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

Abstract

The invention discloses a wind power generation tower pose monitoring method based on a GNSS / MEMS IMU / magnetometer combination, and the method comprises the steps: carrying out the estimation of a state vector containing the position, speed, attitude error, sensor zero offset and scale factor error through error state Kalman filtering based on the GNSS / MEMS IMU combination, and carrying out the correction and fusion of GNSS and IMU data through a lever arm effect; a complementary filtering algorithm is adopted, an error vector is constructed by combining measured values of an accelerometer and a magnetometer, a gyro angular velocity is corrected through a PI controller, an attitude quaternion is recurred, a course angle is calculated, and the course angle is used as an auxiliary observed quantity to update filtering. According to the invention, by adding course information provided by the MEMS IMU and the magnetometer, the position and the attitude are comprehensively estimated, and high-precision six-degree-of-freedom pose monitoring is realized.

Description

Technical Field

[0001] The present invention belongs to the field of precise pose monitoring, and particularly relates to a GNSS / MEMS IMU / magnetometer combined pose monitoring method, specifically a wind power tower pose monitoring method and system based on GNSS / MEMS IMU / magnetometer combination. Background Art

[0002] Wind power generation, as an important and irreplaceable clean energy, plays a key role in the global energy structure transformation and response to climate change. According to the report of the International Energy Agency, wind power generation has become one of the fastest-growing renewable energies globally, and it is expected that by 2030, wind energy will account for more than 20% of the global electricity supply. However, during the operation of wind power towers, they are often affected by complex climate conditions (such as strong winds, temperature changes, etc.), resulting in different degrees of pose dynamic changes in their nacelles and connected fan blades. These changes will not only affect the efficiency of power supply but also pose potential threats to the structural integrity of the tower. Excessive vibration or tilt may cause fatigue damage to the tower body and even lead to serious safety accidents. Therefore, the International Electrotechnical Commission clearly requires in the "IEC 61400-1:2019 Design Standards for Wind Turbines" that wind power systems must have efficient and accurate monitoring capabilities to ensure their safe and stable operation. Currently, the mainstream wind power tower pose monitoring methods include methods based on Global Navigation Satellite System (GNSS) positioning and methods based on vision sensors. Among them, the method based on vision sensors is costly and difficult to popularize. The method based on GNSS positioning focuses on the change of displacement and ignores the precise monitoring of attitude, resulting in many hidden dangers existing during the operation of the system being difficult to detect in time. For example, when the attitude deviation of the tower body is too large, safety hazards such as blade contact with the tower body may be caused.

[0003] Multi-source fusion positioning is the development trend of current navigation and positioning technologies. The combined algorithm based on GNSS and Inertial Navigation System (INS) is currently widely used and can provide complete position and attitude information. However, for scenarios with relatively weak dynamics such as wind turbines, the accuracy of heading angle estimation is often low. Summary of the Invention

[0004] Aiming at the problems that the cost of the existing wind power tower pose monitoring scheme is difficult to control or the attitude information cannot be accurately monitored, the present invention proposes a wind power tower pose monitoring method based on GNSS / MEMS IMU / magnetometer combination. By adding the heading information provided by MEMS IMU and magnetometer, the position and attitude are comprehensively estimated, and high-precision six-degree-of-freedom pose monitoring is realized.

[0005] According to one aspect of the specification of the present invention, a method for monitoring the position and pose of a wind power tower based on a GNSS / MEMS IMU / magnetometer combination is provided, including:

[0006] Based on the GNSS / MEMS IMU combination, use the error-state Kalman filter to estimate the state vector including position, velocity, attitude errors, sensor biases, and scale factor errors, and fuse GNSS and IMU data through lever-arm effect correction.

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

[0008] As a further technical solution, constructing an error vector by combining the measurement values of the accelerometer and magnetometer, and correcting the gyro angular velocity through a PI controller and recursively calculating the attitude quaternion includes:

[0009] Based on the measurement values of the accelerometer and magnetometer, respectively construct the accelerometer and magnetic field error vectors, and then perform error fusion through a PI controller.

[0010] Based on the error fusion result, combine the gyro data to correct the angular velocity output by the gyro and perform recursive calculation of the attitude quaternion.

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

[0012] Calculate the heading angle according to the attitude error of the IMU.

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

[0014] Use the GNSS positioning result at the initial moment as the initial position of the IMU, perform IMU state recursion according to the differential equations of each error in the state vector with respect to time, and perform position observation update after obtaining the GNSS precise positioning result.

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

[0016] At the moment when there is observation information, output the position and pose at the current moment after observation update;

[0017] At the moment when there is no observation information, output the position and pose at the current moment after state recursion.

[0018] According to one aspect of the specification of the present invention, there is provided a wind power tower pose monitoring system based on a GNSS / MEMS IMU / magnetometer combination, including 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. The monitoring data processing device executes the wind power tower pose monitoring method based on the GNSS / MEMS IMU / magnetometer combination based on the acquired data.

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

[0020] A first processing module, configured to estimate a state vector including position, velocity, attitude errors, sensor biases, and scale factor errors based on the GNSS / MEMS IMU combination by using an error state Kalman filter, and fuse GNSS and IMU data through lever arm effect correction;

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

[0022] According to one aspect of the specification of the present invention, there is provided a wind power tower pose monitoring device based on a GNSS / MEMS IMU / magnetometer combination, including 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 the GNSS / MEMS IMU / magnetometer combination.

[0023] According to one aspect of the specification of the present invention, there is provided a non-transitory computer-readable storage medium. The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions cause the computer to execute the wind power tower pose monitoring method based on the GNSS / MEMS IMU / magnetometer combination.

[0024] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0025] 1. For the pose monitoring of wind power towers, the present invention is based on the GNSS / MEMS IMU / magnetometer combination and realizes high-precision six-degree-of-freedom pose monitoring. Compared with traditional GNSS positioning-based methods, attitude measurement is considered, which is of great significance for the safe operation of wind power towers.

[0026] 2. The sensors involved in the present invention are of low cost and easy to popularize. Compared with the traditional GNSS-based positioning method, only a low-cost MEMS IMU and magnetometer are added, and the cost is lower than that of the method based on expensive vision sensors.

[0027] 3. The present invention is verified on a servo platform with physical simulation capabilities: a high pose accuracy can be achieved under the condition of simulating the dynamics of a wind turbine tower. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0029] Figure 1 Schematic diagram of the method for monitoring the pose of a wind turbine tower based on the GNSS / MEMS IMU / magnetometer combination provided by the embodiment of the present invention;

[0030] Figure 2 Position error curve diagrams of GNSS / MEMS IMU and the GNSS / MEMS IMU / magnetometer combination under the condition of simulating the dynamics of a wind turbine tower provided by the embodiment of the present invention;

[0031] Figure 3 Attitude error curve diagrams of GNSS / MEMS IMU and the GNSS / MEMS IMU / magnetometer combination under the condition of simulating the dynamics of a wind turbine tower provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0032] The main dynamic performance of a wind turbine is slow, low-frequency, and small-amplitude vibration. Considering the combined algorithm based on GNSS and inertial navigation systems, the observability of the heading angle is weak and the estimation accuracy is low under such weak dynamics. As a lightweight sensor, a magnetometer can sense the local magnetic field, thereby providing relatively accurate heading information and having a low cost, making it suitable as supplementary information for the GNSS / MEMS IMU combination. Therefore, the present invention provides a high-precision pose monitoring method based on the fusion of low-cost sensors, which has important practical significance for the safe operation of wind power systems.

[0033] Based on the GNSS / MEMS IMU combination, the present invention adds the heading information provided by the MEMS IMU and magnetometer to comprehensively estimate the position and attitude. Compared with the traditional GNSS-based positioning method, only a low-cost MEMS IMU and magnetometer are added, and the cost is lower than that of the method based on expensive vision sensors.

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention. Additionally, the technical features in each embodiment or single embodiment provided by the present invention can be arbitrarily combined with each other to form a new technical solution. Such combination is not restricted by the sequence of steps and / or the structure composition mode, but must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present invention.

[0035] The present invention provides a method for monitoring the position and attitude of a wind power tower based on a GNSS / MEMS IMU / magnetometer combination, including:

[0036] (1) Through the GNSS / MEMS IMU combination, use the error-state Kalman filter to estimate the state vector including position, velocity, attitude errors, sensor biases, and scale factor errors, and fuse the GNSS and IMU data through lever-arm effect correction.

[0037] (2) Adopt a complementary filtering algorithm, construct an error vector by combining the measured values of the accelerometer and magnetometer, correct the gyro angular velocity through a PI controller and recursively calculate the attitude quaternion, calculate the heading angle, and use the heading angle as an auxiliary measurement to update the filter.

[0038] The method of the present invention is specifically used for monitoring the position and attitude of a wind power tower, can monitor the attitude changes of the tower body and nacelle in real time, where the three-axis attitude errors are 0.043 degrees, 0.048 degrees, and 0.579 degrees, can effectively monitor the dynamics during the operation of the wind power tower, and has important practical significance for the safe operation of the wind power system.

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

[0040] The embodiments of the present invention adopt a combination based on the error-state Kalman filter, and the state vector includes three-axis position, velocity, attitude errors, bias error vectors of the gyro and accelerometer, and scale factor error vectors:

[0041]

[0042] where δr n and δv nDenote the position error vector and velocity error vector in the n - frame, φ represents the attitude error vector, b g and b a represent the bias errors of the gyroscope and accelerometer respectively, s g and s a represent the scale factor errors of the gyroscope and accelerometer respectively.

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

[0044]

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

[0046] The present invention constructs the continuous - time system state equation by using the inertial navigation error differential equation, models the attitude error as the φ - angle error model, and models the biases and scale factors of the gyroscope and accelerometer as the first - order Gaussian Markov model. The differential equations of each error in the state vector with respect to time are 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 the 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's angular velocity in the navigation coordinate system in the navigation coordinate system, is the gravity calculation error, is the rotation matrix from the vehicle 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 gyroscope.

[0049] Expanding the above formula gives the F(t) matrix, where T gb 、T ga 、T ab and T as are the correlation times of the first - order Gaussian Markov process, w gb 、w gs 、w ab and w as are the driving white noises of the first - order Gaussian Markov process.

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

[0051]

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

[0053] For the basic equations of discrete-time Kalman filtering, discretization is required. 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] Denote the discretization time interval Δt = t k - t k-1 , when the matrix F(t) changes not too drastically within a relatively short integration interval [t k-1 , t k , and assuming F(t k-1 )Δt << I, where I is the identity 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 not too drastically within a relatively short integration interval [tk-1, tk], Q k can be simplified to 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 do not coincide physically, and the lever arm effect correction is required during the solution. 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 effect matrix, which is calculated according to the following formula:

[0066]

[0067] R M is the radius of the meridian, and R N is the radius of the prime vertical, is the latitude, h is the elevation, 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 deduced by inertial navigation:

[0069]

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

[0071]

[0072] In the formula, the superscript ~ represents the observed value, is the GNSS position observation, and n r is the position error of GNSS. Generally, for simplicity of processing, the error of the GNSS position measurement value is often modeled as a Gaussian white noise sequence, that is, n r,k ~ N(0, R k ), The variance R of the observation error k is obtained from the RTK positioning result.

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

[0074]

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

[0076] The present invention adopts a complementary filtering algorithm based on an accelerometer, a gyroscope and a 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 after normalization is a norm= a / ||a||. Rotate the reference gravity vector to the vehicle coordinate system through the current attitude quaternion. 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 theoretical acceleration value, e a represents the error vector of the acceleration.

[0081] If there is magnetometer data, construct the error based on the geomagnetic field in a similar way and achieve error fusion through a PI controller. Assume that the horizontal component of the geomagnetic field is northward, and the reference vector is m n = [0, m h , m z T , where If the zero bias of the magnetometer has been compensated, rotate the reference geomagnetic vector to the vehicle coordinate system through the attitude quaternion. 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] Achieve error fusion through a PI controller, update the integral error e int , and correct the angular velocity ω obtained from gyro data:

[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 k​i are the parameters of the PI controller.

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

[0091]

[0092] Then, the updated quaternion is normalized according to and then the quaternion q = [q0 q1 q2 q3] T is converted into the form of attitude angles, where the heading angle is calculated as follows:

[0093]

[0094] In the embodiments of the present invention, the heading angle observation is updated as follows:

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

[0096]

[0097] is the element in the i-th row and j-th column of. The heading angle is calculated as follows

[0098]

[0099] obtained by derivative using the chain rule. The observation equation of the auxiliary heading angle is written in the following form:

[0100]

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

[0102] Figure 1The algorithm flow for GNSS / MEMS IMU / magnetometer combined pose monitoring is given. Box ① shows the algorithm flow of the GNSS / MEMS IMU combination. The GNSS positioning result at the initial moment is used as the initial position of the IMU, and the IMU state is recursively calculated according to Equation (3). After obtaining the precise GNSS positioning result, the position observation is updated according to Equation (14). Box ② shows the algorithm flow for heading estimation based on the MEMS IMU and magnetometer. The accelerometer and magnetic field error vectors are respectively constructed through Equations (16) and (18), and then the error fusion is performed through a PI controller. The angular velocity output by the gyroscope is corrected according to Equation (21), and the attitude quaternion is recursively calculated according to Equation (23). Finally, the heading angle is calculated through Equation (24). The heading angle calculated by this method is updated for heading angle observation according to Equation (26). For the moments with observation information, the pose at the current moment is output after the observation update. For the moments without observation information, the pose at the current moment is output after the state recursion.

[0103] As a preferred embodiment, the method of the present invention is implemented and verified based on the PC side in the embodiments of the present invention.

[0104] Figure 2 The displacement error curves of GNSS / MEMS IMU and GNSS / MEMS IMU / magnetometer mounted on a reference platform for the dynamic simulation of a wind power tower are given. The green curve represents the displacement error of the GNSS / MEMS IMU combination, which is basically the same as that of the GNSS / MEMS IMU / magnetometer combination represented by the red curve. The maximum error does not exceed 4 cm. The RMS values of the 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. This is because the positioning accuracy mainly depends on GNSS positioning and hardly changes after adding the magnetometer.

[0105] Figure 3Give the attitude error curves of GNSS / MEMS IMU and GNSS / MEMS IMU / magnetometer mounted on the reference platform of the simulated dynamic wind turbine tower. Compared with the GNSS / MEMS IMU combination represented by the green curve, the error curve of the GNSS / MEMS IMU / magnetometer combination represented by the red curve is closer to 0. Whether viewed from the magnitude of the up and down fluctuations and the magnitude of the maximum error, the error level is reduced, and the accuracy of the roll angle and pitch angle has an improvement effect, which is slightly smaller than that of the heading angle. The RMS values of the errors are 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%. 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%. It significantly improves the accuracy of the heading angle of the GNSS / MEMS IMU combination under weak dynamic conditions, and verifies the feasibility of the present invention in the pose monitoring of wind turbine towers.

[0106] Based on the same inventive concept as the foregoing method embodiment, the embodiment of the present invention further provides a wind turbine tower pose monitoring system based on a GNSS / MEMS IMU / magnetometer combination, including 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. The monitoring data processing device executes the wind turbine tower pose monitoring method based on the GNSS / MEMS IMU / magnetometer combination according to the acquired data.

[0107] The monitoring data processing device includes: a first processing module, configured to estimate a state vector including position, velocity, attitude error, sensor zero bias, and scale factor error based on the GNSS / MEMS IMU combination by using error state Kalman filtering, and fuse GNSS and IMU data through lever arm effect correction; a second processing module, configured to adopt a complementary filtering algorithm, construct an error vector by combining accelerometer and magnetometer measurement values, correct the gyro angular velocity through a PI controller and recursively calculate the attitude quaternion, calculate the heading angle, and update the filter by using the heading angle as an auxiliary observable.

[0108] The wind turbine tower pose monitoring system based on the GNSS / MEMS IMU / magnetometer combination provided by the embodiment of the present invention aims at the problem that the cost of the existing wind turbine tower pose monitoring scheme is difficult to control or the attitude information cannot be accurately monitored. By using the foregoing several devices and modules, by adding the heading information provided by the MEMS IMU and the magnetometer, the position and attitude are comprehensively estimated, and high-precision six-degree-of-freedom pose monitoring is realized.

[0109] Based on the same inventive concept as the foregoing embodiments, an embodiment of the present invention further provides a wind power tower pose monitoring device based on a GNSS / MEMS IMU / magnetometer combination, including a memory and a processor. The memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the above-mentioned wind power tower pose monitoring method based on the GNSS / MEMS IMU / magnetometer combination.

[0110] In an embodiment of the present invention, the memory may be a non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), etc., or may also be a volatile memory, such as a random-access memory (RAM). The memory is any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory in the embodiment of the present invention may also be a circuit or any other device capable of implementing a storage function, for storing program instructions and / or data.

[0111] In an embodiment of the present invention, the processor may be a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present invention may be directly embodied as being executed 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, an embodiment of the present invention further provides a non-transitory computer-readable storage medium. The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions cause the computer to execute the above-mentioned wind power tower pose monitoring method based on the GNSS / MEMS IMU / magnetometer combination as follows:

[0113] Based on the GNSS / MEMS IMU combination, use error-state Kalman filtering to estimate the state vector including position, velocity, attitude errors, sensor biases, and scale factor errors, and fuse GNSS and IMU data through lever-arm effect correction;

[0114] Adopt a complementary filtering algorithm, combine the measured values of the accelerometer and the magnetometer to construct an error vector, correct the gyro angular velocity through a PI controller and recursively calculate the attitude quaternion, calculate the heading angle, and use the heading angle as an auxiliary measurement to update the filtering.

[0115] In summary, the present invention discloses a method for monitoring the position and pose of a wind power tower based on the combination of GNSS / MEMS IMU / magnetometer, specifically including: (1) constructing a state vector and an observation model of the GNSS / INS combination, and using the error state Kalman filter to achieve six-degree-of-freedom position and pose monitoring; (2) optimizing the heading angle estimation by using the complementary filtering algorithm of the accelerometer, gyroscope and magnetometer to improve the attitude accuracy of the GNSS / INS combination; (3) The invention has a relatively low cost compared with the solutions in the same field and a relatively high position and pose monitoring accuracy. The displacement error is centimeter-level, and the three-axis attitude angle errors are 0.043 degrees, 0.048 degrees and 0.579 degrees respectively. The present invention is applicable to the position and pose monitoring of wind power towers, taking into account both a relatively low sensor cost and a relatively high measurement accuracy under such weak dynamic conditions, and providing an effective guarantee for the safe operation of the wind power system.

[0116] The terms "including" and "having" and any variations thereof in the description and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or devices.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A wind power tower posture monitoring method based on GNSS / MEMSIMU / magnetometer combination, characterized in that: include: Based on the GNSS / MEMSIMU combination, the error state Kalman filter is used to estimate the state vector containing position, velocity, attitude error, sensor bias, and scale factor error, and the fusion of GNSS and IMU data is corrected through the lever arm effect. The complementary filtering algorithm is adopted to construct the error vector by combining the accelerometer and magnetometer measurements. The gyro angular velocity is corrected by the PI controller and the attitude quaternion is recursively derived to calculate the heading angle, which is then used as an auxiliary observation to update the filter.

2. The wind power tower posture monitoring method based on GNSS / MEMSIMU / magnetometer combination according to claim 1 is characterized in that: The error vector is constructed by combining the accelerometer and magnetometer measurements, and the gyro angular velocity is corrected by the PI controller and the attitude quaternion is recursively derived, including: Based on the accelerometer and magnetometer measurements, the accelerometer and magnetic field error vectors are constructed respectively, and then the errors are fused through the PI controller. Based on the error fusion result, the angular velocity of the gyro output is corrected in combination with the gyro data, and the attitude quaternion is recursively deduced.

3. The wind power tower posture monitoring method based on GNSS / MEMSIMU / magnetometer combination according to claim 2 is characterized in that: The method further comprises: Calculate the heading angle based on the attitude error of the IMU.

4. The wind power tower posture monitoring method based on GNSS / MEMSIMU / magnetometer combination according to claim 1 is characterized in that: The monitoring process based on the GNSS / MEMSIMU combination includes: The GNSS positioning result at the initial moment is used as the initial position of the IMU. The IMU state is recursively calculated according to the differential equation of the errors in the state vector over time. After obtaining the GNSS precise positioning result, the position observation is updated.

5. The wind power tower posture monitoring method based on GNSS / MEMSIMU / magnetometer combination according to claim 1 is characterized in that: The method further comprises: When there is observation information, the current position and posture are output after observation update; When there is no observation information, the state is recursively deduced and the current position is output.

6. A wind power tower posture monitoring system based on a combination of GNSS / MEMSIMU / magnetometer, characterized in that: The invention comprises a monitoring data acquisition device and a monitoring data processing device, wherein the monitoring data acquisition device acquires GNSS data, accelerometer data and gyroscope data of MEMSIMU and magnetometer data, and the monitoring data processing device executes the wind power tower posture monitoring method based on the GNSS / MEMSIMU / magnetometer combination as described in any one of claims 1 to 5 based on the acquired data.

7. The wind power tower posture monitoring system based on GNSS / MEMSIMU / magnetometer combination according to claim 6 is characterized in that: The monitoring data processing device also includes: The first processing module is used to estimate the state vector including position, velocity, attitude error, sensor bias and scale factor error based on the GNSS / MEMSIMU combination by using the error state Kalman filter, and to correct the fusion of GNSS and IMU data by lever arm effect; The second processing module is used to adopt a complementary filtering algorithm, combine the accelerometer and magnetometer measurements to construct an error vector, correct the gyro angular velocity through a PI controller and recursively derive the attitude quaternion, calculate the heading angle, and use the heading angle as an auxiliary observation to update the filter.

8. A wind power tower posture monitoring device based on GNSS / MEMSIMU / magnetometer combination, characterized in that: It comprises a memory and a processor, wherein the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the wind power tower posture monitoring method based on the GNSS / MEMSIMU / magnetometer combination as described in any one of claims 1 to 5.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the wind power tower posture monitoring method based on the GNSS / MEMSIMU / magnetometer combination as described in any one of claims 1 to 5.

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