A wind farm turbine optimization layout system and method based on multi-factor analysis

Abstract

This invention provides a wind turbine optimization layout system and method for wind farms based on multi-factor analysis, belonging to the field of data optimization technology. It includes: a data acquisition module: collecting wind speed and terrain slope data from various monitoring points within the wind farm area to establish a basic dataset; a region determination module: extracting core performance parameters of the target wind turbines, performing threshold filtering on the wind speed and slope values ​​in the basic dataset to determine effective wind energy areas and flat terrain areas, thereby generating candidate layout areas; a region analysis module: determining the minimum spacing required for a single wind turbine to operate based on blade size, and determining the maximum number of wind turbines that can be arranged in each area based on the area and shape of the candidate layout areas; a scheme determination module: generating several layout schemes for each candidate layout area; and a scheme selection module: determining the final optimized wind turbine layout scheme based on all schemes. This improves the overall power generation efficiency of the wind farm and reduces energy loss.

Description

Technical Field

[0001] This invention relates to the field of data optimization technology, and in particular to a wind farm turbine optimization layout system and method based on multi-factor analysis. Background Technology

[0002] Wind farms are crucial facilities for generating electricity from wind energy, and the arrangement of wind turbines directly impacts their power generation efficiency. In practical applications, the arrangement of wind turbines in a wind farm needs to comprehensively consider multiple factors such as wind speed, terrain, and turbine performance to maximize wind energy utilization. However, current wind farm turbine arrangement methods often rely too heavily on simple rules or experience, failing to fully utilize various geographical and meteorological data for precise optimization, resulting in wind farms not achieving optimal power generation efficiency.

[0003] In existing technologies, most wind farm turbine layout methods are based on wind speed data to determine turbine locations. However, these methods often overlook factors such as terrain, turbine spacing, and specific turbine performance parameters. Furthermore, existing turbine layouts are relatively simple, lacking systematic data analysis and multi-factor optimization, often failing to address issues such as mutual interference between turbines and low wind energy utilization.

[0004] Therefore, the present invention provides a wind farm turbine optimization layout system and method based on multi-factor analysis. Summary of the Invention

[0005] This invention provides a wind farm turbine layout optimization system and method based on multi-factor analysis. It optimizes wind farm turbine layout through multi-factor analysis, combining wind speed, slope, turbine performance, and terrain conditions to determine candidate layout areas and turbine arrangement schemes. By accurately screening effective wind energy areas and flat terrain areas, and considering minimum spacing and area constraints, the number and arrangement of turbines are optimized to maximize wind energy utilization and avoid mutual interference between turbines. The resulting optimized layout scheme improves the overall power generation efficiency of the wind farm, reduces energy loss, and has high economic benefits and operability.

[0006] This invention provides a wind farm turbine optimization layout system based on multi-factor analysis, comprising:

[0007] Data acquisition module: Multiple monitoring devices are evenly set up within the wind farm area according to the preset monitoring point layout rules to collect wind speed data and terrain slope data at each monitoring point, and establish a basic dataset containing location coordinates, wind speed values, and slope values. The monitoring point is the location of the monitoring device within the wind farm area according to the preset monitoring point layout.

[0008] Region determination module: Extract the core performance parameters of the target wind turbine, perform threshold filtering on wind speed data and slope values ​​in the basic dataset, determine the effective wind energy area and flat terrain area, and then generate candidate layout areas that meet the basic conditions for wind turbine operation.

[0009] Regional Analysis Module: Determines the minimum spacing required for a single wind turbine to operate based on the blade size, and determines the maximum number of wind turbines that can be arranged in each region by combining the area and shape of the candidate layout areas;

[0010] Scheme determination module: For each candidate layout area, several layout schemes are generated according to the rated power of the wind turbines, the preset arrangement spacing, and the maximum number of wind turbines to be arranged. Each scheme includes the number of wind turbines, arrangement coordinates, and power configuration parameters.

[0011] Solution selection module: Determines the final optimized wind turbine layout scheme based on the number of wind turbines, their arrangement coordinates, and power configuration parameters of all schemes.

[0012] Preferably, the region determination module includes:

[0013] Performance parameter extraction unit: Extracts core performance parameters such as rated power, blade size, and starting wind speed from the target wind turbine's preset technical documents;

[0014] First area screening unit: Based on core performance parameters, threshold screening is performed on wind speed data in the basic database to determine effective wind energy areas;

[0015] The second region filtering unit: Based on the core performance parameters, the slope values ​​in the basic database are filtered by threshold to determine the gentle terrain regions;

[0016] Regional determination unit: The intersection of the effective wind energy area and the flat terrain area is taken as the candidate layout area that meets the basic conditions for wind turbine operation.

[0017] Preferably, the region filtering unit includes:

[0018] Threshold reduction subunit: Identifies the prevailing wind direction in the local area. If the monitoring point is in the prevailing wind direction, the wind speed threshold reduction will be initiated.

[0019] Wind speed sub-units: The wind speed data in the basic dataset is divided into wind speed data for four seasons, and the ratio of the cumulative time when the wind speed reaches the start-up wind speed threshold at each monitoring point in each season to the total time in each season is calculated.

[0020] Wind speed screening sub-unit: retains the first monitoring point corresponding to the season in which the ratio exceeds the preset ratio;

[0021] Turbulence screening subunit: retrieve turbulence intensity parameters from the preset meteorological database, further screen the retained first monitoring points, remove the first monitoring points whose turbulence intensity parameters exceed the preset turbulence intensity parameters, and obtain several second monitoring points;

[0022] Discrete screening sub-unit: The moving window method is used to remove the second monitoring point whose wind speed reaches the start wind speed but whose single duration is less than the preset duration, and a number of third monitoring points are obtained.

[0023] Regional connection sub-unit: Mark the third monitoring point as the effective wind energy location, connect adjacent effective wind energy locations to form a continuous area, and generate an effective wind energy distribution vector map;

[0024] Region determination sub-unit: The effective wind energy distribution vector map is smoothed, and discrete regions with areas smaller than the minimum layout unit are removed to obtain the effective wind energy region.

[0025] Preferably, the threshold reduction subunit includes:

[0026] Wind direction determination block: Wind direction data acquisition steps: retrieve historical wind direction data of the wind farm area from the meteorological database, generate a wind rose diagram, and identify the prevailing wind direction interval with an annual frequency greater than the preset frequency;

[0027] Monitoring point location determination block: compares the geographical location information of each monitoring point with the prevailing wind direction interval to determine the target monitoring point located in the direction of the incoming flow along the prevailing wind path;

[0028] Wind speed probability statistics block: For the target monitoring point, retrieve its historical wind speed data, statistically analyze the wind speed distribution probability when the prevailing wind occurs, and calculate the ratio of the difference between the average wind speed during the prevailing wind period and the average wind speed in the non-prevailing wind direction.

[0029] Threshold adjustment block: Based on the ratio of the difference between the average wind speed during the prevailing wind direction and the average wind speed in the non-prevailing wind direction, and the preset ratio-amplitude data table, the corresponding reduction range is determined, thereby reducing the start-up wind speed threshold of the target monitoring point by the corresponding range.

[0030] Preferably, the second region filtering unit includes:

[0031] Basic slope filtering sub-unit: Based on the slope values ​​in the basic dataset, the continuous geographical area covered by all monitoring points in the basic dataset whose slope values ​​are less than the preset first slope threshold is marked as a candidate gentle area;

[0032] Mesh sub-units: The candidate smooth area is divided into grids, and the grid size is determined according to the preset accuracy.

[0033] Slope calculation sub-unit: Calculates the absolute value of the slope difference between the center point of each candidate gentle area and its surrounding adjacent grids;

[0034] Region elimination sub-units: Eliminate the regions corresponding to the adjacent grids of the center point of each candidate gentle area whose absolute value of slope difference is greater than the second slope threshold, thereby obtaining gentle terrain regions.

[0035] Preferably, the regional analysis module includes:

[0036] Spacing calculation unit: Calculates the minimum lateral and longitudinal spacing required for the operation of a single fan based on the blade size;

[0037] Region division unit: The grid side length is determined based on the minimum horizontal and vertical spacing, and the candidate layout area is divided into grids based on the grid side length;

[0038] Grid evaluation unit: Statistically calculate the effective wind energy area ratio and slope value within each grid, evaluate whether the grid meets the wind turbine placement conditions, and exclude grids with excessive slope to obtain several effective grids;

[0039] Quantity Calculation Unit: Based on the number of effective grids, combined with the rated power of the wind turbines and site boundary constraints, calculate the maximum number of wind turbines that can be arranged in each candidate layout area.

[0040] Preferably, the spacing calculation unit includes:

[0041] Lateral determination of sub-units: The first preset multiple of the blade diameter is determined as the minimum lateral spacing required for the operation of a single fan;

[0042] Longitudinal determination sub-unit: The second preset multiple of the blade diameter is determined as the minimum longitudinal spacing required for the operation of a single wind turbine.

[0043] Preferably, the scheme determination unit includes:

[0044] Arrangement pattern setting unit: Set various basic arrangement patterns, such as rectangular array, fan-shaped radial, adaptive distributed, etc.

[0045] Power configuration unit: Configures the power of the fans according to their rated power and preset arrangement mode. The power configuration is the combination ratio of high-power fans and medium-power fans.

[0046] Scheme generation unit: In each candidate area, with a preset arrangement spacing as the step size, combined with the preset arrangement mode, power configuration and maximum quantity limit, several preliminary layout schemes are generated;

[0047] Scheme processing unit: Processes each preliminary layout scheme and generates a layout scheme that includes the number of wind turbines, their arrangement coordinates, and power configuration parameters.

[0048] Preferably, the solution filtering module includes:

[0049] Indicator Calculation Unit: For each layout plan, calculate the score of each preset evaluation indicator;

[0050] Comprehensive score calculation unit: Calculates the comprehensive score of each layout scheme based on the weights corresponding to the preset evaluation indicators and the scores of each preset evaluation indicator;

[0051] Scheme ranking unit: Rank all layout schemes according to their comprehensive scores;

[0052] Scheme Determination Unit: Select the layout scheme with the highest comprehensive score as the final optimized wind turbine layout scheme.

[0053] A method for optimizing the layout of wind turbines in a wind farm based on multi-factor analysis includes:

[0054] Step 1: Within the wind farm area, multiple monitoring devices are evenly set up according to the preset monitoring point layout rules. Wind speed data and terrain slope data are collected from each monitoring point to establish a basic dataset containing location coordinates, wind speed values, and slope values. Here, the monitoring point is the location of the monitoring device within the wind farm area according to the preset monitoring point layout.

[0055] Step 2: Extract the core performance parameters of the target wind turbine, perform threshold filtering on the wind speed data and slope values ​​in the basic dataset, determine the effective wind energy area and the flat terrain area, and then generate candidate layout areas that meet the basic conditions for wind turbine operation.

[0056] Step 3: Determine the minimum spacing required for a single fan to operate based on the blade size, and determine the maximum number of fans that can be arranged in each area by combining the area and shape of the candidate arrangement area.

[0057] Step 4: For each candidate layout area, generate several layout schemes according to the rated power of the fans, the preset arrangement spacing, and the maximum number of fans to be arranged. Each scheme includes the number of fans, arrangement coordinates, and power configuration parameters.

[0058] Step 5: Determine the final optimized wind turbine layout scheme based on the number of wind turbines, their arrangement coordinates, and power configuration parameters of all schemes.

[0059] Compared with the prior art, the beneficial effects of this application are as follows:

[0060] By optimizing the wind turbine layout of a wind farm through multi-factor analysis, considering wind speed, slope, turbine performance, and terrain conditions, candidate layout areas and turbine arrangement schemes were determined. Effective wind energy areas and areas with gentle terrain were precisely selected, and the number and arrangement of turbines were optimized based on minimum spacing and area constraints, ensuring maximum wind energy utilization and avoiding mutual interference between turbines. The final optimized layout scheme improved the overall power generation efficiency of the wind farm, reduced energy losses, and demonstrated high economic benefits and feasibility. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in this 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 some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0062] Figure 1 This is a schematic diagram of a wind farm turbine optimization layout system based on multi-factor analysis provided in an embodiment of the present invention;

[0063] Figure 2 This is a flowchart illustrating a wind farm turbine optimization layout method based on multi-factor analysis provided in an embodiment of the present invention.

[0064] Figure 3 This is a schematic diagram of an effective wind energy area provided in an embodiment of the present invention;

[0065] Figure 4 This is a wind rose diagram provided in an embodiment of the present invention. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0067] Example 1:

[0068] This invention provides a wind farm turbine optimization layout system based on multi-factor analysis, such as... Figure 1 As shown, it includes:

[0069] Data acquisition module: Multiple monitoring devices are evenly set up within the wind farm area according to the preset monitoring point layout rules to collect wind speed data and terrain slope data at each monitoring point, and establish a basic dataset containing location coordinates, wind speed values, and slope values. The monitoring point is the location of the monitoring device within the wind farm area according to the preset monitoring point layout.

[0070] Region determination module: Extract the core performance parameters of the target wind turbine, perform threshold filtering on wind speed data and slope values ​​in the basic dataset, determine the effective wind energy area and flat terrain area, and then generate candidate layout areas that meet the basic conditions for wind turbine operation.

[0071] Regional Analysis Module: Determines the minimum spacing required for a single wind turbine to operate based on the blade size, and determines the maximum number of wind turbines that can be arranged in each region by combining the area and shape of the candidate layout areas;

[0072] Scheme determination module: For each candidate layout area, several layout schemes are generated according to the rated power of the wind turbines, the preset arrangement spacing, and the maximum number of wind turbines to be arranged. Each scheme includes the number of wind turbines, arrangement coordinates, and power configuration parameters.

[0073] Solution selection module: Determines the final optimized wind turbine layout scheme based on the number of wind turbines, their arrangement coordinates, and power configuration parameters of all schemes.

[0074] In this embodiment, the system further includes a data update module: a data monitoring submodule, which monitors changes in wind speed, terrain, and other data within the wind farm area in real time; an update triggering submodule, which triggers the data update process when data changes exceed a preset threshold; a data acquisition submodule, which re-acquires relevant data from the changed area; a data integration submodule, which integrates and updates the newly acquired data with the original basic dataset; and a module notification submodule, which notifies other relevant modules that the data has been updated after the data update is complete, so that the wind turbine layout can be re-optimized.

[0075] In this embodiment, the data acquisition module includes: a wind speed acquisition unit: multiple wind speed sensors are reasonably distributed within the wind farm area to collect wind speed data at each monitoring point in real time and record the acquisition time and location coordinates; a terrain acquisition unit: terrain data of the wind farm area is obtained using remote sensing mapping technology, and the slope value at each location is calculated; and a data integration unit: the data acquired by the wind speed acquisition submodule and the terrain acquisition submodule are integrated and matched according to the location coordinates to form a basic dataset containing location coordinates, wind speed values, and slope values.

[0076] In this embodiment, the wind speed data collection process also includes: a sensor calibration step: periodically calibrating the wind speed sensor to ensure the accuracy of the collected data; a data filtering step: using a digital filtering algorithm to filter the collected wind speed data to remove noise interference; a data supplementation step: if data is missing, supplementing the data based on data from adjacent monitoring points and time series; and a data synchronization step: synchronizing the collected wind speed data with time to ensure data time consistency.

[0077] In this embodiment, the "target wind turbine" refers to the specific model of wind turbine to be used in the wind farm project. Its rated power, blade size, starting wind speed and other parameters can be obtained from the technical documents provided by the manufacturer and used as the core input conditions for optimization.

[0078] In this embodiment, the core performance parameters refer to the base rated power, blade size, and starting wind speed.

[0079] The beneficial effects of the above technical solution are as follows: By optimizing the wind turbine layout of the wind farm through multi-factor analysis, and combining wind speed, slope, turbine performance, and terrain conditions, candidate layout areas and turbine layout schemes are determined. Through precise screening of effective wind energy areas and flat terrain areas, combined with minimum spacing and area constraints, the number and arrangement of turbines are optimized to ensure maximum wind energy utilization and avoid mutual interference between turbines. The final optimized layout scheme improves the overall power generation efficiency of the wind farm, reduces energy loss, and has high economic benefits and operability.

[0080] Example 2:

[0081] This invention provides a wind farm turbine optimization layout system based on multi-factor analysis, including a region determination module:

[0082] Performance parameter extraction unit: Extracts core performance parameters such as rated power, blade size, and starting wind speed from the target wind turbine's preset technical documents;

[0083] First area screening unit: Based on core performance parameters, threshold screening is performed on wind speed data in the basic database to determine effective wind energy areas;

[0084] The second region filtering unit: Based on the core performance parameters, the slope values ​​in the basic database are filtered by threshold to determine the gentle terrain regions;

[0085] Regional determination unit: The intersection of the effective wind energy area and the flat terrain area is taken as the candidate layout area that meets the basic conditions for wind turbine operation.

[0086] In this embodiment, the effective wind energy area refers to a continuous geographical space within the wind farm area that meets the target wind turbine startup wind speed requirement and where the duration of wind speed meets a preset threshold. This area is generated by filtering wind speed data from the basic dataset using spatiotemporal thresholds, and must simultaneously meet the following conditions: wind speed threshold condition: wind speed ≥ target wind turbine startup wind speed (e.g., 2.5 m / s); time percentage condition: cumulative duration of wind speed ≥ startup wind speed ≥ preset value (e.g., 40% / season or 60% / year); spatial continuity condition: distance between adjacent effective points ≤ maximum allowable interval (e.g., 500 m), forming a continuous area. For example, in a project, the target wind turbine startup wind speed is 2.5 m / s. By filtering areas with an annual average wind speed ≥ 2.5 m / s and a compliance duration ≥ 65%, an effective wind energy area is formed. Figure 3 The effective wind energy area shown is (shaded area), which covers an area of ​​approximately 15 km². 2 It includes 20 continuous monitoring points. Figure 3The CGCS2000 national geodetic coordinate system is used, with the horizontal axis representing eastward coordinates (X-axis) and the vertical axis representing northward coordinates (Y-axis). The coordinate unit is meters, and the grid spacing is 500 meters × 500 meters. The shaded area represents the effective wind energy area, and the color depth represents the wind energy density level, with darker colors indicating higher wind speeds. The boundaries are drawn with solid black lines. The data was fitted using the Delaunay triangulation algorithm, and areas <0.5 km² were removed. 2 The map shows discrete regions. Circular symbols mark retained third-level monitoring points, while cross symbols mark invalid monitoring points that have been removed. The map also includes dashed contour lines spaced 50 meters apart to represent topographic relief, arrows indicating the prevailing wind direction (WSW~SSW), and light-colored arrows indicating the wake's influence range.

[0087] In this embodiment, a gentle terrain area refers to a continuous geographical space within the wind farm area where the slope value is ≤ a preset threshold and the terrain undulation is within an allowable range. This area is generated by spatially thresholding the slope data in the basic dataset and must meet the following condition: absolute slope value: average slope ≤ θ max (e.g., 15°); Slope change rate condition: Absolute value of slope difference between adjacent grid cells ≤ Δθ max (e.g., 8°); Topographic continuity condition: Area of ​​continuous, gently sloping regions ≥ S min (e.g., 1km) 2 ).

[0088] In this embodiment, the basic operating conditions of the wind turbine refer to the minimum environmental requirements required for the safe and stable operation of the target wind turbine, including but not limited to: wind energy resource conditions: the wind speed parameters in the effective wind energy area meet the requirements for wind turbine start-up and rated operation; terrain and geological conditions: the terrain slope and flatness meet the requirements for wind turbine foundation design and installation; environmental compatibility conditions: avoid unfavorable areas such as high turbulence areas and areas prone to geological disasters.

[0089] The beneficial effects of the above technical solution are as follows: By extracting the core performance parameters of the target wind turbine and combining them with wind speed and slope data, effective wind energy areas and flat terrain areas are accurately selected, and their intersection is determined as candidate layout areas. This method can ensure that the wind turbine operates under optimal wind energy conditions, while avoiding the impact of unsuitable terrain, thus optimizing the layout of the wind farm and improving the overall power generation efficiency and economic benefits of the wind farm.

[0090] Example 3:

[0091] This invention provides a wind farm turbine optimization layout system based on multi-factor analysis, including a regional screening unit:

[0092] Threshold reduction subunit: Identifies the prevailing wind direction in the local area. If the monitoring point is in the prevailing wind direction, the wind speed threshold reduction will be initiated.

[0093] Wind speed sub-units: The wind speed data in the basic dataset is divided into wind speed data for four seasons, and the ratio of the cumulative time when the wind speed reaches the start-up wind speed threshold at each monitoring point in each season to the total time in each season is calculated.

[0094] Wind speed screening sub-unit: retains the first monitoring point corresponding to the season in which the ratio exceeds the preset ratio;

[0095] Turbulence screening subunit: retrieve turbulence intensity parameters from the preset meteorological database, further screen the retained first monitoring points, remove the first monitoring points whose turbulence intensity parameters exceed the preset turbulence intensity parameters, and obtain several second monitoring points;

[0096] Discrete screening sub-unit: The moving window method is used to remove the second monitoring point whose wind speed reaches the start wind speed but whose single duration is less than the preset duration, and a number of third monitoring points are obtained.

[0097] Regional connection sub-unit: Mark the third monitoring point as the effective wind energy location, connect adjacent effective wind energy locations to form a continuous area, and generate an effective wind energy distribution vector map;

[0098] Region determination sub-unit: The effective wind energy distribution vector map is smoothed, and discrete regions with areas smaller than the minimum layout unit are removed to obtain the effective wind energy region.

[0099] In this embodiment, the wind speed division subunit achieves seasonal division through the following steps: Time period definition: The whole year is divided into four seasons according to the Gregorian calendar months, namely spring (March-May), summer (June-August), autumn (September-November) and winter (December-February); Data index construction: The wind speed data in the basic dataset is indexed based on the timestamp field to generate a mapping table between seasons and data; Seasonal data slicing: According to the mapping table, a subset of wind speed data for each season is extracted to form a four-dimensional wind speed tensor V∈R. N×M×D×H Where: N is the number of monitoring points, M is the number of seasons (4 seasons), D is the number of days, and H is the number of hours. In the Nagqu wind farm project in Tibet, the basic dataset contains hourly wind speed data for the whole year from 30 monitoring points (a total of 262,800 records). After seasonal division, the summer wind speed subset (June-August) contains 66,960 records, which are used for subsequent seasonal wind energy assessment.

[0100] In this embodiment, the first monitoring point refers to a monitoring point that meets the following condition: Ratio i ≥T ratio , where: Ratio i T represents the percentage of time during which the wind speed at the i-th monitoring point meets the standard (≥2.5 m / s) in a specific season. ratio The preset ratio threshold ranges from 40% to 70%. For example:

[0101] Monitoring point GNNQ12 achieved wind speed compliance for 1,680 hours during the summer, out of a total summer duration of 2,190 hours, representing 76.7% of the total. Since 76.7% exceeds the preset threshold T... ratio =60%, therefore GNNQ12 was marked as the first monitoring point.

[0102] In this embodiment, the second monitoring point is a monitoring point that further satisfies the turbulence intensity constraint based on the first monitoring point: I t ≤T turb , where: I t T represents the turbulence intensity parameter at the monitoring point. turb The preset turbulence intensity threshold ranges from 0.12 to 0.18. For example, the turbulence intensity at the first monitoring point GNNQ12 is I. t =0.15, and the preset threshold T turb =0.16, therefore GNNQ12 satisfies the turbulence intensity constraint and is retained as the second monitoring point; while the turbulence intensity of monitoring point GNNQ08 is I t =0.19, which exceeds the threshold, so it was removed.

[0103] In this embodiment, the preset turbulence intensity parameter T turb The maximum permissible turbulence intensity threshold for safe operation of a wind turbine is determined as follows: The turbulence intensity tolerance limit I is extracted from the wind turbine design document. limit Safety margin setting: T turb =k×I limit Where k∈[0.7,0.9] is the safety factor; dynamic adjustment mechanism: the value of k is automatically adjusted according to the terrain complexity (such as roughness category), and a lower value is taken for complex terrain. For example: the turbulence intensity tolerance limit I of a certain type of wind turbine. limit =0.20, safety factor k=0.8, then T turb =0.16. In mountainous terrain, the safety factor k is automatically adjusted to 0.75, T turb =0.15.

[0104] In this embodiment, the preset ratio threshold is a critical value for measuring the proportion of time during which the monitoring point is effective in wind energy within a specific season, and the theoretical effective duration ratio benchmark value T is calculated. base The calculation formula is:

[0105]

[0106] Among them, t j T is the duration, after each wind speed reaches the start-up wind speed, until the fan stabilizes. seasonis the total duration of the season; introduce the regional wind energy stability coefficient α, which is determined according to the coefficient of variation CV of the historical wind speed data of the wind farm: when CV ≤ 0.2, α = 1.0; when 0.2 < CV ≤ 0.4, α = 0; when CV > 0.4, α = 0.8, and the final preset ratio threshold T ratio is:

[0107] T ratio = α × T base

[0108] where, T ratio ranges from 40% to 70% and supports manual fine-tuning according to the fan model and regional climate characteristics; for example: in a certain wind farm project, the start-up energy requirement of the target fan is E start = 3MJ, and the average loss during the start-up period is P loss = 20kW. In summer (the total season duration T season = 2190 hours), after the wind speed at the monitoring point GNNQ05 reaches the start-up wind speed (2.5 m / s) each time, it takes an average of 1.5 hours to make the fan operate stably. There are 12 effective start-ups during this season, and the total effective duration is hours, so:

[0109]

[0110] The coefficient of variation of the historical wind speed data in this area is CV = 0.3, so the corresponding regional wind energy stability coefficient is α = 0.9, and thus it is calculated that:

[0111] T ratio = 0.9 × 0.82% ≈ 0.738%

[0112] If the proportion of the wind speed up-to-standard duration of the monitoring point GNNQ10 in summer is 70%, it is less than T ratio , so this monitoring point will not be marked as the first monitoring point. While the proportion of the wind speed up-to-standard duration of the monitoring point GNNQ12 is 76.7%, which is greater than T ratio , so GNNQ12 is marked as the first monitoring point.

[0113] The beneficial effects of the above technical solution: By gradually screening factors such as wind speed, turbulence intensity, and wind speed duration, the effective wind energy area is accurately determined, avoiding errors caused by turbulence and short-term wind speed fluctuations. By smoothing the effective wind energy distribution map and removing unsuitable areas, it ensures that the wind farm fans are arranged in areas with sufficient and stable wind energy. The optimization method improves the accuracy and power generation efficiency of the wind farm fan layout and ensures long-term stable wind energy utilization.

[0114] Example 4:

[0115] This invention provides a wind farm turbine optimization layout system based on multi-factor analysis, including a threshold reduction subunit:

[0116] Wind direction determination block: Wind direction data acquisition steps: retrieve historical wind direction data of the wind farm area from the meteorological database, generate a wind rose diagram, and identify the prevailing wind direction interval with an annual frequency greater than the preset frequency;

[0117] Monitoring point location determination block: compares the geographical location information of each monitoring point with the prevailing wind direction interval to determine the target monitoring point located in the direction of the incoming flow along the prevailing wind path;

[0118] Wind speed probability statistics block: For the target monitoring point, retrieve its historical wind speed data, statistically analyze the wind speed distribution probability when the prevailing wind occurs, and calculate the ratio of the difference between the average wind speed during the prevailing wind period and the average wind speed in the non-prevailing wind direction.

[0119] Threshold adjustment block: Based on the ratio of the difference between the average wind speed during the prevailing wind direction and the average wind speed in the non-prevailing wind direction, and the preset ratio-amplitude data table, the corresponding reduction range is determined, thereby reducing the start-up wind speed threshold of the target monitoring point by the corresponding range.

[0120] In this embodiment, historical wind direction data of the wind farm area is retrieved from a meteorological database to generate a wind rose diagram. The following steps are used to retrieve data from the meteorological database and generate the wind rose diagram: Data retrieval interface: Wind direction data is obtained from a meteorological database (such as the China Meteorological Science Data Sharing Service Network, ERA5 reanalysis database) using a standardized API protocol (such as OpenWeatherMapAPI, WindyAPI). Different data formats are supported, such as GRIB, NetCDF, and CSV, and the time resolution can be configured from 1 hour to 1 year, with a spatial resolution covering a grid from 0.1°×0.1° to 5°×5°. Data cleaning and preprocessing: Outliers in wind direction angles (such as >360° or <0°) are removed, and missing data is filled using linear interpolation. The wind direction data is divided into 16 or 32 sectors (each sector is 22.5° or 11.25°). Wind rose diagram generation algorithm: The frequency f of wind direction occurrence in each sector is calculated. i The formula is:

[0121]

[0122] Where N i N represents the number of wind direction data points in sector i. total The total number of data points; using polar coordinate plotting, with sector angles as polar angles and frequency f. i Assuming the extreme radius, generate a wind rose diagram with wind speed weights (wind speed weights are determined by the average wind speed v in each sector). i and frequency f i(The product is reflected); For example: In the Seni District Jicuo Wind Farm project, hourly wind direction data (105,120 records in total) from 2010 to 2022 were retrieved from the China Meteorological Science Data Sharing Service Network via API. After removing 1,200 outlier data, the wind direction was divided into 16 sectors. The calculated frequency of sector 10 (202.5°-225°) was 32%, with an average wind speed of 8.5 m / s, and the frequency of sector 11 (225°-247.5°) was 28%, with an average wind speed of 7.8 m / s. The final generated wind rose diagram showed that the prevailing wind direction was concentrated in the WSW-SSW interval (e.g., Figure 4 As shown in the figure, the WSW direction is marked as the core prevailing wind direction due to its high frequency and high wind speed.

[0123] In this embodiment, the preset frequency F thres This is a frequency threshold used to determine the prevailing wind direction range. The determination method is as follows: Set a basic threshold F0 based on the wake influence characteristics of the target wind turbine, such as 25%; introduce a terrain correction coefficient k. terrain Flat terrain (roughness length z0 < 0.1m): k terrain =1.0; - Hilly / Mountainous areas (z0≥0.1m): k terrain =1.2-1.5; 3. Final threshold calculation:

[0124] F thres =k terrain ×F0

[0125] F thres The value range is 20%-40%, and manual fine-tuning is supported; for example, the Jicuo Wind Farm in Seni District is located in a plateau and hilly terrain (z0 = 0.2m), with a basic threshold F0 = 25% and a terrain correction coefficient k. terrain =1.3, then:

[0126] F thres =1.3 × 25% = 32.5%

[0127] Based on F thres In the wind rose diagram, only the WSW-SSW range with a frequency greater than 32.5% is identified as the prevailing wind direction range, while other sectors (such as the NNW direction with a frequency of 22%) are not included in the prevailing wind direction range.

[0128] In this embodiment, the prevailing wind direction interval refers to the wind direction frequency greater than the preset frequency F in the wind rose diagram. thres A contiguous sector is mathematically defined as:

[0129] MainSectors={S i |f i ≥F thres And S i+1 -S i ≤22.5°}

[0130] Among them, S i f is the sector angle. i This refers to the sector frequency. The prevailing wind direction interval supports the determination of multiple intervals (e.g., in a dual prevailing wind direction scenario). When multiple discontinuous high-frequency sectors exist, they will be marked as independent prevailing wind direction intervals. For example, in the wind rose diagram of the Jicuo wind farm, sector 10 (202.5°-225°) has a frequency of 32%, and sector 11 (225°-247.5°) has a frequency of 28%, both exceeding the preset frequency F. thres =32.5% and the angle is continuous, therefore the prevailing wind direction range is determined to be 202.5°-247.5° (WSW-SSW). If there is also an independent high-frequency area in the NE direction (frequency 30%), it is marked as the second prevailing wind direction range (0°-45°).

[0131] In this embodiment, the geographical location information of each monitoring point is compared with the prevailing wind direction range. This comparison process is achieved through spatial analysis using a Geographic Information System (GIS). The specific steps are as follows: converting the latitude and longitude coordinates (WGS84 coordinate system) of the monitoring points to the wind rose. Figure 1 A consistent projected coordinate system (e.g., UTMZone44N); based on the center angle θ of the prevailing wind direction zone. main A vector line is generated pointing from the prevailing wind source to the wind farm; buffer analysis is used to determine whether the monitoring point is located within the buffer zone of the vector line, using the following formula:

[0132]

[0133] Among them, P i Let dist(P) be the coordinates of the monitoring point. i Vector represents the shortest distance from a point to a line. For example, after monitoring point GNNQ12 (N31.4821, E92.3145) is converted to UTM coordinates, it is compared with the WSW-SSW prevailing wind direction vector line. The calculated shortest distance from this point to the vector line is 320m, which is less than the preset buffer radius of 500m. Therefore, GNNQ12 is determined to be the target monitoring point. GNNQ25 is 680m away from the vector line and is therefore excluded. Vector: represents the prevailing wind direction vector line, determined by the starting point A and the ending point B. R is the buffer radius in meters, usually ranging from 500 to 2000 meters, and can be dynamically adjusted according to terrain conditions. dist(P i Vector is used to represent monitoring point P. iThe shortest geometric distance to the prevailing wind vector line (Vector) is calculated using the formula for the distance from a point to a line segment. IsTarget = 1: The monitoring point is within the buffer zone and is determined to be a target monitoring point; IsTarget = 0: The monitoring point is outside the buffer zone and is determined to be a non-target monitoring point. Given a vector line Vector (determined by the starting point A and the ending point B) and a monitoring point P, the formula for calculating the shortest distance is as follows:

[0134]

[0135] Where h1 is the perpendicular distance from point P to line AB; the dynamic adjustment mechanism of the buffer radius R is calculated using the following formula:

[0136] R = R0 × k terrain ×k wind

[0137] Wherein, the base radius R0: the default value is 1000 meters, representing the influence range of the prevailing wind direction under flat terrain; the terrain correction coefficient k terrain Adjustments are made based on terrain roughness, specifically: terrain correction coefficient k. terrain Definition: Flat terrain (roughness z0 < 0.1): k terrain =1.0; Hilly terrain (0.1) <z0<0.5):k terrain =1.2; Mountainous / Complex Terrain (z0>0.5): k terrain =1.5; Wind speed correction factor k wind :

[0138]

[0139] in, It is the average wind speed in the prevailing wind direction. It is the average wind speed in the non-dominant wind direction. If k wind When the value is greater than 1.5, then k is taken. wind =1.5.

[0140] In this embodiment, the wind speed distribution probability statistics adopt the kernel density estimation (KDE) method. The specific steps are as follows: extract the wind speed data V = {v1, v2, ..., v} of the target monitoring point within the prevailing wind direction range. n};2. Calculation of probability density function:

[0141]

[0142] Where K is the kernel function (e.g., Gaussian kernel), h is the bandwidth parameter (determined through cross-validation), and n is the number of wind speed data points for the target monitoring point within the prevailing wind direction interval; probability distribution generation: based on f(v), calculate the cumulative distribution probability P(v≤v) for each wind speed interval (e.g., 0-2m / s, 2-4m / s). j The probability distribution curve is generated. For example, 12,000 wind speed data points from the target monitoring point GNNQ12 during the WSW-SSW prevailing wind direction are statistically analyzed. KDE calculations show that the probability of wind speeds between 2-4 m / s is 18%, between 4-6 m / s is 32%, and between 6-8 m / s is 25%. Based on this data, the probability distribution curve shows that the prevailing wind speed at this monitoring point is concentrated in the 4-8 m / s range.

[0143] The beneficial effects of the above technical solution are as follows: By combining historical wind direction data and wind speed probability statistics, the prevailing wind direction area can be accurately identified, and the starting wind speed threshold of the target monitoring point can be adjusted according to the wind speed difference. This method can improve the starting efficiency of wind turbines in the prevailing wind direction area, optimize the layout and operating conditions of wind turbines in the wind farm, improve wind energy utilization, and reduce energy loss during turbine startup, thereby improving the overall power generation efficiency and economic benefits of the wind farm.

[0144] Example 5:

[0145] This invention provides a wind farm turbine optimization layout system based on multi-factor analysis, comprising a second area screening unit, including:

[0146] Basic slope filtering sub-unit: Based on the slope values ​​in the basic dataset, the continuous geographical area covered by all monitoring points in the basic dataset whose slope values ​​are less than the preset first slope threshold is marked as a candidate gentle area;

[0147] Mesh sub-units: The candidate smooth area is divided into grids, and the grid size is determined according to the preset accuracy.

[0148] Slope calculation sub-unit: Calculates the absolute value of the slope difference between the center point of each candidate gentle area and its surrounding adjacent grids;

[0149] Region elimination sub-units: Eliminate the regions corresponding to the adjacent grids of the center point of each candidate gentle area whose absolute value of slope difference is greater than the second slope threshold, thereby obtaining gentle terrain regions.

[0150] In this embodiment, a preset first slope threshold θ1 is used to initially screen the slope critical value of gentle terrain. The method for determining it is as follows: Based on the foundation type of the target wind turbine (such as gravity foundation or pile foundation), the maximum allowable slope value θ specified in the design document is extracted. base Introducing a geological correction factor k geo Its value selection rule is as follows: Rock foundation: kgeo =1.2; Soil foundation: k geo =1.0; Soft soil foundation: k geo =0.8; Final threshold calculation: using the formula θ1=k geo ×θ base The final slope threshold is calculated, ranging from 8° to 15°, and can be manually adjusted based on the wind farm geological survey report. For example, in the Seni District Jicuo Wind Farm project, the target wind turbine uses a gravity foundation, and the maximum allowable slope is 12° (θ). base =12°). The geological survey report shows that the area has a soil foundation (k geo =1.0), therefore the preset first slope threshold θ1 = 1.0 × 12° = 12°. Based on this, areas with slope values ​​less than 12° (such as the main ridge section at an altitude of 4900-5100 meters) will be initially marked as candidate gentle areas.

[0151] In this embodiment, adjacent grids refer to grids that are directly adjacent to the grid where the center point is located in the horizontal direction.

[0152] In this embodiment, a preset second slope threshold θ2 is used to assess the local flatness of the terrain, and its determination method is as follows: based on the maximum allowable tilt angle θ of the hoisting equipment. equip (For example, the maximum allowable tilt angle for a crawler crane is ≤5°), set a baseline value; introduce a safety factor k. safe Its value ranges from 1.2 to 1.5; through the formula θ2=k safe ×θ equip This is used to calculate the final slope threshold, ranging from 6° to 8°, and supports dynamic adjustment based on construction process standards. For example, in this project, the crawler crane used allows a maximum tilt angle of 5° (θ). equip =5°), and take a safety factor k safe =1.5, therefore the preset second slope threshold is θ2 = 1.5 × 5° = 7.5°, rounded to 8°. If the absolute value of the slope difference between the center point of a candidate gentle area and the adjacent grid exceeds 8° (for example, the slope of the center point of an area is 6°, the slope of the adjacent grid is 15°, the difference is 9°, which is greater than 8°), then the area will be eliminated.

[0153] In this embodiment, the preset precision is used to control the fineness of the mesh division, and it is determined as follows: the terrain complexity is quantified by calculating the terrain roughness index RI:

[0154]

[0155] Where, Δh gLet n1 be the elevation difference between adjacent sampling points, n1 be the number of sampling points, and h be the average elevation of the area. The grid precision is determined based on the value of RI, with the following specific rules: When RI < 0.05 (gentle terrain): the preset precision is a 200m × 200m grid; when 0.05 ≤ RI < 0.15 (moderate terrain undulation): the preset precision is a 100m × 100m grid; when RI ≥ 0.15 (complex terrain): the preset precision is a 50m × 50m grid. For example, for the candidate gentle terrain area of ​​the Jicuo Wind Farm, the calculated terrain roughness index RI = 0.08, indicating that this area belongs to "moderate terrain undulation". According to the mapping rules, the preset precision is a 100m × 100m grid, which means that the candidate gentle terrain area is divided into square grid cells with a side length of 100 meters for subsequent slope difference calculation and area elimination operations.

[0156] The beneficial effects of the above technical solution are as follows: By accurately filtering and gridding slope data, it ensures that wind turbines in wind farms are located in areas with gentle and stable slopes. By calculating slope differences and eliminating areas that do not meet the requirements, the accuracy of the solution in areas with gentle terrain is further improved, thereby avoiding the decline in wind turbine operating efficiency caused by excessive terrain undulations, optimizing the wind turbine layout of wind farms, and improving power generation efficiency and economic benefits.

[0157] Example 6:

[0158] This invention provides a wind farm turbine optimization layout system based on multi-factor analysis, including a regional analysis module:

[0159] Spacing calculation unit: Calculates the minimum lateral and longitudinal spacing required for the operation of a single fan based on the blade size;

[0160] Region division unit: The grid side length is determined based on the minimum horizontal and vertical spacing, and the candidate layout area is divided into grids based on the grid side length;

[0161] Grid evaluation unit: Statistically calculate the effective wind energy area ratio and slope value within each grid, evaluate whether the grid meets the wind turbine placement conditions, and exclude grids with excessive slope to obtain several effective grids;

[0162] Quantity Calculation Unit: Based on the number of effective grids, combined with the rated power of the wind turbines and site boundary constraints, calculate the maximum number of wind turbines that can be arranged in each candidate layout area.

[0163] In this embodiment, the minimum lateral and longitudinal spacing required for the operation of a single wind turbine is calculated. According to the industry standard formula: D1=k1×R1, where D1 is the minimum spacing, R1 is the blade radius, and k1 is the safety factor, including the lateral safety factor and the longitudinal safety factor. Different minimum spacings can be calculated based on different safety factors. The minimum lateral spacing is S1, and the minimum longitudinal spacing is S2.

[0164] In this embodiment, determining the grid side length based on the minimum horizontal and vertical spacing includes: L = max(S1,S2), where L is the grid side length in meters;

[0165] In this embodiment, the maximum number of wind turbines that can be deployed in each candidate deployment area is calculated to evaluate the maximum number of wind turbines that can be deployed in the candidate area. This includes: screening effective grids: grids that meet the following three conditions are defined as effective: wind energy percentage ≥ Pmin, average slope ≤ θmax, and distance from the boundary ≥ Dbuffer, where Pmin ∈ [50%, 80%], θmax ∈ [8°, 15°], and Dbuffer ∈ [100m, 300m]; theoretical maximum number N 理论 calculate:

[0166]

[0167] Among them, A 有效 The effective total area of ​​the grid is L, where L is the grid side length.

[0168] Boundary effect correction:

[0169] Where, k edge The boundary penalty coefficient is (0.2-0.5). Let P be the perimeter of the boundary grid. total Total perimeter; Power density constraint:

[0170]

[0171] Where, ρ 功率 For regional power density limitations (1.5-3.0 MW / km) 2 ), P 额定 A is the rated power of a single wind turbine. 总 Total area of ​​candidate placement areas; final placement limit:

[0172] N 上限 =min(N) 边界 N 功率 )

[0173] The beneficial effects of the above technical solution are as follows: By calculating the minimum spacing required for wind turbines and based on grid division and slope assessment, areas that meet the wind turbine layout conditions are accurately selected. By eliminating grids unsuitable for wind turbine placement, the rationality of the wind farm's wind turbine layout is ensured. Simultaneously, by combining the rated power of the wind turbines with site limitations, the calculation of the number of wind turbines is optimized, maximizing the utilization of wind energy resources and improving the power generation efficiency and economic benefits of the wind farm.

[0174] Example 7:

[0175] This invention provides a wind farm turbine optimization layout system based on multi-factor analysis, including a spacing calculation unit:

[0176] The first preset multiple of the blade diameter is determined as the minimum lateral spacing required for the operation of a single wind turbine;

[0177] The second preset multiple of the blade diameter is determined as the minimum longitudinal spacing required for the operation of a single wind turbine.

[0178] In this embodiment, the first preset multiple and the second preset multiple are determined based on the influence range of the wind turbine wake. Specifically, determining the first preset multiple and the second preset multiple based on the influence range of the wind turbine wake includes: establishing a wind turbine wake influence model, which is constructed based on the topography, meteorological conditions, and performance parameters of the wind farm; using the wind turbine wake influence model to simulate the influence of the wind turbine wake on the power generation efficiency of surrounding wind turbines at different spacings; determining the first preset multiple such that, at this multiple, the influence of the wind turbine wake on the power generation efficiency of adjacent horizontal wind turbines is lower than a first preset threshold; and determining the second preset multiple such that, at this multiple, the influence of the wind turbine wake on the power generation efficiency of adjacent vertical wind turbines is lower than a second preset threshold. The first preset threshold and the second preset threshold are determined based on the power generation efficiency requirements of the wind farm.

[0179] The beneficial effects of the above technical solution are as follows: This invention simplifies the wind turbine layout process by determining the minimum lateral and longitudinal spacing required for a single wind turbine based on a preset multiple of the blade diameter. This method ensures a reasonable spacing between wind turbines, avoids mutual interference, improves wind energy utilization efficiency, optimizes the spatial layout of the wind farm, and enhances the overall power generation capacity and economic benefits of the wind farm.

[0180] Example 8:

[0181] This invention provides a wind farm turbine optimization layout system based on multi-factor analysis, including a scheme determination unit comprising:

[0182] Arrangement pattern setting unit: Set various basic arrangement patterns, such as rectangular array, fan-shaped radial, adaptive distributed, etc.

[0183] Power configuration unit: Configures the power of the fans according to their rated power and preset arrangement mode. The power configuration is the combination ratio of high-power fans and medium-power fans.

[0184] Scheme generation unit: In each candidate area, with a preset arrangement spacing as the step size, combined with the preset arrangement mode, power configuration and maximum quantity limit, several preliminary layout schemes are generated;

[0185] Scheme processing unit: Processes each preliminary layout scheme and generates a layout scheme that includes the number of wind turbines, their arrangement coordinates, and power configuration parameters.

[0186] In this embodiment, the wind turbines are arranged in a rectangular array with a fixed row spacing L. row and column distance L col Construct a two-dimensional regular mesh, with the position formula as follows:

[0187] P ij =(x0+i·L) col ,y0+j·L row )

[0188] Among them, P ij Let (x0, y0) be the coordinates of the fan in the i-th column and j-th row, with (x0, y0) as the starting coordinates.

[0189] L row =k r ·D, L col =k c ·D, where D is the blade diameter, k r ∈[3.0,5.0]、k c ∈[4.0,6.0] represents the spacing coefficient; Fan-shaped radial arrangement: Based on the prevailing wind direction, the fans are arranged radially from the center point O(x0,y0), and the coordinates are calculated as follows:

[0190] P i =(x0+r i cos(θ main +Δθ i ),y0+r i sin(θ main +Δθ i ))

[0191] Where, r i =r0+i·Δr is the radius of the i-th layer, Δθ i For angular intervals; adaptive distributed layout: based on wind energy density W(x,y) and slope data S(x,y), particle swarm optimization (PSO) is used to determine the wind turbine coordinates, making the objective function:

[0192]

[0193] Maximize, where I wake The wake influence factor is constrained by factors such as slope; for example, in the Jicuo wind farm: in a rectangular array layout, D = 160m, k is selected. r =4.0, k c=5.0, then the row spacing is 640m and the column spacing is 800m; the fan-shaped radial type uses a prevailing wind direction of 225°, a radius increment of 500m, and an angle interval of 15°; the adaptive distributed type utilizes PSO when W>400W / m 2 Dynamically optimize the layout of wind turbines in areas with a slope of less than 12°.

[0194] In this embodiment, power configuration: Optimization of wind turbine power configuration is achieved through a combination of turbine model library and layout patterns: Constructing turbine model combinations: High-power turbine models: P high ≥4MW, medium power models: 2≤P mid <4MW, corresponding configuration modes: Rectangular array: Configure wind turbines of uniform power (e.g., 5MW) to simplify wake impact analysis; Fan-shaped radial: Configure high-power wind turbines in the central area and medium-power turbines at the edges to reduce the total wake; Adaptive distributed: Deploy high-power turbines in high-density areas and medium-power turbines in low-density areas, optimizing the model:

[0195] max(∑P high ·CF high +∑P mid ·CF mid )-C total

[0196] Where CF is the capacity factor, and C total The total cost is calculated using a genetic algorithm to optimize n. high ,n mid For example, in a radial wind turbine configuration, eight 5MW wind turbines are arranged within a 500m radius of the center, and twelve 3MW wind turbines are arranged at the periphery. Optimization results: Annual power generation increases by 12%, and total cost decreases by 8%.

[0197] In this embodiment, the preset arrangement spacing S preset To ensure minimum wake interference, the wake reference is determined as follows: S wake =k w ·D,k w ∈[5.0,8.0]; Arrangement mode adjustment: Rectangular array: S preset =max(L row ,L col ); Fan-shaped radial type: radial spacing ≥ 1.2·S wake Circumferential spacing ≥ 1.0·S wake Adaptive distributed system: Ensures that the spacing between all fans is ≥ S wake •(1+ΔS), where ΔS∈[0.1,0.3]; Terrain correction: S ′ preset =k terrain ·S preset ,k terrain∈[1.1,1.3]; Example: Fan diameter D=160m, wake coefficient k w =6.0, therefore: S wake =960m, rectangular arrangement spacing is 1000m; in mountainous terrain, k terrain =1.2, the adjusted spacing is 1200m.

[0198] In this embodiment, preliminary layout alternatives are generated using permutations, combinations, and constraint filtering: A parameter space is constructed; layout modes are categorized into three types (rectangular, radial, and adaptive); the proportion of high-power wind turbines is p. high ∈[30%,70%], step size 10%; spacing S preset ∈[1.2,2.0]·S wake Step size 0.2·S wake Generating Scheme Combinations: The above parameters are combined using a full permutation algorithm to generate 3×5×4=60 candidate schemes. Feasibility Screening: Schemes that violate conditions such as slope restrictions, boundary buffers, and insufficient wake spacing are eliminated. For example, in the Jicuo Wind Farm, after screening, 42 feasible configurations were retained from the 60 initial schemes, including the following types: - Regular matrix (spacing 1000m, all high-power wind turbines); Fan-shaped layout (spacing 1200m, high-power ratio 60%); Adaptive optimization (spacing dynamically adjusted, high-power ratio 40%).

[0199] The beneficial effects of the above technical solution are as follows: By setting multiple arrangement patterns and wind turbine power configurations, and combining them with the characteristics of the candidate areas, this invention generates multiple preliminary layout schemes. Through reasonable arrangement patterns and power configurations, the layout of wind turbines within the wind farm is optimized, ensuring efficient utilization of wind energy. Simultaneously, the refined layout schemes provide detailed parameters for the number of wind turbines, their arrangement coordinates, and power configurations, providing a scientific basis for the construction and operation of wind farms and improving power generation efficiency and economic benefits.

[0200] Example 9:

[0201] This invention provides a wind farm turbine optimization layout system based on multi-factor analysis, including a scheme selection module:

[0202] Indicator Calculation Unit: For each layout plan, calculate the score of each preset evaluation indicator;

[0203] Comprehensive score calculation unit: Calculates the comprehensive score of each layout scheme based on the weights corresponding to the preset evaluation indicators and the scores of each preset evaluation indicator;

[0204] Scheme ranking unit: Rank all layout schemes according to their comprehensive scores;

[0205] Scheme Determination Unit: Select the layout scheme with the highest comprehensive score as the final optimized wind turbine layout scheme.

[0206] In this embodiment, preset evaluation indicators, such as terrain adaptability, wind energy utilization rate, spacing compliance, and construction cost, are used, and weights are assigned to each indicator. For example, the terrain adaptability score is calculated based on the ratio of the compliant area to the total area of ​​the region, and the wind energy utilization rate score is calculated based on the ratio of the average effective wind speed duration at each wind turbine location in the plan to the maximum effective wind speed duration in the region.

[0207] In this embodiment, the index calculation unit calculates the preset evaluation index score for each layout scheme through the following steps: establishing a set I = {I1, I2, ..., I...} containing multi-dimensional evaluation indicators. n}, including: Wind energy utilization category: annual equivalent full-load hours I1, wake influence coefficient I2; Terrain adaptation category: slope compliance rate I3, elevation standard deviation I4; Economic cost category: unit kilowatt investment cost I5, investment payback period I6; 2. Calculation of individual scores: For positive indicators (such as annual equivalent full-load hours): For negative indicators (such as investment cost per kilowatt): Where, x i For the plan in indicator I i The actual value of x max and x min These are the maximum and minimum values ​​of the indicator across all scenarios; the Z-score standardization method is used to eliminate the influence of dimensions.

[0208]

[0209] Where, μ i and σ i Indicator I i The mean and standard deviation, for example: For the 10 layout schemes of the Jicuo Wind Farm, the annual equivalent full-load hours of scheme A are 2800 hours, and the minimum value of this indicator among all schemes is 2200 hours, and the maximum value is 3000 hours:

[0210]

[0211] If the investment cost per kilowatt is 12,000 yuan / kW, the maximum is 15,000 yuan / kW, and the minimum is 10,000 yuan / kW:

[0212]

[0213] In this embodiment, the comprehensive score calculation unit calculates the score based on a preset weight vector. The overall score S of the scheme is calculated using the weighted summation formula. total : Among them, Zi For indicator I i The standardized score. Weighting methods include: Analytic Hierarchy Process (AHP): determining the relative importance of each indicator through expert scoring; Entropy weighting: automatically assigning weights based on the degree of data variability; Dynamic adjustment mechanism: manually adjusting weights according to project needs (e.g., increasing the weights of w5 and w6 when prioritizing economic efficiency). For example: if the weight of annual equivalent full-load hours is w1 = 0.3, the weight of unit kilowatt investment cost is w5 = 0.2, and Z1 = 1.2 and Z5 = 0.8 for Scheme A, then:

[0214] S total-A =0.3×1.2+0.2×0.8=0.36+0.16=0.52

[0215] In this embodiment, the scheme sorting unit uses the quicksort algorithm to sort all layout schemes in descending order of their comprehensive scores, and outputs an ordered scheme list L = [P1, P2, ..., P...]. m ], where P i To arrange the plan, satisfy S total (P1)≥S total (P2)≥...≥S total (P m This sorting process supports parallel computing acceleration, enabling the rapid processing of a large number of schemes through multi-threaded or distributed computing frameworks (such as Apache Spark). For example, after sorting 10 layout schemes, the result is:

[0216] L = [Option D, Option A, Option G, ..., Option J]

[0217] Among them, Option D has the highest overall score of 0.68, while Option J has the lowest score of 0.42.

[0218] In this embodiment, the scheme determination unit selects the scheme with the highest comprehensive score from the ranked scheme list as the final optimized scheme. Furthermore, it supports a multi-scheme recommendation mechanism: when the score difference between the highest-scoring scheme and the second-highest-scoring scheme is less than a threshold ΔS (e.g., 0.05), the top three schemes are output for the decision-maker to choose from, along with a sensitivity analysis report for each scheme (e.g., the impact of weight changes on the score). For example, the ranked scheme D is ultimately selected as the optimal scheme, with a comprehensive score of 0.68, significantly higher than scheme A (score of 0.52). If the score difference is only 0.03, schemes D, A, and G are recommended simultaneously, along with a sensitivity analysis report on the score changes of each scheme under different weights.

[0219] The beneficial effects of the above technical solutions are as follows: By evaluating each layout scheme and calculating a comprehensive score based on the weights of various evaluation indicators, the selected scheme is ensured to perform best in multiple aspects. Through scheme ranking and screening, the optimal wind turbine layout scheme can be efficiently determined, thereby optimizing the wind farm turbine layout, improving wind energy utilization efficiency and power generation benefits, reducing resource waste, and providing a scientific decision-making basis for wind farm planning and operation.

[0220] Example 10:

[0221] This invention provides a method for optimizing the layout of wind turbines in a wind farm based on multi-factor analysis, including:

[0222] Step 1: Within the wind farm area, multiple monitoring devices are evenly set up according to the preset monitoring point layout rules. Wind speed data and terrain slope data are collected from each monitoring point to establish a basic dataset containing location coordinates, wind speed values, and slope values. Here, the monitoring point is the location of the monitoring device within the wind farm area according to the preset monitoring point layout.

[0223] Step 2: Extract the core performance parameters of the target wind turbine, perform threshold filtering on the wind speed data and slope values ​​in the basic dataset, determine the effective wind energy area and the flat terrain area, and then generate candidate layout areas that meet the basic conditions for wind turbine operation.

[0224] Step 3: Determine the minimum spacing required for a single fan to operate based on the blade size, and determine the maximum number of fans that can be arranged in each area by combining the area and shape of the candidate arrangement area.

[0225] Step 4: For each candidate layout area, generate several layout schemes according to the rated power of the fans, the preset arrangement spacing, and the maximum number of fans to be arranged. Each scheme includes the number of fans, arrangement coordinates, and power configuration parameters.

[0226] Step 5: Determine the final optimized wind turbine layout scheme based on the number of wind turbines, their arrangement coordinates, and power configuration parameters of all schemes.

[0227] The beneficial effects of the above technical solution are as follows: By optimizing the wind turbine layout of the wind farm through multi-factor analysis, and combining wind speed, slope, turbine performance, and terrain conditions, candidate layout areas and turbine layout schemes are determined. Through precise screening of effective wind energy areas and flat terrain areas, combined with minimum spacing and area constraints, the number and arrangement of turbines are optimized to ensure maximum wind energy utilization and avoid mutual interference between turbines. The final optimized layout scheme improves the overall power generation efficiency of the wind farm, reduces energy loss, and has high economic benefits and operability.

[0228] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A wind farm turbine optimization layout system based on multi-factor analysis, characterized in that, include: Data acquisition module: Multiple monitoring devices are evenly set up within the wind farm area according to the preset monitoring point layout rules to collect wind speed data and terrain slope data at each monitoring point, and establish a basic dataset containing location coordinates, wind speed values, and slope values. The monitoring point is the location of the monitoring device within the wind farm area according to the preset monitoring point layout. Region determination module: Extract the core performance parameters of the target wind turbine, perform threshold filtering on wind speed data and slope values ​​in the basic dataset, determine the effective wind energy area and flat terrain area, and then generate candidate layout areas that meet the basic conditions for wind turbine operation. Regional Analysis Module: Determines the minimum spacing required for a single wind turbine to operate based on the blade size, and determines the maximum number of wind turbines that can be arranged in each region by combining the area and shape of the candidate layout areas; Scheme determination module: For each candidate layout area, several layout schemes are generated according to the rated power of the wind turbines, the preset arrangement spacing, and the maximum number of wind turbines to be arranged. Each scheme includes the number of wind turbines, arrangement coordinates, and power configuration parameters. Solution selection module: Determines the final optimized wind turbine layout scheme based on the number of wind turbines, their arrangement coordinates, and power configuration parameters of all schemes; The region determination module includes: Performance parameter extraction unit: Extracts core performance parameters such as rated power, blade size, and starting wind speed from the target wind turbine's preset technical documents; First area screening unit: Based on core performance parameters, threshold screening is performed on wind speed data in the basic database to determine effective wind energy areas; The second region filtering unit: Based on the core performance parameters, the slope values ​​in the basic database are filtered by threshold to determine the gentle terrain regions; Regional Determination Unit: The intersection of the effective wind energy area and the flat terrain area is taken as the candidate layout area that meets the basic conditions for wind turbine operation; The first area filtering unit includes: Threshold reduction subunit: Identifies the prevailing wind direction in the local area. If the monitoring point is in the prevailing wind direction, the wind speed threshold reduction will be initiated. The threshold reduction subunit includes: Wind direction determination block: Wind direction data acquisition steps: retrieve historical wind direction data of the wind farm area from the meteorological database, generate a wind rose diagram, and identify the prevailing wind direction interval with an annual frequency greater than the preset frequency; Monitoring point location determination block: compares the geographical location information of each monitoring point with the prevailing wind direction interval to determine the target monitoring point located in the direction of the incoming flow along the prevailing wind path; Wind speed probability statistics block: For the target monitoring point, retrieve its historical wind speed data, statistically analyze the wind speed distribution probability when the prevailing wind occurs, and calculate the ratio of the difference between the average wind speed during the prevailing wind period and the average wind speed in the non-prevailing wind direction. Threshold adjustment block: Based on the ratio of the difference between the average wind speed during the prevailing wind direction and the average wind speed in the non-prevailing wind direction, and the preset ratio-amplitude data table, the corresponding reduction range is determined, thereby reducing the start-up wind speed threshold of the target monitoring point by the corresponding range. The second region filtering unit includes: Basic slope filtering sub-unit: Based on the slope values ​​in the basic dataset, the continuous geographical area covered by all monitoring points in the basic dataset whose slope values ​​are less than the preset first slope threshold is marked as a candidate gentle area; Mesh sub-units: The candidate smooth area is divided into grids, and the grid size is determined according to the preset accuracy. Slope calculation sub-unit: Calculates the absolute value of the slope difference between the center point of each candidate gentle area and its surrounding adjacent grids; Region elimination sub-units: Eliminate the regions corresponding to the adjacent grids of the center point of each candidate gentle area whose absolute value of slope difference is greater than the second slope threshold, thereby obtaining gentle terrain regions.

2. The wind turbine optimization layout system for wind farms based on multi-factor analysis according to claim 1, characterized in that, The first area filtering unit also includes: Wind speed sub-units: The wind speed data in the basic dataset is divided into wind speed data for four seasons, and the ratio of the cumulative time when the wind speed reaches the start-up wind speed threshold at each monitoring point in each season to the total time in each season is calculated. Wind speed screening sub-unit: retains the first monitoring point corresponding to the season in which the ratio exceeds the preset ratio; Turbulence screening subunit: retrieve turbulence intensity parameters from the preset meteorological database, further screen the retained first monitoring points, remove the first monitoring points whose turbulence intensity parameters exceed the preset turbulence intensity parameters, and obtain several second monitoring points; Discrete screening sub-unit: The moving window method is used to remove the second monitoring point whose wind speed reaches the start wind speed but whose single duration is less than the preset duration, and a number of third monitoring points are obtained. Regional connection sub-unit: Mark the third monitoring point as the effective wind energy location, connect adjacent effective wind energy locations to form a continuous area, and generate an effective wind energy distribution vector map; Region determination sub-unit: The effective wind energy distribution vector map is smoothed, and discrete regions with areas smaller than the minimum layout unit are removed to obtain the effective wind energy region.

3. The wind farm turbine optimization layout system based on multi-factor analysis according to claim 1, characterized in that, The regional analysis module includes: Spacing calculation unit: Calculates the minimum lateral and longitudinal spacing required for the operation of a single fan based on the blade size; Region division unit: The grid side length is determined based on the minimum horizontal and vertical spacing, and the candidate layout area is divided into grids based on the grid side length; Grid evaluation unit: Statistically calculate the effective wind energy area ratio and slope value within each grid, evaluate whether the grid meets the wind turbine placement conditions, and exclude grids with excessive slope to obtain several effective grids; Quantity Calculation Unit: Based on the number of effective grids, combined with the rated power of the wind turbines and site boundary constraints, calculate the maximum number of wind turbines that can be arranged in each candidate layout area.

4. The wind turbine optimization layout system for wind farms based on multi-factor analysis according to claim 3, characterized in that, Spacing calculation unit, including: Lateral determination of sub-units: The first preset multiple of the blade diameter is determined as the minimum lateral spacing required for the operation of a single fan; Longitudinal determination sub-unit: The second preset multiple of the blade diameter is determined as the minimum longitudinal spacing required for the operation of a single wind turbine.

5. The wind farm turbine optimization layout system based on multi-factor analysis according to claim 1, characterized in that, The solution determination module includes: Arrangement pattern setting unit: Sets various basic arrangement patterns, such as rectangular array, fan-shaped radial, and adaptive distributed; Power configuration unit: Configures the power of the fans according to their rated power and preset arrangement mode. The power configuration is the combination ratio of high-power fans and medium-power fans. Scheme generation unit: In each candidate area, with a preset arrangement spacing as the step size, combined with the preset arrangement mode, power configuration and maximum quantity limit, several preliminary layout schemes are generated; Scheme processing unit: Processes each preliminary layout scheme and generates a layout scheme that includes the number of wind turbines, their arrangement coordinates, and power configuration parameters.

6. The wind turbine optimization layout system for wind farms based on multi-factor analysis according to claim 1, characterized in that, The solution selection module includes: Indicator Calculation Unit: For each layout plan, calculate the score of each preset evaluation indicator; Comprehensive score calculation unit: Calculates the comprehensive score of each layout scheme based on the weights corresponding to the preset evaluation indicators and the scores of each preset evaluation indicator; Scheme ranking unit: Rank all layout schemes according to their comprehensive scores; Scheme Determination Unit: Select the layout scheme with the highest comprehensive score as the final optimized wind turbine layout scheme.

7. A method for optimizing the layout of wind turbines in a wind farm based on multi-factor analysis, characterized in that, include: Step 1: Within the wind farm area, multiple monitoring devices are evenly set up according to the preset monitoring point layout rules. Wind speed data and terrain slope data are collected from each monitoring point to establish a basic dataset containing location coordinates, wind speed values, and slope values. Here, the monitoring point is the location of the monitoring device within the wind farm area according to the preset monitoring point layout. Step 2: Extract the core performance parameters of the target wind turbine, perform threshold filtering on the wind speed data and slope values ​​in the basic dataset, determine the effective wind energy area and the flat terrain area, and then generate candidate layout areas that meet the basic conditions for wind turbine operation. Step 3: Determine the minimum spacing required for a single fan to operate based on the blade size, and determine the maximum number of fans that can be arranged in each area by combining the area and shape of the candidate arrangement area. Step 4: For each candidate layout area, generate several layout schemes according to the rated power of the fans, the preset arrangement spacing, and the maximum number of fans to be arranged. Each scheme includes the number of fans, arrangement coordinates, and power configuration parameters. Step 5: Determine the final optimized wind turbine layout scheme based on the number of wind turbines, their arrangement coordinates, and power configuration parameters of all proposed schemes; wherein, Step 2 above includes: Extract the core performance parameters, such as rated power, blade size, and starting wind speed, from the target wind turbine's pre-defined technical documents; Effective wind energy areas are determined by threshold filtering of wind speed data in the basic database based on core performance parameters. Based on core performance parameters, the slope values ​​in the basic database are used to filter for thresholds to determine areas with gentle terrain. The intersection of the effective wind energy area and the flat terrain area is selected as the candidate layout area that meets the basic conditions for wind turbine operation; Among these measures, effective wind energy areas are determined by threshold filtering of wind speed data in the basic database based on core performance parameters, including: Identify the prevailing wind direction in the area; if the monitoring point is in the prevailing wind direction, the wind speed threshold will be lowered. This includes identifying the prevailing wind direction in the area; if the monitoring point is in the prevailing wind direction, a wind speed threshold reduction will be initiated, including: Wind direction data acquisition steps: retrieve historical wind direction data of the wind farm area from the meteorological database, generate a wind rose diagram, and identify the prevailing wind direction interval with an annual frequency greater than the preset frequency; By comparing the geographical location information of each monitoring point with the prevailing wind direction interval, the target monitoring point located in the direction of the incoming flow along the prevailing wind path is determined. For the target monitoring point, retrieve its historical wind speed data, statistically analyze the wind speed distribution probability when the prevailing wind occurs, and calculate the ratio of the difference between the average wind speed during the prevailing wind period and the average wind speed in the non-prevailing wind direction. Based on the ratio of the difference between the average wind speed during the prevailing wind direction and the average wind speed in the non-prevailing wind direction, and the preset ratio-amplitude data table, the corresponding reduction range is determined, and then the starting wind speed threshold of the target monitoring point is reduced by the corresponding range. Among these measures, the determination of gentle terrain areas involves threshold filtering of slope values ​​in the basic database based on core performance parameters, including: Based on the slope values ​​in the basic dataset, the continuous geographical area covered by all monitoring points in the basic dataset whose slope values ​​are less than the preset first slope threshold is marked as a candidate gentle area. The candidate flat areas are divided into grids, and the grid size is determined according to the preset precision. Calculate the absolute value of the slope difference between the center point of each candidate gentle area and its adjacent grid. The regions corresponding to the adjacent grids of the center point of each candidate gentle terrain area with an absolute value of slope difference greater than the second slope threshold are removed, thereby obtaining the gentle terrain region.

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