A new detection method of power quality disturbance based on VMD

被引：0
|
作者：
Huang C. [1 ,2 ]
Zhou T. [3 ]
机构：
[1] Electromechanical and Vehicle Engineering, Zhongzhou Institute of Technology, Zhengzhou
[2] School of Mechanical Engineering, Zhejiang University, Hangzhou
[3] Research Department, Zhongzhou Institute of Technology, Zhengzhou
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2018年 / 38卷 / 03期
关键词：
Disturbance signal detection; Hilbert transform; Instantaneous amplitude; Instantaneous frequency; Power quality; Variational mode decomposition;
D O I：
10.16081/j.issn.1006-6047.2018.03.016
中图分类号：
学科分类号：
摘要：
In order to extract the characteristics of disturbance signal more accurately, a new method based on VMD (Variational Mode Decomposition) is proposed to detect power quality disturbance. The proposed method is composed of VMD and HT (Hilbert Transform). Firstly, Fourier transform of disturbance signal is used to determine the preset decomposition scale of VMD. Then, the disturbance signal is decomposed into the sum of a series of AM-FM functions by VMD. Finally, the instantaneous amplitude and instantaneous frequency of AM-FM signal are obtained by HT and then the characteristics of disturbance signal are determined. Compared with Hilbert-Huang transform and local mean decomposition method, VMD method can analyze the disturbance signals with different time sets, handle complex disturbances and odd harmonics with similar frequency, has no mode confusion and can obtain the instantaneous amplitude and instantaneous frequency more accurately. Analysis results of simulation signals and voltage signals when switching capacitor bank verify the feasibility and effectiveness of the proposed method. © 2018, Electric Power Automation Equipment Press. All right reserved.
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页码：116 / 123
页数：7
相关论文
共 17 条
  • [1] Zhu L., Liu Z., Hu Q., Et al., Recognition of transient power quality disturbances based on CWD spectral kurtosis, Electric Power Automation Equipment, 34, 2, pp. 125-131, (2014)
  • [2] Ding J., Zhang P., Online power quality disturbance classification based on Hoeffding Tree, Electric Power Automation Equipment, 34, 9, pp. 84-89, (2014)
  • [3] Wang C., Wang J., Classification method of power quality disturbance based on wavelet packet decomposition, Power System Technology, 28, 15, pp. 78-82, (2004)
  • [4] Feng H., Zhou L., Liu Y., Detection and classification of transient power quality disturbances based on complex wavelet transform, Power System Technology, 34, 3, pp. 91-95, (2010)
  • [5] Xu F., Yang H., Ye M., Et al., Classification for power quality short duration disturbances based on generalized S-transform, Proceedings of the CSEE, 32, 4, pp. 77-84, (2012)
  • [6] Xu Z., Yang L., Li X., Power quality disturbance identification based on clustering-modified S-transform and direct support vector machine, Electric Power Automation Equipment, 35, 7, (2015)
  • [7] Wang N., Li L., Jia Q., Et al., Classification of power quality disturbance signals using atomic decomposition method, Proceedings of the CSEE, 31, 4, pp. 51-58, (2011)
  • [8] Qu Z., Hao W., Wang N., Application of atomic rapid decomposition algorithm in power quality disturbance analysis, Electric Power Automation Equipment, 35, 10, pp. 145-150, (2015)
  • [9] Li T., Zhao Y., Li N., Et al., A new method for power quality detection based on HHT, Proceedings of the CSEE, 25, 17, pp. 52-56, (2005)
  • [10] Huang H., Wu J., A method to locate power quality disturbing signal based on empirical mode decomposition, Power System Technology, 34, 5, pp. 41-45, (2010)
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