A wind turbine protection enhancement method, medium, and system

Abstract

The application discloses a kind of wind turbine protection enhancement method, medium and system, method includes the following steps: S1, obtain wind turbine SCADA data, filter effective operating condition data satisfying preset power threshold;Wherein SCADA data includes variable pitch motor current, wind wheel azimuth, wind speed and pitch angle;S2, the variable pitch motor current, wind wheel azimuth, wind speed and pitch angle in wind turbine SCADA data are aligned on time stamp, obtain the data after alignment;S3, based on the data after alignment of step S2, carry out clearance protection, wind wheel imbalance detection and variable pitch motor early warning;Wherein the specific steps of clearance protection are: based on the data after alignment of step S2, dynamically delimit dangerous angle interval;Based on dangerous angle interval, according to impeller azimuth, carry out hierarchical control protection strategy, realize clearance analysis and protection to unit.The application has the advantages of significantly improving the safety, economy and reliability of unit.

Description

Technical Field

[0001] This invention mainly relates to the field of wind power technology, specifically to a method, medium, and system for enhancing the protection of wind turbine generators. Background Technology

[0002] Existing wind turbine protection technologies mostly rely on traditional sensors (such as accelerometers and position encoders) for status monitoring, which suffers from problems such as slow response, high cost, and susceptibility to environmental interference. For example, air clearance protection relies on mechanical limit switches or single-angle signals, which are difficult to cope with dynamic changes in rotor attitude under complex wind conditions; rotor imbalance detection requires manual calibration after shutdown, affecting power generation efficiency; and pitch motor fault warning relies on temperature or vibration sensors, which suffers from a high false alarm rate.

[0003] In summary, the existing technology has the following shortcomings: 1. The existing airspace protection mechanism is not adaptable to dynamic operating conditions and has a high risk of collision between blades and tower. It mainly relies on mechanical limit, fixed threshold control or lidar that is susceptible to environmental interference. It cannot cope with the dynamic deformation of blades under complex operating conditions (such as flapping and oscillation caused by gusts), dynamic attitude changes and signal distortion. It is prone to collision due to response lag when wind speed changes suddenly or in severe weather. 2. Wind turbine imbalance detection relies on manual intervention, which has low correction efficiency and interrupts power generation continuity. Traditional methods require manual installation of counterweights or use of special instruments after shutdown, which is time-consuming. 3. The pitch motor abnormality warning is inaccurate, the identification of potential faults is delayed, and there is a lack of working condition correlation analysis. The fixed threshold setting cannot adapt to the current difference at different pitch speeds, and cannot meet the needs of preventive maintenance. Summary of the Invention

[0004] To address the technical problems existing in the prior art, this invention provides a wind turbine protection enhancement method, medium, and system that significantly improves the safety, economy, and reliability of the unit.

[0005] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows: A method for enhancing the protection of wind turbine generators, comprising the following steps: S1. Obtain SCADA data of the wind turbine and filter valid operating condition data that meet the preset power threshold; the SCADA data includes pitch motor current, rotor azimuth angle, wind speed and pitch angle. S2. Align the pitch motor current, rotor azimuth, wind speed and pitch angle in the SCADA data of the wind turbine with the timestamps to obtain the aligned data. S3. Based on the data aligned in step S2, perform air clearance protection, wind turbine imbalance detection, and pitch motor early warning. The specific steps for airspace protection are as follows: Based on the data aligned in step S2, the dangerous angle range is dynamically defined; based on the dangerous angle range, a graded control and protection strategy is implemented according to the impeller azimuth angle to achieve airspace analysis and protection for the unit.

[0006] Preferably, the specific process for dynamically defining the danger angle range is as follows: The basic interval [A,B] is preset according to the unit design parameters; Real-time monitoring of wind speed v; when v exceeds the preset threshold v0, the dynamic danger angle range is divided into [A-Angle, B-Angle]; Where Angle = k(v - v0); k is set according to the blade stiffness and aerodynamic characteristics.

[0007] Preferably, the specific process of implementing a graded control and protection strategy based on the impeller azimuth angle within the dangerous angle range is as follows: When the impeller azimuth angle first enters the first 10% range of the dynamic danger angle interval, the duration t is less than the first preset time, and the wind speed is less than the first preset wind speed, then the blade pitch angle will be increased by 1-3° from the current value β. When the impeller azimuth angle enters the dynamic danger angle range of 10%-50% and remains within the second preset time, and the wind speed is at the second preset wind speed threshold, the current pitch angle will be increased by 3-6°, and the generator speed will be reduced from the current value by 5%-10%. When the impeller azimuth angle enters more than 50% of the dynamic danger angle range or exceeds the range for a third preset time, and the wind speed reaches near the cut-out wind speed, quickly feather the blades and reduce the speed to less than 50% of the rated speed.

[0008] Preferably, the specific steps for wind turbine imbalance detection are as follows: S301. Determine the stable wind speed range: Calculate the standard deviation of wind speed using the sliding window algorithm to confirm that the wind speed is within the stable range. S302. Constructing sample groups: Divide the impeller azimuth angle into 72 intervals every 5°, collect the pitch motor current amplitude of 3 blades in each interval, and accumulate at least 20 sample groups in each azimuth angle interval to ensure statistical significance. S303, Imbalance Judgment Algorithm: Under the same wind speed or azimuth angle, calculate the standard deviation of the three-phase blade current. If the standard deviation rate is greater than the preset threshold M, it is judged that the wind turbine is unbalanced, triggering automatic correction or alarm.

[0009] Preferably, the stable range in step S301 is from an inlet wind speed of 3 m / s to an outlet wind speed of 25 m / s.

[0010] Preferably, in the pitch motor early warning, under a given wind speed, there are different pitch currents at different pitch angles. The correlation between the pitch motor current of each blade and the pitch angle is analyzed to form 3 sets of variable pairs corresponding to the 3 blades. The grey relational coefficient of each set of variable pairs is calculated to obtain three sets of correlation coefficients to assess the health status of the pitch system of each blade.

[0011] Preferably, the specific process of pitch motor early warning is as follows: Pitch speed range division: Based on K-means clustering, wind speed is divided into low-speed, medium-speed, and high-speed ranges; Establish an interval current threshold model: fit the relationship between the pitch motor current and the pitch angle for each blade based on different wind speed intervals; Grey relational analysis: Calculate the grey relational coefficient of the pitch motor current-pitch angle for each blade; if the grey relational coefficient is less than the preset threshold N, an anomaly is determined to exist.

[0012] The present invention also discloses a computer program product, comprising a computer program that, when executed by a processor, performs the steps of the method described above.

[0013] The present invention further discloses a computer-readable storage medium having a computer program stored thereon, the computer program executing the steps of the method described above when run by a processor.

[0014] The present invention also discloses a wind turbine protection enhancement system, including a memory and a processor connected to each other, wherein the memory stores a computer program, and the computer program executes the steps of the method described above when run by the processor.

[0015] Compared with the prior art, the advantages of the present invention are as follows: The wind turbine protection enhancement method of this invention achieves multi-dimensional signal fusion, dynamic threshold adaptation, and full-process automation, significantly improving the safety, economy, and reliability of the turbine. Specifically, this invention collects SCADA data such as pitch motor current, rotor azimuth angle, and wind speed in real time, and performs multi-source data fusion and synchronization to provide reliable input for airspace protection, rotor imbalance detection, and pitch motor early warning; dynamically delineates dangerous angle ranges to effectively cope with dynamic blade deformation under complex wind conditions and reduce the risk of blade-to-tower collision; automatically identifies imbalance states through statistical sample construction and standard deviation analysis, reducing the need for manual calibration and improving power generation continuity; and detects potential faults in advance through clustering and grey relational analysis, reducing false alarm rates and extending equipment life. Overall, this invention improves the operational stability of the turbine, reduces annual power generation loss, lowers operation and maintenance costs, and is adaptable to various gust wind conditions, possessing high engineering application value.

[0016] This invention, based on SCADA data from wind turbines, constructs a multi-dimensional protection mechanism by real-time acquisition of pitch motor current signals and impeller azimuth angle, thereby improving the safety and stability of turbine operation under complex conditions, reducing manual intervention, and extending equipment lifespan. Specifically, it integrates pitch motor current signals with multi-dimensional parameters such as azimuth angle and wind speed to achieve enhanced protection without additional sensors; dynamically adjusts the current threshold based on operating conditions to improve detection accuracy in complex environments; and implements closed-loop control from fault detection to correction, eliminating the need for manual intervention and reducing operation and maintenance costs.

[0017] The present invention reduces the risk of blade-to-tower collision, provides early warning of pitch motor failure, and ensures high safety; improves the efficiency of wind turbine imbalance correction, reduces annual power generation loss, and is economical; the algorithm has a low false alarm rate, is adaptable to various gust wind conditions, and has high reliability. Attached Figure Description

[0018] Figure 1 This is a flowchart of an embodiment of the wind turbine protection enhancement method of the present invention.

[0019] Figure 2 This is a flowchart of an embodiment of the airspace protection method of the present invention.

[0020] Figure 3 This is a flowchart of an embodiment of the imbalance detection method of the present invention.

[0021] Figure 4 This is a flowchart of an embodiment of the pitch motor early warning method of the present invention. Detailed Implementation

[0022] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0023] like Figure 1 As shown, the wind turbine protection enhancement method provided in this embodiment of the invention includes the following steps: S1. Collect SCADA data of wind turbine in real time, check whether the data contains key variables such as pitch motor current, wind speed, wind turbine azimuth angle and pitch angle, filter valid operating condition data that meet the preset power threshold conditions, and perform data cleaning to ensure data validity and integrity. S2. The multi-source data such as pitch motor current, wind speed, wind turbine azimuth angle and pitch angle are fused together and timestamps are aligned to obtain the fused data, ensuring the spatiotemporal correlation of the data. S3. Based on the data fused in step S2, perform airspace protection, wind turbine imbalance detection, and pitch motor early warning. like Figure 2As shown, the specific steps of the airspace protection are as follows: Based on the data fused in step S2, the dangerous angle range is dynamically defined, and the impeller azimuth angle is monitored in real time to see if it enters the dangerous angle range; if it does, a graded control protection strategy is implemented (such as adjusting the pitch angle or reducing the speed) to achieve airspace analysis and protection for the unit. The process of dynamically defining the dangerous angle range is as follows: a basic range is preset according to the unit design parameters, and it automatically expands to both sides under high wind speed conditions to cope with the dynamic attitude changes caused by the increase in blade flapping. The basic range is the "benchmark framework" of the danger angle. It is pre-set based on the unit design parameters (such as blade length, tower height, minimum clearance safety distance, obstacle location, etc.) and reflects the fixed risk range of the unit under "ideal no wind or low wind speed" operating conditions.

[0024] Specifically, the minimum safe distance S between the blade and the tower. min Determine the starting angle of the base section; when the distance between the blade and the tower is ≤ S min When the azimuth range is defined, the corresponding range is the base interval. For example, assuming the base interval is preset to [A,B] (e.g., [350,10]), it corresponds to the downwind region where the blade is close to the tower.

[0025] The danger angle range is not a fixed value, but is adjusted in real time according to the change of real-time wind speed (the core influencing factor): the basic range is only applicable to low wind speed conditions, while at high wind speeds, the blades will generate flapping due to increased aerodynamic load (the amplitude of the blade's swing around the flapping axis; the higher the wind speed, the greater the flapping, and the actual trajectory of the blade tip deviates towards the tower). At this time, the fixed basic range may not be able to cover the actual risk range, and it needs to be expanded to both sides to achieve dynamic adjustment. Specifically: real-time monitoring of wind speed v, when v exceeds the preset threshold v0 (such as a value above the cut-in wind speed and below the rated wind speed), the expansion logic is activated: the changing angle is expanded according to the wind speed: Angle = k(v-v0), where k is set according to the blade stiffness and aerodynamic characteristics, and the final expanded dynamic danger angle range is defined as [A-Angle, B-Angle].

[0026] Based on the aforementioned dangerous angle range, graded control and protection are implemented according to the actual impeller azimuth angle, specifically including: 1. Warning Level (Low Risk) Triggering conditions: The duration of the impeller azimuth angle entering the edge of the danger angle range is less than 2 seconds, and the wind speed is low; Corresponding action: Slightly increase the propeller pitch angle (to reduce blade flapping) + system warning; Specifically, the duration t (first preset time) during which the impeller azimuth first enters the "edge region" of the dynamic dangerous angle interval (e.g., the first 10% range of the interval, for example, when the dangerous angle interval is [349, 11], its edge region is [349, 349.5] or [10.5, 11]) is short (t<2s), and the wind speed is low (v≤v1 (first preset wind speed), e.g., v1=12m / s, which does not reach the high risk threshold), then the pitch angle is increased by 1-3° from the current value β 2. Mild intervention level (medium risk) Trigger condition: The impeller azimuth enters the middle part of the dangerous angle interval and lasts for 2-5 seconds (second preset time), and the wind speed is medium; Corresponding operation: Significantly increase the pitch angle + slightly reduce the rotation speed + audible and visual alarm; Specifically, when the impeller azimuth enters the "middle region" of the dynamic dangerous angle interval (e.g., the 10%-50% range of the dynamic dangerous angle interval) and lasts for 2-5 seconds, and the wind speed is within the second preset threshold (v1<v≤v2, v2 is close to the rated wind speed), the current pitch angle is increased by 3-6°, and the generator rotation speed is reduced by 5%-10% from the current value at the same time.

[0027] 3. Emergency intervention level (high risk) Trigger condition: The impeller azimuth enters the deep region of the dangerous angle interval and lasts for ≥5 seconds (third preset time), and the wind speed is high; Corresponding operation: Rapid feathering (close to the shutdown angle) + significant rotation speed reduction, and emergency shutdown in extreme cases.

[0028] Specifically, when the impeller azimuth enters the "deep region" (more than 50% of the interval) of the dynamic dangerous angle interval or exceeds the interval, and the wind speed reaches near the cut-out wind speed, rapid feathering is performed, and the rotation speed is reduced to below 50% of the rated rotation speed.

[0029] As Figure 3 shown, the specific steps of wind wheel unbalance detection are: performing sample reconstruction on data such as the pitch motor current and wind wheel azimuth obtained in step S2, and calculating the current standard deviation; if it is greater than the threshold M, it is abnormal, otherwise it is normal, so as to realize the detection and analysis of blade unbalance. The specific steps are: S301. First determine the wind speed stable interval: use the sliding window algorithm to calculate the wind speed standard deviation, and calculate whether the wind speed is in a stable interval (cut-in wind speed 3m / s to cut-out wind speed 25m / s); S302. Construct a sample group: within the stable interval, divide 72 intervals every 5° of the impeller azimuth, collect the pitch motor current amplitude (three-phase blade current effective value) of the 3 blades in each interval, and accumulate at least 20 sets of samples in each azimuth interval to ensure statistical significance; S303, Imbalance Judgment Algorithm: Under the same wind speed or azimuth angle, calculate the standard deviation of the three-phase blade current. If the standard deviation rate is greater than the preset threshold M, it is judged that the wind turbine is unbalanced, triggering automatic correction or alarm.

[0030] like Figure 4 As shown, in the pitch motor early warning, under a certain wind speed, there are different pitch currents at different pitch angles. The correlation between the pitch motor current and the pitch angle of each blade is analyzed (pitch current - pitch angle data). Since wind turbines usually have 3 blades, 3 sets of variable pairs (i.e., 3 sets of "current - pitch angle" data) are formed. The grey relational coefficient of each set of variable pairs is calculated to obtain three sets of correlation coefficients to assess the health status of the pitch system of each blade.

[0031] The specific steps are as follows: S31. Pitch speed range division: Based on K-means clustering, the speed is divided into low-speed, medium-speed and high-speed ranges; S32. Establish an interval current threshold model: Fit the relationship between the current and pitch angle of the variable pitch motor based on different wind speed intervals, covering a 99.7% confidence interval. S33. Grey Relational Analysis: Calculate the grey relational coefficient between the three-phase motor current and the pitch angle. If the correlation is greater than the preset threshold N, then it is correlated; otherwise, it is determined that there is a local anomaly, thus achieving the early warning effect of the pitch motor.

[0032] Specifically, the Grey Relational Coefficient (GRC) measures the similarity between two sequences (variables) in their trends of change. Its value ranges from [0,1], with a value closer to 1 indicating a higher degree of correlation. The calculation steps are as follows: S331. Determine the sequence type: Reference sequence: Usually, the target variable is chosen, and the propeller pitch angle sequence is set as X_0; Comparison sequence: Influencing factors to be analyzed, pitch motor current sequence X_1; S332, Dimensionlessization: Since current and pitch angle have different dimensions, it is necessary to eliminate the influence of dimensions. Initialization is commonly used: each data point is divided by the first data point in the sequence to convert the sequence into a multiple relative to the initial value. S333. Calculate the absolute difference sequence: For the preprocessed sequence, calculate the absolute difference A between the comparison sequence and the reference sequence at each time step. i ; S334. Determine the range values: Minimum absolute difference: E min (Minimum difference across all time points and all comparison sequences); Maximum absolute difference: E max(the maximum difference at all moments and for all comparison sequences); S335. Calculate the grey correlation coefficient:

[0033] is the correlation coefficient of the i-th comparison sequence at the k-th moment; p: distinguishing coefficient, with a value range of (0,1), usually 0.5 (to balance sensitivity and stability) S336. Calculate the grey correlation: Average the correlation coefficients of the i-th comparison sequence to obtain the grey correlation degree R i :

[0034] Judgment rule: If R i <N (preset threshold), it is determined that the correlation between the current of the blade and the pitch angle is abnormal, and it is prompted that the pitch motor may have a fault (such as jamming, abnormal noise, abnormal current fluctuation, etc.).

[0035] The present invention realizes multi-dimensional signal fusion, dynamic threshold self-adaptation and full-process automation through the wind turbine protection enhancement algorithm based on the pitch motor current, which significantly improves the safety, economy and reliability of the unit. Specifically, the present invention collects SCADA data such as pitch motor current, impeller azimuth and wind speed in real time, and performs multi-source data fusion and synchronization to provide reliable inputs for clearance protection, wind wheel unbalance detection and pitch motor early warning; dynamically delineates the dangerous angle interval, effectively copes with the dynamic deformation of the blade under complex wind conditions, and reduces the risk of collision between the blade and the tower; through statistical sample construction and standard deviation analysis, the unbalanced state is automatically identified, the demand for manual calibration is reduced, and the power generation continuity is improved; through clustering and grey correlation analysis, potential faults are detected in advance, the false alarm rate is reduced, and the service life of the equipment is prolonged. On the whole, the present invention improves the operation stability of the unit, reduces the annual power generation loss, lowers the operation and maintenance cost, is suitable for various gust working conditions, and has high engineering application value.

[0036] Based on the SCADA data of wind turbines, the present invention constructs a multi-dimensional protection mechanism by collecting real-time data such as pitch motor current signals and impeller azimuth, which improves the safety and stability of unit operation under complex working conditions, reduces manual intervention, and prolongs the service life of equipment. Specifically, the pitch motor current signal is fused with multi-dimensional parameters such as azimuth angle and wind speed to realize protection enhancement without additional sensors; the current threshold is dynamically adjusted based on working conditions to improve the detection accuracy in complex environments; from fault detection to correction closed-loop control, no manual intervention is required, which reduces operation and maintenance costs.

[0037] The present invention reduces the risk of blade-to-tower collision, provides early warning of pitch motor failure, and ensures high safety; improves the efficiency of wind turbine imbalance correction, reduces annual power generation loss, and is economical; the algorithm has a low false alarm rate, is adaptable to various gust wind conditions, and has high reliability.

[0038] The present invention also discloses a computer program product, comprising a computer program that, when executed by a processor, performs the steps of the method described above.

[0039] The present invention further discloses a computer-readable storage medium having a computer program stored thereon, the computer program executing the steps of the method described above when run by a processor.

[0040] The present invention also discloses a wind turbine protection enhancement system based on pitch motor current, including a memory and a processor connected to each other. The memory stores a computer program, which executes the steps of the method described above when run by the processor.

[0041] The products, media, and systems of the present invention, corresponding to the methods described above, also possess the advantages described above.

[0042] The present invention can implement all or part of the processes in the methods of the above embodiments, or it can be implemented by hardware related to computer program instructions. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium includes: any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. The memory is used to store computer programs and / or modules. The processor implements various functions by running or executing the computer programs and / or modules stored in the memory, and by calling data stored in the memory. The memory may include high-speed random access memory, as well as non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital (SD) cards, flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0043] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method for enhancing the protection of wind turbine generators, characterized in that, Including the following steps: S1. Obtain SCADA data of the wind turbine and filter valid operating condition data that meet the preset power threshold; the SCADA data includes pitch motor current, rotor azimuth angle, wind speed and pitch angle. S2. Align the pitch motor current, rotor azimuth, wind speed and pitch angle in the SCADA data of the wind turbine with the timestamps to obtain the aligned data. S3. Based on the data aligned in step S2, perform air clearance protection, wind turbine imbalance detection, and pitch motor early warning. The specific steps for airspace protection are as follows: Based on the data aligned in step S2, the dangerous angle range is dynamically defined; based on the dangerous angle range, a graded control and protection strategy is implemented according to the impeller azimuth angle to achieve airspace analysis and protection for the unit. The specific process for dynamically defining the dangerous angle range is as follows: The basic interval [A,B] is preset according to the unit design parameters; Real-time monitoring of wind speed v; when v exceeds the preset threshold v0, the dynamic danger angle range is divided into [A-Angle, B-Angle]; Where Angle = k(v - v0); k is set according to the blade stiffness and aerodynamic characteristics; The specific steps for wind turbine imbalance detection are as follows: S301. Determine the stable wind speed range: Calculate the standard deviation of wind speed using the sliding window algorithm to confirm that the wind speed is within the stable range. S302. Constructing sample groups: Divide the impeller azimuth angle into 72 intervals every 5°, collect the pitch motor current amplitude of 3 blades in each interval, and accumulate at least 20 sample groups in each azimuth angle interval to ensure statistical significance. S303, Imbalance Judgment Algorithm: Under the same wind speed or azimuth angle, calculate the standard deviation of the three-phase blade current. If the standard deviation rate is greater than the preset threshold M, it is judged that the wind turbine is unbalanced, triggering automatic correction or alarm. In the pitch motor early warning system, given a fixed wind speed, different pitch currents occur at different pitch angles. The correlation between the pitch motor current and pitch angle for each blade is analyzed, forming three sets of variable pairs corresponding to the three blades. The grey relational coefficient of each set of variable pairs is calculated to obtain three sets of correlation coefficients to assess the health status of the pitch system for each blade.

2. The wind turbine protection enhancement method according to claim 1, characterized in that, The specific process of implementing a graded control and protection strategy based on the impeller azimuth angle within the dangerous angle range is as follows: When the impeller azimuth angle first enters the first 10% range of the dynamic danger angle interval, the duration t is less than the first preset time, and the wind speed is less than the first preset wind speed, then the blade pitch angle will be increased by 1-3° from the current value β. When the impeller azimuth angle enters the dynamic danger angle range of 10%-50% and remains within the second preset time, and the wind speed is at the second preset wind speed threshold, the current pitch angle will be increased by 3-6°, and the generator speed will be reduced from the current value by 5%-10%. When the impeller azimuth angle enters more than 50% of the dynamic danger angle range or exceeds the range for a third preset time, and the wind speed reaches near the cut-out wind speed, quickly feather the blades and reduce the speed to less than 50% of the rated speed.

3. The wind turbine protection enhancement method according to claim 1, characterized in that, The stable range in step S301 is from the cut-in wind speed of 3 m / s to the cut-out wind speed of 25 m / s.

4. The wind turbine protection enhancement method according to claim 1, characterized in that, The specific process of pitch motor early warning is as follows: Pitch speed range division: Based on K-means clustering, wind speed is divided into low-speed, medium-speed, and high-speed ranges; Establish an interval current threshold model: fit the relationship between the pitch motor current and the pitch angle for each blade based on different wind speed intervals; Grey relational analysis: Calculate the grey relational coefficient of the pitch motor current-pitch angle for each blade; If the gray relational coefficient is less than the preset threshold N, an anomaly is determined to exist.

5. A computer program product, comprising a computer program, characterized in that, The computer program is executed by the processor to perform the steps of the method as described in any one of claims 1-4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program, when run by a processor, performs the steps of the method as described in any one of claims 1-4.

7. A wind turbine protection enhancement system, comprising a memory and a processor interconnected, wherein the memory stores a computer program, characterized in that, The computer program, when run by a processor, performs the steps of the method as described in any one of claims 1-4.

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