Method and device for determining main scale of fan floating body, storage medium and electronic device

Abstract

The invention discloses a method and device for determining the main scale of a fan floating body, a storage medium and an electronic device. The method comprises the steps that an initial population is generated according to floating body variables needed for calculating the cost of the fan floating body; generating a hydrodynamic calculation model of the fan floating body according to the structure model corresponding to the fan floating body and the population parameters in the initial population; calculating response data and load data of the fan floating body in different environments by using a hydrodynamic calculation model; using a preset optimization algorithm to iteratively optimize the initial population according to the response data and the load data to obtain a target population, and using a cost function to calculate to obtain a target floating body cost corresponding to the target population, the target population satisfying an optimization condition of stopping iteratively optimizing the initial population; and under the condition that the target floating body cost is determined to be smaller than the floating body cost threshold value, determining the main scale of the fan floating body based on the target group floating body variables of the target population.

Description

Technical Field

[0001] The present application relates to the field of design of wind turbine floating bodies, and in particular, to a method and device for determining the main dimensions of a wind turbine floating body, a storage medium and an electronic device. Background Art

[0002] With the continuous transformation of the energy industry, renewable energy technologies such as floating wind power and floating photovoltaics are gradually advancing from concept verification to small-scale demonstration projects. However, the high development cost constitutes the main obstacle in the commercialization process of these emerging technologies, severely limiting their potential for large-scale application. Therefore, reducing the cost of floating structures is regarded as a key link in reducing costs and increasing efficiency, and is crucial to promoting the entire industry to enter the mass development stage. At present, the optimization of the main scale of the floating body mainly relies on the designer's own practical experience for manual tuning, and then through repeated trial and error and iterative tuning, find the balance between performance and cost. Although this experience-oriented method can provide a preliminary design scheme with reliable performance and reasonable cost, it is difficult to explore the global optimal solution in the parameter space because it is essentially a local optimization strategy. In addition, the manual optimization process is time-consuming and inefficient, especially when the project design cycle is tight, there is a disadvantage of delaying the project progress.

[0003] It can be seen that in the related technology, there is a technical problem of low optimization efficiency of the main dimensions of the floating body.

[0004] With regard to the technical problem of low optimization efficiency of the main dimensions of the floating body in the related technologies, no effective solution has been proposed so far.

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

[0006] The embodiments of the present application provide a method and device for determining the main dimensions of a wind turbine floating body, a storage medium, and an electronic device, so as to at least solve the problem of low optimization efficiency of the main dimensions of the floating body.

[0007] According to one aspect of an embodiment of the present application, a method for determining the main scale of a wind turbine float is provided, comprising: generating an initial population according to float variables required for calculating the cost of the wind turbine float, wherein the initial population includes a plurality of population individuals, and each population individual represents a group of float variables; generating a hydrodynamic calculation model of the wind turbine float according to a structural model corresponding to the wind turbine float and population parameters in the initial population, wherein the structural model is generated based on the float variables, and the structural model is used to represent the float structure of the wind turbine float; using the hydrodynamic calculation model to perform frequency domain calculation on the population individuals to obtain response data of the wind turbine float under different environments; using a preset optimization algorithm to iteratively optimize the initial population according to the response data to obtain a target population, and using a cost function to calculate a target float cost corresponding to the target population, wherein the target population satisfies an optimization condition for stopping iterative optimization of the initial population; and determining the main scale of the wind turbine float based on the target group of float variables of the target population when it is determined that the target float cost is less than a float cost threshold.

[0008] In an exemplary embodiment, before generating an initial population based on the float variables required to calculate the wind turbine float cost, the method also includes: obtaining the pre-set float variables, wherein the float variables satisfy a preset variable range; the float variables include at least one of the following: column spacing, column diameter, and draft depth.

[0009] In an exemplary embodiment, a hydrodynamic calculation model of a wind turbine float is generated according to a structural model corresponding to the wind turbine float and population parameters in the initial population, including: obtaining the floating body parameters of the wind turbine float from the population parameters in the initial population, and converting the floating body construction parameters and the population parameters of the structural model from the current format to the model format using a self-compiled program, and obtaining a model file for generating the hydrodynamic calculation model according to the converted floating body construction parameters and the population parameters, wherein the model format is a format supported by the hydrodynamic calculation model; and generating the hydrodynamic calculation model by running the model file.

[0010] In an exemplary embodiment, a preset optimization algorithm is used to iteratively optimize the initial population according to the response data to obtain a target population, including: using a genetic algorithm to evaluate the multiple population individuals, generating new population individuals based on the multiple population individuals, wherein each population individual has a fitness, and the fitness is determined based on the response data; iteratively selecting, crossover and mutation operations are performed on the new population individuals in sequence until the optimization conditions are met to obtain a target individual, and obtaining the target population based on the target individual.

[0011] In an exemplary embodiment, generating new population individuals based on the multiple population individuals includes at least one of the following: randomly selecting a preset number of first individuals from the initial population, and selecting a second individual from the preset number of first individuals, and determining the second individuals as the new population individuals, wherein the second individuals have the highest fitness; according to a preset crossover probability, crossing two second individuals to generate a third individual, and determining the third individual as the new population individual; according to a preset mutation probability, mutating each gene of the first individual to obtain a fourth individual, according to the preset crossover probability, crossing two fourth individuals to generate a fifth individual, and determining the fifth individual as the new population individual.

[0012] In an exemplary embodiment, it is determined that the target population satisfies the optimization condition for stopping iterative optimization of the initial population in the following manner: when it is determined that the iteration time for iterative optimization of the initial population is equal to or greater than a preset optimization time, it is determined that the target population satisfies the optimization condition for stopping iterative optimization of the initial population; when it is determined that the number of iterations for iterative optimization of the initial population is equal to a preset number of times, it is determined that the target population satisfies the optimization condition for stopping iterative optimization of the initial population.

[0013] According to another aspect of an embodiment of the present application, a device for determining the main dimensions of a wind turbine float is also provided, comprising: a first generating module, for generating an initial population according to float variables required for calculating the cost of the wind turbine float, wherein the initial population includes a plurality of population individuals, and each population individual represents a set of float variables; a second generating module, for generating a hydrodynamic calculation model of the wind turbine float according to a structural model corresponding to the wind turbine float and population parameters in the initial population, wherein the structural model is generated based on the float variables, and the structural model is used to represent the float structure of the wind turbine float; a first obtaining module, for The hydrodynamic calculation model is used to perform frequency domain calculations on the individuals of the population to obtain response data of the wind turbine float under different environments; a second obtaining module is used to use a preset optimization algorithm to iteratively optimize the initial population according to the response data to obtain a target population, and use a cost function to calculate a target float cost corresponding to the target population, wherein the target population satisfies the optimization condition for stopping iterative optimization of the initial population; a determination module is used to determine the main scale of the wind turbine float based on the target group float variables of the target population when it is determined that the target float cost is less than a float cost threshold.

[0014] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned method for determining the main dimensions of the wind turbine floating body when running.

[0015] According to another aspect of an embodiment of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method for determining the main dimensions of a wind turbine floating body through the computer program.

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

[0017] According to the present application, an initial population is generated according to the floating body variables required for calculating the cost of a wind turbine floating body, wherein the initial population includes a plurality of population individuals, and each population individual represents a set of floating body variables; a hydrodynamic calculation model of the wind turbine floating body is generated according to a structural model corresponding to the wind turbine floating body and population parameters in the initial population, wherein the structural model is generated based on the floating body variables, and the structural model is used to represent the floating body structure of the wind turbine floating body; the hydrodynamic calculation model is used to perform frequency domain calculation on the population individuals to obtain response data of the wind turbine floating body under different environments; a preset optimization algorithm is used to iteratively optimize the initial population according to the response data to obtain a target population, and a cost function is used to calculate the target population. The target floating body cost corresponding to the target population is determined, wherein the target population satisfies the optimization condition for stopping the iterative optimization of the initial population; when it is determined that the target floating body cost is less than the floating body cost threshold, the main scale of the wind turbine floating body is determined based on the target group floating body variables of the target population, so that the optimal design parameters of the floating body structure can be determined quickly and accurately without sacrificing the safety of the floating body, and the relationship between the floating body cost, the floating body performance and the floating body safety can be systematically evaluated, so as to quickly form an overall floating body design plan in the design stage of the wind turbine floating body, and the floating body variables can be flexibly adjusted according to actual needs to solve the low optimization efficiency of the main scale of the floating body, thereby improving the optimization efficiency of the main scale of the floating body and improving the design accuracy of the main scale of the floating body. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0020] Figure 1 It is a hardware structure block diagram of a mobile terminal of a method for determining the main dimensions of a wind turbine floating body according to an embodiment of the present application;

[0021] Figure 2 is a flow chart of a method for determining the main dimensions of a wind turbine floating body according to an embodiment of the present application;

[0022] Figure 3 is a flow chart of a method for determining the main dimensions of a wind turbine floating body according to an embodiment of the present application;

[0023] Figure 4 is a schematic diagram of a wind turbine float according to an embodiment of the present application;

[0024] Figure 5 It is a structural block diagram of a device for determining the main dimensions of a wind turbine floating body according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal for determining the main dimensions of a wind turbine floating body according to an embodiment of the present application. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor (Central Processing Unit, MCU) or a programmable logic device (Field Programmable Gate Array, FPGA)) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0028] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for determining the main dimensions of the wind turbine floating body in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0029] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0030] In this embodiment, a method for determining the main dimensions of a wind turbine float is provided. The wind turbine float refers to a wind turbine support structure used in an offshore wind farm, such as a semi-submersible, tension leg or single column float. The main dimensions of the wind turbine float refer to the key geometric dimension parameters of the wind turbine float, which have a direct impact on the performance, stability and cost of the float. The main dimensions of the wind turbine float may include the following parameters:

[0031] 1. Column diameter: For semi-submersible floating bodies, the diameter of the columns directly affects the buoyancy and structural strength of the floating body, as well as the manufacturing cost.

[0032] 2. Column spacing: The distance between columns affects the stability of the float. Too small a spacing may affect the structural rigidity, while too large a spacing may increase material usage and construction costs.

[0033] 3. Draft depth: The depth of the floating body underwater affects the buoyancy and stability of the floating body, and is also related to marine environmental conditions (such as seabed depth, waves and currents).

[0034] 4. Float length and width: These dimensional parameters determine the overall size of the float, affecting the hydrodynamic performance, structural strength and compatibility with wind turbines.

[0035] 5. The shape of the floating body: such as the number and arrangement of columns of a semi-submersible floating body. The shape has an important influence on the hydrodynamic characteristics and structural efficiency of the floating body.

[0036] 6. Center of gravity and center of buoyancy: The relative position of the center of gravity and the center of buoyancy is crucial to the stability of the floating body. Optimizing these parameters can improve the floating body's ability to resist wind and waves.

[0037] 7. Ballast distribution: The distribution of ballast can adjust the buoyancy and stability of the floating body. Appropriate ballast distribution helps to optimize the performance of the floating body.

[0038] By optimizing the main scale parameters, the present application can ensure that the floating body meets the structural safety and performance requirements while minimizing the construction and operation costs and improving the economic benefits of the offshore wind farm. In the design process of the floating body, the present application can comprehensively consider and adjust the main scale parameters by combining numerical simulation, theoretical analysis and engineering experience.

[0039] Figure 2 is a flow chart of a method for determining the main dimensions of a wind turbine floating body according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps:

[0040] Step S202: generating an initial population according to the floating body variables required for calculating the floating body cost of the wind turbine, wherein the initial population includes a plurality of population individuals, each of which represents a group of floating body variables;

[0041] The initial population may be generated, for example, by randomly sampling the floating variables to obtain the sampled variables. The floating variables may be understood as the variables corresponding to the parameters selected from the main scale parameters.

[0042] Step S204: generating a hydrodynamic calculation model of the wind turbine float according to the structural model corresponding to the wind turbine float and the population parameters in the initial population, wherein the structural model is generated based on the float variables, and the structural model is used to represent the float structure of the wind turbine float;

[0043] Optionally, each individual in the initial population can represent a set of design parameters, which can be regarded as the "genes" of the semi-submersible floating body. Therefore, a structural model can be generated using a set of floating body variables.

[0044] Step S206: using the hydrodynamic calculation model to perform frequency domain calculation on the individuals of the population to obtain response data of the wind turbine floating body under different environments;

[0045] Step S208: using a preset optimization algorithm to iteratively optimize the initial population according to the response data to obtain a target population, and using a cost function to calculate a target floating body cost corresponding to the target population, wherein the target population satisfies an optimization condition for stopping iterative optimization of the initial population;

[0046] Optionally, the cost function can be expressed as: cost function = steel unit price * steel weight + ballast unit price * ballast weight. The steel weight and ballast weight can be obtained based on the response data, while the steel unit price and ballast unit price can be based on the industry average price or the latest market quotation. It is recommended to use preset fixed values ​​in the cost function for preliminary optimization, and then update these prices to reflect market changes. For example, you can subscribe to industry information. Use price prediction models or keep in touch with suppliers to update price data in a timely manner.

[0047] Step S210: when it is determined that the target floating body cost is less than the floating body cost threshold, determining the wind turbine floating body main dimension based on the target group floating body variables of the target population.

[0048] Through the above steps, an initial population is generated according to the floating body variables required for calculating the cost of the wind turbine floating body, wherein the initial population includes multiple population individuals, and each population individual represents a set of floating body variables; a hydrodynamic calculation model of the wind turbine floating body is generated according to the structural model corresponding to the wind turbine floating body and the population parameters in the initial population, wherein the structural model is generated based on the floating body variables, and the structural model is used to represent the floating body structure of the wind turbine floating body; the hydrodynamic calculation model is used to perform frequency domain calculation on the population individuals to obtain response data of the wind turbine floating body under different environments; a preset optimization algorithm is used to iteratively optimize the initial population according to the response data to obtain a target population, and a cost function is used to calculate the target population. The target floating body cost corresponding to the target population, wherein the target population satisfies the optimization condition for stopping the iterative optimization of the initial population; when it is determined that the target floating body cost is less than the floating body cost threshold, the main scale of the wind turbine floating body is determined based on the target group floating body variables of the target population, so that the optimal design parameters of the floating body structure can be determined quickly and accurately without sacrificing the safety of the floating body, and the relationship between the floating body cost, the floating body performance and the floating body safety can be systematically evaluated, so as to quickly form an overall design plan of the floating body in the design stage of the wind turbine floating body, and the floating body variables can be flexibly adjusted according to actual needs to solve the low optimization efficiency of the main scale of the floating body, thereby improving the optimization efficiency of the main scale of the floating body and improving the design accuracy of the main scale of the floating body.

[0049] In an exemplary embodiment, before generating an initial population based on the float variables required to calculate the wind turbine float cost, it is further possible to obtain the pre-set float variables, wherein the float variables satisfy a preset variable range; the float variables include at least one of the following: column spacing, column diameter, and draft depth.

[0050] The preset variable range can be set manually in advance.

[0051] Optionally, the column spacing is set to belong to a preset spacing range, and the preset spacing range represents the spacing when the wind turbine float remains stable. The column diameter is set to belong to a preset diameter range. Alternatively, the draft is set to belong to a preset depth range.

[0052] like Figure 4 In the three-column floating body shown, the column spacing of the floating body represents the spacing between adjacent columns, the column diameter represents the diameter of the circle of the bottom surface of each column, and the draft depth represents the depth of the column buried underwater.

[0053] In an exemplary embodiment, the implementation process of generating a hydrodynamic calculation model of a wind turbine float according to the structural model corresponding to the wind turbine float and the population parameters in the initial population in step S204 specifically includes: obtaining the floating body parameters of the wind turbine float from the population parameters in the initial population, and converting the floating body construction parameters and the population parameters of the structural model from the current format to the model format using a self-compiled program, and obtaining a model file for generating the hydrodynamic calculation model according to the converted floating body construction parameters and the population parameters, wherein the model format is a format supported by the hydrodynamic calculation model; and generating the hydrodynamic calculation model by running the model file.

[0054] Optionally, the structural model is, for example, an ANSYS structural model. The ANSYS model represents a model for analyzing structural strength, stiffness, stability and other performance. In this embodiment, a set of population parameters corresponding to each population individual can be used to generate a specific ANSYS structural model to describe the geometric shape and structural composition of the floating body corresponding to the population individual.

[0055] Optionally, the above hydrodynamic calculation model is, for example, an AQWA hydrodynamic calculation model, and AQWA represents a model for calculating the dynamic response data and load data of a floating body under marine environmental conditions such as waves and currents. Parameters such as the weight and center of gravity of the floating body directly affect the buoyancy, stability and interaction with the water flow of the floating body, and are important scale parameters. The geometric information of the ANSYS model (i.e., the above-mentioned floating body structural parameters) and the weight, center of gravity and other data obtained from the population parameters can be converted into a format that can be processed by AQWA using a self-compiled program to obtain an input file for generating an AQWA model, and the AQWA model generated by the input file is used to perform hydrodynamic calculations to obtain the response data of the floating body under different environmental loads, such as displacement, velocity, acceleration, force and torque, etc.

[0056] Optionally, in the above steps, the API or scripting language of ANSYS and AQWA can be used to automatically generate the ANSYS structural model and the AQWA hydrodynamic calculation model, and automatically perform the calculations, thereby improving the degree of automation, reducing manual intervention, speeding up the optimization process, and ensuring data consistency and accuracy.

[0057] In this embodiment, the above calculation results of displacement, velocity, acceleration, force and torque can be further fed back to the genetic algorithm to evaluate the fitness of the individual, and then the next individual is obtained by iterating according to the fitness cycle. In this process, through the intelligent search of the genetic algorithm and the professional computing power of ANSYS and AQWA, a large number of design variable combinations can be explored in a short time, so as to quickly find the optimal or nearly optimal floating body design solution. This highly integrated and automated optimization cycle method fully utilizes the advantages of modern software tools and optimization algorithms, and greatly improves the efficiency of floating body optimization design.

[0058] In an exemplary embodiment, a specific implementation scheme for iteratively optimizing the initial population according to the response data using a preset optimization algorithm to obtain a target population may include: using a genetic algorithm to evaluate the multiple population individuals, generating new population individuals based on the multiple population individuals, wherein each population individual has a fitness, and the fitness is determined based on the response data; iteratively selecting, crossover and mutation operations are performed on the new population individuals in sequence until the optimization conditions are met to obtain a target individual, and obtaining the target population based on the target individual.

[0059] Optionally, for the above-mentioned process of using the hydrodynamic calculation model to perform frequency domain calculation on the individuals of the population, the response data of the floating body to waves and water currents within a specific frequency range can be calculated to obtain the displacement, velocity, acceleration, force and torque and other parameters of the floating body, which are used as quantitative indicators of the hydrodynamic performance of the floating body. Among them, displacement represents the displacement of the floating body in the horizontal and vertical directions, reflecting its stability and dynamic performance. Velocity and acceleration are the derivatives of displacement, which are used to evaluate the velocity and acceleration characteristics of the dynamic response of the floating body. Forces and moments are the torques generated by wave forces, water flow forces, wind forces and other forces acting on the floating body on the floating body, which are used to evaluate the structural bearing capacity and stability of the floating body.

[0060] The above response data will be used to evaluate the fitness of the individual (i.e., float design) in combination with the fitness function.

[0061] The fitness function is an important tool for evaluating the quality of individuals in genetic algorithms. For wind turbine floating body optimization, the fitness function can comprehensively consider the cost function, the hydrodynamic response index of maximum displacement and maximum acceleration, and multiple performance indicators such as construction difficulty and maintainability to evaluate the economy and safety of the design. Among them, the fitness function can use a weighted summation method or a hierarchical optimization strategy to balance multiple performance indicators to ensure that the optimization result achieves the best balance between cost and performance.

[0062] Furthermore, in the process of obtaining the target population using the genetic algorithm, each individual in the population represents a set of design parameters, which can be regarded as the "genes" of the semi-submersible floating body. The genetic algorithm continuously evolves these individuals (parameter combinations) through selection, crossover and mutation operations to find the optimal solution. This embodiment uses a genetic algorithm to evaluate the fitness of each individual, determines the individual with a high fitness value as the parent, and then repeatedly performs selection, crossover and mutation to gradually optimize the design of the floating body, thereby improving the design efficiency.

[0063] Optionally, the above-mentioned preset optimization algorithm may also include a particle swarm optimization algorithm and a simulated annealing algorithm. The particle swarm optimization algorithm is suitable for problems with continuous search space, while the simulated annealing algorithm is better at avoiding local optimal solutions. When selecting an algorithm, the characteristics of the problem, the nature of the solution, and the complexity of the optimization target should be considered. Perform a comparative analysis to determine which algorithm is more effective in a specific scenario.

[0064] In an exemplary embodiment, a plurality of implementation schemes for generating new population individuals based on the plurality of population individuals are proposed, specifically including: 1. randomly selecting a preset number of first individuals from the initial population, and selecting a second individual from the preset number of first individuals, and determining the second individuals as the new population individuals, wherein the second individuals have the highest fitness; 2. according to a preset crossover probability, crossovering two second individuals to generate a third individual, and determining the third individual as the new population individual; 3. according to a preset mutation probability, mutating each gene of the first individual to obtain a fourth individual, according to the preset crossover probability, crossovering two fourth individuals to generate a fifth individual, and determining the fifth individual as the new population individual.

[0065] In an exemplary embodiment, further, it can be determined in the following manner that the target population satisfies the optimization condition for stopping iterative optimization of the initial population: when it is determined that the iteration time for iterative optimization of the initial population is equal to or greater than a preset optimization time, it is determined that the target population satisfies the optimization condition for stopping iterative optimization of the initial population; when it is determined that the number of iterations for iterative optimization of the initial population is equal to a preset number of times, it is determined that the target population satisfies the optimization condition for stopping iterative optimization of the initial population.

[0066] Optionally, in addition to the number of iterations and fitness changes, it is also possible to determine whether to stop iteration based on the degree of fitness change, computing resource consumption, or a specific cost-performance ratio. For example, when the fitness change is small, the iteration is stopped. When the computing resource consumption reaches a limit, the iteration is stopped. When a specific cost-performance ratio is reached, it is determined whether to stop iteration.

[0067] Obviously, the embodiments described above are only some embodiments of the present application, not all embodiments. In order to better understand the above method, the above process is described below in conjunction with the embodiments, but it is not intended to limit the technical solutions of the embodiments of the present application, specifically:

[0068] In an optional embodiment, the present application proposes a design idea for the main scale of a floating body in the preliminary design stage of a floating body, specifically including: generating an initial population containing a certain number of individuals, each individual representing a combination of a set of design variables (column spacing, column diameter, draft depth). Using a self-compiled program to batch generate ANSYS structural models and AQWA hydrodynamic calculation models, and automatically write the floating body design parameters. Using the AQWA hydrodynamic calculation model to perform hydrodynamic frequency domain calculations, the calculation results of all individuals are obtained. Genetic algorithms are used to evaluate individuals based on the calculation results, retain the best individuals of the previous generation, and generate a new generation of populations. Through an iterative process of repeated selection, crossover and mutation, a population that meets a predetermined number of iterations or convergence conditions is obtained. This design idea combines self-programming with commercial software (such as ANSYS and AQWA) to achieve batch modeling, automatic calculation of hydrodynamic characteristics and optimization of structural costs, and uses genetic algorithms to optimize semi-submersible floating bodies in the preliminary design stage of floating bodies, thereby improving the efficiency and accuracy of determining the main scale of floating bodies.

[0069] Optionally, based on the above ideas, a method for determining the main dimensions of a wind turbine floating body is proposed, and the specific steps are as follows:

[0070] Step 1: Define the optimization target as the floating body cost. For example, you can set a cost reduction of 10% as a target, or specify that the cost does not exceed a certain budget value.

[0071] Step 2: Define design variables such as column spacing, column diameter, and draft. The range of design variables should be determined based on engineering practice and theoretical analysis. For example, column spacing may be constrained by the stability and manufacturability of the floating body, and draft is limited by the marine environment and operational restrictions. The corresponding constraints may include but are not limited to: stability requirements of the floating body under different environmental loads, structural bearing capacity, material performance limitations, construction technology, and cost limitations. Through these constraints, it can be ensured that the optimized floating body is practically feasible.

[0072] Step three, encode the design variables, for example, use a real number to represent each design variable, for example, use a floating point number to represent the length of the mooring line.

[0073] Step 4: Set the cost function: Total cost = steel unit price * steel weight + ballast unit price * ballast weight.

[0074] Step 5: Initialize the population, such as Figure 3In step 1 shown, the initial population can be randomly generated and the population size (such as 100 individuals) can be set. Each individual represents a combination of design variables and can be generated by random sampling. The choice of population size has an important impact on the optimization results. The population size is usually set to 100-500 to maintain sufficient exploration and diversity.

[0075] Step 6: Perform iterative optimization process, as follows Figure 3 Steps 2-10 shown:

[0076] Step 2, population assessment;

[0077] In this step, the evaluation process can be realized by combining the design ideas of the main scale of the above-mentioned floating body. That is, the ANSYS structural model and the AQWA hydrodynamic calculation model are generated in batches using a self-compiled program, and then the floating body design parameters such as the floating body weight, the floating body center of gravity and the moment of inertia of the initial population are automatically written into the AQWA hydrodynamic calculation model. Then, different environmental conditions are set, and the AQWA hydrodynamic calculation model is used to perform hydrodynamic frequency domain calculations to obtain individual calculation results, evaluate the individuals based on the calculation results, and calculate the cost of the floating body based on the evaluation results. On the basis of meeting the requirements of the engineering structure, a floating body with the lowest possible cost is obtained.

[0078] Optionally, in this step, more constraints can be set, as follows:

[0079] (1) The heave motion period is greater than the set value.

[0080] (2) The pitch and roll motion periods are greater than the set values.

[0081] (3) Stability meets requirements.

[0082] (4) The displacement of the floating body is less than the set value.

[0083] (5) If the draft of the floating body is less than the set value and any constraint condition is violated, the optimization will continue.

[0084] Step 3, sort the individuals by fitness;

[0085] Step 4, randomly select the initial individual;

[0086] Step 5, crossover or mutation processing is performed on the initial individuals;

[0087] Step 6, obtaining the offspring population;

[0088] Among them, for step 4, for example, several individuals can be randomly selected, and the one with the best fitness can be selected as the parent. For step 5, two parent individuals can be crossed according to the set crossover probability to generate new offspring individuals to simulate the binary crossover process. It is also possible to mutate each gene of the individual according to the set mutation probability to introduce diversity. The selection of parameters such as crossover probability and mutation probability has an important influence on the optimization results. The crossover probability and mutation probability can be set to 0.8-0.9 and 0.1-0.2 respectively.

[0089] Step 7, merge the best individual in the parent generation with the offspring;

[0090] Step 8, obtain a new parent population;

[0091] Step 9, number of iterations + 1;

[0092] Step 10, determine whether the number of iterations is greater than the maximum number of iterations, if so, end, otherwise continue to step 2.

[0093] By using the above steps, iterative calculation can be achieved by repeated selection, crossover and mutation until the predetermined number of iterations and other conditions are reached. Thus, the main scale of the floating body of the semi-submersible wind turbine is optimized using the genetic algorithm, which improves the accuracy of the main scale of the floating body and improves the optimization efficiency of the main scale of the floating body.

[0094] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0095] In this embodiment, a device for determining the main dimensions of a wind turbine floating body is also provided, and the device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made are not repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0096] Figure 5 : is a structural block diagram of a device for determining the main dimensions of a wind turbine floating body according to an embodiment of the present application, the device comprising:

[0097] A first generating module 502 is used to generate an initial population according to the floating body variables required for calculating the floating body cost of the wind turbine, wherein the initial population includes a plurality of population individuals, each of which represents a set of floating body variables;

[0098] A second generation module 504 is used to generate a hydrodynamic calculation model of the wind turbine floating body according to a structural model corresponding to the wind turbine floating body and population parameters in the initial population, wherein the structural model is generated based on the floating body variables, and the structural model is used to represent the floating body structure of the wind turbine floating body;

[0099] A first obtaining module 506 is used to perform frequency domain calculation on the population individuals using the hydrodynamic calculation model to obtain response data of the wind turbine floating body under different environments;

[0100] The second obtaining module 508 is used to iteratively optimize the initial population according to the response data using a preset optimization algorithm to obtain a target population, and use a cost function to calculate a target floating body cost corresponding to the target population, wherein the target population satisfies an optimization condition for stopping iterative optimization of the initial population;

[0101] The determination module 510 is configured to determine the wind turbine floating body main dimension based on the target group floating body variables of the target population when it is determined that the target floating body cost is less than a floating body cost threshold.

[0102] Through the above-mentioned device, an initial population is generated according to the floating body variables required for calculating the cost of the wind turbine floating body, wherein the initial population includes multiple population individuals, and each population individual represents a set of floating body variables; a hydrodynamic calculation model of the wind turbine floating body is generated according to the structural model corresponding to the wind turbine floating body and the population parameters in the initial population, wherein the structural model is generated based on the floating body variables, and the structural model is used to represent the floating body structure of the wind turbine floating body; the hydrodynamic calculation model is used to perform frequency domain calculation on the population individuals to obtain response data of the wind turbine floating body under different environments; a preset optimization algorithm is used to iteratively optimize the initial population according to the response data to obtain a target population, and a cost function is used to calculate the target population. The target floating body cost corresponding to the target population, wherein the target population satisfies the optimization condition for stopping the iterative optimization of the initial population; when it is determined that the target floating body cost is less than the floating body cost threshold, the main scale of the wind turbine floating body is determined based on the target group floating body variables of the target population, so that the optimal design parameters of the floating body structure can be determined quickly and accurately without sacrificing the safety of the floating body, and the relationship between the floating body cost, the floating body performance and the floating body safety can be systematically evaluated, so as to quickly form an overall design plan of the floating body in the design stage of the wind turbine floating body, and the floating body variables can be flexibly adjusted according to actual needs to solve the low optimization efficiency of the main scale of the floating body, thereby improving the optimization efficiency of the main scale of the floating body and improving the design accuracy of the main scale of the floating body.

[0103] In an exemplary embodiment, the first generation module is also used to obtain the pre-set floating body variables before generating the initial population according to the floating body variables required to calculate the wind turbine floating body cost, wherein the floating body variables satisfy the preset variable range; the floating body variables include at least one of the following: column spacing, column diameter, and draft depth.

[0104] In an exemplary embodiment, the second generation module is also used to: obtain the floating body parameters of the wind turbine float from the population parameters in the initial population, and use a self-compiled program to convert the floating body construction parameters and the population parameters of the structural model from the current format to the model format, and obtain a model file for generating the hydrodynamic calculation model based on the converted floating body construction parameters and the population parameters, wherein the model format is a format supported by the hydrodynamic calculation model; and generate the hydrodynamic calculation model by running the model file.

[0105] In an exemplary embodiment, the second obtaining module is also used to: use a genetic algorithm to evaluate the multiple population individuals, generate new population individuals based on the multiple population individuals, wherein each population individual has a fitness, and the fitness is determined based on the response data; sequentially perform iterative selection, crossover and mutation operations on the new population individuals until the optimization conditions are met to obtain a target individual, and obtain the target population based on the target individual.

[0106] In an exemplary embodiment, the second obtaining module is also used to: randomly select a preset number of first individuals from the initial population, and select second individuals from the preset number of first individuals, and determine the second individuals as the new population individuals, wherein the second individuals have the highest fitness; cross two second individuals according to a preset crossover probability to generate a third individual, and determine the third individual as the new population individual; mutate each gene of the first individual according to a preset mutation probability to obtain a fourth individual, cross two fourth individuals according to the preset crossover probability to generate a fifth individual, and determine the fifth individual as the new population individual.

[0107] In an exemplary embodiment, the determination module is further used to determine whether the target population satisfies the optimization condition for stopping iterative optimization of the initial population in the following manner: when it is determined that the iteration time for iterative optimization of the initial population is equal to or greater than a preset optimization time, it is determined that the target population satisfies the optimization condition for stopping iterative optimization of the initial population; when it is determined that the number of iterations for iterative optimization of the initial population is equal to a preset number of times, it is determined that the target population satisfies the optimization condition for stopping iterative optimization of the initial population.

[0108] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when run.

[0109] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0110] S1, generating an initial population according to the floating body variables required for calculating the floating body cost of the wind turbine, wherein the initial population includes a plurality of population individuals, each of which represents a set of floating body variables;

[0111] S2, generating a hydrodynamic calculation model of the wind turbine floating body according to a structural model corresponding to the wind turbine floating body and population parameters in the initial population, wherein the structural model is generated based on the floating body variables, and the structural model is used to represent the floating body structure of the wind turbine floating body;

[0112] S3, using the hydrodynamic calculation model to perform frequency domain calculation on the individuals of the population to obtain response data of the wind turbine floating body under different environments;

[0113] S4, using a preset optimization algorithm to iteratively optimize the initial population according to the response data to obtain a target population, and using a cost function to calculate a target floating body cost corresponding to the target population, wherein the target population satisfies an optimization condition for stopping iterative optimization of the initial population;

[0114] S5, when it is determined that the target floating body cost is less than a floating body cost threshold, determining the wind turbine floating body main dimension based on the target group floating body variables of the target population.

[0115] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0116] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0117] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0118] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0119] S1, generating an initial population according to the floating body variables required for calculating the floating body cost of the wind turbine, wherein the initial population includes a plurality of population individuals, each of which represents a set of floating body variables;

[0120] S2, generating a hydrodynamic calculation model of the wind turbine floating body according to a structural model corresponding to the wind turbine floating body and population parameters in the initial population, wherein the structural model is generated based on the floating body variables, and the structural model is used to represent the floating body structure of the wind turbine floating body;

[0121] S3, using the hydrodynamic calculation model to perform frequency domain calculation on the individuals of the population to obtain response data of the wind turbine floating body under different environments;

[0122] S4, using a preset optimization algorithm to iteratively optimize the initial population according to the response data to obtain a target population, and using a cost function to calculate a target floating body cost corresponding to the target population, wherein the target population satisfies an optimization condition for stopping iterative optimization of the initial population;

[0123] S5, when it is determined that the target floating body cost is less than a floating body cost threshold, determining the wind turbine floating body main dimension based on the target group floating body variables of the target population.

[0124] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0125] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0126] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0127] An embodiment of the present application also provides a computer program, which includes computer instructions stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in any one of the above method embodiments.

[0128] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0129] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0130] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining the main dimensions of a wind turbine floating body, characterized in that: include: Generate an initial population according to the floating body variables required for calculating the floating body cost of the wind turbine, wherein the initial population includes a plurality of population individuals, each of which represents a set of floating body variables; Generate a hydrodynamic calculation model of the wind turbine float according to the structural model corresponding to the wind turbine float and the population parameters in the initial population, wherein the structural model is generated based on the float variables, and the structural model is used to represent the float structure of the wind turbine float; Using the hydrodynamic calculation model to perform frequency domain calculation on the individuals of the population, to obtain response data of the wind turbine floating body under different environments; Iteratively optimizing the initial population according to the response data using a preset optimization algorithm to obtain a target population, and using a cost function to calculate a target floating body cost corresponding to the target population, wherein the target population satisfies an optimization condition for stopping iterative optimization of the initial population; When it is determined that the target floating body cost is less than a floating body cost threshold, the wind turbine floating body main dimension is determined based on the target group floating body variables of the target population.

2. The method according to claim 1, characterized in that Before generating an initial population according to the floating body variables required for calculating the floating body cost of the wind turbine, the method further includes: Acquire the preset floating body variable, wherein the floating body variable satisfies a preset variable range; The floating body variables include at least one of the following: column spacing, column diameter, and draft depth.

3. The method according to claim 1, characterized in that Generating a hydrodynamic calculation model of the wind turbine floating body according to a structural model corresponding to the wind turbine floating body and population parameters in the initial population includes: Acquire the floating body parameters of the wind turbine floating body from the population parameters in the initial population, and convert the floating body construction parameters and the population parameters of the structural model from the current format to the model format using a self-compiled program, and obtain a model file for generating the hydrodynamic calculation model according to the converted floating body construction parameters and the population parameters, wherein the model format is a format supported by the hydrodynamic calculation model; The hydrodynamic calculation model is generated by running the model file.

4. The method according to claim 1, characterized in that Iteratively optimizing the initial population according to the response data using a preset optimization algorithm to obtain a target population includes: Using a genetic algorithm to evaluate the plurality of population individuals, and generating new population individuals based on the plurality of population individuals, wherein each of the population individuals has a fitness, and the fitness is determined based on the response data; Iterative selection, crossover and mutation operations are performed on the new population individuals in turn until the optimization condition is met to obtain a target individual, and the target population is obtained based on the target individual.

5. The method according to claim 4, characterized in that Generating a new population individual according to the plurality of population individuals comprises at least one of the following: Randomly selecting a preset number of first individuals from the initial population, and selecting a second individual from the preset number of first individuals, and determining the second individuals as the new population individuals, wherein the second individuals have the highest fitness; According to a preset crossover probability, crossover is performed on the two second individuals to generate a third individual, and the third individual is determined as the new population individual; According to a preset mutation probability, each gene of the first individual is mutated to obtain a fourth individual, and according to the preset crossover probability, two fourth individuals are crossed to generate a fifth individual, and the fifth individual is determined as the new population individual.

6. The method according to claim 1, characterized in that Determine that the target population satisfies the optimization condition for stopping iterative optimization of the initial population by the following method: When it is determined that the iterative optimization time for the initial population is equal to or greater than the preset optimization time, determining that the target population satisfies the optimization condition for stopping the iterative optimization for the initial population; When it is determined that the number of iterations of iterative optimization for the initial population is equal to a preset number of times, it is determined that the target population meets the optimization condition for stopping iterative optimization for the initial population.

7. A device for determining the main dimensions of a wind turbine floating body, characterized in that: include: A first generating module is used to generate an initial population according to the floating body variables required for calculating the floating body cost of the wind turbine, wherein the initial population includes a plurality of population individuals, each of which represents a group of floating body variables; A second generation module is used to generate a hydrodynamic calculation model of the wind turbine floating body according to a structural model corresponding to the wind turbine floating body and population parameters in the initial population, wherein the structural model is generated based on the floating body variables, and the structural model is used to represent the floating body structure of the wind turbine floating body; A first obtaining module is used to perform frequency domain calculation on the population individuals using the hydrodynamic calculation model to obtain response data of the wind turbine floating body under different environments; The second obtaining module is used to use a preset optimization algorithm to iteratively optimize the initial population according to the response data to obtain a target population, and use a cost function to calculate a target floating body cost corresponding to the target population, wherein the target population satisfies an optimization condition for stopping iterative optimization of the initial population; The determination module is configured to determine the wind turbine floating body main dimension based on the target group floating body variables of the target population when it is determined that the target floating body cost is less than a floating body cost threshold.

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

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

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

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