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CFD and deep learning-mixed extreme strong wind speed prediction method and system

A technology of wind speed prediction and deep learning, applied in speed/acceleration/shock measurement, prediction, fluid velocity measurement, etc., can solve problems such as inability to guarantee generalization ability and long calculation time

Active Publication Date: 2019-07-05
CENT SOUTH UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, the commonly used wind speed prediction methods are mostly statistical methods, which generally have the disadvantages of long calculation time and inability to guarantee generalization ability.

Method used

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  • CFD and deep learning-mixed extreme strong wind speed prediction method and system
  • CFD and deep learning-mixed extreme strong wind speed prediction method and system
  • CFD and deep learning-mixed extreme strong wind speed prediction method and system

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Experimental program
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Effect test

Embodiment 1

[0071] The extreme wind speed prediction method of a kind of mixed CFD (Computational Fluid Dynamics, i.e. calculation fluid dynamics) and deep learning that this embodiment provides is used for carrying out wind speed prediction to extreme wind when the target area is a railway track, such as Figure 5 As shown, the specific steps are:

[0072] Step 1, build the wind measuring device and construct the wind speed measurement module;

[0073] Such as figure 1 As shown, starting from the first station of the railway, along the railway track, each distance length interval Dist 1 It is recorded as 1 wind measuring point, and the total number of wind measuring points is Num 1 . For each wind measuring point in turn, on both sides of the railway track, perpendicular to the direction of the tangent of the railway track at the wind measuring point, the distance from the railway track Dist 1 A boundary wind measuring device is established at / 2, and an internal wind measuring devic...

Embodiment 2

[0119] This embodiment provides a mixed CFD and deep learning extreme wind speed prediction method, which is used to predict the wind speed of extreme wind when the target area is along the bridge. The distribution of the wind measuring devices is as follows figure 2 As shown, and the wind speed prediction method is the same as the embodiment.

Embodiment 3

[0121] This embodiment provides a mixed CFD and deep learning extreme wind speed prediction method, which is used to predict the wind speed of extreme wind when the target area is a wind farm. The distribution of each wind measuring device is as follows image 3 As shown, with respect to the prediction method that the target area of ​​embodiment one is the railway track, the difference is that step 1 and step 2 are adjusted as follows:

[0122] Step 1, build wind measuring device;

[0123] Step 1.1, as in image 3 As shown, the length and width are Dist 1 A rectangle of integer multiples, covering the wind farm area, with the side length being Dist 1 The square network divides the rectangle, and the center of each grid is recorded as 1 wind measuring point, and the total number of wind measuring points is Num 1 ; in this embodiment Dist 1 The value is 10 kilometers.

[0124] Step 1.2, for each grid in turn, build a wind measuring device at the wind measuring point, and bu...

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Abstract

The invention discloses a CFD and deep learning-mixed extreme strong wind speed prediction method and system. The method comprises the steps of simulating a large airflow field of a target area basedon the wind speed test sample and the wind direction test sample so as to calculate the response time and the simulation errors of each grid division mode of each sub-area and a plurality of simulation wind speed sequences of each internal wind measurement device; training a wind speed conversion model based on the corresponding simulated wind speed sequence and the wind speed test sample, and training a wind speed prediction model based on the wind speed test sample, so that when the target prediction place is predicted in real time, the output values of the wind speed conversion model and the wind speed prediction model are intelligently matched according to the distance relation between the target prediction place and the nearest internal wind measurement device, and the optimal wind speed prediction value of the target prediction place is outputted. According to the method, the deep learning model is trained offline, the prediction instantaneity is improved, meanwhile, by fusing aCFD method, the large airflow field of the target site is simulated, the optimum deep learning model is matched, and the generalization ability of the prediction system is improved.

Description

technical field [0001] The invention belongs to the field of wind speed forecasting, and in particular relates to a method and system for forecasting wind speed of extremely strong winds by mixing CFD and deep learning. Background technique [0002] High winds are one of the common extreme weather. The strong wind affects the normal running of the train. When the train runs to special track sections such as curves and hills, derailment and overturning accidents are very likely to occur under the action of the strong wind, threatening transportation safety. Sudden strong winds will change the characteristics of the wind field around the bridge. The strong wind randomly affects the stability of the bridge in time and space, reduces the comfort of passing vehicles, and hinders or even blocks traffic. Sudden strong winds will affect the output power of the wind farm, aggravate the volatility of wind power power, reduce the quality of power energy, and hinder the safe and stable...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/04G01P5/00
CPCG06Q10/04G01P5/00G06N3/045
Inventor 刘辉陈浩林李周欣尹恒鑫张馨雨
Owner CENT SOUTH UNIV
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