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An Induction Curve Model for Prediction of Power Output of Wind Turbines in Complex Conditions

Author

Listed:
  • Mohsen Vahidzadeh

    (IIHR-Hydroscience and Engineering, The University of Iowa, Iowa City, IA 52242, USA
    Civil and Environmental Engineering, The University of Iowa, Iowa City, IA 52242, USA)

  • Corey D. Markfort

    (IIHR-Hydroscience and Engineering, The University of Iowa, Iowa City, IA 52242, USA
    Civil and Environmental Engineering, The University of Iowa, Iowa City, IA 52242, USA)

Abstract

Power generation from wind farms is traditionally modeled using power curves. These models are used for assessment of wind resources or for forecasting energy production from existing wind farms. However, prediction of power using power curves is not accurate since power curves are based on ideal uniform inflow wind, which do not apply to wind turbines installed in complex and heterogeneous terrains and in wind farms. Therefore, there is a need for new models that account for the effect of non-ideal operating conditions. In this work, we propose a model for effective axial induction factor of wind turbines that can be used for power prediction. The proposed model is tested and compared to traditional power curve for a 2.5 MW horizontal axis wind turbine. Data from supervisory control and data acquisition (SCADA) system along with wind speed measurements from a nacelle-mounted sonic anemometer and turbulence measurements from a nearby meteorological tower are used in the models. The results for a period of four months showed an improvement of 51% in power prediction accuracy, compared to the standard power curve.

Suggested Citation

  • Mohsen Vahidzadeh & Corey D. Markfort, 2020. "An Induction Curve Model for Prediction of Power Output of Wind Turbines in Complex Conditions," Energies, MDPI, vol. 13(4), pages 1-23, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:891-:d:321760
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    References listed on IDEAS

    as
    1. Mohsen Vahidzadeh & Corey D. Markfort, 2019. "Modified Power Curves for Prediction of Power Output of Wind Farms," Energies, MDPI, vol. 12(9), pages 1-19, May.
    2. Dongheon Shin & Kyungnam Ko, 2019. "Application of the Nacelle Transfer Function by a Nacelle-Mounted Light Detection and Ranging System to Wind Turbine Power Performance Measurement," Energies, MDPI, vol. 12(6), pages 1-15, March.
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    Cited by:

    1. Evangelos Spiliotis & Fotios Petropoulos & Konstantinos Nikolopoulos, 2020. "The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece," Energies, MDPI, vol. 13(8), pages 1-18, April.
    2. Adolfo Dannier & Emanuele Fedele & Ivan Spina & Gianluca Brando, 2022. "Doubly-Fed Induction Generator (DFIG) in Connected or Weak Grids for Turbine-Based Wind Energy Conversion System," Energies, MDPI, vol. 15(17), pages 1-5, September.

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