Miniature site selection optimization method suitable for high-altitude complex mountain wind power plant

Abstract

The invention provides a micro site selection optimization method suitable for a high-altitude complex mountain wind power plant, and relates to the technical field of data processing, and the method comprises the steps: collecting the basic data of the high-altitude complex mountain wind power plant; processing the topographic data, constructing a digital topographic model, generating topographic feature distribution, calculating wind energy resources, and determining an applicable fan type; identifying a plurality of restrictive area types based on the topographic feature distribution map, generating a site selection forbidden area, preliminarily arranging a plurality of primary selection machine positions by avoiding the forbidden area, and obtaining coordinates, altitude and surrounding topographic roughness parameters of each primary selection machine position; carrying out wake flow influence analysis, determining wake flow speed loss between adjacent primary selection machine positions, carrying out wake flow optimization, and generating a machine position layout scheme after wake flow optimization; and based on the machine position layout scheme after wake flow optimization, current collection line path planning is carried out, and a final fan layout diagram is generated. The power generation efficiency and economic benefits of the wind power plant are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a micro-site selection optimization method suitable for wind farms in complex mountainous areas at high altitudes. Background Art

[0002] With the increasing global demand for renewable energy, wind power, as a key green energy source, has garnered widespread attention in wind farm site selection. Wind farm site selection is particularly challenging in high-altitude, complex mountainous areas, due to the unique topography, climate, and wind energy resources. Traditional site selection methods often fail to fully consider complex terrain and meteorological factors, resulting in suboptimal site selection results and impacting the wind farm's power generation efficiency and economic benefits.

[0003] Several wind farm site selection methods exist, but most focus on plains or relatively simple terrain. For high-altitude wind farms in complex mountainous areas, existing solutions often overlook the impact of topography and weather on wind energy resources, lacking systematic optimization of factors such as wake effects, turbine placement, and line planning.

[0004] Therefore, the present invention provides a micro-site selection optimization method suitable for high-altitude complex mountain wind farms. Summary of the Invention

[0005] The present invention provides a micro-site selection optimization method suitable for high-altitude complex mountain wind farms, solving the problem of unreasonable site selection caused by the failure to fully consider the complex high-altitude mountain terrain and meteorological factors in the existing technology. Through precise calculation of wind energy resources and matching with wind turbines, the energy efficiency of the wind farm is improved, and the utilization of limited resources is maximized; wake optimization reduces wind energy loss between machine sites and improves power generation efficiency; collector line path planning effectively reduces voltage drop and transmission loss, reducing construction and maintenance costs; avoids high-cost construction in prohibited areas, reducing investment risks; at the same time, takes into account environmental adaptability to ensure that the ecological impact of the wind farm is minimized and meets the needs of sustainable development. The power generation efficiency, economy and long-term stability of the wind farm are improved.

[0006] The present invention provides a micro-site selection optimization method applicable to high-altitude complex mountainous wind farms, comprising:

[0007] Step 1: Collect basic data of wind farms in high-altitude and complex mountainous areas, including regional terrain data, meteorological data, and wind tower measured data;

[0008] Step 2: Process the terrain data to construct a digital terrain model. Generate terrain feature distribution based on the digital terrain model. Simultaneously, calculate wind energy resources based on meteorological data and measured data from wind towers. Determine the wind turbine safety level based on pre-set standards and determine the appropriate wind turbine model.

[0009] Step 3: Based on the terrain feature distribution map, several restricted area types are identified, and prohibited areas for site selection are generated. The prohibited areas are avoided on the digital terrain model, and several preliminary camera positions are preliminarily arranged. The coordinates, altitude, and surrounding terrain roughness parameters of each preliminary camera position are obtained.

[0010] Step 4: Perform wake turbulence analysis on each preliminary aircraft position based on its coordinates, altitude, and surrounding terrain roughness parameters. Determine the wake turbulence velocity loss between adjacent preliminary aircraft positions and perform wake optimization to generate a wake-optimized aircraft position layout plan.

[0011] Step 5: Based on the turbine layout plan after wake optimization, the collection line path is planned to generate the final turbine layout diagram including the turbine coordinates, turbine model, and collection line path.

[0012] Preferably, basic data of wind farms in high-altitude and complex mountainous areas are collected, including:

[0013] Obtain long-term meteorological data from regional meteorological stations within the target area, including annual average temperature, extreme temperature range, wind speed distribution, and wind rose characteristics;

[0014] Collect measured data from multiple height levels of the wind tower, including wind speed series, wind power density distribution and turbulence intensity parameters at different heights;

[0015] Obtain three-dimensional terrain data of the site through topographic mapping, including elevation distribution, slope gradient and aspect characteristics;

[0016] Meteorological data, wind measurement data and terrain data are temporally and spatially aligned to form a basic data set.

[0017] Preferably, processing the terrain data to construct a digital terrain model, and generating terrain feature distribution based on the digital terrain model includes:

[0018] Use GIS spatial analysis technology to convert terrain surveying and mapping data into digital terrain models of preset resolution;

[0019] The slope extraction tool is used to calculate the slope value of each grid cell of the digital elevation model and mark the areas where the slope is greater than the preset angle;

[0020] Use hydrological analysis tools to identify ridge lines and valley lines in areas with slopes greater than a preset angle and generate a distribution of terrain features.

[0021] Preferably, wind energy resource calculation is performed based on meteorological data and measured data from a wind tower, and the wind turbine safety level is determined in combination with preset standards to determine the applicable wind turbine model and hub height, including:

[0022] Based on meteorological data and wind tower measured data, wind energy resources are calculated to obtain comprehensive wind shear index and turbulence intensity;

[0023] Determine the applicable wind turbine safety level based on the comprehensive wind shear index and turbulence intensity matching the preset standards;

[0024] Determine the applicable wind turbine model and hub height based on the applicable wind turbine safety level.

[0025] Preferably, wind energy resource calculation is performed based on meteorological data and measured data from a wind tower to obtain a comprehensive wind shear index and turbulence intensity, including:

[0026] The wind shear index at different heights is calculated based on the measured data from the wind tower. The formula is:

[0027]

[0028] Where α is the wind shear index, z1 and z2 are different measurement heights, z1 is the first measurement height, z2 is the second measurement height, v1 and v2 correspond to the wind speeds at the first and second measurement heights respectively;

[0029] The wind shear index at different heights is fitted to determine the average wind shear index of the site, and the average wind shear index of the site is determined as the comprehensive wind shear index;

[0030] Based on meteorological data, the wind speed standard deviation and average wind speed of the preset wind speed segment are determined, and then the turbulence intensity is determined. The formula is:

[0031]

[0032] Where IT is the turbulence intensity, σ is the standard deviation of the wind speed in the preset wind speed segment, and V is the average wind speed in the preset wind speed segment.

[0033] Preferably, based on the terrain feature distribution map, several restricted area types are identified and a prohibited area for site selection is generated. On the digital terrain model, with the ridgeline as the main axis, several preliminary camera positions are preliminarily arranged to avoid the prohibited area. The coordinates, altitude, and surrounding terrain roughness parameters of each preliminary camera position are obtained, including:

[0034] Based on the superimposed terrain feature distribution map and preset land use attribute data, the restricted area types including basic farmland and ecological protection areas are identified;

[0035] Taking the ridgeline as the core axis, a candidate camera position grid is generated within the buffer range on both sides;

[0036] Eliminate candidate camera positions that fall into the prohibited area, retain candidate camera positions that meet the altitude and slope conditions, and then preliminarily arrange several preliminary camera positions and obtain the coordinates and altitude of each preliminary camera position;

[0037] The roughness parameters of the surrounding terrain are determined based on the coordinates and altitude of each preliminary camera position.

[0038] Preferably, a wake impact analysis is performed on each preliminary aircraft position based on its coordinates, altitude, and surrounding terrain roughness parameters, the wake velocity loss between adjacent preliminary aircraft positions is determined, and wake optimization is performed to generate a wake-optimized aircraft position layout plan, including:

[0039] Based on the coordinates, altitude and surrounding terrain roughness parameters of each preliminary selected position, a fluid dynamics model is established to simulate the wind energy flow path;

[0040] Calculate the wake velocity loss ratio between adjacent aircraft positions based on the simulated wind energy flow path results and identify aircraft position pairs that exceed a preset threshold;

[0041] For aircraft positions that exceed the preset threshold, adjustments are made according to the minimum spacing requirements between the main wind direction and the vertical direction;

[0042] Repeat simulation and adjustment until the wake effect is controlled within the preset reasonable range;

[0043] Generate a wake-optimized aircraft stand layout plan, and mark the relationship between the optimized aircraft stand spacing and the main wind direction angle.

[0044] Preferably, the calculation of the wake velocity loss ratio between adjacent aircraft positions based on the simulated wind energy flow path results includes:

[0045] Determine the incoming wind speed distribution at each machine position based on the simulated wind energy flow path results;

[0046] The incoming wind speed is determined based on the incoming wind speed distribution at each aircraft position, and then the initial loss ratio of the wake speed between adjacent aircraft positions is calculated:

[0047]

[0048] v wake is the initial loss ratio of the wake velocity at the downstream position, v0 is the incoming wind speed at the upstream position, C T is the thrust coefficient, d is the impeller diameter, x is the distance between the upstream and downstream positions along the main wind direction, and a is the preset wake attenuation coefficient;

[0049] Based on the fluid mechanics model, the initial loss ratio of wake velocity between adjacent aircraft positions is corrected to obtain the loss ratio of wake velocity between adjacent aircraft positions.

[0050] Preferably, the initial loss ratio of the wake velocity between adjacent aircraft positions is corrected based on a fluid dynamics model to obtain the loss ratio of the wake velocity between adjacent aircraft positions, including:

[0051] In the fluid mechanics model, the incoming wind speed in the main wind energy direction is exponentially corrected according to the average altitude of the site to obtain the altitude corrected wind speed;

[0052] In the fluid dynamics model, the terrain roughness index and altitude-corrected wind speed are substituted into the wake velocity loss formula to obtain the wake velocity loss ratio between adjacent aircraft positions:

[0053]

[0054] Among them, v wake ' is the ratio of wake velocity loss between adjacent aircraft stands, v0' is the altitude-corrected wind speed, and k is the terrain roughness index.

[0055] Preferably, based on the turbine layout plan after wake optimization, the collector line path planning is performed to generate a final turbine layout diagram including the turbine coordinates, turbine model, and collector line path, including:

[0056] Based on the aircraft layout plan after wake optimization, the path optimization algorithm is used to generate the initial collection route;

[0057] Calculate the voltage drop of each line segment in the initial collector routing and adjust the line path if the voltage drop exceeds the preset allowable range;

[0058] Verify the adjusted line path, generate the final wind turbine layout diagram, and mark the machine position coordinates, collection line direction and key node parameters.

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

[0060] Through precise wind energy resource calculation and wind turbine matching, the energy efficiency of wind farms is improved, maximizing the utilization of limited resources. Wake optimization reduces wind energy loss between turbines, improving power generation efficiency. Collector line routing effectively reduces voltage drop and transmission losses, reducing construction and maintenance costs. High-cost construction in restricted areas is avoided, reducing investment risk. Furthermore, environmental adaptability is taken into account to minimize the ecological impact of wind farms and meet the needs of sustainable development. This improves the power generation efficiency, economic efficiency, and long-term stability of wind farms. 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 This is a flow chart of a micro-site selection optimization method applicable to high-altitude complex mountainous wind farms provided by an embodiment of the present invention;

[0063] Figure 2 This is a wind turbine layout diagram of a micro-site selection scheme for a micro-site selection optimization method applicable to high-altitude complex mountainous wind farms provided by an embodiment of the present invention;

[0064] Figure 3 This is the final wind turbine layout diagram of a micro-site selection optimization method applicable to high-altitude complex mountainous wind farms provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0065] 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.

[0066] Example 1:

[0067] The embodiment of the present invention provides a micro-site selection optimization method suitable for high-altitude complex mountainous wind farms, such as Figure 1 Shown, including:

[0068] Step 1: Collect basic data of wind farms in high-altitude and complex mountainous areas, including regional terrain data, meteorological data, and wind tower measured data;

[0069] Step 2: Process the terrain data to construct a digital terrain model. Generate terrain feature distribution based on the digital terrain model. Simultaneously, calculate wind energy resources based on meteorological data and measured data from wind towers. Determine the wind turbine safety level based on pre-set standards and determine the appropriate wind turbine model.

[0070] Step 3: Based on the terrain feature distribution map, several restricted area types are identified, and prohibited areas for site selection are generated. The prohibited areas are avoided on the digital terrain model, and several preliminary camera positions are preliminarily arranged. The coordinates, altitude, and surrounding terrain roughness parameters of each preliminary camera position are obtained.

[0071] Step 4: Perform wake turbulence analysis on each preliminary aircraft position based on its coordinates, altitude, and surrounding terrain roughness parameters. Determine the wake turbulence velocity loss between adjacent preliminary aircraft positions and perform wake optimization to generate a wake-optimized aircraft position layout plan.

[0072] Step 5: Based on the turbine layout plan after wake optimization, the collection line path is planned to generate the final turbine layout diagram including the turbine coordinates, turbine model, and collection line path.

[0073] In this example, the appropriate wind turbine model is determined based on the site's wind energy parameters and climatic conditions, using wind shear index and turbulence intensity, and according to the IEC61400-1 standard to determine if it meets the IEC IIIb safety level. Considering the extremely low temperature (-41.2°C), the DEW200 / 5560-115 low-temperature wind turbine was selected. It has a single unit capacity of 5.56MW, a hub height of 115m, and an impeller diameter of 200m, meeting the cold resistance and aerodynamic performance requirements for high-altitude, low-pressure environments.

[0074] In this example, the wake-optimized aircraft position layout is based on the initial aircraft position coordinates (e.g., GNNQ01:426413.00,3488672.03). The Park model is used to calculate wake velocity loss, with a threshold of 15%. The spacing in the main wind direction (WSW to SSW) is adjusted to ≥1000m (5 times the impeller diameter) and ≥600m (3 times the impeller diameter) in the vertical direction. After optimization, 18 aircraft positions are retained.

[0075] The beneficial effects of the above technical solution are: by systematically collecting and analyzing basic data of high-altitude complex mountain wind farms, combined with digital terrain models and wind energy resource calculations, it is possible to accurately determine the applicable wind turbine model and safety level. By identifying restricted areas and avoiding prohibited areas, the reasonable layout of wind turbines is ensured. At the same time, wake impact analysis and optimization further improve the layout efficiency between wind turbines, reduce wake losses between adjacent machine positions, and thus improve the power generation efficiency of the wind farm. Finally, the collection line path planning optimizes the line design, ensures the efficiency and stability of wind turbine layout and power transmission, and comprehensively improves the economic benefits and sustainability of the wind farm.

[0076] Example 2:

[0077] The embodiment of the present invention provides a micro-site selection optimization method applicable to high-altitude complex mountainous wind farms, which collects basic data of high-altitude complex mountainous wind farms, including:

[0078] Obtain long-term meteorological data from regional meteorological stations within the target area, including annual average temperature, extreme temperature range, wind speed distribution, and wind rose characteristics;

[0079] Collect measured data from multiple height levels of the wind tower, including wind speed series, wind power density distribution and turbulence intensity parameters at different heights;

[0080] Obtain three-dimensional terrain data of the site through topographic mapping, including elevation distribution, slope gradient and aspect characteristics;

[0081] Meteorological data, wind measurement data and terrain data are temporally and spatially aligned to form a basic data set.

[0082] In this embodiment, long-term meteorological data of a regional meteorological station within a 50km radius of the target site (such as the Nagqu Meteorological Station, geographically located at N31°*'*°*', with an average altitude of 4518m) is obtained from the local government meteorological bureau, including an annual average temperature of -0.59°C, an extreme minimum temperature of -41.2°C, an extreme maximum temperature of 24.2°C, an annual average wind speed of 2.4m / s, and a wind rose diagram (the main wind direction is southwest (SW) to west northwest (WNW), and the prevailing wind direction is stable).

[0083] In this embodiment, measured data from multiple height layers of wind towers are collected. 100-meter gradient wind towers (equipped with anemometers, wind vanes, and temperature and humidity sensors) are set up in a triangular layout within the site. Wind speed sequences at multiple height layers are recorded in real time through a data collector (such as NRGSymphonie) (sampling frequency ≥ 1 Hz, for one year). For example, the wind speed sequences at heights of 10, 50, 80, 100, and 120 meters at the 14218# wind tower (tower height 120 meters, wind measurement period 2023.11.15 to 2024.11.20) are recorded. The annual average wind speed at a height of 115 meters is 7.18 m / s, and the wind power density is 306 W / m 2 , the turbulence intensity in the 15m / s wind speed range at an altitude of 100m is 0.091~0.101.

[0084] In this embodiment, three-dimensional terrain data of the site is obtained through terrain surveying, including altitude distribution, slope gradient and slope aspect characteristics. For example, three-dimensional terrain data of the site is obtained through 1:2000 scale terrain surveying, including altitude distribution of 4800-5100m, slope gradient of 0.5°-15° and slope aspect characteristics.

[0085] The beneficial effect of this technical solution is that by systematically collecting and integrating basic data for wind farms in complex, high-altitude mountainous areas, including basic field data, meteorological data, wind tower data, and topographic mapping data, a comprehensive understanding of the climate, wind speed distribution, and topographic characteristics of the site can be achieved. By spatiotemporally registering this data, a basic dataset of wind energy resources and topographic conditions can be accurately constructed, providing a reliable basis for subsequent wind farm site selection and optimization. This method helps ensure the rationality of wind farm site selection in complex mountainous environments, improves wind farm power generation efficiency and economic benefits, and reduces potential risks in wind turbine layout and collection line planning.

[0086] Example 3:

[0087] The embodiment of the present invention provides a micro-site selection optimization method applicable to high-altitude complex mountainous wind farms. The method processes terrain data, constructs a digital terrain model, and generates terrain feature distribution based on the digital terrain model, including:

[0088] Use GIS spatial analysis technology to convert terrain surveying and mapping data into digital terrain models of preset resolution;

[0089] The slope extraction tool is used to calculate the slope value of each grid cell of the digital elevation model and mark the areas where the slope is greater than the preset angle;

[0090] Use hydrological analysis tools to identify ridge lines and valley lines in areas with slopes greater than a preset angle and generate a distribution of terrain features.

[0091] In this embodiment, GIS spatial analysis technology is used to convert the 1:2000 scale topographic mapping data of the site (including contour lines and distribution of land features) into a digital elevation model (DEM) with a resolution of 50m to form a three-dimensional terrain grid.

[0092] In this embodiment, calculating the slope value of each grid cell of the digital elevation model by the slope extraction tool includes: calculating the slope value of each grid cell of the DEM by using a three-dimensional surface differential algorithm through the slope extraction tool, setting a preset angle to 15°, and marking steep areas with a slope greater than 15°.

[0093] In this embodiment, a hydrological analysis tool is used to process the marked area and automatically identify ridgelines and valley lines. This includes: identifying key terrain feature lines based on hydrological analysis principles within a constrained area with a slope greater than 25°; depression filling processing: filling the DEM to a depth of 0.5m to eliminate interference from small terrain depressions on flow direction analysis; D8 flow direction algorithm: calculating the flow direction of each grid (8-neighborhood maximum slope method); water accumulation calculation: counting the number of confluence grids to generate a cumulative matrix; ridgeline identification: extracting the 0.1% area with the lowest water accumulation (i.e., watershed), characterized by continuous linear highlands; valley line identification: calibrating the 5% area with the highest water accumulation (watershed), focusing on identifying V-shaped canyon areas, generating ridgeline (blue) / valley line (green) vector layers, and overlaying them with slope zoning to output a composite terrain feature map: the main ridgeline extends 12.4km long (average altitude 3180m), and typical valley lines are concentrated on the northeast slope (8.3km long, slope between 28-35°).

[0094] The beneficial effects of the above technical solution are: by using GIS spatial analysis technology to construct a digital terrain model, it is possible to accurately process the terrain data of complex mountainous areas and generate detailed terrain feature distribution. Through the slope extraction tool, areas with larger slopes are automatically calculated and marked, providing an important reference for subsequent wind farm site selection. Combining hydrological analysis tools to identify ridge lines and valley lines helps to clarify the appropriate location of the wind farm, avoid unsuitable areas, and ensure the safe and efficient operation of wind turbines. This method can comprehensively improve the accuracy and efficiency of terrain data processing, optimize the wind farm site selection plan, and thus improve the power generation efficiency and overall economic benefits of the wind farm.

[0095] Example 4:

[0096] The present invention provides a micro-site selection optimization method for wind farms in high-altitude and complex mountainous areas. The method calculates wind energy resources based on meteorological data and measured data from wind towers. The method determines the wind turbine safety level based on preset standards and determines the applicable wind turbine model and hub height. The method includes:

[0097] Based on meteorological data and wind tower measured data, wind energy resources are calculated to obtain comprehensive wind shear index and turbulence intensity;

[0098] Determine the applicable wind turbine safety level based on the comprehensive wind shear index and turbulence intensity matching the preset standards;

[0099] Determine the applicable wind turbine model and hub height based on the applicable wind turbine safety level.

[0100] In this embodiment, based on the comprehensive wind shear index and turbulence intensity matching preset standards, the applicable wind turbine safety level is determined, including: matching the calculated parameters with the preset safety standards of the International Electrotechnical Commission (IEC): wind shear level classification: comparing the three thresholds in IEC61400-1 (such as S level: α>0.2), judging whether the site is a strong shear or weak shear environment; turbulence intensity classification: determining airflow turbulence based on the range of turbulence intensity values ​​(such as Class A ≤ 0.12, Class C ≥ 0.16); comprehensive classification: locking the wind turbine safety level based on a dual parameter combination (such as "strong shear + high turbulence" matching Class III), which defines the wind turbine's structural strength, control system, and other capabilities to withstand extreme wind conditions.

[0101] In this embodiment, determining the applicable wind turbine model and hub height based on the applicable wind turbine safety level includes: optimizing equipment selection according to the safety level and the vertical distribution of wind resources, model selection: screening customized plateau models that have passed GL certification (such as low-temperature resistant gearboxes, UV-resistant coated blades), and their design standards must strictly match the safety level (Class III models correspond to harsh working conditions with a comprehensive shear index > 0.25 and a turbulence intensity > 0.16); hub height optimization: based on the wind speed-height change curve, determining the altitude layer corresponding to the peak wind power density, and selecting the optimal hub height (must meet both the safety level requirements and the principle of maximizing power generation benefits), for example, in high shear areas, preferentially raising the hub height to capture the wind speed jump gain.

[0102] The beneficial effects of the above technical solution are: by calculating wind energy resources based on meteorological data and measured data from wind towers, the comprehensive wind shear index and turbulence intensity can be accurately obtained, providing a scientific basis for wind farm site selection. By comparing these data with preset standards, a scientific basis for wind farm site selection is provided. By matching these data with preset standards, the safety level of the wind turbine can be effectively determined, ensuring the safe and stable operation of the wind turbine in complex high-altitude mountainous environments. Furthermore, the applicable wind turbine model and hub height are determined based on the wind turbine safety level, the wind farm's wind turbine configuration is optimized, the wind farm's power generation efficiency and economic benefits are improved, while reducing equipment loss and failure risks, and enhancing the long-term sustainability of the wind farm.

[0103] Example 5:

[0104] The present invention provides a micro-site selection optimization method for wind farms in high-altitude and complex mountainous areas. The method calculates wind energy resources based on meteorological data and wind tower measured data to obtain a comprehensive wind shear index and turbulence intensity, including:

[0105] The wind shear index at different heights is calculated based on the measured data from the wind tower. The formula is:

[0106]

[0107] Among them, α is the wind shear exponent, z1 and z2 are different measurement heights respectively, z1 is the first measurement height, z2 is the second measurement height, and v1 and v2 are the wind speeds corresponding to the first measurement height and the second measurement height respectively;

[0108] Fitting the wind shear exponents at different heights to determine the average wind shear exponent in the field area, and determining the average wind shear exponent in the field area as the comprehensive wind shear exponent;

[0109] Based on meteorological data, determine the wind speed standard deviation and average wind speed in the preset wind speed section, and then determine the turbulence intensity. The formula is:

[0110] <00002�9>

[0111] Among them, IT is the turbulence intensity, σ is the wind speed standard deviation in the preset wind speed section, and V is the average wind speed in the preset wind speed section.

[0112] In this embodiment, the wind shear exponent represents the gradient of the wind speed change with height and is dimensionless. Engineering significance: The wind shear exponent in high-altitude areas is relatively small (0.0515), indicating that the increase in wind speed with height is limited. Therefore, the tower barrel height should not be too high.

[0113] In this embodiment, the wind shear exponents at different heights are fitted to determine the average wind shear exponent in the field area, and the average wind shear exponent in the field area is determined as the comprehensive wind shear exponent. Select height combinations such as 10m and 100m, 50m and 120m, etc., and obtain the wind shear exponent 0.0515 through logarithmic fitting;

[0114] In this embodiment, the preset wind speed section is 15m / s. This wind speed section is the key reference point for the fan design because 15m / s is close to the rated wind speed of most models (such as the rated wind speed of the DEW200 / 5560-115 type fan is 11.5m / s, see). At this time, the influence of turbulence on the blade fatigue load and the stress of the transmission system is the most significant.

[0115] In this embodiment, the different measurement heights can be: Measurement height 1: 10 meters (near the ground), and the wind speed measured at a height of 10 meters is 4m / s, and the wind speed change is small; Measurement height 2: 50 meters (higher layer), and the wind speed measured at a height of 50 meters is 6m / s. The wind speed is stronger than that of the ground layer, and the amplitude of the wind speed change is larger.

[0116] In this embodiment, the turbulence intensity represents the degree of wind speed pulsation and is dimensionless. <000027I>

[0117] In this embodiment, σ is the wind speed standard deviation in the preset wind speed section, which reflects the wind speed fluctuation amplitude. For the 100m height, the average wind speed in the interval "14.5m / s < V ≤ 15.5m / s" is statistically calculated for the standardized turbulence intensity calculation.

[0118] The beneficial effect of the above technical solution is: by calculating the wind shear index at different heights based on the measured data of the wind tower, and fitting the average wind shear index of the site, the wind speed distribution and wind energy resources can be accurately evaluated. This provides a reliable basis for the site selection and wind turbine configuration of the wind farm. Furthermore, it provides a reliable basis for determining the turbulence intensity based on meteorological data. Furthermore, by determining the turbulence intensity based on meteorological data and calculating the standard deviation and average wind speed of the wind speed segment, the turbulence characteristics of the wind farm can be effectively evaluated, and the selection and layout of wind turbines can be optimized. This method can accurately reflect the wind energy conditions in complex mountainous environments at high altitudes, improve the design efficiency of wind farms, ensure the safety and power generation performance of wind turbines, and maximize the economic benefits of wind farms.

[0119] Example 6:

[0120] The present invention provides a micro-site selection optimization method for wind farms in high-altitude and complex mountainous areas. Based on a terrain feature distribution map, several restrictive area types are identified, and prohibited areas for site selection are generated. On a digital terrain model, several preliminary selection sites are initially arranged, with the ridgeline as the main axis, avoiding the prohibited areas. The coordinates, altitude, and surrounding terrain roughness parameters of each preliminary selection site are obtained, including:

[0121] Based on the superimposed terrain feature distribution map and preset land use attribute data, the restricted area types including basic farmland and ecological protection areas are identified;

[0122] Taking the ridgeline as the core axis, a candidate camera position grid is generated within the buffer range on both sides;

[0123] Eliminate candidate camera positions that fall into the prohibited area, retain candidate camera positions that meet the altitude and slope conditions, and then preliminarily arrange several preliminary camera positions and obtain the coordinates and altitude of each preliminary camera position;

[0124] The roughness parameters of the surrounding terrain are determined based on the coordinates and altitude of each preliminary camera position.

[0125] In this embodiment, several preliminary selection positions are initially arranged as follows: Figure 2The wind turbine layout diagram for the preliminary micro-site selection scheme is shown, noting the spatial relationship between the preliminary turbine sites and the prohibited areas. The corresponding wind turbine coordinates for the preliminary micro-site selection scheme are shown in Table 1. In Table 1, "GNNQ01-GNNQ20" are the numbers for the conventional preliminary wind turbine sites, arranged in logical planning order, representing the 20 core locations for proposed wind turbine placement. "bx1" and "bx2" may represent special function sites (such as standby sites and substation-related sites, which need to be confirmed in conjunction with other sections of the report) to supplement the wind farm functional layout. The coordinate data represents the plane coordinates (X and Y values) based on the project's coordinate system (e.g., CGCS2000), accurately marking the spatial location of each turbine site within the wind farm. These coordinates are generated through the process of "terrain data processing → digital terrain model construction → prohibited area identification → candidate site screening" and serve as the spatial reference for subsequent engineering design (such as foundation construction positioning and collector line routing), equipment installation, and operation and maintenance management.

[0126] Table 1 Wind turbine coordinates for the preliminary micro-site selection scheme

[0127] GNNQ01 426413.00 3488672.03 GNNQ02 426775.00 3488332.30 GNNQ03 427223.80 3488208.50 GNNQ04 427699.71 3488141.22 GNNQ05 428126.40 3487842.90 GNNQ06 428558.00 3487540.40 GNNQ07 429047.20 3487433.40 GNNQ08 429509.20 3487190.30 GNNQ09 429912.30 3486895.50 GNNQ10 430165.29 3486529.14 GNNQ11 431372.70 3486725.70 GNNQ12 431273.30 3486171.00 GNNQ13 431871.68 3485392.54 GNNQ14 432369.86 3484931.85 GNNQ15 433441.17 3484780.33 GNNQ16 426888.98 3486789.38 GNNQ17 427004.38 3486379.30 GNNQ18 427218.93 3485969.99 GNNQ19 427636.54 3485192.75 GNNQ20 427890.47 3484839.48 bx1 432705.86 3486466.52 bx2 434093.93 3484229.83

[0128] In this embodiment, based on the superimposed terrain feature distribution map and the preset land use attribute data, the site selection prohibited areas including basic farmland and ecological protection areas are identified: in the micro-site selection optimization of high-altitude complex mountain wind farms, it is first necessary to clearly define the areas where the machine positions are prohibited through spatial data analysis. In the specific operation, the terrain feature distribution map (including ridge lines, valley lines, steep slope boundaries, etc.) generated by the digital terrain model constructed in the early stage is superimposed and analyzed with the preset land use attribute data (such as basic farmland distribution, ecological protection area boundaries, forestry land scope, etc. vector layers) using the GIS platform. Through the "spatial overlay" and "intersection analysis" tools of GIS, areas that meet both terrain restrictions (such as steep areas with a slope greater than 15°) and land use restrictions (such as basic farmland and ecological red lines) are identified to form a composite prohibited area layer. In addition, the vector boundaries of legally prohibited construction areas such as military control areas and cultural relics protection units are manually supplemented to ensure that the prohibited area fully covers all kinds of restrictive factors. The resulting distribution map of prohibited areas must include dual constraint information on topography and land use, providing a clear basis for avoidance for subsequent machine site layout. For example, in the Jicuo wind power project in Senni District, this step clearly avoided the ecological protection area in the north of the site and the basic farmland area in the south.

[0129] In this embodiment, the surrounding terrain roughness parameters are determined based on the coordinates and altitude of each preliminary camera position. For example, according to the terrain roughness quantification modeling method, based on the preliminary camera position coordinates, the terrain data within a range of 500 meters is extracted to calculate the terrain roughness index k, which is calculated using the formula:

[0130] k=0.5+0.3(1-C v )+0.2R e +0.01θ ′ +0.0002H ′

[0131] Among them, C v is the vegetation coverage (decimal), which indicates the proportion of the ground covered by vegetation. The larger the value, the higher the vegetation coverage and the lower the terrain roughness. e is the rock rate (decimal), which indicates the proportion of rock coverage in the terrain. The higher the rock coverage, the greater the roughness of the terrain. ′ The slope characteristic coefficient (dimensionless) indicates the slope of the terrain. The greater the slope, the greater the roughness of the terrain. ′ is the altitude characteristic coefficient;

[0132]

[0133] Where θ is the actual slope (unit: °), and H is the actual altitude (unit: m).

[0134] In this embodiment, the ridgeline is used as the core axis, and candidate site grids are generated within the buffer range on both sides. This includes: after completing the identification of the prohibited area, the main ridgeline extracted from the terrain feature distribution map (such as the main ridges in the central and southern parts of the site) is used as the core layout axis. Using the GIS "buffer analysis" tool, a reasonable buffer range is delineated on both sides of the ridgeline (determined based on the wind turbine spacing requirements and the flatness of the terrain, usually 200-500 meters on each side of the ridgeline), forming a strip-shaped candidate area extending along the ridge. The "fishing net tool" is used to generate regular or irregular candidate site grid points within this area. The grid density is dynamically adjusted based on the uniformity of wind energy resource distribution. The grid is denser (e.g., every 500m×500m grid) in the gentle ridge section with rich wind energy, and appropriately relaxed to 1000m×1000m in the terrain transition area. This process needs to be combined with the main wind energy direction (such as WSW to SSW), and the points are preferentially denser on the windward side of the ridgeline to capture more stable incoming wind speeds. For example, in the Senni District project, this method was used to generate a total of 20 candidate machine position grids on both sides of the two main ridges, preliminarily covering areas with high-quality wind energy resources.

[0135] In this embodiment, candidate camera positions that fall into the prohibited area are excluded, and candidate camera positions that meet the altitude and slope conditions are retained, and then a number of preliminary camera positions are preliminarily arranged: the generated candidate camera position grid points are screened point by point, and the "spatial query" function of GIS is used to determine whether each candidate point falls within the prohibited area layer. If there is a spatial intersection between the coordinates of the candidate point and the prohibited area polygon (such as being located in basic farmland or ecological protection area), it is marked as an invalid point and eliminated; for candidate points that do not fall into the prohibited area, their altitude (usually need to match the altitude range of 4800-5100 meters in the site) and slope conditions (preferentially retaining gentle areas of ≤10°). For example, the Seni District project excluded 20 candidate points in the northern steep slope area through this step, and retained 18 valid points located in the gentle section of the main ridge.

[0136] In this embodiment, spatial coordinates and altitude are obtained: first, a digital terrain model (DEM) is constructed using the 1:2000 scale topographic surveying data of the site, ridge lines are extracted through GIS spatial analysis, and candidate camera position grids are generated within the buffer range on both sides. The prohibited areas are excluded by combining the superimposed land use attribute data such as basic farmland and ecological protection areas; then, the geographic coordinates (CGCS2000 coordinate system, X, Y values) and altitude (Z value, accuracy of ±0.5m) of the compliant candidate camera positions are automatically extracted from the DEM model, and the coordinates are verified through RTK-GPS field measurement, and the altitude is calibrated using leveling measurement to ensure that the error is ≤1m.

[0137] The beneficial effects of the above technical solution are: through the superposition analysis based on the terrain feature distribution map and the land use attribute data, the prohibited areas for wind farm site selection can be effectively identified, and the impact of unsuitable areas (such as basic farmland and ecological protection areas) on wind power projects can be avoided. On the digital terrain model, with the ridge line as the core, combined with the slope and altitude conditions, the candidate site grid is reasonably arranged, and areas that do not meet the conditions are excluded to ensure that the preliminary sites meet the basic requirements for wind farm site selection. This method optimizes the wind turbine layout plan, improves the rationality and feasibility of site selection, while avoiding potential conflicts in ecology and environment, and improving the sustainability and environmental friendliness of the project.

[0138] Example 7:

[0139] The present invention provides a micro-site selection optimization method for wind farms in high-altitude and complex mountainous areas. The method analyzes the wake impact of each preliminary site based on its coordinates, altitude, and surrounding terrain roughness parameters, determines the wake velocity loss between adjacent preliminary sites, performs wake optimization, and generates a wake-optimized site layout plan, including:

[0140] Based on the coordinates, altitude and surrounding terrain roughness parameters of each preliminary selected position, a fluid dynamics model is established to simulate the wind energy flow path;

[0141] Calculate the wake velocity loss ratio between adjacent aircraft positions based on the simulated wind energy flow path results and identify aircraft position pairs that exceed a preset threshold;

[0142] For aircraft positions that exceed the preset threshold, adjustments are made according to the minimum spacing requirements between the main wind direction and the vertical direction;

[0143] Repeat simulation and adjustment until the wake effect is controlled within the preset reasonable range;

[0144] Generate a wake-optimized aircraft stand layout plan, and mark the relationship between the optimized aircraft stand spacing and the main wind direction angle.

[0145] In this embodiment, the identification of the aircraft pair exceeding the preset threshold is to set the wake velocity loss threshold to 15% (a conventional threshold in the industry) and mark the aircraft pair whose loss exceeds the threshold;

[0146] In this embodiment, the simulation and adjustment are repeated until the wake impact is controlled within a preset reasonable range: the adjusted aircraft position coordinates are re-entered, the wake velocity loss under the new layout is calculated, and the loss ratio of all aircraft position pairs is compared. If the loss ratio still exceeds 15%, the spacing is further fine-tuned (each adjustment range is ±50m). The cycle is repeated until the loss between all aircraft positions is ≤15%, forming the final optimized layout. As shown in the table of power generation results of the recommended solution per unit in Table 2, the average wake coefficient is 5.75%.

[0147] Table 2 Single-unit power generation results of recommended solutions

[0148]

[0149] In this embodiment, a wake-optimized aircraft stand layout is generated, and the relationship between the optimized aircraft stand spacing and the main wind direction angle is marked: a coordinate list containing 18 final aircraft stands is output (such as F1: 31426799, 3488332; F9: 31429917, 3486884), ensuring that the main wind direction spacing is 1000-1200m and the vertical spacing is 600-800m; the main wind direction (WSW-SSW, angle of approximately 247.5°-262.5°) is marked with an arrow, and the spacing between typical aircraft stand pairs is marked with line segments.

[0150] The beneficial effects of the above technical solution are: by establishing a fluid mechanics model based on the coordinates, altitude and terrain roughness parameters of the preliminary selected machine positions, it is possible to accurately simulate the wind energy flow path and calculate the wake velocity loss, identify and adjust the machine position pairs with greater wake influence, ensure that the wake interference between the machine positions is controlled in the large machine position pairs, and ensure that the wake interference between the machine positions is controlled within a reasonable range. By repeatedly optimizing the machine position spacing, the goal of reducing wake losses is achieved and the overall power generation efficiency of the wind farm is improved. At the same time, the optimized machine position layout plan not only conforms to the actual environment of the wind farm, but also takes into account the minimum spacing requirements and the main wind direction angle between wind turbines to ensure the safety and efficient operation of the wind turbines. This method effectively improves the energy output and economic benefits of the wind farm.

[0151] Example 8:

[0152] An embodiment of the present invention provides a micro-site selection optimization method applicable to high-altitude complex mountainous wind farms. The method calculates the wake velocity loss ratio between adjacent wind farm sites based on the results of simulating wind energy flow paths, including:

[0153] Determine the incoming wind speed distribution at each machine position based on the simulated wind energy flow path results;

[0154] The incoming wind speed is determined based on the incoming wind speed distribution at each aircraft position, and then the initial loss ratio of the wake speed between adjacent aircraft positions is calculated:

[0155]

[0156] v wake is the initial loss ratio of the wake velocity at the downstream position, v0 is the incoming wind speed at the upstream position, C T is the thrust coefficient, d is the impeller diameter, x is the distance between the upstream and downstream positions along the main wind direction, and a is the preset wake attenuation coefficient;

[0157] Based on the fluid mechanics model, the initial loss ratio of wake velocity between adjacent aircraft positions is corrected to obtain the loss ratio of wake velocity between adjacent aircraft positions.

[0158] In this embodiment, the inflow wind speed distribution at each machine position is determined based on the results of the simulated wind energy flow path, including: by importing the coordinates of the preliminary machine position (such as GNNQ01~GNNQ20), terrain parameters (altitude, slope, roughness) and wind turbine parameters (impeller diameter 200m, hub height 115m), the simulation obtains the following key data: Wind energy flow path cloud map: showing the acceleration, circumference and turbulence distribution of the airflow under the main wind energy direction (WSW~SSW) affected by the terrain; Inflow wind speed distribution at each machine position: based on the annual average wind speed of 7.18m / s at the height of the wind measurement tower of 115m, combined with terrain correction, the actual inflow wind speed of each machine position is obtained; Relative position relationship between machine positions: The spatial coordinate difference x of the upstream and downstream machine positions in the wind energy flow path is determined by the model, which is used to calculate the distance parameter;

[0159] In this embodiment, the upstream wind speed (unit: m / s) is the average wind speed at a height of 115 m of the wind tower, which is 7.18 m / s.

[0160] In this embodiment, C T The thrust coefficient reflects the aerodynamic characteristics of the fan and is taken from the parameters of the DEW200 / 5560-115 fan provided by the manufacturer. The value range is usually 0.7 to 0.9, which is determined by the fan model;

[0161] In this embodiment, d is the impeller diameter, which is input into the model as a fixed parameter, d = 200 m;

[0162] In this embodiment, x is the distance between the upstream and downstream wind turbines along the main wind direction, indicating the horizontal distance between the two wind turbines;

[0163] In this embodiment, a is a preset wake attenuation coefficient, which is related to atmospheric stability and terrain roughness and is dimensionless. The terrain roughness of high-altitude mountainous areas is relatively low (the surface is mainly plateau meadows), and the value of a is generally 1.5 to 2.0 (industry experience value);

[0164] In this embodiment, when the distance between the upstream and downstream stations increases, the term The wake effect weakens with increasing spacing, and the wind speed loss gradually decreases. For example, by adjusting x to above 1000m, the wake wind speed loss is reduced from the initial 20% to 5.75% (refer to the recommended single-unit power generation results table in Table 1), which proves the engineering applicability of the formula in the micro-site selection of wind farms in complex mountainous areas at high altitudes.

[0165] In this embodiment, the effect of the thrust coefficient is: the larger the thrust coefficient, the greater the The smaller the Increases, thereby increasing the loss of wake wind speed; Physical meaning: higher C T This means that the fan has a stronger disturbance on the incoming wind speed and the wake effect is more significant. For example, a low turbulence intensity model (IEC III b level, DEW200 / 5560-115 fan) is selected, and its design C T The lower the value, the less the influence of the wake effect on the wind speed at the downstream turbines, and the higher the overall power generation efficiency of the wind farm.

[0166] The beneficial effects of the above technical solution are: by calculating the wake velocity loss ratio between adjacent machine positions based on the results of the simulated wind energy flow path, the wake velocity loss ratio between adjacent machine positions can be calculated more accurately than the results of the simulated wind energy flow path, the wake effect between wind turbines can be accurately evaluated, and the machine position layout can be optimized. By determining the incoming wind speed distribution of each machine position and calculating the wake loss ratio of adjacent machine positions, the operating efficiency of each wind turbine is maximized. This method effectively reduces wake losses and reduces interference between wind turbines by considering factors such as thrust coefficient, impeller diameter, and machine position spacing, thereby improving the overall power generation efficiency of the wind farm. At the same time, the layout plan after wake optimization helps to ensure the long-term stable operation of the wind farm and enhance economic benefits.

[0167] Example 9:

[0168] An embodiment of the present invention provides a micro-site selection optimization method applicable to high-altitude complex mountainous wind farms. The method corrects the initial loss ratio of wake velocity between adjacent wind farms based on a fluid mechanics model to obtain the wake velocity loss ratio between adjacent wind farms, including:

[0169] In the fluid mechanics model, the incoming wind speed in the main wind energy direction is exponentially corrected according to the average altitude of the site to obtain the altitude corrected wind speed;

[0170] In the fluid dynamics model, the terrain roughness index and altitude-corrected wind speed are substituted into the wake velocity loss formula to obtain the wake velocity loss ratio between adjacent aircraft positions:

[0171]

[0172] Among them, v wake ‘ is the ratio of wake velocity loss between adjacent aircraft positions, v0 ’ is the altitude-corrected wind speed, and k is the terrain roughness index.

[0173] In this embodiment, the wind speed in the main wind direction is subjected to exponential correction to obtain the altitude-corrected wind speed, for example: v0 ’ =v0·e 0.00012H , where v0 is the wind speed at standard altitude (1000m), v0 ’ is the corrected wind speed;

[0174] In this embodiment, -0.00012 is an empirical correction coefficient for the effect of altitude on wind speed attenuation. Its physical meaning and function are as follows: This coefficient is derived from the exponential attenuation relationship between altitude and wind speed in atmospheric boundary layer theory. Under standard atmospheric conditions, air density decreases by approximately 11.6% for every 1000-meter increase in altitude. Wind speed exhibits a nonlinear change due to the reduction in frictional losses caused by the thinning of the atmosphere. By fitting measured data from high-altitude areas (such as the Qinghai-Tibet Plateau), an exponential law of wind speed attenuation with increasing altitude is derived. This law is primarily applicable to high-altitude areas with a range of 2000 m ≤ H ≤ 6000 m. Beyond this range, the coefficient needs to be revalidated.

[0175] The beneficial effect of the above technical solution is that by correcting the wake velocity loss between adjacent turbines based on a fluid dynamics model, the impact of wake turbulence in high-altitude, complex mountainous wind farms can be more accurately assessed. By introducing altitude-corrected wind speed, the impact of different altitudes on wind speed is taken into account, ensuring the accuracy of wake analysis. In combination with the terrain roughness index, the calculation of wake velocity loss is further optimized, making the wind farm layout more reasonable. This method can effectively control wake turbulence losses, improve the power generation efficiency of the wind farm and the safe spacing between wind turbines, thereby maximizing the overall economic benefits and sustainability of the wind farm.

[0176] Example 10:

[0177] The present invention provides a micro-site selection optimization method for wind farms in high-altitude and complex mountainous areas. Based on the wake-optimized turbine layout plan, the method performs collector line path planning and generates a final wind turbine layout diagram containing turbine coordinates, wind turbine models, and collector line paths. The method includes:

[0178] Based on the aircraft layout plan after wake optimization, the path optimization algorithm is used to generate the initial collection route;

[0179] Calculate the voltage drop of each line segment in the initial collector routing and adjust the line path if the voltage drop exceeds the preset allowable range;

[0180] Verify the adjusted line path, generate the final wind turbine layout diagram, and mark the machine position coordinates and collector lines.

[0181] In this embodiment, adjusting the line path is adjusting the position of the machine or upgrading the conductor specifications;

[0182] In this embodiment, the use of a path optimization algorithm to generate an initial collection route is to use a path optimization algorithm (such as a Dijkstra algorithm) to generate the shortest path for the collection line, and determine an initial line connection plan based on the geographic coordinates of the machine layout.

[0183] In this embodiment, the voltage drop of each line segment in the initial collector route is calculated using the formula:

[0184]

[0185] △U is the voltage drop, P is the transmission power (maximum single circuit 27.8MW), R is the line resistance, X is the line inductance, U e The rated voltage is 35kV; if the calculated voltage drop exceeds 5% of the rated voltage, the machine position should be adjusted to shorten the line length, or the conductor cross-section should be changed (such as upgrading from JL / G1A-120 / 25 to JL / G1A-185 / 30), and the voltage drop should be recalculated until the requirements are met.

[0186] In this embodiment, the adjusted line path is verified, and the final wind turbine layout diagram is generated, with the machine coordinates and collector lines marked, including electrical and structural performance verification such as short-circuit current withstand capability, insulation coordination, and mechanical strength (such as icing and strong wind loads). For example, for low-pressure environments in high-altitude areas, it is necessary to verify whether the wire spacing and insulator creepage distance meet the insulation requirements after altitude correction (insulation correction coefficient outside the area of ​​5000m above sea level); after verification, the final wind turbine layout diagram is generated as shown in the figure. Figure 3 shown.

[0187] The beneficial effects of the above technical solution are: by planning the collector line path based on the wake-optimized machine position layout scheme, the impact of line voltage drop on the overall performance of the wind farm can be effectively reduced. The path optimization algorithm is used to generate the initial collector route, and through voltage drop calculation and adjustment, the line voltage drop is ensured to be within the preset range, thereby improving the stability and efficiency of power transmission. At the same time, through the verification of the adjusted line path, the final wind turbine layout diagram is generated with accurate marking of the machine position coordinates and the collector line path, providing an efficient and feasible layout plan for the implementation of the wind farm, optimizing the overall design of the wind farm, and improving power generation efficiency and economic benefits.

[0188] 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 micro-site selection optimization method for wind farms in high-altitude and complex mountainous areas, characterized by: include: Step 1: Collect basic data of wind farms in high-altitude and complex mountainous areas, including regional terrain data, meteorological data, and wind tower measured data; Step 2: Process the terrain data to construct a digital terrain model. Generate terrain feature distribution based on the digital terrain model. Simultaneously, calculate wind energy resources based on meteorological data and measured data from wind towers. Determine the wind turbine safety level based on pre-set standards and determine the appropriate wind turbine model. Step 3: Based on the terrain feature distribution map, several restricted area types are identified, and prohibited areas for site selection are generated. The prohibited areas are avoided on the digital terrain model, and several preliminary camera positions are preliminarily arranged. The coordinates, altitude, and surrounding terrain roughness parameters of each preliminary camera position are obtained. Step 4: Perform wake turbulence analysis on each preliminary aircraft position based on its coordinates, altitude, and surrounding terrain roughness parameters. Determine the wake turbulence velocity loss between adjacent preliminary aircraft positions and perform wake optimization to generate a wake-optimized aircraft position layout plan. Step 5: Based on the turbine layout plan after wake optimization, the collection line path is planned to generate the final turbine layout diagram including the turbine coordinates, turbine model, and collection line path.

2. The micro-site selection optimization method for wind farms in high-altitude complex mountainous areas according to claim 1 is characterized in that: Collect basic data for high-altitude and complex mountainous wind farms, including: Obtain long-term meteorological data from regional meteorological stations within the target area, including annual average temperature, extreme temperature range, wind speed distribution, and wind rose characteristics; Collect measured data from multiple height levels of the wind tower, including wind speed series, wind power density distribution and turbulence intensity parameters at different heights; Obtain three-dimensional terrain data of the site through topographic mapping, including elevation distribution, slope gradient and aspect characteristics; Meteorological data, wind measurement data and terrain data are temporally and spatially aligned to form a basic data set.

3. The micro-site selection optimization method for high-altitude complex mountainous wind farms according to claim 1 is characterized in that: Process terrain data, build a digital terrain model, and generate terrain feature distribution based on the digital terrain model, including: Use GIS spatial analysis technology to convert terrain surveying and mapping data into digital terrain models of preset resolution; The slope extraction tool is used to calculate the slope value of each grid cell of the digital elevation model and mark the areas where the slope is greater than the preset angle; Use hydrological analysis tools to identify ridge lines and valley lines in areas with slopes greater than a preset angle and generate a distribution of terrain features.

4. The micro-site selection optimization method for wind farms in high-altitude complex mountainous areas according to claim 1 is characterized in that: Based on meteorological data and actual wind tower data, wind energy resource calculations are performed. Combined with preset standards, the wind turbine safety level is determined, and the applicable wind turbine model and hub height are determined, including: Based on meteorological data and wind tower measured data, wind energy resources are calculated to obtain comprehensive wind shear index and turbulence intensity; Determine the applicable wind turbine safety level based on the comprehensive wind shear index and turbulence intensity matching the preset standards; Determine the applicable wind turbine model and hub height based on the applicable wind turbine safety level.

5. The micro-site selection optimization method for wind farms in high-altitude complex mountainous areas according to claim 4 is characterized in that: Based on meteorological data and wind tower measured data, wind energy resource calculations are performed to obtain comprehensive wind shear index and turbulence intensity, including: The wind shear index at different heights is calculated based on the measured data from the wind tower. The formula is: Where α is the wind shear index, z1 and z2 are different measurement heights, z1 is the first measurement height, z2 is the second measurement height, v1 and v2 correspond to the wind speeds at the first and second measurement heights respectively; The wind shear index at different heights is fitted to determine the average wind shear index of the site, and the average wind shear index of the site is determined as the comprehensive wind shear index; Based on meteorological data, the wind speed standard deviation and average wind speed of the preset wind speed segment are determined, and then the turbulence intensity is determined. The formula is: Where IT is the turbulence intensity, σ is the standard deviation of the wind speed in the preset wind speed segment, and V is the average wind speed in the preset wind speed segment.

6. The micro-site selection optimization method for wind farms in high-altitude complex mountainous areas according to claim 3, characterized in that: Based on the terrain feature distribution map, several restricted area types are identified and prohibited areas are generated for site selection. On the digital terrain model, with the ridgeline as the main axis, several preliminary camera positions are initially arranged, avoiding the prohibited areas. The coordinates, altitude, and surrounding terrain roughness parameters of each preliminary camera position are obtained, including: Based on the superimposed terrain feature distribution map and preset land use attribute data, the restricted area types including basic farmland and ecological protection areas are identified; Taking the ridgeline as the core axis, a candidate camera position grid is generated within the buffer range on both sides; Eliminate candidate camera positions that fall into the prohibited area, retain candidate camera positions that meet the altitude and slope conditions, and then preliminarily arrange several preliminary camera positions and obtain the coordinates and altitude of each preliminary camera position; The roughness parameters of the surrounding terrain are determined based on the coordinates and altitude of each preliminary camera position.

7. The micro-site selection optimization method for wind farms in high-altitude complex mountainous areas according to claim 1, characterized in that: The wake impact analysis is performed on each preliminary aircraft position based on its coordinates, altitude, and surrounding terrain roughness parameters. The wake velocity loss between adjacent preliminary aircraft positions is determined and wake optimization is performed. A wake-optimized aircraft position layout plan is generated, including: Based on the coordinates, altitude and surrounding terrain roughness parameters of each preliminary selected position, a fluid dynamics model is established to simulate the wind energy flow path; Calculate the wake velocity loss ratio between adjacent aircraft positions based on the simulated wind energy flow path results and identify aircraft position pairs that exceed a preset threshold; For aircraft positions that exceed the preset threshold, adjustments are made according to the minimum spacing requirements between the main wind direction and the vertical direction; Repeat simulation and adjustment until the wake effect is controlled within the preset reasonable range; Generate a wake-optimized aircraft stand layout plan, and mark the relationship between the optimized aircraft stand spacing and the main wind direction angle.

8. The micro-site selection optimization method for wind farms in high-altitude complex mountainous areas according to claim 7, characterized in that: Calculate the wake velocity loss ratio between adjacent aircraft positions based on the simulated wind energy flow path results, including: Determine the incoming wind speed distribution at each machine position based on the simulated wind energy flow path results; The incoming wind speed is determined based on the incoming wind speed distribution at each aircraft position, and then the initial loss ratio of the wake speed between adjacent aircraft positions is calculated: v wake is the initial loss ratio of the wake velocity at the downstream position, v0 is the incoming wind speed at the upstream position, C T is the thrust coefficient, d is the impeller diameter, x is the distance between the upstream and downstream positions along the main wind direction, and a is the preset wake attenuation coefficient; Based on the fluid mechanics model, the initial loss ratio of wake velocity between adjacent aircraft positions is corrected to obtain the loss ratio of wake velocity between adjacent aircraft positions.

9. The micro-site selection optimization method for wind farms in high-altitude complex mountainous areas according to claim 8, characterized in that: Based on the fluid dynamics model, the initial loss ratio of the wake velocity between adjacent aircraft positions is corrected to obtain the wake velocity loss ratio between adjacent aircraft positions, including: In the fluid mechanics model, the incoming wind speed in the main wind energy direction is exponentially corrected according to the average altitude of the site to obtain the altitude corrected wind speed; In the fluid dynamics model, the terrain roughness index and altitude-corrected wind speed are substituted into the wake velocity loss formula to obtain the wake velocity loss ratio between adjacent aircraft positions: Among them, v wake ‘ is the ratio of wake velocity loss between adjacent aircraft positions, v0 ’ is the altitude-corrected wind speed, and k is the terrain roughness index.

10. The micro-site selection optimization method for wind farms in high-altitude complex mountainous areas according to claim 1, characterized in that: Based on the turbine layout plan after wake optimization, the collection line path is planned to generate the final turbine layout diagram containing the turbine coordinates, turbine model, and collection line path, including: Based on the aircraft layout plan optimized based on wake turbulence, the path optimization algorithm is used to generate the initial collection route; Calculate the voltage drop of each line segment in the initial collector routing and adjust the line path if the voltage drop exceeds the preset allowable range; Verify the adjusted line path, generate the final wind turbine layout diagram, and mark the machine position coordinates, collection line direction and key node parameters.

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