A floating wind turbine array optimization layout method and system based on wake effect

Abstract

The present invention discloses a floating wind turbine array optimization layout method and system based on wake effect, which relates to the field of data analysis technology, and includes the following steps: conducting wind resource assessment on the wind farm area where floating wind turbines are deployed, synchronously collecting marine environmental data of the wind farm area, and analyzing wind speed and direction indicators and marine environmental indicators of the wind farm area; analyzing the existing wake model, combining the motion characteristics of floating wind turbines under the dual effects of wave and wind loads, analyzing its influence on the wake effect, and establishing a floating wind turbine wake model according to the actual operating conditions of the floating wind turbines. The present invention significantly reduces the wind speed reduction and turbulence increase caused by the wake effect by optimizing the layout of the floating wind turbine array, thereby improving the wind energy utilization efficiency of the entire wind farm. The optimized layout scheme can ensure that the distance between the wind turbines is large enough to avoid mutual wake interference, so that each wind turbine can operate under better wind speed conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a method and system for optimizing the arrangement of a floating wind turbine array based on a wake effect. Background Art

[0002] With the continuous development of wind power generation technology, floating wind turbines, as a new type of wind power generation equipment, are gradually being used in complex environments such as the deep sea. Floating wind turbines have the advantages of flexible installation location and strong adaptability. They can generate electricity in sea areas far away from land and with high wind speeds. However, under the dual effects of wave and wind loads, the motion characteristics of floating wind turbines are significantly different from those of onshore wind turbines. Therefore, in the layout process of floating wind turbine arrays, it is necessary to fully consider the wake effect and the motion characteristics of floating wind turbines to ensure that the mutual influence between wind turbines is minimized, thereby improving the power generation efficiency of the entire wind farm.

[0003] In the prior art, floating wind turbines operating in a marine environment are easily affected by marine environmental factors and wind speed and direction factors, which in turn leads to deviations between the optimization results of the floating wind turbine array layout and the actual situation. Therefore, how to comprehensively consider the influence of marine environmental factors and wind speed and direction factors on floating wind turbines and improve the optimization efficiency of floating wind turbine arrays is a problem we need to solve. To this end, a floating wind turbine array optimization layout method and system based on wake effect are proposed. Summary of the invention

[0004] The object of the present invention is to provide a method and system for optimizing the arrangement of a floating wind turbine array based on the wake effect, so as to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] In a first aspect, a method for optimizing the arrangement of a floating wind turbine array based on a wake effect comprises the following steps:

[0007] Step 1: Conduct wind resource assessment on the wind farm area where the floating wind turbines are deployed, simultaneously collect marine environmental data of the wind farm area, and analyze wind speed and direction indicators and marine environmental indicators of the wind farm area;

[0008] Step 2: Analyze the existing wake model, combine the motion characteristics of the floating wind turbine under the dual effects of wave and wind loads, analyze its influence on the wake effect, and establish a floating wind turbine wake model based on the actual operating conditions of the floating wind turbine;

[0009] Step 3: According to the operating characteristics of the floating wind turbines and the marine environmental indicators, the constraints for the optimal layout are established, and the floating wind turbine wake model, the marine environmental indicators and the wind speed and direction indicators are combined to construct an optimization model for the floating wind turbine array;

[0010] Step 4: according to the solution requirements of the floating wind turbine array optimization model, an optimization algorithm is selected and the floating wind turbine array optimization model is solved to obtain the optimal layout plan of the floating wind turbine array;

[0011] Step 5: Use simulation software to simulate the operation of the optimized floating wind turbine array to verify the correctness and effectiveness of the optimization results, and conduct field tests to verify and compare the power generation efficiency indicators before and after optimization to further evaluate the optimization effect;

[0012] Step 6: Analyze the results of simulation and field test to find out the deficiencies in the optimization process, and adjust and optimize the optimization scheme according to the analysis results to improve the power generation efficiency and stability of the floating wind turbine array.

[0013] A further improvement of the technical solution of the present invention is that in step 1, the analysis process of the wind speed and direction index and the marine environment index of the wind farm area is:

[0014] Collect wind resource data on wind speed, wind direction, and temperature in the wind farm area through weather stations, wind towers, and buoy equipment;

[0015] Use ocean monitoring stations and buoy equipment to collect ocean environmental data on waves, tides, temperature, and salinity in the wind farm area;

[0016] Analyze the collected wind resource data to determine the average wind speed, wind direction frequency distribution, and key indicators of wind speed variability in the wind farm area, and analyze the ocean environment indicators of wave height, wave period, current speed and direction, and sea level change;

[0017] Integrate wind resource data and marine environment data, analyze the relationship between wind speed and direction in the wind farm area and the marine environment, and identify the potential connection and interaction mechanism between changes in wind speed and direction and wave and tidal marine environmental factors.

[0018] A further improvement of the technical solution of the present invention is that in step 2, the process of establishing the floating wind turbine wake model is as follows:

[0019] Analyze the existing wake model, clarify the basic principles and applicable conditions of the wake model, and use numerical simulation methods to analyze and determine the motion characteristics of floating wind turbines under wave and wind loads, including the sway, yaw, and pitch motion of the wind turbine;

[0020] Combined with the motion characteristics of floating wind turbines, the influence of the floating wind turbines on the wake effect is analyzed, and the influence of waves, wind loads, wind speed and wind direction on the wake effect is analyzed. The multi-body dynamic model of floating wind turbines is established, including the rigidity and flexibility characteristics of floating platforms, towers, and blade components, as well as the connection and interaction between the components. According to the marine environmental conditions and wind turbine characteristics, the size and direction of waves and wind loads are determined, and the finite difference method is used to solve the motion equations of floating wind turbines to obtain their motion response.

[0021] Based on the motion characteristics of floating wind turbines and the influencing factors of wake effect, the multi-body dynamics model of floating wind turbines is combined with the CFD model to establish a floating wind turbine wake model to simulate the motion characteristics and wake effect of floating wind turbines under wave and wind loads.

[0022] According to the actual size of the floating wind turbine, marine environmental conditions and characteristics of the floating wind turbine, the parameters and boundary conditions of the numerical simulation are set. The computational fluid dynamics numerical simulation method is used to simulate and analyze the wake of the floating wind turbine. The numerical simulation results are compared with the experimental data to verify the accuracy and reliability of the floating wind turbine wake model.

[0023] A further improvement of the technical solution of the present invention is that in step 3, the construction process of the floating wind turbine array optimization model is:

[0024] Analyze wind resource data and marine environment data, clarify the operating characteristics and marine environmental indicators of floating wind turbines, and establish the spatial constraints, safety constraints and economic constraints for optimal layout;

[0025] The floating wind turbine wake model is used to simulate the wake effect between floating wind turbines under different layouts. Through simulation, the impact of different layouts on wind speed and power generation efficiency is analyzed;

[0026] The floating wind turbine wake model is combined with marine environmental indicators and wind speed and direction indicators to construct a floating wind turbine array optimization model to evaluate the effective power generation efficiency of floating wind turbine arrays under different layouts.

[0027] A further improvement of the technical solution of the present invention is that the calculation process of the effective power generation efficiency of the floating wind turbine array is to calculate the swept area of ​​each floating wind turbine. , considering the wake effect and the spacing between floating wind turbines, the wake velocity of each floating wind turbine is estimated , and combined with wind speed, power coefficient of floating wind turbine and swept area of ​​floating wind turbine, the theoretical power generation of each floating wind turbine is calculated, and the wake attenuation coefficient is used Analyze the wake effect with the floating wind turbine spacing L, adjust the power generation of each floating wind turbine, and analyze the wind direction change angle in combination with the wave influence factor k and the influence of the marine environment. The influence of temperature T and salinity S is used to obtain the final effective power generation efficiency, which is calculated as follows:

[0028] ;

[0029] ;

[0030] Where E is the effective power generation efficiency of the floating wind turbine array, is the wind speed, D is the diameter of the floating wind turbine, L is the distance between floating wind turbines, is the power coefficient of the floating wind turbine, is the wake attenuation coefficient, k is the wave influence factor, which is related to the wave height and period. is the wind direction change angle, T is the temperature, S is the salinity, is the air density, the reference value is 1.225kg / m³, is the swept area of ​​the i-th fan, is the wake velocity of the i-th wind turbine, n is the total number of wind turbines, is the wake attenuation, The effect of wind direction change on power generation efficiency. The effect of temperature and salinity on air density.

[0031] A further improvement of the technical solution of the present invention is that in step 4, the analysis process of the optimal arrangement of the floating wind turbine array is as follows:

[0032] According to the characteristics and solution requirements of the floating wind turbine array optimization model, the genetic algorithm is selected as the solution tool to solve the floating wind turbine array optimization model;

[0033] The effective power generation efficiency and economic cost of the floating wind turbine array are analyzed, and a fitness function is constructed to evaluate the pros and cons of each solution (i.e., a layout scheme of the floating wind turbine array). The higher the power generation efficiency, the greater the fitness value, and the lower the cost, the greater the fitness value;

[0034] The floating wind turbine array optimization model is used as input, and the genetic algorithm is run to generate a series of solutions (layout plans) and evaluate them through the fitness function. After multiple iterations, the algorithm will gradually converge to the approximate optimal solution.

[0035] For each generated layout scheme, the floating wind turbine wake model and marine environment data are used to comprehensively analyze the wake effect between floating wind turbines, changes in wind speed and direction, and the impact of the marine environment on wind turbine performance, and calculate the effective power generation efficiency of the floating wind turbine array;

[0036] Based on the evaluation results of effective power generation efficiency and combined with economic cost factors, the optimal layout of floating wind turbine arrays that meets all constraints (space constraints, safety constraints, and economic constraints) is selected to achieve the best balance between power generation efficiency and economy.

[0037] The calculation expression of the fitness function is:

[0038] ;

[0039] Among them, A is the fitness function, which represents the pros and cons of a floating wind turbine array layout scheme. is the effective power generation of the i-th wind turbine, which is affected by the wake effect and is calculated using the floating wind turbine wake model. is the installation and operation cost of the i-th wind turbine, including materials, labor, maintenance and other costs, is the weight factor used to balance power generation efficiency and cost, R is the proportion of operation and maintenance costs exceeding the budget, The budget cap.

[0040] A further improvement of the technical solution of the present invention is that in step 5, the process of field testing and verification is:

[0041] According to the characteristics of the floating wind turbine array and the content that needs to be verified, OpenFAST is used to perform aerodynamic-hydrodynamic-servo-elastic simulation calculations of the floating wind turbine. In the simulation software, a corresponding simulation model is built according to the optimized floating wind turbine array layout plan. After the model is built, a preliminary verification is carried out to ensure that the model can correctly simulate the operating characteristics of the floating wind turbine.

[0042] The optimized floating wind turbine array layout scheme is input into OpenFAST for simulation. During the simulation, the power generation efficiency and output power data of the floating wind turbine array are collected and compared with the data before optimization to verify the correctness and effectiveness of the optimization results.

[0043] Install sensors on the actual floating wind turbine array, conduct field tests on the floating wind turbine array, collect the actual power generation efficiency and output power data of the floating wind turbines, and compare them with the simulation results to analyze the differences and causes between the two;

[0044] Compare the power generation efficiency indicators before and after optimization, calculate the evaluation value of the optimization effect, and further evaluate the optimization effect.

[0045] A further improvement of the technical solution of the present invention is that the calculation expression of the optimization effect evaluation value is:

[0046] ;

[0047] Among them, X is the optimization effect evaluation value, is the total power generation of the optimized floating wind turbine array, is the total power generation of the floating wind turbine array before optimization, is the total cost of the optimized floating wind turbine array, is the total cost of the floating wind turbine array before optimization, Score the environmental impact of the optimized floating wind turbine array. Score the environmental impact of the floating wind turbine array before optimization.

[0048] A further improvement of the technical solution of the present invention is that in step 6, the process of adjusting and optimizing the optimization scheme is:

[0049] Based on the optimization effect evaluation results of the power generation efficiency indicators before and after optimization, identify the deficiencies in the optimization process;

[0050] According to the error analysis results, the simulation model is improved to improve the model accuracy, the environmental conditions are analyzed, and the optimization scheme is adjusted to improve the environmental adaptability of the floating wind turbine array. According to the results of simulation and field tests, the layout of the floating wind turbine array is adjusted to optimize the power generation efficiency and stability.

[0051] Input the adjusted optimization scheme into the simulation software, conduct a new round of simulation calculations, conduct field tests on the adjusted floating wind turbine array to verify the optimization effect, and iteratively adjust the optimization scheme based on the results of simulation and field tests until the required power generation efficiency is achieved;

[0052] Summarize the entire optimization process, including the results of simulation and field tests, the adjustment and optimization of the optimization plan, and the final power generation efficiency and stability performance; write a detailed report to record the optimization process, result analysis, adjustment and optimization of the optimization plan, and the final conclusion.

[0053] In a second aspect, a floating wind turbine array optimization layout system based on wake effect is used to implement a floating wind turbine array optimization layout method based on wake effect, including an optimization analysis platform, wherein the optimization analysis platform is communicatively connected with a data collection and analysis module, a wake effect simulation model module, an optimization algorithm module, a fitness function evaluation module, a test verification module, an iterative optimization module, and a report generation module, wherein electrical signals are connected between the modules;

[0054] The data collection and analysis module is used to collect wind resource data and ocean environment data required for optimization analysis of floating wind turbine arrays, and pre-process the collected data to ensure the accuracy and completeness of the data, thereby providing strong support for subsequent optimization analysis;

[0055] The wake effect simulation model module establishes a wake effect simulation model of a floating wind turbine array to simulate the wake interference between floating wind turbines under different layouts;

[0056] The optimization algorithm module uses OpenFAST software to perform aerodynamic-hydrodynamic-servo-elastic simulation calculations of floating wind turbines, simulates the wake effect between floating wind turbines under different layouts, and optimizes the layout of the floating wind turbine array;

[0057] The fitness function evaluation module constructs a fitness function to evaluate the pros and cons of each solution (i.e., a layout scheme of the floating wind turbine array);

[0058] The test verification module installs sensors on the actual floating wind turbine array to collect actual power generation efficiency and output power data, and compares them with the simulation results to verify the actual effect of the optimization scheme and ensure the reliability and effectiveness of the optimization scheme;

[0059] The iterative optimization module identifies deficiencies in the optimization process and adjusts and optimizes the optimization scheme based on the results of simulation and field testing;

[0060] The report generation module summarizes the entire optimization process, including the results of simulation and field tests, the adjustment and optimization process of the optimization plan, and the final power generation efficiency and stability performance. At the same time, it generates an optimization report to record the optimization process, result analysis, adjustment and optimization of the optimization plan, and the final conclusion.

[0061] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0062] The present invention provides a floating wind turbine array optimization layout method and system based on wake effect. By optimizing the layout of the floating wind turbine array, the wind speed reduction and turbulence increase caused by the wake effect are significantly reduced, thereby improving the wind energy utilization efficiency of the entire wind farm. The optimized layout scheme can ensure that the distance between the wind turbines is large enough to avoid wake interference between each other, so that each wind turbine can operate under better wind speed conditions.

[0063] The present invention provides a floating wind turbine array optimization layout method and system based on wake effect. By integrating the wake effect simulation model and the optimization algorithm, efficient optimization of the floating wind turbine array layout is achieved. The simulation model can accurately simulate the wake interference between wind turbines under different layouts. The optimization algorithm continuously adjusts the position and direction of the wind turbine according to the simulation results to maximize the power generation efficiency of the entire array. It breaks through the limitations of traditional layout methods and can quickly find the optimal solution under large-scale and multi-variable conditions. In addition, through continuous iteration and optimization, it can gradually approach the global optimal layout, thereby significantly improving the power generation performance of the floating wind turbine array. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] 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 will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0065] Figure 1 is a flow chart of the method of the present invention;

[0066] Figure 2 A flow chart for constructing a floating wind turbine array optimization model of the present invention;

[0067] Figure 3 The following is a flow chart of the analysis of the optimal arrangement of the floating wind turbine array of the present invention;

[0068] Figure 4 A flow chart for field testing and verification of the present invention;

[0069] Figure 5 It is a module structure diagram of the present invention. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] Embodiment 1, as Figure 1-Figure 3 As shown, the present invention provides a floating wind turbine array optimization layout method based on wake effect, comprising the following steps:

[0072] Step 1: Conduct a wind resource assessment on the wind farm area where floating wind turbines are deployed, collect marine environmental data on the wind farm area simultaneously, analyze wind speed and direction indicators and marine environmental indicators on the wind farm area, collect wind resource data on wind speed, wind direction, and temperature on the wind farm area through meteorological stations, wind towers, and buoy equipment, and conduct statistical analysis on the collected wind resource data, calculate annual power generation and power generation capacity factor indicators, use mathematical models and algorithms to assess wind energy potential, determine the technical feasibility and power generation capacity of the wind farm, draw a wind resource distribution map on the wind farm area through data visualization technology, analyze the wind resource intensity and change trend at different locations, and provide a scientific basis for the layout of wind turbines, and use marine monitoring stations and buoy equipment to collect marine environmental data on waves, tides, temperature, and salinity on the wind farm area. The collected marine environment data are statistically analyzed to calculate key parameters such as wave height, tidal cycle, and temperature range. Mathematical models and algorithms are used to evaluate the impact of the marine environment on the construction and operation of wind farms. The impact of the size and frequency of waves and tides on the stability and operation of wind turbines is analyzed. The impact of temperature changes on the efficiency of wind turbines is evaluated. The collected wind resource data are analyzed to determine the average wind speed in the wind farm area, the frequency distribution of wind direction, and the key indicators of wind speed variability. The wave height, wave cycle, current speed and direction, and sea level change marine environment indicators are analyzed. The wind resource data and marine environment data are integrated to analyze the relationship between the wind speed and direction in the wind farm area and the marine environment. The potential connection and interaction mechanism between the changes in wind speed and direction and the wave and tidal marine environment factors are identified.

[0073] Step 2: Analyze the existing wake model, combine the motion characteristics of floating wind turbines under the dual effects of wave and wind loads, analyze their impact on the wake effect, and establish a floating wind turbine wake model based on the actual operating conditions of the floating wind turbine. Analyze the existing wake model, clarify the basic principles and applicable conditions of the wake model, and use numerical simulation methods to analyze and determine the motion characteristics of floating wind turbines under wave and wind loads, including the swaying, yaw, and pitching motion of the wind turbine. Combined with the motion characteristics of the floating wind turbine, analyze its impact on the wake effect, and analyze the impact of wave, wind load, wind speed, and wind direction factors on the wake effect. Establish a floating wind turbine multi-body dynamics model: including the rigidity and flexibility characteristics of the floating platform, tower, and blade components, as well as the connection and Interaction, according to the marine environmental conditions and wind turbine characteristics, determine the size and direction of waves and wind loads, use the finite difference method to solve the motion equation of the floating wind turbine, and obtain its motion response. Based on the motion characteristics of the floating wind turbine and the influencing factors of the wake effect, combine the multi-body dynamics model of the floating wind turbine with the CFD model to establish a floating wind turbine wake model to simulate the motion characteristics and wake effects of the floating wind turbine under the action of waves and wind loads. According to the actual size of the floating wind turbine, the marine environmental conditions and the characteristics of the floating wind turbine, set the parameters and boundary conditions of the numerical simulation, use the computational fluid dynamics numerical simulation method to simulate and analyze the wake of the floating wind turbine, compare the numerical simulation results with the experimental data, and verify the accuracy and reliability of the floating wind turbine wake model;

[0074] Step 3: According to the operating characteristics and marine environmental indicators of floating wind turbines, establish the constraints for optimal layout, combine the floating wind turbine wake model, marine environmental indicators and wind speed and direction indicators, build a floating wind turbine array optimization model, analyze wind resource data and marine environmental data, clarify the operating characteristics and marine environmental indicators of floating wind turbines, and establish spatial constraints, safety constraints and economic constraints for optimal layout. Among them, the layout of the floating wind turbine array should take into account the spatial distribution of the marine environment to avoid arranging wind turbines under extreme marine environmental conditions. The distance between floating wind turbines should be large enough to avoid reduced wind speed and power generation efficiency caused by the wake effect. The design of floating wind turbines should meet the extreme marine environment. End conditions should be met to ensure safe operation in severe weather such as storms and huge waves. The mooring system of floating wind turbines should be strong enough to resist the impact of external forces such as ocean currents and waves. The construction and operation costs of floating wind turbines should be taken into account, including the costs of equipment purchase, installation, maintenance, and transportation. The layout of the floating wind turbine array should optimize the power generation efficiency to improve economic benefits. The floating wind turbine wake model is used to simulate the wake effect between floating wind turbines under different layouts. Through simulation, the impact of different layouts on wind speed and power generation efficiency is analyzed. The floating wind turbine wake model is combined with marine environmental indicators and wind speed and direction indicators to construct a floating wind turbine array optimization model to evaluate the effective power generation efficiency of floating wind turbine arrays under different layouts.

[0075] Furthermore, the calculation process of the effective power generation efficiency of the floating wind turbine array is to calculate the swept area of ​​each floating wind turbine , considering the wake effect and the spacing between floating wind turbines, the wake velocity of each floating wind turbine is estimated , and combined with wind speed, power coefficient of floating wind turbine and swept area of ​​floating wind turbine, the theoretical power generation of each floating wind turbine is calculated, and the wake attenuation coefficient is used Analyze the wake effect with the floating wind turbine spacing L, adjust the power generation of each floating wind turbine, and analyze the wind direction change angle in combination with the wave influence factor k and the influence of the marine environment. The influence of temperature T and salinity S is used to obtain the final effective power generation efficiency, which is calculated as follows:

[0076] ;

[0077] ;

[0078] Where E is the effective power generation efficiency of the floating wind turbine array, is the wind speed, D is the diameter of the floating wind turbine, L is the distance between floating wind turbines, is the power coefficient of the floating wind turbine, is the wake attenuation coefficient, k is the wave influence factor, which is related to the wave height and period. is the wind direction change angle, T is the temperature, S is the salinity, is the air density, the reference value is 1.225kg / m³, is the swept area of ​​the i-th fan, is the wake velocity of the i-th wind turbine, n is the total number of wind turbines, is the wake attenuation, The effect of wind direction change on power generation efficiency. The influence of temperature and salinity on air density, which in turn affects wind speed and power generation efficiency, The value range of L is between 3m / s and 25m / s. The higher the wind speed, the greater the power generation. L is between 3D and 10D.

[0079] Step 4. According to the solution requirements of the floating wind turbine array optimization model, an optimization algorithm is selected and the floating wind turbine array optimization model is solved to obtain the optimal layout plan of the floating wind turbine array. According to the characteristics and solution requirements of the floating wind turbine array optimization model, a genetic algorithm is selected as a solution tool to solve the floating wind turbine array optimization model. The effective power generation efficiency and economic cost of the floating wind turbine array are analyzed, and a fitness function is constructed to evaluate the pros and cons of each solution (i.e., a layout plan of the floating wind turbine array). The higher the power generation efficiency, the greater the fitness value, and the lower the cost, the greater the fitness value. The floating wind turbine array optimization model is used as input, and the genetic algorithm is run to generate a series of solutions (layout plans), which are evaluated through the fitness function. After multiple iterations, the algorithm will gradually converge to an approximate optimal solution. The specific process is: a set of floating wind turbine arrays is randomly generated. The layout scheme is used as the initial population, and each solution is evaluated according to the fitness function. The solution with higher fitness is selected as the parent generation to generate the next generation. New child solutions are generated by exchanging some genes of the parent solution. The child solutions are randomly mutated to increase the diversity of solutions. The selection, crossover and mutation operations are repeated until the fitness value is no longer significantly improved. For each generated layout scheme, the floating wind turbine wake model and marine environmental data are used to comprehensively analyze the wake effect between floating wind turbines, the changes in wind speed and wind direction, and the impact of the marine environment on wind turbine performance, and calculate the effective power generation efficiency of the floating wind turbine array. According to the evaluation results of the effective power generation efficiency and combined with the economic cost factor, the optimal layout scheme of the floating wind turbine array that meets all constraints (space constraints, safety constraints, and economic constraints) is screened out, thereby achieving the best balance between power generation efficiency and economy.

[0080] Furthermore, the calculation expression of the fitness function is:

[0081] ;

[0082] Among them, A is the fitness function, which represents the pros and cons of a floating wind turbine array layout scheme. is the effective power generation of the i-th wind turbine, which is affected by the wake effect and is calculated using the floating wind turbine wake model. is the installation and operation cost of the i-th wind turbine, including materials, labor, maintenance and other costs, is the weight factor used to balance power generation efficiency and cost, R is the proportion of operation and maintenance costs exceeding the budget, is the budget upper limit, reflecting the impact of cost control on fitness. The fitness function comprehensively considers power generation efficiency and economic cost, and adjusts the weight factor and budget cap , adapt to different optimization goals and project requirements, and find the wind turbine layout solution that maximizes the fitness function through optimization algorithms such as genetic algorithms, that is, to achieve the maximum power generation efficiency while meeting the cost constraints;

[0083] Step 5: Use simulation software to simulate the operation of the optimized floating wind turbine array to verify the correctness and effectiveness of the optimization results, and conduct field tests to verify and compare the power generation efficiency indicators before and after optimization to further evaluate the optimization effect;

[0084] Step 6: Analyze the results of simulation and field test to find out the deficiencies in the optimization process, and adjust and optimize the optimization scheme according to the analysis results to improve the power generation efficiency and stability of the floating wind turbine array.

[0085] Embodiment 2, as Figure 4 As shown, based on Example 1, the present invention provides a technical solution: Preferably, in step 5, the process of field testing and verification is:

[0086] According to the characteristics of the floating wind turbine array and the content that needs to be verified, OpenFAST is used to perform aerodynamic-hydrodynamic-servo-elastic simulation calculations of the floating wind turbine, and in the simulation software, a corresponding simulation model is built according to the optimized floating wind turbine array layout plan. After the model is built, a preliminary verification is carried out to ensure that the model can correctly simulate the operating characteristics of the floating wind turbine. The optimized floating wind turbine array layout plan is input into OpenFAST for simulation operation. During the simulation process, the power generation efficiency and output power data of the floating wind turbine array are collected and compared with the data before optimization to verify the correctness and effectiveness of the optimization results. Sensors are installed on the actual floating wind turbine array, and the floating wind turbine array is field tested. The actual power generation efficiency and output power data of the floating wind turbine are collected and compared with the simulation results. The differences and reasons between the two are analyzed, the power generation efficiency indicators before and after optimization are compared, and the evaluation value of the optimization effect is calculated to further evaluate the optimization effect.

[0087] Furthermore, the calculation expression of the optimization effect evaluation value is:

[0088] ;

[0089] Among them, X is the optimization effect evaluation value, is the total power generation of the optimized floating wind turbine array, is the total power generation of the floating wind turbine array before optimization, is the total cost of the optimized floating wind turbine array, is the total cost of the floating wind turbine array before optimization, Score the environmental impact of the optimized floating wind turbine array. Score the environmental impact of the floating wind turbine array before optimization. and The larger the ratio is, the more the power generation efficiency is improved after optimization, and the higher the value of X is. and The smaller the ratio, the greater the cost reduction after optimization, and the higher the X value. and The closer the ratio is to 1, the better the optimization effect is. The higher the value of X is, the closer the value is to 1.

[0090] In step 6, the process of adjusting and optimizing the optimization scheme is as follows:

[0091] Based on the optimization effect evaluation results of the power generation efficiency indicators before and after optimization, identify the deficiencies in the optimization process. If the simulation results are significantly different from the field test results, the model accuracy is insufficient and a more accurate description of the aerodynamic, hydrodynamic, servo and elastic characteristics of the floating wind turbine is required. If the floating wind turbine array performs poorly under specific environmental conditions, the optimization scheme is not adaptable enough to environmental changes. If the floating wind turbine exhibits large vibrations or output power fluctuations in simulation or field tests, the control strategy needs to be optimized. According to the error analysis results, the simulation model is improved to improve the model accuracy, the environmental conditions are analyzed, and the optimization scheme is adjusted to improve the environmental adaptability of the floating wind turbine array. According to the results of simulation and field tests, the layout of the floating wind turbine array is adjusted to optimize the power generation efficiency and stability. The adjusted optimization scheme is input into the simulation software for a new round of simulation calculations. Field tests are carried out on the adjusted floating wind turbine array to verify the optimization effect. According to the results of simulation and field tests, the optimization scheme is iteratively adjusted until the required power generation efficiency is achieved. The entire optimization process is summarized, including the results of simulation and field tests, the adjustment and optimization of the optimization scheme, and the final power generation efficiency and stability performance. A detailed report is written to record the optimization process, result analysis, adjustment and optimization of the optimization scheme, and the final conclusion.

[0092] Embodiment 3, as Figure 5 As shown, on the basis of Embodiment 1-2, the present invention further provides a floating wind turbine array optimization layout system based on wake effect, which is used to implement a floating wind turbine array optimization layout method based on wake effect, including an optimization analysis platform, the optimization analysis platform is communicatively connected with a data collection and analysis module, a wake effect simulation model module, an optimization algorithm module, a fitness function evaluation module, a test verification module, an iterative optimization module and a report generation module, wherein the modules are connected by electrical signals;

[0093] The data collection and analysis module is used to collect wind resource data and ocean environment data required for the optimization analysis of floating wind turbine arrays, and pre-process the collected data to ensure the accuracy and completeness of the data, providing strong support for subsequent optimization analysis;

[0094] The wake effect simulation model module establishes a wake effect simulation model for floating wind turbine arrays, simulates the wake interference between floating wind turbines under different layouts, and can accurately describe the aerodynamic and hydrodynamic characteristics of floating wind turbines and the impact of wake on downstream wind turbines. Through the simulation model, the power generation efficiency of floating wind turbine arrays under different layouts is predicted, providing a scientific basis for optimizing the layout.

[0095] The optimization algorithm module uses OpenFAST software to perform aerodynamic-hydrodynamic-servo-elastic simulation calculations of floating wind turbines, simulates the wake effect between floating wind turbines under different layouts, optimizes the layout of the floating wind turbine array, and continuously adjusts the position and direction of the wind turbines according to the results of the wake effect simulation model to maximize the power generation efficiency of the entire array. Through the optimization algorithm, the optimal floating wind turbine array layout solution is found to significantly improve the power generation efficiency;

[0096] The fitness function evaluation module constructs a fitness function to evaluate the pros and cons of each solution (i.e., a layout scheme of the floating wind turbine array) and determines the optimal layout scheme through a comprehensive evaluation of power generation efficiency, cost, and environmental impact;

[0097] The test verification module installs sensors on the actual floating wind turbine array to collect actual power generation efficiency and output power data, and compares them with the simulation results to verify the actual effect of the optimization scheme and ensure its reliability and effectiveness;

[0098] Iterative optimization module, based on the results of simulation and field tests, identifies deficiencies in the optimization process, adjusts and optimizes the optimization scheme, improves the power generation efficiency and stability of the floating wind turbine array, and achieves the best balance between power generation efficiency and economy;

[0099] The report generation module summarizes the entire optimization process, including the results of simulation and field tests, the adjustment and optimization process of the optimization plan, and the final power generation efficiency and stability performance. At the same time, it generates an optimization report to record the optimization process, result analysis, adjustment and optimization of the optimization plan, and the final conclusion.

[0100] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A floating wind turbine array optimization layout method based on wake effect, characterized in that: The following steps are involved: Step 1: Conduct wind resource assessment on the wind farm area where the floating wind turbines are deployed, simultaneously collect marine environmental data of the wind farm area, and analyze wind speed and direction indicators and marine environmental indicators of the wind farm area; Step 2, analyzing the existing wake model, combining the motion characteristics of the floating wind turbine under the dual effects of wave and wind loads, analyzing its influence on the wake effect, and establishing a floating wind turbine wake model according to the actual operating conditions of the floating wind turbine. In step 2, the process of establishing the floating wind turbine wake model is as follows: Analyze the existing wake model, clarify the basic principles and applicable conditions of the wake model, and use numerical simulation methods to analyze and determine the motion characteristics of floating wind turbines under wave and wind loads, including the sway, yaw, and pitch motion of the wind turbine; Combined with the motion characteristics of floating wind turbines, the influence of floating wind turbines on wake effects is analyzed, and the influence of wave, wind load, wind speed and wind direction on wake effects is analyzed. A multi-body dynamic model of floating wind turbines is established. According to the marine environmental conditions and wind turbine characteristics, the magnitude and direction of wave and wind loads are determined. The motion equation of floating wind turbines is solved by finite difference method to obtain its motion response. Based on the motion characteristics of floating wind turbines and the influencing factors of wake effect, the multi-body dynamics model of floating wind turbines is combined with the CFD model to establish a floating wind turbine wake model to simulate the motion characteristics and wake effect of floating wind turbines under wave and wind loads. According to the actual size of the floating wind turbine, the marine environment conditions and the characteristics of the floating wind turbine, the parameters and boundary conditions of the numerical simulation are set, and the wake of the floating wind turbine is simulated and analyzed using the computational fluid dynamics numerical simulation method. The numerical simulation results are compared with the experimental data to verify the floating wind turbine wake model. Step 3, according to the operating characteristics of the floating wind turbine and the marine environmental index, establish the constraint conditions for the optimal layout, combine the floating wind turbine wake model, the marine environmental index and the wind speed and direction index, and construct the floating wind turbine array optimization model. In step 3, the construction process of the floating wind turbine array optimization model is as follows: Analyze wind resource data and marine environment data, clarify the operating characteristics and marine environmental indicators of floating wind turbines, and establish the spatial constraints, safety constraints and economic constraints for optimal layout; The floating wind turbine wake model is used to simulate the wake effect between floating wind turbines under different layouts. Through simulation, the impact of different layouts on wind speed and power generation efficiency is analyzed; The floating wind turbine wake model is combined with the marine environment index and the wind speed and direction index to construct a floating wind turbine array optimization model to evaluate the effective power generation efficiency of the floating wind turbine array under different layouts. The calculation process of the floating wind turbine array effective power generation efficiency is to calculate the swept area A of each floating wind turbine. i , considering the wake effect and the distance between floating wind turbines, the wake velocity V of each floating wind turbine is estimated. i , and combined with wind speed, power coefficient of floating wind turbine and swept area of ​​floating wind turbine, the theoretical power generation of each floating wind turbine is calculated, and the wake attenuation coefficient λ and floating wind turbine spacing L are used to analyze the wake effect, adjust the power generation of each floating wind turbine, and combine the wave influence factor k and the influence of the marine environment to analyze the influence of wind direction change angle α and temperature T, salinity S, and obtain the final effective power generation efficiency. The calculation expression is: Where E is the effective power generation efficiency of the floating wind turbine array, V ws is the wind speed, D is the diameter of the floating wind turbine, L is the distance between floating wind turbines, C P is the power coefficient of the floating wind turbine, λ is the wake attenuation coefficient, k is the wave influence factor, α is the wind direction change angle, T is the temperature, S is the salinity, ρ is the air density, and A i is the swept area of ​​the ith fan, V i is the wake velocity of the i-th wind turbine, n is the total number of wind turbines, e -λL is the wake attenuation, cos(α) is the effect of wind direction change on power generation efficiency, for the effects of temperature and salinity on air density; Step 4: according to the solution requirements of the floating wind turbine array optimization model, an optimization algorithm is selected and the floating wind turbine array optimization model is solved to obtain the optimal layout plan of the floating wind turbine array; Step 5: Use simulation software to simulate the operation of the optimized floating wind turbine array to verify the correctness and effectiveness of the optimization results, and conduct field tests to verify and compare the power generation efficiency indicators before and after optimization to further evaluate the optimization effect; Step 6: Analyze the results of simulation and field testing to find out the deficiencies in the optimization process, and adjust and optimize the optimization plan based on the analysis results.

2. The method for optimizing the arrangement of a floating wind turbine array based on wake effect according to claim 1, characterized in that: In step 1, the analysis process of the wind speed and direction index and the marine environment index of the wind farm area is as follows: Collect wind resource data on wind speed, wind direction, and temperature in the wind farm area through weather stations, wind towers, and buoy equipment; Use ocean monitoring stations and buoy equipment to collect ocean environmental data on waves, tides, temperature, and salinity in the wind farm area; Analyze the collected wind resource data to determine the average wind speed, wind direction frequency distribution, and key indicators of wind speed variability in the wind farm area, and analyze the ocean environment indicators of wave height, wave period, current speed and direction, and sea level change; Integrate wind resource data and marine environment data, analyze the relationship between wind speed and direction in the wind farm area and the marine environment, and identify the potential connection and interaction mechanism between changes in wind speed and direction and wave and tidal marine environmental factors.

3. The method for optimizing the arrangement of a floating wind turbine array based on wake effect according to claim 2, characterized in that: In step 4, the analysis process of the optimal arrangement of the floating wind turbine array is as follows: According to the characteristics and solution requirements of the floating wind turbine array optimization model, the genetic algorithm is selected as the solution tool to solve the floating wind turbine array optimization model; Analyze the effective power generation efficiency and economic cost of floating wind turbine arrays, and construct a fitness function to evaluate the pros and cons of each solution; The floating wind turbine array optimization model is used as input, and the genetic algorithm is run to generate a series of solutions, which are evaluated by the fitness function. After multiple iterations, the algorithm gradually converges to a near-optimal solution. For each generated layout scheme, the floating wind turbine wake model and marine environment data are used to comprehensively analyze the wake effect between floating wind turbines, changes in wind speed and direction, and the impact of the marine environment on wind turbine performance, and calculate the effective power generation efficiency of the floating wind turbine array; Based on the evaluation results of effective power generation efficiency and combined with economic cost factors, the optimal layout scheme of floating wind turbine arrays that meets all constraints is selected to achieve the best balance between power generation efficiency and economy. The calculation expression of the fitness function is: Among them, A is the fitness function, P i is the effective power generation of the i-th wind turbine, C i is the installation and operation and maintenance cost of the i-th wind turbine, β is the weight factor used to balance power generation efficiency and cost, R is the proportion of operation and maintenance cost exceeding the budget, and θ is the budget upper limit.

4. The method for optimizing the arrangement of a floating wind turbine array based on wake effect according to claim 3 is characterized in that: In step 5, the process of field testing and verification is as follows: According to the characteristics of the floating wind turbine array and the content that needs to be verified, OpenFAST is used to perform aerodynamic-hydrodynamic-servo-elastic simulation calculations of the floating wind turbine, and a corresponding simulation model is built in the simulation software according to the optimized floating wind turbine array layout plan; The optimized floating wind turbine array layout scheme is input into OpenFAST for simulation. During the simulation, the power generation efficiency and output power data of the floating wind turbine array are collected and compared with the data before optimization to verify the correctness and effectiveness of the optimization results. Install sensors on the actual floating wind turbine array, conduct field tests on the floating wind turbine array, collect the actual power generation efficiency and output power data of the floating wind turbines, and compare them with the simulation results to analyze the differences and causes between the two; Compare the power generation efficiency indicators before and after optimization, calculate the evaluation value of the optimization effect, and further evaluate the optimization effect.

5. The method for optimizing the arrangement of a floating wind turbine array based on wake effect according to claim 4, characterized in that: The calculation expression of the optimization effect evaluation value is: Among them, X is the optimization effect evaluation value, P opt is the total power generation of the optimized floating wind turbine array, P base is the total power generation of the floating wind turbine array before optimization, C opt is the total cost of the optimized floating wind turbine array, C base is the total cost of the floating wind turbine array before optimization, R env R is the environmental impact score of the optimized floating wind turbine array. base Score the environmental impact of the floating wind turbine array before optimization.

6. The method for optimizing the arrangement of a floating wind turbine array based on wake effect according to claim 5, characterized in that: In step 6, the process of adjusting and optimizing the optimization scheme is as follows: Based on the optimization effect evaluation results of the power generation efficiency indicators before and after optimization, identify the deficiencies in the optimization process; Based on the error analysis results, the simulation model was improved, environmental conditions were analyzed, the optimization scheme was adjusted, and the layout of the floating wind turbine array was adjusted based on the results of simulation and field tests; Input the adjusted optimization scheme into the simulation software, conduct a new round of simulation calculations, conduct field tests on the adjusted floating wind turbine array to verify the optimization effect, and iteratively adjust the optimization scheme based on the results of simulation and field tests until the required power generation efficiency is achieved; Summarize the entire optimization process, including the results of simulation and field tests, the adjustment and optimization of the optimization plan, and the final power generation efficiency and stability performance; write a detailed report to record the optimization process, result analysis, adjustment and optimization of the optimization plan, and the final conclusion.

7. A floating wind turbine array optimization layout system based on wake effect, used to implement the floating wind turbine array optimization layout method based on wake effect as described in any one of claims 1 to 6, comprising an optimization analysis platform, characterized in that: The optimization and analysis platform is communicatively connected with a data collection and analysis module, a wake effect simulation model module, an optimization algorithm module, a fitness function evaluation module, a test verification module, an iterative optimization module and a report generation module, wherein electrical signals are connected between the modules; The data collection and analysis module is used to collect wind resource data and ocean environment data required for optimization analysis of the floating wind turbine array, and pre-process the collected data; The wake effect simulation model module establishes a wake effect simulation model of a floating wind turbine array to simulate the wake interference between floating wind turbines under different layouts; The optimization algorithm module uses OpenFAST software to perform aerodynamic-hydrodynamic-servo-elastic simulation calculations of floating wind turbines, simulates the wake effect between floating wind turbines under different layouts, and optimizes the layout of the floating wind turbine array; The fitness function evaluation module constructs a fitness function and evaluates the quality of each solution; The test verification module installs sensors on the actual floating wind turbine array to collect actual power generation efficiency and output power data, and compares them with the simulation results to verify the actual effect of the optimization scheme; The iterative optimization module identifies deficiencies in the optimization process and adjusts and optimizes the optimization scheme based on the results of simulation and field testing; The report generation module summarizes the entire optimization process and generates an optimization report.

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