Mixed Weibull Distribution: A Case Study on Ichanda, India

Abstract

This paper presents a case study of a new methodology to accurately characterize and predict the annual variation of wind conditions applied on Ichanda site, Tamil Nadu, India showing dual behaviour. The estimate of the distribution of wind conditions is necessary to quantify the available energy (power density) at a site, and to design an optimum wind farm. Wind speed frequency distribution for some sites with two distinct peaks is not represented accurately by the typical two parameter Weibull distribution. The wind characteristics of Ichanda has been analysed by using wind data recorded by meteorological mast installed at that location. By examining the analysis, it shows that wind speed distribution is not demonstrated precisely by two parameter Weibull distribution. A mixed Weibull probability distribution function (PDF) is applied to analyse wind speed frequency distribution in that region. This model can be applied to regions where the wind speed distribution presents a bimodal distribution to predict wind speed probability distribution and annual energy production accurately.

Determination of Weibull Parameters for Wind Applications in Two Different Regions

“…The shape parameter determines the shape of the Weibull distribution curve. This reveals the regularity of the winds, that is, for greater values of k, winds will present greater regularities in terms of velocity, and the distribution curve has a narrow format, the scale parameter c is the characteristic factor of the distribution (Kumaravel et al, 2014). The CDF indicates the probability of finding a velocity value between 0 and V. By multiplying this value by the number of hours of the period in which the data were collected, one has the fraction of the time in which the velocity remained between the two values.…”

A novel probability density function applied to wind characterization in order to evaluate the wind power potential in Tungurahua, Ecuador’s Andean region

“…A joint PDF consisting of a Weibull and a Dirac delta function was used, being the latter responsible for modeling the calms. If long periods are considered, seasonal effects substantially modify the wind histogram, usually producing a curve with local maximums associated with each season (Kumaravel et al, 2014). Again, a joint probability function, that is, a weighted average of simple PDFs, is used in these cases (Cook, 2015).…”