Construction method and system of wind pressure distribution map of power transmission tower based on CFD typhoon wind field

Abstract

The present application relates to the technical field of power transmission line disaster prevention, and more particularly to a method and system for constructing a wind pressure distribution map of a power transmission tower based on a CFD typhoon wind field, which comprises data preprocessing, adaptive grid generation, wind field CFD simulation, aerodynamic mapping calculation, equivalent wind pressure conversion, closed-loop calibration iteration and hierarchical mapping output. The method uses an adaptive calculation grid driven by terrain and tower double factors, combines a standard tower type aerodynamic coefficient library to complete structure wind pressure mapping, converts equivalent cumulative wind pressure through a multi-damage mode coupling evaluation criterion, calculates the wind pressure spatial distribution of different return periods, and generates a corresponding wind pressure zoning distribution map after parameter iteration calibration. In the present application, the fatigue accumulation and extreme value overturning disaster effects of typhoons on towers can be comprehensively reflected, while the simulation accuracy of key areas and overall calculation efficiency are also taken into account, the matching degree of the wind pressure distribution map and the actual working condition is improved, and reliable data support can be provided for fine wind resistance and risk control of power transmission towers.

Description

Technical Field

[0001] This invention relates to the field of disaster prevention technology for power transmission lines, and in particular to a method and system for constructing wind pressure distribution maps of transmission towers based on CFD typhoon wind fields, which can be applied to wind resistance planning and wind disaster risk assessment of transmission towers. Background Technology

[0002] Typhoon activity is frequent in the southeastern coastal areas of my country. As the core infrastructure of the power transmission network, transmission towers are vulnerable to structural damage and even collapse caused by typhoons, leading to widespread power outages. Wind pressure distribution maps of transmission towers are crucial technical data for line route planning, tower wind-resistant design, and wind disaster risk management. They provide data support for formulating tower protection standards in different regions and are of significant engineering importance for ensuring the stable operation of the power system.

[0003] Currently, typhoon wind pressure distribution assessments typically use extreme wind pressure calculated from instantaneous maximum wind speeds as the core indicator, primarily focusing on the risk of tower overturning caused by instantaneous strong winds. However, the typhoon process encompasses the continuous action of different wind speed levels, and the cumulative fatigue effect of prolonged strong winds can also damage the tower structure. A single extreme wind pressure indicator cannot fully reflect the comprehensive disaster-causing effect of typhoons on towers, resulting in insufficient matching between wind pressure distribution maps and actual disaster damage, and making it difficult to adequately support the refined wind-resistant design of transmission towers. Summary of the Invention

[0004] To overcome the above shortcomings, this invention provides a method and system for constructing wind pressure distribution maps of transmission towers based on CFD typhoon wind fields. It aims to improve the existing technology that uses a single extreme wind pressure as an evaluation index, which makes it difficult to fully reflect the comprehensive disaster-causing effect of typhoon processes, and the resulting wind pressure distribution maps do not match the actual disaster damage of the towers, thus failing to fully support the refined wind resistance design of transmission towers.

[0005] In a first aspect, the present invention provides the following technical solution: a method for constructing wind pressure distribution maps of transmission towers based on CFD typhoon wind fields, comprising the following steps:

[0006] S1. Data preprocessing: Collect multi-source basic data of the target area and perform standardization processing to obtain a basic dataset for spatial matching;

[0007] S2. Adaptive Grid: Based on the basic dataset, analyze the terrain geometry features and combine them with the importance level of the towers to generate a two-factor driven adaptive computational grid;

[0008] S3. Wind field CFD simulation: Numerical simulation is performed based on the parameterized wind field model and the adaptive computing grid to obtain the near-surface wind speed time history dataset;

[0009] S4. Aerodynamic mapping calculation: Call the standard tower type aerodynamic coefficient library to map the near-surface wind speed time history dataset to the tower structure wind pressure time history dataset;

[0010] S5. Equivalent wind pressure conversion: Based on the multi-damage mode coupling evaluation criterion, the wind pressure time history dataset of the tower structure is converted to obtain the equivalent cumulative wind pressure, and the spatial distribution data of the equivalent cumulative wind pressure at different return periods is calculated.

[0011] S6. Closed-loop calibration iteration: Compare the equivalent cumulative wind pressure spatial distribution data with the reference data, adjust the wind field parameters and aerodynamic parameters in layers and iterate until convergence, and output the converged equivalent cumulative wind pressure spatial distribution data.

[0012] S7. Graded Mapping Output: Based on the converged equivalent cumulative wind pressure spatial distribution data, wind pressure levels are divided by tower type differential grading thresholds to generate a wind pressure zoning distribution map.

[0013] By adopting the above technical solutions: the equivalent cumulative wind pressure index is calculated based on the multi-damage mode coupling evaluation criterion, which can comprehensively reflect the fatigue cumulative damage and extreme overturning risk of towers under typhoon action, and is more in line with the actual disaster characteristics compared with a single extreme wind pressure; an adaptive computational grid driven by two factors, topographic geometry features and tower importance level, is adopted to ensure the simulation accuracy of key areas while taking into account the overall computational efficiency; the deviation between simulation results and actual working conditions can be reduced by closed-loop iterative hierarchical calibration of wind field and aerodynamic parameters; and the wind pressure distribution map output by the tower type differentiated grade threshold can be directly matched with the wind resistance requirements of different tower types, and has good engineering adaptability.

[0014] Preferably, in step S2, the adaptive computational grid is generated in the following manner: extracting terrain elevation data from the basic dataset, calculating terrain curvature and slope and identifying wind pressure sensitive areas, and dividing the initial grid densification level; then extracting the structural importance level of the tower locations, and correspondingly improving the grid densification accuracy for the areas containing tower locations according to the tower importance level.

[0015] Preferably, in step S3, the near-surface wind speed time history dataset is generated in the following manner: typhoon meteorological parameters are extracted from the basic dataset, and the upper-level gradient wind field is calculated based on the parameterized wind field model; the surface roughness correction factor is calculated by combining land use data and the terrain correction factor is calculated by combining terrain data; the upper-level gradient wind field is first corrected by the surface roughness correction factor and then by the terrain correction factor, and then the boundary layer correction is performed sequentially.

[0016] Preferably, in step S4, the tower structure wind pressure time history dataset is obtained by mapping in the following way: extracting the hourly near-surface wind speed of the corresponding tower location in the near-surface wind speed time history dataset; matching the wind load shape coefficient of the corresponding tower type in the standard tower type aerodynamic coefficient library; correcting the wind load shape coefficient by using the wind angle of attack correction coefficient and the wind speed interval correction coefficient, and then calculating the actual wind pressure of each part of the tower hourly.

[0017] Preferably, in step S5, the equivalent cumulative wind pressure is obtained by the following method: dividing the wind pressure time history of the tower structure into multiple intervals according to the wind pressure amplitude, and counting the duration of each interval; calculating the wind-induced fatigue damage and the extreme overturning damage respectively, and coupling them to obtain the total damage; based on the principle of damage equivalence, converting the total damage into the constant wind pressure value corresponding to the set reference duration, and obtaining the equivalent cumulative wind pressure; establishing an extreme value distribution model based on the equivalent cumulative wind pressure of historical typhoon samples, and calculating the spatial distribution data of equivalent cumulative wind pressure for different return periods.

[0018] Preferably, in step S6, the closed-loop calibration iteration is performed in the following manner: the reference data includes historical disaster damage level data and measured wind pressure data; according to terrain type and tower type, the matching degree between the equivalent cumulative wind pressure classification results and historical disaster damage level data, and the deviation between the equivalent cumulative wind pressure value and the measured wind pressure data are statistically analyzed to form deviation characteristics; the terrain correction factor of the CFD simulation and the local correction coefficient of the standard tower type aerodynamic coefficient library are adjusted according to the deviation characteristics; the calculations of steps S3 to S5 are repeated based on the adjusted parameters until the matching degree and the deviation both meet the preset convergence conditions.

[0019] Preferably, in step S7, the tower type differentiated classification threshold is set in the following way: wind pressure thresholds for three damage levels—minor damage, severe damage, and collapse—are set for different tower types; threshold adjustment coefficients are set for different return periods, and the wind pressure thresholds for each damage level of the target tower type are corrected using the threshold adjustment coefficients for the corresponding return periods; after matching the target tower type with the target return period, the equivalent cumulative wind pressure spatial distribution data after convergence is classified into levels.

[0020] Secondly, the present invention provides the following technical solution: a system for constructing wind pressure distribution maps of transmission towers based on CFD typhoon wind fields, the system comprising:

[0021] The preprocessing module is used to collect multi-source basic data of the target area and perform standardization processing to output a basic dataset;

[0022] The grid generation module, connected to the preprocessing module, is used to receive the basic dataset, parse the terrain geometry features and combine them with the importance level of the towers, and output a two-factor driven adaptive computational grid.

[0023] The wind field simulation module, connected to the grid generation module, is used to receive the adaptive computation grid, perform CFD numerical simulation based on the parameterized wind field model, and output a near-surface wind speed time history dataset.

[0024] The aerodynamic mapping module, connected to the wind field simulation module, is used to receive the near-surface wind speed time history dataset, call the standard tower aerodynamic coefficient library to perform mapping calculations, and output the tower structure wind pressure time history dataset.

[0025] The wind pressure conversion module, connected to the aerodynamic mapping module, is used to receive the wind pressure time history dataset of the tower structure, convert the equivalent cumulative wind pressure based on the multi-damage mode coupling evaluation criterion, and output the spatial distribution data of the equivalent cumulative wind pressure with different return periods.

[0026] The calibration iteration module is connected to the wind pressure conversion module and is also signal-connected to the wind field simulation module and the aerodynamic mapping module. It is used to receive the equivalent cumulative wind pressure spatial distribution data, compare it with the reference data, adjust the parameters in layers and iterate until convergence, and output the converged equivalent cumulative wind pressure spatial distribution data.

[0027] The hierarchical mapping module, connected to the calibration iteration module, is used to receive the converged equivalent cumulative wind pressure spatial distribution data, classify wind pressure levels by combining the tower type differential grading threshold, and generate a wind pressure zoning distribution map.

[0028] Preferably, the mesh generation module includes:

[0029] The terrain analysis unit is used to receive the basic dataset, extract terrain elevation data from it, calculate terrain curvature and slope, identify wind pressure sensitive areas, and output terrain feature parameters.

[0030] The classification unit, connected to the terrain parsing unit, is used to receive the terrain feature parameters, classify the initial grid encryption level according to the terrain complexity, and output the initial encryption level information.

[0031] The grid adjustment unit, connected to the level division unit, is used to receive the initial encryption level information, adjust the grid encryption accuracy in combination with the structural importance level of the tower points, and output the final adaptive calculation grid.

[0032] Preferably, the calibration iteration module includes:

[0033] The data comparison unit is used to receive the equivalent cumulative wind pressure spatial distribution data, compare it with the benchmark data according to terrain type and tower type classification, statistically analyze the deviation characteristics, and output the deviation statistical results.

[0034] The parameter adjustment unit, connected to the data comparison unit, is used to receive the deviation statistics, adjust the terrain correction factor and aerodynamic local correction coefficient in layers, and output parameter adjustment instructions.

[0035] The iterative control unit is connected to the parameter adjustment unit and is also signal-connected to the wind field simulation module and the aerodynamic mapping module. It is used to receive the parameter adjustment command, control the repeated calculation of the wind field simulation to wind pressure conversion process, and output the converged equivalent cumulative wind pressure spatial distribution data when the graded matching degree and numerical deviation of the equivalent cumulative wind pressure both meet the preset convergence conditions.

[0036] The present invention has the following beneficial effects:

[0037] 1. In this invention, the equivalent cumulative wind pressure is calculated based on the multi-damage mode coupling evaluation criterion, and the spatial distribution data of different return periods is inferred from this. This can simultaneously reflect the comprehensive disaster-causing effect of wind-induced fatigue damage and extreme overturning damage. Compared with a single extreme wind pressure index, it is more in line with the actual load on the tower under typhoon action, which helps to improve the engineering reference value of the wind pressure distribution map.

[0038] 2. In this invention, an adaptive computational grid driven by two factors—topographic geometry features and the importance level of the towers—is used. This grid can improve the computational accuracy in wind-pressure sensitive terrain areas and for high-level tower locations, while controlling the overall computational load across the entire domain. This ensures the simulation accuracy of key areas while also taking into account overall computational efficiency.

[0039] 3. In this invention, a closed-loop calibration iteration process is set up. By combining historical disaster damage level data and measured wind pressure data, the wind field parameters and aerodynamic parameters are adjusted in layers. Through multiple rounds of iteration to converge and optimize the calculation results, the deviation between the simulated values ​​and the actual working conditions can be reduced, and the accuracy and reliability of the wind pressure distribution data can be improved.

[0040] 4. In this invention, the mapping from the environmental wind field to the wind pressure of the tower structure is completed through the standard tower type aerodynamic coefficient library, and the level classification is completed by using the tower type differentiated classification threshold. The output wind pressure distribution map can directly match the wind resistance requirements of different tower types, reducing the secondary conversion link in engineering applications. Attached Figure Description

[0041] Figure 1 This is a flowchart of the method for constructing wind pressure distribution maps of transmission towers based on CFD typhoon wind fields proposed in this invention.

[0042] Figure 2 This is a framework diagram of the transmission tower wind pressure distribution map construction system based on CFD typhoon wind field proposed in this invention;

[0043] Figure 3 This is a diagram showing the data flow of internal cells within the mesh generation module proposed in this invention.

[0044] Figure 4 This is a diagram showing the data flow within the internal units of the calibration iteration module proposed in this invention. Detailed Implementation

[0045] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] Example 1: In the first embodiment of the present invention, the present invention provides a method for constructing a wind pressure distribution map of transmission towers based on CFD typhoon wind fields, such as... Figure 1 As shown; this embodiment takes a group of 110kV and above voltage level transmission towers in a typhoon-prone area in southeastern coastal my country as the implementation object, and carries out the construction of a wind pressure distribution map of transmission towers based on CFD typhoon wind field. The specific implementation process is as follows:

[0047] S1. Data preprocessing: Collect multi-source basic data of the target area and perform standardization processing to obtain a basic dataset for spatial matching;

[0048] Specifically, multi-source basic data of the target area are collected and standardized to obtain a basic dataset for spatial matching.

[0049] Data was collected from the target area, including a 30m resolution digital elevation model (DEM), 10m resolution land use classification data, historical typhoon best paths and meteorological parameters from the past 40 years, and spatial coordinates and attribute records of transmission towers across the entire region. Typhoon path data underwent time interpolation to achieve a 1-hour time resolution; design reference height and structural type fields were added to the tower record data. All data underwent coordinate system transformation, adopting UTM projection and the WGS84 coordinate system; invalid data such as typhoon path anomalies and elevation jumps were removed; all spatial data were resampled to a 30m spatial resolution; and tower location data was spatially registered with topographic and land use data to ensure complete consistency in coverage, projection coordinate system, and resolution, ultimately forming a standardized basic dataset to provide fundamental input for subsequent grid generation and wind field simulation.

[0050] S2. Adaptive Grid: Based on the basic dataset, analyze the terrain geometry features and combine them with the importance level of the towers to generate a two-factor driven adaptive computational grid;

[0051] Furthermore, the adaptive computational grid is generated in the following manner: topographic elevation data is extracted from the basic dataset, topographic curvature and slope are calculated and wind pressure-sensitive areas are identified, and an initial grid densification level is defined; then the structural importance level of the tower locations is extracted, and the grid densification accuracy is correspondingly improved for the areas containing tower locations according to the tower importance level.

[0052] Specifically, based on the aforementioned basic dataset, terrain geometric features are analyzed and combined with the importance level of the towers to generate a two-factor driven adaptive computational grid.

[0053] Terrain elevation data was extracted from the basic dataset. A third-order inverse distance squared weighted algorithm was used to calculate terrain curvature and slope cell by cell, identifying wind pressure-sensitive areas such as ridgelines, canyon passes, and windward steep slopes. Slopes greater than 25° were classified as steep slopes, and areas with an absolute curvature value greater than 0.05 were classified as complex terrain areas. Four initial densification levels were established based on terrain complexity. Voltage level and structural type attributes of tower locations were extracted, classifying towers into three importance levels: 500kV and above towers were classified as Level 1 importance, 220kV towers as Level 2 importance, and 110kV towers as Level 3 importance. For grid cells containing higher-level towers, the precision was increased by one level based on the initial densification level. The final grid size was calculated using the following formula:

[0054] ;

[0055] In the formula, h is the final size of the target mesh; h0 is the size of the global basic mesh; C is the normalized terrain curvature value, ranging from 0 to 1; k is the tower importance level coefficient, ranging from 0 to 1; α and β are the weighting coefficients of the terrain factor and the tower factor, respectively, which can be adjusted according to the regional terrain characteristics. After the mesh is generated, a quality check is performed to ensure that the mesh aspect ratio is less than 3 and the skewness is less than 0.7, meeting the mesh quality requirements for CFD numerical calculation, and finally forming a dual-factor driven adaptive computational mesh.

[0056] S3. Wind field CFD simulation: Numerical simulation is performed based on the parameterized wind field model and the adaptive computing grid to obtain the near-surface wind speed time history dataset;

[0057] Furthermore, the near-surface wind speed time history dataset is generated in the following manner: typhoon meteorological parameters are extracted from the basic dataset, and the upper-level gradient wind field is calculated based on the parameterized wind field model; the surface roughness correction factor is calculated by combining land use data and the terrain correction factor is calculated by combining terrain data; the upper-level gradient wind field is first corrected by the surface roughness correction factor and then by the terrain correction factor, and then the boundary layer correction is performed sequentially.

[0058] Specifically, numerical simulations were performed based on a parameterized wind field model and an adaptive computational grid to obtain a near-surface wind speed time history dataset.

[0059] Parameters such as central pressure, maximum wind speed, path coordinates, and movement speed of historical typhoons were extracted from the basic dataset. The upper-level gradient wind field of the target area was calculated based on the Holland parametric wind field model. Surface roughness lengths corresponding to each land parcel were extracted using land use classification data, and surface roughness correction factors were calculated. Topographic elevation data and an adaptive grid were used to calculate topographic acceleration correction factors. Wind speed correction was performed using the atmospheric boundary layer wind profile exponential law. First, the vertical distribution of the wind profile was adjusted through surface roughness, and then the wind speed increase caused by topographic lifting and funneling effects was superimposed. The near-surface wind speed calculation formula is as follows:

[0060] ;

[0061] In the formula, v(z) is the near-surface wind speed at height z; v g For gradient wind height z g Gradient wind speed at z; g The gradient wind height is 300m for plains and 400m for mountainous areas; m is the surface roughness index, with values ​​corresponding to land use type: 0.16 for farmland, 0.22 for urban built-up areas, and 0.28 for forest land. The wind speed field for the entire typhoon process is calculated at a fixed time step of 10 minutes. The wind speed sequence at the tower design reference height is extracted to form a near-surface wind speed time history dataset, providing incoming wind field input for subsequent tower wind pressure mapping.

[0062] S4. Aerodynamic mapping calculation: Call the standard tower type aerodynamic coefficient library to map the near-surface wind speed time history dataset to the tower structure wind pressure time history dataset;

[0063] Furthermore, the tower structure wind pressure time history dataset is obtained by mapping in the following way: extracting the hourly near-surface wind speed of the corresponding tower location in the near-surface wind speed time history dataset; matching the wind load shape coefficient of the corresponding tower type in the standard tower type aerodynamic coefficient library; and after correcting the wind load shape coefficient with wind angle of attack correction coefficient and wind speed interval correction coefficient, calculating the actual wind pressure of each part of the tower hourly.

[0064] Specifically, the standard tower aerodynamic coefficient library is called to map the near-surface wind speed time history dataset to the tower structure wind pressure time history dataset.

[0065] For each tower location, the hourly incoming wind speed corresponding to its spatial location and height is extracted from the near-surface wind speed time history dataset. A pre-built standard tower aerodynamic coefficient library is used, based on scaled-down models of mainstream straight-line towers, tension towers, and angle towers. The shape coefficients of the main tower structure, crossarms, and insulator strings at wind attack angles of 0° to 90° are determined through wind tunnel tests, and interpolation yields the full angle-of-attack coefficient matrix. After matching the wind load shape coefficients for the corresponding tower type, wind attack angle correction coefficients and wind speed range correction coefficients are introduced to correct the shape coefficients. The wind attack angle correction coefficient is determined based on the angle between the incoming wind direction and the axis of the tower's main structure, while the wind speed range correction coefficient corrects for aerodynamic interference effects at different wind speeds. The actual wind pressure at each part of the tower is calculated hourly using the following formula:

[0066] ;

[0067] In the formula, w s The actual wind pressure at the corresponding location on the tower; μ s This represents the wind load shape coefficient for the corresponding location; μ α For wind angle of attack correction factor; μ v ρ is the wind speed range correction factor; ρ is the air density, taken as 1.225 kg / m³ under standard atmospheric pressure; v is the incoming wind speed at the corresponding height. After calculation, wind pressure time history datasets for tower structures by tower type and location are generated and input into the next equivalent conversion step.

[0068] S5. Equivalent wind pressure conversion: Based on the multi-damage mode coupling evaluation criterion, the wind pressure time history dataset of the tower structure is converted to obtain the equivalent cumulative wind pressure, and the spatial distribution data of the equivalent cumulative wind pressure at different return periods is calculated.

[0069] Furthermore, the equivalent cumulative wind pressure is obtained by the following conversion method: the wind pressure time history of the tower structure is divided into multiple intervals according to the wind pressure amplitude, and the duration of each interval is counted; the wind-induced fatigue damage and extreme overturning damage are calculated separately and coupled to obtain the total damage; based on the damage equivalence principle, the total damage is converted into the constant wind pressure value corresponding to the set reference duration to obtain the equivalent cumulative wind pressure; an extreme value distribution model is established based on the equivalent cumulative wind pressure of historical typhoon samples to estimate the spatial distribution data of equivalent cumulative wind pressure at different return periods.

[0070] Specifically, based on the multi-damage mode coupling assessment criterion, the equivalent cumulative wind pressure is obtained by converting the wind pressure time history dataset of the tower structure, and the spatial distribution data of the equivalent cumulative wind pressure at different return periods are calculated.

[0071] The tower wind pressure time history of a single typhoon is divided into several continuous intervals according to the wind pressure amplitude, and the duration of action corresponding to each wind pressure interval is statistically analyzed. Wind-induced fatigue damage and extreme overturning damage are calculated separately. Fatigue damage corresponds to the cumulative fatigue failure of the structure under cyclic wind loads, and overturning damage corresponds to the overturning resistance failure of the structure under extreme wind loads. The total damage is obtained by coupling these factors, and the calculation formula is as follows:

[0072] ;

[0073] In the formula, D represents the total damage; n represents the total number of wind pressure ranges; t i T represents the duration of the i-th level wind pressure; i w represents the total time it takes for the structure to reach the corresponding damage state under the action of wind pressure level i; max This represents the maximum wind pressure value during the typhoon process; w u This represents the ultimate wind pressure value for the corresponding part of the tower. A standard time interval of 10 minutes is set as the baseline duration. Based on the principle of damage equivalence, the total damage is converted into a constant wind pressure value that produces the same damage under the baseline duration; this is the equivalent cumulative wind pressure.

[0074] An extreme value probability distribution model was established based on equivalent cumulative wind pressure samples from multiple historical typhoons. The Gumbel extreme value type I distribution was adopted, and the location and scale parameters of the distribution were fitted by the maximum likelihood estimation method. The quantile values ​​corresponding to different return periods were calculated by the inverse operation of the cumulative distribution function, so as to obtain the spatial distribution data of equivalent cumulative wind pressure with different return periods in the whole region, providing core indicators for subsequent calibration and hierarchical mapping.

[0075] S6. Closed-loop calibration iteration: Compare the equivalent cumulative wind pressure spatial distribution data with the reference data, adjust the wind field parameters and aerodynamic parameters in layers and iterate until convergence, and output the converged equivalent cumulative wind pressure spatial distribution data.

[0076] Furthermore, the closed-loop calibration iteration is performed in the following manner: the reference data includes historical disaster damage level data and measured wind pressure data; according to terrain type and tower type, the matching degree between the equivalent cumulative wind pressure classification results and historical disaster damage level data, and the deviation between the equivalent cumulative wind pressure value and the measured wind pressure data are statistically analyzed to form deviation characteristics; the terrain correction factor of the CFD simulation and the local correction coefficient of the standard tower type aerodynamic coefficient library are adjusted according to the deviation characteristics; based on the adjusted parameters, the calculations of steps S3 to S5 are repeated until the matching degree and the deviation both meet the preset convergence conditions.

[0077] Specifically, the equivalent cumulative wind pressure spatial distribution data is compared with the benchmark data, the wind field parameters and aerodynamic parameters are adjusted in layers and iteratively calculated until convergence, and the converged equivalent cumulative wind pressure spatial distribution data is output.

[0078] The selected benchmark data includes tower damage level data obtained from historical typhoon disaster surveys and measured wind pressure data from anemometers at tower sites. Data is categorized and statistically analyzed according to terrain type and tower type. The matching degree between the equivalent cumulative wind pressure classification results and the disaster damage level, as well as the relative deviation between the equivalent cumulative wind pressure value and the measured data, are calculated to form deviation characteristics including the direction and magnitude of the deviation. Based on the deviation characteristics, the terrain correction factor in the CFD simulation and the local correction coefficients in the standard tower type aerodynamic coefficient library are adjusted accordingly. Each parameter adjustment does not exceed 10% of the original value to avoid iterative divergence. Based on the adjusted parameters, the wind field simulation, aerodynamic mapping, and equivalent wind pressure conversion steps are repeated until the classification matching degree is not less than 90% and the relative numerical deviation does not exceed 15%, satisfying the preset convergence condition. The maximum number of iterations is set to 5. If convergence is not achieved after reaching the maximum number of iterations, the current optimal result is output. Finally, the calibrated spatial distribution data of the equivalent cumulative wind pressure is output.

[0079] S7. Graded Mapping Output: Based on the converged equivalent cumulative wind pressure spatial distribution data, wind pressure levels are divided by tower type differential grading thresholds to generate a wind pressure zoning distribution map.

[0080] Furthermore, the tower type differentiated classification thresholds are set in the following way: wind pressure thresholds for three damage levels—minor damage, severe damage, and collapse—are set for different tower types; threshold adjustment coefficients are set for different return periods, and the wind pressure thresholds for each damage level of the target tower type are corrected using the threshold adjustment coefficients for the corresponding return periods; after matching the target tower type with the target return period, the equivalent cumulative wind pressure spatial distribution data after convergence is classified into levels.

[0081] Specifically, based on the converged equivalent cumulative wind pressure spatial distribution data, wind pressure levels are divided by combining the tower type differential grading threshold, and a wind pressure zoning distribution map is generated.

[0082] For different standard tower types, based on structural wind load-bearing test data and historical disaster damage statistics, equivalent cumulative wind pressure thresholds corresponding to three damage levels—minor damage, severe damage, and collapse—were calibrated. Minor damage refers to component deformation and hardware damage; severe damage refers to yielding of the main tower material and crossarm fracture; and collapse refers to the overall overturning and instability of the tower. Corresponding threshold adjustment coefficients were set for different return periods; the longer the return period, the higher the threshold adjustment coefficient, and the higher the corresponding fortification standard. After matching the target tower type and target return period, the corresponding thresholds were used to classify the calibrated equivalent cumulative wind pressure spatial distribution data into levels. A 30m resolution continuous wind pressure zoning raster map was generated using ordinary Kriging spatial interpolation, overlaid with transmission line and tower location layers, and finally outputting a wind pressure level zoning vector map, contour map, and corresponding attribute table, which can be directly used for wind resistance design of transmission towers and line route planning.

[0083] Example 2: This example uses a newly built 220kV transmission line in a mountainous area along the southeast coast of my country as an application scenario. This area includes complex micro-topography such as ridges and valleys. Existing general wind pressure distribution maps have insufficient resolution and do not take into account the characteristics of the tower structures, thus failing to meet the differentiated design requirements of the line. The transmission tower wind pressure distribution map construction system based on CFD typhoon wind fields in this example is used to complete the wind zone distribution map drawing. Its structure is as follows: Figure 2 As shown, the specific implementation methods of each module are as follows:

[0084] The preprocessing module is used to collect multi-source basic data of the target area and perform standardization processing to output a basic dataset;

[0085] Specifically, this module connects to a geographic information database and a power ledger system, collecting 30m resolution digital elevation data, 10m resolution land use cover data, the best typhoon path dataset for the Northwest Pacific over the past 45 years, and spatial coordinates, tower type, and voltage level ledger data of planned and existing transmission towers along the route. It incorporates built-in data validation rules to automatically remove invalid data with out-of-limit coordinates or missing attributes, generating a data quality report; it automatically performs coordinate system transformation and resampling of all spatial data, unifying them to the WGS84 coordinate system and 30m spatial resolution; and it completes spatial correlation matching between tower locations and terrain and land use data, forming a standardized, spatially matched basic dataset, which is then output to the grid generation module.

[0086] The grid generation module, connected to the preprocessing module, is used to receive the basic dataset, parse the terrain geometry features and combine them with the importance level of the towers, and output a two-factor driven adaptive computational grid.

[0087] Furthermore, the mesh generation module includes:

[0088] The terrain analysis unit is used to receive the basic dataset, extract terrain elevation data from it, calculate terrain curvature and slope, identify wind pressure sensitive areas, and output terrain feature parameters.

[0089] The classification unit, connected to the terrain parsing unit, is used to receive the terrain feature parameters, classify the initial grid encryption level according to the terrain complexity, and output the initial encryption level information.

[0090] The grid adjustment unit, connected to the level division unit, is used to receive the initial encryption level information, adjust the grid encryption accuracy in combination with the structural importance level of the tower points, and output the final adaptive calculation grid.

[0091] Specifically, this module comprises three serially connected units: a terrain analysis unit receives the basic dataset output by the preprocessing module, extracts digital elevation data from it, calculates terrain slope and curvature grid by grid using the third-order finite difference method, extracts three types of wind pressure-sensitive terrain units—ridgelines, canyon passes, and windward steep slopes—using a neighborhood feature recognition algorithm, and outputs terrain feature parameters including slope, curvature, and terrain type labels; a level division unit connected to the terrain analysis unit receives the terrain feature parameters, divides the entire region into four levels—extremely complex, complex, general, and gentle—according to terrain complexity, sets four initial grid encryption levels, and outputs the initial encryption level grid information; and a grid adjustment unit connected to the level division unit receives the initial encryption level information, combines the importance level of the tower locations based on voltage levels, increases the encryption accuracy of grid units containing important towers by one level, calculates the grid size in the same way as in Example 1, generates the final adaptive calculation grid file, and outputs it to the wind field simulation module.

[0092] The wind field simulation module, connected to the grid generation module, is used to receive the adaptive computation grid, perform CFD numerical simulation based on the parameterized wind field model, and output a near-surface wind speed time history dataset.

[0093] Specifically, this module incorporates the Holland wind field calculation engine and CFD solver. It imports the central pressure, maximum wind speed, path, and velocity parameters of historical typhoons, first calculating the upper-level gradient wind field at the gradient wind height. Then, it extracts the surface roughness length from land use data and calculates the terrain correction factor from topographic data. Finally, it uses the atmospheric boundary layer exponential wind profile to complete the correction sequentially. The wind speed calculation method is consistent with Example 1. It supports batch processing of multiple historical typhoon data, calculating the time-by-time three-dimensional wind speed field of the entire typhoon process at a fixed time step, extracting the wind speed sequence at the tower design reference height, forming a standardized near-surface wind speed time history dataset, and outputting it to the aerodynamic mapping module.

[0094] The aerodynamic mapping module, connected to the wind field simulation module, is used to receive the near-surface wind speed time history dataset, call the standard tower aerodynamic coefficient library to perform mapping calculations, and output the tower structure wind pressure time history dataset.

[0095] Specifically, this module incorporates a standard tower aerodynamic coefficient library and a tower matching engine. After receiving the near-surface wind speed time history dataset, it automatically calls the corresponding shape coefficient matrix based on the tower type number of the tower location, introduces wind attack angle correction coefficients and wind speed interval correction coefficients to complete the correction, and calculates the actual wind pressure of each structural part at each time step. The wind pressure calculation method is consistent with that in Example 1. After the calculation is completed, a tower structure wind pressure time history dataset is generated by tower type and by part, which is then output to the wind pressure conversion module.

[0096] The wind pressure conversion module, connected to the aerodynamic mapping module, is used to receive the wind pressure time history dataset of the tower structure, convert the equivalent cumulative wind pressure based on the multi-damage mode coupling evaluation criterion, and output the spatial distribution data of the equivalent cumulative wind pressure with different return periods.

[0097] Specifically, this module divides the wind pressure time history of a single typhoon into multiple wind pressure intervals at equal intervals based on amplitude, and calculates the duration of each interval. It then calculates fatigue damage and overturning damage separately, coupling them to obtain the total damage. The damage calculation method is consistent with Example 1. A unified baseline duration is set, and the equivalent cumulative wind pressure is calculated based on the damage equivalence principle. A Gumbel extreme value probability distribution model is established based on calculation samples from multiple historical typhoons to estimate the equivalent cumulative wind pressure corresponding to different return periods (10 years, 30 years, 50 years, 100 years), generating spatial distribution data of the global equivalent cumulative wind pressure, which is output to the calibration iteration module. The module supports weight configuration for multiple damage modes, allowing adjustment of the proportion of fatigue damage and overturning damage according to engineering needs, adapting to different fortification targets.

[0098] The calibration iteration module is connected to the wind pressure conversion module and is also signal-connected to the wind field simulation module and the aerodynamic mapping module. It is used to receive the equivalent cumulative wind pressure spatial distribution data, compare it with the reference data, adjust the parameters in layers and iterate until convergence, and output the converged equivalent cumulative wind pressure spatial distribution data.

[0099] Furthermore, the calibration iteration module includes:

[0100] The data comparison unit is used to receive the equivalent cumulative wind pressure spatial distribution data, compare it with the benchmark data according to terrain type and tower type classification, statistically analyze the deviation characteristics, and output the deviation statistical results.

[0101] The parameter adjustment unit, connected to the data comparison unit, is used to receive the deviation statistics, adjust the terrain correction factor and aerodynamic local correction coefficient in layers, and output parameter adjustment instructions.

[0102] The iterative control unit is connected to the parameter adjustment unit and is also signal-connected to the wind field simulation module and the aerodynamic mapping module. It is used to receive the parameter adjustment command, control the repeated calculation of the wind field simulation to wind pressure conversion process, and output the converged equivalent cumulative wind pressure spatial distribution data when the graded matching degree and numerical deviation of the equivalent cumulative wind pressure both meet the preset convergence conditions.

[0103] Specifically, this module comprises three units: a data comparison unit receives the equivalent cumulative wind pressure spatial distribution data output by the wind pressure conversion module, imports pre-stored historical disaster damage level data and measured wind pressure data, classifies and statistically analyzes the matching degree and relative deviation according to terrain type and tower type, and outputs a deviation statistical result table; a parameter adjustment unit is connected to the data comparison unit, receives the deviation statistical results, adjusts the terrain correction factor according to the terrain type, adjusts the aerodynamic local correction coefficient according to the tower type, and outputs parameter adjustment instructions; and an iterative control unit is connected to the parameter adjustment unit and is also signal-connected to the wind field simulation module and the aerodynamic mapping module. After receiving the parameter adjustment instructions, it sends updated parameters to downstream modules, controls the repeated calculation of the wind field simulation to wind pressure conversion process, collects the results after each round of calculation and judges the convergence. When the preset convergence condition is met, the iteration is terminated, and the converged equivalent cumulative wind pressure spatial distribution data is output to the hierarchical mapping module.

[0104] The hierarchical mapping module, connected to the calibration iteration module, is used to receive the converged equivalent cumulative wind pressure spatial distribution data, classify wind pressure levels by combining the tower type differential grading threshold, and generate a wind pressure zoning distribution map.

[0105] Specifically, this module has a built-in tiered threshold library, which calibrates equivalent cumulative wind pressure benchmark thresholds for three damage levels for different standard tower types, and configures threshold adjustment coefficients for different return periods. After selecting the target tower type and target return period, it automatically calculates the corresponding tiered thresholds and performs tiered classification on the spatial distribution data of wind pressure. It supports customizable output styles and layer configurations, and can batch generate wind pressure distribution maps of different tower types and different return periods. After overlaying line and tower locations, the final output is provided and can be directly imported into power engineering design software.

[0106] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for constructing wind pressure distribution maps of transmission towers based on CFD typhoon wind fields, characterized in that, Includes the following steps: S1. Data preprocessing: Collect multi-source basic data of the target area and perform standardization processing to obtain a basic dataset for spatial matching; S2. Adaptive Grid: Based on the basic dataset, analyze the terrain geometry features and combine them with the importance level of the towers to generate a two-factor driven adaptive computational grid; S3. Wind field CFD simulation: Numerical simulation is performed based on the parameterized wind field model and the adaptive computing grid to obtain the near-surface wind speed time history dataset; S4. Aerodynamic mapping calculation: Call the standard tower type aerodynamic coefficient library to map the near-surface wind speed time history dataset to the tower structure wind pressure time history dataset; S5. Equivalent wind pressure conversion: Based on the multi-damage mode coupling evaluation criterion, the wind pressure time history dataset of the tower structure is converted to obtain the equivalent cumulative wind pressure, and the spatial distribution data of the equivalent cumulative wind pressure at different return periods is calculated. S6. Closed-loop calibration iteration: Compare the equivalent cumulative wind pressure spatial distribution data with the reference data, adjust the wind field parameters and aerodynamic parameters in layers and iterate until convergence, and output the converged equivalent cumulative wind pressure spatial distribution data. S7. Graded Mapping Output: Based on the converged equivalent cumulative wind pressure spatial distribution data, wind pressure levels are divided by tower type differential grading thresholds to generate a wind pressure zoning distribution map.

2. The method for constructing wind pressure distribution maps of transmission towers based on CFD typhoon wind fields according to claim 1, characterized in that, In step S2, the adaptive computational grid is generated in the following way: extracting terrain elevation data from the basic dataset, calculating terrain curvature and slope and identifying wind pressure sensitive areas, and dividing the initial grid densification level; then extracting the structural importance level of the tower locations, and improving the grid densification accuracy of the areas containing tower locations according to the tower importance level.

3. The method for constructing wind pressure distribution maps of transmission towers based on CFD typhoon wind fields according to claim 1, characterized in that, In step S3, the near-surface wind speed time history dataset is generated in the following way: typhoon meteorological parameters are extracted from the basic dataset, and the upper-level gradient wind field is calculated based on the parameterized wind field model; the surface roughness correction factor is calculated by combining land use data and the terrain correction factor is calculated by combining terrain data; the upper-level gradient wind field is first corrected by the surface roughness correction factor and then by the terrain correction factor.

4. The method for constructing wind pressure distribution maps of transmission towers based on CFD typhoon wind fields according to claim 1, characterized in that, In step S4, the tower structure wind pressure time history dataset is obtained by mapping in the following way: extracting the hourly near-surface wind speed of the corresponding tower location in the near-surface wind speed time history dataset; matching the wind load shape coefficient of the corresponding tower type in the standard tower type aerodynamic coefficient library; after correcting the wind load shape coefficient with wind angle of attack correction coefficient and wind speed interval correction coefficient, calculating the actual wind pressure of each part of the tower hourly.

5. The method for constructing wind pressure distribution maps of transmission towers based on CFD typhoon wind fields according to claim 1, characterized in that, In step S5, the equivalent cumulative wind pressure is calculated in the following way: the wind pressure time history of the tower structure is divided into multiple intervals according to the wind pressure amplitude, and the duration of each interval is counted. The wind-induced fatigue damage and the extreme overturning damage were calculated separately and then coupled to obtain the total damage. Based on the principle of damage equivalence, the total damage is converted into the constant wind pressure value corresponding to the set reference duration to obtain the equivalent cumulative wind pressure; an extreme value distribution model is established based on the equivalent cumulative wind pressure of historical typhoon samples to estimate the spatial distribution data of equivalent cumulative wind pressure at different return periods.

6. The method for constructing wind pressure distribution maps of transmission towers based on CFD typhoon wind fields according to claim 1, characterized in that, In step S6, the closed-loop calibration iteration is performed as follows: the reference data includes historical disaster damage level data and measured wind pressure data; according to terrain type and tower type, the matching degree between the equivalent cumulative wind pressure classification results and historical disaster damage level data, and the deviation between the equivalent cumulative wind pressure value and the measured wind pressure data are calculated to form deviation characteristics; the terrain correction factor of the CFD simulation and the local correction coefficient of the standard tower type aerodynamic coefficient library are adjusted according to the deviation characteristics; the calculations of steps S3 to S5 are repeated based on the adjusted parameters until the matching degree and the deviation both meet the preset convergence conditions.

7. The method for constructing wind pressure distribution maps of transmission towers based on CFD typhoon wind fields according to claim 1, characterized in that, In step S7, the tower type differentiated classification threshold is set in the following way: wind pressure thresholds for three damage levels—minor damage, severe damage, and collapse—are set for different tower types; threshold adjustment coefficients are set for different return periods, and the wind pressure thresholds for each damage level of the target tower type are corrected using the threshold adjustment coefficients for the corresponding return periods; after matching the target tower type with the target return period, the equivalent cumulative wind pressure spatial distribution data after convergence is classified into levels.

8. A system for constructing wind pressure distribution maps of transmission towers based on CFD typhoon wind fields, characterized in that, The system used in the method for constructing wind pressure distribution maps of transmission towers based on CFD typhoon wind fields according to any one of claims 1-7, the system comprising: The preprocessing module is used to collect multi-source basic data of the target area and perform standardization processing to output a basic dataset; The grid generation module, connected to the preprocessing module, is used to receive the basic dataset, parse the terrain geometry features and combine them with the importance level of the towers, and output a two-factor driven adaptive computational grid. The wind field simulation module, connected to the grid generation module, is used to receive the adaptive computation grid, perform CFD numerical simulation based on the parameterized wind field model, and output a near-surface wind speed time history dataset. The aerodynamic mapping module, connected to the wind field simulation module, is used to receive the near-surface wind speed time history dataset, call the standard tower aerodynamic coefficient library to perform mapping calculations, and output the tower structure wind pressure time history dataset. The wind pressure conversion module, connected to the aerodynamic mapping module, is used to receive the wind pressure time history dataset of the tower structure, convert the equivalent cumulative wind pressure based on the multi-damage mode coupling evaluation criterion, and output the spatial distribution data of the equivalent cumulative wind pressure with different return periods. The calibration iteration module is connected to the wind pressure conversion module and is also signal-connected to the wind field simulation module and the aerodynamic mapping module. It is used to receive the equivalent cumulative wind pressure spatial distribution data, compare it with the reference data, adjust the parameters in layers and iterate until convergence, and output the converged equivalent cumulative wind pressure spatial distribution data. The hierarchical mapping module, connected to the calibration iteration module, is used to receive the converged equivalent cumulative wind pressure spatial distribution data, classify wind pressure levels by combining the tower type differential grading threshold, and generate a wind pressure zoning distribution map.

9. The system for constructing wind pressure distribution maps of transmission towers based on CFD typhoon wind fields according to claim 8, characterized in that, The mesh generation module includes: The terrain analysis unit is used to receive the basic dataset, extract terrain elevation data from it, calculate terrain curvature and slope, identify wind pressure sensitive areas, and output terrain feature parameters. The classification unit, connected to the terrain parsing unit, is used to receive the terrain feature parameters, classify the initial grid encryption level according to the terrain complexity, and output the initial encryption level information. The grid adjustment unit, connected to the level division unit, is used to receive the initial encryption level information, adjust the grid encryption accuracy in combination with the structural importance level of the tower points, and output the final adaptive calculation grid.

10. The system for constructing wind pressure distribution maps of transmission towers based on CFD typhoon wind fields according to claim 8, characterized in that, The calibration iteration module includes: The data comparison unit is used to receive the equivalent cumulative wind pressure spatial distribution data, compare it with the benchmark data according to terrain type and tower type classification, statistically analyze the deviation characteristics, and output the deviation statistical results. The parameter adjustment unit, connected to the data comparison unit, is used to receive the deviation statistics, adjust the terrain correction factor and aerodynamic local correction coefficient in layers, and output parameter adjustment instructions. The iterative control unit is connected to the parameter adjustment unit and is also signal-connected to the wind field simulation module and the aerodynamic mapping module. It is used to receive the parameter adjustment command, control the repeated calculation of the wind field simulation to wind pressure conversion process, and output the converged equivalent cumulative wind pressure spatial distribution data when the graded matching degree and numerical deviation of the equivalent cumulative wind pressure both meet the preset convergence conditions.

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