Layout method and apparatus for wind power plant, and storage medium and electronic apparatus

Abstract

Disclosed in the present application are a layout method and apparatus for a wind power plant, and a storage medium and an electronic apparatus. The method comprises: acquiring basic data of a wind power plant, wherein the basic data at least comprises: the speed of wind in the wind power plant, terrain data of the position where the wind power plant is located, and meteorological data of the position; by means of the speed of the wind and the meteorological data, simulating the dynamics of airflows inside the wind power plant; analyzing the impact of the terrain data on wind speed distribution, so as to obtain an analysis result; and by means of the dynamics of the airflows and the analysis result, designing a layout for the wind power plant. Therefore, the problem of it being impossible for the layout of a wind power plant to take into consideration the impacts, on the performance of wind turbines, of both complex dynamics of airflows inside the wind power plant and terrain data of the position where the wind power plant is located is solved, and the layout difficulty of the wind power plant is reduced.

Description

Wind farm layout methods and devices, storage media and electronic devices

[0001] Cross-reference to related applications

[0002] This disclosure claims priority to Chinese patent application No. 202410923519.0, filed on July 10, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of wind power generation technology, and more specifically, to a method and apparatus for the layout of a wind farm, a storage medium, and an electronic device. Background Technology

[0004] Wind power is an important renewable energy source, and its power generation efficiency and economics are directly affected by the layout of wind farms. Traditional wind farm layout designs are usually based on experience and simplified models, which cannot fully consider the impact of complex airflow dynamics and geographical features such as topography on wind turbine performance. Technical issues

[0005] There is still no effective solution to the problem that wind farm layout cannot simultaneously consider the complex airflow dynamics inside the wind farm and the impact of the local terrain data on wind turbine performance. Technical solutions

[0006] This application provides a wind farm layout method and apparatus, storage medium and electronic device, to at least solve the problem in the prior art that the wind farm layout cannot simultaneously consider the complex airflow dynamics inside the wind farm and the impact of the local terrain data on the wind turbine performance.

[0007] According to one embodiment of this application, a method for laying out a wind farm is provided, comprising: acquiring basic data of the wind farm, wherein the basic data includes at least: wind speed in the wind farm, topographic data of the location of the wind farm, and meteorological data of the location; simulating the airflow dynamics inside the wind farm using the wind speed and the meteorological data; analyzing the influence of the topographic data on the wind speed distribution to obtain analysis results; and laying out the wind farm using the airflow dynamics and the analysis results.

[0008] In an exemplary embodiment, obtaining the basic data of the wind farm includes: obtaining raw data from a target data source, wherein the raw data includes: the raw wind speed corresponding to the wind speed, the raw terrain data corresponding to the terrain data, and the raw meteorological data corresponding to the meteorological data; performing preprocessing operations on the raw data to obtain the basic data, wherein the preprocessing operations include at least: data cleaning operations, format conversion operations, and data standardization operations.

[0009] In an exemplary embodiment, simulating the airflow dynamics within the wind farm using the wind speed and meteorological data includes: setting simulation conditions using the terrain data and meteorological data, wherein the simulation conditions include at least boundary conditions and initial conditions; establishing a simulation region of the wind farm using the simulation conditions; dividing the simulation region using a mesh generation technique to obtain multiple first units; and solving the flow equations within each first unit using a preset solver to obtain the airflow dynamics.

[0010] In an exemplary embodiment, the simulated region is divided into multiple first units using a grid partitioning technique, including: dividing the simulated region into multiple second units using the grid partitioning technique to obtain a first partitioning result, wherein the first partitioning result includes multiple second units; determining the vorticity and velocity gradient of any grid point corresponding to the first partitioning result using the wind speed, and determining a decision value corresponding to any grid point using the vorticity and velocity gradient, wherein any grid point is any point among the grid endpoints of the grid used to partition the multiple second units; if the decision value exceeds a preset threshold, further partitioning the third unit adjacent to the any grid point to obtain a fourth unit; and determining the multiple first units using the multiple second units and the fourth unit.

[0011] In an exemplary embodiment, analyzing the impact of the terrain data on wind speed distribution to obtain analysis results includes: analyzing the terrain data to obtain terrain features in the terrain data that affect the wind speed distribution; and determining the terrain features as the analysis results.

[0012] In an exemplary embodiment, the layout of the wind farm based on the airflow dynamics and the analysis results includes: determining at least one installation location for wind turbines based on the airflow dynamics and the analysis results; determining the wind power generation efficiency corresponding to the at least one installation location, wherein the wind power generation efficiency is the power generation efficiency obtained after excluding mutual interference between wind turbines installed at the at least one installation location; and determining the layout of the wind farm based on the wind power generation efficiency.

[0013] In an exemplary embodiment, the wind farm is laid out using the airflow dynamics and the analysis results, including: when there are multiple layout results for laying out the wind farm, iteratively optimizing the multiple layout results using a genetic algorithm; and laying out the wind farm using the optimal layout result obtained from the iterative optimization when the iteration termination condition is met.

[0014] According to another embodiment of this application, a wind farm layout device is also provided, comprising: an acquisition module for acquiring basic data of the wind farm, wherein the basic data includes at least: wind speed in the wind farm, topographic data of the location of the wind farm, and meteorological data of the location; a simulation module for simulating the airflow dynamics inside the wind farm using the wind speed and the meteorological data; an analysis module for analyzing the influence of the topographic data on the wind speed distribution and obtaining analysis results; and a layout module for laying out the wind farm using the airflow dynamics and the analysis results.

[0015] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer program, and the computer program is configured to execute the above-described method at runtime.

[0016] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-described method through the computer program.

[0017] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps in any of the above method embodiments. Beneficial effects

[0018] In this embodiment, basic data of the wind farm is obtained, including at least: wind speed in the wind farm, topographic data of the wind farm's location, and meteorological data of the location; the airflow dynamics within the wind farm are simulated using the wind speed and meteorological data; the influence of the topographic data on wind speed distribution is analyzed to obtain analysis results; and the wind farm is laid out based on the airflow dynamics and the analysis results. This embodiment solves the problem in the prior art where wind farm layout cannot simultaneously consider the complex airflow dynamics within the wind farm and the influence of the wind farm's topographic data on wind turbine performance, thus reducing the difficulty of wind farm layout. Attached Figure Description

[0019] Figure 1 is a hardware structure block diagram of a computer terminal for a wind farm layout method according to an embodiment of this application.

[0020] Figure 2 is a flowchart of a wind farm layout method according to an embodiment of this application;

[0021] Figure 3 is a schematic diagram of the layout system of a wind farm according to an embodiment of this application;

[0022] Figure 4 is another flowchart of a wind farm layout method according to an embodiment of this application;

[0023] Figure 5 is a structural block diagram of a wind farm layout device according to an embodiment of this application. Embodiments of the present invention

[0024] The methods and embodiments provided in this application can be executed on a computer terminal or similar computing device, cloud platform, independent physical server, or software platform, wherein the software platform runs through one or more servers. Taking running on a computer terminal as an example, FIG1 is a hardware structure block diagram of a computer terminal for a wind farm layout method according to an embodiment of this application. As shown in FIG1, the computer terminal may include one or more (only one is shown in FIG1) processors 102 and a memory 104 for storing data. In an exemplary embodiment, the computer terminal may also include a transmission device 106 for communication functions and an input / output device 108, wherein the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA. Those skilled in the art will understand that the structure shown in FIG1 is only illustrative and does not limit the structure of the computer terminal. For example, the computer terminal may also include more or fewer components than shown in FIG1, or have different configurations with the same or more functions than those shown in FIG1.

[0025] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thus implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0026] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider for the computer terminal. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0027] This embodiment provides a wind farm layout method, applied in the aforementioned computer terminal. Figure 2 is a flowchart of the wind farm layout method according to an embodiment of this application, which includes the following steps:

[0028] Step S202: Obtain basic data of the wind farm, wherein the basic data includes at least: wind speed in the wind farm, topographic data of the location of the wind farm, and meteorological data of the location;

[0029] Step S204: Simulate the airflow dynamics inside the wind farm using the wind speed and meteorological data;

[0030] Step S206: Analyze the impact of the terrain data on wind speed distribution and obtain the analysis results;

[0031] Step S208: The wind farm is laid out based on the airflow dynamics and the analysis results.

[0032] Through the above steps, basic data of the wind farm is obtained, including at least: wind speed in the wind farm, topographic data of the wind farm's location, and meteorological data of the location. The airflow dynamics within the wind farm are simulated using the wind speed and meteorological data. The impact of the topographic data on wind speed distribution is analyzed to obtain analysis results. The wind farm is then laid out based on the airflow dynamics and the analysis results. This embodiment solves the problem in the prior art where wind farm layout cannot simultaneously consider the complex airflow dynamics within the wind farm and the impact of the wind farm's topographic data on wind turbine performance, thus reducing the difficulty of wind farm layout.

[0033] Optionally, obtaining the basic data of the wind farm includes: obtaining raw data from a target data source, wherein the raw data includes: the raw wind speed corresponding to the wind speed, the raw terrain data corresponding to the terrain data, and the raw meteorological data corresponding to the meteorological data; performing preprocessing operations on the raw data to obtain the basic data, wherein the preprocessing operations include at least: data cleaning operations, format conversion operations, and data standardization operations.

[0034] This application embodiment automatically collects key information such as raw wind speed corresponding to wind speed, raw terrain data corresponding to terrain data, and raw meteorological data corresponding to meteorological data through various methods such as application programming interface (API) calls, file reading, and real-time data stream access. This application embodiment pre-defines the type, location, access protocol, and data format of the target data source using configuration files, making it possible to collect data from multiple different target data sources. Furthermore, this application embodiment triggers data acquisition tasks at a preset frequency using a pre-set scheduler to obtain raw data. Further, after obtaining the raw data, data preprocessing operations are performed on the raw data to obtain high-quality basic data.

[0035] In an optional embodiment, simulating the airflow dynamics within the wind farm using the wind speed and meteorological data includes: setting simulation conditions using the terrain data and meteorological data, wherein the simulation conditions include at least boundary conditions and initial conditions; establishing a simulation region of the wind farm using the simulation conditions; dividing the simulation region into multiple first units using a mesh generation technique; and solving the flow equations within each first unit using a preset solver to obtain the airflow dynamics.

[0036] In this embodiment, the airflow dynamics are determined using a computational fluid dynamics (CFD) simulation module. Specifically, simulation conditions are set based on topographic and meteorological data from the baseline data, and a simulation region of the wind farm is established according to these conditions. The simulation conditions include: boundary conditions defining the extent of the simulation region, initial conditions limiting the number of grid cells allowed in the simulation region, and physical parameters set for the simulation region, such as the number of layers within the simulation region. After dividing the simulation region using mesh generation technology, the flow equations for each first cell obtained from the division are solved using a preset solver to obtain the airflow dynamics.

[0037] Furthermore, the simulated region is divided using a grid partitioning technique to obtain multiple first units, including: dividing the simulated region using the grid partitioning technique to obtain a first partitioning result, wherein the first partitioning result includes multiple second units; determining the vorticity and velocity gradient of any grid point corresponding to the first partitioning result using the wind speed, and determining the decision value corresponding to any grid point using the vorticity and velocity gradient, wherein any grid point is any point among the grid endpoints of the grid used to partition the multiple second units; if the decision value exceeds a preset threshold, further partitioning the third unit adjacent to the any grid point to obtain a fourth unit; determining the multiple first units using the multiple second units and the fourth unit.

[0038] Optional, vorticity The calculation formula is shown in formula (1) below:

[0039] (1);

[0040] in, Represents the velocity field vector. Represents a unit vector. This indicates the wind speed after correction based on meteorological data.

[0041] Optional, velocity gradient The calculation formula is shown in formula (2) below:

[0042] (2);

[0043] in, Indicates a point in space The velocity gradient at a point (equivalent to any grid point in the above embodiments, where a spatial point is equivalent to a grid point) is a scalar value that quantifies the degree of change in fluid velocity at that spatial point. The larger the velocity gradient, the more drastic the change in fluid velocity near that point.

[0044] Indicates wind speed at Velocity component in the direction;

[0045] Furthermore, by combining eddies and velocity gradients, a decision formula is used to calculate the decision value at any grid point. :

[0046] (3);

[0047] in, and The pre-defined weighting parameters determine the relative importance of velocity gradient and vorticity in grid adjustment decisions.

[0048] Therefore, if the decision value of any grid point exceeds a preset threshold, the second unit surrounding that grid point is further subdivided into a more fine-grained fourth unit. The second and fourth units that are not further subdivided are then designated as the first unit.

[0049] In an optional embodiment, analyzing the impact of the terrain data on wind speed distribution to obtain analysis results includes: analyzing the terrain data to obtain terrain features in the terrain data that have an impact on the wind speed distribution; and determining the terrain features as the analysis results.

[0050] Specifically, in this embodiment of the application, the Geographic Information System (GIS) analysis module is used to analyze the terrain, landforms and other geographical features included in the terrain data, determine the terrain features that affect the wind speed distribution, and obtain the analysis results.

[0051] Further, after determining the airflow dynamics and analysis results, the wind farm is laid out based on the airflow dynamics and analysis results, including: determining at least one installation location for the wind turbines based on the airflow dynamics and analysis results; determining the wind power generation efficiency corresponding to the at least one installation location, wherein the wind power generation efficiency is the power generation efficiency obtained after excluding mutual interference between the wind turbines installed at the at least one installation location; and determining the layout of the wind farm based on the wind power generation efficiency.

[0052] Optionally, the terrain features that affect wind speed distribution in the analysis results can be set as at least one installation location of the wind turbine, and the wind power generation efficiency corresponding to the at least one installation location can be calculated. Specifically, the wind power generation efficiency is determined by the following formulas (4) and (5), including:

[0053] First, the foundation wind power generation efficiency at at least one installation site is calculated using formula (4):

[0054] (4);

[0055] in, The coefficient of performance (COP) quantifies the energy conversion efficiency of a wind turbine at a specific wind speed. Different models of wind turbines have different COPs, which are obtained directly from performance data provided by the wind turbine manufacturer or through field testing.

[0056] The probability density function representing wind speed is the wind speed distribution obtained through statistical analysis based on long-term wind speed observation data.

[0057] The wind power curve represents the energy output from the minimum cut-in wind speed. Maximum cut-out wind speed Energy capture;

[0058] This represents the terrain influence function, based on the wind turbine location. To adjust energy output to take into account the impact of terrain on wind speed distribution.

[0059] It should be noted that the basic wind power generation efficiency A scalar value representing wind speed.

[0060] Secondly, considering the impact of mutual interference between wind turbines on energy output, a coefficient is used to adjust the base wind power generation efficiency to obtain the aforementioned wind power generation efficiency. :

[0061] (5);

[0062] in, It is a coefficient that measures the intensity of mutual interference between the i-th and j-th wind turbines, and is related to the distance and relative position between the wind turbines;

[0063] It is a correction factor for the influence of turbulence between wind turbines, reflecting the impact of turbulence on the energy capture efficiency of adjacent wind turbines.

[0064] After determining the wind power generation efficiency corresponding to at least one installation location, the W installation locations with the highest wind power generation efficiency among the at least one installation locations are determined as the expected installation locations of the wind turbines, and the wind farm is laid out accordingly.

[0065] In an exemplary embodiment, the wind farm is laid out using the airflow dynamics and the analysis results, including: when there are multiple layout results for laying out the wind farm, iteratively optimizing the multiple layout results using a genetic algorithm; and laying out the wind farm using the optimal layout result obtained from the iterative optimization when the iteration termination condition is met.

[0066] It is understandable that multiple layouts are typically designed during the wind farm layout planning process. Different layouts usually have different turbine installation locations. This application's embodiment uses a genetic algorithm to iteratively optimize these multiple layouts to obtain the optimal layout, and then uses this optimal layout to deploy the wind farm.

[0067] To better understand the process of the above-mentioned wind farm layout method, the following description will be based on optional embodiments, but it is not intended to limit the technical solutions of the embodiments of this application.

[0068] The purpose of this optional embodiment is to provide a wind farm micro-layout optimization system based on CFD and GIS. By comprehensively applying computational fluid dynamics simulation and geographic information system data, it accurately analyzes the airflow dynamics and terrain features inside the wind farm, optimizes the micro-layout of wind turbines, and thus improves the power generation efficiency and economy of the wind farm.

[0069] To achieve the above objectives, a wind farm layout system is provided. The architecture of the wind farm layout system is shown in Figure 3, and includes:

[0070] Data Acquisition Module: This module automatically collects and integrates key information (equivalent to raw data) such as wind speed, terrain, and meteorological conditions from various sources (the sources are equivalent to the target data source). It supports multiple data formats, including CSV, XML, and SHP files, and can also acquire real-time meteorological data. Furthermore, it features data preprocessing and verification mechanisms to ensure data integrity and accuracy.

[0071] The CFD simulation module uses collected wind speed and meteorological data to simulate the airflow dynamics within a wind farm using computational fluid dynamics. This module can consider the influence of factors such as terrain and buildings, supports airflow simulation in complex terrains, and provides visualization of the simulation results.

[0072] GIS Analysis Module: This module processes and analyzes terrain data to identify the most suitable terrain features for wind turbine installation. It utilizes GIS technology to analyze the impact of terrain on wind speed distribution and outputs the terrain analysis results.

[0073] Optimization Algorithm Module: This module integrates CFD simulation results and GIS analysis results, applying a multi-objective optimization algorithm to optimize the micro-layout of the wind turbines. Considering wind farm design requirements and environmental protection requirements, iterative calculations using a genetic algorithm are employed to find the optimal solution.

[0074] User interface: Provides an intuitive and user-friendly interface, allowing users to easily input basic wind farm data, select simulation and optimization parameters, and view the optimized wind farm layout scheme.

[0075] Feedback and Adjustment Module: Based on user feedback provided through the user interface, adjust and optimize parameters and targets to achieve dynamic adjustment and iterative optimization of the optimization process.

[0076] Furthermore, the working principle and operating range of the data acquisition module are as follows:

[0077] The data acquisition module is designed as the system's data entry point. Its core principle is to automatically collect key information such as wind speed, terrain, and weather conditions through various methods, including API calls, file reading, and real-time data stream access. This module uses configuration files to define the data source type, location, access protocol, and data format, making it possible to collect data from multiple different sources. A built-in scheduler triggers data acquisition tasks at a preset frequency.

[0078] The operations involved in the data acquisition module include, but are not limited to:

[0079] Configure data sources: Configure the required data sources according to the specific needs of the wind farm project, including online meteorological databases, terrain databases, real-time meteorological stations, etc.

[0080] Automated data collection: Based on the configured collection frequency and data source, automatically execute data scraping tasks, including initiating API requests, reading data files, and subscribing to data streams.

[0081] Data preprocessing: Perform format conversion, cleaning (removing outliers and filling missing values) and standardization on the collected raw data to ensure data quality.

[0082] Storage and Indexing: The processed data is stored in the system's database, and appropriate indexes are created to facilitate efficient retrieval and analysis later.

[0083] Furthermore, the working principle and operating range of the CFD simulation module are as follows:

[0084] The CFD simulation module simulates airflow dynamics within a wind farm by numerically analyzing the equations for gas or liquid flow (Navier-Stokes equations). Based on topographic and meteorological data acquired from the data acquisition module, this module sets boundary and initial conditions, and uses mesh generation technology to divide the simulation area into tens of thousands of small cells. By solving the flow equations within each cell, the airflow dynamics of the entire simulation area are ultimately obtained. The operations involved include, but are not limited to:

[0085] Set simulation conditions: Based on the collected terrain and meteorological data, set the boundary conditions, initial conditions and physical parameters for the CFD simulation.

[0086] Mesh generation: The wind farm area is divided into meshes, and finer meshes are used in complex terrain areas to improve simulation accuracy.

[0087] Specifically, to improve the accuracy of adaptive CFD simulations, the calculations of vorticity and velocity gradients are refined, and the mesh adjustments are determined by using vorticity and velocity gradients.

[0088] The vorticity calculation formula is shown in formula (1) in the above embodiment, and the velocity gradient calculation formula is shown in formula (2) in the above embodiment.

[0089] It should be noted that the velocity gradient is a scalar measure of the rate of change of velocity, and can be used to identify regions where the fluid velocity changes drastically.

[0090] Furthermore, by combining eddies and velocity gradients, it is assessed whether the current mesh needs to be refined. The decision formula used for the assessment is shown in formula (3) in the above embodiment.

[0091] Therefore, for each grid point, the decision formula is used to calculate... The value (equivalent to the decision value). If the decision value exceeds the preset threshold, it indicates that the fluid flow near that grid point is changing drastically, and it is necessary to improve the local accuracy of the simulation by refining the grid (equivalent to refining the second element to obtain the fourth element).

[0092] After dividing the simulation region into grids, the flow equations are solved for each grid (equivalent to each first cell). Specifically, the CFD solver is run to solve the flow equations within each grid cell, calculating parameters such as airflow velocity and pressure.

[0093] Results Analysis and Visualization: Analyze the simulation results, identify key information such as airflow acceleration regions and turbulence regions, and display airflow dynamics through visualization tools.

[0094] Furthermore, the working principle and scope of the GIS analysis module are as follows:

[0095] The GIS analysis module utilizes a geographic information system to analyze topography, landforms, and other geographic features, assessing their impact on wind speed distribution and wind farm layout. This module can process spatial data and perform terrain analysis (such as slope and elevation analysis), visualization analysis, and spatial overlay analysis.

[0096] Specifically, the base wind power generation efficiency of each potential installation location (equivalent to at least one installation location) is calculated using formula (1) in the above embodiments; and the base wind power generation efficiency is adjusted using formula (5) in the above embodiments to avoid the impact of mutual interference between wind turbines on energy output.

[0097] The operations involved in the GIS analysis module include, but are not limited to:

[0098] Import terrain data: Import terrain data from the data acquisition module, including spatial data such as elevation and slope.

[0099] Terrain Analysis: Perform terrain analysis to identify terrain features that may affect wind speed distribution, such as valleys and ridges.

[0100] Visualization: The terrain analysis results are visualized using GIS software to help understand the potential impact of terrain on wind speed distribution.

[0101] Wind energy resource assessment: Combining topographic analysis results and CFD simulation wind speed data (i.e. airflow dynamics), assess the wind energy resource potential of different locations and identify the most suitable locations for installing wind turbines.

[0102] Environmental impact assessment: GIS tools are used to analyze the potential impacts of wind farm layout on local ecology, landscape and residential areas, in order to guide the optimization and adjustment of wind turbine locations and ensure environmental friendliness.

[0103] Formulate final layout recommendations: Based on the results of terrain analysis, wind energy resource assessment and environmental impact assessment, formulate final layout recommendations for wind farms to provide decision support for the optimization algorithm module.

[0104] Furthermore, the working principle and scope of the optimization algorithm module are as follows: The optimization algorithm module uses a genetic algorithm to find the optimal wind farm layout scheme (equivalent to the optimal layout result). This module aims to maximize the overall power generation efficiency of the wind farm, minimize construction and operation costs, and reduce environmental impact. Through iterative search, it continuously adjusts the location and configuration of the wind turbines to find the optimal solution that satisfies all conditions and constraints.

[0105] Specifically, iterative optimization is performed using a genetic algorithm, including:

[0106] 1. Initialize the population and define individuals: Each individual represents a layout scheme of a wind farm (equivalent to one of multiple layout results), and each layout scheme includes the location information of the wind turbines.

[0107] Generate initial population: Randomly generate N individuals as the initial population. Ensure that the initial position of each wind turbine satisfies the basic constraints of the project, such as the minimum distance to other wind turbines, the minimum distance to the boundary, etc., where N is a positive integer.

[0108] 2. Fitness function evaluation: Define the fitness function, see formula (6):

[0109] (6)

[0110] in, The power generation efficiency takes into account the mutual interference between wind turbines; C represents the total construction and operation and maintenance cost of the wind turbine. This method simplifies environmental impact to the reciprocal of the minimum distance from the wind turbines in a wind farm to the nearest sensitive ecological area or residential area. It assumes that the closer the distance, the greater the potential environmental impact; α, β, and γ are weighting coefficients.

[0111] Therefore, the power generation efficiency is calculated based on each layout scheme. Estimate cost C and determine the minimum distance to the nearest sensitive area. Then calculate the fitness F.

[0112] 3. Selection: Selection method: Roulette or tournament selection method is used, and selection is based on the fitness of individuals. Superior individuals have a higher chance of being selected to generate the next generation.

[0113] 4. Crossover: A crossover operation is performed by randomly selecting a pair of "parent" individuals and exchanging some of their genes (i.e., the location information of the wind turbine) to generate two "offspring" individuals. Crossover rate. Control the probability of crossover occurring.

[0114] 5. Mutation, Mutation Operation: Mutation operations are performed on the "offspring" individuals, randomly changing the values ​​of certain genes (i.e., changing the location of the fan). Mutation Rate Controlling the probability of mutations to introduce new genetic diversity.

[0115] 6. New generation population formation and replacement strategy: Merge the "offspring" individuals produced through crossover and mutation with the current population, and then select N individuals based on fitness to form a new generation population. An elite preservation strategy can be used to ensure that the best individuals are not lost.

[0116] 7. Iteration Termination Condition: Set an iteration termination condition, such as reaching the maximum number of iterations or the fitness change being less than a certain threshold. The algorithm terminates when the condition is met.

[0117] Furthermore, the working principle and scope of the user interface are as follows: The user interface provides users with an intuitive and user-friendly operating environment. Users can input project data, configure optimization parameters, initiate the optimization process, and view optimization results and analysis reports through this interface. The interface design emphasizes user experience and supports graphical display of wind farm layout and airflow dynamics. The operations involved include, but are not limited to:

[0118] Project configuration: Users can input or upload basic information about the wind farm through the interface, including geographical location, project scale, etc.

[0119] Parameter settings: Users can configure CFD simulation parameters, GIS analysis options, and optimization algorithm configuration parameters as needed.

[0120] Startup optimization: After the user submits the configuration, the system automatically executes the entire process of data collection, CFD simulation, GIS analysis and layout optimization.

[0121] Results Display and Analysis: After optimization, the interface displays the optimal wind farm layout scheme, including wind turbine locations, expected power generation, and environmental impact assessment, and provides a detailed analysis report for users to download or view.

[0122] Furthermore, the working principle and operating range of the feedback adjustment module are as follows:

[0123] The module receives feedback from the user interface, including satisfaction with the current optimization results, specific improvement suggestions, and adjusted parameters. It analyzes the user feedback to identify optimization parameters or strategies that need adjustment, which may involve changing weight coefficients, redefining the optimization objective, or adjusting constraints. Based on the feedback analysis results, it adjusts the parameter settings of the genetic algorithm or other optimization algorithms, such as population size, crossover rate, and mutation rate, or adjusts the weight coefficients in the optimization objective function. It restarts the optimization process using the adjusted parameters and strategies, which may require starting from scratch or from an intermediate state to explore a more satisfactory layout scheme. Through continuous feedback collection, analysis, and adjustment, the module supports iterative improvement of wind farm layout schemes until the optimal solution that satisfies the user is reached.

[0124] The operations involved in the feedback adjustment module include, but are not limited to:

[0125] Parameter tuning: Provides a mechanism to adjust the running parameters of the optimization algorithm, including but not limited to crossover rate, mutation rate, population size, etc., to improve search efficiency and solution quality; allows users to adjust the weight coefficients in the optimization objectives according to actual needs and optimization results to reflect the relative importance between different objectives.

[0126] Scheme Evaluation and Selection: Combining optimization results and user feedback, different wind farm layout schemes are evaluated, supporting users to make selections based on comprehensive factors (such as economics, environmental impact, and technical feasibility); scheme comparison and selection tools are provided to help users understand the advantages and disadvantages of different schemes and make more informed decisions.

[0127] User interaction optimization: Optimize the user interaction process to ensure that the collected feedback is accurate and comprehensive, including users' intuitive feelings about the current solution and specific optimization suggestions; provide an easy-to-understand and easy-to-operate interface to reduce the difficulty for users to provide feedback and encourage more users to participate in the optimization process.

[0128] Dynamic Iteration Process: Supports a dynamic iteration process based on user feedback to continuously adjust and optimize wind farm layout schemes; implements a flexible feedback mechanism that allows for adjustments and fine-tuning at any time during the optimization process.

[0129] Therefore, the above-mentioned wind farm layout system combines multiple modules such as genetic algorithm, CFD simulation, GIS analysis, feedback adjustment mechanism and user interface to achieve scientific optimization of wind farm layout.

[0130] Specifically, the above system has the following advantages:

[0131] 1. **Layout Optimization:** By accurately simulating airflow dynamics within the wind farm and its interaction with the terrain, the system can find the optimal installation location for each wind turbine, optimizing the overall layout of the wind farm. This not only maximizes the power generation efficiency of individual turbines but also reduces mutual interference between turbines, improving the overall power generation efficiency of the entire wind farm. 2. **Reduced Environmental Impact:** Through a comprehensive assessment of terrain and environmentally sensitive areas using the GIS analysis module, the system avoids installing turbines in ecologically sensitive or topographically unfavorable areas, thereby reducing the wind farm's environmental impact. Simultaneously, the optimized layout reduces interference with ground cover and the natural landscape. 3. **Cost-Benefit Analysis:** The optimization algorithm module considers not only maximizing power generation efficiency but also construction and operation costs, including turbine installation and maintenance costs. Through scientific layout optimization, this system helps reduce the overall lifecycle cost of the wind farm and improves the return on investment. 4. **Dynamic Adjustment and Iterative Optimization:** The feedback adjustment module enables the system to dynamically adjust optimization parameters and objectives based on user feedback and actual operating data. This iterative optimization mechanism ensures that the system can adapt to changing environments and market demands, continuously improving the performance of the wind farm. 5. Ease of Use and Interactivity: The system features an intuitive and user-friendly interface, allowing users to easily input data, set simulation and optimization parameters, and view and export results. This enhances the system's ease of use and interactivity, enabling even non-professional users to perform complex wind farm layout optimization.

[0132] Furthermore, Figure 4 is another flowchart of a wind farm layout method according to an embodiment of this application. As shown in Figure 4, the workflow of the above system is as follows:

[0133] Step S401: Data preparation and preprocessing.

[0134] Data collection: Collecting wind speed data, topographic data, and data on ecologically and socially sensitive areas, etc.

[0135] Data preprocessing: The collected data is cleaned, formatted, and standardized to ensure its accuracy and consistency.

[0136] Step S402: User interface operation. This includes project configuration, where users input basic information, optimization objectives, and constraints for the wind farm project through the interactive interface.

[0137] Step S403: Parameter settings: The user sets the genetic algorithm parameters, such as population size, crossover rate, mutation rate, and related parameters for quantifying environmental impact.

[0138] Step S404: GIS Analysis. This includes topographic and environmental analysis, using GIS tools to analyze the topography and assess its impact on wind speed; simultaneously, it analyzes the potential impacts of wind farm layout on ecology, landscape, and residential areas.

[0139] Step S405: Wind Energy Resource Assessment: Based on terrain analysis and wind speed data, assess the wind energy resource potential of different locations.

[0140] Step S406: Optimize algorithm execution.

[0141] Initialize the population: Based on user input and GIS analysis results, a series of wind farm layout schemes are randomly generated as the initial population.

[0142] Step S407: Fitness Calculation: For each layout scheme, calculate its fitness value, which reflects the scheme's power generation efficiency, cost, and environmental impact.

[0143] Iterative genetic algorithm: Through operations such as selection, crossover, and mutation, the layout of wind farms is iteratively optimized until the termination condition is met.

[0144] Step S408: Determine whether the termination condition has been met. If it has, proceed to step S409; otherwise, return to step S407.

[0145] Step S409: Results presentation and analysis.

[0146] Optimization results display: The optimized wind farm layout scheme is displayed through the user interface, including wind turbine locations, expected power generation, cost estimates and environmental impact assessments.

[0147] Results Analysis: Provides detailed analysis and comparison of the optimization process to help users understand the advantages and disadvantages of each solution.

[0148] Step S410: Collect user feedback: Users provide feedback based on the displayed results, including satisfaction with the layout scheme and specific suggestions for improvement. If user feedback indicates that adjustments are needed, proceed to step S411; if user feedback indicates satisfaction, proceed to step S412.

[0149] Step S411: Solution adjustment. Based on user feedback, adjust the optimization parameters or directly modify the layout solution, and then re-execute the optimization algorithm, i.e., re-execute step S406, until the user is satisfied.

[0150] Step S412: Final Solution Confirmation and Implementation

[0151] Solution Confirmation: The user confirms the final wind farm layout plan. Implementation Planning: Based on the confirmed plan, detailed design and implementation planning for the wind farm are carried out.

[0152] Therefore, the wind farm layout system in the optional embodiments of this application provides a precise and practical optimization scheme for wind farm layout through advanced simulation and spatial data analysis techniques combined with genetic algorithms. It optimizes the energy capture efficiency of the wind farm while considering reducing environmental impact and improving economic benefits. The development of this system demonstrates the application potential of technology in wind farm planning and design, providing a valuable tool for the wind power industry.

[0153] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0154] This application also provides a structural block diagram of a wind farm layout device in this embodiment. Figure 5 is a structural block diagram of a wind farm layout device according to an embodiment of this application; as shown in Figure 5, it includes:

[0155] The acquisition module 52 is used to acquire basic data of the wind farm, wherein the basic data includes at least: wind speed in the wind farm, terrain data of the location of the wind farm, and meteorological data of the location;

[0156] Simulation module 54 is used to simulate the airflow dynamics inside the wind farm using the wind speed and the meteorological data;

[0157] Analysis module 56 is used to analyze the impact of the terrain data on wind speed distribution and obtain analysis results;

[0158] The layout module 58 is used to lay out the wind farm based on the airflow dynamics and the analysis results.

[0159] The aforementioned device acquires basic data of the wind farm, including at least: wind speed within the wind farm, topographic data of the wind farm's location, and meteorological data of the location. The wind speed and meteorological data are used to simulate the airflow dynamics within the wind farm. The influence of the topographic data on wind speed distribution is analyzed to obtain analysis results. The wind farm is then laid out based on the airflow dynamics and the analysis results. This embodiment solves the problem in the prior art where wind farm layout cannot simultaneously consider the complex airflow dynamics within the wind farm and the influence of the wind farm's topographic data on wind turbine performance, thus reducing the difficulty of wind farm layout.

[0160] In an exemplary embodiment, the acquisition module 52 is configured to: acquire raw data from a target data source, wherein the raw data includes: the raw wind speed corresponding to the wind speed, the raw terrain data corresponding to the terrain data, and the raw meteorological data corresponding to the meteorological data; and perform preprocessing operations on the raw data to obtain the basic data, wherein the preprocessing operations include at least: data cleaning operations, format conversion operations, and data standardization operations.

[0161] In an exemplary embodiment, the simulation module 54 is configured to: set simulation conditions using the terrain data and the meteorological data, wherein the simulation conditions include at least: boundary conditions and initial conditions; establish a simulation region of the wind farm using the simulation conditions; divide the simulation region using a grid partitioning technique to obtain multiple first units; and solve the flow equations within each first unit using a preset solver to obtain the airflow dynamics.

[0162] In an exemplary embodiment, the simulation module 54 is configured to: divide the simulation area using the grid division technique to obtain a first division result, wherein the first division result includes: a plurality of second units; determine the vorticity and velocity gradient of any grid point corresponding to the first division result using the wind speed, and determine a decision value corresponding to any grid point using the vorticity and the velocity gradient, wherein any grid point is: any point among the grid endpoints of the grid used to divide the plurality of second units; if the decision value exceeds a preset threshold, further divide the third unit adjacent to the any grid point to obtain a fourth unit; and determine the plurality of first units using the plurality of second units and the fourth unit.

[0163] In an exemplary embodiment, the analysis module 56 is configured to: analyze the terrain data to obtain terrain features in the terrain data that affect the wind speed distribution; and determine the terrain features as the analysis result.

[0164] In an exemplary embodiment, the layout module 58 is configured to: determine at least one installation location of a wind turbine based on the airflow dynamics and the analysis results; determine the wind power generation efficiency corresponding to the at least one installation location, wherein the wind power generation efficiency is the power generation efficiency obtained after excluding mutual interference between wind turbines installed at the at least one installation location; and determine the layout of the wind farm based on the wind power generation efficiency.

[0165] In an exemplary embodiment, the layout module 58 is configured to: when there are multiple layout results for laying out the wind farm, iteratively optimize the multiple layout results using a genetic algorithm; and when the iteration termination condition is met, lay out the wind farm using the optimal layout result obtained through iterative optimization.

[0166] Embodiments of this application also provide a storage medium including a stored program, wherein the program executes any of the methods described above when it is run.

[0167] Optionally, in this embodiment, the storage medium may be configured to store program code for performing the following steps:

[0168] S1, acquire basic data of the wind farm, wherein the basic data includes at least: wind speed in the wind farm, topographic data of the location of the wind farm, and meteorological data of the location; S2, simulate the airflow dynamics inside the wind farm using the wind speed and the meteorological data; S3, analyze the influence of the topographic data on the wind speed distribution and obtain the analysis results; S4, lay out the wind farm using the airflow dynamics and the analysis results.

[0169] Embodiments of this application also provide an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0170] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0171] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0172] S1, acquire basic data of the wind farm, wherein the basic data includes at least: wind speed in the wind farm, topographic data of the location of the wind farm, and meteorological data of the location; S2, simulate the airflow dynamics inside the wind farm using the wind speed and the meteorological data; S3, analyze the influence of the topographic data on the wind speed distribution and obtain the analysis results; S4, lay out the wind farm using the airflow dynamics and the analysis results.

[0173] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0174] Embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0175] Embodiments of this application also provide another computer program product, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0176] Embodiments of this application also provide a computer program that includes computer instructions stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps in any of the above method embodiments.

[0177] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

Claims

1. A method for the layout of a wind farm, characterized in that, include: Acquire basic data of the wind farm, wherein the basic data includes at least: wind speed in the wind farm, topographic data of the location of the wind farm, and meteorological data of the location; The airflow dynamics inside the wind farm are simulated using the wind speed and meteorological data. The impact of the terrain data on wind speed distribution was analyzed, and the analysis results were obtained. The wind farm is laid out based on the airflow dynamics and the analysis results.

2. The wind farm layout method according to claim 1, characterized in that, Obtain the basic data of the wind farm, including: Obtain raw data from the target data source, wherein the raw data includes: the raw wind speed corresponding to the wind speed, the raw terrain data corresponding to the terrain data, and the raw meteorological data corresponding to the meteorological data; The raw data is preprocessed to obtain the basic data, wherein the preprocessing operations include at least: data cleaning, format conversion, and data standardization.

3. The wind farm layout method according to claim 1, characterized in that, Simulating the airflow dynamics within the wind farm using the wind speed and meteorological data includes: Simulation conditions are set using the terrain data and the meteorological data, wherein the simulation conditions include at least: boundary conditions and initial conditions; The simulation area of ​​the wind farm is established using the simulation conditions described above; The simulation region is divided using a mesh generation technique to obtain multiple first units; The airflow dynamics are obtained by using a preset solver and solving the flow equations within each first unit.

4. The wind farm layout method according to claim 3, characterized in that, The simulation region is divided using a mesh generation technique to obtain multiple first units, including: The simulation region is divided using the aforementioned meshing technique to obtain a first division result, wherein the first division result includes: a plurality of second units; The vorticity and velocity gradient of any grid point corresponding to the first division result are determined by the wind speed, and the decision value corresponding to any grid point is determined by the vorticity and velocity gradient. The any grid point is any point among the grid endpoints used to divide the plurality of second units. If the decision value exceeds a preset threshold, the third unit adjacent to any of the grid points will be further divided to obtain a fourth unit; The plurality of first units are determined by the plurality of second units and the plurality of fourth units.

5. The wind farm layout method according to claim 1, characterized in that, The impact of the terrain data on wind speed distribution was analyzed, and the analysis results include: The terrain data is analyzed to obtain the terrain features that affect the wind speed distribution. The terrain features are identified as the analysis result.

6. The wind farm layout method according to claim 1, characterized in that, The wind farm is laid out based on the airflow dynamics and the analysis results, including: At least one installation location for the fan is determined based on the airflow dynamics and the analysis results; Determine the wind power generation efficiency corresponding to the at least one installation location, wherein the wind power generation efficiency is the power generation efficiency obtained after eliminating the mutual interference between the wind turbines installed at the at least one installation location. The layout of the wind farm is determined by the wind power generation efficiency.

7. The wind farm layout method according to claim 1, characterized in that, The wind farm is laid out based on the airflow dynamics and the analysis results, including: In the presence of multiple layout results for the wind farm, the multiple layout results are iteratively optimized using a genetic algorithm; If the iteration termination condition is met, the wind farm is laid out using the optimal layout result obtained through iterative optimization.

8. A layout device for a wind farm, characterized in that, include: The acquisition module is used to acquire basic data of the wind farm, wherein the basic data includes at least: wind speed in the wind farm, terrain data of the location of the wind farm, and meteorological data of the location; The simulation module is used to simulate the airflow dynamics inside the wind farm using the wind speed and the meteorological data; The analysis module is used to analyze the impact of the terrain data on wind speed distribution and obtain analysis results; A layout module is used to lay out the wind farm based on the airflow dynamics and the analysis results.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when executed, performs the method described in any one of claims 1 to 7.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the method described in any one of claims 1 to 7 through the computer program.

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