Assessment of wind energy potential by Weibull distribution in isolated islands

In the study, the statistical fittings of wind speed distribution for isolated islands has been carried out. Ten years wind speed data has been collected from Bangladesh meteorological department. The data has been shorted in sequence of appropriate frequency like hourly, daily, monthly and annually mean wind speed. Two important parameters like Weibull shape factor “k” and Weibull scale factor “c” have been calculated by three methods such as Graphical method, Empirical method and Energy method. Accordingly, wind power density, availability factor and electrical energy output from the ideal turbine were assessed by using the Weibull parameters. From the analysis of the data the characteristics of wind pattern and potential of wind energy of the island has been carried out. Theoretical available power and practically extractable power by wind (watt/m2) have also been calculated. The proposed methodology can be used in any windy site to easily identify the potentiality of wind power.

[1]  Tsang-Jung Chang,et al.  Assessment of wind characteristics and wind turbine characteristics in Taiwan , 2003 .

[2]  J. N. Baker,et al.  Electrical energy storage at the turn of the Millennium , 1999 .

[3]  Arvid Naess.  Statistical Extrapolation of Extreme Value Data Based on the Peaks Over Threshold Method , 1998 .

[4]  R. Hanitsch,et al.  Evaluation of wind energy potential and electricity generation on the coast of Mediterranean Sea in Egypt , 2006 .

[5]  J. A. Carta,et al.  The use of wind probability distributions derived from the maximum entropy principle in the analysis of wind energy. A case study , 2006 .

[6]  Qiusheng Li,et al.  Probability distributions of extreme wind speed and its occurrence interval , 2005 .

[7]  A. Celik.  A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey , 2004 .

[8]  E. Akpinar,et al.  A statistical analysis of wind speed data used in installation of wind energy conversion systems , 2005 .

[9]  T. Hennessy,et al.  The multiple benefits of integrating electricity storage with wind energy , 2005, IEEE Power Engineering Society General Meeting, 2005.

[10]  Ali Mostafaeipour,et al.  Using different methods for comprehensive study of wind turbine utilization in Zarrineh, Iran , 2013 .

[11]  S. Watson,et al.  Short-term prediction of local wind conditions , 1994 .

[12]  Matthew A. Lackner,et al.  Probability distributions for offshore wind speeds , 2009 .

[13]  Peter Meibom,et al.  Wind power impacts and electricity storage – A time scale perspective , 2012 .

[14]  A. M. Razali,et al.  Fitting of statistical distributions to wind speed data in Malaysia , 2009 .

[15]  A. Naess,et al.  Estimation of Long Return Period Design Values for Wind Speeds , 1998 .

[16]  B. Yağcitekin,et al.  An Energy Management Strategy for a Wind Power Plant Based on Energy Storage and Wind Power Prediction , 2012 .

[17]  Sinan Akpinar,et al.  An Analysis of the Wind Energy Potential of Elazig, Turkey , 2004 .

[18]  K. P. Pandey,et al.  Analysis of wind regimes for energy estimation , 2002 .

[19]  Shyi-Shiun Kuo,et al.  Wind characterization analysis incorporating genetic algorithm: A case study in Taiwan Strait , 2011 .

[20]  Henrik Madsen,et al.  The Zephyr project - The next generation prediction system (poster) , 2001 .

[21]  Benoit Robyns,et al.  A global supervision for wind/hydro power plant and storage system connected to AC grid , 2009, 2009 13th European Conference on Power Electronics and Applications.