Method, equipment and medium for constructing parameterized model of mesoscale and microscale coupled wind farm

Abstract

The present invention discloses a method, device, and medium for constructing a mesoscale and microscale coupled wind farm parameterized model, relating to the field of wind power generation technology. The method primarily comprises: model input being basic parameter information of a target wind farm; re-determining the background wind speed of the wind farm region based on simulation results in a mesoscale model and a pre-constructed query table mapping the average wind speed and background wind speed of the wind farm region; determining key physical quantities of the wind turbine based on the microscale model and calculating the momentum sink term, turbulent energy term, and power output generated by the wind turbine; and further calculating the momentum sink term, turbulent energy term, and power output within the mesoscale model grid. The method of the present invention significantly improves the prediction accuracy of the momentum sink term, turbulent energy term, and power, and improves the characterization accuracy of the wind farm in the mesoscale model.

Description

Technical Field

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

[0002] Mesoscale models are widely used in wind farm group flow simulation, wind power base planning, and the study of the impact of wind farms on the local environment. Wind farm parameterization models are a crucial component of mesoscale models and serve as a bridge for quantifying the impact of wind farms on the atmospheric boundary layer. It is well known that wind turbines will cause a decrease in downstream wind speed and an increase in turbulent kinetic energy. Therefore, wind farm parameterization models usually equate wind farms to sink terms in the momentum equation and source terms in the turbulent kinetic energy transport equation. However, existing wind farm parameterization models generally use the grid wind speed in the mesoscale model as the reference wind speed, which is significantly different from the local inflow wind speed of the wind turbine. This makes the model calculation results very sensitive to the wind farm density and fails to consider the influence of the sub-grid wake effect, resulting in systematic errors in the momentum sink term, turbulent kinetic energy term, and wind turbine power prediction values.

[0003] To this end, this application proposes a method for constructing a mesoscale-microscale coupled wind farm parameterized model suitable for mesoscale mode to solve the above problems. Summary of the Invention

[0004] In order to overcome the above-mentioned defects, the present invention is proposed to provide a solution or at least partially solve the technical problem that there are systematic errors in the momentum sink term, turbulent kinetic energy 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 parameterized model of a mesoscale and microscale coupled wind farm, the method comprising:

[0006] Step S1: Obtain basic parameter information of the target wind farm as model input;

[0007] Step S2: obtaining simulation results in the mesoscale mode and a pre-constructed mapping relationship query table of average wind speed and 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;

[0008] 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;

[0009] 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;

[0010] Step S5: integrating the output of the wind farm parameterized model based on the momentum sink term, the turbulent kinetic energy term and the power output.

[0011] In some embodiments, the above step S5 further includes:

[0012] Step S6: integrating a mesoscale model control equation based on the momentum sink term, the turbulent kinetic energy term, and the power output, and obtaining a mesoscale model simulation result based on an output of the mesoscale model control equation;

[0013] The simulation result under the mesoscale mode obtained in step S2 is specifically a pre-set simulation result, or the mesoscale mode simulation result output in step S6.

[0014] In some embodiments, the re-determining of the background wind speed of the wind farm area based on the simulation result and the mapping relationship query table in step S2 includes:

[0015] Calculating the average wind speed and average wind direction of the wind farm area based on the simulation results, and selecting an upstream area of ​​the wind farm according to the average wind direction to calculate the environmental turbulence intensity;

[0016] 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 lookup table to reconstruct the background wind speed of the wind farm area.

[0017] In some embodiments, the method further comprises: constructing an offline query table of the mapping relationship between the average wind speed of the target wind farm hub height plane and the background wind speed under different wind direction angles and environmental turbulence intensities based on the microscale wind farm flow analytical model.

[0018] In some embodiments, the calculation of the operating data of each wind turbine in the micro-scale model based on the re-determined background wind speed of the wind farm area in step S3 includes:

[0019] Determining 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 rotor equivalent wind speed, thrust coefficient, and power coefficient;

[0020] Based on the equivalent wind speed of the wind turbine, the momentum sink and power output of each wind turbine are calculated based on the one-dimensional momentum theory of the ideal wind turbine, and the turbulent energy term is calculated based on the energy conservation assumption.

[0021] The operating data includes the momentum sink term, the turbulent kinetic energy term and the power output.

[0022] Furthermore, the micro-scale wind farm flow analytical model is implemented based on a single-machine wake model and a wake superposition method.

[0023] Furthermore, the target wind farm basic parameters 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:

[0024] 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;

[0025] 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.

[0026] Furthermore, 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:

[0027] The momentum sink and turbulent energy terms corresponding to each wind turbine are projected onto the computational grid using the discrete counting method.

[0028] 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.

[0029] In a second aspect, the present invention provides an electronic device comprising at least one processor and at least one memory, wherein the memory is suitable for storing multiple program codes, and the program codes are suitable for being loaded and run by the processor to execute the method for constructing a mesoscale and microscale coupled wind farm parameterized model as described in any one of the technical solutions of the above-mentioned method for constructing a mesoscale and microscale coupled wind farm parameterized model.

[0030] In a third aspect, the present invention provides a computer-readable storage medium storing a plurality of program codes, wherein the program codes are suitable for being loaded and run by a processor to execute the method for constructing a parameterized model of a mesoscale and microscale coupled wind farm as described in any one of the technical solutions for the method for constructing a parameterized model of a mesoscale and microscale coupled wind farm.

[0031] The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:

[0032] In implementing the technical solution of the present invention, based on the concept of actuated wind farms and the assumption of meso- and micro-scale velocity matching, the present invention reconstructs the background wind speed of the wind farm region through online interpolation of the average wind speed of the mesoscale wind farm region. It further couples the microscale wind farm flow analytical model to calculate the key physical quantities required in the wind farm parameterization model, ultimately calculating the momentum sink term, turbulent energy term, and unit power output. The present invention uses the reconstructed background wind speed as a benchmark and couples the microscale wind farm flow analytical model to calculate the equivalent wind speed of the wind turbine. This explicitly considers the wake interference effect at the scale of the wind turbine, significantly improving the prediction accuracy of the momentum sink term, turbulent energy term, and power. It also improves the representation accuracy of wind farms in the mesoscale model, helping to improve the simulation accuracy of wind farm group flows and more scientifically carry out wind power base planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The disclosure of the present invention will become more easily understood with reference to the accompanying drawings. Those skilled in the art will readily appreciate that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of the present invention. Among them:

[0034] Figure 1 This is a schematic flow chart of the main steps of a method for constructing a mesoscale and microscale coupled wind farm parameterized model provided in this application;

[0035] Figure 2 This is a schematic diagram of an application scenario of combining a wind farm parameterized model with a mesoscale model provided in an embodiment of the present application;

[0036] Figure 3 This is a flowchart of specific implementation steps for obtaining the average wind speed in a wind farm area based on a micro-scale wind farm flow analysis model provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] Some embodiments of the present invention are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0038] In the description of the present invention, "module" and "processor" may include hardware, software or a combination of the two. A module may include hardware circuits, various suitable sensors, communication ports, and memories, and may also include software components, such as program code, or a combination of software and hardware. The 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, hardware, or a combination of the two. Non-transitory computer-readable storage media include any suitable media that can store program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, and the like.

[0039] Glossary:

[0040] A mesoscale model (MEM) is a mathematical model used to describe atmospheric mesoscale motion and is widely used in the study of various mesoscale phenomena. Capable of handling high-density data and complex computations, MEMs are particularly well-suited for simulating and forecasting atmospheric circulation at mesoscale and regional scales. MEMs, as mathematical models describing atmospheric mesoscale motion, belong to the field of dynamic meteorology within atmospheric science. They can handle systems of nonlinear partial differential equations and are suitable for simulating or forecasting atmospheric circulation at mesoscale and regional scales.

[0041] WRF (Weather Research and Forecasting) model: This model is primarily used for wind power forecasting in the wind farm sector. As an advanced mesoscale model with high-resolution simulation capabilities, the WRF model can simulate various weather phenomena and climate conditions. The WRF model can simulate future meteorological changes, providing fundamental data support for wind power forecasting.

[0042] WFP (Wind Farm Parameterization): refers to a wind farm parameterization model, which is usually used in conjunction with the WRF (Weather Research and Forecasting) model to simulate the wind resource characteristics and wake effects of a wind farm.

[0043] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart of the main steps of a method for constructing a parameterized model of a mesoscale and microscale coupled wind farm according to an embodiment of the present application. Figure 1 As shown, the method for constructing a wind farm parameterized model (WFP) in the embodiment of the present invention mainly includes the following steps S1 to S5.

[0044] Step S1: Obtain basic parameter information of the target wind farm as model input;

[0045] In this embodiment, the basic parameter information of the target wind farm obtained includes the location of the wind turbine, hub height, rotor diameter, wind turbine power and thrust coefficient curves, mesoscale grid indexes corresponding to different wind turbines, and a collection of wind turbine numbers in different mesoscale grids.

[0046] For example, the spatial positions of different wind turbines can be expressed as ( , , ), the hub height is H, the rotor diameter is D, and the mesoscale grid index corresponding to different wind turbines determined according to the spatial position of the wind turbine can be expressed as , grid The set of internal wind turbine group numbers can be expressed as .

[0047] Step S2: obtaining simulation results in the mesoscale mode and a pre-constructed mapping relationship query table of average wind speed and 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;

[0048] In practical applications, the simulation results under the mesoscale model obtained in this step can be understood as the simulation output of the mesoscale model (WRF), wherein the simulation results obtained for the first time can be specifically a pre-set simulation result, 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.

[0049] 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;

[0050] In this embodiment, the key physical parameters of the wind turbine can be determined based on the re-determined background wind speed of the wind farm area coupled with the micro-scale wind farm flow model, and then the operating data of the wind turbine can be calculated based on the key parameters of the wind turbine, wherein the key physical parameters include the equivalent wind speed of the wind rotor, the thrust coefficient and the power coefficient, and the operating data include the momentum sink term, the turbulent kinetic energy term and the power output generated by the wind turbine.

[0051] 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;

[0052] Step S5: integrating the output of the wind farm parameterized model based on the momentum sink term, the turbulent kinetic energy term and the power output.

[0053] In combination with practical applications, the above method may further include 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.

[0054] It should be understood that step S6 is an implementation method of the mesoscale model of the coupled wind farm parameterized model. In practical applications, the momentum sink term and turbulent energy term calculated in step S4 can be integrated into the control equation of the mesoscale model. On this basis, the influence of other physical processes on the control equation is further considered, and the mesoscale model is integrated in one step time to calculate the physical quantity of the next time step. Steps S2 to S6 are repeated until the end of the simulation. Accordingly, the simulation result under the mesoscale model obtained in step S2 is specifically the mesoscale model simulation result output by step S6.

[0055] Based on the above step S2, in a specific embodiment, the construction of the mapping relationship query table can be specifically as follows: based on the micro-scale wind farm flow analytical model, an offline mapping relationship query table of the average wind speed of the target wind farm hub height plane wind farm area and the background wind speed under different wind direction angles and environmental turbulence intensities is constructed. For example, different wind direction angles and ambient turbulence intensity Constructed wind farm area Average wind speed and background wind speed The mapping query table can be reflected as establishing The mapping relationship.

[0056] In the embodiment of the present application, the microscale wind farm flow analytical model may be a microscale analytical model implemented based on a single-machine wake model and a wake superposition method, or may be a microscale simulation method based on CFD (computational fluid dynamics).

[0057] Based on the above step S2, in a specific implementation manner, the re-determining of the background wind speed of the wind farm area based on the simulation result and the mapping relationship query table may specifically include the following steps 21 and 22:

[0058] Step 21: calculating the average wind speed and average wind direction of the wind farm area based on the simulation results, and calculating the environmental turbulence intensity of the upstream area of ​​the wind farm according to the average wind direction;

[0059] Step 22: 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.

[0060] For example, the wind farm area is calculated based on the simulation results of the mesoscale model. Average wind speed and average wind direction , then according to Select the upstream area of ​​the wind farm to calculate the environmental turbulence intensity ,by Based on online interpolation in the lookup table, reconstruct the background wind speed corresponding to the current state ,Right now In theory, this wind speed corresponds to the regional average wind speed when there is no wind farm in the mesoscale model.

[0061] Based on the above step S3, in a specific implementation manner, the calculation of the operating data of each wind turbine in the micro-scale model based on the re-determined background wind speed of the wind farm area may specifically include steps 31 and 32:

[0062] Step 31: determining 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 rotor equivalent wind speed, thrust coefficient, and power coefficient;

[0063] Specifically in this embodiment, the re-determined background wind speed of the wind farm area and the corresponding average wind direction and environmental turbulence intensity can be input into the micro-scale wind farm flow analytical model to output the wind rotor equivalent wind speed; the power coefficient and thrust coefficient are determined according to the wind rotor equivalent wind speed and the wind turbine power and thrust coefficient curve.

[0064] For example, the background wind speed of a wind farm , average wind direction and ambient turbulence intensity Input into the micro-scale wind farm flow analytical model, thereby explicitly considering the mutual interference of wind turbine wake scales and calculating the flow of different wind turbines in the wind farm. The equivalent wind speed of the wind wheel , thrust coefficient and power factor .

[0065] Step 32: Based on the equivalent wind speed of the wind rotor, calculate the momentum sink and power output of each wind turbine using the one-dimensional momentum theory of the ideal wind rotor, and calculate the turbulent energy term based on the energy conservation assumption.

[0066] For example, the equivalent wind speed of the wind rotor is , wind turbines Produced -Momentum sink 、 -Momentum sink , turbulent energy term and power The calculation formula is as follows:

[0067]

[0068]

[0069]

[0070]

[0071] In the formula Representing wind turbines The horizontal grid index where it is located, Represents the vertical index of the grid, is the grid vertical coordinate, It's a wind wheel The swept area, It's a wind wheel With the Layer and The intersection area of ​​the layer grid, is the horizontal grid area, and Grid The streamwise and spanwise wind speeds at It's a grid The horizontal velocity at is the turbulent kinetic energy correction factor, where ; Indicates air density, standard value can be taken .

[0072] Based on the above step S4, in a specific implementation manner, the calculation of 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 may specifically include the following steps 41 and 42:

[0073] Step 41: Project the momentum sink term and turbulent energy term corresponding to each wind turbine onto the computational grid using a discrete counting method;

[0074] 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 group numbers in different mesoscale grids.

[0075] For example, a mesoscale grid index based on a computational grid The calculated momentum sink term, turbulent energy term and power output within the mesoscale model grid are expressed as follows:

[0076]

[0077]

[0078]

[0079]

[0080] In the formula Represents a mesoscale horizontal grid The wind turbine index collection contained in it.

[0081] See attached Figure 2 , Figure 2 This is a schematic diagram of an application scenario in which the wind farm parameterization model provided in the embodiment of the present application is combined with the mesoscale model. In this application scenario, the first step is to construct a mesoscale-microscale coupled wind power parameterization model and calculate the momentum sink term, turbulent energy term and power within the mesoscale grid. Its specific implementation includes inputting the basic parameter information of the target wind farm, building a velocity query table in combination with the microscale wind farm flow analytical model, determining the background wind speed in the wind farm area based on the mesoscale model output and the velocity query table, determining the key physical parameters of the wind turbine in combination with the microscale wind farm flow analytical model, and then calculating the momentum sink term, turbulent energy term and power generated by the wind turbine, and then 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 wind farm parameterization model output, and carrying out time integration to output the mesoscale model simulation results. It should be noted that the specific implementation of each component shown in the figure and the calculation of the relevant formulas can be found in the specific description in the embodiment of the method of the present application, and will not be repeated here.

[0082] The following is an explanation of the micro-scale wind farm flow analysis model preferably adopted in the embodiment of the present application. The micro-scale wind farm flow analysis model adopted in this embodiment is implemented based on a single-machine wake model and a wake superposition method. Specifically: the target wind farm arrangement, wind turbine geometric parameters and wind environment parameters are input; the physical parameters required for the single-machine wake model are calculated based on the empirical formula, the thrust coefficient and wake variable of the wind turbine are updated one by one using an iterative method, and the equivalent wind speed of the wind wheel is calculated. Based on this, the momentum conservation superposition method, the local linear superposition method or the wind speed product and other wake superposition methods can be used to obtain the spatial distribution of the wind farm flow velocity. The micro-scale wind farm flow analysis model only inputs the geometric information of the wind turbine and the wind environment parameters that are more easily obtained in actual engineering applications. The wake model and the superposition method can determine the spatial distribution of the wake velocity under any wind farm arrangement, quickly output the equivalent wind speed of the wind wheel, and construct a query table for the mapping relationship between the average wind speed of the wind farm area and the background wind speed in the wind farm hub height plane.

[0083] See attached Figure 3 , Figure 3 The average wind speed in the target wind farm area is obtained based on the micro-scale wind farm flow analytical model in the embodiment of the present application. The main implementation steps flow chart is as follows: Figure 3 The main features shown include:

[0084] First determine the model input: background wind speed , ambient turbulence , wind direction angle and unit thrust coefficient curve and ; Assume that the thrust coefficient of each wind turbine is equal to And set the number of iterations jt=0 to start iterating to determine the equivalent wind speed of the wind wheel The iterative process is as follows: the turbulence intensity of each wind turbine is determined 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 , starting point of far wake , wake width and speed loss ; For n=1,...,N, determine one by one in the following way and : Calculate wind turbine Wind wheel sampling point ( ) , where nt represents the unit number index; calculate the equivalent wind speed of the wind turbine rotor of the nth wind turbine unit : ;according to Update unit thrust coefficient and power factor , record the updated unit thrust coefficient and power factor , so far N wind turbines have been completed 、 and Determination; calculation and The mean absolute difference : ;judge Is it less than 0.001? If so, the iteration ends and the output is 、 and ; Otherwise jt=jt+1, and continue the above iterative process.

[0085] Set the wind farm area according to the coordinates of the wind farm units The velocity sampling point set within , of which the wind farm area is a rectangle containing the simulated wind farm; calculate the wind speed at the speed sampling point : , where H represents the hub height; output wind farm area Average wind speed: .

[0086] The mesoscale and microscale coupled wind farm parameterized model proposed in this invention can greatly improve the prediction accuracy of momentum sink terms, turbulent energy terms and power, improve the characterization accuracy of wind farms in mesoscale models, help to improve the simulation accuracy of wind farm group flows, and carry out wind power base planning more scientifically.

[0087] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effect of the present application, different steps do not have to be performed in such an order. They can be performed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present application.

[0088] It will be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment of the present invention may also be completed by instructing the relevant hardware through a computer program. The computer program may be stored in a computer-readable storage medium. When the computer program is executed by a processor, it may implement the steps of each of the above method embodiments. The computer program includes computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal, and software distribution medium, etc., which can carry the computer program code.

[0089] Furthermore, the present invention also provides an electronic device. In one electronic device embodiment 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 executing the method for constructing a mesoscale and microscale coupled wind farm parameterized model according to the above method embodiment. The processor can be configured to execute the program in the storage device, which includes but is not limited to executing the method for constructing a mesoscale and microscale coupled wind farm parameterized model according to the above method embodiment. For ease of explanation, only the portions related to the embodiments of the present invention are shown. For specific technical details not disclosed, please refer to the method section of the embodiments of the present invention.

[0090] In the embodiment of the present application, the electronic device may be a control device device formed by various devices. In some possible implementations, the electronic device may include multiple memories and multiple processors. The program for executing the method for constructing the parameterized model of the meso-microscale coupled wind farm in the above method embodiment can be divided into multiple subroutines, and each subroutine can be loaded and run by the processor to execute different steps of the method for constructing the parameterized model of the meso-microscale coupled wind farm in the above method embodiment. Specifically, each subroutine can be stored in different memories respectively, and each processor can be configured to execute the program in one or more memories to jointly implement the method for constructing the parameterized model of the meso-microscale coupled wind farm in the above method embodiment, that is, each processor executes different steps of the method for constructing the parameterized model of the meso-microscale coupled wind farm in the above method embodiment respectively to jointly implement the method for constructing the parameterized model of the meso-microscale coupled wind farm in the above method embodiment.

[0091] The aforementioned multiple processors may be processors deployed on the same device. For example, the aforementioned electronic device may be a high-performance device composed of multiple processors, and the aforementioned multiple processors may be processors configured on the high-performance device. Furthermore, the aforementioned multiple processors may also be processors deployed on different devices. For example, the aforementioned electronic device may be a server cluster, and the aforementioned multiple processors may be processors on different servers in the server cluster.

[0092] Furthermore, the present invention also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present invention, the computer-readable storage medium can be configured to store a program for executing the method for constructing a meso-microscale coupled wind farm parameterized model of the above-mentioned method embodiment, and the program can be loaded and run by a processor to implement the above-mentioned method for constructing a meso-microscale coupled wind farm parameterized model. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present invention is a non-temporary computer-readable storage medium.

[0093] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection 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 simulation results under the mesoscale mode and a pre-constructed mapping relationship query table of average wind speed and 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; the mapping relationship query table is constructed based on a microscale wind farm flow analytical model, and the microscale wind farm flow analytical model is a microscale analytical model implemented based on a single-machine wake model and a wake superposition method; Step S3: determining key physical parameters of the wind turbines based on the re-determined background wind speed in the wind farm area and the micro-scale wind farm flow analytical model, and calculating operating data of each wind turbine in the micro-scale model based on the key physical parameters; the key physical parameters include the rotor equivalent wind speed, thrust coefficient, and power coefficient; The target wind farm basic parameters include wind turbine power and thrust coefficient curves; 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 analytical model includes: inputting the re-determined background wind speed in the wind farm area into the micro-scale wind farm flow analytical model to output an equivalent wind speed for the wind rotor; and determining the power coefficient and thrust coefficient based on the equivalent wind speed for the wind rotor and the wind turbine power and thrust coefficient curves; Step S4: Projecting the momentum sink term and turbulent energy term corresponding to each wind turbine onto the computational grid using a discrete counting method; calculating 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 numbers within different mesoscale grids; the target wind farm basic parameters include the mesoscale grid index of different wind turbines and the set of wind turbine numbers within different mesoscale grids; Step S5: integrating the output of the wind farm parameterized model based on the momentum sink term, the turbulent kinetic energy term and the power output.

2. The method according to claim 1, characterized in that After step S5, the following steps are further included: Step S6: integrating a mesoscale model control equation based on the momentum sink term, the turbulent kinetic energy term, and the power output, and obtaining a mesoscale model simulation result based on an 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 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 of the wind farm area based on the simulation result and the mapping relationship query table in step S2 includes: Calculating the average wind speed and average wind direction of the wind farm area based on the simulation results, and selecting an 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 lookup table to reconstruct the background wind speed of the wind farm area.

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

5. The method according to claim 1, wherein The operation data of each wind turbine generator set in the micro-scale model calculated in step S3 includes: 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 kinetic energy term and the power output.

6. An electronic device comprising at least one processor and at least one memory, wherein the memory is adapted to store a plurality of program codes, wherein: The program code is suitable for being loaded and run by the processor to execute the method for constructing a meso-microscale coupled wind farm parameterized model according to any one of claims 1 to 5.

7. 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 the method for constructing a meso-microscale coupled wind farm parameterized model according to any one of claims 1 to 5.

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