Wind turbine generator blade zero offset detection method and equipment

Abstract

The invention discloses a method and equipment for detecting zero offset of blades of a wind turbine generator. The method comprises the following steps: acquiring respective variable-pitch motor torque values, impeller azimuth angles, wind speeds, unit output power and variable-pitch modes of three blades in a running state of the unit in real time; carrying out data screening, and judging that the collected data is effective analysis data when a screening condition is met; aiming at the effective analysis data, performing Coleman transformation by taking an impeller azimuth angle as a transformation angle, and calculating a direct-axis component and a quadrature-axis component of a variable-pitch motor torque under a rotating coordinate system; calculating a direct-axis component average value and a quadrature-axis component average value in a continuous period of time, and if the average values exceed a preset threshold value, judging that zero offset of the blade exists; and a two-dimensional diagnosis vector space is constructed, calculation and analysis of diagnosis vectors are carried out, and the specific blade with zero offset, the direction of the zero offset and the degree of the zero offset are obtained. Existing signals during operation of the fan are completely utilized, shutdown is not needed, power generation is not affected, cost is low, and accuracy is high.

Description

Technical Field

[0001] This invention relates to the field of wind power generation technology, and more specifically to a method and device for detecting zero-position offset of wind turbine blades. Background Technology

[0002] A wind turbine rotor consists of three blades. The accuracy of the blade's installation zero position (i.e., the mechanical position of the pitch mechanism when the aerodynamic angle of attack is zero degrees) is crucial to the turbine's aerodynamic balance, power generation efficiency, and fatigue load on key mechanical components (such as main bearings, gearboxes, and towers). If the zero position deviates, it will lead to aerodynamic imbalance of the rotor, causing periodic additional loads, reducing power generation, and shortening equipment lifespan.

[0003] Currently, the detection and correction of blade zero-position mainly relies on two methods: First, manual measurement and calibration are performed on-site using equipment such as theodolites and laser trackers after the turbine is shut down. This method is time-consuming, labor-intensive, costly, and impacts power generation. Second, additional sensors are installed, such as fiber optic strain sensors at the blade root, to directly measure load imbalance and then infer the zero-position problem. This method increases system complexity and initial investment.

[0004] In the field of intelligent operation and maintenance of wind power, although various fault diagnosis methods exist, such as control methods for dynamic blade stall, detection of yaw anomalies, or optimization control of the entire power generation system, a solution specifically utilizing the drive signal (pitch motor torque) of the existing pitch system of the wind turbine for online monitoring of blade zero position remains a gap. The pitch motor torque signal directly reflects the resistance that needs to be overcome to drive the blade rotation, containing rich information about aerodynamic loads. However, this signal is simultaneously subject to multiple interferences such as active intervention from the control system, friction, and inertial forces. How to stably and accurately extract the characteristic quantities representing zero position deviation from it has been a long-standing technical challenge. Summary of the Invention

[0005] The purpose of this invention is to propose a method for detecting the zero-position offset of wind turbine blades in order to solve the problems existing in the prior art.

[0006] To achieve the above objectives, the first aspect of this invention proposes a method for detecting the zero-position offset of wind turbine blades, comprising: S1. Real-time acquisition of the pitch motor torque value, impeller azimuth angle, wind speed, unit output power, and pitch mode of each of the three blades under the unit's operating status. S2. Filter the data according to the set filtering conditions. When the filtering conditions are met, the collected data is determined to be valid analysis data. S3. Based on the valid analysis data, the Coleman transformation is performed on the torque values ​​of the three pitch motors using the impeller azimuth angle as the transformation angle, and the direct-axis and quadrature-axis components of the pitch motor torque in the rotating coordinate system are calculated. S4. For the direct axis component sequence and the quadrature axis component sequence within a continuous time window, calculate the average value of the direct axis component and the average value of the quadrature axis component respectively. If the absolute value of the average value of the direct axis component or the absolute value of the average value of the quadrature axis component exceeds the preset threshold, it is determined that there is a blade zero position offset. S5. Construct a two-dimensional diagnostic vector space with the average value of the direct axis component Td_mean as the abscissa and the average value of the cross axis component Tq_mean as the ordinate. Combine this two-dimensional diagnostic vector space to calculate and analyze the diagnostic vectors to obtain the specific blade that has zero position shift, the direction of the zero position shift, and the degree of the zero position shift.

[0007] Furthermore, the screening criteria for step S2 are: the unit is in a uniform pitch mode, the wind speed and the unit output power are within a preset range, and the standard deviation of the wind speed within a predetermined time period does not exceed the set value.

[0008] Furthermore, the Coleman transform formula for step S3 is: ; Where θ is the impeller azimuth angle; T1, T2, and T3 are the pitch motor torque values ​​of the three blades respectively; Td is the direct axis component, which mainly represents the aerodynamic unbalance torque in the direction perpendicular to the wind turbine plane, i.e., the flapping direction; and Tq is the quadrature axis component, which mainly represents the aerodynamic unbalance torque in the wind turbine plane, i.e., the oscillation direction.

[0009] Furthermore, the preset threshold in step S4 is determined statistically based on the standard deviation of the direct-axis component or quadrature-axis component under normal operating conditions of the unit.

[0010] Furthermore, step S5 specifically includes the following steps: The diagnostic vector space is divided into three angular intervals corresponding to the three blades respectively; The phase angle of the diagnostic vector is calculated based on the average value of the direct axis component and the average value of the quadrature axis component; Based on the angle range of the diagnostic vector phase angle, determine the blade number that has experienced zero-position offset.

[0011] Furthermore, the formula for calculating the phase angle of the diagnostic vector is: .

[0012] Furthermore, step S5 specifically includes the following steps: Establish a circular region with the ideal aerodynamic equilibrium state coordinates as the center and a preset threshold as the radius, as the normal fluctuation range; The degree of blade zero-position offset is determined by the degree to which the coordinate position of the diagnostic vector in the diagnostic vector space deviates from the circular region.

[0013] Furthermore, the degree of zero-position offset in step S5 is determined based on the magnitude of the diagnostic vector. The formula for calculating the magnitude of the diagnostic vector is: .

[0014] Furthermore, the direction of the zero-position offset in step S5 is determined based on the sign of the average value of the direct-axis component of the pitch motor torque: When Tdmean>0, it is determined that the zero point is too large, that is, the actual installation angle is greater than the theoretical value; When Td_mean < 0, it is determined that the zero position is too small, that is, the actual installation angle is less than the theoretical value.

[0015] A second aspect of the present invention provides a computing device comprising: Memory, used to store computer programs; A processor is used to implement the above-described method for detecting zero-position offset of wind turbine blades when executing the computer program.

[0016] Compared with the prior art, the present invention has the following advantages: 1) Online non-destructive monitoring: It makes full use of the existing signals during the operation of the wind turbine, without stopping the machine for testing, and does not affect power generation, realizing the transformation from "periodic check-up" to "real-time monitoring".

[0017] 2) Low cost: No additional special sensors (such as expensive load sensors) are required, which greatly reduces the implementation cost and makes it easy to promote on a large scale in existing and new wind turbines.

[0018] 3) Dual-component joint diagnosis with strong anti-interference capability: The direct-axis component Td and quadrature-axis component Tq of the pitch motor torque are obtained simultaneously through Coleman transformation, and a diagnostic vector space is constructed for analysis. The direct-axis component Td is sensitive to zero-position offset and is the main diagnostic indicator; the quadrature-axis component Tq provides phase information to assist in the location of faulty blades. The combined use of the two effectively eliminates the periodic fluctuations caused by impeller rotation and the random interference caused by turbulence, directly pointing to the essential characteristic of steady-state aerodynamic imbalance caused by zero-position offset, thus improving the accuracy of detection.

[0019] 4) Early warning: It can sensitively detect minute zero-point offset trends and issue warnings before problems cause serious performance degradation or component damage, supporting predictive maintenance.

[0020] 5) Distinct from existing technical approaches: Unlike physical measurement methods based on laser scanning, and unlike other patents targeting stall, yaw, or system control, this invention opens up a completely new technical path for aerodynamic balance diagnosis based on drive system feedback signals and coordinate transformation theory.

[0021] 6) A complete diagnostic chain is realized: This invention can not only detect the existence of zero position offset, but also accurately locate the specific faulty blade, determine the offset direction and estimate the offset amount, forming a complete diagnostic chain from detection to location and then to quantification, which greatly improves maintenance efficiency.

[0022] 7) Clear physical principles: By establishing a diagnostic vector space model, complex time-domain signal analysis is transformed into intuitive geometric space analysis, with clear physical meaning and strong interpretability of diagnostic results.

[0023] 8) Support intelligent maintenance decision-making: The diagnostic results preliminarily determine the blade number and adjustment direction that need to be adjusted, so that the on-site maintenance work is transformed from "experience-driven" to "data-driven". Attached Figure Description

[0024] The following figures are included as part of this invention for understanding its principles. The figures illustrate embodiments of the invention and their descriptions, serving to explain the apparatus and principles of the invention. In the figures, Figure 1 This is a flowchart of the wind turbine blade zero-position offset detection method according to an embodiment of the present invention; Figure 2 A schematic diagram comparing the direct-axis component of the pitch motor torque signal when the impeller is aerodynamically balanced with when there is zero position offset; Figure 3 A schematic diagram comparing the cross-axis component signals of the pitch motor torque when the impeller is aerodynamically balanced with when there is zero position offset; Figure 4 This is a schematic diagram of the diagnostic vector space according to an embodiment of the present invention. Detailed Implementation

[0025] The present application will now be described in more detail with reference to the accompanying drawings. The following embodiments are only used to illustrate the technical solutions of the present application more clearly and are not intended to limit the scope of protection of the present application.

[0026] This invention provides a method for detecting the zero-position offset of wind turbine blades, such as... Figure 1 As shown, the method includes: Step 1: Real-time acquisition of the pitch motor torque values ​​T1, T2, and T3 of each of the three blades, impeller azimuth angle θ, wind speed, unit output power, and pitch mode under the operating status of the unit.

[0027] The output torque of the pitch motor can be calculated from the current of the unit's frequency converter, and the impeller azimuth angle can be obtained from the absolute encoder located at the hub.

[0028] Step 2: Filter the data according to the set filtering conditions. When the filtering conditions are met, the collected data is determined to be valid analysis data.

[0029] To ensure accurate testing, the generator set must be in a uniform pitch mode, with wind speed and power output within a preset range, and the standard deviation of wind speed within a predetermined time period must not exceed a set value. If these conditions are not met, the data is simply stored without diagnostic calculations. Preferably, before this step, friction compensation is applied to the pitch motor torque value to eliminate the influence of inconsistent friction torque in the pitch bearings and gearbox on the diagnostic results.

[0030] Step 3: Based on the valid analysis data, use the impeller azimuth angle as the transformation angle to perform Coleman transformation on the torque values ​​of the three pitch motors, and calculate the direct-axis and quadrature-axis components of the pitch motor torque in the rotating coordinate system.

[0031] The Coleman transformation formula is: Where θ is the impeller azimuth angle; T1, T2, and T3 are the pitch motor torque values ​​of the three blades respectively; Td is the direct axis component, which mainly represents the aerodynamic unbalance torque in the direction perpendicular to the wind turbine plane, i.e., the flapping direction; and Tq is the quadrature axis component, which mainly represents the aerodynamic unbalance torque in the wind turbine plane, i.e., the oscillation direction.

[0032] Step 4: For the direct axis component sequence and the quadrature axis component sequence within a continuous time window, calculate the average value of the direct axis component and the average value of the quadrature axis component respectively. If the absolute value of the average value of the direct axis component or the absolute value of the average value of the quadrature axis component exceeds the preset threshold, it is determined that there is a blade zero position offset.

[0033] Under ideal zero-position conditions (no zero-position offset) and without asymmetrical loads, the average values ​​of the direct-axis component (Td_mean) and the quadrature-axis component (Tq_mean) should approach zero. Therefore, the presence of blade zero-position offset can be determined by comparing the set threshold values ​​with the average values ​​of the direct-axis and quadrature-axis components. The preset threshold is determined statistically based on the standard deviation of the direct-axis or quadrature-axis components under normal operating conditions of the unit.

[0034] Step 5: Construct a two-dimensional diagnostic vector space with the average value of the direct axis component Td_mean as the abscissa and the average value of the quadrature axis component Tq_mean as the ordinate. Combine this two-dimensional diagnostic vector space to calculate and analyze the diagnostic vectors to obtain the specific blade that has zero position shift, the direction of the zero position shift, and the degree of the zero position shift.

[0035] By obtaining the average value of the direct-axis component and the average value of the quadrature-axis component of the pitch motor torque in step 4, the phase angle and amplitude of the diagnostic vector can be calculated respectively.

[0036] The formula for calculating the phase angle of the diagnostic vector is: The formula for calculating the magnitude of the diagnostic vector is: Where Td_mean is the mean of the direct axis components and Tq_mean is the mean of the quadrature axis components.

[0037] The phase angle θv reflects the spatial position of the faulty blade in the impeller, and the amplitude R reflects the severity of the zero-position offset.

[0038] In addition, the zero-position offset direction can be determined based on the sign of the average value of the direct-axis component of the pitch motor torque. Specifically, when Td_mean>0, it is determined that the zero position is too large, that is, the actual installation angle is greater than the theoretical value; when Td_mean<0, it is determined that the zero position is too small, that is, the actual installation angle is less than the theoretical value.

[0039] The principle behind this offset direction determination is as follows: a larger blade zero position leads to an increase in aerodynamic angle of attack and blade lift. To maintain a uniform pitch angle, the torque required for the pitch motor increases, which is ultimately reflected in a positive offset of the average value of the direct-axis component of the pitch motor torque.

[0040] This step can be aided by constructing a two-dimensional diagnostic vector space, with the average value of the direct-axis component Td_mean as the abscissa and the average value of the quadrature-axis component Tq_mean as the ordinate. A circular region is established with the ideal aerodynamic balance coordinates as the center and a preset threshold as the radius, representing the normal fluctuation range (exceeding this region indicates blade zero-position offset). This preset threshold is determined statistically based on the standard deviations of the direct-axis and quadrature-axis components under normal operating conditions. Alternatively, since the variation amplitude of the average value of the direct-axis component Td_mean is usually greater than that of the average value of the quadrature-axis component Tq_mean under offset conditions, this preset threshold can be determined statistically based on the standard deviation of the direct-axis component Td under normal operating conditions. The degree to which the diagnostic vector deviates from this circular region reflects the severity of the blade zero-position offset.

[0041] In addition, based on the 120° spatial symmetry of the three-bladed wind turbine, the diagnostic vector space is divided into three sector regions. The blade number that has zero position offset can be determined according to the sector region where the phase angle θv of the diagnostic vector is located. Specifically, when θv∈[330°, 30°], it corresponds to blade 1; when θv∈[90°, 150°], it corresponds to blade 2; and when θv∈[210°, 270°], it corresponds to blade 3.

[0042] After the above steps are completed, a complete diagnostic report can be generated based on the test results. The report may include "zero position normal", "zero position offset alarm", "possible offset blade number", and "possible offset direction", and the report will be sent to the wind farm monitoring center.

[0043] This invention also proposes an implementation of a wind turbine blade zero-position offset detection system for implementing the above-mentioned wind turbine blade zero-position offset detection method. The system includes: The data acquisition module is used to acquire pitch motor torque, azimuth angle, pitch mode, and wind speed power signals from the wind turbine main controller and pitch system; the operating condition screening module is connected to the data acquisition module and is used to determine whether the current data is suitable for zero-point analysis based on preset screening conditions; the signal preprocessing module is connected to the operating condition screening module and is used to perform friction compensation and other functions on the three torque signals. The transformation unit module is used to perform Coleman transformation on the preprocessed three torque signals and calculate Td and Tq; The feature calculation module is used to calculate the statistical features of Td and Tq within the sliding time window, including their average values ​​Td_mean and Tq_mean. The zero-position offset diagnosis module, connected to the feature calculation module, is used to determine whether the absolute value of Td_mean or Tq_mean exceeds a preset threshold, and thus determine whether a blade zero-position offset has occurred.

[0044] The vector analysis module, connected to the feature zero-position offset diagnosis module, is used to construct the diagnostic vector space, calculate the phase angle and amplitude of the diagnostic vector, and perform fault blade location and offset direction determination. The integrated diagnostic and output module, connected to the vector analysis module, is used to evaluate the blade positioning and offset direction.

[0045] The blade zero-position offset online detection method of the present invention is executed periodically in the background of the wind farm SCADA system or the wind turbine main controller. The following describes the solution of the present invention using a doubly-fed wind turbine generator set with a rated power of 2MW and a rated wind speed of 12m / s as an example.

[0046] Data acquisition: The system reads the output torque value of each pitch motor (calculated from the inverter current), the impeller speed sensor signal, and the azimuth angle signal provided by the absolute encoder located at the hub in real time at a frequency of 10Hz.

[0047] Operating condition screening: The system will initiate the current analysis cycle when the following conditions are met simultaneously: The pitch control mode is "unified pitch"; The average wind speed is between 11 m / s and 13 m / s, and the standard deviation of the wind speed within 10 seconds is less than 0.5 m / s; The output power is between 1.8MW and 2.0MW. If the conditions are not met, the data for this round will only be stored and no diagnostic calculations will be performed.

[0048] Signal Processing and Diagnosis: For 36 consecutive points that meet the conditions, perform the following operations: a. For each time step... a. Using the quaternion, apply the Coleman transform formula to obtain (Td, Tq) at that moment. b. Calculate the average of these 36 Td values, Td_mean, and the average of the Tq values, Tq_mean. c. Set a threshold and statistically analyze the standard deviation of Td under normal operating conditions of the wind turbine. If the absolute value of Td_mean or Tq_mean > 3, then... d. Calculate the phase angle of the diagnostic vector. Normalize to the range [0°, 360°]. e. Determine the faulty blade based on the angle interval where θv is located: θv ∈ [330°, 30°] → Leaf 1 θv ∈ [90°, 150°] → Leaf 2 θv ∈ [210°, 270°] → Leaf 3. f. Determine the offset direction based on the sign of Td_mean: Td_mean > 0 → The zero point is too large. Td_mean < 0 → The zero point is too small.

[0049] Output: The diagnostic module generates a log: "Timestamp: XXXX; Diagnostic result: Zero position offset alarm; Inference: Blade 1 has a large zero position offset; Recommendation: Arrange to check and calibrate the zero position of blade 1." This log is uploaded to the wind farm central monitoring platform.

[0050] The above detection is performed using a diagnostic vector space, such as Figure 4 As shown, the diagnostic vector space is a two-dimensional plane with Td_mean as the horizontal axis and Tq_mean as the vertical axis. The application of the diagnostic vector space first requires determining the healthy baseline point: theoretically, the origin (0,0) represents the unit in an ideal aerodynamic equilibrium state. In practical applications, due to factors such as wind shear and tower shadow effects, the healthy baseline may be slightly offset, requiring statistical learning to determine (…). Figure 4 The healthy baseline coordinates are (-0.4, 11.3). Then, the threshold boundary needs to be determined: centered on the baseline point with a radius equal to the preset threshold value of 3. The circular region represents the normal fluctuation range. When the diagnostic vector exceeds this circular region, a significant zero-point offset is determined. Figure 4 The diagnostic vector amplitude is 28.0 Nm, which is greater than the preset threshold of 17.5 Nm.

[0051] The division of the angle interval is based on the 120° spatial symmetry of the three-bladed wind turbine, and the diagnostic vector space is divided into three sector regions: Region 1 (angle range [330°, 30°]) corresponds to the zero-position offset of blade 1. Region 2 (angle range [90°, 150°]) corresponds to the zero-position offset of blade 2. Region 3 (angle range [210°, 270°]) corresponds to the zero position offset of blade 3.

[0052] When the diagnostic vector falls within a certain area, it can be preliminarily determined that the corresponding blade has a zero-position offset.

[0053] This scheme combines the direct-axis component Td and quadrature-axis component Tq of the pitch motor torque for vector analysis, which can effectively eliminate the periodic fluctuations caused by impeller rotation and the random disturbances caused by turbulence, and more accurately reflect the steady-state aerodynamic imbalance caused by blade zero-position offset.

[0054] Figure 2 The diagram shows a comparison of the Coleman-transformed Td signal of the pitch motor torque under aerodynamic balance and zero-point offset conditions. Under balanced conditions, the Td signal fluctuates around the zero line with a mean of zero. Under offset conditions, the Td signal exhibits a DC bias, with the mean significantly deviating from the zero line. The sign and magnitude of Td_mean directly reflect the direction and severity of the zero-point offset. Figure 3 The diagram shows a comparison of the Coleman-transformed Tq signal of the pitch motor torque under aerodynamically balanced and zero-position offset conditions. Under balanced conditions, the Tq signal exhibits periodic fluctuations, with the mean value reflecting the inherent asymmetric loads caused by wind shear, tower shadow effects, etc. Under offset conditions, the mean value of the Tq signal may change, but the amplitude of the change is usually smaller than that of the Td signal. Tq_mean provides phase information to distinguish faults in different blades.

[0055] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software may depend on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods for each specific application to implement the described functions, but such implementation should not be considered beyond the scope of the embodiments of this disclosure.

[0056] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0057] The methods and products (including but not limited to devices and equipment) disclosed in the embodiments herein can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may only be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0058] Unless otherwise defined, the technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used herein is for descriptive purposes only and is not intended to limit the invention. Terms such as “part” or “component” appearing herein can refer to a single part or a combination of multiple parts. Terms such as “installation” or “installation” appearing herein can refer to one component being directly attached to another component or one component being attached to another component via an intermediary. A feature described in one embodiment herein may be applied, alone or in combination with other features, to another embodiment, unless that feature is not applicable in that other embodiment or is otherwise stated.

[0059] The present invention has been described through the above embodiments. However, it should be understood that the above embodiments are for illustrative purposes only and are not intended to limit the present invention to the described embodiments. Furthermore, those skilled in the art will understand that the present invention is not limited to the above embodiments, and many variations and modifications can be made based on the teachings of the present invention, all of which fall within the scope of protection claimed by the present invention. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for detecting zero-position offset of wind turbine blades, characterized in that, include: S1. Real-time acquisition of the pitch motor torque value, impeller azimuth angle, wind speed, unit output power, and pitch mode of each of the three blades under the unit's operating status. S2. Filter the data according to the set filtering conditions. When the filtering conditions are met, the collected data is determined to be valid analysis data. S3. Based on the valid analysis data, the Coleman transformation is performed on the torque values ​​of the three pitch motors using the impeller azimuth angle as the transformation angle, and the direct-axis and quadrature-axis components of the pitch motor torque in the rotating coordinate system are calculated. S4. For the direct axis component sequence and the quadrature axis component sequence within a continuous time window, calculate the average value of the direct axis component and the average value of the quadrature axis component respectively. If the absolute value of the average value of the direct axis component or the absolute value of the average value of the quadrature axis component exceeds the preset threshold, it is determined that there is a blade zero position offset. S5. Construct a two-dimensional diagnostic vector space with the average value of the direct axis components as the abscissa and the average value of the quadrature axis components as the ordinate. Combine this two-dimensional diagnostic vector space to calculate and analyze the diagnostic vectors, and obtain the specific blade that has zero position offset, the direction of the zero position offset, and the degree of the zero position offset.

2. The method for detecting zero-position offset of wind turbine blades according to claim 1, characterized in that, The selection criteria for step S2 are: the unit is in a uniform pitch mode, the wind speed and the unit output power are within a preset range, and the standard deviation of the wind speed within a predetermined time period does not exceed the set value.

3. The method for detecting zero-position offset of wind turbine blades according to claim 1, characterized in that, The Coleman transform formula for step S3 is: Where θ is the impeller azimuth angle; T1, T2, and T3 are the pitch motor torque values ​​of the three blades respectively; Td is the direct axis component, which mainly represents the aerodynamic unbalance torque in the direction perpendicular to the wind turbine plane, i.e., the flapping direction; and Tq is the quadrature axis component, which mainly represents the aerodynamic unbalance torque in the wind turbine plane, i.e., the oscillation direction.

4. The method for detecting zero-position offset of wind turbine blades according to claim 1, characterized in that, The preset threshold in step S4 is determined statistically based on the standard deviation of the direct-axis component or quadrature-axis component under normal operating conditions of the unit.

5. The method for detecting zero-position offset of wind turbine blades according to claim 1, characterized in that, Step S5 specifically includes the following steps: The diagnostic vector space is divided into three angular intervals corresponding to the three blades respectively; The phase angle of the diagnostic vector is calculated based on the average value of the direct axis component and the average value of the quadrature axis component; Based on the angle range of the diagnostic vector phase angle, determine the blade number that has experienced zero-position offset.

6. The method for detecting zero-position offset of wind turbine blades according to claim 5, characterized in that, The formula for calculating the phase angle of the diagnostic vector is: ， Where Td_mean is the mean of the direct axis component and Tq_mean is the mean of the quadrature axis component.

7. The method for detecting zero-position offset of wind turbine blades according to claim 1, characterized in that, Step S5 specifically includes the following steps: Establish a circular region with the ideal aerodynamic equilibrium state coordinates as the center and a preset threshold as the radius, as the normal fluctuation range; The degree of blade zero-position offset is determined by the degree to which the coordinate position of the diagnostic vector in the diagnostic vector space deviates from the circular region.

8. The method for detecting zero-position offset of wind turbine blades according to claim 1, characterized in that, The degree of zero-position offset in step S5 is determined based on the magnitude of the diagnostic vector. The formula for calculating the magnitude of the diagnostic vector is: ， Where Td_mean is the mean of the direct axis component and Tq_mean is the mean of the quadrature axis component.

9. The method for detecting zero-position offset of wind turbine blades according to claim 1, characterized in that, The direction of the zero-position offset in step S5 is determined based on the sign of the average value of the direct-axis component of the pitch motor torque: When Tdmean>0, it is determined that the zero point is too large, that is, the actual installation angle is greater than the theoretical value; When Td_mean < 0, it is determined that the zero position is too small, that is, the actual installation angle is less than the theoretical value.

10. A computing device, characterized in that, include: Memory, used to store computer programs; A processor is configured to implement the wind turbine blade zero-position offset detection method as described in any one of claims 1-9 when executing the computer program.

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