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

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (3329 KB)
输出: BibTeX | EndNote (RIS)

摘要为实现对万能式断路器分合闸故障的非侵入式监测和诊断,以分合闸过程中所产生的包含丰富机械特性信息的振动作为信号来源,提出一种基于振动信号互补总体平均经验模态分解(CEEMD)-样本熵和相关向量机(RVM)相结合的万能式断路器故障诊断方法。该方法首先将振动信号通过改进的小波包阈值去噪算法处理;其次采用CEEMD提取若干个反映断路器状态信息的固有模态函数(IMF)分量,依据各IMF分量的能量分布特点,选择其中前7阶进行处理,计算其样本熵形成有效的特征样本;最后通过计算不同故障类型的样本间欧氏距离来定量评价类间样本平均距离,建立基于RVM的二叉树多分类器,诊断得出万能式断路器故障类型。基于所设计的分合闸典型故障模型进行实验。与其他方法的对比实验表明,所提方法可利用相对较少的故障数据样本实现对万能式断路器故障类型的识别并具有较高的识别率;同时实验表明,辅以同一故障类型的样本间欧氏距离,可实现对分合闸故障中三相不同期故障严重程度的初步评估。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	孙曙光
	于晗
	杜太行
	王景芹
	赵黎媛

关键词 ：万能式断路器, 分合闸故障诊断, 振动信号, 互补总体平均经验模态分解, 样本熵相关向量机

Abstract：In order to realize the non-invasive monitoring and diagnosis of switching fault for conventional circuit breaker, a method that combines complementary ensemble empirical mode decomposition (CEEMD)-sample entropy and relevance vector machine (RVM) is proposed for fault diagnosis by using vibration signals during the switching process. Firstly, the vibration signal is denoised by improved threshold wavelet packet denoising algorithm. Secondly, several intrinsic mode function (IMF) components which reflect main state information are extracted by CEEMD, and the top seven based on energy distribution characteristic are chosen to calculate the sample entropy as the effective feature sample. Finally, the euclidean distance of different fault samples is calculated to evaluate average sample distance between classes. The binary tree classifier based on RVM is established to diagnose the fault type. By results of contrast experiments based on designed typical switching fault models, the method realizes the conventional circuit breaker fault type identification accurately with relatively less fault data samples. And the experiments also show that it can realize the preliminary evaluation of three-phase non-synchronous fault degree based on the euclidean distance of fault samples.

Key words： Conventional circuit breaker switching fault diagnosis vibration signal complementary ensemble empirical mode decomposition (CEEMD) sample entropy relevance vector machine

收稿日期: 2015-12-28 出版日期: 2017-04-17

PACS:

TM561

基金资助:河北省教育厅资助科研项目(ZD2016108)。

通讯作者: 孙曙光男,1979年生,副教授,研究方向为电器检测与试验。E-mail:sunshuguang_ 2000@163.com

作者简介: 于晗男,1990年生,硕士研究生,研究方向为计算机智能控制与应用。E-mail:345969161@qq.com

引用本文:

孙曙光,于晗,杜太行,王景芹,赵黎媛. 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 Shuguang,Yu Han,Du Taihang,Wang Jingqin,Zhao Liyuan. Diagnosis on the Switching Fault of Conventional Circuit Breaker Based on Vibration Signal Sample Entropy and RVM. Transactions of China Electrotechnical Society, 2017, 32(7): 20-30.

链接本文:

https://dgjsxb.ces-transaction.com/CN/Y2017/V32/I7/20

[1] 王建华,张国钢,耿英三,等.智能电器最新技术研究及应用发展前景[J].电工技术学报,2015,30(9):1-11.
Wang Jianhua,Zhang Guogang,Geng Yingsan,et al.The latest technology research and application prospects of the intelligent electrical apparatus[J].Transactions of China Electrotechnical Society,2015,30(9):1-11.
[2] 缪希仁,吴晓梅,石敦义,等.采用HHT振动分析的低压断路器合闸同期辨识[J].电工技术学报,2014,29(11):154-161.
Miao Xiren,Wu Xiaomei,Shi Dunyi,et al.Switching synchronism identification of low voltage circuit breaker utilizing HHT analysis to vibration signal[J].Transactions of China Electrotechnical Society,2014,29(11):154-161.
[3] 吕锦柏,王毅,谢将剑,等.基于线圈电流的永磁真空断路器控制方法[J].高电压技术,2013,39(4):860-868.
Lü Jinbo,Wang Yi,Xie Jiangjian,et al.Control method for permantent magnetic vacuum circuit breaker based on coil current[J].High Voltage Engineering,2013,39(4) :860-868.
[4] Strachan S M,McArthur S D J,Stephen B,et al.Providing decision support for the condition-based maintenance of circuit breakers through data mining of trip coil current signatures[J].IEEE Transactions on Power Delivery,2007,22(1):178-186.
[5] 李静,曹云东,侯春光,等.交流电弧微观动态形成机理及影响因素[J].电工技术学报,2015,30(17):45-54.
Li Jing,Cao Yundong,Hou Chunguang,et al.Microscopic dynamic formation mechanism and influencing factors of AC vacuum arc[J].Transactions of China Electrotechnical Society,2015,30(17):45-54.
[6] Tang Jianzhong,Lu Shisong,Xie Jing,et al.Contact force monitoring and its application in vacuum circuit breakers[J].IEEE Transactions on Power Delivery,2015,DOI:10.1109/TPWRD.2015.2423686.
[7] 常广,王毅,王玮.采用振动信号零相位滤波时频熵的高压断路器机械故障诊断[J].中国电机工程学报,2013,33(3):155-162.
Chang Guang,Wang Yi,Wang Wei.Mechanical fault diagnosis of high voltage circuit breakers utilizing zero-phase filter time-frequency entropy of vibration signal[J].Proceedings of the CSEE,2013,33(3):155-162.
[8] Charbkaew N,Suwanasri T,Bunyagul T,et al.Vibration signal analysis for condition monitoring of puffer-type high-voltage circuit breakers using wavelet transform[J].IEEJ Transactions on Electrical and Electronic Engineering,2012,7(1):13-22.
[9] 孙一航,武建文,廉世军,等.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,et al.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.
[10]张佩,赵书涛,申路,等.基于改进EEMD的高压断路器振声联合故障诊断方法[J].电力系统保护与控制,2014,42(8):77-81.
Zhang Pei,Zhao Shutao,Shen Lu.Research on vibration and acoustic joint mechanical fault diagnosis method of high voltage circuit breaker based on improved EEMD[J].Power System Protection and Control,2014,42(8):77-81.
[11]Christopher B,Medeiros F A,Zuohua Z,et al.Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements[J].Investigative Ophthalmology & Visual Science,2005,46(4):1322-1329.
[12]Donoho D L,Johnstone J M.Ideal spatial adaptation by wavelet shrinkage[J].Biometrika,1994,81(3):425-455.
[13]杨恢先,王绪四,谢鹏鹤,等.改进阈值与尺度间相关的小波红外图像去噪[J].自动化学报,2011,37(10):1167-1174.
Yang Huixian,Wang Xusi,Xie Penghe,et al.Infrared image denoising based on improved threshold and inter-scale correlations of wavelet transform[J].Acta Automatica Sinica,2011,37(10):1167-1174.
[14]唐进元,陈维涛,陈思雨,等.一种新的小波阈值函数及其在振动信号去噪分析中的应用[J].振动与冲击,2009,28(7):118-121,217.
Tang Jinyuan,Chen Weitao,Chen Siyu,et al.Wavelet-based vibration signal denoising with a new adaptive threshold function[J].Journal of Vibration and Shock,2009,28(7):118-121,217.
[15]李庆民,王冠,徐国政,等.高压断路器振动信号的指数衰减振荡波建模方法[J].高压电器,2004,40(3):177-179.
Li Qingmin,Wang Guan,Xu Guozheng,et al.Modelling method for vibration signals of high voltage circuit breaker based on exponentially damped oscillatory waves[J].High Voltage Apparatus,2004,40(3):177-179.
[16]Wu Zhaohua,Huang N E.Ensemble empirical mode decomposition:a noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2011,1(1):1-41.
[17]Yeh J R,Shieh J S.Complementary ensemble empirical mode decomposition:a noise enhanced data analysis method[J].Advances in Adaptive Data Analysis,2010,2(2):1-41.
[18]Alcaraz R,Rieta J J.A review on sample entropy applications for the non-invasive analysis of atrial fibrillation electrocardiograms[J].Biomedical Signal Processing and Control,2010,5(1):1-14.
[19]尹金良,朱永利,俞国勤.基于多分类相关向量机的变压器故障诊断新方法[J].电力系统保护与控制,2013,41(5):77-82.
Yin Jinliang,Zhu Yongli,Yu Guoqin.New transformer fault diagnosis method based on multi-class relevance vector machine[J].Power System Protection and Control,2013,41(5):77-82.
[20]Smola A.Sparse Bayesian learning and the relevance vector machine[J].Journal of Machine Learning Research,2001,1(3):211-244.
[21]李鹏飞,周文俊,曾国,等.高压断路器合闸弹簧动态特性及储能状态检测方法[J].电工技术学报,2016,31(3):104-112.
Li Pengfei,Zhou Wenjun,Zeng Guo,et al.The dynamic characteristics and energy storage state detection method of high-voltage circuit breaker closing spring[J].Transactions of China Electrotechnical Society,2016,31(3):104-112.