Method and device for site selection of wind observation point, equipment and readable storage medium

Abstract

The application relates to the technical field of wind farm wind measurement equipment site selection, and discloses a wind measurement observation point site selection method, device, equipment and readable storage medium. The site selection method comprises the following steps: carrying out grid division on a global region covering multiple wind farms, taking the grid center points of the grid regions as candidate points; obtaining the grid wind farm data, the grid terrain data and the grid wind resource data corresponding to each grid region; performing three-layer constraint screening on each candidate point to obtain a compliant candidate point; wherein the three-layer constraint screening comprises wind condition threshold constraint screening, terrain shelter constraint screening and stock avoidance constraint screening; obtaining a point comprehensive score according to the wind farm distribution evaluation in the effective coverage region corresponding to the compliant candidate point; and determining an observation point layout result in the global region according to the comprehensive scores of the points. The technical scheme of the application considers the balance of global coverage of the wind measurement points and the demand intensity of the key regions, and provides support for obtaining comprehensive wind measurement data in the global region.

Description

Technical Field

[0001] This invention relates to the field of wind farm wind measurement equipment site selection technology, and in particular to a method, apparatus, equipment and computer-readable storage medium for selecting wind measurement observation points. Background Technology

[0002] With the development and application of wind power technology, the effective prediction of wind farm power generation is crucial for the refined dispatch of the power grid; and the accurate prediction of wind farm power generation relies on comprehensive and reliable wind measurement data; this requires the deployment of a large number of wind measurement points in the area where the wind farm is located to continuously and reliably monitor the wind measurement data.

[0003] Currently, the mainstream wind measurement site selection in the industry is mainly implemented for a single wind farm. Based on the terrain factors, obstacles and other conditions around a single wind farm, multiple preferred wind measurement points for that wind farm are selected and wind measurement equipment is deployed. This leads to problems such as disordered layout of observation points, dense redundancy of observation points in some areas and blind spots in remote areas when scheduling multiple wind farms on a large scale.

[0004] Therefore, how to rationally arrange wind measurement points in the area where multiple wind farms are located, so as to provide a basis for comprehensive and reliable wind measurement data for the joint and refined grid scheduling of multiple wind farms, is one of the technical problems that need to be solved by those skilled in the art. Summary of the Invention

[0005] Therefore, the purpose of this invention is to provide a method, apparatus, device and storage medium for selecting wind measurement observation points, which can realize the full-area layout and automated batch screening of wind measurement observation points covering multiple wind farms, thereby improving the comprehensiveness and reliability of wind measurement data in the scenario of large-scale scheduling of multiple wind farms.

[0006] To solve the above-mentioned technical problems, the present invention provides a method for selecting the location of wind measurement observation points, comprising: The entire area covering multiple wind farms is divided into grids, and the center point of each grid area is used as a candidate point. Based on the wind farm data, terrain data and wind resource data of each wind farm in the entire region, determine the grid wind farm data, grid terrain data and grid wind resource data corresponding to each grid region; Based on the grid wind farm data, the grid terrain data, and the grid wind resource data, a three-layer constraint screening is performed on the candidate points corresponding to each grid area to obtain compliant candidate points. The three-layer constraint screening includes wind condition threshold constraint screening to remove candidate points whose wind condition indicators do not meet the corresponding wind condition adaptive threshold; terrain shading constraint screening to remove candidate points whose terrain shading angle is greater than the shading angle threshold; and existing avoidance constraint screening to remove candidate points whose distance from existing observation stations is less than the avoidance distance threshold. The wind condition adaptive threshold is determined based on the wind condition data of the neighboring wind farms corresponding to the candidate points. Based on the distribution of wind farms within the effective coverage area corresponding to the compliant candidate sites, the comprehensive site score of the compliant candidate sites is evaluated and obtained; Based on the comprehensive score of each compliant candidate point, the observation point layout result within the entire region is determined.

[0007] In one embodiment, the process of filtering each candidate location by wind condition threshold constraints includes: Based on the wind farm data and the wind resource data, determine at least one year of hourly wind speed time series data for each grid area within the preset area corresponding to each wind farm; The wind speed standard deviation is calculated by performing hourly wind speed time series data on each of the grid regions to obtain the regional wind speed standard deviation. The product of the wind speed standard deviation and the preset proportional coefficient is used as the wind condition adaptive threshold for each candidate point corresponding to all grid regions within the preset region. The wind speed standard deviation is calculated on the hourly wind speed time series data corresponding to the grid area where the candidate point is located to obtain the wind condition index corresponding to the candidate point. Candidate points whose wind condition index is greater than the corresponding wind condition adaptive threshold are removed.

[0008] In one embodiment, the process of performing terrain occlusion constraint screening on each of the candidate points includes: Based on the grid terrain data, digital terrain analysis technology is used to calculate and determine the maximum terrain occlusion angle within a 360° omnidirectional range centered on the candidate point. Candidate points whose maximum terrain occlusion angle is greater than the occlusion angle threshold are removed.

[0009] In one embodiment, the process of screening each candidate point using existing avoidance constraints includes: Determine whether there are existing observation stations within the avoidance distance threshold range of the candidate points. If so, remove the candidate points.

[0010] In one embodiment, after performing three-layer constraint screening on the candidate points corresponding to each of the grid regions to obtain compliant candidate points, the method further includes: The wind condition index corresponding to the candidate point is subjected to reverse Min-Max normalization, the maximum terrain shading angle is subjected to reverse Min-Max normalization, and the station spacing index is subjected to Min-Max normalization to obtain the normalized wind condition index, the normalized maximum shading angle, and the normalized station spacing index; wherein, the wind condition index is the standard deviation of wind speed in the grid area where the candidate point is located; the station spacing index is the straight-line distance between the candidate point and the nearest existing observation station; The normalized wind condition index, the normalized maximum occlusion angle, and the normalized station spacing index corresponding to the candidate points are weighted and summed to obtain a comprehensive screening score; Candidate points whose comprehensive screening scores do not meet the preset scoring threshold are removed.

[0011] In one embodiment, based on the distribution of wind farms within the effective coverage area corresponding to the compliant candidate sites, an evaluation of the comprehensive site score is obtained, including: Based on the wind farm data, the total number of wind farms within the effective coverage area centered on the compliant candidate sites and with the grid side length of the grid area as the radius is used as the deployment adaptation frequency of the compliant candidate sites. Based on the wind farm data, the total installed capacity and number of wind turbines in the grid area where the compliant candidate site is located are obtained; and the total installed capacity and number of wind turbines in the grid are weighted and summed to obtain the regional wind power development density corresponding to the compliant candidate site. The results of the Min-Max normalization processing of the deployment adaptation frequency and the regional wind power development density corresponding to the compliant candidate sites are weighted and summed to obtain the comprehensive score of the sites.

[0012] In one embodiment, after obtaining the comprehensive score of each location, and before determining the observation point deployment result within the entire region based on the comprehensive score of each compliant candidate location, the method further includes: Determine whether there is a grid area with coastal / land-sea attributes within the grid area where the compliant candidate point is located; For compliant candidate points whose grid area has coastal and land-sea attributes, the corresponding point comprehensive score is adjusted according to a preset correction coefficient; Based on the terrain data of the grid area where each of the aforementioned candidate compliance points is located, the slope standard deviation of the grid area where the candidate compliance points are located is obtained; Compliant candidate points whose slope standard deviation is greater than the slope standard deviation threshold are marked with complex interference. Accordingly, based on the comprehensive score of each of the compliant candidate points, the observation point deployment result within the entire region is determined, including: The compliant candidate points whose comprehensive score is greater than or equal to the first score threshold are classified as first-level high-priority points. The priority level of the compliant candidate points whose comprehensive score is less than the first score threshold but greater than or equal to the second score threshold is divided into secondary medium priority points. The priority of the compliant candidate points whose overall score is less than the second score threshold is divided into three levels of backup points. The priority level of the compliant candidate points with the aforementioned complex interference markers shall be reduced by one level. The output includes the observation point deployment results, which include the priority levels of each of the compliant candidate points.

[0013] The present invention also provides a device for selecting the location of a wind measurement observation point, comprising: The grid division module is used to divide the entire area covering multiple wind farms into grids, and the center point of each grid area is used as a candidate point. The data acquisition module is used to determine the grid wind farm data, grid terrain data, and grid wind resource data corresponding to each grid area based on the wind farm data, terrain data, and wind resource data of each wind farm in the entire region. The site selection module is used to perform three-layer constraint screening on candidate sites corresponding to each grid area based on the grid wind farm data, the grid terrain data, and the grid wind resource data to obtain compliant candidate sites. The three-layer constraint screening includes wind condition threshold constraint screening to remove candidate sites whose wind condition indicators do not meet the corresponding wind condition adaptive threshold; terrain shading constraint screening to remove candidate sites whose terrain shading angle is greater than the shading angle threshold; and existing avoidance constraint screening to remove candidate sites whose distance from existing observation stations is less than the avoidance distance threshold. The wind condition adaptive threshold is determined based on the wind condition data of the neighboring wind farms corresponding to the candidate sites. The site scoring module is used to evaluate and obtain the comprehensive site score of the compliant candidate site based on the distribution of wind farms within the effective coverage area corresponding to the compliant candidate site. The deployment calculation module is used to determine the deployment result of observation points within the entire region based on the comprehensive score of each of the compliant candidate points.

[0014] The present invention also provides a site selection device for wind measurement observation points, comprising: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the wind measurement observation point location method as described in any of the preceding claims.

[0015] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the wind measurement observation point selection method as described in any of the preceding claims.

[0016] The wind measurement observation point selection method provided by this invention includes: dividing the entire area covering multiple wind farms into grids, and using the center point of each grid area as a candidate point; determining the corresponding grid wind farm data, grid topographic data, and grid wind resource data for each grid area based on wind farm data, topographic data, and wind resource data for each grid area; and performing a three-layer constraint screening on the candidate points corresponding to each grid area based on the grid wind farm data, grid topographic data, and grid wind resource data to obtain compliant candidate points; wherein, the three-layer constraint screening includes eliminating points whose wind condition indicators do not meet the requirements. The selection process involves several steps: first, screening candidate sites based on wind condition adaptive thresholds; second, filtering candidate sites by terrain shading constraints (removing those with terrain shading angles greater than the threshold); and third, filtering candidate sites by existing site avoidance constraints (removing those with distances less than the avoidance distance threshold from existing observation stations). The wind condition adaptive threshold is determined based on wind condition data from neighboring wind farms corresponding to the candidate sites. The comprehensive score of each compliant candidate site is then evaluated based on the distribution of wind farms within the effective coverage area. Finally, the overall observation point deployment results for the entire region are determined based on the comprehensive scores of each compliant candidate site.

[0017] This application employs a gridded approach to divide the entire area covering multiple wind farms, using the grid center point as candidate locations. This expands the selection scope of observation points from a single wind farm to a region where multiple wind farms are distributed. Furthermore, it combines wind condition adaptive threshold screening, terrain obstruction screening, and existing site avoidance screening into a three-layer constraint for batch automated screening of each candidate location. The wind condition adaptive threshold is independently determined by the wind condition data of the neighboring wind farms corresponding to the candidate location, ensuring that the wind condition screening criteria for different wind power areas change with the actual wind resource fluctuation characteristics. Terrain obstruction screening eliminates locations severely obstructed by terrain, and existing site avoidance screening eliminates locations that overlap with or are redundant with existing observation equipment. Finally, the distribution of wind farms within the effective coverage area corresponding to the compliant candidate locations is evaluated to obtain a comprehensive score for each location, and the final deployment result of all observation points within the entire region is determined based on the comprehensive score. Therefore, the decision-making process for the deployment of wind measurement observation points in this application not only focuses on the eligibility of individual observation points, but also enables automated batch screening of observation points in the entire region where multiple wind farms are distributed. This ensures that the spatial layout of the selected observation points balances the balance of coverage across the entire region with the demand intensity of key areas, effectively alleviating the problems of disordered, dense, and blind spot layout of observation points, as well as insufficient data integrity and consistency in the scenario of large-scale multi-wind farms. This provides reliable support for the wind measurement data required for wind power prediction across the entire region.

[0018] The site selection device, equipment, and computer-readable storage medium for wind measurement observation points provided in this application also have the same beneficial effects as described above. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating the method for selecting wind measurement observation points provided in this application embodiment; Figure 2 This is a structural block diagram of the wind measurement observation point location selection device provided in the embodiments of this application. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] like Figure 1 As shown, Figure 1 This is a flowchart illustrating the method for selecting wind measurement observation points according to an embodiment of this application.

[0023] In one specific embodiment of this application, the method for selecting the location of the wind measurement observation point can be as follows.

[0024] S1: Divide the entire area covering multiple wind farms into grids, and use the center point of each grid area as a candidate point.

[0025] In this embodiment, the "global region" refers to the target geographical area where wind measurement observation points need to be strategically located, covering multiple wind farms. The global region encompasses the entire geographical area where wind measurement observation point site selection is required, and multiple wind farms are distributed within this area. The global region is divided into several grid areas according to a preset grid size. The grid size can be set based on the density of wind farm distribution and the required wind power prediction accuracy within the global region. For example, for conventional application scenarios, a basic grid size of 10km × 10km can be used; for scenarios with high wind farm density, a grid size of 8km × 8km can be used to improve point location accuracy; and for scenarios with sparse wind farm distribution, a grid size of 12km × 12km can be used to reduce redundant points.

[0026] After completing the grid division, the geometric center point of each grid region is used as the candidate location for the corresponding wind measurement observation point. In this way, a full-coverage gridded candidate point library covering the entire region can be constructed, and all subsequent filtering, avoidance, and sorting operations are performed based on this candidate point library.

[0027] S2: Based on the wind farm data, terrain data and wind resource data of each wind farm in the entire region, determine the grid wind farm data, grid terrain data and grid wind resource data corresponding to each grid area.

[0028] This step first requires collecting three core basic data types: wind farm data, terrain data, and wind resource data for the entire region.

[0029] The wind farm data covers the attribute information of all grid-connected and under-construction wind farms in the entire region; specifically, it may include the name of the wind farm, geographical coordinates (longitude and latitude), installed capacity (unit: megawatt, MW), number of wind turbines, and grid connection status (grid-connected, under construction, planned, etc.).

[0030] Specifically, terrain data can be high-precision Digital Elevation Model (DEM) data. DEM data stores the surface elevation information of the entire area in raster form, with each raster point recording its elevation value (in meters). DEM data can be used to subsequently calculate the 360° all-around terrain occlusion angle of candidate points and the slope standard deviation of each grid area. DEM data can be obtained from publicly available geographic data platforms.

[0031] Wind resource data can be obtained based on meteorological data, specifically long-term meteorological reanalysis data for the entire region, including hourly wind speed time-series data for the entire region over at least one year. In other words, the meteorological data contains a sequence of hourly wind speed observations continuously recorded for 8760 hours per year for each geographical location within the entire region over the past many years (e.g., 5 or 10 years). Meteorological reanalysis data can be obtained from general meteorological databases or reanalysis datasets.

[0032] After collecting the above three types of basic data, all raw data are subjected to unified standardized preprocessing, including: converting all data into the WGS84 coordinate system, removing abnormal extreme values ​​in the data (such as wind speed values ​​that exceed the reasonable physical range), and deleting invalid wind speed time series data with a missing percentage exceeding a preset ratio (such as 5%).

[0033] Based on the gridded data, the aforementioned global-level wind farm data, topographic data, and wind resource data are mapped to each grid region, determining the corresponding grid wind farm data, grid topographic data, and grid wind resource data for each grid region. For any given grid region, its grid wind farm data includes: the name of the wind farm, its installed capacity, and the number of wind turbines within that grid region. For large wind farms spanning multiple grids, the wind farm is included in the grid wind farm data of multiple grid regions. Grid topographic data includes the topographic elevation values ​​of the grid region extracted from the DEM data. Grid wind resource data includes the hourly wind speed time-series data corresponding to the grid region extracted from meteorological reanalysis data, used for subsequent calculation of wind condition adaptive thresholds.

[0034] S3: Based on grid wind farm data, grid terrain data, and grid wind resource data, candidate locations corresponding to each grid area are screened using a three-layer constraint to obtain compliant candidate locations. The three-layer constraint screening includes wind condition threshold constraint screening to remove candidate locations whose wind condition indicators do not meet the corresponding wind condition adaptive threshold, terrain shading constraint screening to remove candidate locations whose terrain shading angle is greater than the shading angle threshold, and existing avoidance constraint screening to remove candidate locations whose distance from existing observation stations is less than the avoidance distance threshold. The wind condition adaptive threshold is determined based on the wind condition data of the neighboring wind farms corresponding to the candidate locations.

[0035] After constructing a global gridded candidate point database and determining the various data types corresponding to each grid area, a three-layer constraint screening process can be applied to each candidate point. The three-layer constraint screening is executed sequentially in the order of wind condition threshold constraint screening, terrain shading constraint screening, and existing stock avoidance constraint screening. Each layer of screening is automatically determined based on quantitative indicators and preset thresholds. It should be noted that the execution order of the above three-layer constraint screening is not unique and fixed; in practical applications, the execution order can be adjusted according to the specific terrain characteristics and engineering requirements of the area.

[0036] The specific implementation methods of the three-layer constraint filtering are explained in detail below.

[0037] The purpose of wind condition adaptive threshold constraint screening is to more accurately eliminate candidate sites with excessively volatile wind resources and poor observation representativeness. Compared with the traditional approach of using a uniform fixed threshold, this embodiment calculates the wind condition judgment threshold for the vicinity of each wind farm independently based on the temporal statistical characteristics of its own wind resources. This ensures that the screening criteria for different wind power areas correspond to their actual wind conditions, and thus the wind condition adaptive threshold for each candidate site can be determined based on the wind condition data of the neighboring wind farms corresponding to that candidate site.

[0038] In one specific embodiment, the process of screening candidate locations based on wind condition threshold constraints includes: S311: Based on wind farm data and wind resource data, determine at least one year of hourly wind speed time series data for each grid area within the preset area corresponding to each wind farm; S312: Perform wind speed standard deviation calculation on the hourly wind speed time series data of each grid area to obtain the regional wind speed standard deviation, and use the product of the wind speed standard deviation and the preset proportional coefficient as the wind condition adaptive threshold for each candidate point corresponding to all grid areas within the preset area. S313: Calculate the standard deviation of wind speed for the hourly wind speed time series data corresponding to the grid area where the candidate point is located, and obtain the wind condition index corresponding to the candidate point. S314: Remove candidate points whose wind condition indicators are greater than the corresponding wind condition adaptive threshold.

[0039] In this embodiment, for each independent wind farm, the corresponding preset area is a region consisting of 10×10 grid areas centered on the wind farm. For example, assuming the grid size is 10km×10km, the preset area is an area of ​​100km×100km, and the wind farm is located near the center of this area.

[0040] Based on wind resource data, hourly wind speed time-series data corresponding to all grid areas within the preset area can be extracted; the time span of this hourly wind speed time-series data is at least one year (i.e., 8760 hours of continuous observation data). The hourly wind speed time-series data of all grid areas within the preset area can be merged into a single dataset, and the regional wind speed standard deviation σ of this dataset can be calculated uniformly; specifically, the formula for calculating the regional wind speed standard deviation σ is: Where N is the total number of hourly wind speed data points for all grid areas within the preset area over many years. For example, the total number of wind speed data points for 10×10 grid units over 8760 consecutive hours is 876000 data points. For the first Wind speed data, This is the average value of all wind speed data within the preset area.

[0041] The wind condition adaptive threshold T for the wind farm is generated based on the regional wind speed standard deviation σ. Specifically, the regional wind speed standard deviation σ is multiplied by a preset scaling factor α to obtain the wind condition adaptive threshold T = α × σ. The preset scaling factor α can range from 0.6 to 0.9. For example, α can be 0.8. This is necessary to filter grid points with drastic wind speed fluctuations and poor observation representativeness, while avoiding overly strict thresholds that could lead to blind spots in the entire observation area. In practical applications, the value of α can be flexibly adjusted within the range of 0.6 to 0.9 according to the requirements of observation accuracy and coverage.

[0042] The wind condition adaptive threshold T is the wind condition judgment standard for all candidate points within the preset area. For each candidate point (i.e., each grid area) within the preset area, the hourly wind speed time series data of the grid area itself is extracted, and the wind speed standard deviation σ_i (i represents the i-th grid area) of the grid area is calculated separately as the wind condition index of the candidate point.

[0043] For each candidate location, its wind condition index σ_i is compared with the wind condition adaptive threshold T: if σ_i>T, it means that the temporal fluctuation of the wind speed at the candidate location is greater than the overall average level of the region, and the wind speed data observed at the location is not representative enough to accurately characterize the wind resource characteristics of the region. Therefore, the candidate location is removed from the candidate location database; if σ_i≤T, the candidate location passes the wind condition threshold constraint screening, and is retained and enters the next layer of screening.

[0044] Furthermore, for candidate locations not located within the preset area corresponding to any wind farm, since no wind condition data from any neighboring wind farms can be obtained for adaptive threshold determination, they can all be retained in the wind condition threshold constraint screening. For candidate locations located within overlapping areas corresponding to two or more wind farms, the adaptive wind condition threshold corresponding to any of the aforementioned wind farms can be used for determination, or the most stringent threshold (i.e., the minimum value) among the thresholds of each wind farm can be used for determination to ensure the reliability of the screening results.

[0045] The purpose of terrain occlusion constraint screening is to eliminate candidate locations that are severely obstructed by terrain and where the wind-measuring lidar beam cannot effectively measure the wind field in all directions. The process of terrain occlusion constraint screening for each candidate location may include: S321: Based on the grid terrain data, digital terrain analysis technology is used to calculate and determine the maximum terrain occlusion angle within a 360° omnidirectional range centered on the candidate point. S322: Remove candidate points whose maximum terrain occlusion angle is greater than the occlusion angle threshold.

[0046] In this embodiment, the candidate point is the center point of the grid area requiring terrain occlusion assessment. The process of determining its maximum terrain occlusion angle can be as follows: First, using the candidate point as the center, extract the terrain elevation data within a radius R around the candidate point from the DEM data. The value of this radius R can be flexibly set according to the regional terrain characteristics; for example, R can be 10 km. Then, using the elevation value of the candidate point as a reference, divide the 360° omnidirectional area into 72 directional sectors at 5° azimuth intervals (or other intervals). Within each directional sector, calculate the elevation angle of each grid point relative to the candidate point in that direction. The formula for calculating the elevation angle is: Elevation angle = arctan[(terrain elevation value - point elevation value) / horizontal distance]; where the terrain elevation is the altitude value of a grid point in that directional sector, the point elevation is the altitude value of the candidate point, and the horizontal distance is the horizontal straight-line distance between the grid point and the candidate point.

[0047] Within each directional sector, the maximum elevation angle of all grid points is taken as the terrain occlusion angle for that sector. After traversing all 72 directional sectors, the maximum occlusion angle among all directions is taken as the maximum terrain occlusion angle corresponding to the candidate point. Candidate points with a maximum terrain occlusion angle greater than the occlusion angle threshold are uniformly eliminated. The occlusion angle threshold serves as a reference for whether the beam of the wind-measuring lidar will be truncated by the terrain. When the occlusion angle in a certain direction is greater than the occlusion angle threshold, the lidar beam will not be able to complete the wind field measurement in that direction. The value range of the occlusion angle threshold can be 10° to 20°, for example, it can be 15°.

[0048] Furthermore, while performing terrain occlusion constraint filtering, the slope standard deviation of each grid region can be extracted simultaneously. The slope standard deviation is used to characterize the complexity of terrain undulation within the grid region. Specifically, the elevation values ​​of each raster cell within the grid region are extracted from the DEM data, the slope value of each raster point (i.e., the rate of elevation change between the raster point and its adjacent raster points) is calculated, and then the standard deviation of the slope values ​​of all raster points is calculated to obtain the slope standard deviation of the grid region. For grid regions with a slope standard deviation greater than a slope standard deviation threshold, even if their comprehensive occlusion angle is not greater than the occlusion angle threshold, complex interference will be marked. The value range of this slope standard deviation threshold can be 12° to 20°; for example, the slope standard deviation threshold is 18°.

[0049] The purpose of screening for existing site avoidance constraints is to prevent new wind measurement observation points from overlapping with existing observation equipment and causing data redundancy within the region. Existing observation sites refer to existing observation equipment already built within the entire region; for example, existing wind measurement lidar sites, meteorological reference stations, etc. These existing sites are already continuously observing wind resource data. If a new site is too close to an existing site, their observation data will highly overlap, wasting observation resources and failing to provide any new information gain to the overall wind measurement grid.

[0050] When screening for existing observation sites to avoid constraints, all candidate points and all existing observation stations are first transformed to the same coordinate system, such as the WGS84 coordinate system. Then, all candidate points are traversed, and the horizontal straight-line distance between each candidate point and all existing observation stations in the region is calculated. For any candidate point, if the horizontal straight-line distance to any existing observation station is less than the avoidance distance threshold, it is determined that there is observation overlap and data redundancy between the candidate point and existing stations, and the candidate point is removed from the candidate point database. Only when the distance between the candidate point and all existing observation stations is not less than the avoidance distance threshold is the candidate point retained and proceeded to the next step. The avoidance distance threshold ranges from 6km to 10km, for example, it can be 8km. The selection of the avoidance distance threshold can be flexibly adjusted according to the distribution density of wind farms in the region: for areas with high-density wind farm distribution, the avoidance distance threshold can be appropriately reduced to retain more candidate points; for areas with low-density wind farm distribution, the avoidance distance threshold can be appropriately increased to avoid excessive concentration of observation resources.

[0051] After the above three-layer constraint screening, the points that remain in the candidate point pool are considered compliant candidate points and will proceed to the subsequent point evaluation and ranking process. In addition, in practical applications, besides using the three-layer constraint screening to eliminate non-compliant candidate points, screening can also be performed based on a multi-indicator normalized comprehensive scoring method.

[0052] In one specific embodiment, after performing three-layer constraint screening on the candidate points corresponding to each grid region, the process may further include: S323: Perform reverse Min-Max normalization on the wind condition index corresponding to the candidate point, reverse Min-Max normalization on the maximum terrain shading angle, and Min-Max normalization on the station spacing index to obtain the normalized wind condition index, normalized maximum shading angle, and normalized station spacing index; where the wind condition index is the standard deviation of wind speed in the grid area where the candidate point is located; the station spacing index is the straight-line distance between the candidate point and the nearest existing observation station. S324: The normalized wind condition index, normalized maximum occlusion angle and normalized site spacing index corresponding to the candidate sites are weighted and summed to obtain a comprehensive screening score; S325: Remove candidate points whose comprehensive screening scores do not meet the preset scoring threshold.

[0053] In this embodiment, the wind condition index is the standard deviation of wind speed in the grid area where the candidate point is located. The smaller the value, the more stable the wind conditions in the grid area where the candidate point is located. As mentioned earlier, the smaller the value of the maximum terrain obstruction angle, the more open the terrain in the grid area where the candidate point is located. The station spacing index is the straight-line distance (straight-line distance in the horizontal plane) between the candidate point and the nearest existing observation station. The larger the value, the better the avoidance. After normalizing the above three indices, weights are assigned, and a weighted sum is obtained to obtain the comprehensive screening score of each candidate point. Candidate points whose comprehensive screening scores do not meet the preset score threshold (below the preset score threshold) are eliminated.

[0054] In addition, in efficient operation scenarios such as rapid planning or emergency networking in high-density wind power areas, the above-mentioned comprehensive screening and scoring can be used directly to select candidate sites, instead of being limited to the three-layer constraint screening.

[0055] Based on the above discussion, after three-level constraint screening and comprehensive screening and scoring of each candidate point, the remaining candidate points are compliant candidate points and can proceed to the subsequent operation process.

[0056] S4: Based on the distribution of wind farms within the effective coverage area corresponding to the compliant candidate sites, evaluate the overall score of the compliant candidate sites.

[0057] This step involves a comprehensive evaluation of each compliant candidate site, calculating a comprehensive score for each site. This comprehensive score characterizes the overall deployment value of the candidate site from the perspective of multi-wind farm coordinated planning. Therefore, this comprehensive score is determined based on the wind farm distribution.

[0058] In one specific embodiment, the process of determining the overall score of each compliant candidate location may include: S41: Based on wind farm data, the total number of wind farms within the effective coverage area centered on the compliant candidate sites and with the grid side length of the grid area as the radius is used as the deployment adaptation frequency of the compliant candidate sites. S42: Based on wind farm data, obtain the total installed capacity and number of wind turbines in the grid area where the candidate compliant site is located; and perform a weighted summation of the total installed capacity and number of wind turbines in the grid to obtain the regional wind power development density corresponding to the candidate compliant site. S43: The results of the Min-Max normalization of the deployment adaptation frequency and regional wind power development density corresponding to the compliant candidate sites are weighted and summed to obtain the comprehensive score of the sites.

[0059] In this embodiment, the deployment adaptation frequency of each compliant candidate point can be calculated first. Deployment adaptation frequency refers to the total number of independent wind farms within the effective coverage radius centered on the compliant candidate point and the grid side length of the grid area where the point is located. For example, if the effective coverage area of ​​the circle centered on the compliant candidate point and the grid side length contains two independent wind farms, wind farm A and wind farm B, then the deployment adaptation frequency of this compliant candidate point is 2. The deployment adaptation frequency characterizes how many wind farms' wind measurement data needs a compliant candidate point can simultaneously support; the higher the deployment adaptation frequency, the greater the comprehensive deployment value of the compliant candidate point in the overall layout of multiple wind farms. Deploying wind measurement equipment using this compliant candidate point as a wind measurement point can simultaneously provide data support for wind power prediction of multiple wind farms.

[0060] Next, obtain the wind power development density index within the grid area where each compliant candidate site is located. Wind power development density is a composite quantitative parameter, integrating two data points: the total installed capacity of grid-connected and under-construction wind farms within the grid area, and the total number of wind turbines. For example, regional wind power development density = total grid installed capacity × 0.7 + total grid wind turbines × 0.3; where the total grid installed capacity is the total installed capacity of wind farms within the grid area, in megawatts (MW), and the number of grid wind turbines is the total number of wind turbines within the grid area; the installed capacity weight is 0.7, and the number of wind turbines weight is 0.3. In wind power industry demand forecasting, installed capacity has a greater impact on the intensity of demand for power forecasting, therefore it is assigned a higher weight.

[0061] The wind power development density in this region characterizes the overall scale of wind power development and the intensity of predicted demand within a grid area. A higher development density indicates a more urgent need for wind measurement data in that grid area, and therefore, a higher priority should be given to deploying observation points in that region.

[0062] After obtaining the deployment adaptation frequency and regional wind power development density of each compliant candidate site, the two types of indicators are subjected to Min-Max normalization to eliminate the dimensional differences between different indicators.

[0063] The formula for Min-Max normalization is Norm=(X-X_min) / (X_max-X_min); where X is the index value of a certain compliance candidate point (deployment adaptation frequency or regional wind power development density), Norm is the normalized value, X_max is the maximum value of the index value corresponding to all compliance candidate points in the entire region, and X_min is the minimum value of the index value corresponding to all compliance candidate points in the entire region.

[0064] After normalization, the weighted sum of the normalization results of the two types of indicators can be used to obtain the comprehensive score of the site. For example, the comprehensive score of the site = normalized score of deployment adaptation frequency × 0.6 + normalized score of regional wind power development density × 0.4. Among them, the weights of 0.6 and 0.4 correspond to the importance of deployment adaptation frequency and regional wind power development density in the comprehensive evaluation, respectively. In practical applications, other weight values ​​can also be used.

[0065] The overall score of this location represents the priority of a compliant candidate location in the overall deployment of multiple wind farms in the region. The higher the score, the greater the wind measurement benefits that the location can generate after deployment, and it should be prioritized for deployment.

[0066] S5: Determine the layout of observation points within the entire region based on the comprehensive score of each compliant candidate point.

[0067] As mentioned above, after obtaining the comprehensive score of each compliant candidate point, the higher the comprehensive score, the greater the wind measurement benefit that the point can generate after deployment, and the point should be deployed first. Therefore, in practical applications, the final deployment result of observation points in the entire region can be determined based on the size of the comprehensive score.

[0068] In one embodiment, all compliant candidate sites can be sorted in descending order of their comprehensive scores, with the top-ranked sites being prioritized for deployment. Furthermore, if there is a limited budget for the number of observation devices to be deployed, a corresponding number of sites can be selected as the final deployment sites based on the ranking results, from highest to lowest.

[0069] In another embodiment, the overall score of the locations can be adjusted by incorporating other auxiliary factors before ranking and deployment decisions are made; specifically including: S501: Determine whether there is a grid area with coastal and marine attributes within the grid area where the compliant candidate point is located; S502: For compliant candidate points whose grid area has coastal and land-sea attributes, the comprehensive score of the corresponding points is adjusted according to the preset correction coefficient; S503: Based on the topographic data of the grid area where each compliant candidate point is located, obtain the slope standard deviation of the grid area where the compliant candidate point is located; S504: Mark compliant candidate points with complex interference if the corresponding slope standard deviation is greater than the slope standard deviation threshold.

[0070] Accordingly, the process of determining the layout of observation points across the entire region may include: S51: The priority level of compliant candidate points with a comprehensive score greater than or equal to the first score threshold is classified as a first-level high-priority point. S52: The priority level of compliant candidate points whose comprehensive score is less than the first score threshold but greater than or equal to the second score threshold is classified as secondary medium priority points. S53: The priority level of compliant candidate sites whose comprehensive score is less than the second score threshold is divided into three levels of backup sites. S54: Reduce the priority level of compliant candidate sites with complex interference markers by one level; S55: Outputs the observation point layout results, including the priority levels of each compliant candidate point.

[0071] In this embodiment, for compliant candidate sites located in coastal areas (coastal areas refer to the range of coastline extending inland at a preset distance based on the DEM land-sea vector boundary, such as within 15km), due to the drastic intraday fluctuations in wind resources and the higher requirements for wind measurement accuracy, the comprehensive score of such compliant candidate sites is multiplied by a coastal correction coefficient (e.g., 1.1, adjustable range of 1.05 to 1.2) to improve the deployment priority of sites in coastal areas.

[0072] After obtaining the revised overall site score, all compliant candidate sites can be divided into multiple deployment priority levels based on the revised overall site score. For example, three priority levels can be defined: 1) Compliant candidate sites with a revised comprehensive score of 0.7 or higher are classified as first-level high-priority sites, indicating that the comprehensive deployment conditions of such sites are optimal and should be deployed first. 2) Compliant candidate sites with a revised overall score greater than or equal to 0.4 and less than 0.7 are classified as secondary medium-priority sites, indicating that the overall deployment conditions of such sites are moderate and they can be deployed if resources permit. 3) Compliant candidate sites with a revised comprehensive score of less than 0.4 are classified as Level 3 backup sites, indicating that the comprehensive deployment conditions of such sites are relatively limited and they can be used as backup sites for subsequent expansion or supplementary deployment.

[0073] Based on this, for the aforementioned compliant candidate points marked with complex interference (i.e., points with slope standard deviation greater than the slope standard deviation threshold), the priority level corresponding to their comprehensive score is reduced by one level on the basis of the classification.

[0074] Finally, the output includes the priority level of each compliant candidate point and the corresponding geographical location of the observation point layout. This layout result can be presented in list form or visualized on a Geographic Information System (GIS) map.

[0075] In summary, this application employs a gridded approach to divide the entire area covering multiple wind farms, using the grid center point as candidate locations. This expands the selection scope of observation points from a single wind farm to a comprehensive area where multiple wind farms are distributed. Furthermore, it combines wind condition adaptive threshold screening, terrain obstruction screening, and existing resource avoidance screening into a three-layer constraint for batch automated screening of each candidate location. The wind condition adaptive threshold is independently determined by the wind condition data of the neighboring wind farms corresponding to the candidate location, ensuring that the wind condition screening criteria for different wind power areas change with the actual wind resource fluctuation characteristics. Terrain obstruction screening eliminates locations severely obstructed by terrain, and existing resource avoidance screening eliminates locations that overlap with or are redundant with existing observation equipment. Finally, the distribution of wind farms within the effective coverage area corresponding to the compliant candidate locations is evaluated to obtain a comprehensive score for each location, and the final deployment result of all observation points within the entire area is determined based on the comprehensive score. Therefore, the decision-making process for the deployment of wind measurement observation points in this application not only focuses on the eligibility of individual observation points, but also enables automated batch screening of observation points in the entire region where multiple wind farms are distributed. This ensures that the spatial layout of the selected observation points balances the balance of coverage across the entire region with the demand intensity of key areas, effectively alleviating the problems of disordered, dense, and blind spot layout of observation points, as well as insufficient data integrity and consistency in the scenario of large-scale multi-wind farms. This provides reliable support for the wind measurement data required for wind power prediction across the entire region.

[0076] The following describes the site selection device for wind measurement observation points provided in the embodiments of this application. The site selection device for wind measurement observation points described below and the site selection method for wind measurement observation points described above can be referred to in correspondence with each other.

[0077] Figure 2 The structural block diagram of the wind measurement observation point site selection device provided in the embodiments of this application is shown below. Figure 2 The site selection device for the wind measurement observation point may include: The grid division module 100 is used to divide the entire area covering multiple wind farms into grids, and the center point of each grid area is used as a candidate point. The data acquisition module 200 is used to determine the grid wind farm data, grid terrain data, and grid wind resource data corresponding to each grid area based on the wind farm data, terrain data, and wind resource data of each wind farm in the entire region. The site selection module 300 is used to perform three-layer constraint screening on candidate sites corresponding to each grid area based on the grid wind farm data, the grid terrain data, and the grid wind resource data to obtain compliant candidate sites. The three-layer constraint screening includes wind condition threshold constraint screening (removing candidate sites whose wind condition indicators do not meet the corresponding wind condition adaptive threshold), terrain shading constraint screening (removing candidate sites whose terrain shading angle is greater than the shading angle threshold), and existing site avoidance constraint screening (removing candidate sites whose distance from existing observation stations is less than the avoidance distance threshold). The wind condition adaptive threshold is determined based on the wind condition data of the neighboring wind farms corresponding to the candidate sites. The site scoring module 400 is used to evaluate and obtain the comprehensive site score of the compliant candidate site based on the distribution of wind farms within the effective coverage area corresponding to the compliant candidate site. The deployment calculation module 500 is used to determine the deployment result of observation points within the entire region based on the comprehensive score of each of the compliant candidate points.

[0078] In one optional embodiment of this application, the site selection module 300 is specifically used to determine, based on wind farm data and wind resource data, at least one year's worth of hourly wind speed time-series data for each grid area within a preset area corresponding to each wind farm; perform wind speed standard deviation calculation on the hourly wind speed time-series data of each grid area to obtain the regional wind speed standard deviation, and use the product of the wind speed standard deviation and a preset proportional coefficient as the wind condition adaptive threshold for each candidate site corresponding to all grid areas within the preset area; perform wind speed standard deviation calculation on the hourly wind speed time-series data corresponding to the grid area where the candidate site is located to obtain the wind condition index corresponding to the candidate site; and eliminate candidate sites whose wind condition index is greater than the corresponding wind condition adaptive threshold.

[0079] In one optional embodiment of this application, the point selection module 300 is specifically used to calculate the maximum terrain occlusion angle within a 360° omnidirectional range centered on the candidate point based on the grid terrain data and digital terrain analysis technology; and to remove candidate points whose maximum terrain occlusion angle is greater than the occlusion angle threshold.

[0080] In one optional embodiment of this application, the point selection module 300 is specifically used to determine whether there are existing observation stations within the avoidance distance threshold range of the candidate point; if so, the candidate point is removed.

[0081] In an optional embodiment of this application, the point selection module 300 is further configured to perform three-layer constraint selection on the candidate points corresponding to each grid area, and then perform reverse Min-Max normalization on the wind condition index, the maximum terrain occlusion angle, and the station spacing index corresponding to the candidate points, to obtain normalized wind condition index, normalized maximum occlusion angle, and normalized station spacing index; wherein, the wind condition index is the standard deviation of wind speed in the grid area where the candidate point is located; the station spacing index is the straight-line distance between the candidate point and the nearest existing observation station; the normalized wind condition index, the normalized maximum occlusion angle, and the normalized station spacing index corresponding to the candidate point are weighted and summed to obtain a comprehensive selection score; and candidate points whose comprehensive selection score does not meet the preset score threshold are eliminated.

[0082] In one optional embodiment of this application, the site scoring module 400 is specifically used to obtain, based on wind farm data, the total number of wind farms within the effective coverage area centered on the compliant candidate site and with the grid side length of the grid area as the radius, as the deployment adaptation frequency of the compliant candidate site; based on the wind farm data, obtain the total installed capacity of the grid and the number of wind turbines in the grid area where the compliant candidate site is located; and perform a weighted summation of the total installed capacity of the grid and the number of wind turbines in the grid to obtain the regional wind power development density corresponding to the compliant candidate site; and perform a weighted summation of the results of the deployment adaptation frequency and the regional wind power development density corresponding to the compliant candidate site after performing Min-Max normalization processing, respectively, to obtain the site comprehensive score.

[0083] In one optional embodiment of this application, the point scoring module 400 is further configured to, after obtaining the comprehensive point score, determine whether there is a grid area with coastal and land-sea attributes in the grid area where the compliant candidate point is located; for compliant candidate points whose grid area has coastal and land-sea attributes, the corresponding comprehensive point score is corrected according to a preset correction coefficient; based on the terrain data of the grid area where each compliant candidate point is located, the slope standard deviation of the grid area where the compliant candidate point is located is obtained; and compliant candidate points whose slope standard deviation is greater than the slope standard deviation threshold are marked with complex interference. Accordingly, the deployment calculation module 500 is specifically used to classify the priority levels of compliant candidate points whose comprehensive scores are greater than or equal to the first score threshold as first-level high-priority points; to classify the priority levels of compliant candidate points whose comprehensive scores are less than the first score threshold but greater than or equal to the second score threshold as second-level medium-priority points; to classify the priority levels of compliant candidate points whose comprehensive scores are less than the second score threshold as third-level backup points; to reduce the priority level of compliant candidate points with complex interference markers by one level; and to output the observation point deployment results containing the priority levels of each compliant candidate point.

[0084] The wind measurement observation point location device in this embodiment is used to implement the aforementioned wind measurement observation point location method. Therefore, the specific implementation of the wind measurement observation point location device can be found in the embodiment section of the wind measurement observation point location method above. The specific implementation can be referred to the description of the corresponding embodiment, which will not be repeated here.

[0085] This application also provides a device for selecting the location of wind measurement observation points, including: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the wind measurement observation point location method as described in any of the preceding claims.

[0086] The steps of the wind measurement observation point location method executed by this processor include: The entire area covering multiple wind farms is divided into grids, and the center point of each grid area is used as a candidate point. Based on the wind farm data, terrain data, and wind resource data of each wind farm in the entire area, the corresponding grid wind farm data, grid terrain data, and grid wind resource data for each grid area are determined. Based on the grid wind farm data, grid terrain data, and grid wind resource data, a three-layer constraint screening is performed on the candidate points corresponding to each grid area to obtain compliant candidate points. The three-layer constraint screening includes eliminating points whose wind condition indicators do not meet the corresponding wind condition adaptive threshold. The candidate sites are selected based on wind condition threshold constraints, and candidates with terrain shading angles greater than the shading angle threshold are selected based on terrain shading constraints, while candidates with distances less than the avoidance distance threshold are selected based on existing avoidance constraints. The wind condition adaptive threshold is determined based on wind condition data from neighboring wind farms corresponding to the candidate sites. The comprehensive score of the compliant candidate sites is evaluated based on the distribution of wind farms within the effective coverage area corresponding to the compliant candidate sites. The deployment results of observation points in the entire region are determined based on the comprehensive scores of each compliant candidate site.

[0087] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the wind measurement observation point selection method as described in any of the preceding claims.

[0088] The computer-readable storage medium may include random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, register, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0089] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that the elements inherent in a process, method, article, or apparatus that includes a list of elements are included. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. Additionally, portions of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of corresponding technical solutions in the prior art have not been described in detail to avoid excessive elaboration.

[0090] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. It should be noted that those skilled in the art can make several improvements and modifications to the present invention without departing from the principles of the present invention, and these improvements and modifications also fall within the protection scope of the present invention.

Claims

1. A method for selecting the location of wind measurement observation points, characterized in that, include: The entire area covering multiple wind farms is divided into grids, and the center point of each grid area is used as a candidate point. Based on the wind farm data, terrain data and wind resource data of each wind farm in the entire region, determine the grid wind farm data, grid terrain data and grid wind resource data corresponding to each grid region; Based on the grid wind farm data, the grid terrain data, and the grid wind resource data, a three-layer constraint screening is performed on the candidate points corresponding to each grid area to obtain compliant candidate points. The three-layer constraint screening includes wind condition threshold constraint screening to remove candidate points whose wind condition indicators do not meet the corresponding wind condition adaptive threshold, terrain shading constraint screening to remove candidate points whose terrain shading angle is greater than the shading angle threshold, and existing avoidance constraint screening to remove candidate points whose distance from existing observation stations is less than the avoidance distance threshold. The wind condition adaptive threshold is determined based on the wind condition data of the neighboring wind farms corresponding to the candidate points. Based on the distribution of wind farms within the effective coverage area corresponding to the compliant candidate sites, the comprehensive site score of the compliant candidate sites is evaluated and obtained; Based on the comprehensive score of each compliant candidate point, the observation point layout result within the entire region is determined.

2. The method for selecting the location of wind measurement observation points as described in claim 1, characterized in that, The process of filtering each candidate location by wind condition threshold constraints includes: Based on the wind farm data and the wind resource data, determine at least one year of hourly wind speed time series data for each grid area within the preset area corresponding to each wind farm; The wind speed standard deviation is calculated by performing hourly wind speed time series data on each of the grid regions to obtain the regional wind speed standard deviation. The product of the wind speed standard deviation and the preset proportional coefficient is used as the wind condition adaptive threshold for each candidate point corresponding to all the grid regions within the preset region. The wind speed standard deviation is calculated on the hourly wind speed time series data corresponding to the grid area where the candidate point is located to obtain the wind condition index corresponding to the candidate point. Candidate points whose wind condition index is greater than the corresponding wind condition adaptive threshold are removed.

3. The method for selecting the location of wind measurement observation points as described in claim 1, characterized in that, The process of performing terrain occlusion constraint screening on each of the aforementioned candidate points includes: Based on the grid terrain data, digital terrain analysis technology is used to calculate and determine the maximum terrain occlusion angle within a 360° omnidirectional range centered on the candidate point. Candidate points whose maximum terrain occlusion angle is greater than the occlusion angle threshold are removed.

4. The method for selecting the location of wind measurement observation points as described in claim 1, characterized in that, The process of screening each candidate point for stock avoidance constraints includes: Determine whether there are existing observation stations within the avoidance distance threshold range of the candidate points. If so, remove the candidate points.

5. The method for selecting the location of wind measurement observation points as described in claim 1, characterized in that, After performing three-layer constraint filtering on the candidate points corresponding to each of the aforementioned grid regions, the process further includes: The wind condition index corresponding to the candidate point is subjected to reverse Min-Max normalization, the maximum terrain shading angle is subjected to reverse Min-Max normalization, and the station spacing index is subjected to Min-Max normalization to obtain the normalized wind condition index, the normalized maximum shading angle, and the normalized station spacing index; wherein, the wind condition index is the standard deviation of wind speed in the grid area where the candidate point is located; the station spacing index is the straight-line distance between the candidate point and the nearest existing observation station; The normalized wind condition index, the normalized maximum occlusion angle, and the normalized station spacing index corresponding to the candidate points are weighted and summed to obtain a comprehensive screening score; Candidate points whose comprehensive screening scores do not meet the preset scoring threshold are removed.

6. The method for selecting the location of wind measurement observation points as described in any one of claims 1 to 5, characterized in that, Based on the distribution of wind farms within the effective coverage area corresponding to the compliant candidate sites, an overall site score is obtained for each compliant candidate site, including: Based on the wind farm data, the total number of wind farms within the effective coverage area centered on the compliant candidate sites and with the grid side length of the grid area as the radius is used as the deployment adaptation frequency of the compliant candidate sites. Based on the wind farm data, the total installed capacity and number of wind turbines in the grid area where the compliant candidate site is located are obtained; and the total installed capacity and number of wind turbines in the grid are weighted and summed to obtain the regional wind power development density corresponding to the compliant candidate site. The results of the Min-Max normalization processing of the deployment adaptation frequency and the regional wind power development density corresponding to the compliant candidate sites are weighted and summed to obtain the comprehensive score of the sites.

7. The method for selecting the location of wind measurement observation points as described in claim 6, characterized in that, After obtaining the comprehensive score of each location, before determining the observation point deployment result within the entire region based on the comprehensive score of each compliant candidate location, the process further includes: Determine whether there is a grid area with coastal / land-sea attributes within the grid area where the compliant candidate point is located; For compliant candidate points whose grid area has coastal and land-sea attributes, the corresponding point comprehensive score is adjusted according to a preset correction coefficient; Based on the terrain data of the grid area where each of the aforementioned candidate compliance points is located, the slope standard deviation of the grid area where the candidate compliance points are located is obtained; Compliant candidate points whose slope standard deviation is greater than the slope standard deviation threshold are marked with complex interference. Accordingly, based on the comprehensive score of each of the compliant candidate points, the observation point deployment result within the entire region is determined, including: The compliant candidate points whose comprehensive score is greater than or equal to the first score threshold are classified as first-level high-priority points. The priority level of the compliant candidate points whose comprehensive score is less than the first score threshold but greater than or equal to the second score threshold is divided into secondary medium priority points. The priority of the compliant candidate points whose overall score is less than the second score threshold is divided into three levels of backup points. The priority level of the compliant candidate points with the aforementioned complex interference markers shall be reduced by one level. The output includes the observation point deployment results, which include the priority levels of each of the aforementioned compliant candidate points.

8. A device for selecting the location of a wind measurement observation point, characterized in that, include: The grid division module is used to divide the entire area covering multiple wind farms into grids, and the center point of each grid area is used as a candidate point. The data acquisition module is used to determine the grid wind farm data, grid terrain data, and grid wind resource data corresponding to each grid area based on the wind farm data, terrain data, and wind resource data of each wind farm in the entire region. The site selection module is used to perform three-layer constraint screening on candidate sites corresponding to each grid area based on the grid wind farm data, the grid terrain data, and the grid wind resource data to obtain compliant candidate sites. The three-layer constraint screening includes wind condition threshold constraint screening to remove candidate sites whose wind condition indicators do not meet the corresponding wind condition adaptive threshold; terrain shading constraint screening to remove candidate sites whose terrain shading angle is greater than the shading angle threshold; and existing avoidance constraint screening to remove candidate sites whose distance from existing observation stations is less than the avoidance distance threshold. The wind condition adaptive threshold is determined based on the wind condition data of the neighboring wind farms corresponding to the candidate sites. The site scoring module is used to evaluate and obtain the comprehensive site score of the compliant candidate site based on the distribution of wind farms within the effective coverage area corresponding to the compliant candidate site. The deployment calculation module is used to determine the deployment result of observation points within the entire region based on the comprehensive score of each of the compliant candidate points.

9. A device for selecting the location of a wind measurement observation point, characterized in that, include: Memory, used to store computer programs; A processor is configured to execute the computer program to implement the steps of the method for selecting wind measurement observation points as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which is executed by a processor to implement the steps of the method for selecting the location of wind measurement observation points according to any one of claims 1 to 7.

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