Wind turbine blade static test load optimization method, device, equipment, storage medium and program product

Abstract

The present application discloses a method, device, equipment, storage medium and program product for optimizing the static test load of wind turbine blades, and belongs to the technical field of static testing of wind turbine blades. The method includes obtaining static test loading parameters; initializing a test load optimization model with the upper and lower limits of the overload ratio as constraints and minimizing the overload ratio as the optimization goal; iteratively calculating the test load bending moment based on the load quantity and the first loading position; calculating the first overload ratio based on the test load bending moment and the preset target load bending moment; for each first loading position, extracting multiple test load samples to be optimized from the upper and lower limits of the test load based on the first overload ratio; optimizing the loads of multiple test load samples to be optimized to obtain at least one optimization result; and determining the first test load based on the optimization result. According to the embodiment of the present application, the optimized loading load can be obtained quickly and accurately, thereby improving the efficiency of full-size static testing of wind turbine blades.

Description

Technical Field

[0001] The present application belongs to the technical field of static testing of wind turbine blades, and in particular, relates to a method, device, equipment, storage medium and program product for optimizing the static testing load of wind turbine blades. Background Art

[0002] Full-scale static testing of wind turbine blades is a necessary means to verify the service safety of wind turbine blades. The purpose of static testing is mainly to evaluate the bearing capacity, structural strength and deformation characteristics of the blades under static loads, so as to ensure the safety and reliability of wind turbine blades in actual operation.

[0003] At present, when conducting static tests on wind turbine blades, the blades are first leveled, then clamped by a loading bracket, and loading force is applied to each loading point of the blade. The cross-sectional bending moment of the wind turbine blade under the action of the loading force is collected to determine whether the wind turbine blade meets the service standards. During the loading process, the loading load needs to be iterated multiple times until the optimal test load distribution is found. Under the optimal test load distribution, the wind turbine blade is subjected to a full-size static test.

[0004] For the above-mentioned related technologies, manual input and adjustment are required when performing loading load iteration, and the search for the optimal solution relies on the experience of engineers, which is inefficient. Determining the loading load through the model will increase the complexity of loading load optimization and cannot meet actual testing needs. Therefore, there is an urgent need for a wind turbine blade static test load optimization method, device, equipment, storage medium and program product. Summary of the invention

[0005] The embodiments of the present application provide a method, device, equipment, computer storage medium, and program product for optimizing the static test load of wind turbine blades, which can quickly and accurately obtain the optimized loading load and improve the efficiency of full-size static testing of wind turbine blades.

[0006] On the one hand, an embodiment of the present application provides a method for optimizing static test load of a wind turbine blade, the method comprising:

[0007] Obtaining static test loading parameters, wherein the static test loading parameters include load quantity, multiple first loading positions, upper and lower limits of test load, and upper and lower limits of overload ratio;

[0008] Taking the upper and lower limits of the overload ratio as constraints and minimizing the overload ratio as the optimization goal, initializing the test load optimization model;

[0009] iteratively calculating a test load bending moment based on the load quantity and the first loading position;

[0010] When the test load bending moment satisfies a first optimization condition, calculating a first overload ratio based on the test load bending moment and a preset target load bending moment;

[0011] For each of the first loading positions, extracting a plurality of test load samples to be optimized from the upper and lower limits of the test load based on the first overload ratio;

[0012] Using a preset optimization algorithm to perform load optimization on the multiple test load samples to be optimized, and obtaining at least one optimization result;

[0013] When the optimization results corresponding to each of the first loading positions satisfy a second optimization condition, a first test load is determined based on the optimization results.

[0014] Optionally, before obtaining the static test loading parameters, the method further includes:

[0015] Obtaining the size information of the loading fixture and the parameter information of the wind turbine blade;

[0016] Performing finite element analysis on the wind turbine blade based on the size information and parameter information to obtain a failure position of the wind turbine blade;

[0017] A first loading position is determined based on the failure position.

[0018] Optionally, the iterative calculation of the test load bending moment based on the load quantity and the first loading position comprises:

[0019] For each first loading position, calculating a first distance between the first loading position and the blade root of the wind turbine blade, and a second distance between the cross section where the test position is located and the blade root of the wind turbine blade;

[0020] Obtain the current test load, blade gravity bending moment, loading fixture gravity bending moment and upward load bending moment;

[0021] A test load moment is determined based on the first distance, the second distance, the current test load, the blade gravity bending moment, the loading fixture gravity bending moment, and the upward load bending moment.

[0022] Optionally, in the case of iteratively calculating the test load bending moment based on the load quantity and the first loading position, the method further comprises:

[0023] Dividing the wind turbine blade into sections based on the first loading position to obtain a plurality of test sections;

[0024] For each of the test intervals, the test load corresponding to the test interval is optimized in sequence according to a preset step size based on the overload ratio upper limit;

[0025] In a case where the optimization converges or there is no test load satisfying the constraint condition, it is determined that the corresponding test load bending moment satisfies the first optimization condition.

[0026] Optionally, in the case of optimizing the test loads corresponding to the test intervals in sequence according to a preset step length based on the upper limit of the overload ratio, optimizing the test loads for the test intervals corresponding to the tip of the wind turbine blade, the body of the wind turbine blade and the root of the wind turbine blade in a preset order;

[0027] The optimizing the test load corresponding to the test interval in sequence according to the preset step length based on the overload ratio upper limit includes:

[0028] The overload ratio upper limit is taken as the initial overload ratio, and the overload ratio is gradually reduced in accordance with the preset step length to optimize the test load corresponding to the test interval.

[0029] Optionally, the load optimization of the plurality of test load samples to be optimized is performed using a preset optimization algorithm to obtain at least one optimization result, including:

[0030] For multiple test load samples to be optimized in each test interval, a gradient-type optimization algorithm is used to perform gradient-decreasing iterative optimization. When the test load samples to be optimized gradually converge or the difference between the objective function value corresponding to the test load samples to be optimized and the preset objective function value is less than a preset threshold, the optimization result is output.

[0031] Optionally, when the optimization result does not satisfy the second optimization condition, the method further includes:

[0032] Discarding the test load sample to be optimized corresponding to the optimization result;

[0033] In a case where the plurality of test load samples to be optimized are all discarded, the overload ratio upper limit is increased, and the process returns to the step of iteratively calculating the test load bending moment based on the load quantity and the first loading position.

[0034] Optionally, after increasing the overload ratio upper limit, the method further includes:

[0035] When the increased overload ratio upper limit is the maximum overload ratio upper limit that the wind turbine blade can withstand, a second loading position is determined based on the first loading position and the failure position, the second loading position is determined as the new first loading position, and the step of iteratively calculating the test load bending moment based on the load quantity and the second loading position is returned.

[0036] Optionally, after determining the first test load based on the optimization result, the method further includes:

[0037] Obtaining blade stiffness distribution information and weight distribution information of the wind turbine blade;

[0038] Determining a loading position and a loading angle of the loaded blade based on the blade stiffness distribution information and the weight distribution information;

[0039] Acquiring clamping information of multiple loading brackets on the wind turbine blade;

[0040] Under the condition that the preset clamping conditions are met; the first test load is corrected based on the loading position, loading angle and clamping information of the loaded blade to obtain a corrected test load;

[0041] Gradient optimization is performed on each of the modified test loads to obtain a second test load.

[0042] Optionally, the clamping information includes a first distance between two adjacent loading brackets and a clamping angle of the loading bracket;

[0043] The preset clamping condition includes: the first distance is less than a preset distance threshold and the axial angle formed by the clamping angle and the wind turbine blade is greater than a preset angle.

[0044] On the other hand, an embodiment of the present application provides a wind turbine blade static test load optimization device, the device comprising:

[0045] An acquisition module, used for acquiring static test loading parameters, wherein the static test loading parameters include load quantity, multiple first loading positions, upper and lower limits of test load, and upper and lower limits of overload ratio;

[0046] An initialization module is used to initialize the test load optimization model with the upper and lower limits of the overload ratio as constraints and minimization of the overload ratio as an optimization goal;

[0047] a calculation module, configured to iteratively calculate a test load bending moment based on the load quantity and the first loading position;

[0048] The calculation module is further used to calculate a first overload ratio based on the test load bending moment and a preset target load bending moment when the test load bending moment satisfies a first optimization condition;

[0049] An extraction module, configured to extract, for each of the first loading positions, a plurality of test load samples to be optimized from the upper and lower limits of the test load based on the first overload ratio;

[0050] An optimization module, configured to use a preset optimization algorithm to perform load optimization on the plurality of test load samples to be optimized, and obtain at least one optimization result;

[0051] A determination module is used to determine a first test load based on the optimization result when the optimization result corresponding to each of the first loading positions satisfies a second optimization condition.

[0052] In another aspect, an embodiment of the present application provides an electronic device, the device comprising: a processor and a memory storing computer program instructions;

[0053] When the processor executes the computer program instructions, the method for optimizing the static test load of a wind turbine blade as described in the first aspect is implemented.

[0054] On the other hand, 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 wind turbine blade static test load optimization method described in the first aspect is implemented.

[0055] On the other hand, an embodiment of the present application provides a computer program product. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the wind turbine blade static test load optimization method as described in the first aspect.

[0056] The wind turbine blade static test load optimization method, device, equipment and computer storage medium of the embodiments of the present application can, when performing full-scale static testing on wind turbine blades, continuously iteratively calculate the test load bending moment in the test load optimization model through static test loading parameters, and when the test load bending moment satisfies the first optimization condition, use the current test load bending moment as the optimal test load bending moment, and then calculate the first overload ratio based on the optimal test load bending moment and the preset target load bending moment as the optimal overload ratio, and then for each first loading position, use the first overload ratio as the upper limit of the test load, sample multiple test loads from the range of the upper and lower limits of the test load, optimize the multiple test loads, and obtain the first test load, so as to quickly and accurately obtain the optimized loading load and improve the efficiency of full-scale static testing of wind turbine blades. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0058] Figure 1 It is a flow chart of a method for optimizing a static test load of a wind turbine blade provided by an embodiment of the present application;

[0059] Figure 2 is a flow chart of a first loading position determination method provided by an embodiment of the present application;

[0060] Figure 3 It is a flow chart of a method for determining a test load bending moment provided by an embodiment of the present application;

[0061] Figure 4 is a structural schematic diagram of a wind turbine blade static test load optimization device provided by another embodiment of the present application;

[0062] Figure 5 It is a structural diagram of an electronic device provided by yet another embodiment of the present application. DETAILED DESCRIPTION

[0063] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, 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 the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0064] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0065] In order 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 optimizing the static test load of a wind turbine blade.

[0066] When conducting a full-size static test on a wind turbine blade, the test load bending moment is continuously iterated and calculated in a test load optimization model through the static test loading parameters. When the test load bending moment satisfies the first optimization condition, the current test load bending moment is used as the optimal test load bending moment. Then, the first overload ratio calculated based on the optimal test load bending moment and the preset target load bending moment is used as the optimal overload ratio. Then, for each first loading position, the first overload ratio is used as the upper limit of the test load. Multiple test loads are sampled from the range of the upper and lower limits of the test load. After optimizing the multiple test loads, the first test load is obtained. The optimized loading load can be obtained quickly and accurately, thereby improving the efficiency of full-size static testing of wind turbine blades.

[0067] The following first introduces the wind turbine blade static test load optimization method provided in the embodiment of the present application.

[0068] Figure 1 FIG. 1 is a flow chart of a method for optimizing a static test load of a wind turbine blade provided by an embodiment of the present application. Figure 1 As shown, the wind turbine blade static test load optimization method may include the following steps S101-S107:

[0069] S101, obtaining static test loading parameters.

[0070] In some embodiments, when performing a full-scale static test on a wind turbine blade, the wind turbine blade is placed on a test bench, clamped by a loading fixture, and then a test load is applied through the loading fixture to achieve a full-scale static test. During the test, a technician first inputs initial static test loading parameters to provide parameters for determining the test load. As an example, the static test loading parameters may include the number of loads, multiple first loading positions, upper and lower limits of the test load, and upper and lower limits of the overload ratio.

[0071] Reference Figure 2 , in order to improve the optimization efficiency of the test load, before obtaining the static test loading parameters, it can also include:

[0072] S1011, obtaining size information of the loading fixture and parameter information of the wind turbine blade;

[0073] S1012, performing finite element analysis on the wind turbine blade based on the size information and parameter information to obtain a failure position of the wind turbine blade;

[0074] S1013, determining a first loading position based on the failure position.

[0075] In this embodiment, before the staff inputs the first loading position, the first loading position can also be determined by an electronic device, that is, the staff inputs the parameter information of the wind turbine blade and the size information of the loading fixture. As an example, the parameter information may include blade mass distribution information, blade stiffness distribution information and blade size information. Then, the size information and parameter information of the loading fixture are used to perform overall and local buckling failure, fiber static and fatigue failure, fiber-to-fiber failure and bonding failure analysis in the finite element software to determine the failure position of the wind turbine blade, and then the first loading position is set according to a preset distance away from the failure position. For example, the preset distance is 2 meters, and the position at least 2 meters away from the aging position can be determined as the first loading position.

[0076] Specifically, before performing failure analysis, it is necessary to construct a shell model of the wind turbine blade in the finite element software, and then divide the shell model into multiple shell unit grids, define material properties for each shell unit grid, the material properties may include elastic modulus, Poisson's ratio, strength limit and fatigue parameters, and then set preset conditions, such as fixed end and hinged end; apply appropriate static loads to the shell model, and then calculate the overall buckling failure load and buckling mode, calculate the corresponding overall buckling safety factor through the overall buckling failure load and buckling mode, determine the minimum safety factor of the overall buckling failure mode and its axial position, and determine this axial position as the failure position.

[0077] Accordingly, when conducting fiber static analysis, the stress distribution and damage of the fiber under static load are analyzed. The minimum safety factor of the fiber static and its axial position are determined, and this axial position is taken as the failure position.

[0078] Accordingly, when performing failure analysis of the sandwich structure, the local compressive stress of the sandwich structure and the stress level of the core material body are analyzed, and the minimum safety factor of the sandwich structure and its axial position are calculated using a suitable failure criterion, and this axial position is used as the failure position, wherein the suitable failure criterion can be set by technical personnel in this field according to the sandwich structure, and is not limited here.

[0079] In some other embodiments, when performing the bonding failure analysis, the shear and peel properties of the bonding layer under static load can be analyzed to evaluate the possibility of bonding failure. Appropriate models and methods are used to calculate the safety factors of these failure modes, and the axial position with the minimum safety factor is determined, and this axial position is determined as the failure position.

[0080] After the failure position is obtained, when determining the first loading position, in order to reduce the influence of the failure position on the static test, the failure position should be avoided, that is, the distance between the determined first loading position and the aging position should not be less than the preset distance.

[0081] S102, initializing the test load optimization model with the upper and lower limits of the overload ratio as constraints and minimizing the overload ratio as the optimization goal.

[0082] In some embodiments, in order to optimize the test load more quickly and meet the standards of static testing, it is first necessary to establish a test load optimization model, that is, to set the upper and lower limits of the overload ratio as constraints of the test load optimization model, and minimize the overload ratio as the optimization target test load size as the involved variable to achieve optimization of the test load.

[0083] It is worth noting that the overload ratio is the ratio of the test load bending moment of the wind turbine blade under the test load to the theoretical target bending moment. Each loading position of the wind turbine blade has a corresponding overload ratio, and this overload ratio is affected by the material properties of the wind turbine blade. There is a maximum and minimum value, that is, the maximum value is the upper limit of the overload ratio, and the minimum value is the lower limit of the overload ratio.

[0084] In the embodiment of the present application, a particle swarm optimization algorithm can be used as a basis to build a test load optimization model. Of course, other optimization algorithms can also be used to build a test load optimization model, which is not limited here.

[0085] In some other embodiments, when initializing the test load optimization model, the following optimization formula may be used for optimization:

[0086] Find: F i ,i=1,2,3…,n (1)

[0087]

[0088]

[0089]

[0090] Among them, F i is the size of the i-th test load, i.e., the optimization design variable; n is the number of test loads; M j is the test bending moment load of the jth section, i.e., the bending moment generated at the jth section by the loading force, blade weight, fixture weight and upward load; m is the number of target test sections; is the target moment load of the jth section, The lower limit of the ith test load, The upper limit of the ith test load, L i is the distance from the i-th first loading position to the blade root, S j is the distance from the section where the jth test position is located to the blade root, M G is the bending moment caused by blade gravity, M rigis the bending moment caused by the gravity of the loading fixture, M up The bending moment caused by the upward load is The lower limit of the moment overload ratio of the jth section is: is the upper limit of the moment overload ratio of the jth section.

[0091] In the optimization formula (1), F i It indicates that the test load size is the optimization variable, the optimization formula (2) indicates that minimizing the overload ratio is the optimization goal, and the optimization formula (3) indicates that the upper and lower limits of the overload ratio are constraints.

[0092] In addition, during the initialization process, the installation angle of the wind turbine blade also needs to be initialized according to the direction of the target test load, so that the test load applied to the wind turbine blade during the static test is more accurate.

[0093] In one embodiment, during the process of initializing the installation angle, the installation angles of 0°, 90°, 180° and 270° of the target test load in the blade coordinate system can be initialized. The installation angle can be quickly determined through the four angle initializations.

[0094] S103, iteratively calculating the test load bending moment based on the load quantity and the first loading position.

[0095] In some embodiments, after completing the initialization of the test load optimization model, the load quantity and the first loading position are input into the test load optimization model, and then iterative calculations are performed in the test load optimization model. Each iteration determines a test load bending moment based on the above optimization formula, and calculates the first overload ratio by the ratio of the test load bending moment to the theoretical target bending moment load.

[0096] See also Figure 3 In some other embodiments, S103 may include:

[0097] S1031, for each first loading position, calculating a first distance between the first loading position and the blade root of the wind turbine blade, and a second distance between the cross section where the test position is located and the blade root of the wind turbine blade;

[0098] S1032, obtaining the current test load, blade gravity bending moment, loading fixture gravity bending moment and upward load bending moment;

[0099] S1033, determining the test load bending moment based on the first distance, the second distance, the current test load, the blade gravity bending moment, the loading fixture gravity bending moment and the upward load bending moment.

[0100] In this embodiment, when iteratively calculating the test load bending moment, since the test load bending moment corresponding to each first loading position is different, it is necessary to iteratively calculate the test load bending moment for each first loading position.

[0101] Based on the above optimization formulas (2) and (3), the optimization formula (4) can be derived:

[0102] M j =(L i -S j )F i +M G +M rig +M up (4)

[0103] That is, the test load bending moment is determined by a first distance between the first loading position and the root of the wind turbine blade and a second distance between the cross-section at the second loading position and the root of the wind turbine blade. Therefore, the electronic device must calculate the first distance and the second distance before calculating the test load bending moment. In addition, the blade gravity bending moment, the loading fixture gravity bending moment and the upward load bending moment can be determined when the wind turbine blade is placed on the test bench. The blade gravity bending moment, the loading fixture gravity bending moment and the upward load bending moment can be input into the electronic device by technicians to calculate the test load bending moment.

[0104] S104, when the test load bending moment satisfies the first optimization condition, calculating a first overload ratio based on the test load bending moment and a preset target load bending moment.

[0105] In some embodiments, when the test load optimization model is used to iteratively calculate the test load bending moment, the first optimization condition may include optimization convergence or failure to find an optimal solution that satisfies the constraint conditions, that is, the first overload ratio calculated based on the current test load is not within the constraint conditions.

[0106] In some other embodiments, S104 may include:

[0107] Dividing the wind turbine blade into sections based on the first loading position to obtain a plurality of test sections;

[0108] For each test interval, the test load corresponding to the test interval is optimized in sequence according to the preset step length based on the upper limit of the overload ratio;

[0109] When the optimization converges or there is no test load that satisfies the constraint, it is determined that the corresponding test load bending moment satisfies the first optimization condition.

[0110] Specifically, in the case of iterative calculation of the test load bending moment, in order to facilitate rapid static testing based on the determined first loading position, the wind turbine blade is divided into intervals to obtain multiple test intervals, in which the first loading position represents the entire corresponding test interval.

[0111] Moreover, when optimizing the test load, it is first optimized according to the overload ratio, that is, for each test interval, the test load corresponding to the test interval is optimized in sequence according to the preset step size based on the upper limit of the overload ratio until the optimization converges, the number of iterations is reached, or there is no test load that meets the constraints, it is determined that the current test load bending moment meets the first optimization condition, and the test load corresponding to the test load bending moment is the current optimal test load.

[0112] Furthermore, in order to quickly obtain the optimal test load, when performing sequential optimization, the test load is optimized for the test intervals corresponding to the tip of the wind turbine blade, the body of the wind turbine blade and the root of the wind turbine blade in a preset order.

[0113] As an implementation mode, the preset sequence may be optimized in the order from the tip of the wind turbine blade to the blade body of the wind turbine blade and then to the blade root of the wind turbine blade, or may be optimized in the order from the tip of the wind turbine blade to the blade root of the wind turbine blade and then to the blade body of the wind turbine blade, or may be other preset sequences, which are not limited here.

[0114] Taking the blade tip area as an example, a blade tip area may include multiple test intervals. When optimizing the test loads of multiple test intervals in the blade tip area, the test interval farthest from the blade tip area to the blade root area is optimized. When optimizing the test load of the test interval, the overload ratio determined above is first used as the initial overload ratio. When the obtained test load optimization converges, the first overload ratio is reduced according to a preset step size. For example, when the first overload ratio of the initial test is 1.5 and the preset step size is 0.05, when the test load is optimized for the second time, the first overload ratio of 1.45 is used for optimization until the optimization does not converge or no solution that satisfies the constraints is found, that is, the first overload ratio determined by satisfying the first optimization condition is used as the optimal overload ratio upper limit.

[0115] In some other embodiments, each test interval may be set to the same overload ratio upper limit, for example, may be set to 1.5, or different overload ratios may be set, that is, an initial overload ratio is set for each test interval based on experience.

[0116] S105 , for each first loading position, extract a plurality of test load samples to be optimized from the upper and lower limits of the test load based on the first overload ratio.

[0117] In some embodiments, after the test load is optimized for the first time based on the first overload ratio, in order to make the test load closer to reality during full-scale static testing, sampling is performed within the range of the test load upper limit and the test load lower limit based on the first overload ratio, and the test load is optimized again, that is, the above-mentioned optimal first overload ratio is used as the overload ratio upper limit as a constraint condition, and then multiple test load samples to be optimized are extracted. It can be understood that the test load samples to be optimized belong to different test loads within the range of the upper and lower limits of the test load.

[0118] It is worth noting that, in some embodiments, the sampling method may be a Latin hypercube sampling method, or other methods capable of performing global sampling, which are not limited here.

[0119] S106, using a preset optimization algorithm to perform load optimization on a plurality of test load samples to be optimized, and obtaining at least one optimization result.

[0120] In some embodiments, after extracting a plurality of test load samples to be optimized, a preset optimization algorithm is used to perform load optimization on each test load sample to be optimized.

[0121] As an example, the preset optimization algorithm may be a gradient optimization algorithm such as the interior point method, the steepest descent method, or other optimization algorithms, as long as they can meet the requirements of sample optimization, and no limitation is made here.

[0122] When the gradient optimization algorithm is used to optimize the load, S106 may include:

[0123] For multiple test load samples to be optimized in each test interval, a gradient-based optimization algorithm is used to perform gradient-decreasing iterative optimization. When the test load samples to be optimized gradually converge or the difference between the objective function value corresponding to the test load samples to be optimized and the preset objective function value is less than a preset threshold, the optimization result is output.

[0124] In this embodiment, when optimizing the test load samples to be optimized, gradient decreasing iterative optimization is performed with each test load sample to be optimized as the initial point. When the test load samples to be optimized gradually converge or the difference between the objective function value corresponding to the test load sample to be optimized and the preset objective function value is less than a preset threshold, it indicates that the optimization is completed and the optimization result can be output.

[0125] S107, when the optimization result corresponding to each first loading position satisfies the second optimization condition, determining a first test load based on the optimization result.

[0126] In some embodiments, as an example, the second optimization condition may include optimization convergence or finding the optimal solution, or the difference between the objective function value corresponding to the test load sample to be optimized and the preset objective function value is less than a preset threshold.

[0127] When the optimization results corresponding to each test load sample to be optimized meet the second optimization condition, it means that each first loading position can optimally perform full-size static testing on the wind turbine blade under the loading of the test load sample to be optimized and obtain the best test results.

[0128] When conducting a full-size static test on a wind turbine blade, the test load bending moment is continuously iterated and calculated in a test load optimization model through the static test loading parameters. When the test load bending moment satisfies the first optimization condition, the current test load bending moment is used as the optimal test load bending moment. Then, the first overload ratio calculated based on the optimal test load bending moment and the preset target load bending moment is used as the optimal overload ratio. Then, for each first loading position, the first overload ratio is used as the upper limit of the test load. Multiple test loads are sampled from the range of the upper and lower limits of the test load. After optimizing the multiple test loads, the first test load is obtained. The optimized loading load can be obtained quickly and accurately, thereby improving the efficiency of full-size static testing of wind turbine blades.

[0129] In some other embodiments, when the optimization result does not satisfy the second optimization condition, the method further includes:

[0130] Discard the test load samples to be optimized corresponding to the optimization results;

[0131] When multiple test load samples to be optimized are discarded, the upper limit of the overload ratio is increased, and the step of iteratively calculating the test load bending moment based on the load quantity and the first loading position is returned.

[0132] Specifically, in the process of continuous iterative optimization of the optimization results, until the number of iterations is reached, the optimization results have not converged or the optimal solution is found, that is, the optimization results do not meet the second optimization condition, which means that the test load sample to be optimized corresponding to the optimization result cannot meet the requirements of the static test. At this time, the test load sample to be optimized needs to be discarded.

[0133] When all the test load samples to be optimized are discarded, it means that the overload ratio obtained in the above manner cannot obtain the optimal test load, so it is necessary to increase the upper limit of the overload ratio and return to the step of iteratively calculating the test load bending moment based on the load quantity and the first loading position to redetermine the overload ratio.

[0134] In some other embodiments, after increasing the overload ratio upper limit, the method further includes:

[0135] When the increased overload ratio upper limit is the maximum overload ratio upper limit that the wind turbine blade can withstand, the second loading position is determined based on the first loading position and the failure position, the second loading position is determined as the new first loading position, and the step of iteratively calculating the test load bending moment based on the load quantity and the second loading position is returned.

[0136] Specifically, when the overload ratio is repeatedly increased until the overload ratio is increased to the upper limit of the overload ratio, it indicates that at this time, at the current first loading position, the test loads corresponding to all overload ratios cannot meet the test requirements, and the first loading position needs to be adjusted, that is, the second loading position is determined according to the first loading position and the failure position, and a static test is performed on the second loading position.

[0137] In some embodiments, due to the influence of the environment during the actual load test, after determining the first test load based on the optimization result, the method further includes:

[0138] Obtain blade stiffness distribution information and weight distribution information of wind turbine blades;

[0139] determining a loading position and a loading angle of the loaded blade based on the blade stiffness distribution information and the weight distribution information;

[0140] Obtaining clamping information of multiple loading brackets on a wind turbine blade;

[0141] Under the condition that the preset clamping conditions are met; the first test load is corrected based on the loading position, loading angle and clamping information after loading to obtain a corrected test load;

[0142] Perform gradient optimization on each corrected test load to obtain a second test load.

[0143] Specifically, due to the influence of the blade stiffness distribution information and weight distribution information when conducting a full-size static test on the wind turbine blade, the first loading position on the wind turbine blade will be offset after loading. Therefore, it is necessary to calculate the loading point position and loading angle of the blade after loading on the beam model according to the blade stiffness distribution information and weight distribution information to accurately position the loading point so that the position after loading is the first loading position determined in the above manner.

[0144] In addition, since the first test load is generally transmitted through the loading bracket when conducting a full-size static test on a wind turbine blade, when the first test load is applied to the loading point position after loading, the clamping information of the loading bracket is taken into consideration. When the clamping information meets the preset clamping conditions, the first test load is corrected based on the loading position, loading angle and clamping information of the blade after loading, thereby reducing the influence of the loading bracket on the first test load, so that the test load applied to the wind turbine blade is closer to the first test load.

[0145] In a specific embodiment, the clamping information includes a first distance between two adjacent loading brackets and a clamping angle of the loading brackets;

[0146] The preset clamping conditions include: the first distance is less than a preset distance threshold and the axial angle formed by the clamping angle and the wind turbine blade is greater than a preset angle.

[0147] Since multiple first loading positions are set on the wind turbine blade and due to the size limitation of the wind turbine blade, multiple loading brackets are required to support the wind turbine blade. Therefore, in order to reduce the impact of the loading brackets on the test, it is necessary to meet the requirements that the distance between adjacent loading brackets is less than the preset distance threshold and the axial angle formed by the clamping angle and the wind turbine blade is greater than the preset angle, so as to better perform static testing on the wind turbine blade.

[0148] In addition, in order to enable the modified test load to better test the wind turbine blade, it is also necessary to perform gradient decreasing optimization on the modified test load corresponding to each first loading position to obtain the second test load to ensure the static test result.

[0149] In other embodiments, for each optimization result, the electronic device will output on the display device the second test load corresponding to each first loading position, the test bending moment distribution and overload ratio of all interfaces in the wind turbine blade, as well as the position of the loading bracket and the loading position of the blade after loading, so as to facilitate technicians to perform full-scale static testing on the wind turbine blade.

[0150] See also Figure 4 In some embodiments, the present application also provides a wind turbine blade static test load optimization device. A wind turbine blade static test load optimization device 400 may include:

[0151] An acquisition module 401 is used to acquire static test loading parameters, where the static test loading parameters include load quantity, multiple first loading positions, upper and lower limits of test load, and upper and lower limits of overload ratio;

[0152] Initialization module 402, used to initialize the test load optimization model with the upper and lower limits of the overload ratio as constraints and minimization of the overload ratio as the optimization goal;

[0153] A calculation module 403, for iteratively calculating a test load bending moment based on the load quantity and the first loading position;

[0154] The calculation module 403 is further used to calculate a first overload ratio based on the test load bending moment and a preset target load bending moment when the test load bending moment satisfies the first optimization condition;

[0155] An extraction module 404 is used to extract a plurality of test load samples to be optimized from the upper and lower limits of the test load based on the first overload ratio for each first loading position;

[0156] The optimization module 405 is used to optimize the load of a plurality of test load samples to be optimized by using a preset optimization algorithm to obtain at least one optimization result;

[0157] The determination module 406 is used to determine the first test load based on the optimization results when the optimization results corresponding to each first loading position meet the second optimization condition.

[0158] As an optional implementation manner, the acquisition module 401 is further specifically configured to:

[0159] Obtaining the size information of the loading fixture and the parameter information of the wind turbine blade;

[0160] Based on the size information and parameter information, the finite element analysis of the wind turbine blade is performed to obtain the failure position of the wind turbine blade;

[0161] A first loading location is determined based on the failure location.

[0162] As an optional implementation manner, the calculation module 403 is further specifically configured to:

[0163] For each first loading position, calculating a first distance between the first loading position and the blade root of the wind turbine blade, and a second distance between the cross section where the test position is located and the blade root of the wind turbine blade;

[0164] Obtain the current test load, blade gravity bending moment, loading fixture gravity bending moment and upward load bending moment;

[0165] A test load bending moment is determined based on the first distance, the second distance, the current test load, the blade gravity bending moment, the loading fixture gravity bending moment, and the upward load bending moment.

[0166] As an optional implementation manner, the calculation module 403 is further specifically configured to:

[0167] Dividing the wind turbine blade into sections based on the first loading position to obtain a plurality of test sections;

[0168] For each test interval, the test load corresponding to the test interval is optimized in sequence according to the preset step length based on the upper limit of the overload ratio;

[0169] When the optimization converges or there is no test load that satisfies the constraint condition, it is determined that the corresponding test load bending moment satisfies the first optimization condition.

[0170] As an optional implementation, when the test load corresponding to the test interval is optimized in sequence according to the preset step length based on the upper limit of the overload ratio, the test load is optimized for the test interval corresponding to the tip of the wind turbine blade, the blade body of the wind turbine blade, and the blade root of the wind turbine blade in a preset order; the calculation module 403 is further specifically used for:

[0171] The overload ratio upper limit is taken as the initial overload ratio, and the overload ratio is gradually reduced according to the preset step size to optimize the test load corresponding to the test interval.

[0172] As an optional implementation, the optimization module 405 is further specifically configured to:

[0173] For multiple test load samples to be optimized in each test interval, a gradient-based optimization algorithm is used to perform gradient-decreasing iterative optimization. When the test load samples to be optimized gradually converge or the difference between the objective function value corresponding to the test load samples to be optimized and the preset objective function value is less than a preset threshold, the optimization result is output.

[0174] As an optional implementation, when the optimization result does not satisfy the second optimization condition, the determination module 406 is further specifically configured to:

[0175] Discard the test load samples to be optimized corresponding to the optimization results;

[0176] When multiple test load samples to be optimized are discarded, the upper limit of the overload ratio is increased, and the step of iteratively calculating the test load bending moment based on the load quantity and the first loading position is returned.

[0177] As an optional implementation, after increasing the overload ratio, the determination module 406 is further specifically configured to:

[0178] When the increased overload ratio upper limit is the maximum overload ratio upper limit that the wind turbine blade can withstand, the second loading position is determined based on the first loading position and the failure position, the second loading position is determined as the new first loading position, and the step of iteratively calculating the test load bending moment based on the load quantity and the second loading position is returned.

[0179] As an optional implementation manner, the determination module 406 is further specifically configured to:

[0180] Obtain blade stiffness distribution information and weight distribution information of wind turbine blades;

[0181] determining a loading position and a loading angle of the loaded blade based on the blade stiffness distribution information and the weight distribution information;

[0182] Obtaining clamping information of multiple loading brackets on a wind turbine blade;

[0183] Under the condition that the preset clamping conditions are met; the first test load is corrected based on the loading position, loading angle and clamping information of the loaded blade to obtain a corrected test load;

[0184] Perform gradient optimization on each corrected test load to obtain a second test load.

[0185] As an optional implementation, the clamping information includes a first distance between two adjacent loading brackets and a clamping angle of the loading brackets;

[0186] The preset clamping conditions include: the first distance is less than a preset distance threshold and the axial angle formed by the clamping angle and the wind turbine blade is greater than a preset angle.

[0187] Figure 5 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown.

[0188] The electronic device may include a processor 501 and a memory 502 storing computer program instructions.

[0189] Specifically, the processor 501 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0190] The memory 502 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 502 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 502 may include a removable or non-removable (or fixed) medium, or the memory 502 is a non-volatile solid-state memory. The memory 502 may be inside or outside the integrated gateway disaster recovery device.

[0191] In one example, the memory 502 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.

[0192] The memory 502 may include a read-only memory (ROM), a random access memory (RAM), a disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Therefore, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) 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 wind turbine blade static test load optimization method according to the first aspect of the present disclosure.

[0193] The processor 501 reads and executes the computer program instructions stored in the memory 502 to implement Figure 1 A method for optimizing static test load of a wind turbine blade in the illustrated embodiment.

[0194] In one example, the electronic device may further include a communication interface 503 and a bus 504. Figure 5 As shown, the processor 501, the memory 502, and the communication interface 503 are connected via a bus 504 and communicate with each other.

[0195] The communication interface 503 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0196] Bus 504 includes hardware, software or both, and the parts of electronic equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (Accelerated Graphics Port, AGP) or other graphics bus, enhanced industry standard architecture (Extended Industry Standard Architecture, EISA) bus, front side bus (Front Side Bus, FSB), hypertransmission (Hyper Transport, HT) interconnection, industry standard architecture (IndustryStandard Architecture, ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 504 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0197] The electronic device can execute the video query method in the embodiment of the present application, thereby realizing the combination Figure 1-Figure 4 The invention describes a method and device for optimizing the static test load of a wind turbine blade.

[0198] In addition, in combination with the wind turbine blade static test load optimization method in the above embodiment, the present application embodiment can provide a computer storage medium to implement. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any one of the video query methods in the above embodiment is implemented.

[0199] In an optional embodiment, in combination with the wind turbine blade static test load optimization method in the above-mentioned embodiment, the embodiment of the present application can provide a computer program product for implementation, and the instructions in the computer program product are executed by a processor of an electronic device, so that the electronic device can implement any one of the wind turbine blade static test load optimization methods in the above-mentioned embodiments.

[0200] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is 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 the steps after understanding the spirit of the present application.

[0201] The functional blocks shown in the above 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, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0202] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0203] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram 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 so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. 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 box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0204] The above are only specific implementation methods of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A method for optimizing static test load of wind turbine blades, characterized in that: include: Obtaining static test loading parameters, wherein the static test loading parameters include load quantity, multiple first loading positions, upper and lower limits of test load, and upper and lower limits of overload ratio; Taking the upper and lower limits of the overload ratio as constraints and minimizing the overload ratio as the optimization goal, initializing the test load optimization model; iteratively calculating a test load bending moment based on the load quantity and the first loading position; When the test load bending moment satisfies a first optimization condition, calculating a first overload ratio based on the test load bending moment and a preset target load bending moment; For each of the first loading positions, extracting a plurality of test load samples to be optimized from the upper and lower limits of the test load based on the first overload ratio; Using a preset optimization algorithm to perform load optimization on the multiple test load samples to be optimized, and obtaining at least one optimization result; When the optimization results corresponding to each of the first loading positions satisfy a second optimization condition, determining a first test load based on the optimization results; The iterative calculation of the test load bending moment based on the load quantity and the first loading position comprises: After the initialization of the test load optimization model is completed, the load quantity and the first loading position are input into the test load optimization model, and an iterative calculation is performed in the test load optimization model to obtain the test load bending moment.

2. The method according to claim 1, characterized in that Before obtaining the static test loading parameters, the method further includes: Obtaining the size information of the loading fixture and the parameter information of the wind turbine blade; Performing finite element analysis on the wind turbine blade based on the size information and parameter information to obtain a failure position of the wind turbine blade; A first loading position is determined based on the failure position.

3. The method according to claim 1, characterized in that The iterative calculation of the test load bending moment based on the load quantity and the first loading position comprises: For each first loading position, calculating a first distance between the first loading position and the blade root of the wind turbine blade, and a second distance between the cross section where the test position is located and the blade root of the wind turbine blade; Obtain the current test load, blade gravity bending moment, loading fixture gravity bending moment and upward load bending moment; A test load moment is determined based on the first distance, the second distance, the current test load, the blade gravity bending moment, the loading fixture gravity bending moment, and the upward load bending moment.

4. The method according to claim 1, characterized in that: In the case of iteratively calculating the test load bending moment based on the load quantity and the first loading position, the method further comprises: Dividing the wind turbine blade into sections based on the first loading position to obtain a plurality of test sections; For each of the test intervals, the test load corresponding to the test interval is optimized in sequence according to a preset step size based on the overload ratio upper limit; In a case where the optimization converges or there is no test load satisfying the constraint condition, it is determined that the corresponding test load bending moment satisfies the first optimization condition.

5. The method according to claim 4, characterized in that In the case where the test loads corresponding to the test intervals are optimized in sequence according to the preset step length based on the upper limit of the overload ratio, the test loads are optimized for the test intervals corresponding to the tip of the wind turbine blade, the body of the wind turbine blade and the root of the wind turbine blade in a preset order; The optimizing the test load corresponding to the test interval in sequence according to the preset step length based on the overload ratio upper limit includes: The overload ratio upper limit is taken as the initial overload ratio, and the overload ratio is gradually reduced in accordance with the preset step length to optimize the test load corresponding to the test interval.

6. The method according to claim 4, characterized in that The method of using a preset optimization algorithm to perform load optimization on the plurality of test load samples to be optimized to obtain at least one optimization result includes: For multiple test load samples to be optimized in each test interval, a gradient-type optimization algorithm is used to perform gradient-decreasing iterative optimization. When the test load samples to be optimized gradually converge or the difference between the objective function value corresponding to the test load samples to be optimized and the preset objective function value is less than a preset threshold, the optimization result is output.

7. The method according to claim 1, characterized in that When the optimization result does not satisfy the second optimization condition, the method further includes: Discarding the test load sample to be optimized corresponding to the optimization result; In a case where the plurality of test load samples to be optimized are all discarded, the overload ratio upper limit is increased, and the process returns to the step of iteratively calculating the test load bending moment based on the load quantity and the first loading position.

8. The method according to claim 7, characterized in that After increasing the overload ratio upper limit, the method further includes: When the increased overload ratio upper limit is the maximum overload ratio upper limit that the wind turbine blade can withstand, the second loading position is determined based on the first loading position and the failure position, the second loading position is determined as the new first loading position, and the step of iteratively calculating the test load bending moment based on the load quantity and the new first loading position is returned.

9. The method according to claim 1, characterized in that: After determining the first test load based on the optimization result, the method further includes: Obtaining blade stiffness distribution information and weight distribution information of the wind turbine blade; Determining a loading position and a loading angle of the loaded blade based on the blade stiffness distribution information and the weight distribution information; Acquiring clamping information of multiple loading brackets on the wind turbine blade; Under the condition that the preset clamping conditions are met; the first test load is corrected based on the loading position, loading angle and clamping information of the loaded blade to obtain a corrected test load; Gradient optimization is performed on each of the modified test loads to obtain a second test load.

10. The method according to claim 9, characterized in that The clamping information includes a first distance between two adjacent loading brackets and a clamping angle of the loading brackets; The preset clamping condition includes: the first distance is less than a preset distance threshold and the axial angle formed by the clamping angle and the wind turbine blade is greater than a preset angle.

11. A wind turbine blade static test load optimization device, characterized in that: include: An acquisition module, used for acquiring static test loading parameters, wherein the static test loading parameters include load quantity, multiple first loading positions, upper and lower limits of test load, and upper and lower limits of overload ratio; An initialization module is used to initialize the test load optimization model with the upper and lower limits of the overload ratio as constraints and minimization of the overload ratio as an optimization goal; a calculation module, configured to iteratively calculate a test load bending moment based on the load quantity and the first loading position; The calculation module is further used to calculate a first overload ratio based on the test load bending moment and a preset target load bending moment when the test load bending moment satisfies a first optimization condition; An extraction module, configured to extract, for each of the first loading positions, a plurality of test load samples to be optimized from the upper and lower limits of the test load based on the first overload ratio; An optimization module, configured to use a preset optimization algorithm to perform load optimization on the plurality of test load samples to be optimized, and obtain at least one optimization result; A determination module, configured to determine a first test load based on the optimization result when the optimization result corresponding to each of the first loading positions satisfies a second optimization condition; The computing module is specifically used for: After the initialization of the test load optimization model is completed, the load quantity and the first loading position are input into the test load optimization model, and an iterative calculation is performed in the test load optimization model to obtain the test load bending moment.

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 wind turbine blade static test load optimization method 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 optimizing the static test 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 wind turbine blade static test load optimization method according to any one of claims 1 to 10.

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