Research on Parallel Ensemble Empirical Mode Decomposition Denoising Method for Partial Discharge Signals Based on Cloud Platform
Song Yaqi1, 2, Zhou Guoliang1, 2, Zhu Yongli1, 2, Li Li1, 2, Wang Dewen1
1. North China Electric Power University Baoding 071003 China; 2. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing 102206 China
Abstract：Signal denoising is the primary issue when conducting online monitoring and diagnosing of electric transmission and transformation equipments. In view of the advantage of ensemble empirical mode decomposition (EEMD) for partial discharge signal denoising, the parallel EEMD algorithm based on Map Reduce model, named MR-EEMD, is designed to improve the computational efficiency by taking advantage of the cloud platform. In consideration of the inherent defects of the rectangular window, the local flatness-adaptive segmentation envelope reconstruction algorithm (LF-ASER) is proposed to compensate segmented boundary so that the envelope error can be reduced to a given threshold range. The experimental results show that MR-EEMD can be executed much faster than EEMD for the transformer partial discharge high sampling rate signal and maintains good denoising results, high scalability, and speedup.
宋亚奇, 周国亮, 朱永利, 李莉, 王德文. 云平台下并行总体经验模态分解局部放电信号去噪方法[J]. 电工技术学报, 2015, 30(18): 213-222.
Song Yaqi, Zhou Guoliang, Zhu Yongli, Li Li, Wang Dewen. Research on Parallel Ensemble Empirical Mode Decomposition Denoising Method for Partial Discharge Signals Based on Cloud Platform. Transactions of China Electrotechnical Society, 2015, 30(18): 213-222.
[1] 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. [2] 李化, 杨新春, 李剑, 等. 基于小波分解尺度系数能量最大原则的GIS局部放电超高频信号自适应小波去噪[J]. 电工技术学报, 2012, 27(5): 84-91. Li Hua, Yang Xinchun, Li Jian, et al. The maximum energy of wavelet decomposition approximation- related adaptive wavelet de-nosing for partial discharge UHF pulse in GIS[J]. Transactions of China Electro- technical Society, 2012, 27(5): 84-91. [3] 江天炎, 李剑, 杜林, 等. 粒子群优化小波自适应阈值法用于局部放电去噪[J]. 电工技术学报, 2012, 27(5): 77-83. Jiang Tianyan, Li Jian, Du Lin, et al. De-nosing for partial discharge signals using PSO adaptive wavelet threshold estimation[J]. Transactions of China Electrotechnical Society, 2012, 27(5): 77-83. [4] Huang Norden E, Zhang Shen, Long Steven R. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. The Royal Society, 1998, 454(1971): 903-995. [5] 钱勇, 黄成军, 陈陈, 等. 基于经验模态分解的局部放电去噪方法[J]. 电力系统自动化, 2005, 29(12): 53-60. Qian Yong, Huang Chenjun, Chen Chen, et al. Denoising of partial discharge based on empirical mode decomposition[J]. Automation of Electric Power Systems, 2005, 29(12): 53-60. [6] Lin Meiyan, Tai Chengchi, Tang Yawen, et al. Partial discharge signal extracting using the empirical mode decomposition with wavelet transform[C]. Inter- national Conference on Lightning, Chengdu, 2011: 420- 424. [7] 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. [8] Xu Jia, Yang Fan, Ma Fenghai. Research on nonstationary signal denoising based on EEMD filter[C]. International Conference on Multimedia Technology (ICMT), Ningbo, 2010: 1-3. [9] 姚林朋, 郑文栋, 钱勇, 等. 基于集合经验模态分解的局部放电信号的窄带干扰抑制[J]. 电力系统保护与控制, 2011, 39(22): 133-139. Yao Linpeng, Zheng Wendong, Qian Yong, et al. A narrow-band interference suppression method based on EEMD for partial discharge[J]. Power System Protection and Control, 2011, 39(22): 133-139. [10] 胡利萍, 宋恩亮, 李宝清, 等. 一种适用于流数据分析的快速EMD算法[J]. 振动与冲击, 2012, 31(8): 116-120. Hu Liping, Song Enliang, Li Baoqing, et al. A new fast EMD algorithm for streaming data analysis[J]. Journal of Vibration and Shock, 2012, 31(8): 116-120. [11] Damerval C, Meignen S, Perrier V. A fast algorithm for bidimensional EMD[J]. IEEE Signal Processing Letters, 2005, 12(10): 701-704. [12] Chen Q, Huang N, Riemenschneider S, et al. A b- spline approach for empirical mode decompositions[J]. Advances in Computational Mathematics, 2006, 24(1): 171-195. [13] 胡劲松, 杨世锡. 基于有效数据的经验模态分解快速算法研究[J]. 振动、测试与诊断, 2006, 26(2): 119-121. Hu Jingsong, Yang Shixi. Study on valid-data-based EMD fast algorithm[J]. Journal of Vibration, Measurement & Diagnosis, 2006, 26(2): 119-121. [14] Qin S R, Qin Y, Mao Y F. Fast implementation of orthogonal empirical mode decomposition and its application into singular signal detection[C]. IEEE International Conference on Signal Processing and Communications, Dubai United Arab Emirates, 2007: 1215-1218. [15] White T. Hadoop: The Definitive Guide[M]. 1st ed. O’Reilly Media Inc, 2009. [16] Dean J, Ghemawat S. MapReduce: Simplified data processingon large clusters[J]. Communications of the ACM, 2008, 51(1): 107-113. [17] 刘鹏. 云计算[M]. 2版. 北京: 电子工业出版社, 2011. [18] Wu Z, Huang N E. Ensemble empirical mode decom- position: A noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41. [19] Wu T Y, Chung Y L. Misalignment diagnosis of rotating machinery through vibration analysis via the hybrid EEMD and EMD approach[J]. Smart Materials and Structures, 2009, 18(9): 7566-7579. [20] Flandrin P, Rilling G, Gonçalvès P. EMD equivalent filter banks, from interpretation to applications[J]. Hilbert-Huang Transform and Its Applications, 2005: 57-74. [21] George Tsolis, Thomas D Xenos. Signal Denoising using empirical mode decomposition and higher order statistics[J]. International Journal of Signal Processing, Image Processing and Pattern Recognition, 2011, 4(2): 91-106. [22] 唐炬, 许中荣, 孙才新, 等. 应用复小波变换抑制GIS局部放电信号中白噪声干扰的研究[J]. 中国电机工程学报, 2005, 25(16): 30-34. Tang Ju, Xu Zhongrong, Sun Caixin, et al. Application of complex wavelet transform to suppress white- 23 noise interference in GIS PD signals[J]. Proceedings of the CSEE, 2005, 25(16): 30-34. [23] 苗莎, 郑晓薇. 三次插值样条曲线拟合多核并行算法[J]. 计算机应用, 2010,30(12): 3194-3196. Miao Sha, Zheng Xiaowei. Multi-core parallel algorithm for cubic spline curve fitting[J]. Journal of Computer Applications, 2010, 30(12): 3194-3196. [24] Michael G Noll. Benchmarking and Stress Testing an Hadoop Cluster With TeraSort, TestDFSIO & Co. [EB/OL]. 2011.4. http://www.michael-noll.com/blog/ 2011/04/09/benchmarking-and-stress-testing-an-hadoop-cluster-with-terasort-testdfsio-nnbench-mrbench/.
2015, Vol. 30 
