A large-scale wind power cluster-oriented coupling yaw control wind farm parameterized mesoscale numerical simulation method, system and device

Abstract

The application discloses a large-scale wind power cluster-oriented coupling yaw control wind farm parameterization mesoscale numerical simulation method, system and device. First, the method obtains basic data of target wind power cluster wind turbines; numerical modeling is carried out on each wind turbine by using the basic data; then, in the numerical modeling, yaw control commands are added, the influence of yaw on the rear row of wind turbines is calculated through a yaw wake model, so that mesoscale numerical simulation under the wind farm yaw control is realized. Finally, the wind farm parameterization model considering the yaw control is coupled to a numerical weather prediction model, and numerical simulation research on the large-scale wind power cluster is carried out. Compared with the traditional wind farm mesoscale numerical modeling method, the yaw control parameterization is coupled, and the flow and operation characteristics of the large-scale wind power cluster under the operation control can be simulated.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of numerical simulation, and particularly relates to a large-scale wind power cluster-oriented coupling yaw control wind farm parameterization mesoscale numerical simulation method, system and device. BACKGROUND

[0002] In recent years, the scale and large-scale of wind power industry have become an inevitable trend, and the wake effect of wind turbine wake has become more prominent, which will lead to power loss of downstream wind turbines, thereby affecting the operation efficiency of the entire wind power cluster. In order to weaken the interference effect of flow inside and between wind farms and improve the overall power output of the wind power cluster, active flow control technology has become a hot research topic at home and abroad. At present, the active flow control strategy of wind farm includes two types: the limited power method represented by pitch angle adjustment and the wake redirection method represented by yaw angle adjustment. Compared with the limited power method, the active yaw control strategy has better effect on reducing the influence of wake and increasing the energy capture of downstream wind turbines by actively yawing the upstream wind turbine.

[0003] At present, the numerical simulation method of wind farm yaw optimization control mostly deviates from the actual atmospheric environment, the physical mechanism is not analyzed, it is difficult to cope with complex and diverse external environment, resulting in poor practicability of the model, and most of them are only for several wind turbines or small-scale single wind farms. With the large-scale and scale of wind farms, if the microscale numerical simulation method is used, the limited computing resources cannot meet the real-time requirements of optimization control. The mesoscale numerical simulation analysis method has the advantages of low economic and time cost and wide research range, and is a common tool for analyzing atmospheric flow conditions. For large-scale wind farm mesoscale numerical simulation, the commonly used simulation means at home and abroad is the coupling wind farm parameterization model, i.e. Fitch model. This model regards the wind turbine as a brake disc acting on the incoming flow, causing changes in atmospheric momentum and turbulent kinetic energy, which can effectively analyze the effect of wind farm on atmospheric flow and the mutual influence between wind turbines. For example, application number: CN202210802451.1 adds a wind turbine sub-grid interference model to the wind farm parameterization method, improves the accuracy and robustness of the traditional wind farm mesoscale modeling method, and is more suitable for research on the interference in large-scale wind farms and the wake characteristics of the entire wind farm. Application number: CN202310905456.1 uses a mesoscale wind farm parameterization scheme to simulate each wind farm under different conditions, calculates the theoretical value of the wake influence of the surrounding wind farms on the affected wind farm, and corrects it combined with actual operation data to efficiently evaluate the wake effect between wind farms.

[0004] However, the above wind farm parameterization methods do not consider the influence of wind farm operation control, cannot realize yaw control at the mesoscale, and the model lacks direct analysis of the wake of wind turbines. Therefore, the wind farm parameterization method needs to be further developed and improved to realize the numerical simulation of yaw control of mesoscale wind farms in real atmospheric environment. SUMMARY

[0005] The purpose of the present application is to solve the problems existing in the prior art and provide a large-scale wind power cluster-oriented coupled yaw control wind farm parameterization mesoscale numerical simulation method, system and device.

[0006] The purpose of the present application is achieved by the following technical solutions: in the first aspect, the present application provides a large-scale wind power cluster-oriented coupled yaw control wind farm parameterization mesoscale numerical simulation method, which comprises the following steps:

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

[0008] (2) Introducing a yaw wake model in the wind farm parameterization model to realize numerical modeling of mesoscale wind farm yaw control and calculate the interaction of wind turbine wakes after yaw control to obtain the inflow velocity of each wind turbine;

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

[0010] Further, the specific process of obtaining the inflow velocity of each wind turbine is as follows: in each time step, the wind turbines are sorted from upstream to downstream, and the calculation is carried out in turn; when calculating the inflow velocity of each wind turbine, all wind turbines in the upstream sector affected by the wake of the current wind turbine are first selected; the upstream wind turbine reads the yaw angle according to the wind speed and wind direction at the current time step, calculates the speed loss caused by each upstream wind turbine to the current wind turbine using the yaw wake model, and updates the inflow velocity of the wind turbine using the wake superposition method, finally obtains the momentum change term, the turbulent kinetic energy change term and the power output after yaw.

[0011] Further, the mesoscale numerical simulation is based on a terrain-following non-hydrostatic pressure sigma vertical coordinate, and adopts Arakawa C staggered grid; in the wind farm parameterization model, each wind turbine is regarded as a brake disc, the wind turbine exerts a momentum sink on the incoming flow air, and converts kinetic energy of the incoming flow air into electric energy and turbulent kinetic energy; based on basic information data of the wind turbine, an action source term of the wind turbine and the air in each grid is calculated, i.e. a transient momentum change term, a turbulent kinetic energy change term and power output; the basic information data includes geographic location information, wind turbine geometric and physical information and wind turbine operating characteristics.

[0012] Further, the geographic location information includes latitude and longitude information of the wind farm site and each wind turbine; the wind turbine geometric and physical information includes hub height and wind wheel rotating surface radius; and the wind turbine operating characteristics include power curve and thrust curve.

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

[0014] Further, the wake superposition method is:

[0015]

[0016] wherein, U i is the incoming flow speed of the wind turbine i after superposition of the wake influence, U h is the original incoming flow wind speed at the hub height of the wind turbine i, U ij is the speed loss caused by the wind turbine j to the wind turbine i, N i is the number of upstream wind turbines that have an impact on the wind turbine i.

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

[0018]

[0019] wherein, N t is the number of wind turbines in the grid (i, j), U i is the incoming flow wind speed of the wind turbine after superposition of the wake influence calculated by the yaw wake model, U h is the original incoming flow wind speed at the hub height of the wind turbine, U k is the velocity scalar of the grid (i, j, k), u k , v k are horizontal velocity components in the grid, A k is the area of the wind wheel rotating surface intercepted on the vertical layer k of the grid (i, j), (z k+1 -z k) is the vertical distance between the vertical layer k and the 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 turbine, and α is the yaw loss coefficient.

[0020] In a second aspect, the present application further provides a large-scale wind power cluster-oriented coupling yaw control wind farm parameterized mesoscale numerical simulation system, which comprises:

[0021] A parameter acquisition module is configured to calculate the transient momentum change item, the turbulent kinetic energy change item and the power output of each wind turbine in the target wind power cluster affected by the atmospheric kinetic energy, and to perform parameterized modeling on the entire wind power cluster.

[0022] A model coupling module is configured to introduce a yaw wake model into the wind farm parameterized model, to realize numerical modeling of mesoscale wind farm yaw control, to calculate the interaction of the wake of the wind turbine after yaw control, and to obtain the inflow velocity of each wind turbine.

[0023] A numerical evaluation and prediction module is configured to obtain the momentum change item, the turbulent kinetic energy change item and the power output of each wind turbine in the target wind power cluster after yawing based on the inflow velocity, and to realize mesoscale numerical evaluation and prediction of the flow and operating characteristics of a large-scale wind power base considering wind turbine yaw control.

[0024] In a third aspect, the present application further provides a large-scale wind power cluster-oriented coupling yaw control wind farm parameterized mesoscale numerical simulation device, comprising a memory and one or more processors, wherein the memory stores executable code, and the processor executes the executable code to realize the large-scale wind power cluster-oriented coupling yaw control wind farm parameterized mesoscale numerical simulation method.

[0025] In a fourth aspect, the present application further provides a computer readable storage medium having a program stored thereon, wherein the program is executed by a processor to realize the large-scale wind power cluster-oriented coupling yaw control wind farm parameterized mesoscale numerical simulation method.

[0026] The beneficial effects of the present application: the large-scale wind power cluster-oriented coupling yaw control wind farm parameterization mesoscale numerical simulation method, system and device established by the present application is an innovation and improvement of the traditional mesoscale numerical simulation method, the yaw control function is introduced into the wind farm parameterization model, the new incoming flow wind speed of each wind turbine of the wind farm after yawing is calculated through the coupling yaw control model, thereby updating the momentum change, turbulent kinetic energy change and power output in the wind farm parameterization model, and the mesoscale wind farm yaw control numerical simulation under the real atmospheric environment is realized. The improved mesoscale numerical simulation can be aimed at large-scale wind farms or wind power clusters, and can control the yaw operation of the whole wind farm by providing the best yaw angle lookup table, simulate the flow and operating characteristics under the operation control, etc. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 The wind turbine / wind farm yaw control comparison diagram;

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

[0029] Figure 3 The whole wind turbine hub height wind speed summation comparison diagram;

[0030] Figure 4 The whole wind turbine power summation comparison diagram.

[0031] Figure 5 The structure diagram of a large-scale wind power cluster-oriented coupling yaw control wind farm parameterization mesoscale numerical simulation device provided by the present application. DETAILED DESCRIPTION

[0032] The present application will be further described and explained in conjunction with the drawings and specific embodiments.

[0033] The present application mainly constructs a large-scale wind power cluster-oriented coupling yaw control wind farm parameterization mesoscale numerical simulation method, simulates and analyzes the atmospheric motion and operating characteristics around the wind turbine or wind farm under the yaw control of the large-scale wind power base.

[0034] The specific steps are as follows:

[0035] 1) Collect the basic information data of each wind turbine of the target wind farm. Mainly including geographic location information (the latitude and longitude information of the wind farm site and each wind turbine), wind turbine geometric physical information (hub height and wind wheel rotating 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 geometric physical and operating characteristic information of the wind turbine is used for subsequent parameterization modeling calculation steps.

[0036] 2) Parameterized modeling of the entire wind farm using the basic data in step 1). WRF is an open-source mesoscale numerical model, which has a series of parameterization methods of atmospheric physical motion, including the wind farm parameterization model developed by predecessors. However, the traditional model does not directly analyze the wind turbine wake, and the total atmospheric action term of the wind turbine in the same grid is simplified as the N t th t wind turbine, where N k is the total number of wind turbines in the grid (i, j), and each wind turbine is regarded as a drag disk. The wind turbine exerts a momentum sink on the incoming air, converting the incoming kinetic energy into electrical energy and turbulent kinetic energy. The wind turbine and atmospheric action source term, i.e. the instantaneous momentum change term, the turbulent kinetic energy change term and the power output, is calculated based on the wind turbine basic information data in step 1). The total momentum and turbulent kinetic energy action term expression is:

[0037]

[0038] where u k , v k are the horizontal velocity components 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, C TKE = C T -C P , C P is the power coefficient. A k is the area of the wind wheel rotation plane intercepted in the vertical layer k of the grid (i, j); P is the power output, U h is the wind speed at the hub height, A is the wind wheel swept area. N t is the number of wind turbines in the grid (i, j), (z k+1 -z k ) is the vertical distance between the vertical layer k and the vertical layer k+1, γ is the wind turbine yaw angle, 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) In the parameterization process of step 2), the wind turbine yaw control cannot be simulated. Figure 1The influence of upstream wind turbine / wind farm yaw control is shown, the wake will be offset, the wind speed of downstream wind turbine / wind farm will increase, and the power will also be improved accordingly. Therefore, for the wind farm implementing yaw control, the wake offset and power output change after yawing need to be considered. In order to realize this function, the yaw wake model is introduced, the parameterized model of wind farm coupled with yaw control is constructed, and the inflow speed of each wind turbine is obtained by calculating the interaction of wind turbine wakes after yaw control. Figure 2 The construction process of the parameterized model of wind farm coupled with yaw control will be described in detail. First, in a time step, the present application sorts the n wind turbines in the whole field according to the wind direction. From upstream to downstream, each wind turbine is calculated in turn, denoted as wind turbine i, and all wind turbines in the upstream sector area (the sector area angle selected by the present application is ±60°, the radius is 10D, and D is the radius of the wind turbine rotor) that will directly affect wind turbine i are screened out, and the number is denoted as N i . For each wind turbine in the sector area, denoted as wind turbine j, the best yaw angle γ is read from the lookup table according to the wind speed and wind direction at this moment; the lookup table of yaw angle needs to be provided by the user, and different wind turbines have different best yaw angles for different wind speeds and wind directions (the interval is set by the user, and the rule of rounding off is adopted when reading). 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] wherein σ is the wake width, D is the rotor diameter, x is the downstream wake axial distance of the wind turbine, k * and ε * are wake width characteristic parameters, 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 item TKE in the planetary boundary layer (PBL) parameterization scheme of the WRF model, y d (x) is the wake offset distance at the downstream axial distance x, θ0 is the wake deflection angle, calculated by x0 is the near-wake length, calculated by σ0 is the wake width at x0, and the calculation expression is U ijis the velocity deficit caused by wind turbine j to wind turbine i, a, b, c are velocity deficit 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 velocity deficit caused by all upstream wind turbines to wind turbine i needs to be superimposed to update the incoming wind speed of wind turbine i. The wake superposition method is expressed as follows:

[0044]

[0045] where, U i is the incoming wind speed at the hub height of wind turbine i after superimposing the wake effect.

[0046] The calculated U i needs to be interpolated to the k-th layer of the grid, so the velocity scalar U k in the grid of each wind turbine in step 2) needs to be replaced by At the same time, after the yaw of the wind turbine, the force of the wind turbine on the atmosphere and the kinetic energy loss rate of the atmosphere change:

[0047]

[0048] where, is the force of 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 yawed wind wheel, A is the wind wheel area, KE drag is the atmospheric energy loss caused by the force of the wind turbine, is the atmospheric kinetic energy loss rate caused by the force of the wind turbine.

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

[0050]

[0051] where, α is the yaw power loss coefficient.

[0052] 4) Coupling the step 3) to the numerical prediction model WRF (Weather Research and Forecasting Model, WRF version 4.6), i.e. embedding the wind farm modeling process into the planetary boundary layer (PBL) parameterization scheme of the WRF model (MYNN2.5 scheme) to realize the model closure; and then simulating 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 the large-scale wind power base considering the yaw control of the wind farm.

[0053] The specific implementation effects of the above method are demonstrated below in combination with an example.

[0054] Example

[0055] In this example, a classic offshore wind farm located on the southern coast of Sweden is taken as the research object, and the specific implementation effects and advantages of the improved mesoscale numerical modeling method of large wind farms are explored. The parameter settings corresponding to the WRF version 4.6 used are as follows:

[0056] Table 1 WRF parameter settings

[0057]

[0058] By comparing with the measured data, the parameter a of the improved mesoscale numerical modeling method of large 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 stable solution, the first 24h (buffer period) of solution is removed, and the analysis is performed on the 48 wind turbines of the entire wind farm in the last three days. Figure 3 The comparison of the total wind speed at the hub height of the entire wind farm in the yaw condition (yaw) and the non-yaw condition (yaw0) is shown. After the yaw control is performed, the influence of the wake of the front row wind turbines on the rear row wind turbines is reduced, the speed loss is reduced, the wind speed of the entire wind farm is obviously improved, and the average improvement is 1.44%. Figure 4 The comparison of the total wind speed at the hub height of the entire wind farm in the yaw condition (yaw) and the non-yaw condition (yaw0) is shown. After the yaw control is performed, the influence of the wake of the front row wind turbines on the rear row wind turbines is reduced, the speed loss is reduced, the wind speed of the entire wind farm is obviously improved, and the average improvement is 1.44%.

[0060] In addition, compared with the conventional mesoscale model, the wind farm parameterization model coupled with yaw control in the embodiment can be used in the research of a larger scale wind farm or wind cluster.

[0061] Corresponding to the aforementioned embodiment of the method for large-scale wind cluster-oriented coupled yaw control wind farm parameterization mesoscale numerical simulation, the present application also provides an embodiment of a system for large-scale wind cluster-oriented coupled yaw control wind farm parameterization mesoscale numerical simulation. The system comprises a parameter acquisition module, a model coupling module and a numerical evaluation and prediction module, and the specific processes of each module are described in the specific steps of the aforementioned embodiment of the method for large-scale wind cluster-oriented coupled yaw control wind farm parameterization mesoscale numerical simulation.

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

[0063] The model coupling module is configured to introduce a yaw wake model into the wind farm parameterization model to realize numerical modeling of mesoscale wind farm yaw control and calculate the interaction of wind turbine wake after yaw control to obtain the inflow velocity of each wind turbine.

[0064] The numerical evaluation and prediction module is configured to obtain the momentum change term, the turbulent kinetic energy change term and the power output of each wind turbine in the target wind cluster after yaw based on the inflow velocity to realize mesoscale numerical evaluation and prediction of the flow and operating characteristics of the large-scale wind base considering the yaw control of the wind turbine.

[0065] Corresponding to the aforementioned embodiment of the method for large-scale wind cluster-oriented coupled yaw control wind farm parameterization mesoscale numerical simulation, the present application also provides an embodiment of a device for large-scale wind cluster-oriented coupled yaw control wind farm parameterization mesoscale numerical simulation.

[0066] Referring to Figure 5 The device for large-scale wind cluster-oriented coupled yaw control wind farm parameterization mesoscale numerical simulation provided by the embodiment of the present application comprises a memory and one or more processors, the memory stores executable code, and the processor executes the executable code to realize the method for large-scale wind cluster-oriented coupled yaw control wind farm parameterization mesoscale numerical simulation in the aforementioned embodiment.

[0067] The embodiment of the large-scale wind power cluster-oriented coupling yaw control wind farm parameterized mesoscale numerical simulation device can be applied to any device with data processing capability, which can be a device or apparatus such as a computer. The device embodiment can be realized by software, or by hardware or a combination of software and hardware. Taking software implementation as an example, as a logical device, it is formed by reading the corresponding computer program instructions in the non-volatile memory into the memory for running by the processor of the device with data processing capability. From the hardware level, as shown in Figure 5 The embodiment of the large-scale wind power cluster-oriented coupling yaw control wind farm parameterized mesoscale numerical simulation device can be applied to any device with data processing capability, which can be a device or apparatus such as a computer. The device embodiment can be realized by software, or by hardware or a combination of software and hardware. Taking software implementation as an example, as a logical device, it is formed by reading the corresponding computer program instructions in the non-volatile memory into the memory for running by the processor of the device with data processing capability. From the hardware level, as shown in Figure 5 In addition to the processor, the memory, the network interface, and the non-volatile memory shown in the figure, the device with data processing capability in the embodiment can also include other hardware according to the actual function of the device with data processing capability, and details are not described here.

[0068] The implementation process of the functions and roles of each unit in the above device is specifically described in the implementation process of the corresponding steps in the above method, and details are not described here.

[0069] For the device embodiment, since it basically corresponds to the method embodiment, the related parts are described in the method embodiment. The above-described device embodiment is only schematic, and the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. According to actual needs, part or all of the modules can be selected to achieve the purpose of the present application. Those skilled in the art can understand and implement without creative labor.

[0070] The embodiment of the present application also provides a computer readable storage medium, which stores a program, and the program is executed by a processor to realize the large-scale wind power cluster-oriented coupling yaw control wind farm parameterized mesoscale numerical simulation method.

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

[0072] The application further provides a computer program product comprising a computer program, which, when executed by a processor, implements the large wind power cluster-oriented coupling yaw control wind farm parameterized mesoscale numerical simulation method.

[0073] The above-mentioned embodiments are only a preferred scheme of the application, and are not intended to limit the application. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the application. Therefore, any technical scheme obtained by equivalent replacement or equivalent transformation falls within the protection scope of the application.

Claims

1. A parameterized mesoscale numerical simulation method for coupled yaw-controlled wind farms for large wind farm clusters, characterized by: 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) A yaw wake model is introduced into the wind farm parameterization model to realize the numerical modeling of yaw control of mesoscale wind farms. The mesoscale numerical simulation is based on the non-hydrostatic pressure σ vertical coordinate following the terrain and adopts the Arakawa C staggered grid. The interaction of the wind turbine wake after yaw control is calculated to obtain the incoming flow velocity of each wind turbine. (3) After coupling the yaw wake model to the wind farm parameterized model, based on the incoming flow velocity, the momentum effect term, turbulent kinetic energy effect term and power output affected by the wind turbine in the grid are updated as follows: in, is the number of wind turbines in the grid (i, j), U i is the wind speed of the wind turbine after the influence of the superimposed wake calculated by the yaw wake model, U h is the original incoming wind speed at the hub height of the wind turbine, U k is the velocity scalar of the grid (i, j, k), , is the horizontal velocity component in the grid, is the area intercepted by the rotor rotating surface on the vertical layer k of the grid (i, j), 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, is the turbulent kinetic energy, P is the power output, ρ is the atmospheric density, C P is the power coefficient, A is the rotor swept area, and α is the yaw loss coefficient; Finally, the momentum change term, turbulent kinetic energy change term and power output of each wind turbine in the target wind power cluster after yaw are obtained, realizing the mesoscale numerical evaluation and prediction of the flow and operation characteristics of large wind power bases considering wind turbine yaw control.

2. The method for parameterized mesoscale numerical simulation of coupled yaw-controlled 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: within 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-controlled wind farms for large wind power clusters according to claim 1 is characterized in that: In the parameterized model of a wind farm, each wind turbine is regarded as a brake disk. The wind turbine applies a momentum sink to the incoming atmosphere, converting the incoming atmospheric kinetic energy into electrical energy and turbulent kinetic energy. 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-controlled 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; and 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-controlled 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-controlled wind farms for large wind power clusters according to claim 2, 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. 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 6, 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; The model coupling module is used to introduce the yaw wake model into the wind farm parameterized model to achieve 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; 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, and 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.

8. A parameterized mesoscale numerical simulation device for coupled yaw-controlled wind farms for large wind farm 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 6 is implemented.

9. 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 a coupled yaw-controlled wind farm for a large wind power cluster according to any one of claims 1 to 6 is implemented.

Images