全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

盲源分离技术在振动法检测变压器故障中的应用

, PP. 68-78

Keywords: 电力变压器,盲源分离,SDICA,源信号,绕组,铁心

Full-Text   Cite this paper   Add to My Lib

Abstract:

变压器油箱表面的振动信号可以反映其内部绕组与铁心的健康状况,具有良好的应用前景。但在油箱表面测得的信号都是绕组和铁心振动信号的混合,且两者相关程度很高,难以用普通分离算法进行分离,不利于分别对绕组及铁心的故障进行诊断。鉴于此,本文提出一种基于子空间独立分量分离法(SDICA)的变压器绕组和铁心振动信号盲源分离方法。首先,对基于SDICA算法的盲源分离方法进行了说明,利用所构建的仿真信号对该算法进行了验证,并与普通盲源分离算法—快速独立分量分离(fastICA)算法的分离结果进行了对比,证实了SDICA算法适用于变压器振动信号的分离;然后,运用SDICA算法对测得的一台试验变压器的振动信号进行了分离,并探讨了测量位置、电压等级以及负载大小对分离结果的影响;最后,将SDICA盲源分离算法在一台现场实际运行的、有一定故障隐患的变压器中进行应用,成功分离出了绕组和铁心振动信号,且绕组信号中包含有丰富的振动故障特征,与该变压器实际的运行情况相符合。本文提出的盲源分离算法可根据在油箱表面测得的多路振动信号直接分离出绕组和铁心振动信号,这对于振动法在变压器绕组及铁心故障诊断中的推广应用具有重要意义。

References

[1]  Wang M Y, Ling M. Statistics and analysis on short-circuit faults of large power transformers[J]. Transformer, 1997, 34(10): 12-17.
[2]  Belén G, Juan C B, Ángel M A. Transformer tank vibration modeling as a method of detecting winding deformations—part I: theoretical foundation[J]. IEEE Transactions on Power Delivery, 2006, 21(1): 157-163.
[3]  Xie P A, Rao Z S, Zhu Z S. Finite element modeling and analysis on transformer windings[J]. Journal of Vibration and Shock, 2006, 25(2): 134-137.
[4]  Xiong W H, Zhao G Z. Analysis of transformer core vibration characteristics using Hilbert-Huang trans- formation[J]. Transactions of China Electrotechnical Society, 2006, 21(8): 9-13.
[5]  汲胜昌, 刘味果, 单平, 等. 小波包分析在振动法检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.
[6]  Ji S C, Li Y M, Fu C Z. Application of no-load current method in monitoring the condition of transformer’s core based on the vibration analysis method[J]. Proceedings of the CSEE, 2003, 23(6): 154-158.
[7]  Feng Y X, Deng X W, Fan L L, et al. Status and trend of large power transformer’s fault diagnosis with the vibration method[J]. Southern Power System Technology, 2009, 3(3): 49-53.
[8]  洪凯星, 潘再平, 黄海. 短路条件下变压器振动特性研究[J]. 机电工程, 2010, 27(6): 87-90.
[9]  Hong K X, Pan Z P, Huang H. Research on short circuit vibration of power transformer[J]. Journal of Mechanical & Electrical Engineering, 2010, 27(6): 87-90.
[10]  唐卫民, 傅坚, 邵宇鹰, 等. 大型变压器绕组状态振动分析法的试验研究[J]. 变压器, 2010, 47(1): 25-27.
[11]  Tang W M, Fu J, Shao Y Y, et al. Testing research on vibration analysis of large transformer winding[J]. Transformer, 2010, 47(1): 25-27.
[12]  王世山, 汲胜昌, 李彦明. 利用振动法进行变压器在线监测的应用研究[J]. 变压器, 2002, 39(1): 73-76.
[13]  Wang S S, Ji S C, Li Y M. Application research on transformer on-line monitoring with vibration method[J]. Transformer, 2002, 39(1): 73-76.
[14]  Tong L, Inouye Y, Liu R. Waveform-preserving blind estimation of multiple independent sources[J]. IEEE Transactions on Signal Processing, 1993, 41(7): 2461-2470.
[15]  Jiao W D, Yang S X, Qian S X, et al. Waveform reconstruction of multiple spatio-temporal mixed sources by the joint use of MUSIC and FastICA[J]. Journal of Mechanical Engineering, 2010, 46(6): 63-70.
[16]  Ding S X, Huang J, Wei D M, et al. A near real-time approach for convolutive blind source separation[J]. IEEE Transactions on Circuits and Systems—I: Regular Papers, 2006, 53(1): 114-128.
[17]  王梦云, 凌愍. 大型电力变压器短路事故统计与分析[J]. 变压器, 1997, 34(10): 12-17.
[18]  Lavalle J C. Failure detection in transformer using vibrational analysis[M]. MIT: Cambridge, 1986.
[19]  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.
[20]  熊卫华, 赵光宙. 基于希尔伯特-黄变换的变压器铁心振动特性分析[J]. 电工技术学报, 2006, 21(8): 9-13.
[21]  Ji S C, Liu W G, Shan P, et al. The application of the wavelet packet to the monitoring of the core and winding condition of transformer[J]. Proceedings of the CSEE, 2001, 21(12): 24-27.
[22]  汲胜昌, 李彦明, 傅晨钊. 负载电流法在基于振动信号分析法监测变压器铁心状况中的应用[J]. 中国电机工程学报, 2003, 23(6): 154-158.
[23]  Ji S C, Luo Y F, Li Y M. Research on extraction technique of transformer core fundamental frequency vibration based on OLCM[J]. IEEE Transactions on Power Delivery, 2006, 21(4): 1981-1988.
[24]  冯永新, 邓小文, 范立莉, 等. 大型电力变压器振动法故障诊断的现状与趋势[J]. 南方电网技术, 2009, 3(3): 49-53.
[25]  Ju T, Wei L, Liu Y L. Blind source separation of mixed PD signals produced by multiple insulation defects in GIS[J]. IEEE Transactions on Power Delivery, 2010, 25(1): 170-176.
[26]  Aapo H. Fast and robust fixed-point algorithms for independent component analysis[J]. IEEE Transactions on Neural Networks, 1999, 10(3): 626-634.
[27]  焦卫东, 杨世锡, 钱苏翔, 等. 联合应用MUSIC与FastICA算法实现多个时空混叠源信号的波形重建[J]. 机械工程学报, 2010, 46(6): 63-70.
[28]  Cichocki A. General component analysis and blind source separation methods for analyzing multichannel brain signals[C]. Statistical and Process Models of Cognitive Aging, 2007; 320-329.
[29]  Zhang K, Chan L W. An adaptive method for sub-band decomposition ICA[J]. Neural Comput, 2006, 18(2): 191-223.
[30]  Cruces S, Castedo L, Cichocki A. Novel blind source separation algorithms using cumulants[C]. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2000: 3152-3155.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133