Wind resource assessment method, apparatus, device, medium, and product

Abstract

The application discloses a wind resource assessment method, device, equipment, medium and product, and belongs to the technical field of wind power generation. Before wind resource assessment modeling is performed, the wind condition parameters of the location of the wind measurement tower are estimated according to different parameter value combinations, combined with the characteristics of the power field, the wind condition parameter estimation results corresponding to the different parameter value combinations are obtained, the optimal parameter value combination matched with the wind field is determined according to the estimation results and the true results, and then the wind resource assessment is performed according to the optimal parameter value combination, that is, the parameter verification and screening steps are added before the wind resource assessment is performed, the parameters required for the wind resource assessment can be optimized through the steps, and then the assessment accuracy of the wind resource can be improved.

Description

Technical Field

[0001] This application relates to the field of wind power generation technology, and in particular to a wind resource assessment method, apparatus, equipment, medium and product. Background Technology

[0002] Before constructing a wind farm, it is necessary to conduct a feasibility study. Wind resource assessment, as the basis for analyzing the feasibility of wind farm construction, is an important prerequisite for the vigorous development of wind power generation.

[0003] Currently, atmospheric stability parameters are an important factor affecting the accuracy of wind resource assessment results. When conducting wind resource assessments, related technologies usually assume that the atmosphere is neutral, which can easily lead to inaccurate wind resource assessment results, thereby affecting the site selection, design and operation of wind farms. Summary of the Invention

[0004] This application provides a wind resource assessment method, apparatus, equipment, medium, and product, which can improve the accuracy of wind resource assessment results.

[0005] In a first aspect, embodiments of this application provide a wind resource assessment method, including:

[0006] Obtain the electric field characteristics of the candidate wind farm, which include at least one of the following: topographic information of the candidate wind farm and location information of the wind measurement towers in the candidate wind farm;

[0007] Based on the electric field characteristics, a set of parameter value combinations for the target parameters is determined. The target parameters include atmospheric stability parameters, and the target parameters also include at least one of the following: surface roughness parameters, boundary layer height parameters, and friction velocity parameters. The set of parameter value combinations includes multiple combinations of parameter values ​​for the target parameters.

[0008] Based on the electric field characteristics and parameter value combination set, the wind condition parameters at the location of the wind measuring tower are estimated to obtain the estimated wind condition parameters.

[0009] Based on the actual and estimated wind conditions at the location of the meteorological tower, the target parameter value combination corresponding to the electric field characteristics is determined from the parameter value combination set.

[0010] The wind resources of candidate wind farms are evaluated based on the combination of target parameter values ​​to obtain wind resource evaluation results.

[0011] Secondly, embodiments of this application provide a wind resource assessment device, including: an acquisition module, a determination module, a prediction module, and an assessment module;

[0012] The acquisition module is used to acquire the electric field characteristics of the candidate wind farm. The electric field characteristics include at least one of the following: the topographic information of the candidate wind farm and the location information of the wind measurement tower in the candidate wind farm.

[0013] The determination module is used to determine the set of parameter values ​​of the target parameters based on the characteristics of the electric field. The target parameters include atmospheric stability parameters and at least one of the following: surface roughness parameters, boundary layer height parameters, and friction velocity parameters. The set of parameter values ​​includes multiple combinations of parameter values ​​of the target parameters.

[0014] The prediction module is used to predict the wind condition parameters at the location of the wind measurement tower based on the electric field characteristics and parameter value combination set, and obtain the predicted wind condition parameters.

[0015] The determination module is also used to determine the target parameter value combination corresponding to the electric field characteristics from the parameter value combination set based on the actual wind condition parameters and the estimated wind condition parameters at the location of the wind measurement tower.

[0016] The evaluation module is used to evaluate the wind resources of candidate wind farms based on the combination of target parameter values, and obtain the wind resource evaluation results.

[0017] Thirdly, embodiments of this application provide a wind resource assessment device, comprising:

[0018] processor;

[0019] Memory is used to store computer program instructions;

[0020] When computer program instructions are executed by the processor, the method described in the first aspect is implemented.

[0021] Fourthly, embodiments of this application provide a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement the method as described in the first aspect.

[0022] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect.

[0023] Before modeling wind resource assessment, this embodiment estimates wind condition parameters at the location of the wind measurement tower based on different combinations of parameter values ​​and electric field characteristics. This yields wind condition parameter estimation results for different combinations of parameter values. Based on the estimation results and the actual results, the optimal combination of parameter values ​​matching the wind field is determined. Then, wind resource assessment is performed based on the optimal combination of parameter values. In other words, this embodiment adds a parameter verification and screening step before modeling wind resource assessment. This step can optimize the parameters required for wind resource assessment, thereby improving the accuracy of wind resource assessment. Attached Figure Description

[0024] The features, advantages, and technical effects of exemplary embodiments of this application will now be described with reference to the accompanying drawings.

[0025] Figure 1 A flowchart of a wind resource assessment method provided in this application;

[0026] Figure 2 A schematic diagram of wind speed obtained by simulation based on different atmospheric stability levels is provided for this application.

[0027] Figure 3 Another schematic diagram of wind speed obtained by simulation based on different atmospheric stability levels is provided for this application.

[0028] Figure 4 A schematic diagram illustrating the diurnal variation characteristics of atmospheric stability at different altitudes of a wind measurement tower on flat terrain, as provided in this application.

[0029] Figure 5 A flowchart of another wind resource assessment method provided in this application embodiment;

[0030] Figure 6 A flowchart of another wind resource assessment method provided in this application embodiment;

[0031] Figure 7 A structural diagram of a wind resource assessment device provided in an embodiment of this application;

[0032] Figure 8 This is a structural diagram of a wind resource assessment device provided in an embodiment of this application.

[0033] In the accompanying drawings, the same parts use the same reference numerals. The drawings are not drawn to scale. Detailed Implementation

[0034] The features and exemplary embodiments of various aspects of this application will now be described in detail. Numerous specific details are set forth in the following detailed description to provide a comprehensive understanding of this application. However, it will be apparent to those skilled in the art that this application can be implemented without requiring some of these specific details. The following description of embodiments is merely intended to provide a better understanding of this application by illustrating examples. In the accompanying drawings and the following description, at least some well-known structures and techniques are not shown to avoid unnecessarily obscuring the application; and, for clarity, the dimensions of some structures may be exaggerated. Furthermore, the features, structures, or characteristics described below can be combined in any suitable manner in one or more embodiments.

[0035] The directional terms used in the following description refer to the directions shown in the figures and are not intended to limit the specific structure of the cable-stayed tower and wind turbine generator set of this application. It should also be noted in the description of this application that, unless otherwise explicitly specified and limited, the terms "installation" and "connection" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to direct connections or indirect connections. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0036] As mentioned above, wind resource assessment serves as the basis for analyzing the feasibility of wind farm construction and is a crucial prerequisite for the vigorous development of wind power. In wind resource assessment, atmospheric stability is a key factor that significantly impacts the assessment results. Wind formation mechanisms differ across different terrain features, and atmospheric stability also exhibits spatial inconsistencies depending on the geographical distribution and site size of the wind farm.

[0037] However, current wind resource assessments typically assume that the atmosphere is neutral without considering the impact of atmospheric stability on the accuracy of the assessment results. This leads to poor accuracy in wind resource assessments, which in turn affects the site selection, design, and operation of wind farms.

[0038] Therefore, embodiments of this application provide a wind resource assessment method, apparatus, equipment, medium, and product, which can improve the accuracy of wind resource assessment results.

[0039] Before introducing this application, the terms used in this application will be explained as follows.

[0040] Atmospheric stability refers to the degree of stability of air masses moving vertically within the atmosphere. Based on vertical temperature variations, atmospheric stability can be categorized into stable, neutral, and unstable states. In a stable state, an air mass will rise and then return to its original position due to a drop in temperature; in an unstable state, the air mass will continue to rise.

[0041] Boundary layer height: refers to the thickness of the planetary boundary layer, and is an important physical parameter affecting atmospheric numerical models and environmental assessments. It is usually approximated by the height of the lowest inversion layer in the atmosphere, but the numerical values ​​vary greatly and depend on the turbulent properties of the boundary layer.

[0042] Surface roughness: A characteristic parameter describing the roughness of the Earth's surface, commonly used in aerodynamics to represent the influence of the Earth's surface on wind speed. It reflects the interaction between the Earth's surface and the atmosphere, affecting the reduction of wind speed.

[0043] The wind resource assessment method, apparatus, equipment, medium, and product provided in this application will be described below with reference to specific embodiments and accompanying drawings.

[0044] Figure 1 The flowchart provided in this application illustrates a wind resource assessment method. This method can be applied to wind resource assessment equipment, such as laptops, tablets, personal computers, desktops, servers, etc. Figure 1 As shown, the wind resource assessment method may include the following steps:

[0045] S110. Obtain the electric field characteristics of the candidate wind farm. The electric field characteristics include at least one of the following: the topographic information of the candidate wind farm and the location information of the wind measurement tower in the candidate wind farm.

[0046] S120. Based on the electric field characteristics, determine the parameter value combination set of the target parameters. The target parameters include atmospheric stability parameters, and the target parameters also include at least one of the following: surface roughness parameters, boundary layer height parameters, and friction velocity parameters. The parameter value combination set includes multiple parameter value combinations of the target parameters.

[0047] S130. Based on the electric field characteristics and parameter value combination set, the wind condition parameters at the location of the wind measuring tower are estimated to obtain the estimated wind condition parameters.

[0048] S140. Based on the actual and estimated wind conditions at the location of the wind measurement tower, determine the target parameter value combination corresponding to the electric field characteristics from the parameter value combination set.

[0049] S150. Evaluate the wind resources of candidate wind farms based on the combination of target parameter values ​​to obtain wind resource evaluation results.

[0050] Before modeling wind resource assessment, this embodiment estimates wind condition parameters at the location of the wind measurement tower based on different combinations of parameter values ​​and electric field characteristics. This yields wind condition parameter estimation results for different combinations of parameter values. Based on the estimation results and the actual results, the optimal combination of parameter values ​​matching the wind field is determined. Then, wind resource assessment is performed based on the optimal combination of parameter values. In other words, this embodiment adds a parameter verification and screening step before modeling wind resource assessment. This step can optimize the parameters required for wind resource assessment, thereby improving the accuracy of wind resource assessment.

[0051] The above steps are explained in detail below:

[0052] In S110, a candidate wind farm can be a proposed wind farm. The electric field characteristics of a candidate wind farm may include, but are not limited to, the topographic information of the candidate wind farm and the location information of the anemometer towers in the candidate wind farm. The topographic information here may include the flatness of the area where the candidate wind farm is located; that is, the proposed wind farm may be located in a flat area or in an uneven area.

[0053] The location information of the wind measuring tower may include, for example, the height of the wind measuring tower, or the sector to which the wind measuring tower belongs. The sector can be divided according to the wind direction at the height of the wind measuring tower. The specific division process is not limited in this application embodiment.

[0054] In other words, the embodiments of this application can determine a suitable atmospheric stability based on the topography of the candidate wind farm and the location information of the wind measurement tower, and then evaluate the wind resources of the wind farm based on the atmospheric stability, which can improve the accuracy of the wind resource evaluation results.

[0055] In S120, the target parameter can be a parameter that affects the wind resource assessment result. For example, it can include atmospheric stability parameters, and can also include at least one of surface roughness parameters, boundary layer height parameters, and friction velocity parameters.

[0056] For example, the target parameter can be selected by the user from the candidate parameters as needed. For instance, if the user wants to focus on atmospheric stability and surface roughness parameters, these can be selected as the target parameters. Similarly, if the user wants to focus on atmospheric stability, surface roughness, boundary layer height, and friction velocity parameters, these can be selected as the atmospheric stability parameters. Candidate parameters can include the aforementioned atmospheric stability, surface roughness, boundary layer height, and friction velocity parameters, as well as other parameters.

[0057] For example, if the user does not specify a key parameter, all of the above parameters can be identified as target parameters.

[0058] For example, parameters can also be selected based on best practices for different terrains. These best practices could be the optimal parameters selected for different terrains that match the actual wind measurement tower. For instance, parameters selected for similar terrains can be used to determine the target parameters for the candidate wind farm.

[0059] For example, the target parameter can also be selected from the candidate parameters based on correlation analysis or principal component analysis.

[0060] Generally, atmospheric stability parameters can include 10 levels, from low to high: level 0, level 1, level 2, level 3, level 4, level 5, level 6, level 7, level 8, and level 9. The higher the level, the higher the atmospheric stability, that is, the more stable the atmosphere.

[0061] Surface roughness parameters can include five or more levels. Taking five levels as an example, they can be level 0, level 1, level 2, level 3, and level 4. Level 0 is suitable for very smooth surfaces, such as lakes and seas. Level 1 is suitable for open and flat areas, such as deserts and grasslands. Level 2 is suitable for areas with a few trees and scattered buildings, such as farms. Level 3 is suitable for areas with a large number of trees and buildings, such as woodlands and urban areas. Level 4 is suitable for densely built-up urban areas with tall buildings.

[0062] Taking the surface roughness parameter as an example, which includes 9 levels, they can be respectively level 0, level 0.5, level 1, level 1.5, level 2, level 2.5, level 3, level 3.5 and level 4. The applicable scenarios of level 0.5 are between those of level 0 and level 1. For example, level 0.5 can be applied to areas with mixed water and land or very flat ground.

[0063] A set of parameter values ​​can be a collection of different parameter values ​​of a target parameter. The same parameter can have multiple parameter values. Taking the atmospheric stability parameter as an example, this parameter includes 10 parameter values, each representing a different atmospheric stability.

[0064] By combining various values ​​of the target parameters, a set of parameter value combinations can be obtained. Taking the target parameters including atmospheric stability parameters and surface roughness parameters as an example, assuming that the atmospheric stability parameter has 10 levels and the surface roughness parameter has 5 levels, then there are 50 possible combinations of different values ​​for the atmospheric stability parameter and the surface roughness parameter. That is, the set of parameter value combinations corresponding to the atmospheric stability parameter and the surface roughness parameter contains 50 parameter value combinations, with one atmospheric stability level and one surface roughness level constituting one parameter value combination.

[0065] In S130, the wind condition parameters here may include, for example, wind speed and turbulence. That is, the embodiments of this application can estimate the wind speed, turbulence and other parameters at the location of the wind measurement tower, providing a basis for subsequent site selection, design and other processes.

[0066] For example, wind conditions at the location of the wind measuring tower can be simulated using computational fluid dynamics (CFD) software based on the electric field characteristics and parameter value set, to obtain the simulation results of wind conditions, i.e., the predicted wind conditions.

[0067] For example, wind conditions at the location of the anemometer tower can be predicted by combining electric field characteristics and parameter value combinations with a machine learning model. This machine learning model can be trained using a combination of reference parameter values ​​for the target parameters as input and the corresponding reference wind conditions as output. Different electric field characteristics can correspond to different machine learning models; based on the electric field characteristics, a suitable machine learning model can be selected, and then the wind conditions can be predicted using that model.

[0068] For example, for each combination of parameter values, an estimated wind condition parameter can be obtained by any of the methods described above. That is, one combination of parameter values ​​corresponds to one estimated wind condition parameter.

[0069] In S140, by comparing the actual wind condition parameters with the predicted wind condition parameters, the combination of target parameter values ​​corresponding to the electric field characteristics can be obtained.

[0070] For example, the difference between each estimated wind condition parameter and the actual wind condition parameter can be calculated. If the difference meets preset conditions, the parameter value combination corresponding to the estimated wind condition parameter can be determined as the target parameter value combination. For example, the parameter value combination corresponding to the estimated wind condition parameter with the smallest difference can be determined as the target parameter value combination.

[0071] For example, such as Figure 2 As shown, Figure 2 Taking a surface roughness of 0.055 (i.e., a surface roughness level of 1.5) and a wind measurement tower height of 100m as an example, the simulation results of wind speed at this height under different atmospheric stability conditions are obtained. The dashed line represents the actual wind speed at this height. By comparing the simulated wind speeds under different atmospheric stability conditions with the actual wind speeds, it can be determined that S3 is the target atmospheric stability corresponding to this candidate wind farm. That is, the final target parameter value combination corresponding to this candidate wind farm can be determined as (s3, 1.5).

[0072] For example, such as Figure 3 As shown, the horizontal axis represents different sectors (0-360°). Generally, it can be set to 16 sectors by the user. The gray bars represent the percentage of wind frequency recorded by the anemometer tower in different sectors. Different colors represent the results corresponding to different atmospheric stability levels. The black curve represents the true value. Taking wind condition parameters including wind shear as an example, through... Figure 3 As can be seen, the stability levels correspond to different sectors. For example, the target atmospheric stability for the 100° sector is S5, while the target atmospheric stability for the 200° sector is S8 and S9. The atmospheric stability levels corresponding to different colors can be found in [reference needed]. Figure 2 .

[0073] In practical applications, there can be one or more combinations of target parameter values. Let's continue with... Figure 2 For example, the target atmospheric stability can be determined as s3, or the target atmospheric stability can be determined as s3 and s4. In this case, the combination of target parameter values ​​can be (s3, 1.5) and (s4, 1.5).

[0074] For example, users can also specify optimization objectives based on actual and predicted wind parameters. These objectives can include, for example, optimal fitting of wind profiles at different heights, optimal fitting of wind shear, and optimal fitting of turbulence. Generally, the optimization objective can be set to optimal fitting of wind profiles at different heights of the anemometer tower. A wind profile is a curve describing the change in wind speed with height, used to describe the wind speed pattern within the atmospheric boundary layer. It is influenced by topography, stratification stability, and weather conditions, and is typically described using logarithmic or power-law formulas.

[0075] Taking atmospheric stability parameters as an example, in some embodiments, the combination of target parameter values ​​can be further determined by combining the daily or annual variation characteristics of atmospheric stability at different heights of the wind measurement tower.

[0076] like Figure 4 As shown, Figure 4 This example illustrates the diurnal variation characteristics of atmospheric stability at different altitudes of a wind measuring tower on flat terrain. Different colors represent idealized experimental results of atmospheric stability at the corresponding altitudes of the wind measuring tower. Once the location of the wind measuring tower is determined, it can be used in conjunction with... Figure 4 The overall atmospheric stability recommended level is obtained. Then, based on the overall atmospheric stability recommended level and the atmospheric stability level corresponding to the combination of the target parameter values ​​determined above, the final atmospheric stability level is obtained, and thus the final combination of target parameter values ​​is obtained. For example, the intersection or union of the overall atmospheric stability recommended level and the atmospheric stability level corresponding to the combination of the target parameter values ​​can be determined as the final atmospheric stability level.

[0077] In S150, once the target parameter value combination is determined, the wind resources of the candidate wind field can be evaluated based on the target parameter value combination to obtain the wind resource evaluation result. For example, the wind resources can be evaluated by combining the target parameter value combination with CFD software to obtain the wind resource evaluation result.

[0078] The embodiments of this application improve the efficiency and accuracy of wind resource assessment by introducing parameter selection and verification steps before wind resource assessment.

[0079] In some embodiments, the above-described S120 may include the following steps:

[0080] Candidate parameters are obtained, including atmospheric stability parameters, surface roughness parameters, boundary layer height parameters, and friction velocity parameters;

[0081] Determine the degree of influence of candidate parameters on wind condition parameters at the location of the meteorological tower;

[0082] Based on the degree of influence, the target parameter is determined from the candidate parameters;

[0083] Based on the characteristics of the electric field, determine the combination of parameter values ​​for the target parameters.

[0084] For example, the influence of each candidate parameter on the wind condition parameters at the location of the anemometer tower can be determined using correlation analysis, principal component analysis, or similar methods. Based on the influence, the target parameter can be determined from the candidate parameters. For instance, candidate parameters with an influence greater than or equal to an influence threshold can be selected as the target parameters.

[0085] Once the target parameters are determined, the combination of parameter values ​​can be determined based on the electric field characteristics.

[0086] In other words, the embodiments of this application select parameters with greater influence from each candidate parameter based on their influence on wind condition parameters, and then conduct wind resource assessment based on the parameters with greater influence, which can improve the accuracy of wind resource assessment results.

[0087] In some embodiments, the above-described S120 may include the following steps:

[0088] The candidate parameters are displayed, including atmospheric stability parameters, surface roughness parameters, boundary layer height parameters, and friction velocity parameters.

[0089] Upon receiving a first input for the candidate parameters, in response to the first input, the candidate parameter corresponding to the first input is determined as the target parameter;

[0090] Based on the characteristics of the electric field, determine the combination of parameter values ​​for the target parameters.

[0091] For example, candidate parameters can be displayed on the simulation interface of the wind resource assessment equipment for users to select.

[0092] The first input is used to select the target parameter from the candidate parameters. Exemplarily, the first input may include, but is not limited to, touch input of the candidate parameters by the user through a touch device such as a finger or stylus, or a voice command input by the user, or a specific gesture input by the user, or other feasible inputs. The specific input can be determined according to actual usage needs, and this application embodiment does not limit it. The specific gesture in this application embodiment can be any one of a single-click gesture, a swipe gesture, a drag gesture, a pressure-recognition gesture, a long-press gesture, an area-change gesture, a double-press gesture, or a double-click gesture; the click input in this application embodiment can be a single-click input, a double-click input, or any number of clicks, and can also be a long-press input or a short-press input. For example, the first input mentioned above can be: the user's click input on the candidate parameter.

[0093] Users can select a suitable parameter from the candidate parameters as the target parameter according to their needs. For example, if the user's first input on a candidate parameter is received within a preset time period, the candidate parameter corresponding to the first input can be determined as the target parameter. For example, if the candidate parameters include parameter A, parameter B, and parameter C, and the user clicks on parameter A and parameter C, then parameter A and parameter C can be determined as the target parameters. The preset time period can be set according to actual needs, for example, it can be set to 0.1 seconds.

[0094] In other words, the embodiments of this application can use user-specified parameters as target parameters for subsequent wind resource assessment, which can meet the user's personalized needs.

[0095] In some embodiments, the above-described S120 may include the following steps:

[0096] The candidate parameters are displayed, including atmospheric stability parameters, surface roughness parameters, boundary layer height parameters, and friction velocity parameters.

[0097] Without receiving the first input for the candidate parameters, the target parameters are determined from the candidate parameters based on the reference parameters stored in the parameter library. The reference parameters are the target parameters used for the reference electric field.

[0098] Based on the characteristics of the electric field, determine the combination of parameter values ​​for the target parameters.

[0099] The reference electric field can be the electric field corresponding to different terrains or regions, and the reference parameters can be the target parameters corresponding to the idealized experimental results of the reference electric field; that is, the reference parameters can be the optimal parameters corresponding to the reference electric field. The reference parameters can be stored in a parameter library, also known as an experience library, which is mainly used to store historical data and experimental results of relevant parameters of the electric field for different terrains. For example, the experience library can store the relationship between surface roughness and stability, the relationship between surface roughness and boundary layer height, and the relationship between boundary layer height and friction velocity corresponding to different surface roughnesses.

[0100] For example, if no first input from the user for the candidate parameter is received within a preset time period, the target parameter corresponding to the candidate wind field can be selected based on the reference parameters stored in the parameter library.

[0101] For example, if the reference parameters for the electric field of similar terrain are atmospheric stability parameters and surface roughness parameters, then these parameters can be determined as the target parameters for the candidate wind farm. Similarly, if the reference parameters for the electric field of different terrains are primarily atmospheric stability parameters, surface roughness parameters, boundary layer height parameters, and friction velocity parameters, then these parameters can be determined as the target parameters for the candidate wind farm.

[0102] In other words, in the case where the user does not specify parameters, this application embodiment can also select appropriate parameters as target parameters for the candidate electric field by combining the ideal experimental results corresponding to different terrains recorded in the experience database, thereby improving the flexibility of the target parameter determination method.

[0103] Figure 5 A flowchart illustrating another wind resource assessment method provided in this application embodiment. Figure 5 and Figure 1 The difference is that, Figure 1 S130 in the middle can be further refined into Figure 5 S510-S520 in the series.

[0104] S510. For each parameter value combination in the parameter value combination set, simulate the wind condition parameters at the location of the anemometer tower based on the parameter value combination and electric field characteristics, and obtain the simulation results corresponding to each parameter value combination.

[0105] For example, simulation software such as CFD software can be used to combine parameter value combinations and electric field characteristics to simulate the wind condition parameters at the location of the anemometer tower, obtaining simulation results corresponding to each parameter value combination. Let's continue with... Figure 2 For example, each curve represents a combination of parameter values ​​(atmospheric stability level, surface roughness). From this, multiple simulation results can be obtained.

[0106] Based on the simulation results, combined with the user-specified optimization objectives and experience base, one or more sets of target parameter values ​​corresponding to the candidate wind farm can be obtained, reducing the trial-and-error process of optimization and improving the efficiency and accuracy of wind resource assessment.

[0107] S520. Determine the simulation results as the predicted wind condition parameters corresponding to the parameter value combination.

[0108] This application embodiment can use simulation software to simulate the wind conditions at the location of the anemometer tower by combining different combinations of parameter values ​​with electric field characteristics. This allows users to more intuitively understand the changing trend of each parameter value combination with the height of the anemometer tower, providing a more accurate basis for subsequently determining the target parameter value combination.

[0109] In some embodiments, the changing trends of various parameters in the experience base and the degree of influence of different parameters on the simulation results can be summarized to provide guidance for optimizing the target parameters. For example, if the wind shear obtained from the simulation results is too low, the atmospheric stability level can be appropriately increased. For each level increase, the wind speed can be increased by a certain percentage, thereby reducing the number of optimization attempts and improving optimization efficiency.

[0110] In some embodiments, if there are multiple wind measurement towers in the candidate wind field, optimization can be performed on a tower-by-tower basis, and appropriate target parameters and combinations of target parameter values ​​can be selected for each wind measurement tower.

[0111] For example, after obtaining the target parameter value combination corresponding to the candidate wind farm, the electric field characteristics of the candidate wind farm, the corresponding target parameters, the target parameter value combination, and the user's optimization target can be entered into the experience database to provide a basis and guidance for parameter optimization of other wind farms.

[0112] Figure 6 A flowchart illustrating another wind resource assessment method provided in this application embodiment. Figure 6 and Figure 1 The difference is that, Figure 1 S130 in the middle can be further refined into Figure 6 The S610 in the middle.

[0113] S610. Input the electric field characteristics and parameter value combination set into the machine learning model to obtain the predicted wind condition parameters corresponding to each parameter value combination in the parameter value combination set.

[0114] The machine learning model is trained by taking the combination of reference parameter values ​​of the target parameter as input and the reference wind condition parameters corresponding to the combination of reference parameter values ​​as output.

[0115] For example, the machine learning model may be a random forest or gradient boosting machine model, or a support vector machine (SVM) model.

[0116] Taking the SVM model as an example, the SVM model can be trained by taking the combination of reference parameter values ​​of the target parameter as input and the reference wind condition parameters corresponding to the combination of reference parameter values ​​as output.

[0117] For example, the collected dataset can be divided into a training set and a test set, where the training set can account for 70%-80% of the dataset and the test set can account for 20%-30% of the dataset. Each dataset includes a combination of reference parameter values ​​for the target parameter and a reference wind condition parameter corresponding to that combination of reference parameter values.

[0118] The embodiments of this application do not limit the specific data contained in the training set and the test set. For example, 70%-80% of the collected data can be randomly selected as the training set, and the remaining part can be used as the test set, as long as the data distribution in the training set and the test set is consistent.

[0119] The kernel function used in an SVM model can be selected based on the distribution and characteristics of the dataset. For example, for nonlinear problems, radial basis functions can be used as the kernel function of the SVM model.

[0120] Before training, the hyperparameters of the SVM model can be initialized, such as the penalty parameter and the gamma value of the kernel function. After the parameters of the SVM model are set, the SVM model can be trained using the training set, and the hyperparameters of the SVM model can be updated to optimize the performance of the SVM model.

[0121] For example, grid search, random search, and Bayesian optimization can be used to find the optimal combination of hyperparameters. For instance, a search space can be predefined, and then grid search or Bayesian optimization can be used to find the optimal hyperparameters within that space. After finding the optimal parameters, the performance of the SVM model can be evaluated using a test set. For example, the SVM model can be run on the test set, and performance metrics such as accuracy and recall can be calculated. By comparing the performance of the SVM model under different combinations of hyperparameters, the final SVM model can be obtained.

[0122] The embodiments of this application can not only predict wind conditions under different combinations of parameter values ​​through simulation, but also predict wind conditions under different combinations of parameter values ​​through machine learning models, thereby improving the flexibility of wind condition parameter prediction methods.

[0123] In some embodiments, the above S140 may include the following steps:

[0124] Determine the difference between the actual wind condition parameters and the estimated wind condition parameters;

[0125] If the difference is less than the preset difference, the combination of parameter values ​​corresponding to the estimated wind condition parameters will be determined as the target combination of parameter values ​​corresponding to the electric field characteristics.

[0126] For example, the difference between the actual wind condition parameters and the estimated wind condition parameters can be determined based on the location information of the wind measurement tower. If the difference is less than a preset difference, the combination of parameter values ​​corresponding to the estimated wind condition parameters can be determined as the target combination of parameter values ​​corresponding to the candidate wind farm.

[0127] In this embodiment of the application, before wind resource assessment, the parameter values ​​of the target parameters are optimized to obtain the combination of target parameter values ​​corresponding to the candidate wind farm. Subsequently, wind resource assessment can be carried out based on the combination of target parameter values, thereby improving the assessment efficiency and accuracy of wind resources.

[0128] This application's embodiment adds an experience base and a site parameter optimization module before initial flow field modeling. The input to this module is all modeling parameters automatically identified by the model, requiring no user input. The experience base is a collection of idealized experiments with multiple parameter combinations for identified parameters and typical experiments for different application scenarios. The results are user-specified optimization objectives (such as optimal fitting of wind speed profiles at different heights, optimal fitting of wind shear and turbulence, etc.). Combining the experience base, optimization objectives, and wind condition parameter prediction results, it quantitatively recommends one or more optimal combinations of parameter values ​​in the model parameter selection, thereby reducing the trial-and-error process of optimization and improving the accuracy and efficiency of wind resource assessment.

[0129] Based on the same inventive concept, this application also provides a wind resource assessment device, which is described below. Figure 7 The wind resource assessment device provided in the embodiments of this application will be described in detail.

[0130] Figure 7 This is a structural diagram of a wind resource assessment device provided in an embodiment of this application.

[0131] like Figure 7 As shown, the wind resource assessment device 700 may include: an acquisition module 701, a determination module 702, a prediction module 703, and an assessment module 704;

[0132] The acquisition module 701 is used to acquire the electric field characteristics of the candidate wind farm. The electric field characteristics include at least one of the following: the terrain information of the candidate wind farm and the location information of the wind measurement tower in the candidate wind farm.

[0133] The determination module 702 is used to determine the parameter value combination set of the target parameters based on the electric field characteristics. The target parameters include atmospheric stability parameters and at least one of the following: surface roughness parameters, boundary layer height parameters, and friction velocity parameters. The parameter value combination set includes multiple parameter value combinations of the target parameters.

[0134] The prediction module 703 is used to predict the wind condition parameters at the location of the wind measuring tower based on the electric field characteristics and parameter value combination set, and obtain the predicted wind condition parameters.

[0135] The determination module 702 is also used to determine the target parameter value combination corresponding to the electric field characteristics from the parameter value combination set based on the actual wind condition parameters and the estimated wind condition parameters at the location of the wind measurement tower.

[0136] The evaluation module 704 is used to evaluate the wind resources of candidate wind farms based on the combination of target parameter values, and obtain wind resource evaluation results.

[0137] Before modeling wind resource assessment, this embodiment estimates wind condition parameters at the location of the wind measurement tower based on different combinations of parameter values ​​and electric field characteristics. This yields wind condition parameter estimation results for different combinations of parameter values. Based on the estimation results and the actual results, the optimal combination of parameter values ​​matching the wind field is determined. Then, wind resource assessment is performed based on the optimal combination of parameter values. In other words, this embodiment adds a parameter verification and screening step before modeling wind resource assessment. This step can optimize the parameters required for wind resource assessment, thereby improving the accuracy of wind resource assessment.

[0138] In some embodiments, the acquisition module 701 is further configured to acquire candidate parameters, including atmospheric stability parameters, surface roughness parameters, boundary layer height parameters, and friction velocity parameters.

[0139] Module 702 is specifically used for:

[0140] Determine the degree of influence of candidate parameters on wind condition parameters at the location of the meteorological tower;

[0141] Based on the degree of influence, the target parameter is determined from the candidate parameters;

[0142] Based on the characteristics of the electric field, determine the combination of parameter values ​​for the target parameters.

[0143] In some embodiments, the wind resource assessment device 700 may further include:

[0144] The display module is used to display candidate parameters, including atmospheric stability parameters, surface roughness parameters, boundary layer height parameters, and friction velocity parameters.

[0145] Module 702 is specifically used for:

[0146] Upon receiving a first input for the candidate parameters, in response to the first input, the candidate parameter corresponding to the first input is determined as the target parameter;

[0147] Based on the characteristics of the electric field, determine the combination of parameter values ​​for the target parameters.

[0148] In some embodiments, the wind resource assessment device 700 may further include:

[0149] The display module is used to display candidate parameters, including atmospheric stability parameters, surface roughness parameters, boundary layer height parameters, and friction velocity parameters.

[0150] Module 702 is specifically used for:

[0151] Without receiving the first input for the candidate parameters, the target parameters are determined from the candidate parameters based on the reference parameters stored in the parameter library. The reference parameters are the target parameters used for the reference electric field.

[0152] Based on the characteristics of the electric field, determine the combination of parameter values ​​for the target parameters.

[0153] In some embodiments, the estimation module 703 is specifically used for:

[0154] For each parameter value combination in the parameter value combination set, the wind condition parameters at the location of the anemometer tower are simulated based on the parameter value combination and electric field characteristics, and the simulation results corresponding to each parameter value combination are obtained.

[0155] The determination module 702 is also used to determine the simulation results as the predicted wind condition parameters corresponding to the combination of parameter values.

[0156] In some embodiments, the estimation module 703 is specifically used for:

[0157] By inputting the electric field characteristics and parameter value combination set into the machine learning model, the predicted wind condition parameters corresponding to each parameter value combination in the parameter value combination set are obtained.

[0158] The machine learning model is trained by taking the combination of reference parameter values ​​of the target parameter as input and the reference wind condition parameters corresponding to the combination of reference parameter values ​​as output.

[0159] In some embodiments, the location information of the wind measuring tower includes the height of the wind measuring tower or the sector to which the wind measuring tower belongs, and the sector is divided according to the wind direction at the height of the wind measuring tower.

[0160] In some embodiments, the determining module 702 is specifically used for:

[0161] Determine the difference between the actual wind condition parameters and the estimated wind condition parameters;

[0162] If the difference is less than the preset difference, the combination of parameter values ​​corresponding to the estimated wind condition parameters will be determined as the target combination of parameter values ​​corresponding to the electric field characteristics.

[0163] Based on the same inventive concept, embodiments of this application also provide a wind resource assessment device, such as... Figure 8 As shown, the wind resource assessment device 800 may include a processor 801 and a memory 802 for storing computer program instructions.

[0164] The processor 801 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0165] Memory 802 may include mass storage for data or instructions. For example, and not limitingly, memory 802 may include a hard disk drive (HDD), a floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. In one instance, memory 1202 may include removable or non-removable (or fixed) media, or memory 1202 may be non-volatile solid-state memory. In one instance, memory 1202 may be read-only memory (ROM). In one instance, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0166] The processor 801 reads and executes computer program instructions stored in the memory 802 to achieve... Figures 1-6 The method in the illustrated embodiment achieves... Figures 1-6 The corresponding technical effects achieved by the methods in the illustrated embodiments are described briefly and will not be elaborated further here.

[0167] In one example, the wind resource assessment device 800 may also include a communication interface 803 and a bus 804. Wherein, for example... Figure 8 As shown, the processor 801, memory 802 and communication interface 803 are connected through bus 804 and complete communication with each other.

[0168] The communication interface 803 is mainly used to realize communication between various modules, devices and / or equipment in the embodiments of this application.

[0169] Bus 804 includes hardware, software, or both, that couples the components of wind resource assessment device 800 together. For example, and not as a limitation, bus 804 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 804 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0170] After acquiring the electric field characteristics of candidate wind farms, the wind resource assessment device 800 can execute the wind resource assessment method in this application embodiment, thereby achieving a combination of... Figures 1-6 The wind resource assessment method described and Figure 7 The wind resource assessment device described.

[0171] Furthermore, in conjunction with the wind resource assessment methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the wind resource assessment methods in the above embodiments.

[0172] Furthermore, in conjunction with the wind resource assessment methods in the above embodiments, this application embodiment can provide a computer program product to implement them. This computer program product includes a computer program that, when executed by a processor, implements any of the wind resource assessment methods in the above embodiments.

[0173] Although this application has been described with reference to preferred embodiments, various modifications can be made thereto and components can be replaced with equivalents without departing from the scope of this application. In particular, the technical features mentioned in the various embodiments can be combined in any manner, provided there is no structural conflict. This application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A wind resource assessment method, characterized in that, include: Obtain the electric field characteristics of the candidate wind farm, wherein the electric field characteristics include at least one of the following: the topographic information of the candidate wind farm and the location information of the wind measurement tower in the candidate wind farm; Based on the electric field characteristics, a set of parameter value combinations for the target parameters is determined. The target parameters include atmospheric stability parameters and at least one of the following: surface roughness parameters, boundary layer height parameters, and friction velocity parameters. The set of parameter value combinations includes multiple combinations of parameter values ​​for the target parameters. Based on the electric field characteristics and the set of parameter values, the wind condition parameters at the location of the wind measuring tower are estimated to obtain the estimated wind condition parameters. Based on the actual wind conditions at the location of the wind measurement tower and the estimated wind conditions, a target parameter value combination corresponding to the electric field characteristics is determined from the parameter value combination set. The wind resources of the candidate wind farms are evaluated based on the combination of the target parameter values ​​to obtain wind resource evaluation results.

2. The method according to claim 1, characterized in that, The step of determining the set of parameter values ​​for the target parameter based on the electric field characteristics includes: Candidate parameters are obtained, including atmospheric stability parameters, surface roughness parameters, boundary layer height parameters, and friction velocity parameters; Determine the degree of influence of the candidate parameters on the wind condition parameters at the location of the meteorological tower; The target parameter is determined from the candidate parameters based on the influence degree. Based on the electric field characteristics, determine the combination of parameter values ​​for the target parameters.

3. The method according to claim 1, characterized in that, The step of determining the combination of target parameter values ​​based on the electric field characteristics includes: The candidate parameters are displayed, including atmospheric stability parameters, surface roughness parameters, boundary layer height parameters, and friction velocity parameters. Upon receiving a first input for the candidate parameter, in response to the first input, the candidate parameter corresponding to the first input is determined as the target parameter; Based on the electric field characteristics, determine the combination of parameter values ​​for the target parameters.

4. The method according to claim 1, characterized in that, The step of determining the combination of target parameter values ​​based on the electric field characteristics includes: The candidate parameters are displayed, including atmospheric stability parameters, surface roughness parameters, boundary layer height parameters, and friction velocity parameters. In the absence of receiving a first input for the candidate parameters, a target parameter is determined from the candidate parameters based on a reference parameter stored in a parameter library, wherein the reference parameter is the target parameter used for the reference electric field; Based on the electric field characteristics, determine the combination of parameter values ​​for the target parameters.

5. The method according to any one of claims 1-4, characterized in that, The step of estimating the wind condition parameters at the location of the wind measuring tower based on the electric field characteristics and the set of parameter values ​​to obtain the estimated wind condition parameters includes: For each parameter value combination in the parameter value combination set, the wind condition parameters at the location of the anemometer tower are simulated based on the parameter value combination and the electric field characteristics to obtain the simulation results corresponding to each parameter value combination; The simulation results are used to determine the predicted wind conditions corresponding to the combination of parameter values.

6. The method according to any one of claims 1-4, characterized in that, The step of estimating the wind condition parameters at the location of the wind measuring tower based on the electric field characteristics and the set of parameter values ​​to obtain the estimated wind condition parameters includes: The electric field characteristics and the set of parameter values ​​are input into a machine learning model to obtain the predicted wind condition parameters corresponding to each combination of parameter values ​​in the set of parameter values. The machine learning model is trained by taking the combination of reference parameter values ​​of the target parameter as input and the reference wind condition parameter corresponding to the combination of reference parameter values ​​as output.

7. The method according to any one of claims 1-4, characterized in that, The location information of the wind measuring tower includes the height of the wind measuring tower or the sector to which the wind measuring tower belongs. The sector is divided according to the wind direction at the height of the wind measuring tower.

8. The method according to any one of claims 1-4, characterized in that, The step of determining the target parameter value combination corresponding to the electric field characteristics from the parameter value combination set based on the actual wind condition parameters at the location of the wind measuring tower and the estimated wind condition parameters includes: Determine the difference between the actual wind condition parameters and the estimated wind condition parameters; If the difference is less than the preset difference, the combination of parameter values ​​corresponding to the estimated wind condition parameters is determined as the target combination of parameter values ​​corresponding to the electric field characteristics.

9. A wind resource assessment device, characterized in that, include: Acquisition module, determination module, estimation module, and evaluation module; The acquisition module is used to acquire the electric field characteristics of the candidate wind farm, and the electric field characteristics include at least one of the following: the topographic information of the candidate wind farm and the location information of the wind measuring tower in the candidate wind farm; The determining module is used to determine a set of parameter value combinations for the target parameters based on the electric field characteristics. The target parameters include atmospheric stability parameters and at least one of the following: surface roughness parameters, boundary layer height parameters, and friction velocity parameters. The set of parameter value combinations includes multiple combinations of parameter values ​​for the target parameters. The estimation module is used to estimate the wind condition parameters at the location of the wind measuring tower based on the electric field characteristics and the set of parameter values, and obtain the estimated wind condition parameters. The determining module is further configured to determine, from the parameter value combination set, a target parameter value combination corresponding to the electric field characteristics based on the actual wind condition parameters at the location of the wind measuring tower and the estimated wind condition parameters; The evaluation module is used to evaluate the wind resources of the candidate wind farm based on the combination of target parameter values, and obtain wind resource evaluation results.

10. A wind resource assessment device, characterized in that, include: processor; Memory is used to store computer program instructions; When the computer program instructions are executed by the processor, the method as described in any one of claims 1-8 is implemented.

11. A computer-readable storage medium storing computer program instructions thereon, characterized in that, When the computer program instructions are executed by the processor, the method as described in any one of claims 1-8 is implemented.

12. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the method as described in any one of claims 1-8.

Images