Analysis of wind characteristics parameters with the application of lidar and mast

Abstract

AbstractDifferent probability distribution functions have been used to characterize wind resource parameters considering uncertainty in wind potential. However, the application of the best distributions functions is still the most challenging task. In the present work, a comparative statistical analysis of wind speed data, collected from mast and lidar for different distribution model using different numerical methods at different heights, has been performed. It also seeks to select the best alternative method among the maximum likelihood method, modified maximum likelihood method, and WAsP. The 2 years (2015 to 2017) met mast wind data, height ranging from 20 to 100 m, are used to perform wind statistical analysis. One‐month wind data are also monitored by lidar (20, 50, 80, 100, 120, 150, 180, 200, and 220 m). Based on the extrapolated met mast wind data at different heights using power law, log law, and Deaves and Harris model, the results were categorized in two ways. In the first part, results indicate that the R2 and root mean square error for wind speed and direction at different observed heights were equal to or higher than 0.95 and 0.1568, respectively, at Dhanuskhkodi, while in the second part, the Deaves and Harris model merely exhibits 2.12% bias, while it is 5.68% for the other extrapolating models in comparison with the power law and log law.