Blade root load calibration data monitoring method, system, equipment and medium

Abstract

The invention provides a blade root load calibration data monitoring method, system and device and a medium, and belongs to the technical field of wind turbine generator control, and the method comprises the steps: carrying out the iteration of a generator set load according to a wind resource parameter and an IEC load calculation standard, and obtaining a maximum design value of the blade root load of the generator set; carrying out coordinate transformation on the load and carrying out moving mean filtering processing on the load signal; the mean value of the blade root loads of the three blades and the difference between the blade root load of each blade and the mean value of the blade root loads of the three blades are calculated; comparing and judging the difference value with a theoretical maximum value of the blade root flapping direction load, and judging whether the difference value is greater than a load deviation alarm threshold value or not; if yes, a fan fault alarm is given out, and variable pitch optimization action is executed according to the load deviation checking pitch angle limit table. According to the method, the blade root load calibration data can be monitored, the running state of the fan can be continuously optimized, the load deviation is reduced, and the safety and stability of the fan are improved.

Description

Technical Field

[0001] This invention belongs to the field of wind turbine control technology, specifically relating to a method, system, equipment, and medium for monitoring blade root load calibration data. Background Technology

[0002] Wind power, as a renewable energy source, has considerable development potential. Wind power technology is booming, and the trend towards larger wind turbines is a significant one in the current wind power industry. Larger wind turbines can improve power generation efficiency, reduce unit costs, and enhance the competitiveness of wind power. With the continuous increase in wind turbine power ratings, longer blades and larger swept areas are needed to capture more wind energy. However, the growth of wind turbine blades also brings many technical challenges. On the one hand, the overall flexibility of the blades increases; on the other hand, the increased swept area leads to a greater unbalanced load in the rotor plane, which damages the blade clearance and the blade structure itself, posing a significant safety hazard to the turbine.

[0003] Independent pitch control technology for wind turbines has been applied to the control of wind turbines. Independent pitch control allows for independent adjustment of the pitch angle of each blade, rather than a unified pitch control based on related technologies that causes all blades to change at the same angle simultaneously. By using sensors to monitor the wind turbine's operating status and environmental parameters such as wind speed and direction in real time, the control system precisely controls the pitch angle of each blade based on this information.

[0004] In independent pitch control of wind turbines, the relevant technologies suffer from two main problems: First, the lack of accurate theoretical design maximum values ​​for blade root load monitoring makes it impossible to accurately determine whether the load is normal. Second, in blade root load data processing, only raw sensor data is used for pitch control, resulting in large data deviations and affecting turbine stability. Furthermore, the relevant technologies only monitor the load of a single blade and cannot accurately determine whether there are abnormalities in the blade root load. This leads to increased load deviations when adjusting the turbine's operating status, compromising turbine stability. Summary of the Invention

[0005] This invention provides a method for monitoring blade root load calibration data. The method can monitor blade root load calibration data, continuously optimize the operating status of the wind turbine, reduce load deviation, and improve the safety and stability of the wind turbine.

[0006] The methods include: Step 1: Based on wind resource parameters and IEC load calculation standards, iterate the unit load to obtain the maximum design value of the unit blade root load; Step 2: Obtain the load at the blade root detected by the unit's blade root load sensor, perform coordinate transformation on the load, and perform moving mean filtering on the load signal; Step 3: Calculate the average root load of the three blades and the difference between the root load of each blade and the average root load of the three blades; Step 4: Compare the difference with the theoretical maximum value of the load in the blade root flapping direction to determine whether it exceeds the load deviation alarm threshold. Step 5: If the value is greater than the specified value, a wind turbine fault alarm will be issued, and pitch optimization actions will be performed according to the pitch angle limit table based on the load deviation.

[0007] It should be further noted that wind resource parameters include: average wind speed, wind speed standard deviation, wind shear index, and turbulence intensity.

[0008] It should be further explained that the iterative process of the unit load in step one includes: Establish a model of a wind turbine generator set, which includes blades, hub, gearbox, and generator; The actual measured wind resource parameters are input into the wind turbine generator model to simulate the operating status of the generator under different wind conditions. According to IEC standards, the blade root load under different operating conditions is calculated, and the design maximum value is gradually approximated through iterative optimization methods. In the iterative optimization method, a group of particles related to the blade root load are randomly selected under different operating conditions. Each particle represents a set of parameters that affect the blade root load and is randomly assigned an initial velocity as each particle. For each particle, the corresponding leaf root load value is calculated based on its current parameter value using the leaf root load calculation model, and the leaf root load value is used as the particle's fitness value. For each particle, compare its current fitness value with its best fitness value; if the current fitness value is better, update the best position of the particle. The particle's velocity and position are updated based on its current velocity, individual optimal position, and global optimal position. The velocity update formula is:

[0009] The position update formula is:

[0010] in, It is the velocity of particle m in the s-th generation. It is inertial weight. and It is the acceleration constant. and It is a random number between 0 and 1. It is the optimal position for particle m. It is the globally optimal position. It is the position of particle m in the s-th generation; Repeat the above steps until the designed maximum value is obtained.

[0011] It should be further noted that, in this method, the real-time measured value of the blade root load is obtained by using the real-time strain of the blade root load sensor and the calibration coefficient of the load sensor obtained through calibration. M ;

[0012] in, For oscillation load; To swing the load; This is the sensitivity coefficient for oscillation load; For the swing load sensitivity coefficient; , , , For sensor strain;

[0013] in, The strain sensitivity coefficient of the sensor; i The numbers are 1, 2, 3, and 4. For measuring wavelength; This is the initial wavelength.

[0014] It should be further noted that in step two, the measured blade root load is transferred from the blade root load coordinate system. , Transform to the rotating hub coordinate system , The transformation method is as follows:

[0015] Where cone is the angle between the x-axis of the leaf root coordinate system and the x-axis of the hub coordinate system, and the leaf root... It will be projected onto the rotating hub .

[0016] It should be further noted that in step two, the moving average filter window length is defined as P, based on the discrete load signal sequence. The acquired load data is processed by moving mean filtering to obtain discrete load signal sequences. for:

[0017]

[0018] It should be further noted that the methods for calculating the average root load of the three blades include: Let the blade root flapping direction be a certain direction within the wind turbine plane, and let the load on the j-th blade root flapping direction after moving average filtering be Z. hd The average load Z at the blade root flapping direction of the three blades avg The calculation formula is:

[0019] The difference X between the root load of the j-th blade and the average root load of the three blades zc The calculation formula is:

[0020] Let the theoretical maximum value of the blade root flapping direction load be Z. max Then the ratio X of the j-th leaf bj The calculation formula is:

[0021] Let the load deviation alarm threshold be Y. jz The judgment condition is: If it exists , making X bj >Y jz If the wind turbine is found to be in an abnormal condition, a fault alarm will be triggered, and pitch optimization actions will be performed based on the pitch angle limit table according to the load deviation. If for all j=1,2,3, it is X bj ≤Y jz Then the fan will operate normally.

[0022] This application also provides a blade root load calibration data monitoring system, the system comprising: The blade root load maximum value acquisition module is used to iterate the unit load according to wind resource parameters and IEC load calculation standards to obtain the design maximum value of the unit blade root load. The load processing module is used to acquire the load at the blade root detected by the unit's blade root load sensor, perform coordinate transformation on the load, and perform moving mean filtering on the load signal. The load mean calculation module is used to calculate the average load at the root of the three blades and the difference between the load at the root of each blade and the average load at the root of the three blades. The load judgment module is used to compare the difference with the theoretical maximum value of the load in the blade root flapping direction and determine whether it is greater than the load deviation alarm threshold. If the value is greater than the specified value, a wind turbine fault alarm will be issued, and pitch optimization actions will be performed.

[0023] According to another embodiment of this application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the leaf root load calibration data monitoring method.

[0024] According to another embodiment of this application, a storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the leaf root load calibration data monitoring method.

[0025] As can be seen from the above technical solutions, the present invention has the following advantages: The blade root load calibration data monitoring method provided in this application can display the deviation of each blade from the overall average level by calculating the difference between the blade root load of each blade and the average value of the three blades' blade root loads. It can promptly detect abnormal load conditions in individual blades; even if the overall load average appears normal, if the difference in a particular blade is too large, abnormal operating conditions can be detected in time, thus providing early warning. By introducing the theoretical maximum value of the blade root flapping direction load and the load deviation alarm threshold, a standardized judgment criterion is established by comparing the actual load with the theoretical value. This makes the judgment unaffected by factors such as different wind turbine models and different wind farm environments, improving the versatility of the alarm monitoring method.

[0026] This application improves data usability through coordinate transformation and moving average filtering. By calculating the average root load of the three blades and the difference between each blade and the average, and comparing it with the theoretical maximum value and alarm threshold, abnormal root load conditions can be accurately identified. Furthermore, considering that existing technologies lack effective countermeasures when abnormal root loads occur, only issuing alarms without further optimization, this application, during alarm monitoring, determines the magnitude of load deviation in real time by calculating the difference between the root load of each blade and the average value, and the ratio to the theoretical maximum value. Based on the judgment criteria, if the load deviation of a certain blade exceeds the alarm threshold, the corresponding pitch angle adjustment value can be found in the pitch angle limit table. The found pitch angle adjustment value is sent to the wind turbine's pitch control system. The pitch control system, according to the received command, drives the blade to rotate to the specified pitch angle position. By adjusting the pitch angle, the aerodynamic shape of the blade can be changed, thereby adjusting the aerodynamic load on the blade. When the load deviation is positive, appropriately increasing the pitch angle can reduce the windward area of ​​the blades and reduce the load on the blades; when the load deviation is negative, appropriately decreasing the pitch angle can increase the windward area of ​​the blades, increase the output power of the blades, and at the same time make the load between the blades more balanced.

[0027] After performing pitch optimization, the changes in blade root load are continuously monitored. If the load deviation remains large, it indicates that the current pitch angle adjustment may not be ideal. The pitch angle limit table needs to be consulted again based on the new load deviation for further adjustments until the load deviation is reduced below the alarm threshold, at which point the wind turbine returns to normal operation. This closed-loop control method continuously optimizes the wind turbine's operating status, reduces load deviation, and improves the turbine's safety and stability. Attached Figure Description

[0028] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 Flowchart of the leaf root load calibration data monitoring method; Figure 2 Flowchart of an embodiment of the leaf root load calibration data monitoring method; Figure 3 This is a schematic diagram of an electronic device. Detailed Implementation

[0030] The blade root load calibration data monitoring method provided in this application considers that independent pitch control of wind turbines refers to the independent adjustment of the pitch angle of each blade, rather than the simultaneous change of all blades at the same angle as in traditional unified pitch control. Blade root load is one of the bases for the precise control of the pitch angle of each blade by independent pitch control. The measurement of blade root load of the unit in operation is achieved through a blade root load sensor, which is used to measure the load borne by the root of the wind turbine blades, including bending moment, torque, and thrust. The measurement accuracy of the blade load by the blade root load sensor directly affects the accuracy of the unit's independent pitch control. Through calibration, the relationship between the sensor output and the actual load can be determined, thereby improving the accuracy of load measurement. The on-site calibration of the blade root load sensor has strict calibration conditions, requiring not only a standardized calibration process but also high requirements for wind speed, rotor speed, ambient temperature, and humidity. After calibration, verification tests should be conducted to ensure that the sensor's performance in actual operation meets the requirements. The blade root load sensor itself can be displaced due to external factors such as installation stability, its own weight, and inertial forces, resulting in a decrease in measurement accuracy. To improve the accuracy and reliability of calibration results, multiple calibration verifications should be performed, and the results of different calibrations should be compared and analyzed.

[0031] The blade root load sensor calibration involved in this application is a crucial step in ensuring the safe and efficient operation of wind turbines. Inaccurate calibration of the blade root load sensor can lead to inaccurate load measurements, potentially causing misjudgments of the wind turbine's structural strength and increasing the risk of turbine failure. Accurately calibrated sensors can monitor changes in blade root load in a timely and accurate manner. Reliable blade root load information can be used to optimize wind turbine control strategies, improve power generation efficiency and stability, and ensure the safe operation of the wind turbine.

[0032] The following detailed description of the leaf root load calibration data monitoring method includes specific details such as particular system structures and technologies for illustrative purposes rather than limiting them, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details.

[0033] The terms "one embodiment" or "some embodiments" used in this application mean that one or more embodiments of this application include the specific features, structures, or characteristics described in that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this application do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0035] Please see Figure 1 The diagram shows a flowchart of a leaf root load calibration data monitoring method in a specific embodiment. The method includes: S101: Based on wind resource parameters and IEC load calculation standards, iterate the unit load to obtain the maximum design value of the unit blade root load.

[0036] The wind resource parameters in this embodiment include: average wind speed, wind speed standard deviation, wind shear index, and turbulence intensity.

[0037] The average wind speed can be set over a period of one year, one quarter, or one month, and the average wind speed at the wind farm location is statistically analyzed. The standard deviation of wind speed is based on the degree of fluctuation of wind speed around the average value. The wind shear index represents the rate of change of wind speed with height, mainly considering the influence on the aerodynamic distribution and load on the blades.

[0038] In this embodiment, the iteration of unit load is performed based on the following method: Establish a model of a wind turbine generator set, which includes blades, hub, gearbox, and generator; The actual measured wind resource parameters are input into the wind turbine generator model to simulate the operating status of the generator under different wind conditions. According to IEC standards, the blade root load under different operating conditions is calculated, and the design maximum value is gradually approximated through iterative optimization methods. In the iterative optimization method, a group of particles related to the blade root load are randomly selected under different operating conditions. Each particle represents a set of parameters that affect the blade root load and is randomly assigned an initial velocity as each particle. For each particle, the corresponding leaf root load value is calculated based on its current parameter value using the leaf root load calculation model, and the leaf root load value is used as the particle's fitness value. For each particle, compare its current fitness value with its best fitness value; if the current fitness value is better, update the best position of the particle. The particle's velocity and position are updated based on its current velocity, individual optimal position, and global optimal position. The velocity update formula is:

[0039] The position update formula is:

[0040] in, It is the velocity of particle m in the s-th generation. It is inertial weight. and It is the acceleration constant. and It is a random number between 0 and 1. It is the optimal position for particle m. It is the globally optimal position. It is the position of particle m in the s-th generation; Repeat the above steps until the designed maximum value is obtained.

[0041] As can be seen, this embodiment introduces multi-objective optimization, considering not only the maximum blade root load but also multiple objectives such as generator efficiency and fatigue life. By establishing a multi-objective optimization model and utilizing iterative optimization methods such as particle swarm optimization, the optimal balance among multiple objectives is sought. The parameter space is divided into different regions using particle analysis algorithms, each representing a typical operating condition. Then, particles are randomly initialized within each region, allowing for more uniform coverage of the entire parameter space and improving search efficiency.

[0042] During execution, for regions where parameter changes have a significant impact on the blade root load, the initial velocity of the particles is appropriately increased to enable them to explore the region more quickly; for regions where parameter changes have a relatively small impact on the blade root load, the initial velocity of the particles is decreased to prevent excessive particle jumping.

[0043] During the iterative optimization process, the parameter values ​​change continuously as the particles are updated. The blade root load calculation model can be corrected in real time based on the historical information and current state of the particles. In other words, when the parameter values ​​of the particles approach a known specific working condition, the actual measurement data under that working condition is used to correct the blade root load calculation model, thereby improving the accuracy of the calculation results.

[0044] This embodiment adaptively adjusts the update frequency of the individual's optimal position based on the particle's search state and the progress of the optimization process. In the early stages of iteration, the particle's search range is large, so the update frequency can be appropriately reduced to avoid wasting computational resources due to frequent updates. In the later stages of iteration, as the particle gradually approaches the optimal solution, the update frequency can be appropriately increased to ensure that better positions can be captured in a timely manner.

[0045] When a particle goes beyond the boundary, its velocity and position are reinitialized near the boundary based on its direction and speed of motion, allowing it to continue searching within the feasible solution region. This process is repeated until the designed maximum value is obtained.

[0046] It should be noted that when particles gather in a certain area and their search directions tend to be consistent, and the fitness value changes little, it indicates that the algorithm may have converged to a local optimum or a global optimum. In this case, the iteration can be terminated early to improve optimization efficiency.

[0047] S102: Acquire the load at the blade root detected by the unit's blade root load sensor, perform coordinate transformation on the load, and perform moving mean filtering on the load signal.

[0048] In some embodiments, coordinate transformation is based on the principle of vector transformation, converting the original sensor signal into X / Y / Z triaxial forces in the local coordinate system, and then transforming it into the blade flapping-oscillating coordinate system through a rotation matrix to eliminate directional interference caused by blade rotation.

[0049] This embodiment combines moving average filtering with the correlation of signals over time. By averaging, the signal is smoothed, noise and short-term fluctuations are removed, and the signal can better reflect the true trend of load changes. S103: Calculate the average root load of the three blades and the difference between the root load of each blade and the average root load of the three blades.

[0050] In this embodiment, the overall load level can be reflected by calculating the average value, while the difference between each blade and the average value can highlight the relative change of the load on each blade, making it easier to discover abnormal loads that may exist on individual blades.

[0051] S104: Compare the difference with the theoretical maximum value of the load in the blade root flapping direction to determine whether it is greater than the load deviation alarm threshold.

[0052] In some embodiments, comparing the difference with the theoretical maximum value of the blade root flapping load and the load deviation alarm threshold allows for the determination of whether the actual load exceeds the normal range based on established judgment criteria. This clearly determines whether the blade root load is abnormal, providing a basis for issuing alarms and taking appropriate measures, ensuring the wind turbine operates within a safe load range.

[0053] S105: If it is greater than, a wind turbine fault alarm will be issued, and pitch optimization actions will be performed according to the pitch angle limit table based on the load deviation.

[0054] Specifically, in this embodiment, the maximum design value of the blade root load of the unit obtained in step S101 will be used.

[0055] An alarm is triggered when the single-blade load exceeds the alarm threshold. An alarm is also triggered when the deviation value exceeds the deviation alarm threshold. This clearly determines whether abnormal blade root loads have occurred, providing a basis for issuing alarms and taking appropriate measures, ensuring the wind turbine operates within a safe load range.

[0056] This embodiment combines load deviation with a pitch angle limit table to adjust the target pitch angle according to preset rules, such as limiting the target pitch angle to the range of -2° to +5°. By looking up the table, the optimal pitch angle compensation value under the current wind speed and turbulence intensity is quickly matched, reducing abnormal blade aerodynamic loads.

[0057] The load deviation can be set by referring to the pitch angle limit table as follows: when the wind speed is 8-10m / s, the turbulence intensity can be set to 15 and the pitch angle compensation to +1.5.

[0058] When the wind speed is 10-12 m / s, the turbulence intensity can be set to 20 and the pitch angle compensation to +2.0.

[0059] This embodiment addresses the problem in existing technologies where there is a lack of accurate theoretical design maximum values ​​for blade root load monitoring, making it impossible to accurately determine whether the load is normal. This application determines the design maximum value of the blade root load based on wind resource parameters and IEC load calculation standards, providing a reliable reference for monitoring.

[0060] In blade root load data processing, there are instances where sensor data lacks effective coordinate transformation and filtering, resulting in low data quality and affecting the accuracy of monitoring results. This application improves data usability through coordinate transformation and moving average filtering. By calculating the average blade root load of the three blades and the difference between each blade and the average, and comparing it with the theoretical maximum value and alarm threshold, abnormal blade root load conditions can be accurately identified. Moreover, considering that existing technologies lack effective countermeasures when abnormal blade root loads occur, only issuing alarms without further optimization actions, this application not only issues alarms but also performs pitch optimization actions based on load deviations, proactively adjusting the wind turbine's operating status, reducing load deviations, and improving the wind turbine's safety and stability.

[0061] In one embodiment of the present invention, based on step S103, a possible embodiment will be given below, and its specific implementation will be described in a non-limiting manner.

[0062] Methods for calculating the average root load of three blades include: Let the blade root flapping direction be a certain direction within the wind turbine plane, and let the load on the j-th blade root flapping direction after moving average filtering be Z. hd The average load Z at the blade root flapping direction of the three blades avg The calculation formula is:

[0063] In this embodiment, the average root load of the three blades represents the average load level of the three blades under the current operating conditions. If the wind turbine is operating normally, the load on each blade should fluctuate around this average value, and the fluctuation range should be within a reasonable range. By calculating the average value, a benchmark can be provided for subsequent judgment on whether the load on each blade is abnormal.

[0064] The difference X between the root load of the j-th blade and the average root load of the three blades zc The calculation formula is:

[0065] Let the theoretical maximum value of the blade root flapping direction load be Z. max Then the ratio X of the j-th leaf bj The calculation formula is: .

[0066] The theoretical maximum value of the blade root flapping load is derived from the wind turbine's design and theoretical calculations, representing the maximum load the blade can withstand under normal operating conditions. The ratio for each blade is compared to the load deviation alarm threshold. If the ratio exceeds the alarm threshold, it indicates that the load on that blade has exceeded the normal range and could damage the blade, requiring an immediate alarm. This method correlates the actual load with the theoretical safe range, accurately determining whether the blade root load is abnormal.

[0067] For example, let the load deviation alarm threshold be Y. jz The judgment condition is: If it exists , making X bj >Y jz If the wind turbine is found to be malfunctioning, a fault alarm will be triggered, and pitch optimization actions will be performed based on the pitch angle limit table according to the load deviation.

[0068] If for all j=1,2,3, it is X bj ≤Y jz If so, the fan will operate normally.

[0069] This embodiment calculates the difference between the root load of each blade and the average root load of the three blades, thus displaying the degree of deviation of each blade from the overall average level. It can promptly detect abnormal load conditions on individual blades; even if the overall load average appears normal, a large difference in the load of a particular blade can trigger an early warning. By introducing the theoretical maximum value of the root flapping direction load and a load deviation alarm threshold, a standardized judgment criterion is established by comparing the actual load with the theoretical value. This ensures that the judgment is not affected by factors such as different wind turbine models or different wind farm environments, improving the versatility of the alarm monitoring method.

[0070] During alarm monitoring, the magnitude of load deviation is determined in real time by calculating the difference between the root load of each blade and the mean, and the ratio to the theoretical maximum value. Based on the judgment criteria, if the load deviation of a blade exceeds the alarm threshold, the corresponding pitch angle adjustment value can be found in the pitch angle limit table. This value is then sent to the wind turbine's pitch control system. The pitch control system, upon receiving the command, drives the blade to rotate to the specified pitch angle position. Adjusting the pitch angle changes the aerodynamic shape of the blade, thereby adjusting the aerodynamic load on the blade. When the load deviation is positive, appropriately increasing the pitch angle reduces the blade's frontal area, lowering the load on the blade; when the load deviation is negative, appropriately decreasing the pitch angle increases the blade's frontal area, improving the blade's output power, and also makes the load more balanced among the blades.

[0071] After performing pitch optimization, the changes in blade root load are continuously monitored. If the load deviation remains large, it indicates that the current pitch angle adjustment may not be ideal. The pitch angle limit table needs to be consulted again based on the new load deviation for further adjustments until the load deviation is reduced below the alarm threshold, at which point the wind turbine returns to normal operation. This closed-loop control method continuously optimizes the wind turbine's operating status, reduces load deviation, and improves the turbine's safety and stability.

[0072] Based on the above embodiments, in order to further improve the blade root load calibration data monitoring method provided in the above embodiments to effectively solve the blade root load calibration failure problem and improve the unit operation safety and stability, the following is an implementable method. In one embodiment, as follows: Figure 2 As shown, taking a 5MW wind turbine as an example, the 5MW turbine has a rotor diameter of 195m, a tower height of 140m, a cut-in wind speed of 3m / s, a cut-out wind speed of 22m / s, and a rated wind speed of 9.5m / s. The specific steps include: S201: Select a unit equipped with a blade root load sensor and complete the calibration of the blade root load sensor to achieve real-time measurement of the wind turbine blade root load.

[0073] S202: Based on wind resource parameters and the IEC 61400-1 (2019-02) load calculation standard, the unit load is simulated and iterated to obtain the theoretical maximum value of the unit blade root load.

[0074] S203: The real-time measured value of the blade root load is obtained by using the real-time strain of the blade root load sensor and the calibration coefficient of the load sensor obtained through calibration.

[0075]

[0076]

[0077] in, For oscillation load; To swing the load; This is the sensitivity coefficient for oscillation load; For the swing load sensitivity coefficient; , , , For sensor strain;

[0078] in, is the strain sensitivity coefficient of the sensor.

[0079] i is 1, 2, 3, or 4; For measuring wavelength; This is the initial wavelength.

[0080] S204: Transfer the measured blade root load to the blade root load coordinate system. , Transform to the rotating hub coordinate system , .

[0081]

[0082] Where cone is the angle between the x-axis of the leaf root coordinate system and the x-axis of the hub coordinate system, and the leaf root... It will be projected onto the rotating hub .

[0083] S205: Define a moving average filter window of length P, where P is an odd number, and perform moving average filtering on the acquired load data. Discrete load signal sequence. as follows: , .

[0084] S206: Set load deviation rate threshold L Calculate the average value of the root load of the three blades in the rotor plane. Compare the difference between the root load of each blade and the average value of the three blade root loads with the maximum theoretical root load. If the difference exceeds the load deviation rate threshold, the calculation is performed. L At that time, the unit reported an alarm indicating that the blade root load calibration data was invalid.

[0085] S207: When the unit reports an invalid blade root load calibration data alarm, the pitch module executes pitch optimization actions. Otherwise, the unit operates normally. The pitch module will perform different levels of pitch reduction and power control based on the current load deviation rate of the unit using a lookup table. Taking a certain 5MW model as an example, by linearly interpolating the load deviation rate and pitch angle, the load deviation rate and pitch angle limit table is obtained as shown in Table 1.

[0086] Table 1: Load Deviation Rate and Pitch Angle Limits

[0087] The blade root load calibration data monitoring method provided in this application uses coordinate transformation to transform the load at the actual installation location of the load sensor to the wind turbine plane, which can ignore the influence of load differences caused by installation position deviation; by performing moving average filtering on the measured load signal, noise in the signal is reduced, making the load signal smoother and more stable, and improving the stability and reliability of fault alarm monitoring.

[0088] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0089] The following are embodiments of the blade root load calibration data monitoring system provided in this disclosure. This system and the blade root load calibration data monitoring methods in the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the blade root load calibration data monitoring system, please refer to the embodiments of the above blade root load calibration data monitoring methods.

[0090] The system includes: The blade root load maximum value acquisition module is used to iterate the unit load according to wind resource parameters and IEC load calculation standards to obtain the design maximum value of the unit blade root load. The load processing module is used to acquire the load at the blade root detected by the unit's blade root load sensor, perform coordinate transformation on the load, and perform moving mean filtering on the load signal. The load mean calculation module is used to calculate the average load at the root of the three blades and the difference between the load at the root of each blade and the average load at the root of the three blades. The load judgment module is used to compare the difference with the theoretical maximum value of the load in the blade root flapping direction to determine whether it is greater than the load deviation alarm threshold; if it is greater, a wind turbine fault alarm is issued and pitch optimization actions are executed.

[0091] like Figure 3 As shown, this application also provides an electronic device, including a display module 103, a memory 102, a processor 101, and a computer program stored in the memory and executable on the processor 101. When the processor 101 executes the program, it implements the steps of the leaf root load calibration data monitoring method.

[0092] In embodiments of the present invention, electronic devices include, but are not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments described and / or claimed herein.

[0093] In this embodiment, processor 101 may be implemented using at least one of an Application-Specific Integrated Circuit (ASIC), a Programmable Logic Device (PLD), a Field-Programmable Gate Array (FPGA), a processor, a controller, a microcontroller, a microprocessor, or an electronic unit designed to perform the functions described herein. In some cases, such implementations may be implemented within a controller. For software implementations, implementations such as processes or functions may be implemented with separate software modules that allow the performance of at least one function or operation. The software code may be implemented by a software application (or program) written in any suitable programming language, and the software code may be stored in memory and executed by the controller.

[0094] The display module 103 is used to display information input by the user or information provided to the user. The display module 103 may include a display panel, which may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.

[0095] The memory 102 can be used to store software programs and various data. The memory 102 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0096] This application also provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the leaf root load calibration data monitoring method.

[0097] The storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example,, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0098] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring leaf root load calibration data, characterized in that, The methods include: Step 1: Based on wind resource parameters and IEC load calculation standards, iterate the unit load to obtain the maximum design value of the unit blade root load; Step 2: Obtain the load at the blade root detected by the unit's blade root load sensor, perform coordinate transformation on the load, and perform moving mean filtering on the load signal; Step 3: Calculate the average root load of the three blades and the difference between the root load of each blade and the average root load of the three blades; Step 4: Compare the difference with the theoretical maximum value of the load in the blade root flapping direction to determine whether it exceeds the load deviation alarm threshold. Step 5: If the value is greater than the specified value, a wind turbine fault alarm will be issued, and pitch optimization actions will be performed according to the pitch angle limit table based on the load deviation.

2. The leaf root load calibration data monitoring method according to claim 1, characterized in that, Wind resource parameters include: average wind speed, wind speed standard deviation, wind shear index, and turbulence intensity.

3. The leaf root load calibration data monitoring method according to claim 1, characterized in that, Step one involves iterating the unit load, which includes: Establish a model of a wind turbine generator set, which includes blades, hub, gearbox, and generator; The actual measured wind resource parameters are input into the wind turbine generator model to simulate the operating status of the generator under different wind conditions. According to IEC standards, the blade root load under different operating conditions is calculated, and the design maximum value is gradually approximated through iterative optimization methods. In the iterative optimization method, a group of particles related to the blade root load are randomly selected under different operating conditions. Each particle represents a set of parameters that affect the blade root load and is randomly assigned an initial velocity as each particle. For each particle, the corresponding leaf root load value is calculated based on its current parameter value using the leaf root load calculation model, and the leaf root load value is used as the particle's fitness value. For each particle, compare its current fitness value with its best fitness value; if the current fitness value is better, update the best position of the particle. The particle's velocity and position are updated based on its current velocity, individual optimal position, and global optimal position; the velocity update formula is: The position update formula is: in, It is the velocity of particle m in the s-th generation. It is inertial weight. and It is the acceleration constant. and It is a random number between 0 and 1. It is the optimal position for particle m. It is the globally optimal position. It is the position of particle m in the s-th generation; Repeat the above steps until the designed maximum value is obtained.

4. The leaf root load calibration data monitoring method according to claim 1, characterized in that, In this method, the real-time measured value of the blade root load is obtained by using the real-time strain of the blade root load sensor and the calibration coefficient of the load sensor obtained through calibration. M ; in, For oscillation load; To swing the load; This is the sensitivity coefficient for oscillation load; For the swing load sensitivity coefficient; , , , For sensor strain; in, The strain sensitivity coefficient of the sensor; i The numbers are 1, 2, 3, and 4. For measuring wavelength; This is the initial wavelength.

5. The leaf root load calibration data monitoring method according to claim 1, characterized in that, In step two, the measured blade root load is transferred from the blade root load coordinate system. , Transform to the rotating hub coordinate system , The transformation method is as follows: Where cone is the angle between the x-axis of the leaf root coordinate system and the x-axis of the hub coordinate system, and the leaf root... It will be projected onto the rotating hub .

6. The leaf root load calibration data monitoring method according to claim 1, characterized in that, In step two, the moving average filter window length is defined as P, based on the discrete load signal sequence. The acquired load data is processed by moving mean filtering to obtain discrete load signal sequences. for: 。 7. The leaf root load calibration data monitoring method according to claim 1, characterized in that, Methods for calculating the average root load of three blades include: Let the blade root flapping direction be a certain direction within the wind turbine plane, and let the load on the j-th blade root flapping direction after moving average filtering be Z. hd The average load Z at the blade root flapping direction of the three blades avg The calculation formula is: The difference X between the root load of the j-th blade and the average root load of the three blades zc The calculation formula is: Let the theoretical maximum value of the blade root flapping direction load be Z. max Then the ratio X of the j-th leaf bj The calculation formula is: Let the load deviation alarm threshold be Y. jz The judgment condition is: If it exists , making X bj >Y jz If the wind turbine is found to be in an abnormal condition, a fault alarm will be triggered, and pitch optimization actions will be performed based on the pitch angle limit table according to the load deviation. If for all j=1,2,3, it is X bj ≤Y jz Then the fan will operate normally.

8. A leaf root load calibration data monitoring system, characterized in that, The system is used to implement the leaf root load calibration data monitoring method as described in any one of claims 1 to 7; The system includes: The blade root load maximum value acquisition module is used to iterate the unit load according to wind resource parameters and IEC load calculation standards to obtain the design maximum value of the unit blade root load. The load processing module is used to acquire the load at the blade root detected by the unit's blade root load sensor, perform coordinate transformation on the load, and perform moving mean filtering on the load signal. The load mean calculation module is used to calculate the average load at the root of the three blades and the difference between the load at the root of each blade and the average load at the root of the three blades. The load judgment module is used to compare the difference with the theoretical maximum value of the load in the blade root flapping direction and determine whether it is greater than the load deviation alarm threshold. If the value is greater than the specified value, a wind turbine fault alarm will be issued, and pitch optimization actions will be performed.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the leaf root load calibration data monitoring method as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the leaf root load calibration data monitoring method as described in any one of claims 1 to 7.

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