Identification Method of Transformer Inrush Current Based on Skewness Coefficient

被引:0
|
作者
Hu S. [1 ]
Jiang Y. [1 ]
Huang C. [1 ]
机构
[1] College of Electrical and Information Engineering, Hunan University, Changsha, 410082, Hunan Province
来源
Jiang, Yaqun (yaqunjiang@21cn.com) | 1954年 / Power System Technology Press卷 / 42期
基金
中国国家自然科学基金;
关键词
Differential protection; Inrush current; Skewness; Transformer;
D O I
10.13335/j.1000-3673.pst.2017.1613
中图分类号
学科分类号
摘要
This paper analyzes difference of waveform characteristics and probability distribution between inrush current and fault current waveforms, and proposes an identification method of inrush current based on skewness coefficient of transformer differential current. Sine characteristics of internal fault current skewness is negative and remains unchanged, and absolute value of full power cycle skewness is equal to half power frequency cycle skewness; while inrush current is positive, and there is a big difference with half cycle skewness. The skewness coefficients of sampling values of differential current in one power frequency cycle and half power frequency cycles are calculated, and thus the inrush current can be quickly identified. This method is simple in principle and small in calculation quantity. It can recognize inrush current accurately and effectively, and has strong ability of noise immunity and anti-interference. Simulation results verify feasibility and superiority of the proposed method. © 2018, Power System Technology Press. All right reserved.
引用
收藏
页码:1954 / 1959
页数:5
相关论文
共 18 条
  • [1] Wang X., Wang Z., Identification of transformer inrush currents based on waveform distribution characteristics, Transactions of China Electrotechnical Society, 27, 1, pp. 148-154, (2012)
  • [2] Xu Y., Zhou F., A method to distinguish inrush current of power transformer from fault current based on amplitude characteristics, Power System Technology, 35, 9, pp. 205-209, (2011)
  • [3] Yan F., Duan J., Li X., Et al., Identification method of inrush current in distribution network based on analysis of dynamic quadrilateral, Power System Technology, 39, 7, pp. 2017-2022, (2015)
  • [4] Jiao Z., Ma T., Qu Y., Et al., A novel excitation inductance-based power transformer protection scheme, Proceedings of the CSEE, 34, 10, pp. 1658-1666, (2014)
  • [5] Du Z., Jiang Y., Huang C., Et al., New approach of transformer inrush detected based on Tsallis wavelet entropy, Computer Engineering and Applications, 52, 4, pp. 255-260, (2016)
  • [6] Wang Y., Lu Y., Cai C., Et al., A magnetizing inrush identification method applying adaptive data-window currents, Proceedings of the CSEE, 34, 4, pp. 702-711, (2014)
  • [7] Yan F., Li C., Novel method to identify the inrush current based on Pearson correlation coefficient, High Voltage Apparatus, 52, 8, pp. 52-56, (2016)
  • [8] Hoeffding W., A class of statistics with asymptotically normal distribution, The Annals of Mathematical Statistics, 19, 3, pp. 293-325, (1948)
  • [9] Wang R., Wang H., Wang X., Et al., Calculating value-at-risk of electricity market considering the time-varying features of distribution's parameters, Power System Protection and Control, 40, 24, pp. 46-52, (2012)
  • [10] Wang Y., Yuan Y., Gao L., Et al., A algorithm to identify magnetizing inrush current based on FSAD and aperiodic components, Transactions of China Electrotechnical Society, 30, 21, pp. 127-135, (2015)