Optimization method, device and storage medium for pre-bending form of wind power blade

Abstract

The application discloses an optimization method and device for a wind power blade pre-bending form and a storage medium. The method comprises the following steps: determining a self-defined optimization variable corresponding to a blade optimization target from function variables of a blade pre-bending form function of the wind power blade; calculating a blade root bending moment corresponding to the self-defined optimization variable by using a wind power blade simulation software; performing optimization calculation on the blade root bending moment to obtain a target blade root bending moment satisfying the wind power blade optimization target and a parameter value of a target optimization variable corresponding to the target blade root bending moment, wherein the target optimization variable at least comprises one of the following: a target blade tip pre-bending amount, an index value of a target pre-bending form distribution and a target pre-bending starting position; and optimizing the wind power blade pre-bending form based on the parameter value of the target optimization variable. By using the technical scheme, the problem that the power generation efficiency is not high when wind power generation is performed based on an existing wind power blade pre-bending form is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of blade optimization of wind power blades, in particular, to a method and device for optimizing a pre-bending form of a wind power blade and a storage medium. BACKGROUND

[0002] Wind power generation is a clean and efficient renewable energy generation technology. As a core component of wind power generation, the design performance of a wind power blade directly affects the efficiency of wind power generation. At present, the efficiency of wind power generation is generally improved by designing a blade pre-bending form.

[0003] The pre-bending form is achieved by increasing the radius of curvature of the blade root and reducing the radius of curvature of the blade tip during the manufacturing process of the wind power blade. The wind power blade with the pre-bending form has a large bending amount at the blade root and a small bending amount at the end, which can control the power response of wind power generation. However, in the related art, the pre-bending form of the wind power blade is generally designed into a forward bending form according to artificial experience. This design results in a large blade load and is often accompanied by a large amount of trial and error, which consumes a large amount of manpower and material resources. It can be seen that the power generation efficiency of wind power generation based on the existing pre-bending form of the wind power blade is not high in the related art.

[0004] At present, there is no effective solution to the problem that the power generation efficiency of wind power generation based on the existing pre-bending form of the wind power blade is not high in the related art.

[0005] Therefore, it is necessary to improve the related art to overcome the defects in the related art. SUMMARY

[0006] The embodiments of the present application provide a method and device for optimizing a pre-bending form of a wind power blade and a storage medium to at least solve the problem that the power generation efficiency of wind power generation based on the existing pre-bending form of the wind power blade is not high.

[0007] According to an aspect of an embodiment of the present application, a method for optimizing a pre-bending form of a wind power blade is provided, comprising: determining a self-defined optimization variable corresponding to a blade optimization target from function variables of a blade pre-bending form function of the wind power blade; calculating a blade root bending moment corresponding to the self-defined optimization variable by using wind power blade simulation software; performing optimization calculation on the blade root bending moment to obtain a target blade root bending moment satisfying the wind power blade optimization target and a parameter value of a target optimization variable corresponding to the target blade root bending moment, wherein the target optimization variable at least includes one of the following: a target blade tip pre-bending amount, an index value of a target pre-bending form distribution, and a target pre-bending starting position; and optimizing the pre-bending form of the wind power blade based on the parameter value of the target optimization variable.

[0008] In an exemplary embodiment, the blade pre-bending form function is expressed as: D=D tip×((x-x0) / (x tip -x0)) c Where x represents the distance perpendicular to the plane from the leaf root to the leaf tip, which is 0 at the leaf root. tip D indicates the leaf tip. tip The value represents the tip pre-bending amount, x0 represents the pre-bending start position, and c represents the exponential value of the pre-bending pattern distribution.

[0009] In an exemplary embodiment, the optimization objective of the wind turbine blade is to minimize the load on the wind turbine blade. The custom optimization variables include the tip pre-bending amount, the exponent value of the pre-bending pattern distribution, and the pre-bending initiation position. The root bending moment corresponding to the custom optimization variables is calculated using wind turbine blade simulation software, including: obtaining a given exponent value of the pre-bending pattern distribution and obtaining a given pre-bending initiation position; using the given exponent value of the pre-bending pattern distribution, the given pre-bending initiation position, and the pre-bending amount parameter value in the wind turbine blade simulation software to calculate the root bending moment for different tip pre-bending amounts, wherein the pre-bending amount parameter value is the value of the tip pre-bending amount taken from the pre-bending amount value space.

[0010] In an exemplary embodiment, the wind turbine blade simulation software is used to calculate the root bending moment for different tip pre-bending amounts using the given pre-bending pattern distribution index value, the given pre-bending start position, and the pre-bending amount parameter value. This includes: establishing a simulation model of the wind turbine blade using the wind turbine blade simulation software, and inputting the geometric parameters of the wind turbine blade, the material properties of the wind turbine blade, the given pre-bending pattern distribution index value, the given pre-bending start position, and the pre-bending amount parameter value into the simulation model to obtain the root bending moment for different tip pre-bending amounts output by the simulation model.

[0011] In an exemplary embodiment, optimizing the blade root bending moment to obtain a target blade root bending moment that satisfies the optimization objective of the wind turbine blade includes: finding the minimum blade root bending moment from multiple blade root bending moments; obtaining a first simulated power generation of the wind turbine blade under the minimum blade root bending moment; and determining the minimum blade root bending moment as the target blade root bending moment when it is determined that the first simulated power generation is greater than the power generation threshold.

[0012] In an example embodiment, the method further comprises: determining a value range corresponding to the index value of the pre-bending form distribution, sequentially taking different index values from the value range; determining a position range corresponding to the pre-bending starting position, sequentially taking different pre-bending starting positions from the position range; generating a plurality of groups of pre-bending form parameters according to the different index values and the different pre-bending starting positions, wherein each group of pre-bending form parameters comprises at least one index value and one pre-bending starting position; obtaining a plurality of groups of simulated power generation amounts of the wind turbine blade using the plurality of groups of pre-bending form parameters and the target blade root bending moment for simulation power generation; in a case where there is a second simulated power generation amount greater than a power generation amount threshold in the plurality of groups of simulated power generation amounts, determining an index value of a target pre-bending form distribution and a target pre-bending starting position corresponding to the second simulated power generation amount; and determining the parameter value of the target optimization variable using the target blade root bending moment, the index value of the target pre-bending form distribution, and the target pre-bending starting position.

[0013] According to another aspect of the embodiments of the present application, a wind turbine blade pre-bending form optimization device is also provided, comprising: a determination module configured to determine a custom optimization variable corresponding to a blade optimization target from function variables of a blade pre-bending form function of the wind turbine blade; a first calculation module configured to calculate a blade root bending moment corresponding to the custom optimization variable using wind turbine blade simulation software; a second calculation module configured to perform optimization calculation on the blade root bending moment to obtain a target blade root bending moment satisfying the wind turbine blade optimization target and a parameter value of a target optimization variable corresponding to the target blade root bending moment, wherein the target optimization variable comprises at least one of the following: a target blade tip pre-bending amount, an index value of a target pre-bending form distribution, and a target pre-bending starting position; and an optimization module configured to optimize the wind turbine blade pre-bending form based on the parameter value of the target optimization variable.

[0014] According to still another aspect of the embodiments of the present application, a computer readable storage medium having a computer program stored therein is also provided, wherein the computer program is configured to execute the wind turbine blade pre-bending form optimization method when running.

[0015] According to still another aspect of the embodiments of the present application, an electronic device is also provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the wind turbine blade pre-bending form optimization method through the computer program.

[0016] According to still another aspect of the embodiments of the present application, a computer program product is also provided, comprising a computer program, which, when executed by a processor, implements the steps in any of the method embodiments.

[0017] By the present application, the custom optimization variable corresponding to the blade optimization target is determined from the function variable of the blade pre-bending form function of the wind power blade; the root bending moment corresponding to the custom optimization variable is calculated by using the wind power blade simulation software; the root bending moment is optimized to obtain the target root bending moment satisfying the wind power blade optimization target and the parameter value of the target optimization variable corresponding to the target root bending moment, wherein the target optimization variable at least includes one of the following: target tip pre-bending amount, target pre-bending form distribution index value, and target pre-bending starting position; and the wind power blade pre-bending form is optimized based on the parameter value of the target optimization variable. The optimization variable can be customized, and the calculation is combined with the wind power blade simulation software to accurately find the pre-bending form parameter satisfying the optimization target, thereby solving the problem of low power generation efficiency of wind power generation based on the existing wind power blade pre-bending form, and effectively improving the power generation efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and serve to explain the principles of the present application together with the specification.

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, for those skilled in the art, other drawings can also be obtained based on these drawings without creative labor.

[0020] Figure 1 is a hardware structure block diagram of a mobile terminal of a wind power blade pre-bending form optimization method according to an embodiment of the present application;

[0021] Figure 2 is a flowchart of a wind power blade pre-bending form optimization method according to an embodiment of the present application;

[0022] Figure 3 is a structure block diagram of a wind power blade pre-bending form optimization device according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the personnel in the technical field better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for an optimization method of wind turbine blade pre-bending according to an embodiment of this application. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor or a programmable gate array (FPGA) or similar processing device) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0026] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the wind turbine blade pre-bending optimization method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thus implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to a mobile terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0027] The transmission device 106 is configured to receive or send data via a network. The network can include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network interface controller (NIC) that is configured to connect to other network devices through a base station to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module that is configured to communicate with the Internet through a wireless manner.

[0028] In the embodiment, a method for optimizing a pre-bending form of a wind turbine blade is provided, Figure 2 is a flow chart of a method for optimizing a pre-bending form of a wind turbine blade according to the embodiment of the present application, as shown in the figure, the flow includes the following steps: Figure 2

[0029] Step S202: determining a custom optimization variable corresponding to a blade optimization target from function variables of a blade pre-bending form function of the wind turbine blade;

[0030] The custom optimization variable can include, but is not limited to, a blade pre-bending amount, an index value of a pre-bending form distribution, and a pre-bending starting position. Optionally, for the custom optimization variable, the value of at least one optimization variable can be fixed, and the values of other optimization variables can be controlled to be variable.

[0031] Step S204: calculating a blade root bending moment corresponding to the custom optimization variable by using a wind turbine blade simulation software;

[0032] Step S206: performing an optimization calculation on the blade root bending moment to obtain a target blade root bending moment satisfying the wind turbine blade optimization target, and a parameter value of a target optimization variable corresponding to the target blade root bending moment, wherein the target optimization variable at least includes one of the following: a target blade tip pre-bending amount, an index value of a target pre-bending form distribution, and a target pre-bending starting position;

[0033] Step S208: optimizing the pre-bending form of the wind turbine blade based on the parameter value of the target optimization variable.

[0034] ​The custom optimization variable corresponding to the blade optimization target is determined from the function variable of the blade pre-bending form function of the wind power blade through the above steps; the blade root bending moment corresponding to the custom optimization variable is calculated by using the wind power blade simulation software; and the target blade root bending moment satisfying the wind power blade optimization target and the parameter value of the target optimization variable corresponding to the target blade root bending moment are obtained by performing optimization calculation on the blade root bending moment, wherein the target optimization variable at least includes one of the following: a target blade tip pre-bending amount, an index value of a target pre-bending form distribution, and a target pre-bending starting position. The wind power blade pre-bending form can be optimized based on the parameter value of the target optimization variable. The optimization variable can be customized and calculated in combination with the wind power blade simulation software to accurately find the pre-bending form parameter satisfying the optimization target, thereby solving the problem of low power generation efficiency of wind power generation based on the existing wind power blade pre-bending form and effectively improving the power generation efficiency.

[0035] In an exemplary embodiment, the wind power blade pre-bending form function is represented as:

[0036] D = D tip × ((x-x0) / (x tip -x0)) c ; wherein x represents a distance perpendicular to the blade root plane and pointing to the blade tip, x tip represents the blade tip, D tip represents the blade tip pre-bending amount, x0 represents the pre-bending starting position, and c represents the index value of the pre-bending form distribution. By adjusting the variables in the function, fine control of the wind power blade pre-bending shape can be achieved, thereby optimizing the aerodynamic performance and structural strength of the blade, which is suitable for high-precision wind power blade design scenarios such as offshore wind power blades.

[0037] In an exemplary embodiment, the wind power blade optimization target is the minimum load of the wind power blade, and the custom optimization variable includes the blade tip pre-bending amount, the index value of the pre-bending form distribution, and the pre-bending starting position. The process of calculating the blade root bending moment corresponding to the custom optimization variable by using the wind power blade simulation software includes: obtaining a given index value of the pre-bending form distribution and a given pre-bending starting position; and calculating the blade root bending moment under different blade tip pre-bending amounts by using the wind power blade simulation software with the given index value of the pre-bending form distribution, the given pre-bending starting position, and a pre-bending amount parameter value, wherein the pre-bending amount parameter value is the value of the blade tip pre-bending amount taken from the pre-bending amount value space. This method takes the minimum load as the optimization target and can simulate the blade load under different pre-bending amounts by using the simulation software to find the optimal pre-bending amount, thereby reducing the load of the wind power blade and improving the power generation efficiency, which is suitable for scenarios requiring reduction of blade weight and cost.

[0038] It is understood that the minimum load of the wind turbine blade can be achieved by minimizing the root bending moment. In the optimization of the pre-bending design, the weight of the blade can be reduced, i.e. the smaller root load, by reducing the vibration and noise of the blade during operation, and reducing the root bending moment, thereby increasing the operating efficiency of the wind turbine. In addition, the smaller root load also means smaller fatigue damage, prolonging the service life of the blade and the unit.

[0039] Therefore, in the design of the wind turbine blade, optimizing the tip pre-bending amount with the goal of minimizing the root bending moment has a significant impact on the structural strength of the blade.

[0040] Optionally, the following embodiments explain how to fix the pre-bending distribution form and the starting position, and optimize the design model by Bladed software and particle swarm optimization algorithm with the tip pre-bending amount as a variable. Assuming that the length of the blade is 60 meters, the pre-bending starting position is set at 20 meters of the blade in the initial design, and the exponential value of the pre-bending curve is set to 2.5, at this time the pre-bending form has been fixed. Then find the optimal tip pre-bending amount to ensure that the power generation efficiency is met while the root bending moment is minimized. Specifically, it includes:

[0041] Step 1, define the optimization variable: the tip pre-bending amount can be selected as the optimization variable, which can be set to a range of 0 meters to 1 meter.

[0042] Step 2, establish the Bladed model: use Bladed software to establish a simulation model of the wind turbine blade, input the geometric parameters, material properties and pre-set pre-bending form of the blade (here the pre-bending starting position and the exponential value are fixed).

[0043] Step 3, define the objective function: find the value that minimizes the root bending moment. Among them, the output of the Bladed simulation result includes the root bending moment, which represents the bending moment of the blade root during operation.

[0044] Step 4, PSO algorithm application: first, set the parameters of the PSO algorithm, including the number of particles, the maximum number of iterations, the inertia weight, the acceleration factor, etc. Among them, each particle represents a value of the tip pre-bending amount, which is randomly distributed within the given range. Then simulate by Bladed to get the root bending moment corresponding to each value of the tip pre-bending amount. The fitness of the particle can be evaluated, i.e. the value of the root bending moment, the goal is to minimize the fitness. Then update the position and velocity of each particle according to the rules of the PSO algorithm until the convergence condition is reached or the maximum number of iterations is reached.

[0045] Step 5, result analysis: when the PSO algorithm converges, the value of the tip pre-bending amount found is the optimal tip pre-bending amount that minimizes the root bending moment. Further check the Bladed simulation results to ensure that the power generation of the blade under the optimized design meets the design requirements.

[0046] Based on the above steps, an optimal blade tip pre-bend amount under a fixed pre-bend form and a starting position can be found, thereby achieving effective control of the blade load and achieving the purpose of reducing the weight and cost of the entire machine.

[0047] Of course, as part of the optimization design, the entire pre-bend form can be further optimized by adjusting the index value of the pre-bend form distribution and the pre-bend starting position.

[0048] In an exemplary embodiment, the following steps are further proposed to use the wind turbine blade simulation software to calculate the blade root bending moment of different blade tip pre-bend amounts using the given index value of the pre-bend form distribution, the given pre-bend starting position and the pre-bend amount parameter value, specifically including: using the wind turbine blade simulation software to establish a simulation model of the wind turbine blade, and inputting the geometric parameters of the wind turbine blade, the material properties of the wind turbine blade, the given index value of the pre-bend form distribution, the given pre-bend starting position and the pre-bend amount parameter value in the simulation model, to obtain the blade root bending moment of different blade tip pre-bend amounts output by the simulation model. This method can simulate the actual operation of the wind turbine blade under different pre-bend forms by establishing a detailed simulation model, and is suitable for performance prediction and fault analysis of the wind turbine blade, especially in scenarios where durability testing of the blade is required.

[0049] In an exemplary embodiment, further, the process of optimizing the blade root bending moment to obtain a target blade root bending moment that meets the optimization target of the wind turbine blade can include: finding a minimum blade root bending moment from a plurality of blade root bending moments; obtaining a first simulated power generation amount of the wind turbine blade under the minimum blade root bending moment; and determining the minimum blade root bending moment as the target blade root bending moment if it is determined that the first simulated power generation amount is greater than a power generation threshold. This method considers the minimization of blade load, and ensures that the optimized pre-bend form not only reduces the load, but also ensures that the power generation of the wind turbine blade is not lower than a threshold, balancing the performance and economy of the blade, thereby realizing the optimization design of the pre-bend form of the wind turbine blade under the premise of ensuring safety, with significant economic and environmental benefits.

[0050] Optionally, for the process of finding a minimum blade root bending moment from a plurality of blade root bending moments in the present embodiment, intelligent optimization algorithms such as particle swarm optimization algorithms can be used to realize, and specific contents can be referred to the application of the PSO algorithm described above, which will not be repeated here.

[0051] In an exemplary embodiment, further, a value range corresponding to the index value of the pre-bending form distribution can be determined, and different index values can be sequentially taken from the value range; a position range corresponding to the pre-bending starting position can be determined, and different pre-bending starting positions can be sequentially taken from the position range; a plurality of groups of pre-bending form parameters can be generated according to the different index values and the different pre-bending starting positions, wherein each group of pre-bending form parameters includes at least one index value and one pre-bending starting position; a plurality of groups of simulated power generation amounts can be obtained by simulating power generation of the wind power blade using the plurality of groups of pre-bending form parameters and the target blade root bending moment; in a case where a second simulated power generation amount greater than a power generation amount threshold exists in the plurality of groups of simulated power generation amounts, a target pre-bending form distribution index value and a target pre-bending starting position corresponding to the second simulated power generation amount can be determined; and a parameter value of the target optimization variable can be determined using the target blade root bending moment, the target pre-bending form distribution index value, and the target pre-bending starting position. This method can be applied in scenarios such as performance verification before large-scale production of wind power blades, by systematically testing the influence of different pre-bending form parameters on power generation, finding the optimal blade root bending moment, and finding the best pre-bending form suitable for wind power blades.

[0052] Further, for the process of further optimizing the entire pre-bending form by adjusting the index value of the pre-bending form distribution and the pre-bending starting position which is not specifically described in the above embodiments, the following can be supplemented:

[0053] Step 1, define optimization variables: set the index value c of the pre-bending form distribution and the pre-bending starting position x0 as new optimization variables. For example, the range of the index value c is set to 1.5 to 3.5, and the range of the pre-bending starting position x0 is set to 10 meters to 30 meters.

[0054] Step 2, update the Bladed model: based on the optimal blade tip pre-bending amount obtained, fix the D tip value in the Bladed model generated by the Bladed software, and then update the Bladed model, wherein c and x0 are used as variable parameters.

[0055] Step 3, multi-objective optimization: run different pre-bending form parameter combinations in Bladed, and record the values of the blade root bending moment Mxy and the power generation amount of the blade under each group of parameters.

[0056] Optionally, in a case where there is more than one optimization target (for example, in addition to Mxy minimization, there can also be power generation maximization, etc.), a multi-objective optimization algorithm such as NSGA-II (Non-dominated Sorting Genetic Algorithm II) can be used to achieve the best balance between Mxy and power generation in the parameter combination.

[0057] Step 4: Application of intelligent optimization algorithm: Use PSO algorithm or other intelligent optimization algorithms such as genetic algorithm (GA) to find the optimal c and x0. The initial particle distribution of the optimization algorithm should cover the entire search space of c and x0, and each particle represents a parameter combination (c, x0). Through multiple iterations, the parameter combination that minimizes the blade root bending moment Mxy while meeting the power generation requirements will be gradually approached.

[0058] Step 5: Result analysis and verification: After the optimization algorithm converges, select the pre-bending form parameter combination from the results that minimizes Mxy and meets the design requirements of power generation. Then, use the Bladed model to simulate and verify the new pre-bending form, ensure that the simulation results are consistent with the prediction of the optimization algorithm, and evaluate other performance indicators of the blade, such as vibration mode and noise level, to ensure the rationality of the overall design.

[0059] Step 6: Design iteration: Based on the simulation results, if it is found that Mxy still has room for further reduction or the power generation does not meet expectations, adjust the range of optimization variables or the parameters of the optimization algorithm and re-run the optimization process. The design process may require multiple iterations until the most satisfactory pre-bending form is found.

[0060] Through this series of steps, not only the tip pre-bending amount D tip is optimized, but also the pre-bending form distribution index c and the pre-bending starting position x0 are further adjusted to achieve the best structural performance and economic benefits, which can comprehensively optimize the pre-bending form of the wind turbine blade and provide more flexible parameter control means for blade design. Obviously, by using the above method, the pre-bending form of the wind turbine blade can be accurately adjusted, making the structural stress distribution more reasonable when it is subjected to wind, thereby improving the service life and overall performance of the blade.

[0061] In addition, the global emphasis on renewable energy is increasing, and wind power, as one of the important renewable energy sources, has great significance for improving the overall efficiency of wind power generation, reducing power generation costs, and reducing environmental impact. The wind turbine blade pre-bending form optimization method and device provided by the present application can also be applied to rapid design, performance prediction, fault analysis, and performance verification before large-scale production of wind turbine blades, etc., and has a wide application prospect and significant economic benefits. It plays an important role in promoting the development of wind power technology and promoting the widespread application of clean energy.

[0062] Obviously, the above-described embodiments are only a part of the embodiments of the present application, not all. In order to better understand the above method, the following describes the above process in combination with the embodiments, but is not used to limit the technical solutions of the embodiments of the present application, specifically:

[0063] In an alternative embodiment, the application also proposes the following specific implementation steps:

[0064] Step 1: Select the pre-bending form of the blade to meet the following exponential function distribution:

[0065] D = D tip × ((x-x0) / (x tip -x0)) c .

[0066] Wherein, x represents the distance perpendicular to the blade root plane to the blade tip, 0 at the blade root, x tip represents the blade tip, D tip represents the blade tip pre-bending amount, x0 represents the pre-bending starting position, and c represents the exponential value of the pre-bending form distribution.

[0067] Step 2: The pre-bending form of the blade is optimized by the following steps:

[0068] Step 11, fix the exponential value of the pre-bending distribution form and the pre-bending starting position, take the blade tip pre-bending amount as the optimization variable, take the minimum blade root bending moment as the target, use Bladed software to calculate the power generation, and use PSO intelligent optimization algorithm to optimize the blade tip pre-bending amount, so as to obtain the blade tip pre-bending amount D tip value with the lowest load;

[0069] Step 12, set the blade tip pre-bending amount D tip value obtained in step 11 with the lowest load (i.e. the minimum blade root bending moment Mxy) as the latest blade tip pre-bending amount required by Bladed software, still take the exponential value of the pre-bending form distribution and the pre-bending starting position as the design variable, still take the minimum blade root bending moment Mxy as the target, use Bladed software to calculate the power generation, and optimize to obtain the exponential value of the pre-bending form distribution and the pre-bending starting position with the lowest load.

[0070] It should be noted that if the minimum blade tip pre-bending amount has been determined before step 11, step 12 can be directly executed.

[0071] Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be realized by means of software and the necessary general hardware platform, of course, it can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the application can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes a plurality of instructions for making a terminal device (which can be a mobile phone, computer, server, or network device, etc.) execute the methods of various embodiments of the application.

[0072] An optimization device for a pre-bending form of a wind turbine blade is also provided in the present embodiment, which is used to implement the above-mentioned embodiments and preferred embodiments, and will not be described again. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware, or a combination of software and hardware can also be implemented and conceived.

[0073] Figure 3 is a structural block diagram of an optimization device for a pre-bending form of a wind turbine blade according to an embodiment of the present application, which comprises:

[0074] A determination module 32 is configured to determine a custom optimization variable corresponding to a blade optimization target from function variables of a blade pre-bending form function of the wind turbine blade;

[0075] A first calculation module 34 is configured to calculate a root bending moment corresponding to the custom optimization variable by using wind turbine blade simulation software;

[0076] A second calculation module 36 is configured to perform optimization calculation on the root bending moment to obtain a target root bending moment satisfying the wind turbine blade optimization target and a parameter value of a target optimization variable corresponding to the target root bending moment, wherein the target optimization variable at least includes one of the following: a target tip pre-bending amount, an index value of a target pre-bending form distribution, and a target pre-bending starting position;

[0077] An optimization module 38 is configured to optimize the pre-bending form of the wind turbine blade based on the parameter value of the target optimization variable.

[0078] By the above device, a custom optimization variable corresponding to a blade optimization target is determined from function variables of a blade pre-bending form function of the wind turbine blade; a root bending moment corresponding to the custom optimization variable is calculated by using wind turbine blade simulation software; optimization calculation is performed on the root bending moment to obtain a target root bending moment satisfying the wind turbine blade optimization target and a parameter value of a target optimization variable corresponding to the target root bending moment, wherein the target optimization variable at least includes one of the following: a target tip pre-bending amount, an index value of a target pre-bending form distribution, and a target pre-bending starting position; and the pre-bending form of the wind turbine blade is optimized based on the parameter value of the target optimization variable. The optimization variable can be customized, and calculation can be performed in combination with wind turbine blade simulation software to accurately find a pre-bending form parameter satisfying the optimization target, thereby solving the problem of low power generation efficiency based on an existing pre-bending form of a wind turbine blade for power generation, and effectively improving the power generation efficiency.

[0079] In an exemplary embodiment, the wind turbine blade pre-bending form function is represented as: D = D tip × ((x-x0) / (x tip-x0) c ; wherein x represents the distance perpendicular to the blade root plane and pointing to the blade tip, 0 at the blade root, x tip represents the blade tip, D tip represents the blade tip pre-bend amount, x0 represents the pre-bend starting position, and c represents the index value of the pre-bend form distribution.

[0080] In an exemplary embodiment, the wind turbine blade optimization target is the minimum load of the wind turbine blade, and the custom optimization variable includes the blade tip pre-bend amount. The first calculation module is further configured to: obtain a given index value of the pre-bend form distribution and a given pre-bend starting position; and use the wind turbine blade simulation software to calculate the blade root bending moment at different blade tip pre-bend amounts using the given index value of the pre-bend form distribution, the given pre-bend starting position, and a pre-bend amount parameter value, wherein the pre-bend amount parameter value is a value of the blade tip pre-bend amount taken from a pre-bend amount value space.

[0081] In an exemplary embodiment, the first calculation module is further configured to: use the wind turbine blade simulation software to establish a simulation model of the wind turbine blade, and input the geometric parameters of the wind turbine blade, the material properties of the wind turbine blade, the given index value of the pre-bend form distribution, the given pre-bend starting position, and the pre-bend amount parameter value into the simulation model to obtain the blade root bending moment at the different blade tip pre-bend amounts output by the simulation model.

[0082] In an exemplary embodiment, the second calculation module is further configured to: find the minimum blade root bending moment from a plurality of blade root bending moments; obtain a first simulated power generation amount of the wind turbine blade under the minimum blade root bending moment; and determine the minimum blade root bending moment as the target blade root bending moment if it is determined that the first simulated power generation amount is greater than a power generation threshold.

[0083] In an exemplary embodiment, the second calculation module is further configured to: determine an index value interval corresponding to the index value of the pre-bend form distribution, and sequentially take different index values from the index value interval; determine a position interval corresponding to the pre-bend starting position, and sequentially take different pre-bend starting positions from the position interval; generate a plurality of pre-bend form parameter groups according to the different index values and the different pre-bend starting positions, wherein each pre-bend form parameter group includes at least one index value and one pre-bend starting position; obtain a plurality of simulated power generation amounts of the wind turbine blade under the plurality of pre-bend form parameter groups and the target blade root bending moment; and determine an index value and a pre-bend starting position of a target pre-bend form distribution corresponding to a second simulated power generation amount greater than a power generation threshold from the plurality of simulated power generation amounts, and determine the parameter value of the target optimization variable using the target blade root bending moment, the index value of the target pre-bend form distribution, and the target pre-bend starting position.

[0084] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is configured to execute the steps in any one of the method embodiments when being executed.

[0085] Optionally, in the embodiment, the storage medium is configured to store the computer program for executing the following steps.

[0086] S1, determining a custom optimization variable corresponding to a blade optimization target from function variables of a blade pre-bending form function of the wind turbine blade;

[0087] S2, calculating a root bending moment corresponding to the custom optimization variable by using wind turbine blade simulation software;

[0088] S3, performing optimization calculation on the root bending moment to obtain a target root bending moment meeting the wind turbine blade optimization target and a parameter value of a target optimization variable corresponding to the target root bending moment, wherein the target optimization variable at least includes one of the following: a target blade tip pre-bending amount, an index value of a target pre-bending form distribution, and a target pre-bending starting position;

[0089] S4, optimizing the wind turbine blade pre-bending form based on the parameter value of the target optimization variable.

[0090] In an example embodiment, the computer readable storage medium can include, but is not limited to, a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and various media capable of storing computer programs.

[0091] The specific examples in the embodiment can refer to the examples described in the above embodiments and example embodiments, and the embodiment will not be described here again.

[0092] The embodiment of the present application further provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor is configured to execute the computer program to execute the steps in any one of the method embodiments.

[0093] Optionally, in the embodiment, the processor is configured to execute the following steps by the computer program.

[0094] S1, determining a custom optimization variable corresponding to a blade optimization target from function variables of a blade pre-bending form function of the wind turbine blade;

[0095] S2, calculating the root bending moment corresponding to the self-defined optimization variable by using wind turbine blade simulation software;

[0096] S3, performing optimization calculation on the root bending moment to obtain a target root bending moment satisfying the wind turbine blade optimization target and a parameter value of a target optimization variable corresponding to the target root bending moment, wherein the target optimization variable at least includes one of the following: a target blade tip pre-bending amount, an index value of a target pre-bending form distribution, and a target pre-bending starting position;

[0097] S4, optimizing the pre-bending form of the wind turbine blade based on the parameter value of the target optimization variable.

[0098] In one example embodiment, the electronic device described above can further include a transmission device connected to the processor and an input / output device connected to the processor.

[0099] Embodiments of the present application also provide a computer program product, which includes a computer program, and the computer program implements the steps in any of the method embodiments described above when executed by a processor.

[0100] Embodiments of the present application also provide another computer program product, which includes a non-volatile computer readable storage medium, and the non-volatile computer readable storage medium stores a computer program, and the computer program implements the steps in any of the method embodiments described above when executed by a processor.

[0101] Embodiments of the present application also provide a computer program, which includes computer instructions stored in a computer readable storage medium; a processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to enable the computer device to perform the steps in any of the method embodiments described above.

[0102] The specific examples in the present embodiment can refer to the examples described in the above embodiments and example embodiments, and the present embodiment will not be described here again.

[0103] It is apparent that those skilled in the art can, without departing from the spirit of the present application, make various changes and modifications of the modules or steps of the present application described above, which can be implemented by general computing devices, and can be centralized on a single computing device or distributed on a network composed of multiple computing devices, and can be implemented by program codes executable by computing devices, so that they can be stored in storage devices and executed by computing devices, and in some cases, the steps shown or described can be executed in different order, or can be made into individual integrated circuit modules, or multiple modules or steps can be made into a single integrated circuit module. Thus, the present application is not limited to any particular combination of hardware and software.

[0104] The above description is only the preferred embodiments of the present application, and it should be pointed out that those skilled in the art can make several improvements and refinements without departing from the principles of the present application, and these improvements and refinements should be considered as the protection scope of the present application.

Claims

1. A method of optimization of a pre-bend form of a wind turbine blade, characterized in that, The method comprises: determining a custom optimization variable corresponding to a blade optimization target from a function variable of a blade pre-bending form function of the wind turbine blade; calculating a root bending moment corresponding to the custom optimization variable by using wind turbine blade simulation software; performing optimization calculation on the root bending moment to obtain a target root bending moment satisfying the wind turbine blade optimization target and a parameter value of a target optimization variable corresponding to the target root bending moment, wherein the target optimization variable at least includes one of the following: a target tip pre-bending amount, an index value of a target pre-bending form distribution, and a target pre-bending starting position; optimizing the wind turbine blade pre-bending form based on the parameter value of the target optimization variable. The wind turbine blade optimization target is a minimum load of the wind turbine blade, the custom optimization variable includes a tip pre-bending amount, an index value of a pre-bending form distribution, and a pre-bending starting position, and the calculation of the root bending moment corresponding to the custom optimization variable by using the wind turbine blade simulation software comprises: obtaining a given index value of a pre-bending form distribution and a given pre-bending starting position; calculating root bending moments of different tip pre-bending amounts by using the wind turbine blade simulation software with the given index value of the pre-bending form distribution, the given pre-bending starting position, and a pre-bending amount parameter value, wherein the pre-bending amount parameter value is a value of the tip pre-bending amount taken from a pre-bending amount value space.

2. The wind turbine blade pre-bending form optimization method according to claim 1, wherein the wind turbine blade pre-bending form function is expressed as: D = D tip × ((x - x0) / (x tip - x0)) c ; where x represents the distance perpendicular to the blade root plane pointing to the blade tip, 0 at the blade root, x tip represents the blade tip, D tip represents the blade tip pre-bend amount, x0 represents the pre-bend starting position, and c represents the index value of the pre-bend form distribution.

3. A method of optimization of a pre-bend form of a wind turbine blade according to claim 1, characterized in that, calculating root bending moments of different tip pre-bending amounts by using the wind turbine blade simulation software with the given index value of the pre-bending form distribution, the given pre-bending starting position, and a pre-bending amount parameter value, comprises: establishing a simulation model of the wind turbine blade by using the wind turbine blade simulation software, and inputting geometric parameters of the wind turbine blade, material properties of the wind turbine blade, the given index value of the pre-bending form distribution, the given pre-bending starting position, and the pre-bending amount parameter value in the simulation model to obtain the root bending moments of the different tip pre-bending amounts output by the simulation model.

4. A method of optimization of a pre-bend form of a wind turbine blade according to claim 1, characterized in that, performing optimization calculation on the root bending moment to obtain a target root bending moment satisfying the wind turbine blade optimization target, comprises: finding a minimum root bending moment from a plurality of root bending moments; obtaining a first simulation power generation amount of the wind turbine blade under simulation power generation at the minimum root bending moment; determining the minimum root bending moment as the target root bending moment in a case where the first simulation power generation amount is greater than a power generation threshold.

5. A method of optimising a wind turbine blade pre-bend form according to claim 4, characterised in that, The method further comprises: determining a value interval corresponding to the index value of the pre-bending form distribution, and sequentially taking different index values from the value interval; determining a position interval corresponding to the pre-bending starting position, and sequentially taking different pre-bending starting positions from the position interval; generating a plurality of pre-bending form parameters according to the different index values and the different pre-bending starting positions, wherein each pre-bending form parameter at least includes one index value and one pre-bending starting position; obtaining a plurality of simulation power generation amounts of the wind turbine blade under simulation power generation by using the plurality of pre-bending form parameters and the target root bending moment; in a case where a second simulated power generation greater than a power generation threshold exists in the multiple groups of simulated power generations, determining an index value of a target pre-bending form distribution and a target pre-bending starting position corresponding to the second simulated power generation; determining a parameter value of the target optimization variable by using the target blade root bending moment, the index value of the target pre-bending form distribution and the target pre-bending starting position.

6. An apparatus for optimization of a pre-bend form of a wind turbine blade, c h a r a c t e r i s e d in that Comprise: a determining module configured to determine a self-defined optimization variable corresponding to a blade optimization target from function variables of a blade pre-bending form function of the wind turbine blade; a first calculating module configured to calculate a blade root bending moment corresponding to the self-defined optimization variable by using a wind turbine blade simulation software; a second calculating module configured to perform optimization calculation on the blade root bending moment to obtain a target blade root bending moment satisfying the wind turbine blade optimization target and a parameter value of a target optimization variable corresponding to the target blade root bending moment, wherein the target optimization variable at least comprises one of the following: a target blade tip pre-bending amount, an index value of a target pre-bending form distribution and a target pre-bending starting position; an optimization module configured to optimize the pre-bending form of the wind turbine blade based on the parameter value of the target optimization variable. The wind turbine blade optimization target is a minimum load of the wind turbine blade, the self-defined optimization variable comprises a blade tip pre-bending amount, the first calculating module is further configured to: obtain a given index value of a pre-bending form distribution and a given pre-bending starting position; and perform calculation by using the wind turbine blade simulation software to obtain blade root bending moments of different blade tip pre-bending amounts by using the given index value of the pre-bending form distribution, the given pre-bending starting position and a pre-bending amount parameter value, wherein the pre-bending amount parameter value is a value of the blade tip pre-bending amount taken from a pre-bending amount value space.

7. A computer readable storage medium, characterized in that, The computer readable storage medium comprises a stored program, wherein the program performs the method of any one of claims 1 to 5 when executed. 8.An electronic device comprising a memory and a processor, the electronic device comprising: The memory stores a computer program, and the processor is configured to execute the method of any one of claims 1 to 5 by using the computer program.

9. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 5. The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 5.

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