Determine Method of the Transformer Winding Looseness Defeat Based on Vibration

doi:10.11930/j.issn.1004-9649.201711096

Abstract

Abstract: Accurate defeat diagnosis on transformer winding looseness is investigated experimentally and theoretically in this paper. An eigenvalue of transformer winding looseness defeat is defined based on the pre-compression force. The eigenvalue vary monotonously with the pre-compression force, which is used in defeat diagnosis. This method is suitable for on-line monitoring under load fluctuations for the reason that the eigenvalue is unaffected by the current. In the experiments winding looseness faults are set by different pre-compression forces which are measured by the bridge circuit consisted of strain gauges and resistors. Hammering method is used to measure the natural frequency. The impact of pre-compression force's changes on the natural frequency is explored. Effective measurement points are not influenced by the resonance. Defeat threshold values and warning threshold values are selected after analyzing the characteristics of the effective measurement points under the circumstance of different pre-compression force and current to achieve winding looseness defeat diagnosis and defeat phase localization.

Key words: transformer, winding looseness defeat, resonance, eigenvalue, diagnosis method

CLC Number:

TM407

ZHOU Yu, MA Hongzhong, HUANG Fengwen, GU Jinping, CHEN Taotao, GONG Jiewei, ZHU Shizhen. Determine Method of the Transformer Winding Looseness Defeat Based on Vibration[J]. Electric Power, 2018, 51(6): 83-88.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201711096

https://www.electricpower.com.cn/EN/Y2018/V51/I6/83

References

[1] 周宇, 马宏忠, 李凯, 等. 基于相空间重构的大型变压器绕组松动的振动特征识别[J]. 电力自动化设备, 2016, 36(12):169-175.ZHOU Yu, MA Hongzhong, LI Kai, et al. Feature recognition of large transformer winding looseness based on the phase space reconstruction of vibration signals[J], Electric Power Automation Equipment, 2016, 36(12):169-175.
[2] 弓杰伟, 马宏忠, 姜宁, 等. 电力变压器的有限元建模与绕组松动分析[J]. 电力自动化设备, 2016, 36(04):78-84.GONG Jiewei, MA Hongzhong, JIANG Ning, et al. Finite element modeling and winding looseness analysis for power transformer[J]. Electric Power Automation Equipment, 2016, 36(4):78-84.
[3] 陆瑾. 应用机械振动法对变压器绕组状态进行在线测试的研究[D]. 上海:上海交通大学, 2008.
[4] 郝震, 龙凯华, 赵燕坤, 等. 引起变压器异常振动的两种缺陷的诊断[J]. 中国电力, 2014, 47(6):55-60, 65.HAO Zhen, Long Kaihua, ZHAO Yankun, et al. Abnormal vibration diagnostic methods of transformers caused by two defects[J]. Electric Power,2014,47(6):55-60, 65.
[5] 刘军, 张安红. 电力变压器绕组短路动稳定能力的仿真和评估[J]. 变压器, 2012, 49(6):14-25.LIU Jun, ZHANG Anhong. Simulation and evaluation of short circuit dynamic stability of windings in power transformer[J]. Transformer, 2012, 49(6):14-25.
[6] 王涛云, 马宏忠, 姜宁, 等. 基于空载合闸振动信号的变压器绕组松动诊断[J]. 中国电力, 2016, 49(5):39-43.WANG Taoyun, MA Hongzhong, JIANG Ning, et al. Diagnosing of winding looseness of a transformer based on no-load switching-on vibration signals[J]. Electric Power, 2016, 49(5):39-43.
[7] 王钰, 李彦明, 张成良. 变压器绕组变形检测的LVI法和FRA法的比较研究[J]. 高电压技术, 1997, 23(1):13-15.WANG Yu, LI Yanming, ZHANG Chenliang. The comparisonal study on LVI and FRA method of detecting winding deformation faults in transformers[J]. High Voltage Engineering, 1997, 23(1):13-15.
[8] 黄华, 周建国, 姜益明, 等. 阻抗法和频响法诊断电力变压器绕组变形[J]. 高电压技术, 1999, 25(2):70-73.HUANG Hua, ZHOU Jianguo, JIANG Yiming, et al. Diagnosis of winding deformation of transformer by impedance method and FRA[J]. High Voltage Engineering, 1997, 23(1):13-15.
[9] 陶新民, 李震, 张越. 基于DSP的变压器振动信号的小波能量谱熵提取[J]. 中国电力, 2015, 48(8):37-41, 66.TAO Xinmin, LI Zhen, ZHANG Yue.Acquisition system of wavelet energy spectrum entropy of transformer vibration signal based on DSP[J]. Electric Power, 2015, 48(8):37-41, 66.
[10] GARCíA B, BURGOS J C, ALONSO á M. Transformer tank vibration modeling as a method of detecting winding deformations-Part Π:Experimental Verification[J]. IEEE trans on Power Delivery, 2006, 21(1):164-169.
[11] 沈培锋, 赵宏飞, 李凯, 等. 基于振动的变压器铁芯松动定位[J]. 中国电力, 2014, 47(9):66-70.SHEN Peifeng, ZHAO Hongfei, LI Kai, et al. Positioning of transformer core loosening based on the vibration signals[J]. Electric Power, 2014, 47(9):66-70.
[12] 张赢, 马宏忠, 陈楷. 变压器空载分析及实测研究[J]. 中国电力, 2012, 45(5):30-33ZHANG Ying, MA Hongzhong, CHEN Kai. No-load transformer vibration analysis and testing[J]. Electric Power, 2012, 45(5):30-33.
[13] 王春宁, 朱跃光, 马宏忠, 等. 基于振动信号和小波神经网络的变压器故障诊断[J]. 中国电力, 2014, 47(4):75-79.WANG Chunnin, ZHU Yueguang, MA Hongzhong, et al. Fault diagnosis of power transformer based on vibration and wavelet neural network[J]. Electric Power, 2014, 47(4):75-79.
[14] 陆瑾, 金怡. 振动法在变压器绕组状态检测中的应用研究[J]. 中国电力, 2008, 41(12):23-26.LU Jin, JIN Yi. The application of vibration method in transformers'winding state detection[J]. Electric Power, 2008, 41(12):23-26.
[15] 邵宇鹰, 饶柱石, 谢坡岸, 等. 预紧力对变压器绕组固有频率的影响[J]. 噪声与振动控制, 2006, 26(6):51-53.SHAO Yuying, RAO Zhushi, XIE Poan, et al. The influence of pre-compression changing on the natural frequency of electric transformers windings[J]. Noise and Vibration Control, 2006(6):51-53.
[16] 赵宏飞, 马宏忠, 陈楷, 等. 基于振动信号的变压器绕组松动实验研究[J]. 中国电力, 2014, 47(1):13-16.ZHAO Hongfei, MA Hongzhong, CHEN Kai, et al. Experimental study of transformer winding looseness based on vibration signals[J]. Electric Power, 2014, 47(1):13-16.
[17] 马宏忠, 赵宏飞, 陈楷, 等. 基于振动的变压器铁芯松动判定方法[J]. 电力系统自动化, 2013, 37(14):101-106.MA Hongzhong, ZHAO Hongfei, CHEN Kai, et al. Determine method for transformer core looseness based on vibration[J]. Automation of Electric Power Systems, 2013, 37(14):101-106.
[18] 傅坚, 邵宇鹰. 用机械振动频响法诊断大型变压器绕组松动[J]. 华东电力, 2006, 34(4):28-30.FU Jian, SHAO Yuying. The experiment of diagnosing large-scale transformer winding looseness by frequency response analysis[J]. East China Electric Power, 2006, 34(4):28-30.
[19] 吴书有. 基于振动信号分析方法的电力变压器状态监测与故障诊断研究[D]. 合肥:中国科学技术大学, 2009.
[20] 张小凡, 谢大吉, 陈正新. 材料力学实验[M]. 北京:清华大学出版社, 2001.
[21] 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 Hongfei, MA Hongzhong, CHEN Kai, et al. Test and analysis of inherent frequency of power transformer tank[J]. Electric Power Automation Equipment, 2013, 33(11):165-169.