Mountain wind power global wind resource assessment method

Abstract

The invention discloses a mountain wind power global wind resource assessment method. The method comprises the following steps: acquiring topographic data and constructing a mountain wind speed vertical gradient reasonable interval; troubleshooting and eliminating an unsuitable area, and marking a risk early warning area; deploying a high-frequency unmanned aerial vehicle cluster and an encrypted micro sensor; long-term dynamic monitoring is carried out, monitoring data are verified, and seasonal special monitoring is synchronously implemented; performing preliminary screening on the wind resource candidate point locations in the monitoring area; performing secondary screening on the wind resource primary screening point locations through wake flow interference checking and construction cost evaluation to form a plurality of candidate point locations; constructing a terrain and weather coupling interpolation model, and inputting monitoring data for interpolation to generate a high-resolution global wind resource map; turbulence influence quantification and high-altitude environment correction are carried out based on a global wind resource map, and candidate point locations are graded through a comprehensive scoring system; and determining the number of installation point locations and the optimal installation point location. According to the evaluation method, the arrangement of an anemometer tower can be canceled, and the evaluation of wind resources in each region of the project is realized.

Description

Technical Field

[0001] This invention belongs to the technical field of mountain wind power, and specifically relates to a method for assessing wind resources across the entire mountain wind power area. Background Technology

[0002] In the development of mountain wind power projects, factors such as topography, turbulence, altitude, and strong gusts all affect the power generation of wind turbines. Therefore, it is necessary to accurately obtain wind resource data for the entire area and accurately predict the actual power generation potential of each wind turbine location to provide a reliable basis for wind turbine site selection and turbine model selection, and avoid project returns falling short of expectations due to misjudgment of wind conditions.

[0003] Currently, wind resource assessment for mountain wind power mainly relies on traditional steel wind measurement towers. This involves deploying 3-5 wind measurement towers, each 80-120m high, within the project area. Each tower is equipped with sensors such as anemometers and wind direction indicators at different elevations. Through continuous static monitoring for 6-12 months, wind resource data at the monitoring points are obtained. Based on this single-point data and combined with the terrain, the wind conditions of the entire area are roughly estimated, thereby predicting the power generation of the entire project.

[0004] However, the coverage of wind measurement towers is limited. Mountainous terrain is fragmented, and wind speed differences between different slopes and micro-topography within the same area can reach 2-3 m / s. However, the effective representative range of a single wind measurement tower is usually only within 1 km², and it can only record turbulence data at a single point. 3-5 wind measurement towers cannot cover the entire area and cannot reflect the turbulence distribution pattern of the entire area.

[0005] In addition, the construction cost of a single traditional wind measurement tower is 500,000 to 800,000 yuan. In candidate wind turbine locations such as steep mountain slopes and dense forest areas, the transportation of wind measurement towers is difficult and they are hard to deploy. Therefore, the construction cost of wind measurement towers is high, their terrain adaptability is poor, and there are monitoring blind spots, which leads to incomplete and inaccurate assessment of wind resources across the entire region. Summary of the Invention

[0006] This invention provides a method for assessing wind resources across the entire mountainous wind power region, addressing the problems of difficult and costly construction of traditional wind measurement towers and inaccurate measurements.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for assessing wind resources across a mountainous wind power region includes the following steps: Step 1: Conduct preliminary surveys to obtain topographic data and construct a reasonable range for the vertical gradient of wind speed in mountainous areas; Step 2: Investigate and eliminate unsuitable areas, and mark risk warning zones; Step 3: Deploy a high-frequency drone swarm and encrypted micro-sensors, and set up staggered drone operations; Step 4: Conduct long-term dynamic monitoring, verify the monitoring data according to the process of first judging the actual wind conditions and then verifying the equipment deviation, and simultaneously implement seasonal special monitoring. Step 5: Based on the monitoring data, conduct a preliminary screening of candidate wind resource sites within the monitoring area; Step 6: Through wake interference investigation and construction cost assessment, the initial wind resource screening sites are re-screened to form multiple candidate sites; Step 7: Construct a coupled topographic and meteorological interpolation model, input monitoring data, and interpolate to generate a high-resolution global wind resource map; Step 8: Based on the global wind resource map, quantify the impact of turbulence and correct for high-altitude environment, and classify candidate sites through a comprehensive scoring system; Step 9: Based on the project's total power generation target and the estimated power generation per point, work backward to determine the required number of installation points and identify the optimal installation points.

[0008] Furthermore, in step one, a drone equipped with detection equipment is used to scan the entire project area to obtain topographic data including slope, aspect and surface roughness. By conducting pre-scanning surveys using drones, we can collect multi-elevation wind speed data for different terrain units and at different times, and construct a reasonable range for the vertical gradient of wind speed in mountainous areas based on the wind speed data.

[0009] Furthermore, in step two, the investigation and elimination of unsuitable areas includes excluding areas within the project area with excessive slope, ecologically sensitive areas, areas with exposed rocks, and areas where safety and noise control distances do not meet standards. Identify valley bends, turbulence-prone areas, and winter inversion attenuation areas within the project area, and mark them as risk warning zones. These risk warning zones will serve as key data collection targets for subsequent wind condition monitoring.

[0010] Furthermore, in step three: the high-frequency drone cluster includes multiple main drones and at least one backup drone, and each main drone is assigned a staggered scanning mode to be responsible for different areas and different time periods; Encrypted micro sensors are preferentially deployed in areas with uniform terrain and focus on covering the blind spots of drones, and are installed by burying in the soil or attaching to rocks.

[0011] Furthermore, in step four, dynamic monitoring and data verification, the following are included: Long-term dynamic monitoring covering all four seasons will be carried out on a 12-month cycle. Every hour, the drone's scanning data within a preset range around the sensor is extracted, along with the sensor's near-ground wind speed data at the same time. The reasonable range of the vertical gradient of mountain wind speed is then used to determine the reasonableness of the wind speed. For data outside the specified range, special wind conditions are investigated by retrieving turbulence data from the drone and data from surrounding sensors. After excluding special wind conditions, other drones are dispatched for cross-validation to determine equipment deviation, and the deviation equipment is calibrated and the model parameters are updated.

[0012] Furthermore, the initial screening of candidate wind resource sites in step five includes the following steps: Extract the core wind resource indicators for each monitoring point. The core wind resource indicators include the annual average wind speed at the preset elevation, wind direction stability, and turbulence intensity. Set screening thresholds for each core indicator to identify monitoring points with excellent wind resources and controllable risks, and determine them as initial screening points for wind resources.

[0013] Furthermore, in step six, the re-screening of the initial wind resource screening points includes the following steps: Obtain the coordinates of the initial screening points and the diameter parameters of the fan impeller. Set the safety distance standard between adjacent points based on the characteristics of the fan wake. Calculate the point spacing through the geographic information system and remove the initial screening points that do not meet the spacing standard. Determine construction cost assessment indicators including terrain flatness, transportation convenience, and foundation excavation difficulty, construct a cost scoring system to score the initial screening points, eliminate the initial screening points that do not meet the standards, and form candidate points.

[0014] Furthermore, in step seven, when constructing the terrain-meteorological coupled interpolation model, the slope, aspect, and surface roughness obtained from the previous survey are used as weighting factors and incorporated into the Kriging interpolation algorithm to construct the terrain-meteorological coupled interpolation model. When generating a high-resolution global wind resource map, the data collected by UAVs and sensors are input into the terrain and meteorological coupled interpolation model, and the Gaussian variogram is used for interpolation calculation; a global wind resource map with a preset resolution is generated, and the global wind resource map is labeled with the average wind speed, average wind direction and turbulence intensity at multiple elevations within the resolution.

[0015] Furthermore, in step eight, when classifying candidate sites, the following steps are included: Multiple sets of wind turbine location data with different turbulence intensities were extracted from the global wind resource map. Combined with actual power generation data of similar projects, a correlation formula between turbulence intensity and power generation loss was fitted to quantify the impact of turbulence. By fitting the correlation curve between temperature and air density based on temperature data collected by sensors, wind speed correction coefficients at different temperatures are obtained, thus achieving correction for high-altitude environments. A comprehensive scoring system was constructed, which included wind resource potential, risk control capabilities, construction costs, and terrain adaptability. Candidate sites were scored and classified into different levels.

[0016] Furthermore, step nine, "Based on the project's total power generation target and the estimated power generation per point, reverse-engineer the required number of installation points and determine the optimal installation points," includes the following steps: The total power generation target X of the project is determined. Based on the candidate site classification results and the corrected annual power generation per site, the number of installation sites required to meet the total power generation target is calculated by prioritizing the selection of higher-level sites. Adjust the combination of installation points based on the actual capacity of the project area to determine the optimal installation locations.

[0017] The present invention can achieve the following beneficial effects: 1. This application utilizes the collaborative work of drones and sensors to collect wind resource data, replacing the traditional method of deploying wind measurement towers, thus saving detection costs and solving the problems of poor terrain adaptability and limited coverage of wind measurement towers. Through the collaborative efforts of multiple stages, including preliminary surveying and gradient interval establishment, dynamic monitoring closed-loop calibration, and coupled model interpolation, high-resolution wind resource monitoring of the project area is achieved. Overlaying detailed optimizations such as turbulence diffraction and high-altitude correction reduces the prediction deviation of the final power generation, providing accurate data support for wind turbine site selection and turbine model selection.

[0018] 2. The main UAV's off-peak operation and backup UAV's supplementary mode can achieve 24-hour uninterrupted scanning and measurement, avoiding monitoring interruptions caused by single device failure; the deployment of encrypted micro sensors can supplement data for UAV blind spots, and combine with the vertical gradient model to infer high-altitude wind speed; in addition, a dynamic monitoring closed-loop calibration process is set up to first judge the actual wind conditions and then verify the equipment deviation, which can correct the monitoring data deviation in real time and ensure the authenticity and reliability of wind resource data.

[0019] 3. This application establishes a site selection and correction system comprising initial screening, secondary screening, grading, and back-calculation. This system enables precise selection of high-quality wind turbine sites, reduces operational risks, and improves project profitability. Initial screening uses wind speed, wind direction stability, and turbulence intensity thresholds to identify sites with excellent wind resources. Secondary screening eliminates unsuitable sites through wake interference investigation and construction cost assessment. The grading stage combines turbulence quantification and high-altitude environment correction to optimize site scores. Finally, the optimal site combination is calculated back-calculated based on the total power generation target. This design approach can improve the project's annual power generation achievement rate and significantly increase the investment returns of mountain wind power projects. Attached Figure Description

[0020] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a flowchart of a method for assessing wind resources across a mountainous wind power region according to the present invention. Detailed Implementation

[0021] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.

[0022] A method for assessing wind resources across the entire mountainous wind power area, such as Figure 1 As shown, it includes the following steps: Step 1: Conduct preliminary surveys to obtain topographic data and construct a reasonable range for the vertical gradient of wind speed in mountainous areas.

[0023] First, preliminary survey work was carried out: the entire project area was scanned by drones to obtain topographic data, including the slope, aspect and surface roughness of each area within the project area.

[0024] In addition, during this stage, it is necessary to conduct pre-scanning using drones to collect wind speed data at elevations of 2m, 50m, 80m, and 100m at different times for different terrain units in the project area. Based on these measured data, a reasonable range for the vertical gradient of mountain wind speed is constructed. In a specific embodiment, the reasonable range for the vertical gradient of mountain wind speed refers to the reasonable fluctuation range of wind speed at elevation 80m when the wind speed at elevation 2m at point A is 2.0m / s, which is 3.4-3.8m / s.

[0025] Step 2: Investigate and eliminate unsuitable areas, and mark risk warning zones.

[0026] Based on the preliminary survey, a comprehensive investigation was conducted to identify and eliminate unsuitable areas within the project area, such as areas with slopes exceeding 35°, ecologically sensitive areas, areas with exposed rock, and areas where safety and noise control distances are not met. Constructing wind turbine foundations in areas with steep slopes could easily trigger landslides. Ecologically sensitive areas include the core areas of nature reserves and primary protection zones of drinking water sources. Foundation excavation in areas with exposed rock requires extensive blasting operations, significantly increasing the construction period and maintenance difficulty. Areas where safety and noise control distances are not met include those less than 50 meters from existing high-voltage transmission lines or less than 300 meters from villages.

[0027] Simultaneously, risk warning zones within the project area are marked, specifically including valley bends, areas with high turbulence, and areas experiencing winter inversion attenuation. Areas with high turbulence may cause fatigue damage to wind turbine blades, while areas experiencing winter inversion attenuation correspond to areas where wind speeds at higher elevations are lower than under normal operating conditions. These risk warning zones are not directly excluded but are instead used as key data collection targets in subsequent wind condition monitoring to ensure accurate capture of wind condition details in risk areas.

[0028] Step 3: Deploy a high-frequency drone swarm and encrypted micro-sensors, and set up staggered drone operations; First, the high-frequency drone swarm was deployed and its parameters were set. The swarm consisted of four main drones and one backup drone. The main drones were vertical takeoff and landing solar-powered drones, capable of being recharged using solar energy, and equipped with lithium batteries to ensure the specified range on cloudy days. Each drone was equipped with a high-frequency five-hole probe anemometer for collecting wind speed and direction data in multiple directions, a miniature turbulence meter for measuring airflow turbulence intensity, an infrared thermometer for collecting ambient temperature data, and a three-dimensional ultrasonic obstacle avoidance module for detecting obstacles around the drone.

[0029] Based on the topographic data and wind turbine location distribution obtained from the preliminary survey, the project area was divided into several 200m×200m micro-grids. A scheduling model was also established, with four main drones operating at off-peak times and one backup drone providing backup. Specifically, Drone No. 1 was responsible for several micro-grids in the northeast of the project area, operating from 6:00 to 14:00; Drone No. 2 was responsible for several micro-grids in the southeast, operating from 10:00 to 18:00; Drone No. 3 was responsible for several micro-grids in the southwest, operating from 14:00 to 22:00; Drone No. 4 was responsible for several in the northwest, operating from 22:00 to 6:00 the next day; the backup drone was deployed at the central take-off and landing point of the project area, on standby to respond to emergencies. To ensure the integrity of data collection in each area at each time period, the operating hours of the main UAVs 1 to 4 are rotated daily. Specifically, the operating hours of main UAV 1 are 6:00-14:00 on the first day, 14:00-22:00 on the second day, 10:00-18:00 on the third day, 22:00-6:00 the next day on the fourth day, and main UAV 1 ceases operation on the fifth day. The operating hours of other main UAVs are rotated in the same manner.

[0030] During the scanning process, each drone will hover for 1 minute at six elevations: 20m, 50m, 80m, 100m, 120m, and 140m, to collect high-frequency wind speed, wind direction, and turbulence data at each elevation. For previously marked high-turbulence areas, the drone will switch to a low-altitude flight path for intensive scanning, with the hovering time extended to 2 minutes to ensure that the instantaneous fluctuations in wind speed caused by turbulence are fully captured.

[0031] Simultaneously with the deployment of the drone swarm, the deployment of encrypted micro-sensors is carried out. The micro-sensors are evenly distributed throughout the project area at 500m x 500m intervals. However, the sensors are not randomly placed at 500m intervals; instead, they are deployed in areas with uniform terrain, taking into account terrain data, to avoid a single 500m x 500m grid containing various complex terrain features. If the terrain in a certain area is too complex to accommodate a 500m x 500m grid, the sensor deployment can be densified to ensure wind speed data collection across all terrain features, with a focus on covering blind spots inaccessible to drones.

[0032] For soil-covered areas, sensors are installed by drilling holes in the ground with a handheld drill and burying the sensors inside. Only the wind speed acquisition probe and the miniature flexible solar film are exposed. The sensors are secured with gravel or poured concrete to prevent erosion and displacement by rainwater. For areas with exposed rock, sensors are adhered to the rock surface with strong adhesive, and an appropriate installation height is chosen to avoid waterlogging. Monitoring via sensors is simple to install, requires fewer installation points, and significantly reduces environmental impact compared to the large structures of wind measurement towers.

[0033] Each miniature sensor includes a wind speed acquisition module, a wind direction acquisition module, a temperature acquisition module, and a wireless communication module. The wireless communication module transmits the data collected by the wind speed acquisition module, the wind direction acquisition module, and the temperature acquisition module to a remote data processing center.

[0034] Step 4: Conduct long-term dynamic monitoring, verify the monitoring data by first judging the actual wind conditions and then verifying the equipment deviation, and simultaneously implement seasonal special monitoring.

[0035] After equipment deployment, a long-term dynamic monitoring phase begins, with a monitoring cycle of 12 months, covering all four seasons to obtain complete seasonal wind condition data. During monitoring, a closed-loop calibration process between the drone and sensor data must be executed. This process follows the logic of first determining the actual wind conditions and then verifying equipment deviations. The following example illustrates this process: First, the data collected by the UAV within a 500m radius around each sensor is extracted from the data processing center every hour. In this embodiment, an elevation of 80m is used as an example. At the same time, the average wind speed at an elevation of 2m collected by sensor A is also extracted.

[0036] Secondly, the reasonable range of the vertical gradient of mountain wind speed constructed in step one is used to determine whether the measured value of the drone is within a reasonable range. If it exceeds the reasonable range, further investigation is needed to determine whether there is an abnormal wind condition.

[0037] Special wind condition investigation includes the following process: Retrieve the micro turbulence meter data from the corresponding drone (a) in the area where sensor A is deployed. If the data shows a sudden increase in turbulence intensity during the scan, it indicates that the data anomaly is due to fluctuations in actual wind speed caused by turbulence, and no calibration is required. If no turbulence exists, retrieve the concurrent data from two other sensors (B and C) within a 500m radius of sensor A. If the wind speeds at sensors B and C also increase synchronously, it indicates that the overall wind conditions in the area have improved, and calibration of drone a's measurement data is also unnecessary.

[0038] If no special wind conditions exist, multi-device cross-verification is required: Dispatch another main or backup drone operating nearby to fly within 500m of sensor A for retesting. If the retest results are within a reasonable range, and the corresponding drone a's recent three measurements at this elevation in this area are all 0.3-0.4 m / s higher than other drones, it can be determined that drone a's anemometer has a zero-point drift error. Send a calibration command to drone a to adjust its anemometer's zero-point parameters, correcting subsequent measurements by drone a to a reasonable range.

[0039] In addition, during the low-temperature period in winter, three additional vertical profile scans by drones are conducted daily to record wind speed changes under different low-temperature conditions. During the peak seasons of strong gusts in spring and autumn, a drone gust tracking mode is activated. When the ground gateway detects a sudden increase in wind speed exceeding 10 m / s in a certain area, the nearest drone is immediately dispatched to that area to record the peak wind speed, duration, and affected area of ​​the gusts, providing data support for subsequent power generation adjustments.

[0040] Step 5: Based on the monitoring data, conduct a preliminary screening of candidate wind resource sites within the monitoring area.

[0041] After the monitoring period ends, the initial screening stage for wind resource candidate sites begins, which includes the following steps: First, core wind resource indicators for each microgrid monitoring point are extracted from the overall wind condition monitoring dataset. These core indicators include the annual average wind speed at an 80m elevation, wind direction stability, and turbulence intensity. Then, thresholds for these core wind resource indicators are set: an annual average wind speed at an 80m elevation of no less than 4.0 m / s, wind direction stability of no less than 60%, and turbulence intensity not exceeding 0.25. Finally, based on these thresholds, monitoring core points with excellent wind resources and controllable risks are selected, resulting in A initial screening points for wind resources.

[0042] Step 6: Through wake interference investigation and construction cost assessment, the initial screening points for wind resources are re-screened to form multiple candidate points.

[0043] After determining the A initial screening points for wind resources, a secondary screening of the A initial screening points was carried out. The secondary screening process was divided into two parts: wake interference investigation and construction cost assessment.

[0044] In terms of wake interference investigation, the coordinates of the initial screening points for wind resources and the impeller diameter parameters of the wind turbines are first obtained. Based on the wake characteristics of the wind turbines, that is, the principle of wind speed attenuation in the downstream area during operation, a safe distance standard for adjacent points is set. Then, the coordinates of all the initial screening points for wind resources are imported into the geographic information system, the actual distance between adjacent points is calculated, and the points with poor wind resources that do not meet the distance standard are removed by comparing their wind resource data.

[0045] In terms of construction cost assessment, the construction cost assessment indicators are first determined, including the flatness of the surrounding terrain, transportation convenience, and foundation excavation difficulty. Then, a cost scoring system is constructed based on the above indicators, that is, the score ratio of each indicator is determined, the candidate points are scored for cost, points with scores lower than the standard score are eliminated, and finally, B candidate points remain after re-screening.

[0046] Step 7: Construct a terrain and meteorological coupled interpolation model, input monitoring data and interpolate to generate a high-resolution global wind resource map.

[0047] After the secondary screening is completed, the data processing stage begins. The core of this stage is to construct a coupled topographic and meteorological model and complete data fusion and interpolation to provide accurate basis for the classification of candidate points. First, the topographic data obtained from the preliminary survey is used as a weighting factor and integrated into the traditional Kriging interpolation algorithm to construct a coupled topographic and meteorological interpolation model. For example, for every 10% increase in slope, the corresponding interpolation weight increases by 0.1; the weight of south-facing areas is 0.15 higher than that of north-facing areas; and for every 0.05 increase in surface roughness, the weight decreases by 0.08. This weighting setting fully reflects the impact of topography on wind conditions.

[0048] Subsequently, the data collected by the UAV and the data collected by the sensors were input into the model, and interpolation calculations were performed using the Gaussian variogram to generate a 10m×10m resolution global wind resource map. Each 10m×10m grid in this map is labeled with the average wind speed, average wind direction, and turbulence intensity at six elevations: 20m, 50m, 80m, 100m, 120m, and 140m. The method for constructing the global wind resource map is not the focus of this application's description of a method for assessing global wind resources in mountainous wind power; therefore, the specific steps for constructing the global wind resource map are not detailed in this application.

[0049] Step 8: Based on the global wind resource map, quantify the impact of turbulence and correct for high-altitude environment, and classify the B candidate sites through a comprehensive scoring system.

[0050] To quantify the impact of turbulence, 50 sets of wind turbine location data corresponding to different turbulence intensities were extracted from the global wind resource map. Combined with the actual power generation data of similar mountain wind power projects, a correlation formula between turbulence intensity and power generation loss was fitted.

[0051] For high-altitude environment correction, based on the annual temperature data collected by sensors, the correlation curve between temperature and air density is fitted to obtain the wind speed correction coefficient at different temperatures. This coefficient is then applied to the wind speed data for the corresponding temperature period to achieve wind speed correction in low-temperature environments. At the same time, based on the strong gust data collected by drones, the annual power generation loss of each wind turbine due to strong gusts is calculated to be approximately how many kilowatt-hours.

[0052] Based on the above data, the B candidate sites were categorized into three levels: a comprehensive scoring system was constructed, with evaluation dimensions including wind resource potential, risk control capability, construction cost, and terrain adaptability. The scores for each dimension were determined based on the overall wind resource map data and preliminary survey data. It is important to note that the wind resource potential of each candidate site needs to consider the impact of turbulence intensity on annual power generation loss, as well as the impact of high-altitude environment on annual power generation loss. Based on this comprehensive scoring system, each of the B candidate sites was scored individually, and according to the scores, the B candidate sites were divided into three levels: Level 1 sites, Level 2 sites, and Level 3 sites.

[0053] Step 9: Based on the project's total power generation target and the estimated power generation per point, work backward to determine the required number of installation points and identify the optimal installation points.

[0054] Specifically, a method of inverse calculation based on the number of wind power installation sites in mountainous areas is adopted: For example, if the annual total power generation target is X kWh according to the project plan, based on the overall wind resource map and the candidate site classification results, the annual total power generation of all first-level sites is estimated to be X1. If X1 > X, only first-level sites are selected for wind turbine equipment installation; if X1 < X, second-level sites are added for wind turbine equipment installation, until the total power generation of the predicted sites reaches the annual total power generation target of X kWh.

[0055] Meanwhile, a wind risk warning manual was generated based on the wind resource assessment data of the entire region, and the risk types of each wind turbine location were classified: locations with turbulence intensity > 0.25 were classified as high turbulence risk, and it was recommended to select blades with stronger anti-turbulence performance; locations with an average of more than 18 strong gusts per year were classified as high strong gust risk, and it was recommended to reinforce the wind turbine towers, and the probability of occurrence, degree of impact and response suggestions for each type of risk were clearly defined.

[0056] This application presents a method for assessing wind resources across the entire mountainous wind power area. Throughout the entire implementation process, there is no need to deploy wind measurement towers. Through the synergistic effect of high-frequency UAV swarms, encrypted micro sensors, and terrain and meteorological coupling models, the method achieves accurate prediction and assessment of wind resources across the entire mountainous wind power project area and the selection of optimal installation sites.

[0057] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for assessing wind resources across a mountainous wind power region, characterized in that, Includes the following steps: Step 1: Conduct preliminary surveys to obtain topographic data and construct a reasonable range for the vertical gradient of wind speed in mountainous areas; Step 2: Investigate and eliminate unsuitable areas, and mark risk warning zones; Step 3: Deploy a high-frequency drone swarm and encrypted micro-sensors, and set up staggered drone operations; Step 4: Conduct long-term dynamic monitoring, verify the monitoring data according to the process of first judging the actual wind conditions and then verifying the equipment deviation, and simultaneously implement seasonal special monitoring. Step 5: Based on the monitoring data, conduct a preliminary screening of candidate wind resource sites within the monitoring area; Step 6: Through wake interference investigation and construction cost assessment, the initial wind resource screening sites are re-screened to form multiple candidate sites; Step 7: Construct a coupled topographic and meteorological interpolation model, input monitoring data, and interpolate to generate a high-resolution global wind resource map; Step 8: Based on the global wind resource map, quantify the impact of turbulence and correct for high-altitude environment, and classify candidate sites through a comprehensive scoring system; Step 9: Based on the project's total power generation target and the estimated power generation per point, work backward to determine the required number of installation points and identify the optimal installation points.

2. The method for assessing wind resources across a mountainous wind power area according to claim 1, characterized in that: In step one, a drone equipped with detection equipment scans the entire project area to obtain topographic data including slope, aspect, and surface roughness. By conducting pre-scanning surveys using drones, we can collect multi-elevation wind speed data for different terrain units and at different times, and construct a reasonable range for the vertical gradient of wind speed in mountainous areas based on the wind speed data.

3. The method for assessing wind resources across a mountainous wind power area according to claim 1, characterized in that: In step two, the investigation and elimination of unsuitable areas includes areas with excessive slope, ecologically sensitive areas, exposed rock areas, and areas where safety and noise control distances do not meet standards. Identify valley bends, turbulence-prone areas, and winter inversion attenuation areas within the project area, and mark them as risk warning zones. These risk warning zones will serve as key data collection targets for subsequent wind condition monitoring.

4. The method for assessing wind resources across a mountainous wind power area according to claim 1, characterized in that: In step three: the high-frequency drone cluster includes multiple main drones and at least one backup drone, and each main drone is assigned to a staggered scanning mode for different areas and time periods; Encrypted micro sensors are preferentially deployed in areas with uniform terrain and focus on covering the blind spots of drones, and are installed by burying in the soil or attaching to rocks.

5. The method for assessing wind resources across a mountainous wind power area according to claim 1, characterized in that: In step four, dynamic monitoring and data verification, the following is included: Long-term dynamic monitoring covering all four seasons will be carried out on a 12-month cycle. Every hour, the drone's scanning data within a preset range around the sensor is extracted, along with the sensor's near-ground wind speed data at the same time. The reasonable range of the vertical gradient of mountain wind speed is then used to determine the reasonableness of the wind speed. For data outside the specified range, special wind conditions are investigated by retrieving turbulence data from the drone and data from surrounding sensors. After excluding special wind conditions, other drones are dispatched for cross-validation to determine equipment deviation, and the deviation equipment is calibrated and the model parameters are updated.

6. The method for assessing wind resources across a mountainous wind power area according to claim 1, characterized in that: Step five, which involves the initial screening of candidate wind resource sites, includes the following steps: Extract the core wind resource indicators for each monitoring point. The core wind resource indicators include the annual average wind speed at the preset elevation, wind direction stability, and turbulence intensity. Set screening thresholds for each core indicator to identify monitoring points with excellent wind resources and controllable risks, and determine them as initial screening points for wind resources.

7. The method for assessing wind resources across a mountainous wind power area according to claim 1, characterized in that: The sixth step, which involves re-screening the initial wind resource screening points, includes the following steps: Obtain the coordinates of the initial screening points and the diameter parameters of the fan impeller. Set the safety distance standard between adjacent points based on the characteristics of the fan wake. Calculate the point spacing through the geographic information system and remove the initial screening points that do not meet the spacing standard. Determine construction cost assessment indicators including terrain flatness, transportation convenience, and foundation excavation difficulty, construct a cost scoring system to score the initial screening points, eliminate the initial screening points that do not meet the standards, and form candidate points.

8. The method for assessing wind resources across a mountainous wind power area according to claim 1, characterized in that: In step seven, when constructing the terrain-meteorological coupled interpolation model, the slope, aspect, and surface roughness obtained from the previous survey are used as weighting factors and incorporated into the Kriging interpolation algorithm to construct the terrain-meteorological coupled interpolation model. When generating a high-resolution global wind resource map, the data collected by UAVs and sensors are input into the terrain and meteorological coupled interpolation model, and the Gaussian variogram is used for interpolation calculation; a global wind resource map with a preset resolution is generated, and the global wind resource map is labeled with the average wind speed, average wind direction and turbulence intensity at multiple elevations within the resolution.

9. The method for assessing wind resources across a mountainous wind power area according to claim 1, characterized in that: Step eight, when classifying candidate sites, includes the following steps: Multiple sets of wind turbine location data with different turbulence intensities were extracted from the global wind resource map. Combined with actual power generation data of similar projects, a correlation formula between turbulence intensity and power generation loss was fitted to quantify the impact of turbulence. By fitting the correlation curve between temperature and air density based on temperature data collected by sensors, wind speed correction coefficients at different temperatures are obtained, thus achieving correction for high-altitude environments. A comprehensive scoring system was constructed, which included wind resource potential, risk control capabilities, construction costs, and terrain adaptability. Candidate sites were scored and classified into different levels.

10. The method for assessing wind resources across a mountainous wind power area according to claim 1, characterized in that: Step nine, "Based on the project's total power generation target and the estimated power generation per point, reverse-engineer the required number of installation points and determine the optimal installation points," includes the following steps: The total power generation target X of the project is determined. Based on the candidate site classification results and the corrected annual power generation per site, the number of installation sites required to meet the total power generation target is calculated by prioritizing the selection of higher-level sites. Adjust the combination of installation points based on the actual capacity of the project area to determine the optimal installation locations.

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