Wind turbine foundation size optimization method and system

Abstract

The present invention provides a method and system for optimizing the size of a wind turbine foundation, which relates to the technical field of wind turbines. The specific steps include: S1. taking multiple wind turbine foundations as samples to form a sample set, and collecting attribute parameters and environmental load parameters of each sample in the sample set; S2. performing data processing on the environmental load parameters, generating correction coefficients, and obtaining corrected attribute parameters; S3. performing data processing on the corrected attribute parameters to generate objective functions of the foundation's cost, deformation, and stability, optimizing these objective functions separately to obtain a first optimized value, and simultaneously generating and optimizing a comprehensive objective function to obtain a second optimized value; S4. comparing the difference between the first and second optimized values, and comparing them with a preset difference threshold to determine whether the optimization meets expectations. The present invention improves the accuracy and reliability of the optimization results by comprehensively considering the environmental load, and ensures the balance and optimal standards of the design in all aspects through multi-objective comprehensive optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine generator systems, and in particular to a method and system for optimizing the foundation size of a wind turbine generator system. Background Art

[0002] As a clean, renewable energy source with promising prospects for large-scale development and utilization, wind energy is attracting increasing attention worldwide. The tower, supporting the entire unit's weight and providing height for the rotor, is a crucial component of a wind turbine. As the load-bearing component of the entire tower, the tower foundation's design and manufacturing quality directly impact the safety and performance of the entire unit, placing particular emphasis on safety and cost-effectiveness.

[0003] Publication No. CN109543287A discloses a method for optimizing wind turbine foundation dimensions based on a genetic algorithm. The method includes the following steps: Step 1: Determine the encoding rules, variable objective function, and constraints; Step 2: Randomly generate an initial population based on the selected tower foundation dimension range and set the initial parameters required by the genetic algorithm: population size N, maximum evolutionary generation number Mmax, and crossover and mutation probability; Step 3: Using the tower foundation's mechanical properties, deformation, and stability as constraints, the genetic algorithm's objective function is set as the cost of the tower foundation's raw materials, and the fitness value of the individuals is calculated; Step 4: Use a roulette wheel mechanism to select and retain the optimal individuals, and perform crossover, mutation, and inversion to form new individuals, thus forming a new generation of populations; Step 5: Repeat Steps 3 and 4, and use the parameters corresponding to the maximum fitness value as the final optimization result. This method can reduce the cost of raw materials for tower foundations while meeting engineering requirements.

[0004] However, there are still the following deficiencies: As can be seen from the above statements, constraints are usually restrictions that must be strictly met during the optimization process, and solutions that violate these conditions are unacceptable. In the feasible solution space that meets all constraints, there are a large number of solutions. While these solutions meet the deformation and stability requirements, the costs vary greatly. In addition, although the deformation and stability of the tower foundation have been used as constraints, these constraints only guarantee the minimum design standards, not the optimal standards.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The object of the present invention is to provide a method and system for optimizing the foundation size of a wind turbine generator set to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for optimizing the size of a wind turbine foundation comprises the following steps:

[0009] S1. Multiple wind turbine foundations are used as samples to form a sample set, and attribute parameters and environmental load parameters of each sample in the sample set are collected. The attribute parameters include the diameter, depth, height, and slope of the foundation. The environmental load parameters include wind pressure, wind speed, wind direction angle, snow depth, and snow load.

[0010] S2. Process the wind pressure, wind speed, wind direction angle, snow depth, and snow load data and perform correlation analysis to generate correction coefficients for correcting the attribute parameters of the wind turbine foundation. Use the correction coefficients to correct the diameter, depth, height, and slope of the wind turbine foundation to obtain the corrected diameter, depth, height, and slope of the foundation.

[0011] S3. Process the corrected diameter, depth, height, and slope of the foundation and perform a correlation analysis to generate objective functions for the cost, deformation, and stability of the wind turbine foundation, respectively. Optimize the objective functions for the cost, deformation, and stability of the wind turbine foundation to obtain first optimized values for the diameter, depth, height, and slope of the wind turbine foundation. Process the objective functions for the cost, deformation, and stability of the wind turbine foundation to generate a comprehensive objective function. Optimize the comprehensive objective function of the wind turbine foundation to obtain second optimized values for the diameter, depth, height, and slope of the wind turbine foundation.

[0012] S4. Calculate the first optimized value of the diameter, depth, height and slope of the wind turbine foundation and the second optimized value of the diameter, depth, height and slope of the wind turbine foundation respectively, obtain the respective average optimized difference values, compare the respective average optimized difference values with the pre-set difference threshold value, judge whether the optimization of the diameter, depth, height and slope meets expectations, and judge whether the goals of minimum cost, minimum deformation and maximum stability of the wind turbine foundation can be achieved at the same time when achieving the goals of cost, deformation and stability of the wind turbine foundation respectively. If so, an optimal solution is obtained. If the conditions are not met, iterative optimization is required, that is, the parameters and objective function are readjusted, and the optimization calculation is performed again until all goals are met.

[0013] Furthermore, the wind pressure, wind speed, wind direction angle, snow depth and snow load are processed and correlation analysis is performed to generate correction coefficients for the basic attribute parameters. The formula is as follows:

[0014]

[0015] Where C is the correction coefficient, W iis the wind pressure of the i-th sample, P i is the wind speed of the i-th sample, V i is the wind direction angle of the i-th sample, S i is the snow load of the i-th sample, L i is the snow depth of the i-th sample, i is the index of the sample, and N is the number of samples.

[0016] Furthermore, the diameter, depth, height and slope of the wind turbine foundation are corrected by the correction coefficient to obtain the corrected diameter, depth, height and slope of the foundation according to the following formula:

[0017]

[0018] Where D' is the diameter of the base after correction, D is the diameter of the base, α D is the diameter correction weight, d' is the depth of the basis after correction, d is the depth of the basis, α d is the depth correction weight, h' is the height of the basis after correction, h is the height of the basis, α h is the height correction weight, is the slope of the corrected foundation, The basic slope, is the slope correction weight,

[0019] Furthermore, the diameter, depth, height and slope of the corrected foundation are processed and correlation analyzed to generate the objective functions of the cost, deformation and stability of the wind turbine foundation respectively. The process is as follows:

[0020] The cost of the wind turbine foundation and the objective function of each parameter are expressed by polynomials:

[0021]

[0022] Among them, cost is the cost of the wind turbine foundation, a1 is the first weight coefficient of the corrected foundation diameter, a2 is the first weight coefficient of the corrected foundation depth, a3 is the first weight coefficient of the corrected foundation height, a4 is the first weight coefficient of the corrected foundation slope, a0 is the first constant term, 0 <a3<a2<a4<a1<1;

[0023] The deformation of the wind turbine foundation and the objective function of each parameter are expressed by polynomials:

[0024]

[0025] Among them, deformation is the deformation of the wind turbine foundation, b1 is the second weight coefficient of the corrected foundation diameter, b2 is the second weight coefficient of the corrected foundation depth, b3 is the second weight coefficient of the corrected foundation height, b4 is the second weight coefficient of the corrected foundation slope, b0 is the second constant term, 0 <b3<b2<b4<b1<1;

[0026] The stability of the wind turbine foundation and the objective function of each parameter are expressed by polynomials:

[0027]

[0028] Among them, stability is the stability of the wind turbine foundation, c1 is the third weight coefficient of the corrected foundation diameter, c2 is the third weight coefficient of the corrected foundation depth, c3 is the third weight coefficient of the corrected foundation height, c4 is the third weight coefficient of the corrected foundation slope, c0 is the third constant term, 0 <c3<c2<c4<c1<1。

[0029] Furthermore, the objective functions of the cost, deformation, and stability of the wind turbine foundation are optimized respectively, and the process of obtaining the first optimized values of the diameter, depth, height, and slope of the wind turbine foundation is as follows:

[0030] Taking the minimum cost, minimum deformation and maximum stability of the wind turbine foundation as the goal, the objective functions of the cost, deformation and stability of the wind turbine foundation are optimized respectively;

[0031] Set constraints on the diameter, depth, height, and slope of the adjusted foundation:

[0032] D' min ≤D'≤D' max

[0033] d' min ≤d'≤d' max

[0034] h' min ≤h'≤h' max

[0035]

[0036] Among them, D' min and D' max The minimum and maximum values of the diameter of the basis, d' min and d' max The minimum and maximum depth of the basis, h' min and h' max The minimum and maximum values of the base height, and The minimum and maximum values of the slope diameter are taken as the basis; the above objective function is optimized by genetic algorithm, and the specific process is as follows:

[0037] Generate a set of solutions within the constraints, called the "initial population", each solution is a combination of the diameter, depth, height and slope of the corrected foundation;

[0038] For each individual, calculate its value on the three objective functions. Since we hope to meet the minimum cost, minimum deformation, and maximum stability, the fitness function is defined as follows:

[0039] Fitness 1: minimum cost;

[0040] Fitness 2: minimum deformation;

[0041] Fitness 3: Maximum stability;

[0042] According to the fitness value, a roulette wheel is used to select suitable individuals as parents, and crossover and mutation are prepared. The generated offspring individuals are merged with the current population, and fitness evaluation is performed. The best individuals are selected to form a new population. The fitness evaluation, selection, crossover and mutation operations are repeated. After multiple iterations, the Pareto optimal solution is gradually approached. The optimal solution is selected from the solutions on the Pareto frontier as the first optimized value of the diameter, depth, height and slope of the wind turbine foundation, that is, the first optimized value of the diameter, depth, height and slope of the foundation with the lowest cost. The first optimized values of diameter, depth, height and slope of the foundation for minimum deformation First optimized values of diameter, depth, height and slope of the foundation for maximum stability

[0043] Furthermore, the objective functions of the cost, deformation, and stability of the wind turbine foundation are processed to generate a comprehensive objective function. The comprehensive objective function of the wind turbine foundation is optimized to obtain the second optimized values of the diameter, depth, height, and slope of the wind turbine foundation as follows:

[0044] The objective function of the cost of the wind turbine foundation is standardized to the range of [0,1] to obtain cost norm ;

[0045] The objective function of the deformation of the wind turbine foundation is standardized to the range of [0,1] to obtain deformation norm ;

[0046] The objective function of the stability of the wind turbine foundation is standardized to the range of [0,1] to obtain the stability norm ;

[0047] The standardized objective functions are combined according to certain weights to generate a comprehensive objective function based on the following formula:

[0048] zm=ω1·cost norm +ω2·deformation norm +ω3·stability norm

[0049] Where zm is the comprehensive objective function, ω1 is the weight coefficient of the objective function of the standardized cost, ω2 is the weight coefficient of the objective function of the standardized deformation, and ω3 is the weight coefficient of the objective function of the standardized stability, ω1<ω2<ω3, ω1+ω2+ω3=1;

[0050] Satisfy the constraints of diameter, depth, height and slope of the corrected foundation;

[0051] Set the population size to M, and each individual is a solution, that is, a combination of diameter, depth, height and slope;

[0052] Calculate the comprehensive objective function value of each individual as its fitness value. The formula is as follows:

[0053] fitness(γ)=w1·cost norm (γ)+w2·deformation norm (γ)+w3·stability norm (γ)

[0054] Among them, fitness(γ) is the fitness value of the γth individual, and γ is the index of the individual;

[0055] Based on the fitness value, use roulette to select suitable individuals as parents:

[0056] Calculate the cumulative sum G of fitness values according to the following formula:

[0057]

[0058] Calculate the probability of each individual being selected according to the following formula:

[0059]

[0060] Among them, P(γ) is the probability that the γth individual is selected as the parent;

[0061] Perform a crossover operation on the selected parent individuals to generate offspring individuals:

[0062] Randomly select a crossover point k:

[0063] k = rand(1, n)

[0064] Where n is 4, rand(1,n) is a random function that returns an integer between 1 and n;

[0065] Parent individual and Perform a single-point crossover to generate offspring individuals T and Q:

[0066]

[0067] For each offspring individual T and Q, a gene is randomly selected and slightly changed:

[0068] U j =U j +rand(-δ, δ) if rand(0, 1) <p m

[0069] Among them, U j is the jth gene of the offspring individual, j is the index of the gene, rand(0, 1) is a function that generates random floating point numbers, rand(-δ, δ) is a random function that returns a value between -δ and δ, which is used to slightly change the value of the gene, p m is the mutation probability;

[0070] The generated offspring individuals are merged with the current population, and fitness evaluation is performed. The best individuals are selected to form a new population. The fitness evaluation, selection, crossover and mutation operations are repeated. After multiple iterations, the Pareto optimal solution is gradually approached. After multiple iterations, the Pareto frontier is obtained. Each point on the Pareto frontier is an optimized solution that achieves a balance between the objective functions. An optimal solution is selected from the solutions on the Pareto frontier as the second optimized value of the wind turbine foundation, that is, the second optimized value of the diameter, depth, height and slope of the foundation with the lowest cost, minimum deformation and maximum stability.

[0071] Furthermore, the first optimized values of the diameter, depth, height and slope of the wind turbine foundation and the second optimized values of the diameter, depth, height and slope of the wind turbine foundation are calculated to obtain their respective differences, according to the following formula:

[0072] Differences in diameter, depth, height and slope of wind turbine foundations:

[0073]

[0074] Among them, ΔD is the average optimized difference of the diameter of the wind turbine foundation, Δd is the average optimized difference of the depth of the wind turbine foundation, and Δh is the average optimized difference of the height of the wind turbine foundation. is the average optimized difference of the slope of the wind turbine foundation;

[0075] Compare the respective average optimization differences with the pre-set difference threshold to determine whether the optimization of diameter, depth, height, and slope meets expectations, and determine whether the goals of minimum cost, minimum deformation, and maximum stability of the wind turbine foundation can be achieved simultaneously when achieving the goals of cost, deformation, and stability of the wind turbine foundation respectively. The specific process is as follows:

[0076] When ΔD≤ΥZ D , the optimization of the diameter is in line with expectations, otherwise, it is not in line with expectations;

[0077] When Δd≤ΥZ d , the depth optimization meets expectations, otherwise, it does not meet expectations;

[0078] When Δh≤ΥZ h ,The high degree of optimization is as expected, otherwise, it does not meet expectations;

[0079] when The optimization of the slope meets expectations, otherwise, it does not meet expectations;

[0080] Therefore, when the optimization of the diameter, depth, height and slope are all in line with expectations, it means that the first optimized value and the second optimized value of the diameter, depth, height and slope are close. The closeness of the first optimized value and the second optimized value indicates that when the goals of cost, deformation and stability of the wind turbine foundation are achieved respectively, the goals of minimum cost, minimum deformation and maximum stability of the wind turbine foundation can be achieved simultaneously.

[0081] To achieve the above object, the present invention further provides the following technical solutions:

[0082] A wind turbine foundation size optimization system, the system being configured to execute any of the above-mentioned wind turbine foundation size optimization methods, comprising:

[0083] a data acquisition module, configured to take multiple wind turbine foundations as samples to form a sample set, and collect attribute parameters and environmental load parameters of each sample in the sample set, wherein the attribute parameters include the diameter, depth, height and slope of the foundation, and the environmental load parameters include wind pressure, wind speed, wind direction angle, snow depth and snow load;

[0084] A correction module is used to process the wind pressure, wind speed, wind direction angle, snow depth and snow load data, and perform correlation analysis to generate correction coefficients for correcting the attribute parameters of the wind turbine foundation. The diameter, depth, height and slope of the wind turbine foundation are corrected using the correction coefficients to obtain the corrected diameter, depth, height and slope of the foundation;

[0085] an optimization module, configured to process the corrected diameter, depth, height, and slope of the foundation and perform correlation analysis to generate objective functions of the cost, deformation, and stability of the wind turbine foundation, respectively, optimize the objective functions of the cost, deformation, and stability of the wind turbine foundation, respectively, and obtain first optimized values of the diameter, depth, height, and slope of the wind turbine foundation; process the objective functions of the cost, deformation, and stability of the wind turbine foundation to generate a comprehensive objective function; optimize the comprehensive objective function of the wind turbine foundation to obtain second optimized values of the diameter, depth, height, and slope of the wind turbine foundation;

[0086] The judgment module is used to calculate the first optimized value of the diameter, depth, height and slope of the wind turbine foundation and the second optimized value of the diameter, depth, height and slope of the wind turbine foundation respectively, obtain the respective average optimized difference values, compare the respective average optimized difference values with the pre-set difference threshold value, judge whether the optimization of the diameter, depth, height and slope meets expectations, and judge whether the goals of minimum cost, minimum deformation and maximum stability of the wind turbine foundation can be achieved simultaneously when the goals of cost, deformation and stability of the wind turbine foundation are achieved respectively. If so, an optimal solution is obtained. If the conditions are not met, iterative optimization is required, that is, the parameters and objective function are readjusted, and the optimization calculation is performed again until all goals are met.

[0087] Compared with the prior art, the present invention has the following beneficial effects:

[0088] The present invention collects the attribute parameters (including diameter, depth, height and slope) and environmental load parameters (including wind pressure, wind speed, wind direction angle, snow depth and snow load) of the wind turbine foundation, processes the wind pressure, wind speed, wind direction angle, snow depth and snow load, and performs correlation analysis to generate correction coefficients for correcting the attribute parameters of the wind turbine foundation. The diameter, depth, height and slope of the wind turbine foundation are corrected by the correction coefficients to obtain the corrected attribute parameters. Considering the influence of the environmental load parameters on the foundation size, correction is performed by the correction coefficients to improve the accuracy and reliability of the optimization results. The corrected foundation attribute parameters are processed and correlation analysis is performed to generate objective functions of the foundation cost, deformation and stability respectively. These objective functions are optimized to obtain the minimum cost, minimum deformation and stability respectively. The first optimized value of the basic attribute parameters of shape and maximum stability is obtained. At the same time, these objective functions are processed to generate a comprehensive objective function, which is optimized to obtain the second optimized value of the basic attribute parameters of minimum cost, minimum deformation, and maximum stability. Not only a single objective function is optimized, but also a global optimization is performed through the comprehensive objective function to ensure the balance of the design in all aspects. The first optimized value and the second optimized value are calculated to obtain their respective average optimized difference. The difference is compared with the preset threshold to determine whether the optimization meets expectations and whether the goals of minimum cost, minimum deformation and maximum stability can be achieved at the same time. The reliability and rationality of the optimization results are further verified through difference comparison to ensure that the optimization reaches the optimal standard. Through comprehensive evaluation, it is ensured that the overall optimization effect is achieved while meeting various constraints. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 Schematic diagram of the overall method flow of the present invention;

[0090] Figure 2 This is a block diagram of the module composition of the present invention. DETAILED DESCRIPTION

[0091] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0092] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0093] Example:

[0094] See also Figure 1 , the present invention provides a technical solution:

[0095] A method for optimizing the size of a wind turbine foundation comprises the following steps:

[0096] S1. Multiple wind turbine foundations are used as samples to form a sample set, and attribute parameters and environmental load parameters of each sample in the sample set are collected. The attribute parameters include the diameter, depth, height, and slope of the foundation. The environmental load parameters include wind pressure, wind speed, wind direction angle, snow depth, and snow load.

[0097] S2. Process the wind pressure, wind speed, wind direction angle, snow depth, and snow load data and perform correlation analysis to generate correction coefficients for correcting the attribute parameters of the wind turbine foundation. Use the correction coefficients to correct the diameter, depth, height, and slope of the wind turbine foundation to obtain the corrected diameter, depth, height, and slope of the foundation.

[0098] S3. Process the corrected diameter, depth, height, and slope of the foundation and perform a correlation analysis to generate objective functions for the cost, deformation, and stability of the wind turbine foundation, respectively. Optimize the objective functions for the cost, deformation, and stability of the wind turbine foundation to obtain first optimized values for the diameter, depth, height, and slope of the wind turbine foundation. Process the objective functions for the cost, deformation, and stability of the wind turbine foundation to generate a comprehensive objective function. Optimize the comprehensive objective function of the wind turbine foundation to obtain second optimized values for the diameter, depth, height, and slope of the wind turbine foundation.

[0099] S4. Calculate the first optimized value of the diameter, depth, height and slope of the wind turbine foundation and the second optimized value of the diameter, depth, height and slope of the wind turbine foundation respectively, obtain the respective average optimized difference values, compare the respective average optimized difference values with the pre-set difference threshold value, judge whether the optimization of the diameter, depth, height and slope meets expectations, and judge whether the goals of minimum cost, minimum deformation and maximum stability of the wind turbine foundation can be achieved at the same time when achieving the goals of cost, deformation and stability of the wind turbine foundation respectively. If so, an optimal solution is obtained. If the conditions are not met, iterative optimization is required, that is, the parameters and objective function are readjusted, and the optimization calculation is performed again until all goals are met.

[0100] Based on the above embodiment, the wind pressure, wind speed, wind direction angle, snow depth and snow load are processed and correlation analysis is performed to generate correction coefficients for correcting the basic attribute parameters. The formula is as follows:

[0101]

[0102] Where C is the correction coefficient, W i is the wind pressure of the i-th sample, P i is the wind speed of the i-th sample, V i is the wind direction angle of the i-th sample, S i is the snow load of the i-th sample, L i is the snow depth of the i-th sample, i is the index of the sample, and N is the number of samples.

[0103] Wind pressure W i Is the force applied per unit area, the higher the wind pressure W i This means that a greater force acts on the structure. When the wind pressure W i Increases, the structure requires a higher safety margin to cope with this increased force, and the correction factor C is therefore designed to increase with the increase in wind pressure;

[0104] Wind speed P i The square of wind pressure W i Proportional to the higher wind speed P i Means wind pressure W i The increase in wind speed P increases the load on the structure. In order to ensure that the structure is still stable under high wind speed, the correction coefficient C will increase with the wind speed P. i increases with the increase of

[0105] Wind direction V i is the angle of the wind relative to the structure, the wind direction angle V i Wind pressure W i The effect is nonlinear, ln(1+V i2 ) This term results in the wind direction angle V i Negative correlation with the correction coefficient C;

[0106] Snow load S i An increase means an increase in the weight on the structure, leading to higher stress and potential risk. Therefore, in the model, the snow load S i An increase in will usually reduce the correction factor C;

[0107] Greater snow depth L i Means snow load S i The distribution over a larger volume reduces the pressure per unit area, which is beneficial to the structure. Therefore, it is reflected in the model as an increase in the correction coefficient C. From a design perspective, a larger snow depth L i It provides a certain cushioning effect, allowing the structure to better adapt to and distribute the load.

[0108] In summary, the wind pressure W i , wind speed P i , snow depth L i The average and correction coefficient C are positively correlated, and the wind direction angle V i , snow load S i There is a negative correlation between the correction coefficient C.

[0109] Based on the above embodiment, the diameter, depth, height and slope of the wind turbine foundation are corrected by the correction coefficient to obtain the corrected diameter, depth, height and slope of the foundation, according to the following formula:

[0110]

[0111] Where D' is the diameter of the base after correction, D is the diameter of the base, α D is the diameter correction weight, d' is the depth of the basis after correction, d is the depth of the basis, α d is the depth correction weight, h' is the height of the basis after correction, h is the height of the basis, α h is the height correction weight, is the slope of the corrected foundation, The basic slope, is the slope correction weight,

[0112] The larger the diameter D of the foundation, the larger the area in contact with the ground, the higher the overall stability of the foundation, and the stronger the ability to resist overturning and sliding. A larger diameter D can disperse the weight and wind load of the wind turbine, reduce the stress per unit area, and improve the bearing capacity of the foundation. For the foundation of a wind turbine, stability is crucial because wind turbines are often affected by the combined effects of wind force and deadweight. The diameter D directly affects the overall safety and service life of the foundation, so it has the greatest weight. Influence of friction on soil-structure interface, suitable slope It can improve the anti-shear capacity of the foundation. Reasonable slope design can effectively control the overall deformation of the foundation and reduce excessive settlement and tilt. Although the slope It has an important influence on stability and shear resistance, but due to the usual design of slope The range of change is small, and its influence is relatively minor relative to the diameter D. The appropriate slope The design can still significantly improve the stability of the foundation, so its weight is second only to the diameter D; a deeper foundation can more effectively resist the load of the superstructure and reduce local instability of the foundation. A deeply buried foundation helps to reduce the deformation of the foundation and provide better support. The depth d has a significant impact on deformation and stability, but its optimization space and influence are slightly weaker than the slope d. The depth correction weight is important but second only to the slope; the foundation height h directly affects the installation height of the wind turbine, thereby affecting the wind energy capture efficiency. The change in the foundation height h has little direct and short-term impact on the foundation, and has limited impact on the cost and overall stability. Compared with other parameters, the foundation height h has little direct impact on the optimization target, and mainly affects the wind energy capture efficiency rather than the stability and bearing capacity of the foundation. The height correction weight has the lowest importance in the optimization target.

[0113] Based on the above embodiment, the diameter, depth, height and slope of the corrected foundation are processed and correlation analyzed to generate the objective functions of the cost, deformation and stability of the wind turbine foundation respectively as follows:

[0114] The cost of the wind turbine foundation and the objective function of each parameter are expressed by polynomials:

[0115]

[0116] Among them, cost is the cost of the wind turbine foundation, a1 is the first weight coefficient of the corrected foundation diameter, a2 is the first weight coefficient of the corrected foundation depth, a3 is the first weight coefficient of the corrected foundation height, a4 is the first weight coefficient of the corrected foundation slope, a0 is the first constant term, 0 <a3<a2<a4<a1<1。

[0117] The deformation of the wind turbine foundation and the objective function of each parameter are expressed by polynomials:

[0118]

[0119] Among them, deformation is the deformation of the wind turbine foundation, b1 is the second weight coefficient of the corrected foundation diameter, b2 is the second weight coefficient of the corrected foundation depth, b3 is the second weight coefficient of the corrected foundation height, b4 is the second weight coefficient of the corrected foundation slope, b0 is the second constant term, 0 <b3<b2<b4<b1<1。

[0120] The stability of the wind turbine foundation and the objective function of each parameter are expressed by polynomials:

[0121]

[0122] Among them, stability is the stability of the wind turbine foundation, c1 is the third weight coefficient of the corrected foundation diameter, c2 is the third weight coefficient of the corrected foundation depth, c3 is the third weight coefficient of the corrected foundation height, c4 is the third weight coefficient of the corrected foundation slope, c0 is the third constant term, 0 <c3<c2<c4<c1<1。

[0123] Based on the above embodiment, the objective functions of the cost, deformation, and stability of the wind turbine foundation are optimized respectively, and the process of obtaining the first optimized values of the diameter, depth, height, and slope of the wind turbine foundation is as follows:

[0124] Taking the minimum cost, minimum deformation and maximum stability of the wind turbine foundation as the goal, the objective functions of the cost, deformation and stability of the wind turbine foundation are optimized respectively;

[0125] Set constraints on the diameter, depth, height, and slope of the adjusted foundation:

[0126] D' min ≤D'≤D' max

[0127] d' min ≤d'≤d' max

[0128] h' min ≤h'≤h' max

[0129]

[0130] Among them, D' min and D' max The minimum and maximum values of the diameter of the basis, d' min and d' max The minimum and maximum depth of the basis, h'min and h' max The minimum and maximum values of the base height, and The minimum and maximum values of the slope diameter are taken as the basis; the above objective function is optimized by genetic algorithm, and the specific process is as follows:

[0131] Generate a set of possible solutions within the constraints, called the "initial population", each solution is a combination of the diameter, depth, height and slope of the corrected foundation;

[0132] For each individual, calculate its value on the three objective functions (cost, deformation and stability). Since we hope to meet the minimum cost, minimum deformation and maximum stability, the fitness function is defined as follows:

[0133] Fitness 1: minimum cost;

[0134] Fitness 2: minimum deformation;

[0135] Fitness 3: Maximum stability;

[0136] According to the fitness value, a roulette wheel is used to select suitable individuals as parents, and crossover and mutation are prepared. The generated offspring individuals are merged with the current population, and fitness evaluation is performed. The best individuals are selected to form a new population. The fitness evaluation, selection, crossover and mutation operations are repeated. After multiple iterations, the Pareto optimal solution is gradually approached. The optimal solution is selected from the solutions on the Pareto frontier as the first optimized value of the diameter, depth, height and slope of the wind turbine foundation, that is, the first optimized value of the diameter, depth, height and slope of the foundation with the lowest cost. The first optimized values of diameter, depth, height and slope of the foundation for minimum deformation First optimized values of diameter, depth, height and slope of the foundation for maximum stability

[0137] Based on the above embodiment, the objective functions of the cost, deformation, and stability of the wind turbine foundation are processed to generate a comprehensive objective function. The comprehensive objective function of the wind turbine foundation is optimized to obtain the second optimized values of the diameter, depth, height, and slope of the wind turbine foundation as follows:

[0138] The objective function of the cost of the wind turbine foundation is standardized to the range of [0,1] to obtain cost norm ;

[0139] The objective function of the deformation of the wind turbine foundation is standardized to the range of [0,1] to obtain deformation norm ;

[0140] The objective function of the stability of the wind turbine foundation is standardized to the range of [0,1] to obtain the stability norm ;

[0141] The standardized objective functions are combined according to certain weights to generate a comprehensive objective function based on the following formula:

[0142] zm=ω1·cost norm +ω2·deformation norm +ω3·stability norm

[0143] Where zm is the comprehensive objective function, ω1 is the weight coefficient of the objective function of the standardized cost, ω2 is the weight coefficient of the objective function of the standardized deformation, and ω3 is the weight coefficient of the objective function of the standardized stability, ω1<ω2<ω3, ω1+ω2+ω3=1;

[0144] Stability is usually the most important factor in the design of wind turbine foundations, because an unstable foundation may cause the collapse or failure of the entire wind turbine. Therefore, the weight coefficient ω3 of the stability objective function is the largest; deformation has a significant impact on the long-term performance and service life of the foundation. Excessive deformation will affect the normal operation and maintenance of the wind turbine. The weight coefficient ω2 of the deformation objective function should be second only to stability; although economic cost is also an important factor to consider, cost optimization usually takes a relatively secondary position under the premise of ensuring safety and performance. Therefore, the weight coefficient ω1 of the cost objective function is the smallest.

[0145] Satisfy the constraints of diameter, depth, height and slope of the corrected foundation;

[0146] Set the population size to M, and each individual is a solution, that is, a combination of diameter, depth, height and slope;

[0147] Calculate the comprehensive objective function value of each individual as its fitness value. The formula is as follows:

[0148] fitness(γ)=w1·cost norm (γ)+w2·deformation norm (γ)+w3·stability norm (γ)

[0149] Among them, fitness(γ) is the fitness value of the γth individual, and γ is the index of the individual;

[0150] Based on the fitness value, use roulette to select suitable individuals as parents:

[0151] Calculate the cumulative sum G of fitness values according to the following formula:

[0152]

[0153] Calculate the probability of each individual being selected according to the following formula:

[0154]

[0155] Among them, P(γ) is the probability that the γth individual is selected as the parent,

[0156] Perform a crossover operation on the selected parent individuals to generate offspring individuals:

[0157] Randomly select a crossover point k:

[0158] k = rand(1, n)

[0159] Where n is 4, rand(1,n) is a random function that returns an integer between 1 and n;

[0160] Parent individual and Perform a single-point crossover to generate offspring individuals T and Q:

[0161]

[0162] For each offspring individual T and Q, a gene is randomly selected and slightly changed:

[0163] U j =U j +rand(-δ, δ) if rand(0, 1) <p m

[0164] Among them, U j is the jth gene of the offspring individual, j is the index of the gene, rand(0, 1) is a function that generates random floating point numbers, rand(-δ, δ) is a random function that returns a value between -δ and δ, which is used to slightly change the value of the gene, p m is the mutation probability;

[0165] The generated offspring individuals are merged with the current population, and fitness evaluation is performed. The best individuals are selected to form a new population. The fitness evaluation, selection, crossover and mutation operations are repeated. After multiple iterations, the Pareto optimal solution is gradually approached. After multiple iterations, the Pareto frontier is obtained. Each point on the Pareto frontier is an optimized solution that achieves a balance between the objective functions. An optimal solution is selected from the solutions on the Pareto frontier as the second optimized value of the wind turbine foundation, that is, the second optimized value of the diameter, depth, height and slope of the foundation with the lowest cost, minimum deformation and maximum stability.

[0166] Based on the above embodiment, the first optimized values of the diameter, depth, height and slope of the wind turbine foundation and the second optimized values of the diameter, depth, height and slope of the wind turbine foundation are calculated respectively to obtain their respective average optimized differences, according to the following formula:

[0167] Average optimized differences in diameter, depth, height and slope of wind turbine foundations:

[0168]

[0169] Among them, ΔD is the average optimized difference of the diameter of the wind turbine foundation, Δd is the average optimized difference of the depth of the wind turbine foundation, and Δh is the average optimized difference of the height of the wind turbine foundation. is the average optimized difference in the slope of the wind turbine foundation.

[0170] Compare the respective optimized differences with the preset difference thresholds to determine whether the optimization of diameter, depth, height, and slope meets expectations, and determine whether the goals of minimum cost, minimum deformation, and maximum stability of the wind turbine foundation can be achieved simultaneously when achieving the goals of cost, deformation, and stability of the wind turbine foundation respectively. The specific process is as follows:

[0171] When ΔD≤ΥZ D , the optimization of the diameter is in line with expectations, otherwise, it is not in line with expectations;

[0172] When Δd≤ΥZ d , the depth optimization meets expectations, otherwise, it does not meet expectations;

[0173] When Δh≤ΥZ h ,The high degree of optimization is as expected, otherwise, it does not meet expectations;

[0174] when The optimization of the slope is as expected, otherwise, it is not as expected.

[0175] Therefore, when the optimization of the diameter, depth, height and slope are all in line with expectations, it means that the first optimized value and the second optimized value of the diameter, depth, height and slope are close. The closeness of the first optimized value and the second optimized value indicates that when the goals of cost, deformation and stability of the wind turbine foundation are achieved respectively, the goals of minimum cost, minimum deformation and maximum stability of the wind turbine foundation can also be achieved at the same time.

[0176] See also Figure 2 , the present invention provides a technical solution:

[0177] A wind turbine foundation size optimization system, the system being configured to execute any of the above-mentioned wind turbine foundation size optimization methods, comprising:

[0178] a data acquisition module, configured to take multiple wind turbine foundations as samples to form a sample set, and collect attribute parameters and environmental load parameters of each sample in the sample set, wherein the attribute parameters include the diameter, depth, height and slope of the foundation, and the environmental load parameters include wind pressure, wind speed, wind direction angle, snow depth and snow load;

[0179] A correction module is used to process the wind pressure, wind speed, wind direction angle, snow depth and snow load data, and perform correlation analysis to generate correction coefficients for correcting the attribute parameters of the wind turbine foundation. The diameter, depth, height and slope of the wind turbine foundation are corrected using the correction coefficients to obtain the corrected diameter, depth, height and slope of the foundation;

[0180] an optimization module, configured to process the corrected diameter, depth, height, and slope of the foundation and perform correlation analysis to generate objective functions of the cost, deformation, and stability of the wind turbine foundation, respectively, optimize the objective functions of the cost, deformation, and stability of the wind turbine foundation, respectively, and obtain first optimized values of the diameter, depth, height, and slope of the wind turbine foundation; process the objective functions of the cost, deformation, and stability of the wind turbine foundation to generate a comprehensive objective function; optimize the comprehensive objective function of the wind turbine foundation to obtain second optimized values of the diameter, depth, height, and slope of the wind turbine foundation;

[0181] The judgment module is used to calculate the first optimized value of the diameter, depth, height and slope of the wind turbine foundation and the second optimized value of the diameter, depth, height and slope of the wind turbine foundation respectively, obtain the respective average optimized difference values, compare the respective average optimized difference values with the pre-set difference threshold value, judge whether the optimization of the diameter, depth, height and slope meets expectations, and judge whether the goals of minimum cost, minimum deformation and maximum stability of the wind turbine foundation can be achieved simultaneously when the goals of cost, deformation and stability of the wind turbine foundation are achieved respectively. If so, an optimal solution is obtained. If the conditions are not met, iterative optimization is required, that is, the parameters and objective function are readjusted, and the optimization calculation is performed again until all goals are met.

[0182] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0183] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0184] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0185] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A method for optimizing the foundation size of a wind turbine generator system, characterized in that: The specific steps include: S1. Multiple wind turbine foundations are used as samples to form a sample set, and attribute parameters and environmental load parameters of each sample in the sample set are collected. The attribute parameters include the diameter, depth, height, and slope of the foundation. The environmental load parameters include wind pressure, wind speed, wind direction angle, snow depth, and snow load. S2. Process the wind pressure, wind speed, wind direction angle, snow depth, and snow load data and perform correlation analysis to generate correction coefficients for correcting the attribute parameters of the wind turbine foundation. Use the correction coefficients to correct the diameter, depth, height, and slope of the wind turbine foundation to obtain the corrected diameter, depth, height, and slope of the foundation. S3. Process the corrected diameter, depth, height, and slope of the foundation and perform a correlation analysis to generate objective functions for the cost, deformation, and stability of the wind turbine foundation, respectively. Optimize the objective functions for the cost, deformation, and stability of the wind turbine foundation to obtain first optimized values for the diameter, depth, height, and slope of the wind turbine foundation. Process the objective functions for the cost, deformation, and stability of the wind turbine foundation to generate a comprehensive objective function. Optimize the comprehensive objective function of the wind turbine foundation to obtain second optimized values for the diameter, depth, height, and slope of the wind turbine foundation. S4. Calculate the first optimized values of the diameter, depth, height, and slope of the wind turbine foundation and the second optimized values of the diameter, depth, height, and slope of the wind turbine foundation, respectively, and obtain their respective average optimized differences. Compare the respective average optimized differences with a preset difference threshold to determine whether the optimization of the diameter, depth, height, and slope meets expectations, and determine whether, while achieving the respective goals of cost, deformation, and stability of the wind turbine foundation, it is possible to simultaneously achieve the goals of minimum cost, minimum deformation, and maximum stability of the wind turbine foundation. If so, an optimal solution is obtained. If not, iterative optimization is required, i.e., readjusting the parameters and objective function and performing the optimization calculation again until all goals are met. The wind pressure, wind speed, wind direction angle, snow depth and snow load are processed and correlation analysis is performed to generate correction coefficients for the attribute parameters of the correction foundation. The formula is as follows: in, is the correction factor, For the The wind pressure of the samples, For the The wind speed of the samples, For the The wind direction angle of the samples, For the Snow load for each sample, For the The snow depth of each sample, is the index of the sample, is the number of samples.

2. The method for optimizing wind turbine foundation size according to claim 1, characterized in that: The diameter, depth, height and slope of the wind turbine foundation are corrected by the correction coefficient to obtain the corrected diameter, depth, height and slope of the foundation according to the following formula: in, is the diameter of the corrected foundation, The diameter of the base, is the diameter correction weight, is the depth of the foundation after correction, The depth of the foundation, is the depth correction weight, is the height of the corrected foundation, The height of the base, is the height correction weight, is the slope of the corrected foundation, The basic slope, is the slope correction weight, .

3. The method for optimizing wind turbine foundation size according to claim 2, wherein: The process of processing the corrected diameter, depth, height and slope of the foundation and performing correlation analysis to generate the objective functions of the cost, deformation and stability of the wind turbine foundation is as follows: The cost of the wind turbine foundation and the objective function of each parameter are expressed by polynomials: in, The cost of the wind turbine foundation is is the first weight coefficient of the corrected basic diameter, is the first weight coefficient of the corrected foundation depth, is the first weight coefficient of the corrected base height, is the first weight coefficient of the corrected basic slope, is the first constant term, ; The deformation of the wind turbine foundation and the objective function of each parameter are expressed by polynomials: in, is the deformation of the wind turbine foundation, is the second weight coefficient of the corrected basic diameter, is the second weight coefficient of the corrected foundation depth, is the second weight coefficient of the corrected base height, is the second weight coefficient of the corrected basic slope, is the second constant term, ; The stability of the wind turbine foundation and the objective function of each parameter are expressed by polynomials: in, For the stability of the wind turbine foundation, is the third weight coefficient of the corrected basic diameter, is the third weight coefficient of the corrected foundation depth, is the third weight coefficient of the corrected base height, is the third weight coefficient of the corrected basic slope, is the third constant term, .

4. The method for optimizing wind turbine foundation size according to claim 3, wherein: The objective functions of cost, deformation, and stability of the wind turbine foundation are optimized respectively, and the process of obtaining the first optimized values of the diameter, depth, height, and slope of the wind turbine foundation is as follows: Taking the minimum cost, minimum deformation and maximum stability of the wind turbine foundation as the goal, the objective functions of the cost, deformation and stability of the wind turbine foundation are optimized respectively; Set constraints on the diameter, depth, height, and slope of the adjusted foundation: in, and The minimum and maximum diameters of the base, and The minimum and maximum depth values of the basis, and The minimum and maximum values of the base height, and The minimum and maximum values of the slope diameter are the basis; The above objective function is optimized by genetic algorithm. The specific process is as follows: Generate a set of solutions within the constraints, called the "initial population", each solution is a combination of the diameter, depth, height and slope of the corrected foundation; For each individual, calculate its value on the three objective functions. Since we hope to meet the minimum cost, minimum deformation, and maximum stability, the fitness function is defined as follows: Fitness 1: Minimum cost ; Fitness 2: Minimum deformation ; Fitness 3: Maximum Stability ; According to the fitness value, a roulette wheel is used to select suitable individuals as parents, and crossover and mutation are prepared. The generated offspring individuals are merged with the current population, and fitness evaluation is performed. The best individuals are selected to form a new population. The fitness evaluation, selection, crossover and mutation operations are repeated. After multiple iterations, the Pareto optimal solution is gradually approached. The optimal solution is selected from the solutions on the Pareto frontier as the first optimized value of the diameter, depth, height and slope of the wind turbine foundation, that is, the first optimized value of the diameter, depth, height and slope of the foundation with the lowest cost. , the first optimized values of the diameter, depth, height and slope of the foundation with the minimum deformation , the first optimized values of diameter, depth, height and slope of the foundation for maximum stability .

5. The method for optimizing wind turbine foundation size according to claim 4, characterized in that: The objective functions of the cost, deformation, and stability of the wind turbine foundation are processed to generate a comprehensive objective function. The comprehensive objective function of the wind turbine foundation is optimized to obtain the second optimized values of the diameter, depth, height, and slope of the wind turbine foundation as follows: The objective function of the cost of the wind turbine foundation is normalized to the range of [0,1], and the ; The objective function of the deformation of the wind turbine foundation is normalized to the range of [0,1] to obtain ; The objective function of the stability of the wind turbine foundation is normalized to the range of [0,1], and the ; The standardized objective functions are combined according to certain weights to generate a comprehensive objective function based on the following formula: in, is the comprehensive objective function, is the weight coefficient of the objective function of the standardized cost, is the weight coefficient of the objective function of the normalized deformation, is the weight coefficient of the objective function of stability after standardization, , ; Satisfy the constraints of diameter, depth, height and slope of the corrected foundation; Set the population size to , each individual is a solution, that is, a combination of diameter, depth, height and slope; Calculate the comprehensive objective function value of each individual as its fitness value. The formula is as follows: in, For the The fitness value of each individual, is the index of the individual; Based on the fitness value, use roulette to select suitable individuals as parents: Calculate the cumulative sum of fitness values , based on the following formula: Calculate the probability of each individual being selected according to the following formula: in, For the The probability of an individual being selected as a parent; Perform a crossover operation on the selected parent individuals to generate offspring individuals: Randomly select an intersection point : in, is 4, A random function that returns a value from 1 to Integer between ; Parent individual and Perform single-point crossover to generate offspring individuals and : For each offspring individual and , randomly select a gene and make a small change: in, The first genes, is the index of the gene, A function to generate random floating point numbers. Is a random function that returns a arrive The values between are used to slightly change the value of the gene. is the mutation probability; The generated offspring individuals are merged with the current population, and fitness evaluation is performed. The best individuals are selected to form a new population. The fitness evaluation, selection, crossover and mutation operations are repeated. After multiple iterations, the Pareto optimal solution is gradually approached. After multiple iterations, the Pareto frontier is obtained. Each point on the Pareto frontier is an optimized solution that achieves a balance between the objective functions. An optimal solution is selected from the solutions on the Pareto frontier as the second optimized value of the wind turbine foundation, that is, the second optimized value of the diameter, depth, height and slope of the foundation with the lowest cost, minimum deformation and maximum stability. .

6. The method for optimizing wind turbine foundation size according to claim 5, characterized in that: The first optimized values of the diameter, depth, height and slope of the wind turbine foundation and the second optimized values of the diameter, depth, height and slope of the wind turbine foundation are calculated respectively to obtain their respective average optimized differences, according to the following formula: Average optimized differences in diameter, depth, height and slope of wind turbine foundations: in, is the average optimized difference in diameter of the wind turbine foundation, is the average optimized difference in the depth of the wind turbine foundation, is the average optimized difference in the height of the wind turbine foundation, is the average optimized difference of the slope of the wind turbine foundation; Compare the respective average optimization differences with the pre-set difference threshold to determine whether the optimization of diameter, depth, height, and slope meets expectations, and determine whether the goals of minimum cost, minimum deformation, and maximum stability of the wind turbine foundation can be achieved simultaneously when achieving the goals of cost, deformation, and stability of the wind turbine foundation respectively. The specific process is as follows: when , the optimization of the diameter is in line with expectations, otherwise, it is not in line with expectations; when , the depth optimization meets expectations, otherwise, it does not meet expectations; when ,The high degree of optimization is as expected, otherwise, it does not meet expectations; when , the optimization of the slope meets expectations, otherwise, it does not meet expectations; Therefore, when the optimization of the diameter, depth, height and slope are all in line with expectations, it means that the first optimized value and the second optimized value of the diameter, depth, height and slope are close. The closeness of the first optimized value and the second optimized value indicates that when the goals of cost, deformation and stability of the wind turbine foundation are achieved respectively, the goals of minimum cost, minimum deformation and maximum stability of the wind turbine foundation can be achieved simultaneously.

7. A wind turbine foundation size optimization system, the system being configured to execute a wind turbine foundation size optimization method according to any one of claims 1 to 6, characterized in that: include: a data acquisition module, configured to take multiple wind turbine foundations as samples to form a sample set, and collect attribute parameters and environmental load parameters of each sample in the sample set, wherein the attribute parameters include the diameter, depth, height and slope of the foundation, and the environmental load parameters include wind pressure, wind speed, wind direction angle, snow depth and snow load; A correction module is used to process the wind pressure, wind speed, wind direction angle, snow depth and snow load data, and perform correlation analysis to generate correction coefficients for correcting the attribute parameters of the wind turbine foundation. The diameter, depth, height and slope of the wind turbine foundation are corrected using the correction coefficients to obtain the corrected diameter, depth, height and slope of the foundation; an optimization module, configured to process the corrected diameter, depth, height, and slope of the foundation and perform correlation analysis to generate objective functions of the cost, deformation, and stability of the wind turbine foundation, respectively, optimize the objective functions of the cost, deformation, and stability of the wind turbine foundation, respectively, and obtain first optimized values of the diameter, depth, height, and slope of the wind turbine foundation; process the objective functions of the cost, deformation, and stability of the wind turbine foundation to generate a comprehensive objective function; optimize the comprehensive objective function of the wind turbine foundation to obtain second optimized values of the diameter, depth, height, and slope of the wind turbine foundation; The judgment module is used to calculate the first optimized value of the diameter, depth, height and slope of the wind turbine foundation and the second optimized value of the diameter, depth, height and slope of the wind turbine foundation respectively, obtain the respective average optimized difference values, compare the respective average optimized difference values with the pre-set difference threshold value, judge whether the optimization of the diameter, depth, height and slope meets expectations, and judge whether the goals of minimum cost, minimum deformation and maximum stability of the wind turbine foundation can be achieved simultaneously when the goals of cost, deformation and stability of the wind turbine foundation are achieved respectively. If so, an optimal solution is obtained. If the conditions are not met, iterative optimization is required, that is, the parameters and objective function are readjusted, and the optimization calculation is performed again until all goals are met.

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