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Detection of mass imbalance in the rotor of wind turbines using Support Vector Machine

Author

Listed:
  • Hübner, G.R.
  • Pinheiro, H.
  • de Souza, C.E.
  • Franchi, C.M.
  • da Rosa, L.D.
  • Dias, J.P.

Abstract

Condition monitoring systems (CMS) are essential to reduce costs in the wind energy sector. This paper proposes a method based on Support Vector Machine (SVM) to detect rotor mass imbalance for a multi-class imbalance problem, using the estimated speed as an input variable, obtained through a combination of electrical quantities (currents and voltages). Moreover, it is sought to obtain the magnitude of the rotor mass imbalance. With the aid of statistical tools, intermediate classes can be estimated, other than the ones proposed for the SVM. Besides, if the azimuth position is provided, the angular position of the mass imbalance can be also obtained. A 1.5 MW three-bladed wind turbine model with a permanent magnet synchronous generator, was considered, and a database was built numerically using the software Turbsim, FAST, and Simulink. From the database, the Power Spectral Density (PSD) technique was used to transform the input data from the time to the frequency domain. Then, the SVM algorithm and statistical analysis were used to classify the magnitude and the angular position of the imbalance. Different scenarios of mass imbalance were tested under different wind speeds and turbulence intensities. The results demonstrate the satisfactory performance of the proposed method.

Suggested Citation

  • Hübner, G.R. & Pinheiro, H. & de Souza, C.E. & Franchi, C.M. & da Rosa, L.D. & Dias, J.P., 2021. "Detection of mass imbalance in the rotor of wind turbines using Support Vector Machine," Renewable Energy, Elsevier, vol. 170(C), pages 49-59.
    • Handle: RePEc:eee:renene:v:170:y:2021:i:c:p:49-59
    DOI: 10.1016/j.renene.2021.01.080
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    Cited by:

    1. Xing, Zuoxia & Chen, Mingyang & Cui, Jia & Chen, Zhe & Xu, Jian, 2022. "Detection of magnitude and position of rotor aerodynamic imbalance of wind turbines using Convolutional Neural Network," Renewable Energy, Elsevier, vol. 197(C), pages 1020-1033.
    2. Mehlan, Felix C. & Nejad, Amir R., 2023. "Rotor imbalance detection and diagnosis in floating wind turbines by means of drivetrain condition monitoring," Renewable Energy, Elsevier, vol. 212(C), pages 70-81.
    3. Yang Ni & Bin Peng & Jiayao Wang & Farshad Golnary & Wei Li, 2023. "A Short Review on the Time-Domain Numerical Simulations for Structural Responses in Horizontal-Axis Offshore Wind Turbines," Sustainability, MDPI, vol. 15(24), pages 1-19, December.

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