Sinusoidal representation of a transient signal based on the Hilbert transform

被引:0
|
作者
Luo J. [1 ]
Shi J. [1 ]
机构
[1] State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing
基金
中国国家自然科学基金;
关键词
Attenuation characteristics; Fitting function; Hilbert transform; Power system; Transient analysis;
D O I
10.19783/j.cnki.pspc.210309
中图分类号
学科分类号
摘要
To analyze the characteristics of a power system transient signal and calculate its derivative, it is feasible to fit the function expression of the signal based on a sample, but the existing transient signal fitting methods still have shortcomings of certainty and derivability of the fitting function. Given this, a sinusoidal representation method for transient signal analysis of a power system based on a Hilbert transform is proposed. The sine function of the transient signal is used to form a fitting basis function, and the Hilbert transform is used to meet the certainty of the fitting function. Compared with the existing methods, this method can directly decompose the steady-state component and transient component of the fault transient signal, and its transient component can reflect the attenuation characteristics of the fault signal. This method can also fit the function expression of the transient signal, and then calculate the arbitrary order derivative value of the transient voltage and current signal. This is the basis of many signal processing calculations. Through the analysis of a set of given signals and a set of field recorded signals, the effectiveness of the proposed method is verified. © 2022 Power System Protection and Control Press.
引用
收藏
页码:1 / 7
页数:6
相关论文
共 30 条
  • [21] WANG Jialin, XIA Li, WU Zhengguo, Et al., Power system transient signal analysis method based on genetic neural network, High Voltage Engineering, 37, 1, pp. 170-175, (2011)
  • [22] YANG Xuanfang, WANG Jialin, Analysis of transient signals of power using Prony and particle swarm optimization, Proceedings of the CSU-EPSA, 27, 8, pp. 25-30, (2015)
  • [23] SEVEROGLU N, SALOR O., Statistical models of EAF harmonics developed for harmonic estimation directly from waveform samples using deep learning framework, 2020 IEEE Industry Applications Society Annual Meeting, (2020)
  • [24] WANG Taihao, Generalized sinusoidal transform method and its applications in protection of distribution system, (2015)
  • [25] YU B, YANG X., The Hilbert transform of B-spline wavelets, IEEE Signal Processing Letters, 28, pp. 693-697, (2021)
  • [26] LIU Yijiao, A time domain Hilbert transform method, (2016)
  • [27] WANG Yan, LI Qunzhan, ZHOU Fulin, Et al., A new method with Hilbert transform and slip-SVD-based noise-suppression algorithm for noisy power quality monitoring, IEEE Transactions on Instrumentation and Measurement, 68, 4, pp. 987-1001, (2019)
  • [28] ZHANG Guojun, REN Rong, HAN Jingjing, Et al., Application of Hilbert transform in fault line selection of distribution network, Power System Protection and Control, 42, 10, pp. 23-28, (2014)
  • [29] LI Guoxin, FEI Juntao, ZHU Tangyu, Et al., Harmonic detection algorithm based on adaptive variational modal decomposition, Distribution & Utilization, 38, 11, pp. 1-8, (2021)
  • [30] ZAHNG Kaidi, Distributed parameter circuit model and its application in UHV line protection, (2014)