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Identification of optimum wind turbine parameters for varying wind climates using a novel month-based turbine performance index

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  • Gugliani, Gaurav Kumar
  • Sarkar, Arnab
  • Ley, Christophe
  • Matsagar, Vasant

Abstract

The capacity factor (CF) and power coefficient (Cp) are two important wind turbine characteristics. CF describes the power generation capacity during a given period, and Cp describes the efficiency of the wind turbine. Both quantities depend on the rated wind speed. Determining the optimal rated wind speed that maximizes a function of CF and Cp that is directly related to a wind turbine’s output wind power density thus is of utmost importance as it leads to a maximum energy output. This paper proposes a novel Month-based Turbine Performance Index (MTPI) that considers the hourly mean wind speed data month-wise and enables the evaluation of this desired optimum rated turbine speed (Vr,opt) for a given site. Here, the 2-parameter Weibull distribution is employed as a single tool to parameterize the wind speed data and determine the wind speed probability density function, wind power density, vertical wind shear, CF, and Cp of the wind turbine. The examined stations taken for the analysis are from Trivandrum, Ahmedabad, and Calcutta in India. Our index is especially important in regions with intra annular variability, since it is the first to consider monthly instead of annual data.

Suggested Citation

  • Gugliani, Gaurav Kumar & Sarkar, Arnab & Ley, Christophe & Matsagar, Vasant, 2021. "Identification of optimum wind turbine parameters for varying wind climates using a novel month-based turbine performance index," Renewable Energy, Elsevier, vol. 171(C), pages 902-914.
    • Handle: RePEc:eee:renene:v:171:y:2021:i:c:p:902-914
    DOI: 10.1016/j.renene.2021.02.141
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