Wind power generation tower drum steel-concrete composite structure design method based on genetic algorithm optimization

Abstract

The invention discloses a wind power generation tower drum steel-concrete composite structure design method based on genetic algorithm optimization. The method specifically comprises the steps that operation data and design parameters of different types of wind power generation tower drums under different working conditions are obtained to construct an original data set; secondly, determining a design target function and quantifying, and setting each target weight coefficient; a genetic algorithm population is initialized, and individuals are represented by design parameter codes; decoding individuals of the population to obtain an actual design scheme, performing structural mechanical analysis, and calculating mechanical property indexes; calculating an individual fitness value according to the target function and the weight coefficient; new individuals are generated through selection, crossover and mutation operation, whether termination conditions are met or not is judged, if yes, the design scheme corresponding to the individual with the highest fitness is output, and if not, iterative optimization continues. According to the method, the genetic algorithm is used for iterative optimization, the comprehensive performance of the design scheme of the steel-concrete composite structure of the wind power generation tower under multiple targets is effectively improved, and a better design result is obtained.

Description

Technical Field

[0001] The invention relates to the technical field of tower steel-concrete composite structures, and in particular to a design method for a wind power tower steel-concrete composite structure based on genetic algorithm optimization. Background Art

[0002] As the global demand for clean energy continues to grow, wind power generation, as an important way to utilize renewable energy, has become increasingly prominent in the energy field. As the key supporting structure of wind turbines, the performance of wind turbine towers is directly related to the safe and stable operation and power generation efficiency of the entire wind power generation system. In actual application scenarios, wind turbine towers need to face complex and changeable working conditions, such as winds of different intensities, frequent changes in wind direction, their own vibrations, and temperature changes. In order to ensure that the tower can operate reliably under various working conditions, improving its structural performance, reducing costs, and enhancing wind resistance and stability have become urgent issues to be solved.

[0003] Traditional wind power tower designs mostly use a single material, such as steel or concrete. Steel towers have the advantages of high strength, light weight, and easy transportation and installation. However, their disadvantages are also obvious. Steel costs are high, and they are prone to corrosion in harsh environments such as long-term wind and rain, and the maintenance cost is high. Although concrete towers are relatively low in cost and have good durability, they are heavy and have high requirements for foundations. When subjected to strong winds, the flexibility and seismic resistance of their structures are relatively weak, and they are prone to cracks and other problems, affecting the service life and safety of the towers.

[0004] In the prior art, some wind turbine towers have begun to adopt steel-concrete composite structures, attempting to combine the advantages of steel and concrete structures to improve tower performance. This structure improves the shortcomings of a single material to a certain extent, and utilizes the high strength of steel and the durability of concrete to enhance the overall performance of the tower. However, in the design process, it mostly relies on experience and traditional design methods, and lacks scientific and systematic optimization methods for the selection of various design parameters of the steel-concrete composite structure. For example, when determining the types of steel and concrete, the dimensions of each part of the tower, and the parameters of the connecting components, it is often difficult to take into account multiple goals such as structural strength, cost, and wind resistance stability. This results in the designed tower having performance shortcomings in some aspects and failing to achieve optimal comprehensive performance.

[0005] Therefore, whether it is a traditional single - material tower barrel or a steel - concrete composite structure tower barrel in the prior art, there are certain limitations. At present, with the rapid development of the wind power generation industry, in order to meet the growing energy demand, improve the efficiency and economy of wind power generation, there is an urgent need for a method that can comprehensively consider various factors and scientifically optimize the design parameters of the steel - concrete composite structure to enhance the overall performance of the wind power generation tower barrel, reduce costs, enhance wind resistance stability, and ensure the long - term stable operation of the wind power generation system. Summary of the Invention

[0006] Based on the above, this application proposes a design method for the steel - concrete composite structure of a wind power generation tower barrel optimized by a genetic algorithm, including:

[0007] S1. Obtain the operation data of various types of wind power generation tower barrels under different working conditions, as well as the design parameters of the steel - concrete composite structure, and construct an original data set;

[0008] S2. Determine the objective function for the design of the steel - concrete composite structure, quantitatively process the objective function, and set the weight coefficients of each objective;

[0009] S3. Initialize the population of the genetic algorithm, and the individuals in the population are represented as codes containing the design parameters of the steel - concrete composite structure;

[0010] S4. Decode the individuals in the population, convert the codes into actual steel - concrete composite structure design schemes, conduct structural mechanics analysis on each design scheme, and calculate its mechanical performance indicators under various simulated working conditions;

[0011] S5. According to the objective function and weight coefficients, calculate the fitness value of each individual to evaluate the degree to which the design scheme represented by this individual meets the objectives;

[0012] S6. Through the selection operation, select some excellent individuals from the population according to the fitness value as the parent generation of the next - generation population; perform a crossover operation on the parent - generation individuals, randomly select two parent - generation individuals, and exchange some of their genes according to the set crossover probability and crossover method to generate new individuals;

[0013] S7. Perform a mutation operation on the newly generated individuals, randomly change the genes of the individuals with the set mutation probability to simulate minor adjustments of the design parameters;

[0014] S8. Judge whether the termination condition of the genetic algorithm is met. If it is met, output the steel - concrete composite structure design scheme corresponding to the individual with the highest fitness value in the current population as the final design result; if not, return to step S4 and continue the iterative optimization of the genetic algorithm.

[0015] Preferably, the operation data of various types of wind power towers obtained in S1 under different working conditions include the vibration frequency, vibration amplitude, stress change rate of the tower, wind speed and wind direction change data at different heights, and the power generation power fluctuation; the design parameters of the steel-concrete composite structure include the elastic modulus, yield strength, and ultimate strength of the steel, the compressive strength, tensile strength, and elastic modulus of the concrete, the height and radius of each section of the tower, and the type, quantity, and distribution parameters of the steel-concrete connection components in different regions; the operation data and design parameters are standardized, classified and integrated according to the time series and tower types, and the operation data of each type of tower under different working conditions are associated with the corresponding design parameters to form an original data set of multi-dimensional data.

[0016] Preferably, in S2, the specific objective function for determining the design of the steel-concrete composite structure is:

[0017] Construct the objective function as Among them, w1, w2, and w3 are the weight coefficients corresponding to the objectives of maximizing the tower structure strength, minimizing the material cost, and the wind resistance stability of the tower respectively; Represents the sum of the maximum stresses that occur at each key part of the tower under n simulated working conditions, σ a Is the comprehensive allowable stress value of the steel and concrete; C t Is the total material cost of the steel-concrete composite structure, C b Is the upper limit of the material cost budget set in advance; S i Is the instability risk coefficient of the tower; obtain the objective function F m Quantify.

[0018] Preferably, in S2, the objective function is quantified and the weight coefficients of each objective are set, specifically including:

[0019] Construct a multi-objective decision matrix M. The rows of the matrix represent different design schemes, and the columns correspond to maximizing the tower structure strength, minimizing the material cost, and the wind resistance stability of the tower respectively. For the objective of maximizing the tower structure strength, use Quantify, where α j Is the importance coefficient of the jth key part, σ j,a Is the actual stress under the current design scheme, σ j,l Is the theoretically optimal stress value; for the objective of minimizing the material cost, it is quantified as Among them, C d Is the material cost of the current design scheme, C m Is the lowest cost estimated by analyzing historical design data and market prices; for the objective of the wind resistance stability of the tower, it is quantified as Among them, S dis the wind resistance stability index of the current design scheme, S m is the ideal maximum wind resistance stability index; the weight coefficients are determined by using the analytic hierarchy process combined with the entropy weight method. The judgment matrix is constructed by using the analytic hierarchy process, and the subjective weight vector W s is calculated by calculating the eigenvector. According to the quantified target value, the entropy value E is calculated to obtain the objective weight vector W j and the weight coefficient w i is determined. The formula is: w i = λ×W s,i +(1 - λ)×W j where λ is the adjustment coefficient for balancing subjective and objective weights.

[0020] Preferably, the design parameters in S3 at least include steel type, concrete strength grade, steel thickness at different heights of the tower barrel, concrete wall thickness, and reinforcement ratio.

[0021] Preferably, when decoding the individuals in the population in S4, the individual coding is [g1, g2, …, g n , and decoding is performed through the decoding formula: where P i represents the actual steel-concrete composite structure design parameters obtained after decoding, and are the lower and upper limits of the design parameter values, is the maximum coding value of the gene. Each gene in the individual coding is calculated through the decoding formula, and the individual coding is converted into a steel-concrete composite structure design scheme; through structural mechanics analysis of each design scheme, its mechanical performance indexes under various simulation conditions are calculated, including stress distribution, strain condition, and overall stability coefficient.

[0022] Preferably, in S5, according to the objective function and the weight coefficient, the fitness value of each individual is calculated, specifically including:

[0023] The comprehensive optimization coefficient μ is introduced, and the fitness value F S of each individual is calculated through the formula where r is the value of the current individual on the rth objective function. The satisfaction degree of the design scheme represented by the individual to the target is evaluated through the fitness value F S .

[0024] Preferably, in S6, through the selection operation, some excellent individuals are selected from the population according to the fitness value as the parents of the next generation population, specifically including:

[0025] Construct a dynamic selection pressure coefficient S p , which changes with the iteration number t, and the calculation formula is where T maxis the preset maximum number of iterations, β is a regulation factor, and the selection probability of each individual is calculated The formula is Here F S is the fitness value of individual i g The total number of individuals in the population is N. According to the fitness value, some excellent individuals are selected from the population as the parent generation of the next generation population.

[0026] Preferably, when performing the crossover operation on the parent individuals in S7, an adaptive crossover probability is combined with a multi-mode crossover method; the adaptive crossover probability is dynamically adjusted according to the fitness of the parent individuals. For the parent individual pairs with higher fitness, the crossover probability is appropriately reduced to retain their excellent genes; for the parent individual pairs with lower fitness, the crossover probability is increased; the crossover method adopts multi-mode crossover, including single-point crossover, multi-point crossover and uniform crossover. For the gene fragments corresponding to the key structural parameters of the tower barrel in the coding, single-point crossover is preferentially used; for the gene fragments representing the continuously changing parameters such as the steel thickness and concrete wall thickness at different heights of the tower barrel in the coding, multi-point crossover is adopted; for the gene fragments such as the reinforcement ratio that are more sensitive and affect the overall performance, uniform crossover is adopted. By setting these crossover probabilities and crossover methods, part of the genes of the parent individuals are exchanged to generate new individuals.

[0027] Preferably, in S7, a mutation operation is performed on the newly generated individuals to set a mutation probability to randomly change the genes of the individuals, simulating the slight adjustment of the design parameters, specifically including:

[0028] An adaptive mutation probability is adopted, and the formula is: Where is the basic mutation probability, F a is the average fitness of the population, is the individual fitness. If the individual fitness is lower than the average fitness, the mutation probability is increased, otherwise it is decreased to simulate the slight adjustment of the design parameters.

[0029] Compared with the prior art, the technical solution of the present application has the following technical effects:

[0030] By obtaining the operation data of various different types of wind power tower barrels under different working conditions and constructing the original data set with the design parameters of the steel-concrete composite structure, and making full use of these data in the subsequent design process, the present invention solves the technical problem that the design scheme in the traditional design is not accurate and reasonable enough due to the lack of comprehensive data support. The technical effect of being able to optimize the design based on a large amount of actual operation data, making the finally designed steel-concrete composite structure more in line with the actual working condition requirements, and improving the adaptability and stability of the tower barrel under different working conditions is obtained.

[0031] The present invention solves the technical problem of difficultly balancing multiple mutually restrictive objectives such as the structural strength, material cost, and wind resistance stability of the tower barrel during the design process through the technical solution of determining the objective function of the steel-concrete composite structure design, quantifying the objective function, setting the weight coefficients of each objective, and calculating the individual fitness values based on this. The technical effect is obtained that multiple objectives can be comprehensively considered in the design stage, the weight of each objective can be flexibly adjusted according to actual needs, so as to obtain the optimal design scheme that meets different requirements and effectively improve the comprehensive performance of the tower barrel.

[0032] The present invention solves the technical problem that the traditional genetic algorithm is prone to falling into local optimal solutions during the optimization design process, resulting in unsatisfactory search results, through the technical solution of using an adaptive crossover probability combined with a multi-mode crossover method to perform crossover operations on parent individuals, and using an adaptive mutation probability to perform mutation operations on the newly generated individuals. The technical effect is obtained that the genetic algorithm can better balance the global search and local search capabilities during the process of searching for the optimal solution, improve the search efficiency and accuracy of the algorithm, and find the design parameters of the steel-concrete composite structure that meet the design requirements faster and more accurately.

[0033] The present invention solves the technical problem of being unable to accurately evaluate the performance of the design scheme under actual working conditions through the technical solution of decoding the individuals in the population, converting the encoding into the actual steel-concrete composite structure design scheme, and performing structural mechanics analysis on each design scheme to calculate its mechanical performance indicators under various simulated working conditions. The technical effect is obtained that the stress distribution, strain conditions, and overall stability and other mechanical properties of the design scheme under different working conditions can be understood in advance, potential problems in the design scheme can be discovered in time and optimized and improved, so as to ensure the safety and reliability of the finally designed tower barrel during actual operation.

[0034] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, so as to be implemented in accordance with the content of the specification, and in order to make the above and other purposes, features, and advantages of the present application more obvious and understandable, the following describes in detail with reference to the preferred embodiments of the present application and the accompanying drawings.

[0035] Those skilled in the art will understand the above and other purposes, advantages, and features of the present application more clearly according to the following detailed description of the specific embodiments of the present application in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts do not necessarily draw according to the actual scale.

[0037] Figure 1 The flowchart of the design method for the steel-concrete composite structure of a wind power tower barrel optimized based on the genetic algorithm of the present invention. Detailed implementation manners

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. In the following description, specific details such as specific configurations and components are provided only to assist in a comprehensive understanding of the embodiments of the present application. Therefore, those skilled in the art should clearly understand that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Additionally, for the sake of clarity and conciseness, the description of known functions and structures is omitted in the embodiments.

[0039] It should be understood that the term "an embodiment" or "the embodiment" mentioned throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of the present application. Therefore, the appearances of the term "an embodiment" or "the embodiment" throughout the specification do not necessarily refer to the same embodiment. In addition, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0040] In addition, the present application may repeat reference numerals and / or letters in different examples. This repetition is for the purpose of simplicity and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed.

[0041] The term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, B exists alone, and both A and B exist simultaneously. The term " / and" in this article describes another association object relationship, indicating that two relationships can exist. For example, A / and B can represent: A exists alone, and both A and B exist alone. In addition, the character " / " in this article generally represents that the associated objects before and after are in an "or" relationship.

[0042] The term "at least one" in this text is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, at least one of A and B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0043] It should also be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion.

[0044] Embodiment 1

[0045] This embodiment details a design method for the steel-concrete composite structure of a wind power tower optimized based on a genetic algorithm, as Figure 1 described, including the following steps:

[0046] S1. Obtain the operation data of various types of wind power towers under different working conditions, as well as the design parameters of the steel-concrete composite structure, and construct an original data set;

[0047] S2. Determine the objective function for the design of the steel-concrete composite structure, perform quantization processing on the objective function, and set the weight coefficients for each objective;

[0048] S3. Initialize the population of the genetic algorithm, and the individuals in the population are represented as codes containing the design parameters of the steel-concrete composite structure;

[0049] S4. Decode the individuals in the population, convert the codes into actual design schemes for the steel-concrete composite structure, perform structural mechanics analysis on each design scheme, and calculate its mechanical performance indicators under various simulated working conditions;

[0050] S5. According to the objective function and the weight coefficients, calculate the fitness value of each individual to evaluate the degree to which the design scheme represented by this individual meets the objectives;

[0051] S6. Through the selection operation, select some excellent individuals from the population according to the fitness value as the parents of the next generation population; perform a crossover operation on the parent individuals, randomly select two parent individuals, and exchange some of their genes according to the set crossover probability and crossover method to generate new individuals;

[0052] S7. Perform a mutation operation on the newly generated individuals to randomly change the genes of the individuals with the set mutation probability, simulating small adjustments to the design parameters;

[0053] S8. Determine whether the termination condition of the genetic algorithm is satisfied. If it is satisfied, output the design scheme of the steel-concrete composite structure corresponding to the individual with the highest fitness value in the current population as the final design result; if not, return to step S4 and continue the iterative optimization of the genetic algorithm.

[0054] Furthermore, the operation data of various different types of wind power tower barrels under different working conditions obtained in S1 include the vibration frequency, vibration amplitude, stress change rate of the tower barrel, wind speed and wind direction change data at different heights, and the power generation power fluctuation situation; the design parameters of the steel-concrete composite structure include the elastic modulus, yield strength, and ultimate strength of the steel, the compressive strength, tensile strength, and elastic modulus of the concrete, the height and radius of each section of the tower barrel, and the type, quantity, and distribution parameters of the steel-concrete connection components in different regions; standardize the operation data and design parameters, classify and integrate them according to the time series and tower barrel type, and associate the operation data of each type of tower barrel under different working conditions with the corresponding design parameters to form the original data set of multi-dimensional data.

[0055] Furthermore, in S2, the specific objective function for the design of the steel-concrete composite structure is:

[0056] Construct the objective function as where w1, w2, and w3 are the weight coefficients corresponding to the goals of maximizing the tower barrel structure strength, minimizing the material cost, and the wind resistance stability of the tower barrel respectively; represents the sum of the maximum stresses that occur at each key part of the tower barrel under n simulated working conditions, σ a is the comprehensive allowable stress value of the steel and concrete; C t is the total material cost of the steel-concrete composite structure, C b is the upper limit of the pre-set material cost budget; S i is the instability risk coefficient of the tower barrel; obtain the objective function F m for quantification.

[0057] Furthermore, in S2, quantify the objective function and set the weight coefficients of each goal, specifically including:

[0058] Construct a multi-objective decision matrix M. The rows of the matrix represent different design schemes, and the columns respectively correspond to maximizing the tower barrel structure strength, minimizing the material cost, and the wind resistance stability of the tower barrel. For the goal of maximizing the tower barrel structure strength, use for quantification, where α j is the importance coefficient of the jth key part, σ j,a is the actual stress under the current design scheme, σ j,l is the theoretically optimal stress value; for the goal of minimizing the material cost, quantify it as where Cd is the material cost of the current design scheme, C m is the minimum cost estimated through the analysis of historical design data and market prices; the goal of the wind resistance stability of the tower barrel is quantified as where S d is the wind resistance stability index of the current design scheme, S m is the ideal maximum wind resistance stability index; the weight coefficient is determined by using the analytic hierarchy process combined with the entropy weight method. The judgment matrix is constructed by using the analytic hierarchy process, and the eigenvector is calculated to obtain the subjective weight vector W s , the entropy value E is calculated according to the quantified target value to obtain the objective weight vector W j , and the weight coefficient w i is determined. The formula is: w i =λ×W s,i +(1 - λ)×W j , where λ is the adjustment coefficient for balancing the subjective and objective weights.

[0059] Furthermore, the design parameters in S3 at least include the steel type, concrete strength grade, steel thickness at different heights of the tower barrel, concrete wall thickness, and reinforcement ratio.

[0060] Furthermore, when decoding the individuals in the population in S4, the individual coding is [g1, g2, …, g n , and decoding is performed through the decoding formula: where P i represents the actual steel-concrete composite structure design parameters obtained after decoding, and are the lower and upper limits of the design parameter values, is the maximum coding value of the gene. Each gene in the individual coding is calculated through the decoding formula, and the individual coding is converted into a steel-concrete composite structure design scheme; through the structural mechanics analysis of each design scheme, its mechanical performance indexes under various simulation conditions are calculated, including stress distribution, strain condition, and overall stability coefficient.

[0061] Furthermore, in S5, the fitness value of each individual is calculated according to the objective function and the weight coefficient, specifically including:

[0062] The comprehensive optimization coefficient μ is introduced, and the fitness value F of each individual S is calculated through the formula , where r is the value of the current individual on the rth objective function. The degree to which the design scheme represented by the individual meets the goal is evaluated through the fitness value F S .

[0063] Further, in S6, through a selection operation, some excellent individuals are selected from the population according to the fitness value as the parents of the next-generation population, specifically including:

[0064] Construct a dynamic selection pressure coefficient S p , which changes with the iteration number t, and the calculation formula is where T max is the preset maximum number of iterations, and β is a regulation factor. Calculate the selection probability of each individual The formula is Here F S is the fitness value of individual i g , N is the total number of individuals in the population. Some excellent individuals are selected from the population according to the fitness value as the parents of the next-generation population.

[0065] Further, when performing a crossover operation on the parent individuals in S7, an adaptive crossover probability is combined with a multi-mode crossover method; the adaptive crossover probability is dynamically adjusted according to the fitness of the parent individuals. For the pair of parent individuals with higher fitness, the crossover probability is appropriately reduced to retain their excellent genes; for the pair of parent individuals with lower fitness, the crossover probability is increased; the crossover method adopts multi-mode crossover, including single-point crossover, multi-point crossover, and uniform crossover. For the gene segments corresponding to the key structure parameters of the tower barrel in the coding, single-point crossover is preferentially used; for the gene segments representing the continuously changing parameters such as the steel thickness and concrete wall thickness at different heights of the tower barrel in the coding, multi-point crossover is adopted; for the gene segments such as the reinforcement ratio that are more sensitive and affect the overall performance, uniform crossover is adopted. Through these settings of crossover probability and crossover method, part of the genes of the parent individuals are exchanged to generate new individuals.

[0066] Further, in S7, a mutation operation is performed on the newly generated individuals to set a mutation probability to randomly change the genes of the individuals and simulate minor adjustments of the design parameters, specifically including:

[0067] An adaptive mutation probability is adopted, and the formula is: where is the basic mutation probability, F a is the average fitness of the population, is the individual fitness. If the individual fitness is lower than the average fitness, the mutation probability is increased, otherwise it is decreased, and minor adjustments of the design parameters are simulated.

[0068] In this embodiment, it is described in detail that the present application constructs an original data set by obtaining operation data and design parameters, uses a genetic algorithm to encode, decode, and iteratively optimize the design parameters of the steel-concrete composite structure, and simultaneously determines multi-objective functions and weight coefficients to evaluate the design scheme. This technical solution solves the problem that traditional designs are difficult to balance structural strength, cost, and wind resistance stability, realizes precise design, and finally can obtain a design scheme for the steel-concrete composite structure of a wind power tower barrel that has both cost advantages and high wind resistance stability while meeting mechanical performance requirements, improving the comprehensive benefits of the wind power generation system.

[0069] Based on Embodiment 1, in this embodiment, it is described that after converting to the actual steel-concrete composite structure design scheme in S4, structural mechanical analysis is performed on each design scheme, and its mechanical performance indicators under various simulation conditions are calculated. Specifically:

[0070] For the stress distribution index, the tower barrel structure is discretized into a finite number of elements. Under the simulation conditions, through the formula the stress of the element is calculated, where is the axial force acting on the element, is the cross-sectional area of the element, is the bending moment borne by the element, is the distance from the calculation point to the neutral axis of the element, is the moment of inertia of the element; for the strain situation, according to Hooke's law, it is calculated through the formula where is the elastic modulus of the element material; for the overall stability coefficient, a comprehensive influence factor is introduced to consider factors such as the geometric shape of the tower barrel, material nonlinearity, and wind load, and is calculated through the formula where m k and n k are element quantity parameters, is the critical stress of the element under the condition; through the above formulas, the stress distribution, strain situation, and overall stability coefficient mechanical performance indicators of the design scheme under various simulation conditions are comprehensively calculated.

[0071] This embodiment details the technology of performing structural mechanical analysis on the design scheme and calculating mechanical performance indicators, which can comprehensively and precisely analyze the mechanical characteristics of each design scheme under various simulation conditions. Through the constructed calculation methods for stress, strain, and overall stability coefficient, the performance of the tower barrel under different stress conditions can be clearly understood. This helps to identify potential structural weak points and unstable factors at the design stage, providing a key basis for subsequent optimized design, and greatly improving the scientificity and reliability of the design of the steel-concrete composite structure of the wind power tower barrel.

[0072] Based on Embodiment 1, this embodiment describes how to determine whether the termination condition of the genetic algorithm is satisfied. If it is satisfied, the design scheme of the steel-concrete composite structure corresponding to the individual with the highest fitness value in the current population is output as the final design result. Specifically:

[0073] Taking into account the number of iterations, the fitness change rate, and the stagnation of the optimal fitness comprehensively, the maximum number of iterations is set to The current number of iterations is t d , and the fitness change rate R is introduced f . Its calculation formula is where F avg (t d ) is the average fitness of the t d -th generation population, and F avg (t d - 1) is the average fitness of the (t d - 1)-th generation population; at the same time, the threshold S for the number of generations of stagnation of the optimal fitness is set max , and the number of generations s in which the optimal fitness has not been improved continuously is recorded f . When one of the following conditions is met, it is determined that the termination condition is satisfied: Condition 1: t = T max ; Condition 2: R f < ε (ε is a preset extremely small positive number, representing the negligible threshold of fitness change); Condition 3: s f = S max . If the termination condition is satisfied, the design scheme of the steel-concrete composite structure corresponding to the individual with the highest fitness value F S in the current population is output as the final design result.

[0074] This embodiment details the technology of the present application for judging the termination condition of the genetic algorithm and outputting the final design scheme, which can effectively control the operation process of the algorithm and avoid resource waste caused by excessive iteration. Through multi-dimensional judgment, the convergence timing of the algorithm can be accurately captured. The output final design scheme is based on the individual with the highest fitness, ensuring the comprehensive optimality of the scheme in multiple objectives such as maximizing the strength of the tower barrel structure, minimizing the cost, and improving the wind resistance stability, providing high-quality results for the design of the steel-concrete composite structure.

[0075] The above are only the preferred embodiments of the present invention, and it does not limit the protection scope of the present invention accordingly. For those skilled in the art, the present invention can have various changes and modifications; all changes, modifications, substitutions, integrations, and parameter changes made to these embodiments through conventional substitutions or capable of achieving the same functions without departing from the principle and spirit of the present invention fall within the protection scope of the present invention.

Claims

1. A design method for the steel-concrete composite structure of a wind turbine tower optimized by a genetic algorithm, characterized in that, Including: S1. Obtain the operation data of multiple different types of wind power generation tower barrels under different working conditions, as well as the design parameters of the steel-concrete composite structure, and construct an original data set; S2. Determine the objective function for the design of the steel-concrete composite structure, quantitatively process the objective function, and set the weight coefficients of each objective; S3. Initialize the population of the genetic algorithm, and the individuals in the population are represented as codes containing the design parameters of the steel-concrete composite structure; S4. Decode the individuals in the population, convert the codes into actual steel-concrete composite structure design schemes, conduct structural mechanics analysis on each design scheme, and calculate its mechanical performance indicators under various simulated working conditions; S5. Calculate the fitness value of each individual according to the objective function and the weight coefficients to evaluate the degree of satisfaction of the design scheme represented by this individual with the objective; S6. Through the selection operation, select some excellent individuals from the population according to the fitness value as the parents of the next generation population; perform crossover operations on the parent individuals, randomly select two parent individuals, and exchange some of their genes according to the set crossover probability and crossover method to generate new individuals; S7. Perform mutation operations on the newly generated individuals, randomly change the genes of the individuals with the set mutation probability, and simulate the minor adjustments of the design parameters; S8. Judge whether the termination condition of the genetic algorithm is satisfied. If satisfied, output the steel-concrete composite structure design scheme corresponding to the individual with the highest fitness value in the current population as the final design result; if not satisfied, return to step S4 and continue the iterative optimization of the genetic algorithm.

2. The design method of the steel-concrete composite structure of a wind power tower barrel optimized based on a genetic algorithm according to claim 1, wherein The operation data of multiple different types of wind power generation tower barrels obtained in S1 under different working conditions include the vibration frequency, vibration amplitude, stress change rate of the tower barrel, wind speed and wind direction change data at different heights, and the power generation power fluctuation situation; the design parameters of the steel-concrete composite structure include the elastic modulus, yield strength, and ultimate strength of the steel, the compressive strength, tensile strength, and elastic modulus of the concrete, the height and radius of each section of the tower barrel, and the type, quantity, and distribution parameters of the steel-concrete connection components in different regions; the operation data and design parameters are standardized, classified and integrated according to the time series and tower barrel types, and the operation data of each type of tower barrel under different working conditions are associated with the corresponding design parameters to form an original data set of multi-dimensional data.

3. The design method of the steel-concrete composite structure of the wind power tower barrel optimized based on the genetic algorithm according to claim 1, wherein, In S2, the specific determination of the objective function for the design of the steel-concrete composite structure is: The objective function is constructed as where w1, w2, and w3 are the weight coefficients corresponding to the objectives of maximizing the structural strength of the tower barrel, minimizing the material cost, and the wind resistance stability of the tower barrel, respectively; represents the sum of the maximum stresses that occur at each key part of the tower barrel under n simulated working conditions, and σ a is the comprehensive allowable stress value of steel and concrete; C t is the total material cost of the steel-concrete composite structure, and C b is the upper limit of the pre-set material cost budget; S i is the instability risk coefficient of the tower barrel; obtain the objective function F m for quantification.

4. The design method of the steel-concrete composite structure of a wind power tower barrel optimized based on a genetic algorithm according to claim 1, wherein, In S2, the quantitative processing of the objective function and the setting of the weight coefficients of each objective specifically include: Construct a multi-objective decision matrix M. The rows of the matrix represent different design schemes, and the columns correspond to maximizing the tower barrel structural strength, minimizing the material cost, and the wind resistance stability of the tower barrel respectively. For the objective of maximizing the tower barrel structural strength, use for quantification, where α j is the importance coefficient of the jth key part, σ j,a is the actual stress under the current design scheme, and σ j,l is the theoretically optimal stress value; for the objective of minimizing the material cost, it is quantified as where C d is the material cost of the current design scheme, and C m is the lowest cost estimated by analyzing historical design data and market prices; for the objective of the wind resistance stability of the tower barrel, it is quantified as where S d is the wind resistance stability index of the current design scheme, and S m is the ideal maximum wind resistance stability index; the weight coefficient is determined by using the analytic hierarchy process combined with the entropy weight method. The judgment matrix is constructed by using the analytic hierarchy process, and the subjective weight vector W s is obtained by calculating the eigenvector. According to the quantified objective values, the entropy value E is calculated to obtain the objective weight vector W j , and the weight coefficient w i is determined. The formula is: w i =λ×W s,i +(1 - λ)×W j , where λ is the adjustment coefficient for balancing the subjective and objective weights.

5. The design method of the steel-concrete composite structure of a wind power tower barrel optimized based on a genetic algorithm according to claim 1, wherein The design parameters in S3 at least include the steel type, concrete strength grade, steel thickness at different heights of the tower barrel, concrete wall thickness, and reinforcement ratio.

6. The design method of the steel-concrete composite structure of a wind power tower barrel optimized based on a genetic algorithm according to claim 1, wherein, When decoding the individuals in the population in S4, the individual encoding is [g1, g2, …, g n , and the decoding formula is used: for decoding, where P i represents the actual design parameters of the steel-concrete composite structure obtained after decoding, and are the lower and upper limits of the design parameter values, is the maximum encoding value of the gene. Each gene in the individual encoding is calculated through the decoding formula to convert the individual encoding into a design scheme of the steel-concrete composite structure; by performing structural mechanics analysis on each design scheme, its mechanical performance indexes under various simulation conditions are calculated, including stress distribution, strain condition, and overall stability coefficient.

7. The design method of the steel-concrete composite structure of a wind power tower barrel optimized based on a genetic algorithm according to claim 1, wherein, In S5, the calculation of the fitness value of each individual according to the objective function and the weight coefficients specifically includes: Introduce the comprehensive optimization coefficient μ, and the fitness value F of each individual S Through the formula Calculate, where r is the value of the current individual on the r-th objective function, and the satisfaction degree of the design scheme represented by the individual to the objective is evaluated through the fitness value F S Evaluate the satisfaction degree of the design scheme represented by the individual to the objective.

8. The design method of the steel-concrete composite structure of a wind power tower barrel optimized based on a genetic algorithm according to claim 1, characterized in that In S6, through the selection operation, the selection of some excellent individuals from the population according to the fitness value as the parents of the next generation population specifically includes: Construct the dynamic selection pressure coefficient S p , which changes with the number of iterations t, and the calculation formula is where T max is the preset maximum number of iterations, and β is a regulation factor to calculate the selection probability of each individual The formula is where F S is the fitness value of individual i g , and N is the total number of individuals in the population. Some excellent individuals are selected from the population according to the fitness value as the parents of the next generation population.

9. The design method of the steel-concrete composite structure of a wind power tower barrel optimized based on a genetic algorithm according to claim 1, wherein When performing crossover operations on parental individuals in S7, an adaptive crossover probability is combined with a multi-mode crossover method; the adaptive crossover probability is dynamically adjusted according to the fitness of parental individuals. For pairs of parental individuals with higher fitness, the crossover probability is appropriately reduced to retain their excellent genes; for pairs of parental individuals with lower fitness, the crossover probability is increased. The crossover method uses multi-mode crossover, including single-point crossover, multi-point crossover, and uniform crossover. For gene segments corresponding to key structural parameters of the tower barrel in the encoding, single-point crossover is preferentially used; for gene segments representing continuously varying parameters such as the steel thickness and concrete wall thickness at different heights of the tower barrel in the encoding, multi-point crossover is adopted; for gene segments such as the reinforcement ratio that are more sensitive and affect the overall performance, uniform crossover is used. Through these settings of crossover probabilities and crossover methods, some genes of parental individuals are exchanged to generate new individuals.

10. The design method of the steel-concrete composite structure of a wind power tower barrel optimized based on a genetic algorithm according to claim 1, characterized in that In S7, a mutation operation is performed on the newly generated individuals to set the mutation probability Randomly change the genes of the individuals to simulate minor adjustments of the design parameters, specifically including: An adaptive mutation probability is adopted, and the formula is: where is the basic mutation probability, F a is the average fitness of the population, is the individual fitness. If the individual fitness is lower than the average fitness, the mutation probability is increased; otherwise, it is decreased, and a small adjustment of the simulation design parameters is performed.

Images