Wind turbine full wake speed loss evaluation method, device, equipment and medium

Abstract

The application relates to the technical field of wind power generation, and particularly provides a wind turbine full wake speed loss evaluation method, device, equipment and medium, aiming to solve the technical problem that the calculation result of the existing method for the near wake is inaccurate. To this end, the wind turbine full wake speed loss evaluation method comprises the following steps: obtaining an incoming flow wind speed and a wind direction; determining preset parameter information based on the incoming flow wind speed and the wind direction; obtaining a pre-constructed double-Gaussian wake model considering pressure difference correction; determining an amplitude distribution and a shape distribution of the double-Gaussian wake model based on the preset parameter information; and evaluating the wake speed loss of the wind turbine based on the amplitude distribution and the shape distribution of the double-Gaussian wake model. In this way, the pressure difference correction compensates for the defects of the traditional model in the evaluation accuracy of the near wake area.

Description

Technical Field

[0001] This invention relates to the field of wind power generation technology, specifically providing a method, apparatus, equipment, and medium for assessing the velocity loss of the entire wake of a wind turbine. Background Technology

[0002] After absorbing the kinetic energy of the incoming wind, wind turbines create a wake region downstream where wind speed decreases. Currently, analytical wake models based on Gaussian distributions are primarily used to assess the velocity loss in the wind turbine wake. While this model is computationally simple, it neglects pressure changes near the wake and is only applicable to calculations of the far wake, resulting in inaccurate velocity loss calculations in the near-wake region. As the spacing between turbines in wind farms gradually decreases, downstream turbines may be located within the near-wake region of upstream turbines, making existing models insufficient for practical applications.

[0003] Accordingly, there is a need in the field for a new method for assessing the velocity loss of the entire wake of wind turbines to address the above problems. Summary of the Invention

[0004] To overcome the aforementioned deficiencies, this application is proposed to provide a solution, or at least a partial solution, to the technical problem of inaccurate calculation results in the near-wake range of existing methods. This application provides a method, apparatus, equipment, and medium for assessing the velocity loss of a wind turbine's full wake.

[0005] In a first aspect, this application provides a method for assessing the velocity loss of the entire wake of a wind turbine, the method comprising:

[0006] Obtain the incoming wind speed and direction;

[0007] Preset parameter information is determined based on the incoming wind speed and direction;

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

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

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

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

[0012] Based on the law of conservation of mass, a momentum balance equation considering the velocity loss of the wind turbine wake is established.

[0013] Based on the characteristics of the pressure difference distribution, the momentum balance equation is simplified to obtain a simplified momentum balance equation.

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

[0015] 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;

[0016] constructing the double Gaussian wake model based on the shape function and the amplitude function.

[0017] In an embodiment of the present application, the expression of the simplified momentum balance equation is:

[0018]

[0019] wherein, ρ is the air density; A is the wind wheel area, A = πD 2 / 4, D is the wind wheel diameter; 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.

[0020] In an embodiment of the present application, the expression of the shape function G(x, r) of the double Gaussian wake model is:

[0021]

[0022] wherein, σ y and σ z respectively represent the Gaussian standard deviation of the wake in the horizontal and vertical directions; y c is the offset of the wake center; Z h is the hub height; r min is the radial position of the minimum velocity point.

[0023] In an 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 comprises:

[0024] obtaining the expression of the Gaussian distribution:

[0025]

[0026] wherein, 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.

[0027] The expression of the Gaussian distribution is brought into the simplified momentum balance equation, and the simplified momentum balance equation is integrated to obtain an amplitude function of the double Gaussian wake model, and an expression of the amplitude function C(x, y) is:

[0028]

[0029] wherein, σ y and σ z respectively represent Gaussian standard deviations of the wake in horizontal and vertical directions; y c is a displacement of a wake center; r min is a radial position of a minimum value point of a velocity.

[0030] In an embodiment of the present application, the preset parameter information is determined based on the incoming flow wind speed and the wind direction, including:

[0031] The thrust coefficient of the wind turbine is determined based on the incoming flow wind speed;

[0032] The yaw angle of the wind turbine is determined based on the wind direction;

[0033] The preset parameter information is calculated based on the thrust coefficient and the yaw angle, and the preset parameter information includes the radial position of the minimum value point of the velocity, the Gaussian standard deviations of the wake in horizontal and vertical directions, and the displacement of the wake center.

[0034] In an embodiment of the present application, the wake velocity loss of the wind turbine is evaluated based on the amplitude distribution and the shape distribution of the double Gaussian wake model, including: inputting the amplitude distribution, the shape distribution of the double Gaussian wake model, and the incoming flow wind speed of the wind turbine into a double Gaussian distribution expression corresponding to the wake velocity loss, and outputting a wake velocity loss distribution of the wind turbine.

[0035] In a second aspect, a device for evaluating a full wake velocity loss of a wind turbine is provided, and the device includes:

[0036] A first acquisition module configured to acquire an incoming flow wind speed and a wind direction;

[0037] A first determination module configured to determine preset parameter information based on the incoming flow wind speed and the wind direction;

[0038] A second acquisition module configured to acquire a double Gaussian wake model considering pressure difference correction which is constructed in advance;

[0039] A second determination module configured to determine an amplitude distribution and a shape distribution of the double Gaussian wake model based on the preset parameter information;

[0040] an evaluation module configured to evaluate the wake velocity loss of the wind turbine based on the amplitude distribution and the shape distribution of the double Gaussian wake model.

[0041] In a third aspect, an electronic device is provided, comprising:

[0042] at least one processor;

[0043] and a memory connected to the at least one processor in communication;

[0044] wherein the memory has stored therein a computer program, which, when executed by the at least one processor, performs the wind turbine full-wake velocity loss evaluation method as previously described.

[0045] In a fourth aspect, a computer-readable storage medium is provided, which has stored therein a plurality of program codes adapted to be loaded and run by a processor to perform the wind turbine full-wake velocity loss evaluation method as previously described.

[0046] The one or more technical solutions of the present application described above have at least one or more of the following advantages

[0047] Advantages:

[0048] The wind turbine full-wake velocity loss evaluation method in the present application comprises: obtaining an incoming flow wind speed and a wind direction; determining preset parameter information based on the incoming flow wind speed and the wind direction; obtaining a double Gaussian wake model considering pressure difference correction that is constructed in advance; determining an amplitude distribution and a shape distribution of the double Gaussian wake model based on the preset parameter information; and evaluating a wake velocity loss of the wind turbine based on the amplitude distribution and the shape distribution of the double Gaussian wake model. The double Gaussian model more accurately depicts the asymmetric distribution of the wake, and the error is significantly reduced compared to the traditional single Gaussian model. The pressure difference correction compensates for the defects of the traditional model ignoring the influence of the pressure gradient, and improves the evaluation accuracy of the wake velocity loss in the near-wake region. BRIEF DESCRIPTION OF DRAWINGS

[0049] The disclosure of the present application will become more apparent with reference to the drawings. It will be readily understood to those skilled in the art that the drawings are merely intended to illustrate the present application and are not intended to limit the scope of protection of the present application. In addition, similar numbers are used to represent similar components throughout the drawings, in which:

[0050] Figure 1 is a main flowchart of the wind turbine full-wake velocity loss evaluation method in an embodiment of the present application;

[0051] Figure 2 is a schematic diagram of the influence range of the wake pressure difference of the wind turbine in an embodiment of the present application;

[0052] Figure 3 This is 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 this application.

[0053] Figure 4 This is a schematic diagram comparing the results of different models evaluating wake velocity loss in one embodiment of this application;

[0054] Figure 5 This is a schematic diagram of the main structure of a wind turbine full wake velocity loss assessment device in one embodiment of this application;

[0055] Figure 6 This is a schematic diagram of the structure of an electronic device in one embodiment of this application. Detailed Implementation

[0056] Some embodiments of this application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of this application and are not intended to limit the scope of protection of this application.

[0057] In the description of this application, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, memory, and can also include software components, such as program code, or a combination of software and hardware. A processor can be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. The processor has data and / or signal processing capabilities. The processor can be implemented in software, in hardware, or a combination of both. Non-transitory computer-readable storage media includes 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" means all possible combinations of A and B, such as only A, only B, or A and B. The terms "at least one A or B" or "at least one of A and B" have a similar meaning to "A and / or B" and can include only A, only B, or A and B. The singular terms "a" or "this" can also include plural forms.

[0058] The traditional method mainly calculates wake loss 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 the actual needs.

[0059] Therefore, this application proposes a method, device, equipment, and medium for assessing the velocity loss of the entire wake of a wind turbine.

[0060] See appendix Figure 1 , Figure 1This is a schematic flowchart illustrating the main steps of a wind turbine full wake velocity loss assessment method according to an embodiment of this application. Figure 1 As shown, the wind turbine full wake velocity loss assessment method in this application embodiment mainly includes the following steps S10-S50.

[0061] Step S10: Obtain the incoming wind speed and direction.

[0062] Incoming wind speed and direction refer to the free-flow wind speed and direction undisturbed by the wind turbine, which are usually measured by the wind measuring device required for wind turbines.

[0063] Step S20: Determine preset parameter information based on incoming wind speed and direction.

[0064] 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.

[0065] Step S30: Obtain a pre-built double Gaussian wake model that takes into account pressure difference correction.

[0066] The double Gaussian wake model is a mathematical model used to describe the velocity distribution of wind turbine wakes. It uses the superposition of two Gaussian functions to characterize the asymmetric velocity deficit of the wake cross-section. Compared to the single Gaussian model, it can more accurately reflect the true structure of the wake, especially under yaw or complex turbulent conditions. Furthermore, this application introduces a pressure difference correction into the double Gaussian wake model, making its calculation accuracy near the wake even higher.

[0067] Step S40: Determine the amplitude distribution and shape distribution of the double Gaussian wake model based on the parameter information.

[0068] Step S50: Evaluate the velocity loss of the wind turbine's full wake based on the amplitude and shape distribution of the double Gaussian wake model.

[0069] 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 considering pressure difference correction 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 evaluated based on the amplitude distribution and shape distribution of the double Gaussian wake model. The double Gaussian model more accurately characterizes the asymmetric distribution of the wake, significantly reducing errors compared to the traditional single Gaussian model; and the pressure difference correction compensates for the deficiency of the traditional model in ignoring the influence of pressure difference, improving the accuracy of wake velocity loss assessment in the near-wake region.

[0070] The following provides further explanation of steps S20 to S50.

[0071] Specifically, step S20 can be implemented through the following steps S201 to S203.

[0072] Step S201: Determine the thrust coefficient of the wind turbine based on the incoming wind speed.

[0073] Specifically, the thrust coefficient can be determined by referring to the thrust coefficient curve of the wind turbine, based on the incoming wind speed. This curve is usually provided by the wind turbine manufacturer and may vary depending on the type of turbine.

[0074] Step S202: Determine the yaw angle of the wind turbine based on the wind direction.

[0075] 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 specifically using the following methods:

[0076] γ=θ wind -θ nacelle

[0077] Where, θ wind It's the direction of the incoming wind, θ nacelle It refers to the orientation and angle of the cabin.

[0078] Step S203: Calculate the parameter information of the dual Gaussian wake model based on the thrust coefficient and 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.

[0079] Radial position r of the point of minimum velocity min It can be calculated using the following formula:

[0080]

[0081] Where D is the rotor diameter, γ is the yaw angle, x is the downstream distance of the wind turbine, and x0 is the boundary between the near and far wakes. According to the BPA wake model, the expression for x0 is:

[0082]

[0083] Among them, I u The turbulence intensity is denoted as turbulence intensity.

[0084] The Gaussian standard deviations of the wake in the near-wake region in the horizontal and vertical directions are:

[0085]

[0086] Where, σ y σ z denoted as Gaussian standard deviation of the wake in the horizontal and vertical directions.

[0087] The Gaussian standard deviations of the wake in the horizontal and vertical directions within the far wake region are:

[0088]

[0089]

[0090] Where, k y k z These are the wake expansion coefficients in the spanwise and vertical directions, respectively, with a classical value of 0.022.

[0091] The offset y of the wake center in the near-wake region c for:

[0092]

[0093] Where, θ c0 The inclination angle of the airflow near the wake.

[0094] offset y of the wake center in the far wake region c for:

[0095]

[0096] The above is a further explanation of step S20. Step S30 will be further explained below.

[0097] Specifically, step S30 can be implemented through the following steps S301 to S305.

[0098] Step S301: Combining the law of conservation of mass, establish the momentum balance equation for the wake velocity loss of the wind turbine considering the pressure difference correction.

[0099] Specifically, based on the law of conservation of mass, the momentum balance equation for the wake velocity loss of a wind turbine, considering the pressure difference correction, is as follows:

[0100]

[0101] Where ρ is the air density; A is the wind turbine area, A = πD 2 / 4, D is the diameter of the wind turbine; U ∞ C is the inflow velocity. T γ is the thrust coefficient of the wind turbine; γ is the yaw angle; P ∞ For ambient pressure; P w Pressure in the wake region; A p The area covered by the effective pressure difference in the wake; du is the velocity loss of the wind turbine; A w The area of ​​the wake region.

[0102] Step S302: Based on the characteristics of the pressure difference distribution, the momentum balance equation is simplified to obtain a simplified momentum balance equation.

[0103] Specifically, according to the results of large eddy simulation, the pressure difference in the wake is mainly distributed around the wake center, covering an area of ​​2.25 times the projected area of ​​the wind turbine. By dimensionlessly approximating the dynamic pressure of the incoming wind speed on the wind turbine regarding the wake pressure difference, the momentum balance equation can be initially simplified, yielding the initially simplified momentum equation:

[0104]

[0105] Where f(x) is the momentum correction coefficient caused by the pressure difference, and the expression for f(x) is:

[0106]

[0107] Among them (P) ∞ -P w ) represents the average pressure difference at the wake.

[0108] The above formula contains pressure terms that are difficult to measure, but their distribution is regular. Therefore, based on the results of large eddy simulation, the above formula can be fitted to the following expression:

[0109] f(x) = 0.5 / (1+2x / D)

[0110] Substituting the above-fitted f(x) into the initial simplified momentum balance equation, we obtain the final simplified momentum balance equation:

[0111]

[0112] Where ρ is the air density; A is the wind turbine area, A = πD 2 / 4, D is the diameter of the wind turbine; U ∞ C is the incoming air velocity. T γ 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 The area of ​​the wake region.

[0113] Step S303: Obtain the shape function of the double Gaussian wake model.

[0114] Assuming the velocity loss of the wake follows a double Gaussian distribution, the expression is as follows:

[0115]

[0116] Where du is the wake velocity loss of the wind turbine, U ∞Let C(x,γ) be the incoming wind speed, C(x,γ) be the amplitude function of the double Gaussian wake model, and G(x,r) be the shape function of the double Gaussian wake model.

[0117] Specifically, the shape function G(x,r) of the double Gaussian wake model is expressed as:

[0118]

[0119] in, σ y and σ z These represent the Gaussian standard deviations of the wake in the horizontal and vertical directions, respectively; y c Z is the offset of the wake center; h R is the wheel hub height. min This represents the radial position of the point where the velocity is at its minimum.

[0120] Step S304: Obtain the 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.

[0121] Specifically, by substituting the double Gaussian distribution of the wake velocity loss into the simplified momentum balance equation and integrating radially over the range from zero to infinity, the magnitude function C(x,γ) of the double Gaussian wake model can be obtained:

[0122]

[0123] in, σ y and σ z These represent the Gaussian standard deviations of the wake in the horizontal and vertical directions, respectively; y c r is the offset of the wake center. min This represents the radial position of the point where the velocity is at its minimum.

[0124] Step S305: Construct a double Gaussian wake model based on the shape function and the magnitude function.

[0125] Specifically, once the shape function G(x,r) and magnitude function C(x,γ) are obtained, a double Gaussian wake model can be constructed based on the aforementioned wake velocity loss conforming to a double Gaussian distribution.

[0126] The above is a further explanation of step S30. Step S40 will be further explained below.

[0127] Regarding the aforementioned step S40, the preset parameter information obtained in step S20 can be substituted into the amplitude function and shape function of the double Gaussian wake model to obtain the amplitude distribution and shape distribution of the double Gaussian wake model.

[0128] Regarding the aforementioned step S50, in one specific embodiment of this application, the step of evaluating 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.

[0129] Specifically, based on the amplitude distribution C(x,γ), shape distribution G(x,r), and incoming wind speed U obtained from the aforementioned step S40, ∞ By incorporating the expression for the wake velocity loss that conforms to a double Gaussian distribution, the wake velocity loss distribution of the wind turbine can be calculated.

[0130] Figure 2 This diagram illustrates the influence range of the wake pressure difference of a wind turbine, obtained through large eddy numerical simulation. The dashed line represents the range of velocity loss distribution, and the solid line represents the range of pressure difference distribution.

[0131] 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.

[0132] Figure 4 This is a schematic diagram comparing the double Gaussian wake model considering pressure difference correction in this application with 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 fit well with the numerical simulation results.

[0133] It should be noted that although the 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 this application, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders, and these variations are all within the scope of protection of this application.

[0134] Furthermore, this application also provides a velocity loss assessment device for the entire wake of a wind turbine.

[0135] See appendix Figure 5 , Figure 5 This is a main structural block diagram of a wind turbine full wake velocity loss assessment device according to an embodiment of this application. Figure 5As shown, the wind turbine wake velocity loss assessment device in this embodiment 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 a single module.

[0136] In some embodiments, the first acquisition module 11 can be configured to acquire the incoming wind speed and wind direction.

[0137] The first determining module 12 can be configured to determine preset parameter information based on the incoming wind speed and wind direction.

[0138] The second acquisition module 13 can be configured to acquire a pre-built double Gaussian wake model that takes into account pressure difference correction.

[0139] The second determining module 14 can be configured to determine the amplitude distribution and shape distribution of the double Gaussian wake model based on the preset parameter information.

[0140] The evaluation module 15 can be configured to evaluate the wake velocity loss of the wind turbine based on the amplitude and shape distribution of the dual Gaussian wake model.

[0141] In one implementation, a description of the specific function can be found in steps S10 to S50.

[0142] The aforementioned wind turbine wake velocity loss assessment device is used for performing... Figure 1 The embodiments of the wind turbine full wake velocity loss assessment method shown are similar in technical principle, technical problem solved and technical effect. Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process and related instructions of the wind turbine full wake velocity loss assessment device can be found in the embodiments of the wind turbine full wake velocity loss assessment method, which will not be repeated here.

[0143] Furthermore, it should be understood that since the various modules are only provided to illustrate the functional units of the device described in this application, the physical devices corresponding to these modules may be the processor itself, or a part of the processor's software, hardware, or a combination of both. Therefore, the number of modules shown in the figures is merely illustrative.

[0144] Those skilled in the art will understand that the various modules in the device can be adaptively split or combined. Such splitting or combining of specific modules will not cause the technical solution to deviate from the principles of this application; therefore, the technical solutions after splitting or combining will fall within the protection scope of this application.

[0145] Those skilled in the art will understand that all or part of the processes in the method of the above-described embodiment can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0146] Furthermore, this application also provides an electronic device, which may include at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program, which, when executed by the at least one processor, implements the wind turbine full wake velocity loss assessment method described in any of the above embodiments. See also Figure 6 As shown, Figure 6 The structure of an electronic device, including a processor 100 and a memory 200, is illustrated by way of example.

[0147] Furthermore, this application also provides a computer-readable storage medium. In one embodiment of the computer-readable storage medium according to this application, the computer-readable storage medium can be configured to store a program for performing the wind turbine full wake velocity loss assessment method of the above-described method embodiments. This program can be loaded and run by a processor to implement the above-described wind turbine full wake velocity loss assessment method. For ease of explanation, only the parts related to the embodiments of this application are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of this application. The computer-readable storage medium can be a memory device comprising various electronic devices. Optionally, in the embodiments of this application, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0148] The technical solution of this application has been described in conjunction with the specific embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. Without departing from the principles of this 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 scope of protection of this application.

Claims

1. A method for assessing the velocity loss of the entire wake of a wind turbine generator, characterized in that, The method includes: Obtain the incoming wind speed and direction; Preset parameter information is determined based on the incoming wind speed and direction; Obtain a pre-built dual Gaussian wake model that considers pressure difference corrections, including: Based on the law of conservation of mass, a momentum balance equation considering the velocity loss of the wind turbine wake is established. Based on the characteristics of the pressure difference distribution, the momentum balance equation is simplified to obtain a simplified momentum balance equation. Obtain the shape function of the double Gaussian wake model; The amplitude function of the double Gaussian wake model is obtained based on the 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 magnitude function; The amplitude distribution and shape distribution of the double Gaussian wake model are determined based on the preset parameter information; The wake velocity loss of the wind turbine is evaluated based on the amplitude and shape distribution of the double Gaussian wake model.

2. The method for assessing the velocity loss of the entire wake of a wind turbine as described in claim 1, characterized in that, The simplified momentum balance equation is expressed as follows: in, air density; For the area of ​​the wind turbine, , The diameter of the wind turbine; For incoming air velocity; This is the thrust coefficient of the wind turbine. Yaw angle; The downstream distance of the wind turbine; This refers to the wake velocity loss of the wind turbine. The area of ​​the wake region.

3. The method for assessing the velocity loss of the entire wake of a wind turbine as described in claim 1, characterized in that, The shape function of the double Gaussian wake model The expression is: in, , , and These represent the Gaussian standard deviations of the wake in the horizontal and vertical directions, respectively. This is the offset of the wake center; Wheel hub height; This represents the radial position of the point where the velocity is at its minimum.

4. The method for assessing the velocity loss of the entire wake of a wind turbine as described in claim 1, characterized in that, The method for obtaining the 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 includes: Obtain the expression for the Gaussian distribution: in, For the wake velocity loss of the wind turbine, For the incoming wind speed, For the amplitude function of the double Gaussian wake model, The shape function of the double Gaussian wake model; Substituting the expression for the Gaussian distribution into the simplified momentum balance equation and integrating the simplified momentum balance equation, the amplitude function of the double Gaussian wake model is obtained. The expression is: in, , , and These represent the Gaussian standard deviations of the wake in the horizontal and vertical directions, respectively. This is the offset of the wake center; This represents the radial position of the point where the velocity is at its minimum.

5. The method for assessing the velocity loss of the entire wake of a wind turbine as described in claim 1, characterized in that, The determination of preset parameter information based on the incoming wind speed and direction includes: The thrust coefficient of the wind turbine is determined based on the incoming wind speed. The yaw angle of the wind turbine is determined based on the wind direction. The preset parameter information is calculated based on the thrust coefficient and the yaw angle. The preset parameter information includes the radial position of the minimum velocity point, the Gaussian standard deviation of the wake in the horizontal and vertical directions, and the offset of the wake center.

6. The method for assessing the velocity loss of the entire wake of a wind turbine as described in claim 1, characterized in that, The method of evaluating 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.

7. A velocity loss assessment device for the entire wake of a wind turbine generator, characterized in that, The device includes: The first acquisition module is configured to acquire the incoming wind speed and direction; The first determining module is configured to determine preset parameter information based on the incoming wind speed and wind direction; The second acquisition module is configured to acquire a pre-constructed double Gaussian wake model considering pressure difference correction, including: establishing a momentum balance equation for wind turbine wake velocity loss considering pressure difference correction by combining the law of conservation of mass; simplifying the momentum balance equation according to the pressure difference distribution characteristics to obtain a simplified momentum balance equation; acquiring the shape function of the double Gaussian wake model; acquiring the 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; and constructing the double Gaussian wake model based on the shape function and the amplitude function. The 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; The evaluation module is configured to evaluate the wake velocity loss of the wind turbine based on the amplitude and shape distribution of the dual Gaussian wake model.

8. An electronic device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores a computer program, which, when executed by the at least one processor, implements the wind turbine full wake velocity loss assessment method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by a processor to perform the wind turbine full wake velocity loss assessment method as described in any one of claims 1 to 6.

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