Wind resource map generation method, device and equipment based on mesoscale wind field

Abstract

This invention provides a method, apparatus, and device for generating wind resource maps based on mesoscale wind fields. The method includes: acquiring mesoscale wind field data and topographic data at a first resolution, wherein the first resolution is higher than a second resolution of the mesoscale wind field data; determining multiple wind field correction regions based on the target wind resource map region and the mesoscale wind field data, and constructing a target output resolution calculation grid for each region; for each region, calculating the background wind speed at the target height based on the mesoscale wind field data; calculating the perturbation wind speed of the wind field caused by the terrain based on linear terrain perturbation theory, and obtaining the corrected target output resolution wind speed field corresponding to the region based on the background wind speed and the perturbation wind speed; fusing the target output resolution wind speed fields and mapping them onto the grid of the target wind resource map region to generate a target output resolution wind resource map. This invention achieves efficient generation of mesoscale and large-scale wind resource maps.

Description

Technical Field

[0001] This invention relates to the field of wind energy resource assessment technology, and in particular to a method, apparatus and equipment for generating wind resource maps based on mesoscale wind fields. Background Technology

[0002] Wind resource maps are fundamental data products in wind energy resource assessment. They are typically used to describe the spatial distribution characteristics of wind speed and direction at specific heights within a given area, playing a crucial role in wind farm site selection, capacity assessment, and wind energy resource surveys. The accuracy of wind resource maps directly affects the reliability of wind energy resource assessment results.

[0003] In existing technologies, wind resource maps are mostly constructed for relatively limited areas. A common approach is to combine data from on-site anemometers or long-term observations with high-resolution computational fluid dynamics (CFD) numerical simulations of wind fields under complex terrain conditions. However, acquiring measured data from anemometers typically requires a long construction period and high costs, and the number of observation points is limited, making it difficult to achieve uniform coverage over large areas. When the assessment area expands, relying solely on measured data is insufficient to meet the needs of wind resource map construction. In other words, this type of method is highly dependent on data availability and computational resources.

[0004] To compensate for insufficient observational data, some methods use mesoscale numerical weather prediction or reanalysis wind fields as background input. However, under complex terrain conditions, further calculations using high-resolution CFD models are still necessary. However, CFD models are computationally intensive under complex terrain and large-scale conditions, especially when processing long-term series or multiple altitude layers, significantly increasing computational costs and making it difficult to meet the needs of rapid generation of regional or even larger-scale wind resource maps. Furthermore, the configuration, parameter selection, and result stability of CFD models place high demands on users, increasing the complexity of engineering applications.

[0005] Therefore, how to reduce reliance on measured data and computational costs while ensuring the rationality and applicability of wind field calculations, and achieve efficient generation of medium- and large-scale wind resource maps, has become a pressing technical problem in the industry. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method, apparatus, and device for generating wind resource maps based on mesoscale wind fields. While reducing reliance on measured data and computational costs, it also considers the rationality and applicability of wind field calculations, achieving efficient generation of mesoscale and large-scale wind resource maps.

[0007] In a first aspect, the present invention provides a method for generating wind resource maps based on mesoscale wind fields, the method comprising the following steps: Acquire mesoscale wind field data and topographic data at a first resolution; the first resolution is higher than the second resolution of the mesoscale wind field data, and the topographic data at the first resolution is topographic data covering the target wind resource map area; Based on the target wind resource map region and the mesoscale wind field data, multiple wind field correction regions are determined, and a target output resolution calculation grid is constructed for each wind field correction region. For each wind field correction region, the background wind speed at the target height of the wind field correction region is calculated based on the mesoscale wind field data. Based on the linear terrain perturbation theory, using the background wind speed and the terrain data at the first resolution, the perturbation wind speed of the terrain corresponding to the terrain data at the first resolution is calculated, and the corrected target output resolution wind speed field corresponding to the wind field correction area is obtained according to the background wind speed and the perturbation wind speed. The corrected target output resolution wind speed fields corresponding to each wind field correction region are fused and mapped onto the grid of the target wind resource map region to generate a target output resolution wind resource map covering the target wind resource map region.

[0008] According to a method for generating wind resource maps based on mesoscale wind fields provided by the present invention, the step of determining multiple wind field correction regions based on the target wind resource map region and the mesoscale wind field data, and constructing a target output resolution calculation grid for each wind field correction region, includes: Mesoscale grid points that meet preset requirements are selected from the mesoscale wind field data; the preset requirements include that the mesoscale grid points are located within the target wind resource map region, and / or the boundary distance between the mesoscale grid points and the target wind resource map region is less than a preset threshold. Centered on each mesoscale grid point that meets the preset requirements, a wind field correction region corresponding to the mesoscale grid point that meets the preset requirements is constructed according to a preset horizontal scale; adjacent wind field correction regions have overlapping portions; For each wind field correction area, the spatial resolution of the terrain data at the first resolution is used as the computational grid resolution for grid subdivision, and the subdivided grid is assigned corresponding terrain height information to obtain the target output resolution computational grid.

[0009] According to a method for generating wind resource maps based on mesoscale wind fields provided by the present invention, the step of calculating the background wind speed at a target height in the wind field correction area based on the mesoscale wind field data includes: Wind speed data for multiple vertical height layers were extracted from the mesoscale wind field data. Based on the wind speed data of the multiple vertical height layers, the background wind speed at the target height is calculated by interpolation. The background wind speed at the target height obtained by interpolation is constrained between an upper boundary curve and a lower boundary curve; the upper boundary curve and the lower boundary curve are obtained by fitting the wind speed data of the multiple vertical height layers.

[0010] According to the present invention, a method for generating wind resource maps based on mesoscale wind fields is provided, wherein the disturbed wind speed is represented by a wind speed disturbance component; the method for calculating the disturbed wind speed of the wind field corresponding to the terrain data at the first resolution, based on linear terrain disturbance theory and using the background wind speed and terrain data at the first resolution, includes: Using the background wind speed and the terrain data at the first resolution as input, the wind speed disturbance component is obtained by solving the linearized fluid dynamics equations.

[0011] According to the wind resource map generation method based on mesoscale wind fields provided by the present invention, the step of fusing the corrected target output resolution wind speed fields corresponding to each wind field correction region includes: For each grid point in the target wind resource map region, a corresponding rectangular search range is set for the grid point; the scale of the rectangular search range is preset according to the horizontal range of the wind field correction area and the grid resolution. Filter out the wind field correction region where all target output resolution calculation grid points fall within the rectangular search range; Based on the horizontal distance between the grid points in the map and each calculation grid point in the selected wind field correction region, different fusion weights are assigned to each of the selected wind field correction regions. Based on the fusion weights, the wind speed results of each of the selected wind field correction regions are weighted and averaged to obtain the fused wind speed of the map grid points; the corrected target output resolution wind speed field corresponding to the wind field correction region includes the wind speed results of the selected wind field correction region.

[0012] According to the present invention, a method for generating wind resource maps based on mesoscale wind fields is provided, the method further includes: The steps of calculating background wind speed, calculating disturbance wind speed, and fusion are repeatedly performed on mesoscale wind field data from multiple time periods to construct the wind speed time series of each grid point within the target wind resource map region; Based on the wind speed time series, statistical analysis is performed on the wind speed data of each grid point within the target wind resource map area to calculate the wind resource evaluation index for each grid point; the wind resource evaluation index includes at least one of average wind speed, wind speed frequency distribution, wind rose diagram, and power density.

[0013] According to the present invention, a method for generating wind resource maps based on mesoscale wind fields is provided, the method further includes: Multiple preset background wind speeds and multiple preset background wind directions are divided into multiple discrete levels to form a standard combination of multiple preset background wind speeds and preset background wind directions. For each of the aforementioned standard combinations, the corrected target output resolution wind speed field corresponding to each of the aforementioned standard combinations is pre-calculated, and the corrected target output resolution wind speed field corresponding to each of the aforementioned standard combinations is stored in the standard case library. When generating a target wind resource map based on mesoscale wind field data from multiple time periods, for each current time period, the corresponding wind field correction result is directly obtained by searching the standard case library based on the background wind speed and background wind direction of the current time period.

[0014] Secondly, the present invention also provides a wind resource map generation device based on mesoscale wind fields, the device comprising the following modules: The acquisition module is used to acquire mesoscale wind field data and topographic data at a first resolution; the first resolution is higher than the second resolution of the mesoscale wind field data, and the topographic data at the first resolution is topographic data covering the target wind resource map area; The grid construction module is used to determine multiple wind field correction regions based on the target wind resource map region and the mesoscale wind field data, and to construct a target output resolution calculation grid for each wind field correction region. The wind speed calculation module is used to calculate the background wind speed at the target height for each wind field correction area based on the mesoscale wind field data. The terrain disturbance module is used to calculate the disturbance wind speed of the terrain corresponding to the first resolution terrain data based on the background wind speed and the first resolution terrain data, and to obtain the corrected target output resolution wind speed field corresponding to the wind field correction area based on the background wind speed and the disturbance wind speed. The fusion output module is used to fuse the corrected target output resolution wind speed fields corresponding to each wind field correction region and map them onto the grid of the target wind resource map region to generate a target output resolution wind resource map covering the target wind resource map region.

[0015] Thirdly, the present invention also provides 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 computer program to implement the wind resource map generation method based on mesoscale wind fields as described above.

[0016] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the wind resource map generation method based on mesoscale wind fields as described above.

[0017] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the wind resource map generation method based on mesoscale wind fields as described above.

[0018] This invention provides a method, apparatus, and device for generating wind resource maps based on mesoscale wind fields. First, mesoscale wind field data and topographic data at a first resolution (higher than a second resolution of the mesoscale wind field data) are acquired. Based on the target wind resource map region and the mesoscale wind field data, multiple wind field correction regions are determined, and a target output resolution calculation grid is constructed for each correction region. Then, for each correction region, the background wind speed at the target height is calculated based on the mesoscale wind field data. Based on linear topographic perturbation theory, the perturbation wind speed corresponding to the topographic data at the first resolution is calculated using the background wind speed and the topographic data at the first resolution. The corrected target output resolution wind speed field corresponding to the correction region is obtained based on the background wind speed and the perturbation wind speed. Finally, the corrected target output resolution wind speed fields corresponding to each correction region are fused and mapped onto the grid of the target wind resource map region to generate a target output resolution wind resource map covering the target wind resource map region.

[0019] This invention uses mesoscale wind field data as background wind speed input and directly applies it to target height wind speed calculation and terrain disturbance correction. It can reflect the spatiotemporal variation characteristics of the actual background wind field, making it particularly suitable for wind resource assessment scenarios with large spatial scales. Furthermore, by linearizing the wind field disturbances caused by terrain and combining it with Fourier analysis to achieve rapid solutions, it avoids the high computational costs associated with high-resolution CFD numerical simulations while maintaining the main physical mechanisms, making it suitable for the efficient generation of wind resource maps. Thus, while reducing dependence on measured data and computational costs, it balances the rationality and applicability of wind field calculations, achieving efficient generation of mesoscale and large-scale wind resource maps. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is one of the flowcharts illustrating the wind resource map generation method based on mesoscale wind fields provided by this invention.

[0022] Figure 2 This is a schematic diagram of the wind field correction region grid structure provided by the present invention.

[0023] Figure 3 This is a schematic diagram of the calculation of terrain wind speed disturbance effect provided by the present invention.

[0024] Figure 4 This is the second flowchart of the wind resource map generation method based on mesoscale wind fields provided by the present invention.

[0025] Figure 5 This is the third flowchart of the wind resource map generation method based on mesoscale wind fields provided by the present invention.

[0026] Figure 6 This is a schematic diagram of the wind resource map generation device based on mesoscale wind fields provided by the present invention.

[0027] Figure 7 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0029] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same class, not limited in number; for example, a first node can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0030] To more clearly understand the various embodiments provided by the present invention, the technical content involved in the present invention will first be described as follows: In the existing technology, there are also wind field estimation methods based on linear terrain disturbance theory. These methods have high computational efficiency, but they usually rely on idealized background inflow conditions and are difficult to directly reflect the characteristics of mesoscale wind field changes with time and space. Their applicability under complex background wind conditions is limited.

[0031] Therefore, under current technological conditions, how to reduce reliance on measured data and computational costs while taking into account the rationality and applicability of wind field calculations, and achieve efficient generation of medium- and large-scale wind resource maps, remains a technical problem that urgently needs to be solved in the industry.

[0032] The following is combined Figures 1 to 7 The present invention describes a method, apparatus, and device for generating wind resource maps based on mesoscale wind fields.

[0033] Figure 1 This is one of the flowcharts illustrating the wind resource map generation method based on mesoscale wind fields provided by this invention, such as... Figure 1 As shown, the method includes the following: Step 101: Obtain mesoscale wind field data and topographic data at the first resolution; the first resolution is higher than the second resolution of the mesoscale wind field data, and the topographic data at the first resolution is the topographic data covering the target wind resource map area.

[0034] The execution subject of the wind resource map generation method based on mesoscale wind field provided by the present invention can be an electronic device, or any other wind resource map generation system based on mesoscale wind field that can realize the wind resource map generation method based on mesoscale wind field.

[0035] The wind resource map generation method based on mesoscale wind fields provided in this embodiment includes the following steps: First, mesoscale wind field data and topographic data at a first resolution are acquired. The first resolution is higher than the second resolution of the mesoscale wind field data. For example, the first resolution is extremely high resolution and the second resolution is normal resolution.

[0036] The mesoscale wind field data should include at least: grid point latitude and longitude and topographic height information, serving as the basis for constructing the local computational domain and interpolation; the lowest-level topographic height information of the model, as well as the height of the model grid points, for subsequent vertical interpolation; and bottom-level or near-surface wind field elements, including U and V wind speed components, for establishing the initial background field for extremely high-resolution (first-resolution) wind field estimation. The U and V wind speed components of the grid are denoted as u1(i1, j1, k1) and v1(i1, j1, k1), where i1, j1, and k1 are the grid point numbers.

[0037] For example, mesoscale wind field data can be obtained from numerical weather prediction models or reanalysis data, wherein the horizontal resolution of the data is preferably 6 to 9 km and the temporal resolution is hourly.

[0038] Topographic data (typical resolution 10-100 m) covering the target wind resource map area is acquired with significantly higher accuracy than mesoscale data. Specifically, topographic data at a first resolution is acquired, which is higher than the second resolution of the mesoscale wind field data. This extremely high-resolution (first resolution) topographic data is used in subsequent steps to construct an extremely high-resolution grid and to establish a corrected influence field on the wind field using a topographic perturbation approximation method.

[0039] Step 102: Based on the target wind resource map region and mesoscale wind field data, determine multiple wind field correction regions and construct a target output resolution calculation grid for each wind field correction region.

[0040] Specifically, the target wind resource map region is the target area for generating the wind resource map. The target wind resource map region is preset and set by receiving user input. User input includes the spatial range of the target area (target wind resource map region), output resolution (usually high resolution, such as 200m), height, and optional wind energy statistics period (such as 1 year, 2 years, etc.).

[0041] In practical applications, the target wind resource map region is first spatially discretized based on the user-defined target wind resource map region and target output resolution, constructing a grid for subsequent wind field calculations and statistical analysis. This grid covers the entire target wind resource map region, and its grid scale is consistent with the user-defined output resolution. The U and V wind speed components of this grid are denoted as u(i, j) and v(i, j). Then, the high-resolution wind speed results from multiple wind field correction regions can be effectively integrated onto a unified target wind resource map region grid, ultimately resulting in a target height wind speed field that covers the entire target region, has high spatial resolution, and is continuously smooth.

[0042] In this step, after completing the basic data preparation, based on the wind resource map calculation range set by the user (i.e., the target wind resource map area) and the distribution of mesoscale wind field data, various wind field correction regions are constructed for wind field calculation at the target output resolution (usually high resolution).

[0043] Furthermore, a target output resolution computational grid is constructed for each wind field correction region. For example, the corresponding computational grid is constructed based on extremely high resolution terrain data. In practical applications, this process includes, for example, determining the set of mesoscale grid points involved in generating the target wind resource map, constructing the wind field correction region range corresponding to each mesoscale grid point, and meshing each wind field correction region.

[0044] For example, Figure 2 This is a schematic diagram of the wind field correction region grid construction provided by the present invention, as shown below. Figure 2 As shown, the wind resource map range is the area enclosed by the dark box, which is the target wind resource map area. The dark grid points represent mesoscale data grid points (mesoscale grid points), and the light grid points are mesoscale grid points related to map construction. The areas enclosed by the light boxes represent wind field correction areas.

[0045] Step 103: For each wind field correction area, calculate the background wind speed at the target height based on mesoscale wind field data.

[0046] Specifically, after completing the mesh construction for each wind field correction area, this step interpolates and calculates the high-precision background wind speed at the target height (e.g., around 100 meters) set for the target output resolution wind resource map. The background wind speed component at this height is denoted as u. 12 (p), v 12 (p), u 12 (p) represents the eastward component of the background wind speed at the target altitude for the p-th correction region, v 12(p) represents the northward component of the background wind speed at the target altitude for the p-th correction area. This background wind speed provides the basis for subsequent terrain disturbance calculations.

[0047] To ensure the reasonableness of low-altitude wind speeds, two wind speed profiles can be fitted using mesoscale wind field data, corresponding to the lower bound of potentially low wind speeds and the upper bound of potentially high wind speeds, respectively. The background wind speed at the target altitude obtained by interpolation should fall between these two curves as a reasonable range constraint.

[0048] Step 104: Based on the linear terrain perturbation theory, using the background wind speed and the terrain data at the first resolution, calculate the perturbation wind speed of the terrain corresponding to the terrain data at the first resolution, and obtain the corrected target output resolution wind speed field corresponding to the wind field correction area based on the background wind speed and the perturbation wind speed.

[0049] Specifically, linear topographic disturbance theory is the core theoretical foundation for calculating the effects of topographic wind speed disturbance. It is a method that describes the influence of topography on airflow through mathematical simplification while ensuring computational efficiency. The core idea of ​​linear topographic disturbance theory is to decompose the actual wind field into a superposition of "background flow" and "small disturbances," and to ignore the nonlinear interaction between disturbance terms.

[0050] The linear terrain disturbance in this application is applied as follows: the background wind speed, terrain height, and target height are substituted into the linearized disturbance equations; the equations are transformed to the wavenumber domain using Fourier transform; the algebraic equations of the disturbance variables are solved in the wavenumber domain; the disturbance wind speeds u′ and v′ in the spatial domain are obtained through inverse Fourier transform; and the disturbance is superimposed with the background wind speed: U=U0+u′, V=V0+v′, to obtain the corrected high-resolution wind speed field.

[0051] In practical applications, wind fields in the real atmosphere satisfy the Navier-Stokes equations (a set of nonlinear partial differential equations describing fluid motion). When airflow encounters topography, complex turbulence and separation phenomena occur, and directly solving the complete equations (such as using CFD methods) is computationally intensive. The basic assumptions of linearization are: the wind speed changes caused by topography are small disturbances; the background wind field (from mesoscale data) is the dominant term; and the product of the disturbance terms themselves can be ignored.

[0052] The original equations (incompressible Navier-Stokes equations) are expressed as follows: The three-dimensional velocity field can be decomposed into: p represents pressure, and f represents the external force. Since the background wind is mainly horizontal, when the wind speed field is decomposed into background flow and small disturbances, it can be considered as U = U0 + u′, V = V0 + v′, W = W0 + w′. U0 and V0 are the background horizontal wind speeds, that is, u is used. 12 (p), v 12 (p), W0 is the background vertical wind speed, and u′, v′, w′ are the disturbance wind speeds caused by the terrain.

[0053] Thus, under the linearization assumption, substituting the decomposed velocity field into the original equation, subtracting the equation satisfied by the background flow, neglecting the quadratic small quantity of the disturbance term itself, and assuming that the vertical disturbance decays exponentially, the disturbance equation can be simplified to the following set of equations: in, East-west spatial coordinates, These are the spatial coordinates in the north-south direction. is the vertical spatial coordinate, and h is the terrain height in the terrain data at the first resolution (extremely high resolution).

[0054] By using the Fourier transform (to transform the spatial domain to the wavenumber domain), the simplified formula can be obtained as follows: in, Let be the eastward component of the horizontal disturbance wind speed at target height l in the wavenumber domain. Let be the north component of the horizontal disturbance wind speed at target height l in the wavenumber domain. U0 and V0 are the vertical disturbance wind speed components at the target height l in the wavenumber domain, and U0 and V0 are the background wind speeds, i.e., using u 12 (p), v 12 (p); l is the target height set in the target wind resource map; h is the terrain height in the terrain data at the first resolution (extremely high resolution). If roughness is considered, it can be approximated as the increase in terrain height. The other variables i and k x k y k z、 λ and others are temporary variables in the Fourier transform. The terrain height is in the wavenumber domain.

[0055] After solving the equation, an inverse Fourier transform is required to perform a preliminary calculation of the terrain wind speed disturbance, obtaining the terrain disturbance wind speeds u′, v′, and w′ in the spatial domain. Further, by simply adding the background wind speed and the disturbance wind speed, the corrected wind speed in the corrected wind field region can be obtained. Let the wind speed component at this point be u. 2p (p, i2, j2), v 2p (p, i2, j2).

[0056] In this step, the decomposed velocity field is substituted into the original equations, the equations satisfied by the background flow are subtracted, and the quadratic small quantities of the disturbance term are ignored. Simultaneously, it is assumed that the vertical disturbance decays exponentially with height, resulting in a linearized set of disturbance equations. The linearized equations can be solved analytically using Fourier transform (converting the spatial domain to the wavenumber domain), transforming the partial differential equations into algebraic equations, significantly reducing the difficulty of the solution. After solving, an inverse Fourier transform is performed to obtain the topographic disturbance wind speeds u′, v′, and w′ in the spatial domain.

[0057] Thus, the output of this step is the corrected target output resolution wind speed field corresponding to each wind field correction area, that is, the corrected wind speed of each wind field correction area.

[0058] For example, Figure 3 This is a schematic diagram of the calculation of terrain wind speed disturbance effect provided by the present invention, used to visually demonstrate the disturbance effect of terrain on horizontal wind speed within the target wind field correction area, such as... Figure 3 As shown, the horizontal axis x (m) represents the east-west spatial coordinates, and the vertical axis y (m) represents the north-south spatial coordinates. Both axes range from -3000 meters to 3000 meters, forming a square region with sides of 6 kilometers. This region corresponds to a high-resolution computational grid for a specific wind field correction area. The undulating curves in the figure represent the terrain elevation generated based on extremely high-resolution terrain data. It can be seen that there are terrain protrusions (such as hills or ridges) and depressions (such as valleys) within this region, with the maximum elevation difference reaching hundreds of meters. Arrows or streamlines in the figure indicate the direction of the background wind field (e.g., uniform inflow or actual wind direction given by mesoscale data), and the background wind speed has been calculated. In the windward slope region, the airflow is lifted, and the wind speed increases; in the leeward slope region, wind speed may decrease or turbulence may occur. The figure clearly presents the spatial distribution of the terrain-induced wind speed disturbance components (u′, v′) through variations in the length of local arrows, color intensity, or the spacing of contour lines. This schematic diagram verifies the spatial continuity of the disturbance wind speed obtained based on the linear terrain disturbance theory and Fourier transform: the disturbance value is significant in areas with drastic terrain changes, gradually decreases in areas with gentle terrain, and decreases exponentially with distance from the ground surface.

[0059] Step 105: Fuse the corrected target output resolution wind speed fields corresponding to each wind field correction area and map them onto the grid of the target wind resource map area to generate a target output resolution wind resource map covering the target wind resource map area.

[0060] Specifically, after obtaining the corrected target output resolution wind speed field corresponding to each wind field correction area, the correction results of multiple overlapping wind fields are further mapped onto the grid of the target wind resource map area to form a continuous, smooth, and computationally efficient high-resolution target height wind speed field covering the entire target area. In other words, the wind speed of the grid points in the target wind resource map area is updated, and a target output resolution wind resource map covering the target wind resource map area is generated, which can provide basic data for subsequent wind energy statistical analysis and wind resource assessment.

[0061] The method provided in this embodiment of the invention first acquires mesoscale wind field data and topographic data at a first resolution, where the first resolution is higher than the second resolution of the mesoscale wind field data. Based on the target wind resource map region and the mesoscale wind field data, multiple wind field correction regions are determined, and a target output resolution calculation grid is constructed for each wind field correction region. Then, for each wind field correction region, the background wind speed at the target height is calculated based on the mesoscale wind field data. Based on the linear topographic perturbation theory, the perturbation wind speed of the topographic data at the first resolution is calculated using the background wind speed and the topographic data at the first resolution, and the corrected target output resolution wind speed field corresponding to the wind field correction region is obtained based on the background wind speed and the perturbation wind speed. Finally, the corrected target output resolution wind speed fields corresponding to each wind field correction region are fused and mapped onto the grid of the target wind resource map region to generate a target output resolution wind resource map covering the target wind resource map region.

[0062] This invention uses mesoscale wind field data as background wind speed input and directly applies it to target height wind speed calculation and terrain disturbance correction. It can reflect the spatiotemporal variation characteristics of the actual background wind field, making it particularly suitable for wind resource assessment scenarios with large spatial scales. Furthermore, by linearizing the wind field disturbances caused by terrain and combining it with Fourier analysis to achieve rapid solutions, it avoids the high computational costs associated with high-resolution CFD numerical simulations while maintaining the main physical mechanisms, making it suitable for the efficient generation of wind resource maps. Thus, while reducing dependence on measured data and computational costs, it balances the rationality and applicability of wind field calculations, achieving efficient generation of mesoscale and large-scale wind resource maps.

[0063] It should be noted that each implementation method of this application can be freely combined, rearranged, or executed individually, and does not need to rely on or depend on a fixed execution order.

[0064] According to the wind resource map generation method based on mesoscale wind field provided by the present invention, multiple wind field correction regions are determined based on the target wind resource map region and mesoscale wind field data, and a target output resolution calculation grid is constructed for each wind field correction region, including: Mesoscale grid points that meet preset requirements are selected from the mesoscale wind field data. The preset requirements include that the mesoscale grid points are located within the target wind resource map area, and / or the boundary distance between the mesoscale grid points and the target wind resource map area is less than a preset threshold. Centered on each mesoscale grid point that meets the preset requirements, a wind field correction region corresponding to the mesoscale grid point that meets the preset requirements is constructed according to the preset horizontal scale; there is an overlap between adjacent wind field correction regions. For each wind field correction area, the spatial resolution of the terrain data at the first resolution is used as the computational grid resolution for grid subdivision, and the corresponding terrain height information is assigned to the subdivided grid to obtain the computational grid of the target output resolution.

[0065] Specifically, in some embodiments, step 102 includes steps 1-3 as follows.

[0066] Step 1: Determine the set of mesoscale grid points to be used in generating the wind resource map.

[0067] Mesoscale grid points that meet preset requirements are selected from the mesoscale wind field data. The preset requirements (the selection criteria for the mesoscale grid points) include that the mesoscale grid points are located within the target wind resource map region, and / or the boundary distance between the mesoscale grid points and the target wind resource map region is less than a preset threshold (such as half the distance between adjacent mesoscale grid points).

[0068] Step 2: Construct the wind field correction region corresponding to the mesoscale grid points.

[0069] Centered on each mesoscale grid point that meets the preset requirements, a wind field correction region corresponding to the mesoscale grid point is constructed according to a preset horizontal scale (such as 120% of the distance between adjacent mesoscale grid points). Appropriate overlap is formed between adjacent wind field correction regions to ensure the continuity of subsequent high-resolution wind field stitching and transition.

[0070] Step 3: Grid generation of the wind field correction area.

[0071] For each wind field correction area, the spatial resolution of the topographic data at the first resolution (ultra-high resolution topographic data) is used as the computational grid resolution for mesh generation, producing a high-precision topographic grid consistent with the first resolution topographic data, providing a foundation for subsequent calculations of topographic disturbance effects. Furthermore, the meshed grid can be assigned corresponding topographic height information to obtain the computational grid at the target output resolution.

[0072] Let the U and V wind speed components of the target output resolution calculation grid at this time be u2(p, i2, j2) and v2(p, i2, j2), where p is the correction region number and i2 and j2 are the grid point numbers.

[0073] The method provided in this invention decomposes a large area into multiple independent correction regions, avoiding the computational resource consumption caused by global high-resolution calculations while preserving the impact of terrain details on the wind field. The overlapping design of adjacent correction regions provides a smooth physical basis for subsequent multi-region wind speed fusion, effectively solving the boundary abruptness problem caused by region stitching in traditional methods. The one-to-one correspondence between correction regions and mesoscale grid points ensures that the wind field correction of each region is based on its true background wind field characteristics, avoiding information distortion caused by background field interpolation. Using a computational grid consistent with ultra-high-resolution terrain data allows terrain perturbation effects to be accurately mapped into the wind field calculation results, meeting the wind resource assessment needs under complex terrain conditions.

[0074] According to the present invention, a method for generating wind resource maps based on mesoscale wind fields calculates the background wind speed at a target height in a wind field correction area based on mesoscale wind field data, including: Wind speed data from multiple vertical height levels were extracted from mesoscale wind field data; Based on wind speed data from multiple vertical height layers, the background wind speed at the target height is calculated through interpolation. The background wind speed at the target height obtained by interpolation is constrained between the upper and lower bound curves; the upper and lower bound curves are obtained by fitting wind speed data from multiple vertical height layers.

[0075] Specifically, in some embodiments, calculating the background wind speed at the target height in the wind field correction area includes the following steps: First, wind speed data from multiple vertical height layers are extracted from the mesoscale wind field data; that is, wind speed components from all available height layers within the target area are extracted. Since the lowest layer of the mesoscale model may be higher than the target height, this step is not limited to the two layers above and below it, but rather selects the nearest multiple layers for reference based on available data. If the target height is lower than the lowest layer of the mesoscale model, power-law or logarithmic wind speed profile methods can be used to reasonably extrapolate the low-level wind speed.

[0076] Furthermore, based on wind speed data from multiple vertical height layers, the background wind speed at the target height is calculated through interpolation. The background wind speed at the target height obtained by interpolation is constrained between an upper boundary curve and a lower boundary curve, which are obtained by fitting wind speed data from multiple vertical height layers.

[0077] In practical applications, if the interpolated wind speed exceeds the range, it is adjusted to the upper or lower bound to avoid unreasonable values ​​that are too large or too small. Finally, the interpolation results are combined with the above constraints to obtain the target height background wind speed at each grid point in the wind field correction area, providing a basis for subsequent terrain disturbance calculations while ensuring that the low-level wind speed is continuous and consistent with the mesoscale wind field trend. Let the wind speed component at this point be u. 12 (p), v 12 (p).

[0078] The method provided in this invention first extracts wind speed data from multiple vertical height layers from mesoscale wind field data; then, based on the wind speed data from multiple vertical height layers, the background wind speed at the target height is calculated through interpolation; and the interpolated background wind speed at the target height is constrained between an upper and lower bound curve. This invention, by "using wind speed information from multiple vertical layers in a mesoscale model for interpolation" and combining it with an "upper and lower bound constraint mechanism based on wind speed profiles," makes the background wind speed at the target height more realistically reflect the vertical distribution characteristics of the atmospheric boundary layer. Furthermore, by fitting upper and lower bound curves of wind speed variation based on wind speed data from multiple vertical layers, the interpolation results are constrained within a physically reasonable range, avoiding abnormal wind speed values ​​caused by limitations of the interpolation algorithm or data noise, significantly improving the stability and reliability of the background wind speed. In addition, the background wind speed at the target height, after reasonable constraints, exhibits good physical consistency and numerical stability, directly improving the accuracy of subsequent linear terrain disturbance calculations and laying a solid data foundation for generating high-precision, continuous, and reliable wind resource maps.

[0079] According to the present invention, a method for generating wind resource maps based on mesoscale wind fields is provided, wherein the disturbed wind speed is represented by a wind speed disturbance component; based on linear terrain disturbance theory, using background wind speed and terrain data at a first resolution, the method calculates the terrain disturbance wind speed corresponding to the terrain data at the first resolution, including: Using background wind speed and topographic data at first resolution as input, the wind speed disturbance component is obtained by solving the linearized fluid dynamics equations.

[0080] Specifically, in this invention, the three-dimensional velocity field is first decomposed into a superposition of background flow and disturbance. The three-dimensional velocity field can be decomposed into: Where: U0, V0 are the known background horizontal wind speeds; u′, v′, w′ are the disturbance wind speeds caused by the terrain to be solved; it is assumed that the background vertical wind speed is 0, and there is only disturbance in the vertical direction. .

[0081] Under the linearization assumption, substituting the decomposed velocity field into the incompressible Navier-Stokes original equations, subtracting the equations satisfied by the background flow, neglecting the quadratic small quantities of the perturbation term itself, and normalizing, the perturbation equations can be simplified to the following set of equations: in, East-west spatial coordinates, These are the spatial coordinates in the north-south direction. is the vertical spatial coordinate, and h is the terrain height in the terrain data at the first resolution (extremely high resolution).

[0082] By using the Fourier transform (to transform the spatial domain to the wavenumber domain), the simplified formula can be obtained as follows: Where U0 and V0 are the background wind speeds, i.e., using u 12 (p), v 12 (p); l is the target height set in the target wind resource map; h is the terrain height in the terrain data at the first resolution (extremely high resolution). If roughness is considered, it can be approximated as the increase in terrain height. The other variables i and k x k y k z、 λ and others are temporary variables in the Fourier transform.

[0083] After solving the equation, an inverse Fourier transform is performed to complete the preliminary calculation of the terrain wind speed disturbance, that is, to obtain the terrain disturbance wind speeds u′, v′, and w′ in the spatial domain.

[0084] The method provided in this invention first transforms the nonlinear partial differential equation into a linear equation, avoiding iterative solutions and significantly reducing computational load. Then, it uses Fourier transform to convert the partial differential equation into an algebraic equation, achieving rapid analytical solution. Furthermore, it simplifies the vertical solution, requiring only the handling of terrain boundary conditions and directly using mesoscale background wind speeds. The perturbation calculation can reflect the true spatiotemporal variations of the wind field. While preserving the physical mechanisms of terrain perturbation, this invention achieves computational efficiency several orders of magnitude higher than CFD methods, making it particularly suitable for the rapid generation of wind resource maps across large areas, multiple time periods, and multiple altitude layers.

[0085] According to the wind resource map generation method based on mesoscale wind fields provided by the present invention, the corrected target output resolution wind speed fields corresponding to each wind field correction region are fused, including: For each mesoscale grid point in the target wind resource map region, a corresponding rectangular search range is set for the mesoscale grid point; the scale of the rectangular search range is preset according to the horizontal range of the wind field correction area and the grid resolution. Filter out the wind field correction region where all target output resolution calculation grid points fall within the rectangular search range; Based on the horizontal distance between the mesoscale grid points and each computational grid point in the selected wind field correction region, different fusion weights are assigned to each selected wind field correction region. Based on each fusion weight, the wind speed results of each selected wind field correction region are weighted and averaged to obtain the fused wind speed at the mesoscale grid. The corrected target output resolution wind speed field corresponding to the wind field correction region includes the wind speed results of the selected wind field correction region.

[0086] Specifically, in some embodiments, step 105, which involves fusing the corrected target output resolution wind speed fields corresponding to each wind field correction region, includes the following steps: To improve overall computational efficiency while ensuring accuracy, this step does not perform a global traversal of all correction regions. Instead, it uses local search combined with hierarchical weighting, centered on the grid points of the wind resource map, to complete the comprehensive wind speed calculation. The specific process is as follows: First, for each grid point in the target wind resource map region, a corresponding rectangular search range is defined for each grid point. The scale of this rectangular search range is pre-set based on the horizontal range of the wind field correction area and the grid resolution, and is used to quickly filter wind field correction areas that may affect the grid point. Only when a grid point of a wind field correction area falls within this rectangular range is it included in the wind speed calculation for that grid point, thus significantly reducing unnecessary computation and filtering out all wind field correction areas where the target output resolution calculation grid points fall within the rectangular search range.

[0087] After determining the wind field correction regions to be included in the calculation, different weight levels are set according to the horizontal distance between the map grid points and the grid points of each wind field correction region. Specifically, different fusion weights are assigned to each selected wind field correction region based on the horizontal distance between the map grid points and the calculation grid points in the selected wind field correction regions.

[0088] In practical applications, the allocation of different fusion weights is as follows: the closer the computational grid point is to the center of the wind field correction region, the higher the weight of its corresponding wind speed result in the weighted average. That is, computational grid points closer to the center of the wind field correction region represent higher reliability of the wind field correction results in that region, and their weights are relatively larger. As the distance increases, the weights gradually decrease, thereby reducing the influence of edge regions on the final result. This distance-based hierarchical weighting method ensures the dominant role of the wind field correction results within the wind resource map region while avoiding abrupt changes caused by regional boundary splicing.

[0089] Subsequently, the wind speed results of all wind field correction areas within the rectangular area that meet the conditions are weighted and averaged according to the aforementioned weights to obtain the target height wind speed at the grid point of the wind resource map. Specifically, based on each fusion weight, the wind speed results of each selected wind field correction area are weighted and averaged to obtain the fused wind speed at the map grid point. The corrected target output resolution wind speed field corresponding to the wind field correction area includes the wind speed results of the selected wind field correction areas.

[0090] The method provided in this invention effectively integrates high-resolution wind speed results from multiple wind field correction areas onto a unified wind resource map grid, ultimately obtaining a target height wind speed field that covers the entire target area, has high spatial resolution, and is continuous and smooth. This completes the wind speed update of the wind resource map grid points, providing basic data for subsequent wind energy statistical analysis and wind resource assessment.

[0091] The method provided in this invention divides continuously changing wind speed and direction into a finite number of standard combinations and pre-calculates the corresponding wind field correction results. During the generation of multi-time wind resource maps, the results are obtained by looking up tables to reduce redundant calculations.

[0092] According to the present invention, a method for generating wind resource maps based on mesoscale wind fields is provided, the method further includes: The steps of calculating background wind speed, calculating disturbance wind speed, and fusion are repeatedly performed on mesoscale wind field data from multiple time periods to construct the wind speed time series of each grid point within the target wind resource map region; Based on wind speed time series, statistical analysis is performed on the wind speed data of each grid point within the target wind resource map area to calculate the wind resource evaluation index for each grid point. The wind resource evaluation index includes at least one of the following: average wind speed, wind speed frequency distribution, wind rose diagram, and power density.

[0093] Specifically, the above steps mainly calculate and process the wind speed data for a single time period, which can obtain the target height wind speed components of each grid point in the target wind resource map area at that time period.

[0094] In some embodiments, the method further includes: To obtain statistically significant wind resource map results, it is necessary to repeatedly perform the calculation process of background wind speed at the target height, topographic wind speed disturbance effect calculation, and map area wind speed calculation on wind speed data at multiple time points, so as to construct the wind speed time series of each grid point within the wind resource map area.

[0095] After obtaining the complete wind speed time series, statistical analysis is performed on the wind speed data of each grid point in the wind resource map to calculate wind resource evaluation indicators such as average wind speed, wind speed frequency distribution, wind rose diagram, and power density. This ultimately forms a wind resource map for wind energy resource assessment and analysis. Specific variables include Weibull parameter A, Weibull parameter K, sector frequency F, etc.

[0096] The method provided in this invention transforms high-resolution wind speed field data into wind resource mapping results, directly outputting commonly used engineering indicators such as average wind speed, Weibull parameters, and power density, thus solving the problem of the disconnect between the output results of traditional methods and engineering applications. The constructed gridded wind speed time series, as an intermediate data product, can be further used for deeper engineering analyses such as wind resource spatiotemporal variation analysis, climate variability research, and post-evaluation of wind farms, demonstrating high data reuse value.

[0097] According to the present invention, a method for generating wind resource maps based on mesoscale wind fields is provided, the method further includes: Multiple preset background wind speeds and multiple preset background wind directions are divided into multiple discrete levels to form a standard combination of multiple preset background wind speeds and preset background wind directions. For each standard combination, the corrected target output resolution wind speed field corresponding to each standard combination is pre-calculated, and the corrected target output resolution wind speed field corresponding to each standard combination is stored in the standard case library. When generating a target wind resource map based on mesoscale wind field data from multiple time periods, for each current time period, the corresponding wind field correction result is directly obtained by searching the standard case library based on the background wind speed and background wind direction of the current time period.

[0098] Specifically, in some embodiments, to further improve overall computational efficiency, the method also includes the construction of a standard case library.

[0099] The standard case library is constructed as follows: Wind speed and direction are discretized to construct a standardized wind speed-direction case set. For example, multiple continuously changing preset background wind speeds and directions are divided into multiple discrete levels to form multiple standard combinations of preset background wind speeds and directions. For each standard combination, the corrected target output resolution wind speed field (i.e., the terrain disturbance wind speed correction result) is pre-calculated, and the corrected target output resolution wind speed field corresponding to each standard combination is stored in the standard case library.

[0100] Therefore, when performing calculations over multiple time periods, for each current time period, the system first searches the standard case library based on the background wind speed and direction of that time period to directly obtain the corresponding wind field correction result. In other words, it only requires looking up the corresponding wind speed correction result from the case library based on the wind speed level, wind direction level, and the wind field correction area number for the current time period, thus avoiding redundant calculations of terrain disturbance.

[0101] The method provided in this invention can significantly reduce the repetitive overhead of multi-time wind field calculations while ensuring the accuracy of wind resource maps. It is particularly suitable for the rapid generation of wind resource maps for long time series, large areas, and multiple height layers.

[0102] Figure 4 This is the second flowchart illustrating the wind resource map generation method based on mesoscale wind fields provided by this invention. Figure 4 As shown, the method includes S1 to S7: S1 Data Preparation and Calculation Configuration.

[0103] Obtain the mesoscale region (wind field data), and denote the U and V wind speed components of this grid as u1(i1, j1, k1) and v1(i1, j1, k1), where i1, j1, and k1 are the grid point numbers.

[0104] S2 wind resource map regional grid construction.

[0105] Output the wind resource map region (grid), and denote the U and V wind speed components of this grid as u(i, j) and v(i, j).

[0106] S3 wind field correction region grid construction.

[0107] Output the wind field correction region, and denote the U and V wind speed components of the grid at this time as u2(p, i2, j2) and v2(p, i2, j2), where p is the correction region number and i2 and j2 are the grid point numbers.

[0108] S4 Target height wind speed calculation.

[0109] Output the wind speed corresponding to the height of the mesoscale target, and denote the wind speed component at this time as u. 12 (p), v 12 (p).

[0110] S5 Calculation of terrain wind speed disturbance effect.

[0111] The output is the wind speed corresponding to the wind field correction area, denoted as u. 2p (p, i2, j2), v 2p (p, i2,j2).

[0112] Calculation of wind speed in the S6 map region.

[0113] The output is the wind speed corresponding to the wind resource map region. The wind speed components of this grid are denoted as u(i, j) and v(i, j).

[0114] S7 Comprehensive calculation of statistical variables.

[0115] The output consists of various elements of the wind resource map, such as A, K, and N.

[0116] Figure 5 This is the third flowchart illustrating the wind resource map generation method based on mesoscale wind fields provided by this invention. Figure 5 As shown, the method includes: S1 Data Preparation and Calculation Configuration; S2 wind resource map regional grid construction; S3 wind field correction region grid construction; S4 Target Height Wind Speed ​​Calculation; S5 Calculation of terrain wind speed disturbance effect; Calculation of wind speed in the S6 map region; Next, determine whether all time-limited calculations have been completed: If yes, then perform the S7 statistical variable comprehensive calculation; if not, then re-execute the S4 target height wind speed calculation and subsequent steps.

[0117] Compared with existing technologies, the ultra-high resolution wind resource map generation method based on mesoscale wind fields proposed in this invention achieves high accuracy, continuity, and computational efficiency of wind speed in the target area while ensuring computational feasibility and engineering feasibility, and has the following significant technical effects: 1. This invention calculates wind speed at target height and corrects for terrain disturbance by using mesoscale wind fields as background flow. It can obtain a physically reasonable and continuous wind speed distribution without the need for actual wind tower data. It combines multi-level interpolation of target height and reasonable constraints, thereby reducing data acquisition costs and avoiding the limitations of evaluation in areas with scarce data or where it is difficult to deploy observation facilities. It is particularly suitable for cross-regional or large-scale wind resource analysis.

[0118] 2. This invention achieves rapid solution by combining linearized terrain disturbance with Fourier analysis. It can quickly calculate wind field disturbance while preserving the main physical mechanisms of terrain disturbance, avoiding the high computational cost and numerical instability problems of traditional high-resolution CFD models in large-scale regional or long-term series calculations. This enables efficient generation of wind resource maps under multiple height layers and time conditions, significantly improving engineering operability.

[0119] 3. This invention employs a local weighted fusion strategy under multi-wind field correction regional conditions, which enables distance-weighted averaging of overlapping correction regions to achieve a smooth spatial transition of wind speed results. This effectively avoids problems such as abrupt boundary changes and discontinuous splicing, while ensuring the stability and physical consistency of the overall spatial distribution of wind resource maps, providing a reliable foundation for subsequent regional aggregation and analysis.

[0120] 4. This invention, through a multi-layer interpolation and rational constraint method for wind speed at target height, can fully utilize the multi-vertical wind speed information of the mesoscale model and impose rational constraints on the wind speed at target height, so that the interpolated wind speed conforms to the mesoscale trend and avoids outliers that are too high or too low. This improves the problem of low-altitude wind speed deviation, enhances the accuracy of terrain disturbance correction calculation, and provides reliable input for generating high-precision, continuous wind resource maps.

[0121] 5. This invention accelerates computation through a wind speed-direction standard case library, which can discretize continuously changing wind speed and direction into standard cases. During the generation of multi-time wind resource maps, the pre-calculated terrain disturbance correction results are obtained by looking up tables, thereby significantly reducing redundant calculations and improving overall computational efficiency. It is especially suitable for the rapid generation of wind resource maps for long-term series, complex terrain, or large areas, while maintaining the repeatability and engineering feasibility of the results.

[0122] The wind resource map generation device based on mesoscale wind fields provided by the present invention will be described below. The wind resource map generation device based on mesoscale wind fields described below can be referred to in correspondence with the wind resource map generation method based on mesoscale wind fields described above.

[0123] Figure 6 This is a schematic diagram of the wind resource map generation device based on mesoscale wind fields provided by the present invention, as shown below. Figure 6 As shown, the wind resource map generation device 600 based on mesoscale wind fields includes: The acquisition module 610 is used to acquire mesoscale wind field data and topographic data at a first resolution; the first resolution is higher than the second resolution of the mesoscale wind field data, and the topographic data at the first resolution is topographic data covering the target wind resource map area; The grid construction module 620 is used to determine multiple wind field correction regions based on the target wind resource map region and the mesoscale wind field data, and to construct a target output resolution calculation grid for each wind field correction region. The wind speed calculation module 630 is used to calculate the background wind speed at the target height for each wind field correction area based on the mesoscale wind field data. The terrain disturbance module 640 is used to calculate the disturbance wind speed of the terrain corresponding to the first resolution terrain data based on the background wind speed and the first resolution terrain data, and to obtain the corrected target output resolution wind speed field corresponding to the wind field correction area based on the background wind speed and the disturbance wind speed. The fusion output module 650 is used to fuse the corrected target output resolution wind speed fields corresponding to each wind field correction area and map them onto the grid of the target wind resource map area to generate a target output resolution wind resource map covering the target wind resource map area.

[0124] The apparatus provided in this embodiment of the invention includes: an acquisition module 610 for acquiring mesoscale wind field data and terrain data at a first resolution, wherein the first resolution is higher than the second resolution of the mesoscale wind field data; a grid construction module 620 for determining multiple wind field correction regions based on the target wind resource map region and the mesoscale wind field data, and constructing a target output resolution calculation grid for each wind field correction region; a wind speed calculation module 630 for calculating the background wind speed at the target height for each wind field correction region based on the mesoscale wind field data; a terrain disturbance module 640 for calculating the terrain disturbance wind speed corresponding to the first resolution terrain data based on the linear terrain disturbance theory, using the background wind speed and the first resolution terrain data, and obtaining the corrected target output resolution wind speed field corresponding to the wind field correction region based on the background wind speed and the disturbance wind speed; and a fusion output module 650 for fusing the corrected target output resolution wind speed fields corresponding to each wind field correction region, mapping them onto the grid of the target wind resource map region, and generating a target output resolution wind resource map covering the target wind resource map region.

[0125] This invention uses mesoscale wind field data as background wind speed input and directly applies it to target height wind speed calculation and terrain disturbance correction. It can reflect the spatiotemporal variation characteristics of the actual background wind field, making it particularly suitable for wind resource assessment scenarios with large spatial scales. Furthermore, by linearizing the wind field disturbances caused by terrain and combining it with Fourier analysis to achieve rapid solutions, it avoids the high computational costs associated with high-resolution CFD numerical simulations while maintaining the main physical mechanisms, making it suitable for the efficient generation of wind resource maps. Thus, while reducing dependence on measured data and computational costs, it balances the rationality and applicability of wind field calculations, achieving efficient generation of mesoscale and large-scale wind resource maps.

[0126] According to the wind resource map generation device 600 based on mesoscale wind fields provided by the present invention, the grid construction module 620 is specifically used for: Mesoscale grid points that meet preset requirements are selected from the mesoscale wind field data; the preset requirements include that the mesoscale grid points are located within the target wind resource map region, and / or the boundary distance between the mesoscale grid points and the target wind resource map region is less than a preset threshold. Centered on each mesoscale grid point that meets the preset requirements, a wind field correction region corresponding to the mesoscale grid point that meets the preset requirements is constructed according to a preset horizontal scale; adjacent wind field correction regions have overlapping portions; For each wind field correction area, the spatial resolution of the terrain data at the first resolution is used as the computational grid resolution for grid subdivision, and the subdivided grid is assigned corresponding terrain height information to obtain the target output resolution computational grid.

[0127] According to the wind resource map generation device 600 based on mesoscale wind fields provided by the present invention, the wind speed calculation module 630 is specifically used for: Wind speed data for multiple vertical height layers were extracted from the mesoscale wind field data. Based on the wind speed data of the multiple vertical height layers, the background wind speed at the target height is calculated by interpolation. The background wind speed at the target height obtained by interpolation is constrained between an upper boundary curve and a lower boundary curve; the upper boundary curve and the lower boundary curve are obtained by fitting the wind speed data of the multiple vertical height layers.

[0128] According to the present invention, a wind resource map generation device 600 based on a mesoscale wind field is provided, wherein the disturbed wind speed is represented by a wind speed disturbance component; the terrain disturbance module 640 is specifically used for: Using the background wind speed and the terrain data at the first resolution as input, the wind speed disturbance component is obtained by solving the linearized fluid dynamics equations.

[0129] According to the wind resource map generation device 600 based on mesoscale wind fields provided by the present invention, the fusion output module 650 is specifically used for: For each grid point in the target wind resource map region, a corresponding rectangular search range is set for the grid point; the scale of the rectangular search range is preset according to the horizontal range of the wind field correction area and the grid resolution. Filter out the wind field correction region where all target output resolution calculation grid points fall within the rectangular search range; Based on the horizontal distance between the grid points in the map and each calculation grid point in the selected wind field correction region, different fusion weights are assigned to each of the selected wind field correction regions. Based on the fusion weights, the wind speed results of each of the selected wind field correction regions are weighted and averaged to obtain the fused wind speed of the map grid points; the corrected target output resolution wind speed field corresponding to the wind field correction region includes the wind speed results of the selected wind field correction region.

[0130] According to the present invention, a wind resource map generation device 600 based on a mesoscale wind field is provided, the device further includes a statistical variable comprehensive calculation module; The statistical variable comprehensive calculation module is used for: The steps of calculating background wind speed, calculating disturbance wind speed, and fusion are repeatedly performed on mesoscale wind field data from multiple time periods to construct the wind speed time series of each grid point within the target wind resource map region; Based on the wind speed time series, statistical analysis is performed on the wind speed data of each grid point within the target wind resource map area to calculate the wind resource evaluation index for each grid point; the wind resource evaluation index includes at least one of average wind speed, wind speed frequency distribution, wind rose diagram, and power density.

[0131] According to the wind resource map generation method based on mesoscale wind fields provided by the present invention, the statistical variable comprehensive calculation module is further used for: Multiple preset background wind speeds and multiple preset background wind directions are divided into multiple discrete levels to form a standard combination of multiple preset background wind speeds and preset background wind directions. For each of the aforementioned standard combinations, the corrected target output resolution wind speed field corresponding to each of the aforementioned standard combinations is pre-calculated, and the corrected target output resolution wind speed field corresponding to each of the aforementioned standard combinations is stored in the standard case library. When generating a target wind resource map based on the mesoscale wind field data of the multiple time periods, for each current time period, the corresponding wind field correction result is directly obtained by searching the standard case library according to the background wind speed and background wind direction of the current time period.

[0132] Figure 7 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 7 As shown, the electronic device may include: a processor 710, a communications interface 720, a memory 730, and a communication bus 740, wherein the processor 710, the communications interface 720, and the memory 730 communicate with each other via the communication bus 740. The processor 710 can call logical instructions in the memory 730 to execute a wind resource map generation method based on mesoscale wind fields, the method including: Acquire mesoscale wind field data and topographic data at a first resolution; the first resolution is higher than the second resolution of the mesoscale wind field data, and the topographic data at the first resolution is topographic data covering the target wind resource map area; Based on the target wind resource map region and the mesoscale wind field data, multiple wind field correction regions are determined, and a target output resolution calculation grid is constructed for each wind field correction region. For each wind field correction region, the background wind speed at the target height of the wind field correction region is calculated based on the mesoscale wind field data. Based on the linear terrain perturbation theory, using the background wind speed and the terrain data at the first resolution, the perturbation wind speed of the terrain corresponding to the terrain data at the first resolution is calculated, and the corrected target output resolution wind speed field corresponding to the wind field correction area is obtained according to the background wind speed and the perturbation wind speed. The corrected target output resolution wind speed fields corresponding to each wind field correction region are fused and mapped onto the grid of the target wind resource map region to generate a target output resolution wind resource map covering the target wind resource map region.

[0133] Furthermore, the logical instructions in the aforementioned memory 730 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, 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 the present invention. 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.

[0134] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the wind resource map generation method based on mesoscale wind fields provided by the above methods, the method comprising: Acquire mesoscale wind field data and topographic data at a first resolution; the first resolution is higher than the second resolution of the mesoscale wind field data, and the topographic data at the first resolution is topographic data covering the target wind resource map area; Based on the target wind resource map region and the mesoscale wind field data, multiple wind field correction regions are determined, and a target output resolution calculation grid is constructed for each wind field correction region. For each wind field correction region, the background wind speed at the target height of the wind field correction region is calculated based on the mesoscale wind field data. Based on the linear terrain perturbation theory, using the background wind speed and the terrain data at the first resolution, the perturbation wind speed of the terrain corresponding to the terrain data at the first resolution is calculated, and the corrected target output resolution wind speed field corresponding to the wind field correction area is obtained according to the background wind speed and the perturbation wind speed. The corrected target output resolution wind speed fields corresponding to each wind field correction region are fused and mapped onto the grid of the target wind resource map region to generate a target output resolution wind resource map covering the target wind resource map region.

[0135] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the wind resource map generation method based on mesoscale wind fields provided by the above methods, the method comprising: Acquire mesoscale wind field data and topographic data at a first resolution; the first resolution is higher than the second resolution of the mesoscale wind field data, and the topographic data at the first resolution is topographic data covering the target wind resource map area; Based on the target wind resource map region and the mesoscale wind field data, multiple wind field correction regions are determined, and a target output resolution calculation grid is constructed for each wind field correction region. For each wind field correction region, the background wind speed at the target height of the wind field correction region is calculated based on the mesoscale wind field data. Based on the linear terrain perturbation theory, using the background wind speed and the terrain data at the first resolution, the perturbation wind speed of the terrain corresponding to the terrain data at the first resolution is calculated, and the corrected target output resolution wind speed field corresponding to the wind field correction area is obtained according to the background wind speed and the perturbation wind speed. The corrected target output resolution wind speed fields corresponding to each wind field correction region are fused and mapped onto the grid of the target wind resource map region to generate a target output resolution wind resource map covering the target wind resource map region.

[0136] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0137] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for generating wind resource maps based on mesoscale wind fields, characterized in that, include: Acquire mesoscale wind field data and first-resolution terrain data; The first resolution is higher than the second resolution of the mesoscale wind field data, and the terrain data at the first resolution is terrain data covering the target wind resource map area; Based on the target wind resource map region and the mesoscale wind field data, multiple wind field correction regions are determined, and a target output resolution calculation grid is constructed for each wind field correction region. For each wind field correction region, the background wind speed at the target height of the wind field correction region is calculated based on the mesoscale wind field data. Based on the linear terrain perturbation theory, using the background wind speed and the terrain data at the first resolution, the perturbation wind speed of the terrain corresponding to the terrain data at the first resolution is calculated, and the corrected target output resolution wind speed field corresponding to the wind field correction area is obtained according to the background wind speed and the perturbation wind speed. The corrected target output resolution wind speed fields corresponding to each wind field correction region are fused and mapped onto the grid of the target wind resource map region to generate a target output resolution wind resource map covering the target wind resource map region.

2. The method for generating wind resource maps based on mesoscale wind fields according to claim 1, characterized in that, The step of determining multiple wind field correction regions based on the target wind resource map region and the mesoscale wind field data, and constructing a target output resolution calculation grid for each wind field correction region, includes: Mesoscale grid points that meet preset requirements are selected from the mesoscale wind field data; the preset requirements include that the mesoscale grid points are located within the target wind resource map region, and / or the boundary distance between the mesoscale grid points and the target wind resource map region is less than a preset threshold. Centered on each mesoscale grid point that meets the preset requirements, a wind field correction region corresponding to the mesoscale grid point that meets the preset requirements is constructed according to a preset horizontal scale; adjacent wind field correction regions have overlapping portions; For each wind field correction area, the spatial resolution of the terrain data at the first resolution is used as the computational grid resolution for grid subdivision, and the subdivided grid is assigned corresponding terrain height information to obtain the target output resolution computational grid.

3. The method for generating wind resource maps based on mesoscale wind fields according to claim 1, characterized in that, The step of calculating the background wind speed at the target height in the wind field correction area based on the mesoscale wind field data includes: Wind speed data for multiple vertical height layers were extracted from the mesoscale wind field data. Based on the wind speed data of the multiple vertical height layers, the background wind speed at the target height is calculated by interpolation. The background wind speed at the target height obtained by interpolation is constrained between an upper boundary curve and a lower boundary curve; the upper boundary curve and the lower boundary curve are obtained by fitting the wind speed data of the multiple vertical height layers.

4. The method for generating wind resource maps based on mesoscale wind fields according to claim 1, characterized in that, The disturbed wind speed is represented by a wind speed disturbance component; the calculation of the disturbed wind speed of the wind field corresponding to the terrain data at the first resolution, based on the linear terrain disturbance theory and using the background wind speed and the terrain data at the first resolution, includes: Using the background wind speed and the terrain data at the first resolution as input, the wind speed disturbance component is obtained by solving the linearized fluid dynamics equations.

5. The method for generating wind resource maps based on mesoscale wind fields according to claim 1, characterized in that, The step of fusing the corrected target output resolution wind speed fields corresponding to each of the wind field correction regions includes: For each grid point in the target wind resource map region, a corresponding rectangular search range is set for the grid point; the scale of the rectangular search range is preset according to the horizontal range of the wind field correction area and the grid resolution. Filter out the wind field correction region where all target output resolution calculation grid points fall within the rectangular search range; Based on the horizontal distance between the grid points in the map and each calculation grid point in the selected wind field correction region, different fusion weights are assigned to each of the selected wind field correction regions. Based on the fusion weights, the wind speed results of each of the selected wind field correction regions are weighted and averaged to obtain the fused wind speed of the map grid points; the corrected target output resolution wind speed field corresponding to the wind field correction region includes the wind speed results of the selected wind field correction region.

6. The method for generating wind resource maps based on mesoscale wind fields according to any one of claims 1-5, characterized in that, The method further includes: The steps of calculating background wind speed, calculating disturbance wind speed, and fusion are repeatedly performed on mesoscale wind field data from multiple time periods to construct the wind speed time series of each grid point within the target wind resource map region; Based on the wind speed time series, statistical analysis is performed on the wind speed data of each grid point within the target wind resource map area to calculate the wind resource evaluation index for each grid point; the wind resource evaluation index includes at least one of average wind speed, wind speed frequency distribution, wind rose diagram, and power density.

7. The method for generating wind resource maps based on mesoscale wind fields according to claim 1, characterized in that, The method further includes: Multiple preset background wind speeds and multiple preset background wind directions are divided into multiple discrete levels to form a standard combination of multiple preset background wind speeds and preset background wind directions. For each of the aforementioned standard combinations, the corrected target output resolution wind speed field corresponding to each of the aforementioned standard combinations is pre-calculated, and the corrected target output resolution wind speed field corresponding to each of the aforementioned standard combinations is stored in the standard case library. When generating a target wind resource map based on mesoscale wind field data from multiple time periods, for each current time period, the corresponding wind field correction result is directly obtained by searching the standard case library based on the background wind speed and background wind direction of the current time period.

8. A wind resource map generation device based on mesoscale wind fields, characterized in that, include: The acquisition module is used to acquire mesoscale wind field data and topographic data at the first resolution. The first resolution is higher than the second resolution of the mesoscale wind field data, and the terrain data at the first resolution is terrain data covering the target wind resource map area; The grid construction module is used to determine multiple wind field correction regions based on the target wind resource map region and the mesoscale wind field data, and to construct a target output resolution calculation grid for each wind field correction region. The wind speed calculation module is used to calculate the background wind speed at the target height for each wind field correction area based on the mesoscale wind field data. The terrain disturbance module is used to calculate the disturbance wind speed of the terrain corresponding to the first resolution terrain data based on the background wind speed and the first resolution terrain data, and to obtain the corrected target output resolution wind speed field corresponding to the wind field correction area based on the background wind speed and the disturbance wind speed. The fusion output module is used to fuse the corrected target output resolution wind speed fields corresponding to each wind field correction region and map them onto the grid of the target wind resource map region to generate a target output resolution wind resource map covering the target wind resource map region.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the wind resource map generation method based on mesoscale wind fields as described in any one of claims 1 to 6.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the wind resource map generation method based on mesoscale wind fields as described in any one of claims 1 to 6.

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