Wind is a free, clean and replenishable source of energy which can be employed for lifting water or electricity generation. Before assessing wind energy potential, wind data must be analysed stochastically. Entire Orrisa state has been taken into consideration. About 26 representative weather stations, which were located by Orissa Renewable Energy Development Agency (OREDA) have been considered for the study. The wind velocity data of these weather stations were fitted with different probability distributions like i) Normal, ii) Pearson type - III, iii) Gumbel's Extreme value, iv) Log Normal distribution; v) Log Pearson type - III, vi) Log Extreme value distribution and vii) Weibull distribution. With the help of fitted distributions, annual average wind velocities at different levels of probabilities were found out and stochastic isotach (places joining equal wind speed) maps were prepared. These maps may then be well utilized in predicting wind velocity at different probability levels. The methodology originally followed by the author was similar to that of drawing contours using interpolation techniques. But using latest software application i.e. Winsurf (Surfer), the isotach maps can be easily drawn whose application can be widened in many interpretations. The disadvantage of this research work is the limited data of the year 1992 but the methodology adopted may be useful elsewhere where this type of research is being carried out.
