Partial Discharge Detection Method Based on DD-DT CWT and Singular Value Decomposition-Reference-Cited by-同舟云学术

Partial Discharge Detection Method Based on DD-DT CWT and Singular Value Decomposition

Published:2022-04-20 Issue:4 Volume:17 Page:2433-2439
ISSN:1975-0102
Container-title:Journal of Electrical Engineering & Technology
language:en
Short-container-title:J. Electr. Eng. Technol.

Author:

Wu Chao,Gao Yiran,Wang Ruoyan,Wang Kai,Liu Siyang,Nie Yongjie,Wang Ping

Funder

National Key Research and Development Program

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering

Link

https://link.springer.com/content/pdf/10.1007/s42835-022-01081-8.pdf

Reference17 articles.

1. Baug A, Choudhury NR, Ghosh R, Dalai S, Chatterjee B (2017) Identification of single and multiple partial discharge sources by optical method using mathematical morphology aided sparse representation classifier. IEEE Trans Dielectr Electr Insul 24(6):3703–3712

2. Renguifen A, Liuzengli T (2019) An improved EMD adaptive denoising and feature extraction algorithm. In: 2019 IEEE international conference on signal processing, communications and computing (ICSPCC), 2019, pp 1–4

3. Jin T, Li Q, Mohamed MA (2019) A novel adaptive EEMD method for switchgear partial discharge signal denoising. IEEE Access 7:58139–58147

4. Si W, Qin B, Li Q, Liu H (2019) A novel adaptive wavelet threshold estimation based on hybrid particle swarm optimization for partial discharge signal denoising. Optik 181:175–184

5. Soltani AA, EL-Hag A (2021) A new radial basis function neural network-based method for denoising of partial discharge signals. Measurement 2021:10

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