A yaw control method and system for large-scale wind farms based on FLORIS

Abstract

The application discloses a large wind farm yaw control method and system based on FLORIS, belongs to the technical field of wind power generation, and solves the problems of low precision of wind farm yaw optimization scheme, large amount of wind farm related data processing data, long time, and multi-wind direction and multi-wind speed yaw scheme adaptation. The method comprises the following steps: collecting real-time data of a wind farm and real-time data of a wind turbine; improving a wake loss model, a wake deflection model, a wake turbulence model and a wake combination model, calculating a wind farm flow field and output power attenuation affected by a wake, and screening out wind turbines affected by the wake as target wind turbines; determining main upstream wind turbines according to flow field parameters of the target wind turbines; establishing a rectangular coordinate system; placing the wind turbines in the rectangular coordinate system, adjusting yaw angles of the wind turbines, obtaining optimal yaw angles, and controlling the target wind turbines according to the optimal yaw angles, and completing the method. The application is suitable for various complex wind fields and conforms to actual wind farm operation conditions.

Description

Technical Field

[0001] This invention belongs to the field of wind power generation technology, specifically relating to a wind turbine yaw control method. Background Technology

[0002] With the continuous development of the renewable energy industry, the importance of wind energy utilization has become increasingly prominent. As an important part of renewable energy, many wind farms have been established one after another. Wind farms with an installed capacity of more than 100MW are called large wind farms. Compared with small wind farms, large wind farms have a larger wind energy capture capacity and higher output power. However, the output power attenuation problem caused by the wake effect of wind turbines has become increasingly serious.

[0003] Taking two wind turbines arranged in tandem as an example, when the inflow wind direction matches the turbine arrangement direction, the upstream turbine will generate a wake region after it performs its work. The wake generated by the upstream turbine will affect the incoming wind speed of the downstream turbine and increase the turbulence intensity experienced by the downstream turbine, thereby reducing the economic efficiency and safety of large-scale wind farms. Moreover, as the spacing between the turbines gets closer, the impact of the wake effect will become increasingly greater.

[0004] For large-scale wind farms, the wake effect is almost inevitable to wind turbines. To reduce the impact of the wake effect on the economy and safety of wind farms, wind turbine yaw is a quick and convenient way to reduce or even eliminate the wake effect. Yaw control of wind turbines in wind farms can reduce the impact of the wake on downstream wind turbines, improve the stability of wind turbine operation, and increase the output power of wind farms.

[0005] Chinese invention patent publication number CN117287346A discloses a yaw optimization control method and system for wind farms. This method modularizes the wind turbines in a wind farm, performs wake simulation analysis on each turbine group, and calculates yaw optimization for the wake-affected parts. While this method can derive a yaw optimization strategy for the wind farm, the resulting strategy is a turbine group yaw method, which has lower accuracy and slower overall calculation speed compared to a single turbine yaw method.

[0006] Chinese invention patent publication CN117034618A discloses a joint optimization method for wind farm output power considering yaw turbine load. This method designs 4n variables for n wind turbines and uses FLORIS software to calculate the equivalent wind speed of the turbines in the wind farm, optimizing the output power, load, and yaw angle of the target wind farm. This method can derive optimized schemes for wind turbine load, output power, and yaw angle at specific wind speeds, but its adaptability to the complex and variable wind speeds and directions in actual wind farms is limited. Summary of the Invention

[0007] This invention proposes a yaw control method and system for large-scale wind farms based on FLORIS. The purpose is to solve the problems of low accuracy of wind farm yaw optimization schemes, large data volume and long processing time of wind farm related data, and adaptability of yaw schemes to multiple wind directions and speeds.

[0008] The present invention proposes a yaw control method for large-scale wind farms based on FLORIS, comprising:

[0009] S1: Collect real-time data of wind farms and wind turbines;

[0010] S2: Improve the wake deficit model, wake deflection model, wake turbulence model, and wake combination model to obtain improved wake deficit model, improved wake deflection model, improved wake turbulence model, and improved wake combination model; calculate the wind farm flow field and the output power attenuation affected by the wake, and screen out the wind turbines affected by the wake as target wind turbines;

[0011] S3: Determine the main upstream wind turbines based on the flow field parameters of the target wind turbine;

[0012] S4: Establish a rectangular coordinate system with the main upstream wind turbine as the origin and the direction of the target wind turbine as the positive x-axis.

[0013] S5: Group the remaining wind turbines that affect the target wind turbine and place them in the rectangular coordinate system. Adjust the yaw angle of each wind turbine. When the total output power of the grouped wind turbines reaches its maximum, the corresponding yaw angle is the optimal yaw angle of the target wind turbine. Control the target wind turbine according to the optimal yaw angle to complete the method.

[0014] Furthermore, a preferred solution is provided: the real-time data of the wind farm includes: wind farm location data, atmospheric condition data, wind resource data, and historical wind speed and direction data; the real-time data of the wind turbine includes: wind turbine number data and wind turbine parameter data.

[0015] Furthermore, a preferred solution is provided: the improved wake loss model is as follows:

[0016]

[0017] Where ΔU is the change in velocity; U ∞ denoted as the inflow velocity of the wind field; r is the wake radius; y and z represent the longitudinal and vertical coordinates, respectively; y = 0 at the wind turbine hub; z = 0 at the horizontal ground; H is the height of the wind turbine hub; x represents the downstream distance of the wind turbine; C(x) is the amplitude of the velocity loss distribution; σ is the standard deviation of the velocity loss.

[0018] Furthermore, a preferred solution is provided: the improved wake deflection model is as follows:

[0019]

[0020] Where C is the velocity difference at the center of the wake, δ is the wake deflection, and σ y ,σ z These are the wake widths in the y and z directions, respectively.

[0021] Furthermore, a preferred embodiment is provided: the improved wake turbulence model is as follows:

[0022]

[0023] Where I is the wake turbulence intensity, a is the thrust coefficient sensing factor; C C To adjust the scale, C A C is the axial sensing factor. I C represents the intensity of environmental turbulence. D The dependence on downstream distance; x / D is the discriminant for the wake region; C T denoted as , where is the thrust coefficient of the wind turbine; D is the diameter of the wind turbine hub.

[0024] Furthermore, a preferred solution is provided: the improved wake combination model is as follows:

[0025]

[0026] Where i represents the wake of each wind turbine, and the total number of wind turbines is calculated up to N; U i (x) refers to the local average velocity; This refers to the local inflow velocity; Losses due to speed.

[0027] Furthermore, a preferred solution is provided: S3 specifically comprises:

[0028] The upstream wind turbine that has the smallest straight-line distance to the target wind turbine or the greatest impact is selected as the main upstream wind turbine.

[0029] The present invention also proposes a computer device, the computer device including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes a yaw control method for large wind farms based on FLORIS according to any combination of the above schemes.

[0030] The present invention also proposes a computer-readable storage medium for storing a computer program that executes a yaw control method for large wind farms based on FLORIS, as described in any combination of the above-mentioned schemes.

[0031] This invention also proposes a yaw control system for large-scale wind farms based on FLORIS, the system comprising:

[0032] Data acquisition module: Used to collect real-time data from wind farms and wind turbines;

[0033] Improvement module: Used to improve the wake deficit model, wake deflection model, wake turbulence model and wake combination model to obtain improved wake deficit model, improved wake deflection model, improved wake turbulence model and improved wake combination model; calculate the wind farm flow field, the output power attenuation affected by the wake, and screen out the wind turbines affected by the wake as target wind turbines;

[0034] Determining module: Used to determine the main upstream wind turbines based on the flow field parameters of the target wind turbine;

[0035] Establish coordinate module: used to establish a rectangular coordinate system with the main upstream wind turbine as the origin and the direction of the target wind turbine as the positive x-axis;

[0036] Control module: Used to group the remaining related wind turbines that affect the target wind turbine and place them in the rectangular coordinate system. Adjust the yaw angle of each wind turbine. When the total output power increase of the grouped related wind turbines reaches the maximum, the corresponding yaw angle is the optimal yaw angle of the target wind turbine. Control the target wind turbine according to the optimal yaw angle.

[0037] Compared with the prior art, the advantages of the present invention are:

[0038] 1. Rapid wind turbine data analysis methods:

[0039] Traditional wind farm yaw optimization requires a large amount of wind turbine data as the basis for yaw optimization, resulting in a large amount of data analysis and a long analysis time. The method described in this invention uses FLORIS to collect only wind farm location data, wind resource data, atmospheric condition data, historical wind speed and direction data, and wind turbine parameter data, thus reducing the data collection time.

[0040] 2. Precise and high-speed wind turbine yaw solution:

[0041] Compared with other modular yaw optimization schemes, the method described in this invention combines historical actual wind data and uses FLORIS to screen wind turbines in the wind farm that are affected by wake. The yaw optimization is then performed on the screened wind turbines, resulting in a higher accuracy of the yaw scheme design for a single wind turbine and a shorter design time.

[0042] 3. Highly adaptable yaw scheme:

[0043] The yaw optimization method designed by the method described in this invention is based on the condition that the position of the wind turbine in the wind farm remains unchanged. It summarizes the multiple wind directions and wind speeds that the wind turbine may be subjected to. The resulting yaw method can adapt to a variety of complex wind fields and fits the actual operation of the wind farm. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0045] Figure 1 This is a flowchart of a large-scale wind farm yaw control method based on FLORIS, as described in a specific embodiment of the present invention.

[0046] Figure 2 This is an evolution diagram of the wake model and deflection model described in Specific Embodiment 1 of the present invention;

[0047] Figure 3 This is a wake evolution diagram of the wake turbulence model described in a specific embodiment of the present invention;

[0048] Figure 4 This is an evolution diagram of the square sum and free flow wake superposition model described in Specific Embodiment 1 of the present invention. Detailed Implementation

[0049] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[0050] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0051] It should also be understood that the terminology used in this application specification is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this application specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0052] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0053] Many specific details are set forth in the following description in order to provide a full understanding of this application. However, this application may also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0054] Implementation Method 1:

[0055] Reference Figure 1 , Figure 2 , Figure 3 , Figure 4 This implementation method is described below.

[0056] A yaw control method for large wind farms based on FLORIS includes:

[0057] S1: Collect real-time data of wind farms and wind turbines; the real-time data of wind farms includes: wind farm location data, atmospheric condition data, wind resource data, and historical wind speed and direction data; the real-time data of wind turbines includes: wind turbine number data and wind turbine parameter data;

[0058] S2: Improve the wake deficit model, wake deflection model, wake turbulence model, and wake combination model to obtain improved wake deficit model, improved wake deflection model, improved wake turbulence model, and improved wake combination model; calculate the wind farm flow field and the output power attenuation affected by the wake, and screen out the wind turbines affected by the wake as target wind turbines;

[0059] S3: Determine the main upstream wind turbines based on the flow field parameters of the target wind turbine;

[0060] S4: Establish a rectangular coordinate system with the main upstream wind turbine as the origin and the direction of the target wind turbine as the positive x-axis.

[0061] S5: Group the remaining wind turbines that affect the target wind turbine and place them in the rectangular coordinate system. Adjust the yaw angle of each wind turbine. When the total output power of the grouped wind turbines reaches its maximum, the corresponding yaw angle is the optimal yaw angle of the target wind turbine. Control the target wind turbine according to the optimal yaw angle to complete the method.

[0062] Furthermore, a preferred solution is provided: the real-time data of the wind farm includes: wind farm location data, atmospheric condition data, wind resource data, and historical wind speed and direction data; the real-time data of the wind turbine includes: wind turbine number data and wind turbine parameter data.

[0063] Specifically:

[0064] First, install the Python database and download FLORIS version 4. Collect wind farm location data for the target wind farm and number all wind turbines, along with wind resource data, atmospheric condition data, historical wind speed and direction data, and wind turbine parameter data. For data verification, historical operating power output data of the wind turbines in the wind farm can be collected and cross-validated with the data calculated by the software. This implementation method has verified the data for two types of wind turbines in a certain wind farm. The power calculated by FLORIS simulation is basically consistent with the rated power of the corresponding wind turbine models. Root mean square error (RMSE) analysis shows errors of 0.42% and 0.49%, respectively, indicating relatively accurate data.

[0065] The method described in this embodiment utilizes an improved Jensen wake loss model to calculate the velocity decay of the wake along the x-axis direction. The specific formula is as follows:

[0066]

[0067] Where ΔU is the change in velocity; U ∞ denoted as the inflow velocity of the wind field; r is the wake radius; y and z represent the longitudinal and vertical coordinates, respectively; y = 0 at the wind turbine hub; z = 0 at the horizontal ground; H is the height of the wind turbine hub; x represents the downstream distance of the wind turbine; C(x) is the amplitude of the velocity loss distribution; σ is the standard deviation of the velocity loss.

[0068] The method described in this embodiment utilizes an improved Gaussian wake deflection model, which addresses the shortcomings of the Gaussian wake model by analyzing the vortex phenomenon formed in the wake. The specific formula is as follows:

[0069]

[0070] Where C is the velocity difference at the center of the wake, δ is the wake deflection, and σ y ,σ z These are the wake widths in the y and z directions, respectively.

[0071] The method described in this embodiment utilizes a wake turbulence model, which is used to calculate additional uncertainties during wind turbine operation. The wake turbulence intensity is derived by using the thrust coefficient obtained near the wake region and the thrust coefficient obtained far from the wake region, along with environmental turbulence. The specific formula is as follows:

[0072]

[0073] Where I is the wake turbulence intensity, a is the thrust coefficient sensing factor; C C To adjust the scale, C A C is the axial sensing factor. I C represents the intensity of environmental turbulence. D The dependence on downstream distance; x / D is the discriminant for the wake region; C T denoted as , where is the thrust coefficient of the wind turbine; and D is the diameter of the wind turbine hub. The dependence of adjustments on scale, axial sensing factor, and downstream distance can be determined based on the specific wind farm topography and environmental factors.

[0074] The method described in this embodiment utilizes a sum-of-squares free-flow wake superposition model, a wake combination model proposed by Jensen et al. This model combines the wake flow field by the sum of the squares of the new wake to be added and the existing wake. The formula for wake superposition can be expressed as:

[0075]

[0076] Where i represents the wake of each wind turbine, and the total number of wind turbines is calculated up to N; U i (x) refers to the local average velocity; This refers to the local inflow velocity; Losses due to speed.

[0077] The following is a further explanation of the screening of wind turbines affected by wake as described in S2:

[0078] 1. Screening methods for wind turbines susceptible to wake effects:

[0079] First, the FLORIS software was used to simulate a single wind turbine to determine the wind speed at which it can achieve full power operation. Then, the different models of wind turbines were compared to determine the minimum wind speed at which all models of wind turbines can achieve full power operation, which was recorded as wind speed a.

[0080] Import the model parameters and location information of all wind turbines in the wind farm, simulate the wind farm in FLORIS, with the wind turbine blades facing the inflow wind direction and the inflow wind speed being wind speed a, and record the output power of each wind turbine.

[0081] Change the inflow wind direction and repeat the above calculation at 10° intervals. Record 36 sets of wind farm output power data. Divide the data of each wind turbine with the full power of the corresponding model of each wind turbine. If the output power difference is large, it means that the wind turbine is easily affected by the wake under the current wind direction.

[0082] 2. Screening method for wind turbines actually affected by wake:

[0083] In actual large-scale wind farms, the layout of wind turbines is difficult to change after construction. Therefore, the actual geographical factors of the wind turbines are difficult to change, and the wind speed and direction they are subjected to are roughly the same as in previous years. By collecting historical wind speed and direction data of the wind turbines in the wind farm and comparing them with the wind directions that are easily affected by the wake effect, wind turbines with different wind directions and corresponding lower wind speeds can be eliminated, thus identifying the wind turbines in the actual wind farm that are affected by the wake effect.

[0084] The following provides further explanation of S3, S4, and S5:

[0085] In the process of yaw optimization for wind turbines affected by wake effect in actual wind farms, in order to better play the role of yaw optimization, the upstream wind turbine with the smallest straight-line distance to the target wind turbine or the greatest influence is taken as the main upstream wind turbine. Cartesian coordinate transformation is performed, with the origin of the coordinate set as the main upstream wind turbine, and the target wind turbine located on the x-axis with the relative distance remaining unchanged.

[0086] Group all other wind turbines that affect the target wind turbine and place them in the above coordinate system. The yaw angle of the main upstream wind turbine tends to the wind direction with fewer wind turbines. The yaw angle of other upstream wind turbines is related to the quadrant in which they are located. The direction is upward in quadrants 1 and 4; and downward in quadrants 2 and 3.

[0087] The specific yaw angle can be adjusted until the total output power of the wind turbines in the group reaches its maximum, and the corresponding yaw angle is the optimal yaw angle for the target wind turbine.

[0088] By superimposing the obtained yaw angle with the rotation angle during the rectangular coordinate transformation, the yaw angle of the upstream wind turbine when the wind direction is affected by the wake can be obtained.

[0089] When the inflow direction is close to the wake direction of the target wind turbine by +3° to -3°, the yaw angle of the upstream wind turbine can be adjusted by fuzzification based on the relevant positive or negative values. The specific formula is as follows:

[0090] β1 = 8.75|a| + α,

[0091] Where β1 is the yaw angle corresponding to the target wind turbine; a is the angle at which the inflow wind direction approaches the target wind direction; and α is the optimal yaw angle of the upstream wind turbine.

[0092] The method described in this embodiment collects wind turbine data from large wind farms, such as turbine model, hub height, atmospheric conditions, and turbine location information. Using FLORIS software, it simulates and analyzes the wind farm operation under different inflow wind directions and speeds, optimizes the wake effect experienced by the wind turbines during wind farm operation, and derives a yaw optimization scheme for wind farm operation.

[0093] The method described in this embodiment utilizes an improved Gaussian wake deflection model, which improves upon the shortcomings of the Gaussian wake model by analyzing the vortex phenomenon formed in the wake.

[0094] The method described in this embodiment utilizes a wake turbulence model, which is used to calculate additional uncertainties during wind turbine operation. The wake turbulence intensity is obtained by using the thrust coefficient obtained in the near-wake region and the thrust coefficient obtained in the far-wake region along with the environmental turbulence.

[0095] Implementation Method Two:

[0096] A yaw control system for a large wind farm based on FLORIS, the system comprising:

[0097] Data acquisition module: Used to collect real-time data from wind farms and wind turbines;

[0098] Improvement module: Used to improve the wake deficit model, wake deflection model, wake turbulence model and wake combination model to obtain improved wake deficit model, improved wake deflection model, improved wake turbulence model and improved wake combination model; calculate the wind farm flow field, the output power attenuation affected by the wake, and screen out the wind turbines affected by the wake as target wind turbines;

[0099] Determining module: Used to determine the main upstream wind turbines based on the flow field parameters of the target wind turbine;

[0100] Establish coordinate module: used to establish a rectangular coordinate system with the main upstream wind turbine as the origin and the direction of the target wind turbine as the positive x-axis;

[0101] Control module: Used to group the remaining related wind turbines that affect the target wind turbine and place them in the rectangular coordinate system. Adjust the yaw angle of each wind turbine. When the total output power increase of the grouped related wind turbines reaches the maximum, the corresponding yaw angle is the optimal yaw angle of the target wind turbine. Control the target wind turbine according to the optimal yaw angle.

[0102] Those skilled in the art will understand that the above description is merely a preferred embodiment of the present invention, and the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. This is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0103] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0104] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0105] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware implementation, a completely software implementation, or an implementation combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0106] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0107] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure and not to limit its protection scope. Although this disclosure has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this disclosure, they can still make various changes, modifications or equivalent substitutions to the specific embodiments of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims to be published and pending approval.

Claims

1. A FLORIS-based yaw control method for a large wind farm, characterized in that, The method comprises: S1: collecting wind farm real-time data and wind turbine real-time data; S2: improving the wake loss model, the wake deflection model, the wake turbulence model and the wake combination model to obtain an improved wake loss model, an improved wake deflection model, an improved wake turbulence model and an improved wake combination model; calculating the wind farm flow field, the output power attenuation affected by the wake, and screening out the wind turbines affected by the wake as target wind turbines; S3: determining the main upstream wind turbine according to the flow field parameters of the target wind turbine; S4: establishing a rectangular coordinate system with the main upstream wind turbine as the origin and the direction of the target wind turbine as the positive direction of the x-axis; S5: grouping the remaining relevant wind turbines that have an impact on the target wind turbine and placing them in the rectangular coordinate system, adjusting the yaw angles of each wind turbine, and when the total output power of the grouped relevant wind turbines reaches the maximum, the corresponding yaw angle is the optimal yaw angle of the target wind turbine, and the target wind turbine is controlled according to the optimal yaw angle to complete the method; The S3 specifically comprises: Selecting the upstream wind turbine with the smallest linear distance or the largest impact on the target wind turbine as the main upstream wind turbine; In the S2, the wind turbines affected by the wake are screened out as target wind turbines as follows: S2.1: screening wind turbines susceptible to wake effects: Simulate a single wind turbine using FLORIS software to obtain the wind speed at which it can reach full-load operation, and compare different types of wind turbines to obtain the minimum wind speed that allows all types of wind turbines to reach full-load operation, which is recorded as wind speed a; Import the model parameters and location information of all wind turbines in the wind farm into FLORIS, simulate the wind farm, and make the blades of the wind turbines face the incoming wind direction with an incoming wind speed of wind speed a. Record the output power of each wind turbine; Change the incoming wind direction and repeat the above calculation with an interval of 10°. Record 36 sets of wind farm output power data, and subtract the full-load power of each wind turbine from the corresponding model data. The larger the output power difference, the more susceptible the wind turbine is to wake effects under the current wind direction. S2.2: screening wind turbines actually affected by the wake as target wind turbines: Collect historical wind speed and direction data of the wind turbines affected by the wake in the wind farm, and compare them with the wind directions susceptible to wake effects. Exclude wind turbines with different wind directions and low corresponding wind speeds to obtain wind turbines in the actual wind farm affected by the wake effect as target wind turbines; The step S5 further comprises the following steps: Add the optimal yaw angle of the target wind turbine to the angle rotated during the rectangular coordinate transformation to obtain the optimal yaw angle of the upstream wind turbine when the wind direction affected by the wake; When the incoming wind direction is close to the wake wind direction of the target wind turbine +3°~-3°, the optimal yaw angle of the upstream wind turbine is increased or decreased according to the relevant positive and negative properties. The specific formula is as follows: wherein is the optimal yaw angle of the target wind turbine; a is the angle of the incoming wind direction close to the target wind direction; is the optimal yaw angle of the upstream wind turbine.

2. A FLORIS-based yaw control method for a large wind farm according to claim 1, characterized in that, The wind farm real-time data includes: wind farm location data, atmospheric condition data, wind resource data, historical wind speed and direction data; the wind turbine real-time data includes: wind turbine number data, wind turbine parameter data.

3. The FLORIS-based yaw control method for a large wind farm according to claim 1, wherein, The improved wake deficit model is: wherein ∆U is the change in velocity; U ∞ is the inflow velocity of the wind field; r is the wake radius, y and z denote the longitudinal coordinate and the vertical coordinate, respectively, y = 0 at the hub of the wind turbine; z = 0 at the horizontal ground; H is the hub height of the wind turbine; x denotes the distance downstream of the wind turbine, C (1) x is the amplitude of the velocity deficit distribution; σ is the standard deviation of the velocity deficit.

4. The FLORIS-based yaw control method for a large wind farm according to claim 1, wherein, The improved wake deflection model is: wherein is the velocity difference of the wake center, is the wake deflection amount, are respectively the wake width in the direction.

5. The FLORIS-based yaw control method for a large wind farm according to claim 1, wherein, The improved wake turbulence model is: wherein I is the wake turbulence intensity, a is the thrust coefficient induction factor; C C is the adjustment to the scale, C A is the axial induction factor, C I is the ambient turbulence intensity; C D is the dependence on downstream distance; x / D is the discriminant of the wake region; C T is the thrust coefficient of the wind turbine; D is the wind turbine hub diameter.

6. The FLORIS-based yaw control method for a large wind farm according to claim 1, wherein, The improved wake combination model is: where is the total number of wind turbines; is the local inflow velocity; denotes the local mean velocity; is the local inflow velocity; ; is the velocity deficit.

7. Computer device, characterized in that The computer readable storage medium is used to store a computer program, and the computer program executes the FLORIS-based yaw control method for a large wind farm according to any one of claims 1-6.

8. A computer readable storage medium, characterized in that, The system comprises:

9. A FLORIS-based yaw control system for a large wind farm, characterized in that, The acquisition module is used to acquire real-time data of the wind farm and real-time data of the wind turbine; The improvement module is used to improve the wake deficit model, the wake deflection model, the wake turbulence model and the wake combination model, and obtain the improved wake deficit model, the improved wake deflection model, the improved wake turbulence model and the improved wake combination model; calculate the flow field of the wind farm and the output power attenuation affected by the wake, and screen out the wind turbine affected by the wake as the target wind turbine; The determination module is used to determine the main upstream wind turbine according to the flow field parameter of the target wind turbine; The coordinate establishment module is used to establish a rectangular coordinate system with the main upstream wind turbine as the origin and the direction of the target wind turbine as the positive direction of the x-axis; The control module is used to group the related wind turbines that have an impact on the target wind turbine and place them in the rectangular coordinate system, adjust the yaw angles of the wind turbines, and when the total output power improvement of the grouped related wind turbines reaches the maximum, the corresponding yaw angle is the optimal yaw angle of the target wind turbine, and the target wind turbine is controlled according to the optimal yaw angle; The determination module is used to perform the following steps: Select the upstream wind turbine with the smallest linear distance or the largest impact degree as the main upstream wind turbine; In the improvement module, the wind turbine affected by the wake is screened out as the target wind turbine, which specifically comprises the following steps: S2.1, screen the wind turbine susceptible to the wake: Use the FLORIS software to simulate a single wind turbine to obtain the wind speed at which the wind turbine can reach full-load operation, compare different types of wind turbines, and obtain the minimum wind speed at which all types of wind turbines reach full-load state, and record it as wind speed a; Import the model parameters and location information of all wind turbines in the wind farm, simulate the wind farm in FLORIS, and the blades of the wind turbine are perpendicular to the incoming wind direction, and the incoming wind speed is wind speed a. Record the output power of each wind turbine; Change the incoming wind direction, repeat the above calculation with an interval of 10°, record 36 groups of wind farm output power data, and subtract the full-load power of each wind turbine from the corresponding model of each wind turbine. The larger the output power difference, the more susceptible the wind turbine is to the wake under the current wind direction; S2.2, screen the wind turbine actually affected by the wake as the target wind turbine: ​ Collecting the historical wind speed and direction data of the wind turbines affected by the wake effect in the wind farm, comparing the wind directions susceptible to the wake effect, and excluding the wind turbines with different wind directions and low corresponding wind speeds, the wind turbines affected by the wake effect in the actual wind farm are obtained as target wind turbines; The control module is further configured to perform the following steps: The optimal yaw angle of the target wind turbine is superimposed with the angle of rotation when the rectangular coordinate transformation is performed, so as to obtain the optimal yaw angle of the upstream wind turbine when the wind direction is affected by the wake effect; When the inflow wind direction is close to the wake wind direction of the target wind turbine +3°-3°, the optimal yaw angle of the upstream wind turbine is increased or decreased according to the relevant positive and negative, and the specific formula is as follows: wherein is the optimal yaw angle of the target wind turbine; a is the angle of the incoming wind direction close to the target wind direction; is the optimal yaw angle of the upstream wind turbine.

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