基于振动分布特征的电力变压器绕组故障诊断

摘要
图/表
参考文献(23)
相关文章 (6)

全文: PDF (3241 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要振动分析法是实现电力变压器带电监测与故障诊断的重要手段，而基于振动分析法的故障诊断方法的关键在于从复杂的油箱壁振动信号中提取出状态特征（值或矢量）。传统的状态特征提取方法大多选取单个测点的振动信号进行时域或频域特征的提取，往往忽略了各测点间的振动分布特征。从振动重心的角度对振动分布的幅值重心及重心轨迹进行研究与分析，能够提出四个量化参数。在四个量化参数的基础上结合支持向量机分类算法提出基于振动分布特征的变压器绕组故障诊断模型。实际变压器的绕组故障实验以及十余台台电力变压器现场实测数据样本的分析与测试结果均表明，提出的振动分布特征及量化参数能够有效反映变压器绕组变形、压紧力松弛等机械结构变化，而基于振动分布特征的绕组故障诊断模型也可准确的对变压器绕组机械结构状态进行检测与诊断。

	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
	杨毅1
	刘石1
	张楚1
	韩丹1
	孟源源1
	胡异炜2
	郑婧2
	黄海2

关键词 ：电力变压器, 振动分布特征, 绕组故障诊断, 压紧力松弛, 支持向量机(SVM)

Abstract：Vibration analysis method is an important means to realize live monitoring and fault diagnosis of power transformers. The key to the fault diagnosis method based on vibration analysis is to extract state features (values or vectors) from complex vibration signals on oil tank wall. Most of the traditional state feature extraction methods choose a single measured point’s vibration signal to extract its features of time domain or frequency one, and ignore vibration distribution features among various measured points. Here, amplitude barycenter and barycenter trajectory of vibration distribution were studied and analyzed from the view point of vibration barycenter to propose 4 quantized parameters. Based on these 4 quantized parameters and the support vector machine (SVM) classification algorithm, the winding fault diagnosis model for power transformer based on vibration distribution features was proposed. The measurement results of actual power transformer winding fault tests and analysis ones of measured data samples acquired from more than 10 power transformers on site showed that the proposed vibration distribution features and quantized parameters can effectively reflect changes in transformer winding deformation and compression force relaxation, etc.; the winding fault diagnosis model based on vibration distribution features can also correctly detect and diagnose mechanical structure states of power transformer winding.

Key words： power transformer vibration distribution features winding fault diagnosis compression force relaxation;support vector machine (SVM)

收稿日期: 2018-06-20 出版日期: 2019-12-28

引用本文:

杨毅1，刘石1，张楚1，韩丹1，孟源源1，胡异炜2，郑婧2，黄海2. 基于振动分布特征的电力变压器绕组故障诊断[J]. 振动与冲击, 2020, 39(1): 199-208.
YANG Yi1, LIU Shi1, ZHANG Chu1, HAN Dan1, MENG Yuanyuan1, HU Yiwei2, ZHENG Jing2, HUANG Hai2. Winding fault diagnosis of power transformer based on vibration distribution features. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(1): 199-208.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2020/V39/I1/199

[1] Wang M, Vandermaar A J, Srivastava K D. Review of condition assessment of power transformers in service[J]. IEEE Electrical Insulation Magazine, 2003, 18(6):12-25.
[2] 洪凯星, 潘再平, 黄海. 电力变压器绕组轴向振动的建模与分析[J]. 变压器, 2010, 47 (12) :36-40
Hong K X, Pan Z P, Huang H. Modeling and Analysis of Winding Axial Vibration of Power Transformer[J]. Transformer, 2010.
[3] Wang Z, Li J, Sofian D M. Interpretation of transformer FRA responses—Part I: Influence of winding structure[J]. IEEE Transactions on power delivery, 2009, 24(2): 703-710.
[4] Sofian D M, Wang Z, Li J. Interpretation of transformer FRA responses—Part II: Influence of transformer structure[J]. IEEE Transactions on Power Delivery, 2010, 25(4): 2582-2589.
[5] Zhou H, Hong K, Huang H, et al. Transformer winding fault detection by vibration analysis methods[J]. Applied Acoustics, 2016, 114: 136-146.
[6] Hong K, Huang H, Zhou J, et al. A method of real-time fault diagnosis for power transformers based on vibration analysis[J]. Measurement Science and Technology, 2015, 26(11): 115011.
[7] Hong K, Huang H, Fu Y, et al. A vibration measurement system for health monitoring of power transformers[J]. Measurement, 2016, 93: 135-147.
[8] 汲胜昌, 刘味果, 单平,等. 小波包分析在振动法监测变压器铁芯及绕组状况中的应用[J]. 中国电机工程学报, 2001, 21(12):24-27.
Ji S C, Liu W G, Shan P, et al. Application of the wavelet packet to the monitoring of the core and winding condition of transformer[J]. Proceedings of the Csee, 2001.
[9] 张彬, 徐建源, 陈江波,等. 基于电力变压器振动信息的绕组形变诊断方法[J]. 高电压技术, 2015, 41(7):2341-2349.
Zhang B, Jianyuan X U, Chen J, et al. Diagnosis Method of Winding Deformation Based on Power Transformer Vibration Information[J]. High Voltage Engineering, 2015, 41(7):2341-2349.
[10] 黄春梅, 马宏忠, 张艳,等. 基于小波包和模糊自适应共振神经网络的变压器绕组状态识别[J]. 广东电力, 2017, 30(7):89-95.
Huang C M, Ma H Z, Zhang Y, et al. State Recognition for Transformer Winding Based on Wavelet Packet and Fuzzy Adaptive Resonance Theory Neural Network [J]. Guangdong Electric Power, 2017, 30(7):89-95.
[11] 骆波, 王丰华, 廖天明,等. 应用改进复Morlet小波识别电力变压器绕组模态参数[J]. 振动与冲击, 2014, 33(6):131-136.
Bo L, Wang F H, Liao T M, et al. Modal parameters identification of power transformer winding based on the improved complex Morlet wavelet[J]. Journal of Vibration & Shock, 2014, 33(6):131-136.
[12] 杨毅, 王丰华, 段若晨,等. 基于自适应筛选EMD和CFDC的变压器绕组状态检测[J]. 振动与冲击, 2017, 36(19):106-111.
Yang Y, Wang F, Duan R, et al. Detection of transformer winding condition based on the self-adaptive sifting EMD and CFDC[J]. Journal of Vibration & Shock, 2017, 36(19).
[13] 李莉, 朱永利, 宋亚奇. 多尺度熵在变压器振动信号特征提取中的应用[J]. 振动.测试与诊断, 2015, 35(4):757-762.
Li L, Zhu Y L, Song Y Q. Application of MSE in feature extraction of transformer vibration signal [J]. Journal of Vibration，Measurement & Diagnosis, 2015, 35(4):757-762.
[14] Jin M, Pan J, Huang H, et al. Transmission of vibration of a power transformer from the internal structures to the tank[C]//Proceedings of the Acoustics. 2012: 1-7
[15] 赵宏飞, 马宏忠, 李凯,等. 电力变压器油箱固有频率测试及其影响分析[J]. 电力自动化设备, 2013, 33(11):165-169.
Zhao H, Ma H, Li K, et al. Test and analysis of inherent frequency of power transformer tank[J]. Dianli Zidonghua Shebei, 2013, 33(11):165-169.
[16] 周求宽, 王丰华, 万军彪,等. 应用优化限制带宽经验模态分解法识别电力变压器绕组模态参数[J]. 振动与冲击, 2014, 33(13):169-175.
Zhou Q K, Wang F H, Wan J B, et al. Modal parameter identification of power transformer winding based on optimized restricted band empirical mode decomposition[J]. Journal of Vibration & Shock, 2014, 33(13):169-175.
[17] Lin A, Wang Z, Hai H, et al. Study of vibration sensitive areas on 500kV power transformer tank[C]// IEEE International Conference on Industrial Informatics. IEEE, 2012:891-896
[18] Jing Zheng，Jie Pan*，Hai Huang, An Experimental Study of Winding Vibration of A Single-phase Power Transformer Using A Laser Doppler Vibrometer，Applied Acoustics，2015，87：30-37.
[19] J. Zheng, H. Huang, J. Pan, and Y. Zhang, “Identification of the nonlinear vibration system of power transformers”, Meas. Sci. Technol., vol. 28, Nov. 015005 (11pp), 20167. DOI: 10.1088/1361-6501/28/1/015005.
[20] Clauser C E, McConville J T, Young J W. Weight, volume, and center of mass of segments of the human body[R]. ANTIOCH COLL YELLOW SPRINGS OH, 1969.
[21] García B, Burgos J C, Alonso Á. Winding deformations detection in power transformers by tank vibrations monitoring[J]. Electric power systems research, 2005, 74(1): 129-138.
[22] Lin H T, Lin C J, Weng R C. A note on Platt’s probabilistic outputs for support vector machines[J]. Machine Learning, 2007, 68(3): 267-276.
[23] 李琳, 张晓龙. 基于RBF 核的SVM 学习算法的优化计算[J]. 计算机工程与应用, 2006, 42(29): 190-192
Lin L I. Optimization of SVM with RBF Kernel[J]. Computer Engineering & Applications, 2006.