Improved tabu search topology optimization method and device for offshore wind farm power collection system

Abstract

The application provides an improved tabu search topology optimization method of a marine wind farm power collection system, which is based on the positions of wind turbines to intelligently preliminarily group and sort, to form an initial connection scheme, to connect all the wind turbines in each group with the minimum cost, to calculate the group cable cost, to check whether the lines cross, to impose a penalty in the total cost if the lines cross, to ensure that the scheme is feasible, to compare the current scheme with the historical best, to update the record and intelligently exchange the wind turbine groups to explore a new scheme, to repeat the optimization process until the number of iterations is satisfied, and to finally output a globally optimal topology scheme. The application has the advantages of better optimization effect, significantly improved economy, higher calculation efficiency, stronger engineering practicability, better algorithm robustness, better scheme reliability, high design automation degree and strong auxiliary decision-making capacity.

Description

Technical Field

[0001] This invention relates to the field of offshore wind farm electrical system design technology, specifically to an improved tabu search topology optimization method and apparatus for offshore wind farm power collection systems. Background Technology

[0002] The topology design of offshore wind farm collection systems is a typical combinatorial optimization problem, aiming to minimize the cost of laying submarine cables while satisfying electrical and path constraints. Common topologies include single-radial, closed-loop, centralized, and hybrid types, each with its own trade-offs in economics and reliability.

[0003] Existing optimization methods often employ heuristic approaches such as genetic algorithms, simulated annealing, and particle swarm optimization. However, when dealing with large-scale wind turbine layouts and multiple constraints (such as path intersections and cable capacity), they often suffer from slow convergence, susceptibility to local optima, and high computational complexity. Tabu search, as a heuristic algorithm with "memory" capabilities, demonstrates good global search capabilities in path optimization and scheduling problems. However, its application in the topology optimization of offshore wind farm collection systems is not yet systematic, especially in areas such as initialization strategies, constraint handling mechanisms, and inter-group collaborative optimization, where there is still room for improvement. Summary of the Invention

[0004] The purpose of this invention is to solve the problems mentioned in the background section above, and this invention provides the following technical solution:

[0005] An improved tabu search topology optimization method for offshore wind farm collector systems includes the following steps:

[0006] S1. Initialize the algorithm parameters, historical best solutions and taboo list, and based on the wind turbine coordinate information, use polar coordinate mapping and minimum spanning tree algorithm to initially group and sort the wind turbines to generate the initial topology scheme;

[0007] S2. For each wind turbine group, perform optimal connection construction within the group. By selecting the minimum cost edge one by one, connect all wind turbines in the group to the collection node or the substation, and calculate the cable connection cost of the group.

[0008] S3. Determine whether the current topology solution satisfies the path intersection constraint; if it does, directly calculate the total cost; if it does not, add a penalty term to the total cost to form the penalized total objective function value.

[0009] S4. Compare the current total objective function value with the historical best value; if it is better, update the historical best solution and taboo table, and perform inter-wind turbine group exchange operation based on the taboo table to generate a new topology scheme; otherwise, directly perform inter-group exchange operation based on the taboo table.

[0010] S5. Repeat steps S2 to S4 until the preset maximum number of iterations is reached, and output the historical best topology scheme as the optimal topology of the offshore wind farm collection system.

[0011] A further preferred embodiment is that, in step S1, the wind turbines are initially grouped and sorted using polar coordinate mapping and the minimum spanning tree algorithm, specifically including:

[0012] Using the substation as the pole, the rectangular coordinates of the wind turbines are converted to polar coordinates and sorted according to the polar angle. Based on the sorting, the wind turbines are divided into several groups, and within each group, the minimum spanning tree algorithm is used to generate the initial connection relationship based on the wind turbine node coordinates.

[0013] More preferably, in step S2, the construction of the optimal connection within the group adopts a step-by-step addition strategy, specifically including:

[0014] S2.1 Initialize the set of connected nodes and the set of unconnected nodes within the group, wherein the set of connected nodes contains at least one collector node or step-up substation;

[0015] S2.2 Calculate the connection cost between all connected nodes and unconnected nodes, and select the edge with the lowest cost from all candidate connection edges to make the connection;

[0016] S2.3 Update the set of connected nodes and the set of unconnected nodes, and record the connection edge;

[0017] S2.4 Repeat steps S2.2 to S2.3 until all wind turbine nodes in the group are connected.

[0018] More preferably, in step S3, the process of determining whether the path crossing constraint is satisfied is performed with a delay; path crossing detection is only performed when the total cable connection cost of the current solution is lower than the historical best cost; if a crossing is detected, a penalty value positively correlated with the severity of the crossing is added to the total cost.

[0019] More preferably, in step S4, the taboo table is used to record recently executed inter-wind turbine exchange operations; when generating a new solution, exchange operations not in the taboo table are preferentially selected; and entries in the taboo table are automatically unbanned after reaching a preset taboo length.

[0020] More preferably, the wind turbine grouping is a logical set pre-defined based on the polar coordinate position of the wind turbine, electrical capacity constraints, or engineering zoning, and the number of groups and the number of wind turbines within a group can be adjusted according to the scale of the wind farm.

[0021] The present invention also provides an improved tabu search topology optimization device for offshore wind farm collection systems, which applies the improved tabu search topology optimization method for offshore wind farm collection systems described in any of the above claims. The improved tabu search topology optimization device for offshore wind farm collection systems includes a processor, a computer storage medium, and a memory.

[0022] The processor is used to run one or more program instructions to execute any of the steps described in the improved tabu search topology optimization method for offshore wind farm collection systems of the present invention.

[0023] The computer storage medium stores a computer program, which, when executed by a processor, implements any of the steps described in the improved tabu search topology optimization method for the offshore wind farm collection system of the present invention. The computer program is one or more program instructions.

[0024] The memory is used to store one or more executable program instructions of the processor.

[0025] Compared with existing technologies, the improved tabu search topology optimization method for offshore wind farm collection systems provided by this invention can bring the following significant benefits:

[0026] (1) Better optimization effect and significantly improved economy: By integrating the dual-layer optimization mechanism of "fine connection within the group" and "intelligent exchange between groups" and combining the high-quality polar coordinate-minimum spanning tree initialization strategy, the algorithm has stronger global exploration and local development capabilities. This can effectively break out of local optima and find a topology structure with a shorter total cable length and a more reasonable path, thereby directly reducing the cost of submarine cable materials and laying.

[0027] (2) Higher computational efficiency and stronger engineering applicability:

[0028] Intelligent constraint handling: The "delayed verification" mechanism is adopted, and time-consuming path cross detection is only performed on the high-quality solution candidate set, which avoids a lot of computational overhead on invalid solutions and significantly improves the optimization search speed.

[0029] Hierarchical optimization structure: The complex global problem is decomposed into two relatively simple sub-problems: intra-group and inter-group. This reduces the computational complexity of each iteration and enables the algorithm to efficiently handle the topology optimization problem of large-scale wind turbine clusters.

[0030] (3) The algorithm is more robust and the solution is more reliable:

[0031] Dynamic taboo management: By using a taboo table to avoid repeating recent operations, the search is guided to expand to new areas. At the same time, the unbanning strategy is used to prevent missing high-quality solutions, thereby enhancing the adaptability and stability of the algorithm under different wind field layouts.

[0032] Strict constraint satisfaction: Key engineering constraints such as cable capacity and path intersections are precisely incorporated into the objective function in the form of penalty functions, ensuring that all recommended solutions strictly meet engineering safety and specification requirements, thereby improving the direct usability of the design results.

[0033] (4) The design is highly automated and has strong decision-making support capabilities:

[0034] This invention can be embedded in wind power design software or run as an independent module to realize a fully automatic or semi-automatic design process from wind turbine coordinate input to optimal topology output, greatly reducing the workload of manual trial and error and repeated modifications.

[0035] The algorithm can simultaneously output the global optimal solution and multiple suboptimal solutions, providing designers with a variety of feasible technical options and supporting them in making comprehensive decisions based on factors such as cost, redundancy, and construction difficulty. Attached Figure Description

[0036] Figure 1 This is a flowchart of the method of the present invention;

[0037] Figure 2 This is the algorithm optimization part within the group in the overall algorithm architecture diagram of the method of this invention;

[0038] Figure 3 This is the overall algorithm architecture diagram of the method of this invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] The preferred embodiments of the present invention will now be described in further detail with reference to the accompanying drawings.

[0041] Example 1:

[0042] This invention provides an improved tabu search topology optimization method for offshore wind farm collection systems. See the flowchart for the method. Figure 1 ,

[0043] An improved tabu search topology optimization method for offshore wind farm collection systems includes the following steps:

[0044] S1. Initialize the algorithm parameters, historical best solutions, and taboo list. Based on the wind turbine coordinate information, use polar coordinate mapping and minimum spanning tree algorithm to initially group and sort the wind turbines to generate an initial topology scheme.

[0045] S2. For each wind turbine group, perform optimal connection construction within the group. By selecting the minimum cost edge one by one, connect all wind turbines in the group to the collection node or the substation, and calculate the cable connection cost of the group.

[0046] S3. Determine whether the current topology scheme satisfies the path intersection constraint; if it does, calculate the total cost directly; if it does not, add a penalty term to the total cost to form the penalized total objective function value.

[0047] S4. Compare the current total objective function value with the historical best value; if it is better, update the historical best solution and taboo table, and perform the inter-wind turbine group exchange operation based on the taboo table to generate a new topology scheme; otherwise, directly perform the inter-group exchange operation based on the taboo table.

[0048] S5. Repeat steps S2 to S4 until the preset maximum number of iterations is reached, and output the historical best topology scheme as the optimal topology of the offshore wind farm collection system.

[0049] In step S1, the wind turbines are initially grouped and sorted using polar coordinate mapping and the minimum spanning tree algorithm, specifically including:

[0050] Using the substation as the pole, the rectangular coordinates of the wind turbines are converted to polar coordinates and sorted according to the polar angle. Based on the sorting, the wind turbines are divided into several groups, and within each group, the minimum spanning tree algorithm is used to generate the initial connection relationship based on the wind turbine node coordinates.

[0051] In step S2, the construction of the optimal connection within the group adopts a step-by-step addition strategy, specifically including:

[0052] S2.1 Initialize the set of connected nodes and the set of unconnected nodes in the group, wherein the set of connected nodes contains at least one collector node or step-up substation.

[0053] S2.2 Calculate the connection cost between all connected nodes and unconnected nodes, and select the edge with the lowest cost from all candidate connection edges to make the connection.

[0054] S2.3 Update the set of connected nodes and the set of unconnected nodes, and record the connection edge.

[0055] S2.4 Repeat steps S2.2 to S2.3 until all wind turbine nodes in the group are connected.

[0056] In step S3, the process of determining whether the path crossing constraint is satisfied is performed with a delay; path crossing detection is only performed when the total cable connection cost of the current solution is lower than the historical best cost; if a crossing is detected, a penalty value positively correlated with the severity of the crossing is added to the total cost.

[0057] In step S4, the taboo list is used to record recently executed inter-wind turbine exchange operations; when generating a new solution, exchange operations not in the taboo list are selected first; entries in the taboo list are automatically unbanned after reaching a preset taboo length.

[0058] More preferably, the wind turbine grouping is a logical set pre-defined based on the polar coordinate position of the wind turbine, electrical capacity constraints, or engineering zoning, and the number of groups and the number of wind turbines within a group can be adjusted according to the scale of the wind farm.

[0059] See appendix for details. Figure 2 The overall algorithm architecture diagram of this invention shows the intra-group algorithm optimization part and... Figure 3 The overall algorithm architecture diagram of the method of this invention is as follows: Figure 2 The process of optimizing the network route within the group is demonstrated: Starting from the current optimization solution, the set of connected and unconnected nodes is first initialized. Then, the connection cost is calculated based on the wind turbine distance matrix. The optimal candidate route is determined by screening for the minimum cost. The set of nodes and routes is dynamically updated, and the process is iterated until all nodes are connected. Finally, cross-connection avoidance constraints are determined, and the optimal connection scheme within the group is output. This process adopts a route-by-route addition strategy, and local optima are ensured through the cost calculation formula. Figure 3 The overall algorithm flow of this invention is presented as follows: Starting with the input wind turbine layout data, initial grouping is performed using polar coordinate space mapping. A basic connection topology is established based on the minimum spanning tree algorithm. After initializing the submarine cable cost parameters and tabu search algorithm parameters, a dual optimization loop is entered. This loop includes intra-group optimization (adding lines one by one and calculating the target cost) and inter-group optimization (performing neighborhood operations such as swaps / mutations in tabu search). During the process, the tabu table is dynamically updated and cross-constraint verification is performed. Finally, the globally optimal topology that satisfies the current carrying capacity constraint is output.

[0060] The improved tabu search topology optimization method for offshore wind farm collection systems provided by this invention can bring the following significant benefits:

[0061] (1) Better optimization effect and significantly improved economy: By integrating the dual-layer optimization mechanism of "fine connection within the group" and "intelligent exchange between groups" and combining the high-quality polar coordinate-minimum spanning tree initialization strategy, the algorithm has stronger global exploration and local development capabilities. This can effectively break out of local optima and find a topology structure with a shorter total cable length and a more reasonable path, thereby directly reducing the cost of submarine cable materials and laying.

[0062] (2) Higher computational efficiency and stronger engineering applicability:

[0063] Intelligent constraint handling: The "delayed verification" mechanism is adopted, and time-consuming path cross detection is only performed on the high-quality solution candidate set, which avoids a lot of computational overhead on invalid solutions and significantly improves the optimization search speed.

[0064] Hierarchical optimization structure: The complex global problem is decomposed into two relatively simple sub-problems: intra-group and inter-group. This reduces the computational complexity of each iteration and enables the algorithm to efficiently handle the topology optimization problem of large-scale wind turbine clusters.

[0065] (3) The algorithm is more robust and the solution is more reliable:

[0066] Dynamic taboo management: By using a taboo table to avoid repeating recent operations, the search is guided to expand to new areas. At the same time, the unbanning strategy is used to prevent missing high-quality solutions, thereby enhancing the adaptability and stability of the algorithm under different wind field layouts.

[0067] Strict constraint satisfaction: Key engineering constraints such as cable capacity and path intersections are precisely incorporated into the objective function in the form of penalty functions, ensuring that all recommended solutions strictly meet engineering safety and specification requirements, thereby improving the direct usability of the design results.

[0068] (4) The design is highly automated and has strong decision-making support capabilities:

[0069] This invention can be embedded in wind power design software or run as an independent module to realize a fully automatic or semi-automatic design process from wind turbine coordinate input to optimal topology output, greatly reducing the workload of manual trial and error and repeated modifications.

[0070] The algorithm can simultaneously output the global optimal solution and multiple suboptimal solutions, providing designers with a variety of feasible technical options and supporting them in making comprehensive decisions based on factors such as cost, redundancy, and construction difficulty.

[0071] Example 2

[0072] This embodiment provides an improved tabu search topology optimization method for offshore wind farm collection systems. The method aims to minimize submarine cable investment while strictly satisfying cable capacity constraints and path intersection avoidance constraints. The method includes the following steps:

[0073] S1. Initialize the algorithm parameters, historical best solutions, and taboo list. The algorithm parameters are initialized to the maximum number of iterations Tmax, and the historical best solutions are the historical best solutions. The tabu list is the tabu search list r, and based on the wind turbine coordinate information, polar coordinate mapping and minimum spanning tree algorithm are used to initially group and sort the wind turbines. The initial topology scheme is generated. In step S1, the wind turbines are initially grouped and sorted using polar coordinate mapping and the minimum spanning tree algorithm, specifically including:

[0074] Using the substation as the pole, the rectangular coordinates of the wind turbines are converted to polar coordinates and sorted according to the polar angle. Based on the sorting, the wind turbines are divided into several groups, and within each group, the minimum spanning tree algorithm is used to generate the initial connection relationship based on the wind turbine node coordinates.

[0075] S2. For each wind turbine group, perform optimal connection construction within the group. By selecting the minimum cost edge one by one, connect all wind turbines in the group to the collection node or the substation, and calculate the cable connection cost of the group.

[0076] The calculation of cabling connection costs needs to take into account the unit cost of different connection points, as well as the known set of nodes in the current combination. The optimal connection method within a combination is selected by adding the best points one by one. First, the connected points are initialized. and the set of unconnected points The details are as follows:

[0077]

[0078]

[0079]

[0080]

[0081] in The distance matrix is... The cost matrix for the current expected connections. This represents the number of nodes that are already connected and are far from the booster station. This is a set of unconnected nodes. This is the set of connected nodes. and This represents the optimal connection. Connect to existing nodes , This is the optimal connection. Represents the total set of nodes. This is the total set of routes.

[0082] S3. Determine if the current topology solution satisfies the path intersection constraint; if it does, directly calculate the total cost; if not, add a penalty term to the total cost to form the penalized total objective function value. Calculating whether the wind turbine wiring intersection constraint is violated is costly, and is only performed if the cost objective of the current solution is met. Constraint violation checks are added only when necessary. The overall objective and constraint violation calculations are as follows:

[0083]

[0084]

[0085] in For the penalty function, Let F be the penalty coefficient, and F be the objective function for optimization.

[0086] S4. Compare the current overall objective function value with the historical best value; if it is better, update the historical best solution and the taboo table, and perform inter-turbine exchange operations based on the taboo table to generate a new topology scheme; otherwise, directly perform inter-turbine exchange operations based on the taboo table. Specifically: if Update the optimal target and optimal solution set The forbidden search table is updated, and relevant data for replacement combinations is populated. Based on the search table, the wind turbine combinations that need to be replaced are updated.

[0087]

[0088]

[0089] in This is a list of taboos. Forbidden length, , This is the current optimal swap combination number. This is the encoding of the nth individual in the t-th iteration. The solution set is updated by swapping the two selected combinations, where swap represents the function for swapping the wind turbine combinations.

[0090] S5. Repeat steps S2 to S4 until the preset maximum number of iterations is reached, i.e., the number of iterations satisfies t=Tmax, and obtain the current optimal routing set. and optimal goal It is used to generate the topology model of the offshore wind farm collection system for optimal and suboptimal solutions, and outputs the historical best topology scheme as the optimal topology of the offshore wind farm collection system.

[0091] Example 3

[0092] This invention proposes an improved tabu search topology optimization device for offshore wind farm power collection systems, which applies the improved tabu search topology optimization method for offshore wind farm power collection systems described in any of the above claims. The improved tabu search topology optimization device for offshore wind farm power collection systems includes a processor, a computer storage medium, and a memory.

[0093] The processor is used to run one or more program instructions to execute any of the steps described in the improved tabu search topology optimization method for offshore wind farm collection systems of the present invention.

[0094] The computer storage medium stores a computer program, which, when executed by a processor, implements any of the steps described in the improved tabu search topology optimization method for the offshore wind farm collection system of the present invention. The computer program is one or more program instructions.

[0095] The memory is used to store one or more executable program instructions of the processor.

[0096] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. An improved tabu search topology optimization method for offshore wind farm collection systems, characterized in that, Includes the following steps: S1. Initialize the algorithm parameters, historical best solutions and taboo list, and based on the wind turbine coordinate information, use polar coordinate mapping and minimum spanning tree algorithm to initially group and sort the wind turbines to generate the initial topology scheme; S2. For each wind turbine group, perform optimal connection construction within the group. By selecting the minimum cost edge one by one, connect all wind turbines in the group to the collection node or the substation, and calculate the cable connection cost of the group. S3. Determine whether the current topology solution satisfies the path intersection constraint; if it does, directly calculate the total cost; if it does not, add a penalty term to the total cost to form the penalized total objective function value. S4. Compare the current total objective function value with the historical best value; if it is better, update the historical best solution and the taboo table, and perform inter-wind turbine group exchange operations based on the taboo table to generate a new topology scheme; otherwise, directly perform inter-group exchange operations based on the taboo table; in step S4, the taboo table is used to record recently executed inter-wind turbine group exchange operations; when generating a new solution, priority is given to exchange operations not in the taboo table; entries in the taboo table are automatically unbanned after reaching a preset taboo length; S5. Repeat steps S2 to S4 until the preset maximum number of iterations is reached, and output the historical best topology scheme as the optimal topology of the offshore wind farm collection system.

2. The improved tabu search topology optimization method for offshore wind farm collection systems according to claim 1, characterized in that, In step S1, the wind turbines are initially grouped and sorted using polar coordinate mapping and the minimum spanning tree algorithm, specifically including: Using the substation as the pole, the rectangular coordinates of the wind turbines are converted to polar coordinates and sorted according to the polar angle. Based on the sorting, the wind turbines are divided into several groups, and within each group, the minimum spanning tree algorithm is used to generate the initial connection relationship based on the wind turbine node coordinates.

3. The improved tabu search topology optimization method for offshore wind farm collection systems according to claim 1, characterized in that, In step S2, the construction of the optimal connection within the group adopts a step-by-step addition strategy, specifically including: S2.1 Initialize the set of connected nodes and the set of unconnected nodes within the group, wherein the set of connected nodes contains at least one collector node or step-up substation; S2.2 Calculate the connection cost between all connected nodes and unconnected nodes, and select the edge with the lowest cost from all candidate connection edges to make the connection; S2.3 Update the set of connected nodes and the set of unconnected nodes, and record the connection edge; S2.4 Repeat steps S2.2 to S2.3 until all wind turbine nodes in the group are connected.

4. The improved tabu search topology optimization method for offshore wind farm collection systems according to claim 1, characterized in that, In step S3, the process of determining whether the path crossing constraint is satisfied is performed with a delay; path crossing detection is only performed when the total cable connection cost of the current solution is lower than the historical best cost; if a crossing is detected, a penalty value positively correlated with the severity of the crossing is added to the total cost.

5. The improved tabu search topology optimization method for offshore wind farm collection systems according to claim 1, characterized in that, The wind turbine groups are logical sets predefined based on the polar coordinate position of the wind turbines, electrical capacity constraints, or engineering zones. The number of groups and the number of wind turbines within each group can be adjusted according to the scale of the wind farm.

6. An improved tabu search topology optimization device for an offshore wind farm power collection system, characterized in that, The improved tabu search topology optimization method for offshore wind farm power collection systems according to any one of claims 1-5, wherein the improved tabu search topology optimization device for offshore wind farm power collection systems includes a processor, a computer storage medium, and a memory; The processor is used to run one or more program instructions to execute any of the steps described in the improved tabu search topology optimization method for offshore wind farm collection systems of the present invention. The computer storage medium stores a computer program, which, when executed by a processor, implements any of the steps described in the improved tabu search topology optimization method for the offshore wind farm collection system of the present invention. The computer program is one or more program instructions. The memory is used to store one or more executable program instructions of the processor.

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