基于振电结合的高压断路器特征提取及分类方法研究
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摘要
为了对高压断路器操作机构的工作状态进行较准确的评估,提高高压断路器机构的运行稳定性,提出了一种基于振动信号与电流信号结合的高压断路器信号特征提取和分类方法。首先通过对高压断路器分合闸线圈电流信号和振动信号的机理分析,提出利用时间节点参数作为特征向量,然后采用曲线斜率方法提取电流信号时间参数,利用基于短时能量的双门限法提取振动事件的时间参数,将两者的参数作为模式识别的特征向量。最后通过支持向量机(Support Vector Machine, SVM)分类结果表明:线圈电流曲线与振动信号相结合能够准确而全面地反映操作机构的运行状况,利用SVM可以快速准确的判断操作机构的故障类型,对于断路器的故障诊断和检修维护具有重要的意义。
Based on vibration signal and current signal, this paper proposed a method of signal feature extraction and classification of high-voltage circuit breaker. The proposed method can accurately evaluate breaker's operating conditions and improve its operation stability. Firstly, this paper analyzed the vibration signal and current signal of tripping and closing coils of CBs, advocating to take time-node parameter as characteristic vector. Then, this paper extracted the time parameter of current signal by curve slope and that of vibration signal by double-threshold method based on short-term energy. The two parameters were added as feature vectors for pattern recognition. The classification results of SVM indicated that the integration of vibration signal and current signal of tripping and closing coils precisely reflects the operation state of high voltage circuit breaker. In addition, SVM quickly tells the fault type of operating mechanism, contributing to fault diagnosis and maintenance of CBs.
Based on vibration signal and current signal, this paper proposed a method of signal feature extraction and classification of high-voltage circuit breaker. The proposed method can accurately evaluate breaker's operating conditions and improve its operation stability. Firstly, this paper analyzed the vibration signal and current signal of tripping and closing coils of CBs, advocating to take time-node parameter as characteristic vector. Then, this paper extracted the time parameter of current signal by curve slope and that of vibration signal by double-threshold method based on short-term energy. The two parameters were added as feature vectors for pattern recognition. The classification results of SVM indicated that the integration of vibration signal and current signal of tripping and closing coils precisely reflects the operation state of high voltage circuit breaker. In addition, SVM quickly tells the fault type of operating mechanism, contributing to fault diagnosis and maintenance of CBs.
引文
[1] 孙曙光,于晗,杜太行,等.基于多特征融合与改进QPSO-RVM的万能式断路器故障振声诊断方法[J].电工技术学报,2017,32(19):107-117.SUN S G,YU H,DU T H,et al.Fault vibration and acoustic diagnosis method of universal circuit breaker based on multi-feature fusion and improved QPSO-RVM[J].Transactions of China Electrotechnical Society,2017,32(19):107-117.
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[14] 韩中合,韩悦,朱霄珣.基于IMF信息熵与SVM的转子振动故障智能诊断方法[J].华北电力大学学报(自然科学版),2012,39(4):81-85+106.HAN Z H,HAN Y,ZHU X X.Intelligent diagnosis method of rotor vibration fault based on IMF information entropy and SVM[J].Journal of North China Electric Power University (Natural Science Edition),2012,39(4):81-85+106.
[15] 赵书涛,王亚潇,李沐峰,等.基于声振联合特征熵的断路器故障诊断方法[J].华北电力大学学报(自然科学版),2016,43(6):20-24.ZHAO S T,WANG Y X,LI M F,et al.Fault diagnosis method of circuit breaker based on acoustic-vibration joint characteristic entropy[J].Journal of North China Electric Power University (Natural Science Edition),2016,43(6):20-24.
[16] 刘荣海,豆龙江,万书亭,等.基于EEMD样本熵和支持向量机的高压断路器故障诊断[J].华北电力大学学报(自然科学版),2018,45(2):82-88.LIU R H,DOU L H,WAN S T,et al.Fault diagnosis of high voltage circuit breaker based on EEMD sample entropy and support vector machine[J].Journal of North China Electric Power University (Natural Science Edition),2018,45(2):82-88.
[17] 费宇泉,王英健,夏愉乐.语音端点检测算法研究[J].自动化技术与应用,2017,36(8):98-102.FEI Y Q,WANG J Y,XIA Y L.Research on speech endpoint detection algorithms[J].Automation Technology and Application,2017,36(8):98-102.
[18] 王静君,王飞,杨元威,等.短时能量法在断路器机械振动信号分析中的应用[J].高压电器,2017,53(12):14-19.WANG J J,WANG F,YANG Y W,et al.Application of short-time energy method in mechanical vibration signal analysis of circuit breakers[J].High Voltage Apparatus,2017,53(12):14-19.
[19] 张卫正,李永丽,姚创.基于最小二乘支持向量机的高压断路器故障诊断[J].高压电器,2015,51(12):79-83.ZHANG W Z,LI Y L,YAO C.Fault diagnosis of high voltage circuit breaker based on least squares support vector machine[J].High Voltage Apparatus,2015,51(12):79-83.
[2] 段传宗,鄢志平,鄢志辉.高压断路故障检测与诊断技术[M].北京:中国电力出版社,2014.
[3] 陈朋永,赵书涛,李建鹏,等.基于EMD和SVM的高压断路器机械故障诊断方法研究[J].华北电力大学学报(自然科学版),2012,39(6):23-28.CHEN P Y,ZHAO S T,LI J P,et al.Research on mechanical fault diagnosis method of high voltage circuit breaker based on EMD and SVM[J].Journal of North China Electric Power University (Natural Science Edition),2012,39(6):23-28.
[4] 孙曙光,张强,杜太行,等.基于分合闸线圈电流的万能式断路器故障诊断[J].仪器仪表学报,2018,39(2):130-140.SUN S G,ZHANG Q,DU T H,et al.Fault diagnosis of universal circuit breaker based on switching coil current[J].Journal of Instruments and Instruments,2018,39(2):130-140.
[5] HUANG N T,CHEN H J,ZHANG S X,et al.Mechanical fault diagnosis of high voltage circuit breakers based on wavelet time-frequency entropy and one-class support vector machine[J].Entropy,2015,18(1):1-17.
[6] HUANG X,HE X.Design of an on-line monitoring system of mechanical characteristics of high voltage circuit breakers[C]// International Conference on Electronics,Communications and Control.IEEE,2011:3646-3649.
[7] 张永奎,赵智忠,冯旭,等.基于分合闸线圈电流信号的高压断路器机械故障诊断[J].高压电器,2013,49(2):37-42.ZHANG Y K,ZHAO Z Z,FENG X,et al.Mechanical fault diagnosis of high voltage circuit breaker based on current signal of switching coil[J].High Voltage Apparatus,2013,49(2):37-42.
[8] 李春锋,孔海洋,王璇,等.基于PCA和聚类的断路器分合闸线圈电流研究[J].电力与能源,2016,37(1):32-36.LI C F,KONG H Y,WANG X,et al.Research on current of circuit breaker switching coil based on PCA and clustering[J].Electricity and Energy,2016,37(1):32-36.
[9] 孙银山,张文涛,张一茗,等.高压断路器分合闸线圈电流信号特征提取与故障判别方法研究[J].高压电器,2015,51(9):134-139.SUN Y S,ZHANG W T,ZHANG Y M,et al.Research on feature extraction and fault discrimination method of current signal of high voltage circuit breaker switching coil[J].High Voltage Apparatus,2015,51(9):134-139.
[10] 蒋志浩,于群,董骊.基于改进动态时间规整算法的断路器故障诊断[J].工矿自动化,2016,42(8):52-55.JIANG Z H,YU Q,DONG L.Circuit breaker fault diagnosis based on improved dynamic time warping algorithms[J].Industry and Mine Automation,2016,42(8):52-55.
[11] 缪希仁,王燕.低压断路器振动特性分析与合闸同期性研究[J].电工技术学报,2013,28(6):81-85.LIAO X R,WANG Y.Analysis of vibration characteristics of low voltage circuit breakers and study of closing synchronization[J].Transactions of China Electrotechnical Society,2013,28(6):81-85.
[12] LANDRY M,LEONARD F,LANDRY C,et al.An improved vibration analysis algorithm as a diagnostic tool for detecting mechanical anomalies on power circuit breakers[J].IEEE Transactions on Power Delivery,2008,23(4):1986-1994.
[13] 孙曙光,于晗,杜太行,等.基于振动信号样本熵和相关向量机的万能式断路器分合闸故障诊断[J].电工技术学报,2017,32(7):20-30.SUN S G,YU H,DU T H,et al.Fault diagnosis of opening and closing of universal circuit breaker based on sample entropy of vibration signal and relevant vector machine[J].Transactions of China Electrotechnical Society,2017,32(7):20-30.
[14] 韩中合,韩悦,朱霄珣.基于IMF信息熵与SVM的转子振动故障智能诊断方法[J].华北电力大学学报(自然科学版),2012,39(4):81-85+106.HAN Z H,HAN Y,ZHU X X.Intelligent diagnosis method of rotor vibration fault based on IMF information entropy and SVM[J].Journal of North China Electric Power University (Natural Science Edition),2012,39(4):81-85+106.
[15] 赵书涛,王亚潇,李沐峰,等.基于声振联合特征熵的断路器故障诊断方法[J].华北电力大学学报(自然科学版),2016,43(6):20-24.ZHAO S T,WANG Y X,LI M F,et al.Fault diagnosis method of circuit breaker based on acoustic-vibration joint characteristic entropy[J].Journal of North China Electric Power University (Natural Science Edition),2016,43(6):20-24.
[16] 刘荣海,豆龙江,万书亭,等.基于EEMD样本熵和支持向量机的高压断路器故障诊断[J].华北电力大学学报(自然科学版),2018,45(2):82-88.LIU R H,DOU L H,WAN S T,et al.Fault diagnosis of high voltage circuit breaker based on EEMD sample entropy and support vector machine[J].Journal of North China Electric Power University (Natural Science Edition),2018,45(2):82-88.
[17] 费宇泉,王英健,夏愉乐.语音端点检测算法研究[J].自动化技术与应用,2017,36(8):98-102.FEI Y Q,WANG J Y,XIA Y L.Research on speech endpoint detection algorithms[J].Automation Technology and Application,2017,36(8):98-102.
[18] 王静君,王飞,杨元威,等.短时能量法在断路器机械振动信号分析中的应用[J].高压电器,2017,53(12):14-19.WANG J J,WANG F,YANG Y W,et al.Application of short-time energy method in mechanical vibration signal analysis of circuit breakers[J].High Voltage Apparatus,2017,53(12):14-19.
[19] 张卫正,李永丽,姚创.基于最小二乘支持向量机的高压断路器故障诊断[J].高压电器,2015,51(12):79-83.ZHANG W Z,LI Y L,YAO C.Fault diagnosis of high voltage circuit breaker based on least squares support vector machine[J].High Voltage Apparatus,2015,51(12):79-83.