基于VMD和多尺度熵的变压器内绝缘局部放电信号特征提取及分类

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Abstract In order to extract features of partial discharge signals effectively,a method based on variational mode decomposition (VMD) and multiscale entropy (MSE) is proposed for extracting feature vectors,and the back-propagation neural networks is utilized to recognize the discharge types.First of all,the known partial discharge signals are decomposed by VMD and several band-limited intrinsic mode functions (BLIMFs) are extracted.Then the MSE of each intrinsic mode is calculated respectively and the initial feature vector can be acquired by the combination of the MSEs.Finally,the dimension reduction of the feature vectors is carried out by the principal component analysis (PCA).Four types of discharge signals and different degree of corona discharge signals within each discharge type are extracted and recognized using the above methods.Simulation results demonstrate that the proposed method can extract the features of partial discharge signals effectively.With the results as characteristics,it can correctly identify different discharge types and characteristics of the same discharge types under different discharge levels.

Key words： Partial discharge variational mode decomposition multiscale entropy feature extraction pattern recognition

Received: 10 June 2015 Published: 02 November 2016

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TM85

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	Jia Yafei
	Zhu Yongli
	Wang Liuwang
	Li Li

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Jia Yafei,Zhu Yongli,Wang Liuwang等. Feature Extraction and Classification on Partial Discharge Signals of Power Transformers Based on VMD and Multiscale Entropy[J]. Transactions of China Electrotechnical Society, 2016, 31(19): 208-217.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2016/V31/I19/208

[1] 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.
[2] Fuhr J,Haessing 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.
[3] Bozzo R,Guastavino F,Guerra G.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.
[4] 唐炬,王静,李剑,等.统计参数用于局部放电模式识别的研究[J].高电压技术,2002,28(8):4-6.
Tang Ju,Wang Jing,Li Jian,et al.Statistical parameter method for PD pattern recognition[J].High Voltage Engineering,2002,28(8):4-6.
[5] 王瑜,苑津莎,尚海昆,等.组合核支持向量机在放电模式识别中的优化策略[J].电工技术学报,2015,30(2):229-236.
Wang Yu,Yuan Jinsha,Shang Haikun,et al.Optimization strategy research on combined-kernel support vector machine for partial discharge pattern recognition[J].Transactions of China Electrotechnical Society,2015,30(2):229-236.
[6] 任先文,薛雷,宋阳,等.基于分形特征的最小二乘支持向量机局部放电模式识别[J].电力系统保护与控制,2011,39(14):143-147.
Ren Xianwen,Xue Lei,Song Yang,et al.The pattern recognition of partial discharge based on fractal characteristics using LS-SVM[J].Power System Protection and Control,2011,39(14):143-147.
[7] 杨志超,范立新,杨成顺,等.基于GK模糊聚类和LS-SVC的GIS局部放电类型识别[J].电力系统保护与控制,2014,42(20):38-45.
Yang Zhichao,Fan Lixin,Yang Chengshun,et al.Identification of partial discharge in gas insulated switchgears based on GK fuzzy clustering & LS-SVM[J].Power System Protection and Control,2014,42(20):38-45.
[8] 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.
[9] 司文荣,李军浩,袁鹏,等.基于波形非线性映射的多局部放电脉冲群快速分类[J].电工技术学报,2009,24(3):216-221.
Si Wenrong,Li Junhao,Yuan Peng,et al.The fast grouping technique of PD sequence based on the nonlinear mapping of pulse shapes[J].Transactions of China Electrotechnical Society,2009,24(3):216-221.
[10]刘云鹏,律方成,李成榕.局部放电灰度图象数学形态谱的研究[J].中国电机工程学报,2004,24(5):183-187.
Liu Yunpeng,Lü Fangcheng,Li Chengrong.Study on pattern spectrum of partial discharge grayscale image[J].Proceedings of the CSEE,2004,24(5):183-187.
[11]周沙,景亮.基于矩特征与概率神经网络的局部放电模式识别[J].电力系统保护与控制,2016,44(3):98-102.
Zhou Sha,Jing Liang.Pattern recognition of partial discharge based on moment features and probabilistic neural network[J].Power System Protection and Control,2016,44(3):98-102.
[12]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.
[13]唐炬,樊雷,张晓星,等.用谐波小波包变换法提取GIS局部放电信号多尺度特征参数[J].电工技术学报,2015,30(3):250-257.
Tang Ju,Fan Lei,Zhang Xiaoxing,et al.Multi-scale feature parameters extraction of GIS partial discharge signal with harmonic wavelet packet transform[J].Transactions of China Electrotechnical Society,2015,30(3):250-257.
[14]肖燕,黄成军,郁惟镛,等.基于小波和分形分析的GIS局部放电信号特征提取[J].电力系统自动化,2006,30(6):66-69.
Xiao Yan,Huang Chengjun,Yu Weiyong,et al.Signal characteristic extraction of partial discharge in GIS based on wavelet and fractal analysis[J].Automation of Electric Power Systems,2006,30(6):66-69.
[15]Pincus S M.Approximate entropy as a measure of system complexity[J].Proceedings of the National Academy of Sciences of the United States of America,1991,88(6):2297-2301.
[16]Richman J S,Moorman J R.Physiological time-series analysis using approximate entropy and sample entropy[J].American Journal of Physiology Heart & Circulatory Physiology,2000,278(6):H2039-H2049.
[17]郑近德,程军圣,胡思宇.多尺度熵在转子故障诊断中的应用[J].振动.测试与诊断,2013,33(2):294-297.
Zheng Jinde,Cheng Junsheng,Hu Siyu.Rotor fault diagnosis based on multiscale entropy[J].Journal of Vibration,Measurement& Diagnosis,2013,33(2):294-297.
[18]谢平,江国乾,武鑫,等.基于多尺度熵和距离评估的滚动轴承故障诊断[J].计量学报,2013,34(6):548-553.
Xie Ping,Jiang Guoqian,Wu Xin,et al.Rolling bearing fault diagnosis based on multiscale entropy and distance evaluation[J].Acta Metrologica Sinica,2013,34(6):548-553.
[19]杨松山,周灏,赵海洋,等.基于LMD多尺度熵与SVM的往复压缩机轴承故障诊断方法[J].机械传动,2015,39(2):119-123.
Yang Songshan,Zhou Hao,Zhao Haiyang,et al.Fault diagnosis method for the bearing of reciprocating compressor based on LMD multiscale entropy and SVM[J].Journal of Mechanical,2015,39(2):119-123.
[20]谢平,陈晓玲,苏玉萍,等.基于EMD-多尺度熵和ELM的运动想象脑电特征提取和模式识别[J].中国生物医学工程学报,2013,32(6):641-648.
Xie Ping,Chen Xiaoling,Su Yuping,et al.Feature extraction and recognition of motor imagery EEG based on EMD-multiscale entropy and extreme learning machine[J].Chinese Journal of Biomedical Engineering,2013,32(6):641-648.
[21]Dragomiretskiy K,Zosso D.Variational mode decomposition[J].IEEE Transactions on Signal Processing,2014,62(3):531-544.
[22]Aneesh C,Sachin K,Hisham P M,et al.Performance comparison of variational mode decomposition over empirical wavelet transform for the classification of power quality disturbances using support vector machine[J].Procedia Computer Science,2015,46:372-380.
[23]高艳丰,朱永利,闫红艳,等.一种新型的输电线路双端行波故障定位方法[J].电力系统保护与控制,2016,44(8):8-13.
Gao Yanfeng,Zhu Yongli,Yan Hongyan,et al.A new fault location method of transmission lines based on double-terminal traveling wave[J].Power System Protection and Control,2016,44(8):8-13.
[24]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.
[25]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.Rolling bearing fault diagnosis based on variational mode decomposition and fuzzy C means clustering[J].Proceedings of the CSEE,2015,35(13):1-8.
[26]陈慧,张磊,熊国良,等.滚动轴承的MSE和PNN故障诊断方法[J].噪声与振动控制,2014,34(6):169-173.
Chen Hui,Zhang Lei,Xiong Guoliang,et al.Fault diagnosis of rolling bearings using MSE and PNN[J].Noise and Vibration Control,2014,34(6):169-173.
[27]Costa M,Goldberger A L,Peng C K.Multiscale entropy analysis of biological signals[J].Physical Review E,Statistical Nonlinear and Soft Matter Physics,2005,71(2 Pt 1):21906.
[28]Mohanty S, Gupta K K, Raju K S.Comparative study between VMD and EMD in bearing fault diagnosis[C]//9th International Conference on Industrial and Information Systems,Gwalior,2014:1-6.
[29]蔡艳平,李艾华,王涛,等.基于EMD-Wigner-Ville的内燃机振动时频分析[J].振动工程学报,2010,23(4):430-437.
Cai Yanping,Li Aihua,Wang Tao,et al.I.C.engine vibration time-frequency analysis based on EMD-Wigner-Ville[J].Journal of Vibration Engineering,2010,23(4):430-437.
[30]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.