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Identification of the dynamics of the drivetrain and estimating its unknown parts in a large scale wind turbine

Golnary, F ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1016/j.matcom.2021.08.012
  3. Publisher: Elsevier B.V , 2022
  4. Abstract:
  5. In this paper, the drivetrain identification problem of a horizontal axis gear-driven wind turbine has been considered. The identification problem leads to a precise model of the drivetrain of the wind turbines which plays a key role in the production and transmission of electrical energy. This process consists of two stages: First, offline identification which needs the input–output data from the drivetrain system. These data are obtained from the FAST code. FAST (Fatigue, Aerodynamics, Structures, and Turbulence) is a valid aeroelastic code in the simulation aeroelastic field of offshore and onshore wind turbines. In region 2 (wind velocity is between the cut-in and rated velocities), the generator torque is input, and rotor speed is output. It is supposed that the wind velocity is neglected in the identification process (the identification process is not considered in the presence of wind or done in the low wind velocities bellower than the cut-in wind velocity). In the second stage, after completion of the offline identification process, the drivetrain model of the wind turbine is obtained but the aerodynamic torque in the real performance of the wind turbine is still unknown. In this stage, to estimate the aerodynamic torque, high order sliding mode estimator is used and the unknown states are estimated by using the Extended Kalman Filter (EKF) © 2021 International Association for Mathematics and Computers in Simulation (IMACS)
  6. Keywords:
  7. Estimating unknown input ; Extended Kalman Filter (EKF) ; System identification ; Aerodynamics ; Aeroelasticity ; Codes (symbols) ; Extended Kalman filters ; Offshore oil well production ; Offshore wind turbines ; Sliding mode control ; Velocity ; Drivetrain of wind turbine ; Extended kalman filter ; High order sliding mode estimator ; Higher order sliding modes ; Identification process ; Sliding mode estimator ; System-identification ; Unknown inputs ; Wind velocities ; Wind
  8. Source: Mathematics and Computers in Simulation ; Volume 192 , 2022 , Pages 50-69 ; 03784754 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0378475421002895