Current collection system topology optimization method and device based on variable weight minimum spanning tree

Abstract

The invention provides a power collection system topology optimization method and device based on a variable weight minimum spanning tree, and relates to the technical field of offshore wind power topology optimization. The method comprises the following steps: grouping a plurality of fans based on the angle difference among the plurality of fans according to the position information of the offshore booster station to obtain a plurality of fan sets; a Delaunay triangulation method is adopted to construct a reference grid connecting line of each fan set; and based on the reference grid connection line of each fan set, generating the optimal topology of the to-be-processed offshore current collection system based on a variable weight minimum spanning tree algorithm. According to the method, the grouping clustering method, the Delaunay triangulation method and the variable weight minimum spanning tree algorithm are combined, and the topology design of the offshore wind plant current collection system is efficiently, economically and rapidly achieved. According to the invention, topological connection and cable specification type selection can be optimized synchronously, and a global optimal solution is sought between the cable path length and the type cost difference caused by the change of the current-carrying capacity, so that the fine control of the overall cost of the current collection system is realized.

Description

Technical Field

[0001] This disclosure relates to the field of offshore wind power topology optimization technology, and in particular to a method and apparatus for topology optimization of a power collection system based on variable weight minimum spanning tree. Background Technology

[0002] Offshore wind power, as an important clean energy source, has always had its levelized cost (LDC) reduction as a core driving force for industrial development. In offshore wind farms, the collection system is responsible for gathering and transmitting the electricity generated by dozens of dispersed wind turbines to the substation. Its topology and cable investment account for approximately 15% to 20% of the total investment, making it a crucial element for optimizing investment and improving economic efficiency. Traditional collection system topology planning often aims to minimize the total length of submarine cables required to connect all wind turbines and the substation. However, this classic method has significant limitations. It considers only one perspective, using geographical distance as the sole weight. A topology with the shortest total length may be forced to use extremely expensive large-diameter submarine cables due to excessively high current carrying capacity on a critical path, resulting in poor engineering economics. Summary of the Invention

[0003] This disclosure aims to at least partially address one of the technical problems in the related art.

[0004] Therefore, the first aspect of this disclosure proposes a topology optimization method for collector systems based on variable-weight minimum spanning tree, comprising the following steps: Determine the location information of multiple wind turbines and offshore substations in the offshore power collection system to be processed; Based on the location information of the offshore substation, the multiple wind turbines are grouped according to the angle difference between them to obtain multiple wind turbine groups; The reference mesh connection lines for each wind turbine unit were constructed using the Delaunay triangulation method. Based on the reference grid connection lines of each wind turbine, the optimal topology of the offshore power collection system to be processed is generated using the variable weight minimum spanning tree algorithm.

[0005] A second aspect of this disclosure provides a topology optimization device for a collector system based on a variable-weight minimum spanning tree, comprising: The determination module is used to determine the location information of multiple wind turbines and offshore substations in the offshore power collection system to be processed; The grouping module is used to group the multiple wind turbines based on the location information of the offshore substation and the angle difference between the multiple wind turbines to obtain multiple wind turbine groups. The grouping module is used to construct the reference mesh connection lines for each of the wind turbine groups using the Delaunay triangulation method. The optimal topology generation module is used to generate the optimal topology of the offshore power collection system to be processed based on the reference grid connection lines of each wind turbine and the variable weight minimum spanning tree algorithm.

[0006] A third aspect of this disclosure provides an electronic device, including: a processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method described in the first aspect above.

[0007] A fourth aspect of this disclosure provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method described in the first aspect above.

[0008] This disclosure presents a topology optimization method for power collection systems based on variable-weighted minimum spanning tree (VLS). Combining grouping clustering, Delaunay triangulation, and VLS algorithms, it efficiently and economically achieves the topology design of offshore wind farm power collection systems. A wind turbine grouping clustering method based on angle difference is employed, selecting the number of turbines in each group according to the maximum capacity requirements of the submarine cable. To improve computational efficiency, Delaunay triangulation is introduced to sparsify the inter-turbine connection network within a group. A VLS algorithm is proposed to address cost variations caused by different submarine cable selections. This disclosure can simultaneously optimize topology connections and cable specification selection, seeking the globally optimal solution between cable path length and cost differences in cable type due to variations in current carrying capacity, thereby achieving refined control of the overall cost of the power collection system.

[0009] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0010] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which: Figure 1 A flowchart illustrating a topology optimization method for a collector system based on variable weight minimum spanning tree provided in this disclosure embodiment; Figure 2 This is a schematic diagram of a conventional fan grouping provided in an embodiment of the present disclosure; Figure 3 A schematic diagram of a fan grouping based on angle difference is provided for an embodiment of this disclosure; Figure 4A schematic diagram of an inter-node mesh network connection without using Delaunay triangulation is provided for an embodiment of this disclosure; Figure 5 A schematic diagram of an inter-node mesh network connection using the Delaunay triangulation method is provided for an embodiment of this disclosure; Figure 6 This is a schematic diagram of a collector system topology optimization device based on variable weight minimum spanning tree provided in an embodiment of this disclosure. Detailed Implementation

[0011] Embodiments of this disclosure are described in detail below, examples of which are illustrated 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 intended to explain this disclosure, and should not be construed as limiting this disclosure.

[0012] Specifically, the following describes, with reference to the accompanying drawings, a method and apparatus for topology optimization of a collector system based on a variable-weight minimum spanning tree according to embodiments of the present disclosure.

[0013] Figure 1 This is a flowchart illustrating a topology optimization method for a collector system based on variable-weight minimum spanning tree, provided in an embodiment of this disclosure. Figure 1 As shown, the current collector system topology optimization method based on variable weight minimum spanning tree may include the following steps: Step 101: Determine the location information of multiple wind turbines and offshore substations in the offshore power collection system to be processed.

[0014] Step 102: Based on the location information of the offshore substation, the multiple wind turbines are grouped according to the angle difference between them to obtain multiple wind turbine groups.

[0015] Wind turbine grouping is a key step in optimizing the topology of a power collection system. Proper grouping can reduce cable usage, lower investment costs, and improve system reliability. In some embodiments of this disclosure, the topological constraints of the offshore power collection system to be processed can be determined; the angle differences between multiple wind turbines can be determined; the angle bisector of the two wind turbines with the largest angle difference can be used as the 0° angle baseline; starting from the 0° angle baseline, multiple wind turbines are scanned clockwise to obtain multiple turbine groups, each satisfying the topological constraints. This grouping method can largely avoid crossings of power collection cables.

[0016] A reasonable grouping method needs to consider the actual conditions of the offshore wind farm, such as the capacity of the offshore substation, the limit on the number of incoming lines to the substation, the wind turbine capacity, and the maximum transmission capacity of the submarine cable. The number of wind turbines in each feeder is limited by the maximum transmission capacity of the submarine cable, and the number of wind turbines in each feeder should not exceed the limit. At the same time, the limit on the number of feeders allowed to connect to the substation needs to be considered, and the number of wind turbines in each feeder should not be less than the minimum number of wind turbines. Optionally, topology constraints may include: the limit on the number of feeders allowed to connect to the offshore substation in the offshore power collection system to be processed and the limit on the number of wind turbines in each feeder, as shown below:

[0017]

[0018]

[0019] in, This represents the minimum number of fans that can be connected in the feeder. This represents the maximum number of fans that can be connected in the feeder. This represents the number of wind turbines that can be connected to the feeder in this group. This is the maximum current carrying capacity of the submarine cable. For the voltage level of the collector system, For the fan capacity, This represents the total number of wind turbines in the offshore wind farm. This represents the maximum number of incoming lines to the offshore booster station.

[0020] As an example, Figure 2 This is a schematic diagram of a conventional fan grouping provided in an embodiment of the present disclosure. Figure 3 This is a schematic diagram of a fan grouping based on angle difference, provided as an embodiment of this disclosure. Figure 2 As shown, Figure 2 The last group of wind turbines has significantly fewer turbines than the other groups, which will lead to unbalanced feeder power and poor economic efficiency. Figure 3 The more uniform grouping demonstrates the effectiveness of the 0° baseline selection method based on the maximum angle difference.

[0021] Step 103: Use the Delaunay triangulation method to construct the reference mesh connection lines for each wind turbine unit.

[0022] This disclosure provides a Delaunay triangulation method to reduce the number of adjacent mesh lines and sparsify the node distances to the adjacent mesh, which can speed up the algorithm and improve its efficiency to a certain extent. Figure 4 This is a schematic diagram of an inter-node mesh network connection without using Delaunay triangulation, provided in an embodiment of this disclosure. Figure 5This diagram illustrates a node-to-node mesh network connection using the Delaunay triangulation method, as provided in an embodiment of this disclosure. Figure 4 and Figure 5 It can be seen that using the Delaunay triangulation method for sparse networks eliminates most of the redundant connection lines.

[0023] Step 104: Based on the reference grid connection lines of each wind turbine, generate the optimal topology of the offshore power collection system to be processed using the variable weight minimum spanning tree algorithm.

[0024] In the design of submarine cable routing for offshore wind farm power collection systems, the increasing number of wind turbines connected by the submarine cables leads to a greater demand for current carrying capacity, necessitating the selection of higher-specification submarine cables. Different submarine cable models have varying unit-distance costs, meaning the weight values ​​of some cable segments change. Therefore, the minimum spanning tree method considering only distance cost is insufficient to address these issues. This disclosure proposes a variable-weight minimum spanning tree algorithm based on a degree-based approach to solve this problem.

[0025] The topology graph of the power collection system generated using the minimum spanning tree method is an undirected graph and cannot reflect the current flow direction and transmission capacity of the submarine cables in the power collection system. Therefore, a topology identification method based on "degree" is introduced, which can record the current carrying capacity and connection status of each submarine cable. Here, "degree" represents the number of cables connected to each wind turbine node. For example, if the last wind turbine in each wind turbine string is connected to only one cable for power output, then the "degree" of this wind turbine node is 1.

[0026] In some embodiments of this disclosure, generating the optimal topology of the offshore power collection system to be processed based on the reference grid connection lines of each wind turbine unit and the variable weight minimum spanning tree algorithm may include the following steps: S11. For each wind turbine group, with the wind turbine as the node, initialize the degree of the current wind turbine group node to 0, and initialize all line current carrying capacity matrices and node current carrying capacity matrices.

[0027] S12, among the wind turbines not connected to the topology network, iteratively select the next wind turbine to be connected.

[0028] S13. For each wind turbine to be connected, based on the adjacency relationship between the reference grid connection lines and the current topology network, generate all possible connection positions of the wind turbine to be connected, forming a candidate connection set corresponding to the wind turbine to be connected.

[0029] S14. For each candidate connection point in the candidate connection set, simulate connecting the candidate connection point to the topology network, and perform degree-based current carrying capacity analysis and cable cost analysis to obtain the topology network cost corresponding to each candidate connection point after connecting to the topology network.

[0030] S21, Set the current topology network to the state after the candidate connection point is connected to the topology network.

[0031] S22, In the set topology network, iteratively identify all nodes with a degree of 1 until no such nodes remain. For each set of nodes with a degree of 1 identified in each round, perform the following operations: a.1. For each node in the set, determine its uniquely connected parent node and the cable segment between them based on its connection relationship.

[0032] a.2. Calculate the total current collected at this node, which is the sum of the capacities of all downstream wind turbines.

[0033] a.3. Based on the total current value and according to the cable current carrying capacity specification table, select the smallest specification cable model that meets the current carrying capacity requirements for the cable segment.

[0034] As an example, you can select the smallest specification cable model whose rated current carrying capacity is not less than the total current value from the cable current carrying capacity specification table.

[0035] a.4. Calculate the cost of the cable segment based on the unit length cost of the selected cable model and the length of the cable segment, and add it to the total cost.

[0036] a.5. Add the current value collected by the current node to the current value of its parent node, and decrement the "degree" value of both the current node and its parent node by 1 to logically remove the calculated cable segment.

[0037] S15, based on the total cost of each candidate connection point, determine the target connection point in the candidate connection set, and connect the next wind turbine to be connected to the target connection point.

[0038] S16, after all the wind turbines of the current wind turbine unit are connected to the topology network, outputs the topology structure of the current wind turbine unit, the selection results of each cable segment, and the total cost of the topology network.

[0039] In this embodiment, by introducing the node "degree" as the core indicator for topology identification and current flow analysis, the current carrying capacity of each cable segment in the power collection network is dynamically determined and the selection and pricing are based on this, thereby realizing a variable-weight minimum spanning tree construction.

[0040] To better understand the variable-weight minimum spanning tree algorithm in this embodiment, the following steps can also be used to generate the optimal topology: (1) Read the group information of the wind turbine group, initialize the "degree" of the wind turbine node in this group to 0, set the "degree" of the booster station (No. 1) node to 1000 to avoid subsequent judgment errors, and initialize the current carrying capacity matrix of all lines and the current carrying capacity matrix of nodes.

[0041] (2) Read the current topology network information and wind turbine connection status, and find the next unconnected wind turbine.

[0042] (3) Search the connection matrix and select the available fans in order to connect.

[0043] (4) Read the node "degree" matrix, find the number of all nodes with a "degree" of 1, and find the connection information of such nodes.

[0044] (5) Derive the number of the previous level node from the topology information matrix.

[0045] (6) Calculate the output capacity of the wind turbine at this type of node, update the line current carrying capacity matrix and the node current carrying capacity matrix, and mark the corresponding cable type.

[0046] (7) Subtract 1 from the degree of the nodes on both sides of the already calculated line.

[0047] (8) Repeat steps (4)-(7) until there are no nodes with a degree of 1. Calculate the cost of the connected topology based on the current carrying capacity matrix and cable type and save it.

[0048] (9) Repeat steps (3)-(8) until all available wind turbines have been traversed, and select the wind turbine with the smallest change in total topology cost to add to the topology network.

[0049] (10) Repeat steps (2)-(9) until all wind turbines are connected to the collector system topology network.

[0050] (11) Repeat steps (1)-(10) until all wind turbine group feeders are connected to the offshore substation.

[0051] (12) Save the topology matrix of the power collection system, the cable selection matrix, and the total cost of the topology network.

[0052] By implementing the embodiments of this disclosure, combining grouping clustering, Delaunay triangulation, and variable-weighted minimum spanning tree algorithm, the topology design of the offshore wind farm collection system is achieved efficiently and economically. A grouping clustering method based on angle difference is used to select the number of wind turbines in each group according to the maximum capacity requirement of the submarine cable. To improve computational efficiency, Delaunay triangulation is introduced to sparsify the inter-turbine connection network within a group. A variable-weighted minimum spanning tree algorithm is proposed to address the cost variation caused by different submarine cable selections. This disclosure can simultaneously optimize topology connections and cable specification selection, seeking a globally optimal solution between cable path length and model cost differences caused by variations in current carrying capacity, thereby achieving refined control of the overall cost of the collection system.

[0053] Figure 6This is a schematic diagram of a collector system topology optimization device based on variable-weight minimum spanning tree, provided as an embodiment of this disclosure. Figure 6 As shown, the collector system topology optimization device based on variable weight minimum spanning tree may include: a determination module 601, a grouping module 602, a grouping processing module 603, and an optimal topology generation module 604.

[0054] The determination module 601 is used to determine the location information of multiple wind turbines and offshore substations in the offshore power collection system to be processed.

[0055] Grouping module 602 is used to group multiple wind turbines based on the location information of the offshore substation and the angle difference between them, to obtain multiple wind turbine groups.

[0056] The group processing module 603 is used to construct the reference mesh connection lines for each wind turbine unit using the Delaunay triangulation method.

[0057] The optimal topology generation module 604 is used to generate the optimal topology of the offshore power collection system to be processed based on the reference grid connection lines of each wind turbine and the variable weight minimum spanning tree algorithm.

[0058] In some embodiments of this disclosure, the grouping module 602 is specifically used to: determine the topological constraints of the offshore power collection system to be processed; determine the angle difference between multiple wind turbines; take the angle bisector of the two wind turbines with the largest angle difference among the multiple wind turbines as the 0° angle reference line; and scan the multiple wind turbines clockwise starting from the 0° angle reference line to obtain multiple wind turbine groups, each wind turbine group satisfying the topological constraints.

[0059] In some embodiments of this disclosure, topology constraints include: a limit on the number of feeders allowed to connect to the offshore substation in the offshore power collection system to be processed, and a limit on the number of wind turbines in each feeder.

[0060] In some embodiments of this disclosure, the optimal topology generation module 604 is specifically used for: S11, For each wind turbine group, with the wind turbine as the node, initialize the degree of the current wind turbine group node to 0, and initialize all line current carrying capacity matrices and node current carrying capacity matrices; S12, among the wind turbines not connected to the topology network, iteratively select the next wind turbine to be connected; S13. For each wind turbine to be connected, based on the adjacency relationship between the reference grid connection lines and the current topology network, generate all possible connection positions of the wind turbine to be connected, forming a candidate connection set corresponding to the wind turbine to be connected. S14. For each candidate connection point in the candidate connection set, simulate connecting the candidate connection point to the topology network, and then perform degree-based current carrying capacity analysis and cable cost analysis to obtain the topology network cost corresponding to each candidate connection point after connecting to the topology network. S15, Based on the total cost of each candidate connection point, determine the target connection point in the candidate connection set, and connect the next wind turbine to be connected to the target connection point; S16, after all the wind turbines of the current wind turbine unit are connected to the topology network, outputs the topology structure of the current wind turbine unit, the selection results of each cable segment, and the total cost of the topology network.

[0061] In some embodiments of this disclosure, degree-based current carrying capacity analysis and cable cost analysis are performed, including: S21, Set the current topology network to the state after the candidate connection point is connected to the topology network; S22, In the set topology network, identify all nodes with a degree of 1 in a loop until no such nodes exist; for each set of nodes with a degree of 1 identified in each round, perform the following operations: a.1. For each node in the set, determine its uniquely connected parent node and the cable segment between them based on its connection relationship; a.2. Calculate the total current collected at this node, which is the sum of the capacities of all downstream wind turbine units; a.3. Based on the total current value and according to the cable current carrying capacity specification table, select the smallest specification cable model that meets the current carrying capacity requirement for the cable section; a.4. Calculate the cost of the cable segment based on the unit length cost of the selected cable model and the length of the cable segment, and add it to the total cost; a.5. Add the current value collected by the current node to the current value of its parent node, and decrement the "degree" value of both the current node and its parent node by 1 to logically remove the calculated cable segment.

[0062] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0063] To implement the above embodiments, this disclosure also proposes an electronic device, including: a processor and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiments. To implement the above embodiments, this disclosure also proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided in the foregoing embodiments.

[0064] To implement the above embodiments, this disclosure also proposes a computer program product, including a computer program that, when executed by a processor, implements the methods provided in the foregoing embodiments.

[0065] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this disclosure. In this specification, the 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. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0066] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0067] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of this disclosure includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of this disclosure pertain.

[0068] 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. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), 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). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the 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 a computer memory.

[0069] It should be understood that various parts of this disclosure 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.

[0070] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0071] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0072] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A topology optimization method for collector systems based on variable-weight minimum spanning tree, characterized in that, Includes the following steps: Determine the location information of multiple wind turbines and offshore substations in the offshore power collection system to be processed; Based on the location information of the offshore substation, the multiple wind turbines are grouped according to the angle difference between them to obtain multiple wind turbine groups; The reference mesh connection lines for each wind turbine unit were constructed using the Delaunay triangulation method. Based on the reference grid connection lines of each wind turbine, the optimal topology of the offshore power collection system to be processed is generated using the variable weight minimum spanning tree algorithm.

2. The method according to claim 1, characterized in that, The method involves grouping the multiple wind turbines based on the location information of the offshore substation and the angle difference between them, resulting in multiple wind turbine groups, including: Determine the topological constraints of the offshore power collection system to be processed; Determine the angle difference between the plurality of fans; The angle bisectors of the two fans with the largest angle difference among the multiple fans are taken as the 0° angle reference line; Starting from the 0° reference line, the plurality of wind turbines are scanned clockwise to obtain the plurality of wind turbine groups, each of which satisfies the topology constraint conditions.

3. The method according to claim 2, characterized in that, The topology constraints include: a limit on the number of feeders allowed to connect to the offshore substation in the offshore power collection system to be processed, and a limit on the number of wind turbines in each feeder.

4. The method according to claim 1, characterized in that, The process of generating the optimal topology of the offshore power collection system to be processed based on the reference grid connection lines of each wind turbine unit and the variable-weight minimum spanning tree algorithm includes: S11, For each wind turbine group, with the wind turbine as the node, initialize the degree of the current wind turbine group node to 0, and initialize all line current carrying capacity matrices and node current carrying capacity matrices; S12, among the wind turbines not connected to the topology network, iteratively select the next wind turbine to be connected; S13, For each wind turbine to be connected, based on the adjacency relationship between the reference grid connection line and the current topology network, generate all possible connection positions of the wind turbine to be connected, forming a candidate connection set corresponding to the wind turbine to be connected; S14. For each candidate connection point in the candidate connection set, simulate connecting the candidate connection point to the topology network, and perform degree-based current carrying capacity analysis and cable cost analysis to obtain the topology network cost corresponding to each candidate connection point after connecting to the topology network. S15, based on the total cost of each candidate connection point, determine the target connection point in the candidate connection set, and connect the next wind turbine to be connected to the target connection point; S16, after all the wind turbines of the current wind turbine unit are connected to the topology network, outputs the topology structure of the current wind turbine unit, the selection results of each cable segment, and the total cost of the topology network.

5. The method according to claim 4, characterized in that, The execution of degree-based current carrying capacity analysis and cable cost analysis includes: S21, Set the current topology network to the state after the candidate connection point is connected to the topology network; S22, In the set topology network, identify all nodes with a degree of 1 in a loop until no such nodes exist; for each set of nodes with a degree of 1 identified in each round, perform the following operations: a.

1. For each node in the set, determine its uniquely connected parent node and the cable segment between them based on its connection relationship; a.

2. Calculate the total current collected at this node, which is the sum of the capacities of all downstream wind turbine units; a.

3. Based on the total current value, and according to the cable current carrying capacity specification table, select the smallest specification cable model that meets the current carrying capacity requirement for the cable segment; a.

4. Calculate the cost of the cable segment based on the unit length cost of the selected cable model and the length of the cable segment, and add it to the total cost; a.

5. Add the current value collected by the current node to the current value of its parent node, and decrement the "degree" value of both the current node and its parent node by 1 to logically remove the calculated cable segment.

6. The method according to claim 5, characterized in that, The step of selecting the minimum specification cable model that meets the current carrying capacity requirement for the cable segment based on the total current value and according to the cable current carrying capacity specification table includes: In the cable current carrying capacity specification table, select the smallest specification cable model whose rated current carrying capacity is not less than the total current value.

7. A topology optimization device for a collector system based on a variable-weight minimum spanning tree, characterized in that, include: The determination module is used to determine the location information of multiple wind turbines and offshore substations in the offshore power collection system to be processed; The grouping module is used to group the multiple wind turbines based on the location information of the offshore substation and the angle difference between the multiple wind turbines to obtain multiple wind turbine groups. The grouping module is used to construct the reference mesh connection lines for each of the wind turbine groups using the Delaunay triangulation method. The optimal topology generation module is used to generate the optimal topology of the offshore power collection system to be processed based on the reference grid connection lines of each wind turbine and the variable weight minimum spanning tree algorithm.

8. The apparatus according to claim 7, characterized in that, The optimal topology generation module is specifically used for: S11, For each wind turbine group, with the wind turbine as the node, initialize the degree of the current wind turbine group node to 0, and initialize all line current carrying capacity matrices and node current carrying capacity matrices; S12, among the wind turbines not connected to the topology network, iteratively select the next wind turbine to be connected; S13, For each wind turbine to be connected, based on the adjacency relationship between the reference grid connection line and the current topology network, generate all possible connection positions of the wind turbine to be connected, forming a candidate connection set corresponding to the wind turbine to be connected; S14. For each candidate connection point in the candidate connection set, simulate connecting the candidate connection point to the topology network, and perform degree-based current carrying capacity analysis and cable cost analysis to obtain the topology network cost corresponding to each candidate connection point after connecting to the topology network. S15, based on the total cost of each candidate connection point, determine the target connection point in the candidate connection set, and connect the next wind turbine to be connected to the target connection point; S16, after all the wind turbines of the current wind turbine unit are connected to the topology network, outputs the topology structure of the current wind turbine unit, the selection results of each cable segment, and the total cost of the topology network.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

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