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

doi:10.19595/j.cnki.1000-6753.tces.181320

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Abstract In order to detect and diagnose the abnormal state of on-load tap-changer (OLTC) as soon as possible, in the aspect of feature extraction, the specific period of the OLTC vibration signal is selected in combination with the current signal of the drive motor to highlight state features. The intrinsic mode function (IMF) of the vibration signal is obtained by using complementary ensemble empirical mode decomposition (CEEMD). According to the characteristics of the OLTC vibration signal, a denoising algorithm based on the energy feature of IMF components is proposed. The time-frequency matrix partitioning algorithm is designed, and the characteristic parameters, such as dividing line, kurtosis, envelope spectral entropy, time-frequency matrix energy density and time-frequency matrix coefficient of variation, are extracted. In the aspect of anomaly diagnosis, through several vibration measuring points, the anomaly diagnosis of OLTC body and transmission mechanism can be simultaneously realized. An anomaly diagnosis method of OLTC with the local outlier factor (LOF) as the diagnostic parameter is established, where the abnormal state of OLTC is detected and diagnosed by comparing the sample to be tested with the normal sample set. The proposed method shows good universality. The simulation and experimental results indicate that the proposed method can effectively detect and diagnose the abnormal state of OLTC.

Key words： On-load tap-changer (OLTC) vibration signal denoising time-frequency analysis complementary ensemble empirical mode decomposition (CEEMD) local outlier factor (LOF)

Received: 01 August 2018 Published: 18 November 2019

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	Articles by authors
	Zhang Zhixian
	Chen Weigen
	Tang Sirui
	Wang Youyuan
	Wan Fu

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Zhang Zhixian,Chen Weigen,Tang Sirui等. 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.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.181320 OR https://dgjsxb.ces-transaction.com/EN/Y2019/V34/I21/4508

[1] 郑劲. 换流变压器及监造技术[M]. 北京: 中国电力出版社, 2016.
[2] 张德明. 变压器分接开关保养维修技术问答[M]. 北京: 中国电力出版社, 2013.
[3] 赵彤, 李庆民, 陈平. OLTC振动信号特征提取的动力学分析方法[J]. 电工技术学报, 2007, 22(1): 41-46.
Zhao Tong, Li Qingmin, Chen Ping.Dynamic analysis method for feature extraction of mechanical vibration signals of on-load tap changers[J]. Transactions of China Electrotechnical Society, 2007, 22(1): 41-46.
[4] Seo J, Ma Hui, Saha T K.A joint vibration and arcing measurement system for online condition monitoring of onload tap changer of the power transformer[J]. IEEE Transactions on Power Delivery, 2017, 32(2): 1031-1038.
[5] 高鹏, 马宏忠, 张惠峰, 等. 分接开关振动信号EMD熵和小波熵的比较[J]. 电力系统及其自动化学报, 2012, 24(4): 48-53.
Gao Peng, Ma Hongzhong, Zhang Huifeng, et al.Comparison of EMD entropy and wavelet entropy in vibration signals of OLTC[J]. Proceedings of the Chinese Society of Universities for Electric Power System & Its Automation, 2012, 24(4): 48-53.
[6] 段若晨, 王丰华, 周荔丹, 等. 利用窄带噪声辅助多元经验模态分解算法检测换流变压器用有载分接开关机械状态[J]. 电工技术学报, 2017, 32(10): 182-189.
Duan Ruochen, Wang Fenghua, Zhou Lidan, et al.Mechanical condition detection of on-load tap-changer in converter transformer based on narrowband noise assisted multivariate empirical mode decomposition algorithm[J]. Transactions of China Electrotechnical Society, 2017, 32(10): 182-189.
[7] Li Qingmin, Zhao Tong, Zhang Li, et al.Mechanical fault diagnostics of onload tap changer within power transformers based on hidden Markov model[J]. IEEE Transactions on Power Delivery, 2012, 27(2): 596-601.
[8] 周翔, 王丰华, 傅坚, 等. 基于混沌理论和K- means聚类的有载分接开关机械状态监测[J]. 中国电机工程学报, 2015, 35(6): 1541-1548.
Zhou Xiang, Wang Fenghua, Fu Jian, et al.Mechanical condition monitoring of on-load tap changers based on chaos theory and K-means clustering method[J]. Proceedings of the CSEE, 2015, 35(6): 1541-1548.
[9] Yeh J R, Shieh J S, Huang N E.Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method[J]. Advances in Adaptive Data Analysis, 2010, 2(2): 135-156.
[10] Wu Zaohua, Norden Huang.Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.
[11] Huang N E, Shen Zheng, Long S R, et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings Mathematical Physical & Engineering Sciences, 1998, 454(1971): 903-995.
[12] 朱永利, 王刘旺. 并行EEMD算法及其在局部放电信号特征提取中的应用[J]. 电工技术学报, 2018, 33(11): 2508-2519.
Zhu Yongli, Wang Liuwang.Parallel ensemble empirical mode decomposition and its application in feature extraction of partial discharge signals[J]. Transactions of China Electrotechnical Society, 2018, 33(11): 2508-2519.
[13] Huang N E.Hilbert-Huang transform and its appli- cations[M]. Singapore: World Scientific, 2014.
[14] Kopsinis Y, Mclaughlin S.Development of EMD- based denoising methods inspired by wavelet thresholding[J]. IEEE Transactions on Signal Pro- cessing, 2009, 57(4): 1351-1362.
[15] 刘宇舜, 周文俊, 李鹏飞, 等. 基于广义S变换模时频矩阵的局部放电特高频信号去噪方法[J]. 电工技术学报, 2017, 32(9): 211-220.
Liu Yushun, Zhou Wenjun, Li Pengfei, et al.Partial discharge ultrahigh frequency signal denoising method based on generalized S-transform modular time-frequency matrix[J]. Transactions of China Electrotechnical Society, 2017, 32(9): 211-220.
[16] 张德明. 变压器真空有载分接开关[M]. 北京: 中国电力出版社, 2015.
[17] 宋知用. MATLAB在语音信号分析与合成中的应用[M]. 北京: 北京航空航天大学出版社, 2013.
[18] 孙海江, 张滋行, 郑涛, 等. 基于改进EMD与改进灰色相关度的防止变压器差动保护误动的新方法[J]. 电力系统保护与控制, 2017, 45(9): 71-79.
Sun Haijiang, Zhang Zihang, Zheng Tao, et al.A new method based on modified EMD and revised grey relation for avoiding mal-operation of transformer differential protection[J]. Power System Protection and Control, 2017, 45(9): 71-79.
[19] 于德介, 张嵬, 程军圣. 基于EMD的时频熵在齿轮故障诊断中的应用[J]. 振动与冲击, 2005, 24(5): 26-27.
Yu Dejie, Zhang Wei, Cheng Junsheng.Application of time-frequency entropy to gear fault diagnosis based on EMD[J]. Journal of Vibration and Shock, 2005, 24(5): 26-27.
[20] Breunig M M.LOF: identifying density-based local outliers[C]//ACM Sigmod International Conference on Management of Data, Texas, USA, 2000: 93-104.
[21] 陈国灯, 林培杰, 赖云锋, 等. 基于电流时间序列的光伏故障在线监测系统[J]. 电气技术, 2018, 19(6): 20-25.
Chen Guodeng, Lin Peijie, Lai Yunfeng, et al.Fault online monitoring system of photovoltaic based on current time series[J]. Electrical Engineering, 2018, 19(6): 20-25.
[22] 杨晓辉, 尹玉君, 寇晓适. 基于风险评估的特高压受端电网输电设备检修策略研究[J]. 电力工程技术, 2017, 36(2): 72-75.
Yang Xiaohui, Yin Yujun, Kou Xiaoshi.Research on the maintenance strategy of UHV receiving end power grid transmission equipment based on risk assessment[J]. Electric Power Engineering Tech- nology, 2017, 36(2): 72-75.
[23] 陆云才, 胡汉巧, 蔚超, 等. 基于超声波法的变压器重症监护系统研制及应用[J]. 电力工程技术, 2017, 36(2): 94-98.
Lu Yuncai, Hu Hanqiao, Wei Chao, et al.Deve- lopment and application of transformer intensive care system based on ultrasonic method[J]. Electric Power Engineering Technology, 2017, 36(2): 94-98.
[24] 叶昱媛, 沙浩源, 梁君涵, 等. 基于小波包能量的断路器操作机构缺陷诊断技术研究[J]. 电力工程技术, 2018, 37(4): 71-77.
Ye Yuyuan, Sha Haoyuan, Liang Junhan, et al.Defect diagnosis technology of operating mechanism of circuit breakers dased on wavelet packet energy[J]. Electric Power Engineering Technology, 2018, 37(4): 71-77.