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Engineering wind speed short-time prediction method and system based on physical information coupling

A technology of physical information and wind speed, used in forecasting, data processing applications, electrical and digital data processing, etc., can solve problems such as high computational cost, falling into local optimal solutions, poor generalization ability, etc., to achieve comprehensive considerations and improve accuracy. performance, reducing storage costs

Active Publication Date: 2019-07-12
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 high computational cost, poor generalization ability, and easy to fall into local optimal solutions.

Method used

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  • Engineering wind speed short-time prediction method and system based on physical information coupling
  • Engineering wind speed short-time prediction method and system based on physical information coupling
  • Engineering wind speed short-time prediction method and system based on physical information coupling

Examples

Experimental program
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Effect test

Embodiment 1

[0087] A method for short-term prediction of engineering wind speed based on physical information coupling provided in this embodiment is applied to short-term prediction of engineering wind speed when the target area is a railroad, such as Figure 5 shown, including the following steps:

[0088] Step 1, build an internal wind measurement device

[0089] like figure 1 As shown, starting from the railway starting station, along the railway track, each distance length interval Dist 1 It is recorded as 1 wind measurement point, and the total number of recorded wind measurement 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, establish an internal wind measuring device 500 meters away from the railway track; All internal wind measuring devices for 500 meters of railway track are used as cluster wind measuring devices.

[0090] Among them, ...

Embodiment 2

[0148] This embodiment provides a short-term prediction method for engineering wind speed based on physical information coupling, which is used to predict the engineering wind speed in a short time when the target area is along a bridge. The distribution of each wind measuring device is as follows: figure 2 shown, and the prediction method is the same as the embodiment.

Embodiment 3

[0150] The third embodiment provides a method for short-term prediction of engineering wind speed based on physical information coupling, which is used for short-term prediction of engineering wind speed when the target area is a wind farm. The distribution of each wind measuring device is as follows: image 3 As shown, the difference from the prediction method in which the target area of ​​Embodiment 1 is along the railway track is that the following adjustments are made to Step 1 and Step 2:

[0151] Step 1, establish a wind measuring device;

[0152] Step 1.1, as in image 3 As shown, the length and width are Dist 1 Integer rectangle, covering the wind farm area, and the side length is Dist 1 The square network divides the rectangle, and the center of each grid is recorded as 1 wind measurement point, and the total number of wind measurement points is recorded as Num 1 ; in this example Dist 1 The value is 10 kilometers.

[0153] Step 1.2, for each grid in turn, establ...

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Abstract

The invention discloses an engineering wind speed short-time prediction method and system based on physical information coupling. The method includes: simulating a large air flow field of the target area by adopting a CFD method so as to calculate response time and simulation errors of each grid division mode of each sub-area for wind speed simulation and a plurality of simulation wind speed sequences of each internal wind measurement device, and screening dominant grid division modes according to the simulation errors; for each internal wind measurement device, obtaining a wind speed conversion model by offline deep learning based on the simulated wind speed sequence obtained by the fusion CFD method; in addition, obtaining a wind speed prediction model based on wind speed test sample offline deep learning; and finally, intelligently matching the output values of the wind speed conversion model and the wind speed prediction model through the spatial relationship between the target prediction place and the nearest internal wind measurement device, and outputting an optimal wind speed prediction value. According to the method, the calculation and storage cost of offline model training is reduced, and the wind speed prediction precision is improved while the real-time performance and the generalization capability of wind speed prediction are ensured.

Description

technical field [0001] The invention belongs to the field of wind speed prediction, and in particular relates to a method and system for short-term prediction of engineering wind speed based on physical information coupling. Background technique [0002] Sudden high winds are one of the common natural disasters. Sudden strong winds will quickly destroy the stable wind field along the bridge, causing the bridge deck to vibrate and bump, reducing the comfort and safety of passing vehicles and blocking traffic. Sudden strong winds will affect the safety of train operation. Strong winds will damage running equipment such as windows, reduce passenger comfort, hinder train operation, and even overturn trains, causing major safety accidents. Sudden strong winds will aggravate the volatility and instability of the output power of wind farms, pollute electric energy, reduce power quality, and hinder the safe and stable operation of the grid. [0003] Wind speed prediction is one of...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06Q10/04G06F17/50
CPCG06Q10/04G06F30/20G06F18/23
Inventor 刘辉徐一楠王子琪
Owner CENT SOUTH UNIV
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