Wind energy is renewable and environment friendly. It is pollution free source of energy when compared with fossil fuels that pollute the lower layer of atmosphere. A new method depending on the parameters of three parameter mixture Weibull distribution is presented in this paper for calculating the output energy. Parameter estimation is much critical for the application of statistical model and is a challenging problem precisely for a Weibull distribution with more than two parameters. A study on estimation of parameters using maximum likelihood method is discussed. An expression to estimate the Capacity factor is formulated and the average power produced from a turbine is computed using the three parameter mixture Weibull distribution.

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Nowadays wind resource assessment utilize new advanced technologies and appropriate analytical methods which are applied to estimate how much wind as a fossil free fuel will be available from a wind farm over the period of its performance. The utmost important piece of information is determining the expected generation of energy from the power plant and ultimately how much cost effective it will be. In our study we have proposed a new method, the improved mixture Weibull distribution produced from the combination of two and three parameter Weibull distribution with six parameters which include shape, scale, location parameter and the weight component or the mixing parameter. The basic properties of the improved mixture Weibull distribution and the estimation of parameters using maximum likelihood method are discussed. The estimated parameters are used to derive a mathematical model to compute the capacity factor and wind power density.

Owing to the growing environmental concern harnessing renewable energy sources became absolutely necessary like solar, wind etc. Wind energy is more sensitive to variations with topography and wind patterns compared to solar energy. The wind energy distribution is the basis for the assessment of wind energy potential needed for the design of wind farms. Accurate wind speed modeling is critical in estimating wind energy potential for harvesting wind power effectively. The quality of wind speed assessment depends on the capability of chosen pdf to describe the measured wind speed frequency. The objective of this study is to describe (model) wind speed characteristics using proposed generalized skew logistic distribution. In order to decide the most suitable site for installing a wind farm as well as to select a fitting wind turbine model it is necessary to carry out a careful wind energy resource evaluation. In this study we propose the generalized skew logistic distribution for the description of wind speed distribution. This distribution is flexible enough to accommodate the shape of wind speed data and include some well known distribution as special cases, also we evaluate the performance of this distribution by using wind speed data at Mupandal (1) at kanya kumari in India. Results show that this generalized skew logistic distribution is a better fit followed by WW pdf.