Intelligent configuration optimization method for wind-wave coupling unit

Abstract

The application belongs to the technical field of ocean energy, and discloses an intelligent configuration optimization method for a wind-wave coupling unit, which comprises the following steps: determining offshore environment parameters for simulation; determining unit design parameters and an objective function in an optimization process; setting an optimization algorithm; in optimization iteration, an optimization algorithm generates a group of test points, calls an OpenFAST-WECSim solver to start time domain simulation, and performs time domain simulation calculation; the maximum or minimum value of the objective function is found, and the minimum value of the objective function is used as a search guide; new test points are generated according to historical evaluation results, and an OpenFAST-WECSim model input file is updated, so as to be prepared for the next round of simulation and drive parameters to converge to an optimization target; the iteration process continues until a global optimal solution convergence criterion is met. The application combines multi-floater dynamics modeling and intelligent optimization algorithms to realize automatic optimization of unit layout and operation parameters. The application can improve the overall energy capture efficiency, operation stability and adaptability of the unit.

Description

Technical Field

[0001] This invention relates to a marine energy technology, specifically a smart configuration optimization method for wind-wave coupled turbine units. Background Technology

[0002] With the rapid development of offshore renewable energy, the coordinated utilization of wind and wave energy on the same floating platform has become a key approach to improving energy capture efficiency and system economics. Wind-wave coupled turbines can significantly increase the total power output of offshore energy systems by simultaneously converting wind and wave energy. However, existing technologies mainly rely on empirical design or exhaustive trial-and-error methods for optimizing turbine configurations, resulting in low efficiency, poor adaptability, and a lack of systematic and intelligent optimization methods.

[0003] Chinese patent application CN 121503195 A discloses a wind turbine optimization design method based on tidal current-wave-sediment coupling simulation. The method includes: adaptively adjusting the time step of marine environmental data using a physical process intensity discrimination algorithm to obtain tidal current field, wave field, and sediment transport data; performing fluid-structure interaction calculations on the wind turbine structural parameters to obtain structural vibration response and marine load distribution data, and predicting the pile foundation scour depth distribution; correcting the pile foundation bearing capacity parameters using time-varying coefficients to obtain dynamic bearing capacity assessment data; inputting the data into a multi-objective optimization algorithm to obtain the Pareto optimal solution set; and obtaining the optimized design scheme for the wind turbine through engineering feasibility verification. This patented technology is only applicable to optimizing fixed-pile-foundation offshore wind turbines and cannot be applied to wind-wave coupled turbines. Summary of the Invention

[0004] The purpose of this invention is to overcome the problems of insufficient rationality and low efficiency in the process of manually exhaustively calculating the design parameters of a generator unit. It provides an intelligent configuration optimization method for wind-wave coupled generator units, which is developed based on the MATLAB optimization solver. It provides multi-parameter and multi-objective design requirements through online iterative solution, proposes a search strategy that does not rely on data storage, and can provide the optimal design solution under the premise of acceptable computational cost.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A smart configuration optimization method for wind-wave coupled turbine units includes: (1) Determine the marine environmental parameters used for simulation; the marine environmental parameters include at least the wind turbine hub height wind speed, significant wave height, spectral peak period, and ocean current velocity; these parameters are used to generate the unit excitation load and simulate the real sea conditions in subsequent simulations; (2) Determine the unit design parameters and objective function in this optimization process; the parameters to be optimized include, but are not limited to: blade airfoil geometry parameters, tower wall thickness, floating body structure dimensions, structural mass attributes, and anchor chain arrangement; the optimization objectives include, but are not limited to: wind turbine hourly power generation, wave energy device hourly power generation, whole unit hourly power generation, tower base bending moment, platform hourly displacement, platform dynamic equilibrium position, construction cost, or a weighted combination of the above indicators (multi-objective optimization); (3) Set optimization algorithms and configure parameter optimization methods. Develop the MATLAB Global Optimization Toolbox. Select appropriate algorithms from the MATLAB Global Optimization Toolbox according to the nature of the optimization problem. This algorithm library provides a variety of classic global-local hybrid algorithm schemes. The global algorithms include Scatter Search Algorithm (SSA), Multi-Start (MS), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA) and their corresponding variants. The local algorithms can be Interior Point Method (IPM), Sequential Quadratic Programming (SQP) and Active Set Method (ASM). The optimization process is not limited to the MATLAB Global Optimization Toolbox. Various optimization algorithms can be accessed or customized. (4) In the optimization iteration, the optimization algorithm first generates a set of test points, which are numerical combinations of the parameters to be optimized (e.g., the draft and weight of the floating body). These parameters will be used to update the input file of the OpenFAST-WECSim model. (5) Subsequently, the optimization algorithm calls the OpenFAST-WECSim solver to start the time domain simulation; in the MATLAB environment, the OpenFAST-WECSim simulation kernel is automatically started through system call instructions or API interface, loading the environmental parameters determined in step (1) and the parameters to be optimized updated in step (3) to perform time domain simulation calculation; The simulation process simulates the dynamic response of wind turbines under given sea conditions, outputting time-series data including structural loads, power generation, and motion response. After the time-domain simulation is completed, the objective function value of this iteration is calculated based on the user-defined evaluation function (e.g., extracting the time-series power generation results of wind turbines and wave energy devices from the simulation results and calculating their hourly average power as the objective value). After the latter calculation is completed, the obtained statistics are returned to the algorithm to evaluate the objective function and constraints. (6) The core task of the optimization algorithm is to find the maximum or minimum value of the objective function. Therefore, the system evaluation index needs to be uniform in scale and represented as a single value. There are two implementation paths for multi-objective optimization problems. On the one hand, the multi-objectives can be integrated into a single comprehensive index by scalarization methods such as weighted summation. On the other hand, the multi-objective optimization algorithm can be used directly to obtain the Pareto optimal solution set. (7) Both global and local algorithms are search-oriented based on minimizing the objective function; for the energy capture efficiency optimization problem, the instantaneous parameter curves and their representations in the post-processing stage of time-domain simulation need to be converted into time-averaged indices, and then a negative sign is introduced to transform the maximum value problem into a standard minimization solution form: (1) In the formula x To test parameters, f ( x Let ) be the objective function. A , A eq , b and b eq To input constraints, c ( x )and c eq ( x () represents the output constraints. l b and u b This expression indicates the search range; it is general and can be extended to a multi-parameter optimization architecture. (8) The optimization algorithm generates new test points based on historical evaluation results and updates the OpenFAST-WECSim model input file for the next round of simulation, and drives the parameters to converge toward the optimization objective; the iterative process continues until the global optimal solution convergence criterion is met. The convergence conditions include: the change in the objective function value is less than a set threshold, the maximum number of iterations is reached, and the objective function value reaches the expected design index. If the conditions are met, the iteration loop is terminated, and the current optimal parameter combination and the corresponding optimization result are output. If the conditions are not met, the process returns to step (4) and continues the iteration loop until the conditions are met.

[0006] The beneficial effects of this invention are: This invention combines multi-floating body dynamics modeling with intelligent optimization algorithms to achieve automatic optimization of unit layout and operating parameters. Considering nonlinear coupling effects and the randomness of multiple operating conditions, this invention can improve the overall energy capture efficiency, operational stability, and adaptability of the unit, while reducing optimization computation costs, providing effective technical support for the design of intelligent offshore energy systems.

[0007] This invention utilizes an intelligent configuration optimization method to automatically select the optimal structural design, layout, and operating parameters for wind turbines and wave energy devices, enabling wind-wave coupled units to maximize energy output under various sea conditions. Compared to traditional experience-based design or parameter scanning methods, this significantly improves overall energy utilization.

[0008] This invention provides a joint optimization method in the frequency domain and time domain, which can simultaneously consider design parameters such as structural shape, draft of the floating body, mass distribution and inertial properties during the optimization process. Traditional time domain methods are difficult to effectively optimize these parameters due to computational limitations.

[0009] The joint optimization method fully considers the multi-physics coupling effect of aerodynamics, hydrodynamics, elasticity, servo, and mooring, and achieves an accurate description of the overall dynamic response of the floating body and the unit, thereby improving the applicability of the optimization results under real working conditions.

[0010] This invention employs an online optimization scheme, which optimizes design parameters through real-time iteration. This effectively avoids exhaustive traversal of the entire design space and avoids the high storage pressure caused by the need to save all historical data in the traditional offline optimization mode, thereby significantly reducing data storage requirements. This invention addresses the multi-parameter, multi-objective design requirements of wind-wave coupled turbine units, offering greater adaptability to the type and number of design variables. It can be extended to different unit configurations, including floating and stationary wind turbine units or wave energy devices, and includes anchor chain design. Compared to CN 121503195 A, this invention does not rely on offline multiphysics coupling simulation and Pareto post-screening processes. Instead, it employs an online iterative optimization strategy, achieving intelligent configuration of multi-parameter and multi-objective design parameters for wind-wave coupled turbine units within the MATLAB optimization solver framework. This method eliminates the need to store large-scale environmental and response data, and can converge to the optimal solution in real time within a controllable computational cost, demonstrating significant advantages in engineering practicality, computational efficiency, deployment flexibility, and design adaptability. Attached Figure Description

[0011] Figure 1 This is a diagram of a wind-wave coupling system; Figure 2 It is an iterative flowchart; Figure 3 This is a PTO optimization iteration curve graph; Figure 4 This is a diagram of an intelligent configuration optimization method based on the OpenFAST-WECSim simulation model; Figure 5 It is a ring-shaped WEC element diagram generated based on the automatic partitioning code of the structural element. Detailed Implementation

[0012] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0013] The structures, proportions, and sizes illustrated in the accompanying drawings are merely for illustrative purposes and to aid those skilled in the art in understanding and reading the invention. They are not intended to limit the scope of the invention and therefore have no substantial technical significance. Any modifications to the structure, changes in proportions, or adjustments to size, provided they do not affect the effectiveness or purpose of the invention, should still fall within the scope of the technical content disclosed herein. Furthermore, the terms "upper," "lower," "left," "right," "middle," and "one" used in this specification are merely for clarity and not intended to limit the scope of the invention. Changes or adjustments to their relative relationships, without substantially altering the technical content, should also be considered within the scope of the invention's implementation.

[0014] Intelligent configuration optimization methods for wind-wave coupled units include: (1) Determine the marine environmental parameters used for simulation; the marine environmental parameters include at least the wind turbine hub height wind speed, significant wave height, spectral peak period, and ocean current velocity; these parameters are used to generate the unit excitation load and simulate the real sea conditions in subsequent simulations; (2) Determine the unit design parameters and objective function in this optimization process; the parameters to be optimized include, but are not limited to: blade airfoil geometry parameters, tower wall thickness, floating body structure dimensions, structural mass attributes, and anchor chain arrangement; the optimization objectives include, but are not limited to: wind turbine hourly power generation, wave energy device hourly power generation, whole unit hourly power generation, tower base bending moment, platform hourly displacement, platform dynamic equilibrium position, construction cost, or a weighted combination of the above indicators (multi-objective optimization); (3) Set optimization algorithms and configure parameter optimization methods. Develop the MATLAB Global Optimization Toolbox. Select appropriate algorithms from the MATLAB Global Optimization Toolbox according to the nature of the optimization problem. This algorithm library provides a variety of classic global-local hybrid algorithm schemes. The global algorithms include Scatter Search Algorithm (SSA), Multi-Start (MS), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA) and their corresponding variants. The local algorithms can be Interior Point Method (IPM), Sequential Quadratic Programming (SQP) and Active Set Method (ASM). The optimization process is not limited to the MATLAB Global Optimization Toolbox. Various optimization algorithms can be accessed or customized. (4) In the optimization iteration, the optimization algorithm first generates a set of test points, which are numerical combinations of the parameters to be optimized (e.g., the draft and weight of the floating body). These parameters will be used to update the input file of the OpenFAST-WECSim model. (5) Subsequently, the optimization algorithm calls the OpenFAST-WECSim solver to start the time domain simulation; in the MATLAB environment, the OpenFAST-WECSim simulation kernel is automatically started through system call instructions or API interface, loading the environmental parameters determined in step (1) and the parameters to be optimized updated in step (3) to perform time domain simulation calculation; The simulation process simulates the dynamic response of wind turbines under given sea conditions, outputting time-series data including structural loads, power generation, and motion response. After the time-domain simulation is completed, the objective function value of this iteration is calculated based on the user-defined evaluation function (e.g., extracting the time-series power generation results of wind turbines and wave energy devices from the simulation results and calculating their hourly average power as the objective value). After the latter calculation is completed, the obtained statistics are returned to the algorithm to evaluate the objective function and constraints. (6) The core task of the optimization algorithm is to find the maximum or minimum value of the objective function. Therefore, the system evaluation index needs to be uniform in scale and represented as a single value. There are two implementation paths for multi-objective optimization problems. On the one hand, the multi-objectives can be integrated into a single comprehensive index by scalarization methods such as weighted summation. On the other hand, the multi-objective optimization algorithm can be used directly to obtain the Pareto optimal solution set. (7) Both global and local algorithms are search-oriented based on minimizing the objective function; for the energy capture efficiency optimization problem, the instantaneous parameter curves and their representations in the post-processing stage of time-domain simulation need to be converted into time-averaged indices, and then a negative sign is introduced to transform the maximum value problem into a standard minimization solution form: (1) In the formula x To test parameters, f ( x Let ) be the objective function. A , A eq , b and b eq To input constraints, c ( x )and c eq ( x () represents the output constraints. l b and u b This expression indicates the search range; it is general and can be extended to a multi-parameter optimization architecture. (8) The optimization algorithm generates new test points based on historical evaluation results and updates the OpenFAST-WECSim model input file for the next round of simulation, driving the parameters to converge toward the optimization objective; the iterative process continues until the global optimal solution convergence criterion is met (e.g., ...). Figure 2 (as shown) The convergence conditions include: the change in the objective function value is less than the set threshold, the maximum number of iterations is reached, and the objective function value reaches the expected design index. If the conditions are met, the iteration loop is terminated, and the current optimal parameter combination and the corresponding optimization results are output. If the conditions are not met, the process returns to step (4) and continues the iteration loop until the conditions are met.

[0015] The optimization model proposed in this invention is universal and can be used to design any single or multiple structural parameters in any type of wind-wave hybrid system. Here, a coupled system consisting of an IEA-15-MW-VolturnUS floating wind turbine and a ring-shaped WEC is used as an example. Figure 1 Taking the Power Take-Off (PTO) device as the research object, this paper presents the damping optimization problem of the PTO as a test case, with the optimization objective being maximum wave energy capture. During the optimization iteration, the optimization algorithm calls the OpenFAST-WECSim solver to initiate a time-domain simulation. After the latter completes the calculation, it returns the obtained statistics to the algorithm for evaluating the objective function and constraints. This iterative process continues until the global optimal solution convergence criterion is met. This section focuses on showcasing the PTO damping iteration results configured based on the SSA-IPM algorithm.

[0016] The effects of multiphysics coupling can be characterized in the WECSim solver using the platform dynamics equations: (2) In the formula, M1, With F hydro1 F represents the platform's mass-moment-of-inertia matrix, the platform's six-degree-of-freedom acceleration vector, and the hydrodynamic load vector, respectively. PTO1-3 F turbine With F moor The load vectors (F) applied to the platform by the WEC array, wind turbine structure, and mooring system, respectively. turbine The combined aerodynamic-elastic-servo effect is characterized by an OpenFAST simulation model. The wind turbine and mooring loads act indirectly on the WEC unit through the PTO mechanism, while the WEC hydrodynamic effect is ultimately transmitted to the wind turbine and mooring modules via the platform. (3)

[0017] For floating bodies i ( i = 2-4, corresponding to WEC 1-3, k = 1-3), M i , F hydroi With F PTOk These represent the mass-inertia matrix, platform acceleration vector, hydrodynamic load vector, and PTO load vector, respectively. The hydrodynamic coefficients for the platform and WEC are entirely provided by WAMIT. The PTO model, implemented in MATLAB-Simulink, uses prism hinges to construct rigid constraints, completely restricting the relative translation and rotation between the base (platform) and the follower (WEC) coordinate systems, allowing only single-degree-of-freedom translation. The relative vertical load generated by linear PTO damping can be expressed as: (4) B PTOk The damping coefficient of PTO is... For PTO speed. WEC power performance is evaluated using the following formula: (5) The intelligent optimization solver is implemented on the MATLAB development platform, and the OpenFAST-WECSim solver is embedded in it for time-domain simulation. Figure 3The process involves iteratively modifying the input parameters using the OWSmodFile.m function. Additionally, as a necessary file, this patent includes a MATLAB script for iteratively generating the WAMIT hydrodynamic solver.gdf surface structure, which can generate a polar coordinate form of the WAMIT surface input file based on specified geometric parameters, such as... Figure 4 As shown. Furthermore, the WEC mass properties and moment of inertia information are determined through a user-defined function. For a homogeneous ring structure: (6) R and r These are the outer and inner diameters of the WEC, respectively. ρ For water density, d For WEC's water intake, h The height is WEC.

[0018] Figure 5 Showing θ = 0 deg, U hub = 20 m·s -1 , H s = 4.5 m, T p = 10.7 s environmental conditions, the energy efficiency iterative curve in the linear PTO damping iterative optimization process, the optimization objective is the maximum wave energy capture efficiency. <P PTO1-3 > At the same time, the overall power <P total > As a monitoring target, the SSA-IPM algorithm identified 30 local optima (marked by black circles) and the global optimum. B PTOL1 = 2.7942×10 3 kN·m -1 ·s, B PTOL2 = 3.1490×10 3 kN·m -1 •s. PTO design can refer to this damping parameter to achieve the goal of efficient energy harvesting.

[0019] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A smart configuration optimization method for wind-wave coupled turbine units, characterized in that, include: (1) Determine the marine environmental parameters to be used for simulation; (2) Determine the unit design parameters and objective function in the optimization process; (3) Set up optimization algorithms and configure parameter optimization methods using the MATLAB Global Optimization Toolbox; (4) In the optimization iteration, the optimization algorithm first generates a set of test points, which are numerical combinations of the parameters to be optimized. These parameters will be used to update the input file of the OpenFAST-WECSim model. (5) Subsequently, the optimization algorithm calls the OpenFAST-WECSim solver to start the time domain simulation; in the MATLAB environment, the OpenFAST-WECSim simulation kernel is automatically started through system call instructions or API interface, loading the environmental parameters determined in step (1) and the parameters to be optimized updated in step (3) to perform time domain simulation calculation; (6) To find the maximum or minimum value of the objective function, the system evaluation index needs to be standardized in terms of units and represented as a single value; (7) Both global and local algorithms are search-oriented by minimizing the objective function; (8) The optimization algorithm generates new test points based on historical evaluation results and updates the OpenFAST-WECSim model input file for the next round of simulation, and drives the parameters to converge toward the optimization objective; the iterative process continues until the global optimal solution convergence criterion is met.

2. The intelligent configuration optimization method for wind-wave coupled units as described in claim 1, characterized in that, The marine environmental parameters in step (1) include at least the wind turbine hub height wind speed, significant wave height, spectral peak period and ocean current velocity; these parameters are used to generate the unit excitation load and simulate the real sea conditions in subsequent simulations.

3. The intelligent configuration optimization method for wind-wave coupled units as described in claim 1, characterized in that, The unit design parameters in step (2) include, but are not limited to: blade airfoil geometry parameters, tower wall thickness, floating structure dimensions, structural mass attributes, and anchor chain arrangement; the objective function includes, but is not limited to: wind turbine hourly power generation, wave energy device hourly power generation, whole unit hourly power generation, tower base bending moment, platform hourly displacement, platform dynamic equilibrium position, construction cost, or a weighted combination of the above indicators, i.e., multi-objective optimization.

4. The intelligent configuration optimization method for wind-wave coupled units as described in claim 1, characterized in that, In step (3), a suitable algorithm is selected from the MATLAB Global Optimization Toolbox according to the nature of the optimization problem. The algorithm library provides a variety of classic global-local hybrid algorithm schemes, including global algorithms such as scattering search, multiple starting points, genetics, particle swarm optimization, simulated annealing and their corresponding variants, and local algorithms such as interior point selection, quadratic sequence programming and effective set.

5. The intelligent configuration optimization method for wind-wave coupled units as described in claim 1, characterized in that, In step (5), the simulation process simulates the dynamic response of the wind turbine under a given sea condition, outputs time series data including structural load, power generation and motion response, and calculates the objective function value of this iteration based on the user-defined evaluation function after the time domain simulation is completed. After the calculation is completed, the obtained statistics are returned to the algorithm to evaluate the objective function and constraints.

6. The intelligent configuration optimization method for wind-wave coupled units as described in claim 1, characterized in that, In step (6), there are two implementation paths for the multi-objective optimization problem. On the one hand, the multi-objectives are integrated into a single comprehensive index by using the weighted summation scalarization method, and on the other hand, the Pareto optimal solution set is obtained directly by using the multi-objective optimization algorithm.

7. The intelligent configuration optimization method for wind-wave coupled units as described in claim 1, characterized in that, In step (7), for the energy capture efficiency optimization problem, the time-domain simulation post-processing stage needs to convert the instantaneous parameter curves and their representations into time-averaged indices, and then introduce a negative sign to transform the maximum value problem into a standard minimization solution form: （1） In the formula x To test parameters, f ( x Let ) be the objective function. A , A eq , b and b eq To input constraints, c ( x )and c eq ( x ) represents the output constraints. l b and u b This expression indicates the search range; it is general and can be extended to a multi-parameter optimization architecture.

8. The intelligent configuration optimization method for wind-wave coupled units as described in claim 1, characterized in that, In step (8), the convergence conditions include: the change in the objective function value is less than a set threshold, the maximum number of iterations is reached, and the objective function value reaches the expected design index. If the conditions are met, the iteration loop is terminated, and the current optimal parameter combination and the corresponding optimization result are output. If the conditions are not met, the process returns to step (4) and continues the iteration loop until the conditions are met.

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