Multi-Feature Joint Deformation Determination Method Based on Time-Frequency Characteristics of Winding Short-Circuit Impulse Response
Zhang Zikang1, Geng Jianghai1, Wang Xinyu1, Lü Anqiang2, Gao Shuguo3
1. Hebei Provincial Key Laboratory of Power Transmission Equipment Security Defense North China Electric Power University Baoding 071003 China; 2. Department of Electronic and Communication Engineering Hebei Province North China Electric Power University Baoding 071003 China; 3. State Grid Hebei Electric Power Research Institute Shijiazhuang 050021 China
Abstract：The transformer will suffer many external short-circuit impact during long-term operation, and the winding will be affected by many accumulations, which will produce irreversible deformation. However, there are still some problems in the study of on-line monitoring of winding mechanical status. The operation of transformer is affected by the accumulative effect, and the relevant studies have not modified the relevant eigenvalues by de-accumulation. The fault diagnosis is mainly aimed at the fundamental frequency vibration signal of the transformer, but the frequency doubling component within 500 Hz is generally present in normal operation, and the transient state contains more higher harmonics. To solve these problems, a feature extraction and deformation determination method based on time-frequency characteristics of shock response of winding is proposed in this paper, which can accurately judge the deformation of winding. Firstly, based on the relative displacement model of the single degree-of-freedom system, the short-circuit shock response spectrum of the winding is constructed, and the distribution law of the vibration shock response of the winding considering the cumulative effect is obtained. The Fourier spectrum and shock response spectrum distribution of the measured signal are compared to determine the frequency analysis range. Secondly, the time-frequency energy distribution of the short-circuit impulse signal is calculated, and the frequency bands of the energy spectrum are divided according to the response distribution characteristics obtained by the impulse response spectrum to offset the influence of the cumulative effect, and the characteristic changes of the relative energy band entropy are obtained, and the weighted entropy increment is calculated. Thirdly, transform the energy spectrum into time-frequency matrix, decompose the signal by singular value decomposition (SVD), get the singular value vector, and calculate its weight according to the distribution characteristics of the singular value, can calculate the fundamental frequency singular value distortion rate of the signal. Finally, the calculated weighted entropy increment and the singular value distortion of fundamental frequency are compared with the measured cumulative microstrain. After converting the shock response spectrum into a planar strip, it can be seen that there is basically no accumulative effect when the short-circuit current is relatively small, so the first four shock response bands do not change significantly. Since the fifth time, due to the accumulative effect, the base-frequency short-circuit impulse response of A and C two-phase winding increases from about 100 Hz to 105.3 Hz and 104.7 Hz respectively, but the high-frequency part does not change significantly. The shock response of the B-phase fundamental short-circuit increases from 101.5 Hz to 107.6 Hz, and the peak distribution of the main response gradually increases to 200~550 Hz. In the range of 550~1 000 Hz, the shock response of the winding is unevenly distributed. According to this, the frequency bands are divided, the accumulative effect is corrected, and the change of weighted entropy is calculated, which is highly correlated with the accumulative microstrain of the winding. However, because the threshold of entropy increment is not easy to judge, and the requirement of data acquisition is high, it can not be missed or miscollected, so the concept of fundamental frequency singular value distortion is proposed, and the distortion rate does not exceed 2.0 when there is no deformation, which can be used as an auxiliary judgment means. Through the analysis of the feature extraction and deformation determination methods proposed in this paper, the following conclusions are drawn: (1) Less high-frequency components in the spectrum can cause a higher level of shock response in the winding, and it is necessary to conduct a comprehensive analysis of signals within 1 000 Hz. (2) Considering the offset degree of shock response points affected by cumulative effects, the relative energy band entropy in different frequency ranges is calculated, which is consistent with the law of winding burst deformation, indicating that it is correlated with winding deformation. (3) The weighted entropy increment of the short-circuit impact signal is calculated and compared with the cumulative microstrain of the winding. The two are linearly correlated. The calculated singular value distortion rate of the short-circuit impact signal does not exceed 2.0 when the winding is not deformed. The combination of the two can accurately predict whether the winding deformation occurs after short-circuit.
[1] 律方成, 汪鑫宇, 王平, 等. 基于振动偏离及加权熵的多次短路冲击下变压器绕组机械形变辨识[J]. 电工技术学报, 2023, 38(11): 3022-3032. Lü Fangcheng, Wang Xinyu, Wang Ping, et al.Mechanical deformation identification of transformer winding under multiple short-circuit impacts based on vibration deviation and weighted entropy[J]. Transactions of China Electro- technical Society, 2023, 38(11): 3022-3032. [2] 李典阳, 张育杰, 冯健, 等. 变压器故障样本多维诊断及结果可信度分析[J]. 电工技术学报, 2022, 37(3): 667-675. Li Dianyang, Zhang Yujie, Feng Jian, et al.Multi- dimensional diagnosis of transformer fault sample and credibility analysis[J]. Transactions of China Electro- technical Society, 2022, 37(3): 667-675. [3] 朱柯佳. 高噪声环境下变压器声纹特征提取方法研究[D]. 北京: 华北电力大学, 2021. [4] 徐永明, 郭蓉, 张洪达. 电力变压器绕组短路电动力计算[J]. 电机与控制学报, 2014, 18(5): 36-42. Xu Yongming, Guo Rong, Zhang Hongda.Calcu- lation of electrodynamic force with winding short- circuit in power transformers[J]. Electric Machines and Control, 2014, 18(5): 36-42. [5] 李宏达, 黄鼎琨, 张彬, 等. 改进的低压脉冲法对变压器绕组变形的探测研究[J]. 南京理工大学学报, 2020, 44(1): 15-20. Li Hongda, Huang Dingkun, Zhang Bin, et al.Research in detection of winding transformer variation based on improved LVI method[J]. Journal of Nanjing University of Science and Technology, 2020, 44(1): 15-20. [6] 何为, 刘以刚, 胡国辉, 等. 基于短路电抗法的配电变压器绕组变形在线诊断[J]. 电测与仪表, 2014, 51(14): 47-51, 109. He Wei, Liu Yigang, Hu Guohui, et al.On-line diagnosis of the distribution transformer winding deformation based on the short-circuit reactance method[J]. Electrical Measurement & Instrumentation, 2014, 51(14): 47-51, 109. [7] Ludwikowski K, Siodla K, Ziomek W.Investigation of transformer model winding deformation using sweep frequency response analysis[J]. IEEE Transa- ctions on Dielectrics and Electrical Insulation, 2012, 19(6): 1957-1961. [8] 刘云鹏, 王博闻, 李欢, 等. 结合载纤绕组形变测量法的大型变压器绕组多次短路冲击暂态声纹特征[J]. 中国电机工程学报, 2022, 42(1): 434-447. Liu Yunpeng, Wang Bowen, Li Huan, et al.Transient acoustics characteristics of large transformer windings under multiple short-circuit impulse combined with fiber-carrying winding deformation measurement[J]. Proceedings of the CSEE, 2022, 42(1): 434-447. [9] 高树国, 汲胜昌, 孟令明, 等. 基于在线监测系统与声振特征预测模型的高压并联电抗器运行状态评估方法[J]. 电工技术学报, 2022, 37(9): 2179-2189. Gao Shuguo, Ji Shengchang, Meng Lingming, et al.Operation state evaluation method of high-voltage shunt reactor based on on-line monitoring system and vibro-acoustic characteristic prediction model[J]. Transactions of China Electrotechnical Society, 2022, 37(9): 2179-2189. [10] Komatowski E.Amplitude detection of power transformer tank vibrations signal[C]//2016 International Conferenceon Signalsand Electronic Systems (ICSES), Krakow, Poland, 2016: 41-46. [11] 赵书涛, 许文杰, 刘会兰, 等. 基于振动信号谱形状熵特征的高压断路器操动状态辨识方法[J]. 电工技术学报, 2022, 37(9): 2170-2178. Zhao Shutao, Xu Wenjie, Liu Huilan, et al.Identification method for operation state of high voltage circuit breakers based on spectral shape entropy characteristics of vibration signals[J]. Transa- ctions of China Electrotechnical Society, 2022, 37(9): 2170-2178. [12] 王丰华, 段若晨, 耿超, 等. 基于“磁-机械”耦合场理论的电力变压器绕组振动特性研究[J]. 中国电机工程学报, 2016, 36(9): 2555-2562. Wang Fenghua, Duan Ruochen, Geng Chao, et al.Research of vibration characteristics of power transformer winding based on magnetic-mechanical coupling field theory[J]. Proceedings of the CSEE, 2016, 36(9): 2555-2562. [13] 郭俊, 汲胜昌, 沈琪, 等. 盲源分离技术在振动法检测变压器故障中的应用[J]. 电工技术学报, 2012, 27(10): 68-78. Guo Jun, Ji Shengchang, Shen Qi, et al.Blind source separation technology for the detection of transformer fault based on vibration method[J]. Transactions of China Electrotechnical Society, 2012, 27(10): 68-78. [14] 师愉航, 汲胜昌, 张凡, 等. 变压器绕组多倍频振动机理及特性[J]. 高电压技术, 2021, 47(7): 2536-2544. Shi Yuhang, Ji Shengchang, Zhang Fan, et al.Multi- frequency vibration mechanism and characteristics of transformer windings[J]. High Voltage Engineering, 2021, 47(7): 2536-2544. [15] 张凡, 汲胜昌, 师愉航, 等. 电力变压器绕组振动及传播特性研究[J]. 中国电机工程学报, 2018, 38(9): 2790-2798, 2849. Zhang Fan, Ji Shengchang, Shi Yuhang, et al.Research on transformer winding vibration and propagation characteristics[J]. Proceedings of the CSEE, 2018, 38(9): 2790-2798, 2849. [16] 马宏忠, 弓杰伟, 李凯, 等. 基于ANSYS Workbench的变压器绕组松动分析及判定方法[J]. 高电压技术, 2016, 42(1): 192-199. Ma Hongzhong, Gong Jiewei, Li Kai, et al.Analysis and determination method for transformer winding looseness based on ANSYS Workbench[J]. High Voltage Engineering, 2016, 42(1): 192-199. [17] 田聪. 中低频冲击谱测量装置研制及响应谱修正方法研究[D]. 沈阳: 沈阳工业大学, 2021. [18] 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. Xiong Weihua, Zhao Guangzhou.Analysis of transformer core vibration characteristics using Hilbert-Huang transformation[J]. Transactions of China Electrotechnical Society, 2006, 21(8): 9-13. [19] 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.Trans- former winding deformation analysis based on short-circuit reactance and vibration signal analysis[J]. High Voltage Engineering, 2017, 43(6): 2001-2006. [20] 张坤, 王丰华, 廖天明, 等. 应用复小波变换检测突发短路时的电力变压器绕组状态[J]. 电工技术学报, 2014, 29(8): 327-332. Zhang Kun, Wang Fenghua, Liao Tianming, et al.Detection of transformer winding deformation under sudden short-circuit impact based on complex wavelet algorithm[J]. Transactions of China Electrotechnical Society, 2014, 29(8): 327-332. [21] 杜厚贤, 刘昊, 雷龙武, 等. 基于振动信号多特征值的电力变压器故障检测研究[J]. 电工技术学报, 2023, 38(1): 83-94. Du Houxian, Liu Hao, Lei Longwu, et al.Power transformer fault detection based on multi- eigenvalues of vibration signal[J]. Transactions of China Electrotechnical Society, 2023, 38(1): 83-94. [22] 中华人民共和国国家质量监督检验检疫总局. GB 1094.5-2008电力变压器第五部分:承受短路的能力[S]. 北京: 中国标准出版社, 2009. [23] 张凡, 汲胜昌, 祝令瑜, 等. 短路冲击下变压器振动频响函数研究[J]. 西安交通大学学报, 2017, 51(2): 97-103, 154. Zhang Fan, Ji Shengchang, Zhu Lingyu, et al.Frequency response function of short circuit vibration for power transformer[J]. Journal of Xi’an Jiaotong University, 2017, 51(2): 97-103, 154. [24] 张凡, 吴书煜, 徐征宇, 等. 变压器绕组非线性动力学模型及多次短路冲击下的振动特征[J]. 高电压技术, 2022, 48(12): 4882-4892. Zhang Fan, Wu Shuyu, Xu Zhengyu, et al.Nonlinear vibration model of transformer windings and their vibration characteristics during multiple short circuits[J]. High Voltage Engineering, 2022, 48(12): 4882-4892. [25] 马宏忠, 耿志慧, 陈楷, 等. 基于振动的电力变压器绕组变形故障诊断新方法[J]. 电力系统自动化, 2013, 37(8): 89-95. Ma Hongzhong, Geng Zhihui, Chen Kai, et al.A new fault diagnosis method for power transformer winding deformation based on vibration[J]. Automation of Electric Power Systems, 2013, 37(8): 89-95. [26] 姜崇学, 马秀达, 邹强, 等. 柔性直流输电系统的高频谐波保护方法与工程实践[J]. 电力系统自动化, 2024, 48(3): 150-158. Jiang Chongxue, Ma Xiuda, Zou Qiang, et al.High-frequency harmonic protection methods and engineering practice for flexible DC transmission systems[J]. Automation of Electric Power Systems, 2024, 48(3): 150-158. [27] 张知先, 陈伟根, 汤思蕊, 等. 基于互补集总经验模态分解和局部异常因子的有载分接开关状态特征提取及异常状态诊断[J]. 电工技术学报, 2019, 34(21): 4508-4518. Zhang Zhixian, Chen Weigen, Tang Sirui, et al.State feature extraction and anomaly diagnosis of on-load tap-changer based on complementary ensemble empirical mode decomposition and local outlier factor[J]. Transactions of China Electrotechnical Society, 2019, 34(21): 4508-4518. [28] 杨挺, 张璐, 张亚健, 等. 基于信息熵计算模型的电力信息物理系统融合控制方法[J]. 电力系统自动化, 2021, 45(12): 65-74. Yang Ting, Zhang Lu, Zhang Yajian, et al.Fusion control method for cyber-physical power system based on information entropy calculation model[J]. Automation of Electric Power Systems, 2021, 45(12): 65-74. [29] 郭谋发, 徐丽兰, 缪希仁, 等. 采用时频矩阵奇异值分解的配电开关振动信号特征量提取方法[J]. 中国电机工程学报, 2014, 34(28): 4990-4997. Guo Moufa, Xu Lilan, Miao Xiren, et al.A vibration signal feature extraction method for distribution switches based on singular value decomposition of time-frequency matrix[J]. Proceedings of the CSEE, 2014, 34(28): 4990-4997.
2024, Vol. 39 
