Wind power plant fan optimization arrangement system and method based on multi-factor analysis

Abstract

The invention provides a wind power plant fan optimization arrangement system and method based on multi-factor analysis, and relates to the technical field of data optimization, and the system comprises a data collection module which collects wind speed data and topographic slope data of each monitoring point in a wind power plant region, and builds a basic data set; the region determination module is used for extracting core performance parameters of a target fan, performing threshold screening on wind speed data and slope values in the basic data set, determining an effective wind energy region and a gentle terrain region, and further generating a candidate arrangement region; the area analysis module is used for determining the minimum spacing required by operation of a single fan according to the blade size, and determining the maximum number upper limit of the fans capable of being arranged in each area in combination with the area and the shape of the candidate arrangement area; the scheme determination module is used for generating a plurality of groups of arrangement schemes for each candidate arrangement area; and the scheme screening module is used for determining a final fan optimization arrangement scheme based on all the schemes. The overall power generation efficiency of the wind power plant is improved, and the energy loss is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data optimization, and in particular to a system and method for optimizing the layout of wind turbines in a wind farm based on multi-factor analysis. Background Art

[0002] Wind farms are crucial facilities for harnessing wind energy for power generation, and the layout of their turbines directly impacts their power generation efficiency. In practical applications, turbine placement must comprehensively consider multiple factors, including wind speed, topography, and turbine performance, to maximize wind energy utilization. However, current wind farm turbine placement methods often rely too heavily on simple rules or empirical evidence, failing to fully utilize diverse geographic and meteorological data for precise optimization. This results in suboptimal power generation efficiency.

[0003] Most existing wind farm turbine placement methods typically determine turbine location based on wind speed data. However, these methods often overlook factors such as terrain, turbine spacing, and specific turbine performance parameters. Furthermore, existing turbine layouts are relatively simplistic and lack systematic data analysis and multi-factor optimization, making it difficult to address issues such as turbine interference and low wind energy utilization.

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

[0005] This invention provides a system and method for optimizing wind turbine placement in wind farms based on multi-factor analysis. This system optimizes wind turbine placement in wind farms through multi-factor analysis, combining wind speed, slope, turbine performance, and terrain conditions to determine candidate placement areas and turbine placement plans. By accurately screening effective wind energy areas and areas with gentle terrain, and combining minimum spacing and area constraints, the number and arrangement of wind turbines are optimized to maximize wind energy utilization and avoid interference between turbines. The resulting optimized placement plan improves the overall power generation efficiency of the wind farm, reduces energy losses, and offers high economic benefits and operability.

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

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

[0008] Region determination module: This module extracts the core performance parameters of the target wind turbine, performs threshold screening on the wind speed data and slope values ​​in the basic dataset, identifies effective wind energy areas and flat terrain areas, and then generates candidate layout areas that meet the basic operating conditions of the wind turbine.

[0009] Regional analysis module: Determines the minimum spacing required for a single wind turbine to operate based on blade size, and determines the maximum number of wind turbines that can be deployed in each area based on the area and shape of the candidate deployment areas;

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

[0011] Scheme screening module: Determine the final optimized wind turbine layout scheme based on the number of wind turbines, arrangement coordinates and power configuration parameters of all schemes.

[0012] Preferably, the region determination module includes:

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

[0014] The first area screening unit: performs threshold screening on wind speed data in the basic database based on core performance parameters to determine the effective wind energy area;

[0015] The second area screening unit: based on the core performance parameters, the slope values ​​in the basic database are screened by threshold value to determine the flat terrain area;

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

[0017] Preferably, the regional screening unit comprises:

[0018] Threshold reduction subunit: identifies the local main wind direction and initiates wind speed threshold reduction if the monitoring point is in the main wind direction;

[0019] Wind speed division subunit: The wind speed data in the basic data set is divided into four seasons, and the ratio of the cumulative duration of the wind speed reaching the start wind speed threshold in each season to the total duration of each season is calculated for each monitoring point;

[0020] Wind speed screening subunit: retain the first monitoring point corresponding to the season with a ratio exceeding a preset ratio;

[0021] Turbulence screening subunit: retrieves turbulence intensity parameters from a preset meteorological database, further screens the above-retained first monitoring points, eliminates first monitoring points whose turbulence intensity parameters exceed the preset turbulence intensity parameters, and obtains a plurality of second monitoring points;

[0022] Discrete screening subunit: using the moving window method to eliminate the second monitoring points where the wind speed reaches the starting wind speed but the single duration is less than the preset duration, to obtain several third monitoring points;

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

[0024] Region determination subunit: Smooth the effective wind energy distribution vector diagram and remove discrete areas with an area smaller than the minimum layout unit to obtain the effective wind energy area.

[0025] Preferably, the threshold lowering subunit comprises:

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

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

[0028] Wind speed probability statistics block: for the target monitoring point, retrieve its historical wind speed data, count the wind speed distribution probability when the main wind direction occurs, and calculate the difference ratio between the average wind speed during the main wind direction and the average wind speed during the non-main wind direction;

[0029] Threshold adjustment block: Determine the corresponding reduction amplitude based on the difference ratio between the average wind speed during the main wind direction and the average wind speed during the non-main wind direction and the preset ratio-amplitude data table, and then reduce the starting wind speed threshold of the target monitoring point by the corresponding amplitude.

[0030] Preferably, the second area screening unit comprises:

[0031] Basic slope screening subunit: based on the slope values ​​in the basic data set, the continuous geographical range covered by all monitoring points with slope values ​​in the basic data set less than a preset first slope threshold is marked as a candidate flat area;

[0032] Grid division subunit: grid division of candidate flat areas, with the grid size determined according to the preset accuracy;

[0033] Slope calculation subunit: calculates the absolute value of the slope difference between the center point of each candidate flat area and the surrounding adjacent grids;

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

[0035] Preferably, the regional analysis module includes:

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

[0037] Area division unit: determines the grid side length based on the minimum horizontal and vertical spacing, and divides the candidate layout area into grids based on the grid side length;

[0038] Grid evaluation unit: Counts the effective wind energy area ratio and slope value in each grid, evaluates whether the grid meets the wind turbine layout conditions, and eliminates grids with excessive slopes to obtain several valid grids;

[0039] Quantity calculation unit: Based on the number of valid grids, combined with the rated power of the wind turbines and the site boundary restrictions, calculate the maximum number of wind turbines that can be deployed in each candidate layout area.

[0040] Preferably, the distance calculation unit includes:

[0041] Lateral determination subunit: determining a first preset multiple of the blade diameter as the minimum lateral spacing required for operating a single wind turbine;

[0042] The longitudinal determination subunit determines a second preset multiple of the blade diameter as the minimum longitudinal spacing required for the operation of a single wind turbine.

[0043] Preferably, the solution determination unit includes:

[0044] Arrangement mode setting unit: set a variety of basic arrangement modes, such as rectangular array, fan-shaped radiation, adaptive dispersion, etc.

[0045] Power configuration unit: configures the power of the fans according to their rated power and preset arrangement mode, where the power configuration is the combination ratio of high-power fans and medium-power fans;

[0046] Solution generation unit: In each candidate area, using the preset arrangement spacing as the step size, combined with the preset arrangement mode, power configuration and maximum number limit, generates several groups of preliminary layout solutions;

[0047] Scheme arrangement unit: Arrange each set of preliminary layout plans and generate a layout plan including the number of wind turbines, arrangement coordinates and power configuration parameters.

[0048] Preferably, the solution screening module includes:

[0049] Index calculation unit: for each layout plan, calculate the scores of each preset evaluation index;

[0050] Comprehensive score calculation unit: calculates the comprehensive score of each layout plan according to the weight corresponding to the preset evaluation indicators and the scores of each preset evaluation indicator;

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

[0052] Scheme determination unit: select the layout scheme with the highest comprehensive score as the final fan optimization layout scheme.

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

[0054] Step 1: Multiple monitoring devices are evenly distributed within the wind farm area according to the preset monitoring point layout rules. Wind speed data and terrain slope data are collected at each monitoring point to establish a basic data set containing location coordinates, wind speed values, and slope values. The monitoring points are the locations of the monitoring devices arranged according to the preset monitoring point layout within the wind farm area.

[0055] Step 2: Extract the core performance parameters of the target wind turbine, perform threshold screening 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 wind turbine to operate based on the blade size. Combined with the area and shape of the candidate deployment areas, determine the maximum number of wind turbines that can be deployed in each area.

[0057] Step 4: For each candidate layout area, generate several layout plans based on the wind turbine rated power, preset arrangement spacing, and the maximum number of wind turbines to be arranged. Each plan includes the number of wind turbines, arrangement coordinates, and power configuration parameters.

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

[0059] Compared with the prior art, the present invention has the following advantages:

[0060] Through multi-factor analysis, we optimize wind farm turbine layout, identifying candidate locations and turbine placement plans based on factors such as wind speed, slope, turbine performance, and terrain conditions. By accurately screening effective wind energy areas and areas with gentle terrain, and incorporating minimum spacing and area constraints, we optimize the number and arrangement of turbines to maximize wind energy utilization and avoid interference between turbines. The resulting optimized layout improves the overall power generation efficiency of the wind farm, reduces energy losses, and offers high economic benefits and operability. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

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

[0063] Figure 2 This is a flow chart of a method for optimizing wind turbine layout in a wind farm based on multi-factor analysis provided by an embodiment of the present invention;

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

[0065] Figure 4 This is a wind rose diagram provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0066] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0067] Example 1:

[0068] The embodiment of the present invention provides a wind farm wind turbine optimization layout system based on multi-factor analysis, such as Figure 1 As shown, including:

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

[0070] Region determination module: This module extracts the core performance parameters of the target wind turbine, performs threshold screening on the wind speed data and slope values ​​in the basic dataset, identifies effective wind energy areas and flat terrain areas, and then generates candidate layout areas that meet the basic operating conditions of the wind turbine.

[0071] Regional analysis module: Determines the minimum spacing required for a single wind turbine to operate based on blade size, and determines the maximum number of wind turbines that can be deployed in each area based on the area and shape of the candidate deployment areas;

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

[0073] Scheme screening module: Determine the final optimized wind turbine layout scheme based on the number of wind turbines, arrangement coordinates and power configuration parameters of all schemes.

[0074] In this embodiment, the system also includes a data update module: a data monitoring submodule: real-time monitoring of changes in data such as wind speed and terrain in the wind farm area; an update trigger submodule: when the monitored data changes exceed a preset threshold, triggering the data update process; a data acquisition submodule: re-collecting relevant data in the changed area; a data integration submodule: integrating and updating the newly collected data with the original basic data set; a module notification submodule: after the data update is completed, notifying other related modules that the data has been updated so that the wind turbine layout can be re-optimized.

[0075] In this embodiment, the data acquisition module includes: a wind speed acquisition unit: a plurality of wind speed sensors are reasonably distributed in the wind farm area, wind speed data of each monitoring point is collected in real time, and the acquisition time and position coordinates are recorded; a terrain acquisition unit: terrain data of the wind farm area is acquired by remote sensing mapping technology, and the slope value of each position is obtained by calculation; 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 position coordinates to form a basic data set including the position coordinates, wind speed values, and slope values.

[0076] In this embodiment, when collecting wind speed data, it also includes: a sensor calibration step: regularly 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 supplementary collection step: if data is missing, supplementary data is collected based on the data of adjacent monitoring points and time series; a data synchronization step: synchronizing the collected wind speed data with time to ensure the time consistency of the data.

[0077] In this embodiment, the "target wind turbine" refers to a 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 serve as the core input conditions for the optimized layout.

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

[0079] The beneficial effects of this technical solution include optimizing wind farm turbine layout through multi-factor analysis, combining wind speed, slope, turbine performance, and terrain conditions to determine candidate deployment areas and turbine placement plans. By accurately screening effective wind energy zones and areas with gentle terrain, and combining minimum spacing and area constraints, the number and arrangement of turbines are optimized to maximize wind energy utilization and avoid interference between turbines. The resulting optimized layout improves the overall power generation efficiency of the wind farm, reduces energy losses, and offers high economic benefits and operability.

[0080] Example 2:

[0081] An embodiment of the present invention provides a wind farm wind turbine optimization layout system based on multi-factor analysis, and a region determination module, including:

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

[0083] The first area screening unit: performs threshold screening on wind speed data in the basic database based on core performance parameters to determine the effective wind energy area;

[0084] The second area screening unit: based on the core performance parameters, the slope values ​​in the basic database are screened by threshold value to determine the flat terrain area;

[0085] Area determination unit: The intersection of the effective wind energy area and the flat terrain area is determined 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 requirements and where the wind speed duration ratio reaches a preset threshold. This area is generated by performing spatiotemporal threshold screening on the wind speed data in the basic data set, and must simultaneously meet the following conditions: wind speed threshold condition: wind speed ≥ target wind turbine startup wind speed (such as 2.5m / s); time ratio condition: wind speed ≥ cumulative duration ratio of startup wind speed ≥ preset value (such as 40% / season or 60% / year); spatial continuity condition: the distance between adjacent effective points ≤ the maximum allowable interval (such as 500m), forming a continuous area. For example, in the project, the target wind turbine startup wind speed is 2.5m / s. By screening out areas with an average annual wind speed ≥ 2.5m / s and a compliance duration ratio ≥ 65%, a continuous area is formed. Figure 3 The effective wind energy area (shaded area) is about 15 km 2 , including 20 continuous monitoring points, Figure 3The CGCS2000 national geodetic coordinate system is used, with the horizontal axis representing the easting coordinate (X axis) and the vertical axis representing the northing coordinate (Y axis). The coordinate unit is meter, and the grid spacing is 500 m × 500 m. The shaded area represents the effective wind energy area, and the color depth represents the wind energy density level. The darker the color, the higher the wind speed. The boundary is drawn with a black solid line. The area is fitted using the Delaunay triangulation algorithm, and the area < 0.5 km is excluded. 2 Discrete areas. Circles mark the retained third monitoring point, and crosses mark invalid monitoring points that were removed. The figure also includes dashed contour lines at 50-meter intervals to illustrate the terrain. Arrows indicate the prevailing wind direction (WSW to SSW), and light-colored arrows indicate the area affected by the wake.

[0087] In this embodiment, the flat terrain area refers to the continuous geographical space within the wind farm area where the terrain slope value is less than or equal to the preset threshold and the terrain undulation is within the allowable range. This area is generated by filtering the slope data in the basic dataset using spatial thresholds and must meet the following slope absolute value conditions: average slope ≤ θ max (e.g. 15°); Slope change rate condition: the absolute value of the slope difference between adjacent grid cells ≤ Δθ max (e.g. 8°); Terrain continuity condition: area of ​​continuous flat area ≥ 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 wind turbine startup and rated operation requirements; topographic and geological conditions: the terrain slope and flatness meet the wind turbine foundation design and installation requirements; environmental compatibility conditions: avoid unfavorable areas such as high turbulence areas and areas prone to geological disasters.

[0089] The beneficial effect of this technical solution is that by extracting the core performance parameters of the target wind turbines and combining them with wind speed and slope data, it accurately screens effective wind energy areas and areas with gentle terrain, and identifies their intersection as candidate placement areas. This method ensures that wind turbines operate under optimal wind energy conditions while avoiding the impact of unsuitable terrain, 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] An embodiment of the present invention provides a wind farm wind turbine optimization layout system based on multi-factor analysis, and a regional screening unit, comprising:

[0092] Threshold reduction subunit: identifies the local main wind direction and initiates wind speed threshold reduction if the monitoring point is in the main wind direction;

[0093] Wind speed division subunit: The wind speed data in the basic data set is divided into four seasons, and the ratio of the cumulative duration of the wind speed reaching the start wind speed threshold in each season to the total duration of each season is calculated for each monitoring point;

[0094] Wind speed screening subunit: retain the first monitoring point corresponding to the season with a ratio exceeding a preset ratio;

[0095] Turbulence screening subunit: retrieves turbulence intensity parameters from a preset meteorological database, further screens the above-retained first monitoring points, eliminates first monitoring points whose turbulence intensity parameters exceed the preset turbulence intensity parameters, and obtains a plurality of second monitoring points;

[0096] Discrete screening subunit: using the moving window method to eliminate the second monitoring points where the wind speed reaches the starting wind speed but the single duration is less than the preset duration, to obtain several third monitoring points;

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

[0098] Region determination subunit: Smooth the effective wind energy distribution vector diagram and remove discrete areas with an area smaller than the minimum layout unit to obtain the effective wind energy area.

[0099] In this embodiment, the wind speed division subunit implements seasonal division through the following steps: time period definition: the whole year is divided into four seasons according to the Gregorian calendar month, namely spring (March-May), summer (June-August), autumn (September-November) and winter (December-February); data index construction: the wind speed data in the basic data set 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 of 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 (four seasons), D is the number of days, and H is the number of hours. For the Naqu Wind Farm Project in Tibet, the base dataset contains hourly wind speed data from 30 monitoring points throughout the year (a total of 262,800 records). After seasonal division, the summer wind speed subset (June-August) contains 66,960 records and is used for subsequent seasonal wind energy assessment.

[0100] In this embodiment, the first monitoring point refers to a monitoring point that meets the following conditions: Ratio i ≥T ratio , where: Ratio i T is the proportion of time when the wind speed at the i-th monitoring point reaches the standard (≥2.5m / s) in a specific season, ratio is the preset ratio threshold, ranging from 40% to 70%. For example:

[0101] The wind speed at monitoring point GNNQ12 met the standard for 1,680 hours in summer, out of a total of 2,190 hours, accounting for 76.7%. Since 76.7% is greater than the preset threshold T ratio =60%, so GNNQ12 is 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: t ≤T turb , where: I t is the turbulence intensity parameter of the monitoring point, T turb is the preset turbulence intensity threshold, ranging from 0.12 to 0.18. For example, the turbulence intensity of the first monitoring point GNNQ12 is I t =0.15, and the preset threshold T turb =0.16, so GNNQ12 meets 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 and is therefore eliminated.

[0103] In this embodiment, the turbulence intensity parameter T is preset. turb The maximum turbulence intensity threshold allowed for safe operation of the 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: automatically adjust the k value according to the complexity of the terrain (such as roughness category), and take a lower value for complex terrain. For example: the turbulence intensity tolerance limit I of a certain model 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 time proportion of wind energy effectiveness at the monitoring point in a specific season, and the theoretical effective time proportion benchmark value T is calculated. base , the calculation formula is:

[0105]

[0106] Among them, t j T is the time it takes for the fan to operate stably after the wind speed reaches the starting wind speed. 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 has a value range of 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 starting energy requirement of the target fan is E start = 3MJ, and the average loss during the starting 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 starting wind speed (2.5 m / s) each time, it takes an average of 1.5 hours to make the fan run stably. There are 12 effective starts 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 region 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 compliance 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 compliance 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, and the optimization method improves the accuracy and power generation efficiency of the wind farm fan layout, ensuring long-term and stable wind energy utilization.

[0114] Example 4:

[0115] An embodiment of the present invention provides a wind farm wind turbine optimization layout system based on multi-factor analysis, wherein the threshold reduction subunit includes:

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

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

[0118] Wind speed probability statistics block: for the target monitoring point, retrieve its historical wind speed data, count the wind speed distribution probability when the main wind direction occurs, and calculate the difference ratio between the average wind speed during the main wind direction and the average wind speed during the non-main wind direction;

[0119] Threshold adjustment block: Determine the corresponding reduction amplitude based on the difference ratio between the average wind speed during the main wind direction and the average wind speed during the non-main wind direction and the preset ratio-amplitude data table, and then reduce the starting wind speed threshold of the target monitoring point by the corresponding amplitude.

[0120] In this embodiment, historical wind direction data of the wind farm area is retrieved from the meteorological database to generate a wind rose diagram. The following steps are used to retrieve data from the meteorological database and generate a wind rose diagram: Data retrieval interface: Use a standardized API protocol (such as OpenWeatherMapAPI, WindyAPI) to obtain wind direction data from a meteorological database (such as the China Meteorological Science Data Sharing Service Network, ERA5 reanalysis database), support different data formats such as GRIB, NetCDF, CSV, and the time resolution can be configured from 1 hour to 1 year, and the spatial resolution covers 0.1°×0.1° to 5°×5° grids; Data cleaning and preprocessing: Eliminate outliers in wind direction angles (such as >360° or <0°), and use linear interpolation to fill in missing data; Divide the wind direction data into 16 or 32 sectors (each sector is 22.5° or 11.25°); Wind rose diagram generation algorithm: Calculate the frequency of wind direction occurrence f in each sector i , the formula is:

[0121]

[0122] where N i is the number of wind direction data points in the i-th sector, N total is the total number of data points; polar coordinate plotting method is used, sector angle is used as polar angle, frequency f i For the polar diameter, a wind rose diagram with wind speed weight is generated (the wind speed weight is determined by the average wind speed v of each sector). i and frequency f iFor example, in the Jicuo wind farm project in Senni District, hourly wind direction data from 2010 to 2022 (a total of 105,120 records) were retrieved from the China Meteorological Science Data Sharing Service Network through the API. After removing 1,200 abnormal data, the wind direction was divided into 16 sectors. It was calculated that the frequency of sector 10 (202.5°-225°) was 32% and the average wind speed was 8.5m / s, and the frequency of sector 11 (225°-247.5°) was 28% and the average wind speed was 7.8m / s. The final wind rose diagram shows that the main wind direction is concentrated in the WSW-SSW range (such as Figure 4 The WSW direction is marked as the core main wind direction due to its high frequency and high wind speed.

[0123] In this embodiment, the preset frequency F thres It is the frequency threshold used to determine the main wind direction interval. The determination method is as follows: set the basic threshold F0 according to the wake influence characteristics of the target wind turbine, such as 25%; introduce the 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 supports manual fine-tuning; for example: the Jicuo wind farm in Senni District is a plateau hilly terrain (z0=0.2m), the basic threshold F0=25%, and the 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 interval with a frequency greater than 32.5% is determined to be the main wind direction interval, while other sectors (such as the NNW direction with a frequency of 22%) are not included in the main wind direction range.

[0128] In this embodiment, the main wind direction interval refers to the wind direction frequency in the wind rose diagram that is greater than the preset frequency F thres A continuous sector of 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 is the sector angle, f i The main wind direction interval supports the determination of multiple intervals (such as dual main wind direction scenarios). When there are multiple discontinuous high-frequency sectors, they will be marked as independent main wind direction intervals. For example: in the wind rose diagram of the Jicuo wind farm, the frequency of sector 10 (202.5°-225°) is 32%, and the frequency of sector 11 (225°-247.5°) is 28%, both of which exceed the preset frequency F. thres =32.5% and the angle is continuous, so the main wind direction interval 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 main wind direction interval (0°-45°).

[0131] In this embodiment, the geographical location information of each monitoring point is compared with the main wind direction interval. The comparison process is realized by geographic information system (GIS) spatial analysis. The specific steps are as follows: the latitude and longitude coordinates (WGS84 coordinate system) of the monitoring point are converted into the wind rose. Figure 1 consistent projection coordinate system (such as UTMZone44N); based on the central angle θ of the main wind direction interval main , generate a vector line from the main wind direction source to the wind farm; use buffer zone analysis to determine whether the monitoring point is within the vector line buffer zone. The formula is:

[0132]

[0133] Among them, P i is the coordinate of the monitoring point, dist(P i ,Vector) is the shortest distance from a point to a line; for example: after the monitoring point GNNQ12 (N31.4821, E92.3145) is converted to UTM coordinates, it is compared with the WSW-SSW main wind direction vector line. The calculated shortest distance from the 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; while GNNQ25 is 680m away from the vector line and is therefore excluded; Vector: The vector line representing the main wind direction, determined by the starting point A and the end 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 the monitoring point P iThe shortest geometric distance to the main wind direction vector line (Vector) is calculated using the point-to-line distance formula. IsTarget = 1: the monitoring point is within the buffer range and is determined to be the target monitoring point; IsTarget = 0: the monitoring point is outside the buffer range and is determined to be a non-target monitoring point. Given the vector line Vector (determined by the starting point A and the end point B) and the monitoring point P, the shortest distance calculation formula 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 as follows:

[0136] R=R0×k terrain ×k wind

[0137] Among them, the basic radius R0: the default value is 1000 meters, which represents the influence range of the main wind direction under flat terrain; the terrain correction coefficient k terrain : Adjusted according to 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 area / complex terrain (z0>0.5): k terrain =1.5; wind speed correction factor k wind :

[0138]

[0139] in, is the average wind speed in the prevailing direction, is the average wind speed in the non-main wind direction. If k wind >1.5$, then take k wind =1.5.

[0140] In this embodiment, the wind speed distribution probability statistics adopts the kernel density estimation (KDE) method, and the specific steps are as follows: extract the wind speed data V={v1,v2,...,v n}; 2. Probability density function calculation:

[0141]

[0142] Where K is the kernel function (such as Gaussian kernel), h is the bandwidth parameter (determined by cross-validation method), and n is the number of wind speed data of the target monitoring point in the main wind direction interval; Probability distribution generation: Based on f(v), calculate the cumulative distribution probability P(v≤v) of each wind speed interval (such as 0-2m / s, 2-4m / s) j ) and generates a probability distribution curve. For example, statistics on 12,000 wind speed data points at the target monitoring point GNNQ12 during the period of WSW-SSW prevailing winds were collected. KDE calculations revealed that the probability of wind speeds of 2-4 m / s was 18%, the probability of wind speeds of 4-6 m / s was 32%, and the probability of wind speeds of 6-8 m / s was 25%. Based on these data, the probability distribution curve shows that the wind speeds at this monitoring point in the prevailing wind direction are concentrated in the range of 4-8 m / s.

[0143] The beneficial effects of this technical solution include: by combining historical wind direction data with wind speed probability statistics, accurately identifying the prevailing wind direction area and adjusting the startup wind speed threshold at the target monitoring point based on wind speed differences. This method improves wind turbine startup efficiency in the prevailing wind direction area, optimizes the layout and operating conditions of wind turbines within the wind farm, increases wind energy utilization, and reduces energy losses during turbine startup, thereby improving the overall power generation efficiency and economic benefits of the wind farm.

[0144] Example 5:

[0145] An embodiment of the present invention provides a wind farm wind turbine optimization layout system based on multi-factor analysis, wherein the second region screening unit includes:

[0146] Basic slope screening subunit: based on the slope values ​​in the basic data set, the continuous geographical range covered by all monitoring points with slope values ​​in the basic data set less than a preset first slope threshold is marked as a candidate flat area;

[0147] Grid division subunit: grid division of candidate flat areas, with the grid size determined according to the preset accuracy;

[0148] Slope calculation subunit: calculates the absolute value of the slope difference between the center point of each candidate flat area and the surrounding adjacent grids;

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

[0150] In this embodiment, a first slope threshold θ1 is preset to preliminarily screen the critical slope value of the gentle terrain. The determination method 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 ; Introduce geological correction factor k geo , its value selection rules are: Rock foundation: kgeo =1.2; soil foundation: k geo =1.0; soft soil foundation: k geo =0.8; Final threshold calculation: by formula θ1=k geo ×θ base The final slope threshold is calculated, ranging from 8° to 15°, and can be manually adjusted according to the geological survey report of the wind farm. For example, in the Jicuo Wind Farm Project in Senyi District, the target wind turbine adopts a gravity foundation, and the maximum allowable slope is 12° (θ base =12°). The geological survey report shows that the area is a soil foundation (k geo =1.0), so 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 at an altitude of 4900-5100 meters) are preliminarily marked as candidate flat areas.

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

[0152] In this embodiment, the second slope threshold θ2 is preset to evaluate the local flatness of the terrain, and the determination method is as follows: according to the maximum allowable inclination angle θ of the lifting equipment, equip (For example, the maximum tilt angle allowed for crawler cranes is ≤5°), set a reference value; introduce a safety factor k safe , its value range is 1.2 to 1.5; through the formula θ2=k safe ×θ equip To calculate the final slope threshold, the value range is 6° to 8°, and it supports dynamic adjustment according to the construction process standards; for example: in the project, the crawler crane used allows a maximum tilt angle of 5° (θ equip =5°), and take the safety factor k safe =1.5, so 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 flat area and the adjacent grid exceeds 8° (for example, if the slope of the center point of an area is 6° and 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 accuracy is used to control the fineness of the grid division, and its determination method is as follows: the terrain complexity is quantified by calculating the terrain roughness index RI:

[0154]

[0155] Where Δh gThe grid accuracy is determined based on the RI value. The specific rules are as follows: when RI < 0.05 (gentle terrain), the preset accuracy is a 200m × 200m grid; when 0.05 ≤ RI < 0.15 (moderately rugged terrain), the preset accuracy is a 100m × 100m grid; when RI ≥ 0.15 (complex terrain), the preset accuracy is a 50m × 50m grid. For example, for the candidate gentle area of ​​the Jicuo wind farm, the calculated terrain roughness index RI = 0.08 indicates that the area is classified as "moderately rugged terrain." Based on the mapping rules, the preset accuracy is a 100m × 100m grid, which divides the candidate gentle area into square grid cells with a side length of 100 meters for subsequent slope difference calculation and region elimination.

[0156] The beneficial effects of this technical solution include: precise screening and gridding of slope data to ensure that wind farm turbines 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 flat terrain area is further improved, thus avoiding the reduction in wind turbine operating efficiency caused by excessive terrain fluctuations, optimizing the wind farm's wind turbine layout, and improving power generation efficiency and economic benefits.

[0157] Example 6:

[0158] The embodiment of the present invention provides a wind farm wind turbine optimization layout system based on multi-factor analysis, and a regional analysis module, including:

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

[0160] Area division unit: determines the grid side length based on the minimum horizontal and vertical spacing, and divides the candidate layout area into grids based on the grid side length;

[0161] Grid evaluation unit: Counts the effective wind energy area ratio and slope value in each grid, evaluates whether the grid meets the wind turbine layout conditions, and eliminates grids with excessive slopes to obtain several valid grids;

[0162] Quantity calculation unit: Based on the number of valid grids, combined with the rated power of the wind turbines and the site boundary restrictions, calculate the maximum number of wind turbines that can be deployed in each candidate layout area.

[0163] In this embodiment, the minimum transverse 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 transverse safety factor and the longitudinal safety factor. Different minimum spacings can be calculated based on different safety factors. The minimum transverse 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 upper limit of 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, including: screening valid grids: grids that meet the following three conditions are defined as valid: wind energy proportion ≥ Pmin, average slope ≤ θmax, distance to the boundary ≥ Dbuffer, where Pmin∈[50%,80%], θmax∈[8°,15°], Dbuffer∈[100m,300m]; the theoretical maximum number N 理论 calculate:

[0166]

[0167] Among them, A 有效 is the total effective grid area, and L is the grid side length.

[0168] Boundary Effect Modifications:

[0169] Among them, k edge is the boundary penalty coefficient (0.2-0.5), is the perimeter of the boundary grid, P total is the total perimeter; power density constraint:

[0170]

[0171] Among them, ρ 功率 For regional power density limits (1.5-3.0MW / km 2 ), P 额定 is the rated power of a single fan, A 总 is the total area of ​​the candidate layout area; the upper limit of the final layout is:

[0172] N 上限 =min(N 边界 ,N 功率 )

[0173] The beneficial effects of this technical solution include: By calculating the minimum required spacing for wind turbines and, based on gridding and slope assessment, accurately selecting areas that meet the requirements for wind turbine placement. By eliminating grids unsuitable for wind turbine placement, the rationality of the wind farm's wind turbine layout is ensured. Furthermore, by combining wind turbine rated power with site constraints, the calculation of the number of wind turbines to be deployed is optimized, maximizing the utilization of wind energy resources and improving the wind farm's power generation efficiency and economic benefits.

[0174] Example 7:

[0175] An embodiment of the present invention provides a wind farm wind turbine optimization layout system based on multi-factor analysis, and a spacing calculation unit, including:

[0176] determining a first predetermined multiple of the blade diameter as the minimum lateral spacing required for operating a single wind turbine;

[0177] A second predetermined multiple of the blade diameter is determined as the minimum longitudinal spacing required for operating a single wind turbine.

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

[0179] The beneficial effects of the above technical solution are: By determining the minimum horizontal and vertical spacing required for a single wind turbine based on preset multiples of the blade diameter, this method simplifies the wind turbine layout process. This method ensures appropriate spacing between wind turbines, avoids mutual interference, improves wind energy utilization efficiency, and optimizes the spatial layout of the wind farm, enhancing the overall power generation capacity and economic benefits of the wind farm.

[0180] Example 8:

[0181] An embodiment of the present invention provides a wind farm wind turbine optimization layout system based on multi-factor analysis, wherein the solution determination unit includes:

[0182] Arrangement mode setting unit: set a variety of basic arrangement modes, such as rectangular array, fan-shaped radiation, adaptive dispersion, etc.

[0183] Power configuration unit: configures the power of the fans according to their rated power and preset arrangement mode, where the power configuration is the combination ratio of high-power fans and medium-power fans;

[0184] Solution generation unit: In each candidate area, using the preset arrangement spacing as the step size, combined with the preset arrangement mode, power configuration and maximum number limit, generates several groups of preliminary layout solutions;

[0185] Scheme arrangement unit: Arrange each set of preliminary layout plans and generate a layout plan including the number of wind turbines, arrangement coordinates and power configuration parameters.

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

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

[0188] Among them, P ij is the coordinate of the fan in column i and row j, (x0, y0) is the starting coordinate,

[0189] L row =k r D, L col =k c D, D is the blade diameter, k r ∈[3.0,5.0], k c ∈[4.0,6.0] is the spacing coefficient; fan-shaped radial arrangement: based on the main wind direction, the wind turbines are arranged along multiple radial directions from the center point O(x0,y0). 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] Among them, r i =r0+i·Δr is the radius of the i-th layer, Δθ i is the angle interval; Adaptive distributed layout: Based on the wind energy density W(x,y) and slope data S(x,y), particle swarm optimization (PSO) is used to determine the wind turbine coordinates so that the objective function is:

[0192]

[0193] Maximize, where I wake is the wake influence factor, which is subject to constraints such as slope. For example, in the Jicuo wind farm, in a rectangular array arrangement, D = 160m, k is selected. r =4.0, k c=5.0, the row spacing is 640m and the column spacing is 800m; the fan-shaped radial type adopts a main wind direction of 225°, a radius increment of 500m, and an angle interval of 15°; the adaptive distributed type uses PSO when W>400W / m 2 , dynamically optimize wind turbine layout in areas with slopes <12°.

[0194] In this embodiment, power configuration: by combining the model library and the layout mode, the wind turbine power configuration is optimized: constructing a model combination: high power model: P high ≥4MW, medium power model: 2≤P mid <4MW, corresponding mode configuration: Rectangular array: configure uniform power wind turbines (such as 5MW) to simplify the wake impact analysis; Fan-shaped radial: configure high-power wind turbines in the center area and medium-power wind turbines at the edge to reduce the total wake; Adaptive distributed: high-power models are arranged in high wind energy density areas and medium-power models are used in low-density areas. Optimization model:

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

[0196] Where CF is the capacity factor, C total is the total cost; genetic algorithm is used to optimize n high ,n mid For example, in a radial solution, eight 5MW wind turbines are deployed within a 500m radius of the center, and twelve 3MW wind turbines are deployed at the edge. The optimization results: annual power generation increased by 12% and total costs decreased by 8%.

[0197] In this embodiment, the preset arrangement spacing S preset Used to ensure that wake disturbance is minimized and is determined as follows: Wake reference: 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 radiation type: radial spacing ≥1.2·S wake , circumferential spacing ≥ 1.0·S wake ; Adaptive distributed: ensure that the distance 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]; For example: wind turbine diameter D = 160m, wake coefficient k w =6.0 to get: S wake =960m, the rectangular arrangement spacing is 1000m; in mountainous terrain, k terrain =1.2, the adjusted spacing is 1200m.

[0198] In this embodiment, the preliminary layout alternatives are generated by using permutation and combination and constraint screening: constructing parameter space: layout mode: three types (rectangular, radial, adaptive); high power wind turbine proportion p high ∈[30%,70%], step size 10%; arrangement spacing S preset ∈[1.2,2.0]·S wake , step size 0.2·S wake , Generate scheme combinations: Use the full permutation algorithm to combine the above parameters to generate 3×5×4=60 candidate schemes, Feasibility screening: Eliminate schemes that violate conditions such as slope restrictions, boundary buffers, and insufficient wake spacing. For example: In the Jicuo wind farm, 42 feasible configurations are retained after screening of 60 initial schemes, including the following types: - Regular matrix (spacing 1000m, all high-power wind turbines); fan-shaped arrangement (spacing 1200m, high-power ratio 60%); adaptive optimization (dynamic adjustment of spacing, high-power ratio 40%).

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

[0200] Example 9:

[0201] The embodiment of the present invention provides a wind farm wind turbine optimization layout system based on multi-factor analysis, and a solution screening module, including:

[0202] Index calculation unit: for each layout plan, calculate the scores of each preset evaluation index;

[0203] Comprehensive score calculation unit: calculates the comprehensive score of each layout plan according to the weight corresponding to the preset evaluation indicators and the scores of each preset evaluation indicator;

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

[0205] Scheme determination unit: select the layout scheme with the highest comprehensive score as the final fan optimization layout scheme.

[0206] In this embodiment, evaluation indicators are preset, such as terrain suitability, wind energy utilization rate, spacing compliance, construction cost, etc., and weights are assigned to each indicator. For example, the terrain suitability 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 of each wind turbine position in the plan to the maximum effective wind speed duration in the region.

[0207] In this embodiment, the indicator calculation unit calculates the preset evaluation indicator score for each layout scheme through the following steps: establishing a set of multi-dimensional evaluation indicators I = {I1, I2, ..., I n}, where: Wind energy utilization category: annual equivalent full-power hours I1, wake impact 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. Single item score calculation: For positive indicators (such as annual equivalent full-power hours): For negative indicators (such as investment cost per kilowatt): Among them, x i For the scheme in indicator I i The actual value of x max and x min are the maximum and minimum values ​​of the indicator in all solutions respectively; the Z-score normalization method is used to eliminate the dimension effect:

[0208]

[0209] Among them, μ i and σ i Indicator I i For example, for the 10 layout plans of the Jicuo wind farm, the annual equivalent full-power hours of plan A is 2800 hours. The minimum value of this indicator among all plans is 2200 hours, and the maximum value is 3000 hours:

[0210]

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

[0212]

[0213] In this embodiment, the comprehensive score calculation unit calculates the score based on the preset weight vector The comprehensive score S of the solution is calculated by the weighted sum formula total : Among them, Zi For indicator I i Standardized scores. Weight determination methods include: Analytic Hierarchy Process (AHP): Determine the relative importance of each indicator through expert scoring; Entropy Weight Method: Automatically assign weights based on the degree of data variation; Dynamic Adjustment Mechanism: Manually adjust weights based on project requirements (for example, increase the weights of w5 and w6 when economic efficiency is a priority). For example: If the weight of annual equivalent full-power hours, w1, is 0.3, the weight of unit kilowatt investment cost, w5, is 0.2, and Z1 of Plan A is 1.2 and Z5 is 0.8, 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 a quick sorting algorithm to sort the comprehensive scores of all layout schemes in descending order, and outputs an ordered scheme list L = [P1, P2, ..., P m ], where P i For the layout scheme, satisfy S total (P1)≥S total (P2)≥...≥S total (P m The sorting process supports parallel computing acceleration and can quickly process a large number of solutions through multi-threading or distributed computing frameworks (such as Apache Spark). For example, after sorting 10 groups of layout solutions, the results are:

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

[0217] Among them, Plan D has a comprehensive score of 0.68, ranking the highest, and Plan J has a score of 0.42, ranking the lowest.

[0218] In this embodiment, the solution determination unit selects the solution with the highest comprehensive score from the sorted solution list as the final optimized solution. In addition, a multi-solution recommendation mechanism is supported: when the score difference between the highest-scoring solution and the second-highest-scoring solution is less than a threshold ΔS (e.g., 0.05), the top three solutions are output for decision makers to choose from, and a sensitivity analysis report of each solution (such as the impact of weight changes on the score) is provided. For example, the sorted solution D is finally selected as the optimal solution, with a comprehensive score of 0.68, which is significantly higher than solution A (score of 0.52). If the score difference is only 0.03, solution D, solution A, and solution G will be recommended at the same time, and a sensitivity analysis report of the score changes of each solution under different weights will be attached.

[0219] The beneficial effects of this technical solution include: By evaluating each layout option and calculating a comprehensive score based on the weights of various evaluation indicators, the selected option is ensured to perform optimally across multiple dimensions. By ranking and screening the options, the optimal wind turbine layout can be efficiently determined, thereby optimizing the wind farm's turbine layout, improving wind energy utilization efficiency and power generation benefits, reducing resource waste, and providing a scientific basis for decision-making in wind farm planning and operation.

[0220] Example 10:

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

[0222] Step 1: Multiple monitoring devices are evenly distributed within the wind farm area according to the preset monitoring point layout rules. Wind speed data and terrain slope data are collected at each monitoring point to establish a basic data set containing location coordinates, wind speed values, and slope values. The monitoring points are the locations of the monitoring devices arranged according to the preset monitoring point layout within the wind farm area.

[0223] Step 2: Extract the core performance parameters of the target wind turbine, perform threshold screening 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 wind turbine to operate based on the blade size. Combined with the area and shape of the candidate deployment areas, determine the maximum number of wind turbines that can be deployed in each area.

[0225] Step 4: For each candidate layout area, generate several layout plans based on the wind turbine rated power, preset arrangement spacing, and the maximum number of wind turbines to be arranged. Each plan includes the number of wind turbines, arrangement coordinates, and power configuration parameters.

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

[0227] The beneficial effects of this technical solution include optimizing wind farm turbine layout through multi-factor analysis, combining wind speed, slope, turbine performance, and terrain conditions to determine candidate deployment areas and turbine placement plans. By accurately screening effective wind energy zones and areas with gentle terrain, and combining minimum spacing and area constraints, the number and arrangement of turbines are optimized to maximize wind energy utilization and avoid interference between turbines. The resulting optimized layout improves the overall power generation efficiency of the wind farm, reduces energy losses, and offers 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, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A wind farm wind turbine optimization layout system based on multi-factor analysis, characterized in that: include: Data acquisition module: Multiple monitoring devices are evenly arranged within the wind farm area according to the preset monitoring point layout rules. Wind speed data and terrain slope data at each monitoring point are collected to establish a basic data set containing location coordinates, wind speed values, and slope values. The monitoring points are the locations of the monitoring devices arranged according to the preset monitoring points within the wind farm area. Region determination module: This module extracts the core performance parameters of the target wind turbine, performs threshold screening on the wind speed data and slope values ​​in the basic dataset, identifies effective wind energy areas and flat terrain areas, and then generates candidate layout areas that meet the basic operating conditions of the wind turbine. Regional analysis module: Determines the minimum spacing required for a single wind turbine to operate based on blade size, and determines the maximum number of wind turbines that can be deployed in each area based on the area and shape of the candidate deployment areas; Solution determination module: For each candidate layout area, several layout solutions are generated based on the rated power of the wind turbines, the preset arrangement spacing, and the maximum number of wind turbines to be arranged. Each solution includes the number of wind turbines, arrangement coordinates, and power configuration parameters. Scheme screening module: Determine the final optimized wind turbine layout scheme based on the number of wind turbines, arrangement coordinates and power configuration parameters of all schemes.

2. The wind farm wind turbine optimization layout system based on multi-factor analysis according to claim 1 is characterized in that: Region determination module, including: Performance parameter extraction unit: extracts the core performance parameters of rated power, blade size, and starting wind speed from the preset technical documents of the target wind turbine; The first area screening unit: performs threshold screening on wind speed data in the basic database based on core performance parameters to determine the effective wind energy area; The second area screening unit: based on the core performance parameters, the slope values ​​in the basic database are screened by threshold value to determine the flat terrain area; Area determination unit: The intersection of the effective wind energy area and the flat terrain area is determined as the candidate layout area that meets the basic conditions for wind turbine operation.

3. The wind farm wind turbine optimization layout system based on multi-factor analysis according to claim 2 is characterized in that: Regional screening unit, including: Threshold reduction subunit: identifies the local main wind direction and initiates wind speed threshold reduction if the monitoring point is in the main wind direction; Wind speed division subunit: The wind speed data in the basic data set is divided into four seasons, and the ratio of the cumulative duration of the wind speed reaching the start wind speed threshold in each season to the total duration of each season is calculated for each monitoring point; Wind speed screening subunit: retain the first monitoring point corresponding to the season with a ratio exceeding a preset ratio; Turbulence screening subunit: retrieves turbulence intensity parameters from a preset meteorological database, further screens the above-retained first monitoring points, eliminates first monitoring points whose turbulence intensity parameters exceed the preset turbulence intensity parameters, and obtains a plurality of second monitoring points; Discrete screening subunit: using the moving window method to eliminate the second monitoring points where the wind speed reaches the starting wind speed but the single duration is less than the preset duration, to obtain several third monitoring points; Regional connection subunit: Mark the third monitoring point as an effective wind energy location, connect adjacent effective wind energy locations to form a continuous area, and generate an effective wind energy distribution vector diagram; Region determination subunit: Smooth the effective wind energy distribution vector diagram and remove discrete areas with an area smaller than the minimum layout unit to obtain the effective wind energy area.

4. The wind farm wind turbine optimization layout system based on multi-factor analysis according to claim 3 is characterized in that: Threshold reduction subunit, comprising: Wind direction determination block: Wind direction data acquisition step: retrieve historical wind direction data of the wind farm area from the meteorological database, generate a wind rose diagram, and identify the main wind direction interval with an annual frequency greater than the preset frequency; Monitoring point location judgment block: compares the geographical location information of each monitoring point with the main wind direction interval to determine the target monitoring point in the incoming flow direction along the main wind direction path; Wind speed probability statistics block: for the target monitoring point, retrieve its historical wind speed data, count the wind speed distribution probability when the main wind direction occurs, and calculate the difference ratio between the average wind speed during the main wind direction and the average wind speed during the non-main wind direction; Threshold adjustment block: Determine the corresponding reduction amplitude based on the difference ratio between the average wind speed during the main wind direction and the average wind speed during the non-main wind direction and the preset ratio-amplitude data table, and then reduce the starting wind speed threshold of the target monitoring point by the corresponding amplitude.

5. The wind farm wind turbine optimization layout system based on multi-factor analysis according to claim 3 is characterized in that: The second area screening unit includes: Basic slope screening subunit: based on the slope values ​​in the basic data set, the continuous geographical range covered by all monitoring points with slope values ​​in the basic data set less than a preset first slope threshold is marked as a candidate flat area; Grid division subunit: grid division of candidate flat areas, with the grid size determined according to the preset accuracy; Slope calculation subunit: calculates the absolute value of the slope difference between the center point of each candidate flat area and the surrounding adjacent grids; Region elimination subunit: eliminates the area corresponding to the adjacent grids of the center point of each candidate gentle area whose absolute value of the slope difference is greater than the second slope threshold, thereby obtaining the gentle terrain area.

6. The wind farm wind turbine optimization layout system based on multi-factor analysis according to claim 1, characterized in that: Regional analysis module, including: Spacing calculation unit: Calculates the minimum horizontal and vertical spacing required for the operation of a single fan based on the blade size; Area division unit: determines the grid side length based on the minimum horizontal and vertical spacing, and divides the candidate layout area into grids based on the grid side length; Grid evaluation unit: Counts the effective wind energy area ratio and slope value in each grid, evaluates whether the grid meets the wind turbine layout conditions, and eliminates grids with excessive slopes to obtain several valid grids; Quantity calculation unit: Based on the number of valid grids, combined with the rated power of the wind turbines and the site boundary restrictions, calculate the maximum number of wind turbines that can be deployed in each candidate layout area.

7. The wind farm wind turbine optimization layout system based on multi-factor analysis according to claim 6, characterized in that: Spacing calculation unit, including: Lateral determination subunit: determining a first preset multiple of the blade diameter as the minimum lateral spacing required for operating a single wind turbine; The longitudinal determination subunit determines a second preset multiple of the blade diameter as the minimum longitudinal spacing required for the operation of a single wind turbine.

8. The wind farm wind turbine optimization layout system based on multi-factor analysis according to claim 1, characterized in that: The program determination unit includes: Arrangement mode setting unit: set a variety of basic arrangement modes, such as rectangular array, fan-shaped radiation, adaptive dispersion, etc. Power configuration unit: configures the power of the fans according to their rated power and preset arrangement mode, where the power configuration is the combination ratio of high-power fans and medium-power fans; Solution generation unit: In each candidate area, using the preset arrangement spacing as the step size, combined with the preset arrangement mode, power configuration and maximum number limit, generates several groups of preliminary layout solutions; Scheme arrangement unit: Arrange each set of preliminary layout plans and generate a layout plan including the number of wind turbines, arrangement coordinates and power configuration parameters.

9. The wind farm wind turbine optimization layout system based on multi-factor analysis according to claim 1, characterized in that: Solution screening module, including: Index calculation unit: for each layout plan, calculate the scores of each preset evaluation index; Comprehensive score calculation unit: calculates the comprehensive score of each layout plan according to the weight corresponding to the preset evaluation indicators and the scores of each preset evaluation indicator; Scheme ranking unit: sort all layout schemes according to their comprehensive scores; Scheme determination unit: select the layout scheme with the highest comprehensive score as the final fan optimization layout scheme.

10. A method for optimizing the layout of wind turbines in a wind farm based on multi-factor analysis, characterized in that: include: Step 1: Multiple monitoring devices are evenly distributed within the wind farm area according to the preset monitoring point layout rules. Wind speed data and terrain slope data are collected at each monitoring point to establish a basic data set containing location coordinates, wind speed values, and slope values. The monitoring points are the locations of the monitoring devices arranged according to the preset monitoring point layout within the wind farm area. Step 2: Extract the core performance parameters of the target wind turbine, perform threshold screening 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 wind turbine to operate based on the blade size. Combined with the area and shape of the candidate deployment areas, determine the maximum number of wind turbines that can be deployed in each area. Step 4: For each candidate layout area, generate several layout plans based on the wind turbine rated power, preset arrangement spacing, and the maximum number of wind turbines to be arranged. Each plan includes the number of wind turbines, arrangement coordinates, and power configuration parameters. Step 5: Determine the final optimal wind turbine layout scheme based on the number of wind turbines, arrangement coordinates, and power configuration parameters of all schemes.

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