Coupling yaw control wind power plant parameterization mesoscale numerical simulation method, system and device for large wind power cluster

Abstract

The invention discloses a large-scale wind power cluster-oriented coupling yaw control wind power plant parameterization mesoscale numerical simulation method, system and device. Firstly, basic data of wind turbines of a target wind power cluster are obtained; performing numerical modeling on each wind turbine by using the basic data; then, in numerical modeling, a yaw control command is added, the influence of yaw of the front row of wind turbines on the rear row of wind turbines is calculated through a yaw wake flow model, and therefore mesoscale numerical simulation under yaw control of the wind power plant is achieved. And finally, coupling the wind power plant parameterized model considering yaw control to a numerical weather forecast model, and carrying out numerical simulation research on a large-scale wind power cluster. Compared with a traditional wind power plant mesoscale numerical modeling method, yaw control parameterization is coupled, and flowing and operation characteristics and the like of a large wind power cluster under operation control can be simulated.

Description

Technical Field

[0001] The present invention belongs to the field of numerical simulation, and in particular relates to a parameterized mesoscale numerical simulation method, system and device for a coupled yaw-controlled wind farm for a large wind power cluster. Background Art

[0002] In recent years, the scale and large-scale development of the wind power industry has become an inevitable trend. The wake effect of wind turbine wakes has become increasingly prominent, which will cause power losses to downstream wind turbines, thereby affecting the operating efficiency of the entire wind power cluster. In order to weaken the interference effect of flow within and between wind farms and improve the overall power output of wind power clusters, active flow control technology has become a hot topic of research at home and abroad. At present, there are two types of active control strategies for wind farm flow: the limited power method represented by pitch angle adjustment and the wake redirection method represented by yaw angle adjustment. In contrast, the active yaw control strategy actively yaws the upstream wind turbine to deflect the wake, which has a better effect on reducing the impact of the wake and increasing the energy capture of the downstream wind turbine.

[0003] At present, most numerical simulation methods for wind farm yaw optimization control are divorced from the actual atmospheric environment, and the physical mechanism has not been analyzed. It is difficult to cope with the complex and diverse external environment, resulting in poor practicality of the model, and most of them are only for a few wind turbines or a small-scale single wind farm. With the large-scale and large-scale wind farms, if microscale numerical simulation methods are used, limited computing resources cannot meet the requirements of the immediacy of optimization control. Mesoscale numerical simulation analysis methods have the advantages of low economic and time costs and a wide range of research, and are commonly used tools for analyzing atmospheric flow conditions. For mesoscale numerical simulation of large-scale wind farms, the commonly used simulation method at home and abroad is the coupled wind farm parameterized model, namely the Fitch model. This model regards the wind turbine as a brake disc acting on the incoming atmosphere, causing changes in atmospheric momentum and turbulent kinetic energy. It can effectively analyze the effect of wind farms on atmospheric flow and analyze the mutual influence between wind turbines. For example: Application No.: CN202210802451.1 adds a wind turbine subgrid interference model to the wind farm parameterization method to improve the accuracy and robustness of the traditional wind farm mesoscale modeling method, which is more suitable for research on the in-field interference of large wind power and the wake characteristics of the entire field. Application No.: CN202310905456.1 uses a mesoscale wind farm parameter scheme to simulate each wind farm under different conditions, calculates the theoretical value of the wake impact of surrounding wind farms on the affected wind farms, and corrects it in combination with actual operation data, and efficiently evaluates the wake effect between wind farms.

[0004] However, the above wind farm parameterization methods do not consider the impact of wind farm operation control, cannot achieve yaw control at the mesoscale, and the model lacks direct analysis of the wind turbine wake. Therefore, the wind farm parameterization method needs to be further developed and improved to achieve numerical simulation of yaw control of mesoscale wind farms in real atmospheric environments. Summary of the invention

[0005] The purpose of the present invention is to solve the problems existing in the prior art and to provide a method, system and device for parameterized mesoscale numerical simulation of coupled yaw controlled wind farms for large wind power clusters.

[0006] The object of the present invention is achieved through the following technical solutions: In the first aspect, the present invention provides a parameterized mesoscale numerical simulation method for coupled yaw control wind farms for large wind power clusters, the method comprising the following steps:

[0007] (1) Based on the transient momentum change term, turbulent kinetic energy change term and power output of each wind turbine in the target wind power cluster affected by atmospheric kinetic energy, parameterized modeling of the entire wind power cluster is performed;

[0008] (2) The yaw wake model is introduced into the wind farm parameterization model to realize the numerical modeling of yaw control of mesoscale wind farms and calculate the interaction of wind turbine wakes after yaw control to obtain the incoming flow velocity of each wind turbine.

[0009] (3) Based on the incoming flow velocity, the momentum change term, turbulent kinetic energy change term and power output of each wind turbine in the target wind farm cluster after yaw are obtained, so as to realize the mesoscale numerical evaluation and prediction of the flow and operation characteristics of large wind farm bases considering the yaw control of wind turbines.

[0010] Furthermore, the specific process of obtaining the incoming flow velocity of each wind turbine is as follows: in each time step, all wind turbines in the field are sorted from upstream to downstream, and the calculations are carried out in sequence; when calculating the incoming flow velocity of each wind turbine, all wind turbines in the upstream fan-shaped area affected by the wake of the current wind turbine are first screened out; the upstream wind turbine reads the yaw angle according to the wind speed and wind direction of the current time step, and uses the yaw wake model to calculate the speed loss caused by each upstream wind turbine to the current wind turbine, and uses the wake superposition method to update the incoming flow velocity of this wind turbine, and finally obtains the momentum change term, turbulent kinetic energy change term and power output after yaw.

[0011] Furthermore, the mesoscale numerical simulation is based on the non-hydrostatic pressure σ vertical coordinate following the terrain, and adopts the ArakawaC staggered grid; in the parameterized model of the wind farm, each wind turbine is regarded as a brake disc, and the wind turbine imposes a momentum sink on the incoming atmosphere, converting the kinetic energy of the incoming atmosphere into electrical energy and turbulent kinetic energy, and the source terms of the interaction between the wind turbine and the atmosphere in each grid are calculated based on the basic information data of the wind turbine, namely, the transient momentum change term, the turbulent kinetic energy change term and the power output; the basic information data includes geographic location information, wind turbine geometric and physical information and wind turbine operating characteristics.

[0012] Furthermore, the geographical location information includes the site of the wind farm and the longitude and latitude information of each wind turbine; the geometric and physical information of the wind turbine includes the hub height and the radius of the rotor rotation surface; and the operating characteristics of the wind turbine include the power curve and the thrust curve.

[0013] Furthermore, the yaw wake model is a Gaussian yaw wake model.

[0014] Furthermore, the wake superposition method is:

[0015]

[0016] Among them, U i is the incoming flow velocity of wind turbine i after the influence of the superimposed wake, U h is the original incoming wind speed at the hub height of wind turbine i, U ij is the speed loss caused by wind turbine j to wind turbine i, N i is the number of upstream wind turbines that affect wind turbine i.

[0017] Furthermore, in step (2), after coupling the yaw wake model to the wind farm parameterized model, the momentum action term, turbulent kinetic energy action term and power output affected by the wind turbine in the grid are updated as follows:

[0018]

[0019] Among them, N t is the number of wind turbines in the grid (i, j), U i is the wind speed of the wind turbine after the superimposed wake effect calculated by the yaw wake model, U h is the original incoming wind speed at the wind turbine hub height, U k is the velocity scalar of the grid (i, j, k), u k ,v k is the horizontal velocity component in the grid, A k is the area intercepted by the rotor rotating surface on the vertical layer k of the grid (i, j), (z k+1 -z k) is the vertical distance between vertical layer k and vertical layer k+1, γ is the yaw angle of the wind turbine, C T is the thrust coefficient, C TKE is the turbulent kinetic energy coefficient, TKE k is the turbulent kinetic energy, P is the power output, ρ is the atmospheric density, C P is the power coefficient, A is the swept area of ​​the wind rotor, and α is the yaw loss coefficient.

[0020] In a second aspect, the present invention further provides a parameterized mesoscale numerical simulation system for a coupled yaw-controlled wind farm for a large wind power cluster, the system comprising:

[0021] The parameter acquisition module is used to calculate the transient momentum change term, turbulent kinetic energy change term and power output of each wind turbine in the target wind power cluster affected by atmospheric kinetic energy; and to perform parameterized modeling of the entire wind power cluster;

[0022] Model coupling module, which is used to introduce the yaw wake model into the wind farm parameterized model, realize the numerical modeling of the yaw control of the mesoscale wind farm, calculate the interaction of the wind turbine wake after yaw control, and obtain the incoming flow velocity of each wind turbine;

[0023] The numerical evaluation and prediction module is used to obtain the momentum change term, turbulent kinetic energy change term and power output of each wind turbine in the target wind power cluster after yaw based on the incoming flow velocity, so as to realize the mesoscale numerical evaluation and prediction of the flow and operation characteristics of large wind power bases considering the yaw control of wind turbines.

[0024] In the third aspect, the present invention also provides a parameterized mesoscale numerical simulation device for a coupled yaw controlled wind farm for large wind power clusters, comprising a memory and one or more processors, wherein the memory stores executable code, and when the processor executes the executable code, it implements the parameterized mesoscale numerical simulation method for a coupled yaw controlled wind farm for large wind power clusters.

[0025] In a fourth aspect, the present invention further provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the parameterized mesoscale numerical simulation method for coupled yaw-controlled wind farms for large wind power clusters.

[0026] Beneficial effects of the present invention: The method, system and device for parameterized mesoscale numerical simulation of wind farms with coupled yaw control for large wind farm clusters established by the present invention are innovations and improvements to traditional mesoscale numerical simulation methods. The function of yaw control is introduced into the parametric model of the wind farm. By coupling the yaw control model, the new incoming wind speed of each wind turbine in the wind farm after yaw is calculated, thereby updating the momentum change, turbulent kinetic energy change and power output in the parametric model of the wind farm, and realizing the numerical simulation of yaw control of mesoscale wind farms in real atmospheric environments. The improved mesoscale numerical simulation can be targeted at large-scale wind farms or wind farm clusters, and can control the yaw operation of the entire wind turbine field by providing an optimal yaw angle lookup table, and simulate the flow and operation characteristics under operation control. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A schematic diagram showing the comparison of yaw control of wind turbines / wind farms;

[0028] Figure 2 A flowchart of the model running steps;

[0029] Figure 3 This is a comparison chart of the total wind speed at the hub height of the entire wind turbine;

[0030] Figure 4 This is a comparison chart of the total power of the entire field.

[0031] Figure 5 A structural diagram of a parameterized mesoscale numerical simulation device for a coupled yaw-controlled wind farm for a large wind power cluster provided by the present invention. DETAILED DESCRIPTION

[0032] The present invention will be further described and illustrated below in conjunction with the accompanying drawings and specific implementation methods.

[0033] The present invention mainly constructs a parameterized mesoscale numerical simulation method for coupled yaw-controlled wind farms for large wind power clusters, which simulates and analyzes the atmospheric movement and operating characteristics around wind turbines or wind farms under yaw control of large wind power bases.

[0034] The specific steps are as follows:

[0035] 1) Collect basic information data of each wind turbine in the target wind farm. This mainly includes geographical location information (wind farm site and longitude and latitude information of each wind turbine), wind turbine geometric and physical information (hub height and rotor rotation surface radius) and wind turbine operating characteristics (power curve and thrust curve). Among them, the location information is used to determine the calculation domain and the relative position of each wind turbine in the calculation domain, and the wind turbine's geometric physics and operating characteristics information is used for subsequent parametric modeling calculation steps.

[0036] 2) Use the basic data in step 1) to perform parameterized modeling of the entire wind farm. As an open-source mesoscale numerical model, WRF has a series of parameterization methods for atmospheric physical motion, including a wind farm parameterization model developed by previous researchers. However, traditional models do not directly analyze wind turbine wakes, and simplify the total atmospheric action term of wind turbines in the same grid to N for a single wind turbine. t times, where N t is the total number of wind turbines in the grid (i, j). Each wind turbine is regarded as a drag disk. The wind turbine applies a momentum sink to the incoming air, converting the flow energy into electrical energy and turbulent kinetic energy. The source terms of the interaction between the wind turbine and the atmosphere are calculated based on the basic information data of the wind turbine in step 1), namely, the transient momentum change term, the turbulent kinetic energy change term and the power output. The total momentum and turbulent kinetic energy terms are expressed as:

[0037]

[0038] Among them, u k ,v k is the horizontal velocity component in the grid, U k is the velocity scalar, TKE k is the turbulent kinetic energy, C T is the thrust coefficient, C TKE is the turbulent kinetic energy coefficient, and its value C TKE =C T -C P , C P is the power coefficient. A k is the area intercepted by the rotor rotating surface on the vertical layer k of the grid (i, j); P is the power output, U h is the wind speed at the hub height, and A is the swept area of ​​the wind rotor. t is the number of wind turbines in the grid (i, j), (z k+1 -z k ) is the vertical distance between vertical layer k and vertical layer k+1, γ is the yaw angle of the wind turbine, C TKE is the turbulent kinetic energy coefficient, ρ is the atmospheric density, C P is the power coefficient, and α is the yaw loss coefficient.

[0039] 3) During the parameterization process of step 2), the yaw control of the wind turbine cannot be simulated. Figure 1The comparison of the impact of yaw control on upstream wind turbines / wind farms is shown. The wake will shift, and the wind speed of downstream wind turbines / wind farms will increase, and the power will also increase accordingly. Therefore, for wind farms that implement yaw control, it is necessary to consider the wake shift and power output changes after yaw. In order to achieve this function, the present invention introduces a yaw wake model and constructs a parameterized model of a wind farm coupled with yaw control. By calculating the interaction of the wind turbine wake after yaw control, the incoming flow velocity of each wind turbine is obtained. Combined with Figure 2 , the construction process of the wind farm parameterized model coupled with yaw control will be described in detail. First, within a time step, the present invention sorts the n wind turbines in the entire field according to wind direction. Starting from the upstream, each wind turbine is calculated in turn, denoted as wind turbine i, and all wind turbines in the upstream fan-shaped area (the fan-shaped area selected by the present invention has an angle of ±60° and a radius of 10D, where D is the radius of the wind turbine rotor) whose wake will directly affect wind turbine i are screened out, and the number is recorded as N i . For each wind turbine in the fan-shaped area, denoted as wind turbine j, the optimal yaw angle γ is read from the lookup table according to the wind speed and wind direction at this moment; the yaw angle lookup table needs to be provided by the user, and each wind turbine can have a different optimal yaw angle for different wind speeds and wind directions (the interval is set by the user, and the rounding rule is adopted when reading) for different optimization goals. According to an advanced yaw wake model, the yaw wake characteristics of wind turbine j are calculated, and the speed loss caused by wind turbine j at wind turbine i is further calculated:

[0040]

[0041]

[0042] Where σ is the wake width, D is the rotor diameter, x is the axial distance of the wake downstream of the wind turbine, and k * and ε * are the characteristic parameters of wake width, respectively C′ T is the corrected thrust coefficient, C′ T =C T cosγ, γ is the yaw angle of the wind turbine; I a is the turbulence intensity, which is calculated from the turbulent kinetic energy term TKE in the planetary atmospheric boundary layer (PBL) parameterization scheme of the WRF model. y d (x) is the wake displacement distance at the downstream axial distance x, θ 0 is the wake deflection angle, given by Calculate, x 0 is the near wake length, given by Calculate,σ 0 For x 0The wake width at is calculated as U ij is the speed loss caused by wind turbine j to wind turbine i, a, b, c are the speed loss characteristic parameters, respectively r is the radial distance from the wake center, U h is the original incoming wind speed at the hub height of wind turbine i.

[0043] The speed loss caused by all upstream wind turbines to wind turbine i requires wake superposition to update the incoming wind speed of wind turbine i. The expression of wake superposition is as follows:

[0044]

[0045] Among them, U i The incoming flow velocity at the hub height of wind turbine i after the influence of the superimposed wake.

[0046] The calculated U i It needs to be interpolated to the kth grid layer, so the velocity scalar U in each wind turbine grid in step 2) is k Need to use At the same time, after the wind turbine yaws, the force on the atmosphere and the kinetic energy loss rate of the atmosphere change:

[0047]

[0048] in, is the force exerted by the wind turbine on the atmosphere, ρ is the air density, is the horizontal wind speed vector, is the horizontal wind speed vector perpendicular to the yaw wind rotor, A is the wind rotor area, KE drag is the atmospheric energy loss caused by the force of the wind turbine, is the rate of atmospheric kinetic energy loss caused by the force of wind turbines.

[0049] Therefore, the total momentum action of the wind turbine in the grid (i, j, k) is Turbulent kinetic energy term ΔTKE k And the power output term P needs to be multiplied by the power of the cosine value of the yaw angle accordingly and updated as:

[0050]

[0051] Where α is the yaw power loss coefficient.

[0052] 4) Couple step 3) to the numerical forecast model WRF (weather forecast model, version WRF version 4.6), that is, embed the wind farm modeling process into the planetary atmospheric boundary layer (PBL) parameterization scheme of the WRF model (MYNN2.5 scheme) to achieve model closure; then simulate the meteorological field covering hundreds of kilometers including the wind farm, so as to finally realize the mesoscale numerical evaluation and prediction of the flow and operation characteristics of large-scale wind power bases considering the yaw control of wind farms.

[0053] The specific implementation effect of the above method is demonstrated below in conjunction with embodiments.

[0054] Example

[0055] In this example, a classic offshore wind farm located on the south coast of Sweden was used as the research object to explore the specific implementation effect and advantages of this improved large-scale wind farm mesoscale numerical modeling method. The corresponding parameter settings of the adopted WRF version 4.6 are as follows:

[0056] Table 1 WRF parameter settings

[0057]

[0058] By comparing with the measured data, the parameter α required for the improved large-scale mesoscale numerical modeling method of wind farms is set to 1.88. The simulation time is from 2022-10-2-08:00 to 2022-10-6-08:00.

[0059] In order to obtain a stable solution, the initial 24 hours (buffer time) of the solution were excluded, and the 48 wind turbines in the entire wind farm were analyzed for the next three days. Figure 3 The comparison of the total wind speed at the hub height of the entire wind turbine in the yaw condition (yaw) and the non-yaw condition (yaw0) is shown. After yaw control is implemented, the rear-row wind turbines are less affected by the wake of the front-row wind turbines, the speed loss is reduced, and the overall wind speed is significantly improved, with an average increase of 1.44%. Figure 4 The comparison of the total power of the wind farm in the yaw situation (yaw) and the non-yaw situation (yaw0) is shown. Although the power of the front row wind turbines that perform yaw will be reduced, the wind speed of the rear row wind turbines will increase and the power will increase accordingly. The results show that when an effective yaw strategy is implemented, the total power of the entire field is roughly increasing or remaining the same at each moment, and it increases by an average of 2.06% during the simulation. This embodiment proves that the parameterized model of the wind farm coupled with yaw control can realize the numerical simulation of yaw control of mesoscale wind farms, and providing an appropriate yaw control strategy has a certain effect on improving the total power of the entire wind farm.

[0060] In addition, the wind farm parameterized model coupled with yaw control in this embodiment consumes comparable computing resources as the traditional mesoscale model, and can be used in the research of larger-scale wind farms or wind power clusters.

[0061] Corresponding to the aforementioned embodiment of a parameterized mesoscale numerical simulation method for coupled yaw control wind farms for large wind power clusters, the present invention also provides an embodiment of a parameterized mesoscale numerical simulation system for coupled yaw control wind farms for large wind power clusters. The system includes: a parameter acquisition module, a model coupling module, and a numerical evaluation prediction module. For the specific process of each module, please refer to the specific steps of the embodiment of the aforementioned parameterized mesoscale numerical simulation method for coupled yaw control wind farms for large wind power clusters.

[0062] The parameter acquisition module is used to perform parameterized modeling on the entire wind power cluster based on the transient momentum change term, turbulent kinetic energy change term and power output of each wind turbine in the target wind power cluster affected by atmospheric kinetic energy;

[0063] The model coupling module is used to introduce a yaw wake model into the wind farm parameterized model, realize the numerical modeling of the yaw control of the mesoscale wind farm, calculate the interaction of the wind turbine wake after the yaw control, and obtain the incoming flow velocity of each wind turbine;

[0064] The numerical evaluation and prediction module is used to obtain the momentum change term, turbulent kinetic energy change term and power output of each wind turbine in the target wind power cluster after yaw based on the incoming flow velocity, so as to realize the mesoscale numerical evaluation and prediction of the flow and operation characteristics of large wind power bases considering the yaw control of wind turbines.

[0065] Corresponding to the aforementioned embodiment of a parameterized mesoscale numerical simulation method for a coupled yaw control wind farm for a large wind power cluster, the present invention also provides an embodiment of a parameterized mesoscale numerical simulation device for a coupled yaw control wind farm for a large wind power cluster.

[0066] See also Figure 5 An embodiment of the present invention provides a parameterized mesoscale numerical simulation device for a coupled yaw controlled wind farm for a large wind power cluster, comprising a memory and one or more processors, wherein the memory stores executable code, and when the processor executes the executable code, it is used to implement a parameterized mesoscale numerical simulation method for a coupled yaw controlled wind farm for a large wind power cluster in the above embodiment.

[0067] An embodiment of a parameterized mesoscale numerical simulation device for coupled yaw-controlled wind farms for large wind power clusters provided by the present invention can be applied to any device with data processing capabilities, and the device with data processing capabilities can be a device or apparatus such as a computer. The device embodiment can be implemented through software, or through hardware or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, it is formed by the processor of any device with data processing capabilities in which it is located reading the corresponding computer program instructions in the non-volatile memory into the internal memory for execution. From a hardware perspective, if Figure 5 As shown, it is a hardware structure diagram of a device with data processing capability for a coupled yaw control wind farm parameterized mesoscale numerical simulation device for a large wind power cluster provided by the present invention, except Figure 5 In addition to the processor, memory, network interface, and non-volatile memory shown, any device with data processing capabilities in which the apparatus in the embodiments is located may also include other hardware, generally based on the actual functions of the device with data processing capabilities, which will not be described in detail.

[0068] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0069] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of the present invention. Ordinary technicians in this field can understand and implement it without paying creative work.

[0070] An embodiment of the present invention further provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, a parameterized mesoscale numerical simulation method for coupled yaw-controlled wind farms for large wind power clusters in the above embodiment is implemented.

[0071] The computer-readable storage medium may be an internal storage unit of any device with data processing capability described in any of the aforementioned embodiments, such as a hard disk or a memory. The computer-readable storage medium may also be an external storage device of any device with data processing capability, such as a plug-in hard disk, a smart media card (SMC), an SD card, a flash card, etc. equipped on the device. Furthermore, the computer-readable storage medium may also include both an internal storage unit and an external storage device of any device with data processing capability. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capability, and may also be used to temporarily store data that has been output or is to be output.

[0072] The present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the parameterized mesoscale numerical simulation method of a coupled yaw controlled wind farm for a large wind power cluster.

[0073] The above-described embodiment is only a preferred solution of the present invention and is not intended to limit the present invention. A person skilled in the relevant technical field may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention.

Claims

1. A parameterized mesoscale numerical simulation method for coupled yaw control wind farms for large wind farm clusters, characterized in that: The method comprises the following steps: (1) Based on the transient momentum change term, turbulent kinetic energy change term and power output of each wind turbine in the target wind power cluster affected by atmospheric kinetic energy, parameterized modeling of the entire wind power cluster is performed; (2) The yaw wake model is introduced into the wind farm parameterization model to realize the numerical modeling of yaw control of mesoscale wind farms and calculate the interaction of wind turbine wakes after yaw control to obtain the incoming flow velocity of each wind turbine. (3) Based on the incoming flow velocity, the momentum change term, turbulent kinetic energy change term and power output of each wind turbine in the target wind farm cluster after yaw are obtained, so as to realize the mesoscale numerical evaluation and prediction of the flow and operation characteristics of large wind farm bases considering the yaw control of wind turbines.

2. The method for parameterized mesoscale numerical simulation of coupled yaw control wind farms for large wind power clusters according to claim 1 is characterized in that: The specific process of obtaining the incoming flow velocity of each wind turbine is as follows: in each time step, all wind turbines in the field are sorted from upstream to downstream, and the calculation is carried out in sequence; when calculating the incoming flow velocity of each wind turbine, all wind turbines in the upstream fan-shaped area affected by the wake of the current wind turbine are first screened out; the upstream wind turbine reads the yaw angle according to the wind speed and wind direction of the current time step, and uses the yaw wake model to calculate the speed loss caused by each upstream wind turbine to the current wind turbine, and uses the wake superposition method to update the incoming flow velocity of this wind turbine, and finally obtains the momentum change term, turbulent kinetic energy change term and power output after yaw.

3. The method for parameterized mesoscale numerical simulation of coupled yaw control wind farms for large wind power clusters according to claim 1 is characterized in that: The mesoscale numerical simulation is based on the non-hydrostatic pressure σ vertical coordinate following the terrain, and adopts the Arakawa C staggered grid; in the wind farm parameterized model, each wind turbine is regarded as a brake disc, and the wind turbine imposes a momentum sink on the incoming atmosphere, converting the kinetic energy of the incoming atmosphere into electrical energy and turbulent kinetic energy, and the source terms of the interaction between the wind turbine and the atmosphere in each grid, namely, the transient momentum change term, the turbulent kinetic energy change term and the power output, are calculated based on the basic information data of the wind turbine; the basic information data includes geographic location information, wind turbine geometric and physical information, and wind turbine operating characteristics.

4. The method for parameterized mesoscale numerical simulation of coupled yaw control wind farms for large wind power clusters according to claim 3 is characterized in that: The geographical location information includes the site of the wind farm and the longitude and latitude information of each wind turbine; the geometric and physical information of the wind turbine includes the hub height and the radius of the rotor rotation surface; the operating characteristics of the wind turbine include the power curve and the thrust curve.

5. The method for parameterized mesoscale numerical simulation of coupled yaw control wind farms for large wind power clusters according to claim 1 is characterized in that: The yaw wake model is a Gaussian yaw wake model.

6. The method for parameterized mesoscale numerical simulation of coupled yaw control wind farms for large wind power clusters according to claim 2 is characterized in that: The wake superposition method is: Among them, U i is the incoming flow velocity of wind turbine i after the influence of the superimposed wake, U h is the original incoming wind speed at the hub height of wind turbine i, U ij is the speed loss caused by wind turbine j to wind turbine i, N i is the number of upstream wind turbines that affect wind turbine i.

7. The method for parameterized mesoscale numerical simulation of coupled yaw control wind farms for large wind power clusters according to claim 3 is characterized in that: In step (2), after coupling the yaw wake model to the wind farm parameterized model, the momentum action term, turbulent kinetic energy action term and power output affected by the wind turbine in the grid are updated as follows: Among them, N t is the number of wind turbines in the grid (i, j), U i is the wind speed of the wind turbine after the superimposed wake effect calculated by the yaw wake model, U h is the original incoming wind speed at the wind turbine hub height, U k is the velocity scalar of the grid (i, j, k), u k ,v k is the horizontal velocity component in the grid, A k is the area intercepted by the rotor rotating surface on the vertical layer k of the grid (i, j), (z k+1 -z k ) is the vertical distance between vertical layer k and vertical layer k+1, γ is the yaw angle of the wind turbine, C T is the thrust coefficient, C TKE is the turbulent kinetic energy coefficient, TKE k is the turbulent kinetic energy, P is the power output, ρ is the atmospheric density, C P is the power coefficient, A is the swept area of ​​the wind rotor, and α is the yaw loss coefficient.

8. A mesoscale numerical simulation system for implementing the parameterized mesoscale numerical simulation method for coupled yaw control wind farms for large wind power clusters as described in any one of claims 1 to 7, characterized in that: The system includes: The parameter acquisition module is used to calculate the transient momentum change term, turbulent kinetic energy change term and power output of each wind turbine in the target wind power cluster affected by atmospheric kinetic energy; and to perform parameterized modeling of the entire wind power cluster; Model coupling module, which is used to introduce the yaw wake model into the wind farm parameterized model, realize the numerical modeling of the yaw control of the mesoscale wind farm, calculate the interaction of the wind turbine wake after yaw control, and obtain the incoming flow velocity of each wind turbine; The numerical evaluation and prediction module is used to obtain the momentum change term, turbulent kinetic energy change term and power output of each wind turbine in the target wind power cluster after yaw based on the incoming flow velocity, so as to realize the mesoscale numerical evaluation and prediction of the flow and operation characteristics of large wind power bases considering the yaw control of wind turbines.

9. A parameterized mesoscale numerical simulation device for coupled yaw control wind farms for large wind power clusters, comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that: When the processor executes the executable code, a parameterized mesoscale numerical simulation method for a coupled yaw controlled wind farm for a large wind power cluster according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, a parameterized mesoscale numerical simulation method for coupled yaw control wind farms for large wind power clusters according to any one of claims 1 to 7 is implemented.

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