De-noising of partial discharge ultrasonic signal of insulation bar in large motor based on GMC-wavelet-Reference-Cited by-同舟云学术

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

Published:2022-12-01 Issue:6 Volume:73 Page:368-377
ISSN:1339-309X
Container-title:Journal of Electrical Engineering
language:en
Short-container-title:

Author:

Chen Xuejun¹,Ma Lin²,Zhang Lei³,Zhuang Jianhuang⁴

Affiliation:

1. Key Laboratory of Fujian Universities for New Energy Equipment Testing, Putian University , Putian , 351100, P.R. China

2. School of Mechanical Engineering and Automation , Fuzhou University , Fuzhou , PR China

3. College of Mechanical and Electrical Engineering , Fujian Agriculture and Forestry University , Fuzhou 350100, PR China

4. Putian power supply company of State Grid Fujian Electric Power Co., Ltd , Putian , PR China

Abstract

Abstract In view of the bad operation environment of large motor, which often suffers from various strong noise interference, the partial discharge ultrasonic signal is often annihilated, which makes it difficult to detect and analyse. A de-noising method based on generalized minimax concavity (GMC) and wavelet for partial discharge (PD) ultrasonic signal is proposed. GMC is used to enhance the sparsity of PD ultrasonic signal and eliminate the high-frequency noise signal at the same time. Then the residual high-frequency sparse noise and low-frequency noise of the former are de-noised again combined with wavelet. Finally, the signal is reconstructed to achieve the purpose of de-noising the original PD ultrasonic signal with noise. Compared with ℓ1 -norm method, GMC method, wavelet method and ℓ1 -norm-wavelet method, the simulation results show that based on time domain analysis, the de-noising effect of the proposed method is obviously better than the other four methods. The SNR and MSE of the former are better than those of the latter. In addition, the insulation bar discharge model of large motor is constructed to obtain the actual PD ultrasonic signal, which further verifies its effectiveness, and its de-noising effect is also better than the four methods. This method can not only enhance the sparsity of the target signal and improve the estimation accuracy, but also achieve the de-noising effect, while retaining the effective information of PD ultrasonic signal characteristics. This method can provide new ideas for other types of PD signal de-noising, and lay the foundation for later feature analysis.

Publisher

Walter de Gruyter GmbH

Link

https://www.sciendo.com/pdf/10.2478/jee-2022-0051

Reference29 articles.

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.

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

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.

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.

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.