Sensor parameter calibration method, device, system and medium for wind farm cluster

Abstract

The application discloses a kind of wind farm group's sensor parameter calibration method, device, system and medium, belong to wind power generation field.The method comprises: obtaining the unit data of multiple target wind turbine generators in wind farm, unit data includes operating data, sensing data and unit component state data;According to operating data, sensing data and unit component state data, the value of the calibration parameter of sensor is calculated to obtain the minimum absolute value of the difference between theoretical strain and measured strain;Based on the idling unit data of target wind turbine generator, the value of calibration parameter and unit data, the error rate of the theoretical load of the blade of target wind turbine generator and idling calibration load is calculated;In the case where error rate is less than or equal to acceptable error rate threshold, the value of calibration parameter corresponding to target wind turbine generator is respectively pushed to multiple target wind turbine generators.According to the embodiment of the application, the parameter calibration efficiency of sensor can be improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of wind power generation, and particularly relates to a sensor parameter calibration method, device and system for a wind farm cluster and a medium. BACKGROUND

[0002] With the continuous development of wind turbine generators in terms of large impeller diameter, large capacity, high tower, and the like, problems such as fatigue of components of the wind turbine generators, increased load, and reduced clearance have become problems that need to be solved in wind power generation. In order to solve the above problems, it is necessary to monitor the blade root load of the blades of the wind turbine generators by using sensors so as to take corresponding measures.

[0003] In order to ensure the accuracy of the data collected by the sensors, the parameters of the sensors need to be calibrated, and specifically, the parameters of the sensors need to be calibrated manually by humans. However, manually calibrating the parameters of the sensors takes a long time, resulting in low calibration efficiency of the parameters of the sensors. SUMMARY

[0004] The present application provides a sensor parameter calibration method, device, system and medium for a wind farm cluster, which can improve the calibration efficiency of the parameters of the sensors.

[0005] In a first aspect, the present application provides a sensor parameter calibration method for a wind farm cluster, comprising: obtaining unit group data of a plurality of target wind turbine generators in a wind farm, the unit group data comprising operation data, sensing data and unit component state data, the sensing data being obtained by a sensor arranged at a blade root of a blade of the target wind turbine generator; calculating a value of a calibration parameter of the sensor in a case where an absolute value of a difference between a theoretical strain and a measured strain is minimum according to the operation data, the sensing data and the unit component state data, the calibration parameter being used for calculation of the sensing data and the measured strain and calculation of the unit component state data and the theoretical strain; calculating an error rate of a theoretical load of the blade of the target wind turbine generator and an idling calibration load based on idling unit group data of the target wind turbine generator, the value of the calibration parameter and the unit group data; and in a case where the error rate is less than or equal to an acceptable error rate threshold, respectively pushing a calibration message corresponding to the target wind turbine generator to the plurality of target wind turbine generators to update an original value of the calibration parameter of the target wind turbine generator, the calibration message comprising the value of the calibration parameter.

[0006] In some possible embodiments, before the error rate of the theoretical load of the blade of the target wind turbine generator set and the idling calibration load is calculated based on the idling unit data of the target wind turbine generator set, the value of the calibration parameter and the unit data, the method further includes: when the target wind turbine generator set is in a small wind condition and in an equal wind state, controlling the target wind turbine generator set to operate at a limited speed, the limited speed being lower than a normal working speed; when the target wind turbine generator set operates at the limited speed, collecting unit data of the target wind turbine generator set multiple times as idling unit data; when the collected idling unit data is sufficient to calculate the idling calibration load, stopping collecting the idling unit data and controlling the target wind turbine generator set to resume normal operation.

[0007] In some possible embodiments, when the collected idling unit data is sufficient to calculate the idling calibration load, stopping collecting the idling unit data and controlling the target wind turbine generator set to resume normal operation includes: when the number of revolutions of the impeller reaches a preset threshold number of revolutions when the target wind turbine generator set operates at the limited speed, determining that the collected idling unit data is sufficient to calculate the idling calibration load; stopping collecting the idling unit data and controlling the target wind turbine generator set to resume normal operation.

[0008] In some possible embodiments, after the unit data of the target wind turbine generator set is collected multiple times as idling unit data when the target wind turbine generator set operates at the limited speed, the method further includes: filtering the idling unit data that meets a preset first effective filtering condition to obtain idling unit data that meets the first effective filtering condition, the first effective filtering condition being used to simulate the idling state of the target wind turbine generator set.

[0009] In some possible embodiments, the error rate of the theoretical load of the blade of the target wind turbine generator set and the idling calibration load is calculated based on the idling unit data of the target wind turbine generator set, the value of the calibration parameter and the unit data, and includes: calculating multiple theoretical loads of the blade of the target wind turbine generator set according to the unit data of the target wind turbine generator set collected multiple times and the value of the calibration parameter; calculating multiple idling calibration loads of the blade of the target wind turbine generator set according to the idling unit data of the target wind turbine generator set collected multiple times and the value of the calibration parameter; calculating a first error rate of the blade by using a maximum value of the multiple theoretical loads of the blade of the target wind turbine generator set and a maximum value of the multiple idling calibration loads of the target wind turbine generator set; calculating a second error rate of the blade by using a minimum value of the multiple theoretical loads of the blade of the target wind turbine generator set and a minimum value of the multiple idling calibration loads of the target wind turbine generator set; determining the item with the maximum value between the first error rate of the blade and the second error rate of the blade as the error rate.

[0010] In some possible embodiments, the method further comprises: repeatedly calculating values of multiple sets of calibration parameters according to the same operation data, sensor data, and unit component state data; and calculating a variation coefficient of the values of each set of calibration parameters according to the values of the multiple sets of calibration parameters, the variation coefficient representing a degree of variation between the values of a set of calibration parameters.

[0011] In the case where the error rate is less than or equal to the acceptable error rate threshold, the values of the calibration parameters corresponding to the target wind turbine generators are respectively pushed to the target wind turbine generators, including: in the case where the error rate is less than or equal to the acceptable error rate threshold and the variation coefficients of the values of the multiple sets of calibration parameters do not exceed the normal variation range, the calibration message corresponding to the target wind turbine generator is pushed to the target wind turbine generator corresponding to the variation coefficient that does not exceed the normal variation range.

[0012] In some possible embodiments, the calibration message corresponding to the target wind turbine generator is respectively pushed to the target wind turbine generator, including: obtaining a push influencing factor, the push influencing factor including a factor influencing the target wind turbine generator receiving the calibration message; and in the case where the push influencing factor satisfies a preset push judgment condition, the calibration message corresponding to the target wind turbine generator is pushed to the target wind turbine generator.

[0013] In some possible embodiments, the push influencing factor includes one or more of the following: a unit calibration parameter state, a calibration parameter receiving enabling state, a unit operation permission state, a sensor operation state, and a unit operation working condition.

[0014] The unit calibration parameter state is used to represent a state of the values of the calibration parameters in the target wind turbine generator; the calibration parameter push function state is used to represent whether the target wind turbine generator opens a function of receiving the values of the calibration parameters; the unit operation permission state is used to represent whether a working condition of the target wind turbine generator allows the values of the calibration parameters to be received; and the sensor operation state is used to represent a working state of the sensor.

[0015] In some possible embodiments, the push judgment condition includes one or more of the following: the unit calibration parameter state represents that the target wind turbine generator does not receive the values of the calibration parameters; the calibration parameter push function state represents that the target wind turbine generator opens the function of receiving the values of the calibration parameters; the unit operation permission state represents that the working condition of the target wind turbine generator is a small-wind working condition allowing the values of the calibration parameters to be received; and the sensor operation state represents that the working state of the sensor is a normal state.

[0016] In some possible embodiments, after the calibration message corresponding to the target wind turbine generator set is respectively pushed to the plurality of target wind turbine generator sets, the method further includes: reacquiring the unit calibration parameter state of the target wind turbine generator set; in a case where the reacquired unit calibration parameter state indicates that the target wind turbine generator set receives an abnormal value of the calibration parameter, re-pushing the calibration message corresponding to the target wind turbine generator set to the target wind turbine generator set until the reacquired unit calibration parameter state indicates that the target wind turbine generator set receives a normal value of the calibration parameter.

[0017] In some possible embodiments, the calibration message further includes a first check code, the first check code of the target wind turbine generator set being obtained according to a check code conversion algorithm and the value of the calibration parameter corresponding to the target wind turbine generator set before being pushed, and the first check code of the target wind turbine generator set being used to be compared with a second check code calculated by the target wind turbine generator set according to the check code conversion algorithm and the received value of the calibration parameter;

[0018] In a case where the difference between the first check code and the second check code exceeds a normal error range, the unit calibration parameter state is updated to indicate that the target wind turbine generator set receives an abnormal value of the calibration parameter;

[0019] In a case where the difference between the first check code and the second check code is within the normal error range, the unit calibration parameter state is updated to indicate that the target wind turbine generator set receives a normal value of the calibration parameter.

[0020] In some possible embodiments, the sensing data includes a blade azimuth angle,

[0021] Before the value of the sensor calibration parameter in a case where the absolute value of the difference between the theoretical strain and the measured strain is minimum is calculated according to the operation data, the sensing data and the unit component state data, the method further includes: dividing the unit data into preset azimuth angle partitions, the blade azimuth angle in each set of unit data divided into the same azimuth angle partition being located in the azimuth angle partition, and each set of unit data including the operation data, the sensing data and the unit component state data collected at the same time; and counting the number of sets of unit data in each azimuth angle partition.

[0022] The value of the sensor calibration parameter in a case where the absolute value of the difference between the theoretical strain and the measured strain is minimum is calculated according to the operation data, the sensing data and the unit component state data, including: in a case where the number of sets reaches a triggering calibration threshold, triggering the value of the sensor calibration parameter in a case where the absolute value of the difference between the theoretical strain and the measured strain is minimum to be calculated according to the operation data, the sensing data and the unit component state data.

[0023] In some possible embodiments, the value of the sensor calibration parameter at which the absolute value of the difference between the calculated theoretical strain and the measured strain is minimum is calculated according to the operation data, the sensing data and the unit component state data, including: determining, based on the operation data, the sensing data and the unit component state data corresponding to the operation data that satisfy a second effective screening condition, the second effective screening condition being used to screen out unit data affected by error factors; obtaining a first relationship for calculating the measured strain according to the sensing data corresponding to the operation data that satisfy the second effective screening condition and a part of the calibration parameters whose values are unknown; obtaining a second relationship for calculating the theoretical strain according to the sensing data corresponding to the operation data that satisfy the second effective screening condition, the unit component state data corresponding to the operation data that satisfy the second effective screening condition and another part of the calibration parameters whose values are unknown; and calculating, based on the first relationship and the second relationship, the value of the calibration parameter at which the absolute value of the difference between the first relationship and the second relationship is minimum.

[0024] In a second aspect, an embodiment of the present application provides a sensor parameter calibration device for a wind farm group, including: a data acquisition module configured to acquire unit data of a plurality of target wind turbine generators in a wind farm, the unit data including operation data, sensing data and unit component state data, the sensing data being sensed by a sensor arranged at a blade root of a blade of the target wind turbine generator; a calibration module configured to calculate, according to the operation data, the sensing data and the unit component state data, a calibration parameter of the sensor at which the absolute value of the difference between a theoretical strain and a measured strain is minimum, the calibration parameter being used for calculation of the sensing data and the measured strain and calculation of the unit component state data and the theoretical strain; a verification module configured to calculate, based on no-load unit data of the target wind turbine generator, the calibration parameter and the unit data, an error rate of a theoretical load of the blade of the target wind turbine generator and a no-load calibration load; and a pushing module configured to, in a case where the error rate is less than or equal to an acceptable error rate threshold, push, to the plurality of target wind turbine generators respectively, a calibration message corresponding to the target wind turbine generator to update original calibration parameters of the target wind turbine generator, the calibration message including the calibration parameter.

[0025] In a third aspect, an embodiment of the present application provides a wind farm group controller, including a processor and a memory storing computer program instructions, and the processor implements the sensor parameter calibration method for a wind farm group of the first aspect when executing the computer program instructions.

[0026] In some possible embodiments, the wind farm group controller includes a wind farm edge controller.

[0027] In a fourth aspect, an embodiment of the present application provides a sensor parameter calibration system for a wind farm group, comprising: a sensor arranged at a blade root of a blade of a wind turbine generator unit, configured to collect sensing data and transmit the sensing data to a wind farm group controller of the third aspect; the wind farm group controller; and a wind turbine generator unit controller configured to receive a calibration message pushed by the wind farm group controller and update original values of calibration parameters by using calibration parameters in the calibration message.

[0028] In a fifth aspect, an embodiment of the present application provides a computer readable storage medium, and the computer readable storage medium stores computer program instructions. When the computer program instructions are executed by a processor, the wind farm group sensor parameter calibration method of the first aspect is implemented.

[0029] The wind farm group sensor parameter calibration method, device, system and medium provided by the embodiments of the present application can calculate the value of the calibration parameter of the sensor that minimizes the absolute value of the difference between the theoretical strain and the measured strain according to the unit data of the plurality of target wind turbine generators in the wind farm. Based on the obtained value of the calibration parameter, the idling unit data and the unit data, the error rate of the theoretical load of the blade and the idling calibration load can be obtained. The validity of the obtained value of the calibration parameter is verified by the error rate. In the case where the error rate is less than or equal to the acceptable error rate threshold, the value of the calibration parameter is automatically pushed to the corresponding wind turbine generator. The process can automatically calculate, verify and push the value of the calibration parameter without human intervention, thereby improving the parameter calibration efficiency of the sensors of a large number of wind turbine generators in the wind farm. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly introduced. Those skilled in the art can obtain other drawings according to these drawings without creating any creative labor.

[0031] Figure 1 A structural schematic diagram of an embodiment of the wind farm group sensor parameter calibration system provided by the present application;

[0032] Figure 2 A flowchart of an embodiment of the wind farm group sensor parameter calibration method provided by the present application;

[0033] Figure 3 A flowchart of another embodiment of the wind farm group sensor parameter calibration method provided by the present application;

[0034] Figure 4 A flowchart of still another embodiment of the wind farm group sensor parameter calibration method provided by the present application;

[0035] Figure 5 a flow chart of still another embodiment of the sensor parameter calibration method of a wind farm cluster provided in the present application;

[0036] Figure 6 a flow chart of still another embodiment of the sensor parameter calibration method of a wind farm cluster provided in the present application;

[0037] Figure 7 a structural schematic diagram of an embodiment of the sensor parameter calibration device of a wind farm cluster provided in the present application;

[0038] Figure 8 a structural schematic diagram of another embodiment of the sensor parameter calibration device of a wind farm cluster provided in the present application;

[0039] Figure 9 a structural schematic diagram of an embodiment of the wind farm cluster controller provided in the present application. DETAILED DESCRIPTION

[0040] The features and exemplary embodiments of various aspects of the present application will be described in detail below with reference to the drawings. The following detailed description is merely intended to explain the present application, and is not intended to limit the present application. The present application can be implemented without some of the specific details, which will be apparent to those skilled in the art. The following description of the embodiments is merely intended to provide a better understanding of the present application by showing examples of the present application.

[0041] A wind turbine generator is a device for converting wind energy into electric energy. With the continuous development of wind turbine generator technology, the diameter of the impeller of the wind turbine generator gradually increases, the capacity gradually increases, and the tower gradually increases. However, problems such as component fatigue, increased load, and reduced clearance may occur in the wind turbine generator. In order to solve the above problems, a sensor can be used to monitor the blade root load of the wind turbine generator, so that corresponding measures can be taken. However, with the increase of the operation time of the wind turbine generator and other factors, the applicability of the parameters of the sensor will decrease, thereby affecting the accuracy of the data collected by the sensor. In order to ensure the accuracy of the data collected by the sensor, the parameters of the sensor can be calibrated. Specifically, the parameters of the sensor can be manually calibrated by the staff. However, manually calibrating the parameters of the sensor by the staff takes a long time, resulting in low efficiency of calibrating the parameters of the sensor.

[0042] The embodiment of the present application provides a sensor parameter calibration method, device, system and medium of a wind farm group, which can acquire relevant data of a wind turbine generator set in a wind farm, determine the value of a calibration parameter of a sensor by using theoretical strain calculation and measured strain calculation according to the relevant data, and perform error checking on a theoretical load obtained based on the value of the calibration parameter and an idling calibration load of the wind turbine generator set. If the error checking is passed, the value of the calibration parameter is automatically pushed to the corresponding wind turbine generator set to update the value of the parameter of the sensor of the wind turbine generator set, so that the automation of the parameter calibration of the sensor of the wind turbine generator set is realized, and the parameter calibration efficiency of the sensor is improved.

[0043] The sensor parameter calibration method of the wind farm group in the embodiment of the present application can be related to a sensor, a wind farm group controller and a wind turbine generator set controller. Figure 1 An embodiment of a structure diagram of a sensor parameter calibration system of a wind farm group provided by the present application is shown in the figure. Figure 1 As shown in the figure, the sensor parameter calibration system of the wind farm group can include a sensor 11, a wind farm group controller 12 and a wind turbine generator set controller 13.

[0044] The sensor 11 can be arranged at the blade root of a blade of a wind turbine generator set in a wind farm. In some examples, the sensor 11 can include a blade root optical fiber load sensor. The wind farm can include a plurality of wind turbine generator sets. The sensor 11 can be arranged at each wind turbine generator set in the wind farm, or only at part of the wind turbine generator sets in the wind farm, which is not limited herein. One sensor 11 can be arranged at the blade root of one blade, or a plurality of sensors 11 can be arranged at the blade root of one blade, which is not limited herein. The sensor 11 can be used to collect sensing data and transmit the sensing data to the wind farm group controller 12. The sensing data can be related to the blade root load, for example, the sensor 11 can collect data such as the load wavelength at the blade root of the blade and the blade azimuth angle, which is not limited herein. The data collected by the sensor 11 can represent the blade root load and can be used together with other data of the wind turbine generator set to calculate other parameters of the wind turbine generator set.

[0045] The wind farm group controller 12 can be used to monitor the wind farm. The wind farm group controller 12 can be in communication connection with the sensor 11 to acquire the data collected by the sensor 11. The wind farm group controller 12 can be in communication connection with the wind turbine generator set controller 13 of the wind turbine generator set to acquire information from the wind turbine generator set controller 13 and send instructions, information and the like to the wind turbine generator set controller 13. The wind farm group controller 12 can execute the sensor parameter calibration method of the wind farm group in the embodiment of the present application to automatically calibrate the parameter of the sensor and push the value of the calibration parameter to the wind turbine generator set, specifically to the wind turbine generator set controller 13.

[0046] The wind turbine controller 13 is in communication connection with the wind farm group controller 12, and can provide information to the wind farm group controller 12, and can also receive instructions, information, etc. from the wind farm group controller 12. Specifically, the wind turbine controller 13 can transmit operation data of the wind turbine, unit component state data, etc. to the wind farm group controller 12, which is not limited herein. The wind turbine controller 13 can be configured to receive a calibration message of the sensor 11 pushed by the wind turbine controller 13, the calibration message including a value of a calibration parameter, and update an original value of the calibration parameter of the sensor 11 with the value of the calibration parameter in the calibration message, so as to ensure the accuracy of the data collected by the sensor 11.

[0047] In some embodiments, the wind farm group controller 12 can include a wind farm edge controller, i.e. the wind farm edge controller is used to perform the sensor parameter calibration method of the wind farm group in the embodiments of the present application, so as to calibrate the parameters of the sensors of part of the wind turbines in the wind farm. The wind farm edge controller set by the wind farm is used to implement the sensor parameter calibration method of the wind farm group in the embodiments of the present application, which can save the configuration cost, deployment cost, etc. on the one hand, and can also quickly push the value of the calibration parameter to the corresponding wind turbine, so as to speed up the speed of the value of the calibration parameter.

[0048] The sensor parameter calibration method of the wind farm group in the present application will be described below. The present application provides a sensor parameter calibration method of a wind farm group, which can be applied to a wind farm, and can be executed by a sensor parameter calibration device of a wind farm group or a wind farm group controller, which is not limited herein. Figure 2 An embodiment of the sensor parameter calibration method of the wind farm group provided in the present application is shown in a flowchart. As shown in the figure, the sensor parameter calibration method of the wind farm group can include steps S201 to S204. Figure 2

[0049] In step S201, unit data of a plurality of target wind turbines in the wind farm is obtained.

[0050] The target wind turbine is a wind turbine provided with a sensor at the root of the blade. The unit data includes operation data, sensor data and unit component state data.

[0051] The operation data can be collected by the wind turbine controller from the wind turbine, and then transmitted to the sensor parameter calibration device of the wind farm group or the wind farm group controller. The operation data can include data related to the operation state of the wind turbine. In some examples, the operation data can include a pitch angle. The operation data can also include one or more of the following, but is not limited to: yaw action, yaw-to-wind difference, rotational speed, unit operation state, wind speed, ambient temperature. ​

[0052] The sensor data is sensed by sensors disposed at the blade root of the blades of the target wind turbine generator set, i.e., the sensor data is acquired from the sensors. In some examples, the sensor data can include, but is not limited to, blade load wavelength and rotor azimuth angle. The rotor azimuth angle is used to represent the blade position of the rotor, and its value range can be 0° to 360°. For example, the rotor has three blades, the rotor azimuth angle when the first blade is vertically downward can be set as 0°, the rotor azimuth angle when the second blade is vertically downward can be set as 120°, and the rotor azimuth angle when the third blade is vertically downward can be set as 240°.

[0053] The turbine component state data can be collected by the wind turbine generator set controller from the wind turbine generator set, and then transmitted to the sensor parameter calibration device of the wind farm cluster or the wind farm cluster controller. The turbine component state data can include data representing the state of the components of the wind turbine generator set. In some examples, the turbine component state data can include, but is not limited to, main shaft inclination angle, rotor plane cone angle, and blade azimuth angle.

[0054] The turbine data can be acquired periodically. In some examples, in order to save resources occupied by parameter calibration, a first time interval can be set, and parameter calibration can be triggered once every first time interval. If the parameter calibration is successful, parameter calibration does not need to be performed continuously within the first time interval. After each triggering of parameter calibration, turbine data within a second time interval can be acquired every second time interval, and the turbine data within the second time interval can be processed. The first time interval is longer than the second time interval, and the first time interval can be set according to the requirement of parameter calibration of the sensor, for example, parameter calibration of the sensor can be performed once a month to ensure that the sensor can obtain data meeting the accuracy requirement in the month, and then the first time interval can be set as one month, and the first time interval is not limited herein. The second time interval can also be set according to the scene, requirement, experience, etc., and the second time interval can be set to meet the requirement of the number of data in the parameter calibration process and the requirement of saving the computing resources of the sensor parameter calibration device of the wind farm cluster or the wind farm cluster controller, for example, the second time interval can be set as 10 minutes.

[0055] In some examples, the number of groups of turbine data can be counted, and one group of turbine data can include operating data, sensor data, and turbine component state data collected at the same time. When the number of groups of turbine data meets the requirement of parameter calibration calculation, step S202 is performed.

[0056] In step S202, the calibration parameter value of the sensor in the case that the absolute value of the difference between the theoretical strain and the measured strain is minimum is calculated according to the operating data, the sensor data, and the turbine component state data.

[0057] The calibration parameters are used for calculation of the sensing data and the measured strain and calculation of the unit component state data and the theoretical strain. In some examples, the calibration parameters can include, but are not limited to, a blade load initial wavelength, a sensor strain coefficient, a sensor installation position, a blade stiffness coefficient, etc. The blade load initial wavelength is a wavelength measured when no deformation occurs after the sensor is installed at the blade root. The sensor strain coefficient is a coefficient for converting the wavelength measured by the sensor to strain. The sensor installation position is an installation position of the sensor at the blade root. The blade stiffness coefficient is a stiffness coefficient of the blade. The calibration parameters affect the accuracy of the data collected by the sensor. In an ideal state, the measured strain should be substantially the same as the theoretical strain, but due to various factors, there is a certain gap between the theoretical strain and the measured strain. The measured strain is a strain of the wind turbine generator set calculated according to the actually measured data, which can be calculated according to the sensing data and the calibration parameters. The theoretical strain can be calculated according to the actual situation of the unit component and the calibration parameters, and specifically can be calculated according to the unit component state data and the calibration parameters.

[0058] The value of the calibration parameter that makes the absolute value of the difference between the theoretical strain and the measured strain minimum is the value of the calibration parameter that can ensure the accuracy of the data collected by the sensor.

[0059] In some examples, the sensing data includes a blade azimuth angle. The blade azimuth angle can be divided into zones, and the number of groups of unit data in each azimuth angle zone is counted, so that the value of the calibration parameter is calculated in the case that the number of groups of unit data in each azimuth angle zone meets the condition, to avoid the adverse effects of incomplete data on the value of the calibration parameter.

[0060] Specifically, the unit data can be divided according to preset azimuth angle partitions. Each set of unit data in the same azimuth angle partition is divided into the same azimuth angle partition according to the blade azimuth angle. One set of unit data includes operation data, sensor data and unit component state data collected at the same time. The azimuth angle partitions can be preset, for example, the partitions can be 30° each. According to the blade azimuth angle in one set of unit data, the set of unit data can be divided into the azimuth angle partition where the blade azimuth angle is located. The number of sets of unit data in each azimuth angle partition can be counted in real time. When one set of unit data is divided into an azimuth angle partition, the number of sets of unit data in the azimuth angle partition is increased by one. When the number of sets of unit data reaches a triggering threshold, the value of the sensor calibration parameter is triggered to be calculated under the condition that the absolute value of the difference between the theoretical strain and the measured strain is the smallest according to the operation data, the sensor data and the unit component state data. The triggering threshold is a critical value for whether the number of sets of unit data meets the required number for calculation, which can be set according to the scene, demand, experience, etc., which is not limited herein. When the number of sets of unit data in an azimuth angle partition reaches the triggering threshold, it means that the number of sets of unit data in the azimuth angle partition has met the required number for calculation. In the embodiments of the present application, the value of the calibration parameter is triggered to be calculated only when the number of sets of unit data in each azimuth angle partition reaches the triggering threshold.

[0061] In step S203, the error rate between the theoretical load of the blade of the target wind turbine generator unit and the idling calibration load is calculated based on the idling unit data of the target wind turbine generator unit, the value of the calibration parameter and the unit data.

[0062] After obtaining the value of the calibration parameter, the value of the calibration parameter needs to be verified to determine whether the obtained value of the calibration parameter is valid. The idling unit data is the unit data of the wind turbine generator unit in the idling state. The idling calibration load is the load of the wind turbine generator unit in the idling state. Under normal circumstances, the error rate between the theoretical load of the blade and the idling calibration load should be very small. Therefore, the error rate between the theoretical load of the blade and the idling calibration load can be used for verification to determine whether the obtained value of the calibration parameter is valid.

[0063] In step S204, when the error rate is less than or equal to the acceptable error rate threshold, the calibration message corresponding to the target wind turbine generator unit is pushed to the target wind turbine generator unit to update the original value of the calibration parameter of the target wind turbine generator unit.

[0064] The acceptable error rate threshold is a critical value for determining whether the obtained value of the calibration parameter is valid. If the error rate is less than or equal to the acceptable error rate threshold, it indicates that the obtained value of the calibration parameter is valid. If the error rate is greater than the acceptable error rate threshold, it indicates that the obtained value of the calibration parameter is invalid, and the parameter calibration needs to be performed again. The acceptable error rate threshold can be set according to the scene, demand, experience, etc., and is not limited herein. For example, the acceptable error rate threshold can be set to 5%. In a case where the error rates of the theoretical load of each blade of the target wind turbine generator and the idling calibration load are all less than or equal to the acceptable error rate threshold, the calibration message corresponding to the target wind turbine generator is pushed to the target wind turbine generator.

[0065] In a case where the obtained value of the calibration parameter is valid, that is, the verification is successful, the calibration message including the value of the calibration parameter is pushed to the target wind turbine generator corresponding to the value of the calibration parameter. After receiving the calibration message, the target wind turbine generator updates the original value of the calibration parameter with the value of the calibration parameter in the calibration message, so as to realize the parameter calibration of the sensor.

[0066] In some examples, in a plurality of target wind turbine generators, if the values of the calibration parameters obtained according to the unit data of part of the target wind turbine generators are verified successfully, the values of the calibration parameters are first pushed to the corresponding target wind turbine generators, and it is not required that all the values of the calibration parameters are verified successfully and then pushed to all the target wind turbine generators.

[0067] In some examples, the calibration message pushed to the target wind turbine generator and the time when the calibration message is pushed can also be recorded in a log, so as to facilitate a user to view the pushing situation of the value of the calibration parameter.

[0068] In the embodiments of the present application, the values of the calibration parameters of the sensors that minimize the absolute values of the differences between the theoretical strain and the measured strain can be calculated according to the unit data of a plurality of target wind turbine generators in a wind farm. Based on the obtained values of the calibration parameters, the idling unit data and the unit data, the error rates of the theoretical load of the blade and the idling calibration load can be obtained, and whether the obtained values of the calibration parameters are valid is verified through the error rates. In a case where the error rate is less than or equal to the acceptable error rate threshold, the value of the calibration parameter is automatically pushed to the corresponding wind turbine generator. The process can automatically calculate, verify and push the values of the calibration parameters, without human intervention, thereby improving the parameter calibration efficiency of the sensors of a large number of wind turbine generators in a wind farm.

[0069] In some embodiments, the idling unit data can be obtained before the error rate is calculated. Figure 3 The flowchart of another embodiment of the sensor parameter calibration method of the wind farm group provided in the present application. Figure 3 Different from Figure 2 , the difference lies in thatFigure 3 The sensor parameter calibration method of the wind farm cluster shown can further include steps S205 to S208. Figure 3 The specific content of the same steps in the embodiments is described above, and will not be repeated here. Figure 2 The specific content of the same steps in the embodiments is described above, and will not be repeated here.

[0070] In step S205, the target wind turbine is controlled to operate at a limited speed under the condition that the target wind turbine is in a small wind condition and in an equal wind state.

[0071] The small wind condition is a condition in which the wind condition is small and stable, and is less affected by aerodynamic factors. The equal wind state is a state in which the wind turbine is not connected to the grid and not generating electricity. Whether the target wind turbine is in the small wind condition and the equal wind state can be determined by wind speed, wind direction, operating state of the wind turbine, etc. For example, a condition in which the wind speed is less than 5 m / s (meters per second) and the wind direction changes little can be considered as a small wind condition, and a state in which the wind turbine is not connected to the grid and not generating electricity can be determined as an equal wind state. In the small wind condition and the equal wind state, the wind turbine is less affected by the wind, facilitating simulation of the idling state of the target wind turbine. In the small wind condition and the equal wind state, the target wind turbine is controlled to operate at a limited speed, which is lower than the normal operating speed, which can be set according to the scene, demand, experience, etc., and is not limited herein. For example, the limited speed can be 2-3 rpm (revolutions per minute). The idling state of the target wind turbine is simulated by low-speed operation.

[0072] In step S206, the unit data of the target wind turbine is collected multiple times as idling unit data under the condition that the target wind turbine operates at a limited speed.

[0073] The unit data collected multiple times under the condition that the target wind turbine operates at a limited speed, i.e., under the condition that the simulated target wind turbine is in an idling state, can be idling unit data.

[0074] In step S207, the idling unit data satisfying the effective screening condition is obtained by screening according to the preset effective screening condition.

[0075] The idling unit data obtained in step S206 can also include part of the unit data that does not satisfy the idling state. In order to further obtain more accurate idling unit data, the first effective screening condition can be used to screen the idling unit data. The first effective screening condition is used to simulate the idling state of the target wind turbine. The first effective screening condition can include screening conditions for pitch angle, yaw action, yaw wind deviation, and blade direction angle, which can be set according to the scene, demand, experience, etc., and is not limited herein. For example, the first effective screening condition can be as shown in Table 1:

[0076] Table 1

[0077] Screening condition Pitch angle Between 49.5° and 50.5° Yaw action presence No yaw action Yaw to wind deviation Less than 20° Blade azimuth angle Around 30°, 90°, 150°, 210°, 270°, 330°

[0078] The idle unit data satisfying the first effective screening condition is taken as the idle unit data finally used for error rate calculation.

[0079] In step S208, if the collected idle unit data is sufficient for calculating the idle calibration load, the collection of idle unit data is stopped, and the target wind turbine is controlled to resume normal operation.

[0080] In the simulated idle state, the idle unit data of the target wind turbine does not change greatly, and thus a large amount of idle unit data does not need to be collected. If the collected idle unit data is sufficient for calculating the idle calibration load, the collection of idle unit data can be stopped, the normal operation of the target wind turbine is resumed, and the power generation loss of the target wind turbine is further reduced.

[0081] Whether the collected idle unit data is sufficient for calculating the idle calibration load can be determined according to the number of collected idle unit data, the length of time for collecting idle unit data, the state of operation of the target wind turbine, and the like.

[0082] In some examples, if the number of collected idle unit data reaches a certain predetermined number threshold, it can be determined that the collected idle unit data is sufficient for calculating the idle calibration load.

[0083] In other examples, if the length of time for collecting idle unit data reaches a certain predetermined length threshold, it can be determined that the collected idle unit data is sufficient for calculating the idle calibration load.

[0084] In yet other examples, if the number of revolutions of the impeller reaches a preset revolution threshold when the target wind turbine is operated at a limited speed, it can be determined that the collected idle unit data is sufficient for calculating the idle calibration load. The preset revolution threshold can be set according to scenarios, requirements, experience, and the like, and is not limited herein. For example, the preset revolution threshold can be 5 revolutions or 6 revolutions. In a small wind condition and an equal wind condition, the rotational speed of the impeller of the target wind turbine is small, and it takes a long time to run for the impeller to rotate 5 to 6 revolutions, which can ensure that the idle unit data collected in this process is sufficient for calculating the idle calibration load. Moreover, the determination of whether the collected idle unit data is sufficient for calculating the idle calibration load according to the number of revolutions of the impeller can ensure that the target wind turbine has sufficient idle unit data in the vicinity of each impeller azimuth angle in the idle state, thereby ensuring the comprehensiveness of the idle unit data.

[0085] When the idle unit data collected is sufficient to calculate the idle calibration load, the idle unit data collection is stopped, and the target wind turbine is controlled to resume normal operation, so that unnecessary power generation loss of the target wind turbine can be avoided.

[0086] In the embodiments of the present application, the idle state of the target wind turbine can be simulated through monitoring the working condition and state of the target wind turbine and controlling the rotating speed of the target wind turbine, so as to collect the idle unit data. In this process, the target wind turbine does not need to be stopped, the parameters do not need to be modified, and the target wind turbine does not need to be restarted, and no manual intervention is required. The idle unit data is collected without stopping the target wind turbine, which reduces the time required for collecting the idle unit data, reduces the time cost and labor cost, and reduces the loss of power generation of the target wind turbine.

[0087] In some embodiments, after the value of the calibration parameter is obtained, the value of the calibration parameter can be verified according to the multiple sets of unit data, the multiple sets of idle unit data, and the obtained value of the calibration parameter, so as to improve the accuracy of the verification through the multiple sets of data. Figure 4 The flowchart of another embodiment of the sensor parameter calibration method of the wind farm group provided in the present application. Figure 4 Different from Figure 2 The step S203 in Figure 2 The step S203 in Figure 4 The steps S2031 to S2035 in Figure 4 The sensor parameter calibration method of the wind farm group shown in Figure 2 The step S204 in Figure 4 The step S2041 in Figure 4 The specific content of the same step in Figure 2 The specific content of the same step in

[0088] In step S2031, the multiple theoretical loads of the blades of the target wind turbine are calculated according to the multiple sets of unit data of the target wind turbine and the value of the calibration parameter.

[0089] In the process of calibrating the parameters of the sensor each time, the unit data of the target wind turbine is collected multiple times to ensure the accuracy of the unit data. According to the unit data of the target wind turbine collected once and the value of the calibration parameter calculated, a theoretical load can be calculated. The theoretical load is the theoretical gravity moment load. For example, the theoretical load can be calculated according to the following formula (1):

[0090]

[0091] wherein, M is the theoretical load; M G,x is the moment of gravity in the flapwise direction; M G,y is the moment of gravity in the edgewise direction; F G is the moment of gravity in the fixed coordinate system, specifically, the moment of gravity of the impeller mass multiplied by the length of the force arm from the center of gravity position to the sensor installation position; δ is the main shaft inclination angle, specifically, the main shaft inclination angle is positive when the main shaft is inclined upward, and the main shaft inclination angle is negative when the main shaft is inclined downward; θ is the impeller plane cone angle, specifically, the impeller plane cone angle is negative when the impeller plane is gathered toward the windward direction, and the impeller plane cone angle is positive when the impeller plane is gathered toward the leeward direction; Ω is the impeller azimuth angle; and φ is the pitch angle.

[0092] In step S2032, a plurality of idling calibration loads of the blades of the plurality of target wind turbine generators are calculated according to the plurality of times of collected idling turbine data of the target wind turbine generators and the values of the calibration parameters.

[0093] In the process of collecting the idling turbine data, the idling turbine data of the target wind turbine generators are collected multiple times to ensure the accuracy of the idling turbine data. According to the idling turbine data of the target wind turbine generators collected once and the values of the calculated calibration parameters, an idling calibration load can be calculated. The calculation method of the idling calibration load is the same as the calculation method of the theoretical load, except that the turbine data is used to calculate the theoretical load, and the idling turbine data is used to calculate the idling calibration load. The idling calibration load can also be calculated by using the above formula (1).

[0094] In step S2033, a first error rate of the blade is calculated by using the maximum value of the plurality of theoretical loads of the blade of the target wind turbine generator and the maximum value of the plurality of idling calibration loads of the target wind turbine generators.

[0095] The difference between the maximum value of the idling calibration load and the maximum value of the theoretical load can be calculated first, and the ratio of the difference to the maximum value of the theoretical load is taken as the first error rate. That is, the first error rate can be calculated according to the following formula (2):

[0096] e u = [(M m,max -M t,max ) / M t,max ]×100% (2)

[0097] wherein, e u is the first error rate, M m,max is the maximum value of the idling calibration load, and M t,max is the maximum value of the theoretical load.

[0098] In step S2034, the second error rate of the blade is calculated using the minimum value of the plurality of theoretical loadings of the blade of the target wind turbine generator set and the minimum value of the plurality of idling calibration loadings of the target wind turbine generator set.

[0099] The difference between the minimum value of the idling calibration loadings and the minimum value of the theoretical loadings can be calculated first, and the ratio of the difference to the minimum value of the theoretical loadings can be taken as the second error rate. That is, the second error rate can be calculated according to the following formula (3):

[0100] e d =[(M m,min -M t,min ) / M t,min ]×100% (3)

[0101] wherein e d is the first error rate, M m,min is the minimum value of the idling calibration loadings, and M t,min is the minimum value of the theoretical loadings.

[0102] In step S2035, the item with the maximum value between the first error rate and the second error rate of the blade is determined as the error rate.

[0103] Using the maximum value between the first error rate and the second error rate as the error rate to check the value of the calibration parameter can further ensure the accuracy of the value of the calibration parameter.

[0104] In some examples, if the error rate is less than or equal to an acceptable error rate threshold, the calibration message corresponding to the target wind turbine generator set can be pushed to the target wind turbine generator set.

[0105] In other examples, in addition to using the error rate of the theoretical loadings and the idling calibration loadings for checking, the value of the calibration parameter itself can also be used for checking. Details can be seen in steps S209 and S210.

[0106] In step S209, a plurality of sets of values of the calibration parameters are calculated repeatedly according to the same operating data, sensing data and unit component state data.

[0107] Due to hardware or software problems, the calculated values of the calibration parameters can change during the process of calculating the values of the calibration parameters. In order to ensure the accuracy of the calculation of the values of the calibration parameters, the step S202 can be repeated multiple times to calculate multiple sets of values of the calibration parameters for the same operating data, sensing data and component state data of the wind turbine. One set of values of the calibration parameters corresponds to one calculation, and one set of values of the calibration parameters can include values of at least one type of calibration parameters. In the case where the wind turbine is provided with multiple sensors, the values of each type of calibration parameters in each set of values of the calibration parameters can include values of multiple calibration parameters, and the values of one calibration parameter in each type of calibration parameters correspond to one sensor.

[0108] For example, according to the same operating data, sensing data and component state data, the calculation is repeated 10 times to obtain 10 sets of values of the calibration parameters. The wind turbine is provided with 12 sensors. The calibration parameters include four types of parameters, i.e., blade load initial wavelength, sensor strain coefficient, sensor installation position and blade stiffness coefficient. Correspondingly, each set of values of the calibration parameters includes 12 values of blade load initial wavelength, 12 values of sensor strain coefficient, 12 values of sensor installation position and 12 values of blade stiffness coefficient.

[0109] In some examples, in order to ensure the accuracy of the subsequent calculation process, after obtaining multiple sets of values of the calibration parameters, one or more sets of values of the calibration parameters with abnormal values can be removed. The abnormal value is a value that changes abruptly, such as a value that is too different from other values of the calibration parameters. If there is an abnormal value in one set of values of the calibration parameters, the set of values of the calibration parameters is discarded to avoid the influence of the abnormal value on the verification of the values of the calibration parameters themselves.

[0110] In step S210, a variation coefficient of each set of values of the calibration parameters is calculated according to the multiple sets of values of the calibration parameters.

[0111] The variation coefficient represents the degree of variation between the values of one set of calibration parameters. For example, if each set of values of the calibration parameters includes 12 values of blade load initial wavelength, 12 values of sensor strain coefficient, 12 values of sensor installation position and 12 values of blade stiffness coefficient, the variation coefficient of the values of the blade load initial wavelength can be obtained according to the 12 values of the blade load initial wavelength; the variation coefficient of the values of the sensor strain coefficient can be obtained according to the 12 values of the blade sensor strain coefficient; the variation coefficient of the values of the sensor installation position can be obtained according to the 12 values of the sensor installation position; and the variation coefficient of the blade stiffness coefficient can be obtained according to the 12 values of the blade stiffness coefficient.

[0112] The coefficient of variation can be a ratio of a standard deviation of the values of the plurality of calibration parameters to an average of the values of the plurality of calibration parameters. The coefficient of variation can also be a difference between a maximum value and a minimum value of the values of the plurality of calibration parameters. The form of the coefficient of variation is not limited herein and can be set according to the specific calibration parameter. For example, the calibration parameters include the blade load initial wavelength, the sensor strain coefficient, the sensor installation position, and the blade stiffness coefficient. The coefficient of variation of the values of the blade load initial wavelength is a span of the values of the blade load initial wavelength, i.e., a difference between a maximum value and a minimum value of the values of the blade load initial wavelength. The coefficient of variation of the values of the sensor strain coefficient can be a ratio of a standard deviation of the values of the sensor strain coefficient to an average of the values of the plurality of sensor strain coefficients. The coefficient of variation of the values of the sensor installation position can be a ratio of a standard deviation of the values of the sensor installation position to an average of the values of the plurality of sensor installation positions. The coefficient of variation of the values of the blade stiffness coefficient can be a ratio of a standard deviation of the values of the blade stiffness coefficient to an average of the values of the plurality of blade stiffness coefficients.

[0113] In step S2041, in a case where the error rate is less than or equal to the acceptable error rate threshold and the coefficients of variation of the values of the plurality of calibration parameters do not exceed the normal variation range, the target wind turbine corresponding to the coefficient of variation that does not exceed the normal variation range is pushed the calibration message corresponding to the target wind turbine.

[0114] The normal variation range is a normal range of the coefficients of variation of the values of the calibration parameters and can be set according to the calibration parameters, the scene, the requirement, experience, etc. and is not limited herein. The normal variation ranges corresponding to the coefficients of variation of the values of different types of calibration parameters can be different and are not limited herein. For example, the normal variation ranges corresponding to the coefficients of variation of the values of the sensor strain coefficient, the sensor installation position, and the blade stiffness coefficient can be greater than 5%, and the normal variation range corresponding to the coefficient of variation of the values of the blade load initial wavelength can be greater than 0.01 nm (nanometer).

[0115] In a case where the coefficients of variation of the values of the plurality of calibration parameters in the plurality of sets of calibration parameter values do not exceed the normal variation range, it can be considered that the verification using the values of the calibration parameters itself is successful, and the range of the majority is not limited herein and can be adjusted according to the requirement. For example, in a case where the coefficients of variation of the values of at least four-fifths of the calibration parameters in the plurality of sets of calibration parameter values do not exceed the normal variation range, it is considered that the verification using the values of the calibration parameters itself is successful. For another example, if there are N sets of calibration parameter values in total, in a case where the coefficients of variation of the values of at least N-1 sets of calibration parameters do not exceed the normal variation range, it is considered that the verification using the values of the calibration parameters itself is successful.

[0116] In a case where the check using the error rate of the theoretical load and the idling calibration load is successful and the check using the value of the calibration parameter itself is successful, the calibration message including the value of the calibration parameter is pushed to the corresponding wind turbine generator set. The execution order of the check using the error rate of the theoretical load and the idling calibration load and the check using the value of the calibration parameter itself is not limited herein. The value of the calibration parameter pushed by the embodiments of the present application is further improved through the two checks, so that the value of the calibration parameter transmitted to the target wind turbine generator set is more accurate and reliable, to further ensure the accuracy of the sensor collected data.

[0117] In some embodiments, whether to push the calibration message to the target wind turbine generator set can be determined according to factors affecting the reception of the calibration message by the target wind turbine generator set, to improve the success rate of the pushing of the calibration message. Figure 5 The flowchart of still another embodiment of the sensor parameter calibration method of the wind farm group provided by the present application. Figure 5 Different from Figure 2 , the step S204 in Figure 2 may be specifically refined as Figure 5 the step S2042 and the step S2043 in Figure 5 , the sensor parameter calibration method of the wind farm group shown in Figure 5 may further include the step S211 and the step S212. Figure 2 The specific content of the same steps in and

[0118] may refer to the related description in the above embodiments, which will not be repeated here.

[0119] The pushing influencing factor can be acquired periodically, and the period or frequency of acquiring the pushing influencing factor can be set according to scenarios, requirements, experience, etc., which are not limited herein. For example, the acquisition frequency can be 1 Hz (Hertz).

[0120] The pushing influencing factor includes factors affecting the reception of the calibration message by the target wind turbine generator set. In some examples, the pushing influencing factor includes one or more of the following: a unit calibration parameter state, a calibration parameter reception enabling state, a unit operation permission state, and a sensor operation state.

[0121] The unit calibration parameter state can be used to represent the state of the value of the calibration parameter in the target wind turbine generator set. The calibration parameter pushing function state is used to represent whether the target wind turbine generator set opens the function of receiving the value of the calibration parameter. The unit operation permission state is used to represent whether the working condition of the target wind turbine generator set allows the reception of the value of the calibration parameter. The sensor operation state is used to represent the working state of the sensor.

[0122] In some examples, the push influencing factor can be represented by a byte. For example, the unit calibration parameter state can be represented by a unit calibration parameter state word. When the unit calibration parameter state word is 0, it indicates that the target wind turbine has not received the value of the calibration parameter, and the value of the calibration parameter needs to be applied to the target wind turbine. When the unit calibration parameter state word is 1, it indicates that the value of the calibration parameter has been successfully applied to the target wind turbine, i.e., the value of the calibration parameter in the target wind turbine has been successfully applied, and the value of the calibration parameter does not need to be pushed. When the unit calibration parameter state word is 2, it indicates that the value of the calibration parameter is pushed incorrectly, i.e., the value of the calibration parameter in the target wind turbine is incorrect, and needs to be pushed again. For example, the calibration parameter receiving enable state can be represented by a calibration parameter receiving enable state word. When the calibration parameter receiving enable state word is 0, it indicates that the function of receiving the value of the calibration parameter in the target wind turbine is not turned on, and the value of the calibration parameter cannot be pushed to the target wind turbine. When the calibration parameter receiving enable state word is 1, it indicates that the function of receiving the value of the calibration parameter in the target wind turbine is turned on, and the value of the calibration parameter can be pushed to the target wind turbine. For example, the unit operation permission state can be represented by a unit operation permission state word. When the unit operation permission state word is 0, it indicates that the working condition of the target wind turbine does not allow the value of the calibration parameter to be received, and the value of the calibration parameter cannot be pushed to the target wind turbine. When the unit operation permission state word is 1, it indicates that the working condition of the target wind turbine is a small wind condition, and the value of the calibration parameter can be received, and the value of the calibration parameter can be pushed to the target wind turbine. For example, the sensor operation state can be represented by a sensor operation state word. When the sensor operation state word is 0, it indicates that the working state of the sensor is normal. When the sensor operation state word is 1, it indicates that the working state of the sensor is abnormal.

[0123] In step S2043, when the push influencing factor meets the preset push judgment condition, the calibration message corresponding to the target wind turbine is pushed to the target wind turbine.

[0124] The push judgment condition is used to determine whether it is appropriate to push the calibration message to the target wind turbine, which can be set according to the scene, demand, experience, etc., and is not limited herein. In some examples, the push judgment condition includes one or more of the following:

[0125] The unit calibration parameter state indicates that the target wind turbine has not received the value of the calibration parameter;

[0126] The calibration parameter push function state indicates that the target wind turbine turns on the function of receiving the value of the calibration parameter;

[0127] The unit operation permission state indicates that the working condition of the target wind turbine is a small wind condition that allows the value of the calibration parameter to be received;

[0128] The sensor operation state indicates that the working state of the sensor is normal.

[0129] By limiting the push judgment condition as described above, the success rate and safety of pushing the calibration message to the target wind turbine generator set can be improved, and the influence of pushing the calibration message to the target wind turbine generator set on the operation of the target wind turbine generator set can be reduced.

[0130] In some examples, the calibration message can further include a first check code. The first check code of the target wind turbine generator set is obtained according to a check code conversion algorithm and the value of the calibration parameter corresponding to the target wind turbine generator set before pushing. The first check code of the target wind turbine generator set is used to compare with a second check code calculated by the target wind turbine generator set according to the check code conversion algorithm and the value of the received calibration parameter.

[0131] The sensor parameter calibration device of the wind farm group or the wind farm group controller can obtain the first check code by using the check code conversion algorithm according to the obtained value of the calibration parameter after calculating the value of the calibration parameter. The check code conversion algorithm is previously agreed between the target wind turbine generator set and the sensor parameter calibration device of the wind farm group or the wind farm group controller. The target wind turbine generator set obtains the first check code and the value of the calibration parameter from the calibration message after receiving the calibration message. The target wind turbine generator set obtains the second check code by using the check code conversion algorithm according to the obtained value of the calibration parameter. The target wind turbine generator set can compare the first check code and the second check code. In the case that the difference between the first check code and the second check code exceeds the normal error range, the unit calibration parameter state is updated to represent that the target wind turbine generator set abnormally receives the value of the calibration parameter. The difference between the first check code and the second check code exceeding the normal error range indicates that the calibration message is adversely affected in the transmission process, the value of the calibration parameter in the calibration message is abnormal, and the target wind turbine generator set can update the unit calibration parameter state to inform the sensor parameter calibration device of the wind farm group or the wind farm group controller through the unit calibration parameter state in the subsequent process that the target wind turbine generator set abnormally receives the value of the calibration parameter. In the case that the difference between the first check code and the second check code is within the normal error range, the unit calibration parameter state is updated to represent that the target wind turbine generator set normally receives the value of the calibration parameter. The difference between the first check code and the second check code within the normal error range indicates that the calibration message is transmitted normally, the value of the calibration parameter in the calibration message is also normal, and the target wind turbine generator set can update the unit calibration parameter state to inform the sensor parameter calibration device of the wind farm group or the wind farm group controller through the unit calibration parameter state in the subsequent process that the target wind turbine generator set successfully applies the value of the calibration parameter.

[0132] The check code conversion algorithm is not limited herein, and the check code conversion algorithm agreed in advance by the target wind turbine and the sensor parameter calibration device of the wind farm group or the wind farm group controller is the same check code conversion algorithm. The normal error range can be set according to the check code conversion algorithm, scene, requirement, experience, etc., and is not limited herein.

[0133] For example, assuming that the target wind turbine is provided with 12 sensors, the check code conversion algorithm can be shown in the following formula (4):

[0134] P c = (P a1 - 1500) × 100000 × 1.05 + (P a2 - 1500) × 100000 × 1.10 + (P a12 - 1500) × 100000 × 1.60 + … + P b1 × 1.65 + P b2 × 1.70 + … + P b12 × 2.2 + P d1 × 2.25 + … + P d12 × 2.8 + P t × 2.85 (4)

[0135] Wherein, P c is the check code, which can be the first check code or the second check code; P ai , P bi , P di and P t are values of the calibration parameters; 1500, 100000, 1.05, 1.10, …, 2.85 are constant coefficients. Correspondingly, the normal error range can be less than 10 -10 .

[0136] In step S211, the unit calibration parameter state of the target wind turbine is reacquired.

[0137] After the calibration message is pushed to the target wind turbine, the unit calibration parameter state of the target wind turbine can be reacquired to determine whether the values of the calibration parameters are normal and successfully applied to the target wind turbine.

[0138] In step S212, in the case that the reacquired unit calibration parameter state indicates that the target wind turbine abnormally receives the values of the calibration parameters, the calibration message corresponding to the target wind turbine is pushed to the target wind turbine again until the reacquired unit calibration parameter state indicates that the target wind turbine normally receives the values of the calibration parameters.

[0139] In the case that the reacquired unit calibration parameter state representation target wind turbine receives a value of the calibration parameter is abnormal, the calibration message is repushed to enable the target wind turbine to acquire a normal applicable value of the calibration parameter.

[0140] In some examples, to further improve the accuracy of the value of the calibration parameter, data corresponding to a small wind condition can be screened from the unit data, and the value of the calibration parameter is calculated using the data corresponding to the small wind condition to avoid adverse effects of other factors on the value of the calibration parameter. Figure 6 A flowchart of another embodiment of the sensor parameter calibration method of the wind farm group provided in the present application. Figure 6 Different from Figure 2 The step S202 in Figure 2 may be specifically refined as steps S2021 to S2024 shown in Figure 6 . Figure 6 The specific content of the same steps in Figure 2 may be referred to the related description in the above examples, which will not be repeated here.

[0141] In step S2021, based on the operation data, the sensor data and the unit component state data corresponding to the operation data satisfying the second effective screening condition are determined.

[0142] The second effective screening condition is used to screen out unit data affected by error factors. The second effective screening condition can be set according to operation data, scenarios, requirements, experience, etc., which are not limited herein. In some examples, the operation data can include pitch angle, wind speed, impeller speed, yaw action, yaw-to-wind deviation, operation state of the wind turbine, etc. Correspondingly, the second effective screening condition can be as shown in Table Two below: Table Two

[0143] Screening condition Pitch angle Between 30° and 85° Wind speed Less than or equal to 5 m / s Blade rotation speed Less than 2 rpm Yaw action presence No yaw action Yaw to wind deviation Less than 20° Operating state No active pitch

[0144] The second effective screening condition can include the screening conditions of the operation data shown in Table Two above. The screening condition of the pitch angle can avoid the introduction of errors by aerodynamic factors at a smaller pitch angle. The wind speed can be the average wind speed in a period of time, such as the average wind speed in 3 seconds. The screening condition of the wind speed can ensure that the influence of aerodynamic factors on the theoretical load does not exceed a ratio threshold, such as 10%. The screening condition of the impeller speed can avoid the introduction of errors by aerodynamic factors at a high speed. The screening condition of the yaw action can avoid the introduction of aerodynamic loads on the blades by yaw action. The screening condition of the yaw-to-wind deviation can avoid the introduction of aerodynamic loads by a large yaw-to-wind deviation. The operation state of the wind turbine can ensure that a sufficient pitch angle range is introduced under the premise of not actively pitching.

[0145] The sensor data and the unit component state data of the operation data satisfying the second effective screening condition are data avoiding errors caused by aerodynamic factors, and are more accurate and reliable.

[0146] In step S2022, a first relationship for calculating the measured strain is obtained according to the sensor data corresponding to the operation data satisfying the second effective screening condition and a part of the calibration parameters with unknown values.

[0147] The measured strain can be calculated according to the sensor data and a part of the calibration parameters. In the first relationship, the values of the calibration parameters are unknown. For example, the first relationship can include the following formula (5):

[0148]

[0149] wherein, ∈ m is the measured strain; k ∈ is a sensor strain coefficient; λ B is a blade load wavelength, λ B,0 is a blade load initial wavelength. The values of k ∈ and λ B,0 are unknown.

[0150] In step S2023, a second relationship for calculating the theoretical strain is obtained according to the sensor data corresponding to the operation data satisfying the second effective screening condition, the unit component state data corresponding to the operation data satisfying the second effective screening condition, and another part of the calibration parameters with unknown values.

[0151] The theoretical strain can be calculated according to the sensor data, the unit component state data, and the calibration parameters. In the second relationship, the values of the calibration parameters are unknown. For example, the second relationship can include the following formula (6):

[0152]

[0153] wherein, ∈ s is the theoretical strain; K B (φ s ) is a blade stiffness coefficient; P s is a coordinate change matrix of a sensor installation position; is a theoretical load, which can be calculated according to the formula (1) in the above embodiment.

[0154] In the case that four sensors are arranged at the blade root of each blade, the theoretical strain and the coordinate change matrix of the sensor installation position can be obtained according to the following formulas (7) and (8):

[0155]

[0156]

[0157] Where, ∈ si φ represents the strain corresponding to the i-th sensor. s1 Let be the installation location of the i-th sensor.

[0158] In step S2024, based on the first relation and the second relation, the value of the calibration parameter that minimizes the absolute value of the difference between the first relation and the second relation is calculated.

[0159] The absolute value of the difference between the first and second relations can reflect the minimum strain loss. Alternatively, the value of the calibration parameter that minimizes the square of the difference between the first and second relations can be obtained by calculating the value of the calibration parameter that minimizes the square of the difference between the first and second relations. The value of the calibration parameter that minimizes the square of the difference between the first and second relations is the same as the value of the calibration parameter that minimizes the difference between the first and second relations.

[0160] The formula (9) for calculating the minimum value of the square of the difference between the first relation and the second relation is shown below:

[0161]

[0162] Wherein, min is the minimum value to be calculated, and the values ​​of other parameters can be found in the relevant explanations of the above formulas (1) to (8), which will not be repeated here.

[0163] By performing nonlinear optimization on the above formula (9), the value of the calibration parameter that minimizes the square of the difference between the first relation and the second relation is obtained. The obtained value of the calibration parameter is the value that makes the measured strain closest to the theoretical strain, which makes the sensor data acquisition more accurate.

[0164] This application also provides a sensor parameter calibration device for a wind farm cluster. This sensor parameter calibration device for a wind farm cluster can be implemented as a wind farm cluster controller or as a wind farm edge controller, and is not limited thereto. Figure 7 This is a schematic diagram of an embodiment of the sensor parameter calibration device for a wind farm cluster provided in this application. Figure 7 As shown, the sensor parameter calibration device 300 of the wind farm cluster may include a data acquisition module 301, a calibration module 302, a verification module 303, and a push module 304.

[0165] The data acquisition module 301 can be used to acquire unit data of multiple target wind turbine generators in a wind farm.

[0166] The unit data includes operational data, sensor data, and unit component status data. The sensor data is obtained by sensors installed at the blade roots of the target wind turbine.

[0167] The calibration module 302 can be configured to calculate the calibration parameters of the sensors in a case where the absolute value of the difference between the theoretical strain and the measured strain is minimum according to the operation data, the sensing data and the unit component state data.

[0168] The calibration parameters are used for the calculation of the sensing data and the measured strain and the calculation of the unit component state data and the theoretical strain.

[0169] The verification module 303 can be configured to calculate the error rate of the theoretical load of the blade of the target wind turbine generator set and the idling calibration load based on the idling unit data of the target wind turbine generator set, the calibration parameters and the unit data.

[0170] The pushing module 304 can be configured to push the calibration message corresponding to the target wind turbine generator set to the target wind turbine generator set to update the original calibration parameters of the target wind turbine generator set in a case where the error rate is less than or equal to the acceptable error rate threshold, and the calibration message includes the calibration parameters.

[0171] In the embodiments of the present application, the value of the calibration parameters of the sensors that minimize the absolute value of the difference between the theoretical strain and the measured strain can be calculated according to the unit data of the plurality of target wind turbine generator sets in the wind farm. Based on the obtained value of the calibration parameters, the idling unit data and the unit data, the error rate of the theoretical load of the blade and the idling calibration load can be obtained, and whether the obtained value of the calibration parameters is effective is verified through the error rate, and in a case where the error rate is less than or equal to the acceptable error rate threshold, the value of the calibration parameters is automatically pushed to the corresponding wind turbine generator set. The process can automatically calculate, verify and push the value of the calibration parameters, without the need for manual intervention, thereby improving the parameter calibration efficiency of the sensors of a large number of wind turbine generator sets in the wind farm.

[0172] Figure 8 Another embodiment of the sensor parameter calibration device of the wind farm group provided in the present application is shown in the structural schematic diagram. Figure 8 Different from Figure 7 The difference is that Figure 8 The sensor parameter calibration device 300 of the wind farm group shown in the figure further comprises a control module 305 and a screening module 306.

[0173] The control module 305 can be configured to control the target wind turbine generator set to operate at a limited speed in a case where the target wind turbine generator set is in a small wind working condition and in an equal wind state.

[0174] The limited speed is lower than the normal working speed.

[0175] The data acquisition module 301 can also be configured to acquire the unit data of the target wind turbine generator set as idling unit data for multiple times in a case where the target wind turbine generator set operates at a limited speed;

[0176] The control module 305 can also be configured to stop collecting the idling turbine data and control the target wind turbine to resume normal operation when the collected idling turbine data is sufficient to calculate the idling calibration load.

[0177] In some examples, the control module 305 can be configured to determine that the collected idling turbine data is sufficient to calculate the idling calibration load when the number of rotations of the impeller reaches a preset rotation threshold while the target wind turbine is operating at the limited rotation speed, stop collecting the idling turbine data, and control the target wind turbine to resume normal operation.

[0178] The screening module 306 can be configured to screen the idling turbine data to obtain idling turbine data satisfying a preset first effective screening condition.

[0179] The first effective screening condition is used to simulate the idling state of the target wind turbine.

[0180] In some embodiments, the verification module 303 can be configured to calculate a plurality of theoretical loads of the blade of the target wind turbine according to the values of the turbine data and the calibration parameters of the target wind turbine collected multiple times, calculate a plurality of idling calibration loads of the blade of the target wind turbine according to the values of the idling turbine data and the calibration parameters of the target wind turbine collected multiple times, calculate a first error rate of the blade using a maximum value of the plurality of theoretical loads of the blade of the target wind turbine and a maximum value of the plurality of idling calibration loads of the blade of the target wind turbine, calculate a second error rate of the blade using a minimum value of the plurality of theoretical loads of the blade of the target wind turbine and a minimum value of the plurality of idling calibration loads of the blade of the target wind turbine, and determine the error rate as the one with a larger value between the first error rate and the second error rate of the blade.

[0181] In some embodiments, the verification module 303 can also be configured to repeatedly calculate a plurality of sets of values of the calibration parameters multiple times according to the same operating data, sensing data, and turbine component state data, and calculate a variation coefficient of the values of each set of calibration parameters according to the plurality of sets of values of the calibration parameters, the variation coefficient representing a variation degree between the values of a set of calibration parameters.

[0182] The pushing module 304 can be configured to push the calibration message corresponding to the target wind turbine to the target wind turbine corresponding to the variation coefficient that does not exceed the normal variation range when the error rate is less than or equal to the acceptable error rate threshold and the variation coefficients of the plurality of sets of values of the calibration parameters do not exceed the normal variation range.

[0183] In some embodiments, the pushing module 304 can be configured to: acquire a pushing influencing factor, the pushing influencing factor comprising a factor influencing the target wind turbine generator group receiving the calibration message; and push the calibration message corresponding to the target wind turbine generator group to the target wind turbine generator group when the pushing influencing factor satisfies a preset pushing judgment condition.

[0184] In some examples, the pushing influencing factor comprises one or more of: a wind turbine generator group calibration parameter state, a calibration parameter receiving enabling state, a wind turbine generator group operation permission state, a sensor operation state, and a wind turbine generator group operation working condition.

[0185] The wind turbine generator group calibration parameter state is used to represent a state of a value of a calibration parameter in the target wind turbine generator group. The calibration parameter pushing function state is used to represent whether the target wind turbine generator group opens a function of receiving the value of the calibration parameter. The wind turbine generator group operation permission state is used to represent whether a working condition of the target wind turbine generator group allows the value of the calibration parameter to be received. The sensor operation state is used to represent a working state of the sensor.

[0186] In some examples, the pushing judgment condition comprises one or more of:

[0187] The wind turbine generator group calibration parameter state represents that the target wind turbine generator group does not receive the value of the calibration parameter;

[0188] The calibration parameter pushing function state represents that the target wind turbine generator group opens the function of receiving the value of the calibration parameter;

[0189] The wind turbine generator group operation permission state represents that the working condition of the target wind turbine generator group is a light wind working condition allowing the value of the calibration parameter to be received;

[0190] The sensor operation state represents that the working state of the sensor is a normal state.

[0191] In some examples, the calibration message further comprises a first check code. The first check code of the target wind turbine generator group is obtained according to a check code conversion algorithm and the value of the calibration parameter corresponding to the target wind turbine generator group before pushing. The first check code of the target wind turbine generator group is used to be compared with a second check code calculated by the target wind turbine generator group according to the check code conversion algorithm and the received value of the calibration parameter.

[0192] In a case where a difference between the first check code and the second check code exceeds a normal error range, the wind turbine generator group calibration parameter state is updated to represent that the target wind turbine generator group abnormally receives the value of the calibration parameter.

[0193] In a case where the difference between the first check code and the second check code is within the normal error range, the wind turbine generator group calibration parameter state is updated to represent that the target wind turbine generator group normally receives the value of the calibration parameter.

[0194] In some embodiments, the sensing data comprises a blade azimuth angle.

[0195] The data acquisition module 301 can also be configured to reacquire the unit calibration parameter state of the target wind turbine generator set.

[0196] The pushing module 304 can also be configured to re-push the calibration message corresponding to the target wind turbine generator set to the target wind turbine generator set until the reacquired unit calibration parameter state indicates that the target wind turbine generator set receives the value of the calibration parameter normally.

[0197] In some embodiments, the data acquisition module 301 can also be configured to: divide the unit data into preset azimuth angle partitions, the blade azimuth angle of each group of unit data in the same azimuth angle partition is located in the azimuth angle partition, each group of unit data comprises the operating data, the sensing data and the unit component state data collected at the same time; and count the number of groups of unit data in each azimuth angle partition.

[0198] The calibration module 302 can be configured to trigger the value of the calibration parameter of the sensor under the condition that the absolute value of the difference between the calculated theoretical strain and the measured strain is minimum according to the operating data, the sensing data and the unit component state data when the number of groups reaches the triggering calibration threshold.

[0199] In some embodiments, the calibration module 302 can be configured to: determine the sensing data and the unit component state data corresponding to the operating data satisfying the second effective screening condition based on the operating data, the second effective screening condition is used to screen out the unit data affected by error factors; obtain a first relationship formula for calculating the measured strain according to the sensing data corresponding to the operating data satisfying the second effective screening condition and a part of the calibration parameters with unknown values; obtain a second relationship formula for calculating the theoretical strain according to the sensing data corresponding to the operating data satisfying the second effective screening condition, the unit component state data corresponding to the operating data satisfying the second effective screening condition and another part of the calibration parameters with unknown values; and calculate the value of the calibration parameter that makes the absolute value of the difference between the first relationship formula and the second relationship formula minimum based on the first relationship formula and the second relationship formula.

[0200] The application also provides a wind farm cluster controller. Figure 9 An embodiment of the wind farm cluster controller provided by the application is shown in the structure diagram. As shown in the figure, the wind farm cluster controller 400 comprises a memory 401, a processor 402 and a computer program stored in the memory 401 and executable on the processor 402. Figure 9

[0201] ​In one example, the processor 402 described above can include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or can be configured to implement one or more integrated circuits that implement embodiments of the present application.

[0202] The memory 401 can include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical / tangible memory storage devices. Thus, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions that, when executed (e.g., by one or more processors), are operable to perform the operations described with reference to the method of calibrating sensor parameters of a wind farm cluster according to embodiments of the present application.

[0203] The processor 402 runs a computer program corresponding to the executable program code stored in the memory 401 by reading the executable program code, for implementing the method of calibrating sensor parameters of a wind farm cluster in the embodiments described above.

[0204] In one example, the wind farm cluster controller 400 can further include a communication interface 403 and a bus 404. As shown, the memory 401, the processor 402, and the communication interface 403 are connected through the bus 404 and complete communication among each other. Figure 9

[0205] The communication interface 403 is mainly used to realize the communication between the modules, devices, units, and / or equipment in the embodiments of the present application. The input device and / or the output device can also be accessed through the communication interface 403.

[0206] ​Bus 404 includes a hardware, software, or both that couples components of wind farm fleet controller 400 to each other. As an example and not by way of limitation, bus 404 can include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-E) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or another suitable bus or a combination of two or more of these. Where suitable, bus 404 can include one or more buses. Although this embodiment describes and shows a particular bus, this embodiment contemplates any suitable bus or interconnect.

[0207] In some examples, the wind farm fleet controller can include a wind farm edge controller, which is arranged at the edge of the wind farm, and is capable of quickly configuring the values of the calibration parameters to the target wind turbine generator set, saving the configuration and deployment cost, and improving the configuration speed of the values of the calibration parameters.

[0208] The application further provides a computer readable storage medium, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the wind farm fleet sensor parameter calibration method in the above embodiment and achieve the same technical effects. To avoid repetition, details are not described here. The computer readable storage medium can include a non-transitory computer readable storage medium, such as a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like, which is not limited here.

[0209] It should be noted that each of the above-described examples can be implemented in a variety of ways and that the application is not limited to the specific examples described above. Each of the above-described examples can be implemented in combination with one another. Each of the above-described examples can be implemented in any combination, sub-combination, or variation thereof. Each of the above-described examples can be implemented in a device, a wind farm controller, a system, a computer-readable storage medium, or a method. For the sake of brevity, the methodologies implemented in the device, the wind farm controller, the system, the computer-readable storage medium, and the method are not described in detail in this summary. For the sake of brevity, the methodologies implemented in the device, the wind farm controller, the system, the computer-readable storage medium, and the method are not described in detail in this summary. Additional objects, features, and advantages of the present application will become apparent to one of ordinary skill in the art, upon examining the following description in conjunction with the accompanying drawings.

[0210] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. Alternatively, computer program implemented processes can be conveyed, accessed, or retrieved by a computer, other programmable data processing apparatus, or other processing device from a computer readable storage medium to cause a series of steps of a process to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0211] It should be understood that all the above-described examples are merely exemplary and non-limiting. The various technical features appearing in the different examples can be combined to achieve beneficial effects. Based on the drawings, the specification, and the claims, a person skilled in the art should understand and be able to implement other changed examples of the disclosed examples. In the claims, the term “comprising” does not exclude other devices or steps; the numerical term “one” does not exclude more than one; the terms “first”, “second” are used to identify names and not to indicate any particular order. Any reference signs in the claims should not be understood as limiting the scope of protection. The functions of multiple parts appearing in the claims can be implemented by a single hardware or software module. The fact that certain technical features appear in different dependent claims does not mean that these technical features cannot be combined to achieve beneficial effects.

Claims

1. A method for calibrating sensor parameters of a wind farm cluster, characterized in that, The method comprises the following steps: obtaining unit data of a plurality of target wind turbine generators in a wind farm, the unit data comprising operation data, sensing data and unit component state data, the sensing data being sensed by a sensor arranged at a blade root of a blade of the target wind turbine generator; calculating a value of a calibration parameter of the sensor under a condition that an absolute value of a difference between a theoretical strain and a measured strain is minimum, the calibration parameter being used for calculation of the sensing data and the measured strain and calculation of the unit component state data and the theoretical strain, according to the operation data, the sensing data and the unit component state data; calculating an error rate of a theoretical load of a blade of the target wind turbine generator and an idling calibration load, based on idling unit data of the target wind turbine generator, the value of the calibration parameter and the unit data; in a case where the error rate is less than or equal to an acceptable error rate threshold, respectively pushing a calibration message corresponding to the target wind turbine generator to a plurality of the target wind turbine generators to update an original value of the calibration parameter of the target wind turbine generator, the calibration message comprising the value of the calibration parameter; wherein the respectively pushing the calibration message corresponding to the target wind turbine generator to the plurality of the target wind turbine generators comprises: obtaining a push influencing factor, the push influencing factor comprising a factor influencing reception of the calibration message by the target wind turbine generator; in a case where the push influencing factor meets a preset push judgment condition, pushing the calibration message corresponding to the target wind turbine generator to the target wind turbine generator.

2. The method of claim 1, wherein, Before the calculating the error rate of the theoretical load of the blade of the target wind turbine generator and the idling calibration load, based on the idling unit data of the target wind turbine generator, the value of the calibration parameter and the unit data, further comprising: in a case where the target wind turbine generator is in a light wind working condition and in an equal wind state, controlling the target wind turbine generator to operate at a limited speed, the limited speed being lower than a normal working speed; in a case where the target wind turbine generator operates at the limited speed, collecting the unit data of the target wind turbine generator as the idling unit data for a plurality of times; in a case where the collected idling unit data is sufficient for calculation of the idling calibration load, stopping collection of the idling unit data and controlling the target wind turbine generator to resume normal operation.

3. The method of claim 2, wherein, The stopping collection of the idling unit data and controlling the target wind turbine generator to resume normal operation in a case where the collected idling unit data is sufficient for calculation of the idling calibration load comprises: in a case where a number of revolutions of an impeller of the target wind turbine generator reaches a preset revolution threshold when the target wind turbine generator operates at the limited speed, determining that the collected idling unit data is sufficient for calculation of the idling calibration load; stopping collection of the idling unit data and controlling the target wind turbine generator to resume normal operation.

4. The method of claim 2, wherein, In the case that the target wind turbine is running at the limited rotating speed, after the unit data of the target wind turbine is collected multiple times as the idling unit data, the method further comprises: According to a preset first effective screening condition, the idling unit data satisfying the first effective screening condition is screened to obtain the idling unit data, and the first effective screening condition is used to simulate the idling state of the target wind turbine.

5. The method of claim 1, wherein, The error rate of the theoretical load of the blade of the target wind turbine and the idling calibration load is calculated based on the idling unit data of the target wind turbine, the value of the calibration parameter and the unit data, comprising: According to the unit data and the value of the calibration parameter of the target wind turbine collected multiple times, multiple theoretical loads of the blade of the target wind turbine are calculated. According to the idling unit data of the target wind turbine and the value of the calibration parameter collected multiple times, multiple idling calibration loads of the blade of the target wind turbine are calculated. The maximum value of the multiple theoretical loads of the blade of the target wind turbine and the maximum value of the idling calibration load of the target wind turbine are used to calculate the first error rate of the blade. The minimum value of the multiple theoretical loads of the blade of the target wind turbine and the minimum value of the idling calibration load of the target wind turbine are used to calculate the second error rate of the blade. The item with the maximum value of the first error rate and the second error rate of the blade is determined as the error rate.

6. The method of claim 1, wherein, Further comprising: According to the same operating data, sensing data and unit component state data, multiple sets of values of the calibration parameter are repeatedly calculated; According to the multiple sets of values of the calibration parameter, the change coefficient of each set of values of the calibration parameter is calculated, and the change coefficient represents the change degree between the values of the calibration parameter. In the case that the error rate is less than or equal to the acceptable error rate threshold, the values of the calibration parameter corresponding to the target wind turbine are pushed to the target wind turbine, comprising: In the case that the error rate is less than or equal to the acceptable error rate threshold and the change coefficient of the multiple sets of values of the calibration parameter does not exceed the normal change range, the target wind turbine corresponding to the change coefficient that does not exceed the normal change range is pushed to the target wind turbine corresponding to the target wind turbine.

7. The method of claim 1, wherein, The push influencing factors include one or more of the following: Unit calibration parameter state, calibration parameter receiving enabled state, unit operation permission state, sensor operation state, unit operation working condition; The unit calibration parameter state is used to represent the state of the value of the calibration parameter in the target wind turbine; The calibration parameter push function state is used to represent whether the target wind turbine opens the function of receiving the value of the calibration parameter; The unit operation permission state is used to represent whether the working condition of the target wind turbine allows to receive the value of the calibration parameter; The sensor operation state is used to represent the working state of the sensor.

8. The method of claim 7, wherein, The push judgment condition comprises one or more than two of the following: The unit calibration parameter state represents that the target wind turbine does not receive the value of the calibration parameter; The calibration parameter push function state represents that the target wind turbine opens the function of receiving the value of the calibration parameter; The unit operation permission state represents that the working condition of the target wind turbine is a small wind working condition allowing the value of the calibration parameter to be received; The sensor operation state represents that the working state of the sensor is normal.

9. The method of claim 7, wherein, After the respective pushing of the calibration message corresponding to the target wind turbine to a plurality of target wind turbines, further comprising: Re-acquiring the unit calibration parameter state of the target wind turbine; In the case that the re-acquired unit calibration parameter state represents that the target wind turbine abnormally receives the value of the calibration parameter, the calibration message corresponding to the target wind turbine is re-pushed to the target wind turbine until the re-acquired unit calibration parameter state represents that the target wind turbine normally receives the value of the calibration parameter.

10. The method of claim 7, wherein: The calibration message further comprises a first check code, the first check code of the target wind turbine is obtained according to a check code conversion algorithm and the value of the calibration parameter corresponding to the target wind turbine before pushing, and the first check code of the target wind turbine is used to compare with a second check code calculated by the target wind turbine according to the check code conversion algorithm and the received value of the calibration parameter; In the case that the difference between the first check code and the second check code exceeds the normal error range, the unit calibration parameter state is updated to represent that the target wind turbine abnormally receives the value of the calibration parameter; In the case that the difference between the first check code and the second check code is within the normal error range, the unit calibration parameter state is updated to represent that the target wind turbine normally receives the value of the calibration parameter.

11. The method of claim 1, wherein, The sensor data comprises a blade azimuth angle, Before the calculation of the value of the calibration parameter of the sensor under the condition that the absolute value of the difference between the theoretical strain and the measured strain is minimum according to the operation data, the sensor data and the unit component state data, further comprising: Dividing the unit data into preset azimuth angle partitions, the blade azimuth angle in each set of unit data in the same azimuth angle partition is located in the azimuth angle partition, and each set of unit data comprises the operation data, the sensor data and the unit component state data collected at the same time; Counting the number of sets of unit data in each azimuth angle partition; The calculation of the value of the calibration parameter of the sensor under the condition that the absolute value of the difference between the theoretical strain and the measured strain is minimum according to the operation data, the sensor data and the unit component state data, comprises: In a case where the number of the groups reaches a trigger calibration threshold, a value of a calibration parameter of the sensor in a case where an absolute value of a difference between a theoretical strain and a measured strain is minimum is triggered to be calculated according to the operation data, the sensing data and the unit component state data.

12. The method of claim 1, wherein, The value of the calibration parameter of the sensor in the case where the absolute value of the difference between the theoretical strain and the measured strain is minimum is calculated according to the operation data, the sensing data and the unit component state data, including: Based on the operation data, the sensing data and the unit component state data corresponding to the operation data satisfying a second effective screening condition are determined, the second effective screening condition being used to screen out the unit data affected by error factors; A first relationship for calculating the measured strain is obtained according to the sensing data corresponding to the operation data satisfying the second effective screening condition and a part of the calibration parameters with unknown values; A second relationship for calculating the theoretical strain is obtained according to the sensing data corresponding to the operation data satisfying the second effective screening condition, the unit component state data corresponding to the operation data satisfying the second effective screening condition and another part of the calibration parameters with unknown values; Based on the first relationship and the second relationship, a value of the calibration parameter that makes an absolute value of a difference between the first relationship and the second relationship minimum is calculated.

13. A sensor parameter calibration device for a wind farm cluster, characterized in that Including: A data acquisition module is configured to acquire unit data of a plurality of target wind turbine generators in a wind farm, the unit data including operation data, sensing data and unit component state data, the sensing data being sensed by a sensor arranged at a blade root of a blade of the target wind turbine generator; A calibration module is configured to calculate a calibration parameter of the sensor in a case where an absolute value of a difference between a theoretical strain and a measured strain is minimum according to the operation data, the sensing data and the unit component state data, the calibration parameter being used for calculation of the sensing data and the measured strain and calculation of the unit component state data and the theoretical strain; A verification module is configured to calculate an error rate of a theoretical load of a blade of the target wind turbine generator and an idling calibration load based on idling unit data of the target wind turbine generator, the calibration parameter and the unit data. A pushing module is configured to push a calibration message corresponding to the target wind turbine generator to the target wind turbine generator respectively in a case where the error rate is less than or equal to an acceptable error rate threshold, so as to update original calibration parameters of the target wind turbine generator, the calibration message including the calibration parameter. The pushing module is configured to acquire a pushing influence factor, the pushing influence factor including a factor influencing reception of the calibration message by the target wind turbine generator, and push the calibration message corresponding to the target wind turbine generator to the target wind turbine generator in a case where the pushing influence factor satisfies a preset pushing judgment condition.

14. A wind farm cluster controller, characterized in that Including: A processor and a memory having computer program instructions stored therein; The processor implements the sensor parameter calibration method of the wind farm cluster according to any one of claims 1-12 when executing the computer program instructions.

15. The wind farm fleet controller of claim 14, wherein, The wind farm cluster controller comprises a wind farm edge controller.

16. A sensor parameter calibration system for a wind farm fleet, the system comprising: Comprise: a sensor arranged at a blade root of a blade of a wind turbine generator set, configured to collect sensing data and transmit the sensing data to the wind farm cluster controller according to claim 14 or 15; the wind farm cluster controller; a wind turbine generator set controller configured to receive the calibration message pushed by the wind farm cluster controller and update the original value of the calibration parameter with the calibration parameter in the calibration message.

17. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer program instructions, and the computer program instructions are executed by the processor to implement the sensor parameter calibration method of the wind farm cluster according to any one of claims 1-12.

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