Partial Discharge Signal Extraction Method Based on SVD and Low Rank RBF Neural Network

被引：0
|
作者：
Yang X. [1 ]
Huang H. [1 ]
Shu Q. [1 ]
Zhang D. [2 ]
Zhou D. [3 ]
机构：
[1] School of Electrical Engineering, Sichuan University, Chengdu
[2] Chengdu Ruichi Technology Co., Ltd., Chengdu
[3] Electric Power Research Institute of State Grid Sichuan Electric Power Company, Chengdu
来源：
Gaodianya Jishu/High Voltage Engineering | 2021年 / 47卷 / 10期
关键词：
Gaussian window; Neural network; Partial discharge; Periodic narrowband noise; Singular value decomposition; White noise;
D O I：
10.13336/j.1003-6520.hve.20200739
中图分类号：
学科分类号：
摘要：
The detection process of partial discharge(PD) ultrahigh frequency(UHF) signals is severely susceptible to white noise and periodic narrowband noise. In order to extract PD UHF signals and suppress noise effectively, a denoising method based on singular value decomposition(SVD) and low rank radial basis function(RBF) neural network is proposed. Firstly, the noisy PD signals are constructed as a Hankel matrix and SVD matrix is decomposed into a feature matrix space. Then, the singular value mutation point in the feature matrix is set as a threshold to remove the narrowband noise. Finally, RBF neural network is used to approximate the denoised PD signals, and Gaussian window filtering is used to extract the PD signals. The proposed method is compared with reverse separation(RS) and morphology wavelet filter (MWF). The simulation and field-detection results show that the method has a stronger inhibition effect on periodic narrowband noise and white noise, and the evaluation index is more significant. © 2021, High Voltage Engineering Editorial Department of CEPRI. All right reserved.
引用
收藏
页码：3608 / 3616
页数：8
相关论文
共 21 条
  • [1] TANG Zhiguo, TANG Mingze, LI Jinzhong, Et al., Review on partial discharge pattern recognition of electrical equipment, High Voltage Engineering, 43, 7, pp. 2263-2277, (2017)
  • [2] LIU Yushun, ZHOU Wenjun, LI Pengfei, Et al., Partial discharge ultrahigh frequency signal denoising method based on generalized s-transform modular time-frequency matrix, Transactions of China Electrotechnical Society, 32, 9, pp. 211-220, (2017)
  • [3] LI Qingquan, WANG Liangkai, WANG Peijin, Et al., Review on partial discharge and charge distribution characteristics of oil-paper insulation in converter transformer, High Voltage Engineering, 46, 8, pp. 2815-2829, (2020)
  • [4] LIU Yushun, CHENG Dengfeng, XIA Lingzhi, Et al., Partial discharge ultra-high frequency signal de-noising method based on single-channel blind source separation algorithm, Transactions of China Electrotechnical Society, 33, 23, pp. 5625-5636, (2018)
  • [5] TANG J, ZHOU S Y, PAN C., A denoising algorithm for partial discharge measurement based on the combination of wavelet threshold and total variation theory, IEEE Transactions on Instrumentation and Measurement, 69, 6, pp. 3428-3441, (2020)
  • [6] SEO J, MA H, SAHA T., Probabilistic wavelet transform for partial discharge measurement of transformer, IEEE Transactions on Dielectrics and Electrical Insulation, 22, 2, pp. 1105-1117, (2015)
  • [7] QIAN Yong, HUANG Chengjun, CHEN Chen, Et al., Denoising of partial discharge based on empirical mode decomposition, Automation of Electric Power Systems, 29, 12, pp. 53-56, (2005)
  • [8] ZHANG Ruobing, JIN Sen, DU Gang, Method of partial discharge localization for cable under oscillatory wave voltage conditions based on EMD and DTW algorithm, High Voltage Engineering, 46, 1, pp. 273-281, (2020)
  • [9] ZHANG Yuhui, DUAN Weirun, LI Tianyun, A reverse separation method of suppressing periodic narrowband noise in partial discharge signal, Transactions of China Electrotechnical Society, 30, 6, pp. 232-239, (2015)
  • [10] LIU Yunpeng, LU Fangcheng, LI Chengrong, Et al., Study of novel integrated morphology-wavelet filter in suppressing PD'S field noise, High Voltage Engineering, 30, 2, pp. 29-31, (2004)
123