Wind turbine generator tower wall thickness optimization method, device, equipment and medium

Abstract

The invention discloses a method, a device, equipment and a medium for optimizing the wall thickness of a tower of a wind turbine generator, which are applied to the field of wind power and comprise the following steps: acquiring a lightest tower model meeting a preset condition; the preset conditions are other constraint conditions except the buckling margin constraint; working condition checking is conducted on the lightest tower model through an LBA-MNA method; the LBA-MNA method is a linear elastic buckling analysis method and a material nonlinear analysis method; if the verification is passed, the lightest tower model is the optimal tower model; and if the verification is not passed, thickening the wall thickness of the lightest tower model by changing a buckling margin calculated by an engineering algorithm until the thickened tower model passes the working condition verification. According to the method, the LBA-MNA method is used for carrying out working condition checking on the buckling margin, and rapid iteration is carried out on the wind turbine generator tower with the assistance of an engineering algorithm. According to the tower model obtained through optimization of the method, the tower construction cost can be saved, and the economical efficiency of the tower drum is improved.

Description

Technical Field

[0001] The present invention relates to the field of wind power, and in particular to a method, device, equipment and medium for optimizing the wall thickness of a tower of a wind turbine generator set. Background Art

[0002] With the increasing demand for high-power wind turbines, the height of towers is also increasing in order to obtain more abundant wind resources, so the stability of towers has attracted much attention. At the same time, in the context of wind power grid parity, taking into account both structural performance and economy has become the core challenge of tower design. At present, engineering algorithms are generally used to calculate the buckling of towers to optimize the wall thickness in order to achieve lightweight towers. This method is relatively conservative, and the optimized wall thickness is relatively thick, resulting in poor economic efficiency of the tower.

[0003] Therefore, how to optimize the wall thickness of the tower to reduce the production cost and improve the economic performance while ensuring the structural performance. Summary of the invention

[0004] In view of this, an object of the present invention is to provide a method, device, equipment and medium for optimizing the wall thickness of a wind turbine tower, which solves the problem of unreasonable tower wall thickness setting in the prior art, resulting in high economic costs.

[0005] In order to solve the above technical problems, the present invention provides a method for optimizing the wall thickness of a wind turbine tower, comprising:

[0006] Obtaining the lightest tower model that meets preset conditions; the preset conditions are other constraints except the buckling margin constraint;

[0007] The lightest tower model is subjected to working condition verification using the LBA-MNA method; the LBA-MNA method is a linear elastic buckling analysis method and a material nonlinear analysis method;

[0008] If the verification is passed, the lightest tower model is the optimal tower model;

[0009] If the check fails, the wall thickness of the lightest tower model is thickened by changing the buckling margin calculated by the engineering algorithm until the thickened tower model passes the working condition check.

[0010] Optionally, the lightest tower model is subjected to working condition verification using the LBA-MNA method, including:

[0011] For the lightest tower model, the LBA-MNA method is used to perform dangerous working condition verification and full working condition verification;

[0012] Correspondingly, if the verification is passed, the lightest tower model is the optimal tower model, including:

[0013] The tower model that has passed the dangerous working condition verification and the full working condition verification is regarded as the optimal tower model.

[0014] Optionally, the lightest tower model is subjected to a dangerous working condition check and a full working condition check using an LBA-MNA method, including:

[0015] The buckling margin of the lightest tower model under various working conditions is calculated using the LBA-MNA method, and the working condition corresponding to the minimum buckling margin is taken as a dangerous working condition;

[0016] Based on the dangerous working condition, the dangerous working condition verification of the lightest tower model is performed using the LBA-MNA method;

[0017] If the dangerous working condition check fails, the wall thickness of the lightest tower model is thickened by changing the buckling margin calculated by the engineering algorithm until the thickened tower model passes the dangerous working condition check;

[0018] The tower model that has passed the dangerous working condition verification will be verified under full working condition.

[0019] Optionally, the tower model that has passed the dangerous condition verification will be verified under full condition verification, including:

[0020] If the tower model that has passed the dangerous working condition check has not passed the full working condition check, then the dangerous working condition is updated;

[0021] By changing the buckling margin calculated by the engineering algorithm, the wall thickness of the tower model that has not passed the dangerous working condition check is thickened until it can pass the dangerous working condition check and the full working condition check.

[0022] Optionally, the wall thickness of the lightest tower model is thickened by changing the buckling margin calculated by the engineering algorithm until the thickened tower model passes the dangerous working condition check, including:

[0023] The wall thickness of the lightest tower model is increased by increasing the buckling margin of the first adjustment amount, and when the tower model with the increased wall thickness passes the dangerous working condition check, the thickness of the tower model that passes the dangerous working condition check is reduced by reducing the buckling margin of the second adjustment amount;

[0024] If the tower model after reducing the wall thickness passes the dangerous working condition check, continue to reduce the buckling margin of the second adjustment amount until it fails to pass the dangerous working condition check;

[0025] The tower model that has passed the danger check for the last time is used as the thickened tower model; and the first adjustment amount is greater than the second adjustment amount.

[0026] Optionally, the LBA-MNA method is used to perform operating condition verification, including:

[0027] The LBA method is used to verify the working conditions.

[0028] The present invention also provides a wind turbine tower wall thickness optimization device, comprising:

[0029] A tower model acquisition module, used to acquire the lightest tower model that meets preset conditions; the preset conditions are other constraints except the buckling margin constraint;

[0030] A working condition verification module, used for performing working condition verification on the lightest tower model using an LBA-MNA method; the LBA-MNA method is a linear elastic buckling analysis method and a material nonlinear analysis method;

[0031] A first result module, used for determining that the lightest tower model is the optimal tower model if the verification is passed;

[0032] The second result module is used to thicken the wall thickness of the lightest tower model by changing the buckling margin calculated by the engineering algorithm if the verification fails, until the thickened tower model passes the working condition verification.

[0033] Optionally, the operating condition verification module includes:

[0034] A working condition verification unit, used for performing dangerous working condition verification and full working condition verification on the lightest tower model by using the LBA-MNA method;

[0035] Accordingly, the first result module includes:

[0036] The first result unit is used to take the tower model that has passed the dangerous working condition verification and the full working condition verification as the optimal tower model.

[0037] The present invention also provides a wind turbine tower wall thickness optimization device, comprising:

[0038] Memory for storing computer programs;

[0039] The processor is used to implement the steps of the wind turbine tower wall thickness optimization method as described above when executing the computer program.

[0040] The present invention further provides a medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned method for optimizing the wall thickness of a wind turbine tower are implemented.

[0041] It can be seen that the present invention obtains the lightest tower model that meets the preset conditions; the preset conditions are other constraints except the buckling margin constraint; the lightest tower model is checked for working conditions using the LBA-MNA method; the LBA-MNA method is a linear elastic buckling analysis method and a material nonlinear analysis method; if the check passes, the lightest tower model is the optimal tower model; if the check fails, the wall thickness of the lightest tower model is thickened by changing the buckling margin calculated by the engineering algorithm until the thickened tower model passes the working condition check. The present invention uses the LBA-MNA method to check the working conditions of the buckling margin, and uses the engineering algorithm as an aid to quickly iterate the wind turbine tower, forming a set of technical solutions for optimizing the wall thickness of the wind turbine tower. The use of this method is expected to achieve a weight reduction ratio of 3% to 5% on the basis of the original design based on the engineering algorithm, saving the tower construction cost and improving the economy of the tower.

[0042] In addition, the present invention also provides a wind turbine tower wall thickness optimization device, equipment and medium, which also have the above-mentioned beneficial effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention 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, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0044] Figure 1 A flow chart of a method for optimizing the wall thickness of a wind turbine tower provided by an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of a buckling margin calculation method based on LBA-MNA provided in an embodiment of the present invention;

[0046] Figure 3 An example diagram of buckling margin results of an engineering algorithm and an LBA-MNA method provided in an embodiment of the present invention;

[0047] Figure 4 An example diagram of a process for optimizing dangerous working conditions based on wall thickness adjustment provided in an embodiment of the present invention;

[0048] Figure 5 An example flow chart of a method for optimizing the wall thickness of a wind turbine tower provided by an embodiment of the present invention;

[0049] Figure 6 A schematic structural diagram of a wind turbine tower wall thickness optimization device provided by an embodiment of the present invention;

[0050] Figure 7 A schematic structural diagram of a wind turbine tower wall thickness optimization device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Please refer to Figure 1 , Figure 1 A flow chart of a method for optimizing the wall thickness of a wind turbine tower provided by an embodiment of the present invention. The method may include:

[0053] S101: Obtaining a lightest tower model that meets preset conditions; the preset conditions are other constraints except the buckling margin constraint.

[0054] The lightest tower model in this embodiment refers to the lightest tower model that is not limited by the buckling margin but meets other requirements (such as strength and fatigue). The purpose is to facilitate the subsequent thickening of the tower wall based on the lightest tower model, and only the influence of the buckling margin on the tower model needs to be paid attention to.

[0055] S102: For the lightest tower model, use the LBA-MNA method to perform working condition verification; if the verification passes, execute step 103; if the verification fails, execute step S104.

[0056] In this embodiment, the lightest tower model is checked for working conditions to verify whether the tower model can meet various working conditions, that is, the lightest tower model is applicable under various working conditions. The method used is the LBA-MNA (linear elastic bifurcation analysis–materially nonlinear analysis) method. The LBA-MNA method can simulate the buckling process of the structure more realistically and obtain more accurate buckling analysis results. Figure 2 As shown in the figure, the LBA-MNA method combines numerical simulation and formula calculation. The LBA-MNA method is based on the linear elastic buckling analysis simulation to calculate the first-order modal eigenvalue as the elastic instability load. , and based on the material nonlinear analysis simulation calculation, the loading factor when the structure reaches the plastic instability point is the plastic instability load Then combined with the slenderness ratio and related parameters , , and Calculate the elastic-plastic buckling reduction factor to describe the effects of imperfections and elastic-plasticity The elastic-plastic buckling reduction factor is The specific calculation formula is as follows:

[0057] , ;

[0058] , ;

[0059] , .

[0060] in, is the elastic defect reduction factor, is the plastic defect reduction factor, is the interaction coefficient, is the elastic-plastic buckling reduction coefficient of the linear hardening section, is the overall slenderness ratio, is the extrusion slenderness ratio limit, is the plastic slenderness ratio limit. The calculation formula is as follows:

[0061] .

[0062] in, The calculation formula is as follows:

[0063] .

[0064] When satisfied The buckling margin of the LBA-MNA method is The calculation formula is as follows:

[0065] .

[0066] in, is the safety factor, which should be no less than 1.1; is the buckling load, is the critical elastic-plastic instability load, and the calculation formula is as follows:

[0067] .

[0068] Furthermore, in order to improve the verification efficiency, the above-mentioned working condition verification of the lightest tower model using the LBA-MNA method may include the following steps:

[0069] For the lightest tower model, the LBA-MNA method is used to perform dangerous working condition verification and full working condition verification; accordingly, if the above verification is passed, the lightest tower model is the optimal tower model, which can specifically include the following steps: the tower model that passes the dangerous working condition verification and the full working condition verification is used as the optimal tower model.

[0070] Specifically, the verification in this embodiment includes dangerous working condition verification and full working condition verification. That is to say, for each working condition, the lightest tower model is verified using the LBA-MNA method. First, based on the dangerous working condition, the lightest tower model is verified using the LBA-MNA method; if the dangerous working condition verification passes, the working condition verification under other working conditions is then performed.

[0071] Furthermore, in order to further improve the optimization efficiency, the above-mentioned LBA-MNA method is used to check the dangerous working conditions, including:

[0072] Use LBA method to check dangerous working conditions.

[0073] Specifically, considering that the LBA-MNA method involves two parts, LBA and MNA, in actual calculations using a workstation, the LBA calculation time is about 10 minutes, but the MNA calculation time is about 3 hours. Considering that the optimization of hazardous conditions involves iterative optimization but has a long MNA calculation cycle, it is necessary to improve the optimization calculation efficiency.

[0074] Referring to the data in Table 1, it can be seen that the buckling margin results calculated by MNA do not change much during the iterative process of dangerous working condition optimization. If the buckling margin results calculated by MNA are used, the buckling margin results will have little effect. Therefore, in the iterative calculation process, only the buckling margin results calculated by LBA can be iterated and the buckling margin results calculated by MNA of the input model of dangerous working condition optimization can be used for verification. This solution can achieve a significant optimization of calculation efficiency at the expense of a small part of the calculation accuracy, thereby improving the calculation speed of the technical solution.

[0075] Table 1 Comparison of buckling margin calculation results

[0076]

[0077] S103: Taking the lightest tower model as the optimal tower model.

[0078] If the lightest tower model can pass the working condition verification, then the lightest tower model is the optimal tower model, which is the tower model with the thinnest wall thickness that can meet all working conditions.

[0079] S104: If the verification fails, the wall thickness of the lightest tower model is thickened by changing the buckling margin calculated by the engineering algorithm until the thickened tower model passes the working condition verification.

[0080] If the lightest tower module fails to pass the working condition verification, it will be thickened. The thickened tower model that passes the working condition verification is the tower model with the thinnest wall thickness that meets all working conditions.

[0081] Furthermore, in order to improve the efficiency and accuracy of the verification, the dangerous working condition verification and the full working condition verification of the lightest tower model using the LBA-MNA method may include the following steps:

[0082] Step 21: Use the LBA-MNA method to calculate the buckling margin of the lightest tower model under various working conditions, and take the working condition corresponding to the minimum buckling margin as the dangerous working condition;

[0083] Step 22: Based on the dangerous working conditions, the LBA-MNA method is used to check the dangerous working conditions of the lightest tower model;

[0084] Step 23: If the dangerous working condition check fails, the wall thickness of the lightest tower model is thickened by changing the buckling margin calculated by the engineering algorithm until the thickened tower model passes the dangerous working condition check;

[0085] Step 24: Perform full-condition verification on the tower model that has passed the dangerous condition verification.

[0086] Specifically, the lightest tower model is used as input, and the LBA-MNA method is used for modeling and calculation. The working condition with the minimum buckling margin is the current dangerous working condition. For the lightest tower model, the LBA-MNA method is used to check the dangerous working condition. If the check result passes, the full working condition review is directly performed; if the check result fails, iterative optimization is performed for the dangerous working condition, and the lightest tower model is optimized by increasing the wall thickness so that it can meet the dangerous working condition check. Specifically, in order to improve the optimization efficiency, since the engineering algorithm is linearly correlated with the buckling margin result of the LBA-MNA method (such as Figure 3 As shown in the figure, the buckling margin of the engineering algorithm is selected as the optimization variable to assist in the optimization of the tower wall thickness. This method can solve the problem of too many wall thickness parameters and directly affect the buckling margin calculation results of the LBA-MNA method.

[0087] Furthermore, in order to improve the accuracy of tower model optimization, the tower model that has passed the dangerous working condition verification is verified under all working conditions, which may include the following steps:

[0088] Step 241: if the tower model that has passed the dangerous working condition check has not passed the full working condition check, then update the dangerous working condition;

[0089] Step 242: thicken the wall thickness of the tower model that has not passed the dangerous working condition check by changing the buckling margin calculated by the engineering algorithm, until it can pass the dangerous working condition check and the full working condition check.

[0090] Specifically, during the verification of the tower model, if the dangerous working condition verification is passed but the full working condition verification is not passed, the working condition corresponding to the minimum buckling margin in the full working condition verification that has not passed can be used as the dangerous working condition to achieve the update of the dangerous working condition. Considering that the engineering algorithm has a faster calculation speed, the engineering algorithm can be used to calculate the buckling margin during the wall thickness adjustment process to achieve rapid adjustment of the wall thickness.

[0091] Furthermore, in order to improve the accuracy of optimization, the wall thickness of the lightest tower model is thickened by changing the buckling margin calculated by the engineering algorithm until the thickened tower model passes the dangerous working condition check, which may include the following steps:

[0092] Step 2421: increasing the wall thickness of the lightest tower model by increasing the buckling margin of the first adjustment amount, and when the tower model with the increased wall thickness passes the dangerous working condition check, reducing the thickness of the tower model that passes the dangerous working condition check by reducing the buckling margin of the second adjustment amount;

[0093] Step 2422: If the tower model after reducing the wall thickness passes the dangerous working condition check, continue to reduce the buckling margin of the second adjustment amount until it fails to pass the dangerous working condition check;

[0094] Step 2423: The tower model that has passed the danger check for the last time is used as the thickened tower model; the first adjustment amount is greater than the second adjustment amount.

[0095] Specifically, to better understand the above steps, you can refer to Figure 4 . While keeping other element restrictions unchanged, the buckling allowance is increased by 5% each time (the buckling allowance is the buckling margin, which is calculated by the engineering algorithm). This means that the wall thickness of the model (i.e. the tower wall thickness) is thickened in the model, which can improve the buckling margin calculated by the LBA-MNA method for dangerous working conditions. The buckling margin of the thickened tower model is checked again for dangerous working conditions using the LBA-MNA method. If it fails to pass the check, the above-mentioned 5% increase in buckling margin is iteratively executed until the tower model can pass the buckling margin calculation for dangerous working conditions. On the basis of meeting the requirements for dangerous working condition verification, the optimization accuracy can be improved by subsequently reducing the buckling margin calculated by the engineering algorithm by thinning the tower wall by a gradient of 1%. If the structure can still pass the dangerous working condition verification, the buckling margin calculated by the engineering algorithm will continue to be reduced until the result fails to meet the requirements. The wall thickness that meets the requirements for the last time is the final optimized wall thickness.

[0096] The wind turbine tower wall thickness optimization method provided by the embodiment of the present invention is applied, by obtaining the lightest tower model that meets the preset conditions; the preset conditions are other constraints except the buckling margin constraint; the lightest tower model is checked for working conditions using the LBA-MNA method; the LBA-MNA method is a linear elastic buckling analysis method and a material nonlinear analysis method; if the check passes, the lightest tower model is the optimal tower model; if the check fails, the wall thickness of the lightest tower model is thickened by changing the buckling margin calculated by the engineering algorithm until the thickened tower model passes the working condition check. The present invention uses the LBA-MNA method to check the working conditions of the buckling margin, and uses the engineering algorithm as an aid to quickly iterate the wind turbine tower, forming a set of technical solutions for optimizing the wall thickness of the wind turbine tower. The adoption of this solution is expected to achieve a weight reduction ratio of 3% to 5% on the basis of the original design based on the engineering algorithm, saving the tower construction cost and improving the economy of the tower.

[0097] In order to make the present invention easier to understand, please refer to Figure 5 , Figure 5 An example flow chart of a method for optimizing the wall thickness of a wind turbine tower provided by an embodiment of the present invention may specifically include:

[0098] ①Start optimization

[0099] The main purpose of this process is to obtain a lightest tower model that meets other requirements (such as strength and fatigue) in addition to the buckling margin requirements, so as to facilitate the subsequent thickening of the tower wall based on this model to meet the buckling margin requirements.

[0100] ② Optimization of dangerous working conditions

[0101] The process is to use the LBA-MNA method to calculate the buckling margin of the tower model under dangerous working conditions, and to perform verification by checking the buckling margin. If the verification fails, the wall thickness of the tower model is thickened until it can meet the verification under dangerous working conditions; if the verification passes, the LBA-MNA method is directly used to perform full working condition verification.

[0102] ③Working condition verification

[0103] This process is a verification of all working conditions after the calculation of dangerous working conditions to ensure that the tower model can meet the buckling margin calculation requirements of all working conditions.

[0104] The following is an introduction to a wind turbine tower wall thickness optimization device provided by an embodiment of the present invention. The wind turbine tower wall thickness optimization device described below and the wind turbine tower wall thickness optimization method described above can be referenced to each other.

[0105] Please refer to Figure 6 , Figure 6A schematic structural diagram of a wind turbine tower wall thickness optimization device provided by an embodiment of the present invention may include:

[0106] The tower model acquisition module 100 is used to acquire the lightest tower model that meets preset conditions; the preset conditions are other constraints except the buckling margin constraint;

[0107] The working condition verification module 200 is used to perform working condition verification on the lightest tower model using the LBA-MNA method; the LBA-MNA method is a linear elastic buckling analysis method and a material nonlinear analysis method;

[0108] A first result module 300 is used for determining that the lightest tower model is the optimal tower model if the verification is passed;

[0109] The second result module 400 is used to thicken the wall thickness of the lightest tower model by changing the buckling margin calculated by the engineering algorithm if the verification fails, until the thickened tower model passes the working condition verification.

[0110] Based on the above embodiment, the above working condition verification module may include:

[0111] A first working condition verification unit is used to perform dangerous working condition verification and full working condition verification on the lightest tower model using the LBA-MNA method;

[0112] Accordingly, the first result module includes:

[0113] The first result unit is used to take the tower model that has passed the dangerous working condition verification and the full working condition verification as the optimal tower model.

[0114] Based on the above embodiment, the first working condition checking unit may include:

[0115] A dangerous working condition subunit, used for calculating the buckling margin of the lightest tower model under various working conditions by using the LBA-MNA method, and taking the working condition corresponding to the minimum buckling margin as the dangerous working condition;

[0116] A dangerous working condition verification subunit, used for performing a dangerous working condition verification on the lightest tower model using an LBA-MNA method based on the dangerous working condition;

[0117] A first wall thickness adjustment subunit is used for thickening the wall thickness of the lightest tower model by changing the buckling margin calculated by the engineering algorithm if the dangerous working condition check fails, until the thickened tower model passes the dangerous working condition check;

[0118] The full working condition verification subunit is used to conduct full working condition verification on the tower model that has passed the dangerous working condition verification.

[0119] Based on the above embodiment, the full working condition verification subunit may include:

[0120] A dangerous working condition updating subunit, used for updating the dangerous working condition if the tower model that has passed the dangerous working condition verification has not passed the full working condition verification;

[0121] The second wall thickness adjustment subunit is used to thicken the wall thickness of the tower model that has not passed the dangerous working condition check by changing the buckling margin calculated by the engineering algorithm until it can pass the dangerous working condition check and the full working condition check.

[0122] Based on the above embodiment, the second wall thickness adjustment subunit may include:

[0123] A wall thickness increasing subunit is used to increase the wall thickness of the lightest tower model by increasing the buckling margin of the first adjustment amount, and when the tower model with the increased wall thickness passes the dangerous working condition check, the thickness of the tower model that passes the dangerous working condition check is reduced by reducing the buckling margin of the second adjustment amount;

[0124] A wall thickness reduction subunit is used to continue reducing the buckling margin of the second adjustment amount until the tower model fails to pass the dangerous working condition check if the tower model after the wall thickness reduction passes the dangerous working condition check;

[0125] The tower model determination subunit is used to use the tower model that passed the danger check for the last time as the thickened tower model; the first adjustment amount is greater than the second adjustment amount.

[0126] Based on the above embodiment, the operating condition verification module 200 may include:

[0127] The second operating condition verification unit is used to perform operating condition verification using the LBA method.

[0128] It should be noted that the order of the modules and units in the above-mentioned generator tower wall thickness optimization device can be changed before or after without affecting the logic.

[0129] The wall thickness optimization device of the tower of the generator group provided by the embodiment of the present invention is applied, and the tower model acquisition module 100 is used to obtain the lightest tower model that meets the preset conditions; the preset conditions are other constraints except the buckling margin constraint; the working condition verification module 200 is used to verify the working condition of the lightest tower model using the LBA-MNA method; the LBA-MNA method is a linear elastic buckling analysis method and a material nonlinear analysis method; the first result module 300 is used to verify that the lightest tower model is the optimal tower model if the verification passes; the second result module 400 is used to thicken the wall thickness of the lightest tower model by changing the buckling margin calculated by the engineering algorithm if the verification fails, until the thickened tower model passes the working condition verification. The present invention uses the LBA-MNA method to verify the working condition of the buckling margin, and uses the engineering algorithm as an auxiliary to quickly iterate the wind turbine tower, forming a set of technical solutions for optimizing the wall thickness of the wind turbine tower. The use of this device is expected to achieve a weight reduction ratio of 3% to 5% based on the original design based on engineering algorithms, saving tower construction costs and improving the economy of the tower.

[0130] The following is an introduction to a wind turbine tower wall thickness optimization device provided by an embodiment of the present invention. The wind turbine tower wall thickness optimization device described below and the wind turbine tower wall thickness optimization method described above can correspond to each other.

[0131] Please refer to Figure 7 , Figure 7 A schematic structural diagram of a wind turbine tower wall thickness optimization device provided by an embodiment of the present invention may include:

[0132] A memory 10, used for storing computer programs;

[0133] The processor 20 is used to execute a computer program to implement the above-mentioned method for optimizing the wall thickness of a wind turbine tower.

[0134] The memory 10 , the processor 20 , and the communication interface 31 all communicate with each other via the communication bus 32 .

[0135] In the embodiment of the present invention, the memory 10 is used to store one or more programs, and the program may include program code, and the program code includes computer operation instructions. In the embodiment of the present invention, the memory 10 may store programs for implementing the following functions:

[0136] Obtain the lightest tower model that meets preset conditions; the preset conditions are other constraints except the buckling margin constraint;

[0137] For the lightest tower model, the LBA-MNA method is used to check the working condition; the LBA-MNA method is a linear elastic buckling analysis method and a material nonlinear analysis method;

[0138] If the check passes, the lightest tower model is the optimal tower model;

[0139] If the check fails, the wall thickness of the lightest tower model is thickened by changing the buckling margin calculated by the engineering algorithm until the thickened tower model passes the working condition check.

[0140] In a possible implementation, the memory 10 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function, etc.; the data storage area may store data created during use.

[0141] In addition, the memory 10 may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include an NVRAM. The memory stores an operating system and operating instructions, executable modules or data structures, or a subset thereof, or an extended set thereof, wherein the operating instructions may include various operating instructions for implementing various operations. The operating system may include various system programs for implementing various basic tasks and processing hardware-based tasks.

[0142] The processor 20 may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array or other programmable logic device, a microprocessor or any conventional processor, etc. The processor 20 may call a program stored in the memory 10 .

[0143] The communication interface 31 may be an interface of a communication module, and is used to connect to other devices or systems.

[0144] Of course, it should be noted that Figure 7 The structure shown does not constitute a limitation on the wind turbine tower wall thickness optimization device in the embodiment of the present invention. In actual applications, the wind turbine tower wall thickness optimization device may include: Figure 7 More or fewer components than shown, or combinations of certain components.

[0145] The following is an introduction to a readable storage medium provided in an embodiment of the present invention. The medium described below and the wind turbine tower wall thickness optimization method described above can be referenced to each other.

[0146] The present invention also provides a medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for optimizing the wall thickness of a wind turbine tower are implemented.

[0147] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0148] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0149] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0150] Finally, it should be noted that, in this article, relationships such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0151] The above is a detailed introduction to a method, device, and equipment medium for optimizing the wall thickness of a wind turbine tower provided by the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for a person skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for optimizing the wall thickness of a wind turbine tower, characterized in that: include: Obtain the lightest tower model that meets the preset conditions; The preset conditions are other constraints except the buckling margin constraint; The lightest tower model is subjected to working condition verification using the LBA-MNA method; the LBA-MNA method is a linear elastic buckling analysis method and a material nonlinear analysis method; If the verification is passed, the lightest tower model is the optimal tower model; If the check fails, the wall thickness of the lightest tower model is thickened by changing the buckling margin calculated by the engineering algorithm until the thickened tower model passes the working condition check.

2. The wind turbine tower wall thickness optimization method according to claim 1, characterized in that: The lightest tower model is verified using the LBA-MNA method, including: For the lightest tower model, the LBA-MNA method is used to perform dangerous working condition verification and full working condition verification; Correspondingly, if the verification is passed, the lightest tower model is the optimal tower model, including: The tower model that has passed the dangerous working condition verification and the full working condition verification is regarded as the optimal tower model.

3. The method for optimizing the wall thickness of a wind turbine tower according to claim 2, characterized in that: The LBA-MNA method is used to perform dangerous working condition verification and full working condition verification on the lightest tower model, including: The buckling margin of the lightest tower model under various working conditions is calculated using the LBA-MNA method, and the working condition corresponding to the minimum buckling margin is taken as a dangerous working condition; Based on the dangerous working condition, the dangerous working condition verification of the lightest tower model is performed using the LBA-MNA method; If the dangerous working condition check fails, the wall thickness of the lightest tower model is thickened by changing the buckling margin calculated by the engineering algorithm until the thickened tower model passes the dangerous working condition check; The tower model that has passed the dangerous working condition verification will be verified under full working condition.

4. The method for optimizing the wall thickness of a wind turbine tower according to claim 3, characterized in that: The tower model that has passed the dangerous working condition verification will be verified under all working conditions, including: If the tower model that has passed the dangerous working condition check has not passed the full working condition check, then the dangerous working condition is updated; By changing the buckling margin calculated by the engineering algorithm, the wall thickness of the tower model that has not passed the dangerous working condition check is thickened until it can pass the dangerous working condition check and the full working condition check.

5. The method for optimizing the wall thickness of a wind turbine tower according to claim 3, characterized in that: The wall thickness of the lightest tower model is thickened by changing the buckling margin calculated by the engineering algorithm until the thickened tower model passes the dangerous working condition check, including: The wall thickness of the lightest tower model is increased by increasing the buckling margin of the first adjustment amount, and when the tower model with the increased wall thickness passes the dangerous working condition check, the thickness of the tower model that passes the dangerous working condition check is reduced by reducing the buckling margin of the second adjustment amount; If the tower model after reducing the wall thickness passes the dangerous working condition check, continue to reduce the buckling margin of the second adjustment amount until it fails to pass the dangerous working condition check; The tower model that has passed the danger check for the last time is used as the thickened tower model; and the first adjustment amount is greater than the second adjustment amount.

6. The method for optimizing the wall thickness of a wind turbine tower according to claim 1, characterized in that: The LBA-MNA method is used to perform working condition verification, including: The LBA method is used to verify the working conditions.

7. A wind turbine tower wall thickness optimization device, characterized in that: include: A tower model acquisition module, used to acquire the lightest tower model that meets preset conditions; The preset conditions are other constraints except the buckling margin constraint; A working condition verification module, used for performing working condition verification on the lightest tower model using an LBA-MNA method; the LBA-MNA method is a linear elastic buckling analysis method and a material nonlinear analysis method; A first result module, used for determining that the lightest tower model is the optimal tower model if the verification is passed; The second result module is used to thicken the wall thickness of the lightest tower model by changing the buckling margin calculated by the engineering algorithm if the verification fails, until the thickened tower model passes the working condition verification.

8. The wind turbine tower wall thickness optimization device according to claim 7, characterized in that: The working condition verification module comprises: A working condition verification unit, used for performing dangerous working condition verification and full working condition verification on the lightest tower model by using the LBA-MNA method; Accordingly, the first result module includes: The first result unit is used to take the tower model that has passed the dangerous working condition verification and the full working condition verification as the optimal tower model.

9. A wind turbine tower wall thickness optimization device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the wind turbine tower wall thickness optimization method as described in any one of claims 1 to 6 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for optimizing the wall thickness of a wind turbine tower as claimed in any one of claims 1 to 6 are implemented.

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