基于EEMD和关联维数的小电流接地故障选线

doi:10.3969/j.issn.1000-1158.2016.03.16

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Abstract A new fault line selection method for small current grounding system based on ensemble empirical mode of decomposition (EEMD) and correlation dimension is proposed. EEMD is ideal for the fault signal processing of small current grounding system, which could not only achieve the same effect with the empirical mode decomposition (EMD), but also suppress aliasing mode effectively when processing nonlinear and non-stationary signal. The correlation dimension could reflect the feature quantity of the system state, analyze the fault condition quantitatively, and improve the capabilities of fault diagnosis. Phase space reconstruction is necessary before calculating the correlation dimension, and the maximum joint entropy algorithm is introduced to get the optimal delay time, which simplifies the algorithm, and shortens the calculation time of the correlation dimension compared with the mutual information requirements delay time. Finally, G-P algorithm is adopted to calculate the correlation dimension, realizing fault line selection by comparing the numerical of correlation dimension. The experiment results show that the proposed method could select fault line rapidly and accurately, providing an effective method for the small current grounding fault line selection.

Key words： metrology fault selection small current grounding ensemble empirical mode of decomposition correlation dimension maximum joint entropy G-P algorithm

Received: 27 August 2014 Published: 22 March 2016

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TB971

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	Articles by authors
	ZHANG Shu-qing
	ZHAO Peng-cheng
	CHEN Ying
	LIU Zi-yue
	ZHANG Li-guo
	YAN Bing

Cite this article:

ZHANG Shu-qing,ZHAO Peng-cheng,CHEN Ying, et al. Fault Selection for Small Current Grounding System based on EEMD and the Correlation Dimension Algorithm[J]. Acta Metrologica Sinica, 2016, 37(3): 300-305.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2016.03.16 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2016/V37/I3/300

［1］俞阿龙. 基于多重小波变换的力微分信号去噪研究［J］. 计量学报,2008,29（3）:235-237.
［2］钟建林,何友,王红星. 基于多频小波分析和D-S推理的电路故障诊断［J］. 电工技术学报,2010,25（8）:180-184.
［3］黄宜军,汪金友. 小波分析在微弱信号测量中的应用研究［J］. 计量学报,2007,28（2):163-166.
［4］赵妍,高磊,王永,等. 基于S变换奇异值能量熵的单相自适应重合闸故障符号识别法［J］. 电网技术,2010,（12）:209-213.
［5］戴桂平,刘彬. 基于小波去噪和EMD的信号瞬时参数提取［J］. 计量学报,2007,28（2）:158-162.
［6］陈继开,李浩昱,杨世彦. 非广延小波熵及蚁群优化结合的电网暂态电压扰动识别方法［J］. 中国电机工程学报,2010,30(S1):31-36.
［7］Li J L, Qi W, Ke J Y, et al. EMD and spectrum correction-based fault diagnosis method［J］. Chinese Journal of Scientific Instrument, 2011, 32(6):1278-1283.
［8］刘涛,黄其柏. 基于EEMD和HT的轴流泵压力脉动特征信息提取［J］. 机电工程,2012,29(3):278-281.
［9］王建军,徐西鹏. 花岗石抛光表面的粗糙度、分形维数及其关系研究［J］. 计量学报,2007,28(2):124-128.
［10］孟祥海,王金全,李建科. 基于关联维数的空气开关声音信号特征分析［J］. 电子质量,2010,(12):70-72.
［11］Wu Z H, Huang N E. Ensemble empirical mode decomposition:a noise assisted data analysis method［J］.Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.
［12］Yeh J A, Shieh J S, Huang N E. Complementary ensemble enpicical mode decomposition: a novel noise enhanced data analysis method［J］. Advances in Adaptive Data Analysis, 2010, 2(2): 135-156.
［13］Hong H, Zhu X H, Su W M. Detection of time varying pitch in tonal languages: an approach based on ensemble empirical mode decomposition［J］. Zhejiang Univ-Sic C, 2012, 13(2): 139-145.
［14］Wang Z C, Wang S L, Ren K S, et al. Research on the method of hoist head sheave bearing fault diagnosis based on EEMD［J］. Journal of China Coal Society, 2012, 37(4): 689-694.
［15］王皓,孙宏斌,张伯明,等. 基于混合互信息的特征选择方法及其在静态电压稳定评估中的应用［J］. 中国电机工程学报,2006,26（7）:77-81.
［16］张淑清, 贾健, 高敏, 等. 混沌时间序列重构相空间参数选取研究［J］. 物理学报, 2010, 59(3): 1576-1582.
［17］张淑清, 李新新, 张立国, 等. 基于符号分析的极大联合熵延迟时间求取方法［J］. 物理学报,2013,62(11):110506.