Method, system, device and medium for diagnosing abnormalities of wind turbine blades

Abstract

The application provides a wind turbine blade anomaly diagnosis method, system, device and medium, comprising: controlling to execute a first stage diagnosis to determine a target abnormal blade; controlling to execute a second stage diagnosis to diagnose the fault type of the target abnormal blade; the step of controlling to execute the first stage diagnosis comprises: in response to the vibration amplitude signal being an abnormal vibration signal, acquiring a blade azimuth angle signal; performing abnormal blade detection according to the vibration amplitude signal and the blade azimuth angle signal to determine the target abnormal blade; the step of controlling to execute the second stage diagnosis comprises: diagnosing the fault type of the target abnormal blade according to a variable pitch current signal. The application adopts two-stage fault diagnosis, combines dynamic operation monitoring and static point testing with each other, and realizes accurate positioning of a blade abnormal diagnosis fault source through multi-dimensional signal fusion of a vibration amplitude signal, a blade azimuth angle signal and a variable pitch current signal, thereby enhancing the accuracy of a diagnosis result.

Description

Technical Field

[0001] This invention relates to the field of wind power generation equipment, and in particular to a method, system, equipment and medium for diagnosing abnormalities in wind turbine blades. Background Technology

[0002] With the widespread application of wind power in renewable energy, the operating status of wind turbine blades is crucial to the safe operation of wind turbine generators. If the blades crack, it can affect aerodynamic characteristics, preventing the unit from maximizing wind energy harvesting efficiency. In severe cases, it can cause aerodynamic imbalance and damage to the transmission chain. If left unrepaired for a long time, the blades will break during operation, leading to secondary damage such as tower sweeping and tower collapse.

[0003] Existing technologies identify the health status of blades by using video and sound characteristics to determine whether they are cracked, use the characteristic frequencies of vibration sensors installed in the nacelle to identify the risk of blade breakage or falling off, and use analog distance sensors to monitor bolts according to the distance corresponding to different pitch angles. However, these methods have vague fault source localization, are easily affected by external environmental interference, and do not involve early diagnosis of blade structural damage. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the defects of the prior art in blade abnormality diagnosis, such as vague fault source location and low accuracy of diagnosis results, and to provide a method, system, equipment and medium for diagnosing wind turbine blade abnormalities.

[0005] The present invention solves the above-mentioned technical problems through the following technical solution:

[0006] In a first aspect, the present invention provides a method for diagnosing abnormalities in wind turbine blades, the method comprising:

[0007] The control system performs the first stage of diagnosis to identify the target abnormal blade.

[0008] The control system performs the second-stage diagnosis to diagnose the fault type of the target abnormal blade.

[0009] The steps for controlling the execution of the first phase of diagnosis include:

[0010] Acquire vibration amplitude signals for dynamic operation monitoring of wind turbine blades;

[0011] In response to the vibration amplitude signal being an abnormal vibration signal, the blade azimuth angle signal is acquired;

[0012] Abnormal blades are detected based on the vibration amplitude signal and the blade azimuth angle signal to identify the target abnormal blade.

[0013] The steps for controlling the execution of the second-stage diagnostics include:

[0014] After the wind turbine generator is shut down, a static fixed-point test is performed on the wind turbine blades to obtain the pitch current signal corresponding to each blade.

[0015] The fault type is diagnosed for the target abnormal blade based on the pitch current signal.

[0016] Preferably, the step of detecting abnormal blades based on the vibration amplitude signal and the blade azimuth angle signal to determine the target abnormal blade includes:

[0017] The azimuth angle change information of each blade in a specific interval is filtered out from the blade azimuth angle signal;

[0018] Calculate the correlation strength information between the peak value change information of the vibration amplitude signal and the azimuth angle change information;

[0019] The target abnormal blade is determined based on the correlation strength information.

[0020] Preferably, fault type diagnosis of the target abnormal blade is performed based on the pitch current signal, including:

[0021] The key characteristic values ​​of the pitch current for each blade are calculated based on the pitch current signal; the key characteristic values ​​of the pitch current include at least one of the following: maximum current value, minimum current value, average current value, standard current value, peak current, and harmonic distortion rate.

[0022] Calculate the first difference information between the key characteristic value of the pitch current of the target abnormal blade and the set characteristic value threshold;

[0023] Calculate the second difference information between the key characteristic values ​​of the pitch current of each blade;

[0024] Fault type diagnosis is performed based on the first difference information and the second difference information.

[0025] Preferably, the fault type diagnosis based on the first difference information and the second difference information includes:

[0026] If both the first difference information and the second difference information are abnormal, the target abnormal blade is determined to have a serious structural fault.

[0027] If the first difference information is displayed normally and the second difference information is displayed abnormally, the target abnormal blade is determined to have a minor structural fault.

[0028] If both the first and second difference information are displayed normally, the target abnormal blade is determined to be a non-structural fault.

[0029] Preferably, the calculation of the second difference information between the key characteristic values ​​of the pitch current of each blade includes:

[0030] Calculate the ratio of the average current value of the target abnormal blade to the total average current value of all blades;

[0031] And / or, calculate the ratio of the average harmonic distortion rate of the target abnormal leaf to the total average harmonic distortion rate of all leaves.

[0032] Preferably, after the wind turbine generator is shut down, a static fixed-point test is performed on the wind turbine blades to obtain the pitch current signal corresponding to each blade.

[0033] After the wind turbine generator is shut down, each blade is locked to the set azimuth angle in sequence;

[0034] Each blade is controlled to perform uniform pitching motion within a set measurement angle range, and the pitching current signal of each blade is collected during the uniform pitching process.

[0035] Preferably, the diagnostic method further includes:

[0036] The control system performs the first stage of diagnosis, determining that the blade failure is non-structural.

[0037] And / or,

[0038] The vibration amplitude signal includes a first amplitude signal and a second amplitude signal. When either the first amplitude signal or the second amplitude signal exceeds the corresponding set amplitude threshold, the vibration amplitude signal is determined to be the abnormal vibration signal.

[0039] Secondly, the present invention provides a diagnostic system for abnormal wind turbine blades, the diagnostic system comprising:

[0040] The first control module is used to control the execution of the first stage of diagnosis and identify the target abnormal blade.

[0041] The second control module is used to control the execution of the second-stage diagnosis, and to diagnose the fault type of the target abnormal blade.

[0042] The first control module includes:

[0043] The first acquisition unit is used to acquire the vibration amplitude signal of the wind turbine blade dynamic operation monitoring;

[0044] The second acquisition unit is used to acquire the blade azimuth angle signal in response to the vibration amplitude signal being an abnormal vibration signal.

[0045] The first detection unit is used to detect abnormal blades based on the vibration amplitude signal and the blade azimuth angle signal to determine the target abnormal blade.

[0046] The second control module includes:

[0047] The second detection unit is used to perform static fixed-point testing on the wind turbine blades after the wind turbine generator is shut down to obtain the pitch current signal corresponding to each blade.

[0048] The diagnostic module is used to diagnose the fault type of the target abnormal blade based on the pitch current signal.

[0049] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and for running on the processor, wherein the processor executes the computer program to implement the method for diagnosing wind turbine blade abnormalities as described in the first aspect.

[0050] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for diagnosing abnormalities in wind turbine blades as described in the first aspect.

[0051] The significant advantages of this invention are as follows: Abnormal blades are detected based on vibration amplitude and blade azimuth signals to identify target abnormal blades; after controlling the wind turbine generator's start-up and shutdown operations, static fixed-point tests are performed on the wind turbine blades to obtain the pitch current signal corresponding to each blade; and fault type diagnosis is performed on the target abnormal blade based on the pitch current signal. This invention employs a two-stage fault diagnosis approach, combining dynamic operation monitoring and static fixed-point testing. By fusing multi-dimensional signals such as vibration amplitude, blade azimuth, and pitch current, it achieves precise location of the fault source in blade anomaly diagnosis, enhancing the accuracy of diagnostic results. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of the first process of the diagnostic method for wind turbine blade abnormalities according to Embodiment 1 of the present invention.

[0053] Figure 2 This is a schematic diagram of the second process of the diagnostic method for wind turbine blade abnormalities in Embodiment 1 of the present invention.

[0054] Figure 3 This is a schematic diagram of the third process of the diagnostic method for wind turbine blade abnormalities in Embodiment 1 of the present invention.

[0055] Figure 4 This is a schematic diagram of the fourth process of the diagnostic method for wind turbine blade abnormalities in Embodiment 1 of the present invention.

[0056] Figure 5 This is a schematic diagram of the module of the wind turbine blade abnormality diagnosis system of Embodiment 2 of the present invention.

[0057] Figure 6 This is a schematic diagram of the electronic device according to Embodiment 3 of the present invention. Detailed Implementation

[0058] The present disclosure is further illustrated below by way of embodiments, but the present disclosure is not limited to the scope of the embodiments described herein.

[0059] Example 1

[0060] like Figure 1 As shown in the figure, this embodiment provides a diagnostic method for abnormal wind turbine blades, the diagnostic method including:

[0061] S1. Control the execution of the first stage of diagnosis to identify the target abnormal blade;

[0062] S2. Control execution of the second stage of diagnosis, to diagnose the fault type of the target abnormal blade.

[0063] like Figure 2 As shown, step S1 includes:

[0064] S11. Acquire the vibration amplitude signal of the wind turbine blade dynamic operation monitoring;

[0065] S12. In response to the vibration amplitude signal being an abnormal vibration signal, obtain the blade azimuth angle signal;

[0066] S13. Detect abnormal blades based on vibration amplitude signals and blade azimuth angle signals to identify target abnormal blades.

[0067] This diagnostic method also includes:

[0068] The control system performs the first stage of diagnosis, determining that the blade failure is non-structural.

[0069] And / or,

[0070] The vibration amplitude signal includes a first amplitude signal and a second amplitude signal. When either the first amplitude signal or the second amplitude signal exceeds the corresponding set amplitude threshold, the vibration amplitude signal is determined to be an abnormal vibration signal. In some embodiments, the first amplitude signal is a 1P quadrature frequency amplitude signal, and the second amplitude signal is a 2P quadrature frequency amplitude signal.

[0071] Step S2 includes:

[0072] S21. After the wind turbine generator is shut down, a static fixed-point test is performed on the wind turbine blades to obtain the pitch current signal corresponding to each blade.

[0073] S22. Diagnose the fault type of the target abnormal blade based on the pitch current signal.

[0074] For steps S1-S2 above, several wind turbine generators are distributed throughout the large wind power plant. Dynamic operation monitoring is performed on the rotor blades of each wind turbine generator. The control system executes the first stage of diagnosis to determine if the blades are abnormal and to identify the target abnormal blade. Sensors are used to collect the vibration amplitude signal of the rotor. When an abnormal vibration amplitude signal is detected, the blade azimuth angle signal corresponding to each blade during the abnormal period is monitored. The target abnormal blade is identified based on the vibration amplitude signal and the blade azimuth angle signal. If the vibration amplitude signal is abnormal and the blade azimuth angle signal is not abnormal, the target abnormal blade cannot be identified, and the blade is judged to have a non-structural fault. The control system then executes the second stage of diagnosis, using the pitch current signal obtained from static fixed-point testing to diagnose the fault type of the abnormal blade. This two-stage fault diagnosis combines dynamic operation monitoring and static fixed-point testing. By fusing multi-dimensional signals such as vibration amplitude signal, blade azimuth angle signal, and pitch current signal, the system achieves precise location of the fault type for the target blade anomaly diagnosis, enhancing the accuracy of the diagnostic results.

[0075] An anomaly is identified if the first amplitude signal 1P and the second amplitude signal 2P simultaneously exceed their respective set amplitude thresholds, or if either the first amplitude signal 1P or the second amplitude signal 2P exceeds its corresponding set amplitude threshold. Further analysis of the vibration phase change using blade azimuth signals is then conducted to pinpoint the vibration source to each blade and identify potentially abnormal blades. For example, if the peak value of the vibration amplitude signal is strongly correlated with a specific azimuth range of 90°±5°, 180°±5°, and 270°±5° in the azimuth change information of a wind turbine blade, that blade is marked as a target abnormal blade. If no anomalies are found, the vibration amplitude signal anomaly is determined to be caused by external starting factors such as turbulence, tower shadow, or wind shear, and the wind turbine blades have no structural faults.

[0076] For steps S21-S22 above, after the wind turbine generator is shut down, static fixed-point tests are performed on the target abnormal blade and other wind turbine blades. Scanning tests are conducted under uniform pitch motion of the target abnormal blade and other wind turbine blades at a set test azimuth angle to simultaneously acquire pitch current signals from all three blades. A diagnostic report is generated by comparing the absolute threshold judgment results and the relative threshold judgment results of the key characteristic values ​​of the pitch current signals of each blade, accurately determining the fault type of the target abnormal blade: any one of the following: blade root bolt breakage or serious structural fault, blade root bolt breakage or minor structural fault, or no blade structural fault.

[0077] In this embodiment, a dual mechanism of dynamic monitoring of wind turbine blade operation and static current confirmation enables precise diagnosis from system anomalies to the specific fault of a particular blade, greatly reducing false alarms and missed alarms. Pitch current is extremely sensitive to changes in blade load, exhibiting anomalies in the early stages of structural damage, such as partial loss of bolt preload or the appearance of minute cracks in the blade. Early blade diagnosis is achieved through the characteristic values ​​of pitch current. The entire process can be automatically executed by the wind turbine's main controller, eliminating the need for manual onboard inspection, reducing maintenance costs and risks, and significantly improving the accuracy and intelligence of diagnosis.

[0078] In one embodiment, such as Figure 3 As shown, step S13 includes:

[0079] S131. Filter out the azimuth change information of each blade in a specific interval from the blade azimuth signal.

[0080] S132. Calculate the correlation strength information between the peak value change information and the azimuth angle change information of the vibration amplitude signal.

[0081] S133. Determine the target abnormal blade based on the correlation strength information.

[0082] For steps S131-S133 above, it is detected whether the ratio of the first vibration signal 1P and the second vibration signal 2P exceeds a set amplitude threshold. If it exceeds the set amplitude threshold and the duration exceeds a set time threshold, the wind turbine is determined to be vibrating abnormally. Alternatively, it is detected whether the first vibration signal 1P exceeds the corresponding first set amplitude threshold, or whether the second vibration signal 2P exceeds the corresponding second set amplitude threshold. If it exceeds either the first or second set amplitude threshold and the duration exceeds a set time threshold, the wind turbine is determined to be vibrating abnormally. The magnitude of each peak value and the peak time point are obtained from the peak change information. The magnitude of the azimuth angle and the azimuth angle time point corresponding to specific intervals of 90°±5°, 180°±5°, and 270°±5° are obtained from the azimuth angle signal of each blade. The correlation strength information between each peak time point and the azimuth angle time point in the specific interval is further detected. If the synchronous change indicates a strong correlation, the blade can be preliminarily determined to be the target abnormal blade. If the non-synchronous change indicates a weak correlation, the target abnormal blade can be preliminarily determined to be absent.

[0083] In one embodiment, such as Figure 4 As shown, step S22 includes:

[0084] S221. Calculate the key characteristic values ​​of the pitch current for each blade based on the pitch current signal; the key characteristic values ​​of the current include at least one of the following: maximum current value, minimum current value, average current value, standard current value, peak current, and harmonic distortion rate.

[0085] S222. Calculate the first difference information between the key characteristic value of the pitch current of the target abnormal blade and the set characteristic value threshold.

[0086] S223. Calculate the second difference information between the key characteristic values ​​of the pitch current of each blade;

[0087] S224. Perform fault type diagnosis based on the first difference information and the second difference information.

[0088] If both the first and second difference information show abnormalities, the target abnormal blade is determined to be a serious structural failure.

[0089] If the first difference information is normal and the second difference information is abnormal, the target abnormal blade is determined to be a minor structural fault.

[0090] If the first difference information is normal and the second difference information is not abnormal, the target abnormal blade is determined to be a non-structural fault.

[0091] For steps S221-S224 above, the key characteristic values ​​of the pitch current for each blade in each static fixed-point test are calculated based on the pitch current signal. If the first difference information corresponding to any key characteristic value of the pitch current of the target abnormal blade exceeds the absolute safety threshold, the absolute threshold judgment result shows an abnormality; otherwise, the absolute threshold judgment result shows normality. Second difference information is obtained by calculating the ratio of the average current value of the target abnormal blade to the total average current value of all blades, and / or by calculating the ratio of the average harmonic distortion rate of the target abnormal blade to the total average harmonic distortion rate of all blades. When the second difference information exceeds the relative safety threshold, the relative threshold judgment result shows an abnormality; otherwise, the relative threshold judgment result shows normality. If the key characteristic value of the pitch current of the suspected blade shows an abnormality in both the absolute and relative threshold judgments, the final diagnosis is that the blade root bolt is broken or there is a serious structural fault. If the key characteristic value of the pitch current of the suspected blade shows an abnormality in the relative threshold judgment, the final diagnosis is that the blade root bolt is broken or there is a minor structural fault. This method uses first and second difference information to accurately locate faulty blades, effectively eliminating environmental interference factors and improving the accuracy of blade fault diagnosis.

[0092] In one embodiment, step S21 specifically includes:

[0093] After the wind turbine generator is shut down, each blade is locked to the set azimuth angle in sequence;

[0094] Each blade is controlled to perform uniform pitch motion scanning within a set measurement angle range, and the pitch current signal of each blade is collected during the uniform pitch motion process.

[0095] Specifically, during the static constant current test, after the unit is shut down, the three blades are sequentially and precisely rotated to azimuth angles of 90 degrees (vertically upward) and 270 degrees (vertically downward), and the rotor is braked and locked. At each test azimuth angle (e.g., 90 degrees or 270 degrees), the blade under test is scanned with uniform pitch motion within a set measurement angle range (e.g., from 89 degrees to 0 degrees, and then from 0 degrees back to 89 degrees) to synchronously and at high frequency acquire the pitch current signals of the three blade pitch motors. This static test eliminates the interference of aerodynamic loads under complex operating conditions, and the acquired pitch current signals more accurately reflect the mechanical state of the blades and their connecting structures, resulting in a higher signal-to-noise ratio.

[0096] The application steps of the diagnostic method are illustrated below with a specific embodiment:

[0097] Data threshold setting stage: By presetting the vibration threshold of the first amplitude signal 1P and the second amplitude signal 2P, the absolute threshold of the pitch current characteristic value (e.g., the current standard deviation threshold is 1.5A) and the relative threshold of comparison (e.g., the characteristic value difference exceeds 30%) in the wind turbine main control system.

[0098] Triggering the first stage: The system detects the first amplitude signal 1P vibration lasting for 10 minutes and exceeding 0.08 m / s. 2 Analysis revealed that vibration peaks frequently occurred when blade A's azimuth angle was 0° (directly in front of the tower). The system automatically flagged blade A as the target abnormal blade and initiated a shutdown request. The second phase involved shutting down the unit and rotating blade A to a 90° azimuth angle, locking the rotor, and performing a pitch scan from 89° to 0° to 89° while recording the blade pitch current. Blade A was then rotated to a 270° azimuth angle, and the above test was repeated. Blades B and C were scanned in the same manner. Data analysis and diagnosis phase: Calculations showed that at both 90° and 270° positions, the standard deviation of blade A's pitch current reached 2.2A and 2.5A, respectively, far exceeding the absolute threshold of 1.5A. A relative comparison revealed that the standard deviation of blade A's current was approximately 2.8 times that of blades B and C, far exceeding the 30% relative comparison threshold.

[0099] Output report stage: An automatic diagnostic report is generated showing abnormal pitch current fluctuations in blade A at azimuth angles of 90° and 270°, with characteristic values ​​significantly deviating from normal blades, diagnosing it as a high-risk failure of the blade root bolt connection. Simultaneously, a high-level alarm is issued on the monitoring interface, prompting maintenance personnel to prioritize checking the blade root bolts, pitch bearing, and blade appearance of blade A.

[0100] This embodiment provides a diagnostic method for wind turbine blade anomalies. It adopts a two-stage fault diagnosis approach, combining dynamic operation monitoring and static fixed-point testing. By fusing multi-dimensional signals such as vibration amplitude signal, blade azimuth angle signal, and pitch current signal, it can accurately locate the fault source of blade anomaly diagnosis and enhance the accuracy of the diagnostic results.

[0101] Example 2

[0102] This embodiment provides a diagnostic system for wind turbine blade abnormalities, such as... Figure 5 As shown, the diagnostic system includes:

[0103] The first control module 310 is used to control the execution of the first stage diagnosis to identify the target abnormal blade.

[0104] The second control module 320 is used to control the execution of the second-stage diagnosis and to diagnose the fault type of the target abnormal blade.

[0105] The first control module 310 includes:

[0106] The first acquisition unit 311 is used to acquire the vibration amplitude signal of the wind turbine blade dynamic operation monitoring;

[0107] The second acquisition unit 312 is used to acquire the blade azimuth angle signal in response to the vibration amplitude signal being an abnormal vibration signal.

[0108] The first detection unit 313 is used to detect abnormal blades based on vibration amplitude signals and blade azimuth angle signals to determine the target abnormal blade.

[0109] Specifically, the first detection unit 313 is also used for:

[0110] The control system performs the first stage of diagnosis and determines that the blade has a non-structural fault. When the vibration amplitude signal is an abnormal vibration signal and the blade azimuth angle signal is a non-abnormal azimuth angle signal, the blade is determined to have a non-structural fault.

[0111] And / or, the vibration amplitude signal includes a first amplitude signal and a second amplitude signal, and the second acquisition unit 312 is further configured to:

[0112] When either the first amplitude signal or the second amplitude signal exceeds the corresponding set amplitude threshold, the vibration amplitude signal is determined to be an abnormal vibration signal. In some embodiments, the first amplitude signal is a 1P quadrature frequency amplitude signal, and the second amplitude signal is a 2P quadrature frequency amplitude signal.

[0113] The second control module 320 includes:

[0114] The second detection unit 321 is used to perform static fixed-point testing on the wind turbine blades after the wind turbine generator is shut down to obtain the pitch current signal corresponding to each blade.

[0115] The diagnostic unit 322 is used to diagnose the fault type of the target abnormal blade based on the pitch current signal.

[0116] Several wind turbine generators are distributed throughout a large wind power plant. Dynamic operation monitoring is performed on the rotor blades of each generator. The first control module 310 controls the execution of the first-stage diagnosis to determine if the blades are abnormal and to identify the target abnormal blade. Sensors collect vibration amplitude signals of the rotor, and when an abnormal vibration amplitude signal is detected, the blade azimuth angle signal corresponding to each blade during the abnormal period is monitored. The target abnormal blade is identified based on the vibration amplitude signal and the blade azimuth angle signal. If the vibration amplitude signal is abnormal and the blade azimuth angle signal is not abnormal, the target abnormal blade cannot be identified, and the blade is judged to have a non-structural fault. The second control module 320 controls the execution of the second-stage diagnosis, using pitch current signals obtained from static fixed-point testing to diagnose the fault type of the abnormal blade. This two-stage fault diagnosis combines dynamic operation monitoring and static fixed-point testing. By fusing multi-dimensional signals such as vibration amplitude signals, blade azimuth angle signals, and pitch current signals, the fault type of the target blade is accurately located, enhancing the accuracy of the diagnostic results.

[0117] The vibration amplitude signal acquired by the first acquisition unit 311 includes a first amplitude signal 1P corresponding to a first rotation frequency and a second amplitude signal 2P corresponding to a second rotation frequency. If the first amplitude signal 1P and the second amplitude signal 2P continuously exceed limits, or if either the first amplitude signal or the second amplitude signal exceeds a corresponding set amplitude threshold, an abnormality is determined. The first detection unit 313 further analyzes the vibration phase change in conjunction with the blade azimuth angle signal acquired by the second acquisition unit 312, pinpointing the vibration source to each blade to screen out suspicious target abnormal blades. For example, if the peak value of the vibration amplitude signal is strongly correlated with a specific azimuth angle range of 90°±5°, 180°±5°, and 270°±5° in the azimuth angle change information of a certain wind turbine blade, then that blade is marked as a target abnormal blade. When no abnormality is found, it is determined that the abnormality in the vibration amplitude signal is caused by external starting factors such as turbulence, tower shadow, or wind shear, and the wind turbine blades have no structural faults.

[0118] After the wind turbine generator is shut down, the second detection unit 321 performs static fixed-point tests on the target abnormal blade and other wind turbine blades. Through scanning tests under uniform pitch motion of the target abnormal blade and other wind turbine blades at a set test azimuth angle, the pitch current signals of the three blades are simultaneously acquired. The diagnostic module 322 generates a diagnostic report by comparing the absolute threshold judgment results and the relative threshold judgment results of the key characteristic values ​​of the pitch current signals of each blade, accurately determining the fault type of the target abnormal blade: any one of the following: blade root bolt breakage or serious structural fault, blade root bolt breakage or minor structural fault, or no blade structural fault.

[0119] In this embodiment, a dual mechanism of dynamic monitoring of wind turbine blade operation and static current confirmation enables precise diagnosis from system anomalies to the specific fault of a particular blade, greatly reducing false alarms and missed alarms. Pitch current is extremely sensitive to changes in blade load, exhibiting anomalies in the early stages of structural damage, such as partial loss of bolt preload or the appearance of minute cracks in the blade. Early blade diagnosis is achieved through the characteristic values ​​of pitch current. The entire process can be automatically executed by the wind turbine's main controller, eliminating the need for manual onboard inspection, reducing maintenance costs and risks, and significantly improving the accuracy and intelligence of diagnosis.

[0120] In one embodiment, the first detection unit 313 is specifically used for:

[0121] Extract the azimuth angle change information of each blade in a specific interval from the blade azimuth angle signal;

[0122] Calculate the correlation strength information between the peak value variation information and the azimuth angle variation information of the vibration amplitude signal;

[0123] The target anomalous blade is identified based on the correlation strength information.

[0124] Specifically, the ratio of the first vibration signal 1P to the second vibration signal 2P is detected to exceed a set amplitude threshold. If the ratio exceeds the set amplitude threshold and the duration exceeds a set time threshold, the wind turbine is determined to be vibrating abnormally. Alternatively, the first vibration signal 1P is detected to exceed a corresponding first set amplitude threshold, or the second vibration signal 2P is detected to exceed a corresponding second set amplitude threshold. If either the first or second set amplitude threshold is exceeded and the duration exceeds a set time threshold, the wind turbine is determined to be vibrating abnormally. The filtering unit 3211 and the first calculation unit 3212 acquire the magnitude of each peak value and the peak time point from the peak change information, and acquire the magnitude of the azimuth angle and the azimuth angle time point corresponding to specific intervals of 90°±5°, 180°±5°, and 270°±5° from the azimuth angle signal of each blade. Further testing is conducted to determine the correlation strength between each peak time point and the azimuth time point within a specific interval. If the synchronous changes indicate a strong correlation, the blade can be preliminarily identified as the target anomalous blade. If the non-synchronous changes indicate a weak correlation, the absence of a target anomalous blade can be preliminarily identified.

[0125] In one embodiment, the diagnostic unit 322 is specifically used for:

[0126] The key characteristic values ​​of the pitch current for each blade are calculated based on the pitch current signal. The key characteristic values ​​of the current include at least one of the following: maximum current value, minimum current value, average current value, standard current value, peak current, and harmonic distortion rate.

[0127] Calculate the first difference information between the key characteristic value of the pitch current of the target abnormal blade and the set characteristic value threshold;

[0128] Calculate the second difference information between the key characteristic values ​​of the pitch current of each blade;

[0129] Fault type diagnosis is performed based on the first and second difference information.

[0130] If both the first and second difference information show abnormalities, the target abnormal blade is determined to be a serious structural failure.

[0131] If the first difference information is normal and the second difference information is abnormal, the target abnormal blade is determined to be a minor structural fault.

[0132] If the first difference information is normal and the second difference information is not abnormal, the target abnormal blade is determined to be a non-structural fault.

[0133] Specifically, based on the pitch current signal, the key characteristic value of the pitch current for each blade in each static fixed-point test is calculated. If the first difference information corresponding to any key characteristic value of the pitch current of the target abnormal blade exceeds the absolute safety threshold, the absolute threshold judgment result shows an abnormality; otherwise, the absolute threshold judgment result shows normality. Second difference information is obtained by calculating the ratio of the average current value of the target abnormal blade to the total average current value of all blades, and / or by calculating the ratio of the average harmonic distortion rate of the target abnormal blade to the total average harmonic distortion rate of all blades. When the second difference information exceeds the relative safety threshold, the relative threshold judgment result shows an abnormality; otherwise, the relative threshold judgment result shows normality. If the key characteristic value of the pitch current of the suspected blade shows an abnormality in both the absolute and relative threshold judgments, the final diagnosis is that the blade root bolt is broken or there is a serious structural fault. If the key characteristic value of the pitch current of the suspected blade shows an abnormality in the relative threshold judgment, the final diagnosis is that the blade root bolt is broken or there is a minor structural fault. This method uses first and second difference information to accurately locate faulty blades, effectively eliminating environmental interference factors and improving the accuracy of blade fault diagnosis.

[0134] In one embodiment, the second detection unit 321 is specifically used for:

[0135] After the wind turbine generator is shut down, each blade is locked to the set azimuth angle in sequence;

[0136] Each blade is controlled to perform uniform pitch motion scanning within a set measurement angle range, and the pitch current signal of each blade is collected during the uniform pitch motion process.

[0137] Specifically, after the unit stops, the three blades are sequentially and precisely rotated to azimuth angles of 90 degrees (vertically upward) and 270 degrees (vertically downward), and the rotor is braked and locked. At each test azimuth angle (e.g., 90 degrees or 270 degrees), the blade under test is scanned with uniform pitch motion within a set measurement angle range (e.g., from 89 degrees to 0 degrees, and then from 0 degrees back to 89 degrees) to synchronously and at high frequency acquire the pitch current signals of the three blade pitch motors. This static test eliminates the interference of aerodynamic loads under complex operating conditions, and the acquired pitch current signals more accurately reflect the mechanical state of the blades and their connecting structures, resulting in a higher signal-to-noise ratio.

[0138] In this embodiment, a diagnostic system for wind turbine blade anomalies is provided. The first control module and the second control module adopt a two-stage fault diagnosis, which combines dynamic operation monitoring and static fixed-point testing. By fusing multi-dimensional signals such as vibration amplitude signal, blade azimuth angle signal and pitch current signal, the diagnostic module can accurately locate the fault source of blade anomaly diagnosis and improve the accuracy of the diagnostic results.

[0139] Example 3

[0140] Figure 6 This is a schematic diagram of the structure of an electronic device provided in this embodiment. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method for diagnosing wind turbine blade anomalies as described in Embodiment 1. Figure 6 The electronic device 60 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0141] Electronic device 60 may be in the form of a general-purpose computing device, such as a server device. Components of electronic device 60 may include, but are not limited to: at least one processor 61, at least one memory 62, and a bus 63 connecting different system components (including memory 62 and processor 61).

[0142] Bus 63 includes a data bus, an address bus, and a control bus.

[0143] The memory 62 may include volatile memory, such as random access memory (RAM) 621 and / or cache memory 622, and may further include read-only memory (ROM) 623.

[0144] The memory 62 may also include a program tool 625 having a set (at least one) of program modules 624, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0145] The processor 61 executes various functional applications and data processing by running computer programs stored in the memory 62, such as the method for diagnosing abnormal wind turbine blades in Embodiment 1 of the present invention.

[0146] Electronic device 60 can also communicate with one or more external devices 64 (e.g., keyboard, pointing device, etc.). This communication can be performed via input / output (I / O) interface 65. Furthermore, the model-generated device 630 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public network, such as the Internet) via network adapter 66. As shown, network adapter 66 communicates with other modules of model-generated device 60 via bus 63. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with model-generated device 60, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0147] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0148] Example 4

[0149] This embodiment also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method for diagnosing wind turbine blade anomalies in Embodiment 1.

[0150] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0151] In a possible implementation, the present invention can also be implemented as a program product comprising program code, which, when the program product is run on a terminal device, is used to cause the terminal device to perform the steps of the method for diagnosing wind turbine blade anomalies according to Embodiment 1.

[0152] The program code for executing the present invention can be written in any combination of one or more programming languages. The program code can be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0153] While specific embodiments of the present invention have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of the present invention is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of the present invention, but all such changes and modifications fall within the scope of protection of the present invention.

Claims

1. A method for diagnosing abnormalities in wind turbine blades, characterized in that, The diagnostic method includes: The control system performs the first stage of diagnosis to identify the target abnormal blade. The control system performs the second-stage diagnosis to diagnose the fault type of the target abnormal blade. The steps for controlling the execution of the first phase of diagnosis include: Acquire vibration amplitude signals for dynamic operation monitoring of wind turbine blades; In response to the vibration amplitude signal being an abnormal vibration signal, the blade azimuth angle signal is acquired; Abnormal blades are detected based on the vibration amplitude signal and the blade azimuth angle signal to identify the target abnormal blade. The steps for controlling the execution of the second-stage diagnostics include: After the wind turbine generator is shut down, a static fixed-point test is performed on the wind turbine blades to obtain the pitch current signal corresponding to each blade. The fault type is diagnosed for the target abnormal blade based on the pitch current signal.

2. The method for diagnosing wind turbine blade abnormalities as described in claim 1, characterized in that, The step of detecting abnormal blades based on the vibration amplitude signal and the blade azimuth angle signal to determine the target abnormal blade includes: The azimuth angle change information of each blade in a specific interval is filtered out from the blade azimuth angle signal; Calculate the correlation strength information between the peak value change information of the vibration amplitude signal and the azimuth angle change information; The target abnormal blade is determined based on the correlation strength information.

3. The method for diagnosing wind turbine blade abnormalities as described in claim 1, characterized in that, The step of diagnosing the fault type of the target abnormal blade based on the pitch current signal includes: The key characteristic values ​​of the pitch current for each blade are calculated based on the pitch current signal; the key characteristic values ​​of the pitch current include at least one of the following: maximum current value, minimum current value, average current value, standard current value, peak current, and harmonic distortion rate. Calculate the first difference information between the key characteristic value of the pitch current of the target abnormal blade and the set characteristic value threshold; Calculate the second difference information between the key characteristic values ​​of the pitch current of each blade; Fault type diagnosis is performed based on the first difference information and the second difference information.

4. The method for diagnosing wind turbine blade abnormalities as described in claim 3, characterized in that, The fault type diagnosis based on the first difference information and the second difference information includes: If both the first difference information and the second difference information are abnormal, the target abnormal blade is determined to have a serious structural fault. If the first difference information is displayed normally and the second difference information is displayed abnormally, the target abnormal blade is determined to have a minor structural fault. If both the first and second difference information are displayed normally, the target abnormal blade is determined to be a non-structural fault.

5. The method for diagnosing wind turbine blade abnormalities as described in claim 3, characterized in that, The calculation of the second difference information between the key characteristic values ​​of the pitch current of each blade includes: Calculate the ratio of the average current value of the target abnormal blade to the total average current value of all blades; And / or, calculate the ratio of the average harmonic distortion rate of the target abnormal leaf to the total average harmonic distortion rate of all leaves.

6. The method for diagnosing wind turbine blade abnormalities as described in claim 1, characterized in that, After the wind turbine generator is shut down, a static fixed-point test is performed on the wind turbine blades to obtain the pitch current signal corresponding to each blade. After the wind turbine generator is shut down, each blade is locked to the set azimuth angle in sequence; Each blade is controlled to perform uniform pitching motion within a set measurement angle range, and the pitching current signal of each blade is collected during the uniform pitching process.

7. The method for diagnosing wind turbine blade abnormalities as described in claim 1, characterized in that, The diagnostic method further includes: controlling the execution of the first stage of diagnosis to determine that the blade has a non-structural fault; And / or, The vibration amplitude signal includes a first amplitude signal and a second amplitude signal. When either the first amplitude signal or the second amplitude signal exceeds the corresponding set amplitude threshold, the vibration amplitude signal is determined to be the abnormal vibration signal.

8. A diagnostic system for abnormal wind turbine blades, characterized in that, The diagnostic system includes: The first control module is used to control the execution of the first stage of diagnosis and identify the target abnormal blade. The second control module is used to control the execution of the second-stage diagnosis, and to diagnose the fault type of the target abnormal blade. The first control module includes: The first acquisition unit is used to acquire the vibration amplitude signal of the wind turbine blade dynamic operation monitoring; The second acquisition unit is used to acquire the blade azimuth angle signal in response to the vibration amplitude signal being an abnormal vibration signal. The first detection unit is used to detect abnormal blades based on the vibration amplitude signal and the blade azimuth angle signal to determine the target abnormal blade. The second control module includes: The second detection unit is used to perform static fixed-point testing on the wind turbine blades after the wind turbine generator is shut down to obtain the pitch current signal corresponding to each blade. The diagnostic module is used to diagnose the fault type of the target abnormal blade based on the pitch current signal.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and for running on the processor, characterized in that, When the processor executes the computer program, it implements the method for diagnosing abnormal wind turbine blades as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for diagnosing abnormalities in the blades of a wind turbine generator set as described in any one of claims 1-7.

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