Wind profile data prediction method

Abstract

The invention provides a wind profile data prediction method. The laser radar measures the wind speed and direction; whether the wind speed and wind direction are lost and whether the data integrity rate of a single wind speed and wind direction is enough can be automatically judged; an applicable wind speed and wind direction prediction model can be automatically selected to predict and supplement wind speed and wind direction data; therefore, the common unscientific problem of current wind measurement data prediction is well solved, subjective influence on prediction caused by uneven levelsof different engineers is reduced to the maximum extent, and missing of the laser radar and wind measurement data with insufficient single wind speed and wind direction data are scientifically and accurately predicted.

Description

technical field [0001] The invention relates to the technical field of laser radar, in particular to the related technology of laser radar wind measurement, especially to the application in wind resource assessment of wind power generation. Background technique [0002] my country is rich in wind energy resources, and the wind farms that can be developed are widely distributed. In order to ensure the economic operation of wind farms, there is an urgent need for detailed, accurate and reliable assessment of the wind energy resources of the site in all links of the whole life cycle including before the investment and construction of the wind farm, operation and production, and quality assurance. . However, to achieve the above-mentioned effective and accurate measurement and evaluation, it is necessary to ensure that the obtained wind energy resource data must meet the minimum data integrity requirements, otherwise the results of measurement and evaluation will not be used. ...

AI Technical Summary

Problems solved by technology

In a nutshell, the common unscientific aspects in current wind data prediction mainly include: 1. The selected prediction model is unreasonable. For example, for the wind speed and direction at the same height with missing data, the efficiency of different prediction models and wind shear models is Different; 2. The selected reference data is unreasonable. For the wind speed and direction at the same height with missing data, even if the same prediction model is used, different selection of reference data will affect the accuracy of the measurement results

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