Method for measuring an attitude parameter of a wind turbine

Abstract

The application provides a posture parameter measurement method of a wind power generator, and belongs to the technical field of posture monitoring, and comprises the following steps: determining the current angular displacement of each blade in a wind turbine in real time, and shooting a site picture including multiple blades by means of a UAV; and generating a standard picture under the condition that the blades are not disturbed by external interference and are in normal operation at the current position of the UAV. By means of big data and artificial intelligence and according to the influence degree of force on the posture of the blades, whether the difference between the site picture and the standard picture conforms to the expectation is analyzed, if the difference conforms to the expectation, the posture parameters of the wind turbine and the blades are generated according to the standard picture and the site picture; otherwise, the cause of the difference is analyzed, and the factors and positions of the problems are simulated. The posture parameter measurement method of the wind power generator can effectively compare the states of the blades, can timely judge the cause and position of the problems, has low cost, and has great reference significance.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of posture monitoring, and more particularly to a method for measuring the posture parameters of a wind turbine. BACKGROUND

[0002] With the iterative progress of wind power complete machine technology, wind power has become the most widely used new energy power generation method. As one of the key components of a wind turbine, a wind power blade plays a role in capturing wind energy in a wind turbine. Wind energy drives the rotation of the wind wheel, prompting the generator to complete the conversion of wind energy to electric energy. The blade is extremely vulnerable to damage due to its harsh working environment of being exposed to the outdoors all year round. Therefore, regular detection and maintenance of the wind power blade plays a crucial role in the entire wind power generation process.

[0003] Traditional blade inspection is mostly done manually, which is labor-intensive, has a long downtime, and poses a safety risk to personnel. With the development of technology, unmanned aerial vehicles (UAVs) are increasingly being used for inspection work. However, existing UAVs can only monitor the appearance of wind blades by taking pictures, but the more serious problem with blades is their internal connection state. Moreover, some safety issues may be more concealed when the blade rotation angle is different, which makes the data obtained by UAV inspection not intuitive and of little reference value, and it is difficult to effectively analyze the state of the blade and determine the possible problems and their locations. SUMMARY

[0004] The present application aims to provide a method for measuring the posture parameters of a wind turbine, which aims to solve the problem that the data obtained by UAV inspection is not intuitive and has little reference value, and the problem of being unable to determine the blade problems and their locations.

[0005] To achieve the above-mentioned purpose, the technical solution adopted by the present application is to provide a method for measuring the posture parameters of a wind turbine, comprising:

[0006] Starting the UAV and making it stay in the upwind direction of the target wind turbine, determining the environmental parameters of the current location by the UAV and generating environmental forces in combination with the information fed back by the meteorological department;

[0007] Determining the relative position relationship between the UAV and the wind turbine, determining the current angular displacement of each blade in the wind turbine in real time, and taking pictures of the scene including multiple blades by the UAV; generating standard pictures under the condition that the blades are not disturbed externally and are operating normally at the current location of the UAV;

[0008] According to the influence of the force on the blade posture, whether the difference between the field picture and the standard picture is expected is analyzed by means of big data and artificial intelligence, if yes, the posture parameters of the wind turbine and the blade are generated according to the standard picture and the field picture; otherwise, the reason for the difference is analyzed and the factors and positions of the problems are simulated.

[0009] In a possible implementation, the starting and staying of the unmanned aerial vehicle in the upwind direction of the target wind turbine include:

[0010] According to the position of the wind turbine, the unmanned aerial vehicle is raised to a specified height;

[0011] According to the information fed back by the meteorological department and the wind speed and direction detected by the unmanned aerial vehicle on site, the posture of the unmanned aerial vehicle is adjusted so that the unmanned aerial vehicle faces the wind turbine and is located in the upwind direction of the wind turbine.

[0012] In a possible implementation, before the relative position relationship between the unmanned aerial vehicle and the wind turbine is determined, the method further includes:

[0013] A model corresponding to the wind turbine, the blade and the unmanned aerial vehicle is created, material selection parameters are set so that the weight and physical parameters of the model are the same as and one-to-one corresponding to the reality, the model produces the same deformation and stress change as the reality under the action of the force;

[0014] The state of the wind turbine, the angular displacement of the blade and the spatial position and angle of the unmanned aerial vehicle are digitized and displayed in the model in real time.

[0015] In a possible implementation, the standard picture generated at the current position of the unmanned aerial vehicle under the condition that the blade is not disturbed by external interference and operates normally includes:

[0016] After the field picture is obtained, the external force acting on the model is set to zero while maintaining the same motion as the blade and the wind turbine, and the standard picture of the blade under zero external force is obtained according to the same position relationship with the reality.

[0017] In a possible implementation, the analysis of whether the difference between the field picture and the standard picture is expected by means of big data and artificial intelligence and according to the influence of the force on the blade posture includes:

[0018] The blade and the wind turbine are picked up from the standard picture and the field picture, and then the picked-up parts are placed in the same coordinate system for comparison;

[0019] According to the deduction and calculation of big data and artificial intelligence, external force and internal stress change of each position in each field picture are determined based on the standard picture;

[0020] If the force is the reason for the change of the standard picture to the field picture, it is determined that the wind turbine and the blade state are normal, otherwise, the analysis of the internal stress of the blade is performed.

[0021] In a possible implementation, the analysis of the internal stress of the blade includes:

[0022] The force is applied to the model, and then the changed test picture of the model is obtained in the same position relationship;

[0023] The test picture and the field picture are picked up and compared, the internal connection relationship of the wind turbine and the blade is combined, and the position point and the area where the stress of the test picture and the field picture is different are determined, and the actual state and the problem of the blade are inferred.

[0024] In a possible implementation, the determination of the position point and the area where the stress of the test picture and the field picture is different includes:

[0025] The model is changed to the shape in the field picture and the test picture respectively, and is set as a first body and a second body;

[0026] The first body is changed to the second body, the change of the spatial position and the angle of each position in the first body is recorded, the change of the internal stress is recorded, the changed position point and the area are extracted and marked;

[0027] The reason for the problem of the blade is determined through the field inspection of the unmanned aerial vehicle and the related historical experience data.

[0028] In a possible implementation, if the attitude parameter of the wind turbine and the blade is expected, the standard picture and the field picture are generated.

[0029] The spatial position of the unmanned aerial vehicle relative to the blade is changed, the picture of the blade is obtained again, and a three-dimensional frame is generated combined with the field picture;

[0030] The model is changed to the three-dimensional frame, the attitude parameter is generated according to the shape of the model at the current time point, and feedback is performed.

[0031] In a possible implementation, the analysis of the reason for the difference and the simulation of the factor and the position of the problem include:

[0032] The unmanned aerial vehicle generates a next-time field picture again, makes the model change to the shape in the field picture, then rotates the same angle as the field picture, compares the state of the model at this time with the state in the field picture, analyzes the change of the internal stress of the blade under different deflection angles, and finally determines the root cause of the differentiation problem.

[0033] In a possible implementation, the analysis of the change of the internal stress of the blade under different deflection angles and the final determination of the root cause of the differentiation problem include:

[0034] The possible problems are applied to the model, and the stress change and the external shape change of the blade before and after the application are analyzed; different problems are randomly combined until the final determination of the problem.

[0035] The wind turbine posture parameter measurement method provided by the application has the beneficial effects that: compared with the prior art, in the wind turbine posture parameter measurement method, the unmanned aerial vehicle first stays in the upwind direction of the wind turbine, the environmental parameters of the current position are determined by the unmanned aerial vehicle, and the environmental action force is generated in combination with the information fed back by the meteorological department. After the action force is determined, the current angular displacement of each blade in the wind turbine is determined in real time, and the relative position relationship between the unmanned aerial vehicle and the wind turbine is determined. At this time, the field picture and the standard picture when the blade is not disturbed externally and operates normally under the same position relationship are obtained, the difference between the field picture and the standard picture is analyzed by means of big data and artificial intelligence and according to the influence degree of the action force on the blade posture, whether the difference meets the expectation, otherwise, the cause of the difference is analyzed, and the factors and positions causing the problem are simulated. The method of the application can effectively compare the state of the blade, can timely determine the cause and position of the problem, has low cost, and has great reference significance. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0037] Figure 1 The flowchart of the wind turbine posture parameter measurement method provided by the embodiment of the application. DETAILED DESCRIPTION

[0038] In order to make the technical problems, technical solutions and beneficial effects of the present application clearer, the present application will be further described in detail below in conjunction with the drawings and examples. It should be understood that the specific examples described herein are only intended to explain the present application and not to limit the present application.

[0039] Please refer to Figure 1 The attitude parameter measurement method of the wind driven generator provided by the present application will be described. The attitude parameter measurement method of the wind driven generator comprises:

[0040] The UAV is started and is caused to stay in the upwind direction of the target wind driven generator, the environmental parameters of the current position are determined by the UAV, and the environmental force is generated in combination with the information fed back by the meteorological department.

[0041] The relative position relationship between the UAV and the wind driven generator is determined, the current angular displacement of each blade in the wind driven generator is determined in real time, the on-site picture including the plurality of blades is photographed by the UAV, and the standard picture under the condition that the blades are not disturbed by the external interference and are in normal operation is generated at the current position of the UAV.

[0042] Whether the difference between the on-site picture and the standard picture conforms to the expectation is analyzed according to the influence degree of the force on the blade attitude by means of big data and artificial intelligence, if not, the attitude parameters of the wind driven generator and the blades are generated according to the standard picture and the on-site picture, otherwise, the cause of the difference is analyzed, and the factor and the position where the problem occurs are simulated.

[0043] The attitude parameter measurement method of the wind driven generator provided by the present application has the beneficial effects that, compared with the prior art, in the attitude parameter measurement method of the wind driven generator of the present application, the UAV is caused to stay in the upwind direction of the wind driven generator, the environmental parameters of the current position are determined by the UAV, and the environmental force is generated in combination with the information fed back by the meteorological department. After the force is determined, the current angular displacement of each blade in the wind driven generator is determined in real time, and the relative position relationship between the UAV and the wind driven generator is determined. At this time, the on-site picture and the standard picture under the condition that the blades are not disturbed by the external interference and are in normal operation in the same position relationship are obtained, whether the difference between the on-site picture and the standard picture conforms to the expectation is analyzed according to the influence degree of the force on the blade attitude by means of big data and artificial intelligence, otherwise, the cause of the difference is analyzed, and the factor and the position where the problem occurs are simulated. The method of the present application can effectively compare the state of the blades, can timely judge the cause and the position where the problem occurs, has low cost, and has large reference significance.

[0044] In some embodiments of the attitude parameter measurement method of the wind driven generator provided by the present application, starting the UAV and causing the UAV to stay in the upwind direction of the target wind driven generator comprises:

[0045] The drone is raised to a designated altitude based on the location of the wind turbine.

[0046] Based on information from the meteorological department and wind speed and direction detected by the drone on-site, the drone's attitude was adjusted so that it was facing the wind turbine and located upwind of it.

[0047] The meteorological department will provide feedback on the environmental information of the current area at different times. This environmental information includes humidity, wind speed, wind direction and temperature. It is necessary to combine the information from the meteorological department and use the wind speed and direction monitoring equipment on the drone to clarify the environmental conditions of the current wind turbine and blade area.

[0048] Once the environmental conditions are clear, the drone begins to determine the environmental parameters of its current location. It uses its onboard sensors to precisely measure data such as humidity, wind speed, wind direction, and temperature. Then, it integrates these on-site environmental parameters with information from meteorological departments, and uses a specific algorithm to generate environmental forces.

[0049] After generating environmental forces, the drone begins to determine the current angular displacement of each blade in the wind turbine in real time. Angular displacement can be fed back by the wind turbine's own detectors, which accurately capture the blade rotation angle using high-precision optical sensors or other advanced measuring equipment. Simultaneously, using its own positioning system and communication connection with the wind turbine, the drone determines its relative position to the wind turbine.

[0050] At this point, the drone activates its high-definition camera to acquire images of the scene. By comprehensively considering data from various aspects such as environmental forces, blade angular displacement, and relative positional relationships, the factors and locations of the problem are simulated. For example, if abnormal blade angular displacement is detected, the magnitude and direction of the environmental forces are considered to determine whether it is caused by excessive or insufficient wind force; furthermore, based on the relative positional relationships, the specific location of the problem—whether it is at the front, middle, or rear end of the blade—is determined. In this way, the condition of the blades can be comprehensively and accurately compared, and the cause and location of the problem can be determined in a timely manner, ensuring the stable operation of the wind turbine. Moreover, the entire method is low-cost and the data is of high reference value.

[0051] Meanwhile, with continuous technological development and improvement, the wind turbine attitude parameter measurement method of this invention can also be deeply integrated with other monitoring equipment and systems. For example, it can be combined with the sensor network inside the wind turbine to achieve comprehensive, multi-angle data acquisition and analysis. In this way, not only can the operating status of the wind turbine be more fully understood, but potential problems can also be predicted in advance, providing a strong guarantee for the efficient and stable development of the wind power industry.

[0052] In some embodiments of the wind turbine attitude parameter measurement method provided in this application, the method further includes the following steps before determining the relative positional relationship between the UAV and the wind turbine:

[0053] Create models corresponding to wind turbines, blades, and drones, and set material selection parameters to ensure that the weight and physical parameters of the models are the same as those in reality and correspond one-to-one. This will allow the models to produce the same deformation and stress changes as in reality under applied forces.

[0054] The status of the wind turbine, the angular displacement of the blades, and the spatial position and angle of the drone are all digitally displayed in the model in real time.

[0055] Models of the drones, blades, and wind turbines all need to be created. The drone model must correspond one-to-one with the actual object, meaning the positional changes and angles between the drone and its corresponding model must be consistent. Similarly, the rotational speed of the blades and their models must also be consistent. More importantly, the models must scale up to the actual objects, with the ultimate goal of producing the same deformation as the actual objects under the same external forces.

[0056] Based on the above model, the attitude changes of the wind turbine under various conditions were analyzed by simulating different wind conditions. Specifically, the wind speed, direction, and other parameters of the environment in which the model was located were changed, and the corresponding changes of the wind turbine model, blade model, and drone model were observed. Detailed records were made of the wind turbine model's sway amplitude, tilt angle, and other attitude parameters under different wind conditions, as well as the changes in the blade model's rotation angle and speed, and the drone model's positional movement and angle adjustment relative to the wind turbine model.

[0057] Based on the recorded data, we conducted an in-depth study on the relationship between wind turbine attitude and wind force, summarized the wind conditions under which wind turbines are prone to significant attitude deviations, and how to better monitor wind turbine attitude changes by adjusting the position and angle of the drone.

[0058] The model was further optimized by incorporating real-world interference factors, such as the impact of terrain on wind speed and air turbulence, to make it more closely resemble real-world scenarios and more accurately predict the attitude parameters of wind turbines in complex environments. Extensive simulation experiments were conducted using the optimized model to continuously verify and improve the method for measuring the attitude parameters of wind turbines, enhancing the accuracy and reliability of the measurement results and providing stronger support for actual wind turbine operation monitoring.

[0059] In some embodiments of the wind turbine attitude parameter measurement method provided in this application, generating a standard image at the current position of the UAV under conditions where the blades are not subject to external interference and are operating normally includes:

[0060] After acquiring on-site images, the external forces acting on the model are reduced to zero while maintaining the same motion as the blades and wind turbine. Based on the same positional relationship as in reality, a standard image of the blades under zero external forces is obtained.

[0061] The model can simulate the deformation of the blade under different forces. Because the model is identical to reality and has the same angular velocity, by simulating a sufficient number of samples, the motion state of the blade under different external environments can be reconstructed. By comparing standard images with on-site images, it is possible to determine whether the difference is due to external environmental influences or a problem with the blade itself.

[0062] Specifically, during the comparison, feature extraction is first performed on both the standard image and the field image. Key features such as the blade's outline, texture, and angle are extracted. Then, image processing algorithms are used to accurately compare these features. If a significant deviation is found between the field image and the standard image in the blade's outline, such as irregular twisting or deformation of the blade edges, this may indicate that the blade has been subjected to abnormal external forces, causing a change in its shape. Differences in texture features, such as a previously uniform texture becoming blurred or the appearance of new lines, may mean that the blade surface has been damaged, affecting its appearance. Changes in angle may directly reflect changes in the blade's attitude, possibly due to factors such as wind force or mechanical failure.

[0063] In-depth analysis of these differences allows for a more accurate determination of whether the problem stems from external environmental factors or the blades themselves. If it's an external environmental issue, further analysis reveals the specific environmental factors, such as strong winds or abnormal airflow, enabling appropriate measures to mitigate the impact of external conditions on the blades. If the problem lies with the blades themselves, the specific cause—whether it's aging of the blade material, structural damage, or other internal faults—can be determined, allowing for targeted repairs or replacements to ensure stable operation of the wind turbine, improve power generation efficiency, and reduce energy losses and safety hazards caused by blade problems.

[0064] In some embodiments of the wind turbine attitude parameter measurement method provided in this application, the analysis of whether the differences between on-site images and standard images meet expectations based on big data and artificial intelligence and the degree of influence of forces on blade attitude includes:

[0065] Blades and wind turbine units were extracted from standard images and field images, and then the extracted parts were compared under the same coordinate system.

[0066] Based on big data and artificial intelligence analysis and calculations, the external forces and internal stress changes at each location in each field image are determined using standard images as a benchmark.

[0067] If the applied force is the cause of the change from the standard image to the on-site image, then the wind turbine and blades are considered to be in normal condition; otherwise, an analysis of the internal stress of the blades is performed.

[0068] If there are problems inside the blade, the internal stress will inevitably differ from that of a blade under normal conditions, and this difference will be reflected in different blade deformations. However, through big data and artificial intelligence simulations, it is possible to simulate different environmental parameters under the influence of external environmental forces. Specifically, by using wind speed and direction detected by drones, the simulation can show the effects of external winds on the blade at different locations in different areas. It should be noted that the simulation is consistent with information reported by meteorological departments.

[0069] Based on simulations of the external wind effects on the blades under different environmental parameters, and considering the differences in internal blade stress compared to normal conditions, the specific problems reflected by varying blade deformation are further analyzed. Through comparison of numerous similar cases in big data and precise calculations using artificial intelligence, the location, type, and severity of potential defects within the blade are determined. For example, if abnormally increased stress and deformation exceeding the normal range are found in a certain area of ​​the blade, analysis suggests that there may be micro-cracks in the material or loosening of structural connections in that area. Based on these analytical results, targeted repair or adjustment plans are then developed.

[0070] After implementing maintenance or adjustment plans, big data and artificial intelligence are used again to measure and analyze blade attitude parameters to verify the effectiveness of the plan, ensure that wind turbines can operate in a safe and stable state, improve the efficiency and reliability of wind power generation, reduce downtime and maintenance costs caused by blade problems, and provide strong support for the sustainable development of the wind power industry.

[0071] In some embodiments of the wind turbine attitude parameter measurement method provided in this application, the analysis of internal stress in the blades is otherwise performed as follows:

[0072] A force is applied to the model, and then the image of the model after the change is obtained with the same positional relationship.

[0073] By picking and comparing the images to be tested with images from the field, and combining this with the internal connection relationship of the wind turbine and blades, the location points and areas where the stress differs between the images to be tested and the field images are determined, and the actual condition and problems of the blades are inferred.

[0074] Measuring the stress at every location on a blade in practice is impractical. However, models can be used to determine the stress state at different locations on the blade, thus guiding actual blade monitoring. Due to the large size of the blades, comparing on-site images with standard images can clearly identify which areas are experiencing problems. These problems can cause changes in the shape of other areas, and these can all be extrapolated using big data and artificial intelligence.

[0075] During the big data and artificial intelligence simulation process, more data about the blades under different operating conditions will be continuously collected, including environmental parameters and operating time. Through in-depth analysis of massive amounts of data, the model will be further optimized, making the judgment of the blade stress state more accurate. At the same time, virtual reality technology will be used to combine the model with the actual blade operating scenario, simulating various possible situations, identifying potential problems in advance, and providing more forward-looking suggestions for blade maintenance and replacement. For example, when fatigue damage that may occur in the stress concentration area of ​​the blade at a specific wind speed is simulated, inspection and maintenance work can be arranged in advance to avoid failures, ensure the stable operation of the wind turbine, improve power generation efficiency, and reduce operation and maintenance costs.

[0076] In some embodiments of the wind turbine attitude parameter measurement method provided in this application, the location points and regions where the stress difference between the image to be measured and the on-site image is simultaneously determined include:

[0077] The model is transformed into the shapes of the on-site image and the image to be tested, and set as the first body and the second body respectively.

[0078] The first body is transformed into the second body. The changes in spatial position and angle of each position in the first body are recorded, and the changes in internal stress are also recorded. The positions and regions of change are extracted and labeled.

[0079] The cause of the blade problem was determined through on-site inspection using drones and relevant historical experience data.

[0080] First, applying force to the model will change its shape. The reason for obtaining the test image is to ensure it's in the same environment as the actual site image. The second body represents the state of the model after the force is applied. By comparing the changes in the first and second bodies, the changes in internal stress are more intuitively displayed, thus clarifying potential problems with the blades.

[0081] In practice, after completing the above steps, we will further analyze the extracted and labeled change points and regions in detail. By comparing the degree of stress change at different locations, we can determine which areas experience more drastic stress changes, and these areas with drastic changes are often the key to blade problems.

[0082] We categorize and organize the causes of blade problems identified through on-site drone inspections and historical experience data. If abnormal blade stress is caused by excessive wind, we will consider adjusting the wind turbine's control system to appropriately adjust the blade's attitude and reduce stress during high winds. If defects in the blade's material itself are found to cause abnormal stress changes, we will promptly contact the blade manufacturer to discuss blade replacement or material improvement solutions. Simultaneously, we will establish a long-term monitoring mechanism to continuously monitor blade stress changes in order to promptly identify potential problems and take appropriate measures.

[0083] In some embodiments of the wind turbine attitude parameter measurement method provided in this application, generating the wind turbine and blade attitude parameters based on standard images and field images as expected includes:

[0084] The drone's spatial position relative to the blade is changed, and images of the blade are acquired again. These images are then combined with on-site images to generate a 3D frame.

[0085] The model is transformed into a 3D frame, and attitude parameters are generated and fed back based on the shape of the model at the current time point.

[0086] The purpose of setting up a three-dimensional frame is to determine the blade attitude from another angle and position. Because the angle of blade rotation is different, the force it experiences will also be different, and the impact of some problematic areas or points will be more apparent.

[0087] After generating the 3D frame, it undergoes detailed analysis. First, advanced image processing algorithms are used to accurately extract key feature points of the blade within the 3D frame. These feature points reflect the blade's specific morphology in its current position and orientation. Then, these feature points are compared with pre-defined standard orientation feature points. If significant deviations are found in certain feature points, such as deviating from the standard position by more than a certain threshold, it can be preliminarily determined that there is a problem with the blade's orientation. Next, further analysis is conducted on the regions where these deviated feature points are located, considering the importance of these regions when the blade is subjected to stress.

[0088] Attitude deviations occurring in areas of high stress require closer attention, as they can significantly impact the overall performance of the blades. Simultaneously, the relationship between blade rotation angle and stress conditions should be comprehensively assessed to comprehensively evaluate the potential risks of the current attitude deviation to long-term blade operation. If the risk assessment indicates a high risk, an alert should be issued promptly, and relevant personnel should be guided to make precise adjustments to the blade attitude according to the pre-established adjustment strategy. During the adjustment process, changes in blade attitude parameters should be continuously monitored to ensure the effectiveness of the adjustment operation, enabling the blade attitude to return to the normal range as quickly as possible and ensuring the stable and efficient operation of the wind turbine.

[0089] In some embodiments of the wind turbine attitude parameter measurement method provided in this application, the reasons for the differences and the factors and locations of the problems are analyzed and simulated, including:

[0090] The drone generates a new real-world image for the next moment, transforms the model to match the shape in the real-world image, and rotates it by the same angle as the real-world image. The model's current state is then compared with its state in the real-world image. The changes in internal stress of the blade under different deflection angles are analyzed, ultimately identifying the root cause of the discrepancy problem.

[0091] Another embodiment involves keeping the drone's position unchanged, acquiring an image at the next moment, and then comparing the model with the actual blade. Since the model is problem-free, it will never have any stress defect areas during the model's rotation, while the actual blade will be different. Therefore, by comparing at different moments, the problem can be further determined.

[0092] By analyzing and simulating the causes of the discrepancies described above, we can more accurately identify the root causes of problems in the measurement of wind turbine blade attitude parameters. Once the root cause is identified, targeted measures can be taken to improve the situation. For example, if it is found that abnormal stress distribution in a specific part of the blade is causing the attitude parameter deviation, then the structure of that part can be optimized, the material selection adjusted, or its shape changed to reduce stress concentration and make the blade attitude more stable.

[0093] Furthermore, the data obtained using these methods can be used to establish a database of wind turbine blade attitude parameters. By organizing and storing the results analyzed under different conditions, relevant information can be quickly retrieved from the database when similar problems arise, providing a reference for subsequent troubleshooting and resolution. This not only improves work efficiency but also allows for continuous accumulation of experience, further refining the wind turbine attitude parameter measurement methods and the overall system performance.

[0094] Furthermore, with continuous technological advancements, these methods can be combined with other advanced monitoring technologies. For instance, sensor networks can be introduced to monitor various parameter changes of wind turbines in real time during operation, cross-referencing the results obtained through drone and model analysis to achieve more comprehensive and accurate measurement and monitoring of wind turbine attitude parameters. This multi-technology integration approach enables the timely detection of potential problems, allowing for proactive prevention and handling, ensuring stable wind turbine operation, improving power generation efficiency, reducing operation and maintenance costs, and providing stronger support for the development of the new energy industry.

[0095] In some embodiments of the wind turbine attitude parameter measurement method provided in this application, the changes in internal stress of the blades under different deflection angles are analyzed, and the root causes of the differences are finally identified as follows:

[0096] Potential problems are applied to the model, and the stress changes and external shape changes of the blades before and after application are analyzed. Different problems are randomly combined until the problems are finally clarified.

[0097] By analyzing the changes in blade stress and external shape as described above, the root causes of the differentiated problems can be accurately identified. After clarifying the problems, corresponding solutions can be developed for different root causes. For abnormal changes in blade stress caused by specific issues, the blade design structure can be adjusted, and its material distribution optimized to ensure that the internal stress of the blade remains within a reasonable range at different deflection angles, avoiding problems such as excessive stress concentration.

[0098] Simultaneously, the external shape of the blades is fine-tuned to maintain good aerodynamic performance under various operating conditions, reducing additional stress caused by unreasonable shape. For the final problem identified after random combinations of different issues, a systematic evaluation and comprehensive treatment are conducted. A comprehensive monitoring system is established to monitor the blade's attitude parameters and stress changes in real time during actual operation. Once an anomaly is detected, adjustments can be quickly made according to the established solutions to ensure that the wind turbine is always in a stable and efficient operating state, improving energy conversion efficiency, reducing maintenance costs, and providing strong support for the sustainable development of the wind power industry.

[0099] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for measuring the attitude parameters of a wind turbine generator, characterized in that, include: The drone is launched and positioned upwind of the target wind turbine. The drone determines the environmental parameters of the current location and generates environmental forces by combining the information fed back by the meteorological department. The relative positional relationship between the drone and the wind turbine is determined, the current angular displacement of each blade in the wind turbine is determined in real time, and the drone takes on-site pictures including multiple blades; a standard picture of the blade under normal operation without external interference is generated at the current position of the drone. By leveraging big data and artificial intelligence, and based on the degree of influence of the environmental forces on the blade attitude, the difference between the on-site images and the standard images is analyzed to see if it meets expectations. If it does, the attitude parameters of the wind turbine and the blades are generated based on the standard images and the on-site images; otherwise, the reasons for the differences are analyzed and the factors and locations of the problems are simulated. Before clarifying the relative positional relationship between the drone and the wind turbine, the method further includes: Create models corresponding to the wind turbine, the blades, and the drone, and set material selection parameters so that the weight and physical parameters of the model are the same as those in reality and correspond one-to-one, so that the model produces the same deformation and stress change as in reality under the environmental forces. The status of the wind turbine, the angular displacement of the blades, and the spatial position and angle of the UAV are all digitally displayed in the model in real time. The standard image generated at the current location of the drone, assuming the blades are not subject to external interference and are operating normally, includes: After acquiring the on-site images, the external forces acting on the model are reduced to zero while maintaining the same motion as the blades and the wind turbine. Based on the same positional relationship as in reality, the standard images of the blades under zero external forces are acquired. The step of using big data and artificial intelligence to analyze whether the differences between the field images and the standard images meet expectations based on the degree of influence of the environmental forces on the blade attitude includes: The blades and the wind turbine were extracted from the standard images and the field images, and then the extracted parts were compared in the same coordinate system. Based on the extrapolation and calculation of big data and artificial intelligence, the external forces and internal stress changes at each location in each of the aforementioned field images are determined using the standard images as a benchmark. If the external force is the cause of the change from the standard image to the on-site image, then the wind turbine and the blade are determined to be in normal condition; otherwise, the internal stress of the blade is analyzed.

2. The method for measuring the attitude parameters of a wind turbine as described in claim 1, characterized in that, The step of activating the drone and keeping it upwind of the target wind turbine includes: The drone is raised to a specified height based on the location of the wind turbine. Based on information from the meteorological department and the wind speed and direction detected by the drone on-site, the drone's attitude is adjusted so that it faces the wind turbine and is located upwind of it.

3. The method for measuring the attitude parameters of a wind turbine as described in claim 1, characterized in that, Otherwise, the analysis of the internal stress of the blade includes: An external force is applied to the model, and then the image of the model after the change is obtained with the same positional relationship. By picking and comparing the image to be tested with the image on site, and combining the internal connection relationship of the wind turbine and the blade, the location points and areas where the stress in the image to be tested and the image on site are different are determined, and the actual state and problems of the blade are inferred.

4. The method for measuring the attitude parameters of a wind turbine as described in claim 3, characterized in that, The simultaneous determination of the location points and regions where the stress difference between the image to be tested and the on-site image begins includes: The model is transformed to the shapes in the on-site image and the image to be tested, and set as the first body and the second body, respectively. The first body is transformed into the second body. The changes in spatial position and angle of each position in the first body are recorded, and the changes in internal stress are also recorded. The changed position points and regions are extracted and labeled. The cause of the blade problem was determined through on-site inspection by the drone and relevant historical experience data.

5. The method for measuring the attitude parameters of a wind turbine as described in claim 1, characterized in that, If the expected results are met, the generation of attitude parameters for the wind turbine and its blades based on the standard image and the on-site image includes: The spatial position of the UAV relative to the blade is changed, and the image of the blade is acquired again. The image is then combined with the on-site image to generate a three-dimensional frame. The model is transformed into the 3D frame, and posture parameters are generated and fed back based on the shape of the model at the current time point.

6. The method for measuring the attitude parameters of a wind turbine as described in claim 1, characterized in that, The analysis of the reasons for the differences and the simulation of the factors and locations of the problems include: The drone generates a new real-world image for the next moment, causing the model to change to the shape shown in the real-world image and rotate it by the same angle as the real-world image. The current state of the model is compared with the state in the real-world image, and the changes in the internal stress of the blade under different deflection angles are analyzed to ultimately identify the root cause of the difference problem.

7. The method for measuring the attitude parameters of a wind turbine as described in claim 6, characterized in that, The analysis of the changes in internal stress of the blade under different deflection angles ultimately identified the root causes of the differentiation problem as follows: Potential problems are applied to the model, and the stress changes and external shape changes of the blades before and after application are analyzed. Different problems are randomly combined until the problems are finally clarified.

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