scholarly journals High-Temperature Superconducting Cable Fault Location Method Based on Improved Time-Frequency Domain Reflection Method and EEMD Noise Reduction

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Bo Yang ◽  
Jun Tang ◽  
Chen Yang ◽  
Xiaofeng Dong ◽  
Kun Huang ◽  
...  
Keyword(s):  
High Temperature ◽  
Noise Reduction ◽  
Frequency Domain ◽  
Fault Location ◽  
Reflection Method ◽  
Cross Term ◽  
Time Frequency ◽  
High Temperature Superconducting ◽  
The Cross ◽  
Superconducting Cables

Aiming at the operation and maintenance requirements of the fault location of high-temperature superconducting cables, a fault location method of high-temperature superconducting cables based on the improved time-frequency domain reflection method and EEMD noise reduction is proposed. Considering the cross-term interference problem in the traditional time-frequency domain reflection method, this paper introduces the affine transformation to project the time-frequency distribution of the self-term and the cross term and further highlights the characteristic differences between the two through coordinate transformation, and the particle swarm algorithm is employed to solve the optimal stagger angle of the affine transformation. The unscented particle filter is adopted to separate the cross term, and EEMD noise reduction is introduced to solve the signal noise problem. Finally, two software programs, PSCAD and MATLAB, are employed for joint simulation to build a model of high-temperature superconducting cable. The simulation example shows that the proposed method in this paper can eliminate the cross-term interference of the traditional time-frequency domain reflection method, effectively locate the fault of the high-temperature superconducting cable, and improve the positioning accuracy.

Characteristics of Three-Phase High-Temperature Superconducting Cables with Shielded Phases

2020 ◽  
Vol 91 (3) ◽  
pp. 211-216
Author(s):  
V. V. Zheltov ◽  
S. I. Kopylov ◽  
A. Yu. Arkhangel’skii ◽  
L. N. Kopylova ◽  
D. A. Lipa ◽  
...  
Keyword(s):  
High Temperature ◽  
High Temperature Superconducting ◽  
Three Phase ◽  
Superconducting Cables

Review of conductor development for use in high-temperature superconducting cables

1994 ◽  
Vol 16 (12) ◽  
pp. 2067-2072 ◽  
Author(s):  
T. P. Beales ◽  
M. Mölgg ◽  
L. Le Lay ◽  
C. Ferrari ◽  
W. Segir
Keyword(s):  
High Temperature ◽  
High Temperature Superconducting ◽  
Superconducting Cables

scholarly journals Research on Deep Convolutional Neural Network Time-Frequency Domain Seismic Signal Denoising Combined With Residual Dense Blocks

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhitao Gao ◽  
Song Zhang ◽  
Jianxian Cai ◽  
Li Hong ◽  
Jiangshan Zheng
Keyword(s):  
Noise Reduction ◽  
Frequency Domain ◽  
Seismic Signal ◽  
Signal Denoising ◽  
Seismic Signals ◽  
Deep Convolutional Neural Networks ◽  
Time Frequency ◽  
Deep Network ◽  
Network Time ◽  
Feature Information

Deep Convolutional Neural Networks (DCNN) have the ability to learn complex features and are thus widely used in the field of seismic signal denoising with low signal-to-noise ratio (SNR). However, the current convolutional deep network used for seismic signal noise reduction does not make full use of the feature information extracted from all convolution layers in the network, and thus cannot fit the seismic signal with high SNR. To deal with this problem, this paper proposes the DnRDB model, a convolutional deep network time-frequency domain seismic signal denoising model combined with residual dense blocks (RDB). The model is mainly composed of several RDB in series. The input of each convolution layer in each RDB module is formed by the output of all the previous convolution layers. Meanwhile, even if the number of layers is increased, the fusion of the seismic signal features learned by the RDB modules can still achieve full extraction of seismic signals. Furthermore, deepening the model structure by concatenating multiple RDB modules enables further useful feature information to be extracted, which improves the SNR of seismic signals. The DnRDB model was trained and tested using the Stanford Global Seismic Dataset. The experimental results show that the DnRDB model can effectively recover seismic signals and remove various forms of noise. Even in the case of high noise, the denoised signal still has a high SNR. When the DnRDB model is compared with other denoising approaches such as wavelet threshold, empirical mode decomposition, and different deep learning methods, the results indicate that it performs best overall in denoising the same segment of the noisy seismic signal; the denoised signal also has less waveform distortion. Use of the DnRDB model in subsequent seismic signal processing work indicates that it can help the phase recognition algorithm improve the accuracy of seismic recognition through noise reduction.

scholarly journals Vanadium oxide coatings to self-regulate current sharing in high-temperature superconducting cables and magnets

2020 ◽  
Vol 128 (5) ◽  
pp. 055105 ◽  
Author(s):  
Zhenghuai Yang ◽  
Aurora Cecilia Araujo Martínez ◽  
Sachin V. Muley ◽  
Xiaorong Wang ◽  
Qing Ji ◽  
...  
Keyword(s):  
High Temperature ◽  
Vanadium Oxide ◽  
Oxide Coatings ◽  
High Temperature Superconducting ◽  
Current Sharing ◽  
Superconducting Cables

The Certification for Inevitability of Cross-Term in WVD

2014 ◽  
Vol 1030-1032 ◽  
pp. 1930-1933 ◽  
Author(s):  
Zhong Hu Yuan ◽  
Man Yang Xu ◽  
Xiao Xuan Qi
Keyword(s):  
Mathematical Method ◽  
Frequency Analysis ◽  
Cross Term ◽  
Time Frequency Analysis ◽  
Important Method ◽  
Time Frequency ◽  
Multicomponent Signal ◽  
Mathematical Properties ◽  
The Cross

WVD has become an important method for time-frequency analysis because of its mathematical properties. In fact, however, cross-term disturbs its application seriously when the multicomponent signal existed. Now increasing people is going to find pure mathematical method to deal with the cross-term in WVD. But this paper will prove the inevitability of cross-term in WVD.

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