IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v161y2018icp277-283.html
   My bibliography  Save this article

Wake effect measurement in complex terrain - A case study in Brazilian wind farms

Author

Listed:
  • Böhme, Gustavo S.
  • Fadigas, Eliane A.
  • Gimenes, André L.V.
  • Tassinari, Carlos E.M.

Abstract

This study measured the wake effect in a Brazilian onshore windfarm with 38 turbines located in complex terrain. The proposed methodology calculated the wind deficit in 3 different metmasts, by comparing the measurement periods in free-of-wake condition to measurement periods under wake effect. Uncertainties due to wind variability and seasonality effects have been avoided by performing MCP (Measure Correlate Predict) procedures making use of a fourth metmast in free-of-wake condition during the entire concurrent period. This methodology is free of uncertainties from Nacelle anemometry, power curve measurements and micrositing models' spatial extrapolation.

Suggested Citation

  • Böhme, Gustavo S. & Fadigas, Eliane A. & Gimenes, André L.V. & Tassinari, Carlos E.M., 2018. "Wake effect measurement in complex terrain - A case study in Brazilian wind farms," Energy, Elsevier, vol. 161(C), pages 277-283.
    • Handle: RePEc:eee:energy:v:161:y:2018:i:c:p:277-283
    DOI: 10.1016/j.energy.2018.07.119
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218314178
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2018.07.119?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Emami, Alireza & Noghreh, Pirooz, 2010. "New approach on optimization in placement of wind turbines within wind farm by genetic algorithms," Renewable Energy, Elsevier, vol. 35(7), pages 1559-1564.
    2. Marmidis, Grigorios & Lazarou, Stavros & Pyrgioti, Eleftheria, 2008. "Optimal placement of wind turbines in a wind park using Monte Carlo simulation," Renewable Energy, Elsevier, vol. 33(7), pages 1455-1460.
    3. Turner, S.D.O. & Romero, D.A. & Zhang, P.Y. & Amon, C.H. & Chan, T.C.Y., 2014. "A new mathematical programming approach to optimize wind farm layouts," Renewable Energy, Elsevier, vol. 63(C), pages 674-680.
    4. Kusiak, Andrew & Song, Zhe, 2010. "Design of wind farm layout for maximum wind energy capture," Renewable Energy, Elsevier, vol. 35(3), pages 685-694.
    5. Adaramola, M.S. & Krogstad, P.-Å., 2011. "Experimental investigation of wake effects on wind turbine performance," Renewable Energy, Elsevier, vol. 36(8), pages 2078-2086.
    6. 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.
    7. Matthias Ritter & Simone Pieralli & Martin Odening, 2017. "Neighborhood Effects in Wind Farm Performance: A Regression Approach," Energies, MDPI, vol. 10(3), pages 1-16, March.
    8. Leonardo P. Chamorro & Fernando Porté-Agel, 2011. "Turbulent Flow Inside and Above a Wind Farm: A Wind-Tunnel Study," Energies, MDPI, vol. 4(11), pages 1-21, November.
    9. Grassi, Stefano & Junghans, Sven & Raubal, Martin, 2014. "Assessment of the wake effect on the energy production of onshore wind farms using GIS," Applied Energy, Elsevier, vol. 136(C), pages 827-837.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2020. "A review of full-scale wind-field measurements of the wind-turbine wake effect and a measurement of the wake-interaction effect," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    2. Fei Zhao & Yihan Gao & Tengyuan Wang & Jinsha Yuan & Xiaoxia Gao, 2020. "Experimental Study on Wake Evolution of a 1.5 MW Wind Turbine in a Complex Terrain Wind Farm Based on LiDAR Measurements," Sustainability, MDPI, vol. 12(6), pages 1-14, March.
    3. Zhang, Jincheng & Zhao, Xiaowei, 2020. "Quantification of parameter uncertainty in wind farm wake modeling," Energy, Elsevier, vol. 196(C).
    4. Böhme, Gustavo S. & Fadigas, Eliane A. & Soares, Dorel & Gimenes, André L.V. & Macedo, Bruno C., 2020. "Wind speed variability and portfolio effect – A case study in the Brazilian market," Energy, Elsevier, vol. 207(C).
    5. Wang, Qiang & Luo, Kun & Yuan, Renyu & Wang, Shuai & Fan, Jianren & Cen, Kefa, 2020. "A multiscale numerical framework coupled with control strategies for simulating a wind farm in complex terrain," Energy, Elsevier, vol. 203(C).
    6. Wang, Qiang & Luo, Kun & Wu, Chunlei & Zhu, Zhaofan & Fan, Jianren, 2022. "Mesoscale simulations of a real onshore wind power base in complex terrain: Wind farm wake behavior and power production," Energy, Elsevier, vol. 241(C).
    7. Han, Qinkai & Chu, Fulei, 2021. "Directional wind energy assessment of China based on nonparametric copula models," Renewable Energy, Elsevier, vol. 164(C), pages 1334-1349.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dinçer, A.E. & Demir, A. & Yılmaz, K., 2024. "Multi-objective turbine allocation on a wind farm site," Applied Energy, Elsevier, vol. 355(C).
    2. Azlan, F. & Kurnia, J.C. & Tan, B.T. & Ismadi, M.-Z., 2021. "Review on optimisation methods of wind farm array under three classical wind condition problems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    3. Matthias Ritter & Simone Pieralli & Martin Odening, 2017. "Neighborhood Effects in Wind Farm Performance: A Regression Approach," Energies, MDPI, vol. 10(3), pages 1-16, March.
    4. Ulku, I. & Alabas-Uslu, C., 2019. "A new mathematical programming approach to wind farm layout problem under multiple wake effects," Renewable Energy, Elsevier, vol. 136(C), pages 1190-1201.
    5. Wu, Chutian & Yang, Xiaolei & Zhu, Yaxin, 2021. "On the design of potential turbine positions for physics-informed optimization of wind farm layout," Renewable Energy, Elsevier, vol. 164(C), pages 1108-1120.
    6. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2016. "Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model," Applied Energy, Elsevier, vol. 174(C), pages 192-200.
    7. Kyoungboo Yang & Kyungho Cho, 2019. "Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study," Energies, MDPI, vol. 12(23), pages 1-15, November.
    8. repec:hum:wpaper:sfb649dp2016-012 is not listed on IDEAS
    9. Yang, Kyoungboo & Kwak, Gyeongil & Cho, Kyungho & Huh, Jongchul, 2019. "Wind farm layout optimization for wake effect uniformity," Energy, Elsevier, vol. 183(C), pages 983-995.
    10. Syed Ahmed Kabir, Ijaz Fazil & Safiyullah, Ferozkhan & Ng, E.Y.K. & Tam, Vivian W.Y., 2020. "New analytical wake models based on artificial intelligence and rivalling the benchmark full-rotor CFD predictions under both uniform and ABL inflows," Energy, Elsevier, vol. 193(C).
    11. Ritter, Matthias & Pieralli, Simone & Odening, Martin, 2016. "Neighborhood effects in wind farm performance: An econometric approach," SFB 649 Discussion Papers 2016-012, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    12. Cao, Lichao & Ge, Mingwei & Gao, Xiaoxia & Du, Bowen & Li, Baoliang & Huang, Zhi & Liu, Yongqian, 2022. "Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines," Applied Energy, Elsevier, vol. 323(C).
    13. Guirguis, David & Romero, David A. & Amon, Cristina H., 2017. "Gradient-based multidisciplinary design of wind farms with continuous-variable formulations," Applied Energy, Elsevier, vol. 197(C), pages 279-291.
    14. Park, Jinkyoo & Law, Kincho H., 2015. "Layout optimization for maximizing wind farm power production using sequential convex programming," Applied Energy, Elsevier, vol. 151(C), pages 320-334.
    15. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2014. "Investigation into the optimal wind turbine layout patterns for a Hong Kong offshore wind farm," Energy, Elsevier, vol. 73(C), pages 430-442.
    16. Hou, Peng & Hu, Weihao & Soltani, Mohsen & Chen, Cong & Chen, Zhe, 2017. "Combined optimization for offshore wind turbine micro siting," Applied Energy, Elsevier, vol. 189(C), pages 271-282.
    17. Nicolas Kirchner-Bossi & Fernando Porté-Agel, 2018. "Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model," Energies, MDPI, vol. 11(12), pages 1-26, November.
    18. Abdelsalam, Ali M. & El-Shorbagy, M.A., 2018. "Optimization of wind turbines siting in a wind farm using genetic algorithm based local search," Renewable Energy, Elsevier, vol. 123(C), pages 748-755.
    19. Muhammad Nabeel Hussain & Nadeem Shaukat & Ammar Ahmad & Muhammad Abid & Abrar Hashmi & Zohreh Rajabi & Muhammad Atiq Ur Rehman Tariq, 2022. "Micro-Siting of Wind Turbines in an Optimal Wind Farm Area Using Teaching–Learning-Based Optimization Technique," Sustainability, MDPI, vol. 14(14), pages 1-24, July.
    20. Serrano González, Javier & Burgos Payán, Manuel & Santos, Jesús Manuel Riquelme & González-Longatt, Francisco, 2014. "A review and recent developments in the optimal wind-turbine micro-siting problem," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 133-144.
    21. He, Ruiyang & Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2022. "Wind tunnel tests for wind turbines: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:161:y:2018:i:c:p:277-283. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.