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| Winding fault diagnosis of power transformer based on vibration distribution features |
| YANG Yi1, LIU Shi1, ZHANG Chu1, HAN Dan1, MENG Yuanyuan1, HU Yiwei2, ZHENG Jing2, HUANG Hai2 |
1. Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou 510080, China;
2. Department of Instrumentation Science and Engineering, Zhejiang University, Hangzhou 310013, China |
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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.
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Received: 20 June 2018
Published: 28 December 2019
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[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. |
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