ICSET 2008 Economic Index for Selection of Wind Turbine Generator at a Site Sangamesh S. Doddamani Student Member IEEE EEED BVVS Polytechnic, Bagalkot Karnataka India. sangameshdoddamani@rediffmail.com Suresh H. Jangamshetti Senior Member IEEE EEED Basaveshwar Engg, College Bagalkot. Karnataka India suresh.j@ieee.org Abstract: In this paper a new methodology to select a wind turbine generator from the view point of performance and economic considerations is presented. Weibull probability density function is used to analyze the wind data and the performance analysis is based on computing the capacity factors. The economic considerations involve computation of cost of energy based on the energy yield, capital cost, operation and maintenance costs and fixed charge rate of the wind turbines under study at the site. The wind site selected for the study is located at Basaveshwar Engineering College Bagalkot, Karnataka, India. Wind velocities are recorded from a 50-meter wind mast set up under the research grant received from Technical Education Quality Improvement Program of the World Bank. The period of measurement of wind velocities is from June 2007 to May 2008. Four small wind turbine generators of size 0.4kW, 0.65kW, 1.5kW and 4.2kW respectively and locally available in India are used for the study. A novel methodology to compute economic index to select economically viable wind turbine generator is derived and presented in the paper. Economic indices for the four turbines under study are computed. A detailed discussion of the results to select an economically viable wind turbine generator is presented. I. INTRODUCTION Electrical energy needs are in the increasing trend each day. Most of the electrical energy generated today is by burning of fossil fuels causing environment pollution, green house effect and global warming. These concerns have sparked a growth in the renewable energy industry. Employment of wind turbine generators can effectively reduce the environment pollution and meet the electrical energy needs. Governments have come out with supportive energy policies for renewable energy industries to address the growing concerns of environment pollution. This has encouraged the utilities to invest in wind energy systems in a large scale. Now the utilities have a crucial task of selecting an economically viable wind turbine generator for the potential site. Several investigations have been done in the area of site matching of wind turbines. Site matching of a wind turbine generator involves selecting a potential wind site from among several wind turbines. Statistical analysis of recorded wind velocities helps in estimating the wind potential at the site. Researchers have evolved various techniques for accurate assessment of wind power potential at a site [1-3]. They have concluded that using Weibull probability density function to represent the wind speeds with cubic mean cube root [4] gives reliable estimation of wind power potential. This technique is employed for optimum siting of wind turbine generators from the viewpoint of site and wind turbine generator selection [5]. Matching of wind turbine generator to a site using normalized power and capacity factor curves is discussed in [6-7]. The site matching is based on identifying optimum turbine speed parameters from turbine performance index so as to yield higher energy production at higher capacity factor. However the economic considerations of the turbine are not given. Even though economic consideration of wind farms is discussed in [8], the selection of wind turbines is not covered. Hence there is a need for investigation to find a wind turbine generator that can match a potential wind site from the viewpoint of both performance and cost. II. METHODOLOGY 1) Wind Statistics: Monthly and annual cubic means and standard deviations of grouped data are calculated using[4]: = = = = - = = N i i N i i i n N i i n i N i i f v v f and s m f v f v 1 1 2 _ 1 1 1 __ ) ( ] / [ σ (1) Where _ v is mean wind velocity, v is actual wind speed in m/s, N is number of wind speeds recorded, f is frequency of wind speed and n = 3, for cubic root cube (cm) It is evident from the research that the employment of cubic mean wind speeds in Weibull distribution function can estimate the wind power of wind sites more accurately. The Weibull probability density function is a two-parameter distribution, where c and k are the scale parameter and the shape parameter, respectively. Weibull has generally the right shape to fit wind speed frequency curves. There are several methods available for determining the Weibull parameters c and k. If the mean and variance of the wind speed for a site are known, shape parameter k and scale parameter c can be computed using: ( ) ] / [ 1 1 _ 086 . 1 s m k v c and v k v + Γ = - = σ (2) Where Γ is the gamma distribution and is given by () dt t e x x t 1 0 - ∞ - = Γ 2) Energy Production and Capacity Factor: Electrical power output, P e of a wind turbine is a function of the wind speed, the turbine angular velocity, and the efficiencies of each component in the drive train. It is also a function of the type of turbine (propeller, Darrieus, etc.), the inertia of the system, and the gustiness of the wind. The power output can be adequately described as[1]: 622 978-1-4244-1888-6/08/$25.00 c  2008 IEEE Authorized licensed use limited to: Basaveshwar Engineering College. Downloaded on June 4, 2009 at 01:04 from IEEE Xplore. Restrictions apply.