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Identification of the generalized Weibull distribution in wind speed data by the Eigen-coordinates method

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  • Al-Hasan, Mohammed
  • Nigmatullin, Raoul R.

Abstract

The real distributions of local wind speed obtained from random temporal series have been analyzed in the framework of the new Eigen-coordinates method (ECs), which helps to identify a true hypothesis with high level of authenticity. The season variations of the wind speed have been registered in Fjage (Jordan) and considered as initial data. It has been shown that histograms of the local wind speed were precisely described by the generalized Weibull model and the distribution model suggested by C. Tsallis should be rejected for present case. The possibilities and recommendations for the application of the ECs method to identify the theoretical curves needed for description of experimental data containing significant deviations are discussed. It has been shown that the ECs method could be used in these cases as a general approach and could be applied for analysis of temporal random series of different nature. PACS: 02.60.Ed., 02.60.Pn., 06.20.Dk., 07.05.Kf., 07.05.Rm.

Suggested Citation

  • Al-Hasan, Mohammed & Nigmatullin, Raoul R., 2003. "Identification of the generalized Weibull distribution in wind speed data by the Eigen-coordinates method," Renewable Energy, Elsevier, vol. 28(1), pages 93-110.
    • Handle: RePEc:eee:renene:v:28:y:2003:i:1:p:93-110
    DOI: 10.1016/S0960-1481(01)00151-3
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    References listed on IDEAS

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    1. Nigmatullin, R.R., 2000. "Recognition of nonextensive statistical distributions by the eigencoordinates method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 285(3), pages 547-565.
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    1. Nigmatullin, R.R. & Osokin, S.I. & Toboev, V.A., 2011. "NAFASS: Discrete spectroscopy of random signals," Chaos, Solitons & Fractals, Elsevier, vol. 44(4), pages 226-240.
    2. Touré, Siaka, 2005. "Investigations on the Eigen‐coordinates method for the 2‐parameter weibull distribution of wind speed," Renewable Energy, Elsevier, vol. 30(4), pages 511-521.
    3. El Alimi, Souheil & Maatallah, Taher & Dahmouni, Anouar Wajdi & Ben Nasrallah, Sassi, 2012. "Modeling and investigation of the wind resource in the gulf of Tunis, Tunisia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 5466-5478.
    4. Muhammad Ijaz & Syed Muhammad Asim & Alamgir & Muhammad Farooq & Sajjad Ahmad Khan & Sadaf Manzoor, 2020. "A Gull Alpha Power Weibull distribution with applications to real and simulated data," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-19, June.
    5. Gugliani, G.K. & Sarkar, A. & Ley, C. & Mandal, S., 2018. "New methods to assess wind resources in terms of wind speed, load, power and direction," Renewable Energy, Elsevier, vol. 129(PA), pages 168-182.
    6. Andréa Camila dos Santos Martins & Antonio Roberto Balbo & Dylan Jones & Leonardo Nepomuceno & Edilaine Martins Soler & Edméa Cássia Baptista, 2020. "A Hybrid Multi-Criteria Methodology for Solving the Sustainable Dispatch Problem," Sustainability, MDPI, vol. 12(17), pages 1-20, August.

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