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Previsional estimation of the energy output of windgenerators

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  • Pallabazzer, Rodolfo

Abstract

This paper illustrates a simple method for evaluating the energy output of windgenerators of known main characteristics in a site of known wind typology. The method is based on the matching of a model of the WECS with the Weibull model of wind regime. To enter the method the following quantities must be known: Weibull shape parameter and mean wind speed, turbine diameter, hub height, cut-in and nominal wind speeds and nominal power. With these quantities one can enter a diagram that gives the value of the plant utilisation factor for any specific siting. An example shows how to make a choice among several models of small size.

Suggested Citation

  • Pallabazzer, Rodolfo, 2004. "Previsional estimation of the energy output of windgenerators," Renewable Energy, Elsevier, vol. 29(3), pages 413-420.
    • Handle: RePEc:eee:renene:v:29:y:2004:i:3:p:413-420
    DOI: 10.1016/S0960-1481(03)00197-6
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    Cited by:

    1. Carolin Mabel, M. & Fernandez, E., 2008. "Analysis of wind power generation and prediction using ANN: A case study," Renewable Energy, Elsevier, vol. 33(5), pages 986-992.
    2. Calif, Rudy & Emilion, Richard & Soubdhan, Ted, 2011. "Classification of wind speed distributions using a mixture of Dirichlet distributions," Renewable Energy, Elsevier, vol. 36(11), pages 3091-3097.
    3. Blonbou, Ruddy, 2011. "Very short-term wind power forecasting with neural networks and adaptive Bayesian learning," Renewable Energy, Elsevier, vol. 36(3), pages 1118-1124.
    4. Hu, Ssu-yuan & Cheng, Jung-ho, 2007. "Performance evaluation of pairing between sites and wind turbines," Renewable Energy, Elsevier, vol. 32(11), pages 1934-1947.
    5. 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.
    6. Jean Souza dos Reis & Nícolas de Assis Bose & Ana Cleide Bezerra Amorim & Vanessa Dantas Almeida & Luciano Andre Cruz Bezerra & Leonardo de Lima Oliveira & Samira de Azevedo Emiliavaca & Maria de Fáti, 2023. "Wind and Solar Energy Generation Potential Features in the Extreme Northern Amazon Using Reanalysis Data," Energies, MDPI, vol. 16(22), pages 1-27, November.
    7. Mohammed, Y.S. & Mustafa, M.W. & Bashir, N., 2014. "Hybrid renewable energy systems for off-grid electric power: Review of substantial issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 527-539.
    8. 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.
    9. Masseran, Nurulkamal, 2015. "Evaluating wind power density models and their statistical properties," Energy, Elsevier, vol. 84(C), pages 533-541.
    10. Chang, Tsang-Jung & Tu, Yi-Long, 2007. "Evaluation of monthly capacity factor of WECS using chronological and probabilistic wind speed data: A case study of Taiwan," Renewable Energy, Elsevier, vol. 32(12), pages 1999-2010.
    11. Villanueva, D. & Feijóo, A., 2010. "Wind power distributions: A review of their applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(5), pages 1490-1495, June.
    12. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.

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