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Journal of Renewable and Sustainable Energy / Volume 4 / Issue 1 / REGULAR ARTICLES

J. Renewable Sustainable Energy 4, 013114 (2012); http://dx.doi.org/10.1063/1.3683530 (14 pages)

Sizing optimization of a wind pumping plant: Case study in Sfax, Tunisia

Nabiha Brahmi and Maher Chaabene

CMERP-ENIS, University of Sfax, Tunisia

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(Received 27 July 2011; accepted 13 January 2012; published online 16 February 2012)

The effectiveness of wind water pumping plant depends on the available wind potential in the region and on the plant components sizing. This paper presents an algorithm for wind potential assessment based on the widely used Weibull distribution. As many methods are adopted to determine Weibull parameters, an improvement version based on the selection of the most accurate method and the establishment of a huge database using an artificial neural network (ANN) is proposed. Since the site wind performance is evaluated, the wind generator blades surface is computed on the basis of the variation limits of the monthly wind potential and the well height of rise. The sizing principal considers the calculation of the gravity centre of the general function of surface. Results are illustrated using meteorological database provided by the National Institute of Meteorology (INM) corresponding to Sfax, Tunisia. Obtained results confirm that the modified maximum likelihood method (MMLM) is the most accurate one as it provides a monthly error between -11.6% and 2.3%. Hence, a typical pumping plant, with monthly water need of 15  m3month located in Sfax, Tunisia, requires 37  m2 as optimum blades surface.

© 2012 American Institute of Physics

Article Outline

  1. INTRODUCTION
  2. FREQUENCY DISTRIBUTION OF WIND SPEED
    1. Least squares method
    2. Maximum likelihood method
    3. Modified maximum likelihood method
    4. Rayleigh law
  3. WIND POTENTIAL ASSESSMENT
  4. MODELING OF THE WIND PUMPING PLANT
    1. The wind turbine model
    2. The gearbox model
    3. The generator model
    4. The pump motor model
  5. SYSTEM SIZING
  6. RESULTS AND VALORIZATION
  7. CONCLUSIONS AND PERSPECTIVES

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KEYWORDS and PACS

Keywords

blades, maximum likelihood estimation, neural nets, optimisation, Weibull distribution, wind power plants

PACS

  • 88.50.Mp

    Electricity generation, grid integration from wind

  • 07.05.Mh

    Neural networks, fuzzy logic, artificial intelligence

  • 02.50.Ng

    Distribution theory and Monte Carlo studies

  • 02.60.Pn

    Numerical optimization

ARTICLE DATA

Digital Object Identifier
http://dx.doi.org/10.1063/1.3683530

PUBLICATION DATA

ISSN

1941-7012 (online)

Publisher

$abstract.society.value is a Member of CrossRef American Institute of Physics

For access to fully linked references, you need to log in.
    S. M. Moghaddas-Tafreshi, H. A. Zamani, and S. M. Hakimia, J. Renewable Sustainable Energy 3, 043105 (2011)JRSEBH000003000004043105000001.

    X. Sun, W. Zhou, D. Huang, and G. Wu, J. Renewable Sustainable Energy 3, 023109 (2011)JRSEBH000003000002023109000001.


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