Composite material photovoltaic support design optimization method based on group optimization algorithm

Abstract

The invention relates to a composite material photovoltaic support design optimization method based on a group optimization algorithm. The method is suitable for the technical field of photovoltaic support design. According to the technical scheme, the method comprises the steps that the structural form of the composite material photovoltaic support to be optimized and material parameters and length information of all components in the photovoltaic support are obtained; determining constraint conditions of each component in the photovoltaic support based on a structural form and a design standard of the to-be-optimized composite material photovoltaic support; based on a group optimization algorithm and the simulation model of the photovoltaic support, optimizing the section form and the section size of each component in the photovoltaic support in combination with an objective function; the simulation model is used for performing simulation based on the structural form of the photovoltaic support and the length information, the section form, the section size and the material parameters of each component to obtain simulation parameters corresponding to constraint conditions of each component; the objective function comprises a weight function of the photovoltaic support and a penalty function based on constraint conditions and corresponding simulation parameters.

Description

Technical Field

[0001] The invention relates to a composite material photovoltaic bracket design optimization method based on a group optimization algorithm, which is applicable to the photovoltaic bracket design technical field. Background Art

[0002] Photovoltaic brackets are used to support photovoltaic power generation components and are one of the core equipment / devices of photovoltaic power stations. Their cost accounts for about 8% to 15% of the total cost of power station construction. The reliability and economy of their structure have a significant impact on photovoltaic power generation projects. In order to reduce the occupation of existing arable land, more water surface photovoltaic, salt field photovoltaic, tidal flat photovoltaic and floating photovoltaic power stations have emerged. These power stations have put forward higher requirements on the corrosion resistance of photovoltaic brackets. The use of composite material components to design photovoltaic brackets can greatly exert the corresponding performance of its materials, and has broad prospects in photovoltaic power stations in highly corrosive environments.

[0003] The cost of composite materials is relatively high, which leads to the high cost of composite material photovoltaic brackets. How to minimize the use of composite materials and reduce the cost of photovoltaic brackets while ensuring the performance of photovoltaic brackets is a technical difficulty that urgently needs to be solved. Summary of the invention

[0004] The technical problem to be solved by the present invention is: in view of the above-mentioned problems, a composite material photovoltaic bracket design optimization method based on a group optimization algorithm is provided.

[0005] The technical solution adopted by the present invention is: a composite material photovoltaic bracket design optimization method based on a group optimization algorithm, characterized in that it includes:

[0006] Obtaining the structural form of the composite material photovoltaic bracket to be optimized, and the material parameters and length information of each component in the photovoltaic bracket;

[0007] Based on the structural form and design standards of the composite photovoltaic bracket to be optimized, determine the constraints of each component in the photovoltaic bracket;

[0008] Based on the swarm optimization algorithm, the simulation model of the photovoltaic bracket, and the objective function, the cross-sectional form and cross-sectional size of each component in the photovoltaic bracket are optimized;

[0009] The simulation model is used to simulate based on the structural form of the photovoltaic bracket and the length information, cross-sectional form, cross-sectional size and material parameters of each component to obtain simulation parameters corresponding to the constraint conditions of each component;

[0010] The objective function includes a weight function of the photovoltaic support and a penalty function based on constraint conditions and corresponding simulation parameters.

[0011] The constraint conditions of each component in the photovoltaic support include: one or more of the first-order eigenvalue constraint of the overall support structure buckling analysis, purlin deflection constraint, diagonal beam deflection constraint, column top displacement constraint, purlin bending bearing capacity constraint, diagonal beam bending bearing capacity constraint, column tension and compression axial force bearing capacity constraint and diagonal brace tension and compression axial force bearing capacity constraint.

[0012] The swarm optimization algorithm and the simulation model of the photovoltaic bracket are combined with the objective function to optimize the cross-sectional form and cross-sectional size of each component in the photovoltaic bracket, including:

[0013] The group optimization algorithm is used to optimize the cross-sectional form and cross-sectional size of each component in the photovoltaic bracket;

[0014] Each sample individual in the population used by the swarm optimization algorithm includes the cross-sectional form and cross-sectional size of each component in the photovoltaic bracket.

[0015] A composite material photovoltaic bracket design optimization device based on a swarm optimization algorithm, characterized by comprising:

[0016] An information acquisition module is used to obtain the structural form of the composite material photovoltaic support to be optimized, and the material parameters and length information of each component in the photovoltaic support;

[0017] The constraint condition module is used to determine the constraint conditions of each component in the photovoltaic bracket based on the structural form and design standard of the composite photovoltaic bracket to be optimized;

[0018] An optimization calculation module is used to optimize the cross-sectional form and cross-sectional size of each component in the photovoltaic bracket based on a group optimization algorithm, a simulation model of the photovoltaic bracket, and an objective function;

[0019] The simulation model is used to simulate based on the structural form of the photovoltaic bracket and the length information, cross-sectional form, cross-sectional size and material parameters of each component to obtain simulation parameters corresponding to the constraint conditions of each component;

[0020] The objective function includes a weight function of the photovoltaic support and a penalty function based on constraint conditions and corresponding simulation parameters.

[0021] A storage medium stores a computer program that can be executed by a processor, characterized in that when the computer program is executed, the steps of the composite material photovoltaic bracket design optimization method based on the group optimization algorithm are implemented.

[0022] A composite material photovoltaic bracket design optimization device has a memory and a processor, the memory stores a computer program that can be executed by the processor, and is characterized in that when the computer program is executed, the steps of the composite material photovoltaic bracket design optimization method based on the group optimization algorithm are implemented.

[0023] The beneficial effects of the present invention are as follows: the present invention obtains simulation parameters corresponding to the constraints of the components of the photovoltaic bracket through a simulation model based on the structural form of the photovoltaic bracket and the length information, cross-sectional form, cross-sectional size and material parameters of each component, and then determines a penalty function based on the relationship between the constraints and the corresponding simulation parameters. The penalty function affects the objective function of the optimization algorithm, thereby introducing the constraints into the optimization process, so that the photovoltaic bracket corresponding to the optimization result satisfies the relevant constraints and meets the performance requirements for the photovoltaic bracket.

[0024] The present invention introduces the amount of composite materials into the optimization process by influencing the objective function of the optimization algorithm based on the weight function of the photovoltaic support based on the length information, cross-sectional form and cross-sectional size of each component, and obtains an optimization scheme with the least amount of composite materials.

[0025] The present invention fully considers the characteristics of composite materials such as anisotropy and low elastic modulus, optimizes the cross-sectional parameters of photovoltaic support components, reduces the amount of composite materials used, and effectively reduces costs. In the optimization process of the present invention, the finite element software is nested and developed based on the group optimization algorithm. Compared with traditional trial calculations, the process is simpler, the complicated design process is simplified, and the design results are more reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flow chart of Example 1.

[0027] Figure 2 This is a schematic diagram of the structure of the photovoltaic bracket in Example 1.

[0028] Figure 3 This is a graph showing the change in penalty function value during the photovoltaic bracket optimization process in Example 1.

[0029] 1. Purlin; 2. Diagonal beam; 3. Front support; 4. Front diagonal brace; 5. Rear diagonal brace; 6. Rear support. DETAILED DESCRIPTION

[0030] Example 1: Figure 1 The present embodiment is a composite material photovoltaic bracket design optimization method based on a swarm optimization algorithm, which specifically includes the following steps:

[0031] S1. Obtain the structural form of the composite photovoltaic bracket to be optimized, the design standard of the photovoltaic bracket, and the material parameters and length information of each component of the photovoltaic bracket. For example, whether it is a single pile or a double pile, the pile spacing, the overall size of the bracket, the node connection type and other information. In particular, considering that the shear strength between the layers of the composite material is low and the pull-out bearing capacity of the bolts is low, the connection between the purlin and the diagonal beam is connected to the top of the column or diagonal brace to ensure that the components are not damaged, that is, the action position of the connection node of each component of the bracket can be fixed in advance.

[0032] In this example, the composite photovoltaic support structure is double piles, using Figure 2 The structural form shown includes front pillars, rear pillars, oblique beams connecting the front pillars and the rear pillars, front oblique braces connecting the front pillars and the oblique beams, rear oblique braces connecting the rear pillars and the oblique beams, and purlins connecting the oblique beams.

[0033] In this embodiment, special consideration is given to the low interlayer shear strength of composite materials and the low pull-out bearing capacity of bolts. Therefore, the connection between the purlin and the diagonal beam is connected to the column or the top of the diagonal brace to ensure that the components are not damaged. In other words, the action positions of the connection nodes of the bracket components can be fixed in advance. The photovoltaic panel is arranged in 2×26, the pile spacing is 4.0m, the diagonal beam length is 3.95m, the front column length is 1.25m, the rear column length is 3.16m, the front diagonal brace length is 2.06m, and the rear diagonal brace length is 2.46m. Bolts are used to connect the components. The photovoltaic panel has an inclination angle of 31° and a design wind pressure of 0.52kN / m 2 , the design snow pressure is 0.79kN / m 2 .

[0034] S2. Based on the structural form and design standard of the composite photovoltaic bracket to be optimized, determine the constraints of each component in the photovoltaic bracket, which include the first-order eigenvalue constraints of the overall bracket structure buckling analysis, purlin deflection constraints, diagonal beam deflection constraints, column top displacement constraints, purlin bending bearing capacity constraints, diagonal beam bending bearing capacity constraints, column tension and compression bearing capacity constraints, and diagonal brace tension and compression bearing capacity constraints.

[0035] In this embodiment, the first-order eigenvalue constraint of the buckling analysis of the overall support structure includes requiring the first-order eigenvalue f>1 for the buckling analysis of the overall photovoltaic support structure.

[0036] In this example, the purlin deflection constraints, the inclined beam deflection constraints, and the column top displacement constraints include:

[0037] z<(l1 / 250,l2 / 250,l3 / 60) (1)

[0038] Among them, z = (z1, z2, z3), z1, z2, z3 are the purlin deflection, diagonal beam deflection, and column top displacement, respectively, and the unit is mm; l1, l2, l3 are the purlin length, diagonal beam length, and column length, respectively, and the unit is mm.

[0039] In this embodiment, the bending bearing capacity constraints of purlins, the bending bearing capacity constraints of diagonal beams, the tensile and compressive bearing capacity constraints of columns, and the tensile and compressive bearing capacity constraints of diagonal braces include:

[0040]

[0041] Among them, M cFor purlins and diagonal beams, the bending bearing capacity is mainly considered; N c For columns and diagonal braces, the main consideration is the tensile and compressive axial force bearing capacity;

[0042] M r M is the design value of the bending bearing capacity when the component material is damaged, MPa; cr1 is the standard value of the overall stable bending bearing capacity of the pultruded profile of the component section when it is bent around the strong axis, MPa; cr2 It is the standard value of the local stable bending bearing capacity when the flange or web is locally unstable, MPa;

[0043] N s is the design value of the ultimate bearing capacity of the full-section compressive material failure of the component, N; N cr1 is the ultimate bearing capacity of the member section and the overall stability of the member, N; cr2 is the local stability ultimate bearing capacity of the member section, N.

[0044] Taking square tube as an example, M r 、M cr1 、M cr2 、N s 、N cr1 、N cr2 For the corresponding calculation methods, see formulas (3) and (4).

[0045]

[0046]

[0047] Among them, f L,d is the design value of the longitudinal strength of the component, MPa; I y is the weak axis moment of inertia, N·mm2; y is the distance from the neutral axis to the edge fiber of the component, mm; C b is the moment correction factor for the unrestrained members with supports at both ends within the span; is the longitudinal compressive elastic modulus, MPa; D J is the torsional stiffness of the rectangular section; L b f is the length between the lateral restraint points of the compression flange or the length between two adjacent support points that restrain the torsion of the cross section, mm; cr is the design value of critical buckling stress, MPa.

[0048] A n is the net cross-sectional area of ​​the component, mm2; is the design value of longitudinal compressive strength, mm2; λ is the effective slenderness ratio; A g is the total cross-sectional area of ​​the component, mm2; is the transverse compression elastic modulus, MPa; υ LT is the in-plane Poisson’s ratio; GLT is the in-plane shear modulus, MPa; β w It is the ratio of the width to the thickness of the square tube wall.

[0049] The bearing capacity of the bolts of each component when subjected to shear is determined based on the bolt extrusion strength and in-plane shear strength of the composite component. The bearing axial force of the bolts of each composite component when subjected to shear mainly considers the bearing capacity of the screw hole under pressure, the shear bearing capacity and the tensile bearing capacity of the net section, see formula (5)

[0050]

[0051] Where: is the compressive bearing capacity of the bolt hole when the bolt is sheared, N; is the shear bearing capacity when the bolt is subjected to shear, N; is the tensile bearing capacity of the net section when the bolt is subjected to shear, N.

[0052] S3. Taking the genetic algorithm in the swarm optimization algorithm as an example, the simulation model of the photovoltaic bracket based on the finite element software component is combined with the objective function to optimize the cross-sectional form and cross-sectional size of each component in the photovoltaic bracket.

[0053] S3.1. The cross-sectional type and cross-sectional size of each component in each set of photovoltaic brackets are taken as a sample individual, and a set of design schemes are initialized as the initial population. The population size in this embodiment is 10, see Table 1. In this embodiment, considering the requirements of subsequent construction and component cross-sectional types, the purlins and diagonal beams are mainly square tubes and C-shaped sections; the cross-sectional shapes of the front and rear columns are consistent, mainly square tubes and round tubes; the cross-sectional shapes of the front and rear diagonal braces are consistent, mainly square tubes and C-shaped sections.

[0054] Table 1 Initialization population

[0055]

[0056]

[0057] S3.2. Based on the structural form of the photovoltaic bracket, the material parameters and length information of each component in the photovoltaic bracket, and the cross-sectional type and cross-sectional size of each component of the sample individual, the simulation model is used to simulate and calculate the simulation parameters corresponding to the constraints of each component.

[0058] S3.3. Calculate the objective function corresponding to each sample individual. The objective function includes the weight function of the photovoltaic bracket based on the length information, cross-sectional form and cross-sectional size of each component, and the penalty function determined based on the relationship between the constraint conditions and the corresponding simulation parameters.

[0059] The total weight of the photovoltaic support is determined by the cross-sectional area and length of each component in the individual. The penalty function is set to punish the individuals that do not meet the constraints and directly exclude them from the optimization process. In this embodiment, the penalty function is set as an individual that does not meet the constraints, and an additional large constant term is added.

[0060] S3.4. In this example, the termination condition is set to 1000 generations or the difference between the objective function values ​​of adjacent generations is less than 1. Determine whether the termination condition is met. If it is met, end the iteration; otherwise, apply the genetic operators of selection, crossover and mutation to the population to generate a new generation of population, and turn to step 3.2. The penalty function value change curve during the optimization process of this embodiment is shown in Figure 3 , the optimal design was achieved after 265 generations of optimization.

[0061] Embodiment 2: This embodiment is a composite material photovoltaic bracket design optimization device based on a swarm optimization algorithm, including: an information acquisition module, a constraint condition module and an optimization calculation module.

[0062] In this example, the information acquisition module is used to obtain the structural form of the composite material photovoltaic bracket to be optimized, as well as the material parameters and length information of each component in the photovoltaic bracket; the constraint condition module is used to determine the constraints of each component in the photovoltaic bracket based on the structural form and design standards of the composite material photovoltaic bracket to be optimized; the optimization calculation module is used to optimize the cross-sectional form and cross-sectional size of each component in the photovoltaic bracket based on the swarm optimization algorithm, the simulation model of the photovoltaic bracket, and the objective function.

[0063] In this embodiment, the simulation model is used to simulate based on the structural form of the photovoltaic bracket and the length information, cross-sectional form, cross-sectional size and material parameters of each component to obtain simulation parameters corresponding to the constraints of each component. The objective function includes the weight function of the photovoltaic bracket and the penalty function based on the constraints and corresponding simulation parameters.

[0064] Embodiment 3: This embodiment is a storage medium on which a computer program that can be executed by a processor is stored. When the computer program is executed, the steps of the composite material photovoltaic bracket design optimization method based on the swarm optimization algorithm in this example are implemented.

[0065] Example 4: This example is a composite material photovoltaic bracket design optimization device, which has a memory and a processor. The memory stores a computer program that can be executed by the processor. When the computer program is executed, the steps of the composite material photovoltaic bracket design optimization method based on the group optimization algorithm in this example are implemented.

Claims

1. A composite photovoltaic bracket design optimization method based on a swarm optimization algorithm, characterized in that: include: Obtaining the structural form of the composite material photovoltaic bracket to be optimized, and the material parameters and length information of each component in the photovoltaic bracket; Based on the structural form and design standards of the composite photovoltaic bracket to be optimized, determine the constraints of each component in the photovoltaic bracket; Based on the swarm optimization algorithm, the simulation model of the photovoltaic bracket, and the objective function, the cross-sectional form and cross-sectional size of each component in the photovoltaic bracket are optimized; The simulation model is used to simulate based on the structural form of the photovoltaic bracket and the length information, cross-sectional form, cross-sectional size and material parameters of each component to obtain simulation parameters corresponding to the constraint conditions of each component; The objective function includes a photovoltaic support weight function based on the length information, cross-sectional form and cross-sectional size of each component, and a penalty function based on constraint conditions and corresponding simulation parameters.

2. The composite material photovoltaic bracket design optimization method based on the group optimization algorithm according to claim 1 is characterized in that: The constraint conditions of each component in the photovoltaic support include: one or more of the first-order eigenvalue constraint of the overall support structure buckling analysis, purlin deflection constraint, diagonal beam deflection constraint, column top displacement constraint, purlin bending bearing capacity constraint, diagonal beam bending bearing capacity constraint, column tension and compression axial force bearing capacity constraint and diagonal brace tension and compression axial force bearing capacity constraint.

3. The composite material photovoltaic bracket design optimization method based on group optimization algorithm according to claim 1 is characterized in that: The swarm optimization algorithm and the simulation model of the photovoltaic bracket are combined with the objective function to optimize the cross-sectional form and cross-sectional size of each component in the photovoltaic bracket, including: The group optimization algorithm is used to optimize the cross-sectional form and cross-sectional size of each component in the photovoltaic bracket; Each sample individual in the population used by the swarm optimization algorithm includes the cross-sectional form and cross-sectional size of each component in the photovoltaic bracket.

4. A composite material photovoltaic bracket design optimization device based on a swarm optimization algorithm, characterized in that: include: An information acquisition module is used to obtain the structural form of the composite material photovoltaic support to be optimized, and the material parameters and length information of each component in the photovoltaic support; The constraint condition module is used to determine the constraint conditions of each component in the photovoltaic bracket based on the structural form and design standard of the composite photovoltaic bracket to be optimized; An optimization calculation module is used to optimize the cross-sectional form and cross-sectional size of each component in the photovoltaic bracket based on a group optimization algorithm, a simulation model of the photovoltaic bracket, and an objective function; The simulation model is used to simulate based on the structural form of the photovoltaic bracket and the length information, cross-sectional form, cross-sectional size and material parameters of each component to obtain simulation parameters corresponding to the constraint conditions of each component; The objective function includes a weight function of the photovoltaic support and a penalty function based on constraint conditions and corresponding simulation parameters.

5. A storage medium having stored thereon a computer program executable by a processor, characterized in that: When the computer program is executed, the steps of the composite material photovoltaic bracket design optimization method based on the swarm optimization algorithm described in any one of claims 1 to 3 are implemented.

6. A composite photovoltaic support design optimization device, comprising a memory and a processor, wherein the memory stores a computer program executable by the processor, characterized in that: When the computer program is executed, the steps of the composite material photovoltaic bracket design optimization method based on the swarm optimization algorithm described in any one of claims 1 to 3 are implemented.

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