Wind power plant high slope risk assessment method, device and equipment

Abstract

The embodiment of the invention provides a wind power plant high slope risk assessment method, device and equipment. The method comprises the following steps: acquiring a wind power plant surveying and mapping map; according to the wind power plant surveying and mapping map, draught fans with high slope risks in all draught fans in the wind power plant are recognized to serve as preliminary candidate draught fans; selecting a target turbulence model from the plurality of turbulence models; inputting the simulation wind speed of the candidate fan into the target turbulence model to obtain a turbulence result and a simulation back slope wind speed of the candidate fan; and according to the turbulence result of the candidate fan and the simulated back slope wind speed, performing high slope risk assessment on the candidate fan to determine a target fan with a high slope risk. In this way, the installation safety of the fan can be improved, and potential safety hazards of the fan can be reduced.

Description

Technical Field

[0001] The present disclosure relates to the field of wind farms, and particularly to the field of high slope risk assessment technology for wind farms. Background Art

[0002] In recent years, with the large-scale development of the wind power industry, large-scale wind farm projects are often located in complex terrain areas. During the construction process, considering cost factors, the owner and the design unit often only excavate the mountain top to meet the requirements of the fan foundation platform and construction needs and then stop the operation, which is very likely to lead to the formation of high slopes. Therefore, how to accurately identify whether there is a high slope risk for the fans in the wind farm and evaluate the impact of the high slope on the wind resources, so as to improve the safety of the fans and reduce potential safety hazards, has become an urgent problem to be solved. Summary of the Invention

[0003] The present disclosure provides a method, device, equipment and storage medium for high slope risk assessment of wind farms.

[0004] According to a first aspect of the present disclosure, a method for high slope risk assessment of a wind farm is provided. The method includes: Obtain a wind farm surveying and mapping map; According to the wind farm surveying and mapping map, identify the fans with high slope risk among the fans in the wind farm as preliminary candidate fans; Select a target turbulence model from multiple turbulence models; Input the simulated wind speed of the candidate fans into the target turbulence model to obtain the turbulence result and the simulated back slope wind speed of the candidate fans; According to the turbulence result and the simulated back slope wind speed of the candidate fans, conduct a high slope risk assessment on the candidate fans to determine the target fans with high slope risk.

[0005] As described above in the aspect and any possible implementation manner, a further implementation manner is provided. The step of identifying the fans with high slope risk among the fans in the wind farm as preliminary candidate fans according to the wind farm surveying and mapping map includes: Obtain the design parameters of each fan, where the design parameters include: fan coordinate position, construction area, and excavation depth; According to the wind farm surveying and mapping map and the design parameters, generate a target topographic map including the fans; Based on the target topographic map of the fans, identify the candidate fans among the fans.

[0006] As described above in the aspect and any possible implementation manner, a further implementation manner is provided. The step of identifying the candidate fans among the fans based on the target topographic map of the fans includes: Identify the height of the lower tip of the impeller of each wind turbine from the target topographic map; Identify the height of the highest point of the slope corresponding to each wind turbine from the target topographic map; Compare the height of the lower tip of the impeller of each wind turbine with the height of the highest point of the slope corresponding to each wind turbine; Determine, from each of the wind turbines, the wind turbines whose height of the lower tip of the impeller is lower than the height of the highest point of the corresponding slope as the candidate wind turbines.

[0007] In the above-mentioned aspects and any possible implementation manners, a further implementation manner is provided. The selection of the target turbulence model from multiple turbulence models includes: If there are multiple wind turbines among the candidate wind turbines, select pre-identified wind turbines from the multiple wind turbines based on the respective wind turbine coordinate positions of the multiple wind turbines and the coordinate of the anemometer tower of the wind farm; Obtain the first wind measurement data of the first lidar and the second wind measurement data of the second lidar. Among them, the second lidar is installed at the wind turbine coordinate position of the pre-identified wind turbine, and the first lidar is installed on the high slope side of the pre-identified wind turbine and the first lidar and the second lidar are installed at the same height; the first wind measurement data includes the incoming flow wind speed of each sector and each height layer measured by the first lidar; the second wind measurement data includes the measured back slope wind speed of each sector and each height layer measured by the second lidar; Input the first wind measurement data into the multiple turbulence models to obtain the simulated back slope wind speed output by each turbulence model; Select the target turbulence model from the multiple turbulence models according to the simulated back slope wind speed output by each turbulence model and the second wind measurement data.

[0008] In the above-mentioned aspects and any possible implementation manners, a further implementation manner is provided. The selection of the target turbulence model from the multiple turbulence models according to the simulated back slope wind speed output by each turbulence model and the second wind measurement data includes: According to the simulated back slope wind speed of each sector and each height layer output by each turbulence model and the measured back slope wind speed of each sector and each height layer, calculate the root mean square error of the wind speed of each sector corresponding to each turbulence model; Sum up the root mean square errors of the wind speed of each sector corresponding to each turbulence model to obtain the sum of the root mean square errors of the wind speed corresponding to each turbulence model; Compare the magnitudes of the sums of the root mean square errors of the wind speed corresponding to each turbulence model; Select the turbulence model with the smallest sum of the root mean square errors of the wind speed as the target turbulence model.

[0009] For the aspects and any possible implementation manners described above, a further implementation manner is provided. The method for performing a high slope risk assessment on a candidate wind turbine according to the turbulence result of the candidate wind turbine and the simulated back slope wind speed to determine a target wind turbine with a high slope risk includes: Obtain a high slope risk assessment table, where the corresponding relationship between turbulence, wind speed, and wind turbine life is recorded in the high slope risk assessment table; Search in the high slope risk assessment table according to the turbulence result of the candidate wind turbine and the simulated back slope wind speed to obtain the assessed life of the candidate wind turbine; Compare the assessed life of the candidate wind turbine with the preset life of the candidate wind turbine; Screen out the wind turbines with an assessed life lower than the preset life from the candidate wind turbines as the target wind turbines.

[0010] For the aspects and any possible implementation manners described above, a further implementation manner is provided. Obtain the surface roughness of the location where each wind turbine is located; Determine whether the surface roughness is greater than a preset roughness; If the surface roughness is greater than the preset roughness, then inputting the simulated wind speed of the candidate wind turbine into the target turbulence model to obtain the turbulence result and the simulated back slope wind speed of the candidate wind turbine includes: Obtain a preset forest canopy model; Input the simulated wind speed into the preset forest canopy model to obtain the drag force of the plants at the location where each wind turbine is located on the simulated wind speed; Input the simulated wind speed of the candidate wind turbine and the drag force into the target turbulence model to obtain the turbulence result and the simulated back slope wind speed of the candidate wind turbine.

[0011] According to the second aspect of the present disclosure, a high slope risk assessment device for a wind farm is provided. The device includes: An acquisition module, configured to acquire a wind farm surveying and mapping map; An identification module, configured to identify the wind turbines with high slope risks among the wind turbines in the wind farm as preliminary candidate wind turbines according to the wind farm surveying and mapping map; A selection module, configured to select a target turbulence model from multiple turbulence models; An input module, configured to input the simulated wind speed of the candidate wind turbine into the target turbulence model to obtain the turbulence result and the simulated back slope wind speed of the candidate wind turbine; An assessment module, configured to perform a high slope risk assessment on the candidate wind turbine according to the turbulence result and the simulated back slope wind speed of the candidate wind turbine to determine a target wind turbine with a high slope risk.

[0012] According to a third aspect of the present disclosure, an electronic device is provided. The electronic device includes: a memory and a processor, where a computer program is stored on the memory, and when the processor executes the program, the methods described above are implemented.

[0013] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method according to the first aspect of the present disclosure is implemented.

[0014] In the present disclosure, after obtaining the wind farm surveying and mapping map, the wind turbines with high slope risks in each wind turbine in the wind farm can be identified as preliminary candidate wind turbines according to the wind farm surveying and mapping map, and a target turbulence model can be selected from multiple turbulence models, and then the simulated wind speed of the candidate wind turbines is input into the target turbulence model to obtain the turbulence results and simulated back slope wind speeds of the candidate wind turbines. Furthermore, based on the turbulence results and simulated back slope wind speeds of the candidate wind turbines, a high slope risk assessment is performed on the candidate wind turbines to determine the target wind turbines with high slope risks. In this way, the candidate wind turbines with high slope risks can be initially judged from multiple designed wind turbines, and then the impact of the high slope on the wind resources at the candidate wind turbines is evaluated to determine those target wind turbines with high slope risks, thereby improving the safety of wind turbine installation and reducing potential safety hazards of the wind turbines.

[0015] It should be understood that the content described in the summary of the invention is not intended to limit the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] With reference to the accompanying drawings and the following detailed description, the above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent. The drawings are used to better understand the solution and do not constitute a limitation to the present disclosure. In the drawings, the same or similar reference numerals represent the same or similar elements, where: Figure 1 A flowchart showing a method for assessing high slope risks in a wind farm according to an embodiment of the present disclosure is shown; Figure 2 A flowchart showing another method for assessing high slope risks in a wind farm according to an embodiment of the present disclosure is shown; Figure 3 A flowchart showing yet another method for assessing high slope risks in a wind farm according to an embodiment of the present disclosure is shown; Figure 4 A schematic installation diagram of a first lidar and a second lidar relative to a high slope of a pre-identified wind turbine according to an embodiment of the present disclosure is shown.

[0017] Figure 5The block diagram of a high slope risk assessment device for a wind farm according to an embodiment of the present disclosure is shown; Figure 6 The block diagram of an exemplary electronic device capable of implementing the embodiments of the present disclosure is shown. Detailed implementation manners

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Apparently, the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present disclosure without creative efforts shall fall within the protection scope of the present disclosure.

[0019] In addition, the term "and / or" in this document is merely a description of the associated relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after.

[0020] Figure 1 The flowchart of a high slope risk assessment method 100 for a wind farm according to an embodiment of the present disclosure is shown. Method 100 may include: Step 110, obtaining a wind farm surveying and mapping map; The wind farm surveying and mapping map is a map obtained by surveying and mapping a wind farm. Contour lines are marked on this map, which is also called a contour map, such as CGCS2000.

[0021] There are also designed fan positions and the like in the wind farm surveying and mapping map.

[0022] Step 120, according to the wind farm surveying and mapping map, identifying the fans with high slope risks among the fans in the wind farm as preliminary candidate fans; Each fan in the wind farm is only a fan designed based on the wind farm surveying and mapping map, not an already installed fan. The application scenario of the present invention is to identify high slopes and evaluate high slope wind resources for each fan in a wind farm where the fans are designed but not yet installed, so as to determine which designed fan positions in the wind farm cannot install fans, or need to install fans with excellent performance and durability.

[0023] In addition, the high slopes of the present invention may be the high slopes existing in the wind farm surveying and mapping map itself or the high slopes formed after simulating construction at the fan positions according to the fan design parameters; Step 130, selecting a target turbulence model from multiple turbulence models; The multiple turbulence models include but are not limited to - 、 - 、 SST turbulence model.

[0024] Step 140, input the simulated wind speed of the candidate wind turbine into the target turbulence model to obtain the turbulence result and the simulated backslope wind speed of the candidate wind turbine; The simulated wind speed can be the wind speed at different sectors and different height levels at the location of the candidate wind turbine. One height level can be 10 meters, for example, from the ground to the height of the wind turbine hub, and every 10 meters is a height level. One sector can be 30° or 22.5°.

[0025] Step 150, conduct a high slope risk assessment on the candidate wind turbine according to the turbulence result and the simulated backslope wind speed of the candidate wind turbine to determine the target wind turbine with high slope risk. Among them, the high slope risk assessment is to evaluate the impact of the high slope near the candidate wind turbine on the wind resource. If the impact is large, it means that this wind turbine is indeed the target wind turbine with high slope risk, and this location is not suitable for installing a wind turbine or is not suitable for installing a wind turbine with general performance and low durability.

[0026] After obtaining the wind farm surveying and mapping map, based on the wind farm surveying and mapping map, identify the wind turbines with high slope risk among the wind turbines in the wind farm as preliminary candidate wind turbines, select the target turbulence model from multiple turbulence models, then input the simulated wind speed of the candidate wind turbine into the target turbulence model to obtain the turbulence result and the simulated backslope wind speed of the candidate wind turbine, and then conduct a high slope risk assessment on the candidate wind turbine according to the turbulence result and the simulated backslope wind speed of the candidate wind turbine to determine the target wind turbine with high slope risk. In this way, not only can the candidate wind turbines with high slope risk be initially judged from multiple designed wind turbines, and then the impact of the high slope at the candidate wind turbine on the wind resource can be evaluated to determine the target wind turbines with high slope risk. If any, the target wind turbine can be replaced with a wind turbine with better performance, or a wind turbine can be not installed at the location of the target wind turbine, thereby improving the safety of wind turbine installation and reducing potential safety hazards of the wind turbine.

[0027] In some embodiments, the identifying the wind turbines with high slope risk among the wind turbines in the wind farm as preliminary candidate wind turbines according to the wind farm surveying and mapping map includes: Obtain the design parameters of each wind turbine, where the design parameters include: wind turbine coordinate position, construction area, and excavation depth; Generate a target topographic map including the wind turbines according to the wind farm surveying and mapping map and the design parameters; both the target topographic map and the wind farm surveying and mapping map are 3D maps, but the target topographic map is more convenient to view and has a better effect, such as a satellite map.

[0028] Identify candidate wind turbines among the respective wind turbines based on the target topographic map of the wind turbines.

[0029] Based on the wind farm survey map and the design parameters, map conversion, wind turbine excavation simulation, and simulation of the terrain after excavation can be performed to obtain a target topographic map containing the wind turbines. Then, based on the target topographic map of the wind turbines, it is beneficial to accurately identify candidate wind turbines among the respective wind turbines.

[0030] In some embodiments, the identifying candidate wind turbines among the respective wind turbines based on the target topographic map of the wind turbines includes: Identify the height of the lower tip of the impeller of each wind turbine from the target topographic map; The lower impeller of each wind turbine refers to the impeller among the multiple impellers of each wind turbine whose tip points to the ground, and the height of the lower tip of the impeller refers to the height of the lower tip from the ground.

[0031] Identify the height of the highest point of the slope corresponding to each wind turbine from the target topographic map; The height of the highest point of the slope corresponding to each wind turbine refers to the altitude of the highest point of the slope near the location of each wind turbine. For example, it is the altitude of the highest point of the slope within a circular area with a radius of 15 meters centered on the location of each wind turbine.

[0032] Compare the height of the lower tip of the impeller of each wind turbine with the height of the highest point of the slope corresponding to each wind turbine; Determine, from the respective wind turbines, the wind turbines whose height of the lower tip of the impeller is lower than the height of the highest point of the corresponding slope as the candidate wind turbines.

[0033] The height of the lower tip of the impeller of each wind turbine and the height of the highest point of the slope corresponding to each wind turbine can be identified from the target topographic map, and then the two are compared. If the height of the lower tip of the impeller of a certain wind turbine is lower than the height of the highest point of the corresponding slope, it indicates that the impeller will touch the slope during rotation, and this slope belongs to a high slope for this wind turbine. The wind turbine at this location initially has a high slope risk. Therefore, this wind turbine can be initially used as a candidate wind turbine.

[0034] In some embodiments, the selecting a target turbulence model from multiple turbulence models includes: If the candidate wind turbines include multiple wind turbines, select a pre-identified wind turbine from the multiple wind turbines based on the respective wind turbine coordinate positions of the multiple wind turbines and the coordinate of the anemometer tower of the wind farm; The pre-identified wind turbine is the wind turbine located in the middle of the multiple wind turbine coordinate positions and close to the anemometer tower among the multiple wind turbines.

[0035] Obtain the first wind measurement data of the first lidar and the second wind measurement data of the second lidar. Among them, the second lidar is installed at the fan coordinate position of the pre-identified fan, and the first lidar is installed on the high slope side of the pre-identified fan and at the same height as the second lidar; the first wind measurement data includes the oncoming flow wind speeds at each height layer of each sector measured by the first lidar; the second wind measurement data includes the measured back slope wind speeds at each height layer of each sector measured by the second lidar. The installation positions of the first lidar and the second lidar are as Figure 4 shown.

[0036] Input the first wind measurement data into the multiple turbulence models to obtain the simulated back slope wind speeds output by each turbulence model. Select a target turbulence model from the multiple turbulence models according to the simulated back slope wind speeds output by each turbulence model and the second wind measurement data.

[0037] By inputting the first wind measurement data into the multiple turbulence models respectively, the simulated back slope wind speeds output by each turbulence model can be obtained. Then, compare the simulated back slope wind speeds output by each turbulence model with the second wind measurement data to obtain a comparison result. Furthermore, according to the comparison result, select the target turbulence model with the highest simulation accuracy from the multiple turbulence models to improve the accuracy of the assessment of the high slope risk of the fan.

[0038] In some embodiments, the step of selecting a target turbulence model from the multiple turbulence models according to the simulated back slope wind speeds output by each turbulence model and the second wind measurement data includes: According to the simulated back slope wind speeds at each height layer of each sector output by each turbulence model and the measured back slope wind speeds at each height layer of each sector, calculate the root mean square error of the wind speeds of each sector corresponding to each turbulence model. Sum up the root mean square errors of the wind speeds of each sector corresponding to each turbulence model to obtain the sum of the root mean square errors of the wind speeds corresponding to each turbulence model. Compare the magnitudes of the sums of the root mean square errors of the wind speeds corresponding to each turbulence model. Select the turbulence model with the smallest sum of the root mean square errors of the wind speeds from the various turbulence models as the target turbulence model.

[0039] Based on the simulated backslope wind speeds at each height layer of each sector output by each turbulence model and the measured backslope wind speeds at each height layer of each sector, the root mean square error of the wind speed for each sector corresponding to each turbulence model can be obtained. Then, the root mean square errors of the wind speed for each sector corresponding to each turbulence model are summed to obtain the sum of the root mean square errors of the wind speed corresponding to each turbulence model. Furthermore, the sums of the root mean square errors of the wind speed corresponding to different turbulence models are compared in terms of magnitude, and then the turbulence model with the smallest sum of the root mean square errors of the wind speed is selected as the target turbulence model to select the optimal turbulence model through the root mean square error of the wind speed.

[0040] In some embodiments, the high-slope risk assessment of the candidate wind turbines according to the turbulence results and simulated backslope wind speeds of the candidate wind turbines to determine the target wind turbines with high-slope risks includes: Obtain a high-slope risk assessment table, wherein the corresponding relationships among turbulence, wind speed, and wind turbine life are recorded in the high-slope risk assessment table; Search in the high-slope risk assessment table according to the turbulence results and simulated backslope wind speeds of the candidate wind turbines to obtain the evaluated life of the candidate wind turbines; Compare the evaluated life of the candidate wind turbines with the preset life of the candidate wind turbines; Screen out the wind turbines with an evaluated life lower than the preset life from the candidate wind turbines as the target wind turbines.

[0041] After obtaining the high-slope risk assessment table, it is possible to search in the high-slope risk assessment table based on the turbulence results and simulated backslope wind speeds of the candidate wind turbines, so as to obtain the evaluated life of the candidate wind turbines. Then, the evaluated life of the candidate wind turbines is automatically compared with the preset life of the candidate wind turbines. If the evaluated life of a certain wind turbine is lower than the preset life, it means that installing a wind turbine at the location of this wind turbine will cause the wind turbine to be damaged quickly and have a short life. Therefore, the wind turbines with an evaluated life lower than the preset life among the candidate wind turbines are determined as the target wind turbines that actually have high-slope risks.

[0042] In some embodiments, obtain the surface roughness at the locations of each wind turbine; Judge whether the surface roughness is greater than the preset roughness; If the surface roughness is greater than the preset roughness, then inputting the simulated wind speed of the candidate wind turbines into the target turbulence model to obtain the turbulence results and simulated backslope wind speeds of the candidate wind turbines includes: Obtain a preset forest canopy model; Input the simulated wind speed into the preset forest canopy model to obtain the drag force of the plants at the locations of each wind turbine on the simulated wind speed; Since there may be some plants in a wind farm, when the plants are relatively tall and lush, the surface of the location where they are located will be relatively rough. Once the surface roughness is high, these plants will affect the wind resources of nearby wind turbines, such as dragging the wind force and affecting the wind volume. Therefore, if the surface roughness is greater than the preset roughness, the drag force of the plants at the location of the wind turbine can be obtained by using the preset forest canopy model first, and then the simulated wind speed and the drag force of the candidate wind turbine are both input into the target turbulence model to obtain the turbulence result and the simulated back slope wind speed of the candidate wind turbine under the influence of the high slope and plants, improving the accuracy of the turbulence result.

[0043] Specifically as follows: When the surface roughness at the location of a certain wind turbine is above 0.1, it is necessary to confirm the vegetation type at the location of the wind turbine through the target topographic map and add the forest canopy model during the simulation. The formula for calculating the drag force in the forest canopy model is as follows: The formula for the surface drag force is: , is the air density, is the surface drag coefficient, is the wind speed, such as takes 1.10, the surface drag coefficient takes 1, takes 6 m / s.

[0044] The formula for the volume drag force is: , is the air density, is the volume drag coefficient, is the wind speed, such as takes 1.10, the volume drag coefficient takes 0.5, takes 6 m / s.

[0045] Among them, the surface drag force is used to characterize the drag force of low plants on the wind speed, and the volume drag force is used to characterize the drag force of high plants on the wind speed.

[0046] In addition, the thermal stability level can also be set. Then, when inputting to the target turbulence model, the simulated wind speed, the drag force, and the thermal stability level of the candidate wind turbine can be input into the target turbulence model together, so as to obtain a more accurate turbulence result and simulated back slope wind speed; among them, the thermal stability level represents the intensity of heat exchange between the upper and lower layers, and the convection between the upper and lower layers will affect the wind, while the drag force is used to characterize the influence of plants on the wind in the left-right (or front-back) direction. Specifically, the thermal stability level can calculate the solar altitude angle using the Pasquill classification method, and then find the corresponding stability level in the corresponding table according to the solar radiation level number and wind speed corresponding to the solar altitude angle.

[0047] The following will be combined withFigure 2 A description of the high-slope risk assessment method for a wind farm of the present invention is as follows: Step S1: Accurately mark the coordinates of each designed wind turbine in the wind farm on the topographic map of the wind farm surveying and mapping. Analyze the topographic changes after excavating the wind turbine platform, and then conduct pre-identification of slope risks to obtain candidate wind turbines. The specific operations include importing the topographic map of the wind farm surveying and mapping and the coordinates of the wind turbine positions, and combining the construction area of each wind turbine and the required excavation depth to generate a topographic map of the foundation platform containing the wind turbines (i.e., the target topographic map, and the grid resolution of this target topographic map is less than 5 meters). Use this topographic map to identify the risks of high-slope wind turbine positions. The risk identification criterion is: If the height of the lower tip of the impeller of a certain wind turbine is lower than the height of the highest point of the slope, then this wind turbine will be pre-identified as a wind turbine with slope risks, and this wind turbine belongs to the candidate wind turbines. There can be multiple candidate wind turbines. Among them, the surveying and mapping map can be a micro-topographic map containing the center points of each wind turbine with a radius of not less than 1 Km and a scale not greater than 1:1000. The formats include but are not limited to dxf, shp, tiff, map, etc.

[0048] Step S2: Select representative pre-identified wind turbines from the candidate wind turbines, and use different turbulence models to evaluate the wind resource conditions of the pre-identified wind turbines to obtain the target turbulence model with the best performance.

[0049] The core idea of selecting the target turbulence model is to select the most suitable turbulence model by identifying the parameter characteristics of the airflow separation point on the leeward slope of the high slope, so as to improve the simulation accuracy of the wind resources of the oncoming flow in the corresponding wind direction of the leeward slope. The specific implementation steps are as Figure 3 shown: Step P1: Conduct lidar wind measurement on the pre-identified wind turbines in the wind farm. In a wind farm with multiple risk positions of wind turbines, select a representative pre-identified position in the middle for wind measurement. There are two lidar devices: One is marked as F1 and is installed in the oncoming flow direction of the high slope of the wind turbine; the other is marked as F2 and is set at the center point of the pre-identified wind turbine. As Figure 4 shown, lidar F1 and lidar F2 are installed at positions 1 in the oncoming flow direction of the high slope and 2 behind the slope respectively, and continuous wind measurement work is carried out for no less than 3 months.

[0050] Step P2: Use the wind speed measured by F2 in each sector from near the lower tip to the hub center as the measured leeward slope wind speed; at the same time, use the oncoming flow wind speed measured by F1 in each sector as the input data for multiple turbulence models, and obtain the simulated leeward slope wind speed output by the multiple turbulence models.

[0051] The wind measurement height covers from the tip height to the hub height, and the wind speed and wind direction data are recorded every 10 meters.

[0052] Through statistical analysis of lidar data, with the oncoming wind direction of the F1 lidar as the benchmark, a sector is divided every 30° for statistics. For example, the measured oncoming wind speed at each 10m height within the statistical sector of 255° < statistical sector ≤ 285° is statistically analyzed and denoted as , and at the same time, the measured back slope wind speed within the same sector of the F2 lidar behind the slope at the same moment is statistically analyzed , where the value of i is a positive integer, starting from 1, representing the i-th height layer from the tip height of the lower blade of the impeller to the hub center height, and each height layer is 10 meters.

[0053] Respectively apply - , - , SST and other turbulence models, and combine with the measured oncoming wind speed of F1 at the same time period for simulation. The simulation results represent the simulated back slope wind speed of the leeward slope of the high slope where the fan is located, and are respectively denoted as , .

[0054] Among them, when obtaining the simulated back slope wind speed, the roughness and thermal stability level can be combined, that is, when is input into the turbulence model, the drag force corresponding to the roughness and the thermal stability level can be input into the turbulence model together to improve the accuracy of the simulated back slope wind speed.

[0055] Among them, when the surface roughness near the fan is above 0.1, the forest canopy model needs to be used to calculate the drag force of plants near the fan on the wind. The surface roughness is derived from surface roughness data, which includes but is not limited to the ESA or UCL roughness databases, and the resolution should not be lower than 300m resolution. The format includes but is not limited to kmz, shp, dxf and other formats.

[0056] The value of the thermal stability level is not limited to methods such as the Pasquill classification method, Richardson number, and static stability. For example, the Pasquill classification method can be used to calculate the solar altitude angle, and according to the solar radiation grade number and ground wind speed, the corresponding thermal stability level can be found in the corresponding table.

[0057] Step P3: For the oncoming wind speed of each sector measured by F1, compare the simulated back slope wind speeds of multiple turbulence models with the measured back slope wind speeds, and calculate the root mean square error (RMSE) as the evaluation criterion for model optimization.

[0058] Step P4: Continuously perform repeated iteration on each sector, execute the above Step P3 until the root mean square errors corresponding to all sectors are obtained, and select the turbulence model with the smallest root mean square error as the final target turbulence model.

[0059] The RMSE is used to judge different turbulence models. The RMSE index statistics for the same altitude layer at different times in different sectors are as follows:

[0060] For each sector, the simulated backslope wind speed at different altitude layers and the measured backslope wind speed are used to calculate the absolute average error of each sector. Among them, is the simulated backslope wind speed obtained by inputting into the turbulence model.

[0061] Repeat the above iterative process for each sector to obtain the of each sector under each model, calculate the sum of the of each sector under each model, and determine the best turbulence model based on the minimum value of the sum .

[0062] Step S3: Use the target turbulence model to simulate the turbulence results and simulated backslope wind speed corresponding to the simulated wind speed of the candidate wind turbine in different sectors.

[0063] Of course, when obtaining the turbulence results and simulated backslope wind speed, the drag force and thermal stability level corresponding to the surface roughness of the location where the wind turbine is located can also be combined, that is, the simulated wind speed of different sectors and the drag force and thermal stability level corresponding to the surface roughness of the location where the wind turbine is located are input into the target turbulence model together to improve the accuracy of the turbulence results and simulated backslope wind speed.

[0064] Step S4: Perform load safety verification according to the turbulence results and backslope wind speed to obtain the evaluation life of the candidate wind turbine, and then evaluate the safety risk of the high-slope wind turbine to determine the wind turbines that actually have high-slope risks, so as to provide a scientific basis for the high-slope risk assessment of the wind farm and ensure the long-term stable operation of the wind farm.

[0065] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present disclosure is not limited by the described action sequence, because according to the present disclosure, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to the present disclosure.

[0066] The above is the introduction of the method embodiments. The following further illustrates the solution of the present disclosure through device embodiments.

[0067] Figure 5The block diagram of a high slope risk assessment device 500 for a wind farm according to an embodiment of the present disclosure is shown. As Figure 5 shown, the device 500 includes: An acquisition module 510, configured to acquire a wind farm surveying and mapping map; An identification module 520, configured to identify, according to the wind farm surveying and mapping map, the wind turbines with high slope risks among the wind turbines in the wind farm as preliminary candidate wind turbines; A selection module 530, configured to select a target turbulence model from multiple turbulence models; An input module 540, configured to input the simulated wind speed of the candidate wind turbines into the target turbulence model to obtain the turbulence result and the simulated back slope wind speed of the candidate wind turbines; An evaluation module 550, configured to perform a high slope risk assessment on the candidate wind turbines according to the turbulence result and the simulated back slope wind speed of the candidate wind turbines to determine the target wind turbines with high slope risks.

[0068] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the described modules can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0069] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a non-transitory computer-readable storage medium storing computer instructions.

[0070] Figure 6 The schematic block diagram of an electronic device 800 that can be used to implement the embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processing device, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only for illustration and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0071] The device 800 includes a computing unit 801, which can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 802 or the computer program loaded from the storage unit 808 into the random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other through a bus 804. The input / output (I / O) interface 805 is also connected to the bus 804.

[0072] Multiple components in device 800 are connected to I / O interface 805, including: input unit 806, such as a keyboard, mouse, etc.; output unit 807, such as various types of displays, speakers, etc.; storage unit 808, such as a disk, optical disc, etc.; and communication unit 809, such as a network card, modem, wireless communication transceiver, etc. Communication unit 809 allows device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0073] Computing unit 801 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of computing unit 801 include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Computing unit 801 executes the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed onto device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by computing unit 801, one or more steps of method 100 described above can be executed. Alternatively, in other embodiments, computing unit 801 can be configured to execute method 100 in any other suitable manner (e.g., by means of firmware).

[0074] The various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), system on a chip systems (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special or general-purpose programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0075] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general purpose computer, a special purpose computer, or other programmable data processing device, such that the program codes, when executed by the processor or controller, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0076] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0077] In order to provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0078] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), and the Internet.

[0079] The computing system can include clients and servers. The clients and servers are generally far from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, a server of a distributed system, or a server incorporating blockchain.

[0080] It should be understood that the various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and no limitation is imposed herein.

[0081] The above specific embodiments do not constitute a limitation on the protection scope of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the protection scope of this disclosure.

Claims

1. A method for risk assessment of high slopes in a wind farm, characterized in that, Including: Obtain the surveying and mapping map of the wind farm; According to the surveying and mapping map of the wind farm, identify the wind turbines with high slope risk among the wind turbines in the wind farm as preliminary candidate wind turbines; Select a target turbulence model from multiple turbulence models; Input the simulated wind speed of the candidate wind turbines into the target turbulence model to obtain the turbulence result and the simulated back slope wind speed of the candidate wind turbines; According to the turbulence result and the simulated back slope wind speed of the candidate wind turbines, conduct a high slope risk assessment on the candidate wind turbines to determine the target wind turbines with high slope risk.

2. The method according to claim 1, characterized in that The step of according to the surveying and mapping map of the wind farm, identifying the wind turbines with high slope risk among the wind turbines in the wind farm as preliminary candidate wind turbines includes: Obtain the design parameters of each wind turbine, where the design parameters include: wind turbine coordinate position, construction area, and excavation depth; According to the surveying and mapping map of the wind farm and the design parameters, generate a target topographic map including the wind turbines; Based on the target topographic map of the wind turbines, identify the candidate wind turbines among the wind turbines.

3. The method according to claim 2, characterized in that The step of based on the target topographic map of the wind turbines, identifying the candidate wind turbines among the wind turbines includes: Identify the height of the lower tip of the impeller of each wind turbine from the target topographic map; Identify the height of the highest point of the slope corresponding to each wind turbine from the target topographic map; Compare the height of the lower tip of the impeller of each wind turbine with the height of the highest point of the slope corresponding to each wind turbine; Determine the wind turbines with the height of the lower tip of the impeller lower than the height of the highest point of the corresponding slope among the wind turbines as the candidate wind turbines.

4. The method according to claim 1, characterized in that The step of selecting a target turbulence model from multiple turbulence models includes: If there are multiple wind turbines among the candidate wind turbines, based on the respective wind turbine coordinate positions of the multiple wind turbines and the coordinate of the anemometer tower of the wind farm, select a pre-identified wind turbine from the multiple wind turbines; Obtain the first wind measurement data of the first lidar and the second wind measurement data of the second lidar, where the second lidar is installed at the wind turbine coordinate position of the pre-identified wind turbine, the first lidar is installed on the high slope side of the pre-identified wind turbine and the first lidar and the second lidar are installed at the same height; the first wind measurement data includes the incoming flow wind speed of each sector and each height layer measured by the first lidar; the second wind measurement data includes the measured back slope wind speed of each sector and each height layer measured by the second lidar; Input the first wind measurement data into the multiple turbulence models to obtain the simulated back slope wind speed output by each turbulence model; According to the simulated back slope wind speed output by each turbulence model and the second wind measurement data, select a target turbulence model from the multiple turbulence models.

5. The method according to claim 4, characterized in that The step of according to the simulated back slope wind speed output by each turbulence model and the second wind measurement data, selecting a target turbulence model from the multiple turbulence models includes: According to the simulated backslope wind speeds at each height layer of each sector output by each turbulence model and the measured backslope wind speeds at each height layer of each sector, calculate the root mean square error of wind speed for each sector corresponding to each turbulence model; Sum up the root mean square errors of wind speed for each sector corresponding to each turbulence model to obtain the sum of the root mean square errors of wind speed corresponding to each turbulence model; Compare the magnitudes of the sums of the root mean square errors of wind speed corresponding to each turbulence model; Select the turbulence model with the smallest sum of the root mean square errors of wind speed from each turbulence model as the target turbulence model.

6. The method according to claim 1, wherein the high slope risk assessment of the candidate wind turbines based on the turbulence results and simulated backslope wind speeds of the candidate wind turbines to determine the target wind turbines with high slope risks includes: Obtain a high slope risk assessment table, wherein the corresponding relationships among turbulence, wind speed, and wind turbine life are recorded in the high slope risk assessment table; Search in the high slope risk assessment table according to the turbulence results and simulated backslope wind speeds of the candidate wind turbines to obtain the evaluated life of the candidate wind turbines; Compare the evaluated life of the candidate wind turbines with the preset life of the candidate wind turbines; Screen out the wind turbines with an evaluated life lower than the preset life from the candidate wind turbines as the target wind turbines.

7. The method according to any one of claims 1 to 6, wherein Obtain the surface roughness at the locations of each wind turbine; Judge whether the surface roughness is greater than the preset roughness; If the surface roughness is greater than the preset roughness, then the step of inputting the simulated wind speed of the candidate wind turbine into the target turbulence model to obtain the turbulence results and simulated backslope wind speeds of the candidate wind turbine includes: Obtain a preset forest canopy model; Input the simulated wind speed into the preset forest canopy model to obtain the drag force of plants on the simulated wind speed at the locations of each wind turbine; Input the simulated wind speed and the drag force of the candidate wind turbine into the target turbulence model to obtain the turbulence results and simulated backslope wind speeds of the candidate wind turbine.

8. A risk assessment device for a high slope of a wind farm, characterized in that, including: An acquisition module for acquiring a wind farm surveying and mapping map; An identification module for identifying, according to the wind farm surveying and mapping map, the wind turbines with high slope risks among the wind turbines in the wind farm as preliminary candidate wind turbines; A selection module for selecting a target turbulence model from multiple turbulence models; An input module for inputting the simulated wind speed of the candidate wind turbine into the target turbulence model to obtain the turbulence results and simulated backslope wind speeds of the candidate wind turbine; An evaluation module for performing a high slope risk assessment on the candidate wind turbine according to the turbulence results and simulated backslope wind speeds of the candidate wind turbine to determine the target wind turbines with high slope risks.

9. An electronic device, characterized in that, including: A memory and a processor, wherein a computer program is stored on the memory, and when the processor executes the program, the method according to any one of claims 1 - 7 is implemented.

10. A computer-readable storage medium, wherein When the instructions in the storage medium are executed by the processor corresponding to the electronic device, the electronic device can implement the high-slope risk assessment method for a wind farm as described in any one of claims 1-7.

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