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Determination of rated wind speed for maximum annual energy production of variable speed wind turbines

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  • Sedaghat, Ahmad
  • Hassanzadeh, Arash
  • Jamali, Jamaloddin
  • Mostafaeipour, Ali
  • Chen, Wei-Hsin

Abstract

Rated wind speed is recognized as one of the key design factors affecting the overall power production of a wind turbine. No formulation is found in literature to relate the rated wind speed to the wind turbine power curves and the annual energy production (AEP). This paper aims to formulate the suitable rated wind speed for variable speed wind turbines continuously operating at maximum power coefficient for maximizing AEP. A capacity value is introduced which relates AEP to an integral function of the rated wind speed using Weibull distribution of wind speeds and the constant power coefficient of variable speed wind turbines. The capacity values are calculated and presented versus rated wind speeds at different wind classes and Weibull parameters. From the results, the suitable values for the rated wind speeds for maximizing AEP are found which are considerably higher than normally used values and varied from 2 to 5 times of the annual mean wind speed. For instance, for the mean annual wind speed of 4m/s and the shape factors of k=1.2, 1.6, 2.0, 2.4, 2.8, 3.2, and 3.6, the converged rated wind speeds are Vrate=20, 19, 14, 12, 10, 10, and 9m/s, respectively. On this basis, new charts for rated wind speeds are introduced for selecting suitable wind turbines for maximizing AEP. It is concluded that for some selected wind turbines operating at lower rated wind speeds, the AEP may fall below about 43% of actual achievable AEP when employing higher recommended rated wind speeds. Hence, it is shown that selecting the right rated wind speed wind turbines has great impact on overall energy production of a wind site.

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  • Sedaghat, Ahmad & Hassanzadeh, Arash & Jamali, Jamaloddin & Mostafaeipour, Ali & Chen, Wei-Hsin, 2017. "Determination of rated wind speed for maximum annual energy production of variable speed wind turbines," Applied Energy, Elsevier, vol. 205(C), pages 781-789.
    • Handle: RePEc:eee:appene:v:205:y:2017:i:c:p:781-789
    DOI: 10.1016/j.apenergy.2017.08.079
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    as
    1. Ashuri, T. & Zaaijer, M.B. & Martins, J.R.R.A. & van Bussel, G.J.W. & van Kuik, G.A.M., 2014. "Multidisciplinary design optimization of offshore wind turbines for minimum levelized cost of energy," Renewable Energy, Elsevier, vol. 68(C), pages 893-905.
    2. D’Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2015. "Economic performance indicators of wind energy based on wind speed stochastic modeling," Applied Energy, Elsevier, vol. 154(C), pages 290-297.
    3. Masseran, Nurulkamal, 2016. "Modeling the fluctuations of wind speed data by considering their mean and volatility effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 777-784.
    4. Bontempo, R. & Manna, M., 2016. "Effects of the duct thrust on the performance of ducted wind turbines," Energy, Elsevier, vol. 99(C), pages 274-287.
    5. Amirinia, Gholamreza & Kamranzad, Bahareh & Mafi, Somayeh, 2017. "Wind and wave energy potential in southern Caspian Sea using uncertainty analysis," Energy, Elsevier, vol. 120(C), pages 332-345.
    6. Lanzafame, R. & Messina, M., 2010. "Horizontal axis wind turbine working at maximum power coefficient continuously," Renewable Energy, Elsevier, vol. 35(1), pages 301-306.
    7. Abdulrahman, Mamdouh & Wood, David, 2017. "Investigating the Power-COE trade-off for wind farm layout optimization considering commercial turbine selection and hub height variation," Renewable Energy, Elsevier, vol. 102(PB), pages 267-278.
    8. Saleh, H. & Abou El-Azm Aly, A. & Abdel-Hady, S., 2012. "Assessment of different methods used to estimate Weibull distribution parameters for wind speed in Zafarana wind farm, Suez Gulf, Egypt," Energy, Elsevier, vol. 44(1), pages 710-719.
    9. He-Yong Xu & Chen-Liang Qiao & Zheng-Yin Ye, 2016. "Dynamic Stall Control on the Wind Turbine Airfoil via a Co-Flow Jet," Energies, MDPI, vol. 9(6), pages 1-25, June.
    10. 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.
    11. Castellani, Francesco & Astolfi, Davide & Sdringola, Paolo & Proietti, Stefania & Terzi, Ludovico, 2017. "Analyzing wind turbine directional behavior: SCADA data mining techniques for efficiency and power assessment," Applied Energy, Elsevier, vol. 185(P2), pages 1076-1086.
    12. Al-Nassar, W. & Alhajraf, S. & Al-Enizi, A. & Al-Awadhi, L., 2005. "Potential wind power generation in the State of Kuwait," Renewable Energy, Elsevier, vol. 30(14), pages 2149-2161.
    13. Chen, Wei-Hsin & Chen, Ching-Ying & Huang, Chun-Yen & Hwang, Chii-Jong, 2017. "Power output analysis and optimization of two straight-bladed vertical-axis wind turbines," Applied Energy, Elsevier, vol. 185(P1), pages 223-232.
    14. Wang, Huai-zhi & Li, Gang-qiang & Wang, Gui-bin & Peng, Jian-chun & Jiang, Hui & Liu, Yi-tao, 2017. "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 188(C), pages 56-70.
    15. Sedaghat, Ahmad & El Haj Assad, M. & Gaith, Mohamed, 2014. "Aerodynamics performance of continuously variable speed horizontal axis wind turbine with optimal blades," Energy, Elsevier, vol. 77(C), pages 752-759.
    16. Chehouri, Adam & Younes, Rafic & Ilinca, Adrian & Perron, Jean, 2015. "Review of performance optimization techniques applied to wind turbines," Applied Energy, Elsevier, vol. 142(C), pages 361-388.
    17. Yurdusev, M.A. & Ata, R. & Çetin, N.S., 2006. "Assessment of optimum tip speed ratio in wind turbines using artificial neural networks," Energy, Elsevier, vol. 31(12), pages 2153-2161.
    18. Wang, Jian-Zhou & Wang, Yun & Jiang, Ping, 2015. "The study and application of a novel hybrid forecasting model – A case study of wind speed forecasting in China," Applied Energy, Elsevier, vol. 143(C), pages 472-488.
    19. Bontempo, R. & Manna, M., 2014. "Performance analysis of open and ducted wind turbines," Applied Energy, Elsevier, vol. 136(C), pages 405-416.
    20. Song, Dongran & Yang, Jian & Cai, Zili & Dong, Mi & Su, Mei & Wang, Yinghua, 2017. "Wind estimation with a non-standard extended Kalman filter and its application on maximum power extraction for variable speed wind turbines," Applied Energy, Elsevier, vol. 190(C), pages 670-685.
    21. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
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