Wind power tower pose monitoring method and system based on GNSS / MEMS IMU / magnetometer combination

Abstract

The application discloses a wind power tower pose monitoring method based on GNSS / MEMS IMU / magnetometer combination, comprising the following steps: based on GNSS / MEMS IMU combination, using error state Kalman filtering to estimate a state vector containing position, velocity, attitude error and sensor zero offset, proportional factor error, and fusing GNSS and IMU data through a lever arm effect correction; using a complementary filtering algorithm, combining accelerometer and magnetometer measurement values to construct an error vector, correcting gyro angular velocity through a PI controller and recursively calculating attitude quaternions, calculating a heading angle, and taking the heading angle as auxiliary observation to update filtering. The application realizes high-precision six-degree-of-freedom pose monitoring by increasing heading information provided by MEMS IMU and a magnetometer and comprehensively estimating position and attitude.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of precise position and posture monitoring, and particularly relates to a GNSS / MEMS IMU / magnetometer combined position and posture monitoring method, in particular to a wind power tower position and posture monitoring method and system based on GNSS / MEMS IMU / magnetometer combination. BACKGROUND

[0002] Wind power generation, as an important and irreplaceable clean energy, plays a key role in global energy structure transformation and response to climate change. According to the report of the International Energy Agency, wind power has become one of the fastest growing renewable energy sources in the world, and it is expected that wind energy will account for more than 20% of global electricity supply by 2030. However, during the operation of the wind power tower, it is often affected by complex weather conditions (such as strong winds, temperature changes, etc.), resulting in different degrees of dynamic changes in the position and posture of the nacelle and the connected fan parts. These changes not only affect the efficiency of power supply, but also may pose a potential threat to the structural integrity of the tower, excessive vibration or inclination may cause fatigue damage to the tower body, and even cause serious safety accidents. Therefore, the International Electrotechnical Commission clearly requires in the IEC 61400-1:2019 Wind Turbine Generator Design Standard that wind power systems must have efficient and accurate monitoring capabilities to ensure their safe and stable operation. The current mainstream wind power tower position and posture monitoring methods include the method based on Global Navigation Satellite System (GNSS) positioning and the method based on visual sensor, among which the method based on visual sensor is costly and difficult to popularize. The method based on GNSS positioning focuses on the change of displacement and ignores the accurate monitoring of attitude, resulting in that many hidden dangers existing during the operation of the system are difficult to be found in time, for example, when the tower body attitude deviation is too large, it may cause safety hazards such as contact between the blade and the tower body.

[0003] Multi-source fusion positioning is the development trend of current navigation and positioning technology, and the combination algorithm based on GNSS and Inertial Navigation System (INS) is widely used at present, which can provide complete position and attitude information. However, for the scene of dynamic wind power generator, the heading angle estimation accuracy is often low. SUMMARY

[0004] In view of the problem that the existing wind power tower position and posture monitoring scheme is difficult to control the cost or cannot accurately monitor the attitude information, the present application proposes a wind power tower position and posture monitoring method based on GNSS / MEMS IMU / magnetometer combination, which provides heading information by increasing MEMS IMU and magnetometer, estimates position and attitude comprehensively, and realizes high-precision six-degree-of-freedom position and posture monitoring.

[0005] According to an aspect of the present specification, a wind power tower pose monitoring method based on a GNSS / MEMS IMU / magnetometer combination is provided, comprising:

[0006] Based on the GNSS / MEMS IMU combination, the state vector containing position, velocity, attitude error and sensor zero offset, scale factor error is estimated by using error state Kalman filter, and the GNSS and IMU data are fused by correcting the lever arm effect;

[0007] A complementary filter algorithm is used to construct an error vector combining accelerometer and magnetometer measurement values, correct the gyro angular velocity by a PI controller and recursively update the attitude quaternion, calculate the heading angle, and update the filter with the heading angle as auxiliary observation.

[0008] As a further technical solution, an error vector is constructed combining accelerometer and magnetometer measurement values, the gyro angular velocity is corrected by a PI controller and the attitude quaternion is recursively updated, comprising:

[0009] Based on the accelerometer and magnetometer measurement values, the accelerometer and magnetic field error vectors are constructed respectively, and then the error fusion is performed by a PI controller;

[0010] Based on the error fusion result, the gyro output angular velocity is corrected combining the gyro data, and the attitude quaternion is recursively updated.

[0011] As a further technical solution, the method further comprises:

[0012] The heading angle is calculated based on the attitude error of the IMU.

[0013] As a further technical solution, the monitoring process based on the GNSS / MEMS IMU combination comprises:

[0014] The GNSS positioning result at the initial time is taken as the initial position of the IMU, the IMU state is recursively updated according to the differential equation of each error in the state vector with respect to time, and after obtaining the GNSS precise positioning result, the position observation is updated.

[0015] As a further technical solution, the method further comprises:

[0016] At the time when there is observation information, the current time pose is output after observation update;

[0017] At the time when there is no observation information, the current time pose is output after state recursion.

[0018] According to an aspect of the present application, a wind power tower pose monitoring system based on GNSS / MEMS IMU / magnetometer combination is provided, comprising a monitoring data acquisition device and a monitoring data processing device, the monitoring data acquisition device acquires GNSS data, accelerometer data and gyro data of MEMS IMU, and magnetometer data, and the monitoring data processing device performs the wind power tower pose monitoring method based on GNSS / MEMS IMU / magnetometer combination based on the acquired data.

[0019] As a further technical solution, the monitoring data processing device further comprises:

[0020] A first processing module is configured to estimate a state vector comprising position, velocity, attitude error, and sensor zero offset and scale factor error based on GNSS / MEMS IMU combination using error state Kalman filtering, and fuse GNSS and IMU data by correcting the lever effect.

[0021] A second processing module is configured to construct an error vector by combining accelerometer and magnetometer measurement values using a complementary filtering algorithm, correct gyro angular velocity by a PI controller, recursively calculate attitude quaternion, and calculate a heading angle, and update the filter using the heading angle as an auxiliary observation.

[0022] According to an aspect of the present application, a wind power tower pose monitoring device based on GNSS / MEMS IMU / magnetometer combination is provided, comprising a memory and a processor, the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the wind power tower pose monitoring method based on GNSS / MEMS IMU / magnetometer combination.

[0023] According to an aspect of the present application, a non-transitory computer readable storage medium is provided, which stores computer instructions for executing the wind power tower pose monitoring method based on GNSS / MEMS IMU / magnetometer combination.

[0024] Compared with the prior art, the present application has the following advantages:

[0025] 1. The present application is aimed at wind power tower pose monitoring, based on GNSS / MEMS IMU / magnetometer combination, and realizes high-precision six-degree-of-freedom pose monitoring, which is more accurate than the traditional GNSS positioning method and takes into account the measurement of attitude, which is of great significance for the safe operation of wind power towers.

[0026] 2、The sensor related in the application has low cost and is easy to popularize, compared with the traditional method based on GNSS positioning, only low-cost MEMS IMU and magnetometer are added, compared with the method based on expensive visual sensor, the cost is lower.

[0027] 3、The application is verified on a servo platform with physical simulation capability: high pose accuracy can be achieved in the case of simulating wind power tower dynamics. DETAILED DESCRIPTION

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings used in the embodiments or prior art description will be briefly described as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0029] Figure 1 The wind power tower pose monitoring method based on GNSS / MEMS IMU / magnetometer combination provided by the embodiments of the present application is shown in the schematic diagram.

[0030] Figure 2 The position error curve of GNSS / MEMS IMU and GNSS / MEMS IMU / magnetometer combination in the case of simulating wind power tower dynamics provided by the embodiments of the present application is shown in the diagram.

[0031] Figure 3 The attitude error curve of GNSS / MEMS IMU and GNSS / MEMS IMU / magnetometer combination in the case of simulating wind power tower dynamics provided by the embodiments of the present application is shown in the diagram. DETAILED DESCRIPTION

[0032] The main dynamic performance of the wind turbine is slow low-frequency small-amplitude vibration. Considering the combination algorithm based on GNSS and inertial navigation system, the observability of the heading angle is weak and the estimation accuracy is low in such weak dynamic conditions. As a lightweight sensor, the magnetometer can sense the local magnetic field and provide more accurate heading information, and the cost is lower, which is suitable as supplementary information for GNSS / MEMS IMU combination. Therefore, the present application provides a high-precision pose monitoring method based on low-cost sensor fusion, which has important practical significance for the safe operation of the wind power system.

[0033] The present application is based on GNSS / MEMS IMU combination, increases the heading information provided by MEMS IMU and magnetometer, and comprehensively estimates the position and attitude. Compared with the traditional method based on GNSS positioning, only low-cost MEMS IMU and magnetometer are added, compared with the method based on expensive visual sensor, the cost is lower.

[0034] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, 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 some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the protection scope of the present application. In addition, the technical features in each embodiment or in a single embodiment provided by the present application can be combined with each other at will to form new technical solutions, and the combination is not restricted by the sequence of steps and / or the mode of structural composition, but should be based on the realization by those skilled in the art. When the combination of technical solutions appears contradictory or unachievable, it should be considered that the combination of technical solutions does not exist and is not within the protection scope of the present application.

[0035] The present application provides a wind power tower pose monitoring method based on GNSS / MEMS IMU / magnetometer combination, comprising:

[0036] (1) Through the GNSS / MEMS IMU combination, the state vector containing position, velocity, attitude error and sensor zero offset, scale factor error is estimated by using error state Kalman filter, and the GNSS and IMU data are fused by correcting the lever arm effect;

[0037] (2) The error vector is constructed by using the complementary filter algorithm, combining the accelerometer and magnetometer measurement values, the gyro angular velocity is corrected by the PI controller and the attitude quaternion is recursively calculated, the heading angle is calculated, and the heading angle is used as an auxiliary observation to update the filter.

[0038] The method described in the present application is dedicated to the pose monitoring of the wind power tower, and can monitor the attitude change of the tower body and the cabin in real time, wherein the three-axis attitude error is 0.043 degrees, 0.048 degrees and 0.579 degrees, and the dynamic state of the wind power tower during operation can be effectively monitored, which has important practical significance for the safe operation of the wind power system.

[0039] In the embodiments of the present application, the algorithm flow of the GNSS / MEMS IMU combination includes the following steps:

[0040] The combination based on error state Kalman filter is adopted in the embodiments of the present application, and the state vector contains three-axis position, velocity, attitude error, zero offset error vector of the gyro and the accelerometer and scale factor error vector:

[0041]

[0042] In the formula, δr n And δv nrepresents the position error vector and velocity error vector under n, φ represents the attitude error vector, b g and b a respectively represent the zero bias error of the gyro and accelerometer, s g and s a respectively represent the scale factor error of the gyro and accelerometer.

[0043] The continuous-time differential equation of δx(t) is as follows:

[0044]

[0045] In the formula, F(t) is a state transition matrix, w(t) is a system noise vector, and G(t) is a system noise vector coefficient matrix.

[0046] The application adopts an inertial navigation error differential equation to construct a continuous-time system state equation, models the attitude error as a φ angle error model, models the gyro zero bias and scale factor as a first-order Gauss Markov model, and the differential equation of each error in the state vector with respect to time is as follows:

[0047]

[0048] is the angular velocity of the navigation coordinate system relative to the earth coordinate system in the navigation coordinate system, δθ is a misalignment angle vector, v n is the velocity in the navigation coordinate system, f n is the specific force in the navigation coordinate system, is the earth rotation angular velocity in the navigation coordinate system, is a gravity calculation error, a rotation matrix from the carrier coordinate system to the navigation coordinate system, is the angular velocity of the navigation coordinate system relative to the inertial coordinate system, is the angular velocity error of the navigation coordinate system relative to the inertial coordinate system, δf b is the measurement error of the accelerometer, represents the measurement error of the gyro.

[0049] The F(t) matrix can be obtained by expanding the above formula, wherein T gb , T ga , T ab and T as are correlation times of the first-order Gauss Markov process, w gb , w gs , w ab and w as are driving white noises of the first-order Gauss Markov process.

[0050] w(t) is a continuous-time system noise vector:

[0051]

[0052] where w v and w φ are the measurement white noise of accelerometer and gyroscope respectively.

[0053] The basic equation for using the discrete-time Kalman filter needs to be discretized, and the discretized system state equation is:

[0054] δx k = Φ k / k-1 δx k-1 + w k-1 (5)

[0055] In the formula:

[0056]

[0057] Let the discretized time interval Δt = t k -t k-1 , when the F(t) matrix changes less sharply within the shorter integration interval [t k-1 , t k ], and set F(t k-1 )Δt << I, I is the unit matrix, then the one-step transition matrix formula can be approximated as:

[0058] Φ k / k-1 = exp{F(t k-1 )Δt}≈I+F(t k )Δt (7)

[0059] The second moment of w k-1 is calculated as follows:

[0060]

[0061] Similarly, when G(t) changes less sharply within the shorter integration interval [tk-1, tk], Q k can be simplified as trapezoidal integration:

[0062]

[0063] The GNSS positioning solution gives the position coordinates of the antenna phase center, and the MEMS IMU recursion gives the navigation results of the IMU measurement center. The two are not physically coincident, and the lever arm effect correction needs to be performed when solving. The position conversion relationship between the GNSS antenna phase center and the IMU measurement center is:

[0064] ​

[0065] where D R is the lever arm matrix, which is calculated as follows:

[0066]

[0067] R M is the meridian radius, R N is the prime vertical radius, is the latitude, h is the altitude, is the position of the GNSS antenna phase center, is the position of the IMU measurement center, l b is the lever arm vector of the GNSS antenna.

[0068] The position vector of the GNSS antenna phase center calculated by the inertial navigation is:

[0069]

[0070] The position of the GNSS antenna phase center obtained by the GNSS positioning solution is expressed as:

[0071]

[0072] where the superscript ~ indicates the observation value, is the GNSS position observation, n r is the position error of the GNSS. Generally, for the purpose of simplifying the processing, the error of the GNSS position measurement value is modeled as a Gaussian white noise sequence, i.e. r,k ~ N(0, R k ), the variance of the observation error R k is obtained from the RTK positioning result.

[0073] According to the error of each navigation state, the observation vector is expressed as the difference between the position calculated by the MEMS IMU and the GNSS position observation, and its expression is:

[0074]

[0075] In the embodiment of the present application, the heading estimation algorithm based on the MEMS IMU / magnetometer includes the following steps:

[0076] The present application adopts the complementary filtering algorithm based on the accelerometer, gyroscope and magnetometer.

[0077] The normalized gravity reference vector in the navigation coordinate system is g n = [0 0 1] T , and the actual acceleration measurement vector a is normalized as a norm= a / ||a||. The reference gravity vector is rotated to the body frame by the current attitude quaternion, and the error vector is the cross product of the acceleration measurement and the theoretical value:

[0078]

[0079] e a = a norm × g' b (16)

[0080] q represents the attitude quaternion, q * represents the conjugate of the attitude quaternion, g' b represents the acceleration theoretical value, e a represents the error vector of the acceleration.

[0081] If there is magnetometer data, the geomagnetic field-based error is constructed in a similar way, and error fusion is achieved through a PI controller. Assuming that the horizontal component of the geomagnetic field is northward, the reference vector is m n = [0, m h , m z ] T , where If the zero offset of the magnetometer has been compensated, the reference geomagnetic vector is rotated to the body frame by the attitude quaternion, and the error vector is the cross product of the magnetometer measurement and the predicted value:

[0082]

[0083] e m = m norm × m' b (18)

[0084] The total error e is the weighted sum of the acceleration and magnetometer errors:

[0085] e = e a + e m (19)

[0086] Error fusion is achieved through a PI controller, the integral error e int is updated, and the angular velocity ω obtained through gyro data is corrected:

[0087] e int ← e int + k i · e · Δt (20)

[0088] ω corrected = ω + k p · e + e int (21)

[0089] The above k p and ki are parameters of the PI controller.

[0090] The attitude quaternion differential equation is discretized by the first-order Runge-Kutta method, and the attitude quaternion is recursively calculated at discrete time:

[0091]

[0092] The updated quaternion is then normalized and converted to the form of attitude angles, where the heading angle is calculated as: T

[0093]

[0094] In the embodiment of the application, the heading angle observation is updated as follows:

[0095] The auxiliary heading is introduced into the filtering process as a new observation. The attitude error of the IMU is:

[0096]

[0097] is the (i, j) element of the matrix.The heading angle is calculated as:

[0098]

[0099] The derivative is obtained by the chain rule. The observation equation of the auxiliary heading angle is written as follows:

[0100]

[0101] where e ψ is the heading angle observation error.

[0102] Figure 1 ​​The algorithm flow based on GNSS / MEMS IMU / magnetometer combined position and pose monitoring is given. Block ① shows the algorithm flow of GNSS / MEMS IMU combination, taking the GNSS positioning result at the initial moment as the initial position of IMU, performing IMU state recursion according to formula (3), and performing position observation update according to formula (14) after obtaining the GNSS precise positioning result; block ② shows the heading estimation algorithm flow based on MEMS IMU and magnetometer, constructing the accelerometer and magnetic field error vectors through formula (16) and formula (18) respectively, then performing error fusion through a PI controller, correcting the angular velocity output by the gyroscope according to formula (21), and performing attitude quaternion recursion according to formula (23), and finally calculating the heading angle through formula (24), the heading angle obtained by the method is updated according to formula (26). For the moment with observation information, the current moment position is output after observation update, and for the moment without observation information, the current moment position is output after state recursion.

[0103] As a preferred embodiment, the embodiment of the present application is implemented and verified based on a PC terminal.

[0104] Figure 2 The displacement error curves of GNSS / MEMS IMU and GNSS / MEMS IMU / magnetometer mounted on the reference platform of the simulated wind power tower dynamic are given. The green curve represents the displacement error of the GNSS / MEMS IMU combination, and the red curve represents the displacement error of the GNSS / MEMS IMU / magnetometer combination, which is basically consistent, the maximum error is not more than 4 cm, and the RMS values of three-axis displacement errors are 13.6 mm, 14.0 mm, 11.8 mm and 13.2 mm, 14.0 mm, 11.9 mm respectively, because the positioning accuracy mainly depends on GNSS positioning, and the addition of the magnetometer almost does not change.

[0105] Figure 3The attitude error curves of the GNSS / MEMS IMU and the GNSS / MEMS IMU / magnetometer combination on the reference platform simulating the dynamic of the wind power tower are given. Compared with the green curve representing the GNSS / MEMS IMU combination, the error curve of the red curve representing the GNSS / MEMS IMU / magnetometer combination is closer to 0, and the error level is reduced in terms of the size of the up and down fluctuations and the maximum error size, and the accuracy of the roll angle and the pitch angle is improved slightly compared with the heading angle, and the RMS value of the error is reduced from 0.070 degrees and 0.065 degrees to 0.043 degrees and 0.048 degrees, and the accuracy is improved by 38.6% and 26.2%, and the RMS of the heading angle error is reduced from 1.470 degrees to 0.579 degrees, and the accuracy is improved by 60.6%, which significantly improves the accuracy of the heading angle of the GNSS / MEMS IMU combination in the weak dynamic condition, and verifies the feasibility of the application in the wind power tower pose monitoring.

[0106] Based on the same inventive concept as the foregoing method embodiments, the embodiments of the application also provide a wind power tower pose monitoring system based on a GNSS / MEMS IMU / magnetometer combination, comprising a monitoring data acquisition device and a monitoring data processing device, the monitoring data acquisition device acquires GNSS data, accelerometer data and gyro data of the MEMS IMU, and magnetometer data, and the monitoring data processing device performs the wind power tower pose monitoring method based on the GNSS / MEMS IMU / magnetometer combination based on the acquired data.

[0107] The monitoring data processing device comprises: a first processing module for estimating a state vector comprising position, velocity, attitude error and sensor zero bias, proportional factor error based on a GNSS / MEMS IMU combination using error state Kalman filtering, and fusing GNSS and IMU data through a lever effect correction; a second processing module for constructing an error vector by combining accelerometer and magnetometer measurement values using a complementary filtering algorithm, correcting gyro angular velocity through a PI controller and recursively calculating attitude quaternions, calculating a heading angle, and updating filtering by taking the heading angle as an auxiliary observation.

[0108] The wind power tower pose monitoring system based on the GNSS / MEMS IMU / magnetometer combination provided by the embodiments of the application addresses the problem that the existing wind power tower pose monitoring scheme is difficult to control the cost or cannot accurately monitor the attitude information, adopts the foregoing devices and modules, estimates the position and attitude by increasing the heading information provided by the MEMS IMU and the magnetometer, and realizes high-precision six-degree-of-freedom pose monitoring.

[0109] Based on the same inventive concept as the foregoing embodiments, the embodiments of the present application also provide a wind power tower pose monitoring device based on GNSS / MEMS IMU / magnetometer combination, comprising a memory and a processor, the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the GNSS / MEMS IMU / magnetometer combination-based wind power tower pose monitoring method.

[0110] In the embodiments of the present application, the memory can be a non-volatile memory such as a hard disk drive (HDD) or a solid-state drive (SSD), etc., and can also be a volatile memory such as a random-access memory (RAM). The memory can be any other medium capable of carrying or storing desired program codes in the form of instructions or data structures and capable of being accessed by a computer, but is not limited thereto. The memory in the embodiments of the present application can also be a circuit or any other device capable of realizing a storage function, for storing program instructions and / or data.

[0111] In the embodiments of the present application, the processor can be a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, and can realize or execute the disclosed methods, steps and logic block diagrams in the embodiments of the present application. The general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as execution completed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

[0112] Based on the same inventive concept as the foregoing embodiments, the embodiments of the present application also provide a non-transitory computer-readable storage medium storing computer instructions, which cause the computer to execute the GNSS / MEMS IMU / magnetometer combination-based wind power tower pose monitoring method as follows:

[0113] Based on the GNSS / MEMS IMU combination, the error state Kalman filter is used to estimate the state vector containing position, velocity, attitude error and sensor zero offset, scale factor error, and the GNSS and IMU data are fused through the lever arm effect correction;

[0114] The complementary filter algorithm is adopted, the error vector is constructed in combination with the accelerometer and magnetometer measurement values, the PI controller is used to correct the gyro angular velocity and recursively update the attitude quaternion, the heading angle is calculated, and the heading angle is used as an auxiliary observation to update the filter.

[0115] To sum up the embodiments, the application discloses a wind power tower pose monitoring method based on GNSS / MEMS IMU / magnetometer combination, and specifically comprises the following steps: (1) constructing a state vector and an observation model of the GNSS / INS combination, and realizing six-degree-of-freedom pose monitoring by using error state Kalman filtering; (2) optimizing heading angle estimation by using a complementary filtering algorithm of an accelerometer, a gyroscope and a magnetometer, and improving the attitude accuracy of the GNSS / INS combination; (3) the application has relatively low cost compared with the schemes in the same field, has relatively high pose monitoring accuracy, and has a displacement error of centimeter level and three-axis attitude angle errors of 0.043 degrees, 0.048 degrees and 0.579 degrees respectively. The application is suitable for wind power tower pose monitoring, and under the condition of weak dynamics, the application takes into account low sensor cost and high measurement accuracy, thereby providing effective guarantee for safe operation of a wind power system.

[0116] The terms "comprise" and "have" and any variations thereof in the specification and in the claims and the above-described drawings are intended to cover not exclusively including, for example, a process, method, system, product or device comprising a series of steps or units, and can include other steps or units that are not clearly listed or inherent to the process, method, product or device.

[0117] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the application, and not to limit them; although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the application.

Claims

1. A method for monitoring the attitude of wind turbine towers based on a combination of GNSS / MEMS IMU / magnetometer, characterized in that, include: Based on the GNSS / MEMS IMU combination, the GNSS positioning result at the initial moment is used as the initial position of the IMU. The error state Kalman filter is used to estimate the state vector containing position, velocity, attitude errors, sensor bias, and scale factor errors. The IMU state is recursively derived according to the differential equation of time for each error in the state vector. After obtaining the GNSS precise positioning result, the GNSS and IMU data are fused and the position observation is updated by correcting the lever effect. A complementary filtering algorithm is employed, combining accelerometer and magnetometer measurements to construct an error vector. A PI controller corrects the gyroscope angular velocity and recursively calculates the attitude quaternion, then uses the heading angle as an auxiliary observation to update the filter. Specifically, constructing the error vector by combining accelerometer and magnetometer measurements, and correcting the gyroscope angular velocity and recursively calculating the attitude quaternion through the PI controller, includes: constructing accelerometer and magnetic field error vectors based on accelerometer and magnetometer measurements respectively, then fusing these errors through the PI controller; based on the error fusion result, correcting the gyroscope output angular velocity using gyroscope data, and recursively calculating the attitude quaternion. Specifically, the normalized gravity reference vector in the navigation coordinate system is: Actual acceleration measurement vector After normalization , Represents attitude quaternions, Represents the conjugate of a pose quaternion. This represents the theoretical value of acceleration. The error vector representing acceleration; The reference vector is , Total error Weighted sum of acceleration and magnetometer error: , Error fusion is achieved through a PI controller, and the integral error is updated. And correct the angular velocity obtained from the gyroscope data. : ， The above and For the parameters of the PI controller, The attitude quaternion differential equations are discretized using the first-order Runge-Kutta method, and the attitude quaternion recursion is performed in discrete time: ， Then the updated quaternion is... Normalize, then convert the quaternion Converted into attitude angles, the heading angle is calculated as follows: 。 2. The wind turbine tower attitude monitoring method based on GNSS / MEMS IMU / magnetometer combination according to claim 1, characterized in that, The method further includes: The heading angle is calculated based on the attitude error of the IMU.

3. The wind turbine tower attitude monitoring method based on GNSS / MEMS IMU / magnetometer combination according to claim 1, characterized in that, The method further includes: When observation information is available, the current pose is output after updating the observation. When there is no observation information, the current pose is output after state recursion.

4. A wind turbine tower attitude monitoring system based on a combination of GNSS / MEMS IMU / magnetometer, characterized in that, The device includes a monitoring data acquisition device and a monitoring data processing device. The monitoring data acquisition device acquires GNSS data, MEMS IMU accelerometer data, gyroscope data, and magnetometer data. The monitoring data processing device executes the wind power tower attitude monitoring method based on the acquired data as described in any one of claims 1 to 3.

5. The wind turbine tower attitude monitoring system based on GNSS / MEMS IMU / magnetometer combination according to claim 4, characterized in that, The monitoring data processing device also includes: The first processing module is used to take the GNSS positioning result at the initial moment as the initial position of the IMU based on the GNSS / MEMS IMU combination, and use error state Kalman filtering to estimate the state vector containing position, velocity, attitude errors and sensor bias and scaling factor errors. The IMU state is recursively derived according to the differential equation of each error in the state vector with respect to time. After obtaining the GNSS precise positioning result, the GNSS and IMU data are fused and the position observation is updated by correcting the lever effect. The second processing module is used to construct an error vector by combining the measurements from the accelerometer and magnetometer using a complementary filtering algorithm. It then corrects the gyro angular velocity through a PI controller and recursively calculates the attitude quaternion to calculate the heading angle. The heading angle is used as an auxiliary observation to update the filter.

6. A wind turbine tower attitude monitoring device based on a GNSS / MEMS IMU / magnetometer combination, characterized in that, The method includes a memory and a processor, wherein the memory stores program instructions that are executed by the processor, and the processor invokes the program instructions to execute the wind power tower attitude monitoring method based on the GNSS / MEMS IMU / magnetometer combination as described in any one of claims 1 to 3.

7. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions that cause the computer to execute the wind power tower attitude monitoring method based on a GNSS / MEMS IMU / magnetometer combination as described in any one of claims 1 to 3.

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