A wind turbine defect detection method for a wind farm

Abstract

The present invention discloses a method for testing and detecting defects of wind turbines in a wind farm, and relates to the technical field of testing and detecting defects of wind turbines. In the process of analyzing the comprehensive performance of wind turbines under extreme weather conditions, by collecting abnormal environmental information, it is beneficial to dynamically evaluate the operating status of wind turbines under specific environmental conditions. By integrating these environmental factors, potential defect problems can be identified more accurately, ensuring that the wind turbines can take corresponding countermeasures when facing extreme weather challenges, thereby ensuring the safety and reliability of the wind turbines. In the comprehensive performance evaluation link, by combining external visual inspection of the wind turbines with internal equipment operation inspection, it is beneficial to monitor the health status of the wind turbines in all directions. The internal equipment operation inspection provides important quantitative data for comprehensive evaluation by collecting vibration acceleration, noise level and bearing temperature operating performance parameters, thereby ensuring that the equipment status of the wind turbines is accurately reflected.

Description

Technical Field

[0001] The invention relates to the technical field of wind turbine defect testing and detection, and in particular to a wind turbine defect testing and detection method for a wind farm. Background Art

[0002] The frequent occurrence of extreme weather events caused by climate change has brought new challenges to the stable operation of wind farms. Therefore, the wind turbine defect test detection method needs to have the ability to respond flexibly under extreme weather conditions. This can not only ensure the safe operation of wind turbines under harsh conditions, but also shorten the detection cycle, frequently monitor the status of wind turbines, and conduct timely troubleshooting and repair.

[0003] Prior art, such as the invention patent application with announcement number CN116664557B, discloses a method for visual detection of surface defects of wind turbine blades. The method first obtains an abnormal area with abnormal grayscale value in the grayscale image of the front view of the wind turbine blade, obtains a baseline defect probability based on the grayscale distribution and difference characteristics of the abnormal area, obtains the corresponding angle feature conformity according to the extension direction of the abnormal area, obtains the corresponding shape feature conformity according to the length and width distribution of the abnormal area, obtains the corresponding texture feature conformity according to the space and grayscale distribution corresponding to the abnormal area, combines the baseline defect probability, angle feature conformity, shape feature conformity and texture feature conformity, obtains the final defect probability of each abnormal area, and performs wind turbine blade surface defect detection based on the final defect probability. The present invention has a higher accuracy in detecting surface defects of wind turbine blades by calculating the final defect probability.

[0004] With respect to the above scheme, there are at least the following technical problems: 1. The above scheme lacks consideration of the external environmental factors of the wind turbine blades, which leads to neglect of external environmental changes that may have a significant impact on the detection results when detecting defects in wind turbine blades. Specifically, external environmental factors such as extreme weather, wind speed, temperature changes, etc. have a direct impact on the operating state of the wind turbine and the damage on the blade surface. If the external environmental factors are not included in the detection method, it will lead to the inability to fully and accurately evaluate the true state of the blades, and the lack of a mechanism to combine with comprehensive performance evaluation will lead to a lack of sufficient background information to evaluate the severity of blade defects. If the inspection only relies on image processing and defect probability without comparison with the comprehensive performance of the wind turbine, it will lead to misjudgment and failure to identify hidden dangers caused by external factors.

[0005] 2. The above scheme lacks real-time monitoring of abnormal environmental information, which will result in defect detection being unable to respond to changing environmental conditions in a timely manner. The image detection technology itself cannot reflect the additional stress caused by wind speed, rainfall, etc. in real time, which will make the detection results inconsistent with the actual load conditions of the wind turbine, thereby affecting subsequent maintenance decisions. The lack of clear instructions on the subsequent application of the test results will make the use of the test results vague. The lack of guidance on how to combine the test results with the wind turbine operation and maintenance plan will make it impossible to convert the test data into actual maintenance and repair measures, thus losing the meaning of the detection. Summary of the invention

[0006] The purpose of the present invention is to provide a method for testing and detecting defects of wind turbines in a wind farm, which solves the problems existing in the background technology.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a method for defect testing and detection of wind turbines in a wind farm, comprising: S1, evaluating the prerequisites for defect testing and detection of wind turbines in a designated wind farm, the prerequisites including that the wind turbines in the designated wind farm have reached the defect testing and detection cycle and that the comprehensive defects of the wind turbines in the designated wind farm under extreme weather conditions have reached the set defect testing and detection standards.

[0008] S2. Analyze the impact of extreme weather conditions on the overall performance of wind turbines in a specified wind farm.

[0009] S3. When the wind turbines in the designated wind farm reach the defect test detection cycle, the corresponding comprehensive performance of the wind turbines in the designated wind farm is evaluated.

[0010] S4. When the comprehensive defects of wind turbines in a designated wind farm under extreme weather conditions reach the set defect test detection standards, the corresponding comprehensive defects of wind turbines in the designated wind farm are evaluated.

[0011] S5. Analyze the defect test and detection process of the wind turbines in the designated wind farm according to the prerequisites for performing defect test and detection on the wind turbines in the designated wind farm.

[0012] The beneficial effects of the present invention are: 1. A wind turbine defect test detection method for a wind farm provided by the present invention, in the process of analyzing the comprehensive performance of the wind turbine under extreme weather conditions, by collecting abnormal environmental information, is conducive to dynamically evaluating the operating status of the wind turbine under specific environmental conditions. By integrating these environmental factors, potential defect problems can be identified more accurately, ensuring that the wind turbine can take corresponding countermeasures when facing extreme weather challenges, thereby ensuring the safety and reliability of the wind turbine.

[0013] 2. In the comprehensive performance evaluation link, the embodiment of the present invention combines external visual inspection of the wind turbine with internal equipment operation inspection, which is beneficial to all-round monitoring of the health status of the wind turbine. The external visual inspection uses drones to capture images of the wind turbine tower and blades, which improves the efficiency and accuracy of inspection and can also cover a wider inspection area, reducing blind spots for manual inspection. The internal equipment operation inspection collects vibration acceleration, noise level and bearing temperature operating performance parameters to provide important quantitative data for comprehensive evaluation, thereby ensuring that the equipment status of the wind turbine is accurately reflected.

[0014] 3. When analyzing the defect test detection process of a wind turbine in a specified wind farm, the embodiment of the present invention sets a time interval for dynamic monitoring of the collected abnormal environmental information, which is beneficial to real-time tracking of the operation of the wind turbine and ensuring that the detected data reflects the latest status. This timeliness enhances the effectiveness of defect detection and enables operation and maintenance personnel to take quick measures to reduce risks caused by environmental factors.

[0015] 4. The embodiment of the present invention facilitates the determination of whether additional defect tests are required for wind turbines under extreme weather conditions by evaluating the calculation of the comprehensive defect test detection evaluation coefficient, helps to achieve dynamic management of wind turbines, ensures the safety of equipment at critical moments, and enhances the adaptability of detection to meet challenges under different conditions. By obtaining the threshold intervals of each comprehensive performance evaluation index and the threshold intervals of the comprehensive defect test detection evaluation coefficient from the database, it is beneficial to provide a scientific basis for the detection and evaluation of wind turbines. The threshold analysis based on a large amount of data avoids the influence of the randomness of individual data on the evaluation results, thereby improving the accuracy and credibility of defect test detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a schematic flow chart of the implementation steps of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1 As shown, the present invention provides a method for defect testing and detection of wind turbines in a wind farm, the method comprising: S1, evaluating the prerequisites for defect testing and detection of wind turbines in a designated wind farm, the prerequisites comprising that the wind turbines in the designated wind farm have reached a defect testing and detection cycle and that the comprehensive defects of the wind turbines in the designated wind farm under extreme weather conditions have reached a set defect testing and detection standard.

[0020] In a specific embodiment, the evaluation of the prerequisite for the defect test of the wind turbines in the designated wind farm is carried out as follows: A1, obtaining the defect test cycle corresponding to the wind turbines in the designated wind farm from the wind turbine management database, and judging whether the wind turbines in the designated wind farm need to be subjected to defect test according to the defect test cycle corresponding to the wind turbines in the designated wind farm;

[0021] A2. Collect abnormal environmental information corresponding to the specified wind farm under extreme weather conditions. The abnormal environmental information includes rainfall, temperature and wind speed, and combine it with the comprehensive performance of the wind turbines in the specified wind farm to analyze and obtain the comprehensive defects corresponding to the wind turbines in the specified wind farm. This will evaluate whether the wind turbines in the specified wind farm need to undergo defect testing before the defect testing cycle is reached under extreme weather conditions.

[0022] It should be noted that when the wind turbines in a designated wind farm reach the defect test detection cycle, it means that the wind turbines in the designated wind farm need to undergo defect test detection. Otherwise, defect test detection is not required. Extreme weather conditions include strong winds, heavy rains, high temperatures, low temperatures and sandstorms.

[0023] In a specific embodiment, the abnormal environment information corresponding to the designated wind farm under extreme weather conditions is collected, and the specific collection process is as follows: the time point corresponding to the beginning of the extreme weather is used as the time point for starting to collect the abnormal environment information, and the time point corresponding to the end of the extreme weather is used as the time point for ending the collection of the abnormal environment information, and the collection time interval is set, so as to obtain each collection time point corresponding to the abnormal environment information;

[0024] Set various rainfall detection points within the setting range of the wind turbines in the specified wind farm, install rain gauges at corresponding rainfall detection points, obtain the rainfall at corresponding rainfall detection points within the setting range of the wind turbines in the specified wind farm through rain gauge detection, calculate the rainfall at each rainfall detection point by average, and the result obtained is the rainfall corresponding to the wind turbines in the specified wind farm under extreme weather conditions, set various detection points on the tower of the wind turbine, install temperature sensors at corresponding detection points, obtain the temperature value of the wind turbines in the specified wind farm corresponding to each detection point by temperature sensor detection, calculate the temperature value of each detection point by average, and the result obtained is the temperature corresponding to the wind turbines in the specified wind farm under extreme weather conditions;

[0025] A wind speed sensor is set at each detection point. According to each collection time point, the wind speed corresponding to each detection point at each collection time point is detected by the wind speed sensor. The wind speed corresponding to each collection time point at each detection point is calculated by the mean, and then the average wind speed corresponding to each collection time point is obtained. The maximum value of the average wind speed corresponding to each collection time point is selected, which is the peak wind speed. The peak wind speed is used as the wind speed corresponding to the wind turbine of the specified wind farm under extreme weather conditions.

[0026] It should be noted that the time period of extreme weather is defined by combining meteorological models and real-time data. Meteorological models are existing technology and will not be elaborated here. Real-time data refers to meteorological information collected in real time, including environmental monitoring data such as temperature, humidity, wind speed, wind direction, precipitation and atmospheric pressure, as well as observation data from sensors, weather stations and satellites.

[0027] S2. Analyze the impact of extreme weather conditions on the comprehensive performance of wind turbines in a specified wind farm;

[0028] In a specific embodiment, the analysis of the impact of extreme weather conditions on the comprehensive performance of wind turbines in a specified wind farm includes the following specific analysis process: obtaining standard control abnormal environmental information corresponding to wind turbines in the specified wind farm from a database, wherein the standard control abnormal environmental information includes standard control rainfall, standard control temperature, and standard control wind speed;

[0029] By calculating the formula Get the comprehensive impact factor of extreme weather conditions on wind turbines in a specified wind farm α , where h, T, v represent the rainfall, temperature, and wind speed corresponding to the wind turbines in the specified wind farm under extreme weather conditions, respectively; h′, T′, v′ represent the standard control rainfall, standard control temperature, and standard control wind speed, respectively; η1, η2, and η3 represent the weight factors corresponding to the set rainfall, temperature, and wind speed, respectively.

[0030] It should be noted that the values ​​of η1, η2, and η3 are all greater than 0 and less than 1.

[0031] It should also be noted that, first, collect historical weather data on the specific impact of wind turbine operation and damage, and set weights for rainfall, temperature, and wind speed based on correlation analysis. For example, if the study finds that rainfall greater than 50 mm has a significant impact on the wind turbine structure, the weight factor for rainfall is set to 0.5. When the temperature exceeds 35°C, it will cause equipment overheating. At this time, the temperature weight factor is set to 0.3. If the wind speed exceeds 25 m / s, it will affect the stability of the wind turbine blades and tower, and the wind speed weight factor is set to 0.2. Combined with expert opinions and actual abnormal environmental information, the weight factors corresponding to the set rainfall, temperature, and wind speed are finally obtained.

[0032] S3. When the wind turbines in the designated wind farm reach the defect test detection cycle, the corresponding comprehensive performance of the wind turbines in the designated wind farm is evaluated.

[0033] In a specific embodiment, the evaluation of the comprehensive performance corresponding to the wind turbines in the designated wind farm includes the following specific evaluation process: the comprehensive performance corresponding to the wind turbines in the designated wind farm includes external visual inspection and internal equipment operation inspection, and the external visual inspection is realized by taking the tower image and blade image corresponding to the wind turbine by a drone, and observing whether there are cracks and surface damage on the tower and blades by image recognition technology, and according to the preset scoring standard, the tower damage score and blade damage score corresponding to the wind turbine are obtained, and then the external surface damage comprehensive score corresponding to the wind turbine in the designated wind farm is calculated, which is recorded as β, and the operating performance parameters corresponding to the wind turbines in the designated wind farm under the operating state are collected, so as to calculate the performance evaluation index corresponding to the wind turbines in the designated wind farm, so as to realize the internal equipment operation inspection, and the operating performance parameters include vibration acceleration, noise level and bearing temperature, and then the comprehensive performance evaluation index corresponding to the wind turbines in the designated wind farm is calculated.

[0034] It should be noted that the comprehensive external surface damage score β corresponding to the wind turbine in the specified wind farm is obtained by calculating the formula β=x*π1+y*π2, where x and y represent the tower damage score and the blade damage score, respectively, π1 and π2 represent the set tower damage score weight factor and the blade damage score weight factor, respectively, and the values ​​of π1 and π2 are both greater than 0 and less than 1. The setting process of π1 and π2 is the same as that of η1, η2, and η3, and will not be elaborated here.

[0035] It should also be noted that, for example, in the tower image taken by the drone, a crack with a length of 10 cm and a width of 1 cm was found on the surface of the tower. The preset scoring criteria are as follows: For the tower damage score, an assessment is made based on the crack length and width. If the crack length is less than 20 cm and the width is less than 2 cm, it is assessed as minor damage. The corresponding score for minor damage is between 20 and 40 points. The position of the crack in the tower is compared with the preset key positions. If the crack position is not in the preset key positions, the tower damage score is set to 30 points, with a score range of 0-100 points. The scoring criteria for the blade damage score are the same as those for the tower damage score, which will not be elaborated here.

[0036] It should also be noted that the vibration acceleration is measured by acceleration sensors installed on the key components of the fan, including but not limited to the main shaft, bearings and blades. The noise level is detected by noise sensors or sound level meters, and the bearing temperature is monitored by temperature sensors. The vibration acceleration reflects the operating stability of the machine. When the vibration acceleration is greater than the standard control vibration acceleration, it usually indicates that the fan has mechanical failure or imbalance, which may cause equipment damage and affect the operating performance. The noise level is an important indicator for measuring the smoothness of equipment operation. The noise generated by the fan when working is greater than the standard control noise level, indicating mechanical wear, failure or loose components. The bearing temperature is an important indicator of the fan's operating efficiency and life. The normal operating bearing temperature should be kept within the design range. Temperatures higher than the standard control temperature value will cause poor lubrication, accelerated wear or failure.

[0037] In a specific embodiment, the calculation obtains the comprehensive performance evaluation index corresponding to the wind turbine in the specified wind farm, and the specific calculation process is as follows: obtaining standard control operation performance parameters for performance evaluation of the wind turbine in the specified wind farm from a database, the standard control operation performance parameters including standard control vibration acceleration, standard control noise level and standard control bearing temperature;

[0038] By calculating the formula The comprehensive performance evaluation index χ corresponding to the wind turbine in the specified wind farm is obtained, where a, B, and T1 represent the vibration acceleration, noise level, and bearing temperature of the wind turbine in the specified wind farm, respectively; a′, B′, and T1′ represent the standard control vibration acceleration, standard control noise level, and standard control bearing temperature, respectively; γ1, γ2, γ3, and γ4 are the weight factors corresponding to the set vibration acceleration, the weight factor corresponding to the noise level, the weight factor corresponding to the bearing temperature, and the weight factor corresponding to the comprehensive score of external surface damage, respectively.

[0039] It should be noted that the values ​​of γ1, γ2, γ3, and γ4 are all greater than 0 and less than 1.

[0040] It should also be noted that, first of all, an impact assessment should be conducted based on the equipment operation and failure data. By analyzing historical failure cases and operating performance, the contribution of each parameter to the overall safety and efficiency of the wind turbine can be determined. For example, if the study finds abnormal changes in vibration acceleration, it indicates a failure risk. Therefore, the vibration acceleration weight factor is set to 0.4. Although the noise level is important, it usually reflects the equipment condition indirectly. The weight factor is set to 0.2. The bearing temperature is directly related to the operating life and safety of the equipment. The weight factor is set to 0.3. External surface damage directly affects the overall structure and long-term stability of the equipment. The weight factor is set to 0.1. Combined with expert opinions and actual operating performance parameters, the weight factors corresponding to the set vibration acceleration, noise level, bearing temperature, and external surface damage comprehensive score are finally obtained.

[0041] In the comprehensive performance evaluation link, the embodiment of the present invention combines external visual inspection of the wind turbine with internal equipment operation inspection, which is beneficial to all-round monitoring of the health status of the wind turbine. The external visual inspection uses a drone to capture images of the wind turbine tower and blades, which improves the efficiency and accuracy of the inspection and can also cover a wider inspection area and reduce blind spots in manual inspection. The internal equipment operation inspection collects vibration acceleration, noise level and bearing temperature operating performance parameters to provide important quantitative data for comprehensive evaluation, thereby ensuring that the equipment status of the wind turbine is accurately reflected.

[0042] S4. When the comprehensive defects of wind turbines in a designated wind farm under extreme weather conditions reach the set defect test detection standards, the corresponding comprehensive defects of wind turbines in the designated wind farm are evaluated.

[0043] In a specific embodiment, the analysis of the comprehensive defects corresponding to the wind turbines in the specified wind farm is carried out as follows: based on the comprehensive influencing factors of the extreme weather conditions on the wind turbines in the specified wind farm, the comprehensive scores of the external surface damage corresponding to the wind turbines in the specified wind farm, and the comprehensive performance evaluation index, the comprehensive defect test detection evaluation coefficient corresponding to the wind turbines in the specified wind farm under extreme weather conditions is calculated. The comprehensive defect test detection evaluation coefficient includes -1 and 1. When the comprehensive defect test detection evaluation coefficient is -1, it indicates that the wind turbines in the specified wind farm need to undergo defect test detection before the defect test detection cycle is reached under extreme weather conditions. When the comprehensive defect test detection evaluation coefficient is 1, it indicates that the wind turbines in the specified wind farm do not need to undergo defect test detection before the defect test detection cycle is reached under extreme weather conditions.

[0044] In a specific embodiment, the calculation of the comprehensive defect test detection evaluation coefficient corresponding to the wind turbine of the specified wind farm under extreme weather conditions is specifically calculated as follows:

[0045] Evaluate the expression according to the comprehensive defect: The comprehensive defect test detection evaluation coefficient ζ corresponding to the wind turbine in the specified wind farm under extreme weather is obtained, where λ1 and λ2 are the weight factors corresponding to the set comprehensive influencing factors and the weight factors corresponding to the comprehensive performance evaluation index, respectively, and U represents the threshold value of the standard control comprehensive defect test detection evaluation coefficient.

[0046] It should be noted that the values ​​of λ1 and λ2 are both greater than 0 and less than 1. U is used as a basis for evaluating whether the wind turbines in a specified wind farm need to be tested for defects before the defect test detection cycle is reached under extreme weather conditions. When the value of (α*λ1+χ*λ2) is 15 and the value of U is 12, it indicates that the wind turbines in the specified wind farm need to be tested for defects before the defect test detection cycle is reached under extreme weather conditions.

[0047] It should also be noted that external surface damage will affect the aerodynamic performance and structural safety of the fan. The weight factor corresponding to the comprehensive score of external surface damage is set to 0.3. The comprehensive performance evaluation index covers multiple operating parameters and can comprehensively reflect the health status of the equipment. Therefore, the weight factor corresponding to the comprehensive performance evaluation index is set to 0.7. Combined with expert opinions and actual cases, the weight factors corresponding to the set external surface damage comprehensive score and the weight factors corresponding to the comprehensive performance evaluation index are finally obtained.

[0048] When analyzing the defect test detection process of a wind turbine in a specified wind farm, the embodiment of the present invention sets a time interval for dynamic monitoring of the collected abnormal environmental information, which is beneficial for real-time tracking of the operation of the wind turbine and ensuring that the detected data reflects the latest status. This timeliness enhances the effectiveness of defect detection and enables operation and maintenance personnel to take measures quickly to reduce risks caused by environmental factors.

[0049] S5. Analyze the defect test and detection process of the wind turbines in the designated wind farm according to the prerequisites for performing defect test and detection on the wind turbines in the designated wind farm.

[0050] In a specific embodiment, the defect test detection process of the wind turbines in the specified wind farm is analyzed, and the specific analysis process is as follows: the defect test detection of the wind turbines in the specified wind farm is divided into a first defect test detection and a second defect test detection. The first defect test detection is a defect test detection performed when the wind turbines in the specified wind farm have reached a defect test detection cycle. The second defect test detection is a defect test detection that needs to be performed on the wind turbines in the specified wind farm before the defect test detection cycle is reached under extreme weather conditions. Then, the defect test detection process of the wind turbines in the specified wind farm is analyzed by analyzing the first defect test detection process and the second defect test detection process.

[0051] In a specific embodiment, the analysis of the first defect test detection process and the second defect test detection process is as follows: obtaining from a database each comprehensive performance evaluation index threshold interval and each comprehensive defect test detection evaluation coefficient threshold interval corresponding to a wind turbine in a specified wind farm, each comprehensive performance evaluation index threshold interval and each comprehensive defect test detection evaluation coefficient threshold interval respectively corresponding to each normal cycle defect detection level and each defect detection level under extreme weather conditions, and comparing the comprehensive performance evaluation index corresponding to the wind turbine in the specified wind farm with each comprehensive performance evaluation index threshold interval respectively; if the comprehensive performance evaluation index corresponding to the wind turbine in the specified wind farm belongs to a certain comprehensive performance evaluation index threshold interval, then the detection level corresponding to the wind turbine in the specified wind farm during the first defect test detection process is the normal cycle defect detection level corresponding to the comprehensive performance evaluation index threshold interval;

[0052] The comprehensive defect test detection and evaluation coefficients corresponding to the wind turbines in the specified wind farm under extreme weather conditions are compared with the threshold intervals of each comprehensive defect test detection and evaluation coefficient. If the comprehensive defect test detection and evaluation coefficient belongs within a certain comprehensive defect test detection and evaluation coefficient threshold interval, the corresponding detection level of the wind turbines in the specified wind farm during the second defect test detection process is the defect detection level under extreme weather conditions corresponding to the comprehensive defect test detection and evaluation coefficient threshold interval.

[0053] It should be noted that the defect detection levels of each normal cycle and the defect detection levels under each extreme weather condition are used to formulate maintenance and overhaul plans for wind turbines. High normal cycle defect detection levels and high defect detection levels under extreme weather conditions mean that if the defects of the wind turbine are large, emergency overhaul can be arranged. High normal cycle defect detection levels and high defect detection levels under extreme weather conditions also mean that a large number of staff and testing equipment are required in the defect test and inspection process.

[0054] The embodiment of the present invention facilitates the determination of whether additional defect tests are required for wind turbines under extreme weather conditions by evaluating the calculation of the comprehensive defect test detection evaluation coefficient, helps to achieve dynamic management of wind turbines, ensures the safety of equipment at critical moments, and enhances the adaptability of detection to meet challenges under different conditions. By obtaining the threshold intervals of each comprehensive performance evaluation index and the threshold intervals of the comprehensive defect test detection evaluation coefficient from a database, it is beneficial to provide a scientific basis for the detection and evaluation of wind turbines. The threshold analysis based on a large amount of data avoids the influence of the randomness of individual data on the evaluation results, thereby improving the accuracy and credibility of defect test detection.

[0055] The present invention provides a method for testing and detecting defects of wind turbines in a wind farm. In the process of analyzing the comprehensive performance of wind turbines under extreme weather conditions, by collecting abnormal environmental information, it is beneficial to dynamically evaluate the operating status of wind turbines under specific environmental conditions. By integrating these environmental factors, potential defect problems can be identified more accurately, ensuring that the wind turbines can take corresponding countermeasures when facing extreme weather challenges, thereby ensuring the safety and reliability of the wind turbines.

[0056] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they shall all fall within the protection scope of the present invention.

Claims

1. A wind turbine defect detection method for a wind farm, characterized in that: include: S1. Assess the prerequisites for defect testing of wind turbines in designated wind farms, including that the wind turbines in designated wind farms have reached the defect testing cycle and that the comprehensive defects of wind turbines in designated wind farms under extreme weather conditions have reached the set defect testing standards; S2. Analyze the impact of extreme weather conditions on the comprehensive performance of wind turbines in a specified wind farm; The analysis of the impact of extreme weather conditions on the comprehensive performance of wind turbines in a specified wind farm is as follows: Obtaining standard control abnormal environment information corresponding to a wind turbine in a specified wind farm from a database, wherein the standard control abnormal environment information includes standard control rainfall, standard control temperature and standard control wind speed; By calculating the formula The comprehensive impact factor α of extreme weather conditions on the wind turbines in the specified wind farm is obtained, where h, T, and v represent the corresponding rainfall, temperature, and wind speed of the wind turbines in the specified wind farm under extreme weather conditions, respectively; h′, T′, and v′ represent the standard control rainfall, standard control temperature, and standard control wind speed, respectively; η1, η2, and η3 represent the weight factors corresponding to the set rainfall, temperature, and wind speed, respectively; S3. When the wind turbines in the designated wind farm have reached the defect test detection cycle, the corresponding comprehensive performance of the wind turbines in the designated wind farm is evaluated; The specific evaluation process of evaluating the comprehensive performance of wind turbines in a designated wind farm is as follows: The comprehensive performance of wind turbines in a designated wind farm includes external visual inspection and internal equipment operation inspection. The external visual inspection is achieved by taking images of the tower and blades of the wind turbines by drones. The image recognition technology is used to observe whether there are cracks and surface damage on the tower and blades. According to the preset scoring standards, the tower damage score and blade damage score of the wind turbines are obtained, and then the comprehensive score of the external surface damage of the wind turbines in the designated wind farm is calculated, which is recorded as β. The performance evaluation index of the wind turbines in the designated wind farm is calculated by collecting the operating performance parameters of the wind turbines in the designated wind farm under the operating state, so as to achieve internal equipment operation inspection. The operating performance parameters include vibration acceleration, noise level and bearing temperature, and then the comprehensive performance evaluation index of the wind turbines in the designated wind farm is calculated; The calculation obtains the comprehensive performance evaluation index corresponding to the wind turbine in the specified wind farm. The specific calculation process is as follows: Obtaining standard control operating performance parameters for performance evaluation of wind turbines in a designated wind farm from a database, wherein the standard control operating performance parameters include standard control vibration acceleration, standard control noise level, and standard control bearing temperature; By calculating the formula The comprehensive performance evaluation index χ corresponding to the wind turbine of the specified wind farm is obtained, where a, B, and T1 represent the vibration acceleration, noise level, and bearing temperature corresponding to the wind turbine of the specified wind farm, respectively; a′, B′, and T1′ represent the standard control vibration acceleration, the standard control noise level, and the standard control bearing temperature, respectively; γ1, γ2, γ3, and γ4 represent the weight factors corresponding to the set vibration acceleration, the weight factors corresponding to the noise level, the weight factors corresponding to the bearing temperature, and the weight factors corresponding to the comprehensive score of external surface damage, respectively; S4. When the comprehensive defects of wind turbines in a designated wind farm under extreme weather conditions reach the set defect test detection standard, evaluate the corresponding comprehensive defects of wind turbines in the designated wind farm; S5. Analyze the defect test and detection process of the wind turbines in the specified wind farm according to the prerequisites for performing defect test and detection on the wind turbines in the specified wind farm.

2. A wind turbine defect detection method for a wind farm according to claim 1, characterized in that: The assessment specifies the prerequisites for defect testing of wind turbines in wind farms. The specific assessment process is as follows: A1. Obtain the defect test detection cycle corresponding to the wind turbines in the specified wind farm from the wind turbine management database, and determine whether the wind turbines in the specified wind farm need to be subjected to defect test detection according to the defect test detection cycle corresponding to the wind turbines in the specified wind farm; A2. Collect abnormal environmental information corresponding to the specified wind farm under extreme weather conditions. The abnormal environmental information includes rainfall, temperature and wind speed, and combine it with the comprehensive performance of the wind turbines in the specified wind farm to analyze and obtain the comprehensive defects corresponding to the wind turbines in the specified wind farm. This will evaluate whether the wind turbines in the specified wind farm need to undergo defect testing before the defect testing cycle is reached under extreme weather conditions.

3. A wind turbine defect detection method for a wind farm according to claim 2, characterized in that: The specific collection process of collecting abnormal environmental information corresponding to a specified wind farm under extreme weather conditions is as follows: The time point corresponding to the beginning of extreme weather is used as the time point for starting to collect abnormal environment information, and the time point corresponding to the end of extreme weather is used as the time point for ending to collect abnormal environment information, and the collection time interval is set to obtain each collection time point corresponding to the abnormal environment information; Set various rainfall detection points within the setting range of the wind turbines in the specified wind farm, install rain gauges at corresponding rainfall detection points, obtain the rainfall at corresponding rainfall detection points within the setting range of the wind turbines in the specified wind farm through rain gauge detection, calculate the rainfall at each rainfall detection point by average, and the result obtained is the rainfall corresponding to the wind turbines in the specified wind farm under extreme weather conditions, set various detection points on the tower of the wind turbine, install temperature sensors at corresponding detection points, obtain the temperature value of the wind turbines in the specified wind farm corresponding to each detection point by temperature sensor detection, calculate the temperature value of each detection point by average, and the result obtained is the temperature corresponding to the wind turbines in the specified wind farm under extreme weather conditions; A wind speed sensor is set at each detection point. According to each collection time point, the wind speed corresponding to each detection point at each collection time point is detected by the wind speed sensor. The wind speed corresponding to each collection time point at each detection point is calculated by the mean, and then the average wind speed corresponding to each collection time point is obtained. The maximum value of the average wind speed corresponding to each collection time point is selected, which is the peak wind speed. The peak wind speed is used as the wind speed corresponding to the wind turbine of the specified wind farm under extreme weather conditions.

4. A wind turbine defect detection method for a wind farm according to claim 3, characterized in that: The analysis specifies the comprehensive defects corresponding to the wind turbines in the wind farm. The specific analysis process is as follows: According to the comprehensive impact factor of extreme weather conditions on the wind turbines in the designated wind farm, the corresponding comprehensive score of external surface damage and the comprehensive performance evaluation index of the wind turbines in the designated wind farm, the comprehensive defect test and detection evaluation coefficient of the wind turbines in the designated wind farm under extreme weather is calculated. The comprehensive defect test and detection evaluation coefficient includes -1 and 1. When the comprehensive defect test and detection evaluation coefficient is -1, it indicates that the wind turbines in the designated wind farm need to undergo defect test and detection before the defect test and detection cycle is reached under extreme weather conditions. When the comprehensive defect test and detection evaluation coefficient is 1, it indicates that the wind turbines in the designated wind farm do not need to undergo defect test and detection before the defect test and detection cycle is reached under extreme weather conditions.

5. A wind turbine defect detection method for a wind farm according to claim 4, characterized in that: The calculation specifies the comprehensive defect test detection evaluation coefficient of the wind turbine in the wind farm under extreme weather conditions. The specific calculation process is as follows: Evaluate the expression according to the comprehensive defect: The comprehensive defect test detection evaluation coefficient ζ corresponding to the wind turbine in the specified wind farm under extreme weather is obtained, where λ1 and λ2 are the weight factors corresponding to the set comprehensive influencing factors and the weight factors corresponding to the comprehensive performance evaluation index, respectively, and U represents the threshold value of the standard control comprehensive defect test detection evaluation coefficient.

6. A wind turbine defect detection method for a wind farm according to claim 5, characterized in that: The analysis specifies the defect test detection process of wind turbines in a wind farm. The specific analysis process is as follows: The defect test and detection of wind turbines in a designated wind farm is divided into a first defect test and detection and a second defect test and detection. The first defect test and detection is a defect test and detection performed when the wind turbines in the designated wind farm have reached the defect test and detection cycle. The second defect test and detection is a defect test and detection that needs to be performed on the wind turbines in the designated wind farm before the defect test and detection cycle is reached under extreme weather conditions. Therefore, analyzing the defect test and detection process of the wind turbines in the designated wind farm is to analyze the first defect test and detection process and the second defect test and detection process.

7. A wind turbine defect detection method for a wind farm according to claim 6, characterized in that: The analysis of the first defect test detection process and the second defect test detection process is as follows: Obtain from the database the respective comprehensive performance evaluation index threshold intervals and the respective comprehensive defect test detection evaluation coefficient threshold intervals corresponding to the wind turbines in the specified wind farm, wherein the respective comprehensive performance evaluation index threshold intervals and the respective comprehensive defect test detection evaluation coefficient threshold intervals correspond to the defect detection levels in normal cycles and the defect detection levels in extreme weather conditions, respectively; compare the comprehensive performance evaluation index corresponding to the wind turbines in the specified wind farm with the respective comprehensive performance evaluation index threshold intervals; if the comprehensive performance evaluation index corresponding to the wind turbines in the specified wind farm belongs to a certain comprehensive performance evaluation index threshold interval, then the detection level corresponding to the wind turbines in the specified wind farm during the first defect test detection process is the normal cycle defect detection level corresponding to the comprehensive performance evaluation index threshold interval; The comprehensive defect test detection and evaluation coefficients corresponding to the wind turbines in the specified wind farm under extreme weather conditions are compared with the threshold intervals of each comprehensive defect test detection and evaluation coefficient. If the comprehensive defect test detection and evaluation coefficient belongs within a certain comprehensive defect test detection and evaluation coefficient threshold interval, the corresponding detection level of the wind turbines in the specified wind farm during the second defect test detection process is the defect detection level under extreme weather conditions corresponding to the comprehensive defect test detection and evaluation coefficient threshold interval.

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