Large-scale wind power plant yaw control method and system based on FLORIS

Abstract

The invention discloses a large-scale wind power plant yaw control method and system based on FLORIS, belongs to the technical field of wind power generation, and solves the problems that a wind power plant yaw optimization scheme is low in accuracy, wind power plant related data processing is large in data size and long in time, and a multi-wind-direction and multi-wind-speed yaw scheme adapts to the wind power plant yaw optimization scheme. The method comprises the steps that wind power plant real-time data and wind turbine real-time data are collected; the wake flow loss model, the wake flow deflection model, the wake flow turbulence model and the wake flow combination model are improved, the wind power plant flow field and the output power attenuation influenced by the wake flow are calculated, and the wind turbines influenced by the wake flow are screened out to serve as target wind turbines; main upstream wind turbines are determined according to the flow field parameters of the target wind turbine; establishing a rectangular coordinate system; the wind turbines are arranged in the rectangular coordinate system, the yaw angles of all the wind turbines are adjusted, the optimal yaw angle is obtained, the target wind turbine is controlled according to the optimal yaw angle, and the method is completed. The method is suitable for various complex wind fields and fits the actual operation condition of the wind power plant.

Description

Technical Field

[0001] The invention belongs to the technical field of wind power generation, and in particular relates to a yaw control method for a wind turbine. Background Art

[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 installed capacity greater than 100MW are called large wind farms. Compared with small wind farms, large wind farms capture more wind energy and have higher output power. However, the problem of output power attenuation caused by the wake effect of wind turbines is becoming more and more serious.

[0003] Taking two wind turbines arranged in series as an example, when the incoming wind direction coincides with the arrangement direction of the wind turbines, the upstream wind turbine will generate a wake area behind the wind turbine after working. The wake generated by the upstream wind turbine will affect the incoming wind speed of the downstream wind turbine and increase the turbulence intensity of the downstream wind turbine, thereby reducing the economy and safety of large wind farms. Moreover, as the distance between wind turbines approaches, the influence of the wake effect will increase.

[0004] For large wind farms, it is almost inevitable that the wake effect will affect the wind turbines. In order 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 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 wind farm yaw optimization control method and system, which modularizes the wind turbines in the wind farm, performs wake simulation analysis on the wind turbine group of each module, and performs yaw optimization calculation on the part affected by the wake. This method can be used to derive a yaw optimization strategy for the wind farm, but the resulting strategy is a wind turbine group yaw method, which is less accurate than a single wind turbine yaw method and has a slower overall calculation speed.

[0006] China's invention patent publication number CN117034618A discloses a wind farm output power joint optimization method considering the yaw wind turbine load. This method optimizes the target wind farm output power, load, and yaw angle by designing 4n variables of n wind turbines and using FLORIS software to calculate the equivalent wind speed of the wind turbines in the wind farm. This method can be used to obtain an optimization solution for the load, output power, yaw angle, and other data of the wind turbines in the wind farm under a specific wind speed, but it has little adaptability to the complex and changeable wind speed and direction of the wind farm in the actual wind farm. Summary of the invention

[0007] The present invention proposes a large-scale wind farm yaw control method and system based on FLORIS, which aims to solve the problems of low accuracy of wind farm yaw optimization scheme, large amount of data and long time of wind farm related data processing, and adaptability of yaw schemes with multiple wind directions and wind speeds.

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

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

[0010] S2: Improve the wake loss model, wake deflection model, wake turbulence model and 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; calculate the flow field of the wind farm and the output power attenuation affected by the wake, and select the wind turbines affected by the wake as target wind turbines;

[0011] S3: determining the main upstream wind turbine according to 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 direction of the x-axis;

[0013] S5: Group the remaining related wind turbines that have an impact on the target wind turbine and place them in the rectangular coordinate system, adjust the yaw angle of each wind turbine, and when the total output power increase of the grouped related wind turbines reaches a maximum, the corresponding yaw angle is the optimal yaw angle of the target wind turbine, and 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, historical wind speed and direction data; the real-time data of the wind turbine includes: wind turbine number data, wind turbine parameter data.

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

[0016]

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

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

[0019]

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

[0021] Furthermore, a preferred solution is provided: the improved wake turbulence model is:

[0022]

[0023] Where I is the wake turbulence intensity, a is the thrust coefficient induction factor; C C To adjust 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 zone; C T is the thrust coefficient of the wind turbine; D is the hub diameter of the wind turbine.

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

[0025]

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

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

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

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

[0030] The present invention further proposes a computer-readable storage medium, wherein the computer-readable storage medium is used to store a computer program, wherein the computer program executes a large-scale wind farm yaw control method based on FLORIS as described in any one of the above-mentioned combinations of solutions.

[0031] The present invention also proposes a large-scale wind farm yaw control system based on FLORIS, the system comprising:

[0032] Collection module: used to collect real-time data of wind farms and wind turbines;

[0033] Improvement module: used to improve the wake loss model, wake deflection model, wake turbulence model and 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; calculate the flow field of the wind farm, the output power attenuation affected by the wake, and screen out the wind turbines affected by the wake as target wind turbines;

[0034] Determination module: used to determine the main upstream wind turbine according to the flow field parameters of the target wind turbine;

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

[0036] Control module: used to group the remaining related wind turbines that have an impact on the target wind turbine and place them in the rectangular coordinate system, adjust the yaw angle of each wind turbine, and when the total output power increase of the related wind turbines in the group 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.

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

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

[0039] Traditional wind farm yaw optimization requires a large amount of wind turbine data as the basis for yaw optimization, which requires a large amount of data analysis and a long analysis time. The method described in the present invention utilizes 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, thereby reducing data collection time.

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

[0041] Compared with other modular yaw optimization schemes, the method described in the present invention combines historical actual wind data, uses FLORIS to screen wind turbines affected by wake in wind farms, and performs yaw optimization on the screened wind turbines. The resulting yaw design scheme for a single wind turbine has higher accuracy and less design time for the yaw scheme.

[0042] 3. Highly adaptable yaw solution:

[0043] The yaw optimization method designed by the method described in the present invention summarizes the multiple wind directions and wind speeds that the wind turbine may be subjected to under the condition that the position of the wind turbine in the wind farm remains unchanged. The resulting yaw method can adapt to a variety of complex wind farms and fit the actual operation of the wind farm. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 This is a flow chart of a large-scale wind farm yaw control method based on FLORIS according to a specific implementation mode 1 of the present invention;

[0046] Figure 2 This is an evolution diagram of the wake model and the deflection model described in the first specific implementation mode of the present invention;

[0047] Figure 3 The wake evolution diagram of the wake turbulence model described in the first specific embodiment of the present invention;

[0048] Figure 4 This is an evolution diagram of the square sum free stream wake superposition model described in the first specific implementation mode of the present invention. DETAILED DESCRIPTION

[0049] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

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

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

[0052] The following is a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0053] In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present application. Therefore, the present application is not limited to the specific implementation methods disclosed below.

[0054] Implementation method 1:

[0055] Reference Figure 1 , Figure 2 , Figure 3 , Figure 4 This embodiment will be described.

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

[0057] S1: Collecting 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 loss model, wake deflection model, wake turbulence model and 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; calculate the flow field of the wind farm and the output power attenuation affected by the wake, and select the wind turbines affected by the wake as target wind turbines;

[0059] S3: determining the main upstream wind turbine according to 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 direction of the x-axis;

[0061] S5: Group the remaining related wind turbines that have an impact on the target wind turbine and place them in the rectangular coordinate system, adjust the yaw angle of each wind turbine, and when the total output power increase of the grouped related wind turbines reaches a maximum, the corresponding yaw angle is the optimal yaw angle of the target wind turbine, and 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, historical wind speed and direction data; the real-time data of the wind turbine includes: wind turbine number data, wind turbine parameter data.

[0063] Specifically:

[0064] First, install the python database, download the V4 version of FLORIS, collect the wind farm location data of the target wind farm, and number all wind turbines, wind resource data, atmospheric condition data, historical wind speed and direction data, and wind turbine parameter data. If data verification is required, the historical operating power output data of the wind turbines in the wind farm can be collected and cross-verified with the data calculated by the software. This implementation method verifies two types of wind turbines in a certain wind farm. The simulated power calculated by FLORIS is basically consistent with the rated power of the corresponding model wind turbine. The root mean square error (RMSE) analysis is performed on them, and the errors are 0.42% and 0.49% respectively, and the data is relatively accurate.

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

[0066]

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

[0068] The method described in this embodiment utilizes an improved Gaussian wake deflection model, which improves 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, σ y ,σ z 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 the operation of a wind turbine, and obtains the wake turbulence intensity through the thrust coefficient obtained in the near wake region and the thrust coefficient and ambient turbulence obtained in the far wake region. The specific formula is as follows:

[0072]

[0073] Where I is the wake turbulence intensity, a is the thrust coefficient induction factor; C C To adjust 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 zone; C T is the thrust coefficient of the wind turbine; D is the hub diameter of the wind turbine. The dependence of the adjustment on the scale, axial induction factor, and downstream distance can be obtained according to the specific wind farm terrain and environmental factors.

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

[0075]

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

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

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

[0079] First, a single wind turbine is simulated using FLORIS software to obtain the wind speed at which it can reach full power. Different types of wind turbines are compared to obtain the minimum wind speed that can make all types of wind turbines reach full power, which is 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, and set the blades of the wind turbines to face the incoming wind direction. The incoming wind speed is wind speed a, and record the output power of each wind turbine.

[0081] Change the inflow wind direction and repeat the above calculation at intervals of 10°. Record 36 sets of wind farm output power data and subtract the data of each wind turbine from 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 of wind turbines affected by wake:

[0083] After the construction of an actual large-scale wind farm is completed, the layout of the wind turbines is difficult to change. Therefore, the actual geographical factors where the wind turbines are located are difficult to change, and the wind speed and direction they are subject to are generally the same as in previous years. By collecting historical wind speed and direction data for the above-mentioned wind turbines in the wind farm and comparing them with the wind direction that is easily affected by the wake effect, wind turbines with different wind directions and corresponding lower wind speeds can be eliminated, and the wind turbines affected by the wake effect in the actual wind farm can be obtained.

[0084] The S3, S4 and S5 are further described below:

[0085] In the process of yaw optimization of 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 or the greatest impact on the target wind turbine is taken as the main upstream wind turbine, and rectangular coordinate transformation is performed. The origin of the coordinate is set as the main upstream wind turbine, and the target wind turbine is located on the x-axis, and the relative distance remains unchanged.

[0086] All other related wind turbines that affect the target wind turbine are grouped and placed 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 angles of other upstream wind turbines are related to the quadrants they are in, with quadrants 1 and 4 pointing upward and quadrants 2 and 3 pointing downward.

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

[0088] By superimposing the obtained yaw angle with the angle of rotation 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 wind direction is close to the wake wind direction of the target wind turbine at +3° to -3°, the yaw angle of the upstream wind turbine can be fuzzily increased or decreased according to the positive or negative correlation. The specific formula is as follows:

[0090] β 1 =8.75|a|+α,

[0091] where β 1 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; α is the optimal yaw angle of the upstream wind turbine.

[0092] The method described in this embodiment collects wind turbine data in a large wind farm, such as wind turbine model, hub height, atmospheric condition data, and wind turbine location information, etc. The FLORIS software is used to simulate and analyze the operation of the wind farm under different inflow wind directions and wind speeds, optimize the wake effect on the wind turbine during the operation of the wind farm, and obtain a yaw optimization plan for the operation of the wind farm.

[0093] The method described in this embodiment utilizes an improved Gaussian wake deflection model, which improves 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 the operation of a wind turbine, and obtains the wake turbulence intensity through the thrust coefficient obtained in the near wake region and the thrust coefficient and ambient turbulence obtained in the far wake region.

[0095] Implementation method 2:

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

[0097] Collection module: used to collect real-time data of wind farms and wind turbines;

[0098] Improvement module: used to improve the wake loss model, wake deflection model, wake turbulence model and 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; calculate the flow field of the wind farm, the output power attenuation affected by the wake, and screen out the wind turbines affected by the wake as target wind turbines;

[0099] Determination module: used to determine the main upstream wind turbine according to the flow field parameters of the target wind turbine;

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

[0101] Control module: used to group the remaining related wind turbines that have an impact on the target wind turbine and place them in the rectangular coordinate system, adjust the yaw angle of each wind turbine, and when the total output power increase of the related wind turbines in the group 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.

[0102] Those skilled in the art will appreciate that the above are only preferred embodiments of the present invention, and the various embodiments of the present disclosure and / or the features described in the claims may be combined or combined in various ways, even if such combinations or combinations are not explicitly described in the present disclosure. It is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art may still modify the technical solutions described in the aforementioned embodiments, or perform equivalent substitutions on some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

[0103] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0104] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0105] It will be appreciated by those skilled in the art that the embodiments of the present disclosure may be provided as methods, systems or computer program products. Therefore, the present disclosure may be provided in the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Furthermore, the present disclosure may be provided in the form of a computer program product implemented 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] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0107] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0108] Finally, it should be noted that the above implementation modes are only used to illustrate the technical solutions of the present disclosure rather than to limit its protection scope. Although the present disclosure has been described in detail with reference to the above implementation modes, ordinary technicians in the relevant field should understand that after reading the present disclosure, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation modes of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the disclosed claims to be approved.

Claims

1. A large-scale wind farm yaw control method based on FLORIS, characterized in that: The method comprises: S1: Collect real-time data of wind farms and wind turbines; S2: Improve the wake loss model, wake deflection model, wake turbulence model and 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; calculate the flow field of the wind farm and the output power attenuation affected by the wake, and select 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: 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; S5: Group the remaining related wind turbines that have an impact on the target wind turbine and place them in the rectangular coordinate system, adjust the yaw angle of each wind turbine, and when the total output power increase of the grouped related wind turbines reaches a maximum, the corresponding yaw angle is the optimal yaw angle of the target wind turbine, and control the target wind turbine according to the optimal yaw angle to complete the method.

2. A large-scale wind farm yaw control method based on FLORIS according to claim 1, characterized in that: 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.

3. A large-scale wind farm yaw control method based on FLORIS according to claim 1, characterized in that: The improved wake loss model is: Among them, ΔU is the change of speed; U ∞ is the wind farm inflow velocity; r is the wake radius, y and z represent the longitudinal coordinate and vertical coordinate respectively, y = 0 at the wind turbine hub; z = 0 at the horizontal ground; H is the wind turbine hub height; x represents the downstream distance of the wind turbine, C(x) is the velocity loss distribution amplitude; σ is the standard deviation of the velocity loss.

4. A large-scale wind farm yaw control method based on FLORIS according to claim 1, characterized in that: The improved wake deflection model is: Where C is the velocity difference at the center of the wake, δ is the wake deflection, σ y ,σ z are the wake widths in the y and z directions respectively.

5. The FLORIS-based large-scale wind farm yaw control method according to claim 1, characterized in that: The improved wake turbulence model is: Where I is the wake turbulence intensity, a is the thrust coefficient induction factor; C C To adjust 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 zone; C T is the thrust coefficient of the wind turbine; D is the hub diameter of the wind turbine.

6. A large-scale wind farm yaw control method based on FLORIS according to claim 1, characterized in that: The improved wake combination model is: Where i is the wake of each wind turbine, and the total number of wind turbines up to N is calculated; U i (x) refers to the local average velocity; is the local inflow velocity; Loss of speed.

7. A large-scale wind farm yaw control method based on FLORIS according to claim 1, characterized in that: The S3 is specifically: The upstream wind turbine that has the smallest straight-line distance to the target wind turbine or the greatest degree of influence is selected as the main upstream wind turbine.

8. Computer device, characterized in that It comprises a memory and a processor, wherein a computer program is stored in the memory, and when the processor runs the computer program stored in the memory, the processor executes a large-scale wind farm yaw control method based on FLORIS according to any one of claims 1-7.

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

10. A large-scale wind farm yaw control system based on FLORIS, characterized in that: The system comprises: Collection module: used to collect real-time data of wind farms and wind turbines; Improvement module: used to improve the wake loss model, wake deflection model, wake turbulence model and 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; calculate the flow field of the wind farm, the output power attenuation affected by the wake, and screen out the wind turbines affected by the wake as target wind turbines; Determination module: used to determine the main upstream wind turbine according to the flow field parameters of the target wind turbine; Coordinate establishment module: used to establish a rectangular coordinate system with the main upstream wind turbine as the origin and the target wind turbine direction as the positive direction of the x-axis; Control module: used to group the remaining related wind turbines that have an impact on the target wind turbine and place them in the rectangular coordinate system, adjust the yaw angle of each wind turbine, and when the total output power increase of the related wind turbines in the group 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.

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