Analytical Modeling and Analysis Method for Wake Velocity of Wind Turbine, Electronic Device, and Storage Medium

Abstract

This application relates to the technical field of wake velocity analysis of wind turbines, and specifically provides a method for wake velocity analysis and modeling of wind turbines, an electronic device, and a storage medium, aiming to solve the problem of wake prediction of wind turbines after sudden changes in surface roughness. For this purpose, the method for wake velocity analysis and modeling of wind turbines in this application includes: obtaining the background field wind speed in the transition stage of the inner boundary layer under the condition of sudden changes in surface roughness; determining the maximum velocity deficit calculation equation based on the turbulence intensity distribution within a preset range around the wind turbine; determining the wake velocity deficit calculation equation based on the maximum velocity deficit calculation equation; and establishing a wind turbine wake velocity analysis model based on the background field wind speed in the transition stage of the inner boundary layer and the wake velocity deficit calculation equation. The wind turbine wake velocity analysis model of this application can quickly and accurately predict the distribution of the wake velocity of wind turbines in the case of sudden changes in surface roughness.

Description

Technical Field

[0001] The present application relates to the technical field of wake velocity analysis of wind turbines, and specifically provides a method for analytically modeling the wake velocity of a wind turbine, a method for analyzing the wake velocity of a wind turbine, an electronic device, and a storage medium. Background Art

[0002] When the wind blows from a region with high surface roughness to a region with low surface roughness, such as from land to sea or from fields to grasslands, the atmospheric boundary layer will undergo a complex transition process. This process will cause significant changes in the background wind speed and turbulence characteristics of the wind farm and form a new internal boundary layer structure. During the transition stage of the internal boundary layer, the wake of the wind turbine will also undergo complex changes. The wake velocity distribution of the wind turbine under such complex conditions has important guiding significance for the micro-siting of the wind farm in the case of sudden changes in surface roughness.

[0003] Although the existing engineering wake models have been relatively mature in the basic theoretical framework, and researchers have proposed various wake velocity calculation methods for engineering practice, there is currently little research on wakes under sudden roughness changes, and there is still a lack of an accurate wind turbine wake model in this case.

[0004] Correspondingly, there is a need in the art for a new wind turbine wake velocity analysis solution to solve the above problems. Summary of the Invention

[0005] In order to overcome the above defects, the present application is proposed to provide a technical solution for solving or at least partially solving the problem of predicting the wake of a wind turbine after a sudden change in surface roughness.

[0006] In a first aspect, the present application provides a method for analytically modeling the wake velocity of a wind turbine, the method comprising: obtaining the background field wind speed in the transition stage of the internal boundary layer under sudden changes in surface roughness; determining a maximum velocity deficit calculation equation based on the turbulence intensity distribution within a preset range around the wind turbine; determining a wake velocity deficit calculation equation based on the maximum velocity deficit calculation equation; and establishing a wind turbine wake velocity analysis model based on the background field wind speed in the transition stage of the internal boundary layer and the wake velocity deficit calculation equation.

[0007] In a technical solution of the above wind turbine wake velocity analytical modeling method, obtaining the background field wind speed in the transition stage of the inner boundary layer under the condition of sudden change of surface roughness includes: constructing a three-dimensional world coordinate system based on the sudden change point of surface roughness and the position of the wind turbine, where the sudden change point of surface roughness is the origin of the x-axis coordinate, the position of the wind turbine is the origin of the y-axis coordinate and the z-axis coordinate, and the direction parallel to the incoming wind is the x-axis; obtaining the ground information parameters before and after the sudden change of surface roughness, and the ground information parameters at least include surface roughness, mean friction velocity and local friction velocity; determining the height of the inner boundary layer based on the surface roughness before the sudden change; and determining the background field wind speed in the transition stage of the inner boundary layer based on the height of the inner boundary layer.

[0008] In a technical solution of the above wind turbine wake velocity analytical modeling method, determining the background field wind speed in the transition stage of the inner boundary layer based on the height of the inner boundary layer includes: determining the height of the equilibrium layer based on the height of the inner boundary layer; and determining the background field wind speed in the transition stage of the inner boundary layer based on at least one of the z-axis coordinate of the wind turbine, the height of the inner boundary layer, the local friction velocity after the sudden change, the surface roughness before the sudden change, the mean friction velocity before the sudden change, and the height of the equilibrium layer.

[0009] In a technical solution of the above wind turbine wake velocity analytical modeling method, determining the maximum velocity deficit calculation equation based on the turbulence intensity distribution within a preset range around the wind turbine includes: determining the background field non-uniformity factor based on the turbulence intensity distribution; determining the mean convection factor based on the background field wind speed in the transition stage of the inner boundary layer; assuming that the wake velocity deficit of the wind turbine conforms to the self-similarity characteristic and is Gaussian distributed, and determining the standard deviation of the wake velocity Gaussian distribution; omitting the pressure change in the far wake, and determining the integral control equation based on the momentum equation and the mass conservation equation; and determining the maximum velocity deficit calculation equation based on the background field non-uniformity factor, the mean convection factor, the standard deviation of the wake velocity Gaussian distribution, the wind turbine parameters and the integral control equation, where the wind turbine parameters include the wind turbine swept diameter and the wind turbine thrust coefficient.

[0010] In a technical solution of the above analytical modeling method for the wake velocity of a wind turbine, the determination of the average convection factor based on the background field wind speed includes: setting the average convection factor to a preset value, and determining the initial value of the wind turbine wake velocity based on the preset value, the background field non-uniformity factor, the wake expansion rate, the standard deviation of the wake velocity Gaussian distribution, the wind turbine parameters, the background field wind speed at a preset position in front of the wind turbine, and the background field wind speed in the inner boundary layer transition stage; performing iterative calculation on the average convection factor based on the initial value of the wind turbine wake velocity, the background field wind speed in the inner boundary layer transition stage, and the background field wind speed at a preset position in front of the wind turbine until the root mean square error between the currently calculated average convection factor and the average convection factor calculated in the previous time is less than or equal to a preset threshold, so as to obtain the average convection factor.

[0011] In a technical solution of the above analytical modeling method for the wake velocity of a wind turbine, the determination of the wake velocity deficit calculation equation based on the maximum velocity deficit calculation equation includes: determining the wake velocity deficit calculation equation based on the standard deviation of the wake velocity Gaussian distribution, the wind turbine parameters, the background field wind speed at a preset position in front of the wind turbine, and the maximum velocity deficit calculation equation, where the wind turbine parameters include the wind turbine coordinates, the thrust coefficient, the wind turbine swept diameter, the hub height, and the spanwise center position of the wind turbine.

[0012] In a second aspect, the present application provides a method for analyzing the wake velocity of a wind turbine, the method including: obtaining the background field wind speed in the inner boundary layer transition stage under the condition of sudden change of surface roughness; obtaining the turbulence intensity distribution within a preset range around the wind turbine; obtaining a wind turbine wake velocity analysis model, where the wind turbine wake velocity analysis model is obtained by using the wind turbine wake velocity analytical modeling method described in any one of the technical solutions of the above wind turbine wake velocity analytical modeling method; determining the wind turbine wake velocity based at least on the background field wind speed in the inner boundary layer transition stage, the turbulence intensity distribution, and the wind turbine wake velocity analysis model.

[0013] In a technical solution of the above method for analyzing the wake velocity of a wind turbine, the determination of the wind turbine wake velocity based at least on the background field wind speed in the inner boundary layer transition stage, the turbulence intensity distribution, and the wind turbine wake velocity analysis model includes: obtaining the background field wind speed at a preset position in front of the wind turbine; determining the wind turbine wake velocity based on the background field wind speed at a preset position in front of the wind turbine, the turbulence intensity distribution, the background field wind speed in the inner boundary layer transition stage, and the wind turbine wake velocity analysis model.

[0014] In a third aspect, an electronic device is provided, which includes at least one processor and at least one memory. The memory is adapted to store multiple program codes, and the program codes are adapted to be loaded and run by the processor to execute the method described in any one of the technical solutions of the above-mentioned method for analyzing and modeling the wake velocity of a wind turbine.

[0015] In a fourth aspect, a computer-readable storage medium is provided, which stores multiple program codes, and the program codes are adapted to be loaded and run by a processor to execute the method described in any one of the technical solutions of the above-mentioned method for analyzing and modeling the wake velocity of a wind turbine.

[0016] One or more of the above technical solutions of the present application have at least one or more of the following beneficial effects:

[0017] A method for analyzing and modeling the wake velocity of a wind turbine according to the present application includes: obtaining the background wind speed in the transition stage of the inner boundary layer under the condition of sudden change of surface roughness; determining the maximum velocity deficit calculation equation based on the turbulence intensity distribution within a preset range around the wind turbine; determining the wake velocity deficit calculation equation based on the maximum velocity deficit calculation equation; and establishing a wind turbine wake velocity analysis model based on the background wind speed and the wake velocity deficit calculation equation. The wind turbine wake velocity analysis model of the present application fully considers the change of the background wind speed after the sudden change of the surface roughness and the influence of the turbulence characteristics on the wake velocity of the wind turbine, and can quickly and accurately predict the distribution of the wake velocity of the wind turbine under the condition of sudden change of the surface roughness. Description of the Drawings

[0018] Referring to the accompanying drawings, the disclosure of the present application will become easier to understand. It is easy for those skilled in the art to understand that: these drawings are only for illustrative purposes and are not intended to limit the protection scope of the present application. In addition, similar numbers in the drawings are used to represent similar components, where:

[0019] Figure 1 is a schematic diagram of the main steps of the method for analyzing and modeling the wake velocity of a wind turbine according to an embodiment of the present application;

[0020] Figure 2 is a schematic diagram of the inner boundary layer under the condition of sudden change of roughness according to an embodiment of the present application;

[0021] Figure 3 is a schematic diagram of the calculation process for determining the average convection factor according to an embodiment of the present application;

[0022] Figure 4 is a schematic diagram of the main steps of the method for analyzing the wake velocity of a wind turbine according to an embodiment of the present application;

[0023] Figure 5 Schematic diagram of the relative position between a wind turbine and the inner boundary layer according to an embodiment of the present application;

[0024] Figure 6 Comparison diagram of the results of the wind turbine wake velocity analysis model and the large eddy simulation results at the hub center position of a wind turbine according to an embodiment of the present application;

[0025] Figure 7 Comparison diagram of the results of the wind turbine wake velocity analysis model and the large eddy simulation results in the spanwise direction at the hub height of a wind turbine according to an embodiment of the present application;

[0026] Figure 8 Comparison diagram of the results of the wind turbine wake velocity analysis model and the large eddy simulation results in the central vertical axis direction of a wind turbine according to an embodiment of the present application;

[0027] Figure 9 Schematic diagram of the main structural block diagram of an electronic device according to an embodiment of the present application.

[0028] List of Reference Numerals :

[0029] 11: Memory; 12: Processor. Detailed implementation manners

[0030] The following describes some implementation manners of the present application with reference to the accompanying drawings. Those skilled in the art should understand that these implementation manners are only used to explain the technical principle of the present application and are not intended to limit the protection scope of the present application.

[0031] In the description of the present application, "module" and "processor" may include hardware, software, or a combination of both. A module may include a hardware circuit, various suitable sensors, communication ports, memory, and may also include a software part, such as program code, or a combination of software and hardware. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, in hardware, or in a combination of both. The non-transitory computer-readable storage medium includes any suitable medium for storing program code, such as magnetic disks, hard disks, optical disks, flash memories, read-only memories, random access memories, and so on. The term "A and / or B" represents all possible combinations of A and B, such as only A, only B, or A and B. The term "at least one A or B" or "at least one of A and B" has a meaning similar to "A and / or B" and may include only A, only B, or A and B. The singular terms "a" and "this" may also include the plural form.

[0032] When the wind blows from the high surface roughness area to the low surface roughness area, it will cause significant changes in the background wind speed and turbulence characteristics of the wind farm and form a new internal boundary layer structure. During the transition stage of the internal boundary layer, the wake of the wind turbine will also experience complex changes. The wake velocity distribution of the wind turbine under such complex conditions has important guiding significance for the micro-siting of the wind farm in the case of sudden changes in surface roughness. However, in the existing engineering wake model framework, the influence of sudden changes in surface roughness on the wake velocity is lacking.

[0033] Therefore, this application provides a method for analytically modeling the wake velocity of a wind turbine, which specifically includes: obtaining the background field wind speed in the transition stage of the internal boundary layer under the condition of sudden change in surface roughness; determining the maximum velocity deficit calculation equation based on the turbulence intensity distribution within a preset range around the wind turbine; determining the wake velocity deficit calculation equation based on the maximum velocity deficit calculation equation; and establishing a wind turbine wake velocity analysis model based on the background field wind speed and the wake velocity deficit calculation equation. The wind turbine wake velocity analysis model of this application fully considers the change in the background wind speed after the sudden change in surface roughness and the influence of turbulence characteristics on the wake velocity of the wind turbine, and can quickly and accurately predict the distribution of the wake velocity of the wind turbine in the case of sudden changes in surface roughness.

[0034] Refer to the attached Figure 1 , Figure 1 is a schematic diagram of the main step flow of the method for analytically modeling the wake velocity of a wind turbine according to an embodiment of this application. As Figure 1 shown, the method for analytically modeling the wake velocity of a wind turbine in the embodiment of this application mainly includes the following steps S101 - step S104.

[0035] Step S101: Obtain the background field wind speed in the transition stage of the internal boundary layer under the condition of sudden change in surface roughness.

[0036] In this embodiment, the internal boundary layer refers to the new internal boundary layer structure formed during the transformation process of the atmospheric boundary layer after the sudden change in surface roughness, and the transition stage refers to a transition area below the height of the internal boundary layer.

[0037] Step S102: Determine the maximum velocity deficit calculation equation based on the turbulence intensity distribution within a preset range around the wind turbine.

[0038] Step S103: Determine the wake velocity deficit calculation equation based on the maximum velocity deficit calculation equation.

[0039] Step S104: Establish a wind turbine wake velocity analysis model based on the background field wind speed in the transition stage of the internal boundary layer and the wake velocity deficit calculation equation.

[0040] Based on the above steps S101 - S104, the present application determines the maximum velocity deficit calculation equation through the turbulence intensity distribution within a preset range around the wind turbine, and then determines the wake velocity deficit calculation equation based on the maximum velocity deficit calculation equation. An analysis model of the wind turbine wake velocity is established according to the background wind speed in the transition stage of the internal boundary layer and the wake velocity deficit calculation equation under the condition of sudden change of surface roughness. By fully considering the change of the background wind speed after the sudden change of surface roughness and the influence of turbulence characteristics on the wind turbine wake velocity, the distribution of the wind turbine wake velocity under the condition of sudden change of surface roughness can be predicted quickly and accurately.

[0041] The above steps S101 - S104 will be further described below.

[0042] Regarding step S101, in some embodiments, obtaining the background wind speed in the transition stage of the internal boundary layer under the condition of sudden change of surface roughness includes: constructing a three - dimensional world coordinate system based on the surface roughness mutation point and the wind turbine position, where the surface roughness mutation point is the origin of the x - axis coordinate, the wind turbine position is the origin of the y - axis coordinate and the z - axis coordinate, and the direction parallel to the incoming wind is the x - axis; obtaining the ground information parameters before and after the sudden change of surface roughness, where the ground information parameters at least include surface roughness, mean friction velocity, and local friction velocity; determining the internal boundary layer height based on the surface roughness before the mutation; and determining the background wind speed in the transition stage of the internal boundary layer based on the internal boundary layer height.

[0043] The internal boundary layer refers to the new internal boundary layer structure formed during the transformation of the atmospheric boundary layer after the sudden change of surface roughness. The transition stage refers to a transition region below the internal boundary layer height. The internal boundary layer transition stage is as Figure 2 shown.

[0044] Specifically, a three - dimensional world coordinate system is established with the surface roughness mutation point as the origin of the x - axis coordinate, the wind turbine position as the origin of the y - axis coordinate and the z - axis coordinate. The x - axis is the horizontal axis direction, parallel to the incoming wind direction, the y - axis is the vertical axis direction, and the z - axis is the vertical axis direction.

[0045] The surface roughness and mean friction velocity before and after the sudden change of surface roughness are determined through the wind speeds at two heights. The calculation formula is:

[0046]

[0047]

[0048]

[0049]

[0050] Among them, z1 is the first arbitrarily selected height, z2 is the second arbitrarily selected height, and u *1 is the mean friction velocity before the surface roughness mutation, and z 01 is the surface roughness before the surface roughness mutation, and u *2 is the mean friction velocity after the surface roughness mutation, and z 02 is the surface roughness after the surface roughness mutation, is the mean wind speed at the first height z1, is the mean wind speed at the second height z2, and κ represents the von Kármán constant, and its value can be 0.4.

[0051] Based on the surface roughness z 01 before the mutation, the inner boundary layer height is determined, and the calculation formula is as follows:

[0052]

[0053] δ i represents the inner boundary layer height, and z 01 represents the surface roughness before the surface roughness mutation, and x represents the horizontal axis coordinate downstream of the mutation point.

[0054] After determining the inner boundary layer height, the background field wind speed in the inner boundary layer transition stage is determined based on the inner boundary layer height.

[0055] In some embodiments, the determining the background field wind speed in the inner boundary layer transition stage based on the inner boundary layer height includes: determining the equilibrium layer height based on the inner boundary layer height; determining the background field wind speed in the inner boundary layer transition stage based on at least one of the z-axis coordinate of the wind turbine, the inner boundary layer height, the local friction velocity after the mutation, the surface roughness before the mutation, the mean friction velocity before the mutation, and the equilibrium layer height.

[0056] The equilibrium layer is the region below the inner boundary layer transition stage.

[0057] Specifically, the equilibrium layer height is determined according to the inner boundary layer height, and the calculation formula is as follows:

[0058] δ e (x) = 0.04δ i (x) (6)

[0059] Among them, δ i represents the inner boundary layer height, and δ e represents the equilibrium layer height.

[0060] The background field wind speed is determined based on the relationship between the height at any position and the inner boundary layer height and the equilibrium layer height, and the height at any position is the z-axis coordinate at any position.

[0061] When \(z\leq\delta\) e when

[0062]

[0063] When \(\delta\) e \(\leq z\leq\delta\) i when

[0064]

[0065] When \(z > \delta\) i when

[0066]

[0067] where \(z\) is the coordinate in the vertical axis direction, \(U_0(x,z)\) is the background field wind speed, \(z\) 01 represents the surface roughness before the mutation, \(z\) 02 represents the surface roughness after the mutation, \(u\) *1 represents the average friction velocity before the mutation, \(u\) *2,loc is the local friction velocity after the mutation obtained by simultaneously solving Equations (7) and (8) according to the wind speed continuity, \(\delta\) i represents the inner boundary layer height, \(\delta\) e represents the equilibrium layer height, \(\kappa\) represents the von Kármán constant, and its value can be 0.4

[0068] Take the background field wind speed in the area where it is less than or equal to the inner boundary layer height and greater than the equilibrium layer height as the background field wind speed in the inner boundary layer transition stage.

[0069] The above is the description of step S101. Next, step S102 will be further described.

[0070] Regarding step S102, in some embodiments, the determining the maximum velocity deficit calculation equation based on the turbulence intensity distribution within a preset range around the wind turbine includes: determining the background field non-uniformity factor based on the turbulence intensity distribution; determining the average convection factor based on the background field wind speed in the inner boundary layer transition stage; assuming that the wake velocity deficit of the wind turbine conforms to the self-similarity characteristic and is Gaussian distributed, determining the standard deviation of the wake velocity Gaussian distribution; omitting the pressure change in the far wake, and determining the integral control equation based on the momentum equation and the mass conservation equation; determining the maximum velocity deficit calculation equation based on the background field non-uniformity factor, the average convection factor, the standard deviation of the wake velocity Gaussian distribution, the wind turbine parameters, and the integral control equation, where the wind turbine parameters include the wind turbine swept diameter and the wind turbine thrust coefficient.

[0071] Specifically, the Reynolds normal stress is determined based on the turbulence intensity distribution, and then the background field non-uniformity factor is determined based on the Reynolds normal stress. The mean convection factor is determined based on the background field wind speed in the inner boundary layer transition stage. Assuming that the wake velocity deficit of the wind turbine conforms to the self-similarity characteristic and is Gaussian distributed, the standard deviation of the Gaussian distribution of the wake velocity is determined. Based on the momentum equation and the mass conservation equation, the pressure change is omitted at the far wake, and the derived integral control equation is obtained. The background field non-uniformity factor and the mean convection factor are introduced into the integral control equation, and the maximum velocity deficit calculation equation is obtained based on the standard deviation of the Gaussian distribution of the wake velocity, the wind turbine parameters, and the integral control equation.

[0072] In one embodiment, according to the mass conservation equation and the momentum equation, the pressure change is omitted at the far wake, and the derived integral control equation can be obtained. The integral control equation is as follows:

[0073]

[0074] where U is the time-averaged component in the x-axis direction, W is the time-averaged component in the z-axis direction, u' is the pulsation in the x-axis direction, w' is the pulsation in the z-axis direction, T is the thrust acting on the wind turbine, U0 is the background field wind speed, U w represents the wake velocity of the wind turbine, dA represents the differential integration area, dV represents the differential integration volume, represents the Reynolds normal stress, represents the Reynolds shear stress.

[0075] Among them, the thrust of the wind turbine can be determined by the inflow wind speed and the thrust coefficient, and the calculation formula is as follows:

[0076]

[0077] T is the thrust acting on the wind turbine, C T is the thrust coefficient of the wind turbine, ρ is the air density, U 0,h,up is the background field wind speed at a preset position in front of the wind turbine, and the background field wind speed at 2D in front of the wind turbine can be selected. D is the rotor diameter, A is the rotor swept area, and d is the swept diameter of the wind turbine.

[0078] The background field non-uniformity factor γ is introduced, and the four terms on the right side of the equal sign in the integral control equation formula (10) are:

[0079]

[0080] The left side of the equal sign in the integral control equation formula (10) is simplified to:

[0081] ∫∫U w (U0 - U w )dA = ∫∫-(U0 - Uw ) 2 dA+∫∫U0(U0 - U)dA (13)

[0082] Based on equations (11), (12), (13) and equation (10), we get:

[0083]

[0084] Dividing both sides of equation (14) by we get:

[0085]

[0086] Further simplifying equation (15), we get:

[0087]

[0088] Introduce the average convection factor H(x), and the expression of H(x) is as follows:

[0089]

[0090] And represent U0 - U w as the velocity deficit ΔU, then equation (16) can be expressed as:

[0091]

[0092] Integrating equation (18) from 0 to ∞, we get:

[0093]

[0094] where σ is the standard deviation of the wake velocity Gaussian distribution, and C(x) is the maximum velocity deficit.

[0095] After solving equation (19), we get the calculation equation for the maximum velocity deficit at downstream x:

[0096]

[0097] The above is the description of step S102. Next, we will further describe step S103.

[0098] Regarding step S103, in some embodiments, the determining the wake velocity deficit calculation equation based on the maximum velocity deficit calculation equation includes: determining the wake velocity deficit calculation equation based on the standard deviation of the wake velocity Gaussian distribution, wind turbine parameters, the background field wind speed at a preset position in front of the wind turbine, and the maximum velocity deficit calculation equation. The wind turbine parameters include wind turbine coordinates, thrust coefficient, wind turbine swept diameter, hub height, and the lateral center position of the wind turbine.

[0099] Specifically, assuming that the wake of a wind turbine conforms to a self-similar Gaussian distribution in the wake region, the wake velocity deficit can be expressed by the following formula:

[0100]

[0101] where ΔU is the wake velocity deficit, and U 0,h,up is the background field wind speed at a preset position in front of the wind turbine. The background field wind speed at 2D in front of the wind turbine can be selected. D is the rotor diameter, C(x) is the maximum velocity deficit, σ is the standard deviation of the Gaussian distribution of the wake velocity, r is the radial distance from the spatial point to the center of the wake, and x is the horizontal axis coordinate downstream of the wind turbine.

[0102] Substituting the standard deviation of the Gaussian distribution of the wake velocity, the wind turbine parameters, the background field wind speed at a preset position in front of the wind turbine, and the calculation equation of the maximum velocity deficit into the above formula (21), the calculation equation of the wake velocity deficit can be obtained, and the calculation equation of the wake velocity deficit is as follows:

[0103]

[0104] The above is the description of step S103. Next, step S104 will be further described.

[0105] For step S104, a wind turbine wake velocity analysis model is established based on the background field wind speed in the inner boundary layer transition stage and the wake velocity deficit calculation equation.

[0106] Specifically, the wake velocity deficit is defined according to the difference between the background field wind speed in the inner boundary layer transition stage and the wake velocity. Then the wake velocity deficit can be expressed as:

[0107] ΔU = U0(x,z) - U w (x,y,z) (22)

[0108] where ΔU represents the wake velocity deficit, U0 is the background field wind speed in the inner boundary layer transition stage, and U w represents the wind turbine wake velocity.

[0109] It can be seen from formula (22) that the wind turbine wake velocity can be determined by using the background field wind speed in the inner boundary layer transition stage and the wake velocity deficit. Then the wind turbine wake velocity analysis model can be expressed as:

[0110]

[0111] In one embodiment, the Reynolds normal stress is determined based on the turbulence intensity distribution, and then the background field inhomogeneity factor γ is determined based on the Reynolds normal stress.

[0112] Specifically, in the inner boundary layer transition stage, the following assumptions are made for the four terms on the right side of equation (12):

[0113]

[0114]

[0115] Based on the two assumptions of equations (24) and (25), equation (12) can be simplified to:

[0116]

[0117] Among them, x represents the downstream horizontal axis coordinate of the wind turbine, and x up represents the preset position in front of the wind wheel. The preset position in front can be at the 2D position in front of the wind wheel, and D is the wind wheel diameter.

[0118] It can be seen from equation (26) that by determining the distribution of the Reynolds normal stress , the background field non-uniformity factor γ can be determined.

[0119] In one embodiment, the distribution of the Reynolds normal stress is determined by the turbulence intensity distribution.

[0120] Specifically, according to the three-dimensional additional turbulence intensity model of the wind turbine, the additional turbulence intensity ΔIu of the wind turbine is obtained. The three-dimensional additional turbulence intensity model of the wind turbine is as follows:

[0121]

[0122]

[0123]

[0124] Among them, D is the wind wheel diameter, z is the vertical axis direction coordinate, ΔIu is the additional turbulence intensity of the wind turbine, and C T represents the thrust coefficient of the wind turbine, represents the environmental turbulence intensity, and r 1 / 2 is the wake width corresponding to when the velocity deficit reaches 1 / 2ΔU max , σ T represents the standard deviation of the additional turbulence intensity Gaussian distribution, r represents the distance from the spatial coordinate to the wake center, and z h represents the hub height, and α represents the azimuth angle between the spatial position and the wake center position, with the y-axis direction defined as 0°.

[0125] In one embodiment, the thrust coefficient C of the wind turbine under this operating condition is determined based on the correspondence between the incoming flow wind speed, the thrust coefficient of the wind turbine, and the wind speed TThe corresponding relationship between the thrust coefficient of a wind turbine and the wind speed is the corresponding curve of the thrust coefficient of the wind turbine varying with the wind speed.

[0126] Then, based on the additional turbulence intensity ΔIu of the wind turbine, the turbulence intensity distribution Iu within a preset range around the wind turbine is determined, and the calculation formula is as follows:

[0127]

[0128] Among them, ΔIu represents the additional turbulence intensity, represents the environmental turbulence intensity.

[0129] Furthermore, based on the turbulence intensity distribution Iu within a preset range around the wind turbine, the Reynolds normal stress is determined, and the calculation formula is as follows:

[0130]

[0131] Among them, is the Reynolds normal stress, Iu is the turbulence intensity distribution within a preset range around the wind turbine, and U 0,h,up is the background field wind speed at a preset position in front of the wind turbine along the x-axis direction. The background field wind speed at the preset position in front can be the background field wind speed at the 2D position in front of the wind turbine.

[0132] In one embodiment, assuming that the wake velocity deficit of the wind turbine conforms to self-similar characteristics and follows a Gaussian distribution, the standard deviation of the wake velocity Gaussian distribution is determined.

[0133] Specifically, assuming that the wake velocity deficit of the wind turbine conforms to self-similar characteristics and follows a Gaussian distribution, and the wake of the wind turbine expands linearly, the calculation formula for determining the standard deviation of the wake velocity Gaussian distribution is as follows:

[0134]

[0135]

[0136] Among them, σ is the standard deviation of the wake velocity Gaussian distribution, and C T represents the thrust coefficient of the wind turbine, x is the axial coordinate, d is the swept diameter of the wind turbine, and k * represents the wake linear expansion rate, and k * = 0.32I0, where I0 is the measured environmental turbulence intensity.

[0137] In one embodiment, determining the average convection factor based on the background field wind speed includes: setting the average convection factor to a preset value, and determining the initial value of the wind turbine wake velocity based on the preset value, the background field non-uniformity factor, the wake expansion rate, the standard deviation of the wake velocity Gaussian distribution, the wind turbine parameters, the background field wind speed at a preset position in front of the wind turbine, and the background field wind speed in the inner boundary layer transition stage; performing iterative calculation on the average convection factor based on the initial value of the wind turbine wake velocity, the background field wind speed in the inner boundary layer transition stage, and the background field wind speed at a preset position in front of the wind turbine until the root mean square error between the currently calculated average convection factor and the average convection factor calculated last time is less than or equal to a preset threshold, thereby obtaining the average convection factor.

[0138] Specifically, the average convection factor H(x) is determined based on the background field wind speed U0 in the inner boundary layer transition stage, the wind turbine wake velocity U w , the background field wind speed U 0,h,up at a preset position in front of the wind turbine, and the above formula (17).

[0139] The following combines the attached Figure 3 to illustrate the calculation process of determining the average convection factor.

[0140] As Figure 3 shown, the calculation process of determining the average convection factor includes the following steps:

[0141] Step S1: Assume that the average convection factor H(x) is a preset value H1(x), and H1(x) = 1;

[0142] Step S2: Substitute the average convection factor H(x) into the wind turbine wake velocity analysis model to obtain the initial value of the wake velocity;

[0143] Step S3: Based on the initial value of the wind turbine wake velocity, the background field wind speed in the inner boundary layer transition stage, the background field wind speed at a preset position in front of the wind turbine, and the average convection factor calculation formula (17), recalculate and determine the average convection factor;

[0144] Step S4: Calculate the root mean square error between the latest obtained average convection factor and the average convection factor input into the wind turbine wake velocity analysis model last time;

[0145] Step S5: Determine whether the root mean square error between the two is less than or equal to a preset threshold, and the preset threshold can be 1%;

[0146] Step S6: If the root mean square error is less than or equal to the preset threshold, then use the latest obtained average convection factor as the average convection factor and input it into the wind turbine wake velocity analysis model to calculate the wake velocity;

[0147] Step S7: If the root mean square error is greater than the preset threshold, repeat Step S2, input the latest obtained average convection factor into the wind turbine wake velocity analysis model to obtain the wind turbine wake velocity, and perform iterative calculations using the wind turbine wake velocity until the average convection factor is determined.

[0148] See the appendix Figure 4 , Figure 4 is a schematic diagram of the main steps of a wind turbine wake velocity analysis method according to an embodiment of the present application. As Figure 4 shown, the wind turbine wake velocity analysis method in the embodiment of the present application mainly includes the following Steps S201 - Step S204.

[0149] Step S201: Obtain the background field wind speed in the transition stage of the internal boundary layer under the condition of sudden change of surface roughness.

[0150] Step S202: Obtain the turbulence intensity distribution within a preset range around the wind turbine.

[0151] Step S203: Obtain a wind turbine wake velocity analysis model, which is obtained by using the wind turbine wake velocity analytical modeling method described in any one of the above technical solutions of the wind turbine wake velocity analytical modeling method.

[0152] Step S204: Determine the wind turbine wake velocity based on at least the background field wind speed in the transition stage of the internal boundary layer, the turbulence intensity distribution, and the wind turbine wake velocity analysis model.

[0153] Based on the above Steps S201 - Step S204, in this embodiment, the background field wind speed in the transition stage of the internal boundary layer under the condition of sudden change of surface roughness, the turbulence intensity distribution within a preset range around the wind turbine, and the wind turbine wake velocity analysis model are obtained, and the wind turbine wake velocity is determined based on the background field wind speed in the transition stage of the internal boundary layer under the condition of sudden change of surface roughness and the turbulence intensity distribution within a preset range around the wind turbine through the wind turbine wake velocity analysis model. It realizes fully considering the influence of the background wind speed and turbulence characteristics under the condition of sudden change of surface roughness when calculating the wind turbine wake velocity, and improves the accuracy of wind turbine wake velocity analysis.

[0154] In some embodiments, the determining the wind turbine wake velocity based on at least the background field wind speed in the transition stage of the internal boundary layer, the turbulence intensity distribution, and the wind turbine wake velocity analysis model includes: obtaining the background field wind speed at a preset position in front of the wind turbine; determining the wind turbine wake velocity based on the background field wind speed at the preset position in front of the wind turbine, the turbulence intensity distribution, the background field wind speed in the transition stage of the internal boundary layer, and the wind turbine wake velocity analysis model.

[0155] Specifically, the background wind speed of the wind farm is obtained, and the background wind speed of the preset position in front of the wind turbine is determined based on the background wind speed of the wind farm. The preset position in front can be a 2D position in front of the wind turbine coordinate position along the x-axis direction, and D is the diameter of the wind rotor. The wake velocity of the wind turbine is determined based on the background wind speed at the preset position in front of the wind turbine, the turbulence intensity distribution, the background wind speed in the transition stage of the inner boundary layer, and the wake velocity analysis model of the wind turbine.

[0156] In one embodiment, the method for analyzing the wake velocity of a wind turbine generator system comprises the following steps:

[0157] The surface roughness mutation point is taken as the origin of the x-axis coordinate, the foundation position of the wind turbine is taken as the origin of the y-axis coordinate and the origin of the z-axis coordinate, the wind rotor rotation axis is the x-axis, the x-axis is the horizontal axis direction, parallel to the incoming flow direction, the longitudinal axis direction is the y-axis, perpendicular to the incoming flow direction, and the z-axis is the vertical axis direction.

[0158] According to formula (1) and formula (2), the surface roughness z before the surface roughness mutation is determined 01 and the average friction velocity u *1 ; Determine the surface roughness z after mutation by formula (3) and formula (4) 02 ; Determine the inner boundary layer height δ by formula (5) i , the height of the inner boundary layer is compared with the height of the wind turbine to ensure that the wake area of the wind turbine is affected by the gradual characteristics of wind speed and turbulence intensity.

[0159] After determining the surface roughness z 02 and the inner boundary layer height δ i Then, according to the continuity of velocity, formula (7) and (8) are combined to obtain the local friction velocity u corresponding to each x position after the mutation. *2,loc Finally, the background field wind speed U0(x,z) in the inner boundary layer transition stage is obtained according to formulas (7), (8) and (9).

[0160] Obtain the incoming wind speed, and determine the thrust coefficient C of the wind turbine under this working condition based on the corresponding relationship between the incoming wind speed, the wind turbine thrust coefficient and the wind speed. T The corresponding relationship between the thrust coefficient of the wind turbine and the wind speed can be a corresponding curve of the thrust coefficient of the wind turbine and the wind speed change.

[0161] The additional turbulence intensity of the wind turbine is determined according to formulas (27), (28) and (29); the turbulence intensity distribution within a preset range around the wind turbine is determined based on the additional turbulence intensity of the wind turbine and formula (30); the Reynolds normal stress is determined based on the turbulence intensity distribution within a preset range around the wind turbine and formula (31). Based on Reynolds normal stress Determine the background field non-uniformity factor γ using Equation (26).

[0162] Obtain the environmental turbulence intensity, and determine the wake expansion rate k of the wind turbine based on the environmental turbulence intensity * , and the wake expansion rate can be expressed as k * = 0.32I0, where I0 represents the environmental turbulence intensity.

[0163] Determine the mean convection factor H(x) based on the background field wind speed in the inner boundary layer transition stage.

[0164] Finally, determine the wake velocity of the wind turbine based on the background field wind speed, mean convection factor, background field non-uniformity factor, background field wind speed at a preset position in front of the wind turbine, wind turbine parameters, wind turbine thrust coefficient, and wind turbine wake velocity analysis model in the inner boundary layer transition stage. The wind turbine parameters include the hub height, the spanwise center position of the wind turbine, and the swept diameter of the wind turbine.

[0165] Refer to the appendix Figure 5 , Figure 5 is a schematic diagram of the relative position between the wind turbine and the inner boundary layer according to an embodiment of the present application.

[0166] In this embodiment, large eddy simulation data is used for calculation, and the calculation result of the wind turbine wake velocity analysis model is verified according to the calculation result of the large eddy simulation data.

[0167] Specifically, set 4 groups of calculation cases as shown in Table 1. The prototype of the wind turbine model uses the NREL-5MW unit, and the hub height z h = 90 m, and the rotor diameter D = 126 m. The surface roughness before the mutation is set to 0.2 m, the surface roughness after the mutation is set to 0.024 m, and the atmospheric boundary layer height is set to 500 m. Four groups of WTx (x ∈ [1, 2, 3, 4]) represent different types of wind turbine wake simulation cases. In this embodiment, the set WTs 1, 2, 3, and 4 represent the wind turbine cases at positions 625 m, 2100 m, 3125 m, and 5625 m downstream of the mutation point, respectively. To eliminate the influence of the flow spanwise non-uniformity, in this embodiment, in addition to using the shifted periodic boundary condition, three wind turbine cases with different spanwise positions (500 m, 1500 m, 2500 m) are set in each WTx case. Except for the different spanwise position parameters, the other set parameters are the same. The result of each WTx case in this embodiment is the average of the three cases. The distance of the wind turbine model from the mutation point is shown in Table 1.

[0168] Table 1 Settings of large eddy simulation calculation cases

[0169]

[0170] According to the set calculation example, the x-axis coordinate position of the wind turbine can be determined.

[0171] According to formulas (1) and (2), the surface roughness z before the surface roughness mutation is calculated: 01 and the average friction velocity u *1 , the surface roughness z after mutation is determined by equations (3) and (4) 02 ; Determine the inner boundary layer height δ by formula (5) i . By calculating z 01 =0.4 meters,u *1 =0.54, z 02 =0.007 meters.

[0172] Inner boundary layer height δ i like Figure 5 As shown by Figure 5 It can be seen that WT1 is less affected by the inner boundary layer, while WT2, WT3 and WT4 are all in the inner boundary layer and are more affected.

[0173] Determine the surface roughness z after mutation 02 and the inner boundary layer height δ i Then, according to the continuity of velocity, formula (7) and (8) are combined to determine the local friction velocity u corresponding to each x position after the mutation. *2,loc Finally, the background field wind speed U0(x,z) corresponding to the transition stage of the inner boundary layer after the mutation is determined according to formulas (7), (8) and (9).

[0174] Obtain the incoming wind speed, and obtain the thrust coefficient C of the wind turbine under this condition based on the corresponding relationship between the incoming wind speed, the thrust coefficient of the wind turbine and the wind speed change. T , the corresponding relationship between the thrust coefficient of the wind turbine and the wind speed is the corresponding curve between the thrust coefficient of the wind turbine and the wind speed change.

[0175] In the large eddy simulation, the rotating actuator disk model is selected to represent the thrust coefficient of the wind turbine. The fixed blade tip speed ratio control method is adopted. The thrust coefficient of the wind turbine is shown in Table 2.

[0176] Obtain the environmental turbulence intensity and determine the wind turbine wake expansion rate k based on the environmental turbulence intensity * , k can be used in engineering * =0.32I0, where I0 represents the ambient turbulence intensity.

[0177] In the large eddy simulation example, the wake expansion rate k is obtained by Gaussian fitting the velocity loss. * As shown in Table 2.

[0178] Table 2 Thrust coefficient and wake expansion rate of large eddy simulation wind turbine

[0179] Calculation Example <![CDATA[Wind turbine C T > <![CDATA[k * > WT1 0.88 0.0284 WT2 0.86 0.0346 WT3 0.87 0.0322 WT4 0.86 0.0328

[0180] Determine the additional turbulence intensity of the wind turbine according to formulas (27), (28), and (29). Based on the additional turbulence intensity of the wind turbine and formulas (30) and (31), determine the Reynolds normal stress Finally, based on the Reynolds normal stress and formula (26), determine the background field inhomogeneity factor γ

[0181] Since WT1 is less affected by the inner boundary layer transition stage, in this case, let γ(x)≡0 and H(x)≡1. For the other three cases, assume H1(x)≡1. The following steps are used to determine the mean convection factor H(x):

[0182] Step S301: Assume that the mean convection factor H(x) is the preset value H1(x), where H1(x)=1, and substitute it into the wind turbine wake velocity analysis model to obtain the initial wake velocity U w ;

[0183] Step S302: Based on the initial wake velocity of the wind turbine, the background field wind speed in the inner boundary layer transition stage, the background field wind speed at a preset position in front of the wind turbine, and the mean convection factor calculation formula (17), recalculate and determine the mean convection factor

[0184] Step S303: Calculate the root mean square error between the newly obtained mean convection factor and the mean convection factor input into the wind turbine wake velocity analysis model last time. When the root mean square error between the two is greater than 1%, repeat step 301; when the difference between the two is less than or equal to 1%, then use the newly obtained mean convection factor as the mean convection factor, and determine the wind turbine wake velocity U based on the mean convection factor w

[0185] Comparison of the calculation results of the wind turbine wake velocity analysis model at the hub center position of the wind turbine with the large eddy simulation results Figure 6 As shown, comparison of the results of the wind turbine wake velocity analysis model in the spanwise direction at the hub height of the wind turbine with the large eddy simulation results Figure 7 As shown, comparison of the results of the wind turbine wake velocity analysis model in the vertical axis direction of the wind turbine center with the large eddy simulation results Figure 8 ​As shown in the figure, the figure also includes the calculation results using the BP wake model. In the WT1 case, γ(x)≡0 and H(x)≡1 are set, and the obtained results are the same as those of the BP wake model. In the other three groups of cases, compared with the BP model, the calculation results of the wind turbine wake velocity analysis model of the present application are closer to the large-eddy simulation data, and the error is smaller than that of the BP wake model. Therefore, the present application improves the prediction accuracy of the wind turbine wake velocity under the condition of sudden change of surface roughness.

[0186] It should be noted that although the above steps are described in a specific order in the above embodiments, those skilled in the art can understand that in order to achieve the effects of the present application, different steps do not necessarily have to be executed in such an order, and they can be executed simultaneously (in parallel) or in other orders, and these changes are within the protection scope of the present application.

[0187] Those skilled in the art can understand that all or part of the processes in the method of the above-mentioned embodiment of the present application can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable storage medium can include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal, and software distribution medium that can carry the computer program code. It should be noted that the content included in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0188] Furthermore, the present application also provides an electronic device.

[0189] Refer to the appendix Figure 9 , Figure 9 which is a schematic diagram of the main structure block diagram of the electronic device according to an embodiment of the present application. As Figure 9As shown, in an embodiment of an electronic device according to the present application, the electronic device includes at least one processor and at least one memory. The memory can be configured to store a program for implementing the method for analyzing and modeling the wake velocity of a wind turbine in the above method embodiment. The processor can be configured to execute the program in the memory, and the program includes, but is not limited to, the program for implementing the method for analyzing and modeling the wake velocity of a wind turbine in the above method embodiment. For the sake of convenience of description, only the parts related to the embodiments of the present application are shown. For those specific technical details not disclosed, please refer to the method part of the embodiments of the present application. The electronic device can be an electronic device formed by various electronic devices.

[0190] Furthermore, the present application also provides a computer-readable storage medium. In an embodiment of a computer-readable storage medium according to the present application, the computer-readable storage medium can be configured to store a program for implementing the method for analyzing and modeling the wake velocity of a wind turbine in the above method embodiment. The program can be loaded and run by a processor to implement the above method for analyzing and modeling the wake velocity of a wind turbine. For the sake of convenience of description, only the parts related to the embodiments of the present application are shown. For those specific technical details not disclosed, please refer to the method part of the embodiments of the present application. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, in the embodiments of the present application, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0191] Furthermore, it should be understood that since the setting of each module is only for explaining the functional units of the device of the present application, the corresponding physical devices of these modules can be the processor itself, or a part of the software in the processor, a part of the hardware, or a part combined by software and hardware. Therefore, the number of each module in the figure is only illustrative.

[0192] Those skilled in the art can understand that the various modules in the device can be adaptively split or combined. Such splitting or combination of specific modules will not cause the technical solution to deviate from the principle of the present application. Therefore, the technical solutions after splitting or combination will all fall within the protection scope of the present application.

[0193] So far, the technical solutions of the present application have been described in conjunction with the preferred embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Without departing from the principle of the present application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present application.

Claims

1. A method for analytically modeling the wake velocity of a wind turbine unit, characterized in that, The method includes: Obtaining the background wind speed in the transition stage of the inner boundary layer under the condition of sudden change of surface roughness, wherein the background wind speed in the transition stage of the inner boundary layer is determined by the ground information parameters before and after the sudden change of surface roughness; Determining the maximum velocity deficit calculation equation based on the turbulence intensity distribution within a preset range around the wind turbine; Determining the wake velocity deficit calculation equation based on the maximum velocity deficit calculation equation; Establishing a wind turbine wake velocity analysis model based on the background wind speed in the transition stage of the inner boundary layer and the wake velocity deficit calculation equation.

2. The wake velocity analytical modeling method for a wind turbine unit according to claim 1, characterized in that, The obtaining the background wind speed in the transition stage of the inner boundary layer under the condition of sudden change of surface roughness includes: Constructing a three-dimensional world coordinate system based on the surface roughness mutation point and the wind turbine position, wherein the surface roughness mutation point is the origin of the x-axis coordinate, the wind turbine position is the origin of the y-axis coordinate and the z-axis coordinate, and the direction parallel to the incoming wind is the x-axis; Obtaining the ground information parameters before and after the sudden change of surface roughness, where the ground information parameters at least include surface roughness, mean friction velocity and local friction velocity; Determining the inner boundary layer height based on the surface roughness before the mutation; Determining the background wind speed in the transition stage of the inner boundary layer based on the inner boundary layer height.

3. The method for analytically modeling the wake velocity of a wind turbine unit according to claim 2, characterized in that, The determining the background wind speed in the transition stage of the inner boundary layer based on the inner boundary layer height includes: Determining the equilibrium layer height based on the inner boundary layer height; Determining the background wind speed in the transition stage of the inner boundary layer based on at least one of the z-axis coordinate of the wind turbine, the inner boundary layer height, the local friction velocity after the mutation, the surface roughness before the mutation, the mean friction velocity before the mutation, and the equilibrium layer height.

4. The analytical modeling method for the wake velocity of a wind turbine unit according to claim 1, characterized in that The determining the maximum velocity deficit calculation equation based on the turbulence intensity distribution within a preset range around the wind turbine includes: Determining the background field inhomogeneity factor based on the turbulence intensity distribution; Determining the mean convection factor based on the background wind speed in the transition stage of the inner boundary layer; Assuming that the wake velocity deficit of the wind turbine conforms to the self-similarity characteristic and follows a Gaussian distribution, determining the standard deviation of the wake velocity Gaussian distribution; Omitting the pressure change in the far wake, and determining the integral control equation based on the momentum equation and the mass conservation equation; Determining the maximum velocity deficit calculation equation based on the background field inhomogeneity factor, the mean convection factor, the standard deviation of the wake velocity Gaussian distribution, the wind turbine parameters and the integral control equation, where the wind turbine parameters include the wind turbine swept diameter and the wind turbine thrust coefficient.

5. The analytical modeling method for the wake velocity of a wind turbine unit according to claim 4, characterized in that The determining the mean convection factor based on the background wind speed in the transition stage of the inner boundary layer includes: Setting the mean convection factor to a preset value, and determining the initial value of the wind turbine wake velocity based on the preset value, the background field inhomogeneity factor, the wake expansion rate, the standard deviation of the wake velocity Gaussian distribution, the wind turbine parameters, the background wind speed at a preset position in front of the wind turbine, and the background wind speed in the transition stage of the inner boundary layer; Iteratively calculate the average convection factor based on the initial value of the wake velocity of the wind turbine, the background field wind speed in the transition stage of the internal boundary layer, and the background field wind speed at a preset position in front of the wind turbine until the root mean square error between the currently calculated average convection factor and the average convection factor calculated in the previous time is less than or equal to a preset threshold, so as to obtain the average convection factor.

6. The wake velocity analytical modeling method for a wind turbine unit according to claim 4, characterized in that Determining the wake velocity deficit calculation equation based on the maximum velocity deficit calculation equation includes: Determine the wake velocity deficit calculation equation based on the standard deviation of the wake velocity Gaussian distribution, the wind turbine parameters, the background field wind speed at a preset position in front of the wind turbine, and the maximum velocity deficit calculation equation, where the wind turbine parameters include the wind turbine coordinates, the thrust coefficient, the wind turbine swept diameter, the hub height, and the spanwise center position of the wind turbine.

7. A method for analyzing the wake velocity of a wind turbine unit, characterized in that, The method includes: Obtain the background field wind speed in the transition stage of the internal boundary layer under the condition of sudden change of surface roughness, where the background field wind speed in the transition stage of the internal boundary layer is determined by the ground information parameters before and after the sudden change of surface roughness; Obtain the turbulence intensity distribution within a preset range around the wind turbine; Obtain a wind turbine wake velocity analysis model, where the wind turbine wake velocity analysis model is obtained by using the wind turbine wake velocity analytical modeling method according to any one of claims 1 to 6; Determine the wake velocity of the wind turbine based at least on the background field wind speed in the transition stage of the internal boundary layer, the turbulence intensity distribution, and the wind turbine wake velocity analysis model.

8. The method for analyzing the wake velocity of a wind turbine according to claim 7, wherein Determining the wake velocity of the wind turbine based at least on the background field wind speed in the transition stage of the internal boundary layer, the turbulence intensity distribution, and the wind turbine wake velocity analysis model includes: Obtain the background field wind speed at a preset position in front of the wind turbine; Determine the wake velocity of the wind turbine based on the background field wind speed at a preset position in front of the wind turbine, the turbulence intensity distribution, the background field wind speed in the transition stage of the internal boundary layer, and the wind turbine wake velocity analysis model.

9. An electronic device, comprising at least one processor and at least one memory, the memory being adapted to store a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by the processor to execute the method according to any one of claims 1 to 6 or 7 to 8.

10. A computer-readable storage medium storing multiple program codes, characterized in that, The program code is adapted to be loaded and run by the processor to execute the method according to any one of claims 1 to 6 or 7 to 8.

Images