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Wind speed modeling for cascade clusters of wind turbines Part 2: Wind speed reduction and aggregation superposition

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  • Dong, Xinghui
  • Li, Jia
  • Gao, Di
  • Zheng, Kai

Abstract

Wind speed calculation is not only the core problem of the wind energy conversion, but also the key to improve the WT conversion efficiency. Wind speed is affected by many factors, so it is difficult to model. The influencing factors of the wind speed and relationship between wind speed are contained in big data of the WTs’ historical monitoring. In this paper, the “coupling element” (CE) and “aggregation element” (AE) of cascade clusters of wind turbines (CCWT) at different wind speeds are taken as the research objects to study the characteristics of the wind speed reduction (WSR) and aggregation superposition. We analyze and calculate the numerical relations in the historical data of the WTs, and obtain the WSR model of CE including the WTs distribution and state parameters. This paper further analyzes the distribution characteristics of the AE, studies the aggregation distribution and convergence form of multiple incoming streams, and puts forward the wind speed aggregation superposition (WSAS) model of AE which considers the friction energy dissipation between the different incoming streams. The WSR and aggregation model of the WTs in CCWT proposed in this paper has been verified by the examples with high accuracy and robustness.

Suggested Citation

  • Dong, Xinghui & Li, Jia & Gao, Di & Zheng, Kai, 2021. "Wind speed modeling for cascade clusters of wind turbines Part 2: Wind speed reduction and aggregation superposition," Energy, Elsevier, vol. 215(PB).
    • Handle: RePEc:eee:energy:v:215:y:2021:i:pb:s0360544220322520
    DOI: 10.1016/j.energy.2020.119145
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    References listed on IDEAS

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    1. Shakoor, Rabia & Hassan, Mohammad Yusri & Raheem, Abdur & Wu, Yuan-Kang, 2016. "Wake effect modeling: A review of wind farm layout optimization using Jensen׳s model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1048-1059.
    2. Song, M.X. & Chen, K. & He, Z.Y. & Zhang, X., 2012. "Wake flow model of wind turbine using particle simulation," Renewable Energy, Elsevier, vol. 41(C), pages 185-190.
    3. Siniscalchi-Minna, Sara & Bianchi, Fernando D. & De-Prada-Gil, Mikel & Ocampo-Martinez, Carlos, 2019. "A wind farm control strategy for power reserve maximization," Renewable Energy, Elsevier, vol. 131(C), pages 37-44.
    4. Göçmen, Tuhfe & Laan, Paul van der & Réthoré, Pierre-Elouan & Diaz, Alfredo Peña & Larsen, Gunner Chr. & Ott, Søren, 2016. "Wind turbine wake models developed at the technical university of Denmark: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 752-769.
    5. Dong, Xinghui & Li, Jia & Gao, Di & Zheng, Kai, 2020. "Wind speed modeling for cascade clusters of wind turbines part 1: The cascade clusters of wind turbines," Energy, Elsevier, vol. 205(C).
    6. Majid Bastankhah & Fernando Porté-Agel, 2017. "A New Miniature Wind Turbine for Wind Tunnel Experiments. Part II: Wake Structure and Flow Dynamics," Energies, MDPI, vol. 10(7), pages 1-19, July.
    7. Dongran Song & Jian Yang & Mei Su & Anfeng Liu & Yao Liu & Young Hoon Joo, 2017. "A Comparison Study between Two MPPT Control Methods for a Large Variable-Speed Wind Turbine under Different Wind Speed Characteristics," Energies, MDPI, vol. 10(5), pages 1-18, May.
    8. González-Longatt, F. & Wall, P. & Terzija, V., 2012. "Wake effect in wind farm performance: Steady-state and dynamic behavior," Renewable Energy, Elsevier, vol. 39(1), pages 329-338.
    9. Majid Bastankhah & Fernando Porté-Agel, 2017. "A New Miniature Wind Turbine for Wind Tunnel Experiments. Part I: Design and Performance," Energies, MDPI, vol. 10(7), pages 1-19, July.
    10. Yin, Peng-Yeng & Wu, Tsai-Hung & Hsu, Ping-Yi, 2017. "Simulation based risk management for multi-objective optimal wind turbine placement using MOEA/D," Energy, Elsevier, vol. 141(C), pages 579-597.
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