Laser radar wind field data reconstruction method, system and equipment

Abstract

The invention relates to a laser radar wind field data reconstruction method, system and device. The method comprises the following steps: acquiring a plurality of wind speed data Vr1, Vr2, Vr3 and Vr4, wherein the Vr1, Vr2, Vr3 and Vr4 are radial wind speeds in four directions of a to-be-measured position and a to-be-measured horizontal height measured by a laser radar; if any one of Vr1, Vr2, Vr3 and Vr4 data is missing, or any two of Vr1, Vr2, Vr3 and Vr4 data perpendicular to each other in the direction are missing, based on the fact that the vertical wind speeds in the four directions at the same position and the same horizontal height are the same, deriving missing radial wind speed data according to the vertical wind speeds in other directions; and calculating the horizontal wind speed and the horizontal wind direction at the horizontal height according to the Vr1, the Vr2, the Vr3 and the Vr4. According to the method, prediction supplement of the radial wind speed data is carried out by using the assumption that the vertical wind speeds at the same position are the same, so that the common unscientific problem of current wind measurement data prediction is well solved, and the wind speed data are scientifically and accurately predicted.

Description

technical field [0001] The present invention relates to the technical field of laser radar, and in particular, to a method, system and device for reconstructing wind field data of laser radar. Background technique [0002] my country is rich in wind energy resources, and wind farms that can be developed are widely distributed. In order to ensure the economic operation of wind farms, there is an urgent need to conduct detailed, accurate and reliable assessment of wind energy resources at the site in all aspects of the entire life cycle including wind farm investment and construction, 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 requirements of the minimum data integrity rate, otherwise the measurement and evaluation results will not be used. However, in the actual measurement process, whether the...

AI Technical Summary

Problems solved by technology

Practice has shown that a 1% difference in wind speed will cause at least a 2% difference in power generation. Because of the unreasonable data forecast, the current average deviation of domestic forecast data is 2%-3%. If 50% of the data is damaged or missing, it will cause a deviation of 4%-6% in the calculation of wind farm power generation, which will have a huge impact on the final result

Generally speaking, whether the forecast data is accurate, reliable, and consistent will be affected by the selection of the forecast model, the selection of reference data, and even the use of software. Level constraints, whether engineers are experienced, well-trained, and their subjective choices will affect the final prediction results

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

[0004] To sum up, there are many shortcomings in interpolating and correcting the missing wind energy resource data by means of prediction, which will affect the accuracy of the final measurement results. Therefore, a simple and scientific prediction model is urgently needed to improve the accuracy of measurement.

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