Wind speed modeling method considering fan positions of plateau mountain area

Abstract

The invention discloses a wind speed modeling method considering fan positions of a plateau mountain area. The method comprises the steps of S1, collecting geographical coordinates of n fans of a wind power plant, a coordinate position-containing topographic map of a wind power plant region, wind speed data of numerical weather forecast of the wind power plant region, and historical wind speed data and a wind direction rose map of the wind power plant region; S2, classifying the positions of the n fans into three types including a flat region, a mountain slope front region and a mountain slope back region; S3, performing canopy clustering on the historical wind speed data of the wind power plant region to obtain m wind speed sections; and S4, obtaining a wind speed model considering the fan positions of the plateau mountain area through a linear regression analysis method by utilizing the historical wind speed data of the wind power plant region. The technical problems of large description error and relatively large prediction precision error during wind speed data description and power prediction of the wind power plant of the plateau mountain area by utilizing a small amount of real-time data of a wind measurement tower and numerical weather forecast in the prior art are solved.

Description

technical field [0001] The invention belongs to the wind speed prediction technology, in particular to a wind speed modeling method considering the position of a fan in a plateau mountain area. Background technique [0002] The wind turbines in the plateau mountainous area are scattered on different hillsides, and each wind turbine is in the superposition of different forward wind and hillside return wind. And when the wind speed reaches a certain speed, the return wind will increase the wind speed to varying degrees. [0003] The wind speed correlation between the different heights of the same wind measuring tower in the plateau mountainous area is good, but the wind speed correlation between the wind measuring towers in the same wind farm is not good, and the number of wind measuring towers in the wind farm is small, so that the data measured by the wind measuring towers are not meaningful. sufficiently representative. At the same time, unlike the natural wind passing th...

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Problems solved by technology

[0003] The wind speed correlation between the different heights of the same wind measuring tower in the plateau mountainous area is good, but the wind speed correlation between the wind measuring towers in the same wind farm is not good, and the number of wind measuring towers in the wind farm is small, so that the data measured by the wind measuring towers are not meaningful. sufficiently representative

At the same time, unlike the natural wind passing through the fan, the wind speed obtained by the numerical weather forecast does not consider the influence of terrain and other factors

Wind speed changes in plateau mountainous wind farms are not determined by the main wind direction. Therefore, using a small amount of real-time data from anemometer towers and numerical weather forecasts to describe the wind speed data of plateau mountainous wind farms has large errors. However, using such wind speed data to calculate power Prediction, the accuracy will also have a large error

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