A wind turbine tower horizontal sway displacement fusion method and storage medium

Abstract

A wind turbine tower horizontal swing displacement fusion method and storage medium, comprising: calibrating an IMU sensor by a least square method to obtain a calibration parameter, compensating acceleration and angular velocity collected by the IMU based on the calibration parameter; obtaining Euler angles of tower swing by an extended Kalman filter based on the compensated acceleration and angular velocity; constructing a rotation matrix by using the Euler angles, converting acceleration under a carrier coordinate system to a navigation coordinate system, and eliminating a gravity acceleration component; performing twice integration on acceleration in a horizontal direction under the navigation coordinate system to generate a first horizontal displacement estimation as a prediction value; generating a second horizontal displacement estimation as an observation value based on a tower installation height and the Euler angles; and performing data fusion on the prediction value and the observation value by a Kalman filter to output an optimal displacement estimation. The present application realizes high-precision real-time monitoring of wind turbine tower horizontal swing displacement through multi-source data fusion and systematic error control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind power generation equipment monitoring, and particularly relates to a wind turbine tower horizontal sway displacement fusion method and a storage medium. BACKGROUND

[0002] With the rapid development of global renewable energy, wind turbine generators tend to be large-scale (such as 10MW+ units), and the tower height is generally more than 100 meters. As a support structure, the tower is prone to horizontal sway under the dynamic action of wind load, unit operation vibration, etc., directly affecting the operation safety (such as tower fatigue life) and power generation efficiency (such as yaw accuracy) of the wind turbine. Accurate measurement of the horizontal sway displacement of the tower is crucial for structural health monitoring and control strategy optimization.

[0003] Currently, wind turbine tower displacement monitoring mainly relies on the following methods, but all have significant defects. 1) Inertial measurement unit (IMU): displacement is indirectly calculated through an accelerometer / gyroscope, but the integral error will accumulate over time, and the long-term monitoring accuracy is poor (>10% error). 2) GPS positioning system: affected by signal delay and multipath effect, the dynamic response speed is insufficient (the sampling rate is usually ≤10Hz), and it is difficult to capture high-frequency sway. 3) Visual measurement (such as laser radar): high cost and limited by environmental light and weather conditions, not suitable for long-term deployment in the field. There is an urgent need for a stable and reliable horizontal sway displacement monitoring algorithm to ensure the safe and stable operation of the wind turbine generator. SUMMARY

[0004] In view of the technical defects and technical disadvantages in the prior art, the present application provides a wind turbine tower horizontal sway displacement fusion method to overcome the above problems or at least partially solve the above problems, and the specific scheme is as follows:

[0005] As a first aspect of the present application, a wind turbine tower horizontal sway displacement fusion method is provided, the method comprising:

[0006] S1. Calibrate the IMU sensor by the least square method to obtain calibration parameters, and compensate the acceleration and angular velocity collected by the IMU based on the calibration parameters; S2. Based on the compensated acceleration and angular velocity, obtain the Euler angle of the tower sway by extended Kalman filtering; S3. Use the Euler angle to construct a rotation matrix to convert the acceleration in the carrier coordinate system to the navigation coordinate system and eliminate the gravity acceleration component; S4. Twice integrate the acceleration in the horizontal direction in the navigation coordinate system to generate a first horizontal displacement estimate as a prediction value; S5. Based on the tower installation height and the Euler angle, generate a second horizontal displacement estimate as an observation value; S6. The predicted and observed values ​​are fused using Kalman filtering to output the optimal displacement estimate.

[0007] Furthermore, S1 specifically includes:

[0008] First, the least squares method is used to calibrate the IMU sensor to obtain calibration parameters, namely the scale factor and zero bias of the accelerometer's x, y, and z axes, and the scale factor and zero bias of the gyroscope's x, y, and z axes.

[0009] Then, IMU sensors were used to collect the triaxial acceleration of the tower's swaying in real time. and triaxial angular velocity The collected triaxial accelerations were respectively... and triaxial angular velocity values After scaling factor compensation and bias compensation, the calibrated triaxial acceleration based on the IMU carrier coordinate system is obtained. and triaxial angular velocity .

[0010] Furthermore, S2 specifically includes:

[0011] Apply extended Kalman filtering to the calibrated and compensated acceleration and angular velocity Obtain the rotation quaternion The Euler angles of the tower's sway can be obtained using q.

[0012] ,in, It is the roll angle, which is the angle of rotation around the X-axis; It is the pitch angle, that is, the angle of rotation around the Y-axis; It is the heading angle, which is the angle of rotation around the Z-axis.

[0013] Furthermore, S3 specifically includes:

[0014] Using Euler angles Calculate the rotation matrix R from the vehicle coordinate system to the navigation coordinate system. rotate The acceleration in the carrier coordinate system is expressed through a rotation matrix. R rotate Perform a coordinate system transformation to obtain the acceleration in the navigation coordinate system, and remove the gravitational acceleration to obtain the motion acceleration in the navigation coordinate system. .

[0015] Furthermore, S4 specifically includes: based on the acceleration in the horizontal direction , The horizontal velocity can be obtained through integration. , and displacement , ; Horizontal displacement , and speed , as a state vector Horizontal acceleration , As an input control variable, it contains process noise ( , ), variance is ( , Let the sampling time interval be... Then the state equation is as follows:

[0016]

[0017]

[0018]

[0019]

[0020] Written in matrix form as , where the state vector State transition matrix Control matrix Control vector , The process noise has a covariance of .

[0021] Predicted value ,in, For the previous moment The optimal estimate, For the current moment The prior estimate, that is, the first horizontal displacement estimator used as the predicted value, is the prior estimate. Error covariance matrix , The best estimate at the previous time step The error covariance matrix.

[0022] Furthermore, S5 includes:

[0023] Based on the tower height h and the pitch angle Roll angle Calculate the horizontal displacement , Since the horizontal sway angle of the tower does not exceed 0.5°, it conforms to the small angle approximation principle, and the observation equation is as follows:

[0024] ;

[0025] ;

[0026] and The magnitude of the displacement represents the magnitude of the displacement, and the sign indicates the direction of the displacement. The sign of the displacement differs in different coordinate systems. The observation equation is written in matrix form. Among them, the observed values That is, the observation matrix serves as the second horizontal displacement estimator for the observed values. , The covariance matrix of the observation noise is: .

[0027] Furthermore, S6 includes:

[0028] Calculate the Kalman gain matrix Through Kalman gain Predicted values and observed values Data fusion is performed to obtain Optimal estimate , Error covariance matrix ;

[0029] According to the optimal estimate and its error covariance Continue to get the next moment Prior estimate and its error covariance matrix Then use the observed values Correct it to obtain the optimal estimate. and its error covariance matrix This process is repeated to obtain the optimal estimate at each time step.

[0030] Among them, for the initial value The selection is based on the acceleration value at time 0. and Calculate the Euler angles at time 0, and then calculate the horizontal displacement at time 0 using the Euler angles and the equipment installation height h. , .

[0031] Furthermore, the method also includes: resetting the optimal estimation result of the Kalman filter by utilizing the sway characteristics of the tower, and correcting the error covariance matrix of the optimal estimate, specifically including:

[0032] Displacement reset: When the pitch angle θ crosses zero, the optimal estimate will be reset. Zero the x-direction displacement dx;

[0033] When the roll angle crosses zero, the optimal estimate is zeroed; the velocity is reset:

[0034] When the magnitude of the pitch angle reaches the current heave cycle maximum, the optimal estimate is zeroed; when the magnitude of the roll angle reaches the current heave cycle maximum, the optimal estimate is zeroed; the covariance matrix is modified: After each reset, the error covariance matrix is zeroed in the rows and columns associated with the state being reset, and the variance of that state is set to a pre-defined minimum value epsilon.

[0035] Further, the zero-crossing determination employs a hysteresis threshold, including:

[0036] A positive threshold thresh is set;

[0037] Pitch angle zero-crossing determination: when the pitch angle

[0038] increases from negative to above thresh, it is determined to be a zero-crossing from negative to positive; when the pitch angle decreases from positive to below -thresh, it is determined to be a zero-crossing from positive to negative. Roll angle zero-crossing determination: when the roll angle increases from negative to above thresh, it is determined to be a zero-crossing from negative to positive; when the roll angle

[0039] decreases from positive to below -thresh, it is determined to be a zero-crossing from positive to negative.

[0040] The present application has the following beneficial effects:

[0041] The present application realizes high-precision real-time monitoring of horizontal swing displacement of a fan tower drum through multi-source data fusion and systematic error control. Specifically, the method first calibrates and compensates IMU sensor data to eliminate inherent hardware errors; secondly, based on the compensated acceleration and angular velocity, the Euler angle is accurately calculated through extended Kalman filtering, then the rotation matrix is constructed using the Euler angle to convert the acceleration in the carrier coordinate system to the navigation coordinate system and eliminate the gravity acceleration component to accurately obtain the horizontal acceleration in the navigation coordinate system, and finally, in the navigation coordinate system, the horizontal acceleration is twice integrated to predict the displacement, and the geometric observation displacement based on the tower drum height and the Euler angle is combined to obtain the optimal value of the horizontal displacement by dynamically fusing the predicted value and the observed value using Kalman filtering. This process effectively suppresses the IMU integral drift and the limitations of a single data source in traditional methods, significantly improves the displacement estimation accuracy and robustness under slight swing, and provides reliable data support for wind turbine structural health monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 A flowchart of a wind turbine tower drum horizontal swing displacement fusion method provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0044] To enable those skilled in the art to better understand the technical solutions of the present application, the exemplary embodiments of the present application are described below with reference to the drawings, which include various details of the embodiments of the present application to help understanding. They should be considered only as exemplary. Therefore, those skilled in the art should realize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and brevity, the description below omits the description of well-known functions and structures.

[0045] In the case of no conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0046] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0047] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. "Coupled" or "connected" or similar terms are not restricted to physical or mechanical connections or associations, but can also include electrical connections, whether direct or indirect.

[0048] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted in an overly literal or overly formal sense unless expressly so defined herein.

[0049] In the technical solutions of the present application, the collection, storage, use, processing, transmission, provision and disclosure of user personal information comply with relevant laws and regulations and do not violate public order and good customs. The use of user data in the technical solutions complies with relevant national laws and regulations (for example, "Information Security Technology Personal Information Security Specification" and the like). For example, appropriate measures are taken for personal information access control; restrictions are given to the display of personal information; the use purpose of personal information does not exceed the direct or reasonably related range; the use of personal information eliminates explicit identity pointing and avoids precise positioning to a specific individual.

[0050] To solve at least one of the technical problems in the related art described above, the present application provides a wind turbine tower horizontal sway displacement fusion method. Figure 1 A flowchart of a wind turbine tower horizontal sway displacement fusion method provided by an embodiment of the present application is shown in FIG. 1. The method includes the following steps.

[0051] S1. Calibrate the IMU sensor using the least squares method to obtain calibration parameters. Based on these calibration parameters, compensate for the acceleration and angular velocity acquired by the IMU. S2. Based on the compensated acceleration and angular velocity, obtain the Euler angles of the tower sway using an extended Kalman filter. S3. Construct a rotation matrix using the Euler angles to transform the acceleration in the carrier coordinate system to the navigation coordinate system and eliminate the gravitational acceleration component. S4. Perform a quadratic integration on the horizontal acceleration in the navigation coordinate system to generate a first horizontal displacement estimate as the predicted value. S5. Generate a second horizontal displacement estimate as the observed value based on the tower installation height and the Euler angles. S6. Fusion the predicted and observed values ​​using a Kalman filter to output the optimal displacement estimate.

[0052] In existing technologies, IMU measurements are susceptible to noise and drift, leading to distorted displacement estimation. This invention, through calibration, filtering, and fusion, significantly improves the accuracy (e.g., reducing drift) and robustness (e.g., adapting to tower sway characteristics) of displacement estimation. At the same time, by integrating predictions and observations, it overcomes the limitations of simple integration or single observation methods in existing technologies, outputting a more reliable optimal displacement estimate, suitable for high-precision monitoring of minute swaying of wind turbine towers.

[0053] In some embodiments, S1 specifically includes:

[0054] First, the least squares method is used to calibrate the IMU sensor to obtain calibration parameters, namely the scale factor and zero bias of the accelerometer's x, y, and z axes, and the scale factor and zero bias of the gyroscope's x, y, and z axes.

[0055] Then, IMU sensors were used to collect the triaxial acceleration of the tower's swaying in real time. and triaxial angular velocity The collected triaxial accelerations were respectively... and triaxial angular velocity values After scaling factor compensation and bias compensation, the calibrated triaxial acceleration based on the IMU carrier coordinate system is obtained. and triaxial angular velocity .

[0056] Let the scale factor of the accelerometer's x, y, z axes be 1. , , Zero bias is , , And the scale factors of the gyroscope's x, y, and z axes are , , Zero bias is , , ;

[0057] The collected acceleration raw measurements and angular velocity raw measurements are compensated by scale factor and bias to obtain calibrated acceleration values ;

[0058] ;

[0059] .

[0060] The IMU measurements of the prior art are often distorted due to temperature drift or manufacturing bias (e.g. acceleration drift affecting the integration result), while the present application provides high-precision parameter estimation through least square calibration, compensates for the data quality, and lays a foundation for subsequent steps, thereby reducing the overall displacement error.

[0061] In some embodiments, S2 specifically comprises:

[0062] applying extended Kalman filter to the calibrated and compensated acceleration and angular velocity to obtain a rotation quaternion , from which the Euler angles of the tower sway can be obtained

[0063] wherein, is the roll angle, i.e. the angle of rotation around the X axis; is the pitch angle, i.e. the angle of rotation around the Y axis; is the yaw angle, i.e. the angle of rotation around the Z axis.

[0064] A general algorithm can be used to apply extended Kalman filter to the calibrated and compensated acceleration and angular velocity to output the roll angle, pitch angle and yaw angle. Hereinafter, a general algorithm of the prior art is introduced as follows:

[0065] (1) Construct a state prediction model, including:

[0066] use the attitude quaternion as the state variable to avoid gimbal lock;

[0067] use the angular velocity to drive the quaternion update ;

[0068] After discretization, the state equation is established as follows:

[0069] ;

[0070] , the state transition Jacobian matrix .

[0071] (2) Constructing observation model

[0072] Accelerometer value Should be with the gravity vector The projection under the navigation coordinate system is consistent, and the observation equation is constructed as follows:

[0073] Z= ;

[0074] Wherein, the rotation matrix R is derived from the quaternion, and the specific process is as follows:

[0075] ;

[0076] (3) EKF data fusion to obtain the optimal estimation value of quaternion

[0077] After constructing the state prediction equation and the observation equation, the optimal estimation value q of the state variable, i.e. the quaternion, is obtained by EKF data fusion.

[0078] (4) Output Euler angle

[0079] According to the quaternion The Euler angle is calculated, and the specific process is as follows:

[0080] According to The Euler angle

[0081] .

[0082] In some embodiments, S3 specifically comprises:

[0083] The Euler angle The rotation matrix R from the navigation coordinate system to the carrier coordinate system is calculated rotate , and the acceleration in the carrier coordinate system is converted to the navigation coordinate system through the rotation matrix R rotate to obtain the acceleration in the navigation coordinate system, and the motion acceleration in the navigation coordinate system is obtained by removing the gravity acceleration .

[0084] Compared with the prior art method of inaccurate coordinate system conversion or insufficient gravity compensation (such as ignoring the influence of gravity leading to acceleration pollution), the present method ensures the purity of the motion acceleration through the formula , directly eliminates the gravity interference, solves the common height direction error problem in the prior art, makes the horizontal acceleration more reliable, and provides clean input for S4 integration.

[0085] In some embodiments, the acceleration in the horizontal direction is twice integrated to obtain the horizontal displacement of the tower, and the integration will produce cumulative errors, affecting the displacement calculation accuracy; at the same time, the installation height h of the IMU sensor and the Euler angle can also be used to calculate the horizontal displacement, and the measurement of the height and the calculation of the Euler angle also have certain errors. In view of this, Kalman filtering algorithm is used to fuse the horizontal displacement obtained by the two methods to obtain the optimal value of the horizontal displacement.

[0086] In the Kalman filter prediction stage, the acceleration driven state equation in the navigation coordinate system is as follows:

[0087] According to the acceleration in the horizontal direction , , the velocity , and displacement , in the horizontal direction can be calculated by integration; the horizontal displacement , and velocity , are taken as the state vector , the horizontal acceleration , is taken as the input control quantity, and there is process noise , , the variance is , , and the sampling time interval is , so the state equation is as follows:

[0088]

[0089]

[0090]

[0091]

[0092] In matrix form , the state vector is , the state transition matrix is , the control matrix is , the control vector is , is the process noise, and the covariance is .

[0093] The predicted value of is , where optimal estimation value of the current time, priori estimation value of the current time, first horizontal displacement estimation quantity as a prediction value, priori estimation error covariance matrix of the current time, , error covariance matrix of the last time optimal estimation error covariance matrix of the last time optimal estimation.

[0094] Compared with the direct integration method in the prior art (easy to cause displacement drift due to noise and sampling error), the method manages error accumulation through state equation and noise modeling, and reduces long-term integration drift.

[0095] In some embodiments, in the Kalman filter update stage, the horizontal displacement calculated by the installation height and the Euler angle is used as an observation value, and the optimal estimation value of the velocity and the displacement is obtained by fusing the above prediction value, as follows:

[0096] According to the tower height h and the pitch angle , the roll angle , the horizontal displacement , is calculated, and the tower cylinder horizontal swing angle does not exceed 0.5°, which meets the small angle approximation principle, and the observation equation is as follows:

[0097]

[0098]

[0099] and The modulus of the displacement size, the positive and negative signs represent the displacement direction, and the positive and negative signs of the displacement are different in different coordinate systems. The above is only the calculation result in one coordinate system, and the positive and negative signs of the displacement need to be adjusted according to the given coordinate system in actual situation.

[0100] The observation equation is written in the form of a matrix , wherein the observation value , that is, the second horizontal displacement estimation quantity as an observation value, the observation matrix , is the observation noise, and the covariance matrix thereof is .

[0101] In the above embodiments, based on the tower height h, the pitch angle θ and the roll angle , the small angle approximation principle (sinθ≈θ, because the swing angle is less than 0.5°) is applied to calculate the horizontal displacement observation value and Compared with the method of using complex trigonometric functions or ignoring small angle characteristics in the prior art, the feature simplifies the calculation and maintains the accuracy.

[0102] In some embodiments, S6 comprises:

[0103] Calculating the Kalman gain matrix , the optimal estimate of the Kalman gain , and the observation value , , , the error covariance matrix of the optimal estimate ;

[0104] According to the optimal estimate and its error covariance , the prior estimate value and its error covariance matrix at the next time are obtained, and the observation value is used to correct them to obtain the optimal estimate value and its error covariance matrix , and the cycle is repeated to obtain the optimal estimate value at each time; wherein, for the selection of the initial value

[0105] , the Euler angle at time 0 is calculated according to the acceleration value and , and the horizontal displacement at time 0 is calculated through the Euler angle and the device installation height h , .

[0106] In the above embodiments, the Kalman gain K(k) is calculated, the predicted value and the observation value Z(k) are fused, and the optimal displacement estimate is output, and the error covariance is recursively updated. Compared with the non-adaptive fusion (such as weighted average) or fixed gain method in the prior art, the feature optimizes the estimation by dynamically adjusting the gain, automatically balances the prediction and observation weights based on the covariance and R, solves the problem of unstable fusion in the prior art when the noise changes, and outputs a more accurate optimal displacement estimate.

[0107] In some embodiments, the method further comprises resetting the optimal estimation result of the Kalman filter by utilizing the sway characteristics of the tower drum, and correcting the error covariance matrix of the optimal estimate, specifically comprising:

[0108] Displacement reset: when the pitch angle θ passes zero, the optimal estimate zero the x-direction displacement dx;

[0109] when the roll angle crosses zero, zero the optimal estimate of the x-direction displacement dx; velocity reset:

[0110] when the magnitude of the pitch angle reaches the current heave period maximum, zero the optimal estimate of the x-direction velocity ; when the magnitude of the roll angle reaches the current heave period maximum, zero the optimal estimate of the y-direction velocity ; covariance matrix correction: after each reset, zero the rows and columns of the error covariance matrix associated with the state being reset, and set the variance of that state to a pre-determined minimum value

[0111] .

[0112] when the pitch angle crosses zero, zero the x-direction displacement dx, and set the variance of the x-direction displacement in the covariance matrix to a small value (e.g. ), and zero the covariances with other states, i.e. ; ; ; , and proceed with the next prediction and update.

[0113] when the roll angle crosses zero, zero the y-direction displacement dy, and set the variance of the y-direction displacement in the covariance matrix to a small value (e.g. ), and zero the covariances with other states, i.e. ; ; ; , and proceed with the next prediction and update.

[0114] when the magnitude of the pitch angle reaches the maximum, zero the x-direction velocity, and set the variance of the x-direction velocity in the covariance matrix to a small value (e.g. ), and zero the covariances with other states, i.e. ; ; ; , and proceed with the next prediction and update.

[0115] when the magnitude of the roll angle When the modulus reaches the maximum value, the velocity in the y direction is set to zero, and the variance of the velocity in the y direction in the covariance matrix is set to a very small value (such as ), and the covariance with other states is set to 0, that is ; ; ; On this basis, the next prediction and update are carried out, so that the calculated displacement is accurate.

[0116] In some embodiments, the zero-crossing determination adopts hysteresis threshold processing, including:

[0117] A positive threshold thresh is set;

[0118] Zero-crossing determination of the pitch angle θ: when the pitch angle θ increases from a negative value to more than thresh, it is determined that the pitch angle θ crosses zero from negative to positive; when the pitch angle θ decreases from a positive value to less than -thresh, it is determined that the pitch angle θ crosses zero from positive to negative;

[0119] Zero-crossing determination of the roll angle : when the roll angle increases from a negative value to more than thresh, it is determined that the roll angle crosses zero from negative to positive; when the roll angle decreases from a positive value to less than -thresh, it is determined that the pitch angle θ crosses zero from positive to negative.

[0120] Through the above zero-crossing determination, multiple false triggers caused by fluctuations near the threshold are effectively avoided, and the found zero-crossing point is accurate enough.

[0121] The embodiment of the application also provides a computer readable storage medium. The computer readable storage medium stores a computer program, wherein the program is executed by a processor to implement the steps in the fan tower horizontal shaking displacement fusion method in any of the above embodiments. The computer readable storage medium can be a volatile or non-volatile computer readable storage medium.

[0122] The embodiment of the application 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 the above fan tower horizontal shaking displacement fusion method.

[0123] Those of ordinary skill in the art will realize and understand that all or some of the steps in the methods disclosed above and the functional modules / units in the systems and devices can be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementation, the division between the functional modules / units referred to in the above description does not necessarily correspond to the division of physical components; for example, one physical component can have multiple functions, or one function or step can be performed by several physical components in cooperation. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software can be distributed on computer readable storage media, which can include computer storage media (or non-transitory media) and communication media (or transitory media).

[0124] As is well known to those of ordinary skill in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable program instructions, data structures, program modules or other data. Computer storage media include, but are 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 technology, portable compact disc read only memory (CD-ROM), digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and that can be accessed by a computer. Further, it is well known to those of ordinary skill in the art that communication media typically embodies computer readable program instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. As used herein, the term "modulated data signal" means a signal that has one or more of its characteristics changed or set in a manner so as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as wireless networks, cellular telephone networks, code division multiple access (CDMA) networks, and other terrestrial and satellite radio frequency communication networks or frequency (RF) media. The computer software modules and program modules described herein can include routines, programs, objects, components, data structures, and the like, which perform particular tasks and / or implement particular abstract data types. The computer software modules and program modules can be written in a variety of programming languages, such as Objective-C, Java, Visual Basic, Python, C, C++, and / or the like, and can be executed on one or more computer processors.

[0125] The computer software modules and program modules described herein can include routines, programs, objects, components, data structures, and the like, which perform particular tasks and / or implement particular abstract data types. The computer software modules and program modules can be written in a variety of programming languages, such as Objective-C, Java, Visual Basic, Python, C, C++, and / or the like, and can be executed on one or more computer processors.

[0126] Computer readable program instructions for carrying out operations of the present application can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present application.

[0127] The computer program product described herein can be embodied in a specific manner by hardware, software, or a combination thereof. In an optional embodiment, the computer program product is embodied as a computer storage medium. In another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK), and the like.

[0128] The computer program product described herein can be embodied in a specific manner by hardware, software, or a combination thereof. In an optional embodiment, the computer program product is embodied as a computer storage medium. In another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK), and the like.

[0129] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can include a non-transitory computer readable storage medium that can be a computer- readable storage medium having no data storage cycles that change state. The instructions can be executed by one or more processors of a computer, to cause a series of operational steps to be performed on the computer to produce a computer-implemented process. Such instructions can include, but are not limited to, instructions for enabling a computer to create and in-part display a graphical user interface on an external output device of the computer or other programmable data processing apparatus.

[0130] The computer readable program instructions can 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 apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable data processing apparatus or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0131] The flow diagrams and the block diagrams in the drawings are presented to illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to the present application. In this regard, each block in the flow diagrams and the block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions ("instructions"). In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and

[0132] Example embodiments have been disclosed and although specific terms are employed, they are used in a generic and descriptive sense only and not for purposes of limitation. In some instances, it will be apparent to those skilled in the art that features, characteristics or elements described with respect to a particular embodiment can be used, alone or in combination, with other embodiments unless specifically recited otherwise in the detailed description. Accordingly, it will be understood that various changes in form and details can be made without departing from the scope of the present application as set forth in the appended claims.

[0133] The above description is only the preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A wind turbine tower horizontal sway displacement fusion method, characterized by, The method comprises: S1. Calibrate the IMU sensor by least square method to obtain calibration parameters, and compensate the acceleration and angular velocity collected by the IMU based on the calibration parameters; S2. Obtain the Euler angle of the tower cylinder swing based on the compensated acceleration and angular velocity by extended Kalman filtering; S3. Construct a rotation matrix using the Euler angle, convert the acceleration in the carrier coordinate system to the navigation coordinate system, and eliminate the gravity acceleration component; S4. Perform secondary integration on the horizontal acceleration in the navigation coordinate system to generate a first horizontal displacement estimation as a prediction value; S5. Generate a second horizontal displacement estimation as an observation value based on the tower cylinder installation height and the Euler angle; S6. Perform data fusion on the prediction value and the observation value by Kalman filtering to output an optimal displacement estimation; Wherein, S4 specifically includes: according to the acceleration of horizontal direction , , by integral calculation can get the horizontal direction velocity , And displacement , ; The horizontal displacement , And velocity , As state vector , horizontal acceleration , As input control quantity, its existence process noise , variance , set sampling time interval , then the state equation is as follows: ； ； In matrix form, this is written as where the state vector , the state transition matrix , the control matrix , the control vector , is the process noise, with covariance ; the predicted value wherein is the optimal estimate value of the previous time instant is the optimal estimate value of the previous time instant is the a priori estimate value of the current time instant is the a priori estimate value of the current time instant is the error covariance matrix of the a priori estimate , is the error covariance matrix of the optimal estimate of the previous time instant is the error covariance matrix of the optimal estimate of the previous time instant Wherein, S5 specifically comprises: According to the tower drum height h and the pitch angle , the roll angle The horizontal displacement is calculated Since the tower drum swing angle in the horizontal direction does not exceed 0.5°, it meets the small angle approximation principle, and the observation equation is as follows: ； ； and The sign of the modulus represents the displacement direction, and the sign of the displacement is different in different coordinate systems. The observation equation is written in the form of a matrix , that is, the second horizontal displacement estimate as an observation value, and the observation matrix , The observation noise has a covariance matrix ; Wherein, S6 specifically comprises: calculating a kalman gain matrix by the kalman gain the predicted value and the observed value data fusion to obtain the optimal estimate , the error covariance matrix ; According to the optimal estimation and its error covariance Continuously obtain the prior estimate value of the next moment and its error covariance matrix , using the observation value Correct it to obtain the optimal estimate value and its error covariance matrix , and then cycle to obtain the optimal estimate value at each moment.

2. The wind turbine tower level sway displacement fusion method of claim 1, wherein, S1 specifically comprises: First, use least square method to calibrate the IMU sensor to obtain calibration parameters, i.e. the scale factors and zero biases of the x, y, z three axes of the accelerometer and the scale factors and zero biases of the x, y, z three axes of the gyroscope; Then use the IMU sensor to collect the three-axis acceleration of the tower cylinder shaking in real time and three-axis angular velocity , respectively, to the collected three-axis acceleration and three-axis angular velocity value Scale factor compensation and zero offset compensation are performed to obtain calibrated three-axis acceleration and three-axis angular velocity based on the IMU carrier coordinate system.

3. The wind turbine tower level sway displacement fusion method of claim 2, wherein, S2 specifically comprises: The application extends the Kalman filter to process the calibrated and compensated acceleration and angular velocity to get the rotation quaternion and the Euler angles of the tower sway through q wherein, is the roll angle, i.e. the angle of rotation about the X-axis; is the pitch angle, i.e. the angle of rotation about the Y-axis; is the yaw angle, i.e. the angle of rotation about the Z-axis.

4. The wind turbine tower level sway displacement fusion method of claim 3, wherein, S3 specifically comprises: Using Euler angles A rotation matrix R from the body frame to the navigation frame is computed rotate The acceleration in the body frame is transformed to the navigation frame using the rotation matrix R R rotate The acceleration in the navigation frame is obtained by removing the gravitational acceleration from the acceleration in the navigation frame =R rotate * .

5. The wind turbine tower level sway displacement fusion method of claim 1, wherein, The method further comprises resetting the optimal estimation result of Kalman filtering by using the swing characteristics of the tower cylinder, and correcting the error covariance matrix of the optimal estimation, specifically comprising: Displacement reset: when the pitch angle θ crosses zero, the optimal estimate zero the middle x-direction displacement dx; When the roll angle The optimal estimate Zero the y-direction displacement dy; velocity reset: When the modulus of the pitch angle θ reaches the current heave period maximum, the optimal estimate The x-direction velocity is zeroed; when the modulus of the roll angle reaches the current heave period maximum, the optimal estimate The y-direction velocity is zeroed; covariance matrix correction: after each reset, the error covariance matrix has the rows and columns associated with the reset state zeroed and the variance of that state set to a pre-determined minimum value .

6. The wind turbine tower level sway displacement fusion method of claim 5, wherein, The zero-crossing determination adopts hysteresis threshold processing, including: Set a positive threshold thresh; Pitch angle θ zero-crossing point determination: when the pitch angle θ increases from negative to more than thresh, it is determined that the pitch angle θ is from negative to positive zero-crossing point, and when the pitch angle θ decreases from positive to lower than -thresh, it is determined that the pitch angle θ is from positive to negative zero-crossing point; Roll angle Zero-crossing determination: when the roll angle increases from a negative value to above thresh, the roll angle Zero-crossing from negative to positive. When the roll angle decreases from a positive value to below -thresh, the roll angle Zero-crossing from positive to negative.

7. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by a computer to make the computer execute the wind turbine tower cylinder horizontal swing displacement fusion method according to any one of claims 1 to 6.

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