A fan load determination method, system, electronic device and storage medium

Abstract

This application discloses a method, system, electronic device, and storage medium for determining wind turbine loads, belonging to the technical fields of wind power new energy technology and atmospheric environmental element detection technology. The method for determining wind turbine loads includes: determining the measured radial wind speed based on measured data collected by lidar; constructing a wind field model based on a wind profile model and a sensing zone model, and determining an expression for calculating the simulated radial wind speed based on the wind field model; fitting the measured radial wind speed and the simulated radial wind speed to obtain a wind shear index; constructing a three-dimensional simulated wind field based on an atmospheric turbulence model using the wind shear index; and operating a wind turbine model based on the three-dimensional simulated wind field to obtain wind turbine load data. This application can generate a three-dimensional simulated wind field that conforms to actual wind conditions, improving the accuracy of wind turbine load assessment.

Description

Technical Field

[0001] This application relates to the fields of wind power new energy technology and atmospheric environmental element detection technology, and in particular to a method, system, electronic device and storage medium for determining wind turbine load. Background Technology

[0002] Currently, the wind power industry is entering a phase of rapid development. With the continuous growth of installed capacity, wind turbine generators are evolving towards larger sizes. However, the increased size of the generators leads to more frequent and significant high wind shear in the operating environment. This non-uniform wind field causes complex aerodynamic load variations, increasing fatigue risks and consequently affecting the safety and lifespan prediction of the generators. Therefore, accurately reproducing high wind shear conditions and conducting load analysis in a simulation environment is a key issue for evaluating the operating performance and optimizing the design of large wind turbines, possessing significant theoretical and engineering value.

[0003] Relevant industry standards define a series of typical wind conditions for load calculation, but they do not cover all operating conditions that may occur in actual operation, especially high wind shear conditions. This leads to simulation results based on relevant industry standards potentially underestimating the load level of wind turbines under complex wind fields, thus posing design risks. To improve the accuracy of load prediction, it is urgent to observe and analyze the characteristics of high wind shear in actual wind farm environments and incorporate them into the simulation analysis process. Traditional observation methods mainly rely on meteorological masts for stratified wind speed measurements, but their limitations of fixed-height, single-point observation make it difficult to meet the characterization needs of large-scale wind shear characteristics, and they are also costly and have poor adaptability.

[0004] Therefore, how to generate a three-dimensional simulated wind field that conforms to actual wind conditions and improve the accuracy of wind turbine load assessment is a technical problem that needs to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of this application is to provide a method, system, electronic device and storage medium for determining wind turbine loads, which can generate a three-dimensional simulated wind field that conforms to actual wind conditions and improve the accuracy of wind turbine load assessment.

[0006] To address the aforementioned technical problems, this application provides a method for determining wind turbine load, the method comprising: The measured radial wind speed is determined based on the measured data collected by the lidar; wherein the lidar is installed in the nacelle of the wind turbine, and the orientation of the lidar is consistent with the direction of the hub axis of the wind turbine. A wind field model is constructed based on a wind profile model and an induction zone model, and an expression for calculating the simulated radial wind speed is determined based on the wind field model; wherein, the induction zone model is a model used to describe the wind speed changes in the upstream area of ​​the wind turbine caused by the wind turbine. The wind shear index is obtained by fitting the measured radial wind speed and the simulated radial wind speed. A three-dimensional simulated wind field based on an atmospheric turbulence model was constructed using the wind shear index. Based on the three-dimensional simulation wind farm operating wind turbine model, wind turbine load data is obtained.

[0007] Optionally, before determining the measured radial wind speed based on the measured data collected by lidar, the following steps are also included: Configure the scanning mode of the lidar so that the lidar emits scanning beams in at least four directions; The maximum range of the lidar is set based on the rotor diameter of the wind turbine; The emission direction of each scanning beam is set based on the hub height of the wind turbine.

[0008] Optionally, the measured radial wind speed can be determined based on the measured data collected by the lidar, including: Determine the jammed sector of the lidar; Data that is not located in the interfered sector from the measured data collected by the lidar is set as valid data; Determine whether the signal-to-noise ratio of the valid data is greater than a first threshold, and obtain a first determination result; Determine whether the acquisition rate of the valid data within the current time window is greater than the second threshold, and obtain the second determination result; Determine whether the maximum consecutive missing duration of the valid data within the current time window is less than a third threshold, and obtain a third determination result; If the first judgment result, the second judgment result, and the third judgment result are all yes, then the measured radial wind speed is determined based on the valid data.

[0009] Optionally, the measured radial wind speed and the simulated radial wind speed are fitted to obtain the wind shear index, including: Step 1: Set the measured radial wind speed collected in the i-th time period and within the preset distance as the current measured data; Step 2: Initialize the wind field parameters in the expression for the simulated radial wind speed; wherein, the wind field parameters include the free-flow wind speed at the hub height, the axial induction factor, the relative tilt angle, and the wind shear index; Step 3: Use the least squares method to fit the current measured data and the simulated radial wind speed to obtain the wind field parameters for the i-th time period; Step 4: Determine if the loop exit condition has been met; if yes, output the wind shear index corresponding to each time period; if no, increment the value of i by 1 and proceed to step 1.

[0010] Optionally, after obtaining the wind field parameters for the i-th time period, the following may also be included: Substitute the wind field parameters of the i-th time period into the expression of the simulated radial wind speed to obtain the current simulated radial wind speed of the target distance library; The measured radial wind speed in the target distance database and the current simulated radial wind speed are subjected to univariate linear regression analysis to obtain the coefficient of determination. If the determination coefficient is greater than or equal to the fourth threshold, then the wind field parameters for the i-th time period are retained; If the determination coefficient is less than the fourth threshold, then the wind field parameters for the i-th time period are removed.

[0011] Optionally, a three-dimensional simulated wind field based on an atmospheric turbulence model is constructed using the wind shear index, including: The wind field space range is set according to the impeller size of the wind turbine, and the computational domain corresponding to the wind field space range is discretized. Configure the model parameters of the atmospheric turbulence model; wherein, the model parameters include anisotropic parameters, turbulence integral scale, and turbulence dissipation intensity, and the anisotropic parameters are parameters determined according to the wind shear index; Within the computational domain, a three-dimensional simulated wind field based on the atmospheric turbulence model is constructed using the wind shear index.

[0012] Optionally, if the number of wind shear indices is greater than 1, then a three-dimensional simulated wind field based on an atmospheric turbulence model is constructed using the wind shear indices, including: For each wind shear index, a wind field construction operation based on an atmospheric turbulence model is performed to obtain three-dimensional simulated wind fields for multiple wind shear conditions.

[0013] This application also provides a wind turbine load determination system, the system comprising: The data acquisition module is used to determine the measured radial wind speed based on the measured data collected by the lidar; wherein the lidar is installed in the nacelle of the wind turbine, and the orientation of the lidar is consistent with the direction of the hub axis of the wind turbine. The modeling module is used to construct a wind field model based on a wind profile model and a sensing zone model, and to determine an expression for calculating the simulated radial wind speed based on the wind field model; wherein, the sensing zone model is a model used to describe the wind speed changes in the upstream area of ​​the wind turbine caused by the wind turbine. The fitting module is used to fit the measured radial wind speed and the simulated radial wind speed to obtain the wind shear index; The wind field construction module is used to construct a three-dimensional simulated wind field based on the atmospheric turbulence model using the wind shear index. The simulation module is used to obtain wind turbine load data based on the three-dimensional simulated wind field operating wind turbine model.

[0014] This application also provides a storage medium storing a computer program thereon, which, when executed, implements the steps of the wind turbine load determination method described above.

[0015] This application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor invokes the computer program in the memory to implement the steps of the wind turbine load determination method described above.

[0016] This application discloses a method for determining wind turbine loads. The method utilizes a lidar system installed in the wind turbine nacelle to detect the actual radial wind speed. It also constructs a wind field model based on a wind profile model and a sensing zone model, thereby deriving an expression for calculating the simulated radial wind speed using the wind field model. The aforementioned wind profile model reflects the change in wind speed with altitude, and the aforementioned sensing zone model reflects the disturbance of the upstream flow field by the wind turbine. The wind field model constructed based on these two models makes the expression for the simulated radial wind speed reasonable. This application fits the measured and simulated radial wind speeds to obtain the wind shear index. Using this wind shear index, a three-dimensional simulated wind field based on an atmospheric turbulence model is constructed to obtain wind turbine load data. Therefore, this application can generate a three-dimensional simulated wind field that conforms to actual wind conditions, improving the accuracy of wind turbine load assessment. This application also provides a wind turbine load determination system, a storage medium, and an electronic device, all with the aforementioned beneficial effects, which will not be elaborated further here. Attached Figure Description

[0017] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a method for determining wind turbine load provided in an embodiment of this application; Figure 2 A scanning schematic diagram of a lidar provided in an embodiment of this application; Figure 3 A schematic diagram of a rainflow counting method provided in an embodiment of this application; Figure 4 A flowchart illustrating a method for simulating and calculating high wind shear loads on wind turbines based on measured data from lidar, provided in this application embodiment. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] Please see below. Figure 1 , Figure 1 This is a flowchart illustrating a method for determining wind turbine load, as provided in an embodiment of this application.

[0021] Specific steps may include: S101: Determine the measured radial wind speed based on the measured data collected by the lidar.

[0022] In this embodiment, it can be applied to electronic devices with data calculation and simulation functions. This application can install a lidar in the nacelle of a wind turbine that needs to perform load detection. The orientation of the lidar (i.e., the direction of the central axis of the lidar) is consistent with the direction of the hub axis of the wind turbine.

[0023] A lidar can emit a scanning beam in a specific direction to obtain measured data on wind speed. This step can determine the measured radial wind speed based on the measured data collected by the lidar.

[0024] S102: Construct a wind field model based on the wind profile model and the sensing zone model, and determine the expression for calculating the simulated radial wind speed based on the wind field model.

[0025] The wind profile model describes the variation of wind speed with height, while the sensing zone model describes the wind speed variation in the upstream region caused by the turbine. The sensing zone model reflects the wind speed deceleration effect caused by energy extraction upstream of the turbine. This step couples the wind profile model and the sensing zone model to construct a wind field model that considers the variation of wind speed with height and the wind speed variation in the upstream region caused by the turbine. Based on the wind field model, the expression for the radial wind speed in the model can be determined. In this scheme, the radial wind speed calculated using the wind field model is called the simulated radial wind speed. The parameters in the wind field model include free-flow wind speed, axial induction factor, relative tilt angle, and wind shear index. Free-flow wind speed represents the undisturbed natural airflow speed at the hub height. Axial induction factor represents the degree of deceleration of the incoming wind speed by the turbine. Relative tilt angle represents the angle between the wind direction and the turbine's main shaft. The wind shear index represents the variation of wind speed in a plane perpendicular to the wind direction, and its magnitude reflects how quickly the wind speed increases with altitude.

[0026] S103: Fit the measured radial wind speed and the simulated radial wind speed to obtain the wind shear index.

[0027] This step involves parameter fitting (e.g., least squares method) between the measured radial wind speed obtained by lidar from the target range database and the simulated radial wind speed calculated based on the wind field model, to derive the optimal wind shear index. In this fitting process, the wind shear index is the parameter to be identified. Adjusting its value minimizes the residual between the simulated and measured values, thus obtaining the most realistic wind shear index under the current atmospheric boundary layer.

[0028] S104: Construct a three-dimensional simulated wind field based on an atmospheric turbulence model using the wind shear index.

[0029] In this step, the fitted wind shear index is used as the key input feature. Combined with the wind turbine operating status and historical wind data, a three-dimensional simulated wind field based on an atmospheric turbulence model (also known as a wind turbulence model, such as Mann) is constructed.

[0030] S105: Based on the three-dimensional simulation wind farm operating wind turbine model, obtain wind turbine load data.

[0031] This step involves controlling the wind turbine model to operate in the aforementioned three-dimensional simulated wind field, simulating the interaction between the blades and the wind field, obtaining load time history data of key components such as the blades and tower, and then calculating the wind turbine load data.

[0032] This embodiment utilizes a lidar system installed in the wind turbine nacelle to detect the actual radial wind speed. A wind field model is constructed based on a wind profile model and a sensing zone model, resulting in an expression for calculating the simulated radial wind speed using this model. The wind profile model reflects the change in wind speed with altitude, and the sensing zone model reflects the disturbance of the upstream flow field by the wind turbine. The wind field model constructed based on these two models ensures the rationality of the expression for the simulated radial wind speed. This embodiment fits the measured and simulated radial wind speeds to obtain the wind shear index. Using this wind shear index, a three-dimensional simulated wind field based on an atmospheric turbulence model is constructed to obtain wind turbine load data. Therefore, this embodiment can generate a three-dimensional simulated wind field that conforms to actual wind conditions, improving the accuracy of wind turbine load assessment.

[0033] As for Figure 1 In a further description of the corresponding embodiment, before determining the measured radial wind speed based on the measured data collected by the lidar, the lidar can be configured as follows: the lidar's scanning mode is configured so that the lidar emits scanning beams in at least four directions; the maximum range of the lidar is set based on the rotor diameter of the wind turbine; and the emission direction of each scanning beam is set based on the hub height of the wind turbine.

[0034] Specifically, the maximum range of the aforementioned lidar is greater than twice the rotor diameter. In this embodiment, the emission direction of the scanning beam can be set with the constraint that the scanning beam at the maximum range does not contact the ground.

[0035] As for Figure 1 As further described in the corresponding embodiment, after acquiring the measured data collected by the lidar, invalid or interfering data in the measured data can be removed to obtain the measured radial wind speed for fitting with the simulated radial wind speed.

[0036] Specifically, the process of determining the measured radial wind speed based on the measured data collected by lidar includes: The process involves: identifying the interference sector of the lidar; setting data from the measured data collected by the lidar that is not within the interference sector as valid data; determining whether the signal-to-noise ratio of the valid data is greater than a first threshold to obtain a first judgment result; determining whether the acquisition rate of the valid data within the current time window is greater than a second threshold to obtain a second judgment result; determining whether the maximum continuous missing measurement duration of the valid data within the current time window is less than a third threshold to obtain a third judgment result; if the first, second, and third judgment results are all yes, then determining the measured radial wind speed based on the valid data; if the first, second, and third judgment results are not all yes, then discarding the currently collected measured data and waiting for the next batch of measured data. The interference sector is defined as the incoming flow direction range where abnormal wind conditions are caused by the fan wake.

[0037] In this application, the detection area of ​​the lidar includes both interfered and non-interfered sectors. The data (i.e., valid data) in the non-interfered mountainous area is judged based on signal-to-noise ratio, acquisition rate, and maximum consecutive missing duration. The measured radial wind speed used for fitting operations is determined based on the valid data that passes the judgment. Acquisition rate refers to the ratio of the actual amount of data acquired within the current time window to the total amount of data that should be acquired. Maximum consecutive missing duration refers to the longest continuous time interval without data among all time periods with missing data within the current time window. The lidar can emit multiple beams to detect radial wind speed. In this embodiment, if the first, second, and third judgment results of the measured data for all beams in the non-interfered sector are all positive, the measured radial wind speed is determined based on the valid data of all beams. If the first, second, and third judgment results of the measured data for at least one beam in the non-interfered sector are not all positive, the currently acquired measured data is discarded, and the process waits for the next batch of measured data.

[0038] As for Figure 1A further description of the corresponding embodiment describes the process of fitting the measured radial wind speed and the simulated radial wind speed to obtain the wind shear index, which includes the following steps: Step A1: Set the measured radial wind speed collected in the i-th time period and within the preset distance as the current measured data.

[0039] The aforementioned preset distance is the target distance library. By using the measured radial wind speed in the target distance library, the interference of the wind turbine wake can be effectively avoided, and representative inflow wind information can be obtained within the controllable range of the sensing zone, thereby providing high-quality observation basis for the inversion of wind field model parameters.

[0040] Step A2: Initialize the wind field parameters in the expression for the simulated radial wind speed.

[0041] The wind field parameters include the free-flow wind speed at hub height, axial induction factor, relative tilt angle, and wind shear index.

[0042] Step A3: Use the least squares method to fit the current measured data and the simulated radial wind speed to obtain the wind field parameters for the i-th time period.

[0043] In this process, based on the wind field parameters obtained in the i-th time period, the wind shear index in the wind field parameters of the i-th time period can be used as the fitting result.

[0044] Step A4: Determine if the loop exit condition has been met; if yes, output the wind shear index corresponding to each time period; if no, increment the value of i by 1 and proceed to step A1.

[0045] The loop exit condition mentioned above can be a condition related to i. For example, if i is greater than the critical value, the loop exit condition is met; if i is less than or equal to the critical value, the loop exit condition is not met.

[0046] The above process allows for the fitting of measured radial wind speeds with simulated radial wind speeds over multiple time periods, yielding the wind shear index for each time period.

[0047] Furthermore, after obtaining the wind field parameters for the i-th time period, the wind field parameters for the i-th time period are substituted into the expression for the simulated radial wind speed to obtain the current simulated radial wind speed of the target distance library; a univariate linear regression analysis is performed on the measured radial wind speed of the target distance library and the current simulated radial wind speed to obtain the coefficient of determination; if the coefficient of determination is greater than or equal to the fourth threshold, the wind field parameters for the i-th time period are retained; if the coefficient of determination is less than the fourth threshold, the wind field parameters for the i-th time period are discarded.

[0048] As for Figure 1A further description of the corresponding embodiment: the process of constructing a three-dimensional simulated wind field based on an atmospheric turbulence model using the wind shear index includes: setting the wind field spatial range according to the impeller size of the wind turbine; discretizing the computational domain corresponding to the wind field spatial range; configuring the model parameters of the atmospheric turbulence model; wherein the model parameters include anisotropic parameters, turbulence integral scale, and turbulence dissipation intensity, and the anisotropic parameters are parameters determined according to the wind shear index; and constructing a three-dimensional simulated wind field based on the atmospheric turbulence model using the wind shear index within the computational domain.

[0049] As for Figure 1 As further described in the corresponding embodiment, if the number of wind shear indices is greater than 1, a wind field construction operation based on an atmospheric turbulence model can be performed for each wind shear index to obtain a three-dimensional simulation wind field for multiple wind shear conditions.

[0050] The process described in the above embodiments is illustrated below through examples in practical applications.

[0051] LiDAR, with its multi-beam scanning and high precision, can achieve multi-dimensional, high spatiotemporal resolution wind speed observation, effectively capturing the spatial distribution patterns of wind shear. However, due to the influence of the wind turbine sensing zone effect, directly using lidar measurements to calculate wind shear introduces bias. LiDAR-based wind field reconstruction methods offer a new approach to compensate for the shortcomings of relevant industry standards in covering extreme wind conditions. Therefore, how to accurately invert high-wind-shear wind fields and integrate them into a simulation environment to achieve load calculation and performance evaluation is a core aspect of improving the accuracy of large wind turbine load prediction and operational safety.

[0052] This embodiment provides a simulation calculation scheme for high wind shear loads on wind turbines based on measured data from lidar. This scheme incorporates high wind shear conditions from actual wind farms into the simulation calculation, improving load calculation accuracy and reducing wind turbine design and operational risks. This embodiment addresses the measurement characteristics of nacelle-type lidar in the upstream sensing zone of the wind turbine by constructing a mathematical model incorporating the sensing zone formula, and building a corrected model to improve wind shear calculation accuracy. This embodiment also includes high wind shear conditions from actual operation into the load simulation process, compensating for the insufficient coverage of traditional standard wind condition models under complex aerodynamic conditions. Compared with traditional methods, this method effectively reduces load calculation errors caused by neglecting the sensing zone effect and high wind shear conditions, providing a more accurate and reliable quantitative basis for the structural optimization design and safety assessment of large wind turbines.

[0053] This embodiment utilizes a nacelle-type lidar to reconstruct the wind field in the upstream sensing area of ​​the wind turbine, aiming to obtain accurate wind shear distribution and further provide a method for estimating wind turbine load under high wind shear conditions. This embodiment selects different combinations of observation modes according to actual observation requirements, designs elevation angles, azimuth angles, and distance ranges that meet the detection range; sequentially determines the validity of single-beam data; combines the sensing area formula to construct a mathematical model-based simulated radial wind speed and fits it with the measured radial wind speed, outputting accurate wind shear values; and constructs a three-dimensional wind field model based on the wind shear distribution values, further performing equivalent load analysis in a simulation environment.

[0054] The aforementioned simulation calculation method for high wind shear loads on wind turbines based on measured data from lidar utilizes a nacelle-type lidar to reconstruct the wind field in the upstream sensing area of ​​the wind turbine. The aim is to obtain accurate wind shear values ​​and further explore methods for estimating wind turbine loads under high wind shear conditions. The lidar scanning mode and parameters are set according to observation requirements. Based on verifying the validity of each beam's data, a mathematical model is established using the sensing area formula. The simulated radial wind speed is then fitted with the measured data to calculate the accurate wind shear value. Finally, this high wind shear condition is imported into a three-dimensional wind field model to complete the load simulation analysis. This method specifically includes the following steps: Step B1: Design of lidar measurement and scanning modes.

[0055] This step allows you to select the four-beam lidar scanning mode. The four beams emitted by the lidar are symmetrically distributed around the detection center. Set the vertical angle θ and the horizontal angle θ. The target range to be acquired is defined by four beams, denoted as LOS1, LOS2, LOS3, and LOS4. Four radial wind speed values ​​are acquired at a distance of x meters from the detection center (along the wind turbine hub axis), denoted as V. los1 V los2 V los3 V los4 Considering the induction zone effect, the furthest range of the lidar should be at least twice the rotor diameter D of the wind turbine to ensure that at least one range point can detect the free-flow wind speed. Generally, the aforementioned vertical and horizontal angles θ and θ... Set to forward scanning and ensure that it does not touch the ground when the distance x is at its maximum, i.e. , This represents the wheel hub height.

[0056] Please see Figure 2 , Figure 2 This is a schematic diagram of a lidar scanning system provided in an embodiment of this application. The diagram shows the spatial coordinate system XYZ, scanning points Vlos1, Vlos2, Vlos3, and Vlos4, the vertical angle θ, and the horizontal angle θ. .

[0057] The vertical angle θ is the angle between plane 1 (or plane 2) and the X-axis direction, and the horizontal angle is... The angle between plane 3 (or plane 4) and the X-axis direction is defined as follows: plane 1 is the plane containing scan points Vlos1, Vlos2 and the origin O; plane 2 is the plane containing scan points Vlos3, Vlos4 and the origin O; plane 3 is the plane containing scan points Vlos1, Vlos4 and the origin O; and plane 4 is the plane containing scan points Vlos2, Vlos3 and the origin O.

[0058] Step B2: Data Acquisition and Preprocessing.

[0059] This step performs quality control on the radial wind speed Vlos obtained in step B1, removing invalid data. This embodiment determines the validity of the data based on the following conditions: Condition 1: When the signal-to-noise ratio is greater than the threshold SNR th The time is considered a valid value. For a frequency domain signal with a single distance Ri, the validity determination formula is: Typically, in wind farm observations based on Doppler lidar under normal meteorological conditions, the signal-to-noise ratio (SNR) threshold is... th It can be set to 20dB.

[0060] Condition 2: Divide the time window T w Within the current time window, when the LiDAR data acquisition rate reaches the threshold R... valid And there is no continuous multi-second T miss When there is missing data (i.e., the critical duration), the current data window is considered valid. The calculation formula is as follows: , where N valid N represents the valid values ​​within the current window that satisfy condition 1. total For all data volumes. Typically, T is set to... w =600 seconds, when R valid >35%, T miss If the time is ≤10 seconds, the current window is considered to contain valid data.

[0061] Condition 3: Referring to the procedures for evaluating free sectors in relevant standards (such as IEC 61400-12-5), the wake formula applicable to naval lidar is used to calculate the jammed sectors of the naval lidar. The formula is as follows: Where k represents the wake diffusion coefficient, typically ranging from 0.05 to 0.1, and θ wake The angle of the sector affected by wake interference is represented by R, and the impeller radius is represented by R. Data from the non-interference sector is selected for calculation.

[0062] Generally, data at the current moment is considered valid when all beams meet all the above conditions.

[0063] Step B3: Construct a wind field model.

[0064] To improve the accuracy of wind field inversion, this step combines the wind profile model with a simple sensing zone model.

[0065] (1) Clearly define the lidar coordinate system (x L , y L , z L ), hub coordinate system (x H , y H , z H ) and wind (x W , y W ,z W The definition of a coordinate system, the location of its origin, the direction of its axes, and the transformation relationships between them.

[0066] The coordinate system is defined as follows: The lidar coordinate system adopts a right-handed Cartesian coordinate system, with (x... L , y L , z L This indicates that the origin of the coordinate system is set at the position of the lidar's emitted beam, and the X-axis of this coordinate system is determined by the lidar's optical centerline, pointing towards the windward side. The coordinates of the measurement point j are denoted as (x...). j,L , y j,L , z j,L Its specific value can be directly derived by measuring the position and combining the geometric parameters of the lidar. The hub coordinate system is based on (x... H , y H , z H This indicates that its origin is located at the center of the rotor plane; in this coordinate system, the X-axis points downwind, and x... H -y H The plane is horizontal, obtained by performing two angular rotations (one around the lidar's Y-axis and one around the lidar's X-axis) and a spatial translation on the lidar coordinate system. Wind coordinate system (x... W , y W , z W The origin of the hub coordinate system is the same, and its X-axis is aligned with the direction of the average wind vector. It is obtained by rotating the original coordinate system twice according to the relative direction of the wind and the vertical flow angle.

[0067] (2) In the hub coordinate system, the distance r from the lidar system Lj Normalized laser vector n measured at the location j,H for: Where j represents the measurement point in space, This represents the unit direction vector from the lidar to the measurement point j. Represents vector The three components in the hub coordinate system; [This indicates the position of the lidar in the hub coordinate system;] ] indicates the position of measurement point j in the hub coordinate system; The symbol [] represents the position of measurement point j in the lidar coordinate system. The line-of-sight wind speed can be calculated using the wind vector U = [u, v, w] and the normalized vector n of the laser beam direction. j,H The projection between them can be expressed as: In the wind vector, u represents the wind speed component along the x-axis, v represents the wind speed component perpendicular to u in the horizontal direction, and w represents the wind speed component in the vertical direction. This indicates the line-of-sight wind speed (i.e., radial wind speed). This represents the wind vector at measurement point j, where the subscript j indicates different measurement points in space.

[0068] (3) In flat terrain, the wind vector U can be simplified to U=[u, v, 0]. Therefore, the wind model can be represented by the following expression: Select the following power-law profile model: Where θ r V0 represents the relative tilt angle, V0 represents the horizontal wind speed at the hub height, and α represents the shear index. This indicates the horizontal wind speed at different altitudes. Represents a function.

[0069] (4) The expression for a simple sensing area is as follows: Where R represents the impeller radius, This represents the free-flow wind speed along the x-axis at the hub height. This represents the induction factor (i.e., the axial induction factor). The wind model, considering both the wind profile model and the induction zone effect model, can be expressed as: .

[0070] Where α represents the vertical wind shear index. Combining this with the power-law wind profile formula, the horizontal wind speed can be obtained. ,in, , , This indicates the free-flow wind speed at the hub height.

[0071] Furthermore, the components of the wind vector U (i.e., the expression for the simulated radial wind speed) can be specifically expressed as follows: , , Combined with the expression mentioned above Model-based simulation of radial wind speed was constructed. .

[0072] Step B4: Based on the measured radial wind speed V (averaged over 10 minutes) los With simulated radial wind speed The least squares fitting method can be specifically expressed as: . This indicates the number of beams. Generally, the formula... , θ r The initial value of α is set to 0, and V is selected within the range of 0.5R to 4R. los Perform fitting analysis. Apply the output wind field parameters to the target distance database, and further refine the data at that location. With V los When performing a univariate linear regression analysis, when the coefficient of determination R... 2 When a certain threshold is exceeded, the current fitting result is considered reliable. Therefore, statistical analysis is performed on the wind shear α to obtain the local wind shear distribution. Generally, the coefficient of determination R... 2 When the value is ≥0.99, the current fitting result is considered reliable.

[0073] This embodiment selects three months of data for analysis, assuming that the obtained wind shear distribution values ​​are representative; and when They believe that the high wind shear causes serious damage to the wind turbine and should be given special attention.

[0074] Step B5: Construct a three-dimensional time-varying wind field.

[0075] Based on the wind profile model and the obtained wind shear value in step B3, a three-dimensional simulated wind field is constructed.

[0076] The wind field area should fully cover the rotor plane of the wind turbine and the upstream flow influence area, with a longitudinal extension of no less than 4 times the rotor radius (≥4R). The computational domain should be discretized, with the vertical grid area covering the entire rotor swept surface, and a recommended height range of [H]. hub -1.2R, H hub +1.2R].

[0077] Turbulent wind fields were generated using the Mann model. By adjusting the anisotropy parameter Γ to correlate it with the target wind shear index α, three-dimensional simulated wind fields under different wind shear conditions were obtained. L is the integral scale of turbulence. The mean free-flow wind speed at hub height is used to achieve parametric modeling for different wind shear conditions.

[0078] The other two parameters of the Mann model can be set to... . This indicates the intensity of turbulent dissipation.

[0079] Step B6: Calculate the wind turbine load.

[0080] Based on the 3D wind field constructed in step B5, run the wind turbine model to obtain load time history data for key components such as the blades and tower. Identify extreme points in the load time history curves. According to the rainflow counting rule: flow begins from the extreme point and terminates the cycle when it encounters a smaller extreme point or an upper inflection point. Calculate the amplitude, mean, and corresponding cycle number for each cycle. Please refer to [link to relevant documentation]. Figure 3 , Figure 3 The diagram below illustrates a raindrop counting method provided in this application. The horizontal axis represents stress, the vertical axis represents time, and the numerical labels (such as 1, 2, 2', 3, 4, 5, 5', 6, 7, 7', 8, 9, 10) represent different peak values ​​and valleys during raindrop flow.

[0081] According to the IEC 61400-13 standard, combined with the material SN curve, the Miner linear cumulative damage theory is used to accumulate the damage caused by each stress cycle obtained in different wind field environments and calculate the equivalent fatigue load.

[0082] Equivalent fatigue load The calculation formula is as follows: ,in, This represents the load cycle amplitude within the i-th range of the cumulative rainfall spectrum; n i represents the cumulative number of load cycles in the i-th range of the cumulative rainflow spectrum; C is a material constant; m represents the slope of the SN curve of the material.

[0083] In steps B1 and B2, a nacelle-type lidar is used to detect the wind field. The corresponding distance library can be calculated according to actual needs, and wind field data that can be used for subsequent calculations can be filtered out according to a certain threshold.

[0084] In steps B3 and B4, the wind speed profile is dynamically corrected by combining the induction zone formula. This method can accurately reflect the aerodynamic response characteristics under different turbulent and high shear conditions. Compared with the traditional method that does not introduce induction zone correction, it can effectively reduce wind shear inversion error and improve model robustness.

[0085] Steps B5 and B6 incorporate high shear and complex wind conditions into the wind turbine load analysis process. Compared with traditional load analysis methods based on standard wind conditions, this method has higher engineering adaptability and reliability in complex aerodynamic scenarios.

[0086] The above-mentioned wind turbine load calculation method incorporates measured data into the simulation design, achieving a precise mapping from actual operating data to the simulation model, and providing a more comprehensive reference for wind turbine structure optimization.

[0087] Please see Figure 4 , Figure 4 The flowchart of a simulation calculation method for high wind shear load of a wind turbine based on measured data from lidar provided in this application embodiment includes the following steps: calculating the ten-minute average value of each beam, filtering the data, constructing a model-based radial wind speed expression based on the filtered data, fitting the simulated radial wind speed with the ten-minute measured radial data between 0.5D and 2D, obtaining n sets of position parameter values ​​by fitting one day's data, verifying the accuracy of unknown parameters using data outside of 2D, outputting accurate wind shear values, constructing a simulation environment, and calculating fatigue load.

[0088] The data filtering process includes: determining whether the signal-to-noise ratio is greater than the threshold, determining whether the data acquisition rate is met, and determining whether it is in the wake region.

[0089] The process of constructing a model-based radial wind speed expression includes: constructing the lidar detection trajectory based on the location of each detection point; constructing the wind vector based on the induction zone formula; and calculating the simulated radial wind speed expression based on the lidar dot product formula.

[0090] Compared with existing methods, this approach utilizes a nacelle-type lidar for wind field reconstruction, enabling high-precision inversion of high wind shear. This effectively corrects calculation errors caused by the wind turbine induction zone effect, more realistically reproducing complex wind field characteristics. This lays the foundation for accurately incorporating actual high wind shear conditions into the simulation environment and improving the reliability of load calculations. This method improves the accuracy of load prediction under high wind shear conditions, compensating for deficiencies in the IEC 61400-1 standard. Lidar can observe high wind shear conditions not covered by the IEC standard, providing more realistic input data for load simulation of large wind turbines in complex wind fields. This effectively avoids underestimation of structural design due to idealized simulation conditions, thereby improving the safety margin of wind turbine design.

[0091] This application embodiment also provides a wind turbine load determination system, which may include: The data acquisition module is used to determine the measured radial wind speed based on the measured data collected by the lidar; wherein the lidar is installed in the nacelle of the wind turbine, and the orientation of the lidar is consistent with the direction of the hub axis of the wind turbine. The modeling module is used to construct a wind field model based on a wind profile model and a sensing zone model, and to determine an expression for calculating the simulated radial wind speed based on the wind field model; wherein, the sensing zone model is a model used to describe the wind speed changes in the upstream area of ​​the wind turbine caused by the wind turbine. The fitting module is used to fit the measured radial wind speed and the simulated radial wind speed to obtain the wind shear index; The wind field construction module is used to construct a three-dimensional simulated wind field based on the atmospheric turbulence model using the wind shear index. The simulation module is used to obtain wind turbine load data based on the three-dimensional simulated wind field operating wind turbine model.

[0092] This embodiment utilizes a lidar system installed in the wind turbine nacelle to detect the actual radial wind speed. A wind field model is constructed based on a wind profile model and a sensing zone model, resulting in an expression for calculating the simulated radial wind speed using this model. The wind profile model reflects the change in wind speed with altitude, and the sensing zone model reflects the disturbance of the upstream flow field by the wind turbine. The wind field model constructed based on these two models ensures the rationality of the expression for the simulated radial wind speed. This embodiment fits the measured and simulated radial wind speeds to obtain the wind shear index. Using this wind shear index, a three-dimensional simulated wind field based on an atmospheric turbulence model is constructed to obtain wind turbine load data. Therefore, this embodiment can generate a three-dimensional simulated wind field that conforms to actual wind conditions, improving the accuracy of wind turbine load assessment.

[0093] Furthermore, it also includes: The radar configuration module is used to configure the scanning mode of the lidar before determining the measured radial wind speed based on the measured data collected by the lidar, so that the lidar emits scanning beams in at least four directions; it is also used to set the maximum range of the lidar based on the rotor diameter of the wind turbine; and it is also used to set the emission direction of each scanning beam based on the hub height of the wind turbine.

[0094] Furthermore, the process by which the data acquisition module determines the measured radial wind speed based on the measured data collected by the lidar includes: The interference sector of the lidar is determined; data in the measured data collected by the lidar that are not in the interference sector are set as valid data; the signal-to-noise ratio of the valid data is determined to be greater than a first threshold to obtain a first judgment result; the acquisition rate of the valid data in the current time window is determined to be greater than a second threshold to obtain a second judgment result; the maximum continuous missing measurement duration of the valid data in the current time window is determined to be less than a third threshold to obtain a third judgment result; if the first judgment result, the second judgment result, and the third judgment result are all yes, the measured radial wind speed is determined based on the valid data.

[0095] Furthermore, the fitting module fits the measured radial wind speed and the simulated radial wind speed to obtain the wind shear index, including the following steps: Step 1: Set the measured radial wind speed collected in the i-th time period and within the preset distance as the current measured data; Step 2: Initialize the wind field parameters in the expression for the simulated radial wind speed; wherein, the wind field parameters include the free-flow wind speed at the hub height, the axial induction factor, the relative tilt angle, and the wind shear index; Step 3: Use the least squares method to fit the current measured data and the simulated radial wind speed to obtain the wind field parameters for the i-th time period; Step 4: Determine if the loop exit condition has been met; if yes, output the wind shear index corresponding to each time period; if no, increment the value of i by 1 and proceed to step 1.

[0096] Furthermore, it also includes: The verification module is configured to, after obtaining the wind field parameters for the i-th time period, substitute the wind field parameters for the i-th time period into the expression for the simulated radial wind speed to obtain the current simulated radial wind speed of the target distance library; it is also configured to perform a univariate linear regression analysis on the measured radial wind speed of the target distance library and the current simulated radial wind speed to obtain a coefficient of determination; it is also configured to, if the coefficient of determination is greater than or equal to a fourth threshold, retain the wind field parameters for the i-th time period; and it is also configured to, if the coefficient of determination is less than the fourth threshold, discard the wind field parameters for the i-th time period.

[0097] Furthermore, the process of constructing a three-dimensional simulated wind field based on the atmospheric turbulence model using the wind shear index in the wind field construction module includes: setting the wind field spatial range according to the impeller size of the wind turbine, and discretizing the computational domain corresponding to the wind field spatial range; configuring the model parameters of the atmospheric turbulence model; wherein the model parameters include anisotropic parameters, turbulence integral scale, and turbulence dissipation intensity, and the anisotropic parameters are parameters determined according to the wind shear index; and constructing a three-dimensional simulated wind field based on the atmospheric turbulence model within the computational domain using the wind shear index.

[0098] Furthermore, if the number of wind shear indices is greater than 1, the process of the wind field construction module constructing a three-dimensional simulated wind field based on the atmospheric turbulence model using the wind shear indices includes: performing a wind field construction operation based on the atmospheric turbulence model for each wind shear index to obtain three-dimensional simulated wind fields for multiple wind shear conditions.

[0099] Since the embodiments of the system part correspond to the embodiments of the method part, please refer to the description of the embodiments of the method part for the embodiments of the system part, and they will not be repeated here.

[0100] This application also provides a storage medium on which a computer program is stored, which, when executed, can perform the steps provided in the above embodiments. The storage medium may include various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0101] This application also provides an electronic device that may include a memory and a processor. The memory stores a computer program, and when the processor calls the computer program in the memory, it can implement the steps provided in the above embodiments. Of course, the electronic device may also include various network interfaces, power supplies, and other components.

[0102] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

[0103] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only 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 one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for determining wind turbine load, characterized in that, include: The measured radial wind speed is determined based on the measured data collected by the lidar; wherein the lidar is installed in the nacelle of the wind turbine, and the orientation of the lidar is consistent with the direction of the hub axis of the wind turbine. A wind field model is constructed based on a wind profile model and an induction zone model, and an expression for calculating the simulated radial wind speed is determined based on the wind field model; wherein, the induction zone model is a model used to describe the wind speed changes in the upstream area of ​​the wind turbine caused by the wind turbine. The wind shear index is obtained by fitting the measured radial wind speed and the simulated radial wind speed. A three-dimensional simulated wind field based on an atmospheric turbulence model was constructed using the wind shear index. Based on the three-dimensional simulation wind farm operating wind turbine model, wind turbine load data is obtained.

2. The method for determining wind turbine load according to claim 1, characterized in that, Before determining the measured radial wind speed based on the measured data collected by lidar, the following steps are also included: Configure the scanning mode of the lidar so that the lidar emits scanning beams in at least four directions; The maximum range of the lidar is set based on the rotor diameter of the wind turbine; The emission direction of each scanning beam is set based on the hub height of the wind turbine.

3. The method for determining wind turbine load according to claim 1, characterized in that, The measured radial wind speed is determined based on the measured data collected by lidar, including: Determine the jammed sector of the lidar; Data that is not located in the interfered sector from the measured data collected by the lidar is set as valid data; Determine whether the signal-to-noise ratio of the valid data is greater than a first threshold, and obtain a first determination result; Determine whether the acquisition rate of the valid data within the current time window is greater than the second threshold, and obtain the second determination result; Determine whether the maximum consecutive missing duration of the valid data within the current time window is less than a third threshold, and obtain a third determination result; If the first judgment result, the second judgment result, and the third judgment result are all yes, then the measured radial wind speed is determined based on the valid data.

4. The method for determining wind turbine load according to claim 1, characterized in that, The wind shear index is obtained by fitting the measured radial wind speed and the simulated radial wind speed, including: Step 1: Set the measured radial wind speed collected in the i-th time period and within the preset distance as the current measured data; Step 2: Initialize the wind field parameters in the expression for the simulated radial wind speed; wherein, the wind field parameters include the free-flow wind speed at the hub height, the axial induction factor, the relative tilt angle, and the wind shear index; Step 3: Use the least squares method to fit the current measured data and the simulated radial wind speed to obtain the wind field parameters for the i-th time period; Step 4: Determine if the loop exit condition has been met; if yes, output the wind shear index corresponding to each time period; if no, increment the value of i by 1 and proceed to step 1.

5. The method for determining wind turbine load according to claim 4, characterized in that, After obtaining the wind field parameters for the i-th time period, the following is also included: Substitute the wind field parameters of the i-th time period into the expression of the simulated radial wind speed to obtain the current simulated radial wind speed of the target distance library; The measured radial wind speed in the target distance database and the current simulated radial wind speed are subjected to univariate linear regression analysis to obtain the coefficient of determination. If the determination coefficient is greater than or equal to the fourth threshold, then the wind field parameters for the i-th time period are retained; If the determination coefficient is less than the fourth threshold, then the wind field parameters for the i-th time period are removed.

6. The method for determining wind turbine load according to claim 1, characterized in that, Constructing a three-dimensional simulated wind field based on an atmospheric turbulence model using the aforementioned wind shear index includes: The wind field space range is set according to the impeller size of the wind turbine, and the computational domain corresponding to the wind field space range is discretized. Configure the model parameters of the atmospheric turbulence model; wherein, the model parameters include anisotropic parameters, turbulence integral scale, and turbulence dissipation intensity, and the anisotropic parameters are parameters determined according to the wind shear index; Within the computational domain, a three-dimensional simulated wind field based on the atmospheric turbulence model is constructed using the wind shear index.

7. The method for determining wind turbine load according to claim 1, characterized in that, If the number of wind shear indices is greater than 1, then a three-dimensional simulated wind field based on an atmospheric turbulence model is constructed using the wind shear indices, including: For each wind shear index, a wind field construction operation based on an atmospheric turbulence model is performed to obtain three-dimensional simulated wind fields for multiple wind shear conditions.

8. A wind turbine load determination system, characterized in that, include: The data acquisition module is used to determine the measured radial wind speed based on the measured data collected by the lidar; wherein the lidar is installed in the nacelle of the wind turbine, and the orientation of the lidar is consistent with the direction of the hub axis of the wind turbine. The modeling module is used to construct a wind field model based on a wind profile model and a sensing zone model, and to determine an expression for calculating the simulated radial wind speed based on the wind field model; wherein, the sensing zone model is a model used to describe the wind speed changes in the upstream area of ​​the wind turbine caused by the wind turbine. The fitting module is used to fit the measured radial wind speed and the simulated radial wind speed to obtain the wind shear index; The wind field construction module is used to construct a three-dimensional simulated wind field based on the atmospheric turbulence model using the wind shear index. The simulation module is used to obtain wind turbine load data based on the three-dimensional simulated wind field operating wind turbine model.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor, when calling the computer program in the memory, implements the steps of the wind turbine load determination method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores computer-executable instructions, which, when loaded and executed by a processor, implement the steps of the wind turbine load determination method as described in any one of claims 1 to 7.

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