Abstract:
This paper presents a novel approach based on Weibull distribution to determine the capacity of wind turbine generators (WTGs) using capacity factor (CF), normalized ave...Show MoreMetadata
Abstract:
This paper presents a novel approach based on Weibull distribution to determine the capacity of wind turbine generators (WTGs) using capacity factor (CF), normalized average power ( P_{N} ), and the product of CF and P_N under different values of tower height and rated wind speed. Five locations for installation of WTG in Taiwan are practically examined. The proposed Weibull distribution is employed to represent probability distribution of wind speed variation, while the important relationships among mean wind speed (MWS), standard deviation of wind speed, and both scale and shape parameters of the Weibull distribution are also derived. The cost of energy (COE) and capital costs of WTG under different tower heights and various locations are also determined. It can be concluded from the simulation results of five installation locations of WTG in Taiwan that suitable values for both shape parameter and scale parameters of Weibull distribution as well as wind turbine capacity are identically important for selecting locations of installing WTG. The scale parameter of Weibull distribution also significantly affects COE, and it is important to determine whether a wind farm is good or not.
Published in: IEEE Transactions on Energy Conversion ( Volume: 23, Issue: 2, June 2008)
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1.
C. G. Justus, "Nationwide assessment of potential power output from aero-generators", 1975-Jun.-22–25.
2.
G. L. Johnson, "Economic design of wind electric systems", IEEE Trans. Power App. Syst., vol. PAS-97, no. 2, pp. 554-562, Mar./Apr. 1978.
3.
J. P. Hennessey, Jr., "Some aspects of wind power statistics andperformance analysis of a 6 MW wind turbine-generator", J. Appl. Meteorol., vol. 16, no. 2, pp. 119-128, Feb. 1997.
4.
S. H. Jangamshetti and V. G. Rau, "Normalized power curves as a tool for identification ofoptimum wind turbine generator parameters", IEEE Trans. Energy Convers., vol. 16, no. 3, pp. 283-288, Sep. 2001.
5.
S. H. Jangamshetti and V. G. Rau, "Optimum siting of wind turbine generators", IEEE Trans. Energy Convers., vol. 16, no. 1, pp. 8-13, Mar. 2001.
6.
S. H. Jangamshetti and V. G. Rau, "Site matching of wind turbine generators: A case study", IEEE Trans. Energy Convers., vol. 14, no. 4, pp. 1537-1543, Dec. 1999.
7.
P. Gray and L. Johnson, Wind Energy System, NJ, Upper Saddle River:Prentice-Hall, 1985.
8.
E. Golding, The Generation of Electricity by Wind Power, New York:Halsted Press, 1976.
9.
J. F. Manwell, J. G. Mcgowan and A. L. Rogers, Wind Energy Explained Theory Design and Application, New York:Wiley, 2002.
10.
E. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions With Formulas Graphs and Mathematical Tables, New York:Wiley, 1970.
11.
I. S. Gradshteyn and I. M. Ryzhik, Tables of Integrals Series and Products, New York:Academic, 1980.
12.
W. H. Press, B. P. Flannery, S. A. Teukolsky and W. T. Vetterling, Numerical Recipes in C: The Art of Scientific Computing, U.K., Cambridge:Cambridge Univ. Press, 1988.
