A method, device and equipment for determining sensor calibration parameters, and a storage medium

Abstract

Embodiments of the present application disclose a method, device and equipment for determining calibration parameters of a sensor, and a storage medium. The method comprises: obtaining first blade root load sensor data and target impeller data, the first blade root load sensor data comprising first calibration data in a first operating state and second calibration data in a second operating state, and the target impeller data comprising first impeller data in the first operating state and second impeller data in the second operating state; determining a first theoretical gravity moment load of a wind turbine according to the first impeller data; determining calibration parameters according to the first calibration data, the first impeller data and the first gravity moment load; determining a second theoretical gravity moment load according to the calibration parameters and the second impeller data; and determining the calibration parameters as target calibration parameters when the first gravity moment load and the second gravity moment load satisfy a preset condition. The method provided by the embodiments of the present application can accurately determine calibration parameters of a blade root load sensor.

Description

Technical Field

[0001] This application belongs to the field of wind power generation technology, and in particular relates to a method, apparatus, equipment and storage medium for determining sensor calibration parameters. Background Technology

[0002] As wind turbines continue to evolve towards larger rotor diameters and taller towers, external factors such as wind shear, tower shadow effect, and turbulence cause imbalances in aerodynamic loads within the rotor surface, leading to increased ultimate loads and reduced lifespan of wind turbines. Accurately detecting blade root loads and adjusting rotor parameters based on these loads is an effective technical means to address these problems. Fiber optic blade root load sensors, with their advantages of accurate measurement, long lifespan, and strong anti-interference capabilities, are increasingly being used in the wind power industry. Calibrating these sensors is a prerequisite for accurate measurement of blade root loads.

[0003] In the past, blade root load sensors were usually calibrated manually, and the calibration parameters obtained by this method had low accuracy. Summary of the Invention

[0004] This application provides a method, apparatus, device, and storage medium for determining sensor calibration parameters, which can accurately determine the calibration parameters of a leaf root load sensor.

[0005] In a first aspect, embodiments of this application provide a method for determining sensor calibration parameters, the method comprising:

[0006] Acquire data from the first blade root load sensor and the target impeller. The first blade root load sensor data includes the first calibration data of the wind turbine in the first operating state and the second calibration data of the wind turbine in the second operating state. The target impeller data includes the first impeller data of the wind turbine in the first operating state and the second impeller data of the wind turbine in the second operating state.

[0007] Based on the data from the first impeller, the first theoretical gravitational moment load of the wind turbine is determined;

[0008] The calibration parameters are determined based on the first calibration data, the first impeller data, and the first gravitational moment load.

[0009] The second theoretical gravitational moment load is determined based on the calibration parameters and the second impeller data;

[0010] When the first gravitational moment load and the second gravitational moment load meet the preset conditions, the calibration parameters are determined as the target calibration parameters.

[0011] In one possible implementation, calibration parameters are determined based on first calibration data, first impeller data, and a first theoretical gravitational moment load, including:

[0012] Based on the first impeller data and the first theoretical gravitational moment load, the theoretical load strain data of the wind turbine were determined;

[0013] Based on the first calibration data, determine the actual load strain data of the wind turbine.

[0014] The calibration parameters are determined based on the actual load strain data and the theoretical load strain data.

[0015] In one possible implementation, acquiring data from the first blade root load sensor and target impeller data includes:

[0016] Acquire wind turbine operating data, second blade root load sensor data, environmental data, and rotor data at each time point in multiple time periods;

[0017] The second blade root load sensor data at the time corresponding to the unit operation data and environmental data that meet the screening criteria is determined to be the first blade root load sensor data.

[0018] The impeller data at the time corresponding to the unit operation data and environmental data that meet the screening criteria are identified as the target impeller data.

[0019] In one possible implementation, the method further includes:

[0020] The blade root load sensor of the wind turbine is calibrated according to the target calibration parameters.

[0021] Secondly, embodiments of this application provide a device for determining calibration parameters, the device comprising:

[0022] The acquisition module is used to acquire data from the first blade root load sensor and data from the target impeller. The data from the first blade root load sensor includes the first calibration data of the wind turbine in the first operating state and the second calibration data of the wind turbine in the second operating state. The data from the target impeller includes the first impeller data of the wind turbine in the first operating state and the second impeller data of the wind turbine in the second operating state.

[0023] The determination module is used to determine the first theoretical gravitational moment load of the wind turbine based on the first impeller data; it is also used to determine the calibration parameters based on the first calibration data, the first impeller data, and the first gravitational moment load; it is also used to determine the second theoretical gravitational moment load based on the calibration parameters and the second impeller data; and it is also used to determine the calibration parameters as the target calibration parameters when the first gravitational moment load and the second gravitational moment load meet preset conditions.

[0024] In one possible implementation, the module is specifically used for:

[0025] Based on the first impeller data and the first theoretical gravitational moment load, the theoretical load strain data of the wind turbine were determined.

[0026] Based on the first calibration data, determine the actual load strain data of the wind turbine.

[0027] The calibration parameters are determined based on the actual load strain data and the theoretical load strain data.

[0028] In one possible implementation, the acquisition module is specifically used for:

[0029] Acquire wind turbine operating data, second blade root load sensor data, environmental data, and rotor data at each time point in multiple time periods;

[0030] The second blade root load sensor data at the time corresponding to the unit operation data and environmental data that meet the screening criteria is determined to be the first blade root load sensor data.

[0031] The impeller data at the time corresponding to the unit operation data and environmental data that meet the screening criteria are identified as the target impeller data.

[0032] In one possible implementation, the device also includes a calibration module for calibrating the blade root load sensor of the wind turbine according to target calibration parameters.

[0033] Thirdly, embodiments of this application provide an electronic device, the device including: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, it implements the method as described in the first aspect or any possible implementation of the first aspect.

[0034] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the method as described in the first aspect or any possible implementation thereof.

[0035] This application provides a method, apparatus, device, and system for determining sensor calibration parameters. First, it acquires first calibration data of a wind turbine in a first operating state and second calibration data of the wind turbine in a second operating state, as well as first impeller data of the wind turbine in the first operating state and second impeller data of the wind turbine in the second operating state. Second, it determines a first theoretical gravitational moment load of the wind turbine based on the first impeller data. Third, it determines calibration parameters based on the first calibration data, the first impeller data, and the first gravitational moment load. Fourth, it determines a second theoretical gravitational moment load based on the calibration parameters and the second impeller data. When the first gravitational moment load and the second gravitational moment load meet preset conditions, the calibration parameters are determined as target calibration parameters. Because the calibration parameters are determined based on the data in the first operating state and then verified based on the calibration parameters and the data in the second operating state, and the calibration parameters are only determined as target calibration parameters for calibrating the blade root load sensor when the preset conditions are met, the calibration parameters of the blade root load sensor can be accurately determined. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a schematic flowchart of a method for determining sensor calibration parameters provided in an embodiment of this application;

[0038] Figure 2 This is a schematic diagram of a sensor calibration parameter determination device provided in an embodiment of this application;

[0039] Figure 3 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application;

[0040] Figure 4 This is a schematic diagram of a sensor calibration parameter determination system provided in an embodiment of this application. Detailed Implementation

[0041] The features and exemplary embodiments of various aspects of this application will now be described in detail. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only configured to explain this application and are not configured to limit this application. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples of this application.

[0042] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0043] As wind turbines continue to evolve towards larger rotor diameters and taller towers, external factors such as wind shear, tower shadow effect, and turbulence cause imbalances in aerodynamic loads within the rotor surface, leading to increased ultimate loads and reduced lifespan of wind turbines. Accurate detection of blade root loads and adjustment of rotor parameters based on these loads are effective technologies for addressing these problems. Fiber optic blade root load sensors, with their advantages of accurate measurement, long lifespan, and strong anti-interference capabilities, are increasingly being used in the wind power industry. Calibrating these sensors is a prerequisite for accurate measurement. Previously, calibration of blade root load sensors was typically done manually, resulting in low-precision calibration parameters.

[0044] To address the aforementioned problems, this application provides a method, apparatus, device, and system for determining sensor calibration parameters. First, it acquires first calibration data of a wind turbine in a first operating state and second calibration data of a wind turbine in a second operating state, as well as first impeller data of the wind turbine in the first operating state and second impeller data of the wind turbine in the second operating state. Second, it determines a first theoretical gravitational moment load of the wind turbine based on the first impeller data. Third, it determines calibration parameters based on the first calibration data, the first impeller data, and the first gravitational moment load. Fourth, it determines a second theoretical gravitational moment load based on the calibration parameters and the second impeller data. When the first gravitational moment load and the second gravitational moment load meet preset conditions, the calibration parameters are determined as target calibration parameters. Because the calibration parameters are determined based on the data in the first operating state and then verified based on the calibration parameters and the data in the second operating state, and the calibration parameters are only determined as target calibration parameters for calibrating the blade root load sensor when the preset conditions are met, the calibration parameters of the blade root load sensor can be accurately determined.

[0045] The method provided in this application can be executed by a terminal such as a computer that has data transmission and data processing capabilities.

[0046] The following will combine Figure 1 This application provides a detailed description of a method for determining sensor calibration parameters according to embodiments.

[0047] like Figure 1 As shown, the method may include the following steps:

[0048] S110, acquire data from the first blade root load sensor and the target impeller.

[0049] The first blade root load sensor data includes first calibration data of the wind turbine in its first operating state and second calibration data of the wind turbine in its second operating state. The target impeller data includes first impeller data of the wind turbine in its first operating state and second impeller data of the wind turbine in its second operating state. The first operating state can be a non-idling state, and the second operating state can be an idling state. The first calibration data may include the measured wavelength and initial wavelength of the blade root load sensor when the wind turbine is in the non-idling state. The first impeller data includes the impeller's gravitational moment, main shaft tilt angle, impeller plane cone angle, blade propeller gravitational moment angle, blade azimuth angle, impeller weight, blade stiffness coefficient, and the installation position of the blade root load sensor when the wind turbine is in the non-idling state. The second calibration data may include the measured wavelength and initial wavelength of the blade root load sensor when the wind turbine is in the idling state. The second impeller data includes the impeller's gravitational torque, main shaft tilt angle, impeller plane cone angle, blade pitch angle, blade azimuth angle, impeller weight, blade stiffness coefficient, and the installation position of the blade root load sensor when the wind turbine is idling.

[0050] The non-idling state of a wind turbine can include operating states, etc.

[0051] The terminal acquires the measured wavelength and initial wavelength of the blade root load sensor when the wind turbine is not idling, as well as the rotor torque, main shaft tilt angle, rotor plane cone angle, blade pitch moment angle, blade azimuth angle, rotor weight, blade stiffness coefficient, and blade root load sensor installation position when the wind turbine is not idling. It also acquires the measured wavelength and initial wavelength of the blade root load sensor when the wind turbine is idling, as well as the rotor torque, main shaft tilt angle, rotor plane cone angle, blade pitch moment angle, blade azimuth angle, rotor weight, blade stiffness coefficient, and blade root load sensor installation position when the wind turbine is idling.

[0052] S120, based on the first impeller data, determines the first theoretical gravitational moment load of the wind turbine.

[0053] The terminal calculates the first theoretical gravitational moment load of the wind turbine based on the rotor's gravitational moment, main shaft tilt angle, rotor plane cone angle, blade gravitational moment angle, blade azimuth angle, rotor weight, blade stiffness coefficient, and the installation position of the blade root load sensor when the wind turbine is not idling.

[0054] In one example, the terminal uses the following formula to calculate the first theoretical gravitational moment load of the wind turbine.

[0055]

[0056] in, M represents the first theoretical gravitational moment load; G,x The torque (M) represents the gravitational torque in the direction of rotation (edge ​​direction), with units of Nm. G,y F represents the gravitational torque in the flap direction, in Nm. G The torque is expressed in Nm in a fixed coordinate system; δ is the main shaft tilt angle in rad; θ is the impeller plane cone angle in rad; Ω is the blade azimuth angle in rad; and φ is the propeller torque angle in rad.

[0057] S130, determine the calibration parameters based on the first calibration data, the first impeller data, and the first gravitational torque load.

[0058] The terminal calculates calibration parameters based on the measured wavelength and initial wavelength of the blade root load sensor when the wind turbine is not idling, as well as the rotor torque, main shaft tilt angle, rotor plane cone angle, blade pitch angle, blade azimuth angle, rotor weight, blade stiffness coefficient, blade root load sensor installation position, and first torque load when the wind turbine is not idling.

[0059] S140, based on the calibration parameters and the second impeller data, determines the second theoretical gravitational moment load.

[0060] The terminal uses calibration parameters and the rotor's gravitational torque, main shaft tilt angle, rotor plane cone angle, blade pitch angle, blade azimuth angle, rotor weight, blade stiffness coefficient, and blade root load sensor installation position when the wind turbine is in idling state to generate a second theoretical gravitational torque load.

[0061] In one example, the terminal uses formula (1) to calculate the second theoretical gravitational moment load based on the calibration parameters and the wind turbine's impeller gravitational moment, main shaft tilt angle, impeller plane cone angle, blade impeller gravitational moment angle, blade azimuth angle, impeller weight, blade stiffness coefficient, and blade root load sensor installation position when the wind turbine is in idling state.

[0062] S150, when the first gravitational moment load and the second gravitational moment load meet the preset conditions, the calibration parameters are determined as the target calibration parameters.

[0063] In some embodiments, when the differential gravitational torque of the first gravitational torque load and the second gravitational torque load is less than or equal to a preset value, the calibration parameter is determined as the target calibration parameter.

[0064] In one example, the calibration parameter is determined as the target calibration parameter when the differential gravitational torque of the first gravitational torque load and the second gravitational torque load is less than or equal to 5%.

[0065] This application provides a method that, firstly, acquires first calibration data of a wind turbine in a first operating state and second calibration data of a wind turbine in a second operating state, as well as first impeller data of the wind turbine in the first operating state and second impeller data of the wind turbine in the second operating state; secondly, determines a first theoretical gravitational moment load of the wind turbine based on the first impeller data; thirdly, determines calibration parameters based on the first calibration data, the first impeller data, and the first gravitational moment load; and fourthly, determines a second theoretical gravitational moment load based on the calibration parameters and the second impeller data; when the first gravitational moment load and the second gravitational moment load meet preset conditions, the calibration parameters are determined as target calibration parameters. Because the calibration parameters are verified based on the data from the first operating state and then verified again based on the data from the second operating state, and the calibration parameters are only determined as target calibration parameters for calibrating the blade root load sensor when the preset conditions are met, the determined calibration parameters for the blade root load sensor are more accurate.

[0066] In some embodiments, the terminal simultaneously uses multiple wind turbine generators. Figure 1 The method shown can simultaneously determine the calibration parameters of the blade root load sensors for multiple wind turbine units.

[0067] The method provided in this application improves the efficiency of determining calibration parameters.

[0068] In some embodiments, determining the calibration parameters, i.e., S130, based on the first calibration data, the first impeller data, and the first theoretical gravitational moment load may include the following steps:

[0069] First, based on the first impeller data and the first theoretical gravitational moment load, determine the theoretical load strain data of the wind turbine.

[0070] The terminal calculates the theoretical load strain data of the wind turbine based on the rotor's gravitational moment, main shaft tilt angle, rotor plane cone angle, blade gravitational moment angle, blade azimuth angle, rotor weight, blade stiffness coefficient, and the installation position of the blade root load sensor, as well as the first theoretical gravitational moment load, when the wind turbine is not idling.

[0071] In one example, the terminal uses the following formula to calculate the theoretical load strain data of the wind turbine.

[0072]

[0073] in, This represents the theoretical load strain data. Four blade root load sensors are installed on each wind turbine. ε s1 This represents the theoretical load-strain data of the first blade root load sensor, ε. s2 This represents the theoretical load-strain data of the second blade root load sensor, ε. s3 This represents the theoretical load-strain data of the third blade root load sensor, ε. s4 This represents the theoretical load strain data of the fourth blade root load sensor; K B (φ s () represents the blade stiffness coefficient, with units of Nm / m / m It can be abbreviated as K B ;φ s Indicates the sensor installation location. Represents the coordinate transformation matrix of the blade root load sensor, φ s1 Indicates the installation location of the first blade root load sensor, φ s2 Indicates the installation location of the second blade root load sensor, φ s3 Indicates the installation location of the third blade root load sensor, φ s4 This indicates the installation location of the fourth leaf root load sensor.

[0074] Then, based on the first calibration data, the actual load strain data of the wind turbine is determined.

[0075] The terminal calculates the actual load strain data of the wind turbine based on the measured wavelength and initial wavelength of the blade root load sensor when the wind turbine is not idling.

[0076] In one example, the terminal uses the following formula to calculate the actual load strain data of the wind turbine.

[0077]

[0078] Where, ε m Represents the actual load strain data, λ B,0 λ represents the initial wavelength. B K represents the measured wavelength. ε K represents the strain coefficient of the sensor. ε This is the default value.

[0079] Finally, the calibration parameters are determined based on the actual load strain data and the theoretical load strain data.

[0080] The terminal calculates calibration parameters based on actual load strain data and theoretical load strain data.

[0081] Calibration parameters may include blade stiffness coefficient, sensor installation location, sensor strain coefficient, and wavelength.

[0082] In some embodiments, the terminal calculates calibration parameters using a nonlinear optimization algorithm based on actual load strain data and theoretical load strain data.

[0083] In one example, the terminal uses the following formula to calculate the calibration parameters.

[0084]

[0085] Based on the above formula, the calibration parameter K is calculated. Ba φ sa K εa and λ Ba K Ba φ represents the calculated blade stiffness coefficient. sa Indicates the calculated sensor installation location; K εa λ represents the calculated strain coefficient of the sensor. Ba This represents the calculated wavelength.

[0086] Based on the first calibration data, the first impeller data, and the first gravitational torque load, calibration parameters were determined. These calibration parameters were used to calibrate the blade root load sensor. However, the calibration parameters may be inaccurate, and using them for calibration may not accurately measure the impeller load.

[0087] In some embodiments, acquiring the first blade root load sensor data and the target impeller data, i.e., S110, may include the following steps:

[0088] First, acquire the wind turbine's operating data, second blade root load sensor data, environmental data, and rotor data at each time point in multiple time periods.

[0089] The terminal acquires wind turbine operating data, second blade root load sensor data, environmental data, and rotor data at each moment across multiple time periods through the wind farm cluster control platform. The turbine operating data may include the rotor's propeller moment angle, yaw action, yaw deviation from wind direction, and rotational speed. The second blade root load sensor data may include the measured wavelength and initial wavelength of the blade root load sensor. Environmental data may include wind speed and ambient temperature. The rotor data may include the rotor's moment of gravity, main shaft tilt angle, rotor plane cone angle, blade propeller moment angle, blade azimuth angle, rotor weight, blade stiffness coefficient, and the installation position of the blade root load sensor.

[0090] In some embodiments, the terminal determines the calibration parameters of the blade root load sensor according to a preset cycle, and automatically collects the first blade root load sensor data and the target impeller data of the wind turbine in each acquisition cycle.

[0091] In one example, the terminal determines the calibration parameters of the blade root load sensor once a month. When determining the calibration parameters of the blade root load sensor, the data acquisition cycle is 10 minutes. Every 10 minutes, the terminal acquires the wind turbine's unit operation data, second blade root load sensor data, environmental data, and impeller data for the previous 10 minutes.

[0092] In the method provided in this application embodiment, the terminal determines the calibration parameters of the leaf root load sensor according to a preset cycle, thereby reducing resource consumption.

[0093] In some embodiments, the terminal can simultaneously acquire unit operation data, second blade root load sensor data, environmental data, and rotor data of multiple wind turbine units through the wind farm cluster control platform.

[0094] Then, the second blade root load sensor data at the time corresponding to the unit operation data and environmental data that meet the screening criteria is determined as the first blade root load sensor data.

[0095] The filtering conditions and their effects are shown in Table 1.

[0096] Table 1. Filtering conditions and the effects of setting filtering conditions.

[0097] variable Filtering criteria Effect Paddlewheel moment angle [30°,85°] To avoid aerodynamic factors introducing calibration errors at small propeller moment angles. 3-second average wind speed <=5 m / s Ensure that the aerodynamic influence on the theoretical load does not exceed 10%. impeller speed <2 revolutions / minute To avoid aerodynamic factors introducing calibration errors at high speeds Left and right yaw maneuvers No action To avoid yaw action introducing aerodynamic loads to the blades Yaw wind deviation <20° To avoid excessive wind deviation introducing aerodynamic loads

[0098] The terminal determines the time corresponding to the unit operation data and environmental data that meet the screening criteria, and uses the second blade root load sensor data at that time as the first blade root load sensor data.

[0099] Finally, the impeller data at the time corresponding to the unit operation data and environmental data that meet the screening criteria are determined as the target impeller data.

[0100] The terminal determines the time corresponding to the unit operation data and environmental data that meet the screening criteria, and uses the impeller data at that time as the target impeller data.

[0101] In some embodiments, the terminal automatically collects data from the first blade root load sensor and the target impeller of the wind turbine according to a collection cycle. After acquiring the data, the data is divided into compartments based on the azimuth angle range of the impeller. Data from the first blade root load sensor and the target impeller corresponding to an azimuth angle within a preset range are grouped into one data compartment. After each collection cycle, it is determined whether the data volume in each data compartment meets the preset data volume. When the data volume in each data compartment meets the preset data volume, the collection ends. If the data volume in at least one data compartment does not meet the preset data volume, the collection of data from the first blade root load sensor and the target impeller continues according to the preset collection cycle until the data volume in each data compartment meets the preset data volume, at which point the collection ends.

[0102] In one example, the acquisition cycle is 10 minutes, the impeller azimuth angle range is 30°, and each 30° azimuth interval is considered a data bin. For example, 0° to 30° is considered one data bin, 30° to 60° is considered another data bin, and the preset data volume is 300. The data from the first blade root load sensor and the target impeller corresponding to each 30° azimuth angle are grouped into one data bin. After each acquisition cycle, it is determined whether the data volume in each data bin is greater than or equal to 300. When the data volume in each data bin is greater than or equal to 300, the acquisition ends. If the data volume in at least one data bin is less than 300, the acquisition of first blade root load sensor data and target impeller data continues according to the preset acquisition cycle until the data volume in each data bin is greater than or equal to 300, at which point the acquisition ends.

[0103] The method provided in this application provides sufficient first blade root load sensor data and target impeller data that meet the screening criteria, providing data support for determining the sensor calibration parameters.

[0104] In some embodiments, after determining that the calibration parameter is the target calibration parameter, the method may further include:

[0105] The blade root load sensor of the wind turbine is calibrated according to the target calibration parameters.

[0106] The terminal calibrates the blade root load sensor of the wind turbine according to the target calibration parameters.

[0107] The terminal updates the target calibration parameters of the wind turbine to the main control equipment of the wind turbine. Based on the target calibration parameters, the blade root load sensor of the wind turbine can obtain higher precision blade root load data.

[0108] In some embodiments, after determining the calibration parameters, the terminal records the calibration result (whether the calibration parameters are determined to be the target calibration parameters or not) and the differential gravity torque of the first gravity torque load and the second gravity torque load, regardless of whether the calibration parameters are determined to be the target calibration parameters, and provides this information for technical personnel to query.

[0109] The terminal records the calibration results, as well as the differential gravity torque of the first and second gravity torque loads, and provides them for technicians to query, thus providing a data basis for technicians to optimize the determination method of sensor calibration parameters.

[0110] In some embodiments, the terminal simultaneously determines the calibration parameters of multiple wind turbines. When the calibration parameter of any wind turbine is determined as the target calibration parameter, the terminal updates the target calibration parameter of the wind turbine to the main control device of the wind turbine, and the blade root load sensor of the wind turbine is based on the target calibration parameter.

[0111] The method provided in this application embodiment automatically calibrates the blade root load sensor of a wind turbine without manual intervention, thereby improving calibration efficiency and reducing labor costs.

[0112] This application embodiment also provides a calibration parameter determination device, as shown in Figure 2. The device 200 may include an acquisition module 210 and a determination module 220.

[0113] The acquisition module 210 is used to acquire data from the first blade root load sensor and data from the target impeller. The data from the first blade root load sensor includes the first calibration data of the wind turbine in the first operating state and the second calibration data of the wind turbine in the second operating state. The data from the target impeller includes the first impeller data of the wind turbine in the first operating state and the second impeller data of the wind turbine in the second operating state.

[0114] The determination module 220 is used to determine the first theoretical gravitational moment load of the wind turbine based on the first impeller data; it is also used to determine the calibration parameters based on the first calibration data, the first impeller data and the first gravitational moment load; it is also used to determine the second theoretical gravitational moment load based on the calibration parameters and the second impeller data; and it is also used to determine the calibration parameters as the target calibration parameters when the first gravitational moment load and the second gravitational moment load meet preset conditions.

[0115] The sensor calibration parameter determination device provided in this application first acquires first calibration data of the wind turbine in a first operating state and second calibration data of the wind turbine in a second operating state, as well as first impeller data of the wind turbine in the first operating state and second impeller data of the wind turbine in the second operating state. Second, based on the first impeller data, a first theoretical gravitational torque load of the wind turbine is determined. Third, based on the first calibration data, the first impeller data, and the first gravitational torque load, calibration parameters are determined. Fourth, based on the calibration parameters and the second impeller data, a second theoretical gravitational torque load is determined. When the first gravitational torque load and the second gravitational torque load meet preset conditions, the calibration parameters are determined as target calibration parameters. Because the calibration parameters are verified based on the data in the first operating state and then verified based on the calibration parameters and the data in the second operating state, and the calibration parameters are only determined as target calibration parameters for calibrating the blade root load sensor when the preset conditions are met, the calibration parameters of the blade root load sensor can be accurately determined.

[0116] In some embodiments, the determining module 220 may be specifically used for:

[0117] Based on the first impeller data and the first theoretical gravitational moment load, the theoretical load strain data of the wind turbine were determined.

[0118] Based on the first calibration data, determine the actual load strain data of the wind turbine.

[0119] The calibration parameters are determined based on the actual load strain data and the theoretical load strain data.

[0120] The device provided in this application embodiment determines calibration parameters based on first calibration data, first impeller data, and first gravitational torque load, thereby providing calibration parameters for calibrating the blade root load sensor.

[0121] In some embodiments, the acquisition module 210 may be specifically used for:

[0122] Acquire wind turbine operating data, second blade root load sensor data, environmental data, and rotor data at each time point in multiple time periods;

[0123] The second blade root load sensor data at the time corresponding to the unit operation data and environmental data that meet the screening criteria is determined to be the first blade root load sensor data.

[0124] The impeller data at the time corresponding to the unit operation data and environmental data that meet the screening criteria are identified as the target impeller data.

[0125] The device provided in this application provides sufficient first blade root load sensor data and target impeller data that meet the screening criteria, providing data support for determining the sensor calibration parameters.

[0126] In some embodiments, the device 200 may further include a calibration module 230.

[0127] The calibration module 230 is used to calibrate the blade root load sensor of the wind turbine according to the target calibration parameters.

[0128] The sensor calibration parameter determination device provided in this application embodiment automatically calibrates the blade root load sensor of the wind turbine without manual intervention, thereby improving calibration efficiency and reducing labor costs.

[0129] The sensor calibration parameter determination device provided in this application embodiment performs... Figure 1 The steps in the method shown are designed to accurately determine the calibration parameters of the blade root load sensor. For the sake of brevity, they will not be described in detail here.

[0130] Figure 3 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application is shown.

[0131] An electronic device may include a processor 301 and a memory 302 storing computer program instructions.

[0132] Specifically, the processor 301 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0133] Memory 302 may include mass storage for data or instructions. For example, and not limitingly, memory 302 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 302 may include removable or non-removable (or fixed) media. Where appropriate, memory 302 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 302 is non-volatile solid-state memory. In a particular embodiment, memory 302 includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0134] The processor 301 reads and executes computer program instructions stored in the memory 302 to achieve... Figure 1 Methods for determining any of the sensor calibration parameters in the illustrated embodiments.

[0135] In one example, the electronic device may also include a communication interface 303 and a bus 310. For example, Figure 3 As shown, the processor 301, memory 302, and communication interface 303 are connected through bus 310 and complete communication with each other.

[0136] The communication interface 303 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0137] Bus 310 includes hardware, software, or both, that couples components of an electronic device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 310 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0138] This electronic device can execute the sensor calibration parameter determination method in the embodiments of this application, thereby achieving the combination Figure 1 The method for determining the sensor calibration parameters is described.

[0139] This application also provides a system for determining sensor calibration parameters, such as... Figure 4 As shown, the system may include a controller 410 and a blade root load sensor 420.

[0140] The controller 410 is used to acquire first blade root load sensor data and target impeller data. The first blade root load sensor data includes first calibration data of the blade root load sensor 420 of the wind turbine in a first operating state and second calibration data of the blade root load sensor 420 of the wind turbine in a second operating state. The target impeller data includes first impeller data of the wind turbine in the first operating state and second impeller data of the wind turbine in the second operating state. The controller is also used to determine a first theoretical gravitational moment load of the wind turbine based on the first impeller data; to determine calibration parameters based on the first calibration data, the first impeller data, and the first gravitational moment load; to determine a second theoretical gravitational moment load based on the calibration parameters and the second impeller data; to determine the calibration parameters as target calibration parameters when the first gravitational moment load and the second gravitational moment load meet preset conditions; and to calibrate the blade root load sensor 420 of the wind turbine based on the target calibration parameters.

[0141] In some embodiments, the sensor calibration parameter determination system 400 includes multiple wind turbine units 420, and the controller 410 can be used to simultaneously determine the calibration parameters of the blade root load sensors 420 of multiple wind turbine units in the system.

[0142] When the system includes multiple wind turbine units, the controller simultaneously determines the calibration parameters of the blade root load sensors of multiple wind turbine units in the system, thereby improving calibration efficiency.

[0143] In the system provided in this application embodiment, firstly, the controller acquires the first calibration data of the blade root load sensor of the wind turbine in the first operating state and the second calibration data of the wind turbine in the second operating state, as well as the first impeller data of the blade root load sensor of the wind turbine in the first operating state and the second impeller data of the wind turbine in the second operating state; secondly, the controller determines the first theoretical gravitational torque load of the wind turbine based on the first impeller data; thirdly, the controller determines the calibration parameters based on the first calibration data, the first impeller data, and the first gravitational torque load; fourthly, the controller determines the second theoretical gravitational torque load based on the calibration parameters and the second impeller data; when the first gravitational torque load and the second gravitational torque load meet preset conditions, the controller determines the calibration parameters as target calibration parameters and calibrates the blade root load sensor of the wind turbine based on the target calibration parameters. Since the calibration parameters are determined based on the data from the first operating state and then verified based on the data from the second operating state, the calibration parameters are determined as the target calibration parameters for calibrating the blade root load sensor only when the preset conditions are met. Therefore, the calibration parameters of the blade root load sensor can be accurately determined, and the blade root load sensor can be calibrated using more accurate calibration parameters. Thus, the blade root load sensor can accurately measure the load on the impeller.

[0144] Furthermore, in conjunction with the sensor calibration parameter determination method in the above embodiments, this application embodiment can provide a computer-readable storage medium for implementation. This computer-readable storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the sensor calibration parameter determination methods in the above embodiments.

[0145] Based on the sensor calibration parameter determination methods described in the above embodiments, this application can provide a computer program product for implementation. When the instructions in this computer program product are executed by the processor of an electronic device, the electronic device implements any of the sensor calibration parameter determination methods described in the above embodiments.

[0146] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0147] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0148] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0149] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method of determining a sensor calibration parameter, characterized by, The method comprises: obtaining first blade root load sensor data and target impeller data, the first blade root load sensor data comprising first calibration data of a wind turbine in a first operating state and second calibration data of the wind turbine in a second operating state, and the target impeller data comprising first impeller data of the wind turbine in the first operating state and second impeller data of the wind turbine in the second operating state; wherein the first operating state comprises a non-idling state, and the second operating state comprises an idling state; determining a first theoretical gravity moment load of the wind turbine according to the first impeller data; determining a calibration parameter according to the first calibration data, the first impeller data and the first theoretical gravity moment load; determining a second theoretical gravity moment load according to the calibration parameter and the second impeller data; when the first theoretical gravity moment load and the second theoretical gravity moment load satisfy a preset condition, determining that the calibration parameter is a target calibration parameter.

2. The method of claim 1, wherein, The determining of the calibration parameter according to the first calibration data, the first impeller data and the first theoretical gravity moment load comprises: determining theoretical load strain data of the wind turbine according to the first impeller data and the first theoretical gravity moment load; determining actual load strain data of the wind turbine according to the first calibration data; determining the calibration parameter according to the actual load strain data and the theoretical load strain data.

3. The method according to claim 1 or 2, characterized in that, The obtaining of the first blade root load sensor data and the target impeller data comprises: obtaining, for each of a plurality of time points, unit operating data, second blade root load sensor data, environmental data and impeller data of the wind turbine; determining, as the first blade root load sensor data, the second blade root load sensor data corresponding to the time point of the unit operating data and the environmental data satisfying a screening condition; determining, as the target impeller data, the impeller data corresponding to the time point of the unit operating data and the environmental data satisfying the screening condition.

4. The method of claim 1, wherein, The method further comprises: calibrating a blade root load sensor of the wind turbine according to the target calibration parameter.

5. A device for determining a sensor calibration parameter, characterized in that The device comprises: an obtaining module configured to obtain first blade root load sensor data and target impeller data, the first blade root load sensor data comprising first calibration data of a wind turbine in a first operating state and second calibration data of the wind turbine in a second operating state, and the target impeller data comprising first impeller data of the wind turbine in the first operating state and second impeller data of the wind turbine in the second operating state; wherein the first operating state comprises a non-idling state, and the second operating state comprises an idling state; The determination module is configured to determine a first theoretical gravity moment load of the wind turbine according to the first blade data; determine a calibration parameter according to the first calibration data, the first blade data and the first theoretical gravity moment load; determine a second theoretical gravity moment load according to the calibration parameter and the second blade data; and determine the calibration parameter as a target calibration parameter when the first theoretical gravity moment load and the second theoretical gravity moment load satisfy a preset condition.

6. The apparatus of claim 5, wherein, The determination module is specifically configured to: determine theoretical load strain data of the wind turbine according to the first blade data and the first theoretical gravity moment load; determine actual load strain data of the wind turbine according to the first calibration data; determine the calibration parameter according to the actual load strain data and the theoretical load strain data.

7. The apparatus of claim 5 or 6, wherein, The acquisition module is specifically configured to: acquire, at each time point in a plurality of time points, unit operation data, second blade root load sensor data, environmental data and blade data of the wind turbine; determine the second blade root load sensor data at the time point corresponding to the unit operation data and the environmental data satisfying a screening condition as the first blade root load sensor data; determine the blade data at the time point corresponding to the unit operation data and the environmental data satisfying the screening condition as the target blade data.

8. The apparatus of claim 5, wherein, The device further comprises a calibration module configured to calibrate a blade root load sensor of the wind turbine according to the target calibration parameter.

9. An electronic device, comprising: The device comprises a processor and a memory storing computer program instructions; and the processor executes the computer program instructions to implement the method for determining a sensor calibration parameter according to any one of claims 1-4.

10. 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 a processor to implement the method for determining a sensor calibration parameter according to any one of claims 1-4.

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