A blade damage repair system and method for a wind farm in a sand and gobi region

Abstract

This invention belongs to the field of wind power equipment operation and maintenance technology, and provides a blade damage repair system and method for wind farms in desert and barren areas. The system includes: a multi-functional UAV swarm for detecting damage to wind turbine blades in desert and barren areas and obtaining blade damage data; a ground-based intelligent control center for receiving blade damage data, establishing a three-dimensional damage model based on the data, and generating and sending multi-dimensional scheduling and control data to the multi-functional UAV swarm based on the three-dimensional damage model, so that the multi-functional UAV swarm can perform surface pretreatment and in-situ repair coating and curing operations in the damaged area according to the multi-dimensional scheduling and control data; and a mobile supply station for providing rapid energy supply and automatic replenishment of repair materials to the multi-functional UAV swarm. This solution improves operational safety, significantly enhances repair accuracy and efficiency, ensures the continuous operation capability of the UAV swarm in desert and barren areas, and achieves integrated closed-loop control throughout the entire process.

Description

Technical Field

[0001] This invention relates to the field of wind power equipment operation and maintenance technology, and in particular to a blade damage repair system and method for wind farms in desert areas. Background Technology

[0002] The desert and Gobi regions are core areas for the construction of large-scale wind power bases in my country. However, these areas are constantly subjected to harsh natural conditions such as strong wind and sand erosion, high-intensity ultraviolet radiation, extreme temperature differences, drought, and frequent lightning and hail. Wind turbine blades are subjected to harsh operating environments for extended periods, making them highly susceptible to damage such as leading-edge abrasion, surface cracks, coating peeling, substrate damage, and structural perforation. If damage is not detected and repaired in situ in a timely manner, it will rapidly propagate under aerodynamic loads and environmental stresses, significantly reducing the structural strength and service life of the blades.

[0003] Currently, high-altitude damage repair of wind turbine blades mainly relies on traditional methods such as large hoisting equipment, high-altitude platform operations, or manual labor suspended by ropes. These methods have several inherent drawbacks: First, they require the deployment of heavy specialized equipment, resulting in long preparation periods and extended turbine downtime, leading to significant power generation losses. Second, equipment rental, transportation, and labor costs are high, further increasing maintenance costs in remote, desert, and barren areas. Third, operations are significantly limited by terrain, wind speed, and sandstorms, resulting in poor on-site feasibility and low efficiency. Fourth, high-altitude manual labor carries extremely high safety risks, with a significant risk of personnel injury or death. Fifth, minor damage is difficult to respond to and accurately repair in a timely manner, easily leading to further damage or even blade failure.

[0004] In recent years, although drone inspection technology has been gradually applied, it can only achieve damage detection and data collection, but does not have repair capabilities. It cannot form an effective closed-loop operation system and is difficult to meet the large-scale, decentralized, and high-frequency blade damage operation and maintenance needs of the Shagohuang Wind Farm.

[0005] This shows that traditional blade damage repair solutions for wind farms in desert and barren areas suffer from technical problems such as low level of intelligence, insufficient safety and reliability, and lack of precision and efficiency. Summary of the Invention

[0006] This invention provides a blade damage repair system and method for wind farms in desert and barren areas, which solves the shortcomings of existing blade damage repair schemes for wind farms in desert and barren areas, such as low level of intelligence, insufficient safety and reliability, and lack of precision and efficiency.

[0007] On one hand, the present invention provides a blade damage repair system for wind farms in desert areas, comprising: A multi-functional drone swarm was used to detect damage to wind turbine blades in the desert region and obtain blade damage data. The ground-based intelligent control center is used to receive the blade damage data, establish a three-dimensional damage model based on the blade damage data, and generate and send multi-dimensional scheduling and control data to the multi-functional UAV cluster based on the three-dimensional damage model, so that the multi-functional UAV cluster can perform surface pretreatment and in-situ repair coating and curing operations in the damaged area according to the multi-dimensional scheduling and control data. A mobile resupply station is used to provide rapid energy replenishment and automatic replenishment of repair materials for the multi-functional UAV swarm.

[0008] According to the blade damage repair system for wind farms in the desert region provided by the present invention, the multi-functional UAV swarm includes: The detection and identification drone was used to detect damage to wind turbine blades in the desert area and obtain blade damage data. Surface treatment drones are used for surface pretreatment of damaged areas; The repair operation drone is used to perform the coating, filling, lay-up and rapid curing of repair materials based on the repair curing parameters in the multi-dimensional scheduling and control data; The detection and identification drone, surface treatment drone, and repair drone all execute their respective tasks sequentially based on the drone swarm operation data and swarm operation path in the multi-dimensional scheduling and control data.

[0009] According to the blade damage repair system for wind farms in the Shago desert region provided by the present invention, the detection and identification drone includes: a first drone body and a data acquisition component and a first data processor installed on the first drone body; The data acquisition component is used to collect measured field data of wind turbine blades in the Shagohuang area; The first data processor is used to filter the measured field data for wind and sand interference, and to perform damage location and feature recognition on the filtered data to obtain blade damage data including damage location, damage type, damage area and damage depth.

[0010] According to the blade damage repair system for wind farms in desert areas provided by the present invention, the first data processor is further configured to, upon receiving a re-inspection instruction issued by the ground intelligent control center, control the data acquisition component to collect surface measurement data of the repaired area, and, based on the surface measurement data, re-inspect the repair quality of the repaired area of ​​the wind turbine blade.

[0011] According to the blade damage repair system for wind farms in the desert region provided by the present invention, the first data processor performs a re-inspection of the repair quality of the repaired area of ​​the wind turbine blade based on the measured surface data, including: The measured surface data were compared with the three-dimensional damage model and repair curing parameters before repair to obtain the comparison results; Based on the measured surface data, defects are identified in the repaired area to obtain the defect identification results; Based on the comparison results and the defect identification results, a repair quality re-inspection result is generated.

[0012] According to the blade damage repair system for wind farms in the desert region provided by the present invention, the surface treatment drone includes: a second drone body and a high-pressure air nozzle, a flexible cleaning brush, a micro adaptive sander head and a second data processor installed on the second drone body; The second data processor is used to determine the ideal abrasion parameters based on the pre-obtained blade surface curvature and the damage depth in the blade damage data, and to generate abrasion control commands based on the ideal abrasion parameters; The high-pressure nozzle is used to perform dust removal operations on the damaged area; The flexible cleaning brush is used to perform descaling operations on damaged areas; The micro adaptive abrasive head is used to perform surface roughening treatment on the damaged area according to the abrasion control command.

[0013] According to the blade damage repair system for wind farms in the desert region provided by the present invention, the repair operation drone includes: a third drone body and a positioning robotic arm, a micro quantitative extrusion device, a UV-LED curing module and a hot air assisted curing module installed on the third drone body; The micro quantitative extrusion device is used to perform coating, filling and lay-up operations of repair material under the drive of the positioning robotic arm, based on the repair curing parameters; The UV-LED curing module is used to perform rapid curing of the repair material under the drive of the positioning robotic arm, based on the repair curing parameters. The hot air-assisted curing module is used to provide hot air assistance during the rapid curing process.

[0014] According to the blade damage repair system for wind farms in the desert region provided by the present invention, the ground-based intelligent control center establishes a three-dimensional damage model based on the blade damage data, including: The blade damage data is subjected to multi-level preprocessing to obtain preprocessed data; Spatial registration and global coordinate unification are performed on the preprocessed data to obtain calibrated data; The calibrated data is fused and calculated using a 3D reconstruction algorithm to construct a 3D point cloud model of the wind turbine blade surface. Based on a pre-built damage identification model, the damage area is segmented in the three-dimensional point cloud model of the wind turbine blade surface; Geometric calculations are performed on the damaged area to determine and mark the damage features, generating a three-dimensional damage model.

[0015] According to the blade damage repair system for wind farms in the desert region provided by the present invention, the ground-based intelligent control center generates multi-dimensional scheduling and control data based on the three-dimensional damage model, including: Damage features are analyzed on the three-dimensional damage model to determine the key damage features of the damaged area; Based on the key damage characteristics and combined with a pre-established process rule library, a drone swarm operation sequence, operation location, and operation procedure are generated to obtain drone swarm operation data. Combining pre-obtained measured meteorological data of the desert area, UAV location information, and spatial constraints, a path optimization algorithm is used to generate a collision-free, shortest-time cluster operation path. Based on the key characteristics of the damage and the preset characteristics of the repair material, the extrusion amount, number of coating layers and total material usage of the repair material are determined. According to the pre-obtained environmental parameters and combined with the pre-established process rule library, the curing data is matched and output to obtain the repair curing parameters. The data on drone swarm operations, swarm operation paths, and fixed parameters are used as multi-dimensional scheduling and control data.

[0016] On the other hand, the present invention also provides a method for repairing blade damage in wind farms in the desert region, comprising: Damage detection of wind turbine blades in the Shago wasteland was carried out by a multi-functional drone swarm to obtain blade damage data. The ground intelligent control center receives the blade damage data, establishes a three-dimensional damage model based on the blade damage data, and generates and sends multi-dimensional scheduling and control data to the multi-functional UAV cluster based on the three-dimensional damage model, so that the multi-functional UAV cluster can perform surface pretreatment and in-situ repair coating and curing operations in the damaged area according to the multi-dimensional scheduling and control data. The mobile supply station provides rapid energy replenishment and automatic replenishment of repair materials for the multi-functional UAV swarm.

[0017] The blade damage repair system and method provided by this invention for wind farms in desert and barren areas achieve fully unmanned and automated operation of wind turbine blade damage detection, pretreatment, in-situ repair, and curing under harsh environments such as strong winds, sandstorms, strong ultraviolet radiation, and extreme temperature changes in desert and barren areas through the coordinated operation of a multi-functional UAV swarm, a ground intelligent control center, and a mobile supply station. This significantly improves operational safety. It eliminates the need for large hoisting equipment and complex site deployment, enabling rapid response and flexible operation, making it more suitable for the remote and unsupported environments of desert and barren wind farms. The ground intelligent control center generates multi-dimensional scheduling and control data based on a three-dimensional damage model, achieving precise multi-machine coordination and intelligent matching of process parameters. This ensures sufficient pretreatment of the damaged area, accurate repair coating, and reliable curing, significantly improving repair accuracy and efficiency. The mobile supply station provides rapid energy and material replenishment, ensuring the continuous operation capability of the UAV swarm in the vast desert and barren areas, achieving integrated closed-loop control throughout the entire process, and significantly improving the intelligence and efficiency of blade operation and maintenance in desert and barren wind farms. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the blade damage repair system for wind farms in the desert region provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure for detecting and identifying drones in an embodiment of the present invention; Figure 3 This is a schematic diagram of the surface-treated drone in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of the repair drone in an embodiment of the present invention; Figure 5 This is a schematic flowchart of a blade damage repair method for wind farms in the desert region provided by an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0021] The following is combined with Figures 1 to 5 This invention describes in detail the blade damage repair system and method for wind farms in the desert region provided by embodiments of the present invention.

[0022] like Figure 1 As shown in the embodiment of the present invention, the blade damage repair system for wind farms in the desert region specifically includes: The multi-functional drone swarm 110 is used to detect damage to wind turbine blades in the Shago desert area and obtain blade damage data.

[0023] In this embodiment, the multi-functional UAV cluster 110 can be deployed at the wind farm site in the desert area to carry out fully automatic damage inspection of wind turbine blades. Specifically, it can collect blade surface information through airborne high-definition imaging and three-dimensional perception equipment, output blade damage data containing relevant information such as damage location, type, size, and depth, and transmit the blade damage data back to the ground intelligent control center in real time.

[0024] The ground-based intelligent control center 120 is used to receive blade damage data, establish a three-dimensional damage model based on the blade damage data, and generate and send multi-dimensional scheduling and control data to the multi-functional UAV cluster based on the three-dimensional damage model, so that the multi-functional UAV cluster can perform surface pretreatment and in-situ repair coating and curing operations in the damaged area according to the multi-dimensional scheduling and control data.

[0025] In this embodiment, the ground intelligent control center 120 can be set up at the wind farm operation and maintenance site. It can receive blade damage data uploaded by the multi-functional UAV cluster 110, construct a three-dimensional damage model based on deep learning and three-dimensional reconstruction algorithms, and generate multi-dimensional scheduling and control data including UAV operation sequence, cluster operation trajectory, and repair solidification parameters based on the three-dimensional damage model through intelligent path planning and process matching algorithms. The multi-dimensional scheduling and control data is then sent to the multi-functional UAV cluster 110.

[0026] The multi-functional drone swarm 110 can sequentially perform surface pretreatment operations such as dust removal, descaling, and grinding on the damaged area based on the received multi-dimensional scheduling and control data, as well as in-situ repair operations such as precise coating, filling, layering, and rapid curing of repair materials.

[0027] Mobile resupply station 130 is used to provide rapid energy replenishment and automatic resupply of repair materials for multi-functional drone swarms.

[0028] In this embodiment, the mobile supply station 130 can adopt a windproof and sand-proof sealed structure and an off-road mobile chassis, which can be flexibly deployed in sandy deserts and arid terrains. It is used to provide the multi-functional drone swarm 110 with fast automatic battery swapping, wireless fast charging and quantitative filling of repair materials, which can ensure the multi-functional drone swarm 110 can operate continuously for a long time.

[0029] In one embodiment, such as Figure 1 As shown, the multi-functional drone swarm 110 specifically includes: The detection and identification drone 1101 is used to detect damage to wind turbine blades in the Shagohuang area and obtain blade damage data.

[0030] The surface treatment drone 1102 is used to perform surface pretreatment on damaged areas. Specifically, it can sequentially perform high-pressure dust removal, flexible cleaning, and adaptive grinding on the damaged areas to achieve surface roughening pretreatment, thereby improving the bonding strength of the repair materials.

[0031] The 1103 repair drone is used to perform coating, filling, lay-up and rapid curing of repair materials based on the repair curing parameters in the multi-dimensional scheduling and control data. Specifically, it can complete the quantitative extrusion, precise coating, defect filling and fiber lay-up of repair materials through a high-precision actuator, and simultaneously achieve rapid curing and molding.

[0032] The detection and identification drone 1101, the surface treatment drone 1102, and the repair operation drone 1103 all execute their corresponding operation tasks in sequence based on the drone swarm operation data and swarm operation path in the multi-dimensional scheduling and control data, thereby achieving collision-free, high-efficiency, and high-precision collaborative operation.

[0033] In one embodiment, such as Figure 2 As shown, the detection and identification of drones specifically includes: a first drone body 210, a data acquisition component 220 and a first data processor 230 installed on the first drone body 210.

[0034] The data acquisition component 220 is used to collect measured field data of wind turbine blades in the desert area.

[0035] In practical applications, the data acquisition component 220 specifically includes a high-definition zoom camera, a lidar, and a 3D scanner, which are used to acquire high-definition images, point cloud data, and 3D contour data of wind turbine blades in the sandy desert environment with strong winds and strong sunlight, forming actual field data.

[0036] The first data processor 230 is used to filter the measured field data for wind and sand interference, and to perform damage location and feature recognition on the filtered data to obtain blade damage data including damage location, damage type, damage area and damage depth.

[0037] In this embodiment, the first data processor 230 has a built-in wind and sand interference suppression algorithm and damage identification model, which are used to denoise, defog, remove sand and dust obstructions and correct image distortion of the measured field data, automatically locate, segment and extract features of the filtered data, and finally output blade damage data including damage location coordinates, damage type (such as leading edge corrosion, surface crack, coating peeling, structural perforation), damage area and damage depth, and upload the blade damage data to the ground intelligent control center.

[0038] In one embodiment, the first data processor can also be used to control the data acquisition component to collect surface measurement data of the repaired area after receiving a re-inspection instruction issued by the ground intelligent control center, and to re-inspect the repair quality of the repaired area of ​​the wind turbine blade based on the surface measurement data.

[0039] In this embodiment, the first data processor also has a repair quality re-inspection control function. After the repair operation drone completes in-situ repair and curing, the ground intelligent control center can issue a re-inspection command to the detection and identification drone. After receiving the re-inspection command, the first data processor can drive the data acquisition component to fly to the repaired area, adjust the exposure, focus, and scanning reference, eliminate the influence of wind, sand, light, and shaking on the measurement accuracy, and acquire the surface image, three-dimensional point cloud, and flatness data of the repaired area to form the measured surface data after repair. Based on the measured surface data, the first data processor can compare and analyze the damage three-dimensional model before repair, the designed repair thickness, and the curing process parameters to complete the automatic judgment of repair quality and realize closed-loop control of detection-repair-re-inspection.

[0040] Specifically, the first data processor performs a re-inspection of the repair quality of the repaired areas of the wind turbine blades based on surface measurement data, including: On the one hand, the measured surface data is compared with the three-dimensional damage model and repair curing parameters before repair to obtain the comparison results.

[0041] In this embodiment, after receiving the surface measurement data of the repaired area, the first data processor can first perform spatial registration and point cloud alignment between the surface measurement data and the three-dimensional damage model generated before repair, so as to complete the one-to-one correspondence between the contour, boundary, thickness, volume of the repaired area and the damage morphology before repair; then, the measured coating thickness, surface flatness, and curing contour data are compared with the preset coating amount, curing size, and material filling thickness in the repair curing parameters, and the thickness deviation, flatness error, contour matching degree, and material filling rate are calculated to form a comparison result containing quantitative deviation values, thereby completing the quantitative verification of the repair size and process conformity.

[0042] On the other hand, based on the surface measurement data, defects are identified in the repaired areas to obtain the defect identification results.

[0043] In practical applications, the first data processor can call the built-in deep learning-based defect recognition model to perform pixel-by-pixel and point-by-point cloud defect detection on the high-definition images and 3D point clouds in the surface measurement data. It can automatically identify whether there are defects such as missing coating, insufficient coating, bubbles, pinholes, cracks, delamination, edge lifting, bonding gaps, and surface protrusions and depressions on the repaired area surface. It can also locate, mark and quantify the size of the identified defects, and generate defect recognition results including defect type, defect location, defect size and defect quantity, thus completing the qualitative and location detection of appearance and structural defects in the repair.

[0044] Finally, based on the comparison results and defect identification results, a repair quality re-inspection result is generated.

[0045] In this embodiment, the first data processor can fuse the quantitative comparison results and the qualitative defect identification results for judgment. It compares the deviation values ​​in the comparison results with the preset qualified threshold, and combines the defect identification results to determine whether there are any defects that affect the use, and comprehensively judges whether the repair area meets the repair standards. If the deviation values ​​are all within the threshold range and there are no unqualified defects, the repair is judged to be qualified. If there are out-of-tolerance or unqualified defects, the repair is judged to be unqualified, and a repair quality re-inspection result containing qualified status, deviation data, defect information, and re-inspection conclusion is generated. Then, the repair quality re-inspection result can be uploaded to the ground intelligent control center to complete the closed-loop control.

[0046] In one embodiment, such as Figure 3 As shown, the surface treatment drone specifically includes: a second drone body 310 and a high-pressure air nozzle 320, a flexible cleaning brush 330, a micro adaptive sanding head 340, and a second data processor 350 installed on the second drone body 310.

[0047] The second data processor 350 is used to determine the ideal abrasion parameters based on the pre-obtained blade surface curvature and damage depth in the blade damage data, and to generate abrasion control commands based on the ideal abrasion parameters.

[0048] The high-pressure nozzle 320 is used to perform dust removal operations on damaged areas.

[0049] The flexible cleaning brush 330 is used to perform descaling operations on damaged areas.

[0050] The miniature adaptive abrasive head 340 is used to perform surface roughening treatment on damaged areas according to abrasion control commands.

[0051] In this embodiment, the second data processor 350 can receive blade damage data and a three-dimensional damage model sent by the ground intelligent control center, and then adaptively calculate and determine ideal grinding parameters such as ideal grinding pressure, grinding speed, and grinding range based on the damage depth and blade surface curvature, and generate grinding control commands.

[0052] In actual operation, the high-pressure air nozzle 320 first blows the damaged area under the control of the second data processor 350 to remove sand, dust and loose coating; the flexible cleaning brush 330 performs fine wiping and descaling of the damaged area under the control of the second data processor 350; the micro adaptive sand grinding head 340 can perform uniform grinding and roughening treatment on the damaged area according to the grinding control command, realizing integrated pretreatment of dust removal, descaling and grinding, thereby ensuring that the repair material is firmly bonded to the blade substrate.

[0053] In one embodiment, such as Figure 4 As shown, the repair operation drone specifically includes: a third drone body 410 and a positioning robotic arm 420, a micro quantitative extrusion device 430, a UV-LED curing module 440, and a hot air-assisted curing module 450 installed on the third drone body 410.

[0054] The micro quantitative extrusion device 430 is used to perform coating, filling and lay-up operations of repair material under the drive of the positioning robotic arm 420, based on the repair curing parameters.

[0055] The UV-LED curing module 440 is used to perform rapid curing of the repair material under the drive of the positioning robotic arm 420, based on the repair curing parameters.

[0056] The hot air-assisted curing module 450 is used to provide hot air assistance during rapid curing operations.

[0057] In this embodiment, the positioning robotic arm 420 employs visual servo and high-precision closed-loop control, enabling millimeter-level precise positioning of the damaged area. The micro-quantitative extrusion device 430, driven by the positioning robotic arm 420, can precisely coat, fill, or lay up a rapidly curing repair material based on epoxy resin or polyurethane to the damaged area according to the repair curing parameters. The UV-LED curing module 440 is used for rapid light curing of the repair material, with curing time controllable from seconds to minutes. The hot air-assisted curing module can be activated in low-temperature, windy, and sandy environments, providing auxiliary heating and drying functions to improve the curing speed and bonding strength of the repair material, ensuring curing reliability in extreme desert environments.

[0058] In one embodiment, the ground-based intelligent control center establishes a three-dimensional damage model based on blade damage data, specifically including: First, the leaf damage data is preprocessed in multiple stages to obtain the preprocessed data.

[0059] In this embodiment, the high-performance computing unit of the ground intelligent control center can perform multi-level preprocessing on the blade damage data transmitted back by the detection and identification drone. Specifically, it can sequentially perform noise reduction, defogging, dust removal, illumination equalization and lens distortion correction on the image data, and perform filtering, redundant point removal, outlier removal and smoothing on the point cloud data. This can eliminate data interference caused by sand, strong light and shaking in the desert environment, and obtain clear, stable and usable preprocessed data.

[0060] Then, spatial registration and global coordinate unification are performed on the preprocessed data to obtain calibrated data.

[0061] In practical applications, the ground-based intelligent control center can spatially register multi-view, multi-frame pose images and point cloud data in the preprocessed data based on the real-time positioning information of the UAV, the attitude data of the inertial measurement unit, and the blade reference coordinates. This unifies the local data collected from different positions and angles into the global coordinate system of the wind farm, completing data alignment and attitude calibration. This can eliminate position offset and angle error, and obtain calibrated data that can be directly used for 3D reconstruction.

[0062] Next, the calibrated data is fused and calculated using a 3D reconstruction algorithm to construct a 3D point cloud model of the wind turbine blade surface.

[0063] Specifically, the ground-based intelligent control center can use a 3D reconstruction algorithm to perform multi-source data fusion calculations on the calibrated data, combining high-definition image texture information with lidar point cloud depth information to reconstruct a high-precision 3D point cloud model of the complete outer contour of the wind turbine blade, fully restoring the blade's curved surface morphology, structural dimensions, and surface details, forming a digital 3D point cloud model of the wind turbine blade surface that can be used for damage location and analysis.

[0064] Subsequently, based on the pre-built damage identification model, the damaged area was segmented in the three-dimensional point cloud model of the wind turbine blade surface.

[0065] In this embodiment, the ground intelligent control center can call a pre-trained deep learning-based damage recognition model to jointly identify and semantically segment the three-dimensional point cloud model and corresponding texture image of the wind turbine blade surface. It can automatically locate and extract various damage areas such as leading edge corrosion, surface cracks, coating peeling, and structural perforation, and complete the boundary delineation and region marking of the damage area on the three-dimensional point cloud model of the wind turbine blade surface to achieve accurate separation of the damaged area and the healthy area.

[0066] Finally, geometric calculations are performed on the damaged area to determine and mark the damage features, generating a three-dimensional damage model.

[0067] In practical applications, the ground-based intelligent control center can perform three-dimensional geometric calculations on the segmented damaged areas to obtain damage features such as damage spatial coordinates, area, depth, volume, length, width, and corresponding blade surface curvature. These feature parameters, along with the damage type and level, are then labeled into the damaged area in the three-dimensional point cloud model, forming a standardized three-dimensional damage model containing complete damage information that can be directly used for path planning and process matching.

[0068] In one embodiment, the ground intelligent control center generates multi-dimensional scheduling and control data based on the three-dimensional damage model, specifically including: First, damage features are analyzed on the three-dimensional damage model to determine the key damage features of the damaged area.

[0069] In this embodiment, the high-performance computing unit of the ground intelligent control center can perform full-domain feature analysis on the three-dimensional damage model, extracting damage spatial coordinates, geometric dimensions, area, depth, volume, edge contour, surface curvature, and damage type, forming key damage features for scheduling and process matching, thereby providing a quantitative basis for subsequent operation planning.

[0070] Then, based on the key damage characteristics and combined with the pre-established process rule library, the drone swarm operation sequence, operation points and operation procedures are generated to obtain drone swarm operation data.

[0071] In practical applications, the ground-based intelligent control center can match the key damage features obtained from the analysis with a preset process rule library, and automatically generate the operation sequence, division of labor points, and execution procedures of detection and identification drones, surface treatment drones, and repair drones according to the standard process of detection, preprocessing, repair, and re-inspection. This forms drone swarm operation data that includes task allocation, operation level, and action instructions.

[0072] Meanwhile, by combining pre-obtained measured meteorological data of the desert area, UAV location information and spatial constraints, a path optimization algorithm is used to generate a collision-free and shortest-time cluster operation path.

[0073] Meanwhile, the ground intelligent control center can access real-time meteorological data such as wind speed, wind direction, temperature, and sunlight in the desert area, and simultaneously obtain the current positioning information of each UAV. Using blade structure, safe distance, and flight airspace as spatial constraints, it adopts path optimization algorithms to perform trajectory planning and conflict resolution, generating cluster operation paths that are non-intersecting, non-collision, have the shortest flight distance, the least time, and are adaptable to wind field disturbances, thus ensuring the safe and stable collaboration of multiple UAVs.

[0074] In addition, based on the key characteristics of the damage and the preset characteristics of the repair material, the extrusion amount of the repair material, the number of coating layers and the total amount of material are determined. Based on the pre-obtained environmental parameters and combined with the pre-established process rule library, the curing data is matched and output to obtain the repair curing parameters.

[0075] In practical applications, the ground-based intelligent control center can also automatically calculate the amount of adhesive applied at a single point, the number of coating layers, the total extrusion amount, and the total material usage based on the area, depth, and volume of the key damage characteristics, combined with the density, coating efficiency, and layup requirements of the repair material. At the same time, based on the ambient temperature, wind and sand intensity, and light conditions, it can match parameters such as curing method, UV power, hot air temperature, and curing time in the process rule library to form complete repair and curing parameters.

[0076] Finally, the drone swarm operation data, swarm operation paths, and repaired and fixed parameters are used as multi-dimensional scheduling and control data.

[0077] Specifically, the ground-based intelligent control center can standardize and encapsulate the generated UAV swarm operation data, swarm operation paths, and repair and solidification parameters to form multi-dimensional scheduling and control data that includes time-series scheduling, spatial trajectory, process control, material supply, and solidification control. This data is then transmitted to the multi-functional UAV swarm via wireless communication links, thereby driving each UAV to perform repair operations in sequence and in a coordinated manner.

[0078] In summary, the blade damage repair system for wind farms in desert areas provided by this invention has at least the following advantages compared with existing solutions: First, it is extremely efficient and significantly reduces costs: It eliminates the need to call up large hoisting equipment and build platforms, shortening the repair preparation time from days to hours, greatly reducing wind turbine downtime and power generation losses, and significantly reducing overall maintenance costs.

[0079] Second, operational safety has been significantly improved: the "people on the ground, operations at high altitudes" model has been realized, avoiding the risks of high-altitude operations for maintenance personnel and effectively improving the level of safety assurance.

[0080] Third, timely repair prevents deterioration: It can quickly respond to and repair early minor damage in situ, thereby effectively preventing damage from spreading and extending the overall service life of the blade.

[0081] Fourth, the operation is precise and the quality is controllable: the visualization programming based on the 3D model and the high-precision positioning of the UAV ensure the consistency and reliability of the repair process, and the repair quality is better than that of traditional manual operation.

[0082] Fifth, strong environmental adaptability: The UAV system is less restricted by the terrain and is particularly suitable for flexible deployment and operation in the vast and complex Shagohuang wind farm.

[0083] Based on the same general inventive concept, this invention also protects a blade damage repair method for wind farms in the desert region. The blade damage repair method for wind farms in the desert region provided by this invention is described below. The blade damage repair method for wind farms in the desert region described below can be referred to in correspondence with the blade damage repair system for wind farms in the desert region described above.

[0084] like Figure 5 As shown in the embodiment of the present invention, the method for repairing blade damage in wind farms in desert areas specifically includes the following steps: Step 510: Use a multi-functional drone swarm to detect damage to wind turbine blades in the Shago wasteland area and obtain blade damage data.

[0085] Step 520: Receive blade damage data through the ground intelligent control center, establish a three-dimensional damage model based on the blade damage data, generate and send multi-dimensional scheduling and control data to the multi-functional UAV cluster based on the three-dimensional damage model, so that the multi-functional UAV cluster can perform surface pretreatment and in-situ repair coating and curing operations in the damaged area according to the multi-dimensional scheduling and control data.

[0086] Step 530: Provide rapid energy replenishment and automatic repair material replenishment to the multi-functional drone swarm via mobile resupply stations.

[0087] The specific implementation of each step in the methods described in the above embodiments has been described in detail in the embodiments of the relevant systems, and will not be elaborated further here.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A blade damage repair system for wind farms in desert and barren areas, characterized in that, include: A multi-functional drone swarm was used to detect damage to wind turbine blades in the desert region and obtain blade damage data. The ground-based intelligent control center is used to receive the blade damage data, establish a three-dimensional damage model based on the blade damage data, and generate and send multi-dimensional scheduling and control data to the multi-functional UAV cluster based on the three-dimensional damage model, so that the multi-functional UAV cluster can perform surface pretreatment and in-situ repair coating and curing operations in the damaged area according to the multi-dimensional scheduling and control data. A mobile resupply station is used to provide rapid energy replenishment and automatic replenishment of repair materials for the multi-functional UAV swarm.

2. The blade damage repair system for wind farms in desert areas according to claim 1, characterized in that, The multi-functional drone swarm includes: The detection and identification drone was used to detect damage to wind turbine blades in the desert area and obtain blade damage data. Surface treatment drones are used for surface pretreatment of damaged areas; The repair operation drone is used to perform the coating, filling, lay-up and rapid curing of repair materials based on the repair curing parameters in the multi-dimensional scheduling and control data; The detection and identification drone, surface treatment drone, and repair drone all execute their respective tasks sequentially based on the drone swarm operation data and swarm operation path in the multi-dimensional scheduling and control data.

3. The blade damage repair system for wind farms in the desert region according to claim 2, characterized in that, The detection and identification drone includes: a first drone body, a data acquisition component and a first data processor installed on the first drone body; The data acquisition component is used to collect measured field data of wind turbine blades in the Shagohuang area; The first data processor is used to filter the measured field data for wind and sand interference, and to perform damage location and feature recognition on the filtered data to obtain blade damage data including damage location, damage type, damage area and damage depth.

4. The blade damage repair system for wind farms in the desert region according to claim 3, characterized in that, The first data processor is also used to control the data acquisition component to collect surface measurement data of the repaired area after receiving the re-inspection instruction issued by the ground intelligent control center, and to re-inspect the repair quality of the repaired area of ​​the wind turbine blade based on the surface measurement data.

5. The blade damage repair system for wind farms in the desert region according to claim 4, characterized in that, The first data processor performs a re-inspection of the repair quality of the repaired area of ​​the wind turbine blade based on the measured surface data, including: The measured surface data were compared with the three-dimensional damage model and repair curing parameters before repair to obtain the comparison results; Based on the measured surface data, defects are identified in the repaired area to obtain the defect identification results; Based on the comparison results and the defect identification results, a repair quality re-inspection result is generated.

6. The blade damage repair system for wind farms in the desert region according to claim 3, characterized in that, The surface treatment drone includes: a second drone body and a high-pressure air nozzle, a flexible cleaning brush, a micro adaptive sanding head, and a second data processor mounted on the second drone body; The second data processor is used to determine the ideal abrasion parameters based on the pre-obtained blade surface curvature and the damage depth in the blade damage data, and to generate abrasion control commands based on the ideal abrasion parameters; The high-pressure nozzle is used to perform dust removal operations on the damaged area; The flexible cleaning brush is used to perform descaling operations on damaged areas; The micro adaptive abrasive head is used to perform surface roughening treatment on the damaged area according to the abrasion control command.

7. The blade damage repair system for wind farms in the desert region according to claim 2, characterized in that, The repair operation drone includes: a third drone body and a positioning robotic arm, a micro quantitative extrusion device, a UV-LED curing module, and a hot air-assisted curing module installed on the third drone body; The micro quantitative extrusion device is used to perform coating, filling and lay-up operations of repair material under the drive of the positioning robotic arm, based on the repair curing parameters; The UV-LED curing module is used to perform rapid curing of the repair material under the drive of the positioning robotic arm, based on the repair curing parameters. The hot air-assisted curing module is used to provide hot air assistance during the rapid curing process.

8. The blade damage repair system for wind farms in the desert region according to claim 1, characterized in that, Based on the blade damage data, the ground-based intelligent control center establishes a three-dimensional damage model, including: The blade damage data is subjected to multi-level preprocessing to obtain preprocessed data; Spatial registration and global coordinate unification are performed on the preprocessed data to obtain calibrated data; The calibrated data is fused and calculated using a 3D reconstruction algorithm to construct a 3D point cloud model of the wind turbine blade surface. Based on a pre-built damage identification model, the damage area is segmented in the three-dimensional point cloud model of the wind turbine blade surface; Geometric calculations are performed on the damaged area to determine and mark the damage features, generating a three-dimensional damage model.

9. The blade damage repair system for wind farms in the desert region according to claim 1, characterized in that, Based on the three-dimensional damage model, the ground-based intelligent control center generates multi-dimensional scheduling and control data, including: Damage features are analyzed on the three-dimensional damage model to determine the key damage features of the damaged area; Based on the key damage characteristics and combined with a pre-established process rule library, a drone swarm operation sequence, operation location, and operation procedure are generated to obtain drone swarm operation data. Combining pre-obtained measured meteorological data of the desert area, UAV location information and spatial constraints, a path optimization algorithm is used to generate a collision-free and shortest time-consuming cluster operation path. Based on the key characteristics of the damage and the preset characteristics of the repair material, the extrusion amount, number of coating layers and total material usage of the repair material are determined. According to the pre-obtained environmental parameters and combined with the pre-established process rule library, the curing data is matched and output to obtain the repair curing parameters. The data on drone swarm operations, swarm operation paths, and fixed parameters are used as multi-dimensional scheduling and control data.

10. A method for repairing blade damage in wind farms in desert areas, characterized in that, include: Damage detection of wind turbine blades in the Shago wasteland was carried out by a multi-functional drone swarm to obtain blade damage data. The ground intelligent control center receives the blade damage data, establishes a three-dimensional damage model based on the blade damage data, and generates and sends multi-dimensional scheduling and control data to the multi-functional UAV cluster based on the three-dimensional damage model, so that the multi-functional UAV cluster can perform surface pretreatment and in-situ repair coating and curing operations in the damaged area according to the multi-dimensional scheduling and control data. The mobile supply station provides rapid energy replenishment and automatic replenishment of repair materials for the multi-functional UAV swarm.

Images