STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Alternate Energy Sources & Technologies Original Research

Seasonal Variation in the Weibull Parameters from In Situ Measurements and Model: A Case Study in the Bay of Bengal

  1. M. Kalyani Ocean Observation Systems Group, National Institute of Ocean Technology, Chennai
  2. K. Jossia Joseph Ocean Observation Systems Group, National Institute of Ocean Technology, Chennai
  3. C. Anoopa Prasad Ocean Observation Systems Group, National Institute of Ocean Technology, Chennai, Tamil Nadu, India
  4. K. Tirumurugan Ocean Observation Systems Group, National Institute of Ocean Technology, Chennai
  5. R. Sundar Ocean Observation Systems Group, National Institute of Ocean Technology, Chennai
  6. M. Arul Muthiah Ocean Observation Systems Group, National Institute of Ocean Technology, Chennai

Abstract

Seasonal variation of wind speed (U10) and its distribution are essential for the design of wind energy converters (WEC). In this study, year-round wind speed observations from a deepwater moored buoy at a location in the Bay of Bengal are used to assess the potential for wind power generation and are used to validate the ERA5 model. Model-U10 distribution is confined to low wind speeds with high occurrence in the medium range while observations spread to higher range. Model under-predicted monthly averaged peak winds (9.5–10 m/s) by 1.5 m/s during Southwest monsoon (June and July). Model power is always underpredicted. The peak power density during both SW (July, 667 W/m2) and NE monsoons (November, 405 W/m2) is under-predicted by 36%. The model performed well during the calm period (March) and the minimum deviation during pre (April) and post (September) monsoon are 12% and 16% respectively. The maximum deviations observed during NE (October) and SW (June) monsoon are 60% and 42% respectively. This study reveals that observations are essential to validate the model. The power density is fitted and expressed as a second order polynomial of U10. The U10 variations are expressed in terms of Weibull shape and scale parameters, which are calculated by graphical Least Squares Fit method. The monthly variation of Weibull scale parameter closely matches with average wind speed. Based on the shape parameter value, the nature of the wind and its stability during different seasons are classified for buoy-U10 and model-U10 and the deviations in the model are detailed.

Keywords

References (20)

  1. Joselin HGM, Iniyanb S, Sreevalsanc E, Rajapandiand S. A review of wind energy technologies. Renewable and Sustainable Energy Reviews. 2007; 11(6): 1117–1145p.
  2. National Hydro Power Corporation Limited; http://nhpcindia.com/English/cripts/Hydro_Scenario.aspx.
  3. Global wind energy council report 2012; http://geospatial.blogs.com/geospatial/2013/08/global- installed-windpower-generation-capacity-reaches-283-gw.html.
  4. Centre for Wind Energy Technology, India (http://www.cwet.tn.nic.in/html/departments_ewpp.html)
  5. Frank CW, Bernhard P, Wahl S, Keller JD, Hense A, Susanne The added value of high resolution regional reanalyses for wind power applications. Renewable Energy. 2020; 148: 1094- 1109pp.ISSN 0960-1481, https://doi.org/10.1016/j.renene.2019.09.138.6.
  6. Gualtieri G. Analysing the uncertainties of reanalysis data used for wind resource assessment: A critical review. Renewable and Sustainable Energy Reviews. 2022; 167:112741, 1-20pp, ISSN 1364-0321, https://doi.org/10.1016/j.rser.2022.112741.7.
  7. Lun Isaac YF, Joseph C, Lam Study of Weibull parameters using long-term wind observations. Renewable Energy. 2000; 20: 145-153p.8.
  8. Olauson J. ERA5: The new champion of wind power modelling? Renewable Energy. 2018;126:322-331. doi:10.1016/j.renene.2018.03.056
  9. Kwon SD. Uncertainty analysis of wind energy potential assessment. Applied Energy. 2010; 87: 856–865p.
  10. Khalid Mohamed N, Mohamed S, Hasimah AR. Feasibility assessment of wind energy resources in Malaysia based on NWP models. Renewable Energy. 2014; 62:147-154p. 2014.
  11. National Institute of Technology Hamirpur @ Centre for Energy and Environment (CEE); http://www.nith.ac.in/energy
  12. Lashin A, Shata A. An analysis of wind power potential in Port Said, Egypt. Renewable and Sustainable Energy Reviews. 2012; 16:6660–6667p.
  13. Murthy KSR, Rahi OP. Estimation of Weibull parameters using graphical method for wind energy applications. 2014 Eighteenth National Power Systems Conference (NPSC). 2014:1-6. doi:10.1109/npsc.2014.7103858
  14. https://www.lambrecht.net/files/downloadse/LAMBRECHT%20meteo%20Catalogue_EN%2040_18.pdf.
  15. Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz‐Sabater J, et al. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society. 2020;146(730):1999-2049. doi:10.1002/qj.3803
  16. Mirhosseini M, Sharifi F, Sedaghat Assessing the wind energy potential locations in province of Semnan in Iran. Renewable and Sustainable Energy Reviews. 2011;15:449–459p.
  17. Shata A. Investigation of wind characteristics and wind energy potential at Ras Ghareb, Egypt. Renewable and Sustainable Energy Reviews. 2011; 15:2750– 2755p.
  18. Mostafaeipour A. Feasibility study of harnessing wind energy for turbine installation in province of Yazd in Iran. Renewable and Sustainable Energy Reviews. 2010; 14:93–111p.
  19. Zaccheus OO, Folly Statistical Analysis of the Wind Resources at Darling for Energy Production. International Journal of Renewable Energy Research. 2012; 2:250-261p.
  20. Akdag SA, Dinler A. A new method to estimate Weibull parameters for wind energy applications. Energy Convers Manage. 2009; 50:1761–1766p.
Support