基于电压阻尼振荡的变压器故障绕组识别方法

doi:10.19595/j.cnki.1000-6753.tces.230314

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (9606 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The voltage damping oscillation method is effective for diagnosing winding faults, but it still has the following problems. The voltage-damping oscillation of transformer winding continues to fluctuate slightly for a period before the end of charging and discharging. In this process, there are a lot of irregular maximum points, minimum points, and burrs, which interfere with the subsequent characteristic processing and oscillation wave analysis. According to the existing literature, the oscillation ends when it attenuates to 5% of the maximum value, and all poles in this band are effective extreme points. Through a large number of experiments, it is observed that when the voltage-damping oscillation is no longer attenuated, its stable value often exceeds this threshold. As a result, the definition of the effective wave band of the above voltage-damping oscillation is inapplicable. Due to the complexity of on-site testing and external interference, two voltage-damping oscillation signals measured at different time intervals on the same transformer may have small deviations. However, the time-domain feature extraction of the oscillation wave is distinguished only by a single curve feature, and the identifiable accuracy is low when diagnosing the fault location and degree.
Therefore, this paper presents a transformer winding fault analysis method for selecting a dynamic wave band of voltage-damping oscillations. First, a transformer winding fault simulation platform is built, considering four kinds of faults: axial displacement, inter-disk capacitance, inter-pie, and inter-turn short circuits. Secondly, the characteristics of oscillation wave attenuation are analyzed from the perspective of energy conversion, selecting the effective wave band through a defined energy attenuation factor. Then, the waveform feature correlation degree (FCD) is obtained for fault type identification. At the same time, considering the rich feature information between the extreme point and the waveform, the oscillating wave binary image is constructed based on mathematical morphology to eliminate interference and extract more stable Tamura texture features. Finally, according to the distribution rule of characteristic parameter combination, the application analysis is carried out through the actual transformer.
The dynamically selected band shows minimal interference, clear attenuation oscillation regularity, and rich feature information. Oscillation waves under four fault types are different from each other, exhibiting significant variation patterns compared to normal windings. The waveform FCD is effective in identifying winding fault types. Oscillation waves exhibit minor differences in fault areas and degrees under the same fault type. Different combinations of four Tamura texture features demonstrate good performance in classifying fault degrees and types. In general, the waveform FCD and Tamura texture feature based on binarization values extracted under different faults have apparent separation and clustering, which can recognize fault type, degree, and region.

Key words： Transformer voltage damping oscillation winding fault dynamic band waveform feature texture feature

Received: 16 March 2023 Published: 07 June 2024

PACS:

TM614

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Zhou Lijun
	Yun Xiucheng
	Wang Dongyang
	Zhou Meng
	Zhang Jun

Cite this article:

Zhou Lijun,Yun Xiucheng,Wang Dongyang等. Transformer Fault Winding Identification Method Based on Voltage Damped Oscillation[J]. Transactions of China Electrotechnical Society, 2024, 39(10): 3218-3231.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.230314 OR https://dgjsxb.ces-transaction.com/EN/Y2024/V39/I10/3218

[1] 张冰倩, 咸日常, 于洋, 等. 匝间短路故障下电力变压器绕组的物理特征分析[J]. 高电压技术, 2021, 47(6): 2177-2185.
Zhang Bingqian, Xian Richang, Yu Yang, et al.Analysis of physical characteristics of power trans- former windings under inter-turn short circuit fault[J]. High Voltage Engineering, 2021, 47(6): 2177-2185.
[2] 韩金华, 张健壮. 大型电力变压器典型故障案例分析与处理[M]. 北京: 中国电力出版社, 2012.
[3] 汲胜昌, 王世山, 李清泉, 等. 用振动信号分析法监测变压器绕组状况[J]. 高电压技术, 2002, 28(4): 12-13, 15.
Ji Shengchang, Wang Shishan, Li Qingquan, et al.The application of vibration method in monitoring the condition of transformer winding[J]. High Voltage Engineering, 2002, 28(4): 12-13, 15.
[4] 黄华, 周建国, 姜益民, 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 Yimin, et al.Diagnosis of winding deformation of transformer by impedance method and FRA[J]. High Voltage Engin- eering, 1999, 25(2): 70-73.
[5] Akhmetov Y, Nurmanova V, Bagheri M, et al.A new diagnostic technique for reliable decision-making on transformer FRA data in interturn short-circuit con- dition[J]. IEEE Transactions on Industrial Informatics, 2021, 17(5): 3020-3031.
[6] Nurmanova V, Bagheri M, Zollanvari A, et al.A new transformer FRA measurement technique to reach smart interpretation for inter-disk faults[J]. IEEE Transactions on Power Delivery, 2019, 34(4): 1508-1519.
[7] Tahir M, Tenbholen S, Miyazaki S.Analysis of statistical methods for assessment of power trans- former frequency response measurements[J]. IEEE Transactions on Power Delivery, 2021, 36(2): 618-626.
[8] 赵仲勇, 唐超, 李成祥, 等. 基于频率响应二值化图像的变压器绕组变形故障诊断方法[J]. 高电压技术, 2019, 45(5): 1526-1534.
Zhao Zhongyong, Tang Chao, Li Chengxiang, et al.Diagnosis method of transformer winding defor- mation faults based on frequency response binary image[J]. High Voltage Engineering, 2019, 45(5): 1526-1534.
[9] 付强, 宁文瑶, 刘代飞. 基于多指标融合的绕组混合故障分析方法[J]. 高电压技术, 2021, 47(2): 537-545.
Fu Qiang, Ning Wenyao, Liu Daifei.Analysis method for mixed faults in winding based on multi-index fusion[J]. High Voltage Engineering, 2021, 47(2): 537-545.
[10] Aljohani O, Abu-Siada A.Application of DIP to detect power transformers axial displacement and disk space variation using FRA polar plot signature[J]. IEEE Transactions on Industrial Informatics, 2017, 13(4): 1794-1805.
[11] Rahimpour E, Jabbari M, Tenbohlen S.Mathematical comparison methods to assess transfer functions of transformers to detect different types of mechanical faults[J]. IEEE Transactions on Power Delivery, 2010, 25(4): 2544-2555.
[12] 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, Zhan 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.
[13] 刘勇, 汲胜昌, 杨帆, 等. 检测变压器故障的扫频阻抗法特性研究及应用[J]. 高电压技术, 2016, 42(10): 3237-3245.
Liu Yong, Ji Shengchang, Yang Fan, et al.Characteri- stics and application of sweep frequency impedance method for detecting transformer fault[J]. High Voltage Engineering, 2016, 42(10): 3237-3245.
[14] 赵新. 变压器绕组变形检测技术分析研究[J]. 技术与市场, 2020, 27(10): 77-78.
Zhao Xin.Analysis and research of transformer winding deformation detection technology[J]. Tech- nology and Market, 2020, 27(10): 77-78.
[15] 吴振宇, 周利军, 周祥宇, 等. 基于振荡波的变压器绕组故障诊断方法研究[J]. 中国电机工程学报, 2020, 40(1): 348-357, 401.
Wu Zhenyu, Zhou Lijun, Zhou Xiangyu, et al.Research on fault diagnosis method of transformer winding based on oscillatory wave[J]. Proceedings of the CSEE, 2020, 40(1): 348-357, 401.
[16] 周利军, 周猛, 李沃阳, 等. 基于振荡波多特征融合的变压器绕组故障诊断方法[J]. 电力自动化设备, 2022, 42(12): 191-196, 203.
Zhou Lijun, Zhou Meng, Li Woyang, et al.Trans- former winding fault diagnosis method based on oscillating wave multi-feature fusion[J]. Electric Power Automation Equipment, 2022, 42(12): 191-196, 203.
[17] Wu Zhenyu, Zhou Lijun, Lin Tong, et al.A new testing method for the diagnosis of winding faults in transformer[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(11): 9203-9214.
[18] Gonzales Arispe J C, Mombello E E. Detection of failures within transformers by FRA using multire- solution decomposition[J]. IEEE Transactions on Power Delivery, 2014, 29(3): 1127-1137.
[19] Wu Zhenyu, Zhou Lijun, Wang Dongyang, et al.Feature analysis of oscillating wave signal for axial displacement in autotransformer[J]. IEEE Transa- ctions on Instrumentation and Measurement, 2021, 70: 1-13.
[20] 周利军, 林桐, 江飞明, 等. 频响多级分解与图像特征在自耦变压器绕组故障的应用研究[J]. 高电压技术, 2021, 47(10): 3617-3627.
Zhou Lijun, Lin Tong, Jiang Feiming, et al.Application research of autotransformer winding fault by using FRA multi-decomposition and image features[J]. High Voltage Engineering, 2021, 47(10): 3617-3627.
[21] 范学鑫, 杨北超, 揭贵生, 等. 考虑漏感频变特性的三相立体卷铁心变压器建模及分析[J]. 电工技术学报, 2023, 38(1): 140-152.
Fan Xuexin, Yang Beichao, Jie Guisheng, et al.Modeling and analysis of 3D wound core transformer considering frequency-dependent characteristics of leakage inductance[J]. Transactions of China Elec- trotechnical Society, 2023, 38(1): 140-152.
[22] 杨北超, 范学鑫, 王瑞田, 等. 考虑铁心非线性的三相立体卷铁心变压器建模及空载特性分析[J]. 电工技术学报, 2022, 37(9): 2263-2274.
Yang Beichao, Fan Xuexin, Wang Ruitian, et al.Modeling and no-load characteristics analysis of 3D wound core transformer considering core non- linearity[J]. Transactions of China Electrotechnical Society, 2022, 37(9): 2263-2274.
[23] Insulation coordination inpart 2: application guide- lines: the international electrotechnical commission IEC 60071-2[S]. Geneva: International Electrotech- nical Commission, 2018.
[24] 严静荷. 基于属性选择和支持向量机的高铁自耦变压器绕组故障诊断研究[D]. 成都: 西南交通大学, 2019.
[25] Hashemnia N, Abu-Siada A, Islam S.Improved power transformer winding fault detection using FRA diagnostics-part 1: axial displacement simulation[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2015, 22(1): 556-563.
[26] Usha K, Usa S.Inter disc fault location in transformer windings using SFRA[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2015, 22(6): 3567-3573.
[27] Abeywickrama N, Serdyuk Y V, Gubanski S M.High- frequency modeling of power transformers for use in frequency response analysis (FRA)[J]. IEEE Transa- ctions on Power Delivery, 2008, 23(4): 2042-2049.
[28] 张宗成. 基于小波分析的变压器绕组匝间短路检测方法研究[D]. 北京: 华北电力大学, 2015.
[29] Bertuccio G.On the physical origin of the electro- mechano-acoustical analogy[J]. The Journal of the Acoustical Society of America, 2022, 151(3): 2066-2076.
[30] Bloch A.Electromechanical analogies and their use for the analysis of mechanical and electromechanical systems[J]. Journal of the Institution of Electrical Engineers-Part I: General, 1945, 92(52): 157-169.
[31] 邱关源原著, 罗先觉修订. 电路[M]. 5版. 北京: 高等教育出版社, 2006.
[32] Ginsberg J H.Mechanical and structural vibrations: theory and applications[M]. New York: Wiley, 2001.
[33] 李谦, 张波, 蒋愉宽, 等. 变电站内短路电流暂态过程及其影响因素[J]. 高电压技术, 2014, 40(7): 1986-1993.
Li Qian, Zhang Bo, Jiang Yukuan, et al.Transient process of short-circuit current in substations and its influential factors[J]. High Voltage Engineering, 2014, 40(7): 1986-1993.
[34] 赵耀, 陆佳煜, 李东东, 等. 基于机电信号融合的电励磁双凸极电机绕组匝间短路故障诊断[J]. 电工技术学报, 2023, 38(1): 204-219.
Zhao Yao, Lu Jiayu, Li Dongdong, et al.A fault diagnosis strategy for winding inter-turn short-circuit fault in doubly salient electro-magnetic machine based on mechanical and electrical signal fusion[J]. Transactions of China Electrotechnical Society, 2023, 38(1): 204-219.
[35] 李斌, 屈璐瑶, 朱新山, 等. 基于多尺度特征融合的绝缘子缺陷检测[J]. 电工技术学报, 2023, 38(1): 60-70.
Li Bin, Qu Luyao, Zhu Xinshan, et al.Insulator defect detection based on multi-scale feature fusion[J]. Transactions of China Electrotechnical Society, 2023, 38(1): 60-70.
[36] 刘会兰, 许文杰, 赵书涛, 等. 面向高压断路器故障分类的电流-振动信号类聚几何敏感特征优选方法[J]. 电工技术学报, 2023, 38(1): 26-36.
Liu Huilan, Xu Wenjie, Zhao Shutao, et al.Opti- mization method of clustering geometric sensitive features of current vibration signals for fault classification of high voltage circuit breakers[J]. Transactions of China Electrotechnical Society, 2023, 38(1): 26-36.
[37] Ghanizadeh A J, Gharehpetian G B.ANN and cross-correlation based features for discrimination between electrical and mechanical defects and their localization in transformer winding[J]. IEEE Transa- ctions on Dielectrics and Electrical Insulation, 2014, 21(5): 2374-2382.
[38] Zhao Zhongyong, Yao Chenguo, Li Chengxiang, et al.Detection of power transformer winding deformation using improved FRA based on binary morphology and extreme point variation[J]. IEEE Transactions on Industrial Electronics, 2018, 65(4): 3509-3519.
[39] Hedberg H, Dokladal P, Owall V.Binary morphology with spatially variant structuring elements: algorithm and architecture[J]. IEEE Transactions on Image Processing, 2009, 18(3): 562-572.
[40] Dai Xiaolong, Khorram S.A feature-based image registration algorithm using improved chain-code representation combined with invariant moments[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5): 2351-2362.
[41] Tamura H, Mori Shunji, Yamawaki T.Textural features corresponding to visual perception[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1978, 8(6): 460-473.
[42] Hashemnia N, Abu-Siada A, Islam S.Improved power transformer winding fault detection using FRA diagnostics-part 2: radial deformation simulation[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2015, 22(1): 564-570.