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| Power Quality Disturbance Identification Based on CEEMD and GG Clustering |
| ZHANG Shu-qing1,QIAO Yong-jing1,JIANG An-qi1,ZHANG Li-guo1,JIN Mei1,YAO Jia-chen2,MU Yong2 |
1.Institute of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
2.Tangshan Power Supply Company of North Hebei Electric Power Co. Ltd, Tangshan, Hebei 063000, China |
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Abstract A method of power quality disturbance identification based on CEEMD and GG clustering is proposed. CEEMD is a kind of CEMD improved algorithm, its characteristic is putting positive and negative pairs of white noise into the original signal, helps to reduce the residual noise in the auxiliary signal reconstruction; and adding special noise in every stage of decomposition, calculating a unique residual to get each IMF, the decomposition result is complete. Superior to EEMD, CEEMD not only effectively solve the problem of EMD mode mixing, but also retains the advantages of EMD processing non-stationary signals. The CEEMD decomposition of the IMF component of the cross approximate entropy as feature vector is inputted into the GG fuzzy classifier to classify the electric disturbance, The simulational experimental results show that this method has better spectrum separation effect, and needs less iteration times, reduce the computational cost.
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Received: 26 May 2017
Published: 07 January 2019
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Corresponding Authors:
Li-guo ZHANG
E-mail: zlgtime@163.com
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［1］张立国,张淑清,李莎莎,等. 电能质量扰动识别的不同时频分析方法研究［J］. 计量学报, 2017,38(3):345-350.
Zhang L G, Zhang SQ, Li S S, et al. Study on the Detection and Classification of Power Quality Disturbances Using Difference Time-frequency Methods［J］. Acta Metrologica Sinica, 2017,38(3):345-350.
［2］唐轶,刘昊,方永丽. 基于时域特征分析的电能质量扰动分类［J］. 电力系统自动化, 2008,32(17):50-54.
Tang Y , Liu H, Fang Y L. Classification of Power Quality Disturbance Based on Time Domain［J］. Automation of Electric Power Systerms, 2008,32(17):50-54.
［3］张淑清,李盼,师荣艳,等. 基于改进S变换的电能质量扰动分类新方法［J］.仪器仪表学报,2015,36(04):927-934.
Zhang S Q, Li P, Shi RY, et al. New Method for Power Quality Distuibance Classification Based on Modified S Transform［J］. Chinese Journal of Science Instrument, 2015, 36(04):927-934.
［4］张淑清,李盼,冯璐,等. 基于LMD能量熵和GK模糊聚类的电能质量扰动识别［J］.计量学报, 2016, 37(1):90-95.
Zhang S Q, Li P, Feng L, et al. Power Quality Disturbance Identification Based on LMD Energy Entrogy and GK Fuzzy Clustering ［J］. Acta Metrologica Sinica,2016,37(1):90-95.
［5］王天伟,张厚江,路敦民,等. 基于小波去噪和经验模态分解算法的足尺人造板动态称重研究［J］. 计量学报, 2017,38(3):300-303.
Wang T W, Zhang H J, Lu D M, et al.Dynamic Weighing of Full-size Wood Composite Panels Based on Wavelet Denoising and Emprical Mode Decomposition Algorithm［J］. Acta Metrologica Sinica,2017,38(3):300-303.
［6］张会敏,唐贵基.基于CEEMD和奇异值差分谱的滚动轴承故障提取［J］.电力科学与工程,2016,32(1):37-42.
Zhang H M, Tang G J. Fault Feature Extraction of Rolling Bearing Based on CEEMD and Singular Value Difference Spectrum［J］. Electric Power Science and Engineering , 2016,32(1):37-42.
［7］Huang N E, Shen Z, Long S, et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis［J］.Procedings Mathematical Physical & Engineering Sciences, 1998, 454(1971):903-905.
［8］Chang K M. Ensemble empirical mode decomposition for high frequency ECG noise reduction［J］.Biomedical Engineering, 2010,55(4):193-201.
［9］张立国,康乐,金梅,等. 一种基于EEMD-SVD和FCM聚类的轴承故障诊断方法［J］.计量学报, 2016,37(1):67-70.
Zhang L G, Kang L, Jin M, et al. A Bearing Fault Diagnosis Method Based on EEMD-SVD and FCM Clustering［J］. Acta Metrologica Sinica, 2016,37(1):67-70.
［10］Torres M E, colominas M A,schlotthauer G,et al. A complete Ensemble Empirical Mode Decomposition with Adaptive Noise［C］// 2011 IEEE International conference on acoustics,speech and signal Processing(ICASSP).2011:4144-4147
［11］张毅,周春雨,罗元. 基于MEMD的运动想象脑电信号的特征提取与分析［J］.重庆邮电大学学报,2015,27(3): 386-391.
Zhang Y , Zhou CY, Luo Y. Feature Extravtion and Analysis of Imaginary Movements in EEG Based on MEMD［J］. Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition), 2015, 27(03): 386-391.
［12］王玉田,严冰,张淑清,等. 一种改进的广义谐波小波分解算法及在信号特征提取中的应用［J］.燕山大学学报, 2013,37(4):358-365.
Wang Y T, Yan B, Zhang S Q, et al. An Improved Generalized Harmonic Wavelet Decomposition Algorithm and Its Application in Signal Feature Extraction［J］. Journal of Yanshan University, 2013,37(4):358-365.
［13］张淑清,包红燕,李盼,等. 基于RQA与GG聚类的滚动轴承故障识别［J］. 中国机械工程,2015,26(10):1385-1390.
Zhang S Q, Bao H Y, Li P, et al. Fault Diagnosis of Rolling Bearing on RQA and GG Clustering［J］. China Mechanical Engineering, 2015,26(10):1385-1390.
［14］张淑清,胡永涛,李盼,等. 基于MEMD互近似熵及FCM聚类的轴承故障诊断方法［J］.中国机械工程, 2015,26(19):2613-2618.
Zhang S Q, Hu Y T, Li P, et al. Fault Diagnosis Method for Bearings Based on MEMD Cross Approximate Entropy and FCM Clustering［J］. China Mechanical Engineering, 2015,26(19):2613-2618.
［15］符玲,何正友,麦瑞坤,等. 近似熵算法在电力系统故障信号分析中的应用［J］.中国电机工程学报, 2008,28(28):68-73.
Fu L, He Z Y, Mai R K, et al. Application of Approximate Entropy to Fault Signal Analysis in Electric Power System ［J］. Proceeding of the CSEE, 2008, 28(28):68-73 .
［16］凌继平,黄定东,邓异. 基于递归图和近似熵的水下目标特征提取方法［J］.计算机与数字工程, 2011,39(11):147-150.
Ling J P, Huang D D, Deng Y. Method of Underwater Target Feature Extraction Based on Recurrence Plot and Approximate Entropy［J］. Computer ＆ Digital Engineering, 2011,39(11):147-150.
［17］孟宗,季艳,闫晓丽. 基于DEMD和模糊熵的滚动轴承故障诊断方法研究［J］.计量学报, 2016,37(1): 56-61.
Meng Z, Ji Y, Yan X L.Rolling Bearing Fault Diagnosis Based on Differential-based Empirical Mode Decomposition and Fuzzy Entroy［J］. Acta Metrologica Sinica,2016,37(1): 56-61.
［18］张立国,李盼,李梅梅,等. 基于ITD模糊熵和GG聚类的滚动轴承故障诊断［J］.仪器仪表学报 , 2014,35(11):2624-2632 .
Zhang L G, Li P, Li M M, et al. Fault diagnosis of rolling bearing on ITD fuzzy entropy and GG clustering ［J］. Chinese Journal of Science Instrument , 2014,35(11):2624-2632.
［19］谢平,江国乾,李兴林,等. 本征时间尺度排序熵及其在滚动轴承故障诊断中的应用［J］.燕山大学学报, 2013,37(2):179-184.
Xie P, Jiang G Q, Li X L, et al. Intrinsic time scale sorting entropy and its application to fault diagnosis of rolling bearings［J］. Journal of Yanshan University , 2013,37(2):179-184. |
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2019, Vol. 40 
