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A comprehensive review of wind resource assessment

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  • Murthy, K.S.R.
  • Rahi, O.P.

Abstract

A minor deviation in wind speed causes large deviation in the output power of wind turbine because of cubic bond association between these two parameters. Therefore, a precise assessment of wind resource over any site is considered of paramount significance. The investigations associated with the wind resource assessment have been proved of immense help for installation of different wind energy technologies such as nano, micro, small, medium, and large scale for wind energy generation. In order to provide a detailed information regarding the research in wind resource assessment a comprehensive literature review encompassing the different techniques, methodologies involved in development of wind power projects, and uncertainties associated with wind resource assessment as well as the preliminary assessment methodologies have been presented in this work. The advanced computational models namely wind atlas analysis and program, WindPro, computational fluid dynamics, and geographical information system are most frequently used software tools for mapping, modelling and annual energy estimation for a single or multiple wind turbines by considering the local conditions such as topography, surrounding obstacles, orography, and surface roughness based on the on-site measurements for a particular site. In addition, the statistical methods for short and long term data analysis, vertical wind speed profile, numerical weather prediction models, optimization of existing wind resource, and scope of hybrid wind energy systems have been delineated in this paper. Furthermore, current review presents a complete approach with reference to all the facets of the present status of research in the area of wind resource assessment worldwide.

Suggested Citation

  • Murthy, K.S.R. & Rahi, O.P., 2017. "A comprehensive review of wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1320-1342.
    • Handle: RePEc:eee:rensus:v:72:y:2017:i:c:p:1320-1342
    DOI: 10.1016/j.rser.2016.10.038
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