A wind farm wind turbine dynamic wake analysis method and system

Abstract

The application provides a wind farm wind turbine dynamic wake analysis method and system, and relates to the technical field of wind power processing. The method comprises the following steps: acquiring terrain data of a wind farm area; extracting terrain parameters from the terrain data; calculating a terrain correction factor according to the terrain parameters; taking a preselected analysis wake model as a basic model, inputting the terrain correction factor into the basic model to obtain a correction model, so as to realize dynamic adjustment of the wake diffusion and recovery process; introducing real-time wind speed and wind direction data to perform numerical simulation on the correction model, and obtaining a prediction result of the correction model; comparing the prediction result with measured data, and if it is determined that the error between the prediction result and the measured data is within a preset range, then deploying the correction model to a wind farm control system. The application can dynamically analyze the wake characteristics under different terrain conditions.

Description

Technical Field

[0001] The present application relates to the technical field of wind power processing, and in particular to a method and system for analyzing dynamic wakes of wind turbines in a wind farm. Background Art

[0002] The wake effect is a key factor affecting power generation efficiency and equipment life in wind power generation. When wind turbines are in operation, they experience velocity decay and increased turbulence in the downstream area, resulting in reduced wind energy absorption by subsequent turbines and increased load fluctuations. This not only reduces overall power generation efficiency but also increases mechanical wear on equipment, increasing maintenance and operating costs. Therefore, accurately analyzing and controlling the wake effect is crucial for optimizing wind farm layout and improving power generation efficiency.

[0003] For onshore wind farms, complex terrain conditions significantly affect the diffusion and recovery process of the wake. The undulation, surface roughness, and local slope of the terrain alter the turbulence intensity distribution in the wind farm, thereby affecting the wake expansion velocity and turbulent isotropy. However, traditional wake models struggle to effectively capture the dynamic coupling between terrain and wake. Therefore, a method is needed to dynamically analyze wake characteristics under different terrain conditions. Summary of the Invention

[0004] The present application provides a method and system for analyzing dynamic wakes of wind turbines in a wind farm, which can dynamically analyze wake characteristics under different terrain conditions.

[0005] In a first aspect of the present application, a method for analyzing dynamic wakes of wind turbines in a wind farm is provided, the method comprising:

[0006] Obtain topographic data of the wind farm area;

[0007] extracting terrain parameters from the terrain data;

[0008] Calculating a terrain correction factor based on the terrain parameters;

[0009] Using a preselected analytical wake model as a base model, inputting the terrain correction factor into the base model to obtain a correction model to achieve dynamic adjustment of the wake diffusion and recovery process;

[0010] Introducing real-time wind speed and wind direction data, performing numerical simulation on the modified model, and obtaining prediction results of the modified model;

[0011] The prediction result is compared with the measured data, and if it is determined that the error between the prediction result and the measured data is within a preset range, the correction model is deployed to the wind farm control system.

[0012] On the basis of the above technical solution, preferably, the calculating of the terrain correction factor according to the terrain parameters specifically includes:

[0013] Calculate the elevation correction factor based on the elevation difference between the wind turbine and each target point;

[0014] Calculating a roughness correction factor according to a roughness length value of the wind farm area;

[0015] Calculate the slope correction factor based on the terrain slope of the wind farm area.

[0016] On the basis of the above technical solution, preferably, the pre-selected analytical wake model is used as the basic model, specifically including:

[0017] The wake velocity defect formula is used to express the degree of wake velocity attenuation. The wake velocity defect formula is as follows:

[0018]

[0019] Where ΔU(x) is the wake velocity defect, U0 is the free stream velocity, D is the fan diameter, k is the expansion coefficient (wake diffusion coefficient), and x is the downstream distance;

[0020] Gaussian distribution is used to represent the wake diffusion width, and the specific expression is as follows:

[0021] σ x (x)=σ x,0 +ε·x

[0022] Among them, σ x (x) is the lateral diffusion width of the wake, σ x,0 is the initial diffusion width, ε is the diffusion coefficient, and x is the downstream distance.

[0023] Based on the above technical solution, preferably, the step of inputting the terrain correction factor into the basic model to obtain a correction model specifically includes:

[0024] Introduce elevation correction factor and roughness correction factor to adjust the expansion coefficient;

[0025] Based on the influence of slope and roughness on wake diffusion, a roughness correction factor and a slope correction factor are introduced into the basic model;

[0026] The comprehensive revised wake model is expressed as follows:

[0027]

[0028] Among them, U(x,y,z) is the wind speed at any point in the wake area, U0 is the free stream wind speed, and U def(x) is the wake velocity defect, f(k h ,k r ,k s ) is the comprehensive correction factor function.

[0029] On the basis of the above technical solution, preferably, the introducing of real-time wind speed and wind direction data, performing numerical simulation on the correction model, and obtaining the prediction result of the correction model specifically includes:

[0030] Dividing the wind farm area into three-dimensional computational grid units, inputting the real-time wind speed and wind direction data as boundary conditions and initial conditions, and driving the modified model to perform dynamic solution;

[0031] For each three-dimensional computational grid cell, the velocity distribution and turbulence intensity of the wake at different spatial locations are calculated by solving fluid mechanics equations, including the continuity equation and the momentum equation, to generate prediction results;

[0032] The wind speed distribution, turbulence intensity and diffusion form of the wake are output to obtain the prediction result.

[0033] Based on the above technical solution, preferably, the step of comparing the prediction result with the measured data and, if it is determined that the error between the prediction result and the measured data is within a preset range, deploying the correction model to the wind farm control system specifically includes:

[0034] Calculate the error between the predicted wind speed and the measured wind speed;

[0035] It is determined whether the error value is less than or equal to a preset threshold value, and if it is determined that the error value is less than or equal to the preset threshold value, the correction model is deployed to the wind farm control system.

[0036] On the basis of the above technical solution, preferably, extracting terrain parameters from the terrain data specifically includes:

[0037] Calculating the elevation difference between the wind turbine and each of the target points based on the acquired wind turbine position and the positions of the multiple target points;

[0038] Matching roughness length values ​​according to surface type based on surface cover data of the wind farm area;

[0039] Dividing the wind farm area into a plurality of grid units;

[0040] Calculate the terrain slope by using the vertical difference and horizontal distance between two adjacent grid cells.

[0041] In a second aspect of the present application, a wind farm wind turbine dynamic wake analysis system is provided, the system comprising an acquisition module, an extraction module, a processing module, and a judgment module, wherein:

[0042] The acquisition module is used to acquire terrain data of the wind farm area;

[0043] The extraction module is used to extract terrain parameters from the terrain data;

[0044] The processing module is used to calculate a terrain correction factor based on the terrain parameters;

[0045] The processing module is configured to use a preselected analytical wake model as a base model, input the terrain correction factor into the base model, and obtain a correction model to achieve dynamic adjustment of the wake diffusion and recovery process;

[0046] The processing module is used to introduce real-time wind speed and wind direction data, perform numerical simulation on the correction model, and obtain the prediction result of the correction model;

[0047] The judgment module is configured to compare the prediction result with the measured data, and deploy the correction model to the wind farm control system if it is determined that the error between the prediction result and the measured data is within a preset range.

[0048] On the basis of the above technical solution, preferably, the processing module is used to calculate the elevation correction factor according to the elevation difference between the wind turbine and each target point;

[0049] The processing module is configured to calculate a roughness correction factor based on the roughness length value of the wind farm area;

[0050] The processing module is used to calculate a slope correction factor according to the terrain slope of the wind farm area.

[0051] On the basis of the above technical solution, preferably, the processing module is used to express the degree of wake velocity attenuation by using a wake velocity defect formula, and the wake velocity defect formula is specifically as follows:

[0052]

[0053] Where ΔU(x) is the wake velocity defect, U0 is the free stream velocity, D is the fan diameter, k is the expansion coefficient (wake diffusion coefficient), and x is the downstream distance;

[0054] The processing module is used to express the wake diffusion width using Gaussian distribution. The specific expression is as follows:

[0055] σ x (x)=σ x,0 +ε·x

[0056] Among them, σ x (x) is the lateral diffusion width of the wake, σ x,0 is the initial diffusion width, ε is the diffusion coefficient, and x is the downstream distance.

[0057] On the basis of the above technical solution, preferably, the processing module is used to introduce an elevation correction factor and a roughness correction factor to adjust the expansion coefficient;

[0058] The processing module is configured to introduce a roughness correction factor and a slope correction factor into the basic model based on the effects of slope and roughness on wake diffusion;

[0059] The comprehensive revised wake model is expressed as follows:

[0060]

[0061] Among them, U(x,y,z) is the wind speed at any point in the wake area, U0 is the free stream wind speed, and U def (x) is the wake velocity defect, f(k h ,k r ,k s ) is the comprehensive correction factor function.

[0062] Based on the above technical solution, preferably, the processing module is used to divide the wind farm area into three-dimensional calculation grid units, input the real-time wind speed and wind direction data as boundary conditions and initial conditions, and drive the correction model to perform dynamic solution;

[0063] The processing module is configured to calculate the velocity distribution and turbulence intensity of the wake at different spatial positions by solving fluid mechanics equations for each three-dimensional computational grid cell, and generate a prediction result, wherein the fluid mechanics equations include a continuity equation and a momentum equation;

[0064] The processing module is used to output the wind speed distribution, turbulence intensity and diffusion form of the wake to obtain the prediction result.

[0065] On the basis of the above technical solution, preferably, the processing module is used to calculate the error value between the predicted wind speed and the measured wind speed;

[0066] The judgment module is configured to judge whether the error value is less than or equal to a preset threshold, and if it is determined that the error value is less than or equal to the preset threshold, deploy the correction model to the wind farm control system.

[0067] On the basis of the above technical solution, preferably, the processing module is used to calculate the elevation difference between the wind turbine and each of the target points according to the acquired wind turbine position and the positions of the multiple target points;

[0068] The processing module is configured to match the roughness length value according to the surface coverage data of the wind farm area and the surface type;

[0069] The processing module is configured to divide the wind farm area into a plurality of grid units;

[0070] The processing module is used to calculate the terrain slope based on the vertical difference and horizontal distance between two adjacent grid cells.

[0071] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs any of the methods described above.

[0072] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions. When the instructions are executed, any one of the methods described above is executed.

[0073] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0074] 1. This application obtains the terrain data of the wind farm, extracts terrain parameters such as elevation difference, surface roughness and slope, calculates the terrain correction factor, and introduces it into the traditional wake model to correct the expansion coefficient and wake diffusion width, thereby dynamically adjusting the wake diffusion and recovery process. Combined with real-time wind speed and direction data, the correction model can perform real-time numerical simulation based on different terrain features and environmental conditions to ensure that the prediction of wake characteristics is consistent with the actual situation. Therefore, it can effectively capture the dynamic coupling effect of terrain and wake, and achieve accurate dynamic analysis of wake characteristics.

[0075] 2. By introducing elevation, roughness, and slope correction factors, key parameters in the wake model, such as the expansion coefficient and wake diffusion width, are dynamically adjusted to effectively correct the wake velocity attenuation and diffusion process, thereby improving the applicability and prediction accuracy of the wake model.

[0076] 3. By dividing the wind farm into three-dimensional grid cells and combining them with real-time wind speed and direction data, the modified model is dynamically solved using the continuity and momentum equations. The terrain correction term accounts for the impact of complex terrain on wind flow, enabling the prediction results to more accurately reflect the velocity distribution, turbulence intensity, and diffusion pattern of the wake at different spatial locations, effectively improving the accuracy of the wake model in complex terrain and dynamic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 This is a flow chart of a method for analyzing dynamic wakes of wind turbines in a wind farm disclosed in an embodiment of the present application;

[0078] Figure 2 This is a module diagram of a wind farm wind turbine dynamic wake analysis system disclosed in an embodiment of the present application;

[0079] Figure 3 This is a structural diagram of an electronic device disclosed in an embodiment of the present application.

[0080] Explanation of the reference numerals: 201, acquisition module; 202, extraction module; 203, processing module; 204, judgment module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0081] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0082] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0083] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0084] The wake effect affects wind farm efficiency and equipment lifespan. Complex terrain exacerbates this impact by altering turbulence intensity and wake dispersion. Traditional models struggle to capture the coupling between terrain and wake. Therefore, a dynamic wake model based on terrain correction factors is proposed to optimize wind farm layout and improve overall efficiency.

[0085] This embodiment discloses a method for analyzing the dynamic wake of a wind turbine in a wind farm. Figure 1 , including the following steps:

[0086] S110: Acquire terrain data of the wind farm area.

[0087] The embodiment of the present application discloses a method for analyzing the dynamic wake of a wind turbine in a wind farm, which is applied to a server. The server includes, but is not limited to, electronic devices such as mobile phones, tablet computers, wearable devices, and personal computers (PCs), and can also be a background server that runs the method. The server can be implemented as a standalone server or a server cluster consisting of multiple servers.

[0088] Satellite imagery can be used to obtain surface elevation information. High-resolution terrain data can be obtained from public data sources such as NASA and ESA. Alternatively, laser scanning can be used to measure the distance from the ground to the sensor, generating high-precision terrain data. This is particularly suitable for obtaining high-resolution ground relief information. Specifically, elevation data for the relevant area can be downloaded from public DEM databases such as the USGS, NASA, and SRTM. For high-resolution requirements, LiDAR or specialized high-resolution satellite imagery can be used. Surface roughness is typically estimated based on ground type, such as forest, grassland, and buildings. Based on topographic data, land cover data or a geographic information system (GIS) can be used to extract surface roughness for the wind farm area. Information such as vegetation type and buildings in remote sensing imagery or LiDAR data can be used to estimate roughness values. Different surface types, such as flat land, hills, and forests, have different roughness coefficients, and the roughness coefficient is typically selected based on the geographic characteristics of the wind farm area.

[0089] S120: Extracting terrain parameters from the terrain data.

[0090] In one possible implementation, terrain parameters are extracted from terrain data, specifically including: calculating the elevation difference between the wind turbine and each target point based on the acquired wind turbine position and the positions of multiple target points; matching the roughness length value according to the surface type based on the surface coverage data of the wind farm area; dividing the wind farm area into multiple grid cells; and calculating the terrain slope based on the vertical difference and horizontal distance between two adjacent grid cells.

[0091] Specifically, the precise location of wind turbines is obtained from wind farm design documents or GPS positioning systems, typically expressed as longitude and latitude coordinates, for example, in the WGS84 coordinate system. The location of wind turbines is the basis for determining the topographical differences between each turbine and other points, such as target points, adjacent turbines, and environmental measurement points. Target points are typically key locations within the wind farm area, such as other turbine locations, terrain analysis points, and meteorological monitoring points. The locations of these points can be determined through measurements or model simulations.

[0092] Use terrain data to obtain the elevation values ​​of each target point and wind turbine location. For example, DEM data is usually a raster data that represents the elevation of each location on the surface. For each wind turbine and target point, calculate the elevation difference ΔH = H target -H turbine , where H target is the elevation of the target point, H turbine is the elevation of the wind turbine location. This elevation difference reflects the terrain changes between the wind turbine and the target point, which can help analyze the terrain impact between the wind turbine and the wake area.

[0093] Each area is assigned a roughness length based on the surface type. The roughness length reflects the degree of surface irregularity and the effects of wind attenuation. Urban areas, forests, grasslands, and other surfaces have different roughness lengths. For example, urban areas have a higher roughness length, typically around 13 meters; grassland and agricultural areas have a lower roughness length, typically 0.1 to 0.5 meters; and flat desert surfaces have a lower roughness length, approaching zero. Using land cover data, each grid cell is assigned a corresponding roughness length value using a predefined surface roughness table or referenced in standard literature.

[0094] Furthermore, the size of the grid unit is determined based on the actual scale of the wind farm and the required accuracy. Common grid resolutions are 1km×1km, 500m×500m, or finer 10m×10m. Smaller grids can provide higher accuracy but require more computing resources. Use GIS software to grid the wind farm area. Each grid cell represents an area of ​​terrain data, which contains terrain parameters such as elevation, slope, and roughness. Based on the geographical boundaries of the wind farm, such as the latitude and longitude coordinate range, the area is divided into several small grids. Each grid has a clear spatial location and can be linked to the corresponding elevation and roughness data.

[0095] For each grid cell, extract the elevation values ​​of the four boundary points or center point of the grid. The slope reflects the degree of ground inclination and is usually calculated by the elevation difference and horizontal distance between adjacent grid cells. The calculation formula is:

[0096]

[0097] Where: P is the slope, ΔH is the vertical elevation difference between adjacent grid cells, and Δd is the horizontal distance between adjacent grid cells. The horizontal distance is the side length of the grid cell.

[0098] S130: Calculate a terrain correction factor based on the terrain parameters.

[0099] The impact of elevation differences on wakes is primarily reflected in the expansion and recovery speed of the wake. Areas with large elevation differences, such as valleys or plateaus, may cause the expansion of the wake to become more complex or delay recovery, while areas with smaller elevation differences may cause the wake to diffuse more quickly. Based on the elevation difference between the wind turbine and each target point, the elevation correction factor is calculated using the following formula:

[0100]

[0101] Among them, k h is the elevation correction factor, α is the sensitivity of the elevation difference to the wake, and its size is usually determined by experiments or numerical simulations. Δh is the elevation difference between the wind turbine and the target point, H t is the turbine hub height. The elevation correction factor is used to adjust the diffusion process in the wake model. It reflects the impact of terrain elevation differences on wind speed and turbulence intensity. In areas with complex terrain, the elevation correction factor will lead to dynamic changes in the wake model, making the wind farm's wake expansion process more realistic.

[0102] Surfaces with high roughness, such as forests or urban areas, increase the rate at which wind speeds decay and introduce stronger turbulence, slowing the recovery of the wake. Surfaces with low roughness, such as flat grasslands or deserts, cause the wake to expand more quickly. The roughness correction factor is calculated based on the roughness length value of the wind farm area using the following formula:

[0103]

[0104] K r is the roughness correction factor, z0 is the roughness length value, z 0,ref is the reference roughness length, used to normalize the roughness lengths of different surface types. β is the turbulent diffusion coefficient. The roughness correction factor adjusts for the effect of different surface types on wake diffusion within a wind farm. Areas with greater roughness, such as urban areas or forests, slow wake diffusion, while areas with less roughness cause wake expansion to accelerate. The roughness correction factor more accurately reflects the wake behavior of different areas within a wind farm.

[0105] A larger slope means the ground changes more quickly, and the wake may be guided by the local terrain, thus affecting the diffusion and recovery of the wake. Areas with smaller slopes usually make the recovery process of the wake smoother. The slope correction factor is calculated based on the terrain slope of the wind farm area, specifically using the following formula:

[0106] k s =1+γ·sin(θ)

[0107] Among them, k s The slope correction factor is used to adjust the effect of terrain slope on wake behavior in the wake model. Areas with steeper slopes may cause the wake to spread faster or recover more slowly, while areas with shallower slopes may experience more gradual wake expansion. The slope correction factor helps more accurately simulate the wake effects of different terrain conditions within a wind farm.

[0108] S140 , using the pre-selected analytical wake model as a base model, inputting the terrain correction factor into the base model to obtain a correction model.

[0109] To accurately describe the velocity decay and diffusion behavior of a wind turbine's wake, the wake velocity defect formula is used to describe wake velocity decay, combining the classic wake velocity defect formula with the Gaussian distribution model. When a wind turbine is operating, the wind speed decreases after passing through the blades, forming a low-speed region. The wake velocity defect formula is used to describe the drop in wind speed within this region. Wake velocity decay is typically described using the wake velocity defect formula, as follows:

[0110]

[0111] Where ΔU(x) is the wake velocity defect, which is the decrease in wind speed in the turbine wake compared to the free stream speed. U0 is the free stream speed, or the wind speed unaffected by the turbine. D is the turbine diameter. k is the expansion coefficient, or the wake diffusion coefficient, which indicates how quickly the wake expands with downstream distance and is related to factors such as turbulence intensity and topography. x is the downstream distance, the horizontal distance from the turbine to a point in the wake. As downstream distance increases, the wake gradually expands, causing the wind speed in the wake region to return to near the free stream speed. The expansion coefficient k controls the rate of wake expansion. The denominator D+2kx in the formula represents the expansion of the wake as the distance x increases. The wake velocity defect formula describes the wind speed attenuation characteristics within the wake region and provides a basis for predicting the wind speed experienced by downstream wind turbines. It helps calculate the wake's impact range and optimize wind turbine layout to minimize the wake effect.

[0112] The wake diffusion width is expressed according to the Gaussian distribution. The specific expression is as follows:

[0113] σ x (x)=σx,0 +ε·x

[0114] Among them, σ x (x) is the lateral diffusion width of the wake, σ x,0 is the initial diffusion width, which usually refers to the width of the wake just generated behind the fan blades. ε is the diffusion coefficient, which indicates the rate of lateral diffusion of the wake. x is the downstream distance.

[0115] Behind the wind turbine blades, the wake initially has a certain width. As the wake propagates downstream, the lateral turbulence causes the wake's diffusion width to gradually increase. The diffusion coefficient controls the diffusion rate and is generally determined by the turbulence intensity and environmental conditions. The formula shows that the wake's diffusion width increases linearly with downstream distance. This is based on the Gaussian distribution assumption, which assumes that turbulence in the wake causes the wake to diffuse at a uniform rate. The Gaussian distribution model assumes that the velocity decay and turbulent diffusion of the wake follow a normal distribution. This assumption is very effective in describing the wind speed distribution across the wake's cross section. The wind speed is lowest in the center of the wake, and after lateral diffusion, the wind speed gradually returns to the free stream speed. By calculating the wake's diffusion width, the impact range and intensity of the wake can be predicted, providing support for assessing the wind speed and turbulence intensity experienced by downstream wind turbines. The larger the diffusion width, the wider the range of downstream wind turbines affected by the wake.

[0116] Combining the wake velocity defect and diffusion model, the wake velocity defect formula is used to calculate the center velocity of the wake at any distance downstream. Assuming that the wake conforms to a Gaussian distribution on the cross section, the diffusion width σ is used. x (x) Calculate the wind speed distribution at any lateral position:

[0117]

[0118] Where y is the lateral position and x is the downstream distance.

[0119] By calculating the velocity variation at different locations downstream of the turbine using the wake velocity defect formula, the modified model can predict the degree of attenuation of the central wake velocity. The Gaussian distribution model describes the lateral diffusion characteristics of the wake, enabling the modified model to simulate the wind speed distribution at different locations in the wind farm.

[0120] In order to more accurately predict wake diffusion and velocity distribution under complex terrain conditions, the model introduces terrain correction factors, including elevation correction factor, roughness correction factor, and slope correction factor, and applies them to the key parameters of the wake model.

[0121] Introduce elevation correction factor and roughness correction factor to adjust the expansion coefficient, as follows:

[0122] k eff =k·k h ·k r

[0123] Among them, k eff To adjust the expansion coefficient, k is the expansion coefficient, which is determined by the wake characteristics and free stream turbulence conditions. h k is the elevation correction factor, which corrects the effect of the height difference between the wind turbine and the target point on the wake diffusion. The greater the height difference, the more significant the change in the wake expansion rate. r Roughness correction factor accounts for the effect of surface roughness on wake dispersion. The rougher the surface, the higher the turbulence intensity and the faster the wake dispersion rate. In complex terrain, elevation differences and surface roughness alter the local turbulence and wind speed distribution. By incorporating elevation and roughness correction factors into the expansion coefficient, the wake dispersion rate can be dynamically adjusted to simulate realistic wake behavior.

[0124] Based on the influence of slope and roughness on wake diffusion, the roughness correction factor and slope correction factor are introduced into the basic model, and the expressions are as follows:

[0125] σ x (x)=σ x,0 +ε·k s ·k r ·x

[0126] Among them, σ x (x) is the lateral diffusion width of the wake, the initial lateral width of the fan outflow, which depends on the fan diameter and the initial turbulence conditions. x,0 is the initial diffusion width, ε is the diffusion coefficient, x is the downstream distance, k r k is the roughness correction factor, which adjusts the effect of rough surface on diffusion speed. s is the slope correction factor, accounting for the effect of terrain slope on wake diffusion. As the slope increases, the wake diffuses more vertically. Terrain slope and roughness affect turbulence intensity, which in turn alters the lateral diffusion rate of the wake. By modifying the diffusion width formula, the lateral diffusion behavior of the wake can be simulated under different terrain conditions.

[0127] The comprehensive revised wake model is expressed as follows:

[0128]

[0129] Among them, U(x,y,z) is the wind speed at any point in the wake area, U0 is the free stream wind speed, that is, the wind speed when there is no wake effect, and U def (x) is the wake velocity defect, which describes the degree of the wake center velocity drop. f(k h ,k r ,k s ) is a comprehensive correction factor function that comprehensively considers the effects of elevation, roughness, and slope to adjust the speed defect.

[0130] By incorporating correction factors for elevation, roughness, and slope, the wake model can dynamically adjust to complex terrain conditions, accurately describing the wake diffusion and velocity recovery process. This revised wake model not only improves wind farm simulation accuracy but also provides data support for optimizing wind turbine layout and improving power generation efficiency.

[0131] S150, introducing real-time wind speed and wind direction data, performing numerical simulation on the modified model, and obtaining prediction results of the modified model.

[0132] First, the wind farm area is divided into three-dimensional computational grid cells, each representing a discrete volume in space for numerical calculations. Meshing should balance terrain complexity and computational accuracy. Unstructured grids are typically used to accommodate irregular terrain, while structured grids are used for simple terrain. The grid size can be adaptively adjusted based on the turbine diameter and key terrain features to ensure detailed resolution of the wake. This grid provides the computational domain for numerical simulations and discretizes the continuous space, facilitating numerical solutions.

[0133] Input real-time wind speed and direction data to define the velocity distribution at the wind farm inlet. Set the initial velocity field and turbulence intensity distribution within the computational domain, typically based on measured data or steady-state simulation results. Boundary conditions and initial conditions provide physical constraints for the wake simulation, ensuring the model operates within the correct physical environment.

[0134] For each three-dimensional computational grid cell, the velocity distribution and turbulence intensity of the wake at different spatial locations are calculated by solving the fluid mechanics equations to generate prediction results. The fluid mechanics equations include the continuity equation and the momentum equation. The continuity equation is expressed as follows:

[0135]

[0136] Where ρ is the fluid density, t is time, and U is the wind speed vector. The continuity equation states that the fluid maintains mass conservation within the computational domain, meaning that the sum of the local mass change rate and the inflow and outflow mass fluxes is zero. The continuity equation is discretized using the finite volume method or finite difference method, and mass conservation is ensured by iteratively updating the density and velocity fields of each grid cell.

[0137] The momentum equation is expressed as follows:

[0138]

[0139] Among them, ρU is momentum density, p is pressure, μ is dynamic viscosity coefficient, is the Laplace operator of the velocity vector, describing the viscous diffusion of the fluid, F terrainThe terrain correction force term reflects the influence of terrain on fluid, such as additional force caused by slope and roughness, which is calculated by parameters such as terrain slope, elevation difference and roughness, and acts on the flow field to adjust the diffusion and recovery rate of the wake.

[0140] By solving the momentum equation, the wind speed vector of each grid cell is obtained. According to the turbulence model, such as k-∈ or k-ω model, the turbulent kinetic energy and turbulent dissipation rate are calculated to obtain the turbulent intensity of each grid cell. Finally, the prediction results are output, including the wake velocity distribution, the wind speed and direction of the wake area in three-dimensional space; the turbulent intensity, the turbulent intensity distribution at each position in the wake area, reflecting the influence of the wake on the downstream wind turbine; and the wake diffusion form, the diffusion range and form change of the wake at different downstream distances.

[0141] S160, compare the prediction results with the measured data, if it is determined that the error between the prediction results and the measured data is within the preset range, deploy the correction model to the wind farm control system.

[0142] The error value of the predicted and measured wind speed is calculated, and the error calculation formula is as follows:

[0143]

[0144] Where D is the error value, N is the number of samples, i.e. the total number of spatial positions or time points participating in error calculation, U sim (x i ) is the predicted wind speed, and U real (x i ) is the measured wind speed. For each sample point, the predicted wind speed and the corresponding measured wind speed are obtained, the relative error of each sample point is calculated, and the average of the relative errors of all sample points is obtained to obtain the overall error value.

[0145] Determine whether the error value meets the preset threshold value. The preset threshold value is usually set according to actual application requirements, and the common value range is 5% to 10%, which can be dynamically adjusted according to the error statistics of historical data or based on the performance requirements of different wind farms. If it is determined that the error value is less than or equal to the preset threshold value, it means that the error between the prediction results of the correction model and the measured data is within an acceptable range, and the model has practical deployment value.

[0146] Finally, the corrected wake model is integrated into the wind farm control system, including model parameters and correction factors. The model is automatically called through PLC (Programmable Logic Controller) or SCADA (Supervisory Control And Data Acquisition). The model input is updated in real time, including wind speed, wind direction and terrain characteristics, to ensure the effectiveness of the model in dynamic environment. The wind farm control system can optimize the wind turbine torque, pitch angle and power setting according to the prediction results of the correction model to maximize power generation efficiency and reduce equipment wear and tear.

[0147] This embodiment also discloses that a wind farm wind turbine dynamic wake analysis system is provided in the second aspect of the present application, referring to Figure 2 The system includes an acquisition module 201, an extraction module 202, a processing module 203 and a judgment module 204, wherein:

[0148] The acquisition module 201 is used to acquire terrain data of the wind farm area.

[0149] The extraction module 202 is used to extract terrain parameters from the terrain data.

[0150] The processing module 203 is used to calculate the terrain correction factor according to the terrain parameters.

[0151] The processing module 203 is used to use the pre-selected analytical wake model as the basic model, input the terrain correction factor into the basic model, and obtain a correction model to achieve dynamic adjustment of the wake diffusion and recovery process.

[0152] The processing module 203 is used to introduce real-time wind speed and wind direction data, perform numerical simulation on the correction model, and obtain the prediction result of the correction model.

[0153] The judgment module 204 is used to compare the prediction result with the measured data, and if it is determined that the error between the prediction result and the measured data is within a preset range, the correction model is deployed to the wind farm control system.

[0154] In a possible implementation, the processing module 203 is configured to calculate an elevation correction factor based on the elevation difference between the wind turbine and each target point, specifically using the following formula:

[0155]

[0156] Among them, k h is the elevation correction factor, α is the sensitivity of the elevation difference to the wake, Δh is the elevation difference between the wind turbine and the target point, H t is the fan hub height.

[0157] The processing module 203 is used to calculate the roughness correction factor according to the roughness length value of the wind farm area, specifically using the following formula:

[0158]

[0159] K r is the roughness correction factor, z0 is the roughness length value, z 0,ref is the reference roughness length, and β is the turbulent diffusion coefficient.

[0160] The processing module 203 is used to calculate the slope correction factor according to the terrain slope of the wind farm area, which is specifically calculated using the following formula:

[0161] k s =1+γ·sin(θ)

[0162] Among them, k s is the slope correction factor, γ is the terrain sensitivity coefficient, and θ is the terrain slope.

[0163] In a possible implementation, the processing module 203 is configured to use a wake velocity defect formula to represent the degree of wake velocity attenuation. The wake velocity defect formula is specifically as follows:

[0164]

[0165] Where ΔU(x) is the wake velocity defect, U0 is the free stream velocity, D is the fan diameter, k is the expansion coefficient (wake diffusion coefficient), and x is the downstream distance.

[0166] The processing module 203 uses Gaussian distribution to represent the wake diffusion width. The specific expression is as follows:

[0167] σ x (x)=σ x,0 +ε·x

[0168] Among them, σ x (x) is the lateral diffusion width of the wake, σ x,0 is the initial diffusion width, ε is the diffusion coefficient, and x is the downstream distance.

[0169] In a possible implementation, the processing module 203 is configured to introduce an elevation correction factor and a roughness correction factor to adjust the expansion coefficient, specifically as follows:

[0170] k eff =k·k h ·k r

[0171] Among them, k eff To adjust the expansion coefficient, k is the expansion coefficient, k h is the elevation correction factor, k r is the roughness correction factor.

[0172] The processing module 203 is used to introduce the roughness correction factor and the slope correction factor into the basic model based on the influence of slope and roughness on wake diffusion. The expressions are as follows:

[0173] σ x (x)=σ x,0 +ε·k s ·k r·x

[0174] Among them, σ x (x) is the lateral diffusion width of the wake, σ x,0 is the initial diffusion width, ε is the diffusion coefficient, x is the downstream distance, k r is the roughness correction factor, k s is the slope correction factor.

[0175] The comprehensive revised wake model is expressed as follows:

[0176]

[0177] Among them, U(x,y,z) is the wind speed at any point in the wake area, U0 is the free stream wind speed, and U def (x) is the wake velocity defect, f(k h ,k r ,k s ) is the comprehensive correction factor function.

[0178] In a possible implementation, the processing module 203 is configured to divide the wind farm area into three-dimensional computational grid units, input real-time wind speed and direction data as boundary conditions and initial conditions, and drive the correction model to perform dynamic solution.

[0179] The processing module 203 is configured to calculate the velocity distribution and turbulence intensity of the wake at different spatial locations by solving the fluid dynamics equation for each three-dimensional computational grid cell, and generate a prediction result. The fluid dynamics equation includes the continuity equation and the momentum equation. The continuity equation is expressed as follows:

[0180]

[0181] Where ρ is the fluid density, t is the time, and U is the wind speed vector.

[0182] The momentum equation is expressed as follows:

[0183]

[0184] Among them, ρU is momentum density, p is pressure, μ is dynamic viscosity coefficient, is the Laplace operator of the velocity vector, F terrain is the terrain correction force term.

[0185] The processing module 203 is used to output the wind speed distribution, turbulence intensity and diffusion form of the wake to obtain a prediction result.

[0186] In one possible implementation, the processing module 203 is configured to calculate the error between the predicted wind speed and the measured wind speed, specifically using the following formula:

[0187]

[0188] Among them, D is the error value, N is the number of samples, and U sim (x i ) is the predicted wind speed, U real (x i ) is the measured wind speed.

[0189] The judgment module 204 is used to judge whether the error value is less than or equal to a preset threshold value, and if it is determined that the error value is less than or equal to the preset threshold value, deploy the correction model to the wind farm control system.

[0190] In a possible implementation, the processing module 203 is configured to calculate the elevation difference between the wind turbine and each target point based on the acquired wind turbine position and the positions of multiple target points.

[0191] The processing module 203 is configured to match the roughness length value according to the surface coverage data of the wind farm area and the surface type.

[0192] The processing module 203 is configured to divide the wind farm area into a plurality of grid units.

[0193] The processing module 203 is used to calculate the terrain slope based on the vertical difference and horizontal distance between two adjacent grid cells.

[0194] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0195] This embodiment also discloses an electronic device, referring to Figure 3 The electronic device may include: at least one processor 301 , at least one communication bus 302 , a user interface 303 , a network interface 304 , and at least one memory 305 .

[0196] The communication bus 302 is used to implement the connection and communication between these components.

[0197] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0198] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0199] The processor 301 may include one or more processing cores. The processor 301 utilizes various interfaces and lines to connect various parts of the entire server. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and calling data stored in the memory 305, the processor 301 performs various server functions and processes data. Optionally, the processor 301 may be implemented in at least one hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 301 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display; and the modem is used to handle wireless communications. It is understood that the modem may not be integrated into the processor 301 and may be implemented separately on a single chip.

[0200] Among them, the memory 305 may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also be at least one storage device located away from the aforementioned processor 301. As a computer storage medium, the memory 305 may include an operating system, a network communication module, a user interface 303 module and an application program for a method for analyzing the dynamic wake of a wind turbine in a wind farm.

[0201] exist Figure 3In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 301 can be used to call an application program stored in the memory 305 for a method for analyzing the dynamic wake of a wind turbine in a wind farm. When executed by one or more processors 301, the electronic device executes one or more methods in the above-mentioned embodiments.

[0202] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0203] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0204] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0205] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0206] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0207] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory 305 and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory 305 includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disk.

[0208] The present application also discloses a computer-readable storage medium storing instructions, which, when executed by one or more processors 301, enable an electronic device to execute one or more of the methods described in the above embodiments.

[0209] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and practice, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any variation, use or adaptive change of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for analyzing dynamic wake of wind turbines in a wind farm, characterized in that: The method comprises: Obtain topographic data of the wind farm area; extracting terrain parameters from the terrain data; Calculating a terrain correction factor based on the terrain parameters; Using a preselected analytical wake model as a base model, inputting the terrain correction factor into the base model to obtain a correction model to achieve dynamic adjustment of the wake diffusion and recovery process; Introducing real-time wind speed and wind direction data, performing numerical simulation on the modified model, and obtaining prediction results of the modified model; Comparing the predicted result with the measured data, and if it is determined that the error between the predicted result and the measured data is within a preset range, deploying the corrected model to the wind farm control system; Inputting the terrain correction factor into the basic model to obtain a correction model specifically includes: Introduce elevation correction factor and roughness correction factor to adjust the expansion coefficient; Based on the influence of slope and roughness on wake diffusion, a roughness correction factor and a slope correction factor are introduced into the basic model; The comprehensive revised wake model is expressed as follows: ； Among them, U(x,y,z) is the wind speed at any point in the wake area, U0 is the free stream wind speed, and U def (x) is the wake velocity defect, f(k h ,k r ,k s ) is the comprehensive correction factor function, k h is the elevation correction factor, k r is the roughness correction factor, k s is the slope correction factor.

2. A wind farm wind turbine dynamic wake analysis method according to claim 1, characterized in that: Calculating the terrain correction factor according to the terrain parameters specifically includes: Calculate the elevation correction factor based on the elevation difference between the wind turbine and each target point; Calculating a roughness correction factor according to a roughness length value of the wind farm area; Calculate the slope correction factor based on the terrain slope of the wind farm area.

3. A wind farm wind turbine dynamic wake analysis method according to claim 1, characterized in that: Based on the pre-selected analytical wake model as the base model, it includes: The wake velocity defect formula is used to express the degree of wake velocity attenuation. The wake velocity defect formula is as follows: ； Where ΔU(x) is the wake velocity defect, U0 is the free stream velocity, D is the fan diameter, k is the expansion coefficient, and x is the downstream distance; Gaussian distribution is used to represent the wake diffusion width, and the specific expression is as follows: ； Among them, σ x (x) is the lateral diffusion width of the wake, σ x,0 is the initial diffusion width, ε is the diffusion coefficient, and x is the downstream distance.

4. A wind farm wind turbine dynamic wake analysis method according to claim 1, characterized in that: The introducing of real-time wind speed and direction data, performing numerical simulation on the modified model, and obtaining the prediction result of the modified model specifically includes: Dividing the wind farm area into three-dimensional computational grid units, inputting the real-time wind speed and wind direction data as boundary conditions and initial conditions, and driving the modified model to perform dynamic solution; For each three-dimensional computational grid cell, the velocity distribution and turbulence intensity of the wake at different spatial locations are calculated by solving fluid mechanics equations, including the continuity equation and the momentum equation, to generate prediction results; The wind speed distribution, turbulence intensity and diffusion form of the wake are output to obtain the prediction result.

5. A wind farm wind turbine dynamic wake analysis method according to claim 1, characterized in that: The comparing the prediction result with the measured data, and if it is determined that the error between the prediction result and the measured data is within a preset range, deploying the correction model to the wind farm control system, specifically includes: Calculate the error between the predicted wind speed and the measured wind speed; It is determined whether the error value is less than or equal to a preset threshold value, and if it is determined that the error value is less than or equal to the preset threshold value, the correction model is deployed to the wind farm control system.

6. A wind farm wind turbine dynamic wake analysis method according to claim 1, characterized in that: The extracting of terrain parameters from the terrain data specifically includes: Calculating the elevation difference between the wind turbine and each of the target points based on the acquired wind turbine position and the positions of the multiple target points; Matching roughness length values ​​according to surface type based on surface cover data of the wind farm area; Dividing the wind farm area into a plurality of grid units; The terrain slope is calculated by the vertical difference and horizontal distance between two adjacent grid cells.

7. A wind farm wind turbine dynamic wake analysis system, characterized in that: The system is used to execute the method according to any one of claims 1 to 6, and the system comprises an acquisition module (201), an extraction module (202), a processing module (203), and a judgment module (204), wherein: The acquisition module (201) is used to acquire topographic data of the wind farm area; The extraction module (202) is used to extract terrain parameters from the terrain data; The processing module (203) is used to calculate a terrain correction factor based on the terrain parameters; The processing module (203) is used to use a pre-selected analytical wake model as a basic model, input the terrain correction factor into the basic model, and obtain a correction model to achieve dynamic adjustment of the wake diffusion and recovery process; The processing module (203) is used to introduce real-time wind speed and wind direction data, perform numerical simulation on the correction model, and obtain the prediction result of the correction model; The judgment module (204) is used to compare the prediction result with the measured data, and if it is determined that the error between the prediction result and the measured data is within a preset range, deploy the correction model to the wind farm control system.

8. An electronic device, characterized in that: The electronic device comprises a processor (301), a communication bus (302), a user interface (303), a network interface (304) and a memory (305), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are both used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device executes the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 6 is executed.

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