Construction method and device for parameterized model of medium-micro-scale coupling wind power plant and medium

Abstract

The invention discloses a method and equipment for constructing a parameterized model of a medium and micro-scale coupling wind power plant and a medium, and relates to the technical field of wind power generation. The method mainly comprises the steps that model input is basic parameter information of a target wind power plant, and the background wind speed of a wind power plant area is re-determined based on a simulation result in a mesoscale mode and a pre-constructed mapping relation query table of the average wind speed and the background wind speed of the wind power plant area; key physical quantities of the wind turbine generator are determined based on the micro-scale model, a momentum sink item, a turbulent energy item and power output generated by the wind turbine generator are calculated, and then the momentum sink item, the turbulent energy item and the power output in the mesoscale mode grid are calculated. And the characterization precision of the wind power plant in the mesoscale mode is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind farms, and specifically provides a method, device, and medium for constructing a meso-microscale coupled wind farm parameterization model. Background Art

[0002] Mesoscale models are widely used in the simulation of the flow of wind farm groups, the planning of wind power bases, and the study of the impact of wind farms on the local environment. The wind farm parameterization model is a crucial part of the mesoscale model and is a bridge for quantifying the impact of wind farms on the atmospheric boundary layer. As is well known, wind turbines will cause a decrease in the downstream wind speed and an increase in the turbulent kinetic energy. Therefore, the wind farm parameterization model usually equates the wind farm to the sink term of the momentum equation and the source term of the turbulent kinetic energy transport equation. However, the existing wind farm parameterization models generally use the grid wind speed in the mesoscale model as the reference wind speed, which has a significant difference from the local inflow wind speed of the wind turbines, making the model calculation results very sensitive to the wind farm density and unable to consider the influence of the sub-grid wake effect, resulting in systematic errors in the momentum sink term, turbulent energy source term, and wind turbine power prediction value.

[0003] Therefore, this application proposes a method for constructing a meso-microscale coupled wind farm parameterization model applicable to the mesoscale model to solve the above problems. Summary of the Invention

[0004] In order to overcome the above defects, the present invention is proposed to provide a technical solution to solve or at least partially solve the problem of systematic errors in the momentum sink term, turbulent energy source term, and wind turbine power prediction value of the existing wind farm parameterization model.

[0005] In a first aspect, the present invention provides a method for constructing a meso-microscale coupled wind farm parameterization model, the method comprising: Step S1: Obtain the basic parameter information of the target wind farm as the model input; Step S2: Obtain the simulation results under the mesoscale model and the mapping relationship query table of the average wind speed and the background wind speed in the pre-constructed wind farm area, and re-determine the background wind speed in the wind farm area based on the simulation results and the mapping relationship query table; Step S3: Calculate the operation data of each wind turbine in the microscale model based on the re-determined background wind speed in the wind farm area; Step S4: Calculate the momentum sink term, turbulent energy source term, and power output in the mesoscale model grid based on the operation data of each wind turbine in the microscale model; Step S5: Integrate the output of the wind farm parameterization model based on the momentum sink term, the turbulent energy source term, and the power output.

[0006] In some embodiments, after the above step S5, it further includes: Step S6: Integrate the mesoscale model control equations based on the momentum sink term, the turbulent energy term, and the power output, and obtain the mesoscale model simulation results based on the output of the mesoscale model control equations; Among them, the simulation results in the mesoscale model obtained in step S2 are specifically preset simulation results, or the mesoscale model simulation results output in step S6.

[0007] In some embodiments, the re - determining the background wind speed in the wind farm area based on the simulation results and the mapping relationship lookup table in step S2 includes: Calculate the average wind speed, average wind direction in the wind farm area based on the simulation results, and select the upstream area of the wind farm according to the average wind direction to calculate the environmental turbulence intensity; Based on the calculated average wind speed, average wind direction, and environmental turbulence intensity corresponding to the simulation results, perform online interpolation in the mapping relationship lookup table to reconstruct the background wind speed in the wind farm area.

[0008] In some embodiments, the method further includes: offline constructing a mapping relationship lookup table of the average wind speed and background wind speed in the plane of the hub height of the target wind farm under different wind direction angles and environmental turbulence intensities based on the micro - scale wind farm flow analysis model.

[0009] In some embodiments, the calculating the operating data of each wind turbine in the micro - scale model based on the re - determined background wind speed in the wind farm area in step S3 includes: Determine the key physical parameters of the wind turbine based on the re - determined background wind speed in the wind farm area and the micro - scale wind farm flow analysis model, where the key physical parameters include the equivalent wind speed of the wind wheel, the thrust coefficient, and the power coefficient; Based on the equivalent wind speed of the wind wheel, calculate the momentum sink term and power output of each wind turbine based on the one - dimensional momentum theory of the ideal wind wheel, and calculate the turbulent energy term based on the energy conservation hypothesis; Wherein the operating data includes the momentum sink term, the turbulent energy term, and the power output.

[0010] Furthermore, the micro - scale wind farm flow analysis model is implemented based on the single - machine wake model and the wake superposition method.

[0011] Furthermore, the basic parameters of the target wind farm include the power and thrust coefficient curves of the wind turbines; the determining the key physical parameters of the wind turbine based on the re - determined background wind speed in the wind farm area and the micro - scale wind farm flow analysis model includes: Input the re-determined background wind speed of the wind farm area into the micro-scale wind farm flow analysis model to output the equivalent wind speed of the wind turbine rotor. Determine the power coefficient and thrust coefficient according to the equivalent wind speed of the wind turbine rotor, the power of the wind turbine unit, and the power and thrust coefficient curve.

[0012] Furthermore, the basic parameters of the target wind farm include the mesoscale grid index of different wind turbine units and the set of wind turbine unit numbers within different mesoscale grids; the calculation of the momentum sink term, turbulent energy term, and power output within the mesoscale model grid based on the operation data of each wind turbine unit in the micro-scale model in step S4 includes: Project the momentum sink term and turbulent energy term corresponding to each wind turbine unit onto the computational grid using the discrete counting method. Calculate the momentum sink term, turbulent energy term, and power output within the mesoscale model grid based on the mesoscale grid index and the set of wind turbine unit numbers within different mesoscale grids.

[0013] In a second aspect, the present invention provides an electronic device, including at least one processor and at least one memory. The memory is adapted to store multiple program codes, and the program codes are adapted to be loaded and run by the processor to execute the construction method of the medium-scale and micro-scale coupled wind farm parameterization model according to any one of the technical solutions in the technical solutions of the construction method of the medium-scale and micro-scale coupled wind farm parameterization model described above.

[0014] In a third aspect, the present invention provides a computer-readable storage medium, which stores multiple program codes therein. The program codes are adapted to be loaded and run by a processor to execute the construction method of the medium-scale and micro-scale coupled wind farm parameterization model according to any one of the technical solutions in the technical solutions of the construction method of the medium-scale and micro-scale coupled wind farm parameterization model described above.

[0015] One or more of the above technical solutions of the present invention have at least one or more of the following beneficial effects: In implementing the technical solution of the present invention, based on the actuated wind farm concept and the medium-scale and micro-scale velocity matching assumption, the present invention realizes the reconstruction of the background wind speed of the wind farm area through online interpolation of the mesoscale wind farm area average wind speed, further couples the micro-scale wind farm flow analysis model to calculate the key physical quantities required in the wind farm parameterization model, and finally calculates the momentum sink term, turbulent energy term, and unit power output. The present invention takes the reconstructed background wind speed as the benchmark and couples the micro-scale wind farm flow analysis model to calculate the equivalent wind speed of the wind turbine rotor, explicitly considers the wake interference effect at the wind turbine unit scale, greatly improves the prediction accuracy of the momentum sink term, turbulent energy term, and power, improves the characterization accuracy of the wind farm in the mesoscale model, helps to improve the simulation accuracy of the wind farm group flow, and more scientifically conducts the planning of the wind power base. Brief Description of the Drawings

[0016] Referring to the accompanying drawings, the disclosure of the present invention will become more readily understandable. It is readily understood by those skilled in the art that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present invention. Among them: Figure 1 is a schematic diagram of the main steps of a method for constructing a parametric model of a mesoscale coupled wind farm provided by this application; Figure 2 is a schematic diagram of an application scenario of the wind farm parametric model combined with a mesoscale model provided by an embodiment of this application; Figure 3 is a schematic diagram of the specific implementation steps for obtaining the average wind speed in the wind farm area based on the microscale wind farm flow analysis model provided by an embodiment of this application. Detailed Embodiments

[0017] Some embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principle of the present invention and are not intended to limit the scope of protection of the present invention.

[0018] In the description of the present invention, a "module" and a "processor" may include hardware, software, or a combination of both. A module may include a hardware circuit, various suitable sensors, communication ports, memories, and may also include a software part, such as program code, or a combination of software and hardware. A processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, in hardware, or in a combination of both. A non-transitory computer-readable storage medium includes any suitable medium for storing program code, such as magnetic disks, hard disks, optical disks, flash memories, read-only memories, random access memories, and the like.

[0019] Glossary of Terms: Mesoscale Model: A mathematical model used to describe mesoscale motions in the atmosphere, widely applied in the study of various mesoscale phenomena. The mesoscale model can handle large amounts of data and complex computations, and is particularly suitable for simulating and forecasting mesoscale and regional-scale atmospheric circulations. The mesoscale model is a mathematical model for describing mesoscale motions in the atmosphere and belongs to the field of dynamic meteorology in atmospheric science. It can handle systems of nonlinear partial differential equations and is suitable for simulating or forecasting mesoscale and regional-scale atmospheric circulations.

[0020] WRF (Weather Research and Forecasting) model: That is, the Weather Research and Forecasting model, which is mainly used for wind power prediction in the wind farm field. The WRF model is an advanced mesoscale model with high-resolution simulation capabilities, capable of simulating various weather phenomena and climate conditions. Through the WRF model, the meteorological changes in the future period can be simulated, providing basic data support for wind power prediction.

[0021] WFP (Wind Farm Parameterization): Refers to the wind farm parameterization model, which is usually used in combination with the WRF (Weather Research and Forecasting) model to simulate the wind resource characteristics and wake effects of the wind farm, etc.

[0022] See Appendix Figure 1 , Figure 1 is the main step flow diagram of the construction method of the mesoscale-coupled wind farm parameterization model according to an embodiment of the present application. As Figure 1 shown, the construction method of the wind farm parameterization model (WFP) in the embodiment of the present invention mainly includes the following steps S1 to S5.

[0023] Step S1: Obtain the basic parameter information of the target wind farm as the model input; In this embodiment, the obtained basic parameter information of the target wind farm includes the position of the wind turbine, the hub height, the rotor diameter, the power and thrust coefficient curve of the wind turbine, the mesoscale grid index corresponding to different wind turbines, and the set of wind turbine numbers in different mesoscale grids.

[0024] For example, the spatial positions of different wind turbines can be expressed as ( , , ), the hub height is H, the rotor diameter is D, the mesoscale grid index corresponding to different wind turbines determined according to the spatial position of the wind turbine can be expressed as , and the set of wind turbine numbers in grid can be expressed as .

[0025] Step S2: Obtain the simulation results under the mesoscale model and the pre-constructed mapping relationship query table of the average wind speed and background wind speed in the wind farm area, and re-determine the background wind speed in the wind farm area based on the simulation results and the mapping relationship query table; In practical applications, the simulation results obtained in this step under the mesoscale model can be understood as the simulation output of the mesoscale model (WRF). The first obtained simulation results can specifically be the preset simulation results, and the simulation results obtained when applied to WRF simulation are specifically the simulation output of WRF. When the simulated physical time reaches the preset simulation time, the simulation will automatically terminate.

[0026] Step S3: Calculate the operating data of each wind turbine in the microscale model based on the re-determined background wind speed in the wind farm area; In this embodiment, it can be to determine the key physical parameters of the wind turbine by coupling the microscale wind farm flow model based on the re-determined background wind speed in the wind farm area, and then calculate the operating data of the wind turbine based on the key parameters of the wind turbine. The key physical parameters include the equivalent wind speed of the wind turbine, the thrust coefficient, and the power coefficient, and the operating data includes the momentum sink term, the turbulent energy term, and the power output generated by the wind turbine.

[0027] Step S4: Calculate the momentum sink term, the turbulent energy term, and the power output in the mesoscale model grid based on the operating data of each wind turbine in the microscale model; Step S5: Integrate the output of the wind farm parameterization model based on the momentum sink term, the turbulent energy term, and the power output.

[0028] When combined with practical applications, the above method may further include Step S6: Integrate the mesoscale model control equations based on the momentum sink term, the turbulent energy term, and the power output, and obtain the mesoscale model simulation results based on the output of the mesoscale model control equations.

[0029] It should be understood that Step S6 is the implementation method of the mesoscale model coupled with the wind farm parameterization model. In practical applications, it can be to integrate the momentum sink term and the turbulent energy term calculated in Step S4 into the control equations of the mesoscale model. On this basis, further consider the influence of other physical processes on the control equations, perform one-step time integration on the mesoscale model, so as to calculate the physical quantities of the next time step, and repeat Steps S2 to S6 until the simulation ends. Correspondingly, the simulation results obtained in the mesoscale model in Step S2 are specifically the mesoscale model simulation results output in Step S6.

[0030] Based on the above Step S2, in a specific implementation manner, the construction of the mapping relationship query table can specifically be: Offline construct a mapping relationship query table of the average wind speed and the background wind speed in the plane wind farm area at the hub height of the target wind farm under different wind direction angles and environmental turbulence intensities based on the microscale wind farm flow analysis model. For example, different wind direction angles and environmental turbulence intensities Constructed wind farm area Average wind speed And the background wind speed Mapping query table, which can be embodied as establishing Mapping relationship

[0031] In the embodiment of the present application, the micro-scale wind farm flow analysis model can be a micro-scale analysis model implemented based on a single-machine wake model and a wake superposition method, or a micro-scale simulation method based on CFD (Computational Fluid Dynamics).

[0032] Based on the above step S2, in a specific embodiment, the re-determination of the background wind speed in the wind farm area based on the simulation result and the mapping relationship query table may specifically include the following steps 21 and 22: Step 21: Calculate the average wind speed, average wind direction in the wind farm area based on the simulation result, and select the upstream area of the wind farm according to the average wind direction to calculate the environmental turbulence intensity; Step 22: Based on the calculated average wind speed, average wind direction and environmental turbulence intensity corresponding to the simulation result, perform online interpolation in the mapping relationship query table to reconstruct the background wind speed in the wind farm area.

[0033] For example, calculate the average wind speed In the wind farm area based on the simulation result of the mesoscale model And average wind direction , and then according to Select the upstream area of the wind farm to calculate the environmental turbulence intensity , and use As the basis for online interpolation in the query table to reconstruct the background wind speed corresponding to the current state , that is . Theoretically, the wind speed corresponding to this is the regional average wind speed in the mesoscale model without a wind farm.

[0034] Based on the above step S3, in a specific embodiment, the calculation of the operating data of each wind turbine in the micro-scale model based on the re-determined background wind speed in the wind farm area may specifically include steps 31 and 32: Step 31: Determine the key physical parameters of the wind turbine based on the re-determined background wind speed in the wind farm area and the micro-scale wind farm flow analysis model. The key physical parameters include the equivalent wind speed of the wind turbine, the thrust coefficient and the power coefficient; Specifically, in this embodiment, the re-determined background wind speed of the wind farm area, as well as the corresponding average wind direction and environmental turbulence intensity, can be input into the micro-scale wind farm flow analysis model to output the equivalent wind speed of the wind turbine; the power coefficient and thrust coefficient are determined according to the equivalent wind speed of the wind turbine and the power and thrust coefficient curves of the wind turbine generator set.

[0035] For example, the background wind speed of the wind farm , average wind direction and environmental turbulence intensity are input into the micro-scale wind farm flow analysis model, so as to explicitly consider the mutual interference of the wake scales of wind turbine generator sets and calculate the equivalent wind speed of the wind turbines in different wind turbine generator sets , thrust coefficient and power coefficient .

[0036] Step 32: Based on the equivalent wind speed of the wind turbine, calculate the momentum sink term and power output of each wind turbine generator set based on the one-dimensional momentum theory of the ideal wind turbine, and calculate the turbulent energy source term based on the law of conservation of energy.

[0037] For example, when the equivalent wind speed of the wind turbine is , the generated by the wind turbine generator set - momentum sink term , - momentum sink term , turbulent energy source term and power are calculated as follows:

[0038] In the formula represents the horizontal grid index where the wind turbine generator set is located, represents the vertical grid index, is the vertical coordinate of the grid, is the swept area of the wind turbine , is the intersection area of the wind turbine with the th and th layer grids, is the horizontal grid area, and are the flow-direction wind speed and spanwise wind speed at the grid respectively, is the grid​​​​​​​ The horizontal velocity at is the turbulence kinetic energy correction coefficient, where ; represents the air density, and the standard value can be taken.

[0039] Based on the above step S4, in a specific implementation manner, calculating the momentum sink term, turbulent energy term, and power output in the mesoscale model grid based on the operation data of each wind turbine in the microscale model may specifically include the following steps 41 and 42: Step 41: Project the momentum sink term and turbulent energy term corresponding to each wind turbine onto the computational grid using the discrete counting method; Step 42: Calculate the momentum sink term, turbulent energy term, and power output in the mesoscale model grid based on the mesoscale grid index and the set of wind turbine numbers in different mesoscale grids.

[0040] For example, based on the mesoscale grid index of the computational grid The calculated momentum sink term, turbulent energy term, and power output in the mesoscale model grid are expressed as follows:

[0041] In the formula represents the set of wind turbine indices included in the mesoscale horizontal grid .

[0042] Refer to Appendix Figure 2 , Figure 2It is a schematic diagram of the application scenario of the combination of the wind farm parameterization model and the mesoscale model provided by the embodiments of the present application. In this application scenario, first, a meso-microscale coupled wind power parameterization model is constructed, and the momentum sink term, turbulent energy term, and power within the mesoscale grid are calculated. The specific implementation includes inputting the basic parameter information of the target wind farm, constructing a velocity lookup table in combination with the microscale wind farm flow analysis model, determining the background wind speed in the wind farm area based on the mesoscale model output and the velocity lookup table, determining the key physical parameters of the wind turbine in combination with the microscale wind farm flow analysis model, and then calculating the momentum sink term, turbulent energy term, and power generated by the wind turbine, and further calculating the momentum sink term, turbulent energy term, and power within the mesoscale grid; finally, coupling the mesoscale model of the wind farm parameterization model, integrating the output of the wind farm parameterization model, and performing time integration to output the simulation results of the mesoscale model. It should be noted that the specific implementation of each component shown in the figure and the relevant formula calculations can be referred to the specific description in the method embodiments of the present application, which will not be elaborated here.

[0043] The following describes the preferred microscale wind farm flow analysis model adopted in the embodiments of the present application. The microscale wind farm flow analysis model adopted in this embodiment is implemented based on the single-machine wake model and the wake superposition method. Specifically: input the layout of the target wind farm, the geometric parameters of the wind turbine, and the wind environment parameters; calculate the physical parameters required for the single-machine wake model based on empirical formulas, use the iterative method to update the thrust coefficient and wake variables of the wind turbine one by one, and calculate the equivalent wind speed of the wind wheel. Based on this, the spatial distribution of the flow velocity in the wind farm can be obtained by using wake superposition methods such as the momentum conservation superposition method, the local linear superposition method, or the wind speed product method. This microscale wind farm flow analysis model can determine the spatial distribution of the wake velocity under any wind farm layout only by inputting the geometric information of the wind turbine and the wind environment parameters that are relatively easy to obtain in actual engineering applications, can quickly output the equivalent wind speed of the wind wheel, and construct a mapping relationship lookup table between the average wind speed and the background wind speed in the wind farm area at the hub height plane of the wind farm.

[0044] Refer to the appendix Figure 3 , Figure 3 It is the main implementation step flow diagram of obtaining the average wind speed in the target wind farm area based on the microscale wind farm flow analysis model in the embodiments of the present application. As shown, it mainly includes: Figure 3 First, determine the model input: background wind speed , environmental turbulence intensity , wind direction angle , and the thrust coefficient curve of the unit and and ; assume that the thrust coefficient of each wind turbine is equal to Set the number of iterations \(j_t = 0\) and start the iteration to determine the equivalent wind speed of the wind turbine , and the iteration process is as follows: Determine the turbulence intensity of each wind turbine based on the single - machine additional turbulence model and the additional turbulence superposition method ; Determine the wake expansion rate of each wind turbine based on the single - machine wake model , the starting position of the far - wake , the wake width and the velocity loss ; For \(n = 1,\cdots,N\), determine and one by one in the following way: Calculate the wind speed at the wind - turbine wind - wheel sampling point ( ), where \(n_t\) represents the unit number index; Calculate the equivalent wind speed of the wind wheel of the \(n\)th wind turbine : ; Update the thrust coefficient and the power coefficient of the unit according to , record the updated thrust coefficient and power coefficient of the unit. So far, the determination of , and for \(N\) wind turbines is completed; Calculate the mean absolute difference and : ; Judge whether is less than 0.001. If so, end the iteration and output , and ; Otherwise, \(j_t=j_t + 1\) and continue the above iteration process.

[0045] Set the set of velocity sampling points in the wind farm area according to the coordinates of the units in the wind farm, where the wind farm area is a rectangle containing the simulated wind farm; Calculate the wind speed at the position of the velocity sampling point: , where \(H\) represents the hub height; Output the average wind speed of the wind farm area : .

[0046] The meso - scale coupled wind farm parameterization model proposed by the present invention can greatly improve the prediction accuracy of the momentum sink term, the turbulent energy term and the power, improve the characterization accuracy of the wind farm in the meso - scale model, help to improve the simulation accuracy of the flow of the wind farm group, and more scientifically carry out the planning of the wind power base.

[0047] It should be noted that although the steps are described in a specific order in the above embodiments, those skilled in the art can understand that in order to achieve the effects of this application, it is not necessary for different steps to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders, and these variations are within the protection scope of this application.

[0048] Those skilled in the art can understand that all or part of the processes in the method of the above-mentioned embodiment of the present invention can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned method embodiments can be realized. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable storage medium can include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal, and software distribution medium, etc., that can carry the computer program code.

[0049] Furthermore, the present invention also provides an electronic device. In an embodiment of the electronic device according to the present invention, the electronic device includes at least one processor and at least one memory. The memory can be configured to store a program for implementing the method for constructing the mesoscale coupled wind farm parameterization model in the above-mentioned method embodiment. The processor can be configured to execute the program in the storage device, and the program includes, but is not limited to, the program for implementing the method for constructing the mesoscale coupled wind farm parameterization model in the above-mentioned method embodiment. For the sake of convenience of description, only the parts related to the embodiments of the present invention are shown. For the specific technical details not disclosed, please refer to the method part of the embodiments of the present invention.

[0050] In the embodiments of the present application, the electronic device may be a control device formed by various devices. In some possible implementation manners, the electronic device may include multiple memories and multiple processors. The program for implementing the method for constructing the mesoscale coupled wind farm parameterization model in the above method embodiments may be divided into multiple sub-programs, and each sub-program may be loaded and run by a processor respectively to execute different steps of the method for constructing the mesoscale coupled wind farm parameterization model in the above method embodiments. Specifically, each sub-program may be stored in a different memory respectively, and each processor may be configured to execute the program in one or more memories to jointly implement the method for constructing the mesoscale coupled wind farm parameterization model in the above method embodiments, that is, each processor executes different steps of the method for constructing the mesoscale coupled wind farm parameterization model in the above method embodiments respectively to jointly implement the method for constructing the mesoscale coupled wind farm parameterization model in the above method embodiments.

[0051] The above-mentioned multiple processors may be processors deployed on the same device. For example, the above-mentioned electronic device may be a high-performance device composed of multiple processors, and the above-mentioned multiple processors may be processors configured on the high-performance device. In addition, the above-mentioned multiple processors may also be processors deployed on different devices. For example, the above-mentioned electronic device may be a server cluster, and the above-mentioned multiple processors may be processors on different servers in the server cluster.

[0052] Furthermore, the present invention also provides a computer-readable storage medium. In an embodiment of the computer-readable storage medium according to the present invention, the computer-readable storage medium may be configured to store a program for implementing the method for constructing the mesoscale coupled wind farm parameterization model in the above method embodiments, and this program may be loaded and run by a processor to implement the method for constructing the mesoscale coupled wind farm parameterization model. For the sake of convenience of description, only the parts related to the embodiments of the present invention are shown. For the specific technical details not disclosed, please refer to the method part of the embodiments of the present invention. The computer-readable storage medium may be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiments of the present invention is a non-transitory computer-readable storage medium.

[0053] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

Claims

1. A method for constructing a parameterized model of a mesoscale and microscale coupled wind farm, characterized in that: The method comprises: Step S1: Obtain basic parameter information of the target wind farm as model input; Step S2: obtaining the simulation results in the mesoscale mode and a pre-constructed mapping relationship query table of the average wind speed and the background wind speed in the wind farm area, and re-determining the background wind speed in the wind farm area based on the simulation results and the mapping relationship query table; Step S3: calculating the operating data of each wind turbine in the micro-scale model based on the re-determined background wind speed in the wind farm area; Step S4: calculating the momentum sink term, turbulent energy term and power output in the mesoscale model grid based on the operating data of each wind turbine in the microscale model; Step S5: integrating the output of the wind farm parameterized model based on the momentum sink term, the turbulent energy term and the power output.

2. The method according to claim 1, characterized in that After step S5, the following steps are also included: Step S6: integrating the mesoscale model control equation based on the momentum sink term, the turbulent kinetic energy term and the power output, and obtaining the mesoscale model simulation result based on the output of the mesoscale model control equation; The simulation result under the mesoscale mode obtained in step S2 is specifically a pre-set simulation result, or is the mesoscale mode simulation result output in step S6.

3. The method according to claim 1, characterized in that The re-determining of the background wind speed in the wind farm area based on the simulation result and the mapping relationship query table in step S2 includes: Calculate the average wind speed and average wind direction of the wind farm area based on the simulation results, and select the upstream area of ​​the wind farm according to the average wind direction to calculate the environmental turbulence intensity; Based on the calculated average wind speed, the average wind direction and the ambient turbulence intensity corresponding to the simulation result, online interpolation is performed in the mapping relationship query table to reconstruct the background wind speed of the wind farm area.

4. The method according to claim 1, characterized in that: The method further comprises: constructing an offline query table of the mapping relationship between the average wind speed and the background wind speed of the target wind farm hub height plane wind farm area under different wind direction angles and environmental turbulence intensities based on the micro-scale wind farm flow analytical model.

5. The method according to claim 1, characterized in that The operation data of each wind turbine in the micro-scale model calculated based on the re-determined background wind speed in the wind farm area in step S3 includes: Determine the key physical parameters of the wind turbine generator system based on the re-determined background wind speed in the wind farm area and the micro-scale wind farm flow analytical model, wherein the key physical parameters include the equivalent wind speed of the wind rotor, the thrust coefficient and the power coefficient; Based on the equivalent wind speed of the wind rotor, the momentum sink term and power output of each wind turbine are calculated based on the one-dimensional momentum theory of the ideal wind rotor, and the turbulent energy term is calculated based on the energy conservation assumption; The operating data includes the momentum sink term, the turbulent energy term and the power output.

6. The method according to claim 4 or 5, characterized in that: The micro-scale wind farm flow analytical model is implemented based on a single-machine wake model and a wake superposition method.

7. The method according to claim 5, characterized in that The basic parameters of the target wind farm include wind turbine power and thrust coefficient curves; the key physical parameters of the wind turbine determined based on the re-determined background wind speed in the wind farm area and the micro-scale wind farm flow analytical model include: Inputting the re-determined background wind speed of the wind farm area into the micro-scale wind farm flow analytical model to output an equivalent wind speed of the wind rotor; The power coefficient and the thrust coefficient are determined according to the wind rotor equivalent wind speed and the wind turbine generator set power and thrust coefficient curve.

8. The method according to claim 5, characterized in that The basic parameters of the target wind farm include mesoscale grid indexes of different wind turbines and a set of wind turbine numbers in different mesoscale grids; The calculation of the momentum sink term, turbulent energy term and power output in the mesoscale model grid based on the operating data of each wind turbine in the microscale model in step S4 includes: The momentum sink term and turbulent energy term corresponding to each wind turbine are projected onto the computational grid using the discrete counting method; The momentum sink term, turbulent energy term and power output in the mesoscale model grid are obtained by collective calculation based on the mesoscale grid index and the wind turbine group numbers in different mesoscale grids.

9. An electronic device comprising at least one processor and at least one memory, wherein the memory is suitable for storing a plurality of program codes, wherein: The program code is suitable for being loaded and run by the processor to execute a method for constructing a meso-microscale coupled wind farm parameterized model according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by a processor to execute a method for constructing a meso-microscale coupled wind farm parameterized model according to any one of claims 1 to 8.

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