Electric power engineering wind speed optimization method and system based on fluid mechanics simulation software

Abstract

The invention relates to an electric power engineering wind speed optimization method and system based on fluid mechanics simulation software. The method comprises the specific steps that a three-dimensional terrain model of a target area is established based on the fluid mechanics simulation software; historical meteorological data of a target area are collected through geographic information software, boundary conditions are set according to the historical meteorological data, and wind field distribution under different weather systems is simulated; extracting characteristic parameters of each micro-terrain along the power transmission line corridor in the target area, wherein the characteristic parameters comprise terrain gradient, relative height difference and surface roughness classification; calculating a wind speed correction coefficient of each microtopography through a gray model based on the boundary conditions and the characteristic parameters of each microtopography; and finally, performing iterative verification on the corrected wind speed through a Monte Carlo method, and outputting a wind speed result meeting a preset confidence coefficient requirement as a final optimized wind speed.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent operation and maintenance of power systems, and specifically to a method and system for optimizing wind speed in power engineering based on a fluid mechanics simulation software. Background Art

[0002] In the field of intelligent operation and maintenance of power systems, the micro-topographic wind field monitoring and safety protection of transmission lines are crucial. In the prior art, the micro-topographic wind speed monitoring of transmission line corridors mainly relies on traditional weather stations and empirical models, and there are the following technical bottlenecks: 1. The design of traditional power facilities mostly relies on regional meteorological statistical data, without fully considering the significant impact of micro-topography on local wind speed and precipitation. Research shows that micro-topographies such as windward slopes and canyons can cause the local wind speed to increase by 1.4 - 2.3 times the benchmark value, and the existing models lack quantitative analysis of influencing factors such as the bottleneck effect and vegetation resistance, and the deviation rate of the warning threshold setting is relatively large; 2. Existing systems mostly rely on data from a single weather station and do not make full use of multi-source data such as typhoon path probability distribution and gradient wind profile. Historical data shows that the wind speed prediction error rate of coastal lines without superimposing the sea-land breeze correction term reaches 28%, and the failure probability of the model increases significantly when a typhoon makes landfall. 3. Traditional designs do not associate the wind speed correction coefficient with the fatigue life of the tower material, resulting in an increase in the accident rate during the operation and maintenance period.

[0003] In the Chinese invention patent document with the existing patent publication number "CN117390992A", a refined wind field prediction method under complex terrain is disclosed, which realizes the refined matching of mesoscale meteorological data and microscale terrain through the coupling of CFD and MOS correction.

[0004] In the prior art, although the influence of terrain on wind speed correction is considered, the different influences of various terrains on wind speed are not specifically considered, and mesoscale data is used without considering the possible differences caused by multi-source data; moreover, in the actual correction process, the influence caused by wind speed correction is not considered based on the safety of power equipment operation and maintenance. Summary of the Invention

[0005] To solve the problems existing in the above prior art, the present invention proposes a method and system for optimizing wind speed in power engineering based on a fluid mechanics simulation software.

[0006] The technical solution of the present invention is as follows:

[0007] On the one hand, the present invention proposes a method for optimizing wind speed in power engineering based on a fluid mechanics simulation software, and the specific steps include:

[0008] Establish a three-dimensional terrain model of the target area based on the fluid mechanics simulation software;

[0009] Collect historical meteorological data of the target area through geographic information software, and set boundary conditions with the historical meteorological data to simulate the wind field distribution under different weather systems;

[0010] Extract the characteristic parameters of each micro-topography along the transmission line corridor in the target area, including terrain slope, relative height difference, and surface roughness classification;

[0011] Calculate the wind speed correction coefficient of each micro-topography through a gray model based on the boundary conditions and the characteristic parameters of each micro-topography;

[0012] Finally, iteratively verify the corrected wind speed through the Monte Carlo method, and output the wind speed result that meets the preset confidence requirement as the final optimized wind speed.

[0013] As a preferred implementation manner, the characteristic parameters of each micro-topography further include:

[0014] When the distance between the coastline and the target area is less than or equal to the preset threshold, superimpose the sea-land breeze correction term coefficient;

[0015] When the valley width is less than or equal to the preset threshold, superimpose the bottleneck effect coefficient;

[0016] When the forest coverage rate of the target area is greater than or equal to the preset threshold, superimpose the vegetation resistance term coefficient;

[0017] As a preferred implementation manner, the historical meteorological data includes:

[0018] The hourly wind speed data set observed synchronously within the preset number of years;

[0019] The wind profile data sets of multiple gradient observation stations;

[0020] The probability distribution of wind speeds greater than the preset level in the historical typhoon paths;

[0021] As a preferred implementation manner, the step of calculating the wind speed correction coefficient of each micro-topography through a gray model based on the boundary conditions and the characteristic parameters of each micro-topography is specifically:

[0022]

[0023] In the formula, K is the wind speed correction coefficient, α is the terrain slope; ΔH is the relative height difference; Z0 is the surface roughness classification.

[0024] As a preferred implementation manner, it further includes:

[0025] Establish a topological relationship matrix between the micro-topography types and the line poles and towers;

[0026] Calculate the weighted average correction coefficient of each pole and tower point, and distribute the loads of each pole and tower according to the calculation results;

[0027] The finite element method is used to analyze the structural dynamic response of each tower, and the fatigue life of the tower material is verified;

[0028] Taking the fatigue life of the tower material as an influencing factor for wind speed optimization, the final optimized wind speed is further optimized.

[0029] On the other hand, the present invention proposes a wind speed optimization system for power engineering based on a fluid dynamics simulation software, including:

[0030] A three-dimensional terrain construction module, which establishes a three-dimensional terrain model of the target area based on the fluid dynamics simulation software;

[0031] A boundary condition limitation module, which collects the historical meteorological data of the target area through geographic information software, and sets the boundary conditions with the historical meteorological data to simulate the wind field distribution under different weather systems;

[0032] A micro-topography feature parameter extraction module, which extracts the feature parameters of each micro-topography along the transmission line corridor in the target area, including terrain slope, relative height difference, and surface roughness classification;

[0033] A wind speed correction coefficient calculation module, which calculates the wind speed correction coefficients of each micro-topography through a gray model based on the boundary conditions and the feature parameters of each micro-topography;

[0034] A wind speed optimization module, which finally performs iterative verification on the corrected wind speed through the Monte Carlo method, and outputs the wind speed result that meets the preset confidence requirement as the final optimized wind speed.

[0035] As a preferred implementation manner, the feature parameters of each micro-topography further include:

[0036] When the distance between the coastline and the target area is less than or equal to a preset threshold, a sea-land breeze correction term coefficient is superimposed;

[0037] When the valley width is less than or equal to a preset threshold, a channeling effect coefficient is superimposed;

[0038] When the forest coverage rate of the target area is greater than or equal to a preset threshold, a vegetation resistance term coefficient is superimposed;

[0039] As a preferred implementation manner, the historical meteorological data includes:

[0040] An hourly wind speed data set observed synchronously within a preset number of years;

[0041] A wind profile data set of multiple gradient observation stations;

[0042] The probability distribution of wind speeds greater than a preset level in the historical typhoon paths;

[0043] As a preferred embodiment, the step of calculating the wind speed correction coefficient of each microtopography through the gray model based on the boundary conditions and the characteristic parameters of each microtopography is specifically as follows:

[0044]

[0045] In the formula, K is the wind speed correction coefficient, α is the terrain slope; ΔH is the relative height difference; Z0 is the surface roughness classification.

[0046] As a preferred embodiment, it further includes:

[0047] Establish a topological relationship matrix between the microtopography type and the line tower;

[0048] Calculate the weighted average correction coefficient of each tower point, and allocate the loads of each tower according to the calculation results;

[0049] Adopt the finite element method to perform structural dynamic response analysis on each tower, and verify the fatigue life of the tower material;

[0050] Take the fatigue life of the tower material as an influencing factor for wind speed optimization, and re-optimize the final optimized wind speed.

[0051] The present invention has the following beneficial effects:

[0052] 1. The present invention dynamically fuses terrain parameters and meteorological correction terms through the gray model to achieve accurate calculation of the wind speed correction coefficient K;

[0053] 2. The present invention constructs a spatio-temporal coupled wind field simulation system by integrating multi-dimensional data such as synchronous observation wind speed, typhoon path probability, and gradient wind profile, improving the accuracy of wind speed correction;

[0054] 3. The present invention innovatively introduces the topological relationship between the microtopography and the line tower, establishes the connection between the microtopography and the line tower, and realizes the accurate distribution of the tower load through the weighted average correction coefficient, reducing the prediction error of the tower fatigue life; Brief Description of the Drawings

[0055] Figure 1 It is a schematic flow chart of the steps of the present invention. Detailed Embodiment

[0056] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0057] It should be understood that the step numbers used in the text are only for convenience of description and do not limit the order of execution of the steps.

[0058] It should be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0059] The terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0060] The term "and / or" refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0061] Embodiment 1:

[0062] See Figure 1 , a method for optimizing the wind speed of a power project based on a fluid dynamics simulation software, the specific steps include:

[0063] Establish a three-dimensional terrain model of the target area based on the fluid dynamics simulation software;

[0064] Collect historical meteorological data of the target area through geographic information software, and set boundary conditions with the historical meteorological data to simulate the wind field distribution under different weather systems;

[0065] Extract the characteristic parameters of each micro-topography along the transmission line corridor in the target area, including terrain slope, relative height difference, and surface roughness classification;

[0066] Calculate the wind speed correction coefficient of each micro-topography through a gray model based on the boundary conditions and the characteristic parameters of each micro-topography;

[0067] Finally, iterate and verify the corrected wind speed through the Monte Carlo method, and output the wind speed result that meets the preset confidence requirement as the final optimized wind speed.

[0068] In this embodiment, the boundary conditions include:

[0069] 1. Physical field constraint:

[0070] By setting parameters such as inlet wind speed, outlet pressure, and ground roughness, the physical boundaries of the simulation model (such as terrain geometry and surface friction characteristics) are defined to ensure that the simulation results conform to the fluid dynamics laws of the actual micro-topography area;

[0071] 2. Meteorological data constraints:

[0072] The input historical meteorological data (such as the synchronous observed wind speed in the recent 5 years, the probability distribution of the maximum wind speed of typhoon paths) is used as boundary conditions to limit the range of input parameters for simulation, so that the wind field distribution matches typical weather scenarios such as coastal typhoons and monsoons.

[0073] 3. Topographic feature constraints:

[0074] The topographic boundary is set through micro-topographic parameters (slope α ∈ [5°, 45°], relative height difference ΔH ≥ 20m, surface roughness classification Z0), and the applicable micro-topographic types (such as windward slope, leeward slope, bay, canyon) are clarified to avoid over-generalization that deviates from the actual topographic features.

[0075] 4. Numerical calculation stability constraints:

[0076] Boundary conditions (such as the selection of turbulence model, grid resolution ≤ 5m) limit the convergence range of the simulation calculation, ensure the numerical stability of the simulation results, and make the correction coefficient K calculated by the grayscale model have the reliability of engineering application.

[0077] Taking the bay topography of Yacheng Bay in Fujian Province as an actual simulation, the settings of boundary conditions include:

[0078] The inlet wind speed is set to 10 m / s outside the typhoon (based on historical typhoon path data);

[0079] The ground roughness Z0 = 0.01m (corresponding to the vegetated area);

[0080] The outlet pressure gradient is set according to the characteristics of land-sea breeze circulation.

[0081] These conditions jointly restrict that the simulation results are only applicable to narrow bay topographies similar to Yacheng Bay, and ensure the applicability of the wind speed correction coefficient K_sea = 1 + 0.03D (D ≤ 5km), where D is the distance from the coastline.

[0082] As a preferred implementation manner of this embodiment, the characteristic parameters of each micro-topography further include:

[0083] When the distance between the coastline and the target area is less than or equal to the preset threshold, the land-sea breeze correction term coefficient is superimposed;

[0084] When the valley width is less than or equal to the preset threshold, the channeling effect coefficient is superimposed;

[0085] When the forest coverage rate of the target area is greater than or equal to the preset threshold, the vegetation resistance term coefficient is superimposed;

[0086] In this embodiment, the specific settings of each meteorological correction term are as follows:

[0087] The distance threshold between the coastline and the target area is set to 5 km. When the distance is greater than 5 km, the calculation formula of the sea and land breeze correction coefficient is as follows:

[0088] K sea =1+0.03D

[0089] Where D is the distance between the coastline and the target area;

[0090] The valley width threshold is set at 2 km. When the width is greater than 2 km, the calculation formula of the narrow pipe efficiency coefficient is as follows:

[0091] K valley =1+0.05W

[0092] W is the valley width;

[0093] The forest coverage threshold of the target area is set at 30%. When the forest coverage is greater than 30%, the calculation formula of the vegetation resistance coefficient is as follows:

[0094]

[0095] Where C is the forest coverage rate.

[0096] As a preferred implementation of this embodiment, the historical meteorological data includes:

[0097] Hourly wind speed data set observed synchronously within a preset number of years;

[0098] Wind profile datasets from multiple gradient observation stations;

[0099] The probability distribution of wind speeds greater than the preset level in historical typhoon paths;

[0100] In this embodiment, the collection period of the hourly wind speed data set is set to 5 years;

[0101] The wind profile dataset must be collected from at least three gradient observation stations;

[0102] This probability distribution forecast is the distribution probability of the maximum wind speed along the typhoon path. The specific probability calculation formula is as follows:

[0103] P=exp(-λv)

[0104] Where λ is a fixed coefficient, and its value is λ=0.002m -1 .

[0105] As a preferred implementation of this embodiment, the step of calculating the wind speed correction coefficient of each micro-terrain through a grayscale model based on the boundary conditions and the characteristic parameters of each micro-terrain is specifically as follows:

[0106]

[0107] In the formula, K is the wind speed correction coefficient, α is the terrain slope; ΔH is the relative height difference; Z0 is the surface roughness classification.

[0108] As a preferred embodiment of this embodiment, it further includes:

[0109] Establish a topological relationship matrix between the micro-topography type and the line towers;

[0110] Calculate the weighted average correction coefficient of each tower point, and distribute the loads of each tower according to the calculation results;

[0111] Use the finite element method to perform structural dynamic response analysis on each tower, and verify the fatigue life of the tower material;

[0112] Take the fatigue life of the tower material as an influencing factor for wind speed optimization, and re-optimize the final optimized wind speed.

[0113] In this embodiment, the process of establishing a topological relationship matrix between the micro-topography type and the line towers is as follows:

[0114] Obtain the tower coordinate data of the transmission line, and the coordinate accuracy needs to reach the centimeter level (such as RTK measurement data) to ensure spatial alignment with the micro-topography model.

[0115] Based on LiDAR point cloud data or high-resolution DEM (Digital Elevation Model), use GIS software (such as ArcGIS, QGIS) to generate a three-dimensional terrain grid of the research area, and the grid resolution is usually ≤5m.

[0116] Automatically extract micro-topography units according to terrain features (slope, elevation difference, curvature, etc.), for example:

[0117] Windward slope / leeward slope: Judged by the angle between the slope direction and the dominant wind direction.

[0118] Valley / ridge: Screened by terrain elevation difference (ΔH≥20m) and slope (α≥5°).

[0119] Bay / valley: Identified by the water boundary and surface roughness (Z0 classification).

[0120] Project the tower coordinates and the micro-topography DEM onto the same coordinate system (such as UTM) to eliminate geographical errors.

[0121] Use spatial analysis algorithms (such as KD-Tree, ball tree algorithm) to calculate the dominant micro-topography unit where each tower is located or affected. [[ID=4 ,

[0122] Taking the tower as the center, delimit a buffer zone with a radius of 500m (adjusted according to the wind field diffusion range), and identify all micro-topography units within the buffer zone.

[0123] Calculate the weight coefficient according to the distance (d) and area ratio (A) between the micro-topographic unit and the pole tower:

[0124]

[0125] In the formula, i is the micro-topography type, and λ is the attenuation coefficient to ensure that the influence weight of the proximal micro-topography is higher.

[0126] Next, define the matrix dimension:

[0127] Row: Corresponding to all transmission line pole towers (N towers) to be analyzed.

[0128] Column: Corresponding to the micro-topographic units (M units) in the study area.

[0129] Matrix element: R mn Represents the influence weight of the m-th micro-topographic unit on the n-th pole tower.

[0130] Fill the matrix regularly:

[0131] Single dominant unit: If a pole tower is only affected by a single micro-topographic unit (such as an isolated hill), then R mn = 1, and the rest are 0.

[0132] Superposition of multiple units: If a pole tower is affected by multiple micro-topographic units (such as the intersection of a canyon and a ridge), then allocate weights according to:

[0133]

[0134] In the formula, K k Is the correction coefficient of the k-th micro-topographic unit (such as the acceleration coefficient of the windward slope).

[0135] In this embodiment, the calculation formula of the weighted average correction coefficient of each pole tower point is as follows:

[0136] K avg = Σ(K i × A i ) / ∑A i

[0137] In the formula, i is the micro-topography type.

[0138] In this embodiment, the fatigue life of the pole tower material needs to be greater than or equal to 20 years to meet the use requirements.

[0139] Embodiment 2:

[0140] A power engineering wind speed optimization system based on fluid mechanics simulation software, including:

[0141] 3D terrain construction module, which establishes a 3D terrain model of the target area based on a fluid dynamics simulation software;

[0142] Boundary condition limitation module, which collects historical meteorological data of the target area through a geographic information software, and sets boundary conditions with the historical meteorological data to simulate the wind field distribution under different weather systems;

[0143] Micro-topography feature parameter extraction module, which extracts the feature parameters of each micro-topography along the transmission line corridor in the target area, including terrain slope, relative height difference, and surface roughness classification;

[0144] Wind speed correction coefficient calculation module, which calculates the wind speed correction coefficient of each micro-topography through a gray model based on the boundary conditions and the feature parameters of each micro-topography;

[0145] Wind speed optimization module, which finally iteratively verifies the corrected wind speed through the Monte Carlo method, and outputs the wind speed result that meets the preset confidence requirement as the final optimized wind speed.

[0146] As a preferred implementation manner of this embodiment, the feature parameters of each micro-topography further include:

[0147] When the distance between the coastline and the target area is less than or equal to a preset threshold, a sea-land breeze correction term coefficient is superimposed;

[0148] When the valley width is less than or equal to a preset threshold, a channeling effect coefficient is superimposed;

[0149] When the forest coverage rate of the target area is greater than or equal to a preset threshold, a vegetation resistance term coefficient is superimposed;

[0150] As a preferred implementation manner of this embodiment, the historical meteorological data includes:

[0151] An hourly wind speed data set observed synchronously within a preset number of years;

[0152] A wind profile data set of multiple gradient observation stations;

[0153] The probability distribution of wind speeds greater than a preset level in the historical typhoon path;

[0154] As a preferred implementation manner of this embodiment, the step of calculating the wind speed correction coefficient of each micro-topography through a gray model based on the boundary conditions and the feature parameters of each micro-topography is specifically:

[0155]

[0156] In the formula, K is the wind speed correction coefficient, α is the terrain slope; ΔH is the relative height difference; Z0 is the surface roughness classification.

[0157] As a preferred implementation manner of this embodiment, it further includes:

[0158] Establish a topological relationship matrix between microtopography types and line towers;

[0159] Calculate the weighted average correction factor of each tower point, and distribute the loads of each tower according to the calculation results;

[0160] Adopt the finite element method to analyze the structural dynamic response of each tower, and verify the fatigue life of the tower material;

[0161] Take the fatigue life of the tower material as an influencing factor for wind speed optimization, and re-optimize the final optimized wind speed.

[0162] The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for optimizing wind speed in power engineering based on a fluid dynamics simulation software, characterized in that, The specific steps include: Establish a three-dimensional terrain model of the target area based on a fluid dynamics simulation software; Collect historical meteorological data of the target area through a geographic information software, and set boundary conditions with the historical meteorological data to simulate the wind field distribution under different weather systems; Extract the characteristic parameters of each micro-topography along the transmission line corridor in the target area, including terrain slope, relative height difference, and surface roughness classification; Calculate the wind speed correction coefficient of each micro-topography through a gray model based on the boundary conditions and the characteristic parameters of each micro-topography; Finally, iteratively verify the corrected wind speed through the Monte Carlo method, and output the wind speed result that meets the preset confidence requirement as the final optimized wind speed.

2. The method for optimizing the wind speed in a power project based on a fluid dynamics simulation software according to claim 1, characterized in that The characteristic parameters of each micro-topography also include: When the distance between the coastline and the target area is less than or equal to the preset threshold, superimpose the sea-land breeze correction term coefficient; When the valley width is less than or equal to the preset threshold, superimpose the bottleneck effect coefficient; When the forest coverage rate of the target area is greater than or equal to the preset threshold, superimpose the vegetation resistance term coefficient.

3. The method for optimizing wind speed in a power project based on a fluid dynamics simulation software according to claim 1, wherein The historical meteorological data includes: An hourly wind speed data set synchronously observed within a preset number of years; A wind profile data set of multiple gradient observation stations; The probability distribution of wind speeds greater than the preset level of historical typhoon paths.

4. The method for optimizing the wind speed in a power project based on a fluid dynamics simulation software according to claim 1, characterized in that, The specific steps of calculating the wind speed correction coefficient of each micro-topography through a gray model based on the boundary conditions and the characteristic parameters of each micro-topography are as follows: In the formula, K is the wind speed correction coefficient, α is the terrain slope; ΔH is the relative height difference; Z0 is the surface roughness classification.

5. The method for optimizing the wind speed of a power project based on a fluid dynamics simulation software according to claim 1, wherein It also includes: Establish a topological relationship matrix between the micro-topography type and the line tower; Calculate the weighted average correction coefficient of each tower point, and allocate the load of each tower according to the calculation result; Adopt the finite element method to conduct a structural dynamic response analysis of each tower, and verify the fatigue life of the tower material; Take the fatigue life of the tower material as an influencing factor for wind speed optimization, and re-optimize the final optimized wind speed.

6. A wind speed optimization system for power engineering based on a fluid dynamics simulation software, characterized in that, It includes: A three-dimensional terrain construction module that establishes a three-dimensional terrain model of the target area based on a fluid dynamics simulation software; A boundary condition restriction module that collects historical meteorological data of the target area through a geographic information software, and sets boundary conditions with the historical meteorological data to simulate the wind field distribution under different weather systems; A micro-topography characteristic parameter extraction module that extracts the characteristic parameters of each micro-topography along the transmission line corridor in the target area, including terrain slope, relative height difference, and surface roughness classification; A wind speed correction coefficient calculation module that calculates the wind speed correction coefficient of each micro-topography through a gray model based on the boundary conditions and the characteristic parameters of each micro-topography; A wind speed optimization module that finally iteratively verifies the corrected wind speed through the Monte Carlo method, and outputs the wind speed result that meets the preset confidence requirement as the final optimized wind speed.

7. The power engineering wind speed optimization system based on fluid dynamics simulation software according to claim 6, characterized in that, The characteristic parameters of each micro-topography also include: When the distance between the coastline and the target area is less than or equal to the preset threshold, superimpose the sea-land breeze correction term coefficient; When the valley width is less than or equal to the preset threshold, superimpose the bottleneck effect coefficient; When the forest coverage rate of the target area is greater than or equal to the preset threshold, superimpose the vegetation resistance term coefficient.

8. The power engineering wind speed optimization system based on fluid dynamics simulation software according to claim 6, characterized in that The historical meteorological data includes: An hourly wind speed data set synchronously observed within a preset number of years; Wind profile datasets of multiple gradient observation stations; Probability distribution of wind speeds greater than a preset level in historical typhoon paths.

9. The power engineering wind speed optimization system based on fluid dynamics simulation software according to claim 6, characterized in that, The step of calculating the wind speed correction coefficient of each microtopography through the gray model based on the boundary conditions and the characteristic parameters of each microtopography is specifically as follows: In the formula, K is the wind speed correction coefficient, α is the terrain slope; ΔH is the relative height difference; Z0 is the surface roughness classification.

10. The power engineering wind speed optimization system based on fluid dynamics simulation software according to claim 6, characterized in that, It also includes: Establishing a topological relationship matrix between the microtopography type and the line towers; Calculating the weighted average correction coefficient of each tower point, and distributing the loads of each tower according to the calculation results; Performing structural dynamic response analysis on each tower using the finite element method to verify the fatigue life of the tower material; Taking the fatigue life of the tower material as an influencing factor for wind speed optimization, and re-optimizing the final optimized wind speed.

Images