A micro-meteorological-based wind field wind power prediction method and system
By using a nested approach of mesoscale WRF model and microscale CFD model, the challenge of predicting wind speed and wind power in wind farms with complex terrain was solved, enabling fast and accurate online wind speed and wind power forecasting, thus improving the economy and stability of wind farms.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-09-29
- Publication Date
- 2026-03-17
AI Technical Summary
Existing technologies struggle to accurately predict wind speed and wind power in wind farms with complex terrain, especially in harsh environments such as mountains, wilderness, beaches, and islands, where complex terrain and surface features make wind power forecasting difficult.
A nested approach of mesoscale WRF model and microscale CFD model is adopted. By establishing a mesoscale WRF model to obtain micro-meteorological reanalysis data, the inflow boundary conditions of the microscale CFD model are constructed, numerical simulation is performed, a basic database of microscale wind field distribution of wind farms is generated, and the wind direction and wind speed data of wind turbines are calculated in combination with mesoscale numerical weather prediction, and the power generation is calculated based on the power curve.
It enables online and rapid wind speed and wind power forecasting for wind farms in complex terrain, improving the accuracy and timeliness of wind power prediction.
Smart Images
Figure CN115470731B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to a method and system for predicting wind power in wind farms based on micrometeorology, belonging to the field of wind power prediction technology. Background Technology
[0002] Wind power, as a mature, environmentally friendly, green, and renewable energy source, has become an important component in building a new power system based on new energy sources. Given the current relatively high cost of wind power generation, improving its economic viability is crucial for ensuring the sustainable development of the wind power industry. Since the cost of wind power generation largely depends on the wind resources available at wind farms, predicting wind speed and power output is an effective way to reduce wind power generation costs and ensure the safe and stable operation of the power system, enabling the power grid to schedule operations in advance and power generation companies to adjust their maintenance tasks. However, due to the influence of meteorological conditions and geographical environment, wind speed exhibits significant intermittency, fluctuation, and randomness. Therefore, achieving more accurate predictions of wind speed and power output remains a key research task.
[0003] Existing methods, categorized according to the specific model principles used in the prediction process, mainly fall into three categories: physical methods, statistical methods, and combined methods. The NWP model (Numerical Weather Prediction) is a typical physical method. First, it uses the NWP model to determine meteorological information for the future time period. Then, it combines this with geographical information around the wind turbine to calculate wind speed and direction at the turbine's rotor height. Finally, it uses the mapping relationship of the original power curve to derive the turbine's output power. Physical methods require high-precision meteorological forecast data. Statistical methods, based on historical wind farm data, establish a mapping relationship between historical data and predicted values through model identification, parameter estimation, and model validation. Alternatively, they utilize machine learning methods to build nonlinear models through learning and training on data relationships. Combined methods are currently the main direction of research in wind speed and wind power prediction. With the development of computer technology, computational fluid dynamics (CFD) methods based on numerical discrete solutions are increasingly being applied to solve microscale flow problems. This allows for refined simulations of wind farm flow and operational characteristics in complex terrain, enabling a better exploration of the details of gas flow within wind farms in complex terrain. Therefore, the combined forecasting method based on the NWP model is the current direction of wind power forecasting system development. It uses methods such as physical downscaling to process numerical weather forecasts provided by meteorological departments to obtain predicted meteorological information in the microscale environment of wind farms, and combines statistical methods to transform this information into predicted power values of wind farms to guide wind farm operation and management.
[0004] However, wind farms operate under complex conditions. Most wind turbines are installed in relatively harsh environments such as high mountains, wilderness, beaches, and islands. The complex terrain and surface features lead to large spatial and temporal variations in the near-surface wind field, making it difficult to accurately predict the wind power output of wind farms. Summary of the Invention
[0005] To address the problems existing in the prior art, this invention proposes a wind power prediction method for wind farms based on micrometeorology.
[0006] The technical solution of the present invention is as follows:
[0007] On the one hand, this invention proposes a wind power prediction method for wind farms based on micrometeorology, including the following steps:
[0008] A mesoscale WRF model was established, and micro-meteorological reanalysis data of the target area, including the target wind farm area, were obtained as background field data. The mesoscale WRF model was then used to calculate the background field data of the target area to obtain mesoscale meteorological analysis data.
[0009] A microscale CFD model was constructed, and wind speed, wind direction and turbulent kinetic energy information were extracted from mesoscale meteorological analysis data as inflow boundary conditions for the microscale CFD model.
[0010] The inflow boundary conditions are input into a microscale CFD model for numerical simulation, resulting in a basic database of microscale wind field distribution in wind farms.
[0011] Based on mesoscale numerical weather prediction, the corresponding wind field distribution is selected from the microscale wind field distribution database. Based on the selected wind field distribution, the wind direction and wind speed data of each wind turbine in the wind farm are calculated. Based on the calculated wind direction and wind speed data and power curve of the wind turbine, the power generation of the corresponding wind turbine is calculated. According to the wind farm operation strategy, the currently operating wind turbine is determined, and the power generation of all currently operating wind turbines is counted to obtain the wind power prediction value of the target wind farm.
[0012] As a preferred embodiment, the mesoscale WRF model employs the mesoscale atmospheric simulation system AMP;
[0013] When using the mesoscale WRF model to calculate the background field data of the target area, parameters for the atmospheric boundary layer and the surface boundary layer were set. The parameter setting framework for the surface boundary layer was based on the Monin-Obuk-Hov simulation theory; the parameter setting framework for the atmospheric boundary layer was based on the TKE type framework of turbulent kinetic energy or the K-profile type based on Richardson number.
[0014] In a preferred embodiment, the steps for constructing the microscale CFD model are as follows:
[0015] CFD simulation software was used to perform CFD simulation calculations, which were performed by dividing the inflow wind direction into at least 36 angles such as wind direction.
[0016] The Reynolds-averaged equation is used as the governing equation for atmospheric boundary layer flow in the wind farm; the finite volume method is used to discretize the computational domain of the wind farm, forming a structured grid to represent each node in the wind farm.
[0017] The structured grid is solved to obtain the wind acceleration factor of each node in the wind farm relative to the inlet of the microscale CFD model.
[0018] On the other hand, the present invention also proposes a wind farm power prediction system based on micrometeorology, comprising:
[0019] The mesoscale model building module is used to build a mesoscale WRF model, obtain micro-meteorological reanalysis data of the target area containing the target wind farm area as background field data, and use the mesoscale WRF model to calculate the background field data of the target area to obtain mesoscale meteorological analysis data.
[0020] The microscale model building module is used to construct microscale CFD models. It extracts wind speed, wind direction, and turbulent kinetic energy information based on mesoscale meteorological analysis data, which serve as the inflow boundary conditions for the microscale CFD models.
[0021] The basic database establishment module is used to input the inflow boundary conditions into the microscale CFD model for numerical simulation, and obtain the basic database of the microscale wind field distribution of the wind farm.
[0022] The wind power prediction module is used to select the corresponding wind field distribution from the microscale wind field distribution database based on mesoscale numerical weather forecasts. Based on the selected wind field distribution, it calculates the wind direction and wind speed data of each wind turbine in the wind farm. Based on the calculated wind direction and wind speed data and power curve of the wind turbine, it calculates the power generation of the corresponding wind turbine. According to the wind farm operation strategy, it determines the currently operating wind turbine and counts the power generation of all currently operating wind turbines to obtain the wind power prediction value of the target wind farm.
[0023] As a preferred embodiment, the mesoscale WRF model employs the mesoscale atmospheric simulation system AMP;
[0024] When using the mesoscale WRF model to calculate the background field data of the target area, parameters for the atmospheric boundary layer and the surface boundary layer were set. The parameter setting framework for the surface boundary layer was based on the Monin-Obuk-Hov simulation theory; the parameter setting framework for the atmospheric boundary layer was based on the TKE type framework of turbulent kinetic energy or the K-profile type based on Richardson number.
[0025] In a preferred embodiment, the steps for constructing the microscale CFD model are as follows:
[0026] CFD simulation software was used to perform CFD simulation calculations, which were performed by dividing the inflow wind direction into at least 36 angles such as wind direction.
[0027] The Reynolds-averaged equation is used as the governing equation for atmospheric boundary layer flow in the wind farm; the finite volume method is used to discretize the computational domain of the wind farm, forming a structured grid to represent each node in the wind farm.
[0028] The structured grid is solved to obtain the wind acceleration factor of each node in the wind farm relative to the inlet of the microscale CFD model.
[0029] In another aspect, the present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the wind power prediction method for wind farms based on micrometeorology as described in any embodiment of the present invention.
[0030] Furthermore, the present invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the wind power prediction method for wind farms based on micrometeorology as described in any embodiment of the present invention.
[0031] The present invention has the following beneficial effects:
[0032] This invention provides a wind power prediction method for wind farms based on micro-meteorology. By establishing and nesting a mesoscale WRF model and a microscale CFD model, a basic database of microscale wind field distribution in wind farms is obtained. Then, based directly on mesoscale numerical weather forecasts, online rapid downscaling of wind speed and power forecasts for wind farms in complex terrain is achieved. Attached Figure Description
[0033] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention. Detailed Implementation
[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0035] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.
[0036] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.
[0037] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.
[0038] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.
[0039] Example 1:
[0040] See Figure 1 This embodiment proposes a wind power prediction method for wind farms based on micro-meteorology. This embodiment uses reanalysis data from a global model to build a mesoscale meteorological model. The resulting mesoscale data is used in nested calculations during microscale hydrodynamic simulations. Finally, the calculation results are used for wind resource assessment in complex terrain wind farms. Weather movement can be predicted using mesoscale numerical models. Since the wind farm terrain is stable, a microscale CFD model is used to simulate wind field distributions under various mesoscale meteorological background conditions in advance, establishing a basic database of microscale wind field distributions for wind farms. Then, based directly on mesoscale numerical weather forecasts, by selecting the corresponding wind field distribution, online rapid downscaling of wind speed and power forecasts for complex terrain wind farms is achieved to meet the timeliness requirements of wind power prediction for wind farms. Specifically, the method includes the following steps:
[0041] S100. Establish a mesoscale WRF (Weather Research and Forecast) model, obtain micro-meteorological reanalysis data of the target area including the target wind farm area as background field data, and use the mesoscale WRF model to calculate the background field data of the target area to obtain mesoscale meteorological analysis data with a horizontal resolution of 1-3 km.
[0042] S200. Construct a microscale CFD (Computational Fluid Dynamics) model. Based on mesoscale meteorological analysis data, extract information such as wind speed, wind direction, and turbulent kinetic energy near the microscale modeling calculation boundary as inflow boundary conditions for the microscale CFD model.
[0043] S300. Input the inflow boundary conditions into the microscale CFD model, drive the CFD model to perform numerical simulation, and establish a basic database of microscale wind field distribution of wind farms under various mesoscale meteorological background conditions.
[0044] S400. Based on mesoscale numerical weather prediction, select the corresponding wind field distribution from the microscale wind field distribution database, downscale and obtain the wind speed and wind direction deviation distribution of the entire wind farm. Based on the selected wind field distribution, calculate the wind direction and wind speed data of each wind turbine in the wind farm, and calculate the power generation of the corresponding wind turbine based on the calculated wind direction and wind speed data and power curve. According to the wind farm operation strategy, determine the currently operating wind turbines, and statistically analyze the power generation of all currently operating wind turbines to obtain the predicted wind power value of the target wind farm.
[0045] In a preferred embodiment of this practice, the mesoscale WRF model employs the mesoscale atmospheric simulation system AMP. WRF is a widely used meteorological model, and many organizations and research institutions have further developed it, establishing a series of derivative models. Extensive practical projects and related engineering experience have demonstrated that, compared to ARPEGE, ALADIN, or LMDZ models, the WRF model exhibits greater efficiency and accuracy. Furthermore, this model avoids some inaccuracies in vertical velocity vector and pressure disturbance propagation caused by the use of inelastic assumptions (such as Meso-NH).
[0046] When using the mesoscale WRF model to calculate the background field data of the target area, it is also necessary to consider the parameter settings of the atmospheric boundary layer (ABL) and the surface boundary layer (RSL). The parameter setting architecture of the surface boundary layer (RSL) is based on the Monin-Obuk-Hov simulation theory. For the atmospheric boundary layer (ABL), there are two types of architectures to choose from: the TKE type architecture based on turbulent kinetic energy, and the K-profile type based on the Richardson number, which is very important for defining the boundary layer extension. In both cases, the provided architectures have been extensively tested and validated under various conditions.
[0047] In a preferred embodiment of this invention, the steps for constructing the microscale CFD model are as follows:
[0048] The establishment of the microscale model needs to meet the requirements for the accuracy of wind flow field simulation on complex terrain. CFD simulation software is used to divide the wind flow into at least 36 inflow wind directions according to the wind direction and other angles for CFD simulation calculation.
[0049] The Reynolds-averaged equations are used as the governing equations for atmospheric boundary layer flow in the wind farm, as follows:
[0050]
[0051] In the formula, ρ is the air density, u is the flow velocity, p is the air pressure, μ is the viscosity, and F is the volume force such as gravity, inertia, and electric field. For the higher-order Reynolds stress term resulting from the product of the disturbance components, it represents the additional momentum transport caused by turbulent disturbances;
[0052] The finite volume method (FVM) is used to discretize the computational domain of a wind farm, forming a structured grid representing each node within the wind farm. Unlike the finite difference method commonly used in atmospheric models, which relies on Taylor expansions of the governing equations at grid points, microscale modeling based on computational fluid dynamics (CFD) employs the FVM for discretizing the governing equations. In the FVM, the computational region is divided into a series of control volumes, each represented by a node. Discrete equations are derived by integrating the conserved governing equations over the control volumes. During this derivation, assumptions are made regarding the composition of the function being solved and its first derivative at the interface; this composition method is the discretization scheme in the FVM. The discrete equations derived using the FVM guarantee conservation properties, and the physical meaning of the coefficients is clear. The FVM allows for the creation of non-orthogonal or even unstructured grids, better adapting to the needs of meshing complex terrain and characterizing irregular regions in the solution process, thus providing technical possibilities for refined numerical analysis. A microscale wind field database describes the relative relationships of microscale wind speed and direction deviations in the wind farm under various atmospheric stability and wind direction conditions. For wind farms with complex terrain, wind speed distribution is closely related to the incoming wind direction and atmospheric stability. Under specified atmospheric stability and wind direction conditions, a computational fluid dynamics model of the wind farm area is established.
[0053] The structured mesh is solved to obtain the wind acceleration factor of each node within the wind farm relative to the inlet of the microscale CFD model, and the wind acceleration factor within the wind farm region.
[0054] V h，0 (X,Y,Z) is a function of wind direction (d) and atmospheric stability (s), expressed as:
[0055] V h，0 (X,Y,Z)=f(d,s).
[0056] Example 2:
[0057] This invention also proposes a wind farm power prediction system based on micrometeorology, comprising:
[0058] The mesoscale model building module is used to build a mesoscale WRF model, obtain micro-meteorological reanalysis data of the target area containing the target wind farm area as background field data, and use the mesoscale WRF model to calculate the background field data of the target area to obtain mesoscale meteorological analysis data; this module is used to implement the function of step S100 in the above embodiment, which will not be described again here.
[0059] The microscale model building module is used to construct a microscale CFD model. It extracts wind speed, wind direction and turbulent kinetic energy information based on mesoscale meteorological analysis data as inflow boundary conditions for the microscale CFD model. This module is used to implement the function of step S200 in the above embodiment, which will not be described again here.
[0060] The basic database establishment module is used to input the inflow boundary conditions into the microscale CFD model for numerical simulation to obtain the basic database of the microscale wind field distribution of the wind farm. This module is used to implement the function of step S300 in the above embodiment, which will not be described in detail here.
[0061] The wind power prediction module is used to select the corresponding wind field distribution from the microscale wind field distribution database based on mesoscale numerical weather forecasts, calculate the wind direction and wind speed data of each wind turbine in the wind farm based on the selected wind field distribution, calculate the power generation of the corresponding wind turbine based on the calculated wind direction and wind speed data and power curve, determine the currently operating wind turbine based on the wind farm operation strategy, and obtain the wind power prediction value of the target wind farm by statistically analyzing the power generation of all currently operating wind turbines. This module is used to implement the function of step S400 in the above embodiment, which will not be described again here.
[0062] As a preferred embodiment of this example, the mesoscale WRF model uses the mesoscale atmospheric simulation system AMP;
[0063] When using the mesoscale WRF model to calculate the background field data of the target area, parameters for the atmospheric boundary layer and the surface boundary layer were set. The parameter setting framework for the surface boundary layer was based on the Monin-Obuk-Hov simulation theory; the parameter setting framework for the atmospheric boundary layer was based on the TKE type framework of turbulent kinetic energy or the K-profile type based on Richardson number.
[0064] In a preferred embodiment of this invention, the steps for constructing the microscale CFD model are as follows:
[0065] CFD simulation software was used to perform CFD simulation calculations, which were performed by dividing the inflow wind direction into at least 36 angles such as wind direction.
[0066] The Reynolds-averaged equation is used as the governing equation for atmospheric boundary layer flow in the wind farm; the finite volume method is used to discretize the computational domain of the wind farm, forming a structured grid to represent each node in the wind farm.
[0067] The structured grid is solved to obtain the wind acceleration factor of each node in the wind farm relative to the inlet of the microscale CFD model.
[0068] Example 3:
[0069] This embodiment proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the wind power prediction method for wind farms based on micrometeorology as described in any embodiment of the present invention.
[0070] Example 4:
[0071] This embodiment proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the wind power prediction method for wind farms based on micrometeorology as described in any embodiment of the present invention.
[0072] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.
[0073] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0074] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0075] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0076] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.
[0077] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.
Claims
1. A micro-meteorologically based wind farm wind power prediction method, characterized in that, The method comprises the following steps: A mesoscale WRF model is established, meteorological reanalysis data of a target region containing a target wind farm region is obtained as background field data, the mesoscale WRF model is used to calculate the background field data of the target region, and mesoscale meteorological analysis data is obtained; A microscale CFD model is constructed, wind speed, wind direction and turbulent kinetic energy information are extracted based on the mesoscale meteorological analysis data, and the information is used as inflow boundary conditions of the microscale CFD model; The inflow boundary conditions are input into the microscale CFD model for numerical simulation, and a wind farm microscale wind field distribution basic database is obtained; According to a mesoscale numerical weather prediction, corresponding wind field distribution is selected from the wind field distribution basic database, wind direction and wind speed data of each wind turbine in the wind farm are calculated based on the selected wind field distribution, power generation of the corresponding wind turbine is calculated according to the calculated wind direction and wind speed data of the wind turbine and the power curve, and wind power prediction value of the target wind farm is obtained by calculating power generation of all currently running wind turbines according to a wind farm operation strategy. The mesoscale WRF model adopts a mesoscale atmospheric simulation system AMP. When the mesoscale WRF model is used to calculate the background field data of the target region, atmospheric boundary layer and surface boundary layer parameters are set, the surface boundary layer parameter setting architecture is based on the Monin-Obuk-Hov simulation theory, and the atmospheric boundary layer parameter setting architecture is based on a TKE type architecture of turbulent kinetic energy or a K-profile type of Richardson number.
2. A micro-meteorology based wind farm wind power prediction method according to claim 1, characterized in that, The step of constructing the microscale CFD model specifically comprises the following steps: A CFD simulation software is used to divide the wind direction into at least 36 inflow wind directions at equal angles for CFD simulation calculation; The Reynolds average equation is used as a control equation of atmospheric boundary layer flow of the wind farm; A finite volume method is used to discretize the wind farm calculation domain to form a structured grid representing each node in the wind farm; The structured grid is solved to obtain wind acceleration factors of each node in the wind farm relative to the inlet of the microscale CFD model.
3. A micro-meteorologically based wind farm wind power prediction system, characterized by, The method comprises the following steps: A mesoscale model establishing module is configured to establish a mesoscale WRF model, obtain meteorological reanalysis data of a target region containing a target wind farm region as background field data, calculate the background field data of the target region by using the mesoscale WRF model, and obtain mesoscale meteorological analysis data; A microscale model establishing module is configured to construct a microscale CFD model, extract wind speed, wind direction and turbulent kinetic energy information based on the mesoscale meteorological analysis data, and use the information as inflow boundary conditions of the microscale CFD model; A basic database establishing module is configured to input the inflow boundary conditions into the microscale CFD model for numerical simulation, and obtain a wind farm microscale wind field distribution basic database. The wind power prediction module is configured to select corresponding wind field distribution in the micro-scale wind field distribution database according to the mesoscale numerical weather prediction, calculate wind direction and wind speed data of each wind turbine in the wind farm based on the selected wind field distribution, calculate power generation of the corresponding wind turbine according to the calculated wind direction and wind speed data of the wind turbine and the power curve, determine the currently running wind turbine according to the wind farm operation strategy, and obtain the wind power prediction value of the target wind farm by summing up the power generation of all the currently running wind turbines. The mesoscale WRF model adopts a mesoscale atmospheric simulation system AMP. When the mesoscale WRF model is used to calculate the background field data of the target region, the atmospheric boundary layer and the surface boundary layer parameters are set, wherein the parameter setting architecture of the surface boundary layer is based on the Monin-Obuk-Hov simulation theory, and the parameter setting architecture of the atmospheric boundary layer is based on the TKE type architecture of turbulent kinetic energy or the K-profile type based on the Richardson number.
4. A micro-meteorology based wind farm wind power prediction system according to claim 3, wherein, The step of constructing the micro-scale CFD model specifically includes: The CFD simulation software is used to divide the wind direction into at least 36 inflow wind directions at equal angles for CFD simulation calculation; The Reynolds average equation is used as the control equation of the atmospheric boundary layer flow of the wind farm; The finite volume method is used to discretize the wind farm calculation domain to form a structured grid representing each node in the wind farm; The structured grid is solved to obtain the wind acceleration factor of each node in the wind farm relative to the inlet of the micro-scale CFD model.
5. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the micro-meteorological-based wind power prediction method of the wind farm according to any one of claims 1 to 2.
6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the micro-meteorological-based wind power prediction method of the wind farm according to any one of claims 1 to 2.