基于经验小波变换的变压器绕组结构衰退特征提取方法
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摘要
根据变压器绕组物理结构建立多自由度数学模型,得到绕组自由振动解析解,建立结构参数与绕组状态之间的关系。在此基础上,利用经验小波变换提取不同固有频率对应的振动衰减曲线,并利用希尔伯特变换提取机械结构参数。实验采用一台三相110 kV变压器的绕组单元,通过调整压紧力大小模拟绕组结构衰退过程,施加机械冲击并采集不同位置的振动响应信号。实验结果表明,经验小波变换能快速有效地得到绕组结构模态信息,且绕组冲击响应中包含的机械结构参数能正确区分绕组结构状态。在现场测试中,对比分析不同状态变压器在下电过程中产生的振动。结果表明,该研究提出的绕组结构特征提取方法不仅有明晰的物理意义,而且有重要的工程应用价值。
According to the physical structure of transformer windings, a multi-degree-of-freedom mathematical model was established, and the analytical solution of the free vibration of windings was obtained.The relationship between the structural parameters and the winding condition was also established. Based on this, empirical wavelet transform was used to extract the vibration decay curves corresponding to different natural frequencies, and Hilbert transform was used to extract the mechanical parameters. In the experiment, a three-phase 110 kV transformer winding unit was adopted as an object. The process of winding structure degradation was simulated by adjusting the clamping force, and the vibration responses at different locations were collected after a mechanical impulse was applied. The experimental results show that the modal information of the winding structure can be obtained quickly and effectively by empirical wavelet transform, and the structural parameters contained in the impulse vibration responses can distinguish the state of winding structure correctly. In the field test, the vibrations produced in the switched-off process were compared and analyzed for the transformers under different conditions.The results show that the proposed feature extraction method for the winding structure not only has clear physical meaning, but also has important engineering application value.
According to the physical structure of transformer windings, a multi-degree-of-freedom mathematical model was established, and the analytical solution of the free vibration of windings was obtained.The relationship between the structural parameters and the winding condition was also established. Based on this, empirical wavelet transform was used to extract the vibration decay curves corresponding to different natural frequencies, and Hilbert transform was used to extract the mechanical parameters. In the experiment, a three-phase 110 kV transformer winding unit was adopted as an object. The process of winding structure degradation was simulated by adjusting the clamping force, and the vibration responses at different locations were collected after a mechanical impulse was applied. The experimental results show that the modal information of the winding structure can be obtained quickly and effectively by empirical wavelet transform, and the structural parameters contained in the impulse vibration responses can distinguish the state of winding structure correctly. In the field test, the vibrations produced in the switched-off process were compared and analyzed for the transformers under different conditions.The results show that the proposed feature extraction method for the winding structure not only has clear physical meaning, but also has important engineering application value.
引文
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[19]BORUCKI S.Diagnosis of technical condition of power transformers based on the analysis of vibroacoustic signals measured in transient operating conditions[J].IEEETransactions on Power Delivery,2012,27(2):670-676.
[20]WANG Y,PAN J.Applications of operational modal analysis to a single-phase distribution transformer[J].IEEETransactions on Power Delivery,2015,30(4):2061-2063.
[21]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.LUO Bo,WANG Fenghua,LIAO Tianming,et al.Modal parameters identification of power transformer winding based on the improved complex Morlet wavelet[J].Journal of Vibration and Shock,2014,33(6):131-136.
[22]GENG C,WANG F,ZHANG J,et al.Modal parameters identification of power transformer winding based on improved empirical mode decomposition method[J].Electric Power Systems Research,2014,108(108):331-339.
[23]GILLES J.Empirical wavelet transform[J].IEEETransactions on Signal Processing,2013,61(16):3999-4010.
[2]MARTIN D,MARKS J,SAHA T.Survey of Australian power transformer failures and retirements[J].IEEEElectrical Insulation Magazine,2017,33(5):16-22.
[3]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.
[4]SAPONARA S,FANUCCI L,BERNARDO F,et al.Predictive diagnosis of high-power transformer faults by networking vibration measuring nodes with integrated signal processing[J].IEEE Transactions on Instrumentation&Measurement,2016,65(8):1749-1760.
[5]孙翔,何文林,詹江杨,等.电力变压器绕组变形检测与诊断技术的现状与发展[J].高电压技术,2016,42(4):1207-1220.SUN Xiang,HE Wenlin,ZHANG Jiangyang,et al.Current status and development of test and diagnostic technique of transformer winding deformation[J].High Voltage Engineering,2016,42(4):1207-1220.
[6]徐建源,陈彦文,李辉,等.基于短路电抗与振动信号联合分析的变压器绕组变形诊断[J].高电压技术,2017,43(6):2001-2006.XU Jianyuan,CHEN Yanwen,LI Hui,et al.Transformer winding deformation analysis based on short-circuit reactance and vibration signal analysis[J].High Voltage Engineering,2017,43(6):2001-2006.
[7]PATEL M R.Dynamic response of power transformers under axial short circuit forces Part I-winding and clamp as individual components[J].Power Apparatus&Systems IEEETransactions on,1973,92(5):1558-1566.
[8]HORI Y,OKUYAMA K.Axial vibration analysis of transformer windings under short circuit conditions[J].Power Apparatus&Systems IEEE Transactions on,1980,99(2):443-451.
[9]王洪方,王乃庆,李同生.短路条件下电力变压器绕组轴向振动等效单自由度分析[J].电工技术学报,2000,15(5):39-41.WANG Hongfang,WANG Naiqing,LI Tongsheng.Axial vibration equivalent one-degree analysis of power transformer winding under short-circuit[J].Transactions of China Electrotechnical Society,2000,15(5):39-41.
[10]谢坡岸,饶柱石,朱子述.大型变压器绕组有限元建模与分析[J].振动与冲击,2006,25(2):134-137.XIE Poan,RAO Zhushi,ZHU Zishu.Finite element modeling and analysis on transformer windings[J].Journal of Vibration and Shock,2006,25(2):134-137.
[11]JIN M,PAN J.Effects of insulation paper ageing on the vibration characteristics of a transformer winding disk[J].IEEE Transactions on Dielectrics&Electrical Insulation,2015,22(6):3560-3566.
[12]ZHOU H,HONG K,HUANG H,et al.Transformer winding fault detection by vibration analysis methods[J].Applied Acoustics,2016,114:136-146.
[13]GARCIA B,BURGOS J C,ALONSO A M.Transformer tank vibration modeling as a method of detecting winding deformations-PartⅠ:theoretical foundation[J].IEEETransactions on Power Delivery,2006,21(1):157-163.
[14]GARCIA B,BURGOS J C,ALONSO A M.Transformer tank vibration modeling as a method of detecting winding deformations-PartⅡ:experimental verification[J].IEEETransactions on Power Delivery,2006,21(1):164-169.
[15]JI S,LUO Y,LI Y.Research on extraction technique of transformer core fundamental frequency vibration based on OLCM[J].IEEE Transactions on Power Delivery,2006,21(4):1981-1988.
[16]HU C,WANG P,YOUN B D,et al.Copula-based statistical health grade system against mechanical faults of power transformers[J].IEEE Transactions on Power Delivery,2012,27(4):1809-1819.
[17]HONG K,HUANG H,ZHOU J.Winding condition assessment of power transformers based on vibration correlation[J].IEEE Transactions on Power Delivery,2015,30(4):1735-1742.
[18]HONG K,HUANG H,FU Y,et al.A vibration measurement system for health monitoring of power transformers[J].Measurement,2016,93:135-147.
[19]BORUCKI S.Diagnosis of technical condition of power transformers based on the analysis of vibroacoustic signals measured in transient operating conditions[J].IEEETransactions on Power Delivery,2012,27(2):670-676.
[20]WANG Y,PAN J.Applications of operational modal analysis to a single-phase distribution transformer[J].IEEETransactions on Power Delivery,2015,30(4):2061-2063.
[21]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.LUO Bo,WANG Fenghua,LIAO Tianming,et al.Modal parameters identification of power transformer winding based on the improved complex Morlet wavelet[J].Journal of Vibration and Shock,2014,33(6):131-136.
[22]GENG C,WANG F,ZHANG J,et al.Modal parameters identification of power transformer winding based on improved empirical mode decomposition method[J].Electric Power Systems Research,2014,108(108):331-339.
[23]GILLES J.Empirical wavelet transform[J].IEEETransactions on Signal Processing,2013,61(16):3999-4010.