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Open AccessArticle

Imbalance Fault Classification Based on VMD Denoising and S-LDA for Variable-Speed Marine Current Turbine

1
Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China
2
Shanghai Investigation, Design and Research Institute Co., Ltd., Shanghai 200335, China
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SPIC Energy Technology & Engineering Co., Ltd., SPIC Wind Power Innovation Center, Shanghai 200233, China
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School of Engineering ISEN Yncréa Ouest, Brest Campus, 20, Rue Cuirassé Bretagne, 29200 Brest, France
*
Authors to whom correspondence should be addressed.
Academic Editor: José A.F.O. Correia
J. Mar. Sci. Eng. 2021, 9(3), 248; https://doi.org/10.3390/jmse9030248
Received: 14 January 2021 / Revised: 22 February 2021 / Accepted: 23 February 2021 / Published: 26 February 2021
Marine current energy as a kind of renewable energy has gradually attracted more and more attention from many countries. However, the blade imbalance fault of marine current turbines (MCTs) will have an effect on the power production efficiency and cause damage to the MCT system. It is hard to classify the severity of an MCT blade imbalance fault under the condition of the current instability and seafloor noise. This paper proposes a fault classification method based on the combination of variational mode decomposition denoising (VMD denoising) and screening linear discriminant analysis (S-LDA). The proposed method consists of three parts. Firstly, phase demodulation of the collected stator current signal is performed by the Hilbert transform (HT) method. Then, the obtained demodulation signal is denoised by variational mode decomposition denoising (VMD denoising), and the denoised signal is analyzed by power spectral density (PSD). Finally, S-LDA is employed on the power signal to determine the severities of fault classification. The effectiveness of the proposed method is verified by experimental results under different severities of blade imbalance fault. The stator current signatures of experiments with different severities of blade imbalance fault are used to validate the effectiveness of the proposed method. The fault classification accuracy is 92.04% based on the proposed method. Moreover, the experimental results verify that the influence of velocity fluctuation on fault classification can be eliminated. View Full-Text
Keywords: marine current turbine; blade imbalance fault; stator current signature; fault classification; VMD denoising; S-LDA marine current turbine; blade imbalance fault; stator current signature; fault classification; VMD denoising; S-LDA
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MDPI and ACS Style

Wei, J.; Xie, T.; Shi, M.; He, Q.; Wang, T.; Amirat, Y. Imbalance Fault Classification Based on VMD Denoising and S-LDA for Variable-Speed Marine Current Turbine. J. Mar. Sci. Eng. 2021, 9, 248. https://doi.org/10.3390/jmse9030248

AMA Style

Wei J, Xie T, Shi M, He Q, Wang T, Amirat Y. Imbalance Fault Classification Based on VMD Denoising and S-LDA for Variable-Speed Marine Current Turbine. Journal of Marine Science and Engineering. 2021; 9(3):248. https://doi.org/10.3390/jmse9030248

Chicago/Turabian Style

Wei, Jiajia; Xie, Tao; Shi, Ming; He, Qianqian; Wang, Tianzhen; Amirat, Yassine. 2021. "Imbalance Fault Classification Based on VMD Denoising and S-LDA for Variable-Speed Marine Current Turbine" J. Mar. Sci. Eng. 9, no. 3: 248. https://doi.org/10.3390/jmse9030248

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