Dynamic wake flow analysis method and system for wind power plant fan

Abstract

The invention provides a wind power plant fan dynamic wake flow analysis method and system, and relates to the technical field of wind power processing, and the method comprises the steps: obtaining the topographic data of a wind power plant region; terrain parameters are extracted from the terrain data; calculating a terrain correction factor according to the terrain parameters; according to a pre-selected analysis wake flow 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 a wake flow diffusion and recovery process; introducing real-time wind speed and wind direction data, performing numerical simulation on the correction model, and obtaining a prediction result of the correction model; and comparing the prediction result with the actually measured data, and if it is determined that the error between the prediction result and the actually measured data is within a preset range, deploying a correction model to the wind power plant control system. According to the method, the wake flow characteristics can be dynamically analyzed under different topographic conditions.

Description

Technical Field

[0001] This application relates to the technical field of wind power processing, and particularly to a method and system for dynamically analyzing the wake 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 the field of wind power generation. When a wind turbine is operating, it will cause a phenomenon of velocity decay and turbulence enhancement in the downstream area, resulting in less wind energy absorbed by subsequent wind turbines and higher load fluctuations at the same time. This not only reduces the overall power generation efficiency but also exacerbates the mechanical wear of the equipment, increasing maintenance and operation costs. Therefore, accurately analyzing and controlling the wake effect is crucial for optimizing the layout of wind farms 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 will change the turbulence intensity distribution of the wind field, thereby affecting the wake expansion speed and turbulence isotropy characteristics. However, traditional wake models are difficult to effectively capture the dynamic coupling effect between the terrain and the wake. Therefore, a method is needed to dynamically analyze the wake characteristics under different terrain conditions. Summary of the Invention

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

[0005] In the first aspect of this application, a method for dynamically analyzing the wake of wind turbines in a wind farm is provided. The method includes:

[0006] Obtaining terrain data of the wind farm area;

[0007] Extracting terrain parameters from the terrain data;

[0008] Calculating a terrain correction factor according to the terrain parameters;

[0009] Using a pre-selected wake analysis model as a basic model, inputting the terrain correction factor into the basic model to obtain a corrected model, so as to realize the dynamic adjustment of the wake diffusion and recovery process;

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

[0011] Comparing the prediction result with the measured data. If it is determined that the error between the prediction result and the measured data is within a preset range, then deploying the corrected model to the wind farm control system.

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

[0013] Calculating the elevation correction factor according to the elevation differences between the wind turbine and each target point;

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

[0015] Calculating the slope correction factor according to the terrain slope of the wind farm area.

[0016] On the basis of the above technical solutions, preferably, using the pre-selected analytical wake model as the basic model specifically includes:

[0017] Using the wake velocity defect formula to represent the degree of wake velocity decay. The wake velocity defect formula is specifically as follows:

[0018]

[0019] where ΔU(x) is the wake velocity defect, U 0 is the free stream velocity, D is the wind turbine diameter, k is the expansion coefficient (wake diffusion coefficient), and x is the downstream distance;

[0020] Using the Gaussian distribution to represent the wake diffusion width. The specific expression is as follows:

[0021] σ x (x) = σ x,0 + ε·x

[0022] where σ x (x) is the wake diffusion width in the lateral direction, σ x,0 is the initial diffusion width, ε is the diffusion coefficient, and x is the downstream distance.

[0023] On the basis of the above technical solutions, preferably, inputting the terrain correction factor into the basic model to obtain the corrected model specifically includes:

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

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

[0026] The comprehensively corrected wake model is expressed as follows:

[0027]

[0028] where U(x,y,z) is the wind speed at any point in the wake region, U 0is the free-stream wind speed, U def δ(x) is the wake velocity deficit, f(k h , k r , k s ) is the comprehensive correction factor function.

[0029] Based on the above technical solutions, preferably, the real-time wind speed and wind direction data are introduced, and numerical simulation is performed on the correction model to obtain the prediction results of the correction model, which specifically includes:

[0030] Divide the wind farm area into three-dimensional computational grid cells, input the real-time wind speed and the wind direction data as boundary conditions and initial conditions, and drive the correction model to perform dynamic solution;

[0031] For each three-dimensional computational grid cell, by solving the fluid mechanics equations, calculate the velocity distribution and turbulence intensity of the wake at different spatial positions, and generate prediction results. The fluid mechanics equations include the continuity equation and the momentum equation;

[0032] Output the wind speed distribution, turbulence intensity and diffusion pattern of the wake to obtain the prediction results.

[0033] Based on the above technical solutions, preferably, the prediction results are compared with the measured data. If it is determined that the error between the prediction results and the measured data is within the preset range, then deploy the correction model to the wind farm control system, which specifically includes:

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

[0035] Judge whether the error value is less than or equal to the preset threshold. If it is determined that the error value is less than or equal to the preset threshold, then deploy the correction model to the wind farm control system.

[0036] Based on the above technical solutions, preferably, the terrain parameters are extracted from the terrain data, which specifically includes:

[0037] According to the obtained positions of the wind turbines and the positions of multiple target points, calculate the elevation differences between the wind turbines and each of the target points;

[0038] According to the surface cover data of the wind farm area, match the roughness length value according to the surface type;

[0039] Divide the wind farm area into multiple grid cells;

[0040] Based on the vertical difference and horizontal distance between two adjacent grid cells, calculate the terrain slope.

[0041] In the second aspect of the present application, a dynamic wake analysis system for wind turbines in a wind farm is provided. The system includes an acquisition module, an extraction module, a processing module, and a judgment module, where:

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

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

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

[0045] The processing module 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 corrected model to realize 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 corrected model, and obtain the prediction result of the corrected model;

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

[0048] Based on the above technical solutions, preferably, the processing module is used to calculate an elevation correction factor according to the elevation difference between the wind turbine and each target point;

[0049] The processing module is used to calculate a roughness correction factor according to 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] Based on the above technical solutions, preferably, the processing module is used to represent the degree of wake velocity attenuation by a wake velocity defect formula, and the wake velocity defect formula is specifically as follows:

[0052]

[0053] Among them, ΔU(x) is the wake velocity defect, U 0 is the free stream velocity, D is the wind turbine diameter, k is the expansion coefficient (wake diffusion coefficient), and x is the downstream distance;

[0054] The processing module is used to represent the wake diffusion width by a Gaussian distribution, and the specific expression is as follows:

[0055] σ x (x) = σx,0 + ε·x

[0056] where 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] Based on 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 used to introduce a roughness correction factor and a slope correction factor into the basic model based on the influence of slope and roughness on wake diffusion;

[0059] The comprehensively corrected wake model is expressed as follows:

[0060]

[0061] where U(x, y, z) is the wind speed at any point in the wake region, U 0 is the free-stream wind speed, U def (x) is the wake velocity deficit, and 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 computational grid cells, input the real-time wind speed and the wind direction data as boundary conditions and initial conditions, and drive the corrected model for dynamic solution;

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

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

[0065] Based on 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 used to judge whether the error value is less than or equal to a preset threshold. If it is determined that the error value is less than or equal to the preset threshold, the corrected model is deployed to the wind farm control system.

[0067] Based on the above technical solutions, preferably, the processing module is configured to calculate the elevation difference between the fan and each of the target points according to the obtained positions of the fan and the multiple target points;

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

[0069] The processing module is configured to divide the wind farm area into multiple grid cells;

[0070] The processing module is configured to calculate the terrain slope according to the vertical difference and the 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 executes the method described in any one of the above.

[0072] In the fourth aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores instructions, and when the instructions are executed, the method described in any one of the 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. By obtaining the terrain data of the wind farm, extracting terrain parameters such as elevation difference, surface roughness, and slope, calculating the terrain correction factor, and introducing it into the traditional wake model, the expansion coefficient and wake diffusion width are corrected, thereby dynamically adjusting the wake diffusion and recovery process. Combining real-time wind speed and wind direction data, the corrected model can perform numerical simulations in real time according to 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 between the terrain and the wake and achieve accurate dynamic analysis of wake characteristics.

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

[0076] 3. By dividing the wind farm into three-dimensional grid cells, combining real-time wind speed and wind direction data, and dynamically solving the modified model using the continuity equation and momentum equation. The terrain correction force term takes into account the influence of complex terrain on the wind flow, enabling the prediction results to more accurately reflect the velocity distribution, turbulence intensity, and diffusion pattern of the wake at different spatial positions, effectively improving the accuracy of the wake model in complex terrain and dynamic environments. Description of the Drawings

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

[0078] Figure 2 is a schematic block diagram of a system for dynamically analyzing the wake of wind turbines in a wind farm disclosed in an embodiment of the present application;

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

[0080] Description 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 Embodiments

[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 accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0082] In the description of the embodiments of the present application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design solution described as "for example" or "for instance" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "for example" or "for instance" is intended to present relevant concepts in a specific manner.

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

[0084] The wake effect affects the power generation efficiency and equipment life of a wind farm. Complex terrain further exacerbates this effect by changing the turbulence intensity and wake diffusion. Traditional models are difficult to capture the coupling effect between terrain and wake. Therefore, a dynamic wake model based on a terrain correction factor is proposed to optimize the layout of the wind farm and improve the overall efficiency.

[0085] This embodiment discloses a method for dynamically analyzing the wake of wind turbines in a wind farm. Refer to Figure 1 , and it includes the following steps:

[0086] S110, obtain the terrain data of the wind farm area.

[0087] The method for dynamically analyzing the wake of wind turbines in a wind farm disclosed in the embodiments of the present application is applied to a server. The server includes but is not limited to electronic devices such as mobile phones, tablet computers, wearable devices, and PCs (Personal Computers). It can also be a background server running a method for dynamically analyzing the wake of wind turbines in a wind farm. The server can be implemented by an independent server or a server cluster composed of multiple servers.

[0088] Satellite images can be used to obtain elevation information of the earth's surface. High-resolution terrain data can be obtained from public data sources such as NASA and ESA. Or the distance from the ground to the sensor can be measured by laser scanning to generate high-precision terrain data, which is especially suitable for obtaining high-resolution ground undulation information. Specifically, download the elevation data of the relevant area from public DEM databases such as USGS, NASA, and SRTM. For high-resolution requirements, LiDAR or specialized high-resolution satellite images can be selected. Surface roughness is usually estimated by ground type. Ground types such as forests, grasslands, and buildings can be used to extract the surface roughness of the wind farm area based on the terrain data using land cover data or Geographic Information System (GIS). The roughness value can be estimated using information such as vegetation types and buildings in remote sensing images or LiDAR data. Different types of surfaces, such as flat ground, hills, and forests, will have different roughness coefficients, which are usually selected according to the geographical characteristics of the area where the wind farm is located.

[0089] S120, extract terrain parameters from the terrain data.

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

[0091] Specifically, obtain the precise position of the wind turbine through the design document of the wind farm or the GPS positioning system, usually represented by longitude and latitude coordinates. For example, in the WGS84 coordinate system. The position of the wind turbine is the basis for determining the terrain differences between each wind turbine and other points, such as target points, adjacent wind turbines, and environmental measurement points. Target points are usually some key positions within the wind farm area, such as the positions of other wind turbines, terrain analysis points, and meteorological monitoring points. The positions of these points can be obtained through measurement or model simulation.

[0092] Use the terrain data to obtain the elevation values of each target point and the wind turbine position. Such as DEM, DEM data is usually a raster data representing the elevation of each position on the earth's 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 and H turbine is the elevation of the wind turbine position. This elevation difference reflects the terrain change between the wind turbine and the target point and can help analyze the terrain influence between the wind turbine and the wake area.

[0093] According to different surface types, a roughness length is assigned to each area, which reflects the surface irregularity and the impact of wind speed attenuation. The roughness lengths of surfaces such as urban areas, forests, and grasslands are different. For example, the roughness length in urban areas is relatively large, usually around 13 meters; that in grasslands and agricultural areas is smaller, usually 0.1 - 0.5 meters; and that of flat desert surfaces is smaller, approaching zero. Through land cover data, the corresponding roughness length values are assigned to each grid cell using a predefined surface roughness table or according to standard literature.

[0094] Furthermore, according to the actual scale and required accuracy of the wind farm, the size of the grid cells is determined. Common grid resolutions are 1 km × 1 km, 500 m × 500 m, or a finer 10 m × 10 m. Smaller grids can provide higher accuracy but require more computing resources. The wind farm area is gridded using GIS software. 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 longitude and latitude coordinate ranges, the area is divided into several small grids. Each grid has a clear spatial position and can be linked to the corresponding elevation and roughness data.

[0095] For each grid cell, the elevation values of the four boundary points or the center point of the grid are extracted. The slope reflects the degree of ground inclination and is usually calculated through 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, Δd is the horizontal distance between adjacent grid cells, and the horizontal distance is the side length of the grid cell.

[0098] S130. Calculate the terrain correction factor according to the terrain parameters.

[0099] The influence of elevation difference on the wake mainly manifests in the expansion and recovery speed of the wake. In areas with large elevation differences, such as valleys or highlands, the expansion of the wake may become more complex or the recovery may be delayed, while in areas with small elevation differences, the wake may spread faster. According to the elevation difference between the wind turbine and each target point, calculate the elevation correction factor, which is specifically calculated through the following formula:

[0100]

[0101] where, k h is the elevation correction factor, α is the sensitivity of the elevation difference to the wake, and its value is usually determined through experiments or numerical simulations. Δh is the elevation difference between the wind turbine and the target point, H tis the hub height of the wind turbine. The elevation correction factor is used to adjust the diffusion process in the wake model. It reflects the influence of terrain elevation differences on wind speed and turbulence intensity. In areas with complex terrain, the elevation correction factor will cause dynamic changes in the wake model, making the wake expansion process in the wind farm more realistic.

[0102] Surfaces with higher roughness, such as forests or urban areas, will increase the attenuation rate of wind speed and introduce stronger turbulence, slowing down the wake recovery process. Surfaces with lower roughness, such as flat grasslands or deserts, will cause the wake to expand faster. According to the roughness length value of the wind farm area, the roughness correction factor is calculated, specifically through the following formula:

[0103]

[0104] K r is the roughness correction factor, z 0 is the roughness length value, z 0,ref is the reference roughness length, used to standardize the roughness lengths of different surface types. β is the turbulent diffusion coefficient. The roughness correction factor is used to adjust the influence of different surface types in the wind farm on wake diffusion. Areas with larger roughness, such as cities or forests, will cause slower wake diffusion, while areas with smaller roughness will cause the wake to expand faster. Through the roughness correction factor, the wake behavior in different regions of the wind farm can be more accurately reflected.

[0105] A larger slope means that the ground changes rapidly, and the wake may be guided by the local terrain, thus affecting the diffusion and recovery of the wake. Areas with smaller slopes usually have a smoother wake recovery process. According to the terrain slope of the wind farm area, the slope correction factor is calculated, specifically through the following formula:

[0106] k s =1 + γ·sin(θ)

[0107] where, k s is the slope correction factor, γ is the terrain sensitivity coefficient, and θ is the terrain slope. The slope correction factor is used to adjust the influence of the terrain slope in the wake model on wake behavior. Areas with larger slopes may cause the wake diffusion speed to increase or the recovery to slow down, while areas with smaller slopes have a more gentle wake expansion. The slope correction factor helps to more accurately simulate the wake effect under different terrain conditions in the wind farm.

[0108] S140, based on the pre-selected analytical wake model as the basic model, input the terrain correction factor into the basic model to obtain the corrected model.

[0109] To accurately describe the velocity decay and diffusion behavior of the wind turbine wake, combining the classical wake velocity deficit formula and the Gaussian distribution model, the wake velocity deficit formula is used to describe the wake velocity decay. When the wind turbine is operating, the wind speed will decrease after passing through the blades, forming a low-speed region. The wake velocity deficit formula is used to describe the wind speed reduction in this region. The wake velocity decay is usually described by the wake velocity deficit formula as follows:

[0110]

[0111] where ΔU(x) is the wake velocity deficit, that is, the reduction in the wind speed in the wind turbine wake region compared to the free-stream velocity, U 0 is the free-stream velocity, that is, the wind speed not affected by the wind turbine, D is the wind turbine diameter, k is the expansion coefficient, that is, the wake diffusion coefficient, which represents the speed at which the wake expands with the downstream distance and is related to factors such as turbulence intensity and terrain, and x is the downstream distance, which refers to the horizontal distance from the wind turbine location to a certain point in the wake. As the downstream distance increases, the wake gradually expands, resulting in the wind speed in the wake region approaching the free-stream velocity. The expansion coefficient k controls the expansion rate of the wake. The denominator D + 2kx in the formula represents the expansion of the wake with the increase in distance x. The wake velocity deficit formula describes the wind speed decay characteristics in the wake region and provides a basis for predicting the wind speed received by downstream wind turbines. It helps to calculate the wake influence range and optimize the wind turbine layout to reduce the wake effect.

[0112] The wake diffusion width is represented according to the Gaussian distribution, and the specific expression is as follows:

[0113] σ x (x) = σ x,0 + ε·x

[0114] where σ x (x) is the diffusion width of the wake in the transverse direction, σ x,0 is the initial diffusion width, usually referring to the width of the wake just generated behind the wind turbine blades, ε is the diffusion coefficient, which represents the rate of wake diffusion in the transverse direction, and x is the downstream distance.

[0115] Behind the wind turbine blade, the wake initially has a certain initial width. As the wake propagates downstream, the lateral turbulent effect causes the wake diffusion width to gradually increase. The diffusion coefficient controls the diffusion rate and is usually determined by the turbulence intensity and environmental conditions. The formula shows that the wake diffusion width increases linearly with the downstream distance. This is based on the Gaussian distribution assumption, which assumes that the turbulent effect in the wake will cause the wake to diffuse at a uniform rate. The Gaussian distribution model assumes that the velocity decay and turbulent diffusion in the wake follow a normal distribution. This assumption is very effective in describing the wind speed distribution in the wake cross-section. The wind speed is the lowest in the central region of the wake, and the wind speed gradually recovers to the free-stream velocity after lateral diffusion. By calculating the wake diffusion width, the influence range and intensity of the wake can be predicted, providing support for evaluating the wind speed and turbulence intensity experienced by downstream wind turbines. The larger the diffusion width, the wider the range of influence of the wake on downstream wind turbines.

[0116] Combining the wake velocity deficit and diffusion model, using the wake velocity deficit formula, calculate the wake center velocity at any downstream distance. Assume that the wake conforms to the Gaussian distribution in the cross-section, and use the diffusion width σ x (x) to calculate the wind speed distribution at any lateral position:

[0117]

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

[0119] Calculate the velocity change at different positions downstream of the wind turbine through the wake velocity deficit formula. The correction model can predict the attenuation degree of the wake center velocity. The Gaussian distribution model describes the lateral diffusion characteristics of the wake, enabling the correction model to simulate the wind speed distribution at different positions in the wind field.

[0120] 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, slope correction factor, and applies them to the key parameters of the wake model.

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

[0122] k eff = k·k h ·k r

[0123] where k eff is the adjusted expansion coefficient, k is the expansion coefficient, determined by the wake characteristics and free-stream turbulence conditions. k h is the elevation correction factor, which corrects the influence of the height difference between the wind turbine and the target point on wake diffusion. The greater the height difference, the more significant the change in the wake expansion rate. k ris the roughness correction factor, which considers the influence of surface roughness on wake diffusion. The rougher the surface, the higher the turbulence intensity and the faster the wake diffusion rate. In complex terrains, the elevation difference and surface roughness change the local turbulence and wind speed distribution. By introducing the elevation and roughness correction factors into the expansion coefficient, the wake diffusion rate can be dynamically adjusted to simulate the real 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] where, σ x (x) is the wake diffusion width in the lateral direction, the initial lateral width of the fan outflow, which depends on the fan diameter and the initial turbulence conditions. 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, which adjusts the influence of the rough surface on the diffusion speed. k s is the slope correction factor, which considers the influence of terrain slope on wake diffusion. When the slope increases, the wake is more likely to diffuse in the vertical direction. The terrain slope and roughness affect the turbulence intensity, and then change the wake lateral diffusion speed. By modifying the diffusion width formula, the wake lateral diffusion behavior can be simulated under different terrain conditions.

[0127] The comprehensively corrected wake model is expressed as follows:

[0128]

[0129] where, U(x,y,z) is the wind speed at any point in the wake area, U 0 is the free-stream wind speed, that is, the wind speed without wake influence, U def (x) is the wake velocity deficit, which describes the degree of the wake center velocity decrease. f(k h , k r , k s ) is the comprehensive correction factor function, which comprehensively considers the influence of elevation, roughness and slope, and adjusts the velocity deficit.

[0130] By introducing the elevation, roughness and slope correction factors, the wake model can achieve dynamic adjustment under complex terrain conditions and accurately describe the wake diffusion and velocity recovery processes. The corrected wake model not only improves the simulation accuracy of the wind farm, but also provides data support for optimizing the fan layout and improving the power generation efficiency.

[0131] S150. Introduce real-time wind speed and direction data, conduct numerical simulation on the correction model, and obtain the prediction results of the correction model.

[0132] First, divide the wind farm area into three-dimensional computational grid cells. Each cell represents a discrete volume in space and is used for numerical calculations. The grid division should take into account both terrain complexity and computational accuracy. Usually, unstructured grids are adopted to adapt to irregular terrains, or structured grids are used in simple terrain areas. The grid size can be adaptively adjusted according to the turbine diameter and key terrain features to ensure the resolution of wake details. The grid division provides the computational domain for numerical simulation, discretizes the continuous space, and facilitates numerical solution.

[0133] Input real-time wind speed and direction data, and set the velocity distribution at the inlet of the wind farm. Set the initial velocity field and turbulence intensity distribution within the computational domain, usually based on measurement data or steady-state simulation results. The boundary conditions and initial conditions provide physical constraints for the wake numerical simulation, ensuring that the model operates in the correct physical environment.

[0134] For each three-dimensional computational grid cell, calculate the velocity distribution and turbulence intensity of the wake at different spatial positions by solving the fluid mechanics equations. The prediction results are generated. 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 the time, and U is the wind speed vector. The continuity equation represents the mass conservation of the fluid within the computational domain, that is, the sum of the local mass change rate and the mass fluxes flowing in and out is zero. The finite volume method or the finite difference method is used to discretize the continuity equation, and the density and velocity fields of each grid cell are updated iteratively to ensure mass conservation.

[0137] The momentum equation is expressed as follows:

[0138]

[0139] where ρU is the momentum density, p is the pressure, μ is the dynamic viscosity coefficient, is the Laplacian operator of the velocity vector, which describes the viscous diffusion of the fluid. F terrain is the terrain correction force term, which reflects the influence of the terrain on the fluid, such as the additional forces caused by slope and roughness. It is calculated from parameters such as terrain slope, elevation difference, and roughness, acts on the flow field, and adjusts the diffusion and recovery rates of the wake.

[0140] By solving the momentum equation, the wind speed vectors of each grid cell are obtained. According to turbulence models such as the k-ε or k-ω model, the turbulent kinetic energy and turbulent dissipation rate are calculated to obtain the turbulence intensity of each grid cell. Finally, the prediction results are output, including the wake velocity distribution, the magnitude and direction of the wind speed in the wake region in three-dimensional space; the turbulence intensity, the distribution of the turbulence intensity at each position in the wake region, reflecting the impact of the wake on downstream wind turbines; the wake diffusion pattern, the diffusion range and morphological changes 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] Calculate the error value between the prediction and the measured wind speed. The error calculation formula is as follows:

[0143]

[0144] where D is the error value, N is the number of samples, that is, the total number of spatial positions or time points participating in the 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, obtain the predicted wind speed and the corresponding measured wind speed, calculate the relative error of each sample point, and average the relative errors of all sample points to obtain the overall error value.

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

[0146] Finally, integrate the corrected wake model into the wind farm control system, specifically including model parameters and correction factors. Automatically call the model through a PLC (Programmable Logic Controller) or SCADA (Supervisory Control and Data Acquisition). Update the model input in real time, including wind speed, wind direction, and terrain features, to ensure the effectiveness of the model in a dynamic environment. The wind farm control system can optimize the wind turbine torque, pitch angle, and power generation setting according to the prediction results of the correction model to maximize the power generation efficiency and reduce equipment losses.

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

[0148] The acquisition module 201 is used to acquire the 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 a terrain correction factor according to the terrain parameters.

[0151] The processing module 203 is used to use a pre-selected analytical wake model as the basic model, input the terrain correction factor into the basic model, and obtain a corrected model to realize the 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 corrected model, and obtain the prediction result of the corrected model.

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

[0154] In a possible implementation manner, the processing module 203 is used to calculate an elevation correction factor according to the elevation difference between the wind turbine and each target point, and specifically calculate it through the following formula:

[0155]

[0156] where 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, and H t is the hub height of the wind turbine.

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

[0158]

[0159] K r is the roughness correction factor, z 0 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 a slope correction factor according to the terrain slope of the wind farm area to which it belongs, and specifically calculate it through the following formula:

[0161] k s= 1 + γ·sin(θ)

[0162] where 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 represent the wake velocity attenuation degree using a wake velocity deficit formula, and the wake velocity deficit formula is specifically as follows:

[0164]

[0165] where ΔU(x) is the wake velocity deficit, U 0 is the free-stream velocity, D is the wind turbine diameter, k is the expansion coefficient (wake diffusion coefficient), and x is the downstream distance.

[0166] The processing module 203 represents the wake diffusion width according to the Gaussian distribution, and the specific expression is as follows:

[0167] σ x (x) = σ x,0 + ε·x

[0168] where σ x (x) is the wake diffusion width in the lateral direction, 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] where k eff is the adjusted expansion coefficient, k is the expansion coefficient, k h is the elevation correction factor, and k r is the roughness correction factor.

[0172] The processing module 203 is configured to introduce a roughness correction factor and a slope correction factor into the basic model based on the influence of slope and roughness on wake diffusion, and the expression is as follows:

[0173] σ x (x) = σ x,0 + ε·k s ·k r ·x

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

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

[0176]

[0177] Among them, U(x, y, z) is the wind speed at any point in the wake region, and U 0 is the free-stream wind speed, and U def (x) is the wake velocity deficit, and 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 cells, input real-time wind speed and wind direction data as boundary conditions and initial conditions, and drive the correction model for dynamic solution.

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

[0180]

[0181] Among them, 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 the momentum density, p is the pressure, μ is the dynamic viscosity coefficient, is the Laplacian operator of the velocity vector, and F terrain is the terrain correction force term.

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

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

[0187]

[0188] Among them, D is the error value, N is the number of samples, and Usim (x i ) is the predicted wind speed, and 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. If it is determined that the error value is less than or equal to the preset threshold, the correction model is deployed to the wind farm control system.

[0190] In a possible implementation manner, the processing module 203 is used to calculate the elevation difference between the fan and each target point according to the obtained positions of the fan and multiple target points.

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

[0192] The processing module 203 is used to divide the wind farm area into multiple grid cells.

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

[0194] It should be noted that: when the device provided in the above embodiment realizes its functions, only the above-mentioned division of each functional module is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is 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 embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which 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] Among them, the communication bus 302 is used to realize the connection and communication between these components.

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

[0198] Among them, the network interface 304 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

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

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

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

[0202] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0203] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0204] In several embodiments provided by this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, 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 displayed or discussed mutual coupling or direct coupling or communication connection can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

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

[0206] In addition, the functional units in each embodiment of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0207] When 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, in essence, 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. This computer software product is stored in a memory 305 and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present application. The aforementioned memory 305 includes various media that can store program codes, such as USB flash drives, mobile hard disks, magnetic disks, or optical discs.

[0208] The present application also discloses a computer-readable storage medium that stores instructions. When executed by one or more processors 301, it causes the electronic device to execute one or more of the methods as described in the above embodiments.

[0209] The above are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereby. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. Those skilled in the art will readily think of other implementation manners of the present disclosure after considering the specification and the practice of the disclosure. The present application aims to cover any variations, uses, or adaptive changes of the present disclosure, and these variations, uses, or adaptive changes follow the general principles of the present disclosure and include the common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for analyzing the dynamic wake of a wind turbine 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 according to the terrain parameters; Using a pre-selected analytical wake model as a basic model, inputting the terrain correction factor into the basic 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 the prediction result of the modified model; 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.

2. A method for analyzing dynamic wake of wind turbines in a wind farm 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 method for analyzing dynamic wake of wind turbines in a wind farm according to claim 1, characterized in that: The pre-selected analytical wake model is used as the basic model, specifically including: The wake velocity defect formula is used to represent 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. The specific expression is as follows: s x (x)=σ x,0 +e·x 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 method for analyzing dynamic wake of wind turbines in a wind farm according to claim 3, characterized in that: The inputting the terrain correction factor into the basic model to obtain the 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 and corrected 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.

5. A method for analyzing dynamic wake of wind turbines in a wind farm according to claim 1, characterized in that: The introducing of real-time wind speed and wind 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 unit, the velocity distribution and turbulence intensity of the wake at different spatial positions are calculated by solving fluid mechanics equations to generate prediction results, wherein the fluid mechanics equations include a continuity equation and a momentum equation; The wind speed distribution, turbulence intensity and diffusion form of the wake are output to obtain the prediction result.

6. A method for analyzing dynamic wake of wind turbines in a wind farm 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.

7. A method for analyzing dynamic wake of wind turbines in a wind farm according to claim 1, characterized in that: The extracting of terrain parameters from the terrain data specifically includes: 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; According to the surface cover data of the wind farm area, matching the roughness length value according to the surface type; 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.

8. A wind farm wind turbine dynamic wake analysis system, characterized in that: 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 terrain 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 according to 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.

9. An electronic device, characterized in that: The electronic device comprises a processor (301), a communication bus (302), a memory (305), 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 7.

10. 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 7 is performed.

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