Have a personal or library account? Click to login
De-noising of partial discharge ultrasonic signal of insulation bar in large motor based on GMC-wavelet Cover

De-noising of partial discharge ultrasonic signal of insulation bar in large motor based on GMC-wavelet

Journal of Electrical Engineering
Volume 73 (2022): Issue 6 (December 2022)
By: Xuejun Chen,  Lin Ma,  Lei Zhang and  Jianhuang Zhuang  
Open Access
|Dec 2022

References

  1. [1] J. Li, X. Han, Z. Liu, and Y. Li, “Review on partial discharge measurement technology of electrical equipment”, High Voltage Engineering, vol. 41, no. 8, pp. 2583-2601, 2015.
    Search in Google ScholarBack to article
  2. [2] L. Tang, R. Luo, M. Deng, and J. Su, “Study of Partial Discharge Localization Using Ultrasonics in Power Transformer Based on Particle Swarm Optimization”, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 15, no. 2, pp. 492-495, 2008.10.1109/TDEI.2008.4483469
    Search in Google ScholarBack to article
  3. [3] D. Li, J. Yang, J. Liang, and L. Wu, “Characteristics of acoustic signal form free moving metallic particles in gas insulated swithchgear”, Journal of Xi’an Jiaotong University, vol. 43, no. 2, pp. 101-105, 2009.
    Search in Google ScholarBack to article
  4. [4] D. Zheng and P. Zhang, “A review of fault diagnosis and online condition monitoring of stator insulation in AC electrical machine”, Proceedings of the CSEE.
    Search in Google ScholarBack to article
  5. [5] X. Luo, H. Niu and L. Lai, “Application of adaptive wavelet neural network based on particle swarm optimization algorithm in online PD pattern recognition”, Transactions of China Electrotechnical Society, vol. 29, no. 10, pp. 326-333, 2014.
    Search in Google ScholarBack to article
  6. [6] Y. Qian, C. Huang and C. Chen, “Denoising of partial discharge based on empirical mode decomposition”, Automation of Electric Power Systems, vol. 60, pp. 53-56, 2005.
    Search in Google ScholarBack to article
  7. [7] H. Li, Y. Sun and C. Xu, “Noise elimination of PD signals by independent component analysis”, Journal of Sichuan University (Engneering Science Editiion, vol. 39, no. 6, pp. 143-148, 2007.
    Search in Google ScholarBack to article
  8. [8] X. Xiong, S. Yang and X. Zhou, “IMF-based denoising method for vibration signal in rotating machinery”, Journal of Zhejiang University (Engneering Science, vol. 45, no. 8, pp. 1376-1381, 2011.
    Search in Google ScholarBack to article
  9. [9] J. Zhang, H. Chen and P. Zhou, “New de-noising method for partial discharge signals based on wavelet threshold”, Advanced Technology of Electrical Engineering and Energy, vol. 36, no. 8, pp. 80-88, 2017.
    Search in Google ScholarBack to article
  10. [10] K. Zhou, Y. Huang, M. Xie, and H. Min, “Mixed noises suppression of partial discharge signal employing short-time singular value decomposition”, Transaction of China Electrotechnical Society, vol. 34, no. 11, pp. 2435-2443, 2019.
    Search in Google ScholarBack to article
  11. [11] J. Xie, Y. Liu, L. Liu, L. Tang, G. Wang, and X. Li, “A partial discharge signal denoising method based on adaptive weighted framing fast sparse representation”, of the CSEE, vol. 39, no. 21, pp. 6428-6439, 2019.
    Search in Google ScholarBack to article
  12. [12] M. Ashtiani and S. Shahrtash, “Partial discharge de-noising employing adaptive singular value decomposition”, IEEE Transactions on Dielectrics Electrical Insulation, vol. 21, no. 2, pp. 775-782, 2014.10.1109/TDEI.2013.003894
    Search in Google ScholarBack to article
  13. [13] M. Xie, K. Zhou, Y. Huang, M. He, and X. Wang, “A white noise suppression method for partial discharge based on short time singular value decomposition”, Proceedings of the CSEE, vol. 39, no. 3, pp. 915-922, 2019.
    Search in Google ScholarBack to article
  14. [14] H. Wei, H. Ma, T. Huang, and H. Huang, “Application of ICA de-noise method based on EMD in partial discharge signal of switch cabinet in power plant”, Proceedings of the CSU-EPSA, vol. 31, no. 5, pp. 110-116, 2019.
    Search in Google ScholarBack to article
  15. [15] J. Zhong, X. Bi, Q. Shu, D. Zhang, and X. Li, “An improved wavelet spectrum segmentation algorithm based on spectral kurtogram for denoising partial discharge signals”, IEEE Transactions on Instrumentation and Measurement, vol. 70, Article ID 3514408, 2021.10.1109/TIM.2021.3071224
    Search in Google ScholarBack to article
  16. [16] C. Wu, Y. Gao, R. Wang, K. Wang, S. Liu, Y. Nie, and P. Wang, “Partial Discharge Detection Method Based on DD-DT CWT and Singular Value Decomposition”, Journal of Electrical Engineering Technology, vol. 17, pp. 2433-2439, 2022.10.1007/s42835-022-01081-8
    Search in Google ScholarBack to article
  17. [17] F. Miao and R. Zhao, “A new method of vibration signal denoising based on improved wavelet”, Journal of Low Frequency Noise, Vibration and Active Control, vol. 41, no. 2, pp. 637-645, 2022.10.1177/14613484211051857
    Search in Google ScholarBack to article
  18. [18] X. Song, C. Zhou, D. Hepburn, G. Zhang, and M. Michel, “Second generation wavelet transform for data denoising in PD measurement”, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 14, no. 6, pp. 1531-1537, 2007.10.1109/TDEI.2007.4401237
    Search in Google ScholarBack to article
  19. [19] A. Kyprianou, P. L. L. V. Efthimiou, A. Stavrou, and G. E. Georghiou, “Wavelet packet denoising for online partial discharge detection in cables and its application to experimental field results”, Measurement Science and Technology, vol. 17, no. 9, pp. 2367-2379, 2006.10.1088/0957-0233/17/9/001
    Search in Google ScholarBack to article
  20. [20] J. Long, X. Wang, D. Dai, M. Tian, G. Zhu, and J. Zhang, “Denoising of UHF PD signals based on optimised VMD and wavelet transform”, IET Science Measurement Technology, vol. 11, no. 6, pp. 753-760, 2017.10.1049/iet-smt.2016.0510
    Search in Google ScholarBack to article
  21. [21] D. L. Donoho, “De-noising by soft-thresholding”, IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613-627, 1995.10.1109/18.382009
    Search in Google ScholarBack to article
  22. [22] P. Chen and I. W. Selesnick, “Group-sparse signal denoising: non-convex regularization, convex optimization”, IEEE Transactions on Signal Processing, vol. 62, no. 13, pp. 3464-3478, 2014.10.1109/TSP.2014.2329274
    Search in Google ScholarBack to article
  23. [23] M. Protter, I. Yavneh, and M. Elad, “Closed-form MMSE estimation for signal denoising under sparse representation modeling over a unitary dictionary”, IEEE Transactions on Signal Processing, vol. 58, no. 7, pp. 3471-3484, 2010.10.1109/TSP.2010.2046596
    Search in Google ScholarBack to article
  24. [24] D. Moloney, D. Geraghty, C. Mcsweeney, and C. Mcelroy, “Streaming sparse matrix compression/decompression”, Lecture Notes in Computer Science, vol. 3793, pp. 116-129, 2005.10.1007/11587514_9
    Search in Google ScholarBack to article
  25. [25] J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210-227, 2009.10.1109/TPAMI.2008.7919110489
    Search in Google ScholarBack to article
  26. [26] I. W. Selesnick, “Sparse Regularization via convex analysis”, IEEE Transactions on Signal Processing, vol. 65, no. 17, pp. 4481-4494, 2017.10.1109/TSP.2017.2711501
    Search in Google ScholarBack to article
  27. [27] S. Wang, I. W. Selesnick, G. Cai, B. Ding, and X. Chen, “Synthesis versus analysis priors via generalized minimax-concave penalty for sparsity-assisted machinery fault diagnosis”, Mechanical systems and signal processing, vol. 127, pp. 202-233, 2019.10.1016/j.ymssp.2019.02.053
    Search in Google ScholarBack to article
  28. [28] O. P. Goswami, A. Shukla, and M. Kumar, “Optimal design and low noise realization of digital differentiator”, Journal of Electrical Engineering, vol. 73, no. 5, pp. 332-336.10.2478/jee-2022-0044
    Search in Google ScholarBack to article
  29. [29] Z. Jin, A. Dong, M. Shu, and Y. Wang, “Sparse ECG denoising with generalized minimax concave penalty”, Sensors, vol. 19, no. 7, Article ID 1718, 2019.10.3390/s19071718648006630974854
    Search in Google ScholarBack to article
DOI: https://doi.org/10.2478/jee-2022-0051 | Journal eISSN: 1339-309X | Journal ISSN: 1335-3632
Journal RSS Feed
Language: English
Page range: 368 - 377
Submitted on: Oct 17, 2022
|
Published on: Dec 24, 2022
Published by: Slovak University of Technology in Bratislava
In partnership with: Paradigm Publishing Services
Publication frequency: 6 issues per year
Keywords:
insulation,
generalized minimax concave,
partial discharge,
ultrasonic,
de-noising
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2022 Xuejun Chen, Lin Ma, Lei Zhang, Jianhuang Zhuang, published by Slovak University of Technology in Bratislava
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 73 (2022): Issue 6 (December 2022)Next article