Accesso libero

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

INFORMAZIONI SU QUESTO ARTICOLO

Cita

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

[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 Scholar

eISSN:
1339-309X
Lingua:
Inglese
Frequenza di pubblicazione:
6 volte all'anno
Argomenti della rivista:
Engineering, Introductions and Overviews, other