Application of lidar and measure correlate predict method in offshore wind resource assessments

Abstract

Currently, wind data measurement using lidars or other remote sensing instruments are increasing. Lidar measures the wind data over the full wind turbine, but it is not available for a complete period. This paper investigates the application of measure-correlate-predict technique to short-term lidar measurements in order to extrapolate wind shear index. The wind shear exponent from lidar measurements was compared from mast measurements because lidar and mast have a different measurement methodology. The short-term measurement campaign was conducted at Dhanushkodi to analyze the wind data. This paper discussed the application of measure-correlate-predict method to evaluate the performance of the measure-correlate-predict to extrapolate the short term wind shear exponent. A quantitative analysis of the measure-correlate-predict method has been made. The new approach of measuring wind shear exponent using lidar and measure-correlate-predict was used to test the measured data. The six metrics used to evaluate the MCP predictions in the present analysis. The five parameters used to compare the predicted distribution with the actual long-term wind data. The shear measured by lidar over a height range from 10 m to 220 m. Subsequently, the mast data and the MCP method are used to extrapolate the lidar measurement to get the long-term shear exponent. The reference wind data is used by the wind shear exponent from 10 m to 100 m from the met mast. Due to the reduction of error in the wind shear exponent measurement, the uncertainty can sufficiently reduce in the estimation of wind shear exponent. Comparison of the two correlations signifies that the LR method shows in linear relations that have higher intercepts and lower slopes. A difference is found for shear exponent in the range of 0.13–0.28.