Anemometer tower anemometer adaptive wind measurement method, device, equipment and medium

Abstract

The invention relates to the technical field of wind power generation, and provides a self-adaptive wind measurement method, device and equipment for an anemometer tower anemometer and a medium. The method comprises the following steps: acquiring real-time attitude data of an anemograph; comparing the real-time attitude data with a preset target attitude to obtain a pose deviation signal; according to the position and posture deviation signal, a position and posture adjusting mechanism connected between the anemometer tower body and the anemograph is controlled to execute motion so as to drive the anemograph to carry out position and posture adjustment; and acquiring original wind speed data measured by the anemograph, and performing coordinate transformation on the original wind speed data according to the real-time attitude data to obtain real-time wind measurement data. According to the invention, high-precision and high-stability wind speed and wind direction real-time measurement in a turbulence tower top environment is realized, and the accuracy and reliability of wind resource evaluation, fan load calculation and wind power plant operation control are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of wind power generation technology, and in particular to an adaptive wind measurement method, device, equipment and medium for anemometers on wind towers. Background Technology

[0002] In the field of wind power generation, wind measurement systems are mainly used in the following four aspects: 1. To assess the wind resources in the area during the early stage of wind farm development; 2. To meet the needs of wind farm micro-site selection and wind turbine load calculation during the construction period of wind farm; 3. To predict the wind power of wind farm during the operation period of wind farm; 4. To test the power characteristics of wind turbine units during the operation period of wind farm.

[0003] However, as wind turbines become larger, the height of wind measurement towers continues to increase. This increase in tower height makes vertical adjustment difficult, and under wind loads, the tower's sway amplitude (i.e., wind-induced vibration) increases significantly. Consequently, the anemometer (such as an ultrasonic anemometer) installed at the top of the tower experiences complex six-degree-of-freedom motion (i.e., three translational degrees of freedom and three rotational degrees of freedom).

[0004] Traditional anemometers only measure the wind vector without considering the motion of their own base. This motion is superimposed on the measurement data, resulting in significant errors in the measured wind speed and direction data, which cannot meet the needs of high-precision wind resource assessment, wind farm micro-site selection, and wind turbine load calculation. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide an adaptive wind measurement method, device, equipment and medium for anemometers, so as to solve the above-mentioned technical problem.

[0006] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: an adaptive wind measurement method for a wind tower anemometer, comprising: acquiring real-time attitude data of the anemometer; comparing the real-time attitude data with a preset target attitude to obtain a position deviation signal; controlling a position adjustment mechanism connected between the wind tower body and the anemometer to perform movement according to the position deviation signal, so as to drive the anemometer to perform position adjustment; acquiring the original wind speed data measured by the anemometer, and performing coordinate transformation on the original wind speed data according to the real-time attitude data to obtain real-time wind measurement data.

[0007] The beneficial effects of this invention are as follows: This invention acquires the attitude data of the anemometer in real time and drives the posture adjustment mechanism to perform active motion compensation based on this data. At the same time, it uses the same set of attitude data to perform coordinate transformation on the original wind speed output by the anemometer. This suppresses the change in instrument posture caused by the swaying of the wind measuring tower at the physical level and eliminates the measurement error introduced by the carrier motion at the data level. Ultimately, it achieves high-precision and high-stability real-time measurement of wind speed and direction in a turbulent tower top environment, which significantly improves the accuracy and reliability of wind resource assessment, wind turbine load calculation and wind farm operation control.

[0008] Based on the above technical solution, the present invention can be further improved as follows.

[0009] Furthermore, the step of controlling the posture adjustment mechanism connected between the wind tower and the anemometer to perform movement based on the posture deviation signal, so as to drive the anemometer to perform posture adjustment, includes: calculating the desired posture change of the posture adjustment mechanism based on the posture deviation signal using a control algorithm; converting the desired posture change into a drive command based on the inverse kinematics model of the posture adjustment mechanism; and controlling the posture adjustment mechanism to move according to the drive command, so as to drive the anemometer to perform posture adjustment.

[0010] Furthermore, the acquisition of real-time attitude data from the anemometer includes: acquiring raw measurement data collected by a nine-axis attitude sensor fixed on the anemometer; and fusing the raw measurement data using an adaptive Kalman filter algorithm to obtain the real-time attitude data.

[0011] Furthermore, the original measurement data includes accelerometer data, gyroscope data, and magnetometer data; the adaptive Kalman filter algorithm is configured to: evaluate the reliability of the magnetometer data in real time, and adjust the weight of the magnetometer data in the fusion solution process based on the obtained evaluation results; identify the non-gravitational acceleration of the anemometer in real time, and adjust the weight of the accelerometer data in the fusion solution process based on the obtained identification results.

[0012] Furthermore, the step of performing coordinate transformation on the original wind speed data based on the real-time attitude data to obtain real-time wind measurement data includes: transforming the original wind speed data to a geodetic coordinate system based on the real-time attitude data to obtain a corrected wind speed vector; calculating the actual horizontal wind speed and wind direction angle based on the corrected wind speed vector; and obtaining real-time wind measurement data based on the actual horizontal wind speed and the wind direction angle.

[0013] Furthermore, the preset target posture is a fixed posture in which the anemometer is kept horizontal and the measurement axis of the anemometer is aligned with the geographic north.

[0014] Furthermore, the real-time attitude data includes roll angle, pitch angle, and yaw angle.

[0015] To address the aforementioned technical problems, the present invention also provides an adaptive wind measurement system for a wind tower anemometer, comprising: The data acquisition module is used to acquire real-time attitude data from the anemometer. The deviation calculation module is used to compare the real-time attitude data with the preset target attitude to obtain the pose deviation signal; The posture adjustment module is used to control the posture adjustment mechanism connected between the wind tower body and the anemometer to perform movement according to the posture deviation signal, so as to drive the anemometer to perform posture adjustment. The coordinate transformation module is used to acquire the raw wind speed data measured by the anemometer, and to perform coordinate transformation on the raw wind speed data according to the real-time attitude data to obtain real-time wind measurement data.

[0016] To address the aforementioned technical problems, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the adaptive wind measurement method for a wind tower anemometer as described above.

[0017] To address the aforementioned technical problems, the present invention also provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the aforementioned adaptive wind measurement method for a wind tower anemometer. Attached Figure Description

[0018] Figure 1 This is a flowchart of an adaptive wind measurement method for a wind tower anemometer according to the present invention; Figure 2 This is a schematic diagram of the wind measurement tower structure according to Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of an adaptive wind measurement system for a wind tower anemometer according to the present invention; Figure 4 This is a schematic diagram of an electronic device according to the present invention.

[0019] The attached diagram lists the components represented by each number as follows: 1. Nine-axis attitude sensor; 2. Central processing unit; 3. Position adjustment mechanism; 31. Moving platform; 32. Static platform; 33. Actuator; 4. Closed-loop feedback controller; 5. Wind tower body; 6. Anemometer. Detailed Implementation

[0020] The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0021] As mentioned earlier, as the height of the wind measuring tower increases, the verticality becomes increasingly difficult to adjust. In addition, the swaying amplitude of the wind measuring tower also increases due to the influence of wind. The anemometer 6 has a six-degree-of-freedom motion state, and the error of the wind measurement data becomes larger, which cannot meet the application requirements.

[0022] Example 1 Based on this, such as Figure 1 As shown, this embodiment provides an adaptive wind measurement method for a wind tower anemometer, including: S101. Obtain the real-time attitude data of the anemometer 6.

[0023] S102. Compare the real-time attitude data with the preset target attitude to obtain the pose deviation signal.

[0024] S103. Based on the position deviation signal, control the position adjustment mechanism 3 connected between the wind tower body 5 and the anemometer 6 to perform movement, so as to drive the anemometer 6 to adjust its position.

[0025] S104. Obtain the raw wind speed data measured by the anemometer 6, and perform coordinate transformation on the raw wind speed data according to the real-time attitude data to obtain the real-time wind measurement data.

[0026] This method monitors the precise attitude of the anemometer 6 in real time and drives the adjustment device to perform reverse motion compensation, ensuring the anemometer 6 remains stable relative to the ground in space, thereby obtaining accurate wind speed and direction data. This method significantly improves the accuracy of wind measurement data, thereby enhancing the precision of wind resource assessment, micro-site selection, wind turbine load calculation, power prediction, and power characteristic testing. It also ensures the effectiveness of economic assessment of wind farm construction and monitoring during operation, promoting the healthy development of the wind power sector.

[0027] In this embodiment, as Figure 2 As shown, the nine-axis attitude sensor 1 is a sensor unit integrating an accelerometer, gyroscope, and magnetometer. It is rigidly fixed to the anemometer 6 body or its rigid support frame, and its coordinate system is consistent with the anemometer 6 coordinate system. It is used to sense the motion attitude of the anemometer 6 itself in real time. The central processing unit 2 (CPU) receives the raw sensor data from the nine-axis attitude sensor 1, runs the sensor fusion algorithm, and calculates high-precision three-dimensional attitude information in real time.

[0028] The pose adjustment mechanism 3, acting as the actuator, adopts a parallel structure (such as a Stewart platform) or a series structure (such as a three-axis servo platform). The pose adjustment mechanism 3 can achieve six degrees of freedom adjustment of three-dimensional translation and three-dimensional rotation. An anemometer 6 is mounted on the moving platform 31 of the device, and its stationary platform 32 is fixedly connected to the anemometer tower body 5. The device internally contains multiple servo-driven electric actuators 33, which can precisely extend, retract, or rotate under the command of the controller, driving the moving platform 31 to move in six degrees of freedom.

[0029] The closed-loop feedback controller 4 is integrated into the CPU. It receives the calculated current attitude, compares it with the target attitude, generates control commands, and drives the actuators 33 of the attitude adjustment mechanism 3. The anemometer 6 is usually an ultrasonic anemometer 6 or a mechanical anemometer 6, which outputs the original wind speed vector in its own coordinate system.

[0030] Optionally, in an embodiment, the real-time attitude data includes roll angle, pitch angle, and yaw angle.

[0031] Optionally, in this embodiment, acquiring the real-time attitude data of the anemometer 6 includes: acquiring the raw measurement data collected by the nine-axis attitude sensor 1 fixed on the anemometer 6; and fusing and solving the raw measurement data using an adaptive Kalman filter algorithm to obtain the real-time attitude data.

[0032] Optionally, in the embodiment, the original measurement data includes accelerometer data, gyroscope data, and magnetometer data; the adaptive Kalman filter algorithm is configured to: evaluate the reliability of the magnetometer data in real time and adjust the weight of the magnetometer data in the fusion solution process based on the obtained evaluation results; identify the non-gravitational acceleration of the anemometer 6 in real time and adjust the weight of the accelerometer data in the fusion solution process based on the obtained identification results.

[0033] Specifically, an adaptive Kalman filter algorithm is used, which can estimate and compensate for the interference of linear motion acceleration caused by the vibration of the wind tower on the attitude calculation online.

[0034] Dynamic credibility weight allocation: The algorithm needs to evaluate the credibility of the magnetometer data in real time. When an excessively high angular velocity is detected or the accelerometer reading indicates an unsteady state, it is determined that the current period is one of severe shaking, and the magnetometer data is completely unreliable due to the movement of the ferromagnetic material in the tower. At this time, the algorithm automatically reduces or completely removes the magnetometer's weight in the fusion process, relying only on the accelerometer and gyroscope for short-term attitude tracking (the heading angle is determined solely by the gyroscope integration, revealing its relative change).

[0035] Interference Detection and Recovery: Once the motion stabilizes, the algorithm initiates a judgment logic: comparing the current magnetometer reading with the calibrated reference geomagnetic field. If the deviation is within a threshold, the interference is determined to have disappeared, and the magnetometer's fusion weights are gradually restored. The geomagnetic field information is then used to slowly correct the drift accumulated by the gyroscope at the yaw angle.

[0036] Online motion acceleration identification: In the standard Kalman filter's "prediction-update" framework, special modeling of the state equation and measurement equation is required. For example, linear motion acceleration can be treated as a disturbance state variable to be estimated and filtered along with the attitude angle. Alternatively, a more advanced adaptive Kalman filter can be used, dynamically adjusting the covariance matrix R of the measurement noise based on changes in accelerometer readings. When a large non-gravitational acceleration is detected, the accelerometer's measurement noise is automatically increased, thereby reducing its weight in attitude estimation updates and allowing the system to place greater trust in the gyroscope's high short-term accuracy.

[0037] The ultimate goal of this method is not to display attitude, but to provide control information. This requires that the attitude calculation serves the control loop.

[0038] Specifically, high frequency, low latency, and state output: The attitude calculation module must operate at a frequency much higher than the tower's swaying frequency (usually several Hz to tens of Hz) (e.g., above 200 Hz), and output estimated values ​​of attitude, angular velocity, and even angular acceleration with extremely low latency. These complete state estimates are directly used as inputs to subsequent predictive controllers (such as Model Predictive Control, MPC), not just PID control.

[0039] Providing covariance information: Advanced Kalman filtering not only outputs the optimal attitude estimate but also the covariance matrix (P matrix) of its estimation error. This confidence level of the estimation result can be passed to the control loop. The controller can dynamically adjust the control gain based on the uncertainty of the attitude estimate (e.g., adopting a more conservative control strategy when the uncertainty is high), improving the robustness of the entire system.

[0040] Stable real-time attitude data obtained from the fusion algorithm. It is usually represented by Euler angles (φ,θ,ψ) or quaternions (q0,q1,q2,q3).

[0041] Optionally, in an embodiment, the preset target posture is a fixed posture in which the anemometer 6 is kept horizontal and the measurement axis of the anemometer 6 is aligned with the geographic north.

[0042] In most application scenarios, the target pose is fixed, that is, absolutely horizontal and pointing due north, represented by Euler angles as (0,0,0). This target pose defines a stable reference.

[0043] If Euler angles are used, the pose deviation signal is a three-dimensional vector ΔPose=[Δφ,Δθ,Δψ], which is the pose error that needs to be compensated.

[0044] If quaternions are used, the quaternion for the pose deviation signal is a unit quaternion q_target = [1, 0, 0, 0]. The current pose is q_current. The quaternion q_error, representing the rotation error required from the current pose to the target pose, can be obtained through quaternion multiplication: q_error = q_target q_current -1 q_error contains information about the rotation axis and angle that need to be compensated.

[0045] Optionally, in an embodiment, based on the pose deviation signal, the pose adjustment mechanism 3 connected between the wind tower body 5 and the anemometer 6 is controlled to perform movement to drive the anemometer 6 to adjust its pose. This includes: calculating the desired pose change of the pose adjustment mechanism 3 based on the pose deviation signal using a control algorithm; converting the desired pose change into a driving command based on the inverse kinematics model of the pose adjustment mechanism 3; and controlling the pose adjustment mechanism 3 to move according to the driving command to drive the anemometer 6 to adjust its pose.

[0046] Drive commands are typically generated by digital controllers (such as PID controllers). Specifically, the control law is calculated (using PID as an example): The controller performs three calculations for the deviation (e.g., Δφ) for each degree of freedom: Ratio (P): Kp Δφ. Generates an instantaneous corrective force proportional to the deviation, determining the response speed.

[0047] Integral (I): Ki ∫Δφdt. Accumulated historical deviations are used to eliminate steady-state errors (i.e., residual deviations that cannot eventually return to zero).

[0048] Differential (D): Kd d(Δφ) / dt. Predicts the future trend of deviation, suppresses overshoot and oscillation, and increases system stability.

[0049] Adding the three terms together, we get the final control output for that degree of freedom: Output_φ = Kp Δφ+Ki ∫Δφdt+Kd d(Δφ) / dt.

[0050] This calculation is performed for each of the six degrees of freedom (X, Y, Z, φ, θ, ψ).

[0051] Command Mapping and Output: The calculated six control output values ​​are mapped to the individual actuators 33 of the six-degree-of-freedom control device. For example, for the Stewart platform, based on its inverse kinematics model, the desired platform pose change (determined by the control output) is converted into the target extension / retraction length change of each of the six electric cylinders. .

[0052] Finally, the controller converts these length changes into specific drive commands (such as PWM wave duty cycle, analog voltage value, CAN bus command) and sends them to the driver of each actuator 33, thereby driving the motor or hydraulic cylinder to move precisely.

[0053] The driving posture adjustment mechanism 3 performs a movement opposite to the posture deviation signal to compensate for the posture change of the anemometer 6 caused by the swaying of the wind measuring tower, so as to keep it stable in the inertial space.

[0054] Optionally, in an embodiment, the original wind speed data is transformed into coordinates based on real-time attitude data to obtain real-time wind measurement data, including: transforming the original wind speed data to the geodetic coordinate system based on the real-time attitude data to obtain a corrected wind speed vector; calculating the actual horizontal wind speed and wind direction angle based on the corrected wind speed vector; and obtaining real-time wind measurement data based on the actual horizontal wind speed and wind direction angle.

[0055] Specifically, the system receives the raw wind speed vector Vb measured by the anemometer 6 in the carrier coordinate system; and uses the attitude quaternion q (or Euler angles [φ,θ,ψ]) that represents the rotational relationship between the carrier coordinate system and the geodetic coordinate system, calculated in real time by the sensor fusion algorithm, to perform a coordinate transformation according to a predetermined coordinate transformation algorithm (e.g., Vn=R(q)). The original wind speed vector Vb is transformed to the northeast-northeast geodetic coordinate system to obtain the corrected wind speed vector Vn; finally, the true horizontal wind speed Vh and the wind direction angle Dir relative to true north are calculated and output based on Vn.

[0056] In this method, the overall architecture is based on the closed-loop control concept of "perception-decision-execution," achieving a fundamental shift from traditional post-event algorithm compensation to pre-event and in-event active physical compensation. Secondly, in harsh environments with strong vibrations and high magnetic interference, advanced sensor fusion algorithms are used to calculate high-precision, drift-free attitude information in real time, establishing a reliable dynamic geodetic coordinate system benchmark, effectively overcoming the problems of heading angle drift and magnetic interference in traditional schemes. Simultaneously, the same set of calculated high-precision attitude data is used for dual purposes: both to drive the actuator for real-time motion compensation and to perform coordinate transformation on the original measurements of the anemometer 6, improving the system's consistency and efficiency. Finally, the six-degree-of-freedom adjustment device, as a dedicated mechanism connecting the tower and the anemometer 6, can accurately reproduce the reverse motion generated by the algorithm, thereby physically achieving vibration isolation and attitude stabilization of the tower's sway, completing the crucial execution from control commands to actual compensation actions.

[0057] Compared with existing technologies, this method has several outstanding advantages, specifically: Significantly improves accuracy: It fundamentally eliminates the impact of wind tower swaying on the accuracy of wind speed and direction measurements, providing a highly accurate data foundation for wind energy assessment.

[0058] Achieving dynamic stability: Through a closed-loop feedback system, the anemometer 6 can always maintain ground stability in the turbulent tower top environment, achieving "stability in motion" measurement.

[0059] Automation and intelligence: The entire solution operates automatically without human intervention and can work in harsh environments for extended periods.

[0060] High versatility: This solution can be adapted to various types of wind measuring towers and anemometers (especially ultrasonic anemometers), and has a wide range of applications.

[0061] Example 2 like Figure 3 As shown, this embodiment provides an adaptive wind measurement system 200 for a wind tower anemometer, including: The data acquisition module 201 is used to acquire the real-time attitude data of the anemometer 6; The deviation calculation module 202 is used to compare the real-time attitude data with the preset target attitude to obtain the pose deviation signal. The posture adjustment module 203 is used to control the posture adjustment mechanism 3 connected between the wind tower body 5 and the anemometer 6 to perform movement according to the posture deviation signal, so as to drive the anemometer 6 to perform posture adjustment. The coordinate transformation module 204 is used to acquire the raw wind speed data measured by the anemometer 6, and to perform coordinate transformation on the raw wind speed data according to the real-time attitude data to obtain real-time wind measurement data.

[0062] Optionally, in an embodiment, the pose adjustment module 203 includes: The pose calculation unit is used to calculate the desired pose change of the pose adjustment mechanism 3 based on the pose deviation signal and through a control algorithm. The instruction generation unit is used to convert the desired pose change into a driving instruction based on the inverse kinematics model of the pose adjustment mechanism 3. The posture adjustment unit is used to control the posture adjustment mechanism 3 according to the drive command, so as to drive the anemometer 6 to perform posture adjustment.

[0063] Optionally, in an embodiment, the data acquisition module 201 includes: The data acquisition unit is used to acquire the raw measurement data collected by the nine-axis attitude sensor 1 fixed on the anemometer 6; The fusion calculation unit is used to fuse and calculate the original measurement data using an adaptive Kalman filter algorithm to obtain real-time attitude data.

[0064] Optionally, in the embodiment, the original measurement data includes accelerometer data, gyroscope data, and magnetometer data; the adaptive Kalman filter algorithm is configured to: evaluate the reliability of the magnetometer data in real time and adjust the weight of the magnetometer data in the fusion solution process based on the obtained evaluation results; identify the non-gravitational acceleration of the anemometer 6 in real time and adjust the weight of the accelerometer data in the fusion solution process based on the obtained identification results.

[0065] Optionally, in an embodiment, the coordinate transformation module 204 includes: The coordinate transformation unit is used to transform the original wind speed data to the geodetic coordinate system based on real-time attitude data, so as to obtain the corrected wind speed vector. The calculation unit is used to calculate the actual horizontal wind speed and wind direction angle based on the corrected wind speed vector; The data generation unit is used to obtain real-time wind measurement data based on the actual horizontal wind speed and wind direction angle.

[0066] Optionally, in an embodiment, the preset target posture is a fixed posture in which the anemometer 6 is kept horizontal and the measurement axis of the anemometer 6 is aligned with the geographic north.

[0067] Optionally, in an embodiment, the real-time attitude data includes roll angle, pitch angle, and yaw angle.

[0068] In some embodiments, the adaptive wind measurement system 200 of the anemometer of the wind tower of the present invention can be implemented by a combination of hardware and software. As an example, the adaptive wind measurement system 200 of the anemometer of the wind tower of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the adaptive wind measurement method of the anemometer of the wind tower of the present invention. For example, the processor in the form of a hardware decoding processor can be one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0069] The modules described in the embodiments of this invention can be implemented in software or hardware. The names of the modules are not, in some cases, limiting the scope of the module itself.

[0070] Example 3 like Figure 3 As shown, this embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements an adaptive wind measurement method for a wind tower anemometer as described in Embodiment 1.

[0071] In other words, an electronic device according to an embodiment of the present invention may include, but is not limited to, a processor and a memory; the memory is used to store computer programs; the processor is used to execute an adaptive wind measurement method for a wind tower anemometer as shown in any embodiment of the present invention by calling the computer program.

[0072] In one alternative embodiment, an electronic device is provided. Figure 3 The illustrated electronic device 300 includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may further include a transceiver 304, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present invention.

[0073] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0074] Bus 302 may include a path for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The bus 302 is represented by only one thick line, but this does not mean that there is only one bus or one type of bus.

[0075] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0076] The memory 303 is used to store application code (computer program) for executing the present invention, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0077] Among them, electronic devices can also be terminal devices, which can be any device that can install applications, including at least one of smartphones, tablets, laptops, desktop computers, smart speakers, smartwatches, smart TVs, and smart in-vehicle devices.

[0078] It should be noted that, Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0079] Example 4 This embodiment provides a non-transitory computer-readable storage medium that stores computer instructions for causing the computer to execute an adaptive wind measurement method for a wind tower anemometer as described in Embodiment 1.

[0080] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.

[0081] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the aforementioned adaptive wind measurement method for a wind tower anemometer.

[0082] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0083] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0084] The computer-readable storage medium provided in this invention can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EEPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0085] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the method shown in the above embodiments.

[0086] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0087] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0088] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this invention can be specifically implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0089] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. An adaptive wind measurement method for a wind tower anemometer, characterized in that, include: Acquire real-time attitude data from the anemometer; The real-time attitude data is compared with the preset target attitude to obtain the pose deviation signal; Based on the posture deviation signal, the posture adjustment mechanism connected between the wind tower and the anemometer is controlled to perform movement, so as to drive the anemometer to adjust its posture. The raw wind speed data measured by the anemometer is obtained, and the raw wind speed data is transformed into coordinates based on the real-time attitude data to obtain real-time wind measurement data.

2. The adaptive wind measurement method for a wind tower anemometer according to claim 1, characterized in that, The step of controlling the posture adjustment mechanism connected between the wind tower body and the anemometer to perform movement based on the posture deviation signal, so as to drive the anemometer to adjust its posture, includes: Based on the pose deviation signal, the desired pose change of the pose adjustment mechanism is calculated by a control algorithm; Based on the inverse kinematics model of the pose adjustment mechanism, the desired pose change is converted into a driving command. The position adjustment mechanism is controlled according to the driving command to drive the anemometer to adjust its position.

3. The adaptive wind measurement method for a wind tower anemometer according to claim 1, characterized in that, The acquisition of real-time attitude data from the anemometer includes: Acquire raw measurement data from a nine-axis attitude sensor fixed to the anemometer; The real-time attitude data is obtained by fusing and solving the original measurement data using an adaptive Kalman filter algorithm.

4. The adaptive wind measurement method for a wind tower anemometer according to claim 3, characterized in that, The raw measurement data includes accelerometer data, gyroscope data, and magnetometer data; the adaptive Kalman filter algorithm is configured as follows: The reliability of the magnetometer data is evaluated in real time, and the weight of the magnetometer data in the fusion calculation process is adjusted based on the evaluation results. The non-gravity acceleration of the anemometer is identified in real time, and the weight of the accelerometer data in the fusion calculation process is adjusted based on the identification results.

5. The adaptive wind measurement method for a wind tower anemometer according to claim 1, characterized in that, The step of performing coordinate transformation on the original wind speed data based on the real-time attitude data to obtain real-time wind measurement data includes: Based on the real-time attitude data, the original wind speed data is transformed to the geodetic coordinate system to obtain the corrected wind speed vector; Based on the corrected wind speed vector, calculate the actual horizontal wind speed and wind direction angle; Real-time wind measurement data is obtained based on the actual horizontal wind speed and the wind direction angle.

6. The adaptive wind measurement method for a wind tower anemometer according to claim 1, characterized in that, The preset target posture is a fixed posture in which the anemometer is kept horizontal and its measurement axis is aligned with the geographic north.

7. The adaptive wind measurement method for a wind tower anemometer according to claim 1, characterized in that, The real-time attitude data includes roll angle, pitch angle, and yaw angle.

8. An adaptive wind measurement system for a wind tower anemometer, characterized in that, include: The data acquisition module is used to acquire real-time attitude data from the anemometer. The deviation calculation module is used to compare the real-time attitude data with the preset target attitude to obtain the pose deviation signal; The posture adjustment module is used to control the posture adjustment mechanism connected between the wind tower body and the anemometer to perform movement according to the posture deviation signal, so as to drive the anemometer to perform posture adjustment. The coordinate transformation module is used to acquire the raw wind speed data measured by the anemometer, and to perform coordinate transformation on the raw wind speed data according to the real-time attitude data to obtain real-time wind measurement data.

9. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements an adaptive wind measurement method for a wind tower anemometer as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the adaptive wind measurement method of the anemometer as described in any one of claims 1 to 7.

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