High-precision simulation method and system for wind energy resources

Abstract

The invention relates to the technical field of wind power plants, and provides a wind energy resource high-precision simulation method and system, and the method comprises the steps: obtaining geographic data, wind measurement data and mesoscale meteorological data of a target region; the method comprises the following steps: constructing a terrain model based on geographic data, dividing the terrain model into a plurality of sectors, and generating a wind profile as a boundary condition of each sector by using mesoscale meteorological data; the method comprises the following steps: selecting multiple groups of turbulence models based on mesoscale data and wind measurement data of a selected wind measurement tower, performing directional calculation in combination with boundary conditions, constructing a relationship between the selected wind measurement tower and a target wind measurement tower based on a directional calculation result, obtaining a simulation value of the target wind measurement tower through interpolation, and evaluating an error between the simulation value of the target wind measurement tower and the wind measurement data. Selecting an optimal turbulence model based on the error; and performing comprehensive calculation based on the wind measurement data and a directional calculation result corresponding to the optimal turbulence model to obtain a whole-field wind energy resource. And the wind resource simulation accuracy is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind farms, and in particular relates to a high-precision simulation method and system for wind energy resources. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] The existing wind energy resource simulation methods have the following shortcomings: Insufficient accuracy of flow field simulation: The current wind energy resource simulation technology has the problem of insufficient accuracy when considering the changes in flow fields under different atmospheric stability conditions. In particular, there is a certain uncertainty in the modeling of factors such as thermal stability, turbulence intensity and underlying surface, which limits the accurate description of wind field behavior.

[0004] Lack of consideration of mesoscale factors: When simulating wind flow within a wind farm, current technologies rarely consider the impact of mesoscale factors such as macroscopic atmospheric circulation on the wind farm. This results in some variables that are not fully considered in wind energy resource assessment, affecting the comprehensiveness and accuracy of the assessment.

[0005] Inadequately optimized boundary conditions: In directional calculations, existing technologies often use specific input boundary conditions, which results in inadequately optimized boundary conditions, which may lead to differences between simulation results and actual observation data.

[0006] Limitations of turbulence model selection: Regarding the relationship between the distribution density of wind towers and the accuracy of wind energy resource assessment, current technology has not yet clarified the distribution law of related errors, which makes it difficult to interpret the errors between simulation results and measured data, limiting the in-depth understanding of model performance.

[0007] The influence of terrain factors is not fully considered: the existing turbulence model selection relies on experience, which may lead to insufficient accuracy in complex terrain and novel engineering scenarios. Summary of the invention

[0008] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a high-precision simulation method and system for wind energy resources, which adopts mesoscale meteorological data such as macroscopic atmospheric circulation to generate wind profiles as boundary conditions, optimizes the wind flow model in the wind farm, and more realistically reflects the characteristics of the wind farm. According to the mesoscale data and wind measurement data, a turbulence model that is more suitable for the target wind farm is selected, and the turbulence model and parameters are used in subsequent calculations to improve the accuracy of wind resource simulation.

[0009] In order to achieve the above object, the present invention adopts the following technical solution: A first aspect of the present invention provides a high-precision simulation method for wind energy resources, comprising: Obtain geographic data, wind measurement data and mesoscale meteorological data of the target area; Based on geographic data, a terrain model is constructed and divided into several sectors. Mesoscale meteorological data are used to generate wind profiles as boundary conditions for each sector. Based on the mesoscale data and the wind data of the selected wind tower, multiple groups of turbulence models are selected, and directional calculations are performed in combination with boundary conditions. Based on the directional calculation results, the relationship between the selected wind tower and the target wind tower is constructed, and the simulation value of the target wind tower is obtained by interpolation. The error between the simulation value of the target wind tower and the wind data is evaluated, and the optimal turbulence model is selected based on the error. Based on the wind measurement data and the directional calculation results corresponding to the optimal turbulence model, a comprehensive calculation is performed to obtain the wind energy resources of the entire field.

[0010] Furthermore, the wind speeds at different heights of the terrain model are calculated based on the mesoscale meteorological data, and the wind profile is generated based on the wind speeds at each layer height.

[0011] Furthermore, the boundary condition includes an inlet boundary, and the inlet boundary is defined by setting a wind profile and a wind direction of the inlet.

[0012] Furthermore, the boundary condition includes an outlet boundary, and the pressure value of the flow field at the outlet boundary is equal to the pressure of the external environment.

[0013] Furthermore, the boundary condition includes an underlying surface boundary, and the underlying surface boundary is set as a boundary condition regarding surface roughness.

[0014] Furthermore, the directional calculation results include flow field and pressure field distribution, wind speed, wind direction, horizontal deviation, inflow angle, turbulence intensity and wind acceleration factor.

[0015] Furthermore, the comprehensive calculation includes equivalent wind data calculation, turbulence intensity correction, Weibull fitting, wake calculation, and air density and power curve correction.

[0016] A second aspect of the present invention provides a high-precision wind energy resource simulation system, comprising: A data acquisition module, which is configured to: acquire geographic data, wind measurement data and mesoscale meteorological data of a target area; A boundary condition setting module is configured to: construct a terrain model based on geographic data, divide the terrain model into several sectors, and use mesoscale meteorological data to generate wind profiles as boundary conditions for each sector; A model selection module is configured to: select multiple groups of turbulence models based on mesoscale data and wind data of a selected wind tower, perform directional calculations in combination with boundary conditions, construct a relationship between the selected wind tower and a target wind tower based on the directional calculation results, obtain a simulation value of the target wind tower by interpolation, evaluate the error between the simulation value of the target wind tower and the wind data, and select the optimal turbulence model based on the error; The comprehensive calculation module is configured to: perform comprehensive calculation based on the wind measurement data and the directional calculation results corresponding to the optimal turbulence model to obtain the wind energy resources of the entire field.

[0017] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method for high-precision simulation of wind energy resources as described above.

[0018] The fourth aspect of the present invention provides a computer device, comprising a computer-readable storage medium, a processor, and a computer program stored on the computer-readable storage medium and executable on the processor, wherein when the processor executes the program, the steps in a high-precision simulation method for wind energy resources as described above are implemented.

[0019] Compared with the prior art, the present invention has the following beneficial effects: The present invention uses mesoscale meteorological data such as macroscopic atmospheric circulation to generate wind profiles as boundary conditions, optimizes the wind flow model in the wind farm, comprehensively considers the impact of these factors on the wind farm, more realistically reflects the wind farm characteristics, and improves the accuracy of wind energy resource simulation.

[0020] The present invention preliminarily selects multiple groups of turbulence models, performs calculations based on mesoscale data and wind measurement data, compares the error rates of different turbulence models, and selects a group of results with the smallest error as the final simulation results. The turbulence model that is more suitable for the target wind field is screened, and the turbulence model and parameters are used in subsequent calculations, which ultimately improves the accuracy of wind resource simulation and provides more accurate simulation results for wind resource assessment.

[0021] The present invention evaluates the simulation results by using indicators such as root mean square error and mean absolute error, and comprehensively evaluates the consistency between the simulation results and the actual observation data, thereby improving the degree of optimization of the selection of the turbulence model and ensuring the applicability of the turbulence model.

[0022] The present invention achieves higher-precision flow field simulation by optimizing the flow field calculation algorithm, especially considering the simulation under different atmospheric stability conditions, thereby improving the modeling accuracy of factors such as thermal stability, turbulence intensity and underlying surface. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0024] Figure 1 is a flow chart of a high-precision simulation method for wind energy resources according to the first embodiment of the present invention; Figure 2 is a schematic diagram of an error analysis process of Embodiment 1 of the present invention; Figure 3 This is an architecture diagram of a high-precision wind energy resource simulation system according to Embodiment 2 of the present invention; Figure 4 It is a structural diagram of a computer device according to a fourth embodiment of the present invention. DETAILED DESCRIPTION

[0025] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0026] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0027] Terminology explanation: Turbulence model: A mathematical model used to describe turbulent flows. In atmospheric science, turbulence models are used to simulate turbulent processes in the wind in the atmosphere.

[0028] Geographic data: Data describing the characteristics of the Earth's surface, which may include information such as topography, landforms, and roughness.

[0029] Meteorological data: Data describing atmospheric conditions, including wind speed, wind direction, temperature, etc.

[0030] Meshing technology: In simulation, the calculation area is divided into small grids for more detailed simulation and calculation.

[0031] Wind profile: A vertical profile that describes wind speed at different heights and is used to set boundary conditions for simulations.

[0032] Mesoscale data: In atmospheric science, it usually refers to data between large and small scales in meteorology, with a resolution generally ranging from a few kilometers to several hundred kilometers.

[0033] Directional calculation: Calculation for a specific direction, which may be used to simulate the characteristics of the wind field in different directions.

[0034] CFD method: The abbreviation of Computational Fluid Dynamics, is a numerical simulation method used to solve problems such as fluid flow and heat transfer.

[0035] Error assessment: Evaluate the accuracy of the model by comparing the differences between the simulation results and the actual observed data.

[0036] Root mean square error (RMSE): A statistic used to measure the error between the observed value and the model value, which is the square root of the mean of the squares of the errors.

[0037] Mean absolute error (MAE): A statistic that measures the error between observed values ​​and model values. It is the average of the absolute values ​​of the errors.

[0038] Wind turbine siting: In the field of wind energy, choosing an appropriate location to build a wind turbine.

[0039] Electricity generation calculation: Predict or calculate the amount of electricity a wind turbine will generate under specific conditions.

[0040] Wake model: The wake model is used to consider the impact of the wake generated by the wind turbine on the wind farm. Common wake models include the PARK model, improved PARK model, Frandsen model, etc.

[0041] Data interpolation: Data interpolation refers to using information between known data points to estimate values ​​at unknown locations to fill in missing or gaps in the data.

[0042] Data Retrieval: Data retrieval is the process of retrieving required data from a storage module, usually by specifying an identifier.

[0043] Data verification: Data verification is the process of ensuring that the loaded data is complete and accurate, and verifying its consistency with the subsequent wind farm directional calculation condition settings.

[0044] Embodiment 1 This embodiment provides a high-precision simulation method for wind energy resources.

[0045] This embodiment provides a high-precision simulation method for wind energy resources, such as Figure 1 As shown, the following steps are included: Step 1: Input the geographic data and meteorological data of the target area (target wind farm).

[0046] Step 101: The user uploads all relevant geographic information data in the study area, including terrain and roughness, through an interactive interface.

[0047] Step 102: Meteorological data includes wind measurement data and mesoscale meteorological data. The wind measurement data and mesoscale meteorological data are uploaded through an interactive interface, including wind speed, wind direction, wind speed frequency distribution, etc.

[0048] Step 2: Data processing: custom terrain, roughness, topographic map expansion, and splicing.

[0049] Step 201: Receive uploaded geographic information data, use geographic information system (GIS) technology to process the data, and construct a terrain model for wind farm flow field calculation; the terrain map can also be expanded and spliced.

[0050] Step 202: Receive meteorological data, perform data cleaning, calibration and format conversion to ensure data quality. For a small amount of missing data, use meteorological models to interpolate the data to obtain a more comprehensive meteorological data set.

[0051] Step 3: Grid division: Construct the computational domain and use unstructured grids to divide the terrain model, taking into account factors such as terrain, wind field range, and computational domain height.

[0052] The use of specific grid division and encryption techniques enables the model to accurately reflect the wind field characteristics of the study area.

[0053] Step 301: Define the range of the grid calculation domain by defining parameter settings such as "center position", "radius", and "calculation domain height".

[0054] Step 302: Define the degree of grid refinement by setting parameters such as "horizontal resolution", "bottom grid scale", and "vertical growth rate".

[0055] Step 303: Set the encryption position and encrypt the grid through "Encryption Area Settings".

[0056] Step 4: Sector definition: Select the physical model, boundary conditions, and calculation parameter settings for each sector to divide the terrain model into several sectors.

[0057] Step 401: define sectors according to the incoming flow direction. The number of sectors is generally defined as 12 or 16 sectors that are evenly distributed, and may also be other customized numbers or uneven distribution.

[0058] Step 402: Set boundary conditions and calculation parameters for sectors respectively.

[0059] The boundary condition settings include: (1) Outlet boundary: The outlet boundary is the pressure outlet boundary condition.

[0060] (2) Surface boundary: The roughness of the underlying surface of the wind field has an effect on the friction and retardation of the wind flow. The boundary is defined based on the roughness.

[0061] (3) Inlet boundary: There are two ways to define the inlet boundary conditions: through the thermal stability model and through wind shear.

[0062] The theoretical basis of the thermal stability model is the Monin-Obukhov similarity theory (MOST), which uses the thermal stability model to describe the thermodynamic state of the atmosphere and thus define the inlet wind profile.

[0063] The definition of wind shear combines mesoscale data and uses mesoscale meteorological data to generate wind profiles as boundary conditions for each sector, including: ① Calculate the wind speed at different heights of the terrain model through mesoscale meteorological data; ② Use the wind speed at each layer height to generate wind profiles; here the wind profiles are affected by the upstream geographical location of different sectors on the wind field, and the wind profiles of different sectors may be different; ③ The mesoscale meteorological data generates wind profiles as the boundary conditions of each sector: ④Compile the wind profile conditions into initial boundary conditions through UDF (user defined function).

[0064] The wind profiles generated by mesoscale meteorological data as boundary conditions are closer to the actual boundary conditions, which can improve the accuracy of calculations. Using the wind profiles generated by mesoscale data as initial conditions can ensure that the simulation model starts calculations from a state closer to the stable moment, thereby improving the stability of the calculation and reducing the time cost.

[0065] Directional calculations are performed for each sector, and wind speed and direction need to be considered in the definition of boundary conditions. The following is a detailed description of the boundary condition definition using the 0° sector as an example: (1) 0° sector (north wind) boundary condition setting: Inlet boundary: Define the north boundary of the calculation area as the inlet boundary, i.e. the velocity inlet. Define the inlet boundary conditions by setting the wind speed (wind profile) and wind direction at the north inlet. You can choose to use the thermal stability model or wind shear definition to determine the inlet wind profile.

[0066] When using the thermal stability model, the Monin-Obukhov similarity theory and the local thermal stability grade are used to characterize parameters such as the coarse excess length, which are applied in the calculation to be closer to the characteristics of the wind farm; When using the wind shear definition, wind shear information can be obtained based on wind measurement data or mesoscale data, and the wind profile can be fitted as a function of wind speed varying with height, which can be applied to directional calculations.

[0067] Outlet boundary: Set the south boundary of the computational region as a pressure outlet boundary condition to simulate the free flow of the flow field at the south outlet. At the boundary, the pressure value of the flow field is set to be equal to the pressure of the external environment, which can accurately simulate the free flow at the outlet.

[0068] Setting of underlying surface boundary: Considering the influence of surface roughness on wind flow friction and retardation, the underlying surface boundary condition is set to the boundary condition that considers surface roughness. In directional calculation, the surface roughness is converted into surface force, that is, a drag force is added to the bottom boundary to simulate the influence of surface friction on wind field flow.

[0069] (2) Setting of other sector boundary conditions: For other sectors, boundary conditions can also be defined based on wind and flow directions. For example, the inlet of the east fan zone can be set to the west, the outlet can be set to the east, and so on.

[0070] Step 403: Turbulence model setting.

[0071] Combined with terrain features, mesoscale data and wind measurement data, the wind field is preliminarily analyzed. According to the turbulence model principle and applicable conditions, multiple turbulence models are preliminarily selected for the wind field directional simulation model. Each turbulence model corresponds to a set of directional calculations for simulating turbulent processes.

[0072] Turbulence models include k-ω model, k-ε model, large eddy simulation, k-equation, etc. Combined with terrain features, mesoscale data and wind measurement data, a preliminary analysis of the wind field is conducted. Turbulence models are set for directional calculations based on the principles and applicable conditions of turbulence models.

[0073] Step 5: Directional calculation: Based on the mesoscale data and wind measurement data A, n groups of turbulence models are preliminarily selected and calculated.

[0074] The directional calculation process is to obtain the flow field characteristics by CFD calculation by specifying the incoming flow direction.

[0075] Directed computing uses a system solver based on CFD methods and adopts a regional decomposition-based approach for parallel design, which ensures that the grid load divided into each CPU is balanced.

[0076] After the directional calculation reaches convergence, the flow field, pressure field distribution, and field data of wind speed, wind acceleration factor, wind direction, horizontal deviation, inflow angle and turbulence intensity are obtained.

[0077] After the calculation is completed, the calculation results are saved to the storage module.

[0078] For local complex terrain, the local area can be simulated in detail and the directional calculation results can be corrected.

[0079] Different sectors correspond to different wind directions. For example, the directional calculation result of a 30° sector is the flow field at a 30° wind direction.

[0080] Step 6: Evaluate the applicability of the turbulence model.

[0081] In order to select the optimal turbulence model, multiple turbulence models are preliminarily selected for the wind field directional simulation model in step 4. Each turbulence model corresponds to a set of directional calculations. The applicability of the turbulence model is evaluated, such as Figure 2 As shown, the following steps are included: Step 601: Select wind measurement data of a specific period of time of one or a group of wind measurement towers (defined as wind measurement tower A) as input data for directional calculation, and obtain directional simulation data of wind measurement tower A.

[0082] Step 602: Use the interpolation method to combine the directional simulation data with the wind data of wind tower A (the selected wind tower), and calculate the wind speed values ​​at each height of the remaining wind tower positions (defined as wind tower B), that is, the simulation value of wind tower B. The specific description is as follows: (1) The 360° incoming flow is divided into multiple sectors (e.g., 16 sectors), and the directional calculation results are used to calculate the directional simulation results for each sector; (2) The directional calculation results include wind speed, turbulence intensity, wind acceleration factor, etc. The relationship between wind tower A and the target location (target wind tower, i.e. wind tower B) is constructed through the wind acceleration factor (including the corresponding relationship between wind speed during the simulation period and the corresponding relationship between wind speed during the measured period). Combined with the measured data of wind tower A, the simulation value of wind tower B (or any target location) can be calculated by interpolation. Take the calculation of turbulence intensity as an example: ΔI(bin, dir) = I meas (bin, dir)-I calcm (bin,dir) I corp (bin, dir)=I calcp (dir)+ ΔI(bin,dir).S m (dir) / S p (dir) Where dir represents the wind direction (sector). I meas (bin, dir) is the directional turbulence intensity actually measured at the wind tower A; I calcm (bin,dir) is the directional turbulence intensity calculated at the wind tower A; I calcp (dir) and Icorp (bin, dir) are the directional turbulence intensity at the result point B; S m (dir) is the directional wind acceleration factor calculated at the wind tower A; S p (dir) is the directional wind acceleration factor calculated at result point B. I calcp (dir) is the directional turbulence intensity obtained by directional calculation at the result point B; I corp (bin, dir) By using the wind acceleration factor and I calcp (dir) as well as ΔI(bin,dir) The final result calculated (the difference between the actual turbulence and the calculated turbulence at the wind tower A) is the final value of the calculated target point.

[0083] Step 603: Perform error analysis: Evaluation object: Wind field simulation results under different working conditions (wind measurement data from different wind towers A are used as input data).

[0084] Assessment basis: Actual data from wind tower B.

[0085] Evaluation Methodology: (1) Evaluate the consistency between the simulated values ​​of wind tower B and the actual observation data (wind measurement data) at different time periods as a basis for evaluating the accuracy of the turbulence model; (2) When there are enough wind towers and wind tower group B contains multiple wind towers, the error between the simulation value of each wind tower and the actual observation data is calculated as the basis for evaluating the accuracy of the turbulence model.

[0086] Evaluation indicators include: root mean square error (RMSE), mean absolute error (MAE), etc., which represent the average value of the absolute error between the simulated value and the observed value.

[0087] Root Mean Square Error:

[0088] Mean absolute error:

[0089] Step 604: Apply turbulence models and parameters.

[0090] By comparing the error rates of different turbulence models, the set of results with the smallest error is selected as the final simulation result. The turbulence model and its parameters corresponding to the directional calculation result with the smallest error are applied to the full simulation process, and the optimal directional calculation result is selected for subsequent other wind resource assessment calculations in this wind farm.

[0091] Step 7: The directional calculation results are combined with meteorological data and wind turbine data for comprehensive calculation.

[0092] Step 701: Directional result loading.

[0093] Load the result data of directional calculation, including field data of wind acceleration factor, wind direction, horizontal deviation, inflow angle and turbulence intensity in each sector.

[0094] The loaded directional calculation result data may be a local refined simulation result. For a wind farm without complex terrain, ordinary directional calculation results that have not been locally refined may be loaded.

[0095] Among them, you can choose one wind measurement tower or multiple wind measurement towers to load the directional calculation results.

[0096] Step 702: Comprehensive calculation: Based on the wind measurement data and the directional calculation results corresponding to the optimal turbulence model, a comprehensive calculation is performed to obtain the wind energy resources of the entire field.

[0097] The comprehensive calculation process includes calculation of equivalent wind data, turbulence intensity correction, Weibull fitting, air density and power curve correction, wake calculation and other processes.

[0098] Comprehensive calculations apply wind data information (wind speed, wind direction, turbulence intensity) to the entire field, and evaluate and analyze wind parameters, annual power generation, wind profiles, and wake losses in the area.

[0099] The wind condition parameters in the comprehensive calculation include average wind speed, Weibull parameter, wind shear, inflow angle, limit wind speed, turbulence intensity, wind power density, etc., which are used for simulation to evaluate the load adaptability of the unit.

[0100] In the comprehensive calculation, the directional calculation results, wind measurement data, and wake effect are combined to calculate the annual power generation AEP, wake loss, capacity factor, etc. with and without considering the wake.

[0101] Step 703: Evaluate wind energy resources of the later wind farm.

[0102] In large-scale wind farms that are constructed in phases and batches, the directional calculation results of the early wind farms are introduced when conducting comprehensive calculations of the later wind farms, thereby taking into account the influence of the wake of the built wind turbine group.

[0103] Step 8: Post-processing and visualization.

[0104] In post-processing and visualization, export simulation result data for further analysis.

[0105] The calculation results include wind acceleration factor, inflow angle, turbulent kinetic energy, wind direction deflection, etc. of directional calculation, as well as wind speed, inflow angle, turbulence intensity, wind direction deflection, etc. of comprehensive calculation.

[0106] This embodiment provides a high-precision simulation method for wind energy resources, which achieves a higher-precision flow field simulation by optimizing the flow field calculation algorithm, especially considering the simulation under different atmospheric stability conditions (i.e., combining meteorological data), improving the modeling accuracy of factors such as thermal stability, turbulence intensity and underlying surface. It can more accurately describe the wind field, improve the accuracy of the simulation, and provide more reliable data support for wind energy resource assessment.

[0107] This embodiment provides a high-precision simulation method for wind energy resources, which uses mesoscale data such as macroscopic atmospheric circulation to generate wind profiles as boundary conditions, optimizes the wind flow model in the wind farm, comprehensively considers the impact of these factors on the wind farm, more realistically reflects the characteristics of the wind farm, and improves the accuracy of simulation calculations; uses UDF to compile the initialization speed of the wind farm, provides initial conditions closer to the stable moment, increases calculation stability, reduces time costs, and thus improves the efficiency and accuracy of wind farm simulation. It considers mesoscale factors more comprehensively, improves the comprehensiveness and accuracy of wind energy resource assessment, better reflects the wind flow conditions in the actual environment, and provides more comprehensive information for wind farm site selection and planning.

[0108] This embodiment provides a high-precision simulation method for wind energy resources, which optimizes the boundary conditions of the model by using measured data, previous simulation data, and mesoscale data, ensuring that the simulation results are closer to the actual data, improving the realism and accuracy of the model, enhancing the credibility of the simulation results, and making the model closer to the actual scene.

[0109] This embodiment provides a high-precision simulation method for wind energy resources, which preliminarily selects multiple groups of turbulence models and performs calculations based on mesoscale data and wind measurement data; compares the error rates of different turbulence models, and selects a group of results with the smallest error as the final simulation result, and uses the turbulence model and parameters in subsequent calculations. By selecting a better turbulence model, a turbulence model that is more suitable for the target wind field is selected, which ultimately improves the accuracy of wind resource simulation and provides more accurate simulation results for wind resource assessment.

[0110] This embodiment provides a high-precision simulation method for wind energy resources, and proposes two methods for evaluating the accuracy of turbulence models. The simulation results are evaluated by using indicators such as root mean square error (RMSE) and mean absolute error (MAE), and a comprehensive consistency evaluation is performed on the simulation results and the actual observation data, thereby improving the degree of optimization of the selection of turbulence models and ensuring the applicability of turbulence models.

[0111] The high-precision simulation method of wind energy resources provided in this embodiment optimizes the establishment of terrain models and more comprehensively considers the impact of terrain factors on wind resource assessment. The error impact of local complex terrain factors on assessment is solved by meshing local complex terrain and key areas of concern, remodeling and meshing, etc., which can better solve the error impact of terrain factors on assessment at real wind farm sites and improve the applicability and accuracy of simulation results in actual environments.

[0112] Embodiment 2 This embodiment provides a high-precision wind energy resource simulation system.

[0113] Most of the existing wind energy resource simulation and fluid calculation systems adopt the Client-Server (CS) architecture, such as user access on different terminal devices is subject to certain restrictions, and system maintenance and updates are relatively cumbersome.

[0114] like Figure 3 As shown, a high-precision wind energy resource simulation system provided in this embodiment adopts BS architecture (browser-based architecture) to better achieve cross-platform access, improve system flexibility and maintainability, provide users with a more convenient user experience, and reduce system maintenance costs.

[0115] lient (client): The client sends requests to the Web / task server, such as submitting computing tasks or querying task status.

[0116] Docker Swarm Network Web / task server: The http server in this server receives client requests, determines the request destination through routing management, allocates computing tasks through task scheduling, and coordinates resources through Docker resource management.

[0117] Data analysis server / node: provides pre- and post-processing services, etc. The processing results can be fed back to the client through the Web / task server.

[0118] Storage center: The storage center stores files and provides data storage for the entire architecture. Each part can access the storage center through the network.

[0119] Computing servers or computing nodes: These nodes manage computing queues, execute CFD computing tasks, use computing databases to store relevant data, receive tasks assigned by Web / task servers, and can interact with storage centers.

[0120] This embodiment provides a high-precision wind energy resource simulation system that transforms the existing CS architecture into a BS architecture, solving the accessibility and collaboration limitations under the CS architecture. This improves the system's usability and user experience, makes the system more flexible and easy to use, promotes technological innovation and expands the scope of application. The application of the BS architecture may improve user experience, enhance system collaboration, and reduce device dependence.

[0121] This embodiment provides a high-precision wind energy resource simulation system, including: A data acquisition module, which is configured to: acquire geographic data, wind measurement data and mesoscale meteorological data of a target area; A boundary condition setting module is configured to: construct a terrain model based on geographic data, divide the terrain model into several sectors, and use mesoscale meteorological data to generate wind profiles as boundary conditions for each sector; A model selection module is configured to: select multiple groups of turbulence models based on mesoscale data and wind data of a selected wind tower, perform directional calculations in combination with boundary conditions, construct a relationship between the selected wind tower and a target wind tower based on the directional calculation results, obtain a simulation value of the target wind tower by interpolation, evaluate the error between the simulation value of the target wind tower and the wind data, and select the optimal turbulence model based on the error; The comprehensive calculation module is configured to: perform comprehensive calculation based on the wind measurement data and the directional calculation results corresponding to the optimal turbulence model to obtain the wind energy resources of the entire field.

[0122] The high-precision wind energy resource simulation system provided in this embodiment adopts the BS architecture, improves the system's availability and user experience, makes the system more flexible and easy to use, and promotes technological innovation and expands the scope of application. Compared with the existing CS architecture system, the new system improves the user experience, improves the system's synergy, and reduces device dependence through the application of the BS architecture, thereby promoting system innovation and expansion of the scope of application.

[0123] A high-precision wind energy resource simulation system provided in this embodiment has achieved remarkable beneficial effects in many aspects. Compared with existing systems, it has higher simulation accuracy, comprehensiveness and user experience, and has brought obvious technological progress to the field of wind energy resource simulation and fluid calculation.

[0124] It should be noted here that each module in this embodiment corresponds to each step in Example 1 one by one, and the specific implementation process is the same, which will not be repeated here.

[0125] Embodiment 3 This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the method for high-precision simulation of wind energy resources as described in the first embodiment above are implemented.

[0126] Embodiment 4 This embodiment provides a computer device, such as Figure 4 As shown, it includes a computer-readable storage medium 1003, a processor 1001, a communication interface 1002, and a computer program stored on the computer-readable storage medium 1003 and executable on the processor 1001, wherein the processor 1001, the communication interface 1002, and the computer-readable storage medium 1003 can be connected via a bus or other means. The communication interface 1002 is used to receive and send data, and when the processor 1001 executes the program, the steps in the method for high-precision simulation of wind energy resources as described in the first embodiment are implemented.

[0127] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0128] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0129] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A high-precision simulation method for wind energy resources, characterized in that: include: Obtain geographic data, wind measurement data and mesoscale meteorological data of the target area; Based on geographic data, a terrain model is constructed and divided into several sectors. Mesoscale meteorological data are used to generate wind profiles as boundary conditions for each sector. Based on the mesoscale data and the wind data of the selected wind tower, multiple groups of turbulence models are selected, and directional calculations are performed in combination with boundary conditions. Based on the directional calculation results, the relationship between the selected wind tower and the target wind tower is constructed, and the simulation value of the target wind tower is obtained by interpolation. The error between the simulation value of the target wind tower and the wind data is evaluated, and the optimal turbulence model is selected based on the error. Based on the wind measurement data and the directional calculation results corresponding to the optimal turbulence model, a comprehensive calculation is performed to obtain the wind energy resources of the entire field.

2. A high-precision simulation method for wind energy resources according to claim 1, characterized in that: The wind speeds at different heights of the terrain model are calculated based on the mesoscale meteorological data, and the wind profile is generated based on the wind speeds at each layer height.

3. A high-precision simulation method for wind energy resources according to claim 1, characterized in that: The boundary conditions include an inlet boundary, and the inlet boundary is defined by setting a wind profile and a wind direction of the inlet.

4. A high-precision simulation method for wind energy resources as claimed in claim 1, characterized in that: The boundary condition includes an outlet boundary, and the pressure value of the flow field at the outlet boundary is equal to the pressure of the external environment.

5. A high-precision simulation method for wind energy resources as claimed in claim 1, characterized in that: The boundary condition includes an underlying surface boundary, and the underlying surface boundary is set as a boundary condition regarding the roughness of the ground surface.

6. A high-precision simulation method for wind energy resources according to claim 1, characterized in that: The directional calculation results include flow field and pressure field distribution, wind speed, wind direction, horizontal deviation, inflow angle, turbulence intensity and wind acceleration factor.

7. A high-precision simulation method for wind energy resources according to claim 1, characterized in that: The comprehensive calculation includes equivalent wind data calculation, turbulence intensity correction, Weibull fitting, wake calculation, and air density and power curve correction.

8. A high-precision wind energy resource simulation system, characterized in that: include: A data acquisition module, which is configured to: acquire geographic data, wind measurement data and mesoscale meteorological data of a target area; A boundary condition setting module is configured to: construct a terrain model based on geographic data, divide the terrain model into several sectors, and use mesoscale meteorological data to generate wind profiles as boundary conditions for each sector; A model selection module is configured to: select multiple groups of turbulence models based on mesoscale data and wind data of a selected wind tower, perform directional calculations in combination with boundary conditions, construct a relationship between the selected wind tower and a target wind tower based on the directional calculation results, obtain a simulation value of the target wind tower by interpolation, evaluate the error between the simulation value of the target wind tower and the wind data, and select the optimal turbulence model based on the error; The comprehensive calculation module is configured to: perform comprehensive calculation based on the wind measurement data and the directional calculation results corresponding to the optimal turbulence model to obtain the wind energy resources of the entire field.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in a high-precision simulation method for wind energy resources as described in any one of claims 1 to 7 are implemented.

10. A computer device comprising a computer-readable storage medium, a processor, and a computer program stored in the computer-readable storage medium and executable on the processor, characterized in that: When the processor executes the program, the steps in the high-precision simulation method for wind energy resources as described in any one of claims 1-7 are implemented.

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