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Performance evaluation of cup-anemometers and wind speed characteristics analysis

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  • Baseer, M.A.
  • Meyer, J.P.
  • Rehman, S.
  • Md. Mahbub Alam,
  • Al-Hadhrami, L.M.
  • Lashin, A.

Abstract

The objective of the present work is to study the performance of cup-anemometers installed at different heights on a 40 m tall wind mast over an approximate period of 55 months between September 13, 2005 and May 09, 2010. The performance of co-located cup anemometers has been analyzed by calculating the annual mean, median, standard deviation, tower distortion factor (TDF), scatter factor (SCF), and developing linear correlations between the co-located sensors. The study showed that the performance of sensors did not deteriorate much with time but slightly higher values of TDF were obtained with passage of time. The annual mean wind speeds, the median values and the standard deviations were almost the same during different years and were comparable with co-located sensors at each height. The SCF values were found to be increasing with decreasing height of wind speed measurements. Finally, a total of 16 wind turbines of 2 MW rated capacity each were used to find the most suitable wind turbine for the location under investigation.

Suggested Citation

  • Baseer, M.A. & Meyer, J.P. & Rehman, S. & Md. Mahbub Alam, & Al-Hadhrami, L.M. & Lashin, A., 2016. "Performance evaluation of cup-anemometers and wind speed characteristics analysis," Renewable Energy, Elsevier, vol. 86(C), pages 733-744.
    • Handle: RePEc:eee:renene:v:86:y:2016:i:c:p:733-744
    DOI: 10.1016/j.renene.2015.08.062
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    Cited by:

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    4. Wang, Jianzhou & Niu, Tong & Lu, Haiyan & Guo, Zhenhai & Yang, Wendong & Du, Pei, 2018. "An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms," Applied Energy, Elsevier, vol. 211(C), pages 492-512.
    5. Muhammad Shahzad Nazir & Fahad Alturise & Sami Alshmrany & Hafiz. M. J Nazir & Muhammad Bilal & Ahmad N. Abdalla & P. Sanjeevikumar & Ziad M. Ali, 2020. "Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend," Sustainability, MDPI, vol. 12(9), pages 1-27, May.
    6. Paweł Ligęza, 2022. "Dynamic Error Correction Method in Tachometric Anemometers for Measurements of Wind Energy," Energies, MDPI, vol. 15(11), pages 1-9, June.
    7. Sudip Basack & Shantanu Dutta & Dipasri Saha, 2022. "Installation and Performance Study of a Vertical-Axis Wind Turbine Prototype Model," Sustainability, MDPI, vol. 14(23), pages 1-29, December.

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