Load prediction method and device for fan blade, equipment, storage medium and program product

Abstract

The invention discloses a fan blade load prediction method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring wind parameter information of a target wind field in a future preset time period, wherein the wind parameter information comprises first wind speeds and wind directions of different blade elements; generating wind regime information of the blade elements in a future preset time period based on the first wind speed and the wind direction corresponding to each blade element; according to a preset blade element momentum model and the wind condition information, the local blade load of each blade element in the future preset time period is determined, and the preset blade element momentum model is used for representing load distribution of different blade elements of the fan blade under different wind condition information; and carrying out integral summation on the local blade load of each blade element to obtain the overall blade load of the fan blade. According to the embodiment of the invention, a load monitoring sensor does not need to be installed, the operation and maintenance cost is reduced, the fan blade load in future time is accurately predicted, guidance is provided for operation and maintenance personnel, and the operation and maintenance efficiency is improved.

Description

Technical Field

[0001] This application belongs to the technical field of wind power generation, and particularly relates to a method, device, equipment, storage medium, and program product for predicting the load of a wind turbine blade. Background Art

[0002] Currently, in the wind power generation industry, in order to ensure the stable operation of wind turbine blades, the loads on the wind turbine blades are usually monitored.

[0003] In the related art, load monitoring sensors are usually installed on a wind turbine generator set to obtain the wind turbine blade loads through the load monitoring sensors. However, only the real-time monitoring of the wind turbine blade loads can be achieved through the load monitoring sensors, resulting in only being able to give an alarm when the wind turbine blade loads exceed a preset threshold, that is, when the wind turbine blades are severely loaded. Maintenance personnel repair the alarmed wind turbine blades, and it is impossible to predict the wind turbine blade loads in the future time period, greatly reducing the maintenance efficiency. Moreover, installing load monitoring sensors on each wind turbine blade increases the monitoring cost and the maintenance cost. Summary of the Invention

[0004] Embodiments of this application provide a method, device, equipment, storage medium, and program product for predicting the load of a wind turbine blade, which do not require installing load monitoring sensors, reduce the maintenance cost, and accurately predict the wind turbine blade loads in the future time, providing guidance for maintenance personnel and improving the maintenance efficiency.

[0005] On the one hand, embodiments of this application provide a method for predicting the load of a wind turbine blade. The wind turbine blade includes elements discretely arranged along the length direction of the wind turbine blade. The method includes:

[0006] Obtain wind parameter information of a target wind farm in a future preset time period. The wind parameter information includes the first wind speed and wind direction of different elements.

[0007] Generate wind condition information of each element in the future preset time period respectively based on the first wind speed and wind direction corresponding to each element.

[0008] Determine the local blade loads of each element in the future preset time period respectively according to a preset element momentum model and the wind condition information. The preset element momentum model is used to represent the load distribution of different elements of the wind turbine blade under different wind condition information.

[0009] Integrate and sum the local blade loads of each element to obtain the overall blade load of the wind turbine blade.

[0010] Optionally, before obtaining the wind parameter information of the target wind farm in the future preset time period, the method may further include:

[0011] Obtain the historical meteorological data and geographical static data of the target wind farm;

[0012] Input the historical meteorological data and geographical static data into a preset initial wind parameter prediction model to obtain the predicted height and the corresponding predicted wind parameter information;

[0013] In the case where the predicted height meets the matching condition with the preset target height and the predicted wind parameter information is inconsistent with the historical meteorological data corresponding to the target wind farm, adjust the calculation parameters of the preset initial wind parameter prediction model until the predicted wind parameter information is consistent with the historical meteorological data to obtain a preset wind parameter prediction model;

[0014] The obtaining of the wind parameter information of the target wind farm within a preset future time period includes:

[0015] Obtain the wind parameter information of the target wind farm within a preset future time period through the preset wind parameter prediction model.

[0016] Optionally, the generating of the wind condition information of each blade element within a preset future time period respectively based on the first wind speed and wind direction corresponding to each blade element includes:

[0017] For each blade element of the wind turbine blade, determine the average wind speed at the position where the blade element is located based on the first wind speed corresponding to each blade element;

[0018] Determine the turbulence intensity corresponding to each blade element according to the first wind speed and the average wind speed;

[0019] Determine the wind condition information corresponding to each blade element according to the turbulence intensity, the first wind speed, and the wind direction.

[0020] Optionally, before respectively determining the local blade loads of each blade element within a preset future time period according to the preset blade element momentum model and the wind condition information, the method further includes:

[0021] Obtain the blade length of the wind turbine blade;

[0022] Perform circular unit division on the length of the wind turbine blade according to a preset division length to obtain a plurality of blade elements;

[0023] Generate a preset blade element momentum model according to the plurality of blade elements and a preset momentum relationship.

[0024] Optionally, the preset future time period includes a plurality of first moments, and the determining of the local blade loads of each blade element within a preset future time period according to the preset blade element momentum model and the wind condition information includes:

[0025] Based on the wind condition information, determine the second wind speed of the wind turbine blade, where the second wind speed includes the wind speed reaching the wind turbine blade at each first moment within the target wind farm;

[0026] For each blade element, obtain the first distance from the blade element to the hub center, as well as the rotational angular velocity, pitch angle, and blade twist angle of the wind turbine blade;

[0027] Based on the second wind speed, first distance, rotational angular velocity, pitch angle, and blade twist angle, determine the induction factor at each first moment within the future preset time period;

[0028] Calculate the deviation value of the induction factor between the first moment and the second moment, where the second moment is the moment adjacent to the first moment;

[0029] In the case where the deviation value is not within the preset deviation range and the induction factor at the first moment is less than the preset threshold, determine the first inflow angle based on the induction factor at the first moment;

[0030] Obtain the first lift - drag coefficient, air density, relative velocity, and blade length corresponding to the first inflow angle;

[0031] Based on the first inflow angle, first lift - drag coefficient, air density, and blade length, determine the local blade load corresponding to the blade element at the first moment.

[0032] Optionally, the induction factor includes an initial axial induction factor and an initial radial induction factor. The step of determining the induction factor at each first moment within the future preset time period based on the second wind speed, first distance, rotational angular velocity, pitch angle, and blade twist angle includes:

[0033] Based on the first distance and the rotational angular velocity, determine the linear velocity of the blade element;

[0034] Based on the ratio of the second wind speed to the linear velocity, determine the initial inflow angle;

[0035] Based on the sum of the pitch angle and the blade twist angle, determine the local pitch angle of the blade element;

[0036] Based on the difference between the local pitch angle and the initial inflow angle, determine the angle of attack of the blade element;

[0037] Based on the relationship between the preset angle of attack and the lift - drag coefficient, determine the initial lift - drag coefficient corresponding to the angle of attack;

[0038] Based on the initial lift - drag coefficient and the initial inflow angle, determine the initial axial thrust coefficient and the initial radial thrust coefficient;

[0039] Determine an initial axial induction factor based on the initial inflow angle and the initial axial thrust coefficient;

[0040] Determine an initial radial induction factor based on the initial inflow angle and the initial radial thrust coefficient.

[0041] Optionally, the local blade load includes an axial thrust and a radial thrust, the first lift-drag coefficient includes a first axial lift-drag coefficient and a first radial lift-drag coefficient, and determining the local blade load corresponding to the blade element at the first moment according to the first inflow angle, the first lift-drag coefficient, the air density, and the blade length includes:

[0042] For each blade element, determine a first axial thrust coefficient according to the first inflow angle and the first axial lift-drag coefficient;

[0043] Determine a first radial thrust coefficient according to the first inflow angle and the first radial lift-drag coefficient;

[0044] Determine the axial thrust according to the first axial thrust coefficient, the air density, the relative velocity, and the blade length;

[0045] Determine the radial thrust according to the first radial thrust coefficient, the air density, the relative velocity, and the blade length.

[0046] Optionally, when the induction factor at the first moment is greater than a preset threshold, the method further includes:

[0047] Determine a tip loss factor based on a preset Prandtl correction factor and the initial inflow angle;

[0048] Determine a root loss factor based on a preset Prandtl correction factor and the initial inflow angle;

[0049] Determine a correction factor according to the product of the tip loss factor and the root loss factor;

[0050] Correct the induction factor according to the correction factor to obtain a corrected induction factor.

[0051] The determining the first inflow angle based on the induction factor at the first moment includes:

[0052] Determine the first inflow angle based on the corrected induction factor corresponding to the first moment.

[0053] Optionally, after integrating and summing the local blade loads of each blade element to obtain the overall blade load of the wind turbine blade, the method further includes:

[0054] Obtain the remaining service life and the ultimate load of the wind turbine blade;

[0055] For each wind turbine blade, based on the overall blade load and the ultimate load, determine a load ratio;

[0056] Based on the load ratio and the remaining service life, determine the safety risk level of the wind turbine blade.

[0057] Optionally, the determining the safety risk level of the wind turbine blade based on the load ratio and the remaining service life includes:

[0058] Based on the load ratio and a preset load threshold, determine an ultimate weight;

[0059] Based on the overall blade load, determine the life loss of the wind turbine blade;

[0060] Based on the life loss and the remaining service life, determine a fatigue weight;

[0061] Accumulate the load ratios in a preset time sequence to obtain an operation ratio;

[0062] Based on the operation ratio and a preset operation threshold, determine an operation weight;

[0063] Based on the ultimate weight, the fatigue weight, and the operation weight, determine the safety risk level of the wind turbine blade.

[0064] On the other hand, an embodiment of the present application provides a load prediction device for a wind turbine blade. The wind turbine blade includes blade elements discrete along the length direction of the wind turbine blade. The device includes:

[0065] An acquisition module, configured to acquire wind parameter information of a target wind farm within a preset future time period. The wind parameter information includes the first wind speed and wind direction of different blade elements;

[0066] A generation module, configured to generate wind condition information of each blade element within a preset future time period respectively based on the first wind speed and wind direction corresponding to each blade element;

[0067] A determination module, configured to determine the local blade load of each blade element within a preset future time period respectively according to a preset blade element momentum model and the wind condition information. The preset blade element momentum model is used to represent the load distribution of different positions of the wind turbine blade under different wind condition information;

[0068] A summation module, configured to perform integral summation on the local blade loads of each blade element to obtain the overall blade load of the wind turbine blade. On yet another aspect, an embodiment of the present application provides an electronic device, which includes: a processor and a memory storing computer program instructions;

[0069] When the processor executes the computer program instructions, the load prediction method of the wind turbine blade as described in the first aspect is implemented.

[0070] In another aspect, an embodiment of the present application provides a computer storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the load prediction method of the wind turbine blade as described in the first aspect is implemented.

[0071] In another aspect, an embodiment of the present application provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device is caused to execute the load prediction method of the wind turbine blade as described in the first aspect.

[0072] Since in the target wind farm, the wind parameter information at different heights is different, resulting in different wind parameter information at different positions of the wind turbine blade, the load prediction method, device, equipment, storage medium and program product of the wind turbine blade in the embodiment of the present application, when performing the load prediction of the wind turbine blade, generate the wind condition information of each blade element within a future preset time period by obtaining the wind parameter information of different blade elements discretely along the length direction of the wind turbine blade, so as to be able to determine the local blade load of each blade element in the wind turbine blade within the future preset time period based on the wind condition information of each blade element, that is, accurately calculate the local blade load of each blade element through the preset blade element momentum model and the wind condition information. Compared with the overall blade load of the wind turbine blade directly obtained by the load monitoring sensor in the related technology, the present application can perform local blade load prediction and calculation on different blade elements of the wind turbine blade, and by integrating and summing the local blade loads of all blade elements, it can more accurately predict the overall blade load of the wind turbine blade within the future preset time period, provide operation and maintenance support for the operation and maintenance of the wind turbine blade within the future preset time period, improve the operation and maintenance efficiency. In addition, the local blade load calculated by the preset blade element momentum model in the present application and the overall blade load obtained by integrating and summing the local blade loads do not require setting a load monitoring sensor, which can reduce the monitoring cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required to be used in the embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0074] Figure 1 is a flowchart of the load prediction method of the wind turbine blade provided by an embodiment of the present application;

[0075] Figure 2 is a flowchart of the load prediction method of the wind turbine blade provided by another embodiment of the present application;

[0076] Figure 3 It is a schematic flow chart of a load prediction method for a wind turbine blade provided by another embodiment of the present application;

[0077] Figure 4 It is a schematic diagram showing wind condition information provided by another embodiment of the present application;

[0078] Figure 5 It is a schematic diagram showing the blade element distribution of a wind turbine blade provided by another embodiment of the present application;

[0079] Figure 6 It is a schematic diagram of an annular unit of a wind turbine blade provided by another embodiment of the present application;

[0080] Figure 7 It is a schematic diagram of the cross-sectional velocity of a wind turbine blade provided by another embodiment of the present application;

[0081] Figure 8 It is a schematic flow chart of a load prediction method for a wind turbine blade provided by another embodiment of the present application;

[0082] Figure 9 It is a schematic flow chart of a load prediction method for a wind turbine blade provided by another embodiment of the present application;

[0083] Figure 10 It is a schematic diagram of a wind turbine blade coordinate system provided by another embodiment of the present application;

[0084] Figure 11 It is a schematic flow chart of a load prediction method for a wind turbine blade provided by another embodiment of the present application;

[0085] Figure 12 It is a schematic flow chart of a load prediction method for a wind turbine blade provided by another embodiment of the present application;

[0086] Figure 13 It is a schematic structural diagram of a load prediction system for a wind turbine blade provided by another embodiment of the present application;

[0087] Figure 14 It is a schematic structural diagram of a load prediction device for a wind turbine blade provided by another embodiment of the present application;

[0088] Figure 15 It is a schematic structural diagram of an electronic device provided by another embodiment of the present application. Detailed implementation manners

[0089] The features and exemplary embodiments of various aspects of the present application will be described in detail below. To make the objectives, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without some of these specific details. The following description of the embodiments is only intended to provide a better understanding of the present application by showing examples of the present application.

[0090] It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, the elements defined by the statement "comprising..." do not exclude the presence of additional identical elements in the process, method, article or device comprising the said elements.

[0091] To solve the problems of the prior art, the embodiments of the present application provide a method, device, equipment, storage medium and program product for predicting the load of a wind turbine blade. Since the wind parameter information at different heights in the target wind farm is different, resulting in different wind parameter information at different positions of the wind turbine blade, when predicting the load of the wind turbine blade, by obtaining the wind parameter information of different blade elements discretely along the length direction of the wind turbine blade, the wind condition information of each blade element within a preset future time period is generated, so as to be able to determine the local blade load of each blade element in the wind turbine blade within the preset future time period based on the wind condition information of each blade element, that is, by using the preset blade element momentum model and the wind condition information to accurately calculate the local blade load of each blade element. Compared with the overall blade load of the wind turbine blade directly obtained by a load monitoring sensor in the related art, the present application can perform local blade load prediction and calculation on different blade elements of the wind turbine blade, and by integrating and summing the local blade loads of all blade elements, it can more accurately predict the overall blade load of the wind turbine blade within the preset future time period, provide operation and maintenance support for the operation and maintenance of the wind turbine blade within the preset future time period, improve the operation and maintenance efficiency. In addition, the local blade load calculated by the preset blade element momentum model and the overall blade load obtained by integrating and summing the local blade loads in the present application do not require the setting of a load monitoring sensor, which can reduce the monitoring cost.

[0092] First, the load prediction method for the wind turbine blade provided by the embodiments of the present application will be introduced below.

[0093] Figure 1 The flowchart of the load prediction method for the wind turbine blade provided by an embodiment of the present application is shown. As Figure 1 shown, the load prediction method for the wind turbine blade may include S101 - S104:

[0094] S101, obtain the wind parameter information of the target wind farm within a preset future time period.

[0095] As an example, the wind parameter information includes the first wind speed and wind direction of different blade elements.

[0096] In this embodiment, when obtaining the wind parameter information of the target wind farm within a preset future time period, it can be obtained through the Weather Research & Forecasting (WRF) platform. Based on the current real - time wind parameters, the WRF platform can generate the wind parameter information within a preset future time period.

[0097] It should be noted that the WRF platform can obtain the wind parameter information of each location and simulate the wind parameter information within a preset future time period according to the obtained wind parameter information, so as to predict the overall blade load of the wind turbine blade.

[0098] S102, respectively generate the wind condition information of the blade element within a preset future time period based on the first wind speed and wind direction corresponding to each blade element.

[0099] In some embodiments, for a wind turbine blade, to facilitate the analysis of its blade load, the wind turbine blade can be discretely divided into multiple blade elements along the length direction of the wind turbine blade. Each blade element can form a wake unit with the hub center as the center of the circle and the position of the wind turbine blade as the radius when the wind turbine blade rotates.

[0100] Since the wind parameter information may be different on each blade element, it is necessary to generate the corresponding wind condition information for each blade element to accurately calculate the blade load for each blade element.

[0101] S103, respectively determine the local blade load of each blade element within a preset future time period according to the preset blade - element momentum model and the wind condition information.

[0102] In some embodiments, the preset blade - element momentum model is used to represent the load distribution of different blade elements of the wind turbine blade under different wind condition information; among them, the preset blade - element momentum model can be a calculation model that combines the momentum theory and the actual situation of the blade elements of the wind turbine blade.

[0103] That is, the local blade loads of different blade elements can be calculated according to the preset blade element momentum model and the wind condition information corresponding to each blade element.

[0104] S104, integrate and sum the local blade loads of each blade element to obtain the overall blade load of the wind turbine blade.

[0105] In some embodiments, after obtaining the local blade loads of each blade element, in order to accurately predict the overall blade load of the wind turbine blade, the local blade loads of each blade element can be summed to obtain the overall blade load of the wind turbine blade.

[0106] In some embodiments, since the wind parameter information at different heights in the target wind farm is different, resulting in different wind parameter information at different positions of the wind turbine blade, when predicting the load of the wind turbine blade, the wind parameter information of different blade elements discretely distributed along the length direction of the wind turbine blade is obtained, and the wind condition information of each blade element within a preset future time period is generated, so as to determine the local blade load of each blade element in the wind turbine blade within the preset future time period based on the wind condition information of each blade element, that is, the local blade load of each blade element is accurately calculated through the preset blade element momentum model and the wind condition information. Compared with the overall blade load of the wind turbine blade directly obtained by the load monitoring sensor in the related art, the present application can predict and calculate the local blade loads of different blade elements of the wind turbine blade, and by integrating and summing the local blade loads of all blade elements, it can more accurately predict the overall blade load of the wind turbine blade within the preset future time period, provide operation and maintenance support for the operation and maintenance of the wind turbine blade within the preset future time period, improve the operation and maintenance efficiency. In addition, the local blade load calculated by the preset blade element momentum model and the overall blade load obtained by integrating and summing the local blade loads in the present application do not require setting a load monitoring sensor, which can reduce the monitoring cost.

[0107] Refer to Figure 2 , in some embodiments, in order to obtain the accurate first wind speed within the preset future time period, before S101, the method may further include:

[0108] S1011, obtain the historical meteorological data and geographical static data of the target wind farm;

[0109] S1012, input the historical meteorological data and geographical static data into the preset initial wind parameter prediction model to obtain the predicted height and the corresponding predicted wind parameter information;

[0110] S1013, when the predicted height meets the matching condition with the preset target height and the predicted wind parameter information is inconsistent with the historical meteorological data of the target wind farm, adjust the calculation parameters of the preset initial wind parameter prediction model until the predicted wind parameter information is consistent with the historical meteorological data to obtain the preset wind parameter prediction model;

[0111] S101 may specifically include:

[0112] Obtain the wind parameter information of the target wind farm within a preset time period in the future through a preset wind parameter prediction model.

[0113] In this embodiment, in wind resource forecasting, the WRF (Weather Research and Forecasting) model, as an advanced numerical weather forecasting tool, has become an important technical means for global wind energy resource assessment and wind power project planning. Therefore, in the embodiments of this application, the WRF model is used as the preset initial wind parameter prediction model to predict the wind parameter information of different blade elements of the target wind farm.

[0114] For target wind farms in different places, the wind parameter information at different positions and different heights is inconsistent. Therefore, for each target wind farm, a corresponding wind parameter prediction model needs to be trained to accurately predict the wind parameter information of the target wind farm within a preset time period in the future.

[0115] Specifically, the preset initial wind parameter prediction model can be trained by obtaining the historical meteorological data and geographical static data of the target wind farm within a certain historical period; as an example, the historical meteorological data may include historical wind speed, historical wind direction, historical temperature, historical air pressure, and historical humidity, and the geographical static data may include the longitude, latitude, and altitude of the target wind farm.

[0116] After obtaining the historical meteorological data and geographical static data, then gradually and accurately determine the historical meteorological data and geographical static data of the target wind farm through the corresponding nesting strategy. For example, taking County C, City B, Province A as an example, since the accuracy of the obtained historical meteorological data and geographical static data is not accurate enough, the position accuracy can be continuously improved through grids to obtain the accurate historical meteorological data and geographical static data of the location where the target wind farm is located.

[0117] Specifically, the grid points included in Province A can be determined first to obtain the historical meteorological data and geographical static data corresponding to the first-layer grid points, and then the grid range is continuously narrowed to obtain the grid points corresponding to City B, that is, the second-layer grid points. Then, the historical meteorological data and geographical static data corresponding to the second-layer grid points are determined from the historical meteorological data and geographical static data corresponding to the first-layer grid points. After that, the grid range is narrowed again to obtain the third-layer grid points corresponding to County C. Then, the historical meteorological data and geographical static data corresponding to the third-layer grid points are determined from the historical meteorological data and geographical static data corresponding to the second-layer grid points, that is, the historical meteorological data and geographical static data corresponding to the target wind farm.

[0118] It should be noted that determining the accurate historical meteorological data and geographical static data of the target wind farm by continuously narrowing the grid range is a conventional method and will not be elaborated here.

[0119] Further, after obtaining the accurate historical meteorological data and geographical static data of the target wind field, the historical meteorological data and geographical static data can be input into a preset initial wind parameter prediction model. The preset initial wind parameter prediction model calculates based on the input historical meteorological data and geographical static data to obtain the predicted height and the corresponding predicted wind parameter information. Then, by judging whether the predicted height meets the matching condition with the preset target height, if the predicted height does not meet the matching condition with the preset target height, the preset target height is modified, and then it is judged again whether the predicted height meets the matching condition with the modified preset target height until the predicted height meets the matching condition with the preset target height.

[0120] When the predicted height meets the matching with the preset target height, it is also necessary to verify the predicted wind parameter information corresponding to the predicted height, that is, to verify whether the predicted wind speed in the predicted wind parameter information is within the error tolerance range through the historical wind speed. If it is within the error range, it means that the predicted wind parameter information is consistent with the historical meteorological data; otherwise, it is inconsistent. When the predicted risk information is inconsistent with the historical meteorological data, the calculation parameters of the preset initial wind parameter prediction model are continuously adjusted until the predicted wind parameter information is consistent with the historical meteorological data, and a preset wind parameter prediction model is obtained, that is, this predicted wind parameter prediction model can predict the wind parameter information of the target wind field at different times.

[0121] In this embodiment, for different target wind fields, the wind parameter information within a preset future time period can be obtained through the preset wind parameter prediction model corresponding to the target wind field.

[0122] Refer to Figure 3 , in some other embodiments, in order to accurately generate wind condition information, S102 may include:

[0123] S1021, for each element of the wind turbine blade, based on the first wind speed corresponding to each element, determine the average wind speed at the position where the element is located;

[0124] S1022, according to the first wind speed and the average wind speed, determine the turbulence intensity corresponding to each element;

[0125] S1023, according to the turbulence intensity, the first wind speed and the wind direction, determine the wind condition information corresponding to each element.

[0126] In some embodiments, when determining the wind condition information of the element, the average wind speed at the position where the element is located within a preset future time period can be first determined according to the first wind speed, and it can also be directly obtained through a meteorological research and forecasting platform. Then, according to the first wind speed, the average wind speed and the number of elements, the turbulence intensity corresponding to the element is determined. Specifically, it can be calculated according to the following formula (1):

[0127]

[0128] Among them, I is the turbulence intensity, and V i is the first wind speed corresponding to the blade element, and is the average wind speed.

[0129] In this embodiment, the wind condition information corresponding to each blade element can be determined according to the turbulence intensity, the first wind speed, and the wind direction calculated above. Among them, the wind condition information can include the turbulence intensity, the first wind speed, and the wind direction. The wind direction can include the angle reaching the wind turbine blade. The specific angles reaching the wind turbine will be introduced in detail below and will not be elaborated here.

[0130] In this embodiment, generating the wind condition information through the turbulence intensity, the first wind speed, and the wind direction can facilitate determining the local blade load through the wind condition information, so as to quickly and accurately calculate the local blade load.

[0131] For the convenience of understanding the wind condition information, refer to Figure 4 , Figure 4 which shows the wind condition information of a blade element.

[0132] In some other embodiments, in order to facilitate calculating the local blade load of each blade element, before S103, the method may further include:

[0133] Obtaining the blade length of the wind turbine blade;

[0134] Dividing the length of the wind turbine blade into annular units according to a preset division length to obtain a plurality of blade elements;

[0135] Generating a preset blade element momentum model according to the plurality of blade elements and a preset momentum relationship.

[0136] In this embodiment, in order to accurately calculate the local blade load of each blade element, it is first necessary to divide the wind turbine blade into annular units. The wind turbine blade can be divided into a plurality of blade elements with a preset division length of dr for N lengths. Then, by combining the momentum theory and the blade elements, a blade element momentum model is generated to quickly and accurately calculate the local blade load of each blade element.

[0137] Specifically, as an example, reference can be made to Figure 5 and Figure 6 , Figure 5 which shows the distribution of blade elements divided for a wind turbine blade, Figure 6 and is the rotation trajectory of each blade element when the wind turbine blade rotates.

[0138] In some other embodiments, the blade elements divided by the above method have the following two characteristics:

[0139] (1) The radial properties are independent of each other, that is, a change in one blade element does not affect other blade elements.

[0140] (2) The force on each blade element is a constant and remains unchanged.

[0141] Referring to Figure 7 , Figure 7 shows the velocity distribution of the wind condition information reaching the wind turbine blade. Among them, V0 is the second wind speed, Vrel is the relative speed, W is the induced speed, φ is the inflow angle, α is the angle of attack, θ is the pitch angle, a is the axial induction factor, and a’ is the tangential induction factor. Specifically, when calculating the local blade load of a blade element, the local blade load can be determined by determining the above calculation parameters. The following details the steps for determining the local blade load based on the above calculation parameters.

[0142] Referring to Figure 8 , in some other embodiments, the future preset time period may include multiple first moments. That is, within the future preset time period, the wind condition information determined by the above method can reach the wind turbine blade within the future preset time period, and the local blade load is determined. Specifically, S103 may include:

[0143] S1031, based on the wind condition information, determine the second wind speed of the wind turbine blade;

[0144] S1032, for each blade element, obtain the first distance from the blade element to the hub center, as well as the rotational angular velocity, pitch angle, and blade twist angle of the wind turbine blade;

[0145] S1033, according to the second wind speed, first distance, rotational angular velocity, pitch angle, and blade twist angle, determine the induction factor at each first moment within the future preset time period;

[0146] S1034, calculate the deviation value of the induction factor between the first moment and the second moment, where the second moment is the moment adjacent to the first moment;

[0147] S1035, when the deviation value is not within the preset deviation range and the induction factor at the first moment is less than the preset threshold, based on the induction factor at the first moment, determine the first inflow angle;

[0148] S1036, obtain the first lift-drag coefficient, air density, relative speed, and blade length corresponding to the first inflow angle;

[0149] S1037, according to the first inflow angle, first lift-drag coefficient, air density, and blade length, determine the local blade load corresponding to the blade element at the first moment.

[0150] In this embodiment, in S1031, as an example, the second wind speed includes the wind speed reaching the wind turbine blade at each first moment within the target wind field. That is, since the wind condition information may not be the wind condition at the location of the wind turbine blade and the wind condition may change when it reaches the wind turbine blade, in order to accurately predict the local blade load of the wind turbine blade, it is necessary to predict the second wind speed at which the first wind speed reaches the wind turbine blade through a meteorological analysis and forecasting platform based on the first wind speed, and then determine the local blade load of the wind turbine blade according to the second wind speed.

[0151] In some other embodiments, in S1032, since the size of each wind turbine blade is fixed, the first distance can be obtained according to the divided blade elements, as well as the rotational angular velocity, pitch angle, and blade twist angle of the wind turbine blade during rotation. Among them, the pitch angle and blade twist angle can be directly calculated according to the wind direction in the wind condition information.

[0152] In some other embodiments, in S1033, the induction factor can include a tangential induction factor and an axial induction factor. The tangential induction factor describes the change in the flow velocity along the tangential direction (i.e., the direction perpendicular to the rotation axis of the wind turbine blade) due to the rotation of the wind turbine blade. It affects the circulation distribution and lift coefficient on the wind turbine blade, thereby affecting the lift and drag of the wind turbine blade. The axial induction factor describes the change in the flow velocity along the axial direction (i.e., the direction of the rotation axis of the wind turbine blade) due to the rotation of the wind turbine blade. It is usually related to the thrust coefficient and power coefficient of the wind turbine blade and affects the efficiency and performance of the wind turbine blade. When determining the local blade load of the blade element, the induction factor is an important calculation parameter, so it is necessary to first determine the induction factor, and then the local blade load can be determined when certain conditions are met.

[0153] Refer to Figure 9 , specifically, in S1033, in order to accurately calculate the local blade load, S1033 can specifically include:

[0154] S10331, determine the linear velocity of the blade element according to the first distance and the rotational angular velocity;

[0155] S10332, determine the initial inflow angle according to the ratio of the second wind speed to the linear velocity;

[0156] S10333, determine the local pitch angle of the blade element according to the sum of the pitch angle and the blade twist angle;

[0157] S10334, determine the angle of attack of the current blade element according to the difference between the local pitch angle and the initial inflow angle;

[0158] S10335, determine the initial lift and drag coefficients corresponding to the angle of attack according to the relationship between the preset angle of attack and the lift and drag coefficients;

[0159] S10336. Determine the initial axial thrust coefficient and the initial radial thrust coefficient based on the initial lift-drag coefficient and the initial inflow angle.

[0160] S10337. Determine the initial axial induction factor according to the initial inflow angle and the initial axial thrust coefficient.

[0161] S10338. Determine the initial radial induction factor according to the initial inflow angle and the initial radial thrust coefficient.

[0162] In this embodiment, when calculating the inflow angle at the first moment corresponding to the second wind speed, generally, the induction factors are initialized first, that is, the axial induction factor and the tangential induction factor are 0 at the first moment corresponding to the second wind speed, and then the initial inflow angle is determined according to the following formula (2):

[0163]

[0164] where φ is the inflow angle, V0 is the second wind speed, ωr is the linear velocity of the blade element, a is the axial induction factor, and a' is the tangential induction factor.

[0165] When it is convenient to calculate the initial inflow angle, the axial induction factor and the tangential induction factor can be initialized with a value of 0.

[0166] When determining the angle of attack, according to Figure 7 the angular relationship in, the following formula (3) can be obtained:

[0167] α = φ - θ (3)

[0168] where α is the angle of attack, Φ is the initial inflow angle, and θ is the local pitch angle; and in this embodiment, the local pitch angle is the sum of the pitch angle and the blade twist angle, and the specific calculation formula is as formula (4):

[0169] θ = θ p + β (4)

[0170] where θ is the local pitch angle, θ P is the pitch angle, and β is the blade twist angle.

[0171] In some embodiments, after obtaining the angle of attack of the blade element, the initial lift-drag coefficient corresponding to the angle of attack can be determined according to the relationship between the preset angle of attack and the lift-drag coefficient, that is, each angle of attack corresponds to a certain initial lift-drag coefficient, and the relationship between the preset angle of attack and the lift-drag coefficient can be obtained through pre-experiments. The initial lift-drag coefficient can include the initial axial lift-drag coefficient and the initial radial lift-drag coefficient.

[0172] In some other embodiments, it can be determined by the following formula (5) and formula (6):

[0173] C n= C l cosφ + C d sinφ (5)

[0174] C t = C l sinφ - C d cosφ (6)

[0175] where C n is the initial axial thrust coefficient, C t is the initial radial thrust coefficient, Φ is the initial inflow angle, C l is the initial axial lift-drag coefficient, C d is the initial radial lift-drag coefficient.

[0176] After obtaining the initial axial thrust coefficient and the initial radial thrust coefficient, the initial axial induction factor and the initial radial induction factor can be determined respectively according to the following formulas (7) and (8):

[0177]

[0178] where a is the initial axial induction factor, a' is the initial radial induction factor, and σ is the proportionality coefficient.

[0179] In some other embodiments, in order to ensure more accurate calculation of the local blade load, in S1034, the deviation value between the first moment and the second moment is judged, and then based on this deviation value, it is judged whether it is within the preset deviation range. If it is within the preset deviation range, it means that the induction factor at this time meets the calculation conditions, and the local blade load of this blade element can be calculated according to this induction factor. If it is not within the preset deviation range, and the induction factor at the first moment is less than the preset threshold, then it proceeds to S1035.

[0180] In this embodiment, the preset threshold can be 0.4, that is, set by the staff according to the actual situation, and will not be elaborated here. The preset error range can be 0 - 0.3, which is also set by the staff according to the actual situation and will not be elaborated here.

[0181] In some embodiments, in S1035 and S1036, after obtaining the accurate first inflow angle, the corresponding first lift-drag coefficient, air density, relative velocity, and blade length can be obtained according to the first inflow angle. Among them, the air density can be obtained through a barometric sensor, the blade length is a constant fixed value, the relative velocity can be obtained through a meteorological research and analysis platform, and the first lift-drag coefficient can be obtained by interpolation calculation. The interpolation calculation method is a conventional technical means and will not be elaborated here.

[0182] In some other embodiments, the local blade load may include an axial thrust and a radial thrust, and the first lift-drag coefficient may include a first axial lift-drag coefficient and a first radial lift-drag coefficient; the above calculation method is the initial axial lift-drag coefficient and the initial radial lift-drag coefficient at the first moment corresponding to the second wind speed. When the wind condition information corresponding to the second wind speed reaches the wind turbine blade, the inflow angle, the angle of attack, the second wind speed, the axial induction factor, and the radial induction factor will all change. During the change process, the corresponding parameters can be calculated iteratively through the initial axial induction factor, the initial radial induction factor, and the above formulas (1)-(8).

[0183] In some embodiments, S1037 may specifically include:

[0184] For each blade element, determine the first axial thrust coefficient according to the first inflow angle and the first axial lift-drag coefficient;

[0185] Determine the first radial thrust coefficient according to the first inflow angle and the first radial lift-drag coefficient;

[0186] Determine the axial thrust according to the first axial thrust coefficient, the air density, the relative velocity, and the blade length;

[0187] Determine the radial thrust according to the first radial thrust coefficient, the air density, the relative velocity, and the blade length.

[0188] In this embodiment, during the process of determining the local blade load, the induction factors determined by the above method are the initial axial induction factor and the initial radial induction factor. For the first moment corresponding to the second wind speed, that is, the initial moment, the initial axial thrust coefficient and the initial radial thrust coefficient can be determined according to the above formulas (5) and (6). For non-initial moments, the first inflow angle can be iteratively calculated according to the initial inflow angle, and the first angle of attack can be calculated through the first inflow angle. Then, according to the first angle of attack, the first axial lift-drag coefficient and the first radial lift-drag coefficient corresponding to the first angle of attack are obtained from the preset angle of attack and lift-drag coefficient. After that, the first axial thrust coefficient corresponding to the first inflow angle and the first axial lift-drag coefficient is calculated according to formula (7), and the first radial thrust coefficient corresponding to the first inflow angle and the first radial lift-drag coefficient is calculated according to formula (8); finally, the axial thrust and the radial thrust are determined respectively according to the following formulas (9) and (10):

[0189]

[0190]

[0191] Wherein, P n is the axial thrust, C n is the first axial thrust coefficient, P t is the radial thrust, Ct is the first radial thrust coefficient, ρ is the air density, V rel is the relative velocity, and c is the chord length of the airfoil of the fan blade.

[0192] In some other embodiments, the local blade load further includes an axial bending moment and a radial bending moment. After obtaining the axial thrust and the radial thrust, the axial bending moment and the radial bending moment can be determined according to the following formulas (11) and (12):

[0193] M x = ∫rP t dr (11)

[0194] M y = ∫rP n dr (12)

[0195] where M x is the axial bending moment, M y is the radial bending moment, r is the first distance, P n is the axial thrust, P t is the radial thrust, and dr is the width of the blade element.

[0196] In some other embodiments, since the second characteristic of the preset blade element momentum model obtained by the above method is that the number of blades is infinite, but in reality, the number of fan blades is limited. Therefore, for the local blade load corresponding to the actual blade element, the theoretical induced factor needs to be corrected. In addition, when the induced factor at the first moment is greater than the preset threshold, it indicates that the error of the induced factor calculated at this time is large and needs to be corrected in order to calculate the local blade load more accurately. That is, when the induced factor at the first moment is greater than the preset threshold, the method may further include:

[0197] Determine the tip loss factor based on the preset Prandtl correction factor and the initial inflow angle;

[0198] Determine the root loss factor based on the preset Prandtl correction factor and the initial inflow angle;

[0199] Determine the correction factor according to the product of the tip loss factor and the root loss factor;

[0200] Correct the induced factor according to the correction factor to obtain the corrected induced factor.

[0201] Determining the first inflow angle based on the induced factor at the first moment includes:

[0202] Determine the first inflow angle based on the corrected induced factor corresponding to the first moment.

[0203] In this embodiment, when correcting the induction factor, it is necessary to determine the tip loss factor and the root loss factor for the tip and the root of the blade, and then determine the correction factor according to the tip loss factor and the root factor, and perform the correction according to the correction factor.

[0204] Specifically, the tip loss factor can be determined according to the following formulas (13) and (14):

[0205]

[0206] Where B is the preset Prandtl correction factor, R is the length of the wind turbine blade, r is the first distance, φ is the first inflow angle, f tip is the tip loss coefficient, and F tip is the tip loss factor.

[0207] The root loss factor can be determined according to the following formulas (15) and (16):

[0208]

[0209] Where B is the preset Prandtl correction factor, R is the length of the wind turbine blade, r is the first distance, φ is the first inflow angle, f hub is the root loss coefficient, and F hub is the root loss factor.

[0210] After obtaining the tip loss factor and the root loss factor, the correction factor can be determined according to the product of the tip loss factor and the root loss factor. Specifically, the correction factor can be determined according to the following formula (17):

[0211] F = F tip F hub (17)

[0212] Where F is the correction factor, F hub is the root loss factor, and F tip is the tip loss factor.

[0213] When correcting the induction factor based on the correction factor, the following formulas (18) and (19) can be used to correct the axial induction factor and the radial induction factor respectively:

[0214]

[0215] Where the meanings of the letters in the above formulas have been described above and will not be elaborated here.

[0216] After that, when calculating the first inflow angle, the corrected induction factor can be used to calculate the first inflow angle in order to calculate the local blade load more accurately.

[0217] In some other embodiments, in S104, after obtaining the axial thrust and radial thrust of each blade element, the axial thrust and radial thrust of multiple blade elements can be respectively integrated and summed over a preset division length to obtain the thrust of the entire wind turbine blade.

[0218] See Figure 10 , as an example, Figure 10 shows the overall blade loads and force directions of the wind turbine blade in the x, y, and z directions.

[0219] As an example, for the convenience of understanding how to calculate the local blade loads of the wind turbine blade, a specific example is given below for illustration.

[0220] Refer to Figure 11 , Figure 11 shows a specific example of a load prediction method for a wind turbine blade. In some embodiments, the load prediction method for a wind turbine blade may include:

[0221] S1101, dividing the wind turbine blade into blade elements to obtain multiple blade elements;

[0222] S1102, initializing the induction factor of the wind turbine blade;

[0223] S1103, determining the inflow angle and angle of attack of the blade element according to the wind parameter information and the initialized induction factor;

[0224] S1104, determining the lift and drag coefficients corresponding to the angle of attack according to the preset angle of attack and lift-drag coefficients;

[0225] S1105, determining the axial thrust coefficient and radial thrust coefficient according to the inflow angle and lift-drag coefficients;

[0226] S1106, determining the initial induction factor according to the axial thrust coefficient and radial thrust coefficient;

[0227] S1107, correcting the initial induction factor;

[0228] S1108, determining whether the difference between the induction factors of the previous and current times is within a preset error range. If the difference is not within the preset error range, then transfer to S1102. If the difference is within the preset error range, then transfer to S1109;

[0229] S1109, determining the axial thrust and radial thrust of the blade element according to the axial thrust coefficient and radial thrust coefficient;

[0230] S1110, determining the local blade load of the blade element according to the axial thrust and radial thrust.

[0231] S1111, Integrate and sum the local blade loads of multiple blade elements to obtain the overall blade load of the wind turbine blade.

[0232] In this embodiment, when calculating the load of the wind turbine blade, first obtain the first wind speed and wind direction at different heights in the target wind farm within a preset future time period, and then generate the wind condition information of the wind turbine blade within the preset future time period through the first wind speed and wind direction, so as to be able to determine the wind condition that will reach the wind turbine blade within the preset future time period based on the wind condition information within the preset future time period. Then, according to the preset blade element momentum model and wind condition information, determine the load of the wind turbine blade exerted by the wind condition on the wind turbine blade. Compared with the related technology of obtaining the wind turbine blade through a load monitoring sensor, the overall blade load of the wind turbine blade within a preset future time period can be determined through the wind parameter information of the target wind farm, thereby saving the cost of obtaining the wind turbine blade load. Moreover, by obtaining the wind condition information within the preset future time period, the wind turbine blade load of the wind turbine blade can be predicted, providing technical support for predictive maintenance and improving the maintenance efficiency.

[0233] Refer to Figure 12 , In some other embodiments, in order to evaluate the safety of the wind turbine blade to determine the maintenance priority of the wind turbine blade and improve the maintenance efficiency, after S104, the method may further include S1201 - S1203:

[0234] S1201, Obtain the remaining service life and ultimate load of the wind turbine blade;

[0235] S1202, For each wind turbine blade, determine the load ratio based on the overall blade load and the ultimate load;

[0236] S1203, Determine the safety risk level of the wind turbine blade according to the load ratio and the remaining service life.

[0237] In some embodiments, in the meteorological analysis and forecasting platform, the wind parameter information of multiple locations can be obtained. Therefore, the overall blade load prediction can be performed for multiple wind turbine blades in multiple locations, or the overall blade load prediction can be performed for multiple wind turbine blades at one location. After obtaining the overall blade load of each wind turbine blade, the safety risk level can be determined for each wind turbine blade to evaluate the safety of the wind turbine blade in service. At the same time, the wind turbine blades can be sorted for maintenance according to the determined safety risk level, so that the maintenance personnel can perform preventive maintenance on the wind turbine blades according to the safety risk level of the wind turbine blades to improve the maintenance efficiency.

[0238] Specifically, in S1201, before the wind turbine blades are produced, their service life and ultimate load are tested and recorded by electronic devices. The remaining service life of the wind turbine blades can be determined by the difference between the service years and the service life. Therefore, the electronic devices can directly obtain the remaining service life and the ultimate load.

[0239] In S1202, for each wind turbine blade, when conducting a safety assessment of the wind turbine blade, it needs to be determined from the ratio of the remaining service life to the load. Therefore, it is necessary to determine the load ratio based on the overall blade load and the ultimate load predicted by the above method, which is used to represent the wear rate of the wind turbine blade, and the remaining service life represents the time that the wind turbine blade can continue to be used.

[0240] In some other embodiments, S1203 may specifically include:

[0241] Determine the ultimate weight based on the load ratio and the preset load threshold;

[0242] Determine the life loss of the wind turbine blade based on the overall blade load;

[0243] Determine the fatigue weight based on the life loss and the remaining service life;

[0244] Accumulate the load ratios in the preset time sequence to obtain the operation ratio;

[0245] Determine the operation weight based on the operation ratio and the preset operation threshold;

[0246] Determine the safety risk level of the wind turbine blade based on the ultimate weight, the fatigue weight, and the operation weight.

[0247] In this embodiment, when determining the safety risk level of the wind turbine blade, the safety risk level can be determined from the operation weight, the fatigue weight, and the ultimate weight, so as to make the determined safety risk level more accurate.

[0248] Specifically, when conducting an ultimate assessment of the wind turbine blade, the ultimate weight can be determined by the load ratio and the preset load threshold. For example, taking the preset load threshold as 1, if the calculated load ratio is greater than 1, at this time, the wind turbine blade may be in danger under the condition of ultimate failure, so the ultimate weight at this time is relatively large.

[0249] When conducting a fatigue assessment of the wind turbine blade, the life loss of the wind turbine blade can be calculated by the predicted overall wind turbine blade load, and then the fatigue weight can be determined by the life loss and the remaining service life. For example, if the life loss is greater than the preset loss threshold or the remaining service life is less than 0, the fatigue weight of the magnet wind turbine blade is relatively large.

[0250] It should be noted that determining the life loss of the wind turbine blade through the overall wind turbine blade load is a conventional technical means, and will not be elaborated here.

[0251] When evaluating the operation of the wind turbine blade, according to the preset time sequence, that is, from the start of service of the wind turbine blade to the cut-off moment of the future preset time period, the load ratios are accumulated to obtain the operation ratio. When the load ratio is greater than the preset operation threshold, it indicates that the risk of damage to the wind turbine blade at this time is greater and the operation weight is greater.

[0252] In some embodiments, after obtaining the ultimate weight, fatigue weight, and operation weight, the ultimate weight, fatigue weight, and operation weight are accumulated to obtain the total weight value. The total weight value is compared with the safety risk ranges corresponding to multiple safety risk levels. Which safety risk range the total weight value is in, then the wind turbine blade is in that safety risk level.

[0253] It should be noted that the lower the safety risk level, the greater the possible safety risk of the wind turbine blade at this time, and maintenance needs to be carried out in a timely manner.

[0254] Therefore, by traversing the safety risk levels of each wind turbine blade, the sorting order of multiple wind turbine blades can be generated to provide support for the operation and maintenance personnel, facilitating the operation and maintenance personnel to perform operation and maintenance on the wind turbine blades according to the sorting order to improve the operation and maintenance efficiency.

[0255] In some embodiments, the present application also provides a load prediction system for a wind turbine blade. Refer to Figure 13 , the load prediction system 1300 of the wind turbine blade may include:

[0256] A wind condition generation module 13201, configured to obtain the wind farm coordinates, model data, and wind parameter information of the meteorological research and forecasting platform, and generate the wind condition information of the wind farm based on the wind parameter information;

[0257] A wind farm blade load prediction module 1302, configured to obtain the control parameters and wind condition information of the wind turbine blade, and predict the overall blade load of the wind turbine blade through the wind condition information and control parameters to obtain the overall blade loads of multiple wind turbine blades;

[0258] An operation and maintenance guidance module 1303, configured to evaluate the wind turbine blade according to the overall blade load to determine the operation and maintenance priority rating of the wind turbine blade.

[0259] In this embodiment, the wind farm blade load prediction module 1302 may also be configured to perform overall blade load statistics on multiple wind turbine blades in multiple wind farms to perform statistical analysis on multiple wind turbine blades, facilitating the operation and maintenance guidance module to perform operation and maintenance sorting on the wind turbine blades to improve the operation and maintenance efficiency.

[0260] Based on the load prediction method for wind turbine blades provided in the above embodiments, correspondingly, the present application also provides a specific implementation manner of a load prediction device for wind turbine blades. Please refer to the following embodiments.

[0261] Referring to Figure 14 , the load prediction device 1400 for wind turbine blades may include:

[0262] An acquisition module 1401, configured to acquire wind parameter information of a target wind farm within a preset future time period, where the wind parameter information includes the first wind speed and wind direction of different blade elements;

[0263] A generation module 1402, configured to generate wind condition information of each blade element within a preset future time period respectively based on the first wind speed and wind direction corresponding to each blade element;

[0264] A determination module 1403, configured to determine the local blade load of each blade element within a preset future time period respectively according to a preset blade element momentum model and the wind condition information, where the preset blade element momentum model is used to represent the load distribution of different positions of the wind turbine blade under different wind condition information;

[0265] A summation module 1404, configured to perform integral summation on the local blade loads of each blade element to obtain the overall blade load of the wind turbine blade.

[0266] As an optional implementation manner, the acquisition module 1401 may specifically be configured to:

[0267] Acquire the historical meteorological data and geographical static data of the target wind farm;

[0268] Input the historical meteorological data and geographical static data into a preset initial wind parameter prediction model to obtain a prediction height and the corresponding predicted wind parameter information;

[0269] In the case where the prediction height meets the matching condition with a preset target height and the predicted wind parameter information is inconsistent with the historical meteorological data corresponding to the target wind farm, adjust the calculation parameters of the preset initial wind parameter prediction model until the predicted wind parameter information is consistent with the historical meteorological data to obtain a preset wind parameter prediction model;

[0270] The acquisition of the wind parameter information of the target wind farm within a preset future time period includes:

[0271] Obtain the wind parameter information of the target wind farm within a preset future time period through the preset wind parameter prediction model.

[0272] As an optional implementation manner, the generation module 1402 may specifically be configured to:

[0273] For each blade element of the wind turbine blade, determine the average wind speed at the position where the blade element is located based on the first wind speed corresponding to each blade element;

[0274] Determine the turbulence intensity corresponding to each blade element according to the first wind speed and the average wind speed;

[0275] Determine the wind condition information corresponding to each blade element according to the turbulence intensity, the first wind speed, and the wind direction.

[0276] As an optional implementation manner, the determining module 1403 may specifically be configured to:

[0277] Obtain the blade length of the wind turbine blade;

[0278] Perform an annular unit division on the length of the wind turbine blade according to a preset division length to obtain a plurality of blade elements;

[0279] Generate a preset blade element momentum model according to the plurality of blade elements and a preset momentum relationship.

[0280] As an optional implementation manner, the future preset time period includes a plurality of first moments, and the determining module 1403 may further specifically be configured to:

[0281] Determine the second wind speed of the wind turbine blade based on the wind condition information, where the second wind speed includes the wind speed reaching the wind turbine blade at each first moment in the target wind field;

[0282] For each blade element, obtain the first distance from the blade element to the hub center, as well as the rotational angular velocity, pitch angle, and blade twist angle of the wind turbine blade;

[0283] Determine the induction factor at each first moment in the future preset time period according to the second wind speed, the first distance, the rotational angular velocity, the pitch angle, and the blade twist angle;

[0284] Calculate the deviation value of the induction factor between the first moment and the second moment, where the second moment is the moment adjacent to the first moment;

[0285] In the case where the deviation value is not within the preset deviation range and the induction factor at the first moment is less than the preset threshold, determine the first inflow angle based on the induction factor at the first moment;

[0286] Obtain the first lift-drag coefficient, air density, relative velocity, and blade length corresponding to the first inflow angle;

[0287] Determine the local blade load corresponding to the blade element at the first moment according to the first inflow angle, the first lift-drag coefficient, the air density, and the blade length.

[0288] As an optional implementation manner, the induction factor includes an initial axial induction factor and an initial radial induction factor, and the determining module 1403 may further specifically be configured to:

[0289] Determine the linear velocity of the blade element according to the first distance and the rotational angular velocity;

[0290] Determine the initial inflow angle according to the ratio of the second wind speed to the linear velocity;

[0291] Determine the local pitch angle of the blade element according to the sum of the pitch angle and the blade twist angle;

[0292] Determine the angle of attack of the blade element according to the difference between the local pitch angle and the initial inflow angle;

[0293] Determine the initial lift-drag coefficient corresponding to the angle of attack according to the relationship between the preset angle of attack and the lift-drag coefficient;

[0294] Determine the initial axial thrust coefficient and the initial radial thrust coefficient based on the initial lift-drag coefficient and the initial inflow angle;

[0295] Determine the initial axial induction factor according to the initial inflow angle and the initial axial thrust coefficient;

[0296] Determine the initial radial induction factor according to the initial inflow angle and the initial radial thrust coefficient.

[0297] As an alternative implementation, the local blade load includes axial thrust and radial thrust, the first lift-drag coefficient includes the first axial lift-drag coefficient and the first radial lift-drag coefficient, and the determining module 1303 can also be specifically configured to:

[0298] For each blade element, determine the first axial thrust coefficient according to the first inflow angle and the first axial lift-drag coefficient;

[0299] Determine the first radial thrust coefficient according to the first inflow angle and the first radial lift-drag coefficient;

[0300] Determine the axial thrust according to the first axial thrust coefficient, air density, relative velocity, and blade length;

[0301] Determine the radial thrust according to the first radial thrust coefficient, air density, relative velocity, and blade length.

[0302] As an alternative implementation, when the induction factor at the first moment is greater than the preset threshold, the determining module 1403 can also be specifically configured to:

[0303] Determine the tip loss factor based on the preset Prandtl correction factor and the initial inflow angle;

[0304] Determine the root loss factor based on the preset Prandtl correction factor and the initial inflow angle;

[0305] Determine the correction factor according to the product of the tip loss factor and the root loss factor;

[0306] Correct the induction factor according to the correction factor to obtain the corrected induction factor.

[0307] Based on the induction factor at the first moment, determining the first inflow angle includes:

[0308] Based on the corrected induction factor corresponding to the first moment, determining the first inflow angle.

[0309] As an alternative implementation, the summation module 1404 may also be specifically configured to:

[0310] Obtain the remaining service life and ultimate load of the wind turbine blade;

[0311] For each wind turbine blade, based on the overall blade load and the ultimate load, determine the load ratio;

[0312] Based on the load ratio and the remaining service life, determine the safety risk level of the wind turbine blade.

[0313] As an alternative implementation, the summation module 1404 may also be specifically configured to:

[0314] Based on the load ratio and a preset load threshold, determine the ultimate weight;

[0315] Based on the overall blade load, determine the life loss of the wind turbine blade;

[0316] Based on the life loss and the remaining service life, determine the fatigue weight;

[0317] Accumulate the load ratios in a preset time sequence to obtain the operation ratio;

[0318] Based on the operation ratio and a preset operation threshold, determine the operation weight;

[0319] Based on the ultimate weight, the fatigue weight, and the operation weight, determine the safety risk level of the wind turbine blade.

[0320] Figure 15 The schematic diagram of the hardware structure of the electronic device provided by the embodiment of the present application is shown.

[0321] The electronic device may include a processor 1501 and a memory 1502 storing computer program instructions.

[0322] Specifically, the above-mentioned processor 1501 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0323] The memory 1502 may include a mass storage for data or instructions. By way of example and not limitation, the memory 1502 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. In one example, the memory 1502 may include removable or non-removable (or fixed) media, or the memory 1502 is a non-volatile solid-state memory. The memory 1502 may be inside or outside the integrated gateway disaster recovery device.

[0324] In one example, the memory 1502 may be a read only memory (ROM). In one example, 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 a flash memory, or a combination of two or more of these.

[0325] The memory 1502 may include a read only memory (ROM), a random access memory (RAM), a magnetic disk storage media device, an optical storage media device, a flash memory device, an electrical, optical, or other physical / tangible memory storage device. Thus, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the load prediction method of the wind turbine blade according to the first aspect of the present disclosure.

[0326] The processor 1501 reads and executes the computer program instructions stored in the memory 1502 to implement Figure 1 a load prediction method for a wind turbine blade in the illustrated embodiment.

[0327] In one example, the electronic device may further include a communication interface 1503 and a bus 1504. Among them, as Figure 15 shown, the processor 1501, the memory 1502, and the communication interface 1503 are connected through the bus 1504 and complete communication with each other.

[0328] The communication interface 1503 is mainly used to implement communication between the various modules, devices, units, and / or devices in the embodiments of the present application.

[0329] Bus 1504 includes hardware, software, or both, and couples components of the electronic device to each other. By way of example and not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable bus or a combination of two or more of these. Where appropriate, bus 1504 may include one or more buses. Although embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0330] The electronic device can execute the load prediction method for the fan blade in the embodiments of the present application, so as to implement the combination of Figures 1 - 14 the load prediction method and device for the fan blade described above.

[0331] In addition, in combination with the load prediction method for the fan blade in the above embodiments, embodiments of the present application can provide a computer storage medium to implement. Computer program instructions are stored on the computer storage medium; when the computer program instructions are executed by a processor, any one of the load prediction methods for the fan blade in the above embodiments is implemented.

[0332] In an alternative embodiment, in combination with the load prediction method for the fan blade in the above embodiments, embodiments of the present application can provide a computer program product to implement. The instructions in the computer program product are executed by a processor of the electronic device, so that the electronic device can implement any one of the load prediction methods for the fan blade in the above embodiments.

[0333] It should be clear that the present 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 the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications, and additions, or change the order between steps after understanding the spirit of the present application.

[0334] The functional blocks shown in the above structural block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, and so on. When implemented in software, the elements of the present application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium or transmitted via a data signal carried in a carrier wave over a transmission medium or a communication link. "Machine-readable medium" can include any medium that can store or transmit information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical discs, hard disks, fiber optic media, radio frequency (RF) links, and so on. The code segment can be downloaded via a computer network such as the Internet, an intranet, and so on.

[0335] It should also be noted that the exemplary embodiments mentioned in the present application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above steps, that is, the steps can be executed in the order mentioned in the embodiments, or different from the order in the embodiments, or several steps can be executed simultaneously.

[0336] Aspects of the present disclosure have been described above with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block in the flowcharts and / or block diagrams, and the combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the functions / actions specified in one or more blocks of the flowcharts and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It can also be understood that each block in the block diagrams and / or flowcharts, and the combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware that performs the specified functions or actions, or by a combination of dedicated hardware and computer instructions.

[0337] The above are only specific implementation manners of the present application. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the foregoing method embodiments, which will not be elaborated herein. It should be understood that the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should be covered within the protection scope of the present application.

Claims

1. A method for predicting load of a wind turbine blade, characterized in that: The fan blade comprises blade elements that are discrete along a length direction of the fan blade, and the method comprises: Acquire wind parameter information of the target wind field in a future preset period of time, the wind parameter information including first wind speed and wind direction of different blade elements; Based on the first wind speed and wind direction corresponding to each blade element, respectively, generating wind condition information of the blade element in a future preset time period; Determine the local blade load of each blade element in a future preset period of time according to a preset blade element momentum model and the wind condition information, wherein the preset blade element momentum model is used to represent the load distribution of different blade elements of the wind turbine blade under different wind condition information; The local blade load of each blade element is integrated and summed to obtain the overall blade load of the fan blade.

2. The method according to claim 1, characterized in that Before obtaining wind parameter information of the target wind farm within a preset future period, the method may further include: Acquire historical meteorological data and geographical static data of the target wind farm; Inputting the historical meteorological data and geographic static data into a preset initial wind parameter prediction model to obtain predicted height and its corresponding predicted wind parameter information; When the predicted height meets the preset target height and the predicted wind parameter information is inconsistent with the historical meteorological data corresponding to the target wind field, the calculation parameters of the preset initial wind parameter prediction model are adjusted until the predicted wind parameter information is consistent with the historical meteorological data, thereby obtaining the preset wind parameter prediction model; The step of obtaining wind parameter information of the target wind farm in a future preset period of time includes: The wind parameter information of the target wind field in a future preset time period is obtained through the preset wind parameter prediction model.

3. The method according to claim 1, characterized in that The generating, based on the first wind speed and wind direction corresponding to each blade element, wind condition information of the blade element in a future preset time period includes: For each blade element of the fan blade, based on the first wind speed corresponding to each blade element, determine the average wind speed at the location of the blade element; Determining the turbulence intensity corresponding to each blade element according to the first wind speed and the average wind speed; The wind condition information corresponding to each blade element is determined according to the turbulence intensity, the first wind speed and the wind direction.

4. The method according to claim 1, characterized in that: Before respectively determining the local blade load of each blade element in a future preset period of time according to the preset blade element momentum model and the wind condition information, the method further includes: Obtaining the blade length of the fan blade; According to a preset division length, dividing the length of the fan blade into annular units to obtain a plurality of blade elements; A preset blade element momentum model is generated according to the plurality of blade elements and the preset momentum relationship.

5. The method according to any one of claims 1 to 4, characterized in that: The future preset time period includes a plurality of first moments, and determining the local blade load of each blade element in the future preset time period according to the preset blade element momentum model and the wind condition information includes: Determine a second wind speed of the wind turbine blade based on the wind condition information, wherein the second wind speed includes the wind speed reaching the wind turbine blade at each first moment in the target wind farm; For each blade element, obtaining a first distance from the blade element to the center of the hub and a rotational angular velocity, a pitch angle, and a blade twist angle of the wind turbine blade; determining an induction factor at each first moment in the future preset time period according to the second wind speed, the first distance, the rotation angular velocity, the pitch angle and the blade twist angle; Calculating a deviation value of an induction factor between a first moment and a second moment, where the second moment is a moment adjacent to the first moment; When the deviation value is not within the preset deviation range and the induction factor at the first moment is less than a preset threshold, determining a first inflow angle based on the induction factor at the first moment; Obtaining a first lift and drag coefficient, air density, relative speed, and blade length corresponding to the first inflow angle; A local blade load corresponding to the blade element at a first moment is determined according to the first inflow angle, the first lift-drag coefficient, the air density and the blade length.

6. The method according to claim 5, characterized in that The induction factor includes an initial axial induction factor and an initial radial induction factor, and determining the induction factor at each first moment in the future preset time period according to the second wind speed, the first distance, the rotation angular velocity, the pitch angle, and the blade twist angle includes: determining a linear velocity of the blade element according to the first distance and the rotational angular velocity; determining an initial inflow angle according to a ratio of the second wind speed to the linear speed; Determining the local pitch angle of the blade element according to the sum of the pitch angle and the blade twist angle; determining the angle of attack of the blade element according to the difference between the local pitch angle and the initial inflow angle; According to a preset relationship between the angle of attack and the lift-drag coefficient, determining an initial lift-drag coefficient corresponding to the angle of attack; Determining an initial axial thrust coefficient and an initial radial thrust coefficient based on the initial lift-drag coefficient and the initial inflow angle; determining an initial axial induction factor according to the initial inflow angle and the initial axial thrust coefficient; An initial radial induction factor is determined according to the initial inflow angle and the initial radial thrust coefficient.

7. The method according to claim 5, characterized in that The local blade load includes an axial thrust and a radial thrust, the first lift-drag coefficient includes a first axial lift-drag coefficient and a first radial lift-drag coefficient, and determining the local blade load corresponding to the blade element at a first moment according to the first inflow angle, the first lift-drag coefficient, the air density, and the blade length includes: For each blade element, determining a first axial thrust coefficient according to the first inflow angle and the first axial lift-drag coefficient; determining a first radial thrust coefficient according to the first inflow angle and the first radial lift-drag coefficient; determining an axial thrust according to the first axial thrust coefficient, air density, relative speed and blade length; The radial thrust is determined according to the first radial thrust coefficient, air density, relative speed and blade length.

8. The method according to claim 6, characterized in that When the induction factor at the first moment is greater than a preset threshold, the method further includes: Determine the tip loss factor based on the preset Prandtl correction factor and the initial inflow angle; Determine the blade root loss factor based on the preset Prandtl correction factor and the initial inflow angle; Determining a correction factor according to the product of the blade tip loss factor and the blade root loss factor; Correcting the induction factor according to the correction factor to obtain a corrected induction factor; The determining of the first inflow angle based on the induction factor at the first moment includes: A first inflow angle is determined based on the corrected induction factor corresponding to the first moment.

9. The method according to any one of claims 1 to 8, characterized in that: After integrating and summing the local blade load of each blade element to obtain the overall blade load of the fan blade, the method further includes: Obtaining the remaining service life and limit load of the fan blade; For each wind turbine blade, determining a load ratio based on the overall blade load and the ultimate load; The safety risk level of the wind turbine blade is determined according to the load ratio and the remaining service life.

10. The method according to claim 9, characterized in that Determining the safety risk level of the wind turbine blade according to the load ratio and the remaining service life includes: Determining a limit weight based on the load ratio and a preset load threshold; Determining the life loss of the wind turbine blade based on the overall blade load; determining a fatigue weight based on the life loss and the remaining useful life; Accumulating the load ratios according to a preset time sequence to obtain an operation ratio; Determining an operation weight based on the operation ratio and a preset operation threshold; Based on the limit weight, fatigue weight and operation weight, a safety risk level of the wind turbine blade is determined.

11. A load prediction device for a wind turbine blade, characterized in that: The fan blade comprises blade elements discrete along the length direction of the fan blade, and the device comprises: An acquisition module, used for acquiring wind parameter information of a target wind field in a future preset period of time, wherein the wind parameter information includes a first wind speed and a wind direction of different blade elements; A generating module, configured to generate wind condition information of each blade element in a future preset time period based on the first wind speed and wind direction corresponding to each blade element; A determination module, used to determine the local blade load of each blade element in a future preset period of time according to a preset blade element momentum model and the wind condition information, wherein the preset blade element momentum model is used to represent the load distribution at different positions of the wind turbine blade under different wind condition information; The summing module is used to integrate and sum the local blade load of each blade element to obtain the overall blade load of the fan blade.

12. An electronic device, characterized in that: The device comprises: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the load prediction method for the wind turbine blade according to any one of claims 1 to 10 is implemented.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the method for predicting the load of a wind turbine blade according to any one of claims 1 to 10 is implemented.

14. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the load prediction method for a wind turbine blade as described in any one of claims 1 to 10.

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