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Dynamic wind estimation based control for small wind turbines

Author

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  • Meyer, David G.
  • Srinivasan, Sriram
  • Semrau, Greg

Abstract

We introduce a novel scheme for small wind turbines that gives dynamic estimation of wind speed from rotor angular velocity measurements. The estimation proceeds in two different dynamic observers, one giving a valid estimate for higher Tip Speed Ratios (TSRs) and which we call the Upper Wind Estimator (UWE) and the other called the Lower Wind Estimator (LWE) valid for lower TSRs. The meaning of “higher” and “lower”, and the precise regions of validity, are quantified. We further propose a coordinated control scheme using the UWE. Simulations are presented showing closed-loop performance of the turbine and the estimators both in the optimal TSR regulation condition, and the dynamic power-shedding condition caused by a wind gust. An analytic analysis of closed-loop stability and of the convergence and bias properties of the estimator is provided. Empirical data showing performance on a real turbine is also presented.

Suggested Citation

  • Meyer, David G. & Srinivasan, Sriram & Semrau, Greg, 2013. "Dynamic wind estimation based control for small wind turbines," Renewable Energy, Elsevier, vol. 50(C), pages 259-267.
    • Handle: RePEc:eee:renene:v:50:y:2013:i:c:p:259-267
    DOI: 10.1016/j.renene.2012.06.034
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    References listed on IDEAS

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    1. Kodama, Naruhito & Matsuzaka, Tomoyuki & Tuchiya, Keiichi & Arinaga, Shinji, 1999. "Power variation control of a wind generator by using feed forward control," Renewable Energy, Elsevier, vol. 16(1), pages 847-850.
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    Cited by:

    1. Rocha, P.A. Costa & Rocha, H.H. Barbosa & Carneiro, F.O. Moura & Vieira da Silva, M.E. & Bueno, A. Valente, 2014. "k–ω SST (shear stress transport) turbulence model calibration: A case study on a small scale horizontal axis wind turbine," Energy, Elsevier, vol. 65(C), pages 412-418.
    2. Belmokhtar, K. & Doumbia, M.L. & Agbossou, K., 2014. "Novel fuzzy logic based sensorless maximum power point tracking strategy for wind turbine systems driven DFIG (doubly-fed induction generator)," Energy, Elsevier, vol. 76(C), pages 679-693.
    3. Bertašienė, Agnė & Azzopardi, Brian, 2015. "Synergies of Wind Turbine control techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 336-342.
    4. Yossri, Widad & Ben Ayed, Samah & Abdelkefi, Abdessattar, 2021. "Airfoil type and blade size effects on the aerodynamic performance of small-scale wind turbines: Computational fluid dynamics investigation," Energy, Elsevier, vol. 229(C).
    5. Rocha, P. A. Costa & Rocha, H. H. Barbosa & Carneiro, F. O. Moura & da Silva, M. E. Vieira & de Andrade, C. Freitas, 2016. "A case study on the calibration of the k–ω SST (shear stress transport) turbulence model for small scale wind turbines designed with cambered and symmetrical airfoils," Energy, Elsevier, vol. 97(C), pages 144-150.
    6. Marwa Hassan & Alsnosy Balbaa & Hanady H. Issa & Noha H. El-Amary, 2018. "Asymptotic Output Tracked Artificial Immunity Controller for Eco-Maximum Power Point Tracking of Wind Turbine Driven by Doubly Fed Induction Generator," Energies, MDPI, vol. 11(10), pages 1-25, October.

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