A wind-resistant optimization design system based on photovoltaic power station layout

Abstract

The present application relates to the technical field of wind-resistant optimization design of photovoltaic power stations, in particular to a wind-resistant optimization design system based on photovoltaic power station layout, comprising: collecting terrain elevation and meteorological data to construct a terrain flow model and extracting a refined wind speed amplification coefficient and turbulence intensity distribution. A plurality of virtual test pieces are set in the finite element model to calculate the static and dynamic response, and a structure resistance performance parameter table is constructed. The array spacing is iteratively adjusted with the structure fundamental frequency as the constraint, the wake interference coefficient is calculated in real time, and the layout scheme is determined when the preset frequency avoidance relationship is satisfied. The present scheme realizes the coupling analysis of wind environment and structure dynamics, avoids the resonance risk induced by the wake from the source, eliminates the load deviation caused by ignoring the distortion of micro-terrain wind field in traditional design, and improves the safety and design accuracy of the power station.

Description

Technical Field

[0001] This invention relates to the field of wind-resistant optimization design technology for photovoltaic power plants, and in particular to a wind-resistant optimization design system based on the layout of photovoltaic power plants. Background Technology

[0002] In conventional photovoltaic (PV) power plant wind resistance design, wind field environment analysis often involves simplified calculations using general wind field data from meteorological stations. Wind resistance analysis of PV support structures typically employs fixed-parameter specimen models, applying general wind loads for finite element analysis. Power plant array layout optimization is often based on illumination conditions and empirical spacing parameters, without integrating wind field characteristics with structural performance. Site features such as topographic elevation and surface roughness are not incorporated into the wind field modeling process, resulting in wind field simulations that only provide single-dimensional wind field parameters, failing to match the wind force characteristics of PV modules at different installation heights.

[0003] Conventional structural wind resistance analysis does not use differentiated specimens for different windward areas and structural stiffnesses. The static and dynamic response calculations cannot cover diverse support structure forms, and the matching degree between wind loads and actual site wind field characteristics is insufficient. Furthermore, the structural fundamental frequency was not used as a core constraint during photovoltaic array layout adjustments. The wake interference coefficients of adjacent arrays were not correlated with the structural fundamental frequency, and the layout parameter adjustments relied on a single indicator, failing to avoid the structural stress anomalies caused by structural resonance and wake superposition.

[0004] To address the issues of insufficient accuracy in extracting wind field features and limited parameters in structural wind resistance analysis for photovoltaic power plants, it is necessary to construct a dedicated wind field model by combining site topography and surface data, extract wind field feature parameters at multiple heights, and simultaneously perform structural wind resistance performance analysis through multi-parameter virtual specimens. The array layout should be iteratively adjusted with the structural fundamental frequency as a constraint so that the wake interference coefficient matches the preset frequency avoidance correlation state with the structural fundamental frequency. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a wind-resistant optimization design system for photovoltaic power plant layout.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a wind-resistant optimization design system for photovoltaic power plant layout, comprising: The site data processing module acquires topographic elevation data, surface roughness data, and historical wind field data from near-surface meteorological stations at the photovoltaic power station construction site. The simulation module imports the terrain elevation data and surface roughness data into the wind field environment modeling program to generate a terrain flow around model. The historical wind field data is then input into the terrain flow around model to simulate the steady-state wind field. The wind speed amplification factor and turbulence intensity distribution at different heights above the site horizontal plane are extracted from the simulation results to form a site wind field characteristic dataset. The mechanical analysis module, based on the site wind field characteristic dataset, defines multiple sets of virtual specimens with different windward areas and structural stiffness in the finite element analysis model of the photovoltaic module support structure. The wind load spectrum derived from the site wind field characteristic dataset is applied to each set of virtual specimens. The static and dynamic response calculations of the structure are performed in the finite element analysis model. The peak displacement of the vertex and the fundamental frequency of the structure of each set of virtual specimens are recorded, and a table of structural wind resistance performance parameters is constructed. The layout optimization module uses the structural fundamental frequency in the structural wind resistance performance parameter table as a constraint to iteratively adjust the array spacing and row spacing of the photovoltaic power station. After each adjustment, it recalculates the wake interference coefficient between adjacent arrays. When the wake interference coefficient and the structural fundamental frequency meet the preset frequency avoidance relationship, the candidate layout scheme is determined.

[0007] As a further aspect of the present invention, the terrain elevation data and surface roughness data are imported into a wind field environment modeling program to generate a terrain flow around a model, including: The terrain-based flow around the site model reflects the obstruction and acceleration effects of obstacles around the site on the incoming wind field; The terrain elevation data is triangulated to generate a digital elevation grid that fits the actual landform undulations. The node spacing of the digital elevation grid is no more than ten meters. The surface roughness data is classified and assigned values ​​according to land use type, and then mapped to the corresponding nodes of the digital elevation grid to form a three-dimensional terrain point cloud with surface attribute labels. A velocity inlet conforming to the logarithmic distribution of the atmospheric boundary layer is set at the boundary of the digital elevation grid, and symmetrical or free-slip boundaries are set at the top and sides of the computational domain to complete the physical domain definition of the terrain flow around model.

[0008] As a further aspect of the present invention, the historical wind field data is input into a terrain flow model for steady-state wind field simulation, including: The historical wind field data of the near-ground meteorological station includes the average wind speed and wind direction distribution at different altitude levels. The average wind speed and wind direction distribution in the historical wind field data are used as boundary conditions and input into the terrain flow around model. The frequency of occurrence of each wind direction sector in historical wind field data is statistically analyzed, and the most frequently occurring dominant wind directions are selected as the input wind directions for steady-state wind field simulation. For each prevailing wind direction, the average wind speed profile data under the prevailing wind direction is interpolated onto the vertical grid layer of the terrain flow model to form a layered inflow wind speed boundary. The solver of the wind field environment modeling program is started, and the Reynolds-averaged Navier-Stokes equations combined with the standard wall function are used to numerically solve the topographic flow model until convergence, and then the velocity vector field and turbulent dissipation rate field under steady-state wind field are obtained.

[0009] As a further aspect of the present invention, the step of extracting the wind speed amplification factor and turbulence intensity distribution at different heights above the site horizontal plane from the simulation results to form a site wind field characteristic dataset includes: Within the computational domain of the terrain flow model, several monitoring columns perpendicular to the ground are set up, with the height of the monitoring columns extending above the highest installation height of the photovoltaic modules. Multiple sampling points are evenly arranged on each monitoring column to collect the ratio of the simulated wind speed value to the free flow wind speed, and the wind speed amplification factor of each sampling point is calculated. The turbulence intensity at each sampling point is calculated by using the ratio of the root mean square value of the fluctuating wind speed to the average velocity at the sampling points on the monitoring column. The wind speed amplification factor and turbulence intensity of all monitoring columns at different height levels were rasterized and summarized to generate a site wind field characteristic dataset.

[0010] As a further aspect of the present invention, based on the site wind field characteristic dataset, multiple sets of virtual specimens with different windward areas and structural stiffness are defined in the finite element analysis model of the photovoltaic module support structure, including: The virtual specimens represent photovoltaic array arrangement units of different specifications; Select the commonly used bracket materials and cross-sectional specifications for photovoltaic power plants, and establish geometric models of single-column, double-column, and tracking brackets in the finite element analysis model respectively; By changing the length and width parameters of the photovoltaic panel, or by adding mass blocks to the support model, multiple sets of virtual specimens with different windward areas and structural stiffness can be generated in the finite element analysis model. Assign each virtual specimen an elastic modulus and Poisson's ratio that match the actual material, and apply fixed constraints to the bottom boundary of the virtual specimen to complete the modeling preparation of the virtual specimen.

[0011] As a further aspect of the present invention, the step of applying a wind load spectrum derived from the site wind field characteristic dataset to each group of virtual specimens and performing structural static and dynamic response calculations in the finite element analysis model includes: Read the turbulence intensity and wind speed amplification factor corresponding to the installation height of the photovoltaic array in the site wind field feature dataset, and convert them into the equivalent aerodynamic pressure acting on the surface of the photovoltaic panel; In the finite element analysis model, the equivalent aerodynamic pressure is decomposed into a concentrated force acting on the center of the panel and a distributed shear force acting on the edge of the panel. The modal analysis module of the finite element analysis model is enabled to calculate the first few natural frequencies and mode shapes of each virtual specimen. Then, switch to the transient dynamics analysis module, use the equivalent aerodynamic pressure as the input load, calculate the displacement response time history curve of each group of virtual specimens under wind load, and extract the peak displacement from the curve.

[0012] As a further aspect of the present invention, the step of iteratively adjusting the array spacing and row spacing of the photovoltaic power station using the structural fundamental frequency in the structural wind resistance performance parameter table as a constraint, and recalculating the wake interference coefficient between adjacent arrays after each adjustment, includes: The fundamental frequency of the structure must be greater than twice the main frequency of the site's turbulent energy, which is a frequency avoidance constraint. The row spacing of the photovoltaic array is initialized to a reference value, and then the array spacing is increased by a fixed step size based on the reference value, and all spacing combinations are traversed. For each spacing combination, a wind field environment modeling program is called to perform simplified calculations, solve the impact of upstream array wake vortex shedding on the wind-receiving area of ​​downstream array, and quantify the wake interference coefficient. The array spacing combinations that satisfy the frequency avoidance constraint of the structure's fundamental frequency and have a wake interference coefficient lower than the preset upper limit value are selected as candidate layout schemes.

[0013] As a further aspect of the present invention, it also includes: The wind field analysis module feeds back the photovoltaic array location information in the candidate layout scheme to the wind field environment modeling program. In the wind field environment modeling program, the near-wall wind pressure distribution under the layout is recalculated, and the pressure center coordinates and pressure extreme values ​​of the photovoltaic panel are extracted. The layout optimization module fine-tunes the installation tilt and azimuth angle of the photovoltaic panel based on the coordinates of the pressure center and the pressure extreme value, so that the pressure center shifts to the area with higher structural rigidity, and generates wind-resistant layout optimization instructions. The model generation module updates the position, tilt angle and azimuth angle of all photovoltaic modules in the three-dimensional digital model of the photovoltaic power station according to the wind-resistant layout optimization instructions, and obtains the final wind-resistant optimized layout model. The construction coordinate file and component installation angle list are exported from the final wind-resistant optimized layout model as the basis for on-site construction. The step of feeding back the photovoltaic array location information from the candidate layout schemes to the wind field environment modeling program, and recalculating the near-wall wind pressure distribution under the layout in the wind field environment modeling program, specifically includes: Read the four corner coordinates and elevation information of each photovoltaic array in the candidate layout scheme, and convert them into porous medium resistance source terms in the wind field environment modeling program; In the wind field environment modeling program, the terrain flow around the wind was reloaded and the porous medium resistance source term was implanted into the corresponding grid cell to simulate the blocking effect of the photovoltaic array on the airflow near the wall. Run the wind field environment modeling program to perform flow field update calculations. After the calculation converges, extract the pressure values ​​of the grid nodes that are close to the surface of the photovoltaic panel and draw the wind pressure distribution cloud map. Identify the areas with the highest pressure values ​​from the wind pressure distribution cloud map, define them as pressure extreme value areas, and record the corresponding pressure center coordinates.

[0014] As a further aspect of the present invention, the step of fine-tuning the installation tilt angle and azimuth angle of the photovoltaic panel based on the pressure center coordinates and pressure extreme values, so that the pressure center shifts towards a region with higher structural rigidity, includes: Analyzing the structural stress characteristics of the photovoltaic support structure, the intersection point of the support beams was identified as the area with high structural stiffness. By comparing the relative positions of the pressure center coordinates and the high structural stiffness region, the angular deviation between the line connecting the pressure center coordinates and the high structural stiffness region and the normal direction of the photovoltaic panel is calculated. Depending on the sign and magnitude of the included angle deviation, the installation tilt angle of the photovoltaic panel can be reduced or increased accordingly, or the azimuth angle of the photovoltaic panel can be rotated clockwise or counterclockwise. Repeatedly perform the fine-tuning process until the coordinates of the pressure center fall within the preset tolerance range centered on the area with high structural stiffness, and lock the tilt angle and azimuth angle at this point as the optimal attitude.

[0015] As a further aspect of the present invention, updating the position, tilt angle, and azimuth angle of all photovoltaic modules in the three-dimensional digital model of the photovoltaic power station according to the wind-resistant layout optimization instructions includes: Read the row and column coordinate indices, adjusted spacing values, and fine-tuned tilt and azimuth parameters contained in the wind-resistant layout optimization command; Traverse all photovoltaic module instances in the 3D digital model of the photovoltaic power station and match the photovoltaic modules that need to be adjusted based on the row and column coordinate index; Call the component editing interface of the 3D digital model to batch modify the geographical coordinates, pitch angle parameters, and horizontal rotation angle parameters of the matched photovoltaic modules; After all parameters have been updated, a Boolean operation is performed on the entire 3D digital model to eliminate geometric interference caused by changes in component positions, thus solidifying the final wind-resistant optimized layout model.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: The site topographic elevation data and surface roughness data are imported into the wind field environment modeling program to generate a topographic flow around the site. Historical wind field data from near-ground meteorological stations are input to complete the steady-state wind field simulation. The wind speed amplification factor and turbulence intensity distribution at different heights above the site horizontal plane are extracted from the simulation results to form a wind field feature dataset. The wind field simulation process closely matches the actual surface and topographic conditions of the site. The wind field feature parameters cover the stress scenarios of photovoltaic modules at different installation heights. The derived wind load spectrum has a higher degree of fit with the real wind field environment of the site. The dimensionality and refinement of the wind field data are in line with the actual deployment requirements of photovoltaic power plants.

[0017] In the finite element analysis model of the photovoltaic module support structure, multiple sets of virtual specimens with different windward areas and structural stiffness are defined. Corresponding wind load spectra are applied to each set of specimens, and the static and dynamic response calculations of the structure are completed. The peak displacement of the apex and the fundamental frequency of the structure are recorded to construct a table of structural wind resistance performance parameters. The array spacing and row spacing of the photovoltaic power station are iteratively adjusted with the fundamental frequency of the structure as a constraint. After each adjustment, the wake interference coefficient of adjacent arrays is recalculated. After the wake interference coefficient and the fundamental frequency of the structure reach the preset frequency avoidance relationship, the candidate layout scheme is determined. The structural wind resistance performance analysis covers support structure forms with different parameters. The structural response calculation results are more in line with the actual structural stress state. The array layout parameters and the wind resistance characteristics of the support structure are adapted and correlated. The wake interference state and the structural dynamic response state are consistent with each other. The matching degree between the layout parameters and the structural wind resistance performance is more in line with the actual working conditions. Attached Figure Description

[0018] Figure 1 This is a timing diagram of a photovoltaic power plant layout wind resistance optimization design system as described in this invention; Figure 2 A flowchart illustrating the process of inputting historical wind field data into a terrain flow model for steady-state wind field simulation; Figure 3 The graph shows the variation of the fundamental frequency of the structure with the array spacing. Figure 4 A graph showing the relationship between wind speed magnification factor and altitude for different terrains; Figure 5 This is a diagram showing the relationship between the tilt angle adjustment of the photovoltaic panel and the offset of the pressure center. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0020] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0021] See Figure 1 The site data processing module acquires topographic elevation data, surface roughness data, and historical wind field data from near-surface meteorological stations at the photovoltaic power station construction site. The simulation module imports the topographic elevation and surface roughness data into a wind field environment modeling program to generate a topographic flow around the site. The historical wind field data is then input into this model for steady-state wind field simulation. The simulation results extract wind speed amplification factors and turbulence intensity distributions at different heights above the site surface, forming a site wind field characteristic dataset. Based on this dataset, the mechanical analysis module defines multiple sets of virtual specimens with different windward areas and structural stiffnesses within the finite element analysis model of the photovoltaic module support structure. A wind load spectrum derived from the site wind field characteristic dataset is applied to each set of virtual specimens. Static and dynamic response calculations are performed in the finite element analysis model, recording the peak displacement at the top of each virtual specimen and the fundamental frequency of the structure, thus constructing a table of structural wind resistance performance parameters. The layout optimization module uses the structural fundamental frequency in the structural wind resistance performance parameter table as a constraint to iteratively adjust the array spacing and row spacing of the photovoltaic power station. After each adjustment, it recalculates the wake interference coefficient between adjacent arrays. When the wake interference coefficient and the structural fundamental frequency meet the preset frequency avoidance relationship, the candidate layout scheme is determined.

[0022] In one embodiment of the present invention, an example scenario is described, taking a photovoltaic power station construction site in a hilly area as an example. The site area is approximately 2 square kilometers. The topographic elevation data is obtained from lidar scanning with a data accuracy of 0.5 meters. The surface roughness data is obtained from a land use classification map interpreted by satellite remote sensing. Historical wind field data from a near-surface meteorological station includes the average wind speed and direction recorded hourly at different heights over 10 years. The topographic flow around the site reflects the obstruction and acceleration effects of obstacles around the site on the incoming wind field. The topographic elevation data is triangulated to generate a digital elevation grid that closely matches the actual topographic undulations. The node spacing of the digital elevation grid is no greater than 10 meters. In the example scenario, the topographic elevation data, after triangulation, generates a digital elevation grid with a node spacing of 8 meters. The digital elevation grid contains 500,000 nodes, accurately representing the ridge and valley morphology within the site. Surface roughness data is categorized and assigned values ​​according to land use types, including grassland, shrubs, bare soil, and construction land. Each type is assigned a specific value based on a surface roughness length lookup table, and these values ​​are mapped to corresponding nodes in the digital elevation grid, forming a 3D terrain point cloud with surface attribute labels. In the example scenario, grassland corresponds to a surface roughness length of 0.03 meters, and shrubs correspond to a surface roughness length of 0.1 meters. These values ​​are mapped to the corresponding nodes in the digital elevation grid, ensuring that the 3D terrain point cloud simultaneously contains both elevation and surface roughness information. A velocity inlet conforming to the logarithmic distribution of the atmospheric boundary layer is set at the boundary of the digital elevation grid. The logarithmic distribution of the atmospheric boundary layer is described by the following formula:

[0023] in: Indicates height Wind speed at the location, Indicates friction speed, This indicates that the von Kármán constant takes a value of 0.4. The physical domain definition of the terrain flow around the surface is completed by setting symmetrical or free-slip boundaries at the top and sides of the computational domain.

[0024] In some embodiments, steady-state wind field simulations are based on historical wind field data. Historical wind field data from near-surface weather stations includes the average wind speed and direction distribution at different altitudes. (See [reference needed]). Figure 2The average wind speed and direction distribution from historical wind field data are used as boundary conditions input into the terrain flow model. The frequency of occurrence of each wind direction sector in the historical wind field data is statistically analyzed. These sectors are divided into 16 sectors at 22.5-degree intervals, and the most frequently occurring prevailing wind directions are selected as input wind directions for steady-state wind field simulation. In the example scenario, the statistical results show that the northerly and northwesterly wind sectors have frequencies of 18% and 15%, respectively, and are thus identified as the prevailing wind directions. For each prevailing wind direction, the average wind speed profile data under the prevailing wind direction is interpolated onto the vertical grid layer of the terrain flow model. The vertical grid layer of the terrain flow model is divided into 30 layers from the ground to a height of 300 meters, forming a stratified inflow wind speed boundary. The solver of the wind field environment modeling program is started, and the Reynolds-averaged Navier-Stokes equations combined with the standard wall function are used to numerically solve the topographic flow around the model until convergence, which yields the velocity vector field and turbulent dissipation rate field under steady-state wind field. In the example scenario, for the prevailing northerly wind direction, the input wind speed profile is 5 meters per second at a height of 10 meters. After 5000 iterations, the residual of the solver decreases to below 10 to the power of -4, and the converged velocity vector field is obtained.

[0025] Optionally, when generating the digital elevation grid, the Delaunay triangulation algorithm is used for triangulation to ensure that the grid quality meets the requirements of computational fluid dynamics. The node spacing of the digital elevation grid can be dynamically adjusted according to the terrain complexity; in flat areas, the node spacing can be set to 10 meters, and in steep areas, the node spacing can be densified to 5 meters. It is understandable that the classification and assignment of surface roughness data depends on field surveys or high-resolution remote sensing products. The assignment of surface roughness length values ​​directly affects the parameters in the logarithmic distribution of the atmospheric boundary layer, thus affecting the accuracy of the velocity inlet setting.

[0026] In practical implementation, the number of prevailing wind directions selected can be determined according to the accuracy requirements of the wind field simulation. Two prevailing wind directions are selected in the example scenario, but three or more can also be selected to cover the main incoming flow directions. It is understood that the solver settings of the wind field environment modeling program include turbulence model selection, discretization scheme and convergence criteria. Standard wall functions are used to handle near-wall flow, and the Reynolds-averaged Navier-Stokes equations are solved using a pressure-velocity coupled algorithm. In some embodiments, the preprocessing of historical wind field data includes removing invalid records and performing homogenization checks to ensure data quality. The average wind speed profile data is obtained by fitting with an exponential or logarithmic law and then interpolated to the vertical grid layer. Optionally, the physical domain definition of the terrain flow model needs to ensure that the computational domain size is large enough to reduce boundary effects. In the example scenario, the horizontal extent of the computational domain extends 500 meters beyond the site boundary, and the vertical height is set to five times the highest point of the site.

[0027] In one embodiment of the present invention, the simulation module has generated the velocity vector field and turbulent dissipation rate field under steady-state wind conditions. Several monitoring columns perpendicular to the ground are set within the computational domain of the terrain flow model, with the height of the monitoring columns extending above the highest installation height of the photovoltaic modules. In the example scenario, the highest installation height of the photovoltaic modules is 5 meters, and the monitoring column height is 12 meters, with a total of 9 monitoring columns deployed, covering the core area of ​​the site in a 3×3 grid. Multiple sampling points are evenly distributed on each monitoring column to collect the ratio of the simulated wind speed value to the free-flow wind speed, and the wind speed amplification factor for each sampling point is calculated. The formula for calculating the wind speed amplification factor β is: ,in This represents the simulated wind speed value at the sampling point on the monitoring column. This represents the free-flow wind speed at the same height, undisturbed by terrain. In the example scenario, each monitoring column starts at a height of 0.5 meters above the ground, with sampling points spaced every 2 meters until the top of the column. The turbulence intensity at each sampling point is calculated using the ratio of the root mean square value of the fluctuating wind speed to the average velocity. From the formula Given, among which This represents the root mean square value of the fluctuating wind speed at the sampling point. This represents the average velocity at the sampling point. The wind speed amplification factor and turbulence intensity of all monitoring columns at different height levels are rasterized. The rasterization process uses the Kriging space interpolation method to interpolate the data from discrete sampling points into a two-dimensional raster data layer with a resolution of 2 meters × 2 meters covering the entire site area. This data is then aggregated to generate a site wind field feature dataset. In the example scenario, wind speed amplification factor raster maps and turbulence intensity raster maps are generated for each height level. The final dataset contains 12 height levels and a total of 24 raster data files.

[0028] In some embodiments, the virtual specimens are defined in a finite element analysis software environment, and the virtual specimens represent photovoltaic array arrangement units of different specifications. Commonly used bracket materials and cross-sectional specifications for photovoltaic power plants are selected, and geometric models of single-column, double-column, and tracking brackets are established in the finite element analysis model. In the example scenario, the bracket material is Q235 steel, the single-column bracket uses a square steel tube with a cross-sectional specification of 100mm×100mm, the double-column bracket uses two square steel tubes with a cross-sectional specification of 80mm×80mm, and the tracking bracket includes a rotatable main shaft and a supporting truss structure. By changing the length and width parameters of the photovoltaic panel, or by adding mass blocks to the support model, multiple sets of virtual specimens with different windward areas and structural stiffnesses are generated in the finite element analysis model. The example defines three standard photovoltaic panel sizes (length × width): 1.6m × 1.0m, 1.96m × 1.0m, and 2.0m × 1.0m. Different configurations of counterweights are added to the support model to simulate stiffness changes caused by different component weights, generating a total of 15 sets of virtual specimens. Each set of virtual specimens is assigned an elastic modulus and Poisson's ratio consistent with the actual material, and fixed constraints are applied to the bottom boundary of the virtual specimens to complete the modeling preparation. In the example, the elastic modulus of Q235 steel is set to 210GPa, the Poisson's ratio is set to 0.3, and the connection between the bottom of the virtual specimen and the ground is set to full constraint.

[0029] Optionally, the location and number of monitoring columns can be adjusted according to the complexity of the site terrain. The number of monitoring columns can be reduced on flat sites, while a denser deployment is required on complex mountainous sites. It is understood that the wind speed amplification factor and the reference height for the free-flow wind speed must be consistent. The free-flow wind speed is typically taken from the wind speed value at the corresponding height in the boundary conditions at the inlet of the computational domain. In specific implementations, the establishment of the virtual specimen's geometric model can be parameterized. The length, width, and thickness of the photovoltaic panel, as well as the cross-sectional dimensions of the support frame and the column spacing, are all set as input parameters, facilitating the rapid generation of various specification combinations. In some embodiments, the addition of mass blocks can be achieved by defining non-structural mass in the finite element analysis model or by directly modifying the material density properties to simulate the weight increase caused by snow load, dust accumulation, or the use of glass of different thicknesses. It is understood that the fixed constraint conditions at the bottom boundary of the virtual specimen simulate the rigid connection between the support foundation and the ground. If foundation flexibility needs to be considered, spring elements can be added at the constraint points.

[0030] In one embodiment of the present invention, the mechanical analysis module has completed the definition of a virtual specimen based on the site wind field feature dataset. The turbulence intensity and wind speed amplification factor corresponding to the photovoltaic array installation height are read from the site wind field feature dataset and converted into equivalent aerodynamic pressure acting on the surface of the photovoltaic panel. In the example scenario, the wind speed amplification factor corresponding to an installation height of 5 meters is 1.2, the turbulence intensity is 0.15, the reference wind speed is 25 meters per second, and the equivalent aerodynamic pressure is... From the formula:

[0031] in: This indicates that the air density is taken as 1.225 kg per cubic meter. This indicates that the shape factor of the photovoltaic panel is taken as 1.3. This represents the wind speed amplification factor. The reference wind speed is indicated. In the finite element analysis model, the equivalent aerodynamic pressure is decomposed into a concentrated force acting on the center of the panel and a distributed shear force acting on the edge of the panel. In the example scenario, for a panel with dimensions of 1.96 m × 1.0 m, the magnitude of the concentrated force is the total wind force obtained by integrating the pressure distribution, while the distributed shear force is applied according to the shear flow distribution pattern around the panel. The modal analysis module of the finite element analysis model is enabled to calculate the first few natural frequencies and mode shapes of each group of virtual specimens. In the example, the first 5 modes of each of the 15 groups of virtual specimens are calculated, and the natural frequency and corresponding mode shape of each mode are recorded. The first bending frequency is recorded as the fundamental frequency of the structure. Then, the transient dynamic analysis module is switched to, and the equivalent aerodynamic pressure is used as the input load to calculate the displacement response time history curve of each group of virtual specimens under wind load, and the peak displacement value at the top is extracted from the curve. In the example scenario, the input load is the time history wind pressure generated according to the wind spectrum of the target site, the calculation time is 600 seconds, and the maximum displacement value of the top of all virtual specimens is extracted from the displacement response time history curve.

[0032] In some embodiments, the layout optimization module iterates using the structural fundamental frequency as a constraint, setting a frequency avoidance constraint that the structural fundamental frequency must be greater than twice the main frequency of the site turbulent energy. The row spacing of the photovoltaic array is initialized to a baseline value, and then the array spacing is increased in fixed steps based on this baseline value, iterating through all spacing combinations. In the example scenario, the baseline row spacing is set to 2.5 meters (based on the height of the photovoltaic modules), the initial array spacing is 3.5 meters, and it increases to 8.5 meters in 0.5-meter steps, iterating through all row spacing and spacing combinations. For each spacing combination, a wind field environment modeling program is called for simplified calculations to solve the impact of upstream array wake vortex shedding on the windward area of ​​the downstream array, quantifying the wake interference coefficient. The calculation formula is:

[0033] in: This indicates the original windward area of ​​the photovoltaic panel. This represents the effective wind-receiving area of ​​the downstream array panel after considering the wake obstruction effect of the upstream array, under specific row spacing and spacing. The array spacing combinations that satisfy the frequency avoidance constraint for the structural fundamental frequency and have a wake interference coefficient below a preset upper limit are selected as candidate layout schemes. In the example, the preset upper limit for the wake interference coefficient is 0.25, and the main frequency of the site turbulence energy is 0.8 Hz. Therefore, the frequency avoidance constraint is that the structural fundamental frequency is greater than 1.6 Hz. Finally, combinations that meet the conditions are selected, such as a row spacing of 2.5 meters and a spacing of 5.5 meters.

[0034] Optionally, the calculation of equivalent aerodynamic pressure can further consider the gust factor, incorporating the influence of turbulence intensity into the generation process of time-history loads, thereby obtaining a more accurate displacement response in transient dynamic analysis. It can be understood that the fundamental frequency of the virtual specimen's structure is directly related to its stiffness and mass distribution. Changing the array spacing and row spacing will affect the overall constraint conditions of the support system, thus indirectly affecting its fundamental frequency. In layout optimization, the fundamental frequency of the corresponding virtual specimen needs to be obtained by consulting a pre-calculated table of structural wind resistance parameters.

[0035] See Figure 3 This graph illustrates the relationship between the structural fundamental frequency and array spacing in a photovoltaic power plant. It represents a core mechanical analysis result for optimizing the wind-resistant layout. The structural fundamental frequency monotonically increases with increasing array spacing, indicating that a larger array spacing results in stronger overall constraint of the support system and a higher structural fundamental frequency. The frequency avoidance constraint threshold (1.6Hz) requires the structural fundamental frequency to be greater than this value to avoid resonance with the main frequency of site turbulence energy (0.8Hz). When the array spacing is <4.5m, the structural fundamental frequency is below 1.6Hz, failing to meet the frequency avoidance requirement. When the array spacing is ≥4.5m, the structural fundamental frequency remains consistently above 1.6Hz, satisfying the frequency avoidance constraint. Provided the wake interference coefficient also meets the upper limit (0.25), the optimal layout scheme can be selected from spacings ≥4.5m. As the array spacing increases, the overall stiffness and boundary constraints of the photovoltaic support system strengthen, leading to an increase in the structural fundamental frequency. This is a key principle in the wind-resistant design of photovoltaic power plants.

[0036] In one embodiment of the present invention, the layout optimization module has determined a row spacing of 2.5 meters and a spacing of 5.5 meters as a candidate layout scheme. The wind field analysis module feeds back the photovoltaic array position information in the candidate layout scheme to the wind field environment modeling program, which recalculates the near-wall wind pressure distribution under the layout and extracts the pressure center coordinates and pressure extreme values ​​of the photovoltaic panel. The four corner coordinates and elevation information of each photovoltaic array in the candidate layout scheme are read and converted into the porous media resistance source term in the wind field environment modeling program. In the example scenario, the candidate layout scheme contains 20 rows, with 15 photovoltaic arrays in each row, totaling 300 array units. The four corner plane coordinates (X1,Y1), (X2,Y2), (X3,Y3), (X4,Y4) of each array unit and its uniform installation elevation of 5.0 meters are read, and the momentum loss coefficient of the porous media resistance source term is obtained. formula:

[0037] in: This represents an empirical coefficient related to the surface geometry of the photovoltaic panel. Indicates the thickness of the photovoltaic panel. This represents the equivalent hydraulic diameter of the photovoltaic array. The terrain flow model is reloaded in the wind field environmental modeling program, and the porous medium resistance source term is implanted into the corresponding mesh cells to simulate the obstruction effect of the photovoltaic array on the near-wall airflow. The wind field environmental modeling program is run to perform flow field update calculations. After the calculation converges, the pressure values ​​of the mesh nodes adjacent to the photovoltaic panel surface are extracted, and a wind pressure distribution cloud map is plotted. Several regions with the highest pressure values ​​are identified from the wind pressure distribution cloud map and defined as pressure extreme value regions. The corresponding pressure center coordinates are recorded. In the example, pressure extreme value regions usually appear at the upper edge and corners of the panel, and the pressure center coordinates are (…). , , The result was obtained through a weighted average method, where The mounting height is fixed to the panel surface. See Table 1.

[0038] Table 1: Coordinates of the pressure center and extreme pressure values ​​of some arrays in the candidate layout schemes

[0039] In some embodiments, the layout optimization module fine-tunes the installation tilt and azimuth of the photovoltaic panels based on the coordinates of the pressure center and the pressure extreme value, causing the pressure center to shift towards an area with higher structural stiffness, thus generating wind-resistant layout optimization instructions. The model generation module updates the position, tilt, and azimuth of all photovoltaic modules in the 3D digital model of the photovoltaic power station according to the wind-resistant layout optimization instructions, obtaining the final wind-resistant optimized layout model. Construction coordinate files and a list of component installation angles are exported from the final wind-resistant optimized layout model as the basis for on-site construction. In the example scenario, the wind-resistant layout optimization instructions are a set of instructions containing the indexes of all photovoltaic modules that need adjustment and their new parameters. The 3D digital model is built in building information modeling software, and the exported construction coordinate file is in .csv format. The list of component installation angles details the longitude, latitude, elevation, tilt, and azimuth of each photovoltaic array.

[0040] In practical implementation, the wind farm environment modeling program can employ either unsteady simulation or steady-state RANS simulation during recalculation, depending on the accuracy requirements for wake oscillation effects. In the initial optimization stage, the more computationally efficient steady-state RANS method is typically used. In some embodiments, identifying pressure extreme regions from the wind pressure distribution cloud map can be done automatically using image processing algorithms, or by setting pressure thresholds for filtering, recording the pressure center coordinates in a separate data file for the layout optimization module to read. It is understood that the generated wind-resistant layout optimization instructions are in text or structured data format, containing a unique identifier for each photovoltaic module instance and its adjusted attribute values. Optionally, Boolean operations on the 3D digital model are used to detect interference between updated modules and between modules and the foundation, ensuring the physical feasibility of the layout.

[0041] See Figure 4 This is a graph showing the relationship between wind speed amplification factor and altitude for different terrains, illustrating the relationship between ground height and wind speed amplification factor under various terrain conditions. It is a key result in the simulation of wind fields for photovoltaic power plants. The wind speed amplification factor increases most gradually on flat terrain, approximately 1.31 at a height of 20m; on hilly terrain, the increase is moderate, approximately 1.46 at a height of 20m; and on mountainous terrain, the increase is most significant, approximately 1.62 at a height of 20m. A typical installation height for photovoltaic modules (5m) is used to extract the wind speed amplification factor required for engineering design. The greater the terrain undulation, the stronger the airflow and lifting effect, resulting in a higher wind speed amplification factor, meaning the photovoltaic modules will bear a greater wind load. Under all terrain conditions, the wind speed amplification factor increases monotonically with altitude, and the rate of increase gradually slows down, consistent with the distribution pattern of wind fields in the atmospheric boundary layer. Terrain complexity is the core factor determining the wind speed amplification factor; the wind amplification effect in mountainous terrain is much greater than that in flat terrain.

[0042] In one embodiment of the present invention, the wind field analysis module has extracted the pressure center coordinates and pressure extreme values ​​of the photovoltaic panel in the candidate layout scheme. Analyzing the structural stress characteristics of the photovoltaic support structure, the intersection point of the support beams is identified as a region of high structural stiffness. In the single-column support model used in the example scenario, the connection point between the top of the column and the two beams is identified as a region of high structural stiffness, and its three-dimensional coordinates are known in the finite element analysis model. Comparing the relative positional relationship between the pressure center coordinates and the region of high structural stiffness, the angle deviation between the line connecting the pressure center coordinates and the region of high structural stiffness and the normal direction of the photovoltaic panel is calculated; the angle deviation... The calculation formula is:

[0043] in: This represents a vector pointing from the coordinate point of the pressure center to a point in the region of high structural stiffness. This represents the outward normal vector of the photovoltaic panel. Based on the included angle deviation... The sign and magnitude of the value correspond to the adjustment of the installation tilt angle of the photovoltaic panel, or the rotation of the azimuth angle of the photovoltaic panel clockwise or counterclockwise. In the example scenario, for the photovoltaic panel with array number A-1, the calculated deviation of its pressure center coordinates from the direction of the line connecting the high stiffness area and the normal direction of the panel is +5 degrees. Therefore, its initial installation tilt angle is reduced from 25 degrees to 23 degrees, and its azimuth angle (initially due south, i.e., 0 degrees) is rotated 2 degrees clockwise. The fine-tuning process is repeated until the pressure center coordinates fall within the preset tolerance range centered on the high structural stiffness area. The tilt angle and azimuth angle at this point are locked as the optimal posture. The preset tolerance range is set as a spherical space with a radius of 0.1 meters centered on the high structural stiffness area. In the example, after 3 iterations of adjustment, the pressure center coordinates of the photovoltaic panel with array number A-1 are adjusted into this tolerance sphere. At this point, the tilt angle is locked at 22.8 degrees and the azimuth angle is locked at 2.5 degrees west of south.

[0044] In some embodiments, the update of the 3D digital model is executed based on the wind-resistant layout optimization instruction, reading the row and column coordinate indices, adjusted spacing values, and fine-tuned tilt and azimuth parameters contained in the wind-resistant layout optimization instruction. All photovoltaic module instances in the 3D digital model of the photovoltaic power station are traversed, and the photovoltaic modules that need adjustment are matched according to the row and column coordinate indices. In the example, the 3D digital model contains 300 photovoltaic module family instances, each instance having a unique row and column coordinate index attribute. The layout optimization instruction contains a list of indices to be adjusted and their new parameters. The component editing interface of the 3D digital model is called to batch modify the geographical coordinates, pitch angle parameters, and horizontal rotation angle parameters of the matched photovoltaic modules. In the building information modeling software used in the example, a script is written through the application programming interface to update the "base elevation," "tilt angle," and "rotation angle" attributes of each component instance in the index list. After all parameters are updated, a Boolean operation check is performed on the entire 3D digital model to eliminate geometric interference caused by changes in component positions, and the final wind-resistant optimized layout model is obtained. In the example, the Boolean operation check found two cases where the edge distance between adjacent components was too close (less than 50 mm design gap) due to azimuth angle adjustment. The system automatically corrected the horizontal position of one of the components by 0.05 meters.

[0045] Optionally, the area of ​​high structural stiffness is not limited to the intersection of beams. For double-column or tracking supports, the geometric center area formed by the connection points of multiple columns and the main beam can be identified as a high-stiffness area. It is understandable that the included angle deviation... The convention for positive and negative signs needs to be consistent; for example, it should be stipulated that when... In panel normals The deviation is positive when it is clockwise and negative when it is counterclockwise, to guide the direction of azimuth rotation.

[0046] See Figure 5This is a graph showing the relationship between photovoltaic panel tilt angle adjustment and pressure center offset, illustrating the correlation between tilt angle adjustment and the reduction rate of pressure center offset. This is a key analytical result in wind-resistant optimization design for adjusting panel attitude and optimizing stress distribution. The curve exhibits a symmetrical U-shaped parabolic distribution, reaching a minimum near 0° and monotonically increasing towards both sides, peaking at ±10°. A larger tilt angle adjustment results in a higher reduction rate of pressure center offset, indicating that a larger tilt angle adjustment can more effectively guide the pressure center towards areas with higher structural stiffness. At ±10° adjustment, the offset reduction rate is close to 0.7, significantly shifting the pressure center to areas with higher support stiffness and improving structural wind resistance stability. When the tilt angle is adjusted to 0°, the offset reduction rate is close to 0, indicating that no attitude fine-tuning has been performed, and the pressure center remains in its initial position, not optimized towards a higher stiffness area. The curve is completely symmetrical about 0°, demonstrating that increasing the tilt angle positively and decreasing it negatively, under the same adjustment range, have equivalent optimization effects on pressure center offset.

[0047] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A wind-resistant optimization design system for photovoltaic power plant layout, characterized in that, include: The site data processing module acquires topographic elevation data, surface roughness data, and historical wind field data from near-surface meteorological stations at the photovoltaic power station construction site. The simulation module imports the terrain elevation data and surface roughness data into the wind field environment modeling program to generate a terrain flow around model. The historical wind field data is then input into the terrain flow around model to simulate the steady-state wind field. The wind speed amplification factor and turbulence intensity distribution at different heights above the site horizontal plane are extracted from the simulation results to form a site wind field characteristic dataset. The mechanical analysis module, based on the site wind field characteristic dataset, defines multiple sets of virtual specimens with different windward areas and structural stiffness in the finite element analysis model of the photovoltaic module support structure. The wind load spectrum derived from the site wind field characteristic dataset is applied to each set of virtual specimens. The static and dynamic response calculations of the structure are performed in the finite element analysis model. The peak displacement of the vertex and the fundamental frequency of the structure of each set of virtual specimens are recorded, and a table of structural wind resistance performance parameters is constructed. The layout optimization module uses the structural fundamental frequency in the structural wind resistance performance parameter table as a constraint to iteratively adjust the array spacing and row spacing of the photovoltaic power station. After each adjustment, it recalculates the wake interference coefficient between adjacent arrays. When the wake interference coefficient and the structural fundamental frequency meet the preset frequency avoidance relationship, the candidate layout scheme is determined.

2. The photovoltaic power plant layout wind resistance optimization design system according to claim 1, characterized in that, The terrain elevation data and surface roughness data are imported into a wind field environment modeling program to generate a terrain flow around the terrain model, including: The terrain-based flow around the site model reflects the obstruction and acceleration effects of obstacles around the site on the incoming wind field; The terrain elevation data is triangulated to generate a digital elevation grid that fits the actual landform undulations. The node spacing of the digital elevation grid is no more than ten meters. The surface roughness data is classified and assigned values ​​according to land use type, and then mapped to the corresponding nodes of the digital elevation grid to form a three-dimensional terrain point cloud with surface attribute labels. A velocity inlet conforming to the logarithmic distribution of the atmospheric boundary layer is set at the boundary of the digital elevation grid, and symmetrical or free-slip boundaries are set at the top and sides of the computational domain to complete the physical domain definition of the terrain flow around model.

3. The photovoltaic power plant layout wind resistance optimization design system according to claim 2, characterized in that, The historical wind field data is input into the terrain flow around the model for steady-state wind field simulation, including: The historical wind field data of the near-ground meteorological station includes the average wind speed and wind direction distribution at different altitude levels. The average wind speed and wind direction distribution in the historical wind field data are used as boundary conditions and input into the terrain flow around model. The frequency of occurrence of each wind direction sector in historical wind field data is statistically analyzed, and the most frequently occurring dominant wind directions are selected as the input wind directions for steady-state wind field simulation. For each prevailing wind direction, the average wind speed profile data under the prevailing wind direction is interpolated onto the vertical grid layer of the terrain flow model to form a layered inflow wind speed boundary. The solver of the wind field environment modeling program is started, and the Reynolds-averaged Navier-Stokes equations combined with the standard wall function are used to numerically solve the topographic flow model until convergence, and then the velocity vector field and turbulent dissipation rate field under steady-state wind field are obtained.

4. The photovoltaic power plant layout wind resistance optimization design system according to claim 3, characterized in that, The simulation results are used to extract wind speed amplification factors and turbulence intensity distributions at different heights above the site surface, forming a site wind field characteristic dataset, including: Within the computational domain of the terrain flow model, several monitoring columns perpendicular to the ground are set up, with the height of the monitoring columns extending above the highest installation height of the photovoltaic modules. Multiple sampling points are evenly arranged on each monitoring column to collect the ratio of the simulated wind speed value to the free flow wind speed, and the wind speed amplification factor of each sampling point is calculated. The turbulence intensity at each sampling point is calculated by using the ratio of the root mean square value of the fluctuating wind speed to the average velocity at the sampling points on the monitoring column. The wind speed amplification factor and turbulence intensity of all monitoring columns at different height levels were rasterized and summarized to generate a site wind field characteristic dataset.

5. The photovoltaic power plant layout wind resistance optimization design system according to claim 4, characterized in that, Based on the site wind field characteristic dataset, multiple sets of virtual specimens with different windward areas and structural stiffness are defined in the finite element analysis model of the photovoltaic module support structure, including: The virtual specimens represent photovoltaic array arrangement units of different specifications; Select the commonly used bracket materials and cross-sectional specifications for photovoltaic power plants, and establish geometric models of single-column, double-column, and tracking brackets in the finite element analysis model respectively; By changing the length and width parameters of the photovoltaic panel, or by adding mass blocks to the support model, multiple sets of virtual specimens with different windward areas and structural stiffness can be generated in the finite element analysis model. Assign each virtual specimen an elastic modulus and Poisson's ratio that match the actual material, and apply fixed constraints to the bottom boundary of the virtual specimen to complete the modeling preparation of the virtual specimen.

6. The photovoltaic power plant layout wind resistance optimization design system according to claim 5, characterized in that, The process of applying wind load spectra derived from the site wind field characteristic dataset to each set of virtual specimens and performing structural static and dynamic response calculations in the finite element analysis model includes: Read the turbulence intensity and wind speed amplification factor corresponding to the installation height of the photovoltaic array in the site wind field feature dataset, and convert them into the equivalent aerodynamic pressure acting on the surface of the photovoltaic panel; In the finite element analysis model, the equivalent aerodynamic pressure is decomposed into a concentrated force acting on the center of the panel and a distributed shear force acting on the edge of the panel. The modal analysis module of the finite element analysis model is enabled to calculate the first few natural frequencies and mode shapes of each virtual specimen. Then, switch to the transient dynamics analysis module, use the equivalent aerodynamic pressure as the input load, calculate the displacement response time history curve of each group of virtual specimens under wind load, and extract the peak displacement from the curve.

7. The photovoltaic power plant layout wind resistance optimization design system according to claim 6, characterized in that, The method involves iteratively adjusting the array spacing and row spacing of the photovoltaic power station using the structural fundamental frequency in the structural wind resistance performance parameter table as a constraint, and recalculating the wake interference coefficient between adjacent arrays after each adjustment, including: The fundamental frequency of the structure must be greater than twice the main frequency of the site's turbulent energy, which is a frequency avoidance constraint. The row spacing of the photovoltaic array is initialized to a reference value, and then the array spacing is increased by a fixed step size based on the reference value, and all spacing combinations are traversed. For each spacing combination, a wind field environment modeling program is called to perform simplified calculations, solve the impact of upstream array wake vortex shedding on the wind-receiving area of ​​downstream array, and quantify the wake interference coefficient. The array spacing combinations that satisfy the frequency avoidance constraint of the structure's fundamental frequency and have a wake interference coefficient lower than the preset upper limit value are selected as candidate layout schemes.

8. The photovoltaic power plant layout wind resistance optimization design system according to claim 7, characterized in that, Also includes: The wind field analysis module feeds back the photovoltaic array location information in the candidate layout scheme to the wind field environment modeling program. In the wind field environment modeling program, the near-wall wind pressure distribution under the layout is recalculated, and the pressure center coordinates and pressure extreme values ​​of the photovoltaic panel are extracted. The layout optimization module fine-tunes the installation tilt and azimuth angle of the photovoltaic panel based on the coordinates of the pressure center and the pressure extreme value, so that the pressure center shifts to the area with higher structural rigidity, and generates wind-resistant layout optimization instructions. The model generation module updates the position, tilt angle and azimuth angle of all photovoltaic modules in the three-dimensional digital model of the photovoltaic power station according to the wind-resistant layout optimization instructions, and obtains the final wind-resistant optimized layout model. The construction coordinate file and component installation angle list are exported from the final wind-resistant optimized layout model as the basis for on-site construction. The step of feeding back the photovoltaic array location information from the candidate layout schemes to the wind field environment modeling program, and recalculating the near-wall wind pressure distribution under the layout in the wind field environment modeling program, specifically includes: Read the four corner coordinates and elevation information of each photovoltaic array in the candidate layout scheme, and convert them into porous medium resistance source terms in the wind field environment modeling program; In the wind field environment modeling program, the terrain flow around the wind was reloaded and the porous medium resistance source term was implanted into the corresponding grid cell to simulate the blocking effect of the photovoltaic array on the airflow near the wall. Run the wind field environment modeling program to perform flow field update calculations. After the calculation converges, extract the pressure values ​​of the grid nodes that are close to the surface of the photovoltaic panel and draw the wind pressure distribution cloud map. Identify the areas with the highest pressure values ​​from the wind pressure distribution cloud map, define them as pressure extreme value areas, and record the corresponding pressure center coordinates.

9. A wind-resistant optimization design system for photovoltaic power plant layout according to claim 8, characterized in that, The step of fine-tuning the installation tilt and azimuth angle of the photovoltaic panel based on the coordinates of the pressure center and the pressure extreme value, so that the pressure center shifts towards a region with higher structural stiffness, includes: Analyzing the structural stress characteristics of the photovoltaic support structure, the intersection point of the support beams was identified as the area with high structural stiffness. By comparing the relative positions of the pressure center coordinates and the high structural stiffness region, the angular deviation between the line connecting the pressure center coordinates and the high structural stiffness region and the normal direction of the photovoltaic panel is calculated. Depending on the sign and magnitude of the included angle deviation, the installation tilt angle of the photovoltaic panel can be reduced or increased accordingly, or the azimuth angle of the photovoltaic panel can be rotated clockwise or counterclockwise. Repeatedly perform the fine-tuning process until the coordinates of the pressure center fall within the preset tolerance range centered on the area with high structural stiffness, and lock the tilt angle and azimuth angle at this point as the optimal attitude.

10. A wind-resistant optimization design system for photovoltaic power plant layout according to claim 9, characterized in that, The process of updating the position, tilt angle, and azimuth angle of all photovoltaic modules in the three-dimensional digital model of the photovoltaic power station based on the wind-resistant layout optimization instructions includes: Read the row and column coordinate indices, adjusted spacing values, and fine-tuned tilt and azimuth parameters contained in the wind-resistant layout optimization command; Traverse all photovoltaic module instances in the 3D digital model of the photovoltaic power station and match the photovoltaic modules that need to be adjusted based on the row and column coordinate index; Call the component editing interface of the 3D digital model to batch modify the geographical coordinates, pitch angle parameters, and horizontal rotation angle parameters of the matched photovoltaic modules; After all parameters have been updated, a Boolean operation is performed on the entire 3D digital model to eliminate geometric interference caused by changes in component positions, thus solidifying the final wind-resistant optimized layout model.

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