Methods and electronic equipment for detecting and warning of uneven settlement and tilt rate of wind turbine towers

Abstract

This invention provides a method for detecting and warning of uneven settlement and tilt rate of wind turbine towers, belonging to the field of wind power monitoring technology. The method includes: calibrating two IMU sensors to obtain calibration parameters and temperature compensation coefficients; deploying the two sensors along a vertical line at the bottom and top of the tower and calculating the installation compensation matrix; during tower operation, intelligently filtering data segments of the tower's continuous static state by analyzing acceleration modulus and variance; sequentially calibrating, applying temperature and installation compensation to these data segments, and calculating the roll and pitch angles of the bottom and top layers; triggering immediate warnings of uneven settlement, overall tilt, and tower deformation based on the bottom tilt angle, the top tilt angle, and the difference between them, combined with preset thresholds; and providing long-term trend warnings based on the historical rate of change of tilt angle. This invention achieves high-precision, interference-resistant monitoring, effectively separates different sources of structural deformation, and constructs a multi-level intelligent early warning system, improving the reliability and operational efficiency of wind turbine tower safety monitoring.

Description

Technical Field

[0001] This invention relates to the field of wind power equipment monitoring technology, and in particular to a method for detecting and warning of uneven settlement and tilt rate of wind turbine towers. Background Technology

[0002] With the rapid development of wind power technology, the capacity of individual wind turbines is constantly increasing, and the height and weight of the towers are also increasing, which places higher demands on the stability of the tower foundations. In actual operation, due to factors such as geological conditions, environmental loads, construction quality, and long-term operation, the wind turbine tower foundation may experience uneven settlement or overall tilting, leading to stress redistribution in the tower structure. This can cause problems such as tower deformation, increased unit vibration, and misalignment of the transmission system, and in severe cases, may even cause tower structural damage or the entire turbine to overturn.

[0003] Currently, monitoring of wind turbine tower settlement and tilt primarily relies on traditional engineering surveying methods and sensor technology. Conventional methods include periodic manual measurements using high-precision levels and total stations, or the installation of tilt sensors and displacement gauges at key locations on the tower. However, these methods have significant limitations: manual measurements are inefficient, time-consuming, and difficult to perform continuously, and are greatly affected by weather and site conditions; while single tilt sensors often struggle to accurately distinguish between overall tower tilt and uneven foundation settlement, and are susceptible to interference from tower elastic deformation and wind-induced swaying. Furthermore, existing monitoring systems mostly focus on data acquisition, lacking intelligent analysis and early warning capabilities for settlement trends and tilt development rates, making it difficult to provide timely and effective safety status assessments and risk warnings for wind farm operation and maintenance decisions.

[0004] Therefore, there is an urgent need to develop a wind turbine tower safety status detection method that can achieve high-precision, automated, real-time continuous monitoring, effectively distinguish between uneven settlement and overall tilting, and has intelligent early warning function, so as to improve the safety operation level and maintenance efficiency of wind farms and reduce the risk of major safety accidents. Summary of the Invention

[0005] In view of the technical defects and drawbacks existing in the prior art, the embodiments of the present invention provide solutions to overcome the above problems or at least partially solve the above problems, and the specific solutions are as follows;

[0006] As a first aspect of the present invention, a method for detecting and warning of uneven settlement and tilt rate of wind turbine towers is provided, comprising the following steps:

[0007] S1. Calibrate the two inertial measurement units (IMUs) sensors separately to obtain the calibration parameters and temperature compensation coefficients of each IMU sensor;

[0008] S2. Deploy one IMU sensor processed by S1 at the bottom of the wind turbine tower near the foundation, and deploy the other at the top of the tower, with both installed along the same vertical direction; after the IMU sensors are deployed, collect data during the continuous static period of the tower to calculate the installation compensation matrix of each IMU sensor due to its installation attitude.

[0009] S3. During the operation of the wind turbine tower, data is synchronously collected using the two IMU sensors. Based on preset filtering conditions, data segments in which the tower is in a continuously stationary state are identified from the data. For the identified continuously stationary data segments, the calibration parameters, the temperature compensation coefficient, and the corresponding installation compensation matrix are applied sequentially to obtain the compensated acceleration data of each IMU sensor in the global horizontal coordinate system. Based on this, the roll angle of the bottom layer of the tower is calculated. With pitch angle θ1 and roll angle at the top of the tower With pitch angle θ2;

[0010] S4. According to the above Calculate the bottom dip angle α1 with θ1, according to the above. Calculate the top-level tilt angle α2 with θ2; based on at least one of the bottom-level tilt angle α1, the top-level tilt angle α2, and the difference between the two (α2-α1), compare it with the corresponding preset threshold to trigger the corresponding safety status warning.

[0011] In some embodiments, step S1 includes:

[0012] The step of calibrating the IMU sensor includes calibrating the accelerometer of the IMU sensor using the least squares method to obtain the calibration parameters, which include the scaling factors of the accelerometer's x, y, and z axes. , , and zero offset , , ;

[0013] And, the step of determining the temperature compensation coefficient includes:

[0014] When the IMU sensor is in a static state, its continuous data is collected over a period of time. The continuous data includes triaxial acceleration values ​​and temperature values ​​collected synchronously with it.

[0015] For the continuous data, establish a linear relationship between the acceleration values ​​of each axis and the corresponding temperature values;

[0016] Based on the aforementioned linear relationship, the slope of the acceleration value of each axis as a function of temperature is determined. , , and the slope , , The temperature compensation coefficient is used as the corresponding axis.

[0017] In some embodiments, the application of the calibration parameters in step S3 is performed using the following calibration compensation formula, utilizing the scaling factor. , , and the zero offset , , Raw triaxial acceleration values ​​acquired by the IMU sensor Processing:

[0018]

[0019] in, These are the calibrated and compensated triaxial acceleration values.

[0020] In some embodiments, the application of the temperature compensation coefficient in step S3 is performed using the following temperature compensation formula, utilizing the temperature compensation coefficient. , , For the calibrated and compensated triaxial acceleration values Processing:

[0021]

[0022] in, These are the temperature-compensated triaxial acceleration values;

[0023] When T is used for temperature compensation, it is related to the above. The corresponding IMU sensor temperature measurement value;

[0024] T b The reference temperature for calibrating the accelerometer is determined in advance.

[0025] In some embodiments, the calculation of the installation compensation matrix for each IMU sensor due to its installation attitude in step S2 includes:

[0026] After the IMU sensor is deployed, the data collected by it during the continuous static period of the tower are processed sequentially using the calibration parameters and the temperature compensation coefficient to obtain the calibrated and temperature-compensated triaxial acceleration sequence.

[0027] Calculate the average value of the triaxial acceleration sequence on each axis, and denote them as follows: ;

[0028] Based on the average value, according to the following formula Calculate the mounting deviation angle of the IMU sensor, including the roll angle. And pitch angle θ:

[0029]

[0030]

[0031] That is, the global horizontal coordinate system is rotated by θ around the Y-axis, and then rotated around the X-axis. This yields the sensor's coordinate system and its rotation matrix.

[0032]

[0033] Then install the compensation matrix

[0034] .

[0035] In some embodiments, the step S3, which involves identifying data segments from the data in which the tower is in a continuously stationary state based on preset screening conditions, includes making the following judgment on the acceleration data collected by the IMU sensor and after calibration and temperature compensation:

[0036] For a continuous series of N triaxial acceleration data points that have undergone calibration and temperature compensation, calculate the acceleration magnitude of the k-th data point. :

[0037] ;

[0038] in, , , This refers to the triaxial acceleration component corresponding to the k-th data point, after calibration and temperature compensation.

[0039] Set an upper threshold A close to the gravitational acceleration g. max and lower limit threshold A min A min < g < A max ;

[0040] Set the acceleration magnitude variance threshold ;

[0041] The k-th data point is considered to satisfy the static condition when both of the following conditions are met:

[0042] Condition 1: ;

[0043] Condition 2: Taking the k-th data point as the endpoint and tracing back N consecutive data points, the variance of their acceleration magnitude values. satisfy < The variance The calculation formula is: ;

[0044] If each of the N consecutive data points is determined to meet the static condition, then the time period corresponding to the N data points is determined to be the continuous static state of the tower.

[0045] In some embodiments, step S3 involves obtaining the compensated acceleration data of each IMU sensor in the global horizontal coordinate system when the tower remains stationary, and calculating the roll angle of the bottom layer of the tower in the stationary state accordingly. With pitch angle θ1 and roll angle at the top of the tower With pitch angle θ2, including:

[0046] For the data segment determined to be in a continuous static state of the tower, the average value of the triaxial acceleration data contained within it, after calibration and temperature compensation, is taken to obtain the mean triaxial acceleration value corresponding to that data segment. ;

[0047] Using the installation compensation matrix M determined for the corresponding IMU sensor in step S2, installation compensation is performed on the triaxial acceleration mean acc_b to obtain the compensated acceleration mean acc in the global horizontal coordinate system. n Its calculation is , where acc n The component is , , ;

[0048] Based on the following formula, the mean acceleration after compensation, acc n The component is used to calculate the roll angle at the IMU sensor mounting location. And pitch angle θ:

[0049]

[0050]

[0051] For the IMU sensor installed at the bottom of the tower, the calculated roll angle is denoted as... The pitch angle is denoted as θ1; for the IMU sensor installed at the top of the tower, the calculated roll angle is denoted as... The pitch angle is denoted as θ2.

[0052] In some embodiments, the comparison in step S4 based on at least one of the bottom tilt angle α1, the top tilt angle α2, and the difference between the two (α2 - α1) with a corresponding preset threshold is used to trigger a corresponding safety status warning. Specifically, this is implemented as follows:

[0053] First, calculate the bottom slope angle. and top slope ;

[0054] Set the bottom dip angle threshold τ1 for judging uneven settlement, the top dip angle threshold τ2 for judging overall tilt, and the inter-layer dip angle difference threshold ε for judging tower deformation;

[0055] Then, the following judgment and warning triggering logic is executed:

[0056] If α1>τ1, then an early warning for uneven settlement exceeding the limit will be triggered;

[0057] If α2 > τ2, then an early warning of overall tower tilt exceeding the limit will be triggered;

[0058] If (α2-α1)>ε, then an abnormal tower deformation warning will be triggered.

[0059] In some embodiments, step S4 further includes a long-term trend warning, specifically including:

[0060] During the process of triggering the corresponding safety status warning, the calculated bottom tilt angle α1 and top tilt angle α2 are continuously recorded and stored;

[0061] Based on the stored historical data of the bottom dip angle α1 and the top dip angle α2 over a preset time period, the annual change rate Δ1 of the bottom dip angle α1 and the annual change rate Δ2 of the top dip angle α2 are calculated respectively.

[0062] Set settlement trend threshold and tilt trend threshold ;

[0063] If Δ1> If this occurs, an early warning of excessive settlement trend will be triggered;

[0064] If Δ2> If this occurs, an over-limit warning for the tilt trend will be triggered.

[0065] As a second aspect of the present invention, an electronic device is provided, comprising:

[0066] One or more processors;

[0067] Memory, used to store one or more programs;

[0068] When the one or more programs are executed by the one or more processors, the one or more processors implement the methods described above.

[0069] The present invention has the following beneficial effects:

[0070] This invention combines least squares calibration, temperature compensation, and installation error compensation to systematically improve the accuracy of IMU sensor data, laying a reliable foundation for high-precision tilt angle calculation. By simultaneously deploying IMUs at the bottom and top of the tower, it achieves separate monitoring of the overall tower tilt and uneven settlement, accurately identifying the sources of different structural deformations. Tilting angle calculation is performed using data from continuous static periods, effectively eliminating dynamic interferences such as wind loads and turbine operation, ensuring that the monitoring results reflect the true structural deformation trend, and improving the reliability and engineering applicability of the monitoring technology. By setting threshold warnings for the bottom tilt angle (settlement), the top tilt angle (overall tilt), and the angle difference between the two (tower deformation), a three-dimensional safety defense line is constructed. This allows for a more comprehensive and hierarchical assessment of the tower's safety status. Long-term trend analysis can detect slowly developing settlement or tilt, providing a basis for predictive maintenance decisions and helping to formulate reinforcement or correction plans before problems become severe, avoiding costly unexpected downtime or structural damage. Attached Figure Description

[0071] Figure 1 A flowchart illustrating a method for detecting and warning of uneven settlement and tilt rate of a wind turbine tower, provided in an embodiment of the present invention;

[0072] Figure 2 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0073] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0074] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0075] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0076] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0077] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0078] In the technical solution of this invention, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information all comply with relevant laws and regulations and do not violate public order and good morals. The use of user data in this technical solution follows relevant national laws and regulations (e.g., the "Information Security Technology - Personal Information Security Specification"). For example: appropriate measures are taken for personal information access control; restrictions are imposed on the display of personal information; the purpose of using personal information does not exceed the scope of direct or reasonable association; and explicit identity targeting is eliminated when using personal information to avoid precisely locating a specific individual.

[0079] To address at least one of the technical problems existing in the aforementioned related technologies, the present invention provides a method for detecting and warning of uneven settlement and tilt rate of wind turbine towers. Figure 1 This is a flowchart illustrating a method for detecting and warning of uneven settlement and tilt rate of a wind turbine tower according to an embodiment of the present invention. The method includes:

[0080] S1. Calibrate the two inertial measurement units (IMUs) sensors separately to obtain the calibration parameters and temperature compensation coefficients of each IMU sensor;

[0081] S2. Deploy one IMU sensor processed by S1 at the bottom of the wind turbine tower near the foundation, and deploy the other at the top of the tower, with both installed along the same vertical line. Record the horizontal distance L between the two installation points. After the IMU sensors are deployed, collect data during the period when the tower is stationary to calculate the installation compensation matrix of each IMU sensor due to its installation attitude.

[0082] S3. During the operation of the wind turbine tower, data is synchronously collected using the two IMU sensors. Based on preset filtering conditions, data segments in which the tower is in a continuously stationary state are identified from the data. For the identified continuously stationary data segments, the calibration parameters, the temperature compensation coefficient, and the corresponding installation compensation matrix are applied sequentially to obtain the compensated acceleration data of each IMU sensor in the global horizontal coordinate system. Based on this, the roll angle of the bottom layer of the tower is calculated. With pitch angle θ1 and roll angle at the top of the tower With pitch angle θ2;

[0083] S4. According to the above Calculate the bottom dip angle α1 with θ1, according to the above. Calculate the top-level tilt angle α2 with θ2; based on at least one of the bottom-level tilt angle α1, the top-level tilt angle α2, and the difference between the two (α2-α1), compare it with the corresponding preset threshold to trigger the corresponding safety status warning.

[0084] This invention provides a systematic and automated method for monitoring and early warning of wind turbine tower settlement and tilt. First, by integrating sensor calibration, installation compensation, real-time data acquisition, and intelligent analysis and early warning into a complete process, it achieves end-to-end automation from raw data to safety decisions. Second, it innovatively adopts a dual-sensor synchronous deployment scheme (bottom-top level) and uses static data for tilt angle calculation, effectively separating different structural deformations caused by uneven foundation settlement (bottom tilt angle α1) and overall tower tilt (top tilt angle α2), thereby achieving accurate tracing and classification assessment of the tower's safety status. Finally, by setting multi-dimensional early warning logic (α1, α2, and the difference between them), it can identify different safety risks (settlement, tilt, tower deformation) in a graded and comprehensive manner, providing more timely, accurate, and comprehensive early warning information for operation and maintenance decisions, thereby improving the wind farm's proactive safety defense capabilities and the level of intelligent operation and maintenance.

[0085] In some embodiments, step S1 includes:

[0086] The step of calibrating the IMU sensor includes calibrating the accelerometer of the IMU sensor using the least squares method to obtain the calibration parameters, which include the scaling factors of the accelerometer's x, y, and z axes. , , and zero offset , , ;

[0087] And, the step of determining the temperature compensation coefficient includes:

[0088] When the IMU sensor is in a static state, its continuous data is collected over a period of time. The continuous data includes triaxial acceleration values ​​and temperature values ​​collected synchronously with it.

[0089] For the continuous data, establish a linear relationship between the acceleration values ​​of each axis and the corresponding temperature values;

[0090] Based on the aforementioned linear relationship, the slope of the acceleration value of each axis as a function of temperature is determined. , , and the slope , , The temperature compensation coefficient is used as the corresponding axis.

[0091] The above embodiments specifically define the sensor front-end processing. Specifically, it uses the least squares method to systematically calibrate the accelerometer, accurately obtaining the scaling factor and zero-point offset of each axis, eliminating the sensor's own nonlinear errors and zero-point drift. Simultaneously, it introduces a dedicated temperature compensation coefficient determination step. By collecting temperature change data of the sensor in a static state and establishing a linear relationship model between acceleration and temperature, it can effectively compensate for the drift effect caused by changes in ambient temperature on the sensor output. This dual preprocessing mechanism of "calibration + temperature compensation" jointly ensures the long-term stability and reliability of the raw measurement data. Even in complex and variable outdoor climate environments, it can obtain acceleration information that accurately reflects the true attitude of the tower, thereby fundamentally improving the accuracy and robustness of the entire monitoring system.

[0092] In some embodiments, the application of the calibration parameters in step S3 is performed using the following calibration compensation formula, utilizing the scaling factor. , , and the zero offset , , Raw triaxial acceleration values ​​acquired by the IMU sensor Processing:

[0093]

[0094] in, These are the calibrated and compensated triaxial acceleration values.

[0095] The above embodiments specifically define the calculation formula for calibration compensation. Through explicit mathematical transformation formulas, the obtained abstract "calibration parameters" (scaling factor Sa and zero offset Ba) are directly and unambiguously combined with the raw acceleration data (acc_m) collected by the sensor.

[0096] In some embodiments, the application of the temperature compensation coefficient in step S3 is performed using the following temperature compensation formula, utilizing the temperature compensation coefficient. , , For the calibrated and compensated triaxial acceleration values Processing:

[0097]

[0098] in, These are the temperature-compensated triaxial acceleration values;

[0099] When T is used for temperature compensation, it is related to the above. The corresponding IMU sensor temperature measurement value;

[0100] T b The predetermined calibration reference temperature for the accelerometer (determined by the factory calibration temperature of the accelerometer, generally 25℃).

[0101] The above embodiments specifically define the calculation formula for temperature compensation, utilizing a determined temperature sensitivity coefficient ( , , The obtained calibrated data is then combined with the real-time temperature measurement (T) and the reference temperature (T). b Linear correction can be performed to effectively offset the drift of sensor internal component characteristics caused by diurnal temperature differences and seasonal changes.

[0102] In some embodiments, the calculation of the installation compensation matrix for each IMU sensor due to its installation attitude in step S2 includes:

[0103] After the IMU sensor is deployed, the data collected by it during the continuous static period of the tower are processed sequentially using the calibration parameters and the temperature compensation coefficient to obtain the calibrated and temperature-compensated triaxial acceleration sequence.

[0104] Calculate the average value of the triaxial acceleration sequence on each axis, and denote them as follows: ;

[0105] Based on the average value, according to the following formula Calculate the mounting deviation angle of the IMU sensor, including the roll angle. And pitch angle θ:

[0106]

[0107]

[0108] That is, the global horizontal coordinate system is rotated by θ around the Y-axis, and then rotated around the X-axis. This yields the sensor's coordinate system and its rotation matrix.

[0109]

[0110] Then install the compensation matrix

[0111] .

[0112] The above embodiments specifically define the calculation method for the installation compensation matrix, achieving precise alignment of the sensor measurement coordinate system to the global horizontal coordinate system. Specifically, this scheme involves acquiring initial static data after sensor installation and using the processed high-precision data to calculate the installation deviation angle (roll angle) of the sensor coordinate system relative to the true horizontal plane. And the pitch angle θ), thus constructing a unique mathematical transformation matrix M. Subsequent steps apply this matrix, and all measurement data are uniformly transformed to a standard horizontal reference frame for analysis, thereby ensuring the comparability of data from different sensors (bottom and top layers), and the calculated tilt angle ( θ) directly represents the attitude of the tower relative to the real horizontal plane, rather than relative to the sensor's own coordinate system which may be tilted, fundamentally improving the accuracy and reliability of the angle reference of the entire monitoring system.

[0113] In some embodiments, the step S3, which involves identifying data segments from the data in which the tower is in a continuously stationary state based on preset screening conditions, includes making the following judgment on the acceleration data collected by the IMU sensor and after calibration and temperature compensation:

[0114] For a continuous series of N triaxial acceleration data points that have undergone calibration and temperature compensation, calculate the acceleration magnitude of the k-th data point. :

[0115] ;

[0116] in, , , This refers to the triaxial acceleration component corresponding to the k-th data point, after calibration and temperature compensation.

[0117] Set an upper threshold A close to the gravitational acceleration g. maxand lower limit threshold A min A min < g < A max ;

[0118] Set the acceleration magnitude variance threshold ;

[0119] The k-th data point is considered to satisfy the static condition when both of the following conditions are met:

[0120] Condition 1: ;

[0121] Condition 2: Taking the k-th data point as the endpoint and tracing back N consecutive data points, the variance of their acceleration magnitude values. satisfy < The variance The calculation formula is: ;

[0122] If each of the N consecutive data points is determined to meet the static condition, then the time period corresponding to the N data points is determined to be the continuous static state of the tower.

[0123] The above embodiments specifically define an intelligent recognition algorithm for the "continuous static state" of the tower. This algorithm can automatically and accurately filter out effective data segments suitable for calculating the actual structural deformation from a continuous sensor data stream, thereby effectively eliminating dynamic interference such as wind turbine operation and wind load sway. The scheme employs dual criteria for filtering: first, it identifies whether the tower as a whole is in a near-static state by judging whether the acceleration modulus is stable near the gravitational acceleration g; second, it determines whether the static state is continuous and stable, rather than instantaneous, by calculating the variance of the acceleration modulus within a time window. This combination of "amplitude + variance" judgment logic constitutes a robust filtering mechanism that effectively excludes periods of drastic acceleration fluctuations caused by unit operation and strong winds, retaining only data from "absolutely static" periods with no wind, light wind, or turbine shutdown for tilt angle calculation. This ensures that the final calculated φ and θ reflect the true static deformation and settlement of the tower foundation and structure under no external force excitation, rather than instantaneous dynamic swaying. This improves the effectiveness of settlement and tilt monitoring results and avoids erroneous warnings caused by misuse of dynamic data.

[0124] In some embodiments, step S3 involves obtaining the compensated acceleration data of each IMU sensor in the global horizontal coordinate system when the tower remains stationary, and calculating the roll angle of the bottom layer of the tower in the stationary state accordingly. With pitch angle θ1 and roll angle at the top of the tower With pitch angle θ2, including:

[0125] For the data segment determined to be in a continuous static state of the tower, the average value of the triaxial acceleration data contained within it, after calibration and temperature compensation, is taken to obtain the mean triaxial acceleration value corresponding to that data segment. ;

[0126] Using the installation compensation matrix M determined for the corresponding IMU sensor in step S2, installation compensation is performed on the triaxial acceleration mean acc_b to obtain the compensated acceleration mean acc in the global horizontal coordinate system. n Its calculation is , where acc n The component is , , ;

[0127] Based on the following formula, the mean acceleration after compensation, acc n The component is used to calculate the roll angle at the IMU sensor mounting location. And pitch angle θ:

[0128]

[0129]

[0130] For the IMU sensor installed at the bottom of the tower, the calculated roll angle is denoted as... The pitch angle is denoted as θ1; for the IMU sensor installed at the top of the tower, the calculated roll angle is denoted as... The pitch angle is denoted as θ2.

[0131] The above embodiments specifically define the calculation process from static data to the final attitude angles (roll and pitch angles). Through a standardized data processing and calculation procedure, the original sensor signals, after multiple compensations, are transformed into precise geometric angles that can be directly used for structural safety assessment. Specifically, this scheme first averages the selected effective static data segments to further smooth random noise, obtaining an acceleration vector representing the average attitude of the tower during that period. Then, using a pre-calibrated installation compensation matrix (M), this average vector is transformed to precisely rotate it from the sensor's local coordinate system, which may have installation deviations, to a unified global horizontal coordinate system, obtaining a pure component reflecting the direction of gravity (acc). n Finally, using the determined arctangent function formula, from acc n The only solution that can calculate the roll and pitch angles is the one that is used in this study.

[0132] In some embodiments, the comparison in step S4 based on at least one of the bottom tilt angle α1, the top tilt angle α2, and the difference between the two (α2 - α1) with a corresponding preset threshold is used to trigger a corresponding safety status warning. Specifically, this is implemented as follows:

[0133] First, calculate the bottom slope angle. and top slope ;

[0134] Set the bottom dip angle threshold τ1 for judging uneven settlement, the top dip angle threshold τ2 for judging overall tilt, and the inter-layer dip angle difference threshold ε for judging tower deformation;

[0135] Then, the following judgment and warning triggering logic is executed:

[0136] If α1>τ1, then an early warning for uneven settlement exceeding the limit will be triggered;

[0137] If α2 > τ2, then an early warning of overall tower tilt exceeding the limit will be triggered;

[0138] If (α2-α1)>ε, then an abnormal tower deformation warning will be triggered.

[0139] The above embodiment specifically defines a multi-dimensional threshold early warning logic based on real-time tilt angle values. Specifically, this scheme calculates the bottom tilt angle (α1) and top tilt angle (α2), and sets corresponding independent thresholds (τ1, τ2, ε): when the bottom tilt angle α1 caused by uneven foundation settlement exceeds the limit, an "uneven settlement exceeding the limit early warning" is triggered, directly indicating a foundation stability problem; when the overall tilt angle α2 of the tower exceeds the limit, an "overall tilt exceeding the limit early warning" is triggered, indicating an abnormal overall tower posture; when the difference between the top and bottom tilt angles (α2 - α1) exceeds the limit, an "abnormal tower deformation early warning" is triggered, which can identify additional bending or deformation of the tower body itself (such as flange connections, cylinder walls) caused by fatigue, loosening, etc., providing maintenance personnel with highly targeted maintenance directions and decision-making basis.

[0140] In some embodiments, step S4 further includes a long-term trend warning, specifically including:

[0141] During the process of triggering the corresponding safety status warning, the calculated bottom tilt angle α1 and top tilt angle α2 are continuously recorded and stored;

[0142] Based on the stored historical data of the bottom dip angle α1 and the top dip angle α2 over a preset time period, the annual change rate Δ1 of the bottom dip angle α1 and the annual change rate Δ2 of the top dip angle α2 are calculated respectively.

[0143] Set settlement trend threshold and tilt trend threshold ;

[0144] If Δ1> If this occurs, an early warning of excessive settlement trend will be triggered;

[0145] If Δ2> If this occurs, an over-limit warning for the tilt trend will be triggered.

[0146] The above embodiments specifically define an early warning mechanism based on the long-term trend of tilt angle changes, enabling early detection and predictive warning of slow, cumulative structural defects in wind turbine towers, thus overcoming the insensitivity of instant threshold warnings to gradual changes. Specifically, this scheme continuously records historical tilt angle data and calculates its annual rate of change (Δ1, Δ2), extending the monitoring perspective from "whether the current state exceeds the limit" to "whether the trend of change is healthy." Even if the current absolute tilt angle value (α1, α2) does not exceed the instant warning threshold, as long as the rate of settlement or tilt development (Δ1, Δ2) exceeds the set trend threshold (ε1, ε2), the system will trigger an early warning of "settlement trend exceeding the limit" or "tilt trend exceeding the limit."

[0147] Based on the same inventive concept, embodiments of the present invention also provide an electronic device. Figure 2 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Figure 2 As shown, an embodiment of the present invention provides an electronic device including: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement a method for detecting and warning of uneven settlement and tilt rate of wind turbine towers as described in any of the above embodiments; the one or more I / O interfaces 103 are connected between the processor and the memory, configured to enable information interaction between the processor and the memory.

[0148] The processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus).

[0149] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.

[0150] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0151] This invention also provides a computer-readable medium. The computer-readable medium stores a computer program, which, when executed by a processor, implements the steps in any of the wind turbine tower uneven settlement and tilt rate detection and early warning methods described in the above embodiments. The computer-readable storage medium can be volatile or non-volatile.

[0152] This invention also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes any of the above-described methods for detecting and warning of uneven settlement and tilt rate of wind turbine towers.

[0153] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0154] As is known to those skilled in the art, computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0155] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0156] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0157] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0158] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0159] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0160] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0161] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0162] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. A method for detecting and warning of uneven settlement and tilt rate of wind turbine towers, characterized in that, Includes the following steps: S1. Calibrate the two inertial measurement units (IMUs) sensors separately to obtain the calibration parameters and temperature compensation coefficients of each IMU sensor; S2. Deploy one IMU sensor processed by S1 at the bottom of the wind turbine tower near the foundation, and deploy the other at the top of the tower, with both installed along the same vertical direction; after the IMU sensors are deployed, collect data during the continuous static period of the tower to calculate the installation compensation matrix of each IMU sensor due to its installation attitude. S3. During the operation of the wind turbine tower, data is collected synchronously using the two IMU sensors, and data segments in which the tower is in a continuous static state are identified from the data based on preset filtering conditions; The identified data segments in the continuous static state are processed sequentially using the calibration parameters, the temperature compensation coefficient, and the corresponding installation compensation matrix to obtain the compensated acceleration data of each IMU sensor in the global horizontal coordinate system. Based on this, the roll angle of the bottom layer of the tower is calculated. With pitch angle θ1 and roll angle at the top of the tower With pitch angle θ2; S4. According to the above Calculate the bottom dip angle α1 with θ1, according to the above. Calculate the top-level tilt angle α2 with θ2; based on at least one of the bottom-level tilt angle α1, the top-level tilt angle α2, and the difference between the two (α2-α1), compare it with the corresponding preset threshold to trigger the corresponding safety status warning.

2. The method according to claim 1, characterized in that, Step S1 includes: The step of calibrating the IMU sensor includes calibrating the accelerometer of the IMU sensor using the least squares method to obtain the calibration parameters, which include the scaling factors of the accelerometer's x, y, and z axes. , , and zero offset , , ; And, the step of determining the temperature compensation coefficient includes: When the IMU sensor is in a static state, its continuous data is collected over a period of time. The continuous data includes triaxial acceleration values ​​and temperature values ​​collected synchronously with it. For the continuous data, establish a linear relationship between the acceleration values ​​of each axis and the corresponding temperature values; Based on the aforementioned linear relationship, the slope of the acceleration value of each axis as a function of temperature is determined. , , and the slope , , The temperature compensation coefficient is used as the corresponding axis.

3. The method according to claim 2, characterized in that, The processing described in step S3, applying the calibration parameters, is performed using the following calibration compensation formula, utilizing the scaling factor. , , and the zero offset , , Raw triaxial acceleration values ​​acquired by the IMU sensor Processing: in, These are the calibrated and compensated triaxial acceleration values.

4. The method according to claim 2, characterized in that, The process described in step S3, applying the temperature compensation coefficient, is performed using the following temperature compensation formula, utilizing the temperature compensation coefficient. , , For the calibrated and compensated triaxial acceleration values Processing: in, These are the temperature-compensated triaxial acceleration values; When T is used for temperature compensation, it is related to the above. The corresponding IMU sensor temperature measurement value; T b The reference temperature for calibrating the accelerometer is determined in advance.

5. The method according to claim 1, characterized in that, Step S2, which involves calculating the installation compensation matrix for each IMU sensor due to its mounting attitude, includes: After the IMU sensor is deployed, the data collected by it during the continuous static period of the tower are processed sequentially using the calibration parameters and the temperature compensation coefficient to obtain the calibrated and temperature-compensated triaxial acceleration sequence. Calculate the average value of the triaxial acceleration sequence on each axis, and denote them as follows: ; Based on the average value, according to the following formula Calculate the mounting deviation angle of the IMU sensor, including the roll angle. And pitch angle θ: That is, the global horizontal coordinate system is rotated by θ around the Y-axis, and then rotated around the X-axis. This yields the sensor's coordinate system and its rotation matrix. Then install the compensation matrix 。 6. The method according to claim 1, characterized in that, Step S3, which involves identifying data segments from the data in which the tower is in a continuously stationary state based on preset screening conditions, includes making the following judgments on the acceleration data collected by the IMU sensor and after calibration and temperature compensation: For a continuous series of N triaxial acceleration data points that have undergone calibration and temperature compensation, calculate the acceleration magnitude of the k-th data point. : ； in, , , This refers to the triaxial acceleration component corresponding to the k-th data point, after calibration and temperature compensation. Set an upper threshold A close to the gravitational acceleration g. max and lower limit threshold A min A min < g < A max ; Set the acceleration magnitude variance threshold ; The k-th data point is considered to satisfy the static condition when both of the following conditions are met: Condition 1: ; Condition 2: Taking the k-th data point as the endpoint and tracing back N consecutive data points, the variance of their acceleration magnitude values. satisfy < The variance The calculation formula is: ; If each of the N consecutive data points is determined to meet the static condition, then the time period corresponding to the N data points is determined to be the continuous static state of the tower.

7. The method according to claim 1, characterized in that, In step S3, the compensated acceleration data of each IMU sensor in the global horizontal coordinate system is obtained when the tower remains stationary, and the roll angle of the bottom layer of the tower is calculated accordingly. With pitch angle θ1 and roll angle at the top of the tower With pitch angle θ2, including: For the data segment determined to be in a continuous static state of the tower, the average value of the triaxial acceleration data contained within it, after calibration and temperature compensation, is taken to obtain the mean triaxial acceleration value corresponding to that data segment. ; Using the installation compensation matrix determined for the corresponding IMU sensor in step S2 The mean triaxial acceleration acc_b is compensated to obtain the compensated mean acceleration acc in the global horizontal coordinate system. n Its calculation is , where acc n The component is , , ; Based on the following formula, the mean acceleration after compensation, acc n The component is used to calculate the roll angle at the IMU sensor mounting location. And pitch angle θ: For the IMU sensor installed at the bottom of the tower, the calculated roll angle is denoted as... The pitch angle is denoted as θ1; for the IMU sensor installed at the top of the tower, the calculated roll angle is denoted as... The pitch angle is denoted as θ2.

8. The method according to claim 1, characterized in that, Step S4, which compares at least one of the bottom tilt angle α1, the top tilt angle α2, and the difference between the two (α2 - α1) with a corresponding preset threshold to trigger a corresponding safety status warning, is specifically implemented as follows: First, calculate the bottom slope angle. and top slope ; Set the bottom dip angle threshold τ1 for judging uneven settlement, the top dip angle threshold τ2 for judging overall tilt, and the inter-layer dip angle difference threshold ε for judging tower deformation; Then, the following judgment and warning triggering logic is executed: If α1>τ1, then an early warning for uneven settlement exceeding the limit will be triggered; If α2 > τ2, then an early warning of overall tower tilt exceeding the limit will be triggered; If (α2-α1)>ε, then an abnormal tower deformation warning will be triggered.

9. The method according to claim 8, characterized in that, Step S4 also includes long-term trend warning, specifically including: During the process of triggering the corresponding safety status warning, the calculated bottom tilt angle α1 and top tilt angle α2 are continuously recorded and stored; Based on the stored historical data of the bottom dip angle α1 and the top dip angle α2 over a preset time period, the annual change rate Δ1 of the bottom dip angle α1 and the annual change rate Δ2 of the top dip angle α2 are calculated respectively. Set settlement trend threshold and tilt trend threshold ; If Δ1> If this occurs, an early warning of excessive settlement trend will be triggered; If Δ2> If this occurs, an over-limit warning for the tilt trend will be triggered.

10. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-9.

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