Wind measurement data interpolation method and device and computer readable storage medium

Abstract

The embodiment of the invention provides a wind measurement data interpolation method and device and a computer readable storage medium. The method comprises the following steps: acquiring complete historical wind speed data of a target anemometer tower and a similar anemometer tower adjacent to the target anemometer tower in the same time period; training a neural network by learning a corresponding relation between historical wind speed data of a target anemometer tower and historical wind speed data of a similar anemometer tower so as to finally obtain a neural network model in which the wind speed changes along with the position and time; and using the neural network model to predict the actually missing wind speed data of the target anemometer tower. According to the embodiment of the invention, the missing data can be interpolated, so that the complete wind measurement data can be obtained.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of wind power generation, and in particular, to a wind measurement data interpolation method, a device thereof, and a computer-readable storage medium. Background technique [0002] With the gradual exhaustion of coal, petroleum and other energy sources, human beings pay more and more attention to the utilization of renewable energy. As a clean and renewable energy, wind energy has been paid more and more attention by countries all over the world. With the continuous development of wind power technology, the application of wind turbines in the power system is increasing. Wind turbines are large-scale equipment that convert wind energy into electrical energy, and are usually installed in areas with abundant wind energy resources. [0003] The evaluation of wind energy resources is an important link in the construction and operation of the entire wind farm, and is the key to achieving g...

AI Technical Summary

Problems solved by technology

The most critical thing in wind energy resource assessment is the effective integrity of wind measurement data. However, due to instrument failures, tower collapses, etc., the effective data period of observation data is often less than a full year.

However, the correlation method is greatly affected by the correlation coefficient, especially for low wind speed wind farms, where the small wind speed sample size is relatively large, which will affect the correlation equation, and then affect the accuracy of data interpolation

However, the wind shear index method has the problem that the wind shear index is different in different wind directions and different heights, and different wind shear indices will affect the accuracy of the calculation results.

Therefore, both existing methods are highly unpredictable

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