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Wind Turbine Power Curve Design for Optimal Power Generation in Wind Farms Considering Wake Effect

Author

Listed:
  • Jie Tian

    (Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark
    Sino-Danish Centre for Education and Research, 8000 Aarhus, Denmark)

  • Dao Zhou

    (Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

  • Chi Su

    (Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

  • Mohsen Soltani

    (Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

  • Zhe Chen

    (Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

  • Frede Blaabjerg

    (Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

Abstract

In modern wind farms, maximum power point tracking (MPPT) is widely implemented. Using the MPPT method, each individual wind turbine is controlled by its pitch angle and tip speed ratio to generate the maximum active power. In a wind farm, the upstream wind turbine may cause power loss to its downstream wind turbines due to the wake effect. According to the wake model, downstream power loss is also determined by the pitch angle and tip speed ratio of the upstream wind turbine. By optimizing the pitch angle and tip speed ratio of each wind turbine, the total active power of the wind farm can be increased. In this paper, the optimal pitch angle and tip speed ratio are selected for each wind turbine by the exhausted search. Considering the estimation error of the wake model, a solution to implement the optimized pitch angle and tip speed ratio is proposed, which is to generate the optimal control curves for each individual wind turbine off-line. In typical wind farms with regular layout, based on the detailed analysis of the influence of pitch angle and tip speed ratio on the total active power of the wind farm by the exhausted search, the optimization is simplified with the reduced computation complexity. By using the optimized control curves, the annual energy production ( AEP ) is increased by 1.03% compared to using the MPPT method in a case-study of a typical eighty-turbine wind farm.

Suggested Citation

  • Jie Tian & Dao Zhou & Chi Su & Mohsen Soltani & Zhe Chen & Frede Blaabjerg, 2017. "Wind Turbine Power Curve Design for Optimal Power Generation in Wind Farms Considering Wake Effect," Energies, MDPI, vol. 10(3), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:3:p:395-:d:93565
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    References listed on IDEAS

    as
    1. Gonzalez-Rodriguez, Angel G. & Burgos-Payan, Manuel & Riquelme-Santos, Jesus & Serrano-Gonzalez, Javier, 2015. "Reducing computational effort in the calculation of annual energy produced in wind farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 656-665.
    2. Serrano González, Javier & Burgos Payán, Manuel & Riquelme Santos, Jesús & González Rodríguez, Ángel Gaspar, 2015. "Maximizing the overall production of wind farms by setting the individual operating point of wind turbines," Renewable Energy, Elsevier, vol. 80(C), pages 219-229.
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    Cited by:

    1. Siyu Tao & Qingshan Xu & Andrés Feijóo & Stefanie Kuenzel & Neeraj Bokde, 2019. "Integrated Wind Farm Power Curve and Power Curve Distribution Function Considering the Wake Effect and Terrain Gradient," Energies, MDPI, vol. 12(13), pages 1-14, June.
    2. Neeraj Bokde & Andrés Feijóo & Nadhir Al-Ansari & Siyu Tao & Zaher Mundher Yaseen, 2020. "The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling," Energies, MDPI, vol. 13(7), pages 1-23, April.
    3. Zheng Li & Wenda Zhang & Hao Dong & Yongsheng Tian, 2019. "Performance Analysis and Structure Optimization of a Nautilus Isometric Spiral Wind Turbine," Energies, MDPI, vol. 13(1), pages 1-20, December.
    4. Zheng, Jiancai & Wang, Nina & Wan, Decheng & Strijhak, Sergei, 2023. "Numerical investigations of coupled aeroelastic performance of wind turbines by elastic actuator line model," Applied Energy, Elsevier, vol. 330(PB).
    5. Saint-Drenan, Yves-Marie & Besseau, Romain & Jansen, Malte & Staffell, Iain & Troccoli, Alberto & Dubus, Laurent & Schmidt, Johannes & Gruber, Katharina & Simões, Sofia G. & Heier, Siegfried, 2020. "A parametric model for wind turbine power curves incorporating environmental conditions," Renewable Energy, Elsevier, vol. 157(C), pages 754-768.
    6. 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.
    7. Yolanda Vidal & Leonardo Acho & Ignasi Cifre & Àlex Garcia & Francesc Pozo & José Rodellar, 2017. "Wind Turbine Synchronous Reset Pitch Control," Energies, MDPI, vol. 10(6), pages 1-16, June.
    8. Zhenzhou Shao & Ying Wu & Li Li & Shuang Han & Yongqian Liu, 2019. "Multiple Wind Turbine Wakes Modeling Considering the Faster Wake Recovery in Overlapped Wakes," Energies, MDPI, vol. 12(4), pages 1-14, February.

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