Wind power plant layout method considering line current-carrying capacity constraint
By determining the maximum number of wind turbines allowed on a single collector line in an offshore wind farm and optimizing the turbine layout using a minimum spanning tree algorithm, the problem of the collector line's current carrying capacity was not considered, thus improving both economy and safety.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-06
- Publication Date
- 2026-04-07
AI Technical Summary
Existing offshore wind farm layout methods do not consider the current carrying capacity of collector lines, leading to cable overload or redundant investment, posing safety hazards and high adjustment costs.
By initializing the population, the maximum number of wind turbines that can be carried on a single-loop set line is determined. The topology is generated by combining the minimum spanning tree algorithm, the wind turbine layout is optimized to meet the current carrying capacity constraint, and the layout scheme is iteratively updated with the goal of maximizing annual net income to generate an economically optimized layout scheme.
This effectively avoids wind turbine overload, achieves the best balance between power generation and line costs, and improves the overall return on investment and engineering feasibility of the project.
Smart Images
Figure CN121808993A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to a wind farm layout method considering line ampacity constraints. BACKGROUND
[0002] According to the International Energy Agency report, the global offshore wind power installed capacity has reached 64.2GW, and is expected to exceed 380GW by 2030. Offshore wind farms will become an important source of renewable energy. At the same time, with the development of deep-sea offshore wind power, the construction cost is gradually increasing. Under this background, wind turbine layout optimization has become a core technical challenge to reduce the levelized cost of electricity.
[0003] In the design of offshore wind farm wind turbine arrangement, the genetic algorithm, particle swarm algorithm, etc. are often used to determine the distance between wind turbines by focusing on the power loss caused by the wake effect. However, such methods often ignore electrical constraints: they do not consider the ampacity limit of the collection line, which may cause cable overload or redundant investment. Thus, there are significant technical defects: these methods do not consider the ampacity limit of the collection line.
[0004] As a key device for collecting and transmitting wind turbine power, the ampacity of the collection cable is naturally constrained, which limits the cable's ability to safely and continuously pass current. In the layout optimization process without considering this constraint, the algorithm may arrange too dense wind turbines in a certain local area. When these wind turbines are connected to the same back collection line, the total output current is likely to exceed the rated ampacity of the cable, causing cable overload. This design flaw can cause two serious consequences: one is to bring serious safety hazards, and the other is to force costly adjustments in the later design stage, such as replacing higher-specification and more expensive cables, or making a revolutionary electrical topology reconstruction, resulting in redundant investment and project delays. SUMMARY
[0005] The technical problem to be solved by the present application is to provide a wind farm layout method considering line ampacity constraints in view of the above-mentioned problems.
[0006] The technical solution adopted by the present application is: a wind farm layout method considering line ampacity constraints, comprising: S100, initializing a population, the parameters of each individual in the population being capable of being used to determine wind turbine layout points in the wind farm area that meet a preset number requirement; S200, determining the maximum number of wind turbines allowed on a single back collection line based on the voltage level, continuous ampacity of the collection line, and the rated power and power factor of the wind turbine; S300, generate the power collection line topology structure in the wind farm based on the fan layout points corresponding to each individual and in combination with the maximum number of fans allowed to be carried on the single-loop power collection line, and then determine the power collection line cost; S400, calculate the annual power generation of the wind farm based on the fan layout points corresponding to each individual and in combination with the wind resource data of the wind farm and the fan performance parameters of the fan, and then determine the annual power generation income of the wind farm; S500, determine the annual equivalent cost of the power collection line based on the power collection line cost, and calculate the annual net income of the wind farm in combination with the annual power generation income of the wind farm; S600, take the maximum annual net income of the wind farm as the optimization target, iteratively update the population, and return to step S300 until the preset convergence requirement is met or the preset iteration number is reached.
[0007] The fan layout points in the wind farm area are arranged in a tabular manner, the spacing between adjacent rows and adjacent fans meets the preset requirement, and the maximum number of fans in each row is M, which is determined based on the shape of the wind farm and the maximum number of fans allowed to be carried on the single-loop power collection line.
[0008] The maximum number of fans M in each row includes: ; Wherein, N_max is the maximum number of fans allowed to be carried on the single-loop power collection line, k is a positive integer, and the value of k is determined according to the length-width ratio R of the wind farm area: when R≥2, k=ceil(R / 1.5); when R<2, k=1.
[0009] The generation of the power collection line topology structure in the wind farm based on the fan layout points corresponding to each individual and in combination with the maximum number of fans allowed to be carried on the single-loop power collection line includes: The minimum spanning tree algorithm is used to generate the power collection line topology structure connecting each point in the wind farm.
[0010] The minimum spanning tree algorithm uses an improved Kruskal algorithm, which calculates the weights of all possible connection paths and preferentially selects the connection scheme that can form a more regular and less branched trunk line.
[0011] The determination of the annual equivalent cost of the power collection line based on the power collection line cost and the calculation of the annual net income of the wind farm in combination with the annual power generation income of the wind farm include: ; Wherein, F is the annual net income of the wind farm, a is the on-grid electricity price, E is the annual power generation of the wind farm, β is the annual equivalent coefficient of the power collection line cost, and C is the power collection line cost.
[0012] A wind farm layout system considering line current carrying capacity constraints includes: The initialization module is used to initialize the population. The parameters of each individual in the population can be used to determine the wind turbine placement locations that meet the preset number requirements within the wind farm area. The electrical constraint module is used to determine the maximum number of wind turbines that can be carried on a single collector line based on the voltage level, continuous current carrying capacity, rated power and power factor of the wind turbines. The line cost calculation module is used to generate the topology of the collection lines in the wind farm based on the wind turbine deployment points corresponding to each entity and the maximum number of wind turbines that can be carried on a single collection line, thereby determining the collection line cost. The power generation revenue calculation module is used to calculate the annual power generation of the wind farm based on the wind turbine deployment locations corresponding to each individual, combined with the wind resource data of the wind farm and the wind turbine performance parameters, and then determine the annual power generation revenue of the wind farm. The net income calculation module is used to determine the annual discounted cost of the collection line based on the collection line cost, and calculate the annual net income of the wind farm in combination with the annual power generation revenue of the wind farm. The optimization iteration module is used to iteratively update the population with the goal of maximizing the annual net income of the wind farm, until the preset convergence requirement is met or the preset number of iterations is reached.
[0013] A storage medium storing a computer program executable by a processor, wherein the computer program, when executed, implements the steps of the wind farm layout method considering line current carrying capacity constraints.
[0014] A wind farm layout device has a memory and a processor. The memory stores a computer program that can be executed by the processor. When the computer program is executed, it implements the steps of the wind farm layout method that takes into account line current carrying capacity constraints.
[0015] The beneficial effects of this invention are as follows: By pre-calculating the maximum number of wind turbines that a single-circuit collector line can carry, this invention constrains the generation process of the collector line topology, eliminating the problem of wind turbine overload on single-circuit lines and ensuring the electrical feasibility of the design. This invention optimizes power generation and line costs under a unified economic function, achieving the best balance between the two and improving the overall return on investment for the project. Attached Figure Description
[0016] Figure 1 This is an overall flowchart of the wind farm layout method proposed in the embodiment.
[0017] Figure 2 The diagram shows the fan layout in a comparative example (without using the layout method of this embodiment).
[0018] Figure 3This is the optimized fan layout diagram in the embodiment. Detailed Implementation
[0019] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.
[0020] In the description of this invention, "multiple" means two or more. The use of "first" and "second" is for distinguishing technical features only and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or the order of the indicated technical features. Furthermore, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.
[0021] Example 1: This example is a wind farm layout method considering line current carrying capacity constraints, specifically including the following steps: S100. Initialize the population. The parameters of each individual in the population can be used to determine the wind turbine placement locations that meet the preset quantity requirements within the wind farm area.
[0022] In this embodiment, N wind turbine deployment points are randomly generated for each individual in the population, located within the wind farm area, where N is the preset number of wind turbines in the wind farm.
[0023] Based on the voltage level U, continuous current carrying capacity I_max, rated power P_i, and power factor cosθ of a single wind turbine, determine the maximum number of wind turbines N_max that can be carried on a single collector line.
[0024] In this embodiment, the maximum number of wind turbines N_max satisfies the following relationship: ; S300: Based on the wind turbine deployment locations corresponding to each individual, and combined with the maximum number of wind turbines that can be carried on a single collector line, the topology of the collector lines within the wind farm is generated, thereby determining the cost of the collector lines.
[0025] In this example, the wind turbine locations within the wind farm area are required to be arranged in a row-column pattern. The spacing between adjacent rows and adjacent turbines must meet the preset requirements, and the maximum number of turbines per row is M. M is determined based on the shape of the wind farm and the maximum number of turbines that can be carried on a single collection line, including: ; Where N_max is the maximum number of wind turbines that can be carried on a single collection line, and k is a positive integer. The value of k is determined according to the length-to-width ratio R of the wind farm area: when R≥2, k=ceil(R / 1.5); when R<2, k=1.
[0026] In this example, based on N wind turbine deployment points and the maximum number of wind turbines M per row, the wind turbine deployment points are restructured using a row-determinant method to form a well-organized deployment array where each row satisfies the maximum number of wind turbines constraint. The row-determinant reconstruction of the deployment points is achieved through the following steps: First, a primary direction needs to be defined for the wind farm area. This direction will serve as the extension direction of the wind turbine "rows". Typically, the longer side of the wind farm area, or a direction parallel / perpendicular to the prevailing wind direction, is chosen. A local coordinate system is established with this primary direction as the X-axis (or Y-axis). All random point coordinates are transformed to this coordinate system. In this example, it is assumed that the "rows" are arranged along the X-axis. All N wind turbine locations are sorted in ascending order according to their Y-coordinates. Starting with the point with the smallest Y-coordinate, the sorted list of points is traversed. Different "rows" are identified by analyzing "jumps" in the Y-coordinate. When the difference in the Y-coordinates of two adjacent points (after Y-sorting) exceeds a preset minimum row spacing threshold, they are classified as belonging to different rows. Simultaneously, the formation of each row is constrained by the maximum number of wind turbines M per row. Points with similar Y-coordinates are grouped into a row until the number of wind turbines in that row reaches M, or a new "jump" in the Y-coordinate is encountered. Then, the row ends, and a new row begins. For each grouped row, the fan locations within it are sorted in ascending order according to their X-axis coordinates. This determines the left-right order of the fans in each row. Since the initial random locations, even after sorting, have uneven spacing, the coordinates of these locations need to be adjusted to ensure that "the spacing between adjacent rows and adjacent fans meets the preset requirements." The average X-coordinate of all locations in the row is taken as the center of the row. Then, using the preset minimum fan spacing as the standard, the X-coordinate of each fan in the row is recalculated and reassigned evenly from the center outwards. The average Y-coordinate of each row is calculated. Then, using the preset minimum row spacing as the standard, the average Y-coordinate of each row is adjusted to ensure even spacing between rows.
[0027] After the above steps, the original random, disordered points with unsatisfactory spacing are "reconstructed" into a new array with clearly defined rows and columns, regular spacing, and satisfying the constraint of the maximum number of turbines M per row. This new array can then be used for subsequent wake calculations and cost accounting. The above process must consider both the approximate location of the initial points and their mapping to a standard array structure.
[0028] When there are a large number of wind turbines, grid optimization and other turbine placement optimization methods can be used. Grid optimization is used for the first stage of macro-layout to quickly identify a high-performance layout scheme that meets the main constraints. Then, based on the results of grid optimization, small-scale continuous coordinate optimization may be performed to fine-tune the positions of the wind turbines.
[0029] In this embodiment, the minimum spanning tree algorithm is used to generate a collector line topology that satisfies the electrical constraint of the maximum number of wind turbines N_max for the reconstructed wind turbine deployment points, and to accurately calculate the total length of the collector lines and the collector line cost C under this topology.
[0030] The minimum spanning tree algorithm uses an improved Kruskal algorithm, which calculates the weights of all possible connection paths and prioritizes connection schemes that can form more regular trunk lines with fewer branches.
[0031] The improved Kruskal algorithm in this embodiment is improved in that: it prioritizes vertical connections between wind turbines in the same column, and secondarily selects diagonal connections across rows; furthermore, the edge weight W_ij connecting two nodes is determined by the following formula: ; Where L_ij is the line length, B_ij is the branch coefficient (value is 1 - number of line branches connecting the main cable), and γ and δ are dynamic adjustment factors.
[0032] S400: Based on the wind turbine locations corresponding to each individual (after reconstruction), combined with the wind resource data of the wind farm (such as wind rose diagrams for various typical wind conditions) and the wind turbine performance parameters (such as power curves, hub height, and impeller diameter), calculate the annual power generation E of the wind farm, and then determine the annual power generation revenue of the wind farm by combining the grid-connected electricity price α.
[0033] S500. Based on the cost of the power collection line C and the annual conversion factor β of the power collection line cost, determine the annual converted cost of the power collection line. Combined with the annual power generation revenue of the wind farm, calculate the annual net income of the wind farm.
[0034] In this embodiment, the annual net income of the wind farm is used as the economic evaluation function, including: ; Where F is the annual net income of the wind farm, α is the on-grid electricity price, E is the annual power generation of the wind farm, β is the annual conversion factor for the cost of the collection line, and C is the cost of the collection line.
[0035] S600. Using a genetic algorithm, with the goal of maximizing the annual net income of the wind farm (using F as the fitness), the population is iteratively updated, and the process returns to step S300 until the preset convergence requirement is met or the preset number of iterations is reached.
[0036] The following comparative analysis uses specific cases: For an offshore wind farm project in an irregularly shaped sea area, the project plans to deploy 17 wind turbine generators, each with a rated capacity of 12MW. The voltage level of the collection line is 66kV, and the location of the substation has been determined. The on-grid electricity price for the project will be calculated uniformly at RMB 0.391 / kWh.
[0037] Option A (Comparative): Conventional layout aimed at maximizing power generation The position layout of the wind turbines in the comparative example is as follows Figure 2 As shown. This layout, through initial optimization, minimizes the wake effect.
[0038] Based on power generation calculations, the total annual on-grid electricity generated by wind farms in this layout is 828.3 million kWh. To connect all the wind turbines in this layout, the total length of the designed collection lines is approximately 40 kilometers, with a unit cost of 2.7 million yuan per kilometer.
[0039] Economic accounting: Annual electricity generation revenue = 828.3 million kWh × 0.391 yuan / kWh = 323.87 million yuan; Cost of power collection line = 40km × 2.7 million yuan / km = 108 million yuan.
[0040] Option B: Adopt the layout method of this embodiment. Since this project uses a 66kV voltage level, and based on the selected submarine cable specifications, the maximum number of wind turbines N_max that a single-circuit collector cable can carry is calculated to be 6 units. This means that any subsequent cable branch cannot connect more than 6 wind turbines in series.
[0041] Based on the elongated shape of the wind farm, with an aspect ratio R of approximately 3, the adjustment coefficient k = ceil(3 / 1.5) = 2 is calculated. Therefore, the maximum number of wind turbines M per row is... M = N_max × k = 6 × 2 = 12 units; An initial population containing hundreds of different layout schemes is randomly generated. Each scheme is treated as an individual and is a dataset containing the planar coordinates (x, y) of 17 wind turbines.
[0042] Based on the constraint M≤12, the discrete coordinates of these 17 wind turbines are reconstructed using a determinant, arranging them into a regular array where each row satisfies the maximum number of wind turbines constraint, such as... Figure 3 As shown.
[0043] Based on the reconstructed layout, an improved Kruskal algorithm is used to plan the power collection line topology for these 17 wind turbines under the electrical constraint that N_max≤6. The algorithm calculates the weights of all possible connection paths and prioritizes connection schemes that can form more regular trunk lines with fewer branches, ultimately generating a cable network with the shortest total length that satisfies all constraints, and calculating its total length C.
[0044] The annual power generation E of the current layout is calculated, and E and C are substituted into the economic evaluation function F to obtain the "fitness score" of the layout. This score is returned to the outer genetic algorithm to guide the evolution of the next generation of the "population". The entire process is repeated continuously. Through hundreds of generations of iteration, the algorithm continuously eliminates schemes with lower F values, retains and evolves schemes with higher F values, and finally converges to a global optimum.
[0045] Once the evaluation function F converges, the optimal solution output by the system is solution B, and the final wind turbine location layout is as follows: Figure 3 As shown in the figure, based on the power generation calculation data, the total annual on-grid electricity generated by the wind farm under the new layout is 820.29 million kWh. The total length of the optimized collection lines is shortened to approximately 30 kilometers, with a unit cost of 2.75 million yuan / kilometer.
[0046] Economic accounting: Annual electricity generation revenue = 820.29 million kWh × 0.391 yuan / kWh = 320.73 million yuan; The cost of the power collection line = 30km × 2.75 million yuan / km = 0.825 billion yuan.
[0047] A detailed comparison in this embodiment shows that, after optimization using the method described in this embodiment, the initial construction cost of the power collection line was reduced from 108 million yuan to 82.5 million yuan, saving 25.5 million yuan in one-time investment, a reduction of 23.6%. Although the adjustment of the wind turbine location led to a slight decrease in annual power generation, the reduction in annual revenue was only 3.14 million yuan, which is negligible relative to total revenue (approximately -0.97%).
[0048] In summary, this embodiment, through its unique two-layer optimization framework and algorithm, successfully finds a balance point while ensuring electrical safety constraints and engineering regularity: sacrificing minimal annual power generation revenue in exchange for significant savings in initial engineering investment. This significantly lowers the initial investment threshold, shortens the investment payback period, and ultimately effectively reduces the levelized cost of electricity (LCOE), fully demonstrating the immense value of this invention in improving the overall economic efficiency and engineering feasibility of offshore wind farm projects.
[0049] Example 2: This example is a wind farm layout system considering line current carrying capacity constraints, specifically including: The initialization module is used to initialize the population. The parameters of each individual in the population can be used to determine the wind turbine placement locations that meet the preset number requirements within the wind farm area. The electrical constraint module is used to determine the maximum number of wind turbines that can be carried on a single collector line based on the voltage level, continuous current carrying capacity, rated power and power factor of the wind turbines. The line cost calculation module is used to generate the topology of the collection lines in the wind farm based on the wind turbine deployment points corresponding to each entity and the maximum number of wind turbines that can be carried on a single collection line, thereby determining the collection line cost. The power generation revenue calculation module is used to calculate the annual power generation of the wind farm based on the wind turbine deployment locations corresponding to each individual, combined with the wind resource data of the wind farm and the wind turbine performance parameters, and then determine the annual power generation revenue of the wind farm. The net income calculation module is used to determine the annual discounted cost of the collection line based on the collection line cost, and calculate the annual net income of the wind farm in combination with the annual power generation revenue of the wind farm. The optimization iteration module is used to iteratively update the population with the goal of maximizing the annual net income of the wind farm, until the preset convergence requirement is met or the preset number of iterations is reached.
[0050] Example 3: This example is a storage medium that stores a computer program that can be executed by a processor. When the computer program is executed, it implements the steps of the wind farm layout method considering line current carrying capacity constraints described in Example 1.
[0051] Example 4: This example is a wind farm layout device, which has a memory and a processor. The memory stores a computer program that can be executed by the processor. When the computer program is executed, it implements the steps of the wind farm layout method considering line current carrying capacity constraints described in Example 1.
[0052] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the aforementioned functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.
[0053] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0054] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.
[0055] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the aforementioned program can be printed, because the aforementioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.
[0056] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.
[0057] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.
[0058] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
[0059] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.
Claims
1. A wind farm layout method considering line current carrying capacity constraints, characterized in that, include: S100. Initialize the population. The parameters of each individual in the population can be used to determine the wind turbine placement locations that meet the preset quantity requirements within the wind farm area. S200. Based on the voltage level and continuous current carrying capacity of the collector line, as well as the rated power and power factor of the wind turbine, determine the maximum number of wind turbines that can be carried on a single collector line. S300: Based on the wind turbine deployment locations corresponding to each individual, and combined with the maximum number of wind turbines that can be carried on a single collection line, the topology of the collection line in the wind farm is generated, and the cost of the collection line is determined. S400. Based on the wind turbine deployment locations corresponding to each individual, combined with the wind resource data of the wind farm and the wind turbine performance parameters, calculate the annual power generation of the wind farm, and then determine the annual power generation revenue of the wind farm. S500. Based on the cost of the collection line, determine the annual depreciated cost of the collection line, and calculate the annual net income of the wind farm in combination with the annual power generation revenue of the wind farm. S600. With the goal of maximizing the annual net income of the wind farm, iteratively update the population and return to step S300 until the preset convergence requirement is met or the preset number of iterations is reached.
2. The wind farm layout method considering line current carrying capacity constraints according to claim 1, characterized in that, The wind turbine locations within the wind farm area are arranged in a row-column pattern. The spacing between adjacent rows and adjacent wind turbines meets the preset requirements, and the maximum number of wind turbines in each row is M. M is determined based on the shape of the wind farm and the maximum number of wind turbines that can be carried on a single collection line.
3. The wind farm layout method considering line current carrying capacity constraints according to claim 2, characterized in that, The maximum number of fans M per row includes: ； Where N_max is the maximum number of wind turbines that can be carried on a single collection line, and k is a positive integer. The value of k is determined according to the length-to-width ratio R of the wind farm area: when R≥2, k=ceil(R / 1.5); when R<2, k=1.
4. The wind farm layout method considering line current carrying capacity constraints according to claim 1, characterized in that, The method generates the collector network topology within the wind farm based on the wind turbine deployment locations corresponding to each individual turbine and the maximum number of wind turbines allowed to be carried on a single collector line, including: The minimum spanning tree algorithm is used to generate the topology of the collection lines connecting each wind turbine location in the wind farm.
5. The wind farm layout method considering line current carrying capacity constraints according to claim 1, characterized in that, The minimum spanning tree algorithm employs an improved Kruskal algorithm, which calculates the weights of all possible connection paths and prioritizes connection schemes that can form more regular trunk lines with fewer branches.
6. The wind farm layout method considering line current carrying capacity constraints according to claim 1, characterized in that, The process involves determining the annual discounted cost of the power collection line based on its cost, and then calculating the annual net income of the wind farm by combining this with the annual power generation revenue of the wind farm. This includes: ； Where F is the annual net income of the wind farm, α is the on-grid electricity price, E is the annual power generation of the wind farm, β is the annual conversion factor for the cost of the collection line, and C is the cost of the collection line.
7. A wind farm layout system considering line current carrying capacity constraints, characterized in that, include: The initialization module is used to initialize the population. The parameters of each individual in the population can be used to determine the wind turbine placement points that meet the preset number requirements within the wind farm area. The electrical constraint module is used to determine the maximum number of wind turbines that can be carried on a single collector line based on the voltage level, continuous current carrying capacity, rated power and power factor of the wind turbines. The line cost calculation module is used to generate the topology of the collection lines in the wind farm based on the wind turbine deployment points corresponding to each entity and the maximum number of wind turbines that can be carried on a single collection line, thereby determining the collection line cost. The power generation revenue calculation module is used to calculate the annual power generation of the wind farm based on the wind turbine deployment locations corresponding to each individual, combined with the wind resource data of the wind farm and the wind turbine performance parameters, and then determine the annual power generation revenue of the wind farm. The net income calculation module is used to determine the annual discounted cost of the collection line based on the collection line cost, and calculate the annual net income of the wind farm in combination with the annual power generation revenue of the wind farm. The optimization iteration module is used to iteratively update the population with the goal of maximizing the annual net income of the wind farm, until the preset convergence requirement is met or the preset number of iterations is reached.
8. A storage medium having a computer program stored thereon that can be executed by a processor, characterized in that, When the computer program is executed, it implements the steps of the wind farm layout method considering line current carrying capacity constraints as described in any one of claims 1 to 6.
9. A wind farm layout device, comprising a memory and a processor, wherein the memory stores a computer program executable by the processor, characterized in that, When the computer program is executed, it implements the steps of the wind farm layout method considering line current carrying capacity constraints as described in any one of claims 1 to 6.