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Wind energy potential in Tamil Nadu India: Prediction and mapping using GIS


D. Jayakumar, Prashanthi Devi. M,
S. Suriyanarayanan,S. Balasubramanian
Dept of Environmental Sciences, Bharathiar University,
Coimbatore-641046. Tamil Nadu. India

C. R. Ranganathan
Dept of Mathematics, Tamil Nadu Agricultural University
Coimbatore -641003 Tamil Nadu. India
E-mail: sbalasubra2001@rediffmail.com
Phone No: 91 0422 425459, Fax No. 091-422-422387

Wind energy is one of the important free renewable clean and non-polluting sources, which vigorously pursued in many countries. Of the several alternative energy sources wind is perhaps the most suitable and cost effective.

Compared with solar energy sources wind is more sensitive to variations with topography and weather patterns. These properties make wind resources assessment - the characterization of wind as an energy resource - a very important part of wind energy applications (Pacific North West Laboratory, 1979). Thus unlike other renewable resources, the wind resource varies with time of day and season of year and even some extent from year to year. Wind energy has inherent variances and hence it has been expressed by distribution methods like Weibull, Bivariate Normal and Gamma distributions (Christofferson and Gillette (1987), Ranganathan et al., 1991). With this in view, the present study has been initiated to attempt to estimate the available wind power potential using Gamma distribution in different parts of Tamil Nadu and mapped using GIS.

Study Area and Data
The study area Tamil Nadu lies within the latitude 8° 5' - 13° 35'N and longitude 76º 15'- 80º 20' E. Tamil Nadu is situated in the southern end of Indian peninsula, which is bounded by Karnataka & Andhra Pradesh in the north, Bay of Bengal in the east and Indian ocean in the south and Kerala in the West. The total area of this state is 1,30,058 Km².

The basic data for this study consists of mean monthly wind speed data of 18 meteorological stations randomly distributed in Tamil Nadu for 11 years (1970-1980). The readings were taken at a height of 10meters above the ground level. The locations of the stations, the altitude and the descriptive statistics of monthly wind speed were analysed.

Wind power estimation and Methodology
The observed mean monthly wind speed has inherent variability and can be best described by a probability distribution method. Gamma distribution is one of the most useful continuous distributions often used to model natural events like precipitation and other climatological data of series (WMO, 1996) In this paper, we have used Gamma distribution to estimate the mean wind power of 18 stations in Tamil Nadu. The mathematical analysis of wind power calculations and fitting procedure of Gamma distribution to observe data are as follows:

The probability density function of the two-parameter Gamma distribution is defined by


where G(b) is the Gamma function defined by


The Gamma distribution has a single peak at x = (b - 1)/a, (b < 1) and it takes a variety of shapes, depending on the values of b, ranging from reverse J shaped for b < 1 to single peaked for b > 1. The constant b is called shape parameter and a is called scale parameter. The cumulative Gamma distribution function is defined by


The value of the above function can't be given in a closed form and it can be evaluated numerically using tables of incomplete Gamma function, which is available in several handbooks (eg. Pearson, 1957). The mean wind speed, says wbar, in any locality is given by the mean of the Gamma distribution and can be shown as


The wind power at any location is given by where r is the air density. So the available mean wind power at any location is given by


To estimate this value, we have to find the value of E (w³). This is done as follows. If G (v) is the cumulative distribution function of w³, then


Hence if g (u) is the density function of u, then,


The above equation gives an expression for available wind power at any location. As an example, for Kodaikanal station, the estimates of values of a and b are22.8 and 7.27 respectively. Hence the average wind speed is given by E (w) = 22.8/7.27 = 3.14 and the mean power, assuming the value of air density to be 1.25 is


In a similar manner, the mean wind speed and wind power were estimated for the remaining stations. The results of the two-parameter Gamma distribution were compared with two-parameter Weibull distribution estimates of both Method I & Method II (Ranganathan et al., 1991). These values were found to be on par with each other.

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