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Higher-Order Spectra Analysis-Based Diagnosis Method of Blades Biofouling in a PMSG Driven Tidal Stream Turbine

Author

Listed:
  • Lotfi Saidi

    (Laboratory of Signal Image and Energy Mastery (SIME, LR 13ES03), Université de Tunis, ENSIT, Tunis 1008, Tunisia
    Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 IRDL), University of Brest, 29238 Brest, France)

  • Mohamed Benbouzid

    (Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 IRDL), University of Brest, 29238 Brest, France
    Engineering Logistics College, Shanghai Maritime University, Shanghai 201306, China)

  • Demba Diallo

    (Engineering Logistics College, Shanghai Maritime University, Shanghai 201306, China
    Génie électrique et électronique de Paris (UMR CNRS 8507 GeePs), Université Paris-Saclay, CentraleSupelec, 91192 Gif-sur-Yvette, France)

  • Yassine Amirat

    (Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 IRDL), ISEN Yncréa Ouest, 29200 Brest, France)

  • Elhoussin Elbouchikhi

    (Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 IRDL), ISEN Yncréa Ouest, 29200 Brest, France)

  • Tianzhen Wang

    (Engineering Logistics College, Shanghai Maritime University, Shanghai 201306, China)

Abstract

Most electrical machines and drive signals are non-Gaussian and are highly nonlinear in nature. A useful set of techniques to examine such signals relies on higher-order statistics (HOS) spectral representations. They describe statistical dependencies of frequency components that are neglected by traditional spectral measures, namely the power spectrum (PS). One of the most used HOS is the bispectrum where examining higher-order correlations should provide further details and information about the conditions of electric machines and drives. In this context, the stator currents of electric machines are of particular interest because they are periodic, nonlinear, and cyclostationary. This current is, therefore, well adapted for analysis using bispectrum in the designing of an efficient condition monitoring method for electric machines and drives. This paper is, therefore, proposing a bispectrum-based diagnosis method dealing the with tidal stream turbine (TST) rotor blades biofouling issue, which is a marine environment natural process responsible for turbine rotor unbalance. The proposed bispectrum-based diagnosis method is verified using experimental data provided from a permanent magnet synchronous generator (PMSG)-based TST experiencing biofouling emulated by attachment on the turbine blade. Based on the achieved results, it can be concluded that the proposed diagnosis method has been very successful. Indeed, biofouling imbalance-related frequencies are clearly identified despite marine environmental nuisances (turbulences and waves).

Suggested Citation

  • Lotfi Saidi & Mohamed Benbouzid & Demba Diallo & Yassine Amirat & Elhoussin Elbouchikhi & Tianzhen Wang, 2020. "Higher-Order Spectra Analysis-Based Diagnosis Method of Blades Biofouling in a PMSG Driven Tidal Stream Turbine," Energies, MDPI, vol. 13(11), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2888-:d:367618
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    References listed on IDEAS

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    1. Chen, Long & Lam, Wei-Haur, 2015. "A review of survivability and remedial actions of tidal current turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 891-900.
    2. Miguel E. Iglesias-Martínez & Jose Alfonso Antonino-Daviu & Pedro Fernández de Córdoba & J. Alberto Conejero, 2019. "Rotor Fault Detection in Induction Motors Based on Time-Frequency Analysis Using the Bispectrum and the Autocovariance of Stray Flux Signals," Energies, MDPI, vol. 12(4), pages 1-16, February.
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