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Statistical distributions of wind parameters at Sydney, Australia

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  • Morgan, Vincent T.

Abstract

The characteristics of the wind at 10 m height were studied over a period of 32 months. The sampling interval was 20 ms and the averaging time was 10 min. Probability density functions are given for the speed, direction, inclination and intensity of turbulence of the wind. Frequency contour plots are given for wind speed vs solar time, wind speed vs wind direction, wind speed vs global solar irradiance and wind speed vs the intensity of turbulence of the wind. Differences between the results for day and night and between various seasons are examined.

Suggested Citation

  • Morgan, Vincent T., 1995. "Statistical distributions of wind parameters at Sydney, Australia," Renewable Energy, Elsevier, vol. 6(1), pages 39-47.
    • Handle: RePEc:eee:renene:v:6:y:1995:i:1:p:39-47
    DOI: 10.1016/0960-1481(94)E0017-Y
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    Cited by:

    1. Arwade, Sanjay R. & Gioffrè, Massimiliano, 2014. "Validity of stationary probabilistic models for wind speed records of varying duration," Renewable Energy, Elsevier, vol. 69(C), pages 74-81.
    2. Tiam Kapen, Pascalin & Jeutho Gouajio, Marinette & Yemélé, David, 2020. "Analysis and efficient comparison of ten numerical methods in estimating Weibull parameters for wind energy potential: Application to the city of Bafoussam, Cameroon," Renewable Energy, Elsevier, vol. 159(C), pages 1188-1198.
    3. 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.
    4. Bahrami, Arian & Teimourian, Amir & Okoye, Chiemeka Onyeka & Khosravi, Nima, 2019. "Assessing the feasibility of wind energy as a power source in Turkmenistan; a major opportunity for Central Asia's energy market," Energy, Elsevier, vol. 183(C), pages 415-427.
    5. Islam, M.R. & Saidur, R. & Rahim, N.A., 2011. "Assessment of wind energy potentiality at Kudat and Labuan, Malaysia using Weibull distribution function," Energy, Elsevier, vol. 36(2), pages 985-992.
    6. Gualtieri, Giovanni, 2015. "Surface turbulence intensity as a predictor of extrapolated wind resource to the turbine hub height," Renewable Energy, Elsevier, vol. 78(C), pages 68-81.
    7. Celik, Ali N. & Kolhe, Mohan, 2013. "Generalized feed-forward based method for wind energy prediction," Applied Energy, Elsevier, vol. 101(C), pages 582-588.
    8. Ettoumi, F.Youcef & Sauvageot, H & Adane, A.-E.-H, 2003. "Statistical bivariate modelling of wind using first-order Markov chain and Weibull distribution," Renewable Energy, Elsevier, vol. 28(11), pages 1787-1802.
    9. Keyhani, A. & Ghasemi-Varnamkhasti, M. & Khanali, M. & Abbaszadeh, R., 2010. "An assessment of wind energy potential as a power generation source in the capital of Iran, Tehran," Energy, Elsevier, vol. 35(1), pages 188-201.
    10. Amr Khaled Khamees & Almoataz Y. Abdelaziz & Makram R. Eskaros & Mahmoud A. Attia & Mariam A. Sameh, 2022. "Optimal Power Flow with Stochastic Renewable Energy Using Three Mixture Component Distribution Functions," Sustainability, MDPI, vol. 15(1), pages 1-21, December.
    11. Kantar, Yeliz Mert & Usta, Ilhan & Arik, Ibrahim & Yenilmez, Ismail, 2018. "Wind speed analysis using the Extended Generalized Lindley Distribution," Renewable Energy, Elsevier, vol. 118(C), pages 1024-1030.

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