Alpine zone wind power plant near wake flow area resource calculation method considering shear and turbulence

Abstract

The invention relates to an alpine zone wind power plant near wake flow area resource calculation method considering shear and turbulence, and the method comprises the steps: obtaining an actual parameter of a target position meeting a preset alpine zone condition, and carrying out the wind speed vertical shear correction based on the actual parameter, so as to obtain corrected wind speed data; calculating the wake effect of the wind driven generator on the basis of a pre-constructed super Gaussian wake model to obtain the wake velocity distribution of the wind driven generator, calculating the turbulence intensity of the wind driven generator, and calculating the total wake velocity loss of the wind driven generator on the basis of the turbulence intensity and the wake velocity distribution; based on the corrected wind speed data and the total wake flow loss, the effective input wind speed of each wind generator in the wind power plant is calculated, and the wind energy resource of each wind generator in the wind power plant is calculated in combination with the actual parameters of the wind generators. Therefore, the problems that in the prior art, due to the fact that an empirical power law model is adopted for wind speed vertical correction, adaptability to complex terrains and special atmospheric conditions is poor, most wind farms in high and cold zones are located in a near wake flow area, turbulence intensity modeling is imperfect, and prediction errors of wind speeds at different heights are large are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind energy resource assessment, in particular to a high-cold zone wind farm near wake area resource calculation method considering shear and turbulence. BACKGROUND

[0002] With the accelerated promotion of global clean energy transformation, wind energy as a renewable and clean energy form is increasingly valued in the development of high-cold zones. High-cold zones usually have low temperature, strong wind, frozen soil and seasonal ice and snow changes, etc. These conditions pose special challenges to wind farm power generation capacity calculation, including abnormal vertical distribution of wind speed, enhanced turbulence intensity, significant wake effect, and high requirements for calculation efficiency. Accurate power generation capacity assessment is the basis for wind farm planning, design and optimization, and is crucial for reducing investment risk and improving wind farm efficiency.

[0003] In related technologies, a simplified method based on statistical and empirical models uses historical wind measurement data statistics and empirical formulas to estimate power generation, such as industry commonly used WASP, WindPro, etc. Software tools use empirical power law models for vertical wind speed correction, and wake models use Jensen model, etc. Simplified methods mainly model the far wake area; high-precision physical models based on computational fluid dynamics solve fluid mechanics control equations and can accurately simulate complex flow fields, such as Reynolds-averaged Navier-Stokes models and large eddy simulations; data aggregation and machine learning-based methods use gridded geographic areas and weighted formulas for statistical evaluation.

[0004] However, in related technologies, due to the use of empirical power law models for vertical wind speed correction, the adaptability to complex terrain and special atmospheric conditions is poor, and high-cold zone wind farms are mostly in the near wake area, resulting in large prediction errors of wind speed at different heights, and high-precision physical models have huge calculation costs, making it difficult to meet the needs of rapid solution and real-time layout adjustment in high-cold zone wind farm planning tasks, and data aggregation and machine learning methods rely too much on historical meteorological statistics and do not fully consider the special physical mechanisms of high-cold environments, gridded processing can mask local flow field details, resulting in decreased accuracy in the near wake area, which needs to be improved. SUMMARY

[0005] The present application provides a high-cold zone wind farm near wake area resource calculation method considering shear and turbulence, to solve the problems in related technologies that due to the use of empirical power law models for vertical wind speed correction, the adaptability to complex terrain and special atmospheric conditions is poor, high-cold zone wind farms are mostly in the near wake area and turbulence intensity modeling is not perfect, resulting in large prediction errors of wind speed at different heights, etc.

[0006] The first aspect embodiment of the application provides a high-cold zone wind farm near wake area resource calculation method considering shear and turbulence, comprising the following steps: obtaining actual parameters of a target position meeting preset high-cold zone conditions, and performing wind speed vertical shear correction based on the actual parameters to obtain corrected wind speed data; calculating the wake effect of a wind turbine based on a pre-constructed hyper-Gaussian wake model to obtain the wake velocity distribution of the wind turbine, and calculating the turbulence intensity of the wind turbine to calculate the total wake velocity loss of the wind turbine based on the turbulence intensity and the wake velocity distribution; calculating the effective input wind speed of each wind turbine in the wind farm based on the corrected wind speed data and the total wake loss, and combining the actual parameters of the wind turbine to calculate the wind energy resource of each wind turbine in the wind farm.

[0007] Through the above technical means, the actual parameters of the target position in the high-cold zone can be obtained and the wind speed vertical shear correction can be performed, the wake effect of the wind turbine can be calculated by means of the pre-constructed hyper-Gaussian wake model to obtain the wake velocity distribution, the turbulence intensity of the wind turbine can be calculated and the total wake velocity loss can be obtained accordingly, the effective input wind speed of each wind turbine and the wind energy resource of the wind farm can be calculated by combining the corrected wind speed data and the total wake loss, thereby adaptively adapting to the special environmental conditions of the high-cold zone, effectively improving the accuracy of the wind speed data and the precision of the wake influence quantification, balancing the calculation precision and the calculation cost, and providing an efficient tool for rapid and accurate assessment of wind energy resources in the high-cold zone.

[0008] Optionally, in an embodiment of the application, the wind speed vertical shear correction calculation formula is: , wherein, is the reference height, is the wind speed data, is the surface roughness length, is the actual height.

[0009] Through the above technical means, the surface roughness length can be introduced to adapt to the change of the surface state of the high-cold zone, the wind speed value corresponding to different actual heights can be calculated, the wind speed calculation deviation caused by ignoring the difference in surface characteristics can be effectively avoided, the corrected wind speed data is more consistent with the actual wind speed distribution in the high-cold zone, and reliable basic data support is provided for subsequent wind energy resource calculation.

[0010] Optionally, in one embodiment of this application, the step of calculating the wake effect of the wind turbine based on a pre-built super-Gaussian wake model to obtain the wake velocity distribution of the wind turbine includes: simulating the radial distribution of the wake velocity deficit based on the pre-built super-Gaussian wake model; calculating wake characteristic parameters based on the radial distribution of the wake velocity deficit; and constructing a three-dimensional wake velocity distribution model considering the longitudinal, lateral, and vertical directions based on the wake characteristic parameters to obtain the wake velocity distribution of the wind turbine.

[0011] Through the above-mentioned technical means, the embodiments of this application can simulate the radial distribution of the wake velocity deficit based on a pre-constructed super-Gaussian wake model, calculate wake characteristic parameters, construct a three-dimensional wake velocity distribution model covering the longitudinal, lateral and vertical directions, obtain the wake velocity distribution of the wind turbine, accurately describe the non-uniform diffusion characteristics near the wake region, and fully characterize the wake effect in each spatial direction.

[0012] Optionally, in one embodiment of this application, calculating the turbulence intensity of the wind turbine to calculate the total wake velocity loss of the wind turbine based on the turbulence intensity and the wake velocity distribution includes: calculating the additional turbulence intensity generated by the wind turbine based on a preset additional turbulence model, and defining an offset weight function considering the influence of wind direction offset on turbulence propagation, and superimposing the background turbulence intensity and the additional turbulence intensity according to weights to obtain the turbulence intensity of the wind turbine; calculating the single wake loss of the upstream wind turbine that affects the target location based on the turbulence intensity and the wake velocity distribution, and calculating the total wake loss based on the single wake loss of the upstream wind turbine using root mean square superposition.

[0013] Through the above-mentioned technical means, the embodiments of this application can use an additional turbulence model and a bias weighting function to calculate turbulence intensity, weighted superposition of background turbulence and additional turbulence, and use the root mean square method to calculate the total wake loss. This fully considers the actual impact of wind direction changes on turbulence propagation, making the turbulence intensity calculation more in line with the actual environment, accurately quantifying the distribution characteristics of turbulence intensity, and improving the accuracy of the total wake loss calculation.

[0014] Optionally, in one embodiment of this application, the effective input wind speed calculation formula for each wind turbine in the wind farm is as follows: , in, To effectively input wind speed, To correct the wind speed data, This represents the total wake loss.

[0015] Through the above-mentioned technical means, the embodiments of this application can combine factors such as wind speed correction and wake loss to determine the effective input wind speed of each wind turbine, improve the accuracy of power generation prediction, and provide a reliable data foundation for the micro-site selection and layout optimization of wind farms.

[0016] A second aspect of this application provides a resource calculation device for the near-wake region of a wind farm in a cold region, taking into account shear and turbulence. The device includes: a correction module for acquiring actual parameters of a target location that meets preset cold region conditions, and performing vertical wind speed shear correction based on the actual parameters to obtain corrected wind speed data; a first calculation module for calculating the wake effect of a wind turbine based on a pre-built super-Gaussian wake model to obtain the wake velocity distribution of the wind turbine, and calculating the turbulence intensity of the wind turbine, so as to calculate the total wake velocity loss of the wind turbine based on the turbulence intensity and the wake velocity distribution; and a second calculation module for calculating the effective input wind speed of each wind turbine in the wind farm based on the corrected wind speed data and the total wake loss, so as to calculate the wind energy resources of each wind turbine in the wind farm by combining the actual parameters of the wind turbine.

[0017] Through the aforementioned technical means, the embodiments of this application can obtain the actual parameters of the target location in high-altitude and cold regions and perform vertical wind speed shear correction. By using a pre-constructed super-Gaussian wake model, the wake effect of the wind turbine can be calculated to obtain the wake velocity distribution. At the same time, the turbulence intensity of the wind turbine can be calculated to obtain the total wake velocity loss. By combining the corrected wind speed data and the total wake loss, the effective input wind speed of each wind turbine and the wind energy resources of the wind farm can be calculated. This allows for targeted adaptation to the special environmental conditions of high-altitude and cold regions, effectively improving the accuracy of wind speed data and the precision of wake influence quantification, balancing calculation accuracy and calculation cost, and providing an efficient tool for the rapid and accurate assessment of wind energy resources in high-altitude and cold regions.

[0018] Optionally, in one embodiment of this application, the formula for calculating the vertical wind shear correction is: , in, For reference height, For wind speed data, The length of the surface roughness. This refers to the actual height.

[0019] Through the above-mentioned technical means, the embodiments of this application can introduce the surface roughness length to adapt to the changes in the surface state of high-altitude and cold regions, and can calculate the wind speed values ​​corresponding to different actual heights. This effectively avoids the deviation in wind speed calculation caused by ignoring the differences in surface characteristics, and makes the corrected wind speed data more consistent with the actual wind speed distribution in high-altitude and cold regions, providing reliable basic data support for subsequent wind energy resource calculations.

[0020] Optionally, in one embodiment of this application, the first calculation module includes: a simulation unit for simulating the radial distribution of the wake velocity deficit based on a pre-built super-Gaussian wake model; a wake characteristic parameter calculation unit for calculating wake characteristic parameters based on the radial distribution of the wake velocity deficit; and a construction unit for constructing a three-dimensional wake velocity distribution model considering the longitudinal, lateral, and vertical directions based on the wake characteristic parameters, so as to obtain the wake velocity distribution of the wind turbine.

[0021] Through the above-mentioned technical means, the embodiments of this application can simulate the radial distribution of the wake velocity deficit based on a pre-constructed super-Gaussian wake model, calculate wake characteristic parameters, construct a three-dimensional wake velocity distribution model covering the longitudinal, lateral and vertical directions, obtain the wake velocity distribution of the wind turbine, accurately describe the non-uniform diffusion characteristics near the wake region, and fully characterize the wake effect in each spatial direction.

[0022] Optionally, in one embodiment of this application, the second calculation module includes: a turbulence intensity calculation unit, used to calculate the additional turbulence intensity generated by the wind turbine based on a preset additional turbulence model, and to define an offset weight function considering the influence of wind direction offset on turbulence propagation, and to superimpose the background turbulence intensity and the additional turbulence intensity according to weights to obtain the turbulence intensity of the wind turbine; and a total wake loss calculation unit, used to calculate the single wake loss of the upstream wind turbine that affects the target location based on the turbulence intensity and the wake velocity distribution, and to calculate the total wake loss based on the single wake loss of the upstream wind turbine using root mean square superposition.

[0023] Through the above-mentioned technical means, the embodiments of this application can use an additional turbulence model and a bias weighting function to calculate turbulence intensity, weighted superposition of background turbulence and additional turbulence, and use the root mean square method to calculate the total wake loss. This fully considers the actual impact of wind direction changes on turbulence propagation, making the turbulence intensity calculation more in line with the actual environment, accurately quantifying the distribution characteristics of turbulence intensity, and improving the accuracy of the total wake loss calculation.

[0024] Optionally, in one embodiment of this application, the effective input wind speed calculation formula for each wind turbine in the wind farm is as follows: , in, To effectively input wind speed, To correct the wind speed data, This represents the total wake loss.

[0025] Through the above-mentioned technical means, the embodiments of this application can combine factors such as wind speed correction and wake loss to determine the effective input wind speed of each wind turbine, improve the accuracy of power generation prediction, and provide a reliable data foundation for the micro-site selection and layout optimization of wind farms.

[0026] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the resource calculation method for near-wake regions of wind farms in cold regions, taking into account shear and turbulence, as described in the above embodiments.

[0027] The fourth aspect of this application provides a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for calculating near-wake resources of wind farms in cold regions, taking into account shear and turbulence.

[0028] The fifth aspect of this application provides a computer program product that stores a computer program that, when executed by a processor, implements the above-described method for calculating near-wake zone resources of wind farms in cold regions, taking into account shear and turbulence.

[0029] This application's embodiments can obtain actual parameters of target locations in high-altitude and cold regions and perform vertical wind speed shear correction. It uses a pre-constructed super-Gaussian wake model to calculate the wake effect of wind turbines to obtain the wake velocity distribution. Simultaneously, it calculates the turbulence intensity of the wind turbines and obtains the total wake velocity loss. Combining the corrected wind speed data and the total wake loss, it calculates the effective input wind speed for each wind turbine and the wind energy resources of the wind farm. This allows for targeted adaptation to the special environmental conditions of high-altitude and cold regions, effectively improving the accuracy of wind speed data and the precision of wake influence quantification, balancing computational accuracy and cost. It provides an efficient tool for the rapid and accurate assessment of wind energy resources in high-altitude and cold regions. This solves the problems in related technologies, such as poor adaptability to complex terrain and special atmospheric conditions due to the use of empirical power-law models for vertical wind speed correction, and the large prediction errors of wind speeds at different altitudes because wind farms in high-altitude and cold regions are mostly located in near-wake regions and turbulence intensity modeling is incomplete.

[0030] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0031] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a method for calculating near-wake region resources of a wind farm in a cold region, taking into account shear and turbulence, according to an embodiment of this application. Figure 2 This is a flowchart of a method for calculating near-wake region resources of a wind farm in a cold region, taking into account shear and turbulence, according to an embodiment of this application; Figure 3 This is a schematic diagram of a resource calculation device for a wind farm near the wake region in a cold region, which takes into account shear and turbulence, according to an embodiment of this application. Figure 4 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0032] Figure label: 10 - Resource calculation device for near-wake region of wind farm in high-altitude cold regions considering shear and turbulence; 100 - Correction module; 200 - First calculation module; 300 - Second calculation module; 401 - Memory; 402 - Processor; 403 - Communication interface. Detailed Implementation

[0033] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0034] The following describes, with reference to the accompanying drawings, a method for calculating near-wake region resources of wind farms in high-altitude, cold regions, taking into account shear and turbulence, according to embodiments of this application. In response to the aforementioned background technologies, the use of empirical power-law models for vertical wind speed correction leads to poor adaptability to complex terrain and special atmospheric conditions. Furthermore, wind farms in high-altitude, cold regions are often located near the wake region, and turbulence intensity modeling is incomplete, resulting in significant errors in wind speed prediction at different altitudes. This application provides a method for calculating wind farm resources in the near-wake region of high-altitude, cold regions, taking into account wind shear and turbulence. This method obtains actual parameters of the target location in high-altitude, cold regions and performs vertical wind speed shear correction. It uses a pre-constructed super-Gaussian wake model to calculate the wake effect of wind turbines to obtain the wake velocity distribution. Simultaneously, it calculates the turbulence intensity of wind turbines and obtains the total wake velocity loss. Combining the corrected wind speed data and the total wake loss, it calculates the effective input wind speed for each wind turbine and the wind energy resources of the wind farm. This method is specifically adapted to the special environmental conditions of high-altitude, cold regions, effectively improving the accuracy of wind speed data and the precision of wake influence quantification, balancing computational accuracy and cost, and providing an efficient tool for rapid and accurate assessment of wind energy resources in high-altitude, cold regions. This solves the problems in related technologies, such as poor adaptability to complex terrain and special atmospheric conditions due to the use of empirical power-law models for vertical wind speed correction, and large prediction errors of wind speed at different altitudes because wind farms in high-altitude and cold regions are mostly located in the near-wake region and the turbulence intensity modeling is incomplete.

[0035] Specifically, Figure 1 This is a flowchart illustrating a method for calculating near-wake zone resources of a wind farm in a cold region that takes into account shear and turbulence, as provided in an embodiment of this application.

[0036] like Figure 1 As shown, the resource calculation method for wind farms in high-altitude and cold regions that takes into account shear and turbulence near the wake region includes the following steps: In step S101, the actual parameters of the target location that meet the preset high-altitude cold zone conditions are obtained, and the vertical wind speed shear is corrected based on the actual parameters to obtain the corrected wind speed data.

[0037] It is understood that the preset high-altitude cold zone conditions in the embodiments of this application can be the criteria for determining the characteristics of a high-altitude cold environment, such as an average annual temperature ≤ -5℃, snow cover days ≥ 100 days in winter, and permafrost distribution ≥ 60%. The preset high-altitude cold zone conditions can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here. The actual parameters may include, but are not limited to, altitude, wind speed and surface roughness length.

[0038] In actual implementation, the embodiments of this application can obtain the actual parameters of the target location that meets the preset high-altitude cold zone conditions, and obtain the reference altitude from the high-altitude cold zone meteorological tower. Wind speed data at the location Based on the terrain characteristics of snowfields in high-altitude and cold regions, the surface roughness length can be set. Based on actual parameters, a logarithmic law model based on atmospheric surface layer turbulence motion theory is used to perform vertical wind speed correction in order to obtain corrected wind speed data.

[0039] The embodiments of this application can perform vertical wind speed shear correction using the actual parameters of the target location to obtain corrected wind speed data, thereby better adapting to complex terrain and special atmospheric conditions, significantly improving the accuracy of wind speed prediction, and providing a reliable data foundation for subsequent calculations.

[0040] Optionally, in one embodiment of this application, the formula for calculating the vertical wind shear correction is: , in, For reference height, For wind speed data, The length of the surface roughness. This refers to the actual height.

[0041] It is understood that the surface roughness length in the embodiments of this application... It can be selected from a predefined set of parameter values ​​based on the surface type of high-altitude and cold regions.

[0042] In practical implementation, the embodiments of this application can use a logarithmic law model based on atmospheric physics laws to perform vertical extrapolation of wind speed, and set a series of roughness lengths with clear physical meaning and adapted for typical surface types in high-altitude and cold regions (such as smooth ice surfaces, stable snow cover, exposed rocks, etc.). Recommended values, such as the surface roughness length, can be set based on the terrain characteristics of snowfields in high-altitude and cold regions. Based on the reference height, wind speed data, and surface roughness length, the corrected wind speed data is obtained by using the wind speed vertical shear correction calculation formula.

[0043] This application embodiment can introduce surface roughness length to adapt to the changes in surface conditions in high-altitude and cold regions, and can calculate the wind speed values ​​corresponding to different actual heights. This effectively avoids the wind speed calculation deviation caused by ignoring the differences in surface characteristics, and makes the corrected wind speed data more consistent with the actual wind speed distribution in high-altitude and cold regions, providing reliable basic data support for subsequent wind energy resource calculations.

[0044] In step S102, the wake effect of the wind turbine is calculated based on the pre-built super Gaussian wake model to obtain the wake velocity distribution of the wind turbine, and the turbulence intensity of the wind turbine is calculated to calculate the total wake velocity loss of the wind turbine based on the turbulence intensity and wake velocity distribution.

[0045] It is understood that the pre-constructed super-Gaussian wake model in the embodiments of this application can be the Blondel super-Gaussian wake model. The pre-constructed super-Gaussian wake model can be set by those skilled in the art according to the actual situation, and no specific restrictions are made here. The wake effect can be understood as the deceleration and turbulence enhancement phenomenon generated by the wind turbine on the downstream airflow during operation. The turbulence intensity can be a parameter characterizing the degree of airflow pulsation.

[0046] For example, embodiments of this application can introduce a super-Gaussian distribution function to describe the non-uniform, non-"top-hat" shaped velocity deficit distribution near the wake region. The Blondel super-Gaussian wake model is selected, and a dynamic decay function is proposed where the super-Gaussian order varies with downstream distance. The super-Gaussian distribution function is then used to describe the wake velocity deficit. , in, For the wake velocity loss, To maximize losses, The characteristic width of the wake. The order of the superGaussian function is a dynamic variable that varies with the downstream distance x, and is determined by an exponentially decaying function.

[0047] Furthermore, the wake effect of the wind turbine is calculated based on a pre-built super-Gaussian wake model to obtain the wake velocity distribution of the wind turbine, and the turbulence intensity of the wind turbine is calculated to calculate the total wake velocity loss of the wind turbine based on the turbulence intensity and wake velocity distribution.

[0048] The embodiments of this application can use the super-Gaussian distribution function to simulate the wake velocity deficit and accurately characterize the physical process of the wake morphological evolution from the near-wake region to the far-wake region behind the wind turbine.

[0049] Optionally, in one embodiment of this application, the wake effect of the wind turbine is calculated based on a pre-built super-Gaussian wake model to obtain the wake velocity distribution of the wind turbine, including: simulating the radial distribution of the wake velocity deficit based on the pre-built super-Gaussian wake model; calculating wake characteristic parameters based on the radial distribution of the wake velocity deficit; and constructing a three-dimensional wake velocity distribution model considering the longitudinal, lateral, and vertical directions based on the wake characteristic parameters to obtain the wake velocity distribution of the wind turbine.

[0050] It is understood that the radial distribution of the wake velocity deficit in the embodiments of this application can be the difference distribution between the wind speed and the incoming wind speed in the wake region of the wind turbine along the direction perpendicular to the airflow direction; the wake characteristic parameters can be including but not limited to the wake characteristic width, super Gaussian order and maximum velocity deficit; the three-dimensional wake velocity distribution model is constructed as a complete wake velocity distribution model considering the longitudinal, lateral and vertical directions.

[0051] The radial distribution of the wake velocity deficit is simulated based on a pre-built super-Gaussian wake model. Based on this radial distribution, wake characteristic parameters are calculated. Finally, based on these parameters, a three-dimensional wake velocity distribution model considering the longitudinal, lateral, and vertical directions is constructed to obtain the wake velocity distribution of the wind turbine. For example, embodiments of this application may select the Blondel super-Gaussian wake model: using a super-Gaussian distribution function to describe the radial distribution of the wake velocity deficit: , in, For the wake velocity loss, To maximize losses, The characteristic width of the wake. The order of the superGaussian function is a dynamic variable that varies with the downstream distance x, and is determined by an exponentially decaying function.

[0052] Based on the radial distribution of the wake velocity deficit, the wake characteristic parameters are calculated, and the wake characteristic width is determined: , in, The characteristic width of the wake. For turbulence intensity, For thrust coefficient, , , The model fitting coefficients need to be calibrated based on measured data from the near-wake region of high-altitude, cold-weather wind farms to adapt to the wake diffusion patterns under high-altitude, cold, and strong-wind conditions. The super-Gaussian order is determined by fitting an exponential decay function. Solve for the maximum velocity deficit. The Qian-Ishihara near-wake correction model is applied to calculate the maximum velocity deficit. .

[0053] Based on wake characteristic parameters, a three-dimensional wake velocity distribution model considering longitudinal, lateral, and vertical directions is constructed to obtain the wake velocity distribution of the wind turbine.

[0054] The embodiments of this application can simulate the radial distribution of the wake velocity deficit based on a pre-constructed super-Gaussian wake model, calculate wake characteristic parameters, construct a three-dimensional wake velocity distribution model covering the longitudinal, lateral and vertical directions, obtain the wake velocity distribution of the wind turbine, accurately describe the non-uniform diffusion characteristics near the wake region, and fully characterize the wake effect in each spatial direction.

[0055] Optionally, in one embodiment of this application, the turbulence intensity of the wind turbine is calculated to calculate the total wake velocity loss of the wind turbine based on the turbulence intensity and wake velocity distribution. This includes: calculating the additional turbulence intensity generated by the wind turbine based on a preset additional turbulence model, and defining an offset weight function to consider the influence of wind direction shift on turbulence propagation, and superimposing the background turbulence intensity and the additional turbulence intensity according to weights to obtain the turbulence intensity of the wind turbine; calculating the single wake loss of the upstream wind turbine that affects the target location based on the turbulence intensity and wake velocity distribution, and calculating the total wake loss based on the single wake loss of the upstream wind turbine using root mean square superposition.

[0056] It is understood that the preset additional turbulence model in the embodiments of this application can be the STF additional turbulence model. The preset additional turbulence model can be set by those skilled in the art according to the actual situation, and no specific restrictions are made here. The offset weight function can be a function that reflects the influence of wind direction change on the turbulence propagation path, and is used to quantify the turbulence superposition effect under different wind direction conditions.

[0057] In actual implementation, the embodiments of this application can apply the STF additional turbulence model to calculate the additional turbulence intensity generated by the wind turbine, as shown in the following formula: , in, To add turbulence intensity, The diameter of the wind turbine rotor. This is the wind turbine thrust coefficient. , These are the calibration coefficients for the STF model.

[0058] Furthermore, considering the impact of wind direction shift on turbulence propagation, a shift weighting function is defined to superimpose the background turbulence and the additional turbulence according to their weights to obtain the turbulence intensity of the wind turbine, as shown in the following formula: , in, For turbulence intensity, To add turbulence intensity, Background turbulence intensity, This represents the weighting for wind direction offset.

[0059] Furthermore, based on the turbulence intensity and wake velocity distribution, the wake loss of a single upstream wind turbine affecting the target location is calculated. Based on the wake loss of a single upstream wind turbine, the total wake loss is calculated using root mean square superposition.

[0060] Specifically, determine the impact of upstream wind turbines: identify all upstream wind turbines that affect the target location. Calculate the velocity loss at the target location for each upstream wind turbine. Calculate the total wake velocity loss using a root mean square stacking model, as shown in the following formula: , in, For total wake loss, The single wake velocity loss generated by the i-th upstream wind turbine at the target location is denoted as .

[0061] The embodiments of this application can use an additional turbulence model and a bias weighting function to calculate turbulence intensity, weighted superposition of background turbulence and additional turbulence, and use the root mean square method to calculate the total wake loss. This fully considers the actual impact of wind direction changes on turbulence propagation, making the turbulence intensity calculation more in line with the actual environment, accurately quantifying the distribution characteristics of turbulence intensity, and improving the accuracy of the total wake loss calculation.

[0062] In step S103, based on the corrected wind speed data and total wake loss, the effective input wind speed of each wind turbine in the wind farm is calculated, and the wind energy resources of each wind turbine in the wind farm are calculated in combination with the actual parameters of the wind turbine.

[0063] It is understood that the actual parameters of the wind turbine in the embodiments of this application may include, but are not limited to, the turbine rotor diameter, swept area, and power characteristic curve.

[0064] In actual implementation, the embodiments of this application can calculate the effective input wind speed of each wind turbine based on the corrected wind speed data and total wake loss. Combined with the actual parameters of the wind turbine, such as the power characteristic curve, air density correction coefficient and equipment operating parameters, the theoretical power generation can be calculated, and the wind energy resources of each wind turbine in the wind farm can be calculated, so as to realize the accurate calculation of wind resources at various locations in the wind farm.

[0065] The embodiments of this application can accurately calculate the actual input wind speed of each wind turbine to calculate the wind energy resources of each wind turbine in the wind farm, providing a reliable basis for wind farm layout optimization and power generation prediction, and significantly improving the economic benefits of wind energy development in high-altitude and cold regions.

[0066] Optionally, in one embodiment of this application, the effective input wind speed calculation formula for each wind turbine in the wind farm is as follows: , in, To effectively input wind speed, To correct the wind speed data, This represents the total wake loss.

[0067] In actual implementation, the embodiments of this application can combine factors such as wind speed correction and wake loss to determine the effective input wind speed of each wind turbine, as shown in the following formula: , in, To effectively input wind speed, To correct the wind speed data, The total wake loss is represented by the formula. The combined weakening effect of the wakes from multiple upstream wind turbines on the target wind turbine's wind speed is quantified using nonlinear mathematical methods, thereby determining the effective input wind speed.

[0068] The embodiments of this application can combine factors such as wind speed correction and wake loss to determine the effective input wind speed of each wind turbine, improve the accuracy of power generation prediction, and provide a reliable data foundation for micro-site selection and layout optimization of wind farms.

[0069] Specifically, it can be combined with Figure 2 As shown, a specific embodiment is used to elaborate in detail on the working principle of the near-wake region resource calculation method for wind farms in high-altitude and cold regions that takes into account shear and turbulence in this application.

[0070] like Figure 2 As shown, the embodiments of this application may include: an input data layer, a core computing layer, an integrated computing layer, and an output result layer.

[0071] The input data layer includes two types of basic input data: meteorological data from high-altitude and cold regions and surface roughness parameters. Meteorological data from high-altitude and cold regions may include, but is not limited to, wind speeds at different altitudes; surface roughness parameters may be surface roughness lengths, selected from a predefined set of parameter values ​​based on the surface type of the high-altitude and cold regions.

[0072] The core computing layer comprises three parallel specialized computing modules: Turbulence intensity modeling: employing the STF additional turbulence model for accurate calculation of the additional turbulence generated by the wind turbine itself; Wake effect calculation: employing the Blondel super-Gaussian model for high-precision simulation of the wake velocity distribution and deficit behind the wind turbine; and Vertical wind speed shear correction: employing a logarithmic law model to extrapolate the wind speed at the wind turbine hub height based on the measured wind speed, correcting the vertical wind speed profile.

[0073] The integrated computing layer integrates and couples multiple intermediate results from the core computing layer to complete the final evaluation: Wake loss superposition: The root mean square superposition method is applied to comprehensively calculate the superposition effect of the wakes of multiple wind turbines on the target location; Wind energy resource calculation: Based on the correction of wind speed and total wake loss, the effective wind speed at each wind turbine location is determined, and the power generation is finally calculated.

[0074] The output results layer ultimately outputs the distribution of wind energy resources in the wind farm, intuitively presenting the quantitative results of wind energy resources in each area of ​​the wind farm, providing a scientific basis for the layout optimization of high-altitude and cold-weather wind farms.

[0075] The resource calculation method for wind farms in high-altitude and cold regions, which considers wind shear and turbulence, proposed in this application, can obtain the actual parameters of the target location in high-altitude and cold regions and perform vertical wind speed shear correction. It uses a pre-constructed super-Gaussian wake model to calculate the wake effect of wind turbines to obtain the wake velocity distribution, and simultaneously calculates the turbulence intensity of wind turbines to obtain the total wake velocity loss. Combining the corrected wind speed data and the total wake loss, it calculates the effective input wind speed for each wind turbine and the wind energy resources of the wind farm. This method is specifically adapted to the special environmental conditions of high-altitude and cold regions, effectively improving the accuracy of wind speed data and the precision of wake influence quantification, balancing calculation accuracy and cost, and providing an efficient tool for the rapid and accurate assessment of wind energy resources in high-altitude and cold regions. This solves the problems in related technologies where the use of empirical power-law models for vertical wind speed correction leads to poor adaptability to complex terrain and special atmospheric conditions, and where wind farms in high-altitude and cold regions are mostly located in near-wake regions with incomplete turbulence intensity modeling, resulting in large prediction errors for wind speeds at different altitudes.

[0076] Next, referring to the accompanying drawings, a resource calculation device for near-wake regions of wind farms in high-altitude and cold regions, taking into account shear and turbulence, is described according to an embodiment of this application.

[0077] Figure 3This is a schematic diagram of the structure of a resource calculation device for a wind farm near the wake region in a cold region that takes into account shear and turbulence, according to an embodiment of this application.

[0078] like Figure 3 As shown, the resource calculation device 10 for wind farms in high-altitude and cold regions that takes into account shear and turbulence near the wake region includes: a correction module 100, a first calculation module 200, and a second calculation module 300.

[0079] The correction module 100 is used to obtain the actual parameters of the target location that meet the preset high-altitude cold zone conditions, and to perform vertical wind speed shear correction based on the actual parameters to obtain corrected wind speed data.

[0080] The first calculation module 200 is used to calculate the wake effect of the wind turbine based on a pre-built super-Gaussian wake model to obtain the wake velocity distribution of the wind turbine, and to calculate the turbulence intensity of the wind turbine, so as to calculate the total wake velocity loss of the wind turbine based on the turbulence intensity and the wake velocity distribution.

[0081] The second calculation module 300 is used to calculate the effective input wind speed of each wind turbine in the wind farm based on the corrected wind speed data and total wake loss, so as to calculate the wind energy resources of each wind turbine in the wind farm in combination with the actual parameters of the wind turbine.

[0082] Optionally, in one embodiment of this application, the formula for calculating the vertical wind shear correction is: , in, For reference height, For wind speed data, The length of the surface roughness. This refers to the actual height.

[0083] Optionally, in one embodiment of this application, the first calculation module 200 includes: a simulation unit, a wake characteristic parameter calculation unit, and a construction unit.

[0084] The simulation unit is used to simulate the radial distribution of the wake velocity deficit based on a pre-built super-Gaussian wake model.

[0085] The wake characteristic parameter calculation unit is used to calculate wake characteristic parameters based on the radial distribution of wake velocity deficit.

[0086] The building unit is used to construct a three-dimensional wake velocity distribution model considering longitudinal, lateral, and vertical directions based on wake characteristic parameters, so as to obtain the wake velocity distribution of the wind turbine.

[0087] Optionally, in one embodiment of this application, the second calculation module 300 includes: a turbulence intensity calculation unit and a total wake loss calculation unit.

[0088] The turbulence intensity calculation unit is used to calculate the additional turbulence intensity generated by the wind turbine based on a preset additional turbulence model. It also considers the influence of wind direction offset on turbulence propagation to define an offset weight function, and superimposes the background turbulence intensity and the additional turbulence intensity according to the weight to obtain the turbulence intensity of the wind turbine.

[0089] The total wake loss calculation unit is used to calculate the wake loss of a single upstream wind turbine that affects the target location based on turbulence intensity and wake velocity distribution. Based on the wake loss of a single upstream wind turbine, the total wake loss is calculated using root mean square superposition.

[0090] Optionally, in one embodiment of this application, the effective input wind speed calculation formula for each wind turbine in the wind farm is as follows: , in, To effectively input wind speed, To correct the wind speed data, This represents the total wake loss.

[0091] It should be noted that the explanation of the above-described embodiment of the resource calculation method for near-wake region of wind farms in high-altitude cold regions that takes into account shear and turbulence also applies to the resource calculation device for near-wake region of wind farms in high-altitude cold regions that takes into account shear and turbulence in this embodiment, and will not be repeated here.

[0092] The wind farm near-wake region resource calculation device for high-altitude cold regions, which considers wind shear and turbulence, proposed in this application, can acquire actual parameters of the target location in high-altitude cold regions and perform vertical wind speed shear correction. It uses a pre-built super-Gaussian wake model to calculate the wake effect of wind turbines to obtain the wake velocity distribution, and simultaneously calculates the turbulence intensity of the wind turbines to obtain the total wake velocity loss. Combining the corrected wind speed data and the total wake loss, it calculates the effective input wind speed for each wind turbine and the wind energy resources of the wind farm. This allows for targeted adaptation to the special environmental conditions of high-altitude cold regions, effectively improving the accuracy of wind speed data and the precision of wake influence quantification, balancing calculation accuracy and cost, and providing an efficient tool for rapid and accurate assessment of wind energy resources in high-altitude cold regions. This solves the problems in related technologies where the use of empirical power-law models for vertical wind speed correction leads to poor adaptability to complex terrain and special atmospheric conditions, and where wind farms in high-altitude cold regions are mostly located in near-wake regions with incomplete turbulence intensity modeling, resulting in large prediction errors for wind speeds at different altitudes.

[0093] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0094] When the processor 402 executes the program, it implements the resource calculation method for the near-wake region of wind farms in high-altitude cold regions, which takes into account shear and turbulence, as provided in the above embodiments.

[0095] Furthermore, electronic devices also include: Communication interface 403 is used for communication between memory 401 and processor 402.

[0096] The memory 401 is used to store computer programs that can run on the processor 402.

[0097] Memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0098] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0099] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0100] Processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0101] This application also provides a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for calculating near-wake zone resources of wind farms in cold regions, taking into account shear and turbulence.

[0102] This application also provides a computer program product storing a computer program that, when executed by a processor, implements the above-mentioned method for calculating near-wake zone resources of wind farms in cold regions, taking into account shear and turbulence.

[0103] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0104] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0105] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0106] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0107] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0108] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0109] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0110] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for calculating near-wake region resources of wind farms in high-altitude, cold regions, taking into account shear and turbulence, characterized in that... Includes the following steps: Obtain the actual parameters of the target location that meet the preset high-altitude cold zone conditions, and perform vertical wind speed shear correction based on the actual parameters to obtain corrected wind speed data; The wake effect of the wind turbine is calculated based on a pre-built super-Gaussian wake model to obtain the wake velocity distribution of the wind turbine, and the turbulence intensity of the wind turbine is calculated to calculate the total wake velocity loss of the wind turbine based on the turbulence intensity and the wake velocity distribution. Based on the corrected wind speed data and the total wake loss, the effective input wind speed of each wind turbine in the wind farm is calculated. Combined with the actual parameters of the wind turbine, the wind energy resources of each wind turbine in the wind farm are calculated.

2. The method according to claim 1, wherein the formula for calculating the vertical wind shear correction is: ， in, For reference height, For wind speed data, The length of the surface roughness. This refers to the actual height.

3. The method according to claim 1, wherein calculating the wake effect of the wind turbine based on a pre-constructed super-Gaussian wake model to obtain the wake velocity distribution of the wind turbine includes: The radial distribution of the wake velocity deficit is simulated based on a pre-built super-Gaussian wake model; Based on the radial distribution of the wake velocity deficit, the wake characteristic parameters are calculated. Based on the wake characteristic parameters, a three-dimensional wake velocity distribution model considering the longitudinal, lateral, and vertical directions is constructed to obtain the wake velocity distribution of the wind turbine.

4. The method according to claim 1, wherein calculating the turbulence intensity of the wind turbine, and calculating the total wake velocity loss of the wind turbine based on the turbulence intensity and the wake velocity distribution, comprises: The additional turbulence intensity generated by the wind turbine is calculated based on a preset additional turbulence model. The influence of wind direction shift on turbulence propagation is considered to define a shift weight function. The background turbulence intensity and the additional turbulence intensity are superimposed according to weight to obtain the turbulence intensity of the wind turbine. Based on the turbulence intensity and the wake velocity distribution, the wake loss of a single upstream wind turbine affecting the target location is calculated. Based on the wake loss of a single upstream wind turbine, the total wake loss is calculated using root mean square superposition.

5. The method according to claim 1, wherein the effective input wind speed calculation formula for each wind turbine in the wind farm is: ， in, The effective input wind speed, For the corrected wind speed data, The total wake loss is given.

6. A resource calculation device for the near-wake region of a wind farm in a cold region, taking into account shear and turbulence, characterized in that, include: The correction module is used to obtain the actual parameters of the target location that meet the preset high-altitude cold zone conditions, and to perform vertical wind speed shear correction based on the actual parameters to obtain corrected wind speed data. The first calculation module is used to calculate the wake effect of the wind turbine based on a pre-built super-Gaussian wake model to obtain the wake velocity distribution of the wind turbine, and to calculate the turbulence intensity of the wind turbine, so as to calculate the total wake velocity loss of the wind turbine based on the turbulence intensity and the wake velocity distribution. The second calculation module is used to calculate the effective input wind speed of each wind turbine in the wind farm based on the corrected wind speed data and the total wake loss, so as to calculate the wind energy resources of each wind turbine in the wind farm in combination with the actual parameters of the wind turbine.

7. The apparatus according to claim 6, wherein the formula for calculating the vertical wind speed shear correction is: ， in, For reference height, For wind speed data, The length of the surface roughness. This refers to the actual height.

8. An electronic device, characterized in that, include: The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the near-wake region resource calculation method for wind farms in high-altitude cold regions, taking into account shear and turbulence, as described in any one of claims 1-5.

9. A non-volatile computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the near-wake region resource calculation method for wind farms in high-altitude cold regions, taking into account shear and turbulence, as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the resource calculation method for near-wake regions of wind farms in high-altitude cold regions, taking into account shear and turbulence, as described in any one of claims 1-5.

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