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Multi-peak Gaussian fit applicability to wind speed distribution

Author

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  • Hossain, Jami
  • Sharma, Suman
  • Kishore, V.V.N.

Abstract

Efforts to harness wind energy on a large scale have gained momentum across the world. By the end of December 2013, a cumulative capacity of more than 300GW of wind power projects had been installed all over the world. One of the key aspects involved in implementing wind power projects is the analysis of wind speeds distributions observed or recorded and assessment of annual energy output from the wind turbines. The wind speed frequency distribution is generally assumed to follow two-parameter Weibull Distribution. In general, across the world, annual energy generation estimations of a wind turbine at a given site are assessed on the basis of Weibull Distribution. However, in this paper, based on a robust analysis carried out on over 208 measurement sites in India, we show that multi-peak Gaussian distribution functions are a significantly improved representation of observed wind speed distributions.

Suggested Citation

  • Hossain, Jami & Sharma, Suman & Kishore, V.V.N., 2014. "Multi-peak Gaussian fit applicability to wind speed distribution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 483-490.
    • Handle: RePEc:eee:rensus:v:34:y:2014:i:c:p:483-490
    DOI: 10.1016/j.rser.2014.03.026
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    References listed on IDEAS

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    1. Dvorak, Michael J. & Archer, Cristina L. & Jacobson, Mark Z., 2010. "California offshore wind energy potential," Renewable Energy, Elsevier, vol. 35(6), pages 1244-1254.
    2. Hossain, Jami & Sinha, Vinay & Kishore, V.V.N., 2011. "A GIS based assessment of potential for windfarms in India," Renewable Energy, Elsevier, vol. 36(12), pages 3257-3267.
    3. Akdag, S.A. & Bagiorgas, H.S. & Mihalakakou, G., 2010. "Use of two-component Weibull mixtures in the analysis of wind speed in the Eastern Mediterranean," Applied Energy, Elsevier, vol. 87(8), pages 2566-2573, August.
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    Cited by:

    1. Volkanovski, Andrija, 2017. "Wind generation impact on electricity generation adequacy and nuclear safety," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 85-92.
    2. Mazzeo, Domenico & Oliveti, Giuseppe & Labonia, Ester, 2018. "Estimation of wind speed probability density function using a mixture of two truncated normal distributions," Renewable Energy, Elsevier, vol. 115(C), pages 1260-1280.
    3. Ally, Clint & Bahadoorsingh, Sanjay & Singh, Arvind & Sharma, Chandrabhan, 2015. "A review and technical assessment integrating wind energy into an island power system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 863-874.
    4. Bagci, Kubra & Arslan, Talha & Celik, H. Eray, 2021. "Inverted Kumarswamy distribution for modeling the wind speed data: Lake Van, Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    5. Jung, Christopher & Schindler, Dirk, 2019. "Wind speed distribution selection – A review of recent development and progress," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.

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