Line optimization method, system and equipment for offshore wind plant and storage medium

Abstract

The invention provides a line optimization method, system and device for an offshore wind plant and a storage medium. The method comprises the steps of obtaining a line design demand; the line design requirements comprise a line voltage grade, a fan capacity, a discount rate, a construction period, a submarine cable length coefficient, a fan coordinate table and a submarine cable parameter table; grouping each fan of the offshore wind plant to form a fan set scheme; generating a candidate submarine cable arrangement scheme package according to the line design requirement and the fan set scheme; determining a constraint algorithm by combining whether the submarine cables are crossed and whether the submarine cables interfere with a fan; on the basis of the constraint algorithm, line schemes of the candidate submarine cable arrangement scheme packages are judged and screened one by one, and an optimization scheme is determined; and selecting a final line design scheme from the optimization schemes. According to the invention, automatic optimization of the design scheme is realized, and the calculation speed of the optimal scheme can be improved.

Description

Technical Field

[0001] This application belongs to the technical field of power collection line design, and relates to a method, system, equipment and storage medium for optimizing power collection lines in offshore wind farms. Background Technology

[0002] The collector lines of an offshore wind farm play a crucial role in its overall operation. These lines are responsible for collecting the electricity generated by the wind turbines and efficiently transmitting it to the substation. The collector lines of an offshore wind farm are quite complex, mainly including several key components such as submarine cables, submarine cable terminals, submarine cable connectors, and wind turbine ring main units.

[0003] When planning the power collection lines for offshore wind farms, multiple factors must be comprehensively considered, including the scale of the wind farm, the generating capacity of a single wind turbine, the voltage level of the submarine cables used, the redundancy or reliability requirements of the system, the cost of engineering construction, and the developer's risk tolerance. The trade-offs and negotiations among these factors constitute the core challenge of power collection line design.

[0004] However, for offshore wind farms, the voltage level and design scheme of the collection line are not unique. Traditional design schemes are cumbersome, have a large influence from human experience, and have limited alternative schemes. Summary of the Invention

[0005] This application provides a method, system, equipment, and storage medium for optimizing the route of an offshore wind farm, which addresses the problem of limited alternative solutions in traditional design schemes.

[0006] In a first aspect, this application provides a method for optimizing the route of an offshore wind farm, comprising: obtaining route design requirements; the route design requirements include route voltage level, wind turbine capacity, discount rate, construction period, submarine cable length coefficient, and wind turbine coordinate table and submarine cable parameter table; grouping the various wind turbines of the offshore wind farm to form a wind turbine group scheme; generating a candidate submarine cable layout scheme package based on the route design requirements and the wind turbine group scheme; determining a constraint algorithm based on whether the submarine cables cross and whether the submarine cables interfere with the wind turbines; judging and filtering the route schemes in the candidate submarine cable layout scheme package one by one based on the constraint algorithm to determine the optimized scheme; and selecting the final route design scheme from the optimized schemes.

[0007] In one implementation of the first aspect, the step of obtaining line design requirements includes: obtaining parameter input instructions from a visual interface; the parameter input instructions include input instructions for line voltage level, wind turbine capacity, discount rate, construction period, and submarine cable length coefficient, as well as upload operation instructions for wind turbine coordinate table and submarine cable parameter table; and determining the line design requirements based on the parameter input instructions.

[0008] In one implementation of the first aspect, the step of grouping the individual wind turbines of an offshore wind farm to form a wind turbine group scheme includes: determining the total number of wind turbines; determining the number of wind turbines in each group according to business requirements; wherein the number of wind turbines is a value within the range of the total number of wind turbines; and forming the wind turbine group scheme based on the minimum and maximum number of wind turbines.

[0009] In one implementation of the first aspect, the steps of determining the constraint algorithm by combining whether the submarine cable crosses and whether the submarine cable interferes with the wind turbine include: taking whether the submarine cable crosses and whether the submarine cable interferes with the wind turbine as constraint conditions; embedding the constraint conditions into the algorithm for finding the minimum spanning tree in the weighted graph to form the constraint algorithm.

[0010] In one implementation of the first aspect, the step of determining the optimal scheme by judging and filtering the route schemes of the candidate submarine cable layout scheme package one by one based on the constraint algorithm includes: sorting the route schemes from low to high according to their prices; selecting a preset number of route schemes based on the principle of low cost being preferred; using the constraint algorithm to perform spatial topology judgment and interference judgment on the selected route schemes; and determining the optimal scheme from the route schemes retained by the constraint algorithm.

[0011] In one implementation of the first aspect, the steps of performing spatial topology judgment and interference judgment on the selected route scheme using the constraint algorithm include: determining the edges of the weighted graph according to the route scheme; sorting all edges of the input weighted graph in ascending order of weight value to form an edge set; wherein the weight value refers to the distance between wind turbines; initializing the spanning tree set to an empty set; sequentially traversing the ascending sorted edge set and determining whether each edge in the edge set should be added to the spanning tree set, until the number of edges in the spanning tree set reaches a set threshold and then terminating.

[0012] In one implementation of the first aspect, after selecting the final route design scheme from the optimized schemes, the method further includes: presenting the route layout path on the wind turbine layout diagram according to the final route design scheme.

[0013] Secondly, this application provides a route optimization system for offshore wind farms, comprising: a demand acquisition module configured to acquire route design requirements; the route design requirements include route voltage level, wind turbine capacity, discount rate, construction period, submarine cable length coefficient, and wind turbine coordinate table and submarine cable parameter table; a wind turbine grouping module configured to group the various wind turbines of the offshore wind farm to form wind turbine group schemes; a scheme candidate module configured to generate a candidate submarine cable layout scheme package based on the route design requirements and the wind turbine group schemes; an algorithm determination module configured to determine a constraint algorithm based on whether the submarine cables cross and whether the submarine cables interfere with the wind turbines; a scheme optimization module configured to determine an optimized scheme by judging and filtering the route schemes in the candidate submarine cable layout scheme package one by one based on the constraint algorithm; and a scheme determination module configured to select the final route design scheme from the optimized schemes.

[0014] Thirdly, this application provides an electronic device, including: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to cause the electronic device to perform the method.

[0015] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the method described thereon.

[0016] As described above, the offshore wind farm line optimization method, system, equipment, and storage medium of this application have the following beneficial effects:

[0017] This application utilizes intelligent algorithm technology to assist in the design of smart wind farms. It designs a visualized intelligent path optimization system interface for offshore wind farms, comprehensively utilizing a visualized operation window, dynamic programming algorithms, and an improved Kruskal algorithm. By fully integrating the design rules of the collection lines, it achieves automatic optimization of design schemes and simultaneous calculation of multiple design schemes, improving the calculation speed of the optimal scheme. It breaks through the characteristics of traditional scheme design processes, such as cumbersome processes, significant influence of human experience, and limited alternative schemes. It replaces a large amount of manual work with machine operation, improving work efficiency, enhancing design quality, and providing technical support for the large-scale development of wind power projects. Attached Figure Description

[0018] Figure 1 The diagram shows an application scenario of the route optimization method for offshore wind farms described in this application embodiment.

[0019] Figure 2 The diagram shown is a schematic flowchart of the route optimization method for offshore wind farms described in this application embodiment.

[0020] Figure 3This is a schematic diagram of the input interface for the offshore wind farm route optimization method described in this application embodiment.

[0021] Figure 4 The flowchart shown is a generation tree set judgment flowchart of the route optimization method for offshore wind farms described in the embodiments of this application.

[0022] Figure 5 This is a schematic diagram of the integrated circuit optimization of the existing manual design scheme.

[0023] Figure 6 This is a schematic diagram showing the optimization results of the existing manual design scheme.

[0024] Figure 7 The diagram shown is an integrated route optimization schematic of the route optimization method for offshore wind farms described in this application embodiment.

[0025] Figure 8 The diagram shown illustrates the optimization results of the route optimization method for offshore wind farms described in this application embodiment.

[0026] Figure 9 The diagram shown is a schematic diagram of the line optimization system for an offshore wind farm as described in an embodiment of this application.

[0027] Figure 10 The diagram shown is a structural connection diagram of the electronic device described in an embodiment of this application.

[0028] Component designation explanation

[0029] 1 electronic devices 11 processor 12 memory 13 Communication interface 14 System bus 9 Offshore wind farm route optimization system 91 Requirements Acquisition Module 92 Fan grouping module 93 Solution candidate module 94 Algorithm determination module 95 Solution optimization module 96 Solution determination module S21~S26 step 3.1~3.4 step Detailed Implementation

[0030] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0031] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0032] The following embodiments of this application provide a method, system, device and storage medium for optimizing the power collection lines of an offshore wind farm, including but not limited to a power collection line design project for an offshore wind farm. The following description will take this application scenario as an example.

[0033] Please see Figure 1 The image shows a schematic diagram illustrating an application scenario of the offshore wind farm route optimization method described in this application embodiment. For example... Figure 1 As shown, taking a 700MW offshore wind farm project in a certain region of China as an example, the project plans to install 41 17.2MW wind turbines. The wind turbine layout of the wind farm is as follows. Figure 1 As shown. For large-capacity wind turbines, 66kV voltage is widely used both domestically and internationally. Therefore, the voltage level of the collector line for this offshore wind farm is 66kV. The offshore wind farm line optimization method of this application is used to design an optimized submarine cable collector line scheme for this offshore wind farm. Relevant parameters are input into the software interface to obtain the optimization results.

[0034] The technical solutions in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0035] Please see Figure 2 The diagram shows the principle flowchart of the offshore wind farm route optimization method described in the embodiments of this application. Figure 2 As shown in the figure, this embodiment provides a method for optimizing the route of an offshore wind farm, which specifically includes the following steps:

[0036] S21, Obtain the line design requirements; the line design requirements include the line voltage level, wind turbine capacity, discount rate, construction period, submarine cable length coefficient, as well as wind turbine coordinate table and submarine cable parameter table.

[0037] In one embodiment, the step of obtaining the line design requirements includes:

[0038] (1) Obtain parameter input instructions from the visualization interface; the parameter input instructions include input instructions for line voltage level, wind turbine capacity, discount rate, construction period, submarine cable length coefficient, as well as upload operation instructions for wind turbine coordinate table and submarine cable parameter table.

[0039] (2) Determine the line design requirements based on the parameter input instructions.

[0040] Please see Figure 3 This is a schematic diagram of the input interface for the offshore wind farm route optimization method described in this application embodiment. Figure 3As shown, by inputting various parameters such as length coefficient, collector line voltage level (kV), wind turbine capacity, discount rate, construction period, and submarine cable length coefficient into the software interface, and uploading wind turbine coordinate tables and submarine cable parameter tables, the line design requirements are determined based on the input and uploaded information. To facilitate scheme calculation, the backend of this application method considers multiple schemes, and the calculation results can be output in tabular form.

[0041] In practical applications, this application uses Python as the backend language and PyQT as the GUI (Graphical User Interface) development framework to visualize the software interface. Software interface design serves as the bridge between the user and the software, directly impacting the user experience and software usability. This application employs an intuitive and clear interface design, using a reasonable layout, clear icons, and color scheme to enable users to quickly understand the software's functions and operational processes.

[0042] This application prioritizes user operating habits and mental models, continuously optimizing interface elements to ensure the smoothness and efficiency of the software system during operation. Furthermore, the software's structural design is simple and clear, requiring no complex operations from the user. The software's interface design strives to provide an efficient, easy-to-use, and inclusive environment, making the user experience simple and straightforward.

[0043] S22, group the individual wind turbines of the offshore wind farm to form a wind turbine group scheme.

[0044] Specifically, this application solves the wind turbine grouping problem using dynamic programming. Dynamic programming is a method used in mathematics, management science, computer science, economics, and bioinformatics to solve complex problems by decomposing the original problem into relatively simpler subproblems. Dynamic programming algorithms are commonly used to solve problems with overlapping subproblems and optimal substructure properties. It improves efficiency by decomposing the problem into smaller problems, storing the solutions to the smaller problems, and avoiding redundant calculations.

[0045] In one embodiment, the step of grouping the individual wind turbines of an offshore wind farm to form a wind turbine group scheme includes:

[0046] (1) Determine the total number of fans.

[0047] In practical applications, for example, there are a total of Typhoon machine:

[0048]

[0049] (2) Determine the number of wind turbines in each group according to business requirements; wherein the number of wind turbines is a value within the range of the total number of wind turbines. Wherein, business requirements may refer to the fact that offshore wind turbines are usually grouped according to foundation structure (monopile foundation, jacket foundation, gravity foundation, suction foundation and floating foundation), number of blades (three-blade wind turbine, two-blade wind turbine and multi-blade wind turbine) or power generation system technology (squirrel-cage asynchronous motor, wound-rotor asynchronous motor, permanent magnet synchronous motor and wound-rotor synchronous motor).

[0050] Specifically, the number of fans within the group is recorded according to business needs. The restrictions are:

[0051]

[0052] in, This indicates the minimum number of fans in each group. This indicates the maximum number of fans in each group.

[0053] (3) The wind turbine group scheme is formed based on the minimum and maximum number of wind turbines.

[0054] Specifically, based on the minimum number of fans in each group. and maximum number of units List the wind turbine unit schemes that meet the requirements. Record the possible wind turbine configurations that can be formed by typhoon generator sets as follows: The number of wind turbines in the wind turbine unit needs to be in the set of integers. In the middle. Break the problem down into subproblems:

[0055]

[0056] The subproblems are then calculated similarly until the initial state is reached. The algorithm incorporates constraints that prohibit submarine cables from crossing each other and from interfering with the wind turbines, ensuring that the wind turbine planning does not violate design rules.

[0057] S23, Generate a candidate submarine cable layout package based on the line design requirements and the wind turbine unit scheme.

[0058] Specifically, the candidate submarine cable deployment schemes are typically sorted according to economic indicators (such as total cost, cable length, etc.), but have not yet undergone engineering constraint screening. Therefore, the system subsequently employs a constraint screening algorithm based on a greedy approach to quickly evaluate each input scheme.

[0059] S24, combining whether the submarine cables cross and whether the submarine cables interfere with the wind turbine to determine the constraint algorithm.

[0060] In one embodiment, the steps of determining the constraint algorithm based on whether the submarine cables cross and whether the submarine cables interfere with the wind turbine include:

[0061] (1) Let whether the submarine cables cross and whether the submarine cables interfere with the wind turbines be used as constraints.

[0062] (2) The constraints are embedded into the algorithm for finding the minimum spanning tree in the weighted graph to form the constraint algorithm.

[0063] S25. Based on the constraint algorithm, the route schemes of the candidate submarine cable layout scheme package are judged and screened one by one to determine the optimal scheme.

[0064] Specifically, the constraint algorithm described in this application includes an improved Kruskal algorithm, or other greedy algorithms besides Kruskal's algorithm for finding the minimum spanning tree in a weighted graph. Kruskal's algorithm constructs the MST (Minimum Spanning Tree) by progressively adding edges, selecting the edge with the smallest weight at each step while ensuring that no cycle is created after addition. Compared to Prim's algorithm for finding the minimum spanning tree, Kruskal's algorithm has better performance for sparse graphs. Kruskal's algorithm is not only efficient but also simple to implement, making it one of the classic methods for solving the minimum spanning tree problem.

[0065] By improving Kruskal's algorithm, most solutions that do not meet the constraints are eliminated. Kruskal's algorithm uses a disjoint-set data structure to quickly determine the existence of cycles and to merge different trees in a forest. This improved algorithm is suitable for sparse graphs because its time complexity is proportional to the logarithm of the number of edges.

[0066] In one embodiment, based on the constraint algorithm, the process of judging and filtering the route schemes of the candidate submarine cable layout scheme package one by one to determine the optimal scheme involves the following steps. In the specific implementation, the Kruskal algorithm utilizes a disjoint-set data structure to achieve efficient connectivity judgment. By querying, the identifier of the connected component to which any vertex belongs is determined, and the union operation is used to merge different components, thereby effectively avoiding the generation of loops during edge selection. This mechanism ensures that the algorithm has high execution efficiency while maintaining correctness. Specifically, it includes the following steps:

[0067] (1) Sort the route options from low to high according to their prices.

[0068] (2) Based on the principle of low cost, select a preset number of route options.

[0069] Specifically, the algorithm in this application takes the price ranking list of the proposed solutions as input, and sequentially selects the current optimal (lowest cost) solution as a candidate, and performs geometric and logical verification on each submarine cable connection path within it.

[0070] (3) Using the constraint algorithm, spatial topology judgment and interference judgment are performed on the selected route scheme.

[0071] In actual engineering projects, submarine cables for power collection lines are usually designed to avoid situations where they cross or interfere with wind turbines. Therefore, a screening algorithm based on the greedy principle is used to remove schemes that do not meet the actual conditions of the project, thereby improving the efficiency of software calculation and the usability of the output schemes.

[0072] Among them, spatial topology judgment refers to calculating the spatial vector relationship of each segment of submarine cable using the coordinate information of the wind turbine and the substation; interference judgment refers to detecting whether there are path intersections, overlaps, or interference with the wind turbine foundation, anchorage area, no-layout area, etc.

[0073] Please see Figure 4 This is a flowchart illustrating the spanning tree set judgment process for the route optimization method for offshore wind farms described in this application embodiment. Figure 4 As shown, the steps for performing spatial topology and interference judgments on the selected route scheme using the constraint algorithm include:

[0074] (3.1) Determine the edges of the weighted graph based on the route scheme.

[0075] (3.2) Sort all edges of the input weighted graph in ascending order of weight value to form an edge set; wherein the weight value refers to the distance between wind turbines.

[0076] (3.3) Initialize the spanning tree set to an empty set.

[0077] (3.4) Iterate through the edge set after ascending sorting in sequence, and determine whether each edge in the edge set should be added to the spanning tree set, until the number of edges in the spanning tree set reaches the set threshold.

[0078] Specifically, the sorted edge set is traversed sequentially. For each edge (u, v): if u and v belong to different connected components, the edge is added to the spanning tree set; otherwise, the edge is skipped. In practical applications, a threshold is set to the number of vertices minus one. When the number of edges in the spanning tree reaches the number of vertices minus one, the algorithm of this application terminates.

[0079] (4) Determine the optimization scheme from the route schemes retained by the constraint algorithm.

[0080] Specifically, the optimal solution for each wind turbine unit scheme is calculated, and schemes within the wind turbine unit are selected. The optimization within the wind turbine unit can be viewed as a graph problem. Given each node in the graph and the weights of each type of edge, the weights and minimum connected paths that meet the conditions are found. An improved weighted minimum spanning tree is used to solve the objective function.

[0081] Among various spanning trees, the chain spanning tree is a special form. Its structure requires all nodes to be connected linearly, meaning the shape of the spanning tree must be a chain-like structure, not an arbitrary tree shape or branching form. This means that in a chain spanning tree, each node has at most one predecessor and one successor node (excluding the first and last nodes), forming a continuous sequence of nodes. The optimization problem within a wind turbine group can be abstracted as a chain spanning tree problem. For example, suppose there are four wind turbines A, B, C, and D. This group optimization is about solving how to connect these four wind turbines A, B, C, and D, which, at the algorithm level, is a chain spanning tree.

[0082] Let the set of nodes be

[0083]

[0084] Let the edge set be The weight of the edge is

[0085]

[0086] The goal of the optimization process is to find a set of edges. Make:

[0087]

[0088] That is:

[0089] 1. Edge connection in All nodes in;

[0090] 2. The number of sides in is (Satisfies the spanning tree property);

[0091] 3. It has a chain structure;

[0092] 4. Minimum.

[0093] The algorithm in this application first starts with the initial node and adds it to the candidate set of the spanning tree. Then, it selects the edge with the smallest weight from all edges connecting to the current candidate set and adds its corresponding node to the spanning tree, repeating this process until all nodes are connected. To satisfy the chain constraint, this application adjusts the edge selection order in the algorithm, allowing expansion only of the start or end point of the current path, thereby ensuring that the generated structure always maintains a chain form and generates an optimal solution that satisfies the constraints.

[0094] The algorithm employs a local optimum retention strategy at each screening stage: once a conflict is detected between a scheme and another under any constraints, the scheme is immediately eliminated without further constraint checks, thus reducing computational complexity. For schemes that meet all constraints, their comprehensive index is recorded and they proceed to the next round of sorting or output.

[0095] Through this rapid screening strategy, the system can efficiently eliminate unsolvable solutions without recalculating path optimization, significantly reducing redundant calculations and improving overall computational efficiency and the engineering feasibility of the output solutions.

[0096] S26, Select the final route design scheme from the optimized schemes.

[0097] Specifically, after screening, solutions that do not meet the design rules are eliminated, and users can select the top ten optimization solutions according to their needs. Combined with... Figure 3 The interface shown, for example, displays a total of 60 solutions. If the user enters 10 in the area for selecting the number of solutions, it means that the user needs to select 10 solutions. Then, the user can click "Extract" to extract the top 10 solutions.

[0098] Please see Figure 5 This displays a schematic diagram of the integrated circuit optimization for an existing manually designed scheme. For example... Figure 5 As shown, the wiring between the various wind turbines includes four wire types: 3×95 (three-core wire, each core with a cross-sectional area of ​​95 square millimeters), 3×150 (three-core wire, each core with a cross-sectional area of ​​150 square millimeters), 3×300 (three-core wire, each core with a cross-sectional area of ​​300 square millimeters), and 3×630 (three-core wire, each core with a cross-sectional area of ​​630 square millimeters). Figure 5 The diagram shows an optimized circuit diagram of the artificial design scheme for 41 wind turbines at the 220kV offshore substation.

[0099] Please see Figure 6 This displays a schematic diagram of the optimized results of existing manual design schemes. For example... Figure 6 As shown, in the optimization of the power collection line in the artificial design scheme, the total cost of the submarine cable for the power collection line is 288.514 million yuan, and the total length of the 66kV submarine cable of each cross section is 88.22km.

[0100] Please see Figure 7 The diagram shows an integrated route optimization schematic of the route optimization method for offshore wind farms described in this application embodiment. Figure 7 As shown, in one embodiment, after selecting the final route design scheme from the optimized schemes, the method further includes: presenting the route layout path on the wind turbine layout diagram according to the final route design scheme. The routes between the various wind turbines include four types of wires: 3×95 (three-core wire, each core with a cross-sectional area of ​​95 square millimeters), 3×150 (three-core wire, each core with a cross-sectional area of ​​150 square millimeters), 3×300 (three-core wire, each core with a cross-sectional area of ​​300 square millimeters), and 3×630 (three-core wire, each core with a cross-sectional area of ​​630 square millimeters). Figure 7 The diagram shows the optimized route generated after implementing the route optimization method for offshore wind farms in this application for 41 wind turbines at the 220kV offshore substation.

[0101] Please see Figure 8 The image shows a schematic diagram illustrating the optimization results of the route optimization method for offshore wind farms described in this application embodiment. Figure 8 As shown, in the scheme generated after implementing the line optimization method of the offshore wind farm in this application, the total cost of the collection line submarine cable is RMB 287,374,200, and the total length of the 66kV submarine cable of each cross section is 95.41km.

[0102] The scope of protection for the offshore wind farm line optimization method described in this application is not limited to the execution order of the steps listed in this embodiment. Any solution implemented by adding, subtracting, or replacing steps in the prior art based on the principles of this application is included within the scope of protection of this application.

[0103] This application also provides a route optimization system for offshore wind farms. The route optimization system for offshore wind farms can implement the route optimization method for offshore wind farms described in this application. However, the implementation device for the route optimization method for offshore wind farms described in this application includes, but is not limited to, the structure of the route optimization system for offshore wind farms listed in this embodiment. All structural modifications and substitutions of the prior art made based on the principles of this application are included within the protection scope of this application.

[0104] Please see Figure 9 The diagram shows the structural principle of the offshore wind farm line optimization system described in this application embodiment. Figure 9 As shown, this embodiment provides a route optimization system 9 for offshore wind farms, including: a demand acquisition module 91, a wind turbine grouping module 92, a scheme candidate module 93, an algorithm determination module 94, a scheme optimization module 95, and a scheme determination module 96.

[0105] The requirement acquisition module 91 is configured to acquire line design requirements; the line design requirements include line voltage level, wind turbine capacity, discount rate, construction period, submarine cable length coefficient, as well as wind turbine coordinate table and submarine cable parameter table.

[0106] The wind turbine grouping module 92 is configured to group the individual wind turbines of the offshore wind farm to form a wind turbine group scheme.

[0107] The candidate module 93 is configured to generate a candidate submarine cable layout package based on the line design requirements and the wind turbine scheme.

[0108] The algorithm determination module 94 is configured to combine whether the submarine cable crosses and whether the submarine cable interferes with the wind turbine to determine the constraint algorithm.

[0109] The scheme optimization module 95 is configured to, based on the constraint algorithm, evaluate and filter the route schemes of the candidate submarine cable layout scheme package one by one to determine the optimal scheme.

[0110] The scheme determination module 96 is configured to select the final line design scheme from the optimized schemes.

[0111] In the embodiments provided in this application, it should be understood that the disclosed systems or methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or modules or units, and may be electrical, mechanical, or other forms.

[0112] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of this application, depending on actual needs. For example, the functional modules / units in the various embodiments of this application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0113] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0114] This application also provides an electronic device, characterized in that it includes: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to cause the electronic device to perform the method described thereon.

[0115] Please see Figure 10 The diagram shows the structural connections of the electronic device described in the embodiments of this application. Figure 10 As shown, the electronic device 1 of this application includes: a processor 11, a memory 12, a communication interface 13 and / or a system bus 14. The memory 12 and the communication interface 13 are connected to the processor 11 through the system bus 14 and communicate with each other. The memory 12 is used to store computer programs, the communication interface 13 is used to communicate with other devices, and the processor 11 is used to run the computer program, causing the electronic device 1 to execute the various steps of the method.

[0116] The processor 11 mentioned above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0117] The aforementioned memory 12 may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.

[0118] The system bus 14 mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This system bus 14 can be divided into an address bus, a data bus, a control bus, etc. The communication interface is used to enable communication between the database access device and other devices (such as clients, read-write databases, and read-only databases).

[0119] In practical applications, the electronic device may be a computer including all or some of its components such as memory, storage controller, one or more processing units (CPU), peripheral interfaces, RF circuitry, audio circuitry, speakers, microphones, input / output (I / O) subsystems, displays, other output or control devices, and external ports; the computer includes, but is not limited to, personal computers such as desktop computers, laptops, tablets, smartphones, and personal digital assistants (PDAs). In other embodiments, the electronic device may also be a server, which may be deployed on one or more physical servers depending on factors such as function and load, or it may be a cloud server composed of distributed or centralized server clusters; this embodiment does not impose any limitations.

[0120] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the method described thereon.

[0121] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing a processor. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. This available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state drive (SSD)).

[0122] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0123] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for optimizing the routing of an offshore wind farm, characterized in that, include: Obtain the line design requirements; the line design requirements include the line voltage level, wind turbine capacity, discount rate, construction period, submarine cable length coefficient, as well as wind turbine coordinate table and submarine cable parameter table; The individual wind turbines in the offshore wind farm are grouped to form a wind turbine group scheme; Based on the line design requirements and the wind turbine unit scheme, a candidate submarine cable layout scheme package is generated. The constraint algorithm is determined by considering whether the submarine cables cross and whether the submarine cables interfere with the wind turbines; Based on the constraint algorithm, the route schemes of the candidate submarine cable layout scheme package are judged and screened one by one to determine the optimal scheme. The final route design scheme is selected from the optimized schemes.

2. The method according to claim 1, characterized in that, The steps for obtaining line design requirements include: Obtain parameter input instructions from the visualization interface; the parameter input instructions include input instructions for line voltage level, wind turbine capacity, discount rate, construction period, submarine cable length coefficient, as well as upload operation instructions for wind turbine coordinate table and submarine cable parameter table; The circuit design requirements are determined based on the parameter input instructions.

3. The method according to claim 1, characterized in that, The steps for grouping the individual wind turbines in an offshore wind farm to form a turbine group scheme include: Determine the total number of fans; The number of fans in each group is determined according to business needs; wherein, the number of fans is a value within the range of the total number of fans; The wind turbine group scheme is formed based on the minimum and maximum number of wind turbines.

4. The method according to claim 1, characterized in that, The steps for determining the constraint algorithm, considering whether the submarine cables cross or interfere with the wind turbine, include: The constraints include whether the submarine cables cross each other and whether the submarine cables interfere with the wind turbines. The constraint conditions are embedded into the algorithm for finding the minimum spanning tree in the weighted graph, forming the constraint algorithm.

5. The method according to claim 4, characterized in that, Based on the constraint algorithm, the steps of evaluating and filtering the route schemes of the candidate submarine cable layout scheme package one by one to determine the optimal scheme include: The route options are sorted from lowest to highest price. Based on the principle of prioritizing low cost, a predetermined number of route options are selected. Using the aforementioned constraint algorithm, spatial topology and interference judgments are performed on the selected route schemes; The optimization scheme is determined from the route schemes retained by the constraint algorithm.

6. The method according to claim 5, characterized in that, The steps of performing spatial topology and interference judgments on the selected route scheme using the aforementioned constraint algorithm include: The edges of the weighted graph are determined based on the described route scheme; All edges of the input weighted graph are sorted in ascending order of their weight values ​​to form an edge set; where the weight value refers to the distance between wind turbines. Initialize the spanning tree set to an empty set; The process involves iterating through the edge set after ascending sorting, determining whether each edge in the edge set should be added to the spanning tree set, and continuing until the number of edges in the spanning tree set reaches a set threshold.

7. The method according to claim 1, characterized in that, After selecting the final route design scheme from the optimized schemes, the process also includes: Based on the final route design plan, the route layout path is presented on the wind turbine layout diagram.

8. A route optimization system for an offshore wind farm, characterized in that, include: The requirement acquisition module is configured to acquire line design requirements; the line design requirements include line voltage level, wind turbine capacity, discount rate, construction period, submarine cable length coefficient, as well as wind turbine coordinate table and submarine cable parameter table; The wind turbine grouping module is configured to group the individual wind turbines in an offshore wind farm to form a wind turbine group scheme; The candidate solution module is configured to generate a package of candidate submarine cable layout schemes based on the line design requirements and the wind turbine scheme. The algorithm determination module is configured to combine whether the submarine cables cross and whether the submarine cables interfere with the wind turbine to determine the constraint algorithm. The scheme optimization module is configured to, based on the constraint algorithm, evaluate and filter the route schemes of the candidate submarine cable layout scheme package one by one to determine the optimal scheme. The scheme determination module is configured to select the final route design scheme from the optimized schemes.

9. An electronic device, characterized in that, include: Processor and memory; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the method described in any one of claims 1 to 7.

Images