Method, device and equipment for evaluating speed loss of full wake flow of wind turbine generator and medium

Abstract

The invention relates to the technical field of wind power generation, in particular to a speed loss evaluation method, device and equipment for full wake flow of a wind turbine generator and a medium, and aims to solve the technical problem that the calculation result of near wake flow is inaccurate in an existing method. In order to achieve the purpose, the speed loss evaluation method for the full wake flow of the wind turbine generator comprises the steps that the incoming flow wind speed and wind direction are obtained; preset parameter information is determined based on the incoming flow wind speed and wind direction; obtaining a pre-constructed double-Gaussian wake flow model considering pressure difference correction; determining amplitude distribution and shape distribution of the double-Gaussian wake flow model based on preset parameter information; and evaluating the wake flow speed loss of the wind turbine generator based on the amplitude distribution and shape distribution of the double-Gaussian wake flow model. In this way, the defect that a traditional model is insufficient in evaluation precision in a near wake flow area is overcome through pressure difference correction.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and specifically provides a method, device, equipment and medium for evaluating the velocity loss of the full wake of a wind turbine. Background Art

[0002] After absorbing the kinetic energy of the incoming wind, a wind turbine forms a wake region downstream where wind speed decreases. Currently, analytical wind turbine wake models based on a Gaussian distribution are primarily used to assess velocity losses in wind turbine wakes. While computationally simple, these models ignore pressure variations near the wake and are only applicable to calculations far from the wake, resulting in inaccurate results for velocity losses near the wake. As turbine spacing within wind farms decreases, downstream turbines may be within the wake of upstream turbines, making existing models inadequate for practical applications.

[0003] Accordingly, a new method for evaluating the velocity loss of the entire wake of a wind turbine is needed in this field to solve the above problems. Summary of the Invention

[0004] To overcome the above-mentioned drawbacks, the present application is proposed to provide a solution or at least partially solve the technical problem of inaccurate calculation results of existing methods in the near-wake range. The present application provides a method, device, equipment and medium for evaluating velocity loss in the full wake of a wind turbine.

[0005] In a first aspect, the present application provides a method for evaluating velocity loss of a full wake of a wind turbine, the method comprising:

[0006] Get incoming wind speed and direction;

[0007] Determining preset parameter information based on the incoming wind speed and wind direction;

[0008] Get the pre-built double-Gaussian wake model that takes into account the pressure difference correction;

[0009] Determining the amplitude distribution and shape distribution of the double Gaussian wake model based on the preset parameter information;

[0010] The wake velocity loss of the wind turbine is evaluated based on the amplitude distribution and shape distribution of the double Gaussian wake model.

[0011] In one embodiment of the present application, the double Gaussian wake model considering pressure difference correction is constructed by the following steps:

[0012] Combined with the law of conservation of mass, the momentum balance equation of the wind turbine wake velocity loss is established taking into account the pressure difference correction;

[0013] Simplifying the momentum balance equation according to the pressure difference distribution characteristics to obtain a simplified momentum balance equation;

[0014] Obtaining a shape function of the double Gaussian wake model;

[0015] Obtaining an amplitude function of a double Gaussian wake model based on a double Gaussian distribution corresponding to the wake velocity loss and the simplified momentum balance equation;

[0016] The double-Gaussian wake model is constructed based on the shape function and the amplitude function.

[0017] In one embodiment of the present application, the simplified momentum balance equation is expressed as:

[0018] Where ρ is the air density; A is the area of ​​the wind wheel, A=πD 2 / 4, D is the diameter of the wind wheel; U ∞ is the incoming wind speed; C T is the thrust coefficient of the wind turbine; γ is the yaw angle; x is the downstream distance of the wind turbine; du is the wake velocity loss of the wind turbine; A w is the area of ​​the wake region.

[0019] In one embodiment of the present application, the shape function G(x, r) of the double Gaussian wake model is expressed as:

[0020] in, σ y and σ z represent the Gaussian standard deviation of the wake in the horizontal and vertical directions respectively; y c is the offset of the wake center; Z h is the hub height; r min is the radial position of the velocity minimum point.

[0021] In one embodiment of the present application, the obtaining of the amplitude function of the double Gaussian wake model based on the double Gaussian distribution and the simplified momentum balance equation includes:

[0022] Get the expression of the Gaussian distribution:

[0023] Among them, du is the wake velocity loss of the wind turbine, U ∞ is the incoming wind speed, C(x,γ) is the amplitude function of the double Gaussian wake model, and G(x,r) is the shape function of the double Gaussian wake model;

[0024] The expression of the Gaussian distribution is substituted into the simplified momentum balance equation, and the simplified momentum balance equation is integrated to obtain the amplitude function of the double Gaussian wake model. The expression of the amplitude function C(x,γ) is:

[0025] in, σ y and σ z represent the Gaussian standard deviation of the wake in the horizontal and vertical directions respectively; y c is the offset of the wake center; r min is the radial position of the velocity minimum point.

[0026] In one embodiment of the present application, determining the preset parameter information based on the incoming wind speed and wind direction includes:

[0027] determining a thrust coefficient of the wind turbine generator system based on the incoming wind speed;

[0028] determining a yaw angle of the wind turbine generator system based on the wind direction;

[0029] The preset parameter information is calculated based on the thrust coefficient and the yaw angle, where the preset parameter information includes a radial position of a velocity minimum point, a Gaussian standard deviation of the wake in horizontal and vertical directions, and an offset of a wake center.

[0030] In one embodiment of the present application, the evaluation of the wake velocity loss of the wind turbine based on the amplitude distribution and shape distribution of the double Gaussian wake model includes: inputting the amplitude distribution, shape distribution of the double Gaussian wake model and the incoming wind speed of the wind turbine into the double Gaussian distribution expression corresponding to the wake velocity loss, and outputting the wake velocity loss distribution of the wind turbine.

[0031] In a second aspect, a device for evaluating velocity loss of a full wake of a wind turbine is provided, the device comprising:

[0032] A first acquisition module is configured to acquire incoming wind speed and wind direction;

[0033] A first determining module is configured to determine preset parameter information based on the incoming wind speed and wind direction;

[0034] A second acquisition module is configured to acquire a pre-built double Gaussian wake model considering pressure difference correction;

[0035] A second determining module is configured to determine the amplitude distribution and shape distribution of the double Gaussian wake model based on the preset parameter information;

[0036] An evaluation module is configured to evaluate the wake velocity loss of the wind turbine generator system based on the amplitude distribution and shape distribution of the double Gaussian wake model.

[0037] In a third aspect, an electronic device is provided, comprising:

[0038] at least one processor;

[0039] and, a memory communicatively coupled to the at least one processor;

[0040] Wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the aforementioned method for evaluating speed loss of the full wake of a wind turbine is implemented.

[0041] In a fourth aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored in the computer-readable storage medium, wherein the program codes are suitable for being loaded and run by a processor to execute any of the aforementioned methods for assessing velocity loss in the full wake of a wind turbine.

[0042] The above one or more technical solutions of this application have at least one or more of the following Beneficial effects:

[0043] The method for assessing the velocity loss of the entire wake of a wind turbine in this application includes: obtaining the incoming wind speed and direction; determining preset parameter information based on the incoming wind speed and direction; obtaining a pre-built double Gaussian wake model that takes into account pressure difference correction; determining the amplitude distribution and shape distribution of the double Gaussian wake model based on the preset parameter information; and assessing the wake velocity loss of the wind turbine based on the amplitude distribution and shape distribution of the double Gaussian wake model. The double Gaussian model more accurately depicts the asymmetric distribution of the wake, significantly reducing the error compared to the traditional single Gaussian model; and the pressure difference correction compensates for the defect of the traditional model that ignores the influence of pressure gradient, thereby improving the accuracy of the assessment of wake velocity loss in the near-wake area. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The disclosure of this application will be more easily understood with reference to the accompanying drawings. Those skilled in the art will readily appreciate that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this application. Furthermore, similar numbers in the figures represent similar components, where:

[0045] Figure 1 This is a schematic diagram of the main process of a method for evaluating the velocity loss of the entire wake of a wind turbine in one embodiment of the present application;

[0046] Figure 2 This is a schematic diagram of the influence range of the wind turbine tail pressure difference in one embodiment of the present application;

[0047] Figure 3is a graph showing the variation of f(x) with the downstream distance of the wind turbine obtained by large eddy numerical simulation in one embodiment of the present application;

[0048] Figure 4 This is a schematic diagram comparing the results of evaluating wake velocity loss using different models in one embodiment of the present application;

[0049] Figure 5 This is a schematic diagram of the main structure of a device for evaluating the velocity loss of the entire wake of a wind turbine generator system according to one embodiment of the present application;

[0050] Figure 6 It is a structural diagram of an electronic device in one embodiment of the present application. DETAILED DESCRIPTION

[0051] Some embodiments of the present application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the scope of protection of the present application.

[0052] In the description of this application, "module" and "processor" may include hardware, software, or a combination of both. A module may include hardware circuitry, various suitable sensors, communication ports, and memory. It may also include software components, such as program code, or a combination of software and hardware. A processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. A processor has data and / or signal processing capabilities. A processor may be implemented in software, hardware, or a combination of both. Non-transitory computer-readable storage media include any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc. The term "A and / or B" refers to all possible combinations of A and B, such as only A, only B, or both A and B. The terms "at least one of A or B" or "at least one of A and B" have similar meanings to "A and / or B" and may include only A, only B, or both A and B. The singular forms "a" and "the" may also include the plural forms.

[0053] The current traditional method mainly calculates wake losses by using a wind turbine wake model based on Gaussian distribution. Although this method is simple to calculate, the calculation results for near-wake are inaccurate and cannot meet actual needs.

[0054] To this end, the present application proposes a method, device, equipment and medium for evaluating velocity loss of the entire wake of a wind turbine.

[0055] See attached Figure 1 , Figure 1FIG. 1 is a flow chart showing the main steps of a method for evaluating the velocity loss of a full wake of a wind turbine according to an embodiment of the present application. Figure 1 As shown, the method for evaluating the velocity loss of the entire wake of a wind turbine in the embodiment of the present application mainly includes the following steps S10 to S50.

[0056] Step S10: Obtain incoming wind speed and direction.

[0057] The incoming wind speed and direction refer to the free stream wind speed and direction that are not disturbed by the wind turbine, and are usually measured by the wind measuring device required by the wind turbine.

[0058] Step S20: Determine preset parameter information based on the incoming wind speed and wind direction.

[0059] The preset parameter information includes the radial position of the velocity minimum point, the Gaussian standard deviation of the wake in the horizontal and vertical directions, and the offset of the wake center.

[0060] Step S30: obtaining a pre-built double-Gaussian wake model that takes pressure difference correction into consideration.

[0061] The double-Gaussian wake model is a mathematical model used to describe the velocity distribution of a wind turbine wake. It uses the superposition of two Gaussian functions to characterize the asymmetric velocity loss in the wake cross section. Compared to a single-Gaussian model, it more accurately reflects the true structure of the wake, especially under yaw or complex turbulent conditions. Furthermore, this application further introduces a pressure difference correction into the double-Gaussian wake model, resulting in even higher accuracy near the wake.

[0062] Step S40: determining the amplitude distribution and shape distribution of the double Gaussian wake model based on the parameter information.

[0063] Step S50: Evaluate the velocity loss of the entire wake of the wind turbine based on the amplitude distribution and shape distribution of the double Gaussian wake model.

[0064] Based on steps S10-S50 above, the incoming wind speed and direction are first obtained; preset parameter information is determined based on the incoming wind speed and direction; a pre-constructed double-Gaussian wake model that takes pressure difference correction into account is obtained; the amplitude distribution and shape distribution of the double-Gaussian wake model are determined based on the parameter information; and the wake velocity loss of the wind turbine is assessed based on the amplitude distribution and shape distribution of the double-Gaussian wake model. The double-Gaussian model more accurately depicts the asymmetric distribution of the wake, significantly reducing the error compared to the traditional single-Gaussian model. Pressure difference correction compensates for the traditional model's lack of ignoring the influence of pressure difference, improving the accuracy of the wake velocity loss assessment in the near-wake region.

[0065] The above steps S20 to S50 are further explained below.

[0066] Specifically, step S20 can be implemented through the following steps S201 to S203.

[0067] Step S201: determining the thrust coefficient of the wind turbine generator system based on the incoming wind speed.

[0068] Specifically, the thrust coefficient can be determined based on the incoming wind speed by consulting the thrust coefficient curve for the wind turbine. This curve is usually provided by the wind turbine manufacturer and varies depending on the type of turbine.

[0069] Step S202: determining the yaw angle of the wind turbine based on the wind direction.

[0070] The yaw angle γ is mainly used to describe the angular deviation of the wind turbine's orientation relative to the wind direction. It can be determined in the following ways: γ=θ wind -θ nacelle

[0071] Among them, θ wind is the direction of the incoming wind, θ nacelle is the orientation angle of the cabin.

[0072] Step S203: Calculating parameter information of the double Gaussian wake model based on the thrust coefficient and the yaw angle. The preset parameter information includes the radial position of the velocity minimum point, the Gaussian standard deviation of the wake in the horizontal and vertical directions, and the offset of the wake center.

[0073] Radial position r of the velocity minimum point min It can be calculated by the following formula:

[0074] Where D is the rotor diameter, γ is the yaw angle, x is the downstream distance of the wind turbine, and x0 is the dividing point between the near and far wakes. According to the BPA wake model, the expression for x0 is:

[0075] Among them, I u is the turbulence intensity.

[0076] The Gaussian standard deviation of the wake in the horizontal and vertical directions in the near-wake region is:

[0077] Among them, σ y , σ z is the Gaussian standard deviation of the wake in the horizontal and vertical directions.

[0078] The Gaussian standard deviation of the wake in the far wake region in the horizontal and vertical directions is:

[0079] Among them, k y 、k z are the wake expansion coefficients in the spanwise and vertical directions, respectively, and the classic value is 0.022.

[0080] The offset y of the wake center in the near wake region c for:

[0081] Among them, θ c0 is the airflow inclination angle near the wake,

[0082] The offset y of the wake center in the far wake region c for:

[0083] The above is a further description of step S20 , and the following further describes step S30 .

[0084] Specifically, step S30 can be implemented through the following steps S301 to S305.

[0085] Step S301: In combination with the law of conservation of mass, a momentum balance equation of the wind turbine wake velocity loss taking into account pressure difference correction is established.

[0086] Specifically, combined with the law of conservation of mass, the momentum balance equation for the wind turbine wake velocity loss considering the pressure difference correction is established as follows:

[0087] Where ρ is the air density; A is the area of ​​the wind wheel, A=πD 2 / 4, D is the diameter of the wind wheel; U ∞ is the inflow wind speed; C T is the thrust coefficient of the wind turbine; γ is the yaw angle; P ∞ is the ambient pressure; P w is the pressure in the wake area; A p is the area covered by the effective pressure difference in the wake; du is the speed loss of the wind turbine; A w is the area of ​​the wake region.

[0088] Step S302: Simplifying the momentum balance equation according to the pressure difference distribution characteristics to obtain a simplified momentum balance equation.

[0089] Specifically, according to the results of large eddy simulation, the pressure difference in the wake is mainly distributed around the center of the wake, covering an area 2.25 times the projected area of ​​the wind rotor. The dynamic pressure of the wind speed on the wind rotor is used to non-dimensionalize the wake pressure difference, and the momentum balance equation can be initially simplified to obtain the initial simplified momentum equation:

[0090] Where f(x) is the momentum correction coefficient caused by the pressure difference, and the expression of f(x) is:

[0091] Among them (P ∞ -P w ) is the average pressure difference at the wake.

[0092] The above formula contains a pressure term that is difficult to measure, but its distribution is regular. Therefore, based on the results of large eddy simulation, the above formula can be fitted into the following expression: f(x)=0.5 / (1+2x / D)

[0093] Substitute f(x) obtained from the above fitting into the momentum balance equation after initial simplification to obtain the final simplified momentum balance equation:

[0094] Where ρ is the air density; A is the area of ​​the wind wheel, A=πD 2 / 4, D is the diameter of the wind wheel; U ∞ is the incoming wind speed; C T is the thrust coefficient of the wind turbine; γ is the yaw angle; x is the downstream distance of the wind turbine; du is the wake velocity loss of the wind turbine; A w is the area of ​​the wake region.

[0095] Step S303: Obtain the shape function of the double Gaussian wake model.

[0096] Assuming that the velocity loss of the wake conforms to the double Gaussian distribution, the expression is:

[0097] Among them, du is the wake velocity loss of the wind turbine, U ∞ is the incoming wind speed, C(x,γ) is the amplitude function of the double Gaussian wake model, and G(x,r) is the shape function of the double Gaussian wake model.

[0098] Specifically, the shape function G(x,r) of the double Gaussian wake model is expressed as:

[0099] in, σ y and σ z represent the Gaussian standard deviation of the wake in the horizontal and vertical directions respectively; y c is the offset of the wake center; Z h is the hub height; r min is the radial position of the velocity minimum point.

[0100] Step S304: obtaining an amplitude function of the double Gaussian wake model based on the double Gaussian distribution corresponding to the wake velocity loss and the simplified momentum balance equation.

[0101] Specifically, by bringing the double Gaussian distribution of the wake velocity loss into the simplified momentum balance equation and integrating it radially from zero to infinity, we can obtain the amplitude function C(x,γ) of the double Gaussian wake model:

[0102] in, σ y and σ z represent the Gaussian standard deviation of the wake in the horizontal and vertical directions respectively; y c is the offset of the wake center; r min is the radial position of the velocity minimum point.

[0103] Step S305: constructing a double-Gaussian wake model based on the shape function and the amplitude function.

[0104] Specifically, after obtaining the shape function G(x, r) and the amplitude function C(x, γ), a double Gaussian wake model can be constructed based on the fact that the velocity loss of the wake conforms to the double Gaussian distribution.

[0105] The above is a further description of step S30 , and the following further describes step S40 .

[0106] With respect to the aforementioned step S40 , the preset parameter information obtained in step S20 may be brought into the amplitude function and shape function of the double Gaussian wake model, thereby obtaining the amplitude distribution and shape distribution of the double Gaussian wake model.

[0107] With respect to the aforementioned step S50, in a specific embodiment of the present application, the evaluation of the wake velocity loss of the wind turbine based on the amplitude distribution and shape distribution of the double Gaussian wake model includes: inputting the amplitude distribution, shape distribution of the double Gaussian wake model and the incoming wind speed of the wind turbine into the double Gaussian distribution expression corresponding to the wake velocity loss, and outputting the wake velocity loss distribution of the wind turbine.

[0108] Specifically, the amplitude distribution C(x,γ), shape distribution G(x,r) and the incoming wind speed U calculated in step S40 are ∞ The wake velocity loss distribution of the wind turbine can be calculated by taking the expression of the double Gaussian distribution into account.

[0109] Figure 2 This is a schematic diagram of the influence range of the wind turbine wake pressure difference obtained through large eddy numerical simulation. The dotted line in the figure represents the range of velocity loss distribution, and the solid line represents the range of pressure difference distribution.

[0110] Figure 3 This is a graph showing the variation of f(x) with the downstream distance of the wind turbine obtained through large eddy numerical simulation.

[0111] Figure 4 This is a comparison diagram of the double Gaussian wake model considering pressure difference correction in this application and other wake models, the purpose of which is to verify the accuracy of the wake model. Figure 4 As shown, the calculation results of the double Gaussian wake model considering pressure difference correction provided in this application are well matched with the numerical simulation results.

[0112] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effect of the present application, different steps do not have to be performed in such an order. They can be performed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present application.

[0113] Furthermore, the present application also provides a device for evaluating the velocity loss of the entire wake of a wind turbine.

[0114] See attached Figure 5 , Figure 5 This is a main structural block diagram of a device for evaluating the velocity loss of the entire wake of a wind turbine according to an embodiment of the present application. Figure 5 As shown, the wind turbine full wake velocity loss assessment device in the embodiment of the present application mainly includes a first acquisition module 11, a first determination module 12, a second acquisition module 13, a second determination module 14, and an assessment module 15. In some embodiments, one or more of the first acquisition module 11, the first determination module 12, the second acquisition module 13, the second determination module 14, and the assessment module 15 can be combined into one module.

[0115] In some embodiments, the first acquisition module 11 may be configured to acquire incoming wind speed and direction.

[0116] The first determining module 12 may be configured to determine preset parameter information based on the incoming wind speed and wind direction.

[0117] The second acquisition module 13 may be configured to acquire a pre-built double-Gaussian wake model that takes pressure difference correction into consideration.

[0118] The second determining module 14 may be configured to determine the amplitude distribution and shape distribution of the double Gaussian wake model based on the preset parameter information.

[0119] The evaluation module 15 may be configured to evaluate the wake velocity loss of the wind turbine based on the amplitude distribution and shape distribution of the double Gaussian wake model.

[0120] In one embodiment, the description of the specific implementation functions can refer to steps S10 to S50.

[0121] The above-mentioned wind turbine full wake velocity loss assessment device is used to perform Figure 1 The embodiment of the method for evaluating the speed loss of the entire wake of a wind turbine shown in the figure has similar technical principles, technical problems solved and technical effects produced. Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working process and relevant instructions of the device for evaluating the speed loss of the entire wake of a wind turbine can refer to the contents described in the embodiment of the method for evaluating the speed loss of the entire wake of a wind turbine, and will not be repeated here.

[0122] Furthermore, it should be understood that since the configuration of each module is merely for the purpose of illustrating the functional units of the apparatus of the present application, the physical devices corresponding to these modules may be the processor itself, or a portion of the software in the processor, a portion of the hardware, or a combination of software and hardware. Therefore, the number of modules in the figure is merely illustrative.

[0123] Those skilled in the art will appreciate that the various modules in the device can be adaptively split or merged. Such splitting or merging of specific modules will not cause the technical solution to deviate from the principles of this application. Therefore, the technical solutions after splitting or merging will fall within the scope of protection of this application.

[0124] It will be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment of the present application can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code.

[0125] Furthermore, the present application also provides an electronic device, which may include at least one processor; and a memory in communication with the at least one processor; wherein the memory stores a computer program, and when the computer program is executed by the at least one processor, the method for evaluating the speed loss of the full wake of a wind turbine as described in any of the above embodiments is implemented. Figure 6 As shown, Figure 6 exemplarily shows the structure of an electronic device, which includes a processor 100 and a memory 200.

[0126] Furthermore, the present application also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present application, the computer-readable storage medium can be configured to store a program for executing the method for evaluating the speed loss of the full wake of a wind turbine set according to the above-mentioned method embodiment. The program can be loaded and run by a processor to implement the above-mentioned method for evaluating the speed loss of the full wake of a wind turbine set. For ease of explanation, only the parts related to the embodiment of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present application. The computer-readable storage medium can be a memory device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present application is a non-temporary computer-readable storage medium.

[0127] Thus far, the technical solutions of the present application have been described in conjunction with the specific embodiments shown in the accompanying drawings. However, it is readily understood by those skilled in the art that the scope of protection of the present application is obviously not limited to these specific embodiments. Without departing from the principles of the present application, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present application.

Claims

1. A method for evaluating velocity loss of the entire wake of a wind turbine, characterized in that: The method comprises: Get incoming wind speed and direction; Determining preset parameter information based on the incoming wind speed and wind direction; Get the pre-built double-Gaussian wake model that takes into account the pressure difference correction; Determining the amplitude distribution and shape distribution of the double Gaussian wake model based on the preset parameter information; The wake velocity loss of the wind turbine is evaluated based on the amplitude distribution and shape distribution of the double Gaussian wake model.

2. The method for evaluating velocity loss of the full wake of a wind turbine according to claim 1, characterized in that: The double Gaussian wake model considering pressure difference correction is constructed by the following steps: Combined with the law of conservation of mass, the momentum balance equation of the wind turbine wake velocity loss is established taking into account the pressure difference correction; Simplifying the momentum balance equation according to the pressure difference distribution characteristics to obtain a simplified momentum balance equation; Obtaining a shape function of the double Gaussian wake model; Obtaining an amplitude function of a double Gaussian wake model based on a double Gaussian distribution corresponding to the wake velocity loss and the simplified momentum balance equation; The double-Gaussian wake model is constructed based on the shape function and the amplitude function.

3. The method for evaluating velocity loss of the full wake of a wind turbine according to claim 2, characterized in that: The simplified momentum balance equation is expressed as: Where ρ is the air density; A is the area of ​​the wind wheel, A=πD 2 / 4, D is the diameter of the wind wheel; U ∞ is the incoming wind speed; C T is the thrust coefficient of the wind turbine; γ is the yaw angle; x is the downstream distance of the wind turbine; du is the wake velocity loss of the wind turbine; A w is the area of ​​the wake region.

4. The method for evaluating velocity loss of the entire wake of a wind turbine according to claim 2, wherein: The shape function G(x,r) of the double Gaussian wake model is expressed as: in, σ y and σ z represent the Gaussian standard deviation of the wake in the horizontal and vertical directions respectively; y c is the offset of the wake center; Z h is the hub height; r min is the radial position of the velocity minimum point.

5. The method for evaluating velocity loss of the entire wake of a wind turbine according to claim 2, characterized in that: The obtaining of the amplitude function of the double Gaussian wake model based on the double Gaussian distribution and the simplified momentum balance equation includes: Get the expression of the Gaussian distribution: Among them, du is the wake velocity loss of the wind turbine, U ∞ is the incoming wind speed, C(x,γ) is the amplitude function of the double Gaussian wake model, and G(x,r) is the shape function of the double Gaussian wake model; The expression of the Gaussian distribution is substituted into the simplified momentum balance equation, and the simplified momentum balance equation is integrated to obtain the amplitude function of the double Gaussian wake model. The expression of the amplitude function C(x,γ) is: in, σ y and σ z represent the Gaussian standard deviation of the wake in the horizontal and vertical directions respectively; y c is the offset of the wake center; r min is the radial position of the velocity minimum point.

6. The method for evaluating velocity loss of the entire wake of a wind turbine according to claim 1, characterized in that: The determining of preset parameter information based on the incoming wind speed and wind direction includes: determining a thrust coefficient of the wind turbine generator system based on the incoming wind speed; determining a yaw angle of the wind turbine generator system based on the wind direction; The preset parameter information is calculated based on the thrust coefficient and the yaw angle, where the preset parameter information includes a radial position of a velocity minimum point, a Gaussian standard deviation of the wake in horizontal and vertical directions, and an offset of a wake center.

7. The method for evaluating velocity loss of the entire wake of a wind turbine according to claim 1, characterized in that: The evaluating the wake velocity loss of the wind turbine generator set based on the amplitude distribution and shape distribution of the double Gaussian wake model includes: inputting the amplitude distribution, shape distribution of the double Gaussian wake model and the incoming wind speed of the wind turbine generator set into a double Gaussian distribution expression corresponding to the wake velocity loss, and outputting the wake velocity loss distribution of the wind turbine generator set.

8. A device for evaluating the velocity loss of the entire wake of a wind turbine, characterized in that: The device comprises: A first acquisition module is configured to acquire incoming wind speed and wind direction; A first determining module is configured to determine preset parameter information based on the incoming wind speed and wind direction; A second acquisition module is configured to acquire a pre-built double Gaussian wake model considering pressure difference correction; A second determining module is configured to determine the amplitude distribution and shape distribution of the double Gaussian wake model based on the preset parameter information; An evaluation module is configured to evaluate the wake velocity loss of the wind turbine generator system based on the amplitude distribution and shape distribution of the double Gaussian wake model.

9. An electronic device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; Wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method for evaluating the speed loss of the full wake of a wind turbine set according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by a processor to execute the method for evaluating velocity loss of the full wake of a wind turbine according to any one of claims 1 to 7.

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