基于振动信号谱形状熵特征的高压断路器操动状态辨识方法

doi:10.19595/j.cnki.1000-6753.tces.211569

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (6273 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The potential energy of the energy storage spring of the high voltage circuit breaker is instantaneously released and converted into mechanical energy, inducing the movement of the mechanism components. This enables the static and dynamic contacts to complete the opening and closing operations. The accompanying vibration signal exhibits non-stationary and non-linear characteristics. In this paper, a feature analysis method called spectral shape entropy is propose . Firstly, the non-stationary signal with complicated frequency structure is gradually split by using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to obtain the components representing different frequency band features. Then the power spectrum of the filtered component signal is transformed to polar coordinates, and the sensitivity of the main peak region is improved by the divergence factor. Calculating the spectral shape entropy characteristics according to the waveform scattered in sub-regions. Finally, the support vector machine optimized by grouped particle swarm algorithm model is used to identify the circuit breaker operating states. The experiments show that the spectral shape entropy can characterize the waveform variation of vibration signals and the distribution of the main peaks of the power spectrum. It has high accuracy in identifying typical mechanical faults of circuit breakers and takes less computing time.

Key words： High voltage circuit breaker operation state vibration signal spectral shape entropy polar coordinates

Received: 07 October 2021

PACS:

TM561

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Zhao Shutao
	Xu Wenjie
	Liu Huilan
	Zeng Rui
	Xia Xiaofei

Cite this article:

Zhao Shutao,Xu Wenjie,Liu Huilan等. Identification Method for Operation State of High Voltage Circuit Breakers Based on Spectral Shape Entropy Characteristics of Vibration Signals[J]. Transactions of China Electrotechnical Society, 2022, 37(9): 2170-2178.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.211569 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/I9/2170

[1] 杨秋玉, 王栋, 阮江军, 等. 基于振动信号的断路器机械零部件故障程度识别[J]. 电工技术学报, 2021, 36(13): 2880-2892.
Yang Qiuyu, Wang Dong, Ruan Jiangjun, et al.Fault severity estimation method for mechanical parts in circuit breakers based on vibration analysis[J]. Transactions of China Electrotechnical Society, 2021, 36(13): 2880-2892.
[2] 王昱皓, 武建文, 马速良, 等. 基于核主成分分析-SoftMax的高压断路器机械故障诊断技术研究[J]. 电工技术学报, 2020, 35(增刊1): 267-276.
Wang Yuhao, Wu Jianwen, Ma Suliang, et al.Mechanical fault diagnosis research of high voltage circuit breaker based on kernel principal component analysis and SoftMax[J]. Transactions of China Electrotechnical Society, 2020, 35(S1): 267-276.
[3] Heising C R, Janssen A L J, Lanz W, et al. Summary of CIGRE 13.06 Working Group world wide reliability data and maintenance cost data on high voltage circuit breakers above 63kV[C]//IEEE Industry Applications Society Meeting, Rome, Italy, 1994: 2226-2234.
[4] 杨秋玉, 阮江军, 张灿, 等. 基于定量递归分析的高压断路器机械缺陷辨识及应用[J]. 电工技术学报, 2020, 35(18): 3848-3859.
Yang Qiuyu, Ruan Jiangjun, Zhang Can, et al.Study and application of mechanical defect identification for high-voltage circuit breakers using recurrence quantification analysis[J]. Transactions of China Electrotechnical Society, 2020, 35(18): 3848-3859.
[5] 游颖敏, 王景芹, 舒亮, 等.断路器保护特性测试电流的自适应控制策略[J]. 电工技术学报, 2020, 35(15): 3203-3213.
You Yingmin, Wang Jingqin, Shu Liang, et al.Research on adaptive current control method in circuit breaker protection characteristic test[J]. Transactions of China Electrotechnical Society, 2020, 35(15): 3203-3213.
[6] 马强, 荣命哲, 贾申利. 基于振动信号小波包提取和短时能量分析的高压断路器合闸同期性的研究[J]. 中国电机工程学报, 2005, 25(13): 149-154.
Ma Qiang, Rong Mingzhe, Jia Shenli.Study of switching synchronization of high voltage breakers based on the wavelet packets extraction algorithm and short time analysis method[J]. Proceedings of the CSEE, 2005, 25(13): 149-154.
[7] 张佩, 周秀萍. 基于振声时三维图的高压断路器机械故障诊断方法[J]. 高压电器, 2014, 50(12): 121-125.
Zhang Pei, Zhou Xiuping.Mechanical fault diagnosis of high voltage circuit breaker based on three-dimensional vibration-acoustics-time diagram[J]. High Voltage Apparatus, 2014, 50(12): 121-125.
[8] 赵洋, 曾庆军. 采用功率谱估计法的真空断路器振动分析[J]. 电气应用, 2009, 28(9): 62-65.
Zhao Yang, Zeng Qingjun.Vibration analysis of vacuum circuit breaker by power spectrum estimation method[J]. Electrical Application, 2009, 28(9): 62-65.
[9] 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): 115-119,125.
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): 115-119, 125.
[10] 曹宇鹏, 罗林, 王乔, 等. 基于卷积深度网络的高压真空断路器机械故障诊断方法[J]. 电力系统保护与控制, 2021, 49(3): 39-47.
Cao Yupeng, Luo Lin, Wang Qiao, et al.Fault diagnosis of high-voltage vacuum circuit breaker with a convolutional deep network[J]. Power System Protection and Control, 2021, 49(3): 39-47.
[11] 赵书涛, 马莉, 朱继鹏, 等. 基于CEEMDAN样本熵与FWA-SVM的高压断路器机械故障诊断[J]. 电力自动化设备, 2020, 40(3): 181-186.
Zhao Shutao, Ma Li, Zhu Jipeng, et al.Mechanical fault diagnosis of high voltage circuit breaker based on CEEMDAN sample entropy and FWA-SVM[J]. Electric Power Automation Equipment, 2020, 40(3): 181-186.
[12] 黄南天, 陈怀金, 林琳, 等. 基于S变换和极限学习机的高压断路器机械故障诊断[J]. 高压电器, 2018, 54(6): 74-80.
Huang Nantian, Chen Huaijin, Lin Lin, et al.Mechanical fault diagnosis of high voltage circuit breakers based on S-transform and extreme learning machine[J]. High Voltage Apparatus, 2018, 54(6): 74-80.
[13] 陈欣昌, 冯玎, 林圣. 基于深度自编码网络的高压断路器操作机构机械故障诊断方法[J]. 高电压技术, 2020, 46(9): 3080-3088.
Chen Xinchang, Feng Ding, Lin Sheng.Mechanical fault diagnosis method of high voltage circuit breaker operating mechanism based on deep auto-encoder network[J]. High Voltage Engineering, 2020, 46(9): 3080-3088.
[14] 范贤浩, 刘捷丰, 张镱议, 等. 融合频域介电谱及支持向量机的变压器油浸纸绝缘老化状态评估[J]. 电工技术学报, 2021, 36(10): 2161-2168.
Fan Xianhao, Liu Jiefeng, Zhang Yiyi, et al.Aging evaluation of transformer oil-immersed insulation combining frequency domain spectroscopy and support vector machine[J]. Transactions of China Electrotechnical Society, 2021, 36(10): 2161-2168.
[15] 罗翔. 一起基于声电联合检测的特高压GIS断路器局部放电缺陷诊断分析与处理[J]. 电气技术, 2018, 19(6): 107-111, 116.
Luo Xiang.Diagnosis analysis and disposal of partial discharge in GIS using ultrasonic associated with electromagnetic wave method[J]. Electrical Engineering, 2018, 19(6): 107-111, 116.
[16] Torres M E, Colominas M A, Schlotthauer G, et al.A complete ensemble empirical mode decomposition with adaptive noise[J]. Brain Research Bulletin, 2011, 125(3): 4144-4147.
[17] 刘友波, 吴浩, 刘挺坚, 等. 集成经验模态分解与深度学习的用户侧净负荷预测算法[J]. 电力系统自动化, 2021, 45(24): 57-64.
Liu Youbo, Wu Hao, Liu Tingjian, et al.User-side net load forecasting method integrating empirical mode decomposition and deep learning[J]. Automation of Electric Power Systems, 2021, 45(24): 57-64.
[18] 张梅军, 唐建, 何晓晖. EEMD方法及其在机械故障诊断中的应用[D]. 北京: 国防工业出版社, 2015.
[19] 毕立恒, 朱彦齐. 基于分群粒子群算法的平面度误差评定研究[J]. 计量学报, 2019, 40(6): 980-985.
Bi Liheng, Zhu Yanqi.Flatness error evaluation based on grouped particle swarm optimization algorithm[J]. Acta Metrologica Sinica, 2019, 40(6): 980-985.
[20] 卢辉斌, 周绯菲, 孙金伟. 一种改进粒子群优化算法在入侵检测中的应用[J]. 燕山大学学报, 2013, 37(2): 124-128,147.
Lu Huibin, Zhou Feifei, Sun Jinwei.Application of improved particle swarm optimization algorithm in intrusion detection[J]. Journal of Yanshan University, 2013, 37(2): 124-128,147.
[21] 谢国民, 倪乐水. 基于IABC优化SVM的变压器故障诊断[J]. 电力系统保护与控制, 2020, 48(15): 156-163.
Xie Guomin, Ni Leshui.Transformer fault diagnosis based on an artificial bee colony-support vector machine optimization algorithm[J]. Power System Protection and Control, 2020, 48(15): 156-163.
[22] Iqbal A, Singh G K.PSO based controlled six-phase grid connected induction generator for wind energy generation[J]. CES Transactions on Electrical Machines and Systems, 2021, 5(1): 41-49.
[23] Ali M Z, Shabbir M N S K, Liang Xiaodong, et al. Machine learning-based fault diagnosis for single-and multi-faults in induction motors using measured stator currents and vibration signals[J]. IEEE Transactions on Industry Applications, 2019, 55(3): 2378-2391.
[24] Miao Di.Research on fault diagnosis of high-voltage circuit breaker based on support vector machine[J]. International Journal of Pattern Recognition & Artificial Intelligence, 2019, 33(6): 603-608.
[25] 谢桦, 亚夏尔·吐尔洪, 陈昊, 等. 基于支持向量机算法的配电线路时变状态预测方法[J]. 电力系统自动化, 2020, 44(18): 74-80.
Xie Hua, Yaxar Turgun, Chen Hao, et al.Prediction method for time-variant states of distribution line based on support vector machine algorithm[J]. Automation of Electric Power Systems, 2020, 44(18): 74-80.