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

Abstract

The invention discloses an optimization method and device for a pre-bending form of a wind power blade and a storage medium. The method comprises the steps that a self-defined optimization variable corresponding to a blade optimization target is determined 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 utilizing wind power blade simulation software; the blade root bending moment is subjected to optimization calculation, a target blade root bending moment meeting the wind power blade optimization target and a parameter value of a target optimization variable corresponding to the target blade root bending moment are obtained, and the target optimization variable at least comprises one of a target blade tip pre-bending amount, an index value of target pre-bending form distribution and a parameter value of a target blade tip pre-bending form distribution. A target pre-bending initial position; and optimizing the pre-bending form of the wind power blade based on the parameter value of the target optimization variable. By the adoption of the technical scheme, the problem that the power generation efficiency of wind power generation based on an existing wind power blade pre-bending mode is not high is solved.

Description

Technical Field

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

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

[0003] The pre-bend form is achieved during the manufacturing process of wind turbine blades by increasing the radius of curvature of the blade root and reducing the radius of curvature of the blade tip. The pre-bend form of wind turbine blades 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-bend form of wind turbine blades is generally designed into a forward bend form based on manual experience. This design makes the blade load larger and is often accompanied by a lot of trial and error, and also consumes a lot of manpower and material resources. It can be seen that in the related art, the power generation efficiency of wind power generation based on the existing pre-bend form of wind turbine blades is not high.

[0004] In the related technology, the power generation efficiency of wind power generation based on the existing pre-bending form of wind turbine blades is not high, and no effective solution has been proposed yet.

[0005] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology. Summary of the invention

[0006] The embodiments of the present application provide a method, device and storage medium for optimizing the pre-bending form of wind turbine blades, so as to at least solve the problem of low power generation efficiency of wind power generation based on the existing pre-bending form of wind turbine blades.

[0007] According to one aspect of an embodiment of the present application, a method for optimizing the pre-bending form of a wind turbine blade is provided, comprising: 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; calculating the blade root bending moment corresponding to the custom optimization variable using wind turbine blade simulation software; optimizing the blade root bending moment to obtain a target blade root bending moment that meets 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 includes 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 optimizing the pre-bending form of the wind turbine blade based on the parameter value of the target optimization variable.

[0008] In an exemplary embodiment, the pre-bending form function of the wind turbine blade 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, which is 0 at the blade root and x tip Indicates the tip of the leaf, D tip represents the amount of blade tip pre-bending, x0 represents the starting position of pre-bending, and c represents the index value of the pre-bending form distribution.

[0009] In an exemplary embodiment, the optimization target of the wind turbine blade is to minimize the load on the wind turbine blade, and the custom optimization variables include the tip pre-bending amount, the index value of the pre-bending form distribution and the pre-bending starting position. The wind turbine blade simulation software is used to calculate the blade root bending moment corresponding to the custom optimization variable, including: obtaining the index value of a given pre-bending form distribution, and obtaining a given pre-bending starting position; using the wind turbine blade simulation software to use the index value of the given pre-bending form distribution, the given pre-bending starting position and the pre-bending amount parameter value to calculate, and obtain the blade root bending moment of different blade tip pre-bending amounts, 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.

[0010] In an exemplary embodiment, the wind turbine blade simulation software is used to calculate using the given index value of the pre-bending form distribution, the given pre-bending starting position and the pre-bending amount parameter value to obtain the blade root bending moment with different blade tip pre-bending amounts, 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-bending form distribution, the given pre-bending starting position and the pre-bending amount parameter value into the simulation model to obtain the blade root bending moment with different blade tip pre-bending amounts output by the simulation model.

[0011] In an exemplary embodiment, the blade root bending moment is optimized and calculated to obtain a target blade root bending moment that meets the optimization target of the wind turbine blade, including: finding a 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 when it is determined that the first simulated power generation is greater than a power generation threshold, determining the minimum blade root bending moment as the target blade root bending moment.

[0012] In an exemplary embodiment, the method also includes: determining a value interval corresponding to the index value of the pre-bending form distribution, and sequentially taking out different index values ​​from the value interval; determining a position interval corresponding to the pre-bending starting position, and sequentially taking out different pre-bending starting positions from the position interval; generating multiple sets of pre-bending form parameters according to the different index values ​​and the different pre-bending starting positions, wherein each set of pre-bending form parameters includes at least one index value and one pre-bending starting position; obtaining multiple sets of simulated power generation of the wind turbine blade using the multiple sets of pre-bending form parameters and the target blade root bending moment for simulated power generation; when there is a second simulated power generation greater than a power generation threshold in the multiple sets of simulated power generation, determining the index value of the target pre-bending form distribution and the target pre-bending starting position corresponding to the second simulated power generation; 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 an embodiment of the present application, a device for optimizing the pre-bending form of a wind turbine blade is also provided, comprising: a determination module, used 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, used to calculate the blade root bending moment corresponding to the custom optimization variable using wind turbine blade simulation software; a second calculation module, used to optimize the blade root bending moment to obtain a target blade root bending moment that meets the optimization target of the wind turbine blade, and a parameter value of a target optimization variable corresponding to the target blade root bending moment, wherein the target optimization variable includes 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; an optimization module, used to optimize the pre-bending form of the wind turbine blade based on the parameter value of the target optimization variable.

[0014] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned method for optimizing the pre-bending form of wind turbine blades when running.

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

[0016] According to another aspect of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

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

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

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

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

[0021] Figure 2 is a flow chart of a method for optimizing the pre-bending form of a wind turbine blade according to an embodiment of the present application;

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

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal for optimizing the pre-bending form of a wind turbine blade according to an embodiment of the present application. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a microprocessor or a processing device such as a programmable logic device (Field Programmable Gate Array, FPGA)) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is for illustration only and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0026] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the optimization method of the pre-bending form of wind turbine blades in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a 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 a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0027] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

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

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

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

[0031] Step S204: using wind turbine blade simulation software to calculate the blade root bending moment corresponding to the user-defined optimization variable;

[0032] Step S206: Optimizing and calculating the blade root bending moment to obtain a target blade root bending moment that meets the optimization target of the wind turbine blade, and a parameter value of a target optimization variable corresponding to the target blade root bending moment, wherein the target optimization variable includes 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;

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

[0034] Through the above steps, the custom optimization variable corresponding to the blade optimization target is determined from the function variables of the blade pre-bending form function of the wind turbine blade; the blade root bending moment corresponding to the custom optimization variable is calculated using the wind turbine blade simulation software; the blade root bending moment is optimized and calculated to obtain the target blade root bending moment that meets the wind turbine blade optimization target, and the parameter value of the target optimization variable corresponding to the target blade root bending moment, wherein the target optimization variable includes at least one of the following: target tip pre-bending amount, index value of target pre-bending form distribution, target pre-bending starting position; the wind turbine blade pre-bending form is optimized based on the parameter value of the target optimization variable. The optimization variables can be customized and calculated in combination with the wind turbine blade simulation software to accurately find the pre-bending form parameters that meet the optimization target, thereby solving the problem of low power generation efficiency of wind power generation based on the existing wind turbine blade pre-bending form, and effectively improving the power generation efficiency.

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

[0036] 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, which is 0 at the blade root and x tip Indicates the tip of the leaf, D tip represents the tip pre-bend amount, x0 represents the pre-bend starting position, and c represents the exponential value of the pre-bend form distribution. By adjusting the variables in the function, the pre-bend shape of the wind turbine blade can be finely controlled, thereby optimizing the aerodynamic performance and structural strength of the blade, which is suitable for high-precision wind turbine blade design scenarios such as offshore wind turbine blades.

[0037] In an exemplary embodiment, the optimization target of the wind turbine blade is to minimize the load of the wind turbine blade, and the custom optimization variables include the tip pre-bending amount, the index value of the pre-bending form distribution and the pre-bending starting position. The process of using the wind turbine blade simulation software to calculate the blade root bending moment corresponding to the custom optimization variable includes: obtaining the index value of the given pre-bending form distribution and obtaining the given pre-bending starting position; using the wind turbine blade simulation software to use the index value of the given pre-bending form distribution, the given pre-bending starting position and the pre-bending amount parameter value to calculate and obtain the blade root bending moment of different blade tip pre-bending amounts, 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, can simulate the blade load under different pre-bending amounts through the simulation software, find the optimal pre-bending amount, and improve the power generation efficiency while reducing the load of the wind turbine blade. It is suitable for scenarios where it is necessary to reduce the weight and cost of the blade.

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

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

[0040] Optionally, the following embodiments are combined to illustrate how to fix the pre-bending distribution form and starting position, and how to optimize the design model by using 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 index value of the pre-bending curve is set to 2.5. At this time, the pre-bending form is fixed. Then find the optimal tip pre-bending amount to ensure that the root bending moment is minimized while meeting the power generation efficiency. Specifically, it includes:

[0041] Step 1, define optimization variables: The tip pre-bend amount can be selected as the optimization variable, and its range can be set between 0 meter and 1 meter.

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

[0043] Step 3, define the objective function: find the value that minimizes the blade root bending moment. The output of the Bladed simulation results includes the blade root bending moment, which represents the bending moment on 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-bend, which is randomly distributed within a given range. Then, through Bladed simulation, the blade root bending moment corresponding to each blade tip pre-bend value is obtained. The fitness of the particle, that is, the value of the blade root bending moment, can be evaluated, and the goal is to minimize the fitness. Then, according to the rules of the PSO algorithm, the position and velocity of each particle are updated until the convergence condition or the maximum number of iterations is reached.

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

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

[0047] Of course, as a part of the optimization design, the above embodiment can further optimize the entire pre-bending form by adjusting the index value of the pre-bending form distribution and the pre-bending starting position.

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

[0049] In an exemplary embodiment, further, the process of optimizing and calculating the blade root bending moment to obtain the target blade root bending moment that meets the optimization target of the wind turbine blade may include: finding the minimum blade root bending moment from multiple blade root bending moments; obtaining the 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. This method takes into account the minimization of blade loads, and by simulating the power generation under different pre-bending forms, ensures that the optimized pre-bending form can not only reduce the load, but also ensure that the power generation of the wind turbine blade is not lower than the threshold, balancing the blade performance and economy, thereby achieving the optimized design of the pre-bending form of the wind turbine blade under the premise of ensuring safety, with significant economic and environmental benefits.

[0050] Optionally, the process of finding the minimum blade root bending moment from multiple blade root bending moments in this embodiment can be implemented in combination with intelligent optimization algorithms such as particle swarm optimization algorithm, for example. For details, please refer to the application of the above-mentioned PSO algorithm, which will not be described in detail in this application.

[0051] In an exemplary embodiment, further, it is also possible to determine a value interval corresponding to the index value of the pre-bending form distribution, and sequentially take out different index values ​​from the value interval; determine the position interval corresponding to the pre-bending starting position, and sequentially take out different pre-bending starting positions from the position interval; generate multiple 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 includes at least one index value and one pre-bending starting position; obtain multiple groups of simulated power generation of the wind turbine blade using the multiple groups of pre-bending form parameters and the target blade root bending moment to simulate power generation; in the case where there is a second simulated power generation greater than the power generation threshold value in the multiple groups of simulated power generation, determine the index value of the target pre-bending form distribution and the target pre-bending starting position corresponding to the second simulated power generation; determine 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. This method systematically tests the impact of different pre-bending parameters on power generation, finds the optimal blade root bending moment, and finds the best pre-bending form suitable for wind turbine blades. It can be used in scenarios such as performance verification of wind turbine blades before large-scale production.

[0052] Furthermore, 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 embodiment, a supplementary description can be provided in combination with the following contents:

[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 between 10 meters and 30 meters.

[0054] Step 2: Update the Bladed model: Fix the D in the Bladed model generated by the Bladed software based on the optimal blade tip prebend that has been obtained. tip The value is then updated in the Bladed model, where c and x0 are used as the changing parameters.

[0055] Step 3, multi-objective optimization: Run different combinations of pre-bending parameters in Bladed, and record the value of the blade root bending moment Mxy and the power generation of the blade under each set of parameters.

[0056] Optionally, for situations where there is more than one optimization objective (e.g., in addition to minimizing Mxy, there may also be maximizing power generation, 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. The combination of parameters.

[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, it will gradually approach the parameter combination that minimizes the blade root bending moment Mxy and meets the power generation requirements.

[0058] Step 5, result analysis and verification: After the optimization algorithm converges, select the pre-bending form parameter combination with the minimum Mxy and the power generation that meets the design requirements from the results. Then, use the Bladed model to simulate and verify the new pre-bending form to ensure that the simulation results are consistent with the predictions of the optimization algorithm. At the same time, evaluate other performance indicators of the blade, such as vibration mode, noise level, etc., 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, the range of optimization variables or the parameters of the optimization algorithm can be adjusted and the optimization process can be rerun. 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 blade tip pre-bend D is considered tip The optimization also further adjusted the index value c of the pre-bending form distribution and the pre-bending starting position x0 to achieve the best structural performance and economic benefits, which can comprehensively optimize the pre-bending form of wind turbine blades and provide more flexible parameter control means for blade design. Obviously, the above method can accurately adjust the pre-bending form of wind turbine blades, making the structural stress distribution more reasonable when subjected to wind force, thereby improving the service life and overall performance of the blades.

[0061] In addition, the world is paying more and more attention to renewable energy. As one of the important renewable energy sources, the optimization of wind power blade design is of great significance for improving the overall efficiency of wind power generation, reducing power generation costs, and reducing the impact on the environment. The method and device for optimizing the pre-bending form of wind turbine blades provided in this application can also be applied to multiple scenarios such as rapid design, performance prediction, fault analysis, and performance verification before large-scale production of wind turbine blades, and has broad application prospects and significant economic benefits. It plays an important role in promoting the development of wind power generation technology and promoting the widespread application of clean energy.

[0062] Obviously, the embodiments described above are only some embodiments of the present application, not all embodiments. In order to better understand the above method, the above process is described below in conjunction with the embodiments, but it is not intended to limit the technical solutions of the embodiments of the present application, specifically:

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

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

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

[0066] Where x represents the distance perpendicular to the blade root plane pointing to the blade tip, which is 0 at the blade root and x tip Indicates the tip of the leaf, D tip represents the amount of blade tip pre-bending, x0 represents the starting position of pre-bending, and c represents the index value of the pre-bending form distribution.

[0067] Step 2: Use the following steps to optimize the design of the pre-bending form of the blade;

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

[0069] Step 12: The tip pre-bending amount D with the lowest load (i.e., the smallest blade root bending moment Mxy) obtained in step 11 is tip The value is set to the latest blade tip pre-bending amount required by the Bladed software, and the index value of the pre-bending form distribution and the pre-bending starting position are taken as design variables. The goal is still to minimize the blade root bending moment Mxy. The Bladed software is used to calculate the power generation, and the index value of the pre-bending form distribution and the pre-bending starting position with the lowest load are optimized.

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

[0071] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, 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 number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0072] In this embodiment, a device for optimizing the pre-bending form of a wind turbine blade is also provided, and the device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. 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, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

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

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

[0075] A first calculation module 34 is used to calculate the blade root bending moment corresponding to the user-defined optimization variable using wind turbine blade simulation software;

[0076] The second calculation module 36 is used to optimize the blade root bending moment to obtain a target blade root bending moment that meets the optimization target of the wind turbine blade, and a parameter value of a target optimization variable corresponding to the target blade root bending moment, wherein the target optimization variable includes 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;

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

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

[0079] In an exemplary embodiment, the pre-bending form function of the wind turbine blade 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, which is 0 at the blade root and x tip Indicates the tip of the leaf, D tip represents the amount of blade tip pre-bending, x0 represents the starting position of pre-bending, and c represents the index value of the pre-bending form distribution.

[0080] In an exemplary embodiment, the optimization goal 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, and the first calculation module is also used to: obtain an index value of a given pre-bending form distribution, and obtain a given pre-bending starting position; use the wind turbine blade simulation software to use the index value of the given pre-bending form distribution, the given pre-bending starting position and the pre-bending amount parameter value to calculate and obtain the blade root bending moment of different blade tip pre-bending amounts, 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.

[0081] In an exemplary embodiment, the first calculation module is also used 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 index value of the given pre-bending form distribution, the given pre-bending starting position and the pre-bending amount parameter value into the simulation model to obtain the blade root bending moment of the different blade tip pre-bending amounts output by the simulation model.

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

[0083] In an exemplary embodiment, the second calculation module is also used to: determine the value interval corresponding to the index value of the pre-bending form distribution, and sequentially select different index values ​​from the value interval; determine the position interval corresponding to the pre-bending starting position, and sequentially select different pre-bending starting positions from the position interval; generate multiple 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 includes at least one index value and one pre-bending starting position; obtain multiple groups of simulated power generation of the wind turbine blade using the multiple groups of pre-bending form parameters and the target blade root bending moment to simulate power generation; when there is a second simulated power generation greater than the power generation threshold in the multiple groups of simulated power generation, determine the index value of the target pre-bending form distribution and the target pre-bending starting position corresponding to the second simulated power generation; determine 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.

[0084] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0085] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

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

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

[0088] S3, optimizing and calculating the blade root bending moment to obtain a target blade root bending moment that meets the optimization target of the wind turbine blade, and a parameter value of a target optimization variable corresponding to the target blade root bending moment, wherein the target optimization variable includes 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;

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

[0090] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0091] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0092] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0093] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

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

[0095] S2, using wind turbine blade simulation software to calculate the blade root bending moment corresponding to the custom optimization variable;

[0096] S3, optimizing and calculating the blade root bending moment to obtain a target blade root bending moment that meets the optimization target of the wind turbine blade, and a parameter value of a target optimization variable corresponding to the target blade root bending moment, wherein the target optimization variable includes 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;

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

[0098] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0099] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0100] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0101] An embodiment of the present application also provides 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, so that the computer device performs the steps in any one of the above method embodiments.

[0102] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0103] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0104] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for optimizing the pre-bending form of a wind turbine blade, characterized in that: include: Determining a custom optimization variable corresponding to a blade optimization target from the function variables of the blade pre-bending form function of the wind turbine blade; Calculating the blade root bending moment corresponding to the user-defined optimization variable using wind turbine blade simulation software; Optimizing and calculating the blade root bending moment to obtain a target blade root bending moment that meets the optimization target of the wind turbine blade, and a parameter value of a target optimization variable corresponding to the target blade root bending moment, wherein the target optimization variable includes 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; The pre-bending form of the wind turbine blade is optimized based on the parameter value of the target optimization variable.

2. The method for optimizing the pre-bending form of wind turbine blades according to claim 1, characterized in that: 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, which is 0 at the blade root and x tip Indicates the tip of the leaf, D tip represents the amount of blade tip pre-bending, x0 represents the starting position of pre-bending, and c represents the index value of the pre-bending form distribution.

3. The method for optimizing the pre-bending form of wind turbine blades according to claim 1, characterized in that: The optimization target of the wind turbine blade is to minimize the load of the wind turbine blade. The custom optimization variables include the tip pre-bending amount, the index value of the pre-bending form distribution and the pre-bending starting position. The blade root bending moment corresponding to the custom optimization variable is calculated using the wind turbine blade simulation software, including: Obtain the index value of the given pre-bending form distribution, and obtain the given pre-bending starting position; The wind turbine blade simulation software is used to calculate the given index value of the pre-bending form distribution, the given pre-bending starting position and the pre-bending amount parameter value to obtain the blade root bending moment with different blade tip pre-bending amounts, 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.

4. The method for optimizing the pre-bending form of wind turbine blades according to claim 3, characterized in that: The wind turbine blade simulation software is used to calculate the given index value of the pre-bending form distribution, the given pre-bending starting position and the pre-bending amount parameter value to obtain the blade root bending moment of different blade tip pre-bending amounts, including: 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 index value of the given pre-bending form distribution, the given pre-bending starting position and the pre-bending amount parameter value into the simulation model to obtain the blade root bending moment of the different blade tip pre-bending amounts output by the simulation model.

5. The method for optimizing the pre-bending form of a wind turbine blade according to claim 1, characterized in that: Optimizing and calculating the blade root bending moment to obtain a target blade root bending moment that meets the wind turbine blade optimization target includes: Find the minimum blade root bending moment from multiple blade root bending moments; Acquire a first simulated power generation of the wind turbine blade under the minimum blade root bending moment; In a case where it is determined that the first simulated power generation is greater than a power generation threshold, the minimum blade root bending moment is determined as the target blade root bending moment.

6. The method for optimizing the pre-bending form of wind turbine blades according to claim 5, characterized in that: The method further comprises: Determine a value interval corresponding to the index value of the pre-bending form distribution, and sequentially select different index values ​​from the value interval; Determine a position interval corresponding to the pre-bending starting position, and sequentially select different pre-bending starting positions from the position interval; 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 includes at least one index value and one pre-bending starting position; Acquire multiple groups of simulated power generation by the wind turbine blade using the multiple groups of pre-bending form parameters and the target blade root bending moment to simulate power generation; In the case where there is a second simulated power generation amount greater than the power generation amount threshold value 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; The parameter value of the target optimization variable is determined 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.

7. An optimization device for the pre-bending form of a wind turbine blade, characterized in that: include: A determination module, used to determine a custom optimization variable corresponding to a blade optimization target from the function variables of the blade pre-bending form function of the wind turbine blade; A first calculation module is used to calculate the blade root bending moment corresponding to the user-defined optimization variable using wind turbine blade simulation software; A second calculation module is used to optimize the blade root bending moment to obtain a target blade root bending moment that meets the optimization target of the wind turbine blade, and a parameter value of a target optimization variable corresponding to the target blade root bending moment, wherein the target optimization variable includes 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; The optimization module is used to optimize the pre-bending form of the wind turbine blade based on the parameter value of the target optimization variable.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 6 when executed.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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