Position Fault of Transmission Line Based on Improved Vanishing Moment Complex Wavelet

被引：0

作者：

Wang M. ^{[1
]}

Xu C. ^{[1
]}

Wang W. ^{[2
]}

Zhao C. ^{[3
]}

机构：

[1] College of Electric and Control Engineering, Xi'an University of Science and Technology, Xi'an

[2] Shanxi Key Laboratory of Prevention and Control of Coal Fire, Xi'an University of Science and Technology, Xi'an

[3] Center of Power Cable Xi'an Limited Liability Company of Electric Cable, Xi'an

来源：

| 2018年 / Nanjing University of Aeronautics an Astronautics卷 / 38期

关键词：

Complex wavelet analysis; Fault location; Information of phase; Singularity detection; Vanishing moment;

D O I：

10.16450/j.cnki.issn.1004-6801.2018.02.018

中图分类号：

学科分类号：

摘要：

Owing to the result of the real wavelet transform is real number, which lacks phase information and the frequency spectrum band exists phenomenon of aliasing, when the real wavelet is used to position the transmission line fault. An improved vanishing moment Gauss complex wavelet is proposed, and which is utilized to analyze fault signals. Firstly, how to change wavelet in time frequency and magnitude frequency by vanishing moment is analyzed; then, the relationship between Lipschitz index and vanishing moment is obtained by research singularity of fault signal, and a criterion is proposed to improve vanishing moment; eventually, fault signal is analyzed by Gauss complex wavelet, and the vanishing moment is optimized by adjacent magnitude ratio and phase distortion ratio. Simulation and experiment show that improved vanishing moment Gauss complex wavelet enhances distinguish ability for singular signal, which not only restrain the interference effectively and decrease it about 17.84%, but also reduce the phase distortion rate in phase information by 4.12%. Meanwhile, the error of improved method decreases by 6.06% compared with ordinary Gauss complex wavelet, and decreases by 5.82% compared with real wavelet. © 2018, Editorial Department of JVMD. All right reserved.

引用

页码：333 / 340

页数：7

共 12 条

[11] Jian T., He Y., Su F., Et al., Selection of vanishing moments of wavelet in denoising the signal with singularity, Radar Science and Technology, 4, 1, pp. 31-35, (2006)
[12] Xu Y., Meng Z., Lu M., Compound fault diagnosis of rolling bearing based on dual-tree complex wavelet packet transform and ICA, Journal of Vibration, Measurement & Diagnosis, 35, 3, pp. 513-519, (2015)

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