丹.基于优化HHT算法与洛仑兹信息量度的换流变用有载分接开关机械特征提取[J].中国电机工程学报,2016,36(11):3101-3109.DUAN Ruochen,WANG Fenghua,ZHOU Lidan. Mechanical features extraction of on-load tap-changer in converter transformer based on optimized HHT algorithm and Lorentz information measure[J]. Proceedings of the CSEE,2016,36(11):3101-3109.
[12]李建鹏,赵书涛,夏燕青.基于双谱和希尔伯特-黄变换的断路器故障诊断方法[J].电力自动化设备,2013,33(2):115-125.LI Jianpeng,ZHAO Shutao,XIA Yanqing. Fault diagnosis based on bispectrum and Hilbert-Huang transform for circuit breaker[J].Electric Power Automation Equipment,2013,33(2):115-125.
[13]侯慧娟,盛戈皞,朱文俊,等.基于高阶累积量的局部放电超高频信号时延估计算法[J].高电压技术,2013,39(2):342-347.HOU Huijuan,SHENG Gehao,ZHU Wenjun,et el. Time-delay estimation algorithm of partial ultra high frequency signals based on higher-order cumulant[J]. High Voltage Engineering,2013,39(2):342-347.
[14]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 A:Mathematical,Physical and Engineering Sciences,1998,454(1971):903-995.
[15]任宜春,翁璞.基于改进Hilbert-Huang变换的结构损伤识别方法研究[J].振动与冲击,2015,34(18):195-199.REN Yichun,WENG Pu. Structural damage detection based on improved Hilbert-Huang transform[J]. Journal of Vibration and Shock,2015,34(18):195-199.
[16]WU Z H,HUANG N E. A study of the characteristics of white noise using the empirical mode decomposition method[J]. Proceedings of the Royal Society A:Mathematical,Physical and Engineering Sciences,2003,460(2046):1597-1611.
[17]LIU Z G,CUI Y,LI W H. A classification method for complex power quality disturbances using EEMD and rank wavelet SVM[J]. IEEE Transactions on Smart Grid,2015,6(4):1678-1685.
[18]孙一航,武建文,廉世军.结合经验模态分解能量总量法的断路器振动信号特征向量提取[J].电工技术学报,2014,29(3):228-236.SUN Yihang,WU Jianwen,LIAN Shijun. Extraction of vibration signal feature vector of circuit breaker based on empirical mode decomposition amount of energy[J]. Transactions of China Electrotechnical Society,2014,29(3):228-236.
[19]陈攀,姚陈果,廖瑞金.分频段能量谱及马氏聚类算法在开关柜局部放电模式识别中的应用[J].高电压技术,2015,41(10):3332-3341.CHEN Pan,YAO Chenguo,LIAO Ruijin. Application of signals separated band energy spectrum and mahalanobis clustering algorithm for switchgear partial discharge pattern recognition[J]. High Voltage Engineering,2015,41(10):3332-3341.
[20]胡伟,李勇,曹一家,等.基于LOF和SVM的智能配电网故障辨识方法[J].电力自动化设备,2016,36(6):7-12.HU Wei,LI Yong,CAO Yiji,et al. Fault identification based on LOF and SVM for smart distribution network[J]. Electric Power Automation Equipment,2016,36(6):7-12.
[21]程序,关永刚,张文鹏.基于因子分析和支持向量机算法的高压断路器机械故障诊断方法[J].电工技术学报,2017,29(7):209-215.CHENG Xu,GUAN Yonggang,ZHANG Wenpeng. Diagnosis method on the mechanical failure of high voltage circuit breakers based on factor analysis and SVM[J]. Transactions of China Electrotechnical Society,2017,29(7):209-215.
[22]REN L K,LV W M,JIANG S W,et al. Fault diagnosis using a joint model based on sparse representation and SVM[J]. IEEE Transactions on Instrumentations and Measurement,2016,65(10):2313-2320.