CN112632729A - Method for improving prediction precision of Gaussian wake flow model on wake field of wind turbine - Google Patents

Method for improving prediction precision of Gaussian wake flow model on wake field of wind turbine Download PDF

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CN112632729A
CN112632729A CN202110013523.XA CN202110013523A CN112632729A CN 112632729 A CN112632729 A CN 112632729A CN 202110013523 A CN202110013523 A CN 202110013523A CN 112632729 A CN112632729 A CN 112632729A
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wake
wind turbine
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张子良
易侃
张皓
王浩
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China Three Gorges Corp
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Abstract

A method for improving the prediction precision of a Gaussian wake flow model on a wake field of a wind turbine comprises the following steps: step 1) obtaining a thrust coefficient of a wind turbine; step 2) obtaining a proportionality coefficient; step 3) obtaining an initial standard deviation coefficient; step 4), obtaining a standard deviation; step 5) combining a Gaussian wake model to obtain the speed loss of the wake region; the invention aims to improve the accuracy of the obtained initial standard deviation coefficient by improving the obtaining means of the initial standard deviation coefficient starting from improving the universality of the initial standard deviation coefficient, and solves the technical problem that the universality of the initial standard deviation coefficient obtained by a conventional method is insufficient, so that the prediction accuracy of the velocity of a wake area is greatly influenced by a Gaussian wake model in different environments.

Description

Method for improving prediction precision of Gaussian wake flow model on wake field of wind turbine
Technical Field
The invention relates to the technical field of wind power generation, in particular to a method for improving the prediction precision of a Gaussian wake flow model on a wake field of a wind turbine, which can be used for work such as wake flow evaluation and micro site selection of a wind power plant.
Background
In the field of wind power generation, after free incoming flow flows through a wind turbine, the wind turbine absorbs a part of energy of the incoming flow, so that the phenomena of wind speed reduction and turbulence increase occur at the downstream of the wind turbine, and a region generating the phenomena is called a wake region. Due to the fact that the wind speed in the wake area is reduced and the turbulence degree is increased, the generated energy of the wind turbine in the wake area is reduced, fatigue loads are increased, and the investment benefits of the wind power plant are seriously affected. Therefore, wake flow evaluation is one of the most important links for micro site selection and power generation prediction of the wind power plant.
The method for evaluating the wake flow of the wind power plant is mainly divided into three types: analytic wake model evaluation, numerical calculation evaluation and experimental evaluation. At present, in practical engineering application, analyzing a wake flow model is a widely adopted evaluation method, and the method predicts the flow field distribution of a wake flow area by providing a distribution function of velocity loss of the wake flow area based on theoretical derivation and hypothesis of fluid mechanics. Researchers have proposed various analytic wake models, such as Jensen wake model, Frandsen wake model, gaussian wake model, etc., wherein the gaussian wake model is considered to be capable of accurately describing the distribution of wake region velocity loss, in the current research of gaussian wake model, the initial standard deviation coefficient epsilon is obtained by fitting the experiment or numerical calculation result, the difference of the relational expressions obtained under different experiment or numerical calculation conditions is large, which results in the lack of generality of epsilon expression, and further affects the prediction precision of gaussian wake model to the wake region velocity under different environments.
Disclosure of Invention
The invention aims to improve the accuracy of the obtained initial standard deviation coefficient by improving the obtaining means of the initial standard deviation coefficient starting from improving the universality of the initial standard deviation coefficient, and solves the technical problem that the universality of the initial standard deviation coefficient obtained by a conventional method is insufficient, so that the prediction accuracy of the velocity of a wake area is greatly influenced by a Gaussian wake model in different environments.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a method for improving the prediction precision of a Gaussian wake flow model on a wake field of a wind turbine comprises the following steps:
step 1) obtaining a thrust coefficient of a wind turbine;
step 2) obtaining a proportionality coefficient;
step 3) obtaining an initial standard deviation coefficient;
step 4), obtaining a standard deviation;
step 5) combining a Gaussian wake model to obtain the speed loss of the wake region;
it also comprises a step 6): and predicting the speed loss distribution of the wake area of the wind turbine, and avoiding arranging the wind turbine in the wake area with reduced wind speed.
In step 1), the thrust coefficient of the wind turbine can be determined by the incoming wind speed and the thrust coefficient curve of the wind turbine.
In step 2), when the proportionality coefficient β is obtained, the following formula is adopted:
Figure BDA0002885858620000021
in the formula: d1The wake diameter corresponding to the flow direction position when the wake pressure starts to recover the incoming flow pressure; d0Is the diameter of the impeller of the wind turbine; cTIs the thrust coefficient of the wind turbine.
In step 2), D1And D0The flow direction positions are the same and are all positioned on the plane of the wind wheel.
In step 3), when obtaining the initial standard deviation coefficient epsilon, the following steps are adopted:
let Dw＝5.16σ (8)
In the formula: dwIs the wake diameter; σ is the standard deviation.
The binding standard deviation σ can be expressed as:
Figure BDA0002885858620000022
in the formula: k is a radical ofBIs the standard deviation diffusion coefficient; x is the flow direction distance from the wake area to the wind turbine; ε is the initial standard deviation factor.
The following can be obtained:
Dw＝5.16kBx+5.16εD0 (9)
recombination of
Dw＝2kwx+D1 (10)
In the formula: k is a radical ofwIs the wake diffusion coefficient.
The following can be obtained:
Figure BDA0002885858620000023
in conjunction with equation (7) one can obtain:
Figure BDA0002885858620000031
in step 3), the wake diameter follows a linear expansion law along the flow direction, i.e. the wake diameter increases linearly with increasing distance of the flow direction.
In step 4), the standard deviation conforms to the linear expansion law along the flow direction, i.e. the standard deviation increases linearly with increasing distance of the flow direction.
In step 4), when the standard deviation σ is obtained, the following formula is adopted:
σ＝kBx+εD0 (13)
in step 4), the value of the flow direction distance should be larger than 2 times of the diameter of the impeller.
In step 5), when the velocity defect of the wake region is obtained by combining the gaussian wake model, the following formula is adopted:
Figure BDA0002885858620000032
in the formula: u shape∞Is the free incoming flow velocity; u shapewIs the wake zone velocity; and r is the distance from the center line of the hub of the wind turbine in the wake flow area.
In the step 5), the speed loss of the wake area is maximum at the position of the center line of the hub of the wind turbine, and the speed loss is gradually reduced towards the periphery by taking the position of the center line of the hub as the center.
Compared with the prior art, the invention has the following technical effects:
1) the method can efficiently and accurately predict the speed loss distribution condition of the wake area of the wind turbine, avoid the wind turbine from being arranged in the wake area with reduced wind speed, keep the efficient operation of the wind turbine, avoid the increase of fatigue load, and provide help for the reasonable arrangement and the efficient work of the wind turbine.
2) The invention provides an initial standard deviation coefficient obtaining method, which solves the technical problems that the universality of an initial standard deviation coefficient expression is insufficient and the prediction precision of a Gaussian wake flow model on the wake flow area speed in different environments is influenced because the traditional obtaining method is obtained by fitting an experiment or numerical calculation result and the relational expression obtained under different experiment or numerical calculation conditions is greatly different.
Drawings
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
FIG. 1 is a schematic diagram of a Gaussian wake model velocity deficit distribution;
FIG. 2 is a schematic flow chart of the present invention;
FIG. 3 is a graph of sigma/D calculated according to the present invention0Comparing the result with other relational expression calculation results and numerical calculation results;
FIG. 4 is a calculated velocity deficit distribution for a wake region of a wind turbine according to the present disclosure.
Detailed Description
A method for improving the prediction precision of a Gaussian wake flow model on a wake field of a wind turbine comprises the following steps:
step 1) obtaining a thrust coefficient C of a wind turbineT；
Step 2) obtaining a proportionality coefficient beta;
step 3) obtaining an initial standard deviation coefficient epsilon;
step 4), obtaining a standard deviation sigma;
step 5) combining a Gaussian wake model to obtain the speed loss of the wake region;
it also comprises a step 6): and predicting the speed loss distribution of the wake area of the wind turbine, and avoiding arranging the wind turbine in the wake area with reduced wind speed.
In step 1), the thrust coefficient of the wind turbine can be determined by the incoming wind speed and the thrust coefficient curve of the wind turbine.
In step 2), when the proportionality coefficient β is obtained, the following formula is adopted:
Figure BDA0002885858620000041
in step 2), D1And D0The flow direction positions are the same and are all positioned on the plane of the wind wheel.
In step 3), the distribution function of the gaussian wake model is shown in formula (1):
Figure BDA0002885858620000042
where σ is generally expressed as:
Figure BDA0002885858620000043
currently, in the research of gaussian wake models, researchers have proposed various expressions of epsilon, mainly including:
ε＝0.23CT -0.25I0 0.17 (3)
ε＝-1.91kB+0.34 (4)
Figure BDA0002885858620000044
Figure BDA0002885858620000051
wherein: i is0β is a proportionality coefficient, which is the turbulence of the incoming flow and can be expressed by the following formula:
Figure BDA0002885858620000052
in the above expression of epsilon, the expressions (3) - (5) are obtained by fitting the experiment or numerical calculation results, and the relational expressions obtained under different experiment or numerical calculation conditions have large differences, which results in insufficient generality of the expression of epsilon. Equation (6) is obtained by comparing the mass flow of the gaussian wake model with the mass flow of the Frandsen wake model at x ═ 0, however, it has been shown that epsilon obtained by using this relation is large compared with the true value, and it is necessary to make corresponding corrections in actual use. The shortages of the expression of epsilon further influence the prediction precision of the Gaussian wake model on the velocity of the wake region.
In step 3), when obtaining the initial standard deviation coefficient epsilon, the following steps are adopted:
let Dw＝5.16σ (8)
The binding standard deviation σ can be expressed as:
Figure BDA0002885858620000053
the following can be obtained:
Dw＝5.16kBx+5.16εD0 (9)
recombination of
Dw＝2kwx+D1 (10)
The following can be obtained:
Figure BDA0002885858620000054
in conjunction with equation (7) one can obtain:
Figure BDA0002885858620000055
in step 3), the wake diameter follows a linear expansion law along the flow direction, i.e. the wake diameter increases linearly with increasing distance of the flow direction.
In step 4), the standard deviation conforms to the linear expansion law along the flow direction, i.e. the standard deviation increases linearly with increasing distance of the flow direction.
In step 4), when the standard deviation σ is obtained, the following formula is adopted:
σ＝kBx+εD0 (13)
in step 4), the value of the flow direction distance should be larger than 2 times of the diameter of the impeller.
In step 5), when the velocity defect of the wake region is obtained by combining the gaussian wake model, the following formula is adopted:
Figure BDA0002885858620000061
in the step 5), the speed loss of the wake area is maximum at the position of the center line of the hub of the wind turbine, and the speed loss is gradually reduced towards the periphery by taking the position of the center line of the hub as the center.
Example (b):
taking a wind turbine simulated by a certain amount of money as an example, as shown in fig. 2 to 4;
1) extracting thrust coefficient C of wind turbineT；
For a certain wind turbine, the thrust coefficient is set to CT＝0.8；
2) Calculating a proportionality coefficient beta;
calculating a proportionality coefficient beta according to the formula (7), wherein beta is 1.618;
3) calculating an initial standard deviation coefficient epsilon;
calculating an initial standard deviation coefficient epsilon according to the formula (12), and obtaining the epsilon 0.2465;
4) calculating a standard deviation sigma;
wind wheel diameter D of wind turbine080m, standard deviation diffusion coefficient kB0.055, flow direction distance x/D0The value range of (2) to (15), the standard deviation sigma is calculated according to the formula (13), and the calculation result is shown in fig. 2. In addition, standard deviations and numerical calculations based on other e relationships are also added to the figure. As can be seen from the figure, sigma obtained by the calculation of the epsilon relational expression provided by the invention is better matched with the numerical calculation result. For other relational expressions, although the σ calculated by the corresponding relational expression of the formula (4) and the formula (5) is well matched with the numerical calculation result, both the relational expressions are obtained by fitting data, and a sufficient theoretical basis is lacking. Therefore, the standard deviation sigma in the Gaussian wake model can be accurately calculated by the relational expression provided by the invention.
5) Calculating the velocity defect of the wake region by combining a Gaussian wake model
Thrust coefficient C of wind turbineT0.8, wind wheel diameter D080m, the distance of the flow direction is x/D0And (5) calculating the speed loss of the wake area of the model wind turbine according to the formula (14), wherein the calculation result is shown in fig. 4.

Claims (10)

1. A method for improving the prediction precision of a Gaussian wake flow model on a wake field of a wind turbine is characterized by comprising the following steps:
step 1) obtaining a thrust coefficient of a wind turbine;
step 2) obtaining a proportionality coefficient;
step 3) obtaining an initial standard deviation coefficient;
step 4), obtaining a standard deviation;
and 5) combining the Gaussian wake model to obtain the velocity loss of the wake region.
2. The method for improving the prediction accuracy of the wake field of the wind turbine by the Gaussian wake model as recited in claim 1, further comprising the step 6): and predicting the speed loss distribution of the wake area of the wind turbine, and avoiding arranging the wind turbine in the wake area with reduced wind speed.
3. The method for improving the prediction accuracy of the Gaussian wake model on the wake field of the wind turbine as claimed in claim 1, wherein in step 1), the thrust coefficient of the wind turbine can be determined by the curve of the incoming wind speed and the thrust coefficient of the wind turbine.
4. The method for improving the prediction accuracy of the wake field of the wind turbine by the Gaussian wake model as claimed in claim 1, wherein in the step 2), when the proportionality coefficient β is obtained, the following formula is adopted:
Figure FDA0002885858610000011
5. the method for improving the prediction accuracy of the wake field of the wind turbine by the Gaussian wake flow model as claimed in claim 4, wherein in the step 2), D1And D0The flow direction positions are the same and are all positioned on the plane of the wind wheel.
6. The method for improving the prediction accuracy of the wake field of the wind turbine by the Gaussian wake model as recited in claim 1, wherein in the step 3), when the initial standard deviation coefficient ε is obtained, the following steps are adopted:
let Dw＝5.16σ (8)
The binding standard deviation σ can be expressed as:
Figure FDA0002885858610000012
the following can be obtained:
Dw＝5.16kBx+5.16εD0 (9)
recombination of Dw＝2kwx+D1 (10)；
The following can be obtained:
Figure FDA0002885858610000021
in conjunction with equation (7) one can obtain:
Figure FDA0002885858610000022
7. the method for improving the prediction accuracy of the wake field of the wind turbine by the Gaussian wake model as claimed in claim 1, wherein in step 4), the following formula is adopted when the standard deviation σ is obtained:
σ＝kBx+εD0 (13)。
8. the method for improving the prediction accuracy of the wake field of the wind turbine by the Gaussian wake model as claimed in claim 7, wherein in the step 4), the value of the flow direction distance is more than 2 times of the diameter of the impeller.
9. The method for improving the prediction accuracy of the wake field of the wind turbine by the gaussian wake model according to claim 1, wherein in the step 5), when the speed loss of the wake region is obtained by combining the gaussian wake model, the following formula is adopted:
Figure FDA0002885858610000023
10. the method for improving the prediction accuracy of the wake field of the wind turbine by the gaussian wake model according to claim 9, wherein in step 5), the velocity loss of the wake region is maximum at the position of the center line of the hub of the wind turbine, and the velocity loss gradually decreases towards the periphery with the position of the center line of the hub as the center.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113239648A (en) * 2021-06-22 2021-08-10 华北电力大学 Method and device for determining turbulence degree of far-field wake flow direction of wind power plant
CN113627097A (en) * 2021-07-22 2021-11-09 中国长江三峡集团有限公司 Method for correcting wake flow evaluation model by using SCADA data of wind turbine

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113239648A (en) * 2021-06-22 2021-08-10 华北电力大学 Method and device for determining turbulence degree of far-field wake flow direction of wind power plant
CN113239648B (en) * 2021-06-22 2023-11-21 华北电力大学 Method and device for determining turbulence of wind power plant far-field wake flow
CN113627097A (en) * 2021-07-22 2021-11-09 中国长江三峡集团有限公司 Method for correcting wake flow evaluation model by using SCADA data of wind turbine
CN113627097B (en) * 2021-07-22 2022-10-04 中国长江三峡集团有限公司 Method for correcting wake flow evaluation model by using SCADA data of wind turbine

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