Offshore wind plant current collection system optimization method considering cable type selection

Abstract

An offshore wind power plant current collection system optimization method considering cable type selection comprises the following steps: S1, acquiring wind power plant basic data including fan and transformer substation coordinates, fan rated power, multi-specification cable electrical and cost parameters and the like; s2, on the basis of the basic data, generating a candidate cable set by using clustering partitions and a distance threshold value, completing candidate set reduction, and synchronously generating a cable crossing avoidance constraint pair set; s3, an optimization model with the minimum total cost of the whole life cycle is established, the double-side ring path does not need scene variables, and the composite ring path needs to integrate scene driving variables; s4, carrying out path accelerated solution, carrying out hot start on a double-sided ring, carrying out cross constraint simplification, and carrying out additional fault set dimension reduction on a composite ring to obtain an initial scheme; and S5, performing full fault set N-1 verification on the initial scheme, bringing a violation scene into a fault set for resolution until the scheme meets an N-1 criterion, and outputting a final result. According to the method, topology and cable type selection integrated optimization is realized, and the solving quality and efficiency are considered.

Description

Technical Field

[0001] The present invention relates to offshore wind farm technology, and in particular to an offshore wind farm power collection system optimization method considering cable selection. Background Art

[0002] In recent years, driven by global renewable energy development goals, offshore wind power, with its abundant resources, mature technology, and enormous potential, has become a key component of energy transformation. As installed capacity continues to expand and wind farm development gradually extends to deeper and farther waters, offshore wind farm collection system planning will face increasingly stringent reliability requirements.

[0003] The collection system, a critical link connecting wind turbines to offshore substations, accounts for approximately 15%–30% of the total wind farm construction cost. The rationality of its planning and design directly impacts the system's economic efficiency and stability. However, this type of optimization problem is NP-hard and non-convex. As the scale of wind farms increases, the complexity of the problem increases dramatically, making it difficult to obtain a global optimal solution. Furthermore, the harsh environmental conditions at sea make its operation and maintenance costs far higher than onshore wind power. Failure of the collection system can have serious economic and social consequences. Therefore, achieving a reasonable balance between economic efficiency and reliability is a pressing issue.

[0004] The collection system of an offshore wind farm must economically and stably transmit the power from distributed wind turbines to an offshore substation for collection and transmission, while meeting reliability constraints. In addition to topology, cable selection (cross-section, type, current-carrying capacity, etc.) has a crucial impact on lifecycle costs and operability under fault conditions. On the one hand, a larger cross-section reduces line losses and thermal constraints, but significantly increases initial investment. On the other hand, a smaller cross-section, while lowering construction costs, may trigger current-carrying capacity or voltage-limit violations during fault current redistribution, rendering the solution unfeasible in the N-1 scenario. Therefore, selecting the appropriate type for each cable, co-optimizing topology and cable selection while meeting the N-1 safety criteria, and obtaining a high-quality planning solution that balances cost and reliability have become key issues that urgently need to be addressed in engineering practice.

[0005] From a computational complexity perspective, the optimization problem of a collection system with cable selection is inherently strong NP-hard: in addition to topological binary variables, each candidate edge introduces multiple discrete choices of specifications, and the scale of variables and the number of constraints rapidly increase with the formula "number of candidate edges × number of specifications × number of scenarios." If the current and capacity constraints for all single-cable fault scenarios are also required to be met, and engineering feasibility constraints such as geometric non-intersection and wind turbine node degree are added, the difficulty and time cost of direct solution will increase dramatically with the scale of the site, becoming a bottleneck restricting industrial implementation. Due to the limitations of model complexity and computational difficulty, current research on collection system planning that considers cable selection is generally limited to the relatively simple radial collection system planning. For collection system planning under the more complex N-1 criterion, there is currently no precedent for a collection system planning model that integrates cable selection and meets the N-1 criterion.

[0006] In summary, the existing technology still has deficiencies in the following aspects: (1) The research on the collection system planning based on the N-1 criterion itself is weak. Most studies are still limited to heuristic methods or traditional bilateral ring structures, which cannot achieve higher economic goals. (2) Considering cable selection will bring high computational complexity to the model. Currently, no research can achieve a balance between the N-1 criterion and cable selection. (3) Faced with the combination of multiple specifications and multiple scenarios, there is a lack of systematic scale control and computational acceleration mechanisms, making it difficult to obtain stable high-quality solutions on large-scale wind farms.

[0007] It should be noted that the information disclosed in the above background technology section is only used to understand the background of this application, and therefore may include information that does not constitute prior art known to ordinary technicians in this field. Summary of the Invention

[0008] The main purpose of the present invention is to overcome the defects existing in the above-mentioned background technology and provide an optimization method for the offshore wind farm collection system taking cable selection into consideration.

[0009] To achieve the above object, the present invention adopts the following technical solutions: A method for optimizing an offshore wind farm power collection system considering cable selection includes the following steps: S1. Obtain basic wind farm data, including the spatial coordinates of wind turbine nodes and substation nodes, wind turbine rated power, electrical parameters and cost parameters of various cable specifications, system life cycle parameters, and electricity price information; S2. Based on the basic data, a candidate cable set reduction strategy based on cluster partitioning and distance threshold is adopted to generate a candidate cable set, and a set of cable crossing avoidance constraint pairs is simultaneously identified and generated. The model scale and the number of crossing constraints are controlled by reducing the candidate set; S3. Construct a scenario set that includes normal scenarios and fault scenarios, and establish an optimization model with the goal of minimizing the total lifecycle cost. The model integrates variables and constraints in different paths: If it is a bilateral ring path, it integrates cable model selection variables and construction state variables, introduces power balance constraints based on DC power flow, ring topology constraints for adaptive cable selection, node degree constraints, and cable crossing avoidance constraints; If it is an N-1 composite ring path, it integrates scenario-driven operating state variables on the basis of the bilateral ring path, and characterizes the full fault set constraints through the scenario dimension; S4. Adopt an accelerated solution strategy for the corresponding path: For bilateral loop paths, adopt two strategies: hot start initialization based on pre-solved solutions and cable cross-constraint simplification based on incremental loading. For N-1 compound loop paths, in addition to the two bilateral loop strategies, adopt an additional fault set dimensionality reduction strategy based on key fault scenario screening. Through this strategy-accelerated solution, the initial topology and cable selection solution are obtained. S5. Perform a safety check on the entire fault set N-1 for the initial solution, incorporate the violation scenarios into the fault set and re-solve it. The N-1 compound ring paths are iteratively expanded to the fault set, while the bilateral ring paths are checked for compliance until the solution meets the N-1 safety criteria. The final topology planning and cable selection results are output.

[0010] Furthermore, the cable model selection constraint in step S3 requires that: for any candidate cable location, at most only one cable model can be selected; if it is an N-1 composite ring path, the scenario-driven operating state variable constraint requires that: for any candidate cable and any scenario, the value of its operating state variable is constrained to be between the sum of all its model construction state variables and zero; for any scenario pre-defined as a fault scenario, the operating state variable of the corresponding specific cable is forced to be set to zero; if it is a bilateral ring path, no scenario-driven operating state variable constraint is required.

[0011] Furthermore, in step S3, for the bilateral ring path, the ring topology constraint of the adapter cable selection is realized by introducing a binary variable representing the direction of the virtual flow and a continuous variable representing the cumulative power of the node. The constraint includes: ensuring that the number of virtual flow inflows and outflows of each wind turbine node is 1 to form a ring structure; establishing a bidirectional virtual flow accumulation equation to calculate the cumulative power value of each node in the positive and negative directions; based on the cumulative power value, constructing a constraint condition for each cable that its carrying power must meet the N-1 safety criterion. This condition uses different expressions for cables connecting substations and cables within wind farms; the N-1 composite ring path does not require this ring topology constraint.

[0012] Furthermore, for a bilateral ring path, the specific implementation process of the ring topology constraint for the adapter cable selection includes: A set of first binary variables is introduced to represent the forward direction of the virtual power flow on the candidate cable; A set of second binary variables is introduced to represent the reverse direction of the virtual power flow on the candidate cable; Constraining the sum of the first binary variable and the second binary variable to be equal to the construction state variable of the cable to ensure that the virtual power flow only exists on the constructed cables and that only one direction of virtual power flow can exist on each cable; For each wind turbine node, the sum of the first binary variables on all incoming cables and the sum of the second binary variables on all outgoing cables are constrained to be equal to 1. At the same time, the sum of the first binary variables on all outgoing cables and the sum of the second binary variables on all incoming cables are constrained to be equal to 1, thereby forcing the formation of a closed ring topology. A set of first continuous variables is introduced to represent the sum of the rated powers of downstream wind turbines accumulated at each node on the assumed virtual positive power flow path; A set of second continuous variables is introduced to represent the sum of the rated powers of downstream wind turbines accumulated at each node on the assumed virtual reverse power flow path; Establishing a recursive relationship constraint for the first continuous variable and the second continuous variable so that their values ​​increase along the direction of the virtual power flow, and the increment is the rated power of the wind turbine node passed through; The first continuous variable and the second continuous variable are constrained to have a lower bound of the rated power of the wind turbine itself and an upper bound of the capacity of the largest cable model; Based on the first and second continuous variables, a lower power limit constraint is established for each cable: for a cable directly connected to a substation, the power capacity it needs to carry must be no less than the maximum value of the first and second continuous variables at the wind turbine node to which it is connected. For a cable connecting two wind turbine nodes, the power capacity it needs to carry must be no less than the maximum value of the node at its two ends with the larger maximum value of the first and second continuous variables, minus one wind turbine rated power. Associating the above power capability requirement constraints with the nominal current carrying capacity of the selected cable model ensures that the capacity of the selected cable meets the power transmission requirements under all possible fault scenarios.

[0013] Furthermore, the first continuous variable and the second continuous variable are further constrained to have values ​​that are integer multiples of the rated power of the wind turbine, so as to comply with the physical reality of discrete power accumulation and improve the efficiency of model solution.

[0014] Furthermore, the power capacity requirement constraint constructed for the cable connecting the two wind turbine nodes is linearized by introducing a sufficiently large constant and combining it with the large M method to ensure that the constraint is only activated when the cable is actually selected and constructed.

[0015] Furthermore, in step S4, for the N-1 composite ring path, the fault set dimensionality reduction strategy based on the screening of key fault scenarios includes: in the initial iteration, if it is a single substation scenario, the cable fault scenario directly connected to the substation node is preferentially included; if it is a multi-substation scenario, there is no need to initially include specific cable fault scenarios; in subsequent iterations, the system overload risk after all candidate cable faults is calculated and ranked based on the line outage distribution factor (LODF), or the fault severity is evaluated by solving a DC optimization subproblem with the goal of minimizing the total system overload, and several scenarios with the highest risk are selected to be added to the main problem fault set, and the iteration is carried out until there is no overload in all single cable fault scenarios.

[0016] Furthermore, the cable crossing constraint simplification strategy based on incremental loading described in step S4 includes: during the solution process, initially only the crossing pairs that may be involved in the cables connected to the substation node are included in the constraint set; after obtaining the initial solution, check whether there are unconstrained cable crossings; if so, add the corresponding cross constraints to the model and re-solve, and gradually approach a feasible solution that meets all crossing avoidance requirements through this iterative method.

[0017] Furthermore, the hot start initialization strategy in step S4 includes: if it is a bilateral ring path, first solve a simplified model that does not consider cable selection or a relatively fixed topology structure, and use its optimal solution as the initial solution of the complex optimization model that considers cable selection; if it is an N-1 compound ring path, first solve the bilateral ring optimization model that considers cable selection, and use its optimal solution as the initial solution of the N-1 compound ring optimization model that considers cable selection; thereby accelerating the solution process.

[0018] A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the method for optimizing an offshore wind farm power collection system is implemented.

[0019] Compared with the prior art, the present invention has the following beneficial effects: Aiming at the obvious limitations of existing offshore wind farm collection system planning methods: most methods carry out planning within the framework of predefined topology (such as bilateral ring, multi-ring), or use heuristic and meta-heuristic algorithms such as "sweep angle clustering / CMST / CVRP" to first determine the topology and then configure the capacity. Although the solution efficiency is acceptable, there is a lack of global optimality guarantee; some other methods use mathematical optimization in the bilateral ring topology to improve the quality of the solution, but do not systematically couple the cable model selection with the full-scene N-1 safety criterion, making it difficult to take into account both reliability and economy; the present invention proposes an offshore wind farm collection system optimization method considering cable selection, the core of which is to construct a field based on the N-1 safety criterion. Scenario-driven planning model: By introducing binary variables for cable types, it enables modeling of cable selection for multiple specifications, and integrates key parameters such as cable cost, resistance, and current carrying capacity into the objective function and constraint system (for example, building current carrying capacity constraints that adapt to fault scenarios). It also jointly optimizes the topology of the collection system and the selection of cable models. The model includes two specific scenarios: "double-sided ring N-1 belt selection" and "N-1 composite ring belt selection." Under the premise of meeting the N-1 safety criterion, it can achieve a high-quality planning solution that minimizes the full life cycle cost (covering construction, operation and maintenance, network loss, and wind curtailment costs) even in the face of a rapid expansion of the "candidate edge × model × fault scenario" dimension.

[0020] Considering that both of the above-mentioned models involving cable selection have high computational complexity, in order to adapt to the actual engineering requirements for solution efficiency and the scalability of the model to larger-scale examples, the present invention also proposes a structured accelerated solution strategy to better balance solution efficiency and quality. Specifically, it includes: reducing the set of candidate cables through clustering partitioning and distance screening to reduce the scale of variables; performing fault set dimensionality reduction based on key fault scenarios (such as cable faults directly connected to the substation in a single substation scenario, and high-risk faults selected by LODF sorting) to reduce the amount of scenario calculations; first solving a simplified model that does not consider cable selection or a relatively fixed topological structure (such as a double-sided ring optimization model), and using its optimal solution as the warm start initial solution to accelerate model convergence; using incremental loading to handle cable cross constraints and simplify the constraint system.

[0021] The important innovations of this invention are reflected in two aspects: first, it constructs a framework for integrated joint optimization of topology and cable model selection, breaking the limitations of the traditional "topology first, then selection" or "topology optimization only"; second, it designs an efficient acceleration strategy that adapts to the main problem, specifically solving difficult problems in large-scale and multi-scenario situations.

[0022] Compared with existing methods, the present invention has three significant advantages: First, it achieves high-quality solutions - an optimization method is designed instead of a heuristic algorithm, which can pursue the global optimal solution. The comparison results of the examples show that it has significant advantages in economy and reliability; Second, it has strong scalability - the model can be naturally expanded to various scenarios such as cable selection, multiple substations, large-scale wind farms, etc., and adapt to different engineering needs; Third, it takes into account both solution quality and efficiency - through the coordination of the core framework and the supporting acceleration strategy, it not only ensures the high quality of the planning scheme, but also effectively reduces the difficulty and time cost of the solution.

[0023] In summary, the present invention ultimately achieves three key goals: first, cable selection variables are deeply involved in model construction to achieve coordinated optimization of topology and selection; second, constraint variables are established based on scenarios to build a planning model that meets the N-1 safety criterion; third, an efficient solution method that conforms to the description characteristics of the acceleration strategy is formed, providing reliable technical support for the optimal planning of offshore wind farm collection systems.

[0024] Other beneficial effects of the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a schematic diagram of the accumulated power of the improved CVRP model according to an embodiment of the present invention; Figure 2 A schematic diagram of vector cross product determination for assisting in determining cable geometric crossover and other relationships according to an embodiment of the present invention; Figure 3 This is a flow chart of a topology planning scheme for an offshore wind farm power collection system according to an embodiment of the present invention; Figure 4 Schematic diagram of wind turbines, substation coordinates and candidate submarine cables for a single-boost station offshore wind farm; Figure 5 This is the sweep+CWS topology planning result diagram of comparative example 1; Figure 6 This is the result diagram of the bilateral ring topology planning of Comparative Example 2; Figure 7 This is the multi-ring structure topology planning result diagram of Comparative Example 3; Figure 8 This is a diagram showing the topology planning results of the double-sided ring belt selection in Example 1 of the present invention; Figure 9 This is a diagram showing the topology planning results of the N-1 composite ring belt selection according to Example 2 of the present invention; Figure 10 This is a schematic diagram of the Sheringham Shoal offshore wind farm with dual booster stations, substation coordinates, and candidate submarine cables; Figure 11 This is the result diagram of the bilateral ring topology planning of Comparative Example 4; Figure 12This is the CSI structure topology planning result diagram of Comparative Example 5; Figure 13 This is a diagram showing the result of the double-sided ring topology planning selected in Example 3 of the present invention; Figure 14 This is a diagram showing the topology planning result of the selected N-1 composite ring according to Example 4 of the present invention; Figure 15 This is a graph showing the convergence of the solution time for the accelerated solution strategy according to an embodiment of the present invention; Figure 16 The overall flow chart of the offshore wind farm collection system optimization method considering cable selection. DETAILED DESCRIPTION

[0026] The following is a detailed description of the embodiments of the present invention. It should be emphasized that the following description is only exemplary and is not intended to limit the scope of the present invention and its application.

[0027] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0028] The present invention aims to solve the problems in the existing offshore wind farm collection system planning, such as the lack of systematic coupling between cable model selection and the full-scenario N-1 safety criterion, high computational complexity under multiple specifications and multiple scenarios, and lack of global optimality. It proposes to construct a scenario-driven planning model based on the N-1 safety criterion to achieve integrated joint optimization of topology and cable selection. It is specifically divided into two relatively independent paths: the bilateral ring path does not require the expansion of scenario dimensions and scenario-driven variables, and naturally meets the N-1 criterion by adapting the ring topology constraints of cable selection. The model only integrates cable model selection, construction state variables and basic constraints; the N-1 composite ring path needs to characterize the entire fault set through the scenario dimension, and the model additionally integrates scenario-driven operating state variables. In order to adapt to the two paths and control the computational complexity, the present invention proposes a step-by-step and path-by-path acceleration strategy: reducing the candidate cable set based on clustering and partitioning (simultaneously affecting the size of the cable cross-constraint set, completed in the stage of generating the candidate cable set, laying the foundation for subsequent model solution). In the solution stage, the bilateral loop path adopts two strategies: hot start initialization based on the pre-solution solution and cable cross-constraint simplification based on incremental loading. On this basis, the N-1 composite loop path additionally adopts a fault set dimensionality reduction strategy based on the screening of key fault scenarios. Compared with existing methods, it can not only pursue the global optimum to achieve high-quality solutions, but also be expanded to multiple substations and large-scale scenarios, taking into account both solution quality and efficiency.

[0029] See Figure 16 The embodiment of the present invention provides an offshore wind farm collection system optimization method considering cable selection, comprising the following steps: Step S1: Obtain basic data of the wind farm, including the spatial coordinates of wind turbine nodes and substation nodes, wind turbine rated power, electrical parameters and cost parameters of various cable specifications, system life cycle parameters, and electricity price information.

[0030] Step S2: Based on the basic data, a candidate cable set reduction strategy based on cluster partitioning and distance threshold is adopted to generate a candidate cable set, and a cable crossing avoidance constraint pair set is simultaneously identified and generated. The model scale and the number of crossing constraints are controlled by reducing the candidate set.

[0031] In some embodiments, the generation of the candidate cable set in step S2 adopts a screening method based on spatial distance and cluster analysis, specifically including: for large-scale wind farm scenarios, first using a clustering algorithm to divide the wind farm area into several partitions, and only retaining node pairs located in the same partition as potential candidate cables; then calculating the Euclidean distance between each two nodes corresponding to the above potential candidate cables based on the coordinate information of all nodes, and setting a distance threshold to filter out potential cable connections whose length exceeds the threshold; for non-large-scale wind farm scenarios, candidate cables can be directly screened by calculating the Euclidean distance between nodes and combining the distance threshold, without the need for a clustering partitioning step; thereby effectively controlling the problem scale while maintaining the optimization potential.

[0032] Step S3: Construct a scenario set including normal scenarios and fault scenarios, and establish an optimization model with the goal of minimizing the total cost of the entire life cycle. The model integrates variables and constraints in different paths: If it is a bilateral ring path: integrate cable model selection variables and construction state variables, introduce power balance constraints based on DC power flow, ring topology constraints for adaptive cable selection, node degree constraints and cable crossing avoidance constraints (no need for scenario-driven operating variables); If it is an N-1 compound ring path: on the basis of the bilateral ring path, additional scenario-driven operating state variables are integrated, and the full fault set constraints are characterized through the scenario dimension.

[0033] In some embodiments, the objective function of the optimization model in step S3 is constructed in the following manner: cable construction cost is calculated by accumulating the product of the construction cost of each candidate cable under different models and its corresponding binary selection decision variable; system operation and maintenance cost is calculated by multiplying the cable construction cost by a fixed proportional coefficient and then evenly amortizing it over the entire system life cycle through an annuity discount formula; system network loss cost, based on the DC power flow assumption, the product of the resistance value of each cable under its selected model and the square of the transmission power is accumulated to obtain the annual network loss energy cost, and then it is extended to the entire life cycle through a discount calculation; wind curtailment cost is achieved by introducing a penalty coefficient with a sufficiently large value and multiplying it by the sum of the wind curtailment power of all wind turbine nodes in all scenarios to ensure that the wind curtailment is forced to zero in the process of minimizing the objective function.

[0034] In some embodiments, the cable model selection constraint in step S3 requires that: for any candidate cable location, at most one cable model can be selected; the scenario-driven operating state variable constraint requires that: for any candidate cable and any scenario, the value of its operating state variable is constrained to be between the sum of all its model construction state variables and zero; for any scenario pre-defined as a fault scenario, the operating state variable of the corresponding specific cable is forced to be set to zero.

[0035] In some embodiments, the power balance constraint based on DC power flow in step S3 is established through the following process: for each wind turbine node and each scenario, its power balance equation constrains the sum of all cable powers flowing out of the node to be equal to the difference between its wind power generation power and the possible wind power abandonment power; for each substation node and each scenario, its power balance equation constrains the sum of all cable powers flowing into the node to be equal to the power collected by it to the grid; for each candidate cable and each scenario, by introducing the node voltage phase angle variable and establishing its DC power flow equation based on the susceptance value of the selected cable model, and at the same time using the large M method to couple the equation with the cable selection and operating state variables; the voltage phase angle of any substation node in the system is set to zero as the reference phase of the entire network; for each candidate cable and each scenario, the absolute value of its transmission power is constrained not to exceed the product of the current carrying capacity of its selected model and the operating state variable; for each wind turbine node and each scenario, its wind abandonment power is constrained to be between zero and the wind power generation power of the node.

[0036] In some embodiments, the ring topology constraint for the selection of the adapter cable in step S3 is implemented by introducing a binary variable representing the direction of the virtual flow and a continuous variable representing the cumulative power of the node. The constraint includes: ensuring that the number of virtual flow inflows and outflows of each wind turbine node is 1 to form a ring structure; establishing a bidirectional virtual flow accumulation equation to calculate the cumulative power value of each node in the positive and negative directions; based on the cumulative power value, constructing a constraint condition for each cable that its carrying power must meet the N-1 safety criterion, and this condition uses different expressions for cables connecting substations and cables within wind farms.

[0037] In a further preferred embodiment, the specific implementation process of the ring topology constraint for the selection of the adapter cable includes: introducing a set of first binary variables for representing the forward direction of the virtual power flow on the candidate cable; introducing a set of second binary variables for representing the reverse direction of the virtual power flow on the candidate cable; constraining the sum of the first binary variable and the second binary variable to be equal to the construction state variable of the cable to ensure that the virtual flow only exists on the constructed cable, and that there can only be a virtual flow in one direction on each cable; for each wind turbine node, constraining the sum of the first binary variables on all its incident cables and the sum of the second binary variables on all its outgoing cables to be equal to 1, and at the same time constraining the sum of the first binary variables on all its outgoing cables and the sum of the second binary variables on all its incident cables to be equal to 1, thereby forcing the formation of a closed-loop ring topology structure; introducing a set of first continuous variables for representing the sum of the downstream wind turbine rated powers accumulated by each node on the assumed virtual forward power flow path; introducing a set of second continuous variables for representing the sum of the downstream wind turbine rated powers accumulated by each node on the assumed virtual forward power flow path. The sum of the rated powers of downstream wind turbines accumulated at each node on the reverse power flow path; a recursive relationship constraint is established for the first continuous variable and the second continuous variable so that their values ​​increase along the direction of the virtual flow, and the increment is the rated power of the wind turbine node passed by; the lower limit of the value of the first continuous variable and the second continuous variable is constrained to be the rated power of the wind turbine itself, and the upper limit is the capacity of the largest cable model; based on the first continuous variable and the second continuous variable, a lower limit constraint on the power that each cable needs to carry is constructed: for a cable directly connected to a substation, the power capacity it needs to carry must not be less than the maximum value of the first continuous variable and the second continuous variable at the wind turbine node to which it is connected; for a cable connecting two wind turbine nodes, the power capacity it needs to carry must not be less than the maximum value of the node at its two end nodes with the larger maximum value of the first continuous variable and the second continuous variable, minus one wind turbine rated power; the above power capacity requirement constraint is associated with the nominal current carrying capacity of the selected cable model to ensure that the capacity of the selected cable meets the power transmission requirements under all possible fault scenarios.

[0038] In a further preferred embodiment, the first continuous variable and the second continuous variable are further constrained to have values ​​that are integer multiples of the rated power of the wind turbine, so as to comply with the physical reality of discrete power accumulation and improve the efficiency of model solution.

[0039] In a further preferred embodiment, the power capacity requirement constraint constructed for the cable connecting the two wind turbine nodes is linearized by introducing a sufficiently large constant and combining it with the large M method to ensure that the constraint is only activated when the cable is actually selected and constructed.

[0040] Step S4: Adopt an accelerated solution strategy for the corresponding path: For bilateral loop paths, adopt two strategies: hot start initialization based on a pre-solution solution and cable cross constraint simplification based on incremental loading. For N-1 compound loop paths, in addition to the two bilateral loop path strategies, adopt an additional fault set dimensionality reduction strategy based on key fault scenario screening (due to the need to process scenario variables). Through this strategy-accelerated solution, the initial topology and cable selection solution are obtained.

[0041] In some embodiments, the fault set dimensionality reduction strategy based on the screening of key fault scenarios described in step S4 includes: in the initial iteration, if it is a single substation scenario, the cable fault scenario directly connected to the substation node is preferentially included; if it is a multi-substation scenario, there is no need to initially include specific cable fault scenarios; in subsequent iterations, the system overload risk after all candidate cable faults is calculated and ranked based on the line outage distribution factor (LODF), or the fault severity is evaluated by solving a DC optimization sub-problem with the goal of minimizing the total system overload, and several scenarios with the highest risk are selected to be added to the main problem fault set, and iterate until there is no overload in all single cable fault scenarios.

[0042] In some embodiments, the cable crossing constraint simplification strategy based on incremental loading described in step S4 includes: during the solution process, initially only the crossing pairs that may be involved in the cables connected to the substation node are included in the constraint set; after obtaining the initial solution, checking whether there are unconstrained cable crossings; if so, adding the corresponding cross constraints to the model and resolving the problem, and gradually approaching a feasible solution that meets all crossing avoidance requirements through this iterative method.

[0043] In some embodiments, the hot start initialization strategy in step S4 includes: if it is a bilateral ring path, first solve a simplified model that does not consider cable selection or has a relatively fixed topology structure (such as a bilateral ring topology model, not considering cable selection), and use its optimal solution as the initial solution of the complex optimization model that considers cable selection; if it is an N-1 compound ring path, first solve the bilateral ring optimization model that considers cable selection (as a simplified model), and use its optimal solution as the initial solution of the N-1 compound ring optimization model that considers cable selection; thereby accelerating the solution process.

[0044] Step S5: Perform a safety check on the entire fault set N-1 for the initial solution, and include the violation scenarios in the fault set re-solution. Only the N-1 composite ring path requires iterative expansion of the fault set, while the bilateral ring path naturally meets N-1 due to its topological characteristics. The verification focuses on selection compliance until the solution meets the N-1 safety criteria, and outputs the final topology planning and cable selection results.

[0045] The present invention proposes an optimization method for offshore wind farm collection systems that takes cable selection into consideration. By constructing an optimization model that integrates cable model selection variables, construction state variables, and scenario-driven operation state variables, it successfully solves the difficult problem of collaborative optimization of the topology structure and cable selection of offshore wind farm collection systems while meeting the N-1 safety criteria. This method innovatively integrates multiple specification parameters such as cable cost, resistance, and current carrying capacity into the planning framework, breaking through the limitations of existing research that is mostly limited to predefined topologies or heuristic methods, and achieving high-quality solutions under the pursuit of global optimality. Faced with the explosive growth in computational complexity brought about by "candidate edge × model × fault scenario", the present invention proposes a systematic acceleration strategy including candidate set reduction, hot start initialization, dimensionality reduction of key fault scenarios, and incremental constraint loading, which significantly reduces the difficulty and time cost of solution, and enables the model to have a powerful scalability to handle large-scale wind farms, multiple substations, and multi-specification cable scenarios. Ultimately, while ensuring high reliability, it significantly improves the economy and engineering feasibility of the planning scheme.

[0046] The following further describes specific embodiments of the present invention, its algorithm examples and experimental verification.

[0047] An optimization method for offshore wind farm collection systems considering cable selection is proposed. A bilateral loop planning model considering cable selection is constructed as follows: Objective function: Assume a complete weighted directed graph ,in Including substation nodes and wind turbine nodes , Represents all candidate cables connecting nodes, each candidate cable The cost under model c is Representative candidate cable A binary decision variable for whether to be constructed, When the candidate cable Each candidate cable is constructed. The resistance under model c is , the power flow on each cable is expressed as Assume that the abandoned air volume is To ensure that the power collection system meets the "N-1" safety constraint, multiply this cost by a very large number ,like To ensure that the wind curtailment cost item in the objective function is 0. The objective function of the economic planning model of the power collection system that meets the "N-1" safety constraint is constructed as follows:

[0048] (1) in 、 、 、 are the cable construction cost of the collection system, system operation and maintenance cost, system network loss cost and wind curtailment cost, which can be expressed as:

[0049] The cable construction cost is the sum of the cost of each candidate cable model multiplied by the construction decision variables; the system operation and maintenance cost can be regarded as the construction cost multiplied by a fixed coefficient; in the system network loss cost, represents the network loss cost of the wind farm throughout its life cycle, is the annual inflation rate. At the same time, the DC power flow assumption is adopted in the model of the present invention, and the per-unit value of each node voltage is 1, then , so after considering the cable selection, the network loss cost of each cable is written as For the wind curtailment cost, M is set to a very large number to ensure that the value is 0.

[0050] Cable selection constraints: Considering the cable selection, it is required that at most one cable type can be selected at each location. The relevant constraints are: (7) Power balance constraints based on DC power flow: The model adopts the following form of DC power flow based transmission planning model: (8) (9) (10) (11) (12) Constraint (8) is the power balance constraint of point i, which represents the power balance equation of each wind turbine node i; constraint (9) is the power balance constraint of substation node j; constraint (10) represents the power balance constraint of cable Considering the DC power flow constraints taking into account the phase angle after cable selection, constraint (11) is the reference node constraint of the substation node for the power flow model; constraint (12) limits the wind abandonment capacity of each wind turbine node i; Improved CVRP ring constraint: Traditional bilateral ring collector system planning often uses a modeling method based on the CVRP model. This paper improves it to adapt to the situation of considering cable selection:

[0051] In constraint (13), and is a binary variable representing the virtual power flow, where Indicates that virtual power flows from node i(j) to node j(i), and these unidirectional flows can only exist on the constructed cables; constraints (14)-(15) limit the inflow and outflow of each wind turbine node to 1, thus ensuring that the topology is a ring; constraints (16)-(19) are extensions of the elimination constraints for MTZ specific sub-loops, and the variables ( ) represent the cumulative power of node i when considering the forward (reverse) power flow, while constraints (20)-(21) limit ( ) as the effective lower bound under the subsequent N-1 criterion.

[0052] Figure 1 An example of a CVRP model with a bidirectional power accumulation variable is shown. In this example, the path Sub→1→2→3→4→Sub represents a virtual power flow path connecting wind turbines 1, 2, 3, and 4 in a bilateral loop. Under constraints (16)-(17), the power accumulated in the forward direction of this virtual power flow is From Node 1 Increase to 4 of node 4 , accordingly, the reverse accumulated power From Node 4 Accumulated to 4 at node 1 This bidirectional accumulated power flow can help to better establish the power constraint that meets the N-1 criterion. The following formula gives the number of wind turbines that the i-th cable needs to bear under the N-1 criterion: , where n is the total number of cables in the ring network, Indicates the number of wind turbines that the i-th cable along the ring needs to support from the substation node:

[0053] (twenty two) Therefore, the power constraint under the N-1 criterion can be expressed by the following two equations:

[0054] Combine Figure 1 Understanding constraints (23)-(24): For the cable directly connected to the substation (e.g. Sub-1), the required current carrying capacity is represented by the maximum value of the bidirectional accumulated power at the wind turbine node (max( , )); For cables not connected to a substation (e.g., 1-2), the required current carrying capacity is represented by the maximum value of the bidirectional accumulated power at the node on the side topologically far from the substation (max( , )). This forms two large M-method constraints (23)-(24), ensuring that they are only effective for the cables being constructed. Indicates the maximum current carrying capacity of the cable (i, j) under the selected model.

[0055] It is worth noting that the min function on the right side of the constraint (24) inequality makes it difficult to implement directly. Given that the output of wind turbines in offshore wind power system planning is often fixed and consistent, for cables not adjacent to substations (such as 1-2), the maximum value of the bidirectional accumulated power (max( , )) compared to the corresponding value at the node topologically close to the substation (max( , )) must be small .

[0056] Therefore, constraint (24) can be expressed in a more convenient programming form as follows:

[0057] Cables do not cross and node degree constraints: Similarly, the model characterizes the avoidance of cable crossings and the constraints on wind turbine node degrees.

[0058] (26) The cable pair (AB, CD) belongs to , if and only if and ,like Figure 2 shown.

[0059] For the ring topology, it is easy to get the degree of each wind turbine node to be equal to 2: (27) In summary, the establishment of a bilateral ring model that meets the N-1 criterion and considers cable selection is completed as follows:

[0060] The N-1 composite ring planning model considering cable selection in the present invention is as follows: The objective function, cable selection constraints, DC power flow constraints, and even cable non-crossing and node degree constraints involved in this section can largely follow the relevant content of the bilateral ring model in terms of principle and form. Here we introduce the parts that differ from the bilateral ring form: Fault set constraints: When modeling an “N-1” failure set, Represents a collection of full scenes, using binary variables , indicating the candidate cables In the scene t Whether the candidate cable is operating normally, to distinguish it from the variable indicating whether the candidate cable is constructed . Indicates that in the scene t Lower cable Normal operation. The fault set constraints determined are:

[0061] (28) (29) Here, constraint (28) means The value is between 0 and The construction of the cable is a necessary condition for normal operation; Constraint (29) is the fault cable constraint, which means that in the fault scenario The corresponding preset fault cable cannot operate normally.

[0062] Power balance constraints based on DC power flow: The following transmission planning model based on DC power flow is introduced in the form of scenario variables:

[0063] Constraint (30) is the power balance constraint of point i, which represents the power balance equation of each wind turbine node i in each scenario; Constraint (31) is the power balance constraint of substation node j in each scenario; Constraint (32) represents the power balance of cable Considering the DC power flow constraints of the phase angle, constraint (33) is the reference node constraint of the substation node for the power flow model; constraints (34) and (35) are respectively applied to each candidate cable. The transmission power and the abandoned wind volume of each wind turbine node i are limited; Node degree constraint:

[0064] In summary, the model of the N-1 composite ring that meets the N-1 criterion and considers cable selection is established as follows:

[0065] Both of the above-mentioned models for cable selection have considerable computational complexity. Considering the actual scale of actual engineering on solution efficiency and the scalability of the proposed model to larger-scale examples, the present invention also proposes the following accelerated solution strategy to better balance solution efficiency and quality.

[0066] Candidates for cable set cuts: Within the framework of power collection system planning, the coordinates of wind turbines and substations are predetermined during the micro-site selection phase and serve as model inputs. Candidate cables are generated from these spatial coordinates. The dimensionality of the decision variables and constraints increases directly with the size of the candidate cable set, necessitating effective means to rationally reduce the candidate cable set. For larger wind farms, this paper employs a "zoning + candidate cable screening" approach.

[0067] Specifically, after obtaining the coordinate information of the wind farm, the partitions to be divided and the number of wind turbines in each partition are confirmed. Kmeans is first used on the wind turbine coordinates to obtain the centroid position of each partition, and the distance matrix from each wind turbine to the centroid is calculated. The 90th percentile distance from the centroid of each cluster sorted by distance is defined as the "core radius" of each cluster. Greedy allocation is preferentially used to allocate the wind turbines within the core radius of each cluster to the cluster under the premise that the capacity is met. Then, the wind turbines that have not yet been allocated are backfilled to the nearest cluster with surplus capacity, thus completing the partitioning step.

[0068] After the partitioning is completed, candidate cables are constructed within each cluster without considering cables across intervals. Specifically, since the method of the present invention aims to obtain a high-quality collection system planning scheme, the construction cost term in the objective function is expressed as minimizing the Euclidean distance between wind turbines. Connections that span too long distances are almost impossible to appear in the optimal layout, so they can be deleted through targeted screening. This scheme first sets the maximum candidate distance, and cables that exceed the maximum candidate distance are not added to the candidate cable set. If partitioning is not required for smaller-scale wind farms, this method is directly used to construct the reduced cable set.

[0069] Hot start speeds up the solution: In large-scale nonlinear, non-convex optimization frameworks, constructing high-quality initial solutions plays a key role in accelerating model convergence and avoiding local optimality. A good initial solution can reduce the number of iterations and effectively shorten the solution time. In the framework of the method proposed in the present invention, a bilateral ring planning method without cable selection is first used to obtain a feasible solution, which serves as a hot start for the bilateral ring planning method considering cable selection. Furthermore, since the bilateral ring solution proposed in the present invention naturally meets the N-1 safety criterion, the solution obtained by the dual variable ring considering cable selection can be used as a hot start for the N-1 composite ring considering cable selection.

[0070] Fault scenario dimensionality reduction: In planning models, computational complexity is highly correlated with the number of fault scenarios. Accurately identifying the most critical emergency scenarios can significantly improve solution efficiency. Geometric features can be used to identify cables close to substations as critical faults, but this principle has limitations when considering cable selection. Instead, a line outage distribution factor (LODF) is used to calculate post-fault power flows and rank each candidate single-line outage by overload risk.

[0071] First, For the selected cable set in the current main model planning result, the node susceptance matrix after dimensionality reduction is constructed by removing the reference bus , for each built cable , specify a fixed direction , and define its associated vector ,in is the standard basis vector of busbar i, let For the candidate outage cable k connecting m and n, let , at this time, for the monitored cable a, define its relative The injected power flow transfer distribution factor (PTDF) is: (37) The corresponding line outage allocation factor (LODF) is:

[0072] This means that the change in power flow per unit on line a caused by removing branch k is , at this time, when k is out of service, the post-accident power flow on a is: (39)

[0073] And because , naturally there is Under the DC power flow model, these expressions are analytically accurate. The post-accident power flow calculated using LODF is consistent with the result obtained by resolving the power flow after removing line k, and only a single sparsity solution is required each time.

[0074] For each candidate outage ,calculate: (40) in is the transmission capacity of cable a, and then Sort all candidate single-line outages in descending order and select the top n scenarios as the "key failure scenarios" for this iteration. Add these key failure scenarios to the failure scenario set of the main problem and continue to iterate the planning problem until each built cable and single-line outage meet , which indicates that there is no overload scenario and the obtained planning result meets the N-1 criterion.

[0075] When the scenario includes multiple substations, the power collected by each substation node is a decision variable. In this scenario, the net injected power of the entire network is uncertain, and the LODF method cannot be used. At this time, for each fault scenario: , solve the following DC optimization sub-problem without abandoning wind power to minimize the overload relaxation, where .

[0076]

[0077] Solving this problem yields the post-accident current and optimal relaxation , the optimal target value , at this time also press Sort the outages in descending order, append the first n scenarios to the scenario set of the main problem, and iterate until all the scenarios are satisfied on the established network. , indicating that no more relaxation is needed and the design has satisfied the N-1 criterion.

[0078] Cables do not cross constraints simplified: Although constraint (26) can effectively prevent the intersection of cable pairs, an excessively large constraint size will introduce significant complexity, which usually dominates the actual planning problem and severely limits the modeling and solution speed. Therefore, when encountering a computational bottleneck, a feasible approach is to adopt a moderately relaxed For example, considering only the cable pairs containing the substation node, the test shows that the relaxed principle can be applied in both bilateral ring and N-1 compound ring planning frameworks. They can all avoid the intersection of cable pairs with a very high probability in the actual planning process, making them sufficiently effective benchmark candidate sets.

[0079] During the solution process, crossing cable pairs involving substation connections are first fed into the solver as high-quality constraints. Crossover checks are then performed after the planning results are obtained. When crossovers are detected, the constraint set is expanded and iteratively refined. This incremental constraint loading mechanism maintains constraint reliability while enabling the model to handle planning tasks of higher complexity.

[0080] Therefore, the overall planning flow chart is as follows Figure 3 shown.

[0081] Experimental verification: Experiment 1: Using 36 wind turbines and a single booster station, the wind farm's wind turbine and substation coordinates, and the candidate submarine cable diagram are as follows: Figure 4 shown.

[0082] This experiment conducts a variety of case comparisons, as shown in Table 1.

[0083] Table 1

[0084] Planning results: Figure 5 The sweep+CWS topology planning results of comparative example 1 are shown; Figure 6 The results of bilateral ring topology planning for comparative example 2 are shown; Figure 7 The multi-ring structure topology planning results of Comparative Example 3 are shown; Figure 8 The topology planning results of the double-sided ring belt selection in Example 1 of the present invention are shown; Figure 9 The topology planning results of the N-1 composite ring belt selection of Example 2 of the present invention are shown.

[0085] The cost comparison results (unit: RMB million) are shown in Table 2.

[0086] Table 2

[0087] Experimental results show that the cost reductions for the selected double-sided ring model (Example 1) of the present invention compared to the traditional methods (Comparative Examples 1-3) are 16.5%, 14.7%, and 14.9%, respectively. The cost reductions for the selected N-1 compound ring model (Example 2) of the present invention compared to the traditional methods (Comparative Examples 1-3) are 21.4%, 19.7%, and 19.9%, respectively.

[0088] Experiment 2: The actual Sheringham Shoal offshore wind farm uses 88 wind turbines, dual booster stations, and the wind turbine and substation coordinates of the wind farm, as well as the schematic diagram of the candidate submarine cables. Figure 10 .

[0089] This experiment conducts a variety of case comparisons, as shown in Table 3.

[0090] Table 3

[0091] Planning results: Figure 11 The results of bilateral ring topology planning for comparative example 4 are shown; Figure 12 The CSI structure topology planning results of comparative example 5 are shown; Figure 13 The results of the double-sided ring topology planning for the third embodiment of the present invention are shown. Figure 14 The results of the selected N-1 composite ring topology planning of Example 4 of the present invention are shown.

[0092] The cost comparison results (unit: RMB million) are shown in Table 4.

[0093] Table 4

[0094] The experimental results show that compared with the traditional methods (Comparative Examples 4-5), the cost of Example 3 of the present invention was reduced by 10.6% and 16.9%, respectively. Compared with the traditional methods (Comparative Examples 4-5), the cost of Example 4 of the present invention was reduced by 17.1% and 23.0%, respectively.

[0095] Verification of accelerated solution effect: To verify the effectiveness of the four proposed acceleration strategies, the following verification experiment was set up. Comparative Examples 6-9 selectively disabled one of the acceleration strategies, while Example 5 used all four acceleration strategies to compare the effects of each strategy: All experiments used the previous 36-turbine wind farm and N-1 compound ring model. Considering the complexity of the model itself and the difficulty of solving some examples after disabling the acceleration strategy, the cable selection was not considered in this verification part. The solution time of each example was calculated to obtain a high-quality feasible solution (Gap < 10%). The results are shown in Table 5.

[0096] Table 5

[0097] Solve the time convergence curve as Figure 15 The results verify the effectiveness of the four acceleration strategies of the embodiment of the present invention.

[0098] An embodiment of the present invention further provides a storage medium for storing a computer program, which at least performs the above method when executed.

[0099] An embodiment of the present invention further provides a control device, comprising a processor and a storage medium for storing a computer program; wherein the processor is configured to execute at least the method described above when executing the computer program.

[0100] An embodiment of the present invention further provides a processor, which executes a computer program and at least performs the method described above.

[0101] The storage medium can be implemented by any type of non-volatile storage device, or a combination thereof. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory (Flash Memory), a magnetic surface memory, an optical disc or a read-only optical disc (CD-ROM); the magnetic surface memory can be a magnetic disk memory or a magnetic tape memory. The storage medium described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0102] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0103] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0104] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0105] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware associated with program instructions, and the aforementioned program may be stored in a computer-readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0106] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0107] The methods disclosed in the several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0108] The features disclosed in several product embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0109] The features disclosed in several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0110] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. Those skilled in the art will recognize that, without departing from the scope of the present invention, several equivalent substitutions or obvious variations can be made, and the performance or use of the same should be considered to fall within the scope of protection of the present invention.

Claims

1. A method for optimizing an offshore wind farm collection system considering cable selection, characterized in that: The following steps are involved: S1. Obtain basic wind farm data, including the spatial coordinates of wind turbine nodes and substation nodes, wind turbine rated power, electrical parameters and cost parameters of various cable specifications, system life cycle parameters, and electricity price information; S2. Based on the basic data, a candidate cable set reduction strategy based on cluster partitioning and distance threshold is adopted to generate a candidate cable set, and a set of cable crossing avoidance constraint pairs is simultaneously identified and generated. The model scale and the number of crossing constraints are controlled by reducing the candidate set; S3. Construct a scenario set that includes normal scenarios and fault scenarios, and establish an optimization model with the goal of minimizing the total lifecycle cost. The model integrates variables and constraints in different paths: If it is a bilateral ring path, it integrates cable model selection variables and construction state variables, introduces power balance constraints based on DC power flow, ring topology constraints for adaptive cable selection, node degree constraints, and cable crossing avoidance constraints; If it is an N-1 composite ring path, it integrates scenario-driven operating state variables on the basis of the bilateral ring path, and characterizes the full fault set constraints through the scenario dimension; S4. Adopt an accelerated solution strategy for the corresponding path: For bilateral loop paths, adopt two strategies: hot start initialization based on pre-solved solutions and cable cross-constraint simplification based on incremental loading. For N-1 compound loop paths, in addition to the two bilateral loop strategies, adopt an additional fault set dimensionality reduction strategy based on key fault scenario screening. Through this strategy-accelerated solution, the initial topology and cable selection solution are obtained. S5. Perform a safety check on the entire fault set N-1 for the initial solution. The violation scenarios are included in the fault set and re-solved. The N-1 composite ring path is iteratively expanded to the fault set, while the bilateral ring path is checked for compliance until the solution meets the N-1 safety criteria. The final topology planning and cable selection results are output.

2. The offshore wind farm power collection system optimization method according to claim 1, characterized in that: The cable model selection constraint requirements in step S3 are: for any candidate cable location, at most only one cable model can be selected; if it is an N-1 composite ring path, the scenario-driven operating state variable constraint requirements are: for any candidate cable and any scenario, the value of its operating state variable is constrained to be between the sum of all its model construction state variables and zero; for any scenario pre-defined as a fault scenario, the operating state variable of the corresponding specific cable is forced to be set to zero; if it is a bilateral ring path, no scenario-driven operating state variable constraint is required.

3. The offshore wind farm power collection system optimization method according to claim 1 or 2, characterized in that: In step S3, for the bilateral ring path, the ring topology constraint of the adaptive cable selection is implemented by introducing a binary variable representing the direction of the virtual flow and a continuous variable representing the cumulative power of the node. The constraints include: ensuring that the number of virtual flow inflows and outflows of each wind turbine node is 1 to form a ring structure; establishing a bidirectional virtual flow accumulation equation to calculate the cumulative power value of each node in the positive and negative directions; based on the cumulative power value, constructing a constraint condition for each cable that its carrying power must meet the N-1 safety criterion. This condition uses different expressions for cables connecting substations and cables within wind farms; the N-1 compound ring path does not require this ring topology constraint.

4. The offshore wind farm power collection system optimization method according to claim 3, characterized in that: For a bilateral ring path, the specific implementation process of the ring topology constraint for adapter cable selection includes: A set of first binary variables is introduced to represent the forward direction of the virtual power flow on the candidate cable; A set of second binary variables is introduced to represent the reverse direction of the virtual power flow on the candidate cable; Constraining the sum of the first binary variable and the second binary variable to be equal to the construction state variable of the cable to ensure that the virtual power flow only exists on the constructed cables and that only one direction of virtual power flow can exist on each cable; For each wind turbine node, the sum of the first binary variables on all incoming cables and the sum of the second binary variables on all outgoing cables are constrained to be equal to 1. At the same time, the sum of the first binary variables on all outgoing cables and the sum of the second binary variables on all incoming cables are constrained to be equal to 1, thereby forcing the formation of a closed ring topology. A set of first continuous variables is introduced to represent the sum of the rated powers of downstream wind turbines accumulated at each node on the assumed virtual positive power flow path; A set of second continuous variables is introduced to represent the sum of the rated powers of downstream wind turbines accumulated at each node on the assumed virtual reverse power flow path; Establishing a recursive relationship constraint for the first continuous variable and the second continuous variable so that their values ​​increase along the direction of the virtual power flow, and the increment is the rated power of the wind turbine node passed through; The first continuous variable and the second continuous variable are constrained to have a lower bound of the rated power of the wind turbine itself and an upper bound of the capacity of the largest cable model; Based on the first and second continuous variables, a lower power limit constraint is established for each cable: for a cable directly connected to a substation, the power capacity it needs to carry must be no less than the maximum value of the first and second continuous variables at the wind turbine node to which it is connected. For a cable connecting two wind turbine nodes, the power capacity it needs to carry must be no less than the maximum value of the node at its two ends with the larger maximum value of the first and second continuous variables, minus one wind turbine rated power. Associating the above power capability requirement constraints with the nominal current carrying capacity of the selected cable model ensures that the capacity of the selected cable meets the power transmission requirements under all possible fault scenarios.

5. The offshore wind farm power collection system optimization method according to claim 4, characterized in that: The first continuous variable and the second continuous variable are further constrained to have values ​​that are integer multiples of the rated power of the wind turbine, so as to conform to the physical reality of discrete power accumulation and improve the efficiency of model solution.

6. The offshore wind farm power collection system optimization method according to claim 4, characterized in that: The power capacity requirement constraint constructed for the cable connecting the two wind turbine nodes is linearized by introducing a sufficiently large constant and combining it with the large M method to ensure that the constraint is only activated when the cable is actually selected and constructed.

7. The offshore wind farm power collection system optimization method according to claim 1 or 2, characterized in that: In step S4, for the N-1 composite ring path, the fault set dimensionality reduction strategy based on the screening of key fault scenarios includes: in the initial iteration, if it is a single substation scenario, the cable fault scenario directly connected to the substation node is preferentially included; if it is a multi-substation scenario, there is no need to initially include specific cable fault scenarios; in subsequent iterations, the system overload risk after all candidate cable faults is calculated and ranked based on the line outage distribution factor (LODF), or the fault severity is evaluated by solving a DC optimization subproblem with the goal of minimizing the total system overload, and several scenarios with the highest risk are selected to be added to the main problem fault set, and the iteration is carried out until there is no overload in all single cable fault scenarios.

8. The offshore wind farm power collection system optimization method according to claim 1 or 2, characterized in that: The cable crossing constraint simplification strategy based on incremental loading in step S4 includes: during the solution process, initially only the crossing pairs that may be involved in the cables connected to the substation node are included in the constraint set; after obtaining the initial solution, check whether there are any unconstrained cable crossings; if so, add the corresponding cross constraints to the model and re-solve, and through this iterative method, gradually approach a feasible solution that meets all crossing avoidance requirements.

9. The offshore wind farm power collection system optimization method according to claim 1 or 2, characterized in that: The hot start initialization strategy in step S4 includes: if it is a bilateral ring path, first solve a simplified model that does not consider cable selection or a relatively fixed topology structure, and use its optimal solution as the initial solution of the complex optimization model that considers cable selection; if it is an N-1 compound ring path, first solve the bilateral ring optimization model that considers cable selection, and use its optimal solution as the initial solution of the N-1 compound ring optimization model that considers cable selection; thereby accelerating the solution process.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for optimizing the offshore wind farm collection system according to any one of claims 1 to 9 is implemented.

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