基于振动信号的高压断路器触头超程状态识别
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摘要
提出了一种利用振动信号识别高压断路器触头超程状态的新方法。高压断路器分/合闸动作过程中产生的振动信号包含有触头超程状态信息,合适的信号处理方法及特征提取技术可以将该信息提取出来。试验获取高压断路器不同触头超程下的振动信号,采用经验模态分解(EMD)对振动信号进行分解,计算分解得到的本征模态函数(IMF) Hilbert边际谱能量,作为触头超程状态特征量,并详细分析该特征量与触头超程之间的变化规律。结果表明,所提方法能够有效提取高压断路器的触头超程状态信息,能够实现触头超程状态的准确识别,为高压断路器触头超程状态在线监测与诊断提供一种新方法。
This paper presents a method for identifying the state of electrical contact's over-travel in highvoltage circuit breaker( HVCB) using vibration signature. The vibration signal generated during the process of HVCB opening/closing operation contains the information of electrical contact' s over-travel,which can be extracted by appropriate feature extraction method. The vibration signals of different overtravels were obtained by experimental investigation. The empirical mode decomposition( EMD) was used to decompose the vibration signal,and the Hilbert marginal spectral energy of the decomposed intrinsic mode function( IMF) was calculated as the feature for identifying over-travel state. Furthermore,the variation rule between the feature and the over-travel was analyzed in detail. The experimental results show that the proposed method effectively extracts the over-travel state information from HVCB's vibration signal,which realizes the accurate identification of the over-travel's state. This method provides a feasible approach for online monitoring and diagnosis of HVCB's electrical contact over-travel.
This paper presents a method for identifying the state of electrical contact's over-travel in highvoltage circuit breaker( HVCB) using vibration signature. The vibration signal generated during the process of HVCB opening/closing operation contains the information of electrical contact' s over-travel,which can be extracted by appropriate feature extraction method. The vibration signals of different overtravels were obtained by experimental investigation. The empirical mode decomposition( EMD) was used to decompose the vibration signal,and the Hilbert marginal spectral energy of the decomposed intrinsic mode function( IMF) was calculated as the feature for identifying over-travel state. Furthermore,the variation rule between the feature and the over-travel was analyzed in detail. The experimental results show that the proposed method effectively extracts the over-travel state information from HVCB's vibration signal,which realizes the accurate identification of the over-travel's state. This method provides a feasible approach for online monitoring and diagnosis of HVCB's electrical contact over-travel.
引文
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[12]史红梅,赵蓉,余祖俊,等.基于钢轨振动响应分析的车轮扁疤检测方法研究[J].振动与冲击,2016,35(10):24.SHI Hongmei,ZHAO Rong,YU Zujun,et al. Detection method for wheel flats based on rail vibration responses analysis[J].Journal of Vibration and Shock,2016,35(10):24.
[13]林慧斌,丁康.基于振动的发动机转速高精度动态测量新方法[J].振动工程学报,2012,25(5):595.LIN Huibin,DING Kang. A high accuracy dynamical measurement method for engine rotation speed based on vibration[J].Journal of Vibration Engineering,2012,25(5):595.
[14]龙海军,孙灿飞,莫固良.直升机振动检测通用算法的研究与实现[J].振动、测试与诊断,2016,36(3):254.LONG Haijun,SUN Canfei,MO Guliang. Research and implementation of general algorithm for helicopter vibration detection[J]. Journal of Vibration,Measurement&Diagnosis,2016,36(3):254.
[15]李辉,李洋,杨东,等.基于EMD相关去噪的风电机组振动噪声抑制及特征频率提取[J].电机与控制学报,2016,20(1):73.LI Hui,LI Yang,YANG Dong,et al. Noise suppression and characteristic frequency extraction of wind turbine vibration based on EMD correlation denoising[J]. Electric Machines and Control,2016,20(1):73.
[16] 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]. Proceeding of the Royal Society of London Series A-Mathematical Physical and Engineering Sciences,1998,454(1971):903.
[17] SMITH J S. The local mean decomposition and its application to EEG perception data[J]. Journal of the Royal Society Interface,2005,2(5):443.
[18] DRAGOMIRETSKIY K,ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing,2014,62(3),531.
[19]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.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.
[20]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.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.
[2] NATTI S,KEZUNOVIC M. Assessing circuit breaker performance using condition-based data and Bayesian approach[J]. Electric Power Systems Research,2011,81(9),1796.
[3] RAZIKAZEMI A A,VAKILIAN M,NIAYESH K,et al. Priority assessment of online monitoring investment for power system circuit breakers-Part I:Qualitative-quantitative approach[J]. IEEE Transactions on Power Delivery,2013,28(2),928.
[4] JUN Z,WENYUAN L,CAISHENG W,et al. A rankboost based data-driven method to determine maintenance priority of circuit breakers[J]. IEEE Transactions on Power Delivery,DOI 10.1109/TPWRD. 2017. 2748146.
[5]赵书涛,吴成坚,李明,等.基于NCC-P-S优化算法的断路器机械特性测试方法研究[J].中国电机工程学报,2017,37(14):4265.ZHAO Shutao,WU Chenjian,JI Ming,et al. Research of circuit breaker mechanical characteristic test method based on normalized cross correlation-pyramid-sector optimization algorithm[J]. Proceedings of the CSEE,2017,37(14):4265.
[6]刘艳,陈丽安.基于SOM的真空断路器机械故障诊断[J].电工技术学报,2017,32(5):49.LIU Yan,CHEN Lian. Mechanical fault diagnosis of vacuum circuit breaker based on SOM[J]. Transactions of China Electrotechnical Society,2017,32(5):49.
[7]张显聪,喻冬梅,杨柳,等.位移传感器在中压断路器机械特性监测中的应用研究[J].高压电器,2015,51(7):37.ZHANG Xiancong,YU Dongmei,YANG Liu,et al. Application study of the displacement sensor used for testing the mechanical properties of medium voltage circuit breaker[J]. High Voltage Apparatus,2015,51(7):37.
[8]陈建平,胡占强,苏晓东.智能化断路器机械特性在线监测关键技术设计[J].高压电器,2014,50(4):108.CHEN Jianping,HU Zhanqiang,SU Xiaodong. Key technology design to on-line monitoring of mechanical characteristics for intelligent vacuum circuit breaker[J]. High Voltage Apparatus,2014,50(4):108.
[9]牛为华,梁贵书,赵鹏.基于稀疏表示和M-ELM的断路器故障诊断模型[J].系统仿真学报,2017,29(11):2828.NIU Weihua,LIANG Guishu,ZHAO Peng. Fault diagnosis model of circuit breaker based on sparse representation and M-ELM[J].Journal of System Simulation,2017,29(11):2828.
[10]姜生,李世民,王继元.真空断路器机械特性的探讨[J].高压电器,2002,38(4):61.JIANG Sheng,LI Shimin,WANG Jiyuan. Discussion on the mechanical characteristic of vacuum circuit breaker[J]. High Voltage Apparatus,2002,38(4):61.
[11]戴煜林.故障树及振动包络分析在风电机组故障诊断中的应用[D].北京:华北电力大学,2016.
[12]史红梅,赵蓉,余祖俊,等.基于钢轨振动响应分析的车轮扁疤检测方法研究[J].振动与冲击,2016,35(10):24.SHI Hongmei,ZHAO Rong,YU Zujun,et al. Detection method for wheel flats based on rail vibration responses analysis[J].Journal of Vibration and Shock,2016,35(10):24.
[13]林慧斌,丁康.基于振动的发动机转速高精度动态测量新方法[J].振动工程学报,2012,25(5):595.LIN Huibin,DING Kang. A high accuracy dynamical measurement method for engine rotation speed based on vibration[J].Journal of Vibration Engineering,2012,25(5):595.
[14]龙海军,孙灿飞,莫固良.直升机振动检测通用算法的研究与实现[J].振动、测试与诊断,2016,36(3):254.LONG Haijun,SUN Canfei,MO Guliang. Research and implementation of general algorithm for helicopter vibration detection[J]. Journal of Vibration,Measurement&Diagnosis,2016,36(3):254.
[15]李辉,李洋,杨东,等.基于EMD相关去噪的风电机组振动噪声抑制及特征频率提取[J].电机与控制学报,2016,20(1):73.LI Hui,LI Yang,YANG Dong,et al. Noise suppression and characteristic frequency extraction of wind turbine vibration based on EMD correlation denoising[J]. Electric Machines and Control,2016,20(1):73.
[16] 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]. Proceeding of the Royal Society of London Series A-Mathematical Physical and Engineering Sciences,1998,454(1971):903.
[17] SMITH J S. The local mean decomposition and its application to EEG perception data[J]. Journal of the Royal Society Interface,2005,2(5):443.
[18] DRAGOMIRETSKIY K,ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing,2014,62(3),531.
[19]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.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.
[20]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.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.