DOI QR코드

DOI QR Code

An Analysis of Wind Data for Development of Energy Independent Village

에너지 자립 마을 개발을 위한 공력 실증 데이터 분석

  • ALI, SAJID (Smart City Construction Engineering, University of Science & Technology (UST)) ;
  • JANG, CHOON-MAN (Smart City Construction Engineering, University of Science & Technology (UST))
  • 사지드 알리 (한국과학기술대학교 스마트시티건설공학과) ;
  • 장춘만 (한국과학기술대학교 스마트시티건설공학과)
  • Received : 2019.10.14
  • Accepted : 2019.12.30
  • Published : 2019.12.30

Abstract

In the present study, the wind characteristics were analyzed according to the time averages to evaluate the performance of small wind turbines required for the development of energy independent village. Measuring data of wind speed were recorded between January 2016 and April 2016 every second. Experimental data is averaged out using 5, 10, 15, 20 and 30 minute time steps. Throughout the experimental data analysis, 5 minutes averaged data is used to analyze the performance of the wind turbine, because it produces a minimum turbulence intensity in wind speed. The measuring power of the wind turbine is less than the designed value due to the unsteady nature wind of sudden changes in magnitude of wind speed and wind angle. Detailed wind conditions are also analysed using two variable Weibull probability density functions.

Keywords

References

  1. Y. Shen, C. Zhang, X. Huang, X. Wang, and S. Cen, "The effect of wind speed averaging time on sand transport estimates", Catena, Vol. 175, 2019, pp. 286-293, doi: https://doi.org/10.1016/j.catena.2018.12.020.
  2. J. E. Stout, "Effect of averaging time on the apparent threshold for aeolian transport", Journal of Arid Environments, Vol. 39, No. 3, 1998, pp. 395-401, doi: https://doi.org/10.1006/jare.1997.0370.
  3. B. A. Harper, J. D. Kepert, and J. Ginger, "Wind speed time averaging conversions for tropical cyclone conditions", 28th Conference on Hurricanes and Tropical Meteorology, 2008. Retrieved from https://ams.confex.com/ams/28Hurricanes/techprogram/paper_138064.htm.
  4. Z. Guo, T. M. Zobeck, J. E. Stout, and K. Zhang, "The effect of wind averaging time on wind erosivity estimation", Earth Surface Processes and Landforms, Vol. 37, No. 7, 2012, pp. 797-802, doi: https://doi.org/10.1002/esp.3222.
  5. S. K. Gulev, "Influence of space-time averaging on the ocean-atmosphere exchange estimates in the North Atlantic midlatitudes", Journal of physical oceanography, Vol. 24, No. 6, 1994, pp. 1236-1255, doi: https://doi.org/10.1175/1520-0485(1994)024<1236:IOSTAO>2.0.CO;2.
  6. P. D. Clausen and D. H. Wood, "Research and development issues for small wind turbines", Renewable Energy, Vol. 16, No. 1-4, 1999, pp. 922-927, doi: https://doi.org/10.1016/S0960-1481(98)00316-4.
  7. K. Mohammadi, O. Alavi, A. Mostafaeipour, N. Goudarzi, and M. Jalilvand, "Assessing different parameters estimation methods of Weibull distribution to compute wind power density", Energy Conversion and Management, Vol. 108, 2016, pp. 322-335, doi: https://doi.org/10.1016/j.enconman.2015.11.015.
  8. E. K. Akpinar, "A statistical investigation of wind energy potential", Energy Sources, Part A, Vol. 28, No. 9, 2006, pp. 807-820, doi: https://doi.org/10.1080/009083190928038.
  9. L. Bilir, M. Imir, Y. Devrim, and A. Albostan, "An investigation on wind energy potential and small scale wind turbine performance at Incek region-Ankara, Turkey", Energy Conversion and Management, Vol. 103, 2015, pp. 910-923, doi: https://doi.org/10.1016/j.enconman.2015.07.017.
  10. Carrasco, J. M., Ortega, E. M., and Cordeiro, G. M., "A generalized modified Weibull distribution for lifetime modelling", Computational Statistics & Data Analysis, Vol. 53, No. 2, 2008, pp. 450-462, doi: https://doi.org/10.1016/j.csda.2008.08.023.
  11. T. P. Chang, "Wind speed and power density analyses based on mixture Weibull and maximum entropy distributions", International Journal of Applied Science and Engineering, Vol. 8, No. 1, 2010, pp. 39-46, doi: https://doi.org/10.6703/IJASE.2010.8(1).39.
  12. F. O. Hocaoglu, M. Fidan, and O. N. Gerek, "Mycielski approach for wind speed prediction", Energy Conversion and Management, Vol. 50, No. 6, 2009, pp. 1436-1443, doi: https://doi.org/10.1016/j.enconman.2009.03.003.
  13. C. Ozay and M. S. Celiktas, "Statistical analysis of wind speed using two-parameter Weibull distribution in Alacati region", Energy Conversion and Management, Vol. 121, 2016, pp. 49-54, doi: https://doi.org/10.1016/j.enconman.2016.05.026.
  14. S. H. Pishgar-Komleh, A. Keyhani, and P. Sefeedpari, "Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran)", Renewable and Sustainable Energy Reviews, Vol. 42, 2015, pp. 313-322, doi: https://doi.org/10.1016/j.rser.2014.10.028.
  15. P. A. C. Rocha, R. C. de Sousa, C. F. de Andrade, and M. E. V. da Silva, "Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil", Applied Energy, Vol. 89, No. 1, 2012, pp. 395-400, doi: https://doi.org/10.1016/j.apenergy.2011.08.003.
  16. H. Saleh, A. Abou El-Azm Aly, and S. Abdel-Hady, "Assessment of different methods used to estimate Weibull distribution parameters for wind speed in Zafarana wind farm, Suez Gulf, Egypt", Energy, Vol. 44, No. 1, 2012, pp. 710-719, doi: https://doi.org/10.1016/j.energy.2012.05.021.