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A comprehensive review on wind turbine power curve modeling techniques

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  • Lydia, M.
  • Kumar, S. Suresh
  • Selvakumar, A. Immanuel
  • Prem Kumar, G. Edwin

Abstract

The wind turbine power curve shows the relationship between the wind turbine power and hub height wind speed. It essentially captures the wind turbine performance. Hence it plays an important role in condition monitoring and control of wind turbines. Power curves made available by the manufacturers help in estimating the wind energy potential in a candidate site. Accurate models of power curve serve as an important tool in wind power forecasting and aid in wind farm expansion. This paper presents an exhaustive overview on the need for modeling of wind turbine power curves and the different methodologies employed for the same. It also reviews in detail the parametric and non-parametric modeling techniques and critically evaluates them. The areas of further research have also been presented.

Suggested Citation

  • Lydia, M. & Kumar, S. Suresh & Selvakumar, A. Immanuel & Prem Kumar, G. Edwin, 2014. "A comprehensive review on wind turbine power curve modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 452-460.
    • Handle: RePEc:eee:rensus:v:30:y:2014:i:c:p:452-460
    DOI: 10.1016/j.rser.2013.10.030
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    References listed on IDEAS

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    1. Fadaee, M. & Radzi, M.A.M., 2012. "Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3364-3369.
    2. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "Models for monitoring wind farm power," Renewable Energy, Elsevier, vol. 34(3), pages 583-590.
    3. Whale, J. & McHenry, M.P. & Malla, A., 2013. "Scheduling and conducting power performance testing of a small wind turbine," Renewable Energy, Elsevier, vol. 55(C), pages 55-61.
    4. Jafarian, M. & Ranjbar, A.M., 2010. "Fuzzy modeling techniques and artificial neural networks to estimate annual energy output of a wind turbine," Renewable Energy, Elsevier, vol. 35(9), pages 2008-2014.
    5. Olaofe, Zaccheus O. & Folly, Komla A., 2013. "Wind energy analysis based on turbine and developed site power curves: A case-study of Darling City," Renewable Energy, Elsevier, vol. 53(C), pages 306-318.
    6. Thapar, Vinay & Agnihotri, Gayatri & Sethi, Vinod Krishna, 2011. "Critical analysis of methods for mathematical modelling of wind turbines," Renewable Energy, Elsevier, vol. 36(11), pages 3166-3177.
    7. Shafiullah, G.M. & M.T. Oo, Amanullah & Shawkat Ali, A.B.M. & Wolfs, Peter, 2013. "Potential challenges of integrating large-scale wind energy into the power grid–A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 306-321.
    8. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    9. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "On-line monitoring of power curves," Renewable Energy, Elsevier, vol. 34(6), pages 1487-1493.
    10. Kusiak, Andrew & Li, Wenyan, 2011. "The prediction and diagnosis of wind turbine faults," Renewable Energy, Elsevier, vol. 36(1), pages 16-23.
    11. Marvuglia, Antonino & Messineo, Antonio, 2012. "Monitoring of wind farms’ power curves using machine learning techniques," Applied Energy, Elsevier, vol. 98(C), pages 574-583.
    12. Schallenberg-Rodriguez, Julieta, 2013. "A methodological review to estimate techno-economical wind energy production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 272-287.
    13. Carrillo, C. & Obando Montaño, A.F. & Cidrás, J. & Díaz-Dorado, E., 2013. "Review of power curve modelling for wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 572-581.
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