基于改进变分模态分解和Hilbert变换的变压器局部放电信号特征提取及分类

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (5373 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Feature extraction of partial discharge (PD) signals is one of the key step in the analysis of PD signals. Owing to the shortcomings of existing feature extraction methods, a novel method based on the variational mode decomposition (VMD) and Hilbert transform (Hilbert-VMD) was put forward in this paper. Meanwhile, a dual threshold method was proposed to determine the number of decomposition modes. Firstly, the number of decomposition modes was obtained by using the dual threshold method according to power spectra of PD signals. Secondly, the known PD signals were decomposed by VMD and several band-limited intrinsic mode functions(BLIMFs) were extracted. Then each BLIMFs was processed by Hilbert transform and the marginal spectrum of each BLIMFs was calculated. Finally, the features of PD signals in the frequency domain were extracted based on the marginal spectrum of each BLIMFs. In order to verify the effectiveness of the proposed feature extraction method, PD signals in laboratory environment and field measured were processed by Hilbert-VMD and Hilbert-Huang respectively and support vector machine (SVM) classifiers were utilized for pattern recognition. Compared with the feature extraction method based on Hilbert-Huang, the feature extracted by the proposed method have a higher correct recognition rate. The experimental results show that the proposed method can effectively extract the features of PD signals. The correct recognition rate of field measured signals using the proposed method is not significantly reduced and it is proved that the proposed method has better noise robustness. In addition, the Hilbert-VMD also provides a new time-frequency analysis method for PD signals.

Key words： Partial discharge variational mode decomposition Hilbert transform dual threshold method feature extraction

Received: 11 November 2015 Published: 12 May 2017

PACS:

TM85

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Zhu Yongli
	Jia Yafei
	Wang Liuwang
	Li Li
	Zheng Yanyan

Cite this article:

Zhu Yongli,Jia Yafei,Wang Liuwang等. Feature Extraction and Classification on Partial Discharge Signals of Power Transformers Based on Improved Variational Mode Decomposition and Hilbert Transform[J]. Transactions of China Electrotechnical Society, 2017, 32(9): 221-235.

URL:

https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2017/V32/I9/221

[1] 尹金良, 朱永利, 俞国勤. 基于多分类相关向量机的变压器故障诊断新方法[J]. 电力系统保护与控制, 2013, 41(5): 77-82.
Yin Jinliang, Zhu Yongli, Yu Guoqin. New transformer fault diagnosis method based on multi-class relevance vector machine[J]. Power System Protection and Control, 2013, 41(5): 77-82.
[2] 唐炬, 董玉林, 樊雷, 等. 基于Hankel矩阵的复小波-奇异值分解法提取局部放电特征信息[J]. 中国电机工程学报, 2015, 35(7): 1808-1817.
Tang Ju, Dong Yulin, Fan Lei, et al. Feature information extraction of partial discharge signal with complex wavelet transform and singular value decomposition based on Hankel matrix[J]. Proceedings of the CSEE, 2015, 35(7): 1808-1817.
[3] Fuhr J, Haessig M, Boss P, et al. Detection and location of internal defects in the insulation of power transformers[J]. IEEE Transactions on Electrical Insulation, 1993, 28(6): 1057-1067.
[4] Bozzo R,Guastavino F. PD detection and localization by means of acoustic measurements on hydogenerator stator bars[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 1995, 2(4): 660-666.
[5] 温敏敏, 宋建成, 宋渊, 等. 基于局部放电统计特征参量分析的矿用干式变压器绝缘状态评估[J]. 高电压技术, 2014, 40(8): 2398-2405.
Wen Minmin, Song Jiancheng, Song Yuan, et al. Insulationcondition estimation of mining flameproof dry-type transformer based on statistical characteristic parameters analysis of partial discharge[J]. High Voltage Engineering, 2014, 40(8): 2398-2405.
[6] 李剑, 孙才新, 杜林, 等. 局部放电灰度图象分维数的研究[J]. 中国电机工程学报, 2002, 22(8): 123-127.
Li Jian, Sun Caixin, Du Lin, et al. Study on fractal dimension of PD gray intensity image[J]. Proceedings of the CSEE, 2002, 22(8): 123-127.
[7] 高凯, 谈克雄, 李福祺, 等. 基于散点集分形特征的局部放电模式识别研究[J]. 中国电机工程学报, 2002, 22(5): 23-27.
Gao Kai, Tan Kexiong, Li Fuqi, et al. Pattern recognition of partial discharges based on fractal features of the scatter set[J]. Proceedings of the CSEE, 2002, 22(5): 23-27.
[8] 尚海昆, 苑津莎, 王瑜, 等. 基于交叉小波变换和相关系数矩阵的局部放电特征提取[J]. 电工技术学报, 2014, 29(4): 274-281.
Shang Haikun, Yuan Jinsha, Wang Yu, et al. Feature extraction for partial discharge based on cross-wavelet transform and correlation coefficient matrix[J]. Transactions of China Electrotechnical Society, 2014, 29(4): 274-281.
[9] 鲍永胜. 局部放电脉冲波形特征提取及分类技术[J]. 中国电机工程学报, 2013, 33(28): 168-175.
Bao Yongsheng. Partial discharge pulse waveform feature extraction and classification techniques[J]. Proceedings of the CSEE, 2013, 33(28): 168-175.
[10] 刘志刚, 李文帆, 孙婉璐. Hilbert-Huang变换及其在电力系统中的应用[J]. 电力自动化设备, 2012, 32(4): 109-116.
Liu Zhigang, Li Wenfan, Sun Wanlu. Hilbert-Huang transform and its applications in power system[J]. Electric Power Automation Equipment, 2012, 32(4): 109-116.
[11] 缪希仁, 吴晓梅, 石敦义, 等. 采用HHT振动分析的低压断路器合闸同期辨识[J]. 电工技术学报, 2014, 29(11): 154-161.
Mou Xiren, Wu Xiaomei, Shi Dunyi, et al. Switching synchronism identification of low voltage circuit breaker utilizing HHT analysis to vibration signal[J]. Transactions of China Electrotechnical Society, 2014, 29(11): 154-161.
[12] 刘毅力, 陶学军, 李佳, 等. 基于HHT的风力发电机组滚动轴承故障特征提取[J]. 电力系统保护与控制, 2012, 40(20): 79-82.
Liu Yili, Tao Xuejun, Li Jia, et al. Feature extraction of rolling bearing for wind generator based on HHT[J]. Power System Protection and Control, 2012, 40(20): 79-82.
[13] 向玲, 朱永利, 唐贵基. HHT方法在转子振动故障诊断中的应用[J]. 中国电机工程学报, 2007, 27(35): 84-89.
Xiang Ling, Zhu Yongli, Tang Guiji. Application of Hilbert-Huang transform method in vibration faults diagnosis for rotor system[J]. Proceedings of the CSEE, 2007, 27(35): 84-89.
[14] 李天云, 赵妍, 季小慧, 等. HHT方法在电力系统故障信号分析中的应用[J]. 电工技术学报, 2005, 20(6): 87-91.
Li Tianyun, Zhao Yan, Ji Xiaohui, et al. Application of HHT method for analysis of fault signal in electric power system[J]. Transactions of China Electrotechnical Society, 2005, 20(6): 87-91.
[15] 刘长良, 武英杰, 甄成刚. 基于变分模态分解和模糊 C 均值聚类的滚动轴承故障诊断[J]. 中国电机工程学报, 2015, 35(13): 1-8.
Liu Changliang, Wu Yingjie, Zhen Chenggang. Rollingbearing fault diagnosis based on variational mode decomposition and fuzzy C means clustering[J]. Proceedings of the CSEE, 2015, 35(13): 1-8.
[16] 雷亚国. 基于改进Hilbert-Huang变换的机械故障诊断[J]. 机械工程学报, 2011, 47(5): 71-77.
Lei Yaguo. Machinery fault diagnosis based on improved Hilbert-Huang transform[J]. Journal of Mechanical Engineering, 2011, 47(5): 71-77.
[17] Dragomiretskiy K, Zosso D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.
[18] 唐贵基, 王晓龙. 参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用[J]. 西安交通大学学报, 2015, 49(5): 73-81.
Tang Guiji, Wang Xiaolong. Parameter optimized variational mode decomposition method with application to incipient fault diagnosis of rolling bearing[J]. Journal of Xi'an Jiaotong University, 2015, 49(5): 73-81.
[19] 高艳丰, 朱永利, 闫红艳, 等. 基于VMD和TEO的高压输电线路雷击故障测距研究[J]. 电工技术学报, 2016, 31(1): 24-33.
Gao Yanfeng, Zhu Yongli, Yan Hongyan, et al. Study on lighting fault locating of high-voltage transmission lines based on VMD and TEO[J]. Transactions of China Electrotechnical Society, 2016, 31(1): 24-33.
[20] Daubechies I, Lu Jianfeng, Wu H T. Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool[J]. Applied and Computational Harmonic Analysis, 2011, 30(2): 243-261.
[21] 郑君里, 应启珩, 杨为理. 信号与系统[M]. 第二版. 北京: 高等教育出版社, 2000.
[22] 周玉钏. 变压器局部放电诊断方法研究[J]. 电气开关, 2012 (2): 47-49.
Zhou Yuchuan. Study on diagnostic method of partial discharge of transformers[J]. Electric Switcher, 2012 (2): 47-49.
[23] 郭俊, 吴广宁, 张血琴, 等. 局部放电检测技术的现状和发展[J]. 电工技术学报, 2005, 20(2): 29-35.
Guo Jun, Wu Guangning, Zhang Xueqin, et al. The actuality and perspective of partial discharge detection techniques[J]. Transactions of China Electrotechnical Society, 2005, 20(2): 29-35.
[24] 李军浩, 韩旭涛, 刘泽辉, 等. 电气设备局部放电检测技术述评[J]. 高电压技术, 2015, 41(8): 2583-2601.
Li Junhao, Han Xutao, Liu Zehui, et al. Review on partial discharge measurement technology of electrical equipment[J]. High Voltage Engineering, 2015, 41(8): 2583-2601.
[25] 许向阳, 宋恩民, 金良海. Otsu准则的阈值性质分析[J]. 电子学报, 2009, 37(12): 2716-2719.
Xu Xiangyang, Song Enmin, Jin Lianghai. Characteristic analysis of threshold based on Otsu criterion[J]. Acta Electronica Sinica, 2009, 37(12): 2716-2719.
[26] Yang J N, Lei Ying, Pan Shuwen, et al. System identification of linear structures based on Hilbert-Huang spectral analysis, part 1: normal modes[J]. Earthquake Engineering & Structural Dynamics, 2003, 32(9): 1443-1467.
[27] Yang J N, Lei Ying, Pan Shuwen, et al. System identification of linear structures based on Hilbert-Huang spectral analysis, part 2: complex modes[J]. Earthquake Engineering & Structural Dynamics, 2003, 32(10): 1533-1554.
[28] 李天云, 高磊, 聂永辉, 等. 基于经验模式分解处理局部放电数据的自适应直接阈值算法[J]. 中国电机工程学报, 2006, 26(15): 29-34.
Li Tianyun, Gao Lei, Nie Yonghui, et al. A new adaptive direct-threshold algorithm to partial discharge data processing based on empirical mode decomposition[J]. Proceedings of the CSEE, 2006, 26(15): 29-34.
[29] 袁娜, 朱永利, 梁涵卿. 结合矩形窗的EEMD局部放电信号去噪[J]. 电力系统及其自动化学报, 2015, 27(3): 54-58.
Yuan Na, Zhu Yongli, Liang Hanqing. EEMD de-noising method using rectangular window[J]. Proceedings of the CSU-EPSA, 2015, 27(3): 54-58.
[30] 尚海昆, 苑津莎, 王瑜, 等. 多核多分类相关向量机在变压器局部放电模式识别中的应用[J]. 电工技术学报, 2014, 29(11): 221-228.
Shang Haikun, Yuan Jinsha, Wang Yu, et al. Partial discharge pattern recognition in power transformer based on multi-kernel multi-class relevance vector machine[J]. Transactions of China Electrotechnical Society, 2014, 29(11): 221-228.