Wind wheel azimuth angle sensor null drift calibration method based on blade root shimmy load, electronic equipment and storage medium

Abstract

The invention belongs to the technical field of wind power generation, and particularly relates to a blade root shimmy load-based wind wheel azimuth angle sensor null drift calibration method, electronic equipment and a storage medium. Comprising the following steps: installing a wind wheel azimuth angle sensor and a blade root load measuring system; the wind wheel azimuth angle and the blade root shimmy load are collected, and the blade root shimmy load at the 0-degree blade angle and the corresponding wind wheel azimuth angle at the maximum blade root shimmy load at the 0-degree blade angle are further obtained; if the wind wheel azimuth angle sensor operates for more than one year, subtracting the wind wheel azimuth angle corresponding to the blade root shimmy maximum load at the 0-degree blade angle obtained before and after the wind wheel azimuth angle sensor operates for one year to obtain a null drift difference value of the wind wheel azimuth angle sensor before and after the wind wheel azimuth angle sensor operates for one year; if the null drift difference value exceeds a set threshold value, the null drift difference value of the wind wheel azimuth angle sensor before and after one year is issued to the wind wheel azimuth angle sensor for calibration according to a communication protocol. Based on the installed blade root load measuring system, hardware does not need to be added, cost is low, and precision is high.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind power generation, and more specifically, relates to a zero drift angle calibration method for a wind wheel azimuth angle sensor based on blade root swing load, an electronic device and a storage medium. Background Art

[0002] In recent years, with the rapid development of the wind power industry, wind turbines have developed rapidly in power level, blade length, and tower height. More and more new control and protection technologies are being applied to wind turbines. As a wind rotor angle measurement device, wind rotor azimuth sensors are increasingly being used in new control and protection technologies for wind turbines. During operation, as the operating time increases, the wind rotor azimuth sensor will have circuit device aging problems, causing zero drift in the wind rotor azimuth measurement data, resulting in inaccurate measurement of the wind rotor azimuth data, which is bound to cause the control technology based on the wind rotor azimuth sensor to increase errors or even fail. In order to ensure the safe and stable operation of wind turbines and enhance the market competitiveness of wind turbines, it is necessary to regularly perform angle zero calibration on the wind rotor azimuth sensor.

[0003] At present, the mainstream wind rotor azimuth angle sensor is mainly composed of an accelerometer and a gyroscope. The azimuth angle of the wind rotor is measured by using different measurement data weights of the two. During the operation of the wind rotor azimuth angle sensor, as time goes by, the gyroscope or accelerometer will have electrical hardware aging problems, causing errors in the measurement data of the wind rotor azimuth angle sensor. This problem is called the zero drift of the wind rotor azimuth angle sensor. Since this problem is caused by hardware aging, the industry cannot completely eliminate it at present. It is mainly avoided by improving hardware quality, but as time goes by, this problem will still occur. In response to the zero drift problem of the wind rotor azimuth angle sensor, various industries will regularly calibrate the measurement angle of the wind rotor azimuth angle sensor under the operating conditions where the wind rotor azimuth angle accuracy is required. The current calibration method is mainly through hardware calibration. By adding hardware sensors and other equipment, the wind rotor azimuth angle sensor is calibrated for zero drift, but the increase in hardware calibration equipment will increase costs.

[0004] There are also technical solutions in the prior art that use software programs to calibrate sensors. Chinese patent document CN 102313543B discloses a measurement method and orthogonal compensation method for a geomagnetic azimuth angle measurement system based on a giant magnetoresistance sensor, which solves the measurement error caused by the non-orthogonality and zero-position deviation of the three axes of the giant magnetoresistance sensor. The specific technical solution for zero-position compensation is to add magnetic fields on the three axes as required, record the geomagnetic values ​​output by the sensor, calculate four angles and the zero-position deviation of the three axes according to the relevant formula model, and take the average for compensation; however, the patent document discloses the calibration of the measurement error of the giant magnetoresistance sensor, which improves the accuracy of geomagnetic measurement. The calibration method is not suitable for the wind wheel azimuth angle sensor in wind power generation. Therefore, it is necessary to develop a new calibration method to overcome the technical disadvantage of increased cost caused by the zero drift problem of the hardware calibration wind wheel azimuth angle sensor. Summary of the invention

[0005] The present invention aims to overcome at least one defect of the above-mentioned prior art and provide a method, electronic device and storage medium for zero drift calibration of a wind rotor azimuth angle sensor based on blade root swing load, so as to solve the problem of increased cost caused by hardware calibration of the zero drift problem of the wind rotor azimuth angle sensor.

[0006] The detailed technical scheme of the present invention is as follows:

[0007] A zero drift calibration method for a wind rotor azimuth angle sensor based on blade root swing load, the method comprising:

[0008] S1: Complete the installation of the wind rotor azimuth sensor and the blade root load measurement system on the wind turbine generator set;

[0009] S2: The wind turbine generator set is connected to the grid and runs for at least 30 minutes, and the wind turbine rotor azimuth angle and blade root swing load are collected;

[0010] S3: Compensate the blade root shimmy load for the influence of the blade angle to obtain the blade root shimmy load at a blade angle of 0°;

[0011] S4: Least square method fitting of blade root swing load under wind rotor azimuth angle and 0° blade angle;

[0012] S5: Obtain the wind rotor azimuth angle corresponding to the maximum load of blade root swing at a blade angle of 0°, and save the wind rotor azimuth angle at this time;

[0013] S6: Start the wind rotor azimuth angle sensor running time timing;

[0014] S7: If the wind rotor azimuth angle sensor has been in operation for more than 1 year, execute step S8; if it has not been in operation for more than 1 year, execute step S6;

[0015] S8: The wind turbine generator set is connected to the grid and operates for at least 30 minutes, and the blade root swing load and the rotor azimuth angle of the wind turbine generator set are collected;

[0016] S9: Compensate the blade root shimmy load for the influence of the blade angle to obtain the blade root shimmy load at a blade angle of 0°;

[0017] S10: Least square method fitting of blade root swing load under wind rotor azimuth angle and 0° blade angle;

[0018] S11: Obtain the rotor azimuth angle corresponding to the maximum load of blade root swing at a blade angle of 0°;

[0019] S12: subtract the wind rotor azimuth angle corresponding to the maximum load of blade root swing at a blade angle of 0° obtained after the wind rotor azimuth angle sensor has been running for one year from the wind rotor azimuth angle corresponding to the maximum load of blade root swing at a blade angle of 0° one year ago to obtain the zero drift difference value before and after the wind rotor azimuth angle sensor has been running for one year;

[0020] S13: When the zero drift difference exceeds the set threshold, it is determined that the wind rotor azimuth angle sensor has zero drift, and the wind rotor azimuth angle sensor needs to be calibrated, and step S14 is executed; if the zero drift difference does not exceed the set threshold, it is determined that the wind rotor azimuth angle sensor has no zero drift, and the wind rotor azimuth angle sensor does not need to be calibrated temporarily;

[0021] S14: Obtaining the zero calibration communication protocol of the wind rotor azimuth sensor;

[0022] S15: sending the zero drift difference of the wind rotor azimuth angle sensor before and after one year to the wind rotor azimuth angle sensor according to the communication protocol, and the wind rotor azimuth angle sensor is calibrated by inputting the zero drift difference;

[0023] S16: The wind rotor azimuth angle sensor operation timer is reset and step S6 is executed.

[0024] Preferably, according to the present invention, the blade root shimmy load is acquired by collecting the blade root load measurement system. Specifically, the blade root shimmy load can be acquired by wavelength conversion of a sensor of the blade root load measurement system.

[0025] Preferably, according to the present invention, the blade root shimmy load is compensated for the influence of the blade angle to obtain the blade root shimmy load at a blade angle of 0°, and the compensation formula is:

[0026] M PlanoutEdgeI =(cos(Pitch_AngleI)sin(Pitch_AngleI)0) (M PlaneInEdgeI ) (3)

[0027] (3) Where M PlanOutEdgeis the blade root vibration load at 0° blade angle, I is 1 / 2 / 3, representing blade 1, blade 2, and blade 3 respectively; Pitch_Angle is the blade angle, M PlaneInEdge is the blade root swing load.

[0028] Preferably, according to the present invention, the least square method is used to fit the blade root swing load at the wind rotor azimuth angle and the blade angle of 0°, and the formula is:

[0029] f(x):y=w T x+b (4)

[0030] w=(w1,w2…,wn) T (5)

[0031]

[0032] (4)-(6) In formulas, f(x) is the linear regression equation; y represents the swing load at a blade angle of 0°; w represents the optimal vector, w T is the optimal vector transpose; x represents the wind rotor azimuth; b is the intercept; w1, w2…, wn represent the 1st to nth optimal vectors; L(w, b) is the cost function of linear regression; m represents the number of data; xi represents the i-th wind rotor azimuth; yi represents the swing load at the i-th blade angle of 0°.

[0033] Preferably, according to the present invention, the blade root shimmy load is obtained by converting the wavelength of the blade root load measurement system sensor using the formula:

[0034]

[0035] (1) Where M RootEdge is the blade root vibration load; k 1，1 , k 1，2 , k 1，3 , k 1，4 is the vibration load sensitivity coefficient, which is obtained by calibrating the blade root load measurement system; ε1, ε2, ε3, and ε4 are the blade root strains measured by the sensor;

[0036] (2) where ε is the blade root strain calculated in real time; λ is the real-time measurement wavelength; i is the initial wavelength, which is obtained by calibrating the blade root load measurement system.

[0037] In another aspect of the present invention, there is provided an electronic device, comprising:

[0038] At least one processor; and a memory storing instructions, which, when executed by the at least one processor, enable the at least one processor to perform the zero-drift calibration method of the wind rotor azimuth angle sensor based on the blade root swing load as described above.

[0039] In another aspect of the present invention, a machine-readable storage medium is provided, on which executable instructions are stored, and when the instructions are executed, the machine executes the wind rotor azimuth angle sensor zero drift calibration method based on blade root swing load as described above.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The present invention provides a zero-drift calibration method for a wind rotor azimuth angle sensor based on blade root swing load, an electronic device and a storage medium. The method comprises installing a wind rotor azimuth angle sensor and a blade root load measurement system on a wind turbine generator set to collect the wind rotor azimuth angle and blade root swing load of the wind turbine generator set, and then performing blade angle compensation on the blade root swing load and least squares fitting to obtain the wind rotor azimuth angle corresponding to the maximum load of the blade root swing at a blade angle of 0°, and making a difference between the wind rotor azimuth angle corresponding to the maximum load of the blade root swing at a blade angle of 0° before and after the wind rotor azimuth angle sensor has been operated for one year to determine whether the wind rotor azimuth angle sensor needs zero-drift calibration, and using the obtained zero-drift difference between the wind rotor azimuth angle sensor before and after the wind rotor azimuth angle sensor has been operated for one year to perform calibration. This eliminates the need to perform zero-drift calibration on the wind rotor azimuth angle sensor by adding hardware sensors and other equipment, effectively reducing the cost of zero-drift calibration of the wind rotor azimuth angle sensor, improving the calibration accuracy and efficiency, and improving the measurement accuracy of the wind rotor azimuth angle. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flow chart of a method for zero drift calibration of a wind wheel azimuth angle sensor based on blade root swing load described in the present invention.

[0043] Figure 2 It is a scatter diagram and fitting curve of the blade root swing load at a blade angle of 0° of a wind rotor azimuth angle sensor zero drift calibration method based on the blade root swing load described in the present invention.

[0044] Figure 3 It is a scatter diagram and fitting curve of the blade root swing load at a blade angle of 0° one year after the zero drift calibration method of the wind rotor azimuth angle sensor based on the blade root swing load described in the present invention.

[0045] Figure 4 This is a comparison diagram of the blade root swing load fitting curves of the wind rotor azimuth angle sensor at a blade angle of 0° before and after one year of operation of the wind rotor azimuth angle sensor according to the wind rotor azimuth angle sensor zero drift calibration method based on the blade root swing load described in the present invention. DETAILED DESCRIPTION

[0046] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0047] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0048] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0049] In the absence of conflict, the embodiments in the present disclosure and the features in the embodiments may be combined with each other.

[0050] In order to solve the problem of increased cost caused by the existing hardware calibration of the zero drift of the wind rotor azimuth angle sensor, the present invention provides a zero drift calibration method for the wind rotor azimuth angle sensor based on the blade root swing load.

[0051] The following is a further description of a zero drift calibration method of a wind rotor azimuth angle sensor based on blade root swing load of the present invention in conjunction with a specific embodiment.

[0052] Embodiment 1,

[0053] like Figure 1 As shown, this embodiment provides a method for zero drift calibration of a wind rotor azimuth angle sensor based on blade root swing load. The method is mainly aimed at a wind turbine generator set that needs to install a blade root load measurement system. The blade root swing load at a blade angle of 0° is obtained by using the blade root swing load measured by the blade root load measurement system. The least squares method is used to fit the wind rotor azimuth angle and the blade root swing load at a blade angle of 0°. The wind rotor azimuth angle corresponding to the maximum load of blade root swing at a blade angle of 0° before and after the wind rotor azimuth angle sensor is operated for one year is obtained, thereby realizing zero drift calibration of the wind rotor azimuth angle sensor. This method is based on an installed blade root load measurement system, does not require additional hardware, and is implemented through a software program. It has low cost, high accuracy, and good executability, and has great advantages in zero drift calibration of the wind rotor azimuth angle sensor.

[0054] Take a fiber grating blade root load measurement device, where the output value is the wavelength value, and a wind rotor azimuth angle sensor device, where the output value is the wind rotor azimuth angle, as examples;

[0055] Specifically, the zero drift angle calibration method of the wind rotor azimuth angle sensor based on the blade root swing load comprises the following steps:

[0056] S1: Complete the installation of the wind rotor azimuth angle sensor and the blade root load measurement system on the wind turbine generator set;

[0057] S2: The wind turbine generator set is connected to the grid and operates for no less than 30 minutes, and the wind turbine rotor azimuth angle and blade root swing load are collected;

[0058] S2.1. The blade root shimmy load can be obtained by converting the wavelength of the blade root load measurement system sensor. The formula used to convert the wavelength of the blade root load measurement system sensor to the blade root shimmy load is:

[0059]

[0060] (1) Where M RootEdg1 is the blade root vibration load; k 1，1 , k 1，2 , k 1，3 , k 1，4 is the vibration load sensitivity coefficient, which is obtained by calibrating the blade root load measurement system; ε1, ε2, ε3, and ε4 are the blade root strains measured by the sensor;

[0061] (2) where ε is the blade root strain calculated in real time; λ is the real-time measurement wavelength; i is the initial wavelength, which is obtained by calibrating the blade root load measurement system.

[0062] S3: Compensate the blade root shimmy load for the influence of the blade angle to obtain the blade root shimmy load at a blade angle of 0°;

[0063] S3.1. In step S3, the blade root shimmy load is compensated for the influence of the blade angle, and the blade root shimmy load at a blade angle of 0° is obtained. The compensation formula is:

[0064] M PlanOutEdgeI =(cos(Pitch_AngleI)sin(Pitch_AngleI)0) (M PlaneInEdgeI ) (3)

[0065] (3) Where M PlanOutEdge is the blade root vibration load at 0° blade angle, I is 1 / 2 / 3, representing blade 1, blade 2, and blade 3 respectively; Pitch_Angle is the blade angle, M PlaneInEdge is the blade root swing load.

[0066] S4: Least squares fitting of blade root swing load at wind rotor azimuth and 0° blade angle:

[0067] S4.1. The least squares formula used is:

[0068] f(x):y=w T x+b (4)

[0069] w=(w1,w2…,wn) T (5)

[0070]

[0071] (4)-(6) In formulas, f(x) is the linear regression equation; y represents the swing load at a blade angle of 0°; w represents the optimal vector, w T is the optimal vector transpose; x represents the wind rotor azimuth; b is the intercept; w1, w2…, wn represent the 1st to nth optimal vectors; L(w, b) is the cost function of linear regression; m represents the number of data; xi represents the i-th wind rotor azimuth; yi represents the swing load at the i-th blade angle of 0°.

[0072] S5: obtaining the wind rotor azimuth angle corresponding to the maximum load of blade root swing at a blade angle of 0°, and recording and saving the wind rotor azimuth angle at this time;

[0073] S6: Start the wind rotor azimuth angle sensor running time timing;

[0074] S7: If the wind rotor azimuth angle sensor has been in operation for more than 1 year, execute step S8; if it has not been in operation for more than 1 year, execute step S6;

[0075] S8: The wind turbine generator set is connected to the grid and operates for no less than 30 minutes, and the blade root swing load and the rotor azimuth angle of the wind turbine generator set are collected;

[0076] Setting the grid-connected power generation operation time of wind turbines to no less than 30 minutes can ensure the amount of data collected and improve the accuracy of data fitting;

[0077] S8.1. The blade root shimmy load can be obtained by converting the wavelength of the blade root load measurement system sensor. The formula used to convert the wavelength of the blade root load measurement system sensor to the blade root shimmy load is:

[0078]

[0079]

[0080] (1) Where M RootEdge is the blade root vibration load; k 1，1 , k 1，2 , k 1，3 , k 1，4is the vibration load sensitivity coefficient, which is obtained by calibrating the blade root load measurement system; ε1, ε2, ε3, and ε4 are the blade root strains measured by the sensor;

[0081] (2) where ε is the blade root strain calculated in real time; λ is the real-time measurement wavelength; i is the initial wavelength, which is obtained by calibrating the blade root load measurement system.

[0082] S9: Compensate the blade root shimmy load for the influence of the blade angle to obtain the blade root shimmy load at a blade angle of 0°;

[0083] S9.1. Compensate the blade root shimmy load for the influence of blade angle, and obtain the blade root shimmy load at 0° blade angle. The compensation formula is:

[0084] M PlanOutEdgeI =(cos(Pitch_AngleI)sin(Pitch_AngleI)0)(M PlaneInEdgeI ) (3)

[0085] (3) Where M PlanoutEdge is the blade root vibration load at 0° blade angle, I is 1 / 2 / 3, representing blade 1, blade 2, and blade 3 respectively; Pitch_Angle is the blade angle, M PlaneInEdge is the blade root swing load.

[0086] S10: Least square method fitting of blade root swing load under wind rotor azimuth angle and 0° blade angle;

[0087] S10.1. The least squares formula used is:

[0088] f(x):y=w T x+b (4)

[0089] w=(w1,w2…,wn) T (5)

[0090]

[0091] (4)-(6) In formulas, f(x) is the linear regression equation; y represents the swing load at a blade angle of 0°; w represents the optimal vector, w T is the optimal vector transpose; x represents the wind rotor azimuth; b is the intercept; w1, w2…, wn represent the 1st to nth optimal vectors; L(w, b) is the cost function of linear regression; m represents the number of data; xi represents the i-th wind rotor azimuth; yi represents the swing load at the i-th blade angle of 0°.

[0092] S11: Obtain the rotor azimuth angle corresponding to the maximum load of blade root swing at a blade angle of 0°;

[0093] S12: subtract the wind rotor azimuth angle corresponding to the maximum load of blade root swing at a blade angle of 0° obtained after the wind rotor azimuth angle sensor has been running for one year from the wind rotor azimuth angle corresponding to the maximum load of blade root swing at a blade angle of 0° one year ago to obtain the zero drift difference value before and after the wind rotor azimuth angle sensor has been running for one year;

[0094] S13: When the zero drift difference exceeds the set threshold, it is determined that the wind rotor azimuth angle sensor has zero drift, and the wind rotor azimuth angle sensor needs to be calibrated, and step S14 is executed; if the zero drift difference does not exceed the set threshold, it is determined that the wind rotor azimuth angle sensor has no zero drift, and the wind rotor azimuth angle sensor does not need to be calibrated temporarily;

[0095] S14: Obtaining the zero calibration communication protocol of the wind rotor azimuth sensor;

[0096] S15: sending the zero drift difference of the wind rotor azimuth angle sensor before and after one year to the wind rotor azimuth angle sensor according to the communication protocol, and the wind rotor azimuth angle sensor is calibrated by inputting the zero drift difference;

[0097] S16: The wind rotor azimuth angle sensor operation timer is reset and step S6 is executed.

[0098] like Figure 2 —3, Figure 2 —3 are the scatter plots and fitting curves of the blade root swing load at a blade angle of 0° corresponding to the operation of the wind rotor azimuth angle sensor for one year, respectively. The blue part represents the scatter plot of the blade root swing load at a blade angle of 0°, and the red part represents the fitting curve of the blade root swing load at a blade angle of 0°; Figure 4 As shown in the figure, the blade root swing load fitting curves at a blade angle of 0° before and after one year are compared. The blue curve represents the blade root swing load fitting curve at a blade angle of 0° one year ago, and the red curve represents the blade root swing load fitting curve at a blade angle of 0° one year later. This can intuitively show the changes in the blade root swing load at a blade angle of 0°, so that technicians can find problems in time and confirm whether it is necessary to perform zero drift calibration on the wind rotor azimuth angle sensor.

[0099] The present invention provides a method for zero drift calibration of a wind rotor azimuth angle sensor based on blade root swing load, by comparing the wind rotor azimuth angles corresponding to the maximum load of blade root swing at a blade angle of 0° before and after one year to determine whether it is necessary to perform zero drift calibration on the wind rotor azimuth angle sensor, and based on the wind rotor azimuth angle sensor calibration communication protocol, the difference is sent to the wind rotor azimuth angle sensor to calibrate the sensor. This method is based on an installed blade root load measurement system, does not require additional hardware, and is implemented through a software program, has low cost, high accuracy, and good executability, and has great advantages in zero drift calibration of the wind rotor azimuth angle sensor.

[0100] Embodiment 2,

[0101] This embodiment also provides an electronic device, including:

[0102] At least one processor; and a memory storing instructions, which, when executed by the at least one processor, enable the at least one processor to perform a zero-drift calibration method for a wind rotor azimuth angle sensor based on blade root swing load as described above.

[0103] In this embodiment, the electronic device may include, but is not limited to: personal computers, server computers, workstations, desktop computers, laptop computers, notebook computers, mobile computing devices, smart phones, tablet computers, cellular phones, personal digital assistants (PDAs), handheld devices, messaging devices, wearable computing devices, consumer electronic devices, and the like.

[0104] Embodiment 3,

[0105] This embodiment also provides a machine-readable storage medium storing executable instructions, which, when executed, enable the machine to execute the above-mentioned method for zero-drift calibration of a wind rotor azimuth angle sensor based on blade root swing load.

[0106] Specifically, a system or device equipped with a readable storage medium can be provided, on which software program codes that implement the functions of any of the above-mentioned embodiments are stored, and a computer or processor of the system or device can read and execute instructions stored in the readable storage medium.

[0107] In this case, the program code itself read from the machine-readable medium can realize the function of any one of the above embodiments, and thus the machine-readable code and the machine-readable storage medium storing the machine-readable code constitute part of this specification.

[0108] Examples of readable storage media include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD-RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, the program code may be downloaded from a server computer or a cloud via a communication network.

[0109] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0110] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0111] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0113] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the technical solution of the present invention, and are not intended to limit the specific implementation methods of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the claims of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. A zero drift calibration method for a wind rotor azimuth angle sensor based on blade root swing load, characterized in that: The method comprises: S1: Complete the installation of the wind rotor azimuth angle sensor and the blade root load measurement system on the wind turbine generator set; S2: The wind turbine generator set is connected to the grid and runs for at least 30 minutes, and the wind turbine rotor azimuth angle and blade root swing load are collected; S3: Compensate the blade root shimmy load for the influence of the blade angle to obtain the blade root shimmy load at a blade angle of 0°; S4: Least square method fitting of blade root swing load under wind rotor azimuth angle and 0° blade angle; S5: Obtain the wind rotor azimuth angle corresponding to the maximum load of blade root swing at a blade angle of 0°, and save the wind rotor azimuth angle at this time; S6: Start the wind rotor azimuth angle sensor running time timing; S7: If the wind rotor azimuth angle sensor has been in operation for more than 1 year, execute step S8; if it has not been in operation for more than 1 year, execute step S6; S8: The wind turbine generator set is connected to the grid and operates for at least 30 minutes, and the blade root swing load and the rotor azimuth angle of the wind turbine generator set are collected; S9: Compensate the blade root shimmy load for the influence of the blade angle to obtain the blade root shimmy load at a blade angle of 0°; S10: Least square method fitting of blade root swing load under wind rotor azimuth angle and 0° blade angle; S11: Obtain the rotor azimuth angle corresponding to the maximum load of blade root swing at a blade angle of 0°; S12: subtract the wind rotor azimuth angle corresponding to the maximum load of blade root swing at a blade angle of 0° obtained after the wind rotor azimuth angle sensor has been running for one year from the wind rotor azimuth angle corresponding to the maximum load of blade root swing at a blade angle of 0° one year ago to obtain the zero drift difference value before and after the wind rotor azimuth angle sensor has been running for one year; S13: When the zero drift difference exceeds the set threshold, it is determined that the wind rotor azimuth angle sensor has zero drift, and the wind rotor azimuth angle sensor needs to be calibrated, and step S14 is executed; if the zero drift difference does not exceed the set threshold, it is determined that the wind rotor azimuth angle sensor has no zero drift, and the wind rotor azimuth angle sensor does not need to be calibrated temporarily; S14: Obtaining the zero calibration communication protocol of the wind rotor azimuth sensor; S15: The zero drift difference of the wind rotor azimuth angle sensor before and after one year is sent according to the communication protocol and inputted into the wind rotor azimuth angle sensor, and the wind rotor azimuth angle sensor is calibrated by inputting the zero drift difference; S16: The wind rotor azimuth angle sensor operation timer is reset and step S6 is executed.

2. The zero drift calibration method of a wind rotor azimuth angle sensor based on blade root swing load according to claim 1, characterized in that: The blade root swing load is obtained by converting the wavelength of a blade root load measurement system sensor.

3. The zero drift calibration method of a wind rotor azimuth angle sensor based on blade root swing load according to claim 1, characterized in that: The blade root shimmy load is compensated for the influence of blade angle to obtain the blade root shimmy load at a blade angle of 0°. The compensation formula is: M PlanOutEdgeI =(cos(Pitch_AngleI) sin(Pitch_AngleI)0) (M PlaneInEdgeI ) (3) (3) Where M PlanOutEdge is the blade root vibration load at 0° blade angle, I is 1 / 2 / 3, representing blade 1, blade 2, and blade 3 respectively; Pitch_Angle is the blade angle, M PlaneInEdge is the blade root swing load.

4. The zero drift calibration method of a wind rotor azimuth angle sensor based on blade root swing load according to claim 1, characterized in that: The least square method is used to fit the blade root swing load under the wind rotor azimuth angle and 0° blade angle, and the formula is: f(x):y=w T x+ b (4) w=(w1,w2…,wn) T (5) (4)-(6) In formulas, f(x) is the linear regression equation; y represents the swing load at a blade angle of 0°; w represents the optimal vector, w T is the optimal vector transpose; x represents the wind rotor azimuth; b is the intercept; w1, w2…, wn represent the 1st to nth optimal vectors; L(w, b) is the cost function of linear regression; m represents the number of data; xi represents the i-th wind rotor azimuth; yi represents the swing load at the i-th blade angle of 0°.

5. The zero drift calibration method of a wind rotor azimuth angle sensor based on blade root swing load according to claim 2, characterized in that: The blade root vibration load is converted from the wavelength of the blade root load measurement system sensor using the formula: (1) Where M RootEdge is the blade root vibration load; k 1,1 , k 1,2 , k 1,3 , k 1,4 is the vibration load sensitivity coefficient, which is obtained by calibrating the blade root load measurement system; ε1, ε2, ε3, and ε4 are the blade root strains measured by the sensor; (2) where ε is the blade root strain calculated in real time; λ is the real-time measurement wavelength; λ i is the initial wavelength, which is obtained by calibrating the blade root load measurement system.

6. An electronic device, characterized in that: The electronic device comprises: At least one processor; and a memory storing instructions, which, when executed by the at least one processor, enable the at least one processor to perform the zero-drift calibration method for a wind rotor azimuth angle sensor based on blade root swing load as described in any one of claims 1 to 5.

7. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores executable instructions, which, when executed, enable the machine to perform the zero-drift calibration method for a wind rotor azimuth angle sensor based on blade root swing loads according to any one of claims 1 to 5.

Images