Plateau mountain wind resource calculation method and device based on large vortex simulation

Abstract

The embodiment of the invention provides a plateau mountain wind resource calculation method and device based on large eddy simulation, and the method comprises the steps: obtaining the atmospheric potential temperature, density, surface roughness and thermal inversion layer height of a plateau atmospheric boundary layer in a first preset target region; obtaining wind speed data of a plateau atmospheric boundary layer according to the atmospheric potential temperature, the density, the surface roughness, the thermal inversion layer height and a preset deflection angle; acquiring topographic data, hub height wind speed, annual average wind speed of the anemometer tower and wind acceleration factors of the anemometer tower in a second preset target area; and according to the wind speed data, the topographic data, the hub height wind speed, the annual average wind speed of the anemometer tower, the wind acceleration factor of the anemometer tower and a preset point location, obtaining the actual annual average wind speed of the preset point location in a second preset target area. Therefore, the wind resource calculation result is more accurate, and the site selection of the wind power plant in the plateau area is optimized.

Description

Technical Field

[0001] This document relates to the field of fluid mechanics technology, and in particular to a method and device for calculating plateau mountain wind resources based on large eddy simulation. Background Art

[0002] Air flow in high-altitude areas is more active than in plain areas, and wind speeds are usually higher than in plain areas. The technological development of wind energy resources in China's high-altitude areas can reach 600 million kilowatts, accounting for about 15% of the country's total. Wind energy resources are extremely rich, and the development potential of plateau wind farms is very outstanding.

[0003] The existing technology mainly uses the RANS method to establish a geometric model of the calculation domain, set boundary conditions, select a suitable turbulence model, and perform numerical simulations to ultimately obtain parameters such as wind speed and turbulence intensity, and combine these parameters to select the site of the wind farm.

[0004] However, the existing RANS method cannot simulate turbulent conditions and requires the introduction of additional turbulence models, which cannot accurately describe the anisotropic turbulence in the plateau boundary layer. In addition, the recirculation zone involves flow separation, reattachment and strong vortex evolution. The RANS model has difficulty capturing the dynamic changes of the separation point and the transient behavior of the vortex, resulting in large calculation errors in the highly turbulent atmospheric boundary layer and the recirculation zone, making it difficult to accurately and quickly evaluate the wind resource parameters in the plateau area, which in turn affects the site selection of wind farms. Summary of the Invention

[0005] In view of the above scheme, the present application aims to propose a method and device for calculating plateau mountain wind resources based on large eddy simulation to solve at least one of the above technical problems.

[0006] In a first aspect, one or more embodiments of this specification provide a method for calculating plateau mountain wind resources based on large eddy simulation, including:

[0007] Obtaining atmospheric potential temperature, density, surface roughness, and inversion layer height of the plateau atmospheric boundary layer within the first preset target area;

[0008] Obtaining wind speed data of the plateau atmospheric boundary layer according to the atmospheric potential temperature, the density, the surface roughness, the inversion layer height, and a preset deflection angle;

[0009] Obtaining terrain data, hub height wind speed, annual average wind speed of the wind tower, and wind acceleration factor of the wind tower within a second preset target area;

[0010] The actual annual average wind speed of the preset point in the second preset target area is obtained according to the wind speed data, the terrain data, the hub height wind speed, the annual average wind speed of the wind tower, the wind acceleration factor of the wind tower and the preset point.

[0011] Furthermore, it also includes:

[0012] Get the wind turbine hub height;

[0013] Obtaining an average wind speed vector of a first preset target area according to the hub height and the wind speed data;

[0014] Calculating the actual deflection angle of the wind speed in the plateau atmospheric boundary layer based on the average wind speed vector of the first preset target area;

[0015] The wind speed data of the plateau atmospheric boundary layer is corrected according to the actual deflection angle of the wind speed.

[0016] Furthermore, the calculation formula of the average wind speed vector of the first preset target area is as follows:

[0017]

[0018] Among them, u avg It represents the average wind speed in the first preset target area and can be expressed as

[0019] t represents time;

[0020] t2 represents the time to end data collection after the flow field has fully developed;

[0021] t1 represents the time when data collection begins after the flow field has fully developed;

[0022] y represents the spanwise coordinate of the computational domain;

[0023] y max Indicates the maximum spanwise coordinate value of the computational domain;

[0024] y0 represents the minimum spanwise coordinate value of the computational domain;

[0025] x represents the flow direction coordinate of the computational domain;

[0026] x max Indicates the maximum coordinate value of the flow direction in the calculation domain;

[0027] x0 represents the minimum coordinate value of the flow direction in the calculation domain;

[0028] z represents the vertical coordinate of the computational domain;

[0029] z hub The vertical coordinate representing the hub height of the computational domain;

[0030] u represents the instantaneous wind speed vector of the entire field collected after the large eddy simulation of the atmospheric boundary layer over the plateau;

[0031] u(x,y,z hub, t) represents the instantaneous wind speed at the hub height collected after the large eddy simulation of the plateau atmospheric boundary layer in the first preset target area.

[0032] Furthermore, the calculation formula for the actual deflection angle of the wind speed in the plateau atmospheric boundary layer is as follows:

[0033]

[0034] Among them, θ represents the actual deflection angle of wind speed;

[0035] Indicates the average wind speed in the span direction in the first preset target area;

[0036] Indicates the average wind speed in the first preset target area.

[0037] Furthermore, obtaining the actual annual average wind speed of the preset point in the second preset target area based on the wind speed data, the terrain data, the hub height wind speed, the annual average wind speed of the wind tower, the wind acceleration factor of the wind tower, and the preset point includes:

[0038] Obtaining a terrain wind acceleration factor based on the hub height wind speed, wind speed data, and terrain data;

[0039] Determining a wind acceleration factor at a preset point based on the preset point and the terrain wind acceleration factor;

[0040] The actual annual average wind speed of the preset point in the second preset target area is obtained according to the wind acceleration factor of the preset point, the annual average wind speed of the wind measurement tower, and the wind acceleration factor of the wind measurement tower.

[0041] Furthermore, the calculation formula for the actual annual average wind speed of the preset point in the second preset target area is as follows:

[0042]

[0043] Among them, U(x,y,z) represents the actual annual average wind speed at the preset point (x,y,z);

[0044] U t_avg It indicates the annual average wind speed in a certain wind direction at the location of the wind tower;

[0045] ΔS(x t ,y t ,z t ) represents the location of the wind tower (x t ,y t ,z t ) wind acceleration factor;

[0046] ΔS(x,y,z) represents the wind acceleration factor at the preset point (x,y,z).

[0047] In a second aspect, an embodiment of the present application provides a device for calculating plateau mountain wind resources based on large eddy simulation, comprising:

[0048] The first acquisition module is used to obtain the atmospheric potential temperature, density, surface roughness and inversion layer height of the plateau atmospheric boundary layer in the first preset target area;

[0049] A first wind speed calculation module is used to obtain wind speed data of the plateau atmospheric boundary layer according to the atmospheric potential temperature, the density, the surface roughness, the inversion layer height and a preset deflection angle;

[0050] A second acquisition module is used to obtain terrain data, hub height wind speed, annual average wind speed of the wind tower and wind acceleration factor of the wind tower within a second preset target area;

[0051] The second wind speed calculation module is used to obtain the actual annual average wind speed of the preset point in the second preset target area based on the wind speed data, the terrain data, the hub height wind speed, the annual average wind speed of the wind measurement tower, the wind acceleration factor of the wind measurement tower and the preset point.

[0052] Furthermore, it also includes a wind speed correction module,

[0053] Get the wind turbine hub height;

[0054] Obtaining an average wind speed vector of a first preset target area according to the hub height and the wind speed data;

[0055] Calculating the actual deflection angle of the wind speed in the plateau atmospheric boundary layer based on the average wind speed vector of the first preset target area;

[0056] The wind speed data of the plateau atmospheric boundary layer is corrected according to the actual deflection angle of the wind speed.

[0057] Furthermore, the wind speed calculation module is used to calculate the average wind speed vector of the first preset target area;

[0058]

[0059] Among them, u avg It represents the average wind speed in the first preset target area and can be expressed as

[0060] t represents time;

[0061] t2 represents the time to end data collection after the flow field has fully developed;

[0062] t1 represents the time when data collection begins after the flow field has fully developed;

[0063] y represents the spanwise coordinate of the computational domain;

[0064] y max Indicates the maximum spanwise coordinate value of the computational domain;

[0065] y0 represents the minimum spanwise coordinate value of the computational domain;

[0066] x represents the flow direction coordinate of the computational domain;

[0067] x max Indicates the maximum coordinate value of the flow direction in the calculation domain;

[0068] x0 represents the minimum coordinate value of the flow direction in the calculation domain;

[0069] z represents the vertical coordinate of the computational domain;

[0070] z hub The vertical coordinate representing the hub height of the computational domain;

[0071] u represents the instantaneous wind speed vector of the entire field collected after the large eddy simulation of the atmospheric boundary layer over the plateau;

[0072] u(x,y,z hub , t) represents the instantaneous wind speed at the hub height collected after the large eddy simulation of the plateau atmospheric boundary layer in the first preset target area.

[0073] In a third aspect, an embodiment of the present application provides a storage medium for storing computer-executable instructions, characterized in that when the computer-executable instructions are executed, the steps of calculating plateau mountain wind resources based on large eddy simulation as described in any one of the first aspects are implemented.

[0074] Compared with the existing technology, this application can at least achieve the following technical effects:

[0075] This application can use the deflection angle to eliminate stripes within the first preset target area during simulation, making the calculation results more accurate. The actual simulated wind speed data is used as the inflow for the second preset target area, thereby more accurately obtaining the actual annual average wind speed at each point, providing support for both macro- and micro-site selection of wind farms in plateau areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0077] Figure 1 A flow chart of a method for calculating plateau mountain wind resources based on large eddy simulation for one or more embodiments of this specification;

[0078] Figure 2 A schematic diagram of the striping effect provided for one or more embodiments of this specification.

[0079] Figure 3 A schematic diagram illustrating the effect of uneven wind speed caused by strips provided in one or more embodiments of this specification.

[0080] Figure 4 A schematic diagram of eliminating the striping effect by deflecting the angle provided in one or more embodiments of this specification.

[0081] Figure 5 A schematic diagram of the wind speed effect of adding a deflection angle provided in one or more embodiments of this specification.

[0082] Figure 6 A schematic structural diagram of a plateau mountain wind resource calculation device based on large eddy simulation provided in one or more embodiments of this specification. DETAILED DESCRIPTION

[0083] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below in conjunction with the drawings in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this document.

[0084] With the continuous development of my country's new energy industry, wind farm development in low-altitude plains is approaching saturation, and an increasing number of wind farms are being built in high-altitude mountainous areas. Due to the lower air density and the prevalence of mountainous terrain, wind resource assessment methods in high-altitude areas differ significantly from those in flat terrain. At the beginning of wind farm construction, wind towers are typically used to measure wind speeds at a few key locations. These speeds are then used to infer wind resources for the rest of the area. (In other words, existing technology involves installing a wind tower at a specific location when building a wind farm in a specific area and using it to measure wind speed data for 1-3 years. However, since wind towers cannot be installed at all locations, if a tower is located on a mountain and 20 wind turbines are to be deployed, the wind speed data below the mountain is unknown.) Currently, there are two main technical approaches to commonly used commercial wind resource assessment software. The first is linear wind resource processing software, represented by WASP, Windfarmer, and Windpro. This type of software uses linear models to account for the impact of complex terrain on wind resources. While fast, it suffers from lower accuracy. In particular, the linear model parameters are typically derived at normal temperature and pressure, further reducing accuracy. The second type of software uses CFD technology to solve wind resources, such as WT and Windsim. However, the wind farm inflow in the calculation process is calculated using a model, and all use the RANS method, which does not adequately simulate the low density, high cold, and high turbulence characteristics of the atmospheric boundary layer in plateau areas. In particular, when calculating turbulence intensity, the RANS method uses a turbulence intensity model to simulate the high turbulence atmospheric boundary layer and the recirculation zone caused by terrain, which will produce certain errors. This will affect the selection of wind turbine placement location (wind speeds can be high or low at different locations. High wind speeds have higher power generation efficiency and can generate more electricity; low wind speeds have relatively poor power generation efficiency and generate relatively less power). Therefore, the choice of wind turbine placement location will affect power generation efficiency.

[0085] In response to the above technical problems, this application proposes a method for calculating plateau mountain wind resources based on large eddy simulation, such as Figure 1 The specific steps are as follows:

[0086] Step S1: Obtain the atmospheric potential temperature, density, surface roughness and inversion layer height of the plateau atmospheric boundary layer in a first preset target area.

[0087] In an embodiment of the present application, first, a first preset target area is determined. The actual temperature and pressure of the air parcel are obtained through a weather station, a sounding instrument, or a weather satellite. The atmospheric potential temperature and air density are calculated using the actual temperature and pressure. Second, a land use classification map of the first preset target area is obtained. GIS software is used to process the remote sensing data, mapping different surface types to known roughness lengths to obtain the surface roughness of the target area. Finally, a wind profiler radar can be used to measure the vertical distribution of atmospheric wind speed and direction, and the height of the inversion layer can be determined by combining the temperature-wind speed relationship.

[0088] For example, if a user wants to install a wind farm in the Himalayas, they can select a large area with the Himalayas as the target for large eddy simulation and calculate the wind speed data of the atmospheric boundary layer of the entire Qinghai-Tibet Plateau. Calculating the wind speed data for a large area first can solve the problem of large deviations in the subsequent wind resource data calculation results caused by the logarithmic wind speed data (false) simulated by formulas in the existing technology.

[0089] Step S2: obtaining wind speed data of the plateau atmospheric boundary layer according to the atmospheric potential temperature, the density, the surface roughness, the inversion layer height, and a preset deflection angle.

[0090] In the embodiment of the present application, first, an idealized computational domain is constructed based on large eddy simulation (for example, using CFD computational software such as OpenFOAM, LESGO, and fluent (less common) for simulation), wherein the computational domain ignores the actual terrain undulation data of the plateau area, and assumes that the plateau is a computational domain without terrain, completely flat, and infinite; then, the flow direction and span direction data of the computational domain are set (to achieve simulation of an infinite area), vertical height, and grid resolution, and a grid is drawn (for example, a rectangular area with a length of 5 km, a width of 5 km, and a height of 1 km, and a grid resolution of less than 10 m); then, according to the simulated height, the idealized computational domain is constructed based on the large eddy simulation. The original atmospheric boundary conditions set information such as atmospheric potential temperature, density, inversion layer height, surface roughness and deflection angle. Among them, the deflection angle refers to a given angle when simulating the atmospheric boundary layer, which can prevent low-speed micro-clusters in the flow field from passing through the low-speed zone again, but passing through other paths, thereby eliminating low-speed strips; the deflection angle range is 1-45 degrees, and the deflection angle is generally set to 3 degrees. The selected angle is essentially to allow the low-speed zone fluid to come out from the outlet and re-enter the inlet through the periodic boundary conditions without passing through the previous low-speed zone; the smaller the wind speed angle, the smaller the lateral displacement of the fluid micro-clusters, and the more uniform the flow field development will be, but at the same time it will take longer.

[0091] Secondly, according to the wind speed characteristics of the plateau atmospheric boundary layer that need to be simulated, the pressure-driven layer height and wind speed are set, and the large eddy simulation subgrid model is selected. This application uses the Lagrangian dynamic model.

[0092] Finally, select a large eddy simulation solver with potential temperature capability and set the time step. The time step size is related to the grid size; smaller grids result in smaller time steps. Solve the flow field for each time step. Calculate for a period of time, then collect wind speed data after the flow field stabilizes. In this application, we collected wind speed data from 20,000s to 30,000s after the calculation stabilized after 20,000s.

[0093] Specifically, when using large eddy simulation to simulate the atmospheric boundary layer of the plateau, periodic boundary conditions need to be set to ensure that the flow field is fully developed. The periodic boundary condition uses the outlet flow velocity as the inlet flow velocity to achieve the simulation of an infinite area. When using periodic boundary conditions, since the fluid particles at the outlet will return to the same spanwise position of the inlet, and the flow field velocity direction is in the x-direction, the fluid will not undergo a large displacement in the y-direction. When the fluid velocity at a certain spanwise position is disturbed and becomes smaller, the fluid velocity at the outlet and inlet of the computational domain will decrease, which will result in a strip with lower wind speed at a certain spanwise position. The strip phenomenon is such as Figure 2 、 Figure 3 As shown, Figure 2 The distribution data of wind speed on the calculation domain plane at a hub height of 100 meters was simulated. At the same height, the wind speed is not constant with the change of span and vertical directions, which is the striping effect. The positive flow of wind speed will cause the striping effect. Figure 3 It indicates the wind speed at different heights. Some of the wind speeds are small while most of the wind speeds are small. This is because the wind speed is uneven due to the strip effect.

[0094] The fundamental reason for the appearance of stripes is that the periodic boundary conditions between the outlet and the inlet cause the atmospheric micro-clusters with smaller flow rates to be in the area with smaller wind speeds during the circulation process. This disturbance is continuously amplified during the fluid circulation process. The most direct way to solve the problem is to set the fluid direction so that the fluid flow direction does not coincide with the x-direction of the calculation domain, but to give the fluid a smaller angle (deflection angle). In this way, the fluid micro-clusters will not return to the previous inlet position during the periodic circulation process, but will traverse the entire calculation domain, thus eliminating the influence of stripes during the atmospheric boundary layer simulation process. Figure 4 、 Figure 5 shown. Figure 4 Represents streamlines, which are the flow direction of wind speed data in the flow field. Different lines represent different wind speeds. The oblique direction of the streamlines is the addition of a 3° deflection angle during large eddy simulation, which can eliminate the striping effect. Figure 5 This means that the wind speed data is more average and has less fluctuation after adding the deflection angle.

[0095] In this application, by giving a deflection angle when simulating the atmospheric boundary layer, the low-speed micro-clusters in the flow field no longer pass through the low-speed zone again, thereby eliminating the low-speed strips. Among them, the strips refer to the actual simulation of the plateau atmospheric boundary layer. Since its grid cannot be drawn to infinity, periodic boundary conditions are used. The periodic boundary conditions force the fluid micro-clusters flowing out of the calculation domain to return to the inlet. When the wind speed of the outflowing micro-clusters is reduced by unavoidable small disturbances, the slow micro-clusters return to the inlet, resulting in a fluid micro-clusters with lower speeds from upstream to downstream at this position. From a macroscopic point of view, a low-speed strip appears in the span direction. Since strips do not exist in real life, but in the simulation, since the grid cannot be infinitely large, the calculated results will lack accuracy when calculating wind farm data.

[0096] Step S3: obtaining terrain data, hub height wind speed, annual average wind speed at the wind tower, and wind acceleration factor at the wind tower within the second preset target area.

[0097] In an embodiment of the present application, high-resolution digital elevation model (DEM) data is downloaded through a geographic information system (GIS) platform to obtain terrain data such as the slope and aspect of the second preset target area. Numerical simulation is performed using the terrain data within the target area to obtain the wind speed at the hub height. Historical data of existing wind towers in the target area are queried to obtain the annual average wind speed of the wind towers in the target area (for example, according to the wind tower observation report published by the Wind and Solar Energy Resource Center of the China Meteorological Administration, the annual average wind speed at an altitude of 80 meters at the Nagqu wind tower (4,500 meters above sea level) on the Qinghai-Tibet Plateau is approximately 6.2 m / s). Wind speed data at the top of the wind tower is then obtained from the historical data. Based on the wind speed data at the top of the tower and the wind speed data at the hub height, a wind acceleration factor is obtained for the wind tower. The wind acceleration factor is used to calculate wind speed data at different locations. In actual calculations, due to the different wind speeds at various locations in the plateau, it is difficult to simulate the flow field size at all wind speeds. Therefore, only the flow field at one wind speed is calculated. When the wind speeds are different, the wind acceleration factor is used to calculate the wind speed on different terrains.

[0098] Step S4, obtaining the actual annual average wind speed of the preset point in the second preset target area according to the wind speed data, the terrain data, the annual average wind speed of the wind tower, the wind acceleration factor of the wind tower and the preset point.

[0099] In an embodiment of the present application, a terrain wind acceleration factor is obtained based on the hub height wind speed, wind speed data and the terrain data; the wind acceleration factor of the preset point is determined based on the preset point and the terrain wind acceleration factor; and the actual annual average wind speed of the preset point in the second preset target area is obtained based on the wind acceleration factor of the preset point, the annual average wind speed of the wind measurement tower and the wind acceleration factor of the wind measurement tower.

[0100] For example: the wind speed at the hub height is 8m / s, and the wind speed data at the top of the mountain is 16m / s. The wind acceleration factor is calculated as 16 / 8, so the wind speed at the top of the mountain is twice the wind speed at the hub height, and the wind acceleration factor is 2. Similarly, the wind acceleration factor can be obtained if there is wind speed. The wind acceleration factor is normalized to obtain the terrain wind speed acceleration factor at each location.

[0101] Specifically, the GIS terrain data for the second target area, including the plateau and mountainous regions, was imported into the new CFD calculation software. A corresponding grid was drawn to capture the terrain features. Using processed atmospheric boundary layer wind speed data as the incoming flow, a refined flow field simulation was conducted on the plateau and mountainous regions of the target area to determine the wind speed across the entire plateau and mountainous regions. The calculated wind speed for the plateau and mountainous regions was then combined with the measured wind speed data at a specific point to calculate the plateau wind resource data.

[0102] Due to the presence of terrain in the second preset target, an unstructured grid is used in the computational domain during modeling. The wind speed data on the grid points are not evenly arranged in space. To facilitate wind speed processing, the wind speed field in the entire computational domain of the plateau mountainous area is rearranged structurally, and m, n, and k new grids are created for the flow direction, span direction, and vertical direction of the rectangular computational domain, respectively. The sizes of m, n, and k are determined according to the accuracy requirements. If the data accuracy is higher, the grid quantity needs to be increased. Due to the existence of the mountain, the grid points of the structured grid will inevitably fall inside the mountain. There is no wind speed at these grid points and they are meaningless. However, for the convenience of calculation, the wind speed at the grid points inside the mountain is set to 0, and the wind speed data of the grid points outside the mountain are interpolated using the wind speed data on the unstructured grid. Finally, the structured wind speed data in the entire computational domain is obtained. The calculation formula is as follows:

[0103]

[0104] Among them, U inner Indicates the wind speed at a point located inside the mountain;

[0105] U out (x, y, z) represents the wind speed at a point outside the mountain;

[0106] represents the time-averaged wind speed of the plateau mountain flow field calculated using large eddy simulation.

[0107] Taking the hub height wind speed as the benchmark, the actual wind speed is normalized to obtain the terrain acceleration factor at each position in the space. The calculation formula is as follows:

[0108]

[0109] Where ΔS(x,y,z) represents the terrain wind speed acceleration factor at position coordinates x,y,z;

[0110] U hub represents the wind speed at the hub height in the atmospheric boundary layer calculation;

[0111] U out (x,y,z) represents the wind speed at the point with coordinates x,y,z outside the mountain.

[0112] Finally, the wind resource conditions at various locations in the plateau and mountainous areas are obtained by using the actual wind measurement data and the local wind speed acceleration ratio at the location of the wind tower. The calculation formula is as follows:

[0113]

[0114] Among them, U(x,y,z) represents the actual annual average wind speed at the preset point (x,y,z);

[0115] U t_avg It indicates the annual average wind speed in a certain wind direction at the location of the wind tower;

[0116] ΔS(x t ,y t ,z t ) represents the wind acceleration factor of the wind tower;

[0117] ΔS(x,y,z) represents the wind acceleration factor at the preset point (x,y,z).

[0118] At this time, the wind resource situation under one wind direction is calculated. By rotating the plateau mountain grid within the calculation domain, the wind resource situation under different incoming wind directions can be simulated. In this application, since the actual wind direction will not be from only one direction, the flow field data on the terrain under 16 wind directions is simulated by rotating the mountain grid, and each wind direction is calculated once. For example: the left side of the grid is the wind speed inlet, the right side of the grid is the wind speed outlet, and the middle of the grid is the terrain. The terrain is rotated, and the positions of the inlet and outlet remain unchanged. As the terrain changes, the wind direction also changes accordingly. Each time the terrain is rotated 22.5 degrees, 16 wind resource conditions will be obtained.

[0119] Furthermore, the hub height of the wind turbine is obtained; based on the hub height and the wind speed data, the average wind speed vector of the first preset target area is obtained; based on the average wind speed vector of the first preset target area, the actual deflection angle of the wind speed in the plateau atmospheric boundary layer is calculated; based on the actual deflection angle of the wind speed, the wind speed data of the plateau atmospheric boundary layer is corrected.

[0120] Specifically, after simulating the high-altitude atmospheric boundary layer over a large area (the first preset target area) using large eddy simulation, the high-altitude atmospheric boundary layer wind speed data stored for a period of time is used for high-altitude mountain wind resource calculation. However, since there is a certain angle between the atmospheric boundary layer wind speed and the flow direction, and the incoming flow angle needs to be precisely controlled to determine the wind speed during wind resource calculation, the saved plateau atmospheric boundary layer wind resource data needs to be processed (that is, since the stripes in the atmospheric boundary layer simulation process need to be eliminated in step S2, a deflection angle is added, but there is actually no deflection angle, so the plateau atmospheric boundary layer wind speed data calculated for the first preset target area needs to be corrected).

[0121] First, all wind speed data on the plane at hub height are selected for temporal and spatial averaging to obtain the average wind speed on the plane at hub height that changes with time. Then, the data at all times are averaged to obtain the average wind speed in different directions of the first preset target area. The calculation formula is as follows:

[0122]

[0123] Among them, u avg It represents the average wind speed in the first preset target area and can be expressed as

[0124] t represents time;

[0125] t2 represents the time to end data collection after the flow field has fully developed;

[0126] t1 represents the time when data collection begins after the flow field has fully developed;

[0127] y represents the spanwise coordinate of the computational domain;

[0128] y max Indicates the maximum spanwise coordinate value of the computational domain;

[0129] y0 represents the minimum spanwise coordinate value of the computational domain;

[0130] x represents the flow direction coordinate of the computational domain;

[0131] x max Indicates the maximum coordinate value of the flow direction in the calculation domain;

[0132] x0 represents the minimum coordinate value of the flow direction in the calculation domain;

[0133] z represents the vertical coordinate of the computational domain;

[0134] z hub The vertical coordinate representing the hub height of the computational domain;

[0135] u represents the instantaneous wind speed vector of the entire field collected after the large eddy simulation of the atmospheric boundary layer over the plateau;

[0136] u(x,y,z hub , t) represents the instantaneous wind speed at the hub height collected after the large eddy simulation of the plateau atmospheric boundary layer in the first preset target area.

[0137] Secondly, through the vector Calculate the actual deflection angle θ of the atmospheric boundary layer using the following formula:

[0138]

[0139] Among them, u y represents the wind speed component in the y direction;

[0140] u x represents the wind speed component in the x direction;

[0141] θ represents the actual deflection angle of the wind speed (theoretically, this angle is a set value, but there will be a slight deviation in the actual calculation, so the atmospheric boundary layer data is used for calculation);

[0142] express The average wind speed in the spanwise and streamwise directions.

[0143] Finally, the wind direction correction is performed on the wind speed variables of all time steps obtained in step S2 to ensure that the wind direction of the average wind speed is in the x direction of the calculation domain.

[0144]

[0145] Among them, v ′ represents the plateau atmospheric boundary layer wind speed after the wind speed angle is corrected;

[0146] R Z (θ) represents the coordinate transformation matrix;

[0147] u x ,u y ,u z Represents the instantaneous wind speed in the x, y, and z directions of the atmospheric boundary layer in large eddy simulation.

[0148] In this application, since the deviation angle may deviate from the preset value during the simulation process, the deviation angle is recalculated to make the final wind resource calculation result more accurate.

[0149] The embodiment of the present application provides a device for calculating plateau mountain wind resources based on large eddy simulation, such as Figure 6 Shown, including:

[0150] The first acquisition module 101 is used to obtain the atmospheric potential temperature, density, surface roughness and inversion layer height of the plateau atmospheric boundary layer in the first preset target area;

[0151] A first wind speed calculation module 102 is configured to obtain wind speed data of the plateau atmospheric boundary layer according to the atmospheric potential temperature, the density, the surface roughness, the inversion layer height, and a preset deflection angle;

[0152] The second acquisition module 103 is used to obtain terrain data, hub height wind speed, annual average wind speed of the wind tower and wind acceleration factor of the wind tower within the second preset target area;

[0153] The second wind speed calculation module 104 is configured to obtain the actual annual average wind speed of the preset point in the second preset target area based on the wind speed data, the terrain data, the hub height wind speed, the annual average wind speed of the wind tower, the wind acceleration factor of the wind tower, and the preset point.

[0154] Furthermore, the wind speed correction module is used to obtain the wind turbine hub height;

[0155] Obtaining an average wind speed vector of a first preset target area according to the hub height and the wind speed data;

[0156] Calculating the actual deflection angle of the wind speed in the plateau atmospheric boundary layer based on the average wind speed vector of the first preset target area;

[0157] The wind speed data of the plateau atmospheric boundary layer is corrected according to the actual deflection angle of the wind speed.

[0158] Furthermore, the wind speed calculation module is used to calculate the average wind speed vector of the first preset target area;

[0159]

[0160] Among them, u avg It represents the average wind speed in the first preset target area and can be expressed as

[0161] t represents time;

[0162] t2 represents the time to end data collection after the flow field has fully developed;

[0163] t1 represents the time when data collection begins after the flow field has fully developed;

[0164] y represents the spanwise coordinate of the computational domain;

[0165] y max Indicates the maximum spanwise coordinate value of the computational domain;

[0166] y0 represents the minimum spanwise coordinate value of the computational domain;

[0167] x represents the flow direction coordinate of the computational domain;

[0168] x max Indicates the maximum coordinate value of the flow direction in the calculation domain;

[0169] x0 represents the minimum coordinate value of the flow direction in the calculation domain;

[0170] z represents the vertical coordinate of the computational domain;

[0171] z hub The vertical coordinate representing the hub height of the computational domain;

[0172] u represents the instantaneous wind speed vector of the entire field collected after the large eddy simulation of the atmospheric boundary layer over the plateau;

[0173] u(x,y,z hub , t) represents the instantaneous wind speed at the hub height collected after the large eddy simulation of the plateau atmospheric boundary layer in the first preset target area.

[0174] An embodiment of the present application provides a storage medium for storing computer-executable instructions, characterized in that when the computer-executable instructions are executed, the steps of calculating plateau mountain wind resources based on large eddy simulation described in any one of the above embodiments are implemented.

[0175] It should be noted that the embodiment of the storage medium in this specification and the embodiment of the method for calculating plateau mountain wind resources based on large eddy simulation in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can refer to the aforementioned corresponding implementation of the method for calculating plateau mountain wind resources based on large eddy simulation, and the repeated parts will not be repeated.

[0176] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0177] In the 1930s, technological improvements could be clearly distinguished as either hardware improvements (for example, improvements to circuit structures like diodes, transistors, and switches) or software improvements (improvements to process flows). However, with the advancement of technology, many process flow improvements today can now be considered direct improvements to hardware circuit structures. Designers almost always create the corresponding hardware circuit structure by programming the improved process flow into the hardware circuit. Therefore, it cannot be said that a process flow improvement cannot be implemented using hardware modules. For example, a programmable logic device (PLD), such as a field programmable gate array (FPGA), is an integrated circuit whose logical function is determined by user programming. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to hire a chip manufacturer to design and manufacture a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly done using "logic compiler" software. This is similar to the software compiler used when developing programs. Before compilation, the original code must also be written in a specific programming language, called a hardware description language (HDL). There is not just one HDL, but many, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art will also understand that by simply programming the method flow in one of these hardware description languages and then programming it into an integrated circuit, a hardware circuit that implements the logic method flow can be easily obtained.

[0178] The controller can be implemented in any suitable manner. For example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also know that in addition to implementing the controller in a purely computer-readable program code format, the controller can be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules that implement the method and structures within the hardware component.

[0179] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0180] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing the embodiments of this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0181] Those skilled in the art will appreciate that one or more embodiments of this specification may be provided as a method, system, or computer program product. Thus, one or more embodiments of this specification may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0182] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0183] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0184] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0185] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0186] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0187] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0188] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0189] One or more embodiments of this specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. One or more embodiments of this specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0190] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0191] The foregoing description is merely an example of the present invention and is not intended to limit the present invention. Persons skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be included within the scope of the claims herein.

Claims

1. A method for calculating plateau mountain wind resources based on large eddy simulation, characterized by include: Obtaining atmospheric potential temperature, density, surface roughness, and inversion layer height of the plateau atmospheric boundary layer within the first preset target area; Obtaining wind speed data of the plateau atmospheric boundary layer according to the atmospheric potential temperature, the density, the surface roughness, the inversion layer height, and a preset deflection angle; Obtaining terrain data, hub height wind speed, annual average wind speed of the wind tower, and wind acceleration factor of the wind tower within a second preset target area; The actual annual average wind speed of the preset point in the second preset target area is obtained according to the wind speed data, the terrain data, the hub height wind speed, the annual average wind speed of the wind tower, the wind acceleration factor of the wind tower and the preset point.

2. The method according to claim 1, characterized in that The method further comprises: Get the wind turbine hub height; Obtaining an average wind speed vector of a first preset target area according to the hub height and the wind speed data; Calculating the actual deflection angle of the wind speed in the plateau atmospheric boundary layer based on the average wind speed vector of the first preset target area; The wind speed data of the plateau atmospheric boundary layer is corrected according to the actual deflection angle of the wind speed.

3. The method according to claim 2, characterized in that The calculation formula of the average wind speed vector of the first preset target area is as follows: Among them, u avg It represents the average wind speed in the first preset target area and can be expressed as t represents time; t2 represents the time to end data collection after the flow field has fully developed; t1 represents the time when data collection begins after the flow field has fully developed; y represents the spanwise coordinate of the computational domain; y max Indicates the maximum spanwise coordinate value of the computational domain; y0 represents the minimum spanwise coordinate value of the computational domain; x represents the flow direction coordinate of the computational domain; x max Indicates the maximum coordinate value of the flow direction in the calculation domain; x0 represents the minimum coordinate value of the flow direction in the calculation domain; z represents the vertical coordinate of the computational domain; z hub The vertical coordinate representing the hub height of the computational domain; u represents the instantaneous wind speed vector of the entire field collected after the large eddy simulation of the atmospheric boundary layer over the plateau; u(x,y,z hub , t) represents the instantaneous wind speed at the hub height collected after the large eddy simulation of the plateau atmospheric boundary layer in the first preset target area.

4. The method according to claim 2, characterized in that The calculation formula for the actual deflection angle of the wind speed in the plateau atmospheric boundary layer is as follows: Among them, θ represents the actual deflection angle of wind speed; Indicates the average wind speed in the span direction in the first preset target area; Indicates the average wind speed in the first preset target area.

5. The method according to claim 1, wherein Obtaining the actual annual average wind speed of the preset point in the second preset target area according to the wind speed data, the terrain data, the hub height wind speed, the annual average wind speed of the wind tower, the wind acceleration factor of the wind tower, and the preset point includes: Obtaining a terrain wind acceleration factor based on the hub height wind speed, wind speed data, and terrain data; Determining a wind acceleration factor at a preset point based on the preset point and the terrain wind acceleration factor; The actual annual average wind speed of the preset point in the second preset target area is obtained according to the wind acceleration factor of the preset point, the annual average wind speed of the wind measurement tower, and the wind acceleration factor of the wind measurement tower.

6. The method according to claim 1, wherein: The calculation formula for the actual annual average wind speed at the preset point in the second preset target area is as follows: Among them, U(x,y,z) represents the actual annual average wind speed at the preset point (x,y,z); U t_avg It indicates the annual average wind speed in a certain wind direction at the location of the wind tower; ΔS(x t ,y t ,z t ) represents the wind acceleration factor of the wind tower; ΔS(x,y,z) represents the wind acceleration factor at the preset point (x,y,z).

7. A device for calculating plateau mountain wind resources based on large eddy simulation, characterized in that include: The first acquisition module is used to obtain the atmospheric potential temperature, density, surface roughness and inversion layer height of the plateau atmospheric boundary layer in the first preset target area; A first wind speed calculation module is used to obtain wind speed data of the plateau atmospheric boundary layer according to the atmospheric potential temperature, the density, the surface roughness, the inversion layer height and a preset deflection angle; A second acquisition module is used to obtain terrain data, hub height wind speed, annual average wind speed of the wind tower and wind acceleration factor of the wind tower within a second preset target area; The second wind speed calculation module is used to obtain the actual annual average wind speed of the preset point in the second preset target area based on the wind speed data, the terrain data, the hub height wind speed, the annual average wind speed of the wind measurement tower, the wind acceleration factor of the wind measurement tower and the preset point.

8. The device according to claim 7, characterized in that The device also includes a wind speed correction module, Get the wind turbine hub height; Obtaining an average wind speed vector of a first preset target area according to the hub height and the wind speed data; Calculating the actual deflection angle of the wind speed in the plateau atmospheric boundary layer based on the average wind speed vector of the first preset target area; The wind speed data of the plateau atmospheric boundary layer is corrected according to the actual deflection angle of the wind speed.

9. The device according to claim 8, characterized in that The device further comprises: A wind speed calculation module, configured to calculate an average wind speed vector of a first preset target area; Among them, u avg It represents the average wind speed in the first preset target area and can be expressed as t represents time; t2 represents the time to end data collection after the flow field has fully developed; t1 represents the time when data collection begins after the flow field has fully developed; y represents the spanwise coordinate of the computational domain; y max Indicates the maximum spanwise coordinate value of the computational domain; y0 represents the minimum spanwise coordinate value of the computational domain; x represents the flow direction coordinate of the computational domain; x max Indicates the maximum coordinate value of the flow direction in the calculation domain; x0 represents the minimum coordinate value of the flow direction in the calculation domain; z represents the vertical coordinate of the computational domain; z hub The vertical coordinate representing the hub height of the computational domain; u represents the instantaneous wind speed vector of the entire field collected after the large eddy simulation of the atmospheric boundary layer over the plateau; u(x,y,z hub , t) represents the instantaneous wind speed at the hub height collected after the large eddy simulation of the plateau atmospheric boundary layer in the first preset target area.

10. A storage medium for storing computer-executable instructions, characterized in that: When the computer executable instructions are executed, the steps of calculating plateau mountain wind resources based on large eddy simulation according to any one of claims 1 to 6 are implemented.

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