Method and system for optimizing dynamic cable slow wave structure of medium-shallow water depth large floating fan based on particle swarm optimization

Abstract

The invention provides a method and a system for optimizing a slow wave structure of a dynamic cable of a medium-shallow water depth large floating fan based on a particle swarm algorithm, and the method and the system are used for completing the optimization of the lengths of a balance weight section, a buoy section, a suspension section and the like of the slow wave dynamic cable by combining a numerical model and the particle swarm algorithm. A numerical model is established based on ocean engineering dynamics analysis software, initial conditions such as cable size, ocean current speed, wave elements, floating platform motion and cable accessory parameters are set in the model, a dynamic cable slow wave structural form is continuously adjusted by using a particle swarm algorithm, and the length of each section of the dynamic cable is changed; performing static analysis and dynamic analysis on the dynamic cables with different slow waveforms through a numerical model, analyzing and checking a calculation result until the maximum effective tension and the maximum curvature of the dynamic cable of the floating platform meet the design requirements, and combining the economic index requirements to obtain the dynamic cable of the floating platform. And a proper slow waveform structure is provided for the dynamic cable of the medium-shallow water depth large floating type fan.

Description

Technical Field

[0001] The present invention belongs to the technical field of offshore wind power generation, and relates to a method and system for optimizing a dynamic cable wave-slowing structure of a large floating wind turbine in medium and shallow waters, and in particular to a method for optimizing a dynamic cable wave-slowing structure of a large floating wind turbine in medium and shallow waters based on a particle swarm algorithm. Background Art

[0002] As offshore wind power continues to develop in deep seas, offshore floating wind turbines present great development potential. However, the development of offshore floating wind turbines still faces severe technical challenges. One of the key challenges is to ensure the reliability and integrity of transmission cables. Dynamic cables are usually connected to the upper floating platform at one end and to the underwater booster station or buried under the seabed at the other end, while the middle section is suspended in the seawater. They are greatly affected by the dynamic loads in the marine environment, and the upper floating platform also moves under the environmental loads, making the mechanical properties of the cables more complex. Dynamic cables are more prone to fatigue failure due to the dynamic tension, bending and torsion loads caused by platform movement, as well as environmental loads of waves, wind and currents. Therefore, it is crucial to optimize the structural form of the dynamic cable of floating wind turbines and adjust the line shape of the dynamic cable to better withstand the complex marine environment for the development of floating wind power.

[0003] Medium and shallow waters require higher adaptability of dynamic cable linear structures, and dynamic cable damage is more likely to occur. However, there is little research on the design of dynamic cable structures for large floating wind turbines in medium and shallow waters, and the marine environment is complex and changeable. The marine environment of different projects requires separate linear designs for floating dynamic cables. Therefore, a universally applicable method for optimizing dynamic cable structures is needed to better design dynamic cable structures.

[0004] However, the traditional slow-wave dynamic cables currently used cannot adapt well to existing working conditions and usually require the addition of a certain amount of counterweight. The research on optimization methods for slow-wave dynamic cables at home and abroad now mostly considers deepwater conditions and is limited to traditional slow-wave structures. The influence of counterweights has not been considered, making it difficult to adapt to medium and shallow water conditions.

[0005] Based on the above background, there is an urgent need for an optimization method that can accurately and quickly design dynamic cable slow-wave structures suitable for large floating wind turbines in medium and shallow water depths. Summary of the invention

[0006] In order to solve the above problems, the present invention provides a method and system for optimizing the dynamic cable wave-slowing structure of large floating wind turbines in medium and shallow waters based on a particle swarm algorithm. The technical solution adopted by the present invention is as follows:

[0007] An optimization method for the slow-wave structure of the dynamic cable of a large floating wind turbine in medium and shallow water depths based on the particle swarm optimization algorithm, comprising the following steps:

[0008] Build a numerical model of the dynamic cable using marine engineering dynamics analysis software;

[0009] Based on the design requirement that the effective tension of the dynamic cable of the large floating wind turbine in medium and shallow water depths under all working conditions is less than the allowable tension, utilize the load characteristics of the dynamic cable under extreme working conditions, and determine the effective tension fitness function according to the relationship between the gravity, buoyancy, hydrostatic pressure and effective stress of the dynamic cable;

[0010] Based on the design requirement that the bending curvature of the dynamic cable of the large floating wind turbine in medium and shallow water depths under all working conditions is less than the allowable curvature, utilize the load characteristics of the dynamic cable under extreme working conditions, and determine the bending curvature fitness function according to the relationship between the bending moment and curvature of the dynamic cable;

[0011] Based on the lengths of the counterweight section, buoy section and suspension section of the dynamic cable, and combined with the cost of each section of the dynamic cable, determine the fitness functions for the appropriate lengths of the buoy section and counterweight section;

[0012] According to the numerical model, effective tension fitness function, bending curvature fitness function, and fitness functions for the lengths of the buoy section and counterweight section, use the particle swarm optimization algorithm to optimize the lengths of each section of the dynamic cable to obtain the layout scheme of the slow-wave structure of the dynamic cable of the large floating wind turbine in medium and shallow water depths.

[0013] Further, the input parameters in the numerical model include dynamic cable parameters, buoy and counterweight ring parameters, seawater depth, sea current velocity, wave elements, and floating platform motion parameters, and the output parameters are the maximum effective tension and maximum bending curvature.

[0014] Further, the numerical analysis methods in the numerical model are the static method and the lumped mass method.

[0015] Further, the dynamic cable in the numerical model satisfies the motion equation:

[0016] M(p,a)+C(p,v)+K(p)=F(p,v,t),

[0017] where M(p,a) is the system inertial load; C(p,v) is the damping load; K(p) is the stiffness load; F(p,v,t) is the external load; p is the position of the dynamic cable, v and a are the velocity and acceleration vectors of the dynamic cable respectively, and t is the simulation time length of the numerical model;

[0018] The extended form of the Morison equation is used in the numerical model to calculate the hydrodynamic load on the dynamic cable, and the extended form of the Morison equation is:

[0019]

[0020] Among them, f is the hydrodynamic force acting on the object, C m is the inertia coefficient of the object, Δ is the fluid mass of the object displacement, a f is the acceleration of the fluid relative to the earth, C a is the added mass coefficient of the object, a b is the acceleration of the object relative to the earth, ρ is the density of water, C d is the drag coefficient of the object, A is the cross-sectional area of the object, u r is the fluid velocity relative to the object.

[0021] Furthermore, the effective tension fitness function is:

[0022]

[0023] Among them, f t is the value of the dynamic cable effective tension fitness evaluation function, T max is the maximum effective tension of the dynamic cable, and T is the allowable tension of the dynamic cable;

[0024] The bending curvature fitness function is:

[0025]

[0026] Among them, fκ is the value of the dynamic cable bending curvature fitness evaluation function, κ max is the maximum bending curvature of the dynamic cable, and κ is the allowable curvature of the dynamic cable;

[0027] The buoy section length and counterweight section length fitness function is:

[0028]

[0029] Among them, f b is the value of the dynamic cable buoy section length fitness evaluation function, f B is the value of the dynamic cable counterweight section length fitness evaluation function, l b is the length of the dynamic cable buoy section, l B is the length of the dynamic cable counterweight section, and L is the total length of the dynamic cable.

[0030] Furthermore, according to the numerical model, the effective tension fitness function, the bending curvature fitness function, and the buoy section length and counterweight section length fitness function, the particle swarm optimization algorithm is used to optimize the lengths of each section of the dynamic cable, and a layout scheme of the slow-wave structure of the dynamic cable for large floating wind turbines in medium and shallow waters is obtained. The specific steps include:

[0031] Initialize the particle swarm parameters, as well as the positions and velocities of each particle. The positions of the particles include the lengths of each section of the dynamic cable.

[0032] For each particle, perform static analysis and dynamic analysis using a numerical model based on its position, and calculate the maximum tension and maximum curvature of the dynamic cable. Calculate the fitness value using the maximum tension and maximum curvature of the dynamic cable and the lengths of each section of the dynamic cable: In the formula, f is the fitness value of the slow-wave line type of the dynamic cable, and α, β, and ω are the weights of the tension fitness function, the curvature fitness function, and the length fitness function of each section of the dynamic cable, respectively.

[0033] For each particle, if its current fitness value is better than its individual historical best fitness value, update its individual historical best position and fitness value.

[0034] Traverse all particles. If the fitness value of a certain particle is better than the group historical best fitness value, update the group historical best position and fitness value.

[0035] Update the velocity and position of each particle according to the inertia weight, learning factor, individual historical best, and group historical best information.

[0036] Perform multiple iterations until the change in the group historical best fitness value between two times is less than the set tolerance or the maximum number of iterations is reached.

[0037] Obtain the optimal combination of the lengths of each section of the cable from the group historical best position to obtain the layout scheme of the slow-wave structure of the dynamic cable for large floating wind turbines in medium and shallow waters.

[0038] Furthermore, the particle swarm parameters include population size, inertia weight, learning factor, total number of iterations, and fitness value tolerance.

[0039] An optimization system for the slow-wave structure of the dynamic cable of a large floating wind turbine in medium and shallow waters based on the particle swarm algorithm includes:

[0040] One or more processors;

[0041] A memory for storing one or more programs;

[0042] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned optimization method for the slow-wave structure of the dynamic cable of a large floating wind turbine in medium and shallow waters based on the particle swarm algorithm.

[0043] A computer-readable storage medium storing computer instructions, which, when executed by one or more processors, cause the one or more processors to execute the steps in the above method.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0045] By combining a numerical model and a particle swarm algorithm, the present invention optimizes the lengths of the counterweight section, buoy section, bottom-lying section, and suspension section of the slow-wave dynamic cable for large floating wind turbines in medium and shallow water depths. A numerical model is established through marine engineering dynamics analysis software, and the particle swarm algorithm is used to continuously adjust the slow-wave structure form of the dynamic cable. An fitness evaluation function is constructed according to the design requirements and economy of the dynamic cable, and an optimization design method considering inertia weight and learning factor is introduced. An optimization design process for the lengths of the buoy section, counterweight section, and cable section of the slow-wave dynamic cable is proposed, providing an optimization method for the structure design of the slow-wave dynamic cable for large floating wind turbines in medium and shallow water depths. The present invention takes into account the specific environmental factors of large floating wind turbines in medium and shallow water depths. Compared with traditional slow-wave dynamic cables, the influence of the counterweight section is additionally considered. For different load conditions and different specifications of dynamic cables in medium and shallow water depth environments, the structure design of the slow-wave dynamic cable can be optimized, the lengths of each section of the slow-wave dynamic cable can be adjusted, the cost can be reduced, the tension and curvature of the slow-wave dynamic cable can be optimized, and the optimization efficiency is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is the flow chart of the optimization algorithm in the embodiment of the present invention.

[0047] Figure 2 is the schematic diagram of the slow-wave dynamic cable structure in the embodiment of the present invention.

[0048] Figure 3 is the implementation logic diagram of the particle swarm algorithm in the embodiment of the present invention.

[0049] Figure 4 is the comparison chart of the maximum effective tension along the dynamic cable before and after optimization in the embodiment of the present invention.

[0050] Figure 5 is the comparison chart of the maximum bending curvature along the dynamic cable before and after optimization in the embodiment of the present invention.

[0051] Figure 6 is the comparison chart of the lengths of each section of the dynamic cable before and after optimization in the embodiment of the present invention.

[0052] Figure 7 is the design drawing of the slow-wave dynamic cable after optimization in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] The technical solutions of the present invention will be further described below in conjunction with the drawings and specific embodiments, but the protection scope of the present invention is not limited to the described embodiments.

[0054] Figure 1It is a schematic flow chart of an optimization method for the slow-wave structure of a dynamic cable of a large floating wind turbine in medium and shallow water depths based on the particle swarm algorithm. The specific implementation steps of this method are as follows:

[0055] Step 1. Establish a numerical model in the marine engineering dynamics analysis software OrcaFlex. Set the dynamic cable parameters, buoy and counterweight ring parameters, seawater depth, sea current velocity, wave elements, and floating platform motion parameters in the numerical model. Divide the slow-wave dynamic cable into a counterweight section, a suspension section, and a buoy section. The slow-wave structure of the dynamic cable is as Figure 2 shown.

[0056] The numerical model performs numerical analysis on the dynamic cable through the quasi-static method and the lumped mass method. The quasi-static method assumes that it remains basically stationary in a short time, that is, no obvious dynamic motion occurs; the lumped mass method divides the dynamic cable into a series of massless line segments, which are connected by nodes. The straight part only has axial and torsional characteristics, and the nodes have the properties of the mass, gravity, and buoyancy of the anchor chain.

[0057] The dynamic cable satisfies the motion equation:

[0058] M(p,a)+C(p,v)+K(p)=F(p,v,t)

[0059] where: M(p,a) is the system inertial load; C(p,v) is the damping load; K(p) is the stiffness load; F(p,v,t) is the external load; p is the position of the dynamic cable, v and a are the velocity and acceleration vectors of the dynamic cable respectively, and t is the simulation time length of the numerical model.

[0060] For the hydrodynamic load on the dynamic cable, the extended form of the Morison equation is used to calculate the wave load on the line model element. The extended form of the Morison equation in Orcaflex software is:

[0061]

[0062] where, f is the hydrodynamic force (per unit length) acting on the object, C m is the inertial coefficient of the object, Δ is the fluid mass of the object displacement, a f is the acceleration of the fluid relative to the earth, C a is the added mass coefficient of the object, a b is the acceleration of the object relative to the earth, ρ is the density of water, C d is the drag coefficient of the object, A is the cross-sectional area of the object, u r is the fluid velocity relative to the object.

[0063] Step 2. Based on the load characteristics of the dynamic cable of large floating wind turbines in medium and shallow waters under all working conditions, according to the relationship between the gravity, buoyancy, hydrostatic pressure and effective stress of the dynamic cable, the effective tension of the dynamic cable is calculated through a numerical model. The formula for the effective tension is as follows:

[0064] T e = T w +(1 - 2v)p0A0 + EAe(dL / dt) / L0,

[0065] In the formula, T e is the effective tension, T w is the wall thickness tension, T w = EAε, v is the Poisson's ratio, p0 is the external pressure, A0 is the cross-sectional area of the dynamic cable, EA is the axial stiffness, e is the damping coefficient of the dynamic cable, dL / dt is the length increase rate, L0 is the initial length of the dynamic cable unit, and ε is the total average axial strain.

[0066] The maximum effective tension of the dynamic cable is the maximum value of the effective tension within the length range from the suspension point to the anchoring point of the dynamic cable during the calculation time of the simulation conditions. Select the maximum effective tension T max of the dynamic cable. Then, the fitness evaluation function of the effective tension of the dynamic cable is where f t is the fitness evaluation function value of the effective tension of the dynamic cable, and T is the allowable tension of the dynamic cable.

[0067] Step 3. Based on the load characteristics of the dynamic cable of large floating wind turbines in medium and shallow waters under all working conditions, according to the bending stiffness and the moment of the dynamic cable, the bending curvature of the dynamic cable is calculated through software. The formula for the bending curvature of the dynamic cable is as follows:

[0068] M = EIκ+(λ t / 100)D c ·dκ / dt,

[0069] In the formula, M is the bending moment of the dynamic cable, κ is the bending curvature of the dynamic cable, EI is the flexural rigidity of the dynamic cable, λ t is the target bending damping, D c is the bending critical damping value, and dκ / dt is the curvature change rate.

[0070] The maximum bending curvature of the dynamic cable is the maximum value of the bending curvature within the length range from the suspension point to the anchoring point of the dynamic cable during the calculation time of the simulation conditions. Select the maximum bending curvature κ max of the dynamic cable. Then, the fitness evaluation function of the bending curvature of the dynamic cable is f κ is the fitness evaluation function value of the bending curvature of the dynamic cable, and κ is the allowable curvature of the dynamic cable.

[0071] Step 4. Based on the lengths of the floating wind turbine's slow-wave structure dynamic cable's counterweight section, buoy section, and suspension section, and combining the cost of each section of the dynamic cable, determine the fitness functions for the appropriate lengths of the buoy section and the counterweight section:

[0072]

[0073] In the formula, f b is the fitness evaluation function value of the buoy section length of the dynamic cable, f B is the fitness evaluation function value of the counterweight section length of the dynamic cable, l b is the length of the buoy section of the dynamic cable, l B is the length of the counterweight section of the dynamic cable, and L is the total length of the dynamic cable.

[0074] Step 5. Determine the implementation logic and control parameters for optimizing the particle swarm algorithm. The real-time logic of the particle swarm algorithm is as Figure 3 shown, including the selection of the fitness evaluation function weight, the update and iteration of the optimization variables for each section length of the dynamic cable; the control parameters include the population size (set to 100), the inertia weight (set to 0.5), the learning factors (set to 0.5, 0.5), the total number of iterations (set to 1000), and the fitness value tolerance.

[0075] The update formula of the particle swarm algorithm is as follows:

[0076] ν i,j = wν i-1,j + c1r1(pbest i - x i-1,j ) + c2r2(gbest - x i-1,j )

[0077] x i,j = x i-1,j + ν i-1,j

[0078] Among them, v is the particle velocity, x is the particle position, i = 1, 2,..., m, m is the particle swarm size, j = 1, 2,..., D, D is the particle swarm dimension; w is the inertia weight, representing the degree of trust in the current velocity direction; c1, c2 are the learning factors, used to adjust the maximum learning step; r l , r2 are two random values in [0, 1], used to increase the search randomness; pbest i is the individual extreme value of the i-th particle, and gbest is the global extreme value of the particle.

[0079] Optimize the lengths of each section of the slow-wave dynamic cable using the particle swarm algorithm. By considering the individual and social attributes of each particle through its inertial weight and learning factor, continuously update the velocity and position of each particle, search for the optimal solution of the particle, and calculate the fitness value of each particle until the change in the values of the two fitness evaluation functions is less than the set tolerance or the maximum number of iterations is reached, thus completing the optimization of the lengths of each section of the slow-wave dynamic cable. Specifically:

[0080] Initialize the position and velocity of each particle, where the position of the particle includes the lengths of each section of the dynamic cable;

[0081] For each particle, perform static and dynamic analyses using the numerical model based on its position, calculate the maximum tension and maximum curvature of the dynamic cable, and use Python to check the calculation results of the numerical model to ensure that the maximum effective tension and maximum bending curvature of the dynamic cable meet the design requirements (Tmax is less than 595 kN, κ max is less than 0.455); calculate the fitness value using the maximum tension and maximum curvature of the dynamic cable and the lengths of each section of the dynamic cable:

[0082]

[0083] In the formula, f is the fitness value of the slow-wave line type of the dynamic cable, and α, β, and ω are the weights of the tension fitness function, curvature fitness function, and length fitness function of each section of the dynamic cable, respectively;

[0084] For each particle, if its current fitness value is better than its individual historical best fitness value, update its individual historical best position and fitness value;

[0085] Traverse all particles. If the fitness value of a certain particle is better than the group historical best fitness value, update the group historical best position and fitness value;

[0086] Update the velocity and position of each particle according to the inertial weight, learning factor, individual historical best, and group historical best information;

[0087] Perform multiple iterations until the change in the group historical best fitness value between two times is less than the set tolerance or the maximum number of iterations is reached;

[0088] Obtain the optimal combination of the lengths of each section of the cable from the group historical best position to get the layout scheme of the slow-wave structure of the dynamic cable for large floating wind turbines in medium and shallow waters.

[0089] Figure 4 This is a comparison chart of the maximum effective tension along the dynamic cable before and after optimization in an embodiment of the present invention. It can be seen that after optimization, the maximum effective tension of the dynamic cable has decreased, which better meets the strength design requirements of the dynamic cable. Figure 5It is a comparison chart of the maximum bending curvature along the dynamic cable before and after optimization. It can be seen that after optimization, the maximum bending curvature of the dynamic cable has decreased, which better meets the strength design requirements of the dynamic cable. Figure 6 It is a comparison chart of the lengths of each section of the dynamic cable before and after optimization. Combining the cost of each section of the dynamic cable, it can be seen that after optimization, the lengths of the buoy section and the counterweight section of the dynamic cable have decreased, and the economic performance of the dynamic cable has been improved.

[0090] Comprehensively Figures 4 - 6 It can be seen that the calculation results of this method are good, and it can well carry out the structural design of the dynamic cable of the floating wind turbine in medium and shallow water depths, optimize the strength and economic indicators of the slow-wave structure of the dynamic cable, and the designed slow-wave structure of the dynamic cable meets the engineering design requirements. Figure 7 The design result of the slow-wave dynamic cable obtained by design can be seen that the design result meets the requirements of conventional engineering structures.

[0091] In summary, the present invention provides an optimization method for the slow-wave structure of the dynamic cable of a large floating wind turbine in medium and shallow water depths based on the particle swarm algorithm. This method establishes a numerical model based on the marine engineering dynamics analysis software OrcaFlex, sets initial conditions such as cable size, sea current velocity, wave elements, floating platform motion, cable accessory parameters, etc. in the model, and uses the particle swarm algorithm to continuously adjust the slow-wave structure form of the dynamic cable, change the lengths of each section of the dynamic cable, and perform static analysis and dynamic analysis on the dynamic cable with different slow-wave forms through OrcFxAPI calling OrcaFlex. Use Python to analyze and check the calculation results to make the maximum effective tension and maximum curvature of the dynamic cable of the floating platform meet the design requirements, and combine the economic index requirements to optimize the slow-wave dynamic cable structure of the large floating wind turbine in medium and shallow water depths.

[0092] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and combinations of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0094] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0096] The above specific embodiments are used to explain and illustrate the present invention, rather than to limit the present invention. Any modifications and changes made to the present invention within the spirit and scope of the claims of the present invention fall within the protection scope of the present invention.

Claims

1. An optimization method for the dynamic cable slow wave structure of large floating wind turbines in medium and shallow water depths based on the particle swarm optimization algorithm, characterized in that, It includes the following steps: Construct a numerical model of the dynamic cable using marine engineering dynamics analysis software; Determine the effective tension fitness function according to the relationship between the gravity, buoyancy, hydrostatic pressure and effective stress of the dynamic cable; Determine the bending curvature fitness function according to the relationship between the bending moment and curvature of the dynamic cable; Based on the lengths of the counterweight section, buoy section and suspension section of the dynamic cable, and combined with the cost of each section of the dynamic cable, determine the fitness functions of the buoy section length and the counterweight section length; According to the numerical model, effective tension fitness function, bending curvature fitness function, and the fitness functions of the buoy section length and the counterweight section length, use the particle swarm algorithm to optimize the lengths of each section of the dynamic cable to obtain the layout scheme of the slow wave structure of the dynamic cable for large floating wind turbines in medium and shallow waters.

2. The optimization method for the slow-wave structure of the dynamic cable of a large floating wind turbine in medium and shallow water depths based on the particle swarm algorithm according to claim 1, characterized in that, The input parameters in the numerical model include dynamic cable parameters, buoy and counterweight ring parameters, sea depth, sea current velocity, wave elements and floating platform motion parameters, and the output parameters are the maximum effective tension and the maximum bending curvature.

3. The optimization method for the dynamic cable slow-wave structure of a large floating wind turbine in medium and shallow water depths based on the particle swarm algorithm according to claim 1, characterized in that, The numerical analysis methods in the numerical model are the static method and the lumped mass method.

4. The optimization method for the dynamic cable slow wave structure of large floating wind turbines in medium and shallow water depths based on the particle swarm algorithm according to claim 1, characterized in that, The dynamic cable in the numerical model satisfies the motion equation: M(p,a)+C(p,v)+K(p)=F(p,v,t), where M(p,a) is the system inertial load; C(p,v) is the damping load, K(p) is the stiffness load; F(p,v,t) is the external load; p is the position of the dynamic cable, v and a are the velocity and acceleration vectors of the dynamic cable respectively, and t is the numerical model simulation time length; In the numerical model, the extended form of the Morison equation is used to calculate the hydrodynamic load on the dynamic cable, and the extended form of the Morison equation is: where f is the fluid force acting on the object, C m is the inertia coefficient of the object, Δ is the fluid mass of the object displacement, a f is the acceleration of the fluid relative to the earth, C a is the added mass coefficient of the object, a b is the acceleration of the object relative to the earth, ρ is the density of water, C d is the drag coefficient of the object, A is the cross-sectional area of the object, u r is the fluid velocity relative to the object.

5. The optimization method of the dynamic cable slow wave structure of large floating wind turbines in medium and shallow water depths based on the particle swarm algorithm according to claim 1, characterized in that, The effective tension fitness function is: Among them, f t is the evaluation function value of the effective tension fitness of the dynamic cable, and T max is the maximum effective tension of the dynamic cable, and T is the allowable tension of the dynamic cable; The bending curvature fitness function is: Among them, f κ is the evaluation function value of the dynamic cable bending curvature fitness, κ max is the maximum bending curvature of the dynamic cable, and κ is the allowable curvature of the dynamic cable; The fitness functions of the buoy section length and the counterweight section length are: Among them, f b is the fitness evaluation function value of the dynamic cable buoy section length, f B is the fitness evaluation function value of the dynamic cable counterweight section length, l b is the length of the dynamic cable buoy section, l B is the length of the dynamic cable counterweight section, and L is the total length of the dynamic cable.

6. The optimization method of the dynamic cable slow wave structure of a large floating wind turbine in medium and shallow water depths based on the particle swarm algorithm according to claim 1, characterized in that According to the numerical model, effective tension fitness function, bending curvature fitness function, and the fitness functions of the buoy section length and the counterweight section length, use the particle swarm algorithm to optimize the lengths of each section of the dynamic cable to obtain the layout scheme of the slow wave structure of the dynamic cable for large floating wind turbines in medium and shallow waters. The specific steps include: Initialize the particle swarm parameters and the position and velocity of each particle. The position of the particle includes the lengths of each section of the dynamic cable; For each particle, perform static analysis and dynamic analysis using the numerical model according to its position, and calculate the maximum tension and maximum curvature of the dynamic cable; calculate the fitness value using the maximum tension and maximum curvature of the dynamic cable and the lengths of each section of the dynamic cable; For each particle, if its current fitness value is better than its individual historical best fitness value, update its individual historical best position and fitness value; Traverse all particles. If the fitness value of a certain particle is better than the group historical best fitness value, update the group historical best position and fitness value; Update the velocity and position of each particle according to the inertia weight, learning factor, individual historical best and group historical best information; Perform multiple iterations until the change in the group historical best fitness value between two times is less than the set tolerance or the maximum number of iterations is reached; Obtain the optimal length combination of each section of the cable from the group historical optimal position to obtain the layout scheme of the dynamic cable slow wave structure for large floating wind turbines in medium and shallow water depths.

7. The optimization method for the dynamic cable slow wave structure of large floating wind turbines in medium and shallow water depths based on the particle swarm algorithm according to claim 6, wherein The particle swarm parameters include population size, inertia weight, learning factor, total number of iterations, and fitness tolerance.

8. The optimization method for the dynamic cable slow-wave structure of a large floating wind turbine in medium and shallow water depths based on the particle swarm algorithm according to claim 6, characterized in that The fitness calculation method is as follows: In the formula, f is the fitness of the dynamic cable slow wave line type, and α, β, and ω are the weights of the tension fitness function, curvature fitness function, and length fitness function of each section of the dynamic cable, respectively.

9. A dynamic cable slow-wave structure optimization system for large floating wind turbines in medium and shallow water depths based on the particle swarm optimization algorithm, characterized in that, It includes: One or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the optimization method of the dynamic cable slow wave structure for large floating wind turbines in medium and shallow water depths according to any one of claims 1-7.

10. A computer-readable storage medium storing computer instructions, characterized in that, When the computer instructions are executed by one or more processors, the one or more processors are caused to execute the steps in the method according to any one of claims 1-7.

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