Wind resource data processing method and system
By introducing terrain similarity filtering and mesoscale weather data correction into wind farms, the problem of wind farm power generation falling short of expectations was solved, and more accurate wind resource data processing and power generation prediction were achieved.
Patent Information
- Application Number
- CN202610016990.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-07
- Publication Date
- 2026-04-14
AI Technical Summary
In existing technologies, the power generation of wind farms is lower than expected after construction, mainly due to inaccurate wind resource assessment. This results in the failure to effectively consider the differences in wind resource characteristics caused by micro-topographical differences in wind farms, and existing interpolation methods have failed to effectively solve this problem.
By introducing terrain similarity filtering, neighboring wind measurement towers with high terrain similarity are selected for interpolation. Combined with mesoscale weather data, data correction and tower shadow correction are performed to ensure that the data processing process closely reflects the real atmospheric flow patterns and improve the accuracy of wind speed/wind direction correlation modeling.
It improves the accuracy and reliability of wind resource data processing, reduces the bias introduced by micro-topographic differences, and enhances the reliability of wind farm power generation prediction.
Smart Images
Figure CN121859020A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of wind resource data processing technology, specifically relating to wind resource data processing methods and systems. Background Technology
[0002] In the wind power industry, accurate wind resource assessment is crucial for wind resource development planning. In many existing wind farms, the average annual power generation after construction and commissioning is 20% to 30% lower than predicted. A significant reason for this is inaccurate wind resource assessments prior to construction, leading to lower-than-expected power generation and substantial economic losses. Accurate wind resource data (e.g., wind resource maps) is a vital component of assessing the wind resources of a region before constructing a wind farm.
[0003] Existing methods and systems for interpolating and correcting heterogeneous wind resource data. Utilizing effective wind resource data from the target meteorological tower and its neighboring towers, along with mesoscale weather data, intra-tower interpolation is performed based on the effective wind resource data at the available height of the target tower at a set time. Then, hetero-tower interpolation is performed based on the effective wind resource data from neighboring towers, adjusting wind direction and / or wind speed. Mesoscale weather data is used to correct the data to representative wind resource data. Finally, tower shadow correction yields the final interpolated and corrected wind resource data for the target meteorological tower.
[0004] However, in the early stages of wind farm development, differences in the micro-topography (such as slope, vegetation, and obstacles) of the two meteorological towers may lead to significant differences in wind resource characteristics (wind shear, turbulence), making the MCP model (wind speed / wind direction relationship) based on them inaccurate. Summary of the Invention
[0005] To overcome the aforementioned shortcomings of existing technologies, this invention proposes a wind resource data processing method and system. By filtering based on "terrain similarity," the microenvironments of the two towers are made comparable, thereby improving the accuracy of wind speed / direction correlation modeling. Terrain factors are introduced as a decision-making basis, making the data processing process closer to the real atmospheric flow patterns and improving the reliability of the results. When multiple suspected wind measurement towers meet the terrain similarity criteria, they can be retained simultaneously to improve interpolation accuracy.
[0006] In a first aspect, the present invention provides a wind resource data processing method, comprising the following steps: S1, using the target wind resource data of the target wind measurement tower, perform same-tower interpolation on the wind resource data of different height layers of the target wind measurement tower; wherein, the target wind resource data is the effective wind resource data of the available height of the target wind measurement tower at a set time; S2, after performing same-tower interpolation on the target wind measurement tower, determine whether there is missing target data in the target wind resource data at different height levels; wherein, the target data includes at least one of wind speed and wind direction; if yes, proceed to S3; if no, proceed to S5; S3, select the neighboring wind measurement towers of the target wind measurement tower by combining the distance between towers and the terrain similarity calculated based on terrain data, and obtain the neighboring wind resource data of the neighboring wind measurement towers. S4, use nearby wind resource data to perform interpolation of the target data in the target wind resource data at different heights of the target wind measurement tower, and then jump to S5; S5. Using mesoscale weather data, the target wind resource data at different heights of the target wind measurement tower are corrected into representative wind resource data, and the representative wind resource data is subjected to tower shadow correction processing to obtain the corrected wind resource data of the target wind measurement tower.
[0007] Furthermore, in step S3, the process of selecting a neighboring anemometer tower of the target tower includes the following steps: S311, search for the meteorological tower with the smallest distance from the target meteorological tower, and mark the meteorological tower with the smallest distance from the target meteorological tower as a suspected meteorological tower; S312, Obtain the measured distance between the target wind measurement tower and the suspected wind measurement tower; S313, determine whether the measured distance between towers is less than or equal to the preset distance between towers. If yes, execute S314; if no, mark the suspected wind measuring tower as a neighboring wind measuring tower. S314, acquire the target terrain data of the target wind measurement tower and the suspected terrain data of the suspected wind measurement tower respectively; S315, Calculate the theoretical similarity between the target terrain data and the suspected terrain data; S316, determine whether the theoretical similarity is greater than or equal to the preset similarity; if yes, mark the suspected wind measurement tower as a neighboring wind measurement tower; if no, delete the suspected wind measurement tower and jump to S31.
[0008] Further, in step S315, the theoretical similarity is calculated by assigning weights to the terrain factors in the terrain data using weighted Euclidean distance, cosine similarity, or a comprehensive scoring method; the formula for calculating the theoretical similarity is as follows: ; in, This is the weighting coefficient for elevation; The difference between the elevation of the target terrain data and the elevation of the suspected terrain data; This is the weighting coefficient for the slope; The slope difference between the target terrain data and the suspected terrain data; This is the weighting coefficient for surface roughness; The difference between the surface roughness of the target terrain data and the surface roughness of the suspected terrain data; This is the weighting coefficient for slope aspect; The slope aspect difference between the target terrain data and the suspected terrain data.
[0009] Further, in step S316, the process of determining whether the theoretical similarity is greater than or equal to the preset similarity includes the following steps: S3161, determine whether the number of theoretical similarities is greater than 1; if yes, proceed to S3162; if no, proceed to S3163. S3162, determine whether the number of theoretical similarity greater than or equal to the preset similarity is less than 1; if yes, delete each of the suspected wind measurement towers and jump to S311; if no, mark the suspected wind measurement towers with theoretical similarity greater than or equal to the preset similarity as neighboring wind measurement towers. S3163, determine whether the number of theoretical similarities greater than or equal to the preset similarity is equal to 1; if yes, mark the suspected wind measurement tower as a neighboring wind measurement tower; if no, delete the suspected wind measurement tower and jump to S311.
[0010] Further, in step S3162, when the number of theoretical similarities greater than or equal to the preset similarities is greater than 1, the theoretical similarities greater than or equal to the preset similarities are marked as target similarities; Obtain the maximum target similarity among all target similarities and determine whether the number of maximum target similarities is greater than 1; if so, mark all suspected anemometers corresponding to the maximum target similarity as neighboring anemometers; if not, mark all suspected anemometers corresponding to the maximum target similarity as neighboring anemometers.
[0011] Further, in step S3, the process of acquiring the nearby wind resource data of the nearby meteorological tower includes: S321, determine whether the number of nearby wind measurement towers is greater than 1; if yes, proceed to S322; if no, proceed to S323. S322, acquire the effective wind resource data of each of the adjacent wind measurement towers respectively, and calculate the effective wind resource data to obtain the adjacent wind resource data; S323, Obtain the effective wind resource data of the nearby wind measurement tower, and record the effective wind resource data as the nearby wind resource data.
[0012] Furthermore, in step S322, the formula for calculating each of the effective wind resource data is as follows: ; in, For nearby wind resource data; This represents the number of nearby meteorological towers. For the first The weight of each neighboring meteorological tower; For the first Effective wind resource data from nearby meteorological towers.
[0013] In a second aspect, the present invention provides a wind resource data processing system, the processing system being applicable to the processing method described in the first aspect, and the processing system comprising: The first processing module is used to execute S1 to perform on-tower interpolation of wind resource data at different heights of the target wind measurement tower using the target wind resource data of the target wind measurement tower; wherein, the target wind resource data is the effective wind resource data of the target wind measurement tower at a set time at the available height. The judgment module is used to execute S2 to determine whether there is missing target data in the target wind resource data at different height levels after performing same-tower interpolation on the target wind measurement tower; wherein the target data includes at least one of wind speed and wind direction; if yes, then execute S3; if no, then execute S5. The acquisition module is used to execute S3 to select the neighboring wind measurement towers of the target wind measurement tower and acquire the neighboring wind resource data of the neighboring wind measurement towers. The second processing module is used to execute S4 to perform interpolation of the target data in the target wind resource data at different heights of the target wind measurement tower using the adjacent wind resource data, and then jump to S5. The third processing module is used to execute S5 to correct the target wind resource data of different height layers of the target wind measurement tower into representative wind resource data using mesoscale weather data, and to perform tower shadow correction processing on the representative wind resource data to obtain the corrected wind resource data of the target wind measurement tower.
[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. Traditional methods select reference towers solely based on distance, which easily introduces terrain interference errors. This scheme uses "terrain similarity" filtering to ensure the comparability of the microenvironments of two towers, improving the accuracy of wind speed / direction correlation modeling. Introducing terrain factors as a decision-making basis makes the data processing process closer to real atmospheric flow patterns, enhancing the reliability of the results. When multiple suspected anemometer towers meet the terrain similarity requirement, they can be retained simultaneously to improve interpolation accuracy.
[0015] 2. Select neighboring anemometer towers of the target tower and obtain nearby wind resource data for those towers. Filter by "terrain similarity" to ensure the microenvironments of the two towers are comparable, thus improving the accuracy of wind speed / direction correlation modeling.
[0016] 3. Obtain the target terrain data of the target anemometer tower and the suspected terrain data of the suspected anemometer tower, respectively; and calculate the theoretical similarity between the target terrain data and the suspected terrain data. By introducing terrain factors as a decision-making basis, the data processing process is made closer to the real atmospheric flow patterns, improving the reliability of the results. Attached Figure Description
[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below.
[0018] Figure 1 This is a flowchart of the wind resource data processing method according to an embodiment of the present invention. Detailed Implementation
[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.
[0020] The wind resource data processing method and system of this invention include: like Figure 1 As shown, in a first aspect, a wind resource data processing method is provided, including the following operations: S1, using the target wind resource data of the target wind measurement tower, perform same-tower interpolation on the wind resource data of different height layers of the target wind measurement tower; whereby, the target wind resource data is the effective wind resource data of the available height of the target wind measurement tower at a set time; For example, during implementation, valid wind resource data collected at a specific time (e.g., a certain day or month) at an available height (e.g., 70m or 80m) of the target anemometer tower is acquired and recorded as the target wind resource data. This target wind resource data includes valid wind speed and valid wind direction data. The target wind resource data is then used to interpolate or extrapolate missing wind resource data at other different height levels of the target anemometer tower (e.g., 50m or 100m).
[0021] For example, a wind profile model or a logarithmic law model can be used, and the wind speed data at other unknown heights on the target wind measurement tower can be inferred based on the effective wind speed data in the target wind resource data.
[0022] Effective wind speed data from the target wind resource data can be used, combined with time series trends, to perform linear / nonlinear fitting interpolation to obtain risk data for other unknown layers at different heights on the target wind measurement tower.
[0023] By prioritizing the use of data from the same tower for interpolation, the aim is to reduce the bias introduced by micro-topographical differences, thereby ensuring the reliability of the interpolation base.
[0024] S2, after performing same-tower interpolation on the target wind measurement tower, determine whether there is missing target data in the target wind resource data at different height levels; wherein, the target data includes at least one of wind speed and wind direction; if yes, proceed to S3; if no, proceed to S5; For example, after interpolating the wind resource data at different heights of the target wind measurement tower, it is determined whether there are still missing wind speed and / or wind direction data at each height of the target wind measurement tower.
[0025] S3, select the neighboring wind measurement towers of the target wind measurement tower and obtain the nearby wind resource data of the neighboring wind measurement towers; For example, during implementation, when wind speed and / or wind direction data are still missing at each height level of the target wind measurement tower, a neighboring wind measurement tower is selected, and the neighboring wind resource data of the neighboring wind measurement tower is obtained.
[0026] In this embodiment, step S3, the process of selecting a neighboring meteorological tower of the target meteorological tower, includes: S311, search for the meteorological tower with the smallest distance from the target meteorological tower, and mark the meteorological tower with the smallest distance from the target meteorological tower as a suspected meteorological tower; For example, during implementation, the location coordinates (latitude and longitude) of all available wind measurement towers are obtained in the database or GIS system, and the Euclidean distance or spherical distance between the target wind measurement tower and other wind measurement towers is calculated. The wind measurement tower that is closest to the target tower is identified and recorded as the suspected wind measurement tower.
[0027] For example, target anemometer A is located at point A. Around the target anemometer A are anemometer B at point B, anemometer C at point C, and anemometer D at point D. Anemometer B at point B is the closest to target anemometer A at point A; therefore, anemometer B at point B is marked as a suspected anemometer.
[0028] S312, Obtain the measured distance between the target wind measurement tower and the suspected wind measurement tower; For example, during implementation, high-precision geographic information system (GIS) or RTK measurement data is used to obtain the actual ground distance between the target wind measurement tower and the suspected wind measurement tower, and this actual ground distance is recorded as the measured distance between the target wind measurement tower and the suspected wind measurement tower.
[0029] S313, determine whether the actual distance between towers is less than or equal to the preset distance between towers. If yes, execute S314; otherwise, mark the suspected wind measurement tower as a neighboring wind measurement tower. For example, during implementation, a preset distance between towers is set based on historical experience; for instance, the preset distance between towers can be 10 km to 30 km. Within the preset distance, the atmospheric boundary layer characteristics and weather systems between the target wind measurement tower and the suspected wind measurement tower are highly similar, meeting the usage requirements.
[0030] Determine whether the measured distance between the target wind measurement tower and the suspected wind measurement tower is less than or equal to the preset distance between towers.
[0031] When the measured distance between the target meteorological tower and the suspected meteorological tower is greater than the preset distance, it is considered that the atmospheric boundary layer characteristics and weather system similarity between the target and suspected meteorological towers may be low. However, if there is no meteorological tower closer to the target tower, the suspected meteorological tower is marked as a neighboring tower.
[0032] When the measured distance between the target meteorological tower and the suspected meteorological tower is less than or equal to the preset distance, it is considered that the atmospheric boundary layer characteristics and weather system similarity between the target meteorological tower and the suspected meteorological tower are high. At this time, proceed to S314.
[0033] S314, acquire the target terrain data of the target wind measurement tower and the suspected terrain data of the suspected wind measurement tower respectively; For example, during implementation, topographic data such as elevation (altitude), slope, aspect, and surface roughness (vegetation type, land cover) within a certain range centered on the target wind measurement tower are acquired, and this topographic data is recorded as the target topographic data. Similarly, topographic data such as elevation (altitude), slope, aspect, and surface roughness (vegetation type, land cover) within a certain range centered on the suspected wind measurement tower are acquired, and this topographic data is recorded as the suspected topographic data.
[0034] S315, calculate the theoretical similarity between the target terrain data and the suspected terrain data; For example, during implementation, weighted Euclidean distance, cosine similarity, or a comprehensive scoring method can be used to assign weights to terrain factors in the terrain data before calculating the theoretical similarity. The formula for calculating this theoretical similarity is as follows: ; in, This is the weighting coefficient for elevation; The difference between the elevation of the target terrain data and the elevation of the suspected terrain data; This is the weighting coefficient for the slope; The slope difference between the target terrain data and the suspected terrain data; This is the weighting coefficient for surface roughness; The difference between the surface roughness of the target terrain data and the surface roughness of the suspected terrain data; This is the weighting coefficient for slope aspect; The slope aspect difference between the target terrain data and the suspected terrain data.
[0035] S316, determine whether the theoretical similarity is greater than or equal to the preset similarity; if yes, mark the suspected wind measurement tower as a neighboring wind measurement tower; if no, delete the suspected wind measurement tower and jump to S31.
[0036] For example, during implementation, a preset similarity is set based on historical experience. Let's assume the preset similarity is 0.8. When the theoretical similarity is greater than or equal to the preset similarity, the suspected terrain data of the suspected wind measurement tower is considered to have a high similarity to the target terrain data of the target wind measurement tower, thus meeting the usage requirements.
[0037] The system determines whether the theoretical similarity is greater than the preset similarity. If the theoretical similarity is greater than or equal to the preset similarity, the suspected terrain data of the suspected anemometer tower is considered to have a high similarity to the target terrain data of the target anemometer tower, meeting the usage requirements. In this case, the suspected anemometer tower is marked as a neighboring anemometer tower. If the theoretical similarity is less than the preset similarity, the suspected terrain data of the suspected anemometer tower is considered to have a low similarity to the target terrain data of the target anemometer tower, not meeting the usage requirements. In this case, the suspected anemometer tower is deleted, and the system jumps back to S311 to search for the next nearest anemometer tower. The goal is to find the suspected anemometer tower that is closest to the target anemometer tower but has a large terrain difference, discard the suspected anemometer tower, and continue searching for a more suitable anemometer tower, thereby ensuring the accuracy of the wind resource data interpolated by different towers.
[0038] In this embodiment, step S316, the process of determining whether the theoretical similarity is greater than or equal to the preset similarity, includes: S3161, determine if the number of theoretical similarities is greater than 1; if yes, proceed to S3162; if no, proceed to S3163. For example, during implementation, it is determined whether the number of theoretical similarities is greater than 1. If the number of theoretical similarities is greater than 1, it is considered that there are multiple suspected wind measurement towers. At this time, proceed to S3162. If the number of theoretical similarities is equal to 1, it is considered that there is only one suspected wind measurement tower. At this time, proceed to S3163.
[0039] S3162, determine whether the number of theoretical similarity greater than or equal to the preset similarity is less than 1; if yes, delete each suspected wind measurement tower and jump to S311; if no, mark the suspected wind measurement towers with theoretical similarity greater than or equal to the preset similarity as neighboring wind measurement towers. For example, during implementation, when multiple suspected meteorological towers exist, it is determined whether the theoretical similarity of each suspected meteorological tower is greater than or equal to a preset similarity. The number of theoretical similarities greater than or equal to the preset similarity is then counted.
[0040] Secondly, determine if the number of theoretical similarities greater than or equal to the preset similarity is less than 1. If the number of theoretical similarities greater than or equal to the preset similarity is less than 1, it is considered that the similarity between the suspected terrain data of each suspected wind measurement tower and the target terrain data of the target wind measurement tower is low, which does not meet the usage requirements. At this time, delete each suspected wind measurement tower and jump back to S311 to search for the next nearest wind measurement tower. If the number of theoretical similarities greater than or equal to the preset similarity is greater than or equal to 1, it is considered that there is at least one suspected wind measurement tower whose suspected terrain data has a high similarity to the target terrain data of the target wind measurement tower, which meets the usage requirements. At this time, mark the suspected wind measurement towers corresponding to the theoretical similarity greater than or equal to the preset similarity as neighboring wind measurement towers.
[0041] S3163, determine whether the number of theoretical similarity values greater than or equal to the preset similarity value is equal to 1; if yes, mark the suspected wind measurement tower as a neighboring wind measurement tower; if no, delete the suspected wind measurement tower and jump to S311.
[0042] For example, during implementation, when only one suspected wind measurement tower exists, it is determined whether the number of theoretical similarities greater than or equal to a preset similarity value for this suspected wind measurement tower is equal to 1. If the theoretical similarity of this suspected wind measurement tower is greater than or equal to the preset similarity value of 1, it is considered that the suspected terrain data of this suspected wind measurement tower has a high similarity to the target terrain data of the target wind measurement tower, meeting the usage requirements. At this time, the suspected wind measurement tower is marked as a neighboring wind measurement tower. If the theoretical similarity of this suspected wind measurement tower is greater than or equal to the preset similarity value of 0, it is considered that the suspected terrain data of this suspected wind measurement tower has a low similarity to the target terrain data of the target wind measurement tower, not meeting the usage requirements. At this time, the suspected wind measurement tower is deleted, and the process jumps back to S311 to search for the next nearest wind measurement tower.
[0043] In this embodiment, in step S3162, when the number of theoretical similarities greater than or equal to the preset similarity is greater than 1, the theoretical similarities greater than or equal to the preset similarity are marked as target similarities. For example, during implementation, when the number of theoretical similarities greater than or equal to the preset similarity in step S3162 is greater than 1, it indicates that at least two suspected anemometer towers have a high similarity to the target anemometer tower, meeting the usage requirements. At this time, the theoretical similarities greater than or equal to the preset similarity are marked as target similarities. And there are at least two target similarities.
[0044] Obtain the maximum target similarity among all target similarities and determine whether the number of maximum target similarities is greater than 1; if so, mark all suspected anemometers corresponding to the maximum target similarity as neighboring anemometers; if not, mark all suspected anemometers corresponding to the maximum target similarity as neighboring anemometers.
[0045] For example, during implementation, the highest target similarity is selected from all target similarities. It is then determined whether the number of highest target similarities is greater than 1. If the number of highest target similarities is greater than 1, it is considered that there are at least two highest target similarities. In this case, all suspected anemometers corresponding to each highest target similarity are marked as neighboring anemometers. If the number of highest target similarities is equal to 1, it is considered that there is only one highest target similarity. In this case, the suspected anemometer corresponding to that highest target similarity is marked as a neighboring anemometer.
[0046] In this embodiment, step S3, the process of acquiring nearby wind resource data from nearby meteorological towers, includes: S321, determine if the number of nearby wind measurement towers is greater than 1; if yes, jump to S322; if no, jump to S323. For example, during implementation, it is determined whether the number of adjacent wind measurement towers is greater than 1. If the number of adjacent wind measurement towers is greater than 1, it is considered that there are at least two adjacent wind measurement towers. At this time, proceed to S322. If the number of adjacent wind measurement towers is equal to 1, it is considered that there is only one adjacent wind measurement tower. At this time, proceed to S323.
[0047] S322: Obtain the effective wind resource data of each nearby wind measurement tower, and calculate the effective wind resource data to obtain the nearby wind resource data. In this embodiment, the formula for calculating each valid wind resource data in step S322 is as follows: ; in, For nearby wind resource data; This represents the number of nearby meteorological towers. For the first The weight of each neighboring meteorological tower; For the first Effective wind resource data from nearby meteorological towers.
[0048] S323, obtain the effective wind resource data of the nearby wind measurement tower, and record the effective wind resource data as the nearby wind resource data.
[0049] S4, use nearby wind resource data to perform interpolation of the target data in the target wind resource data at different heights of the target wind measurement tower, and then jump to S5; For example, during implementation, the wind speed / wind direction correlation between the target wind measurement tower and neighboring wind measurement towers is established (such as linear regression, matrix method, neural network, etc.), and the missing target wind resource data of the target wind measurement tower is interpolated using the neighboring wind resource data of the neighboring wind measurement towers.
[0050] S5. Using mesoscale weather data, the target wind resource data at different heights of the target wind measurement tower are corrected into representative wind resource data, and the representative wind resource data is subjected to tower shadow correction processing to obtain the corrected wind resource data of the target wind measurement tower.
[0051] For example, during implementation, global or regional scale meteorological model data (such as ERA5, MERRA2, WRF, etc.) are introduced, with a temporal resolution of hourly and a spatial resolution of approximately 10-30 km. Long-term relationships (such as scaling factors and bias corrections) are established between the target wind resource data (measured by the target wind measurement tower, interpolated at the same tower, and interpolated at different towers) and mesoscale data to form long-term representative data (e.g., representing 30 years of climate conditions) for long-term power generation prediction. When the wind measurement tower structure itself obstructs wind flow, data from the sector "behind the tower" (e.g., within ±30°) is removed based on wind direction, and weighted compensation is performed using CFD simulation results or adjustments are made based on empirical models. This yields corrected wind resource data for the target wind measurement tower.
[0052] Secondly, a wind resource data processing system is provided, characterized in that the processing system is applicable to the processing method as described in the first aspect, and the processing system includes a first processing module, a judgment module, an acquisition module, a second processing module, and a third processing module. Wherein, The first processing module executes S1 to perform in-tower interpolation on wind resource data at different heights of the target wind measurement tower using the target wind resource data of the target wind measurement tower; wherein, the target wind resource data is the effective wind resource data available at the height of the target wind measurement tower at a set time. The judgment module executes S2 to determine whether there is missing target data in the target wind resource data at different heights after in-tower interpolation on the target wind measurement tower; wherein, the target data contains at least one of wind speed and wind direction; if yes, then execute S3; if no, then execute S5. The acquisition module executes S3 to select the neighboring wind measurement towers of the target wind measurement tower and acquire the neighboring wind resource data of the neighboring wind measurement towers. The second processing module executes S4 to perform out-of-tower interpolation on the target data in the target wind resource data at different heights of the target wind measurement tower using the neighboring wind resource data, and then jumps to S5. The third processing module is used to execute S5 to correct the target wind resource data at different heights of the target wind measurement tower to representative wind resource data using mesoscale weather data, and to perform tower shadow correction processing on the representative wind resource data to obtain the corrected wind resource data of the target wind measurement tower.
[0053] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
Claims
1. A wind resource data processing method, characterized in that: Includes the following steps: S1, using the target wind resource data of the target wind measurement tower, perform same-tower interpolation on the wind resource data of different height layers of the target wind measurement tower; wherein, the target wind resource data is the effective wind resource data of the available height of the target wind measurement tower at a set time; S2, after performing same-tower interpolation on the target wind measurement tower, determine whether there is missing target data in the target wind resource data at different height levels; wherein, the target data includes at least one of wind speed and wind direction; if yes, proceed to S3; if no, proceed to S5; S3, select the neighboring wind measurement towers of the target wind measurement tower by combining the distance between towers and the terrain similarity calculated based on terrain data, and obtain the neighboring wind resource data of the neighboring wind measurement towers. S4, use nearby wind resource data to perform interpolation of the target data in the target wind resource data at different heights of the target wind measurement tower, and then jump to S5; S5. Using mesoscale weather data, the target wind resource data at different heights of the target wind measurement tower are corrected into representative wind resource data, and the representative wind resource data is subjected to tower shadow correction processing to obtain the corrected wind resource data of the target wind measurement tower.
2. The wind resource data processing method according to claim 1, characterized in that, Step S3, the process of selecting a neighboring meteorological tower of the target tower, includes the following steps: S311, search for the wind measurement tower with the smallest distance from the target wind measurement tower, and mark the wind measurement tower with the smallest distance from the target wind measurement tower as a suspected wind measurement tower; S312, Obtain the measured distance between the target wind measurement tower and the suspected wind measurement tower; S313, determine whether the measured distance between towers is less than or equal to the preset distance between towers; if yes, execute S314; if no, mark the suspected wind measuring tower as a neighboring wind measuring tower. S314, acquire the target terrain data of the target wind measurement tower and the suspected terrain data of the suspected wind measurement tower respectively; S315, Calculate the theoretical similarity between the target terrain data and the suspected terrain data; S316, determine whether the theoretical similarity is greater than or equal to the preset similarity; if yes, mark the suspected wind measurement tower as a neighboring wind measurement tower; if no, delete the suspected wind measurement tower and jump to S31.
3. The wind resource data processing method according to claim 2, characterized in that, In step S315, the theoretical similarity is calculated by assigning weights to the terrain factors in the terrain data using weighted Euclidean distance, cosine similarity, or a comprehensive scoring method. The formula for calculating the theoretical similarity is as follows: ； in, This is the weighting coefficient for elevation; The difference between the elevation of the target terrain data and the elevation of the suspected terrain data; This is the weighting coefficient for the slope; The slope difference between the target terrain data and the suspected terrain data; This is the weighting coefficient for surface roughness; The difference between the surface roughness of the target terrain data and the surface roughness of the suspected terrain data; This is the weighting coefficient for slope aspect; The slope aspect difference between the target terrain data and the suspected terrain data.
4. The wind resource data processing method according to claim 2, characterized in that, Step S316, the process of determining whether the theoretical similarity is greater than or equal to the preset similarity, includes the following steps: S3161, Determine whether the number of theoretical similarities is greater than 1; if yes, proceed to S3162; if no, proceed to S3163. S3162, determine whether the number of theoretical similarity greater than or equal to the preset similarity is less than 1; if yes, delete each of the suspected wind measurement towers and jump to S311; if no, mark the suspected wind measurement towers with theoretical similarity greater than or equal to the preset similarity as neighboring wind measurement towers. S3163, determine whether the number of theoretical similarities greater than or equal to the preset similarity is equal to 1; if yes, mark the suspected wind measurement tower as a neighboring wind measurement tower; if no, delete the suspected wind measurement tower and jump to S311.
5. The wind resource data processing method according to claim 2, characterized in that, In step S3162, when the number of theoretical similarities greater than or equal to the preset similarities is greater than 1, the theoretical similarities greater than or equal to the preset similarities are marked as target similarities. Obtain the maximum target similarity among all target similarities and determine whether the number of maximum target similarities is greater than 1; if so, mark all suspected anemometers corresponding to the maximum target similarity as neighboring anemometers; if not, mark all suspected anemometers corresponding to the maximum target similarity as neighboring anemometers.
6. The wind resource data processing method according to claim 1, characterized in that, Step S3, the process of obtaining the nearby wind resource data of the nearby meteorological tower, includes: S321, determine whether the number of nearby wind measurement towers is greater than 1; if yes, proceed to S322; if no, proceed to S323. S322, acquire the effective wind resource data of each of the adjacent wind measurement towers respectively, and calculate the effective wind resource data to obtain the adjacent wind resource data; S323, Obtain the effective wind resource data of the nearby wind measurement tower, and record the effective wind resource data as the nearby wind resource data.
7. The wind resource data processing method according to claim 6, characterized in that, In step S322, the formula for calculating each of the effective wind resource data is as follows: ； in, For nearby wind resource data; This represents the number of nearby meteorological towers. For the first The weight of each neighboring meteorological tower; For the first Effective wind resource data from nearby meteorological towers.
8. A wind resource data processing system, wherein the processing system is applicable to the wind resource data processing method as described in any one of claims 1-7, and the processing system comprises: The first processing module is used to execute S1 to perform on-tower interpolation of wind resource data at different heights of the target wind measurement tower using the target wind resource data of the target wind measurement tower; wherein, the target wind resource data is the effective wind resource data of the target wind measurement tower at a set time at the available height. The judgment module is used to execute S2 to determine whether there is missing target data in the target wind resource data at different height levels after performing same-tower interpolation on the target wind measurement tower; wherein the target data includes at least one of wind speed and wind direction; if yes, then execute S3; if no, then execute S5. The acquisition module is used to execute S3 to select the neighboring wind measurement towers of the target wind measurement tower and acquire the neighboring wind resource data of the neighboring wind measurement towers. The second processing module is used to execute S4 to perform interpolation of the target data in the target wind resource data at different heights of the target wind measurement tower using the adjacent wind resource data, and then jump to S5. The third processing module is used to execute S5 to correct the target wind resource data of different height layers of the target wind measurement tower into representative wind resource data using mesoscale weather data, and to perform tower shadow correction processing on the representative wind resource data to obtain the corrected wind resource data of the target wind measurement tower.