Application of sliding detrended fluctuation analysis in detection of transient power quality disturbances

被引：0
作者：
Zhang, Shuqing [1 ]
Zhai, Xinpei [1 ]
Liu, Yongfu [1 ]
Tang, Baiwen [1 ]
Zhao, Yuchun [1 ]
机构：
[1] Measurement Technology and Instrumentation Key Lab. of Hebei Province, Yanshan University
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2012年 / 36卷 / 08期
关键词：
Fluctuating parameter; Power quality; Sliding detrended fluctuation analysis; Transient disturbance detection;
D O I：
10.3969/j.issn.1000-1026.2012.08.09
中图分类号：
学科分类号：
摘要：
In view of the irregularity and mutability of power quality transient disturbance signals, a new method for detecting the transient power quality disturbances based on sliding detrended fluctuation analysis is introduced. The correlated properties of the original signals change when a transient disturbances occurs, and the fluctuating parameters of the detrended fluctuation analysis are sensitive to them. Fluctuating parameter curves can be constructed according to sliding windows. It can detect the disturbance moment and determine the typical voltage fluctuation amplitudes such as voltage swell and sag. Experimental results show that the method is effective, and has better immunity to noise than the method based on wavelet transform. © 2012 State Grid Electric Power Research Institute Press.
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页码：52 / 57
页数：5
相关论文
共 14 条
  • [1] Wang L., He Z., Dai M., Et al., A new hieratic power quality disturbance signal identification method, Automation of Electric Power Systems, 33, 24, pp. 65-69, (2009)
  • [2] Zhang W., Wang C., Classification of the voltage transient disturbances based on probabilistic neural networks, Automation of Electric Power Systems, 31, 18, pp. 34-38, (2007)
  • [3] Liu X., Yang H., Zhao J., Classification for short duration power quality disturbance based on S-transform with module time-frequency matrixes partial similarity, Automation of Electric Power Systems, 33, 8, pp. 62-67, (2009)
  • [4] Zhao J., He Z., Jia Y., Et al., Power quality disturbance location method utilizing morphological undecimated wavelet, Proceedings of the CSEE, 29, 31, pp. 109-114, (2009)
  • [5] Zhang M., Li K., Hu Y., A power quality signal denoising algorithm based on wavelet domain neighboring thresholding classification, Automation of Electric Power Systems, 34, 10, pp. 84-89, (2010)
  • [6] Jia Q., Yu L., Dong H., Et al., Power quality disturbance features extraction based on atomic decomposition, Automation of Electric Power Systems, 33, 24, pp. 61-64, (2009)
  • [7] Kantelhardt J.M., Koscielny B.E., Rego H.H.A., Et al., Detecting long-range correlations with detrended fluctuation analysis, Physica A, 295, 3-4, pp. 441-454, (2001)
  • [8] Kantelhardt J.M., Zschiegner S.A., Koscielny B.E., Et al., Multifractal detrended fluctuation analysis of nonstationary time series, Physica A, 316, 1-4, pp. 87-114, (2002)
  • [9] Yang P., Hou W., Feng G., Determining the threshold of extreme events with detrended fluctuation analysis, Acta Physica Sinica, 57, 8, pp. 5333-5342, (2008)
  • [10] Norouzzadeh P., A multifractal detrended fluctuation description of Iranian rial-US dollar exchange rate, Physica A, 367, 2, pp. 328-336, (2006)
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