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Method for calculating standard deviation diffusion coefficient in Gaussian wake flow model of wind turbine

A diffusion coefficient, Gaussian wake technology, applied in computing, computer-aided design, electrical digital data processing, etc., can solve problems such as poor accuracy, Gaussian wake model cannot accurately predict the velocity of the wake region, and lack of versatility.

Inactive Publication Date: 2021-04-13
CHINA THREE GORGES CORPORATION
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Problems solved by technology

[0004] The purpose of the present invention is to start from improving the universality of the standard deviation diffusion coefficient, by improving the acquisition means of the standard deviation diffusion coefficient, thereby improving the accuracy of the standard deviation diffusion coefficient obtained, and then solving the standard deviation caused by utilizing conventional methods. Insufficient versatility and poor accuracy of the diffusion coefficient lead to the technical problem that the Gaussian wake model cannot accurately predict the velocity of the wake region under different environmental conditions

Method used

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  • Method for calculating standard deviation diffusion coefficient in Gaussian wake flow model of wind turbine
  • Method for calculating standard deviation diffusion coefficient in Gaussian wake flow model of wind turbine
  • Method for calculating standard deviation diffusion coefficient in Gaussian wake flow model of wind turbine

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Effect test

Embodiment

[0094] Take a model wind turbine in a wind tunnel experiment as an example, such as Figure 2 to Figure 4 shown;

[0095] 1) Extract terrain roughness z 0 and wind turbine hub height z h

[0096] Its terrain roughness z 0 =0.00003m, wind turbine hub height z h =0.125m;

[0097] 2) Obtain the wake diffusion coefficient k w

[0098] According to the formula (9) to calculate the wake diffusion coefficient, it can be known that k w =0.06;

[0099] 3) Obtain the standard deviation diffusion coefficient k B

[0100] According to the formula (13) to calculate the standard deviation diffusion coefficient, it can be known that k B =0.0233;

[0101] 4) Get the standard deviation σ

[0102] The rotor diameter D of the model wind turbine 0 = 0.15m, initial standard deviation coefficient ε = 0.219, flow distance x / D 0 The value range of is from 2 to 15, and the standard deviation σ is calculated according to the formula (14). Calculated as image 3 As shown, in addition, a...

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Abstract

The invention relates to a method for calculating a standard deviation diffusion coefficient in a Gaussian wake flow model of a wind turbine. The method comprises the following steps of: 1) obtaining terrain roughness z0 and the height zh of a hub of the wind turbine; 2) obtaining a wake flow diffusion coefficient kw; 3) obtaining a standard deviation diffusion coefficient kB; 4) obtaining a standard deviation [sigma] in the Gaussian wake flow model; and 5) obtaining a Gaussian wake flow model, and calculating the speed loss of the wake flow area. The purpose of the invention is to improve the prediction precision of the Gaussian wake flow model for the wake flow area speed in different environments from the aspect of improving the universality of the standard deviation diffusion coefficient, so as to provide the calculation method for the standard deviation diffusion coefficient in the Gaussian wake flow model of the wind turbine.

Description

technical field [0001] The invention relates to the technical field of wind power generation, in particular to a method for calculating the standard deviation diffusion coefficient in a Gaussian wake model of a wind turbine, which can be used in work such as wind farm wake evaluation and microscopic site selection. Background technique [0002] The wake effect is one of the main factors affecting the power generation of wind farms. As the wind turbine absorbs part of the free-flow energy, the wind speed in a certain area downstream of the wind turbine decreases, which in turn reduces the power generation of the wind turbine located in this area. During the micro-site selection process of wind farms, wind turbines should be avoided as much as possible in the wake area where the wind speed decreases to maximize the power generation of wind farms. Therefore, accurate assessment of the wake effect of wind turbines can effectively improve the return on investment of wind farms. ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/17G06F30/20G06F113/06G06F119/14
CPCG06F30/17G06F30/20G06F2113/06G06F2119/14
Inventor 张子良易侃张皓王浩
Owner CHINA THREE GORGES CORPORATION
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