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Prediction and Validation of the Annual Energy Production of a Wind Turbine Using WindSim and a Dynamic Wind Turbine Model

Author

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  • Yuan Song

    (Department of Advanced Mechanical Engineering, Kangwon National University, Chuncheon 24341, Gangwon, Korea)

  • Insu Paek

    (Department of Integrated Energy & Infra System, Kangwon National University, Chuncheon 24341, Gangwon, Korea
    Department of Mechatronics Engineering, Kangwon National University, Chuncheon 24341, Gangwon, Korea)

Abstract

In this study, dynamic simulations of a wind turbine were performed to predict its dynamic performance, and the results were experimentally validated. The dynamic simulation received time-domain wind speed and direction data and predicted the power output by applying control algorithms. The target wind turbine for the simulation was a 2 MW wind turbine installed in an onshore wind farm. The wind speed and direction data for the simulation were obtained from WindSim, which is a commercial computational fluid dynamics (CFD) code for wind farm design, and measured wind speed and direction data with a mast were used for WindSim. For the simulation, the wind turbine controller was tuned to match the power curve of the target wind turbine. The dynamic simulation was performed for a period of one year, and the results were compared with the results from WindSim and the measurement. It was found from the comparison that the annual energy production (AEP) of a wind turbine can be accurately predicted using a dynamic wind turbine model with a controller that takes into account both power regulations and yaw actions with wind speed and direction data obtained from WindSim.

Suggested Citation

  • Yuan Song & Insu Paek, 2020. "Prediction and Validation of the Annual Energy Production of a Wind Turbine Using WindSim and a Dynamic Wind Turbine Model," Energies, MDPI, vol. 13(24), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:24:p:6604-:d:462027
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    References listed on IDEAS

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    4. Hyungyu Kim & Kwansu Kim & Insu Paek, 2019. "A Study on the Effect of Closed-Loop Wind Farm Control on Power and Tower Load in Derating the TSO Command Condition," Energies, MDPI, vol. 12(10), pages 1-19, May.
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    Cited by:

    1. Riccardo De Blasis & Giovanni Batista Masala & Filippo Petroni, 2021. "A Multivariate High-Order Markov Model for the Income Estimation of a Wind Farm," Energies, MDPI, vol. 14(2), pages 1-16, January.
    2. Francesco Castellani & Ravi Pandit & Francesco Natili & Francesca Belcastro & Davide Astolfi, 2023. "Advanced Methods for Wind Turbine Performance Analysis Based on SCADA Data and CFD Simulations," Energies, MDPI, vol. 16(3), pages 1-15, January.
    3. Hao Liu & Jixing Chen & Jing Zhang & Yining Chen & Yafeng Wen & Xiaoyang Zhang & Zhongjie Yan & Qingan Li, 2023. "Study on Atmospheric Stability and Wake Attenuation Constant of Large Offshore Wind Farm in Yellow Sea," Energies, MDPI, vol. 16(5), pages 1-15, February.
    4. Dongmyoung Kim & Taesu Jeon & Insu Paek & Daeyoung Kim, 2022. "A Study on Available Power Estimation Algorithm and Its Validation," Energies, MDPI, vol. 15(7), pages 1-14, April.
    5. Shaima A. Alnaqbi & Abdul Hai Alami, 2023. "Sustainability and Renewable Energy in the UAE: A Case Study of Sharjah," Energies, MDPI, vol. 16(20), pages 1-30, October.

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