A method and system for dynamic decoupling planning of large-scale offshore wind farm power systems

Abstract

The present invention discloses a method and system for dynamic decoupling planning of a large-scale offshore wind farm power system. The method includes constructing a two-stage dynamic decoupling planning model based on wind farm data: in the first stage, all wind turbines are pre-coded to divide the wind turbines in the loop into static and dynamic categories; in the second stage, a two-layer dynamic decoupling model of static and dynamic wind turbines in the loop is established, a multi-cable planning model under different operating conditions is constructed, and the objective function of the bilateral ring network collection system topology optimization problem is constructed with the minimum total investment and operating cost and constraints are added; the bilateral ring network collection system topology optimization problem is converted into an integer linear programming problem and the bilateral ring network collection system topology and total cost are solved. The present invention aims to improve and balance the computational efficiency and cost-effectiveness of planning for large-scale offshore wind farm power systems, and to take into account both the minimization of investment costs and operating losses under high reliability requirements.

Description

Technical Field

[0001] The present invention relates to the field of wind power technology, and in particular to a method and system for dynamic decoupling planning of a large-scale offshore wind farm power system. Background Art

[0002] While traditional radial topologies offer low cost, they suffer from insufficient reliability. The highly reliable double-sided ring topology, due to its complex redundancy design and network loss modeling, is considered a non-deterministic polynomial-hard problem with extremely high computational complexity and difficulty in solving efficiently. Specifically, increasing the number of wind turbines triggers a combinatorial explosion, leading to a sharp increase in optimization variables. Furthermore, the ring network must account for cable capacity constraints and power flow distribution under both normal and fault conditions, significantly increasing modeling complexity. Furthermore, the strong coupling between substation capacity configuration and cable layout requires global optimization to coordinate the two. Existing two-stage methods (such as divide-and-conquer and fuzzy clustering) are limited in their efficiency when addressing the "coupling relationship" between wind turbines and substation intervals and loops, and are prone to falling into local optimality. Furthermore, the lack of a dynamic adjustment mechanism makes it difficult to balance computational efficiency and economic efficiency. Therefore, how to ensure the high reliability of the offshore wind farm collection system, overcome the combination explosion problem caused by the surge in the number of units, reduce the modeling complexity of cable capacity and current under normal and fault conditions, and coordinate the strong coupling of substation capacity and cable layout to achieve the global minimization of the investment cost and operating loss of the collection system has become a key technical issue that needs to be solved urgently. Summary of the Invention

[0003] The technical problem to be solved by the present invention is as follows: In response to the above-mentioned problems of the prior art, a method and system for dynamic decoupling planning of a large-scale offshore wind farm power system are provided. The present invention aims to improve and balance the computational efficiency and cost-effectiveness of planning for a large-scale offshore wind farm power system, and to take into account both investment costs and minimization of operating losses under high reliability requirements.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] A large-scale offshore wind farm power system dynamic decoupling planning method includes the following steps:

[0006] 1) Obtain wind farm data for offshore wind farms;

[0007] 2) A two-stage dynamic decoupling planning model is constructed based on wind farm data. In the first stage, wind turbines within the substation area are divided into static and dynamic categories based on their distance from the substation. A fuzzy clustering algorithm modified by a genetic algorithm is used to precode all wind turbines to classify the wind turbines in the loop into static and dynamic categories. In the second stage, a two-layer dynamic decoupling model for static and dynamic wind turbines in the loop is established, and a multi-cable planning model is constructed under different operating conditions. The objective function of the bilateral ring network collection system topology optimization problem is constructed with the minimum total investment and operating costs, and constraints are added.

[0008] 3) The bilateral ring network collection system topology optimization problem is converted into an integer linear programming problem and the bilateral ring network collection system topology and total cost are solved, and the optimal bilateral ring network collection system topology and the minimum total cost are output.

[0009] Optionally, in step 2), the function expression for dividing the wind turbines in the substation area into static and dynamic categories according to the distance between the wind turbines and the substation is:

[0010] ,

[0011] ,

[0012] ,

[0013] In the above formula, and are the collections of all wind turbine nodes and substation nodes in large-scale offshore wind farms, and Respectively The set of static wind turbine nodes and dynamic wind turbine nodes in each substation interval, and Respectively The distance between the typhoon generator and the nearest substation and the second nearest substation. For the Typhoon and The coupling degree between substations, is the critical value of coupling degree.

[0014] Optionally, in step 2), using a genetic algorithm to improve a fuzzy clustering algorithm to precode all wind turbines to divide the wind turbines in the loop into static and dynamic categories includes:

[0015] S101, initializing and setting the two-dimensional population number and value of the genetic algorithm, where the two dimensions are the number of clusters and weights;

[0016] S102, initializing and setting the number of clusters, the weights between azimuth angle and distance, and the number of iterations of the fuzzy clustering algorithm;

[0017] S103, initializing the membership matrix;

[0018] S104, iteratively calculating the membership degree of the wind turbine to each cluster center;

[0019] S105, determining the preset clustering objective function Convergence: If not, jump to step S104 to continue iteration; otherwise, output the clustering situation and jump to step S106; wherein, the preset clustering objective function The function expression is:

[0020] ,

[0021] ,

[0022] ,

[0023] ,

[0024] ,

[0025] in, is the collection of all wind turbine nodes in a large-scale offshore wind farm. is the set of cluster centers in the substation interval, For the Typhoon The membership degree of the cluster centers, is the exponent of the membership matrix, is the coordinate of the i-th wind turbine To the cth cluster center The comprehensive distance, is the comprehensive distance coefficient, is the weight between azimuth and distance, For the Typhoon azimuth and the azimuth of the c-th cluster center The difference, is the absolute distance between the i-th wind turbine and the j-th cluster center, is the distance constant, Substation area Inner Ring Road The number of blowers, is the capacity of a single fan, The maximum allowed capacity of the cable. and are the comprehensive distance coefficients at time t and time t-1 respectively;

[0026] S106, connecting the wind turbines in each cluster into a loop using a single-loop path optimization algorithm according to the clustering situation;

[0027] S107, determine whether the preset first objective function Obj1 has converged. If it has not converged, perform crossover and mutation operations on the two-dimensional population, and jump to step S102 to continue iteration using the genetic algorithm; otherwise, output the membership degree of the wind turbine to the loop within each substation interval obtained by the improved fuzzy clustering algorithm; the function expression of the preset first objective function Obj1 is:

[0028] ,

[0029] ,

[0030] in, Cost of medium voltage cables used for route planning, is the total service life of the cable in years, is the cable power loss cost for one year, is the annual inflation rate of energy prices, t is the time, is the total number of hours in a year, is the medium voltage cable power loss, is the set of wind turbine nodes, For medium voltage cable assembly, For wind turbine nodes The cost of connecting the two rooms via type A cable, For wind turbine nodes Length of medium voltage cables;

[0031] S108, calculate the coupling degree between any i-th wind turbine in the substation section and the two loops with the highest membership, divide the second highest membership by the two highest memberships as the coupling degree between the i-th wind turbine and the loop with the highest membership; if the coupling degree exceeds the preset value, it is determined that the i-th wind turbine is a static wind turbine of the loop with the highest membership, and the i-th wind turbine is classified into the set of static wind turbines of the loop with the highest membership; otherwise, it is determined that the i-th wind turbine is a dynamic wind turbine of the loop with the highest membership, and the i-th wind turbine is classified into the set of dynamic wind turbines of the loop with the highest membership.

[0032] Optionally, the function expression for iteratively calculating the membership degree of the wind turbine to each cluster center in step S104 is:

[0033] ,

[0034] ,

[0035] in, For the Typhoon The membership degree of the cluster centers, is the set of cluster centers in the substation interval, is the coordinate of the i-th wind turbine To Cluster centers The comprehensive distance, is the coordinate of the i-th wind turbine To the kth cluster center The comprehensive distance, is the exponent of the membership matrix, is the index of the cluster center, is the collection of all wind turbine nodes in a large-scale offshore wind farm. for The collection size of For the 1st Typhoon The membership degree of cluster centers of q times the coordinates of the i-th wind turbine The accumulation of For the 1st Typhoon The membership degree of cluster centers of q The accumulation of powers, for The collection size.

[0036] Optionally, in step 2), a two-layer dynamic decoupling model of static and dynamic wind turbines in the loop is established, including an outer model for decoupling the wind turbines to the substation interval and an inner model for decoupling the wind turbines to the ring network cluster. The function expression of the outer model is:

[0037] ,

[0038] ,

[0039] ,

[0040] ,

[0041] ,

[0042] in, 、 and Connected to the substation section Wind turbine nodes for all wind turbines, static wind turbines and dynamic wind turbines The binary variable of the cable between and Connected to the substation section All wind turbines and static wind turbine nodes The binary variable of the cable between is the union, 、 、 and Substation section The set of all wind turbine nodes, substation nodes, static wind turbine nodes and dynamic wind turbine nodes, is the set of substation intervals, and are the upper and lower limits, and Connected to the substation section The binary variables of the cable between the substation node s and the wind turbine node i, and the binary variables of the cable between the wind turbine node j and the substation node s, Substation area The number of loops within Substation area The number of fans, is the capacity of a single fan, is the maximum allowable capacity of the cable; the function expression of the inner layer model is:

[0043] ,

[0044] ,

[0045] ,

[0046] ,

[0047] ,

[0048] in, 、 and Connected to the substation section and loop Cables for all fans, static and dynamic A binary variable, 、 and Connected to the substation section and loop Wind turbine nodes for all wind turbines, static wind turbines and dynamic wind turbines The binary variable of the cable between Substation area A collection of Substation area The loop set, 、 and Substation section and loop The set of all wind turbine nodes, static wind turbine nodes and dynamic wind turbine nodes, and Substation section Loop Wind turbine node i and wind turbine nodes j voltage.

[0049] Optionally, the multi-cable planning model under different operating conditions constructed in step 2) includes a medium voltage multi-cable model and a high voltage multi-cable model, and the function expression of the medium voltage multi-cable model is:

[0050] ,

[0051] ,

[0052] ,

[0053] ,

[0054] ,

[0055] in, For medium voltage cable assembly, Substation area and loop The set of all wind turbine nodes, and Have positive and negative sort positions respectively Wind turbine node Binary variables for medium voltage cables between and Have positive and negative sort positions respectively Wind turbine node Binary variables for medium voltage cables between is the capacity of a single fan, The maximum allowed capacity of the cable. is the set of sorted positions of the medium voltage cables, and Wind turbine nodes The maximum sort position between and the optimal sort position The power flow energy of the cable under normal working conditions, is the index in the set of sorted positions of the medium voltage cable;

[0056] The functional expression of the high-voltage multi-cable model is:

[0057] ,

[0058] ,

[0059]

[0060] in, Substation area The total energy of all fans in the 、 Substation section The collection of all wind turbine nodes and substation nodes, Substation area The energy of each fan, Substation area A collection of is the sorted position set of high-voltage cables, Substation area Has positive sort position The binary variable of the high voltage cable, Substation area Has positive sort position The maximum capacity allowed for high voltage cable operation.

[0061] Optionally, in step 2), the objective function of the topology optimization problem of constructing a bilateral ring network collection system with minimum total investment and operating cost is expressed as follows:

[0062] ,

[0063] ,

[0064] ,

[0065] ,

[0066] ,

[0067] ,

[0068] ,

[0069] ,

[0070] in, The objective function of the topology optimization problem of bilateral ring network collection system is to minimize the total investment and operating costs. The cost of key equipment, For high and medium voltage cable costs, is the total service life of the cable in years, is the cable power loss cost for one year, is the annual inflation rate of energy prices, t is the time, is the total number of hours in a year, is the power loss of high voltage and medium voltage cables; and They are respectively a collection of high voltage and medium voltage cables, is the sorted position set of high-voltage cables, Substation area Has positive sort position The binary variable of the high voltage cable, and are the linear cost coefficients of offshore substation and platform respectively, 、 Substation section Has positive sort position The rated power of the offshore substation and the platform to which the cable is connected, and are the constant terms of offshore substation and platform respectively, is the cost of high voltage cables, is the cost of medium voltage cables, and Substation section Has positive sort position The cost of high-voltage cables, wind turbine nodes The cost of medium voltage cables between and Substation section The length of the high-voltage cable and the wind turbine node The length of the medium voltage cable between and Substation section Has positive sort position The resistance of the high-voltage cable and the wind turbine node There is a positive sort position between The resistance of the medium voltage cable, For wind turbine nodes There is a positive sort position between Binary variable for medium voltage cables, For wind turbine nodes There is a negative sort position between Binary variable for medium voltage cables, For wind turbine nodes Take the positive sort position between , negative sort position The cost of the medium voltage cable in the maximum value, is the high voltage cable power loss, is the medium voltage cable power loss, Substation area The total energy of all fans in the is the capacity of a single fan, For wind turbine nodes Take the positive sort position between , negative sort position The resistance of the cable with the maximum value in step 2); when constructing the objective function of the bilateral ring network collection system topology optimization problem with the minimum total investment and operating cost and adding constraints, the constraints added are:

[0071] ,

[0072] in, For wind turbine nodes The cables between For wind turbine nodes The cables between For wind turbine nodes The cables between For wind turbine nodes The cables between is a collection of cross-cable pairs; when the bilateral ring network collection system topology optimization problem is converted into an integer linear programming problem in step 3), the medium voltage cable cost is and medium voltage cable power losses Linearization, and medium voltage cable costs and medium voltage cable power losses The linearized function expression is:

[0073] ,

[0074] ,

[0075] in, A collection of medium voltage cables, is the set of sorted positions of the medium voltage cables, For wind turbine nodes A binary variable describing the product of the positive and negative sort positions of the medium voltage cable, For wind turbine nodes Take the positive sort position between , negative sort position The cost of the medium voltage cable in the maximum value, For wind turbine nodes The length of the medium voltage cable between For all A collection of For wind turbine nodes Active power on the cable between them.

[0076] In addition, the present invention also provides a large-scale offshore wind farm power system dynamic decoupling planning system, comprising an interconnected microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the large-scale offshore wind farm power system dynamic decoupling planning method.

[0077] In addition, the present invention also provides a computer-readable storage medium, which stores a computer program or instruction, and the computer program or instruction is programmed or configured to execute the large-scale offshore wind farm power system dynamic decoupling planning method through a processor.

[0078] In addition, the present invention also provides a computer program product, comprising a computer program or instructions, which are programmed or configured to execute the large-scale offshore wind farm power system dynamic decoupling planning method through a processor.

[0079] Compared with the existing technology, the present invention can mainly achieve the following beneficial effects: the method of the present invention includes constructing a two-stage dynamic decoupling planning model based on wind farm data: in the first stage, all wind turbines are pre-coded to divide the wind turbines in the loop into static and dynamic categories; in the second stage, a two-layer dynamic decoupling model of static and dynamic wind turbines in the loop is established, and a multi-cable planning model under different operating conditions is constructed. The objective function of the bilateral ring network collection system topology optimization problem is constructed with the minimum total investment and operating cost, and constraints are added; the bilateral ring network collection system topology optimization problem is converted into an integer linear programming problem and the bilateral ring network collection system topology and total cost are solved until the total cost converges. The present invention effectively solves the combinatorial explosion, high modeling complexity and strong coupling optimization problems in the planning of offshore wind farm collection systems through a two-stage dynamic decoupling planning method, and achieves the global optimization goal. The present invention dynamically decouples the relationship between wind turbines and substations through a fuzzy clustering model improved by a genetic algorithm, and combines redundancy design with linear modeling of tidal constraints, significantly improving computational efficiency and cost-effectiveness. It can take into account both investment costs and minimization of operating losses under high reliability requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 Schematic diagram of the basic process of the method of the embodiment of the present invention.

[0081] Figure 2 This is a flow chart of improving the fuzzy clustering algorithm using a genetic algorithm in the first stage of an embodiment of the present invention.

[0082] Figure 3 This is a schematic diagram of modeling the current carrying capacity of cables and normal operating power flow in a medium-voltage power collection system according to an embodiment of the present invention.

[0083] Figure 4 Schematic diagram of the optimal topology structure of a wind farm with 30 wind turbines obtained using three methods in an embodiment of the present invention, where (a) is the optimal topology structure obtained in the first stage of the method of this embodiment, (b) is the optimal topology structure obtained by the method of this embodiment, and (c) is the optimal topology structure obtained by the mathematical programming method.

[0084] Figure 5 Schematic diagram of the optimal topology structure of a wind farm with 42 wind turbines obtained using three methods in an embodiment of the present invention, where (a) is the optimal topology structure obtained in the first stage of the method of this embodiment, (b) is the optimal topology structure obtained by the method of this embodiment, and (c) is the optimal topology structure obtained by the mathematical programming method.

[0085] Figure 6 Schematic diagram of the optimal topology structure of a wind farm with 91 wind turbines obtained using three methods in an embodiment of the present invention, where (a) is the optimal topology structure obtained by the scanning method, (b) is the optimal topology structure obtained by the genetic algorithm improved scanning method, and (c) is the optimal topology structure obtained by the method of this embodiment. DETAILED DESCRIPTION

[0086] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be further described in detail below with reference to the accompanying drawings in the embodiments of the present invention.

[0087] like Figure 1 As shown, the dynamic decoupling planning method for a large-scale offshore wind farm power system in this embodiment includes the following steps:

[0088] 1) Obtain wind farm data for offshore wind farms;

[0089] 2) A two-stage dynamic decoupling planning model is constructed based on wind farm data. In the first stage, wind turbines within the substation area are divided into static and dynamic categories based on their distance from the substation. A fuzzy clustering algorithm modified by a genetic algorithm is used to precode all wind turbines to classify the wind turbines in the loop into static and dynamic categories. In the second stage, a two-layer dynamic decoupling model for static and dynamic wind turbines in the loop is established, and a multi-cable planning model is constructed under different operating conditions. The objective function of the bilateral ring network collection system topology optimization problem is constructed with the minimum total investment and operating costs, and constraints are added.

[0090] 3) The bilateral ring network collection system topology optimization problem is converted into an integer linear programming problem and the bilateral ring network collection system topology and total cost are solved, and the optimal bilateral ring network collection system topology and the minimum total cost are output.

[0091] Step 2) involves precoding the wind turbines. This precoding process uses a fuzzy clustering model based on a genetic algorithm to obtain the membership of wind turbines within each substation interval to the loop. Then, using the aforementioned method for classifying wind turbines into static and dynamic categories, the wind turbines are classified into static and dynamic categories based on the membership of each loop. In step 2) of this embodiment, the function expression for classifying wind turbines within the substation interval into static and dynamic categories based on their distance from the substation is:

[0092] ,

[0093] ,

[0094] ,

[0095] In the above formula, and are the collections of all wind turbine nodes and substation nodes in large-scale offshore wind farms, and Respectively The set of static wind turbine nodes and dynamic wind turbine nodes in each substation interval, and Respectively The distance between the typhoon generator and the nearest substation and the second nearest substation. For the Typhoon and The coupling degree between substations, is the critical value of coupling degree.

[0096] like Figure 2 As shown, in step 2) of this embodiment, the fuzzy clustering algorithm improved by the genetic algorithm is used to precode all wind turbines to divide the wind turbines in the loop into static and dynamic categories, including:

[0097] S101, initializing and setting the two-dimensional population number and value of the genetic algorithm, where the two dimensions are the number of clusters and weights;

[0098] S102, initializing and setting the number of clusters, the weights between azimuth angle and distance, and the number of iterations of the fuzzy clustering algorithm;

[0099] S103, initializing the membership matrix;

[0100] S104, iteratively calculating the membership degree of the wind turbine to each cluster center;

[0101] S105, determining the preset clustering objective function Whether it converges, if not, jump to step S104 to continue iteration; otherwise, output the clustering situation and jump to step S106; the improved fuzzy clustering algorithm first sets the number of clusters, the weights between the azimuth and the distance, and the number of iterations, and then initializes the membership matrix, and continuously iterates the cluster centers and membership according to the objective function until convergence. In this embodiment, the preset clustering objective function The function expression is:

[0102] ,

[0103] ,

[0104] ,

[0105] ,

[0106] ,

[0107] in, is the collection of all wind turbine nodes in a large-scale offshore wind farm. is the set of cluster centers in the substation interval, For the Typhoon The membership degree of the cluster centers, is the exponent of the membership matrix, is the coordinate of the i-th wind turbine To the cth cluster center The comprehensive distance, is the comprehensive distance coefficient, is the weight between azimuth and distance, For the Typhoon azimuth and the azimuth of the c-th cluster center The difference, is the absolute distance between the i-th wind turbine and the j-th cluster center, is the distance constant, Substation area Inner Ring Road The number of blowers, is the capacity of a single fan, The maximum allowed capacity of the cable. and are the comprehensive distance coefficients at time t and time t-1 respectively;

[0108] S106, connecting the wind turbines in each cluster into a loop using a single-loop path optimization algorithm according to the clustering situation;

[0109] S107, determining whether the preset first objective function Obj1 has converged; if not, performing crossover and mutation operations on the two-dimensional population, and jumping to step S102 to continue iteration using the genetic algorithm; otherwise, outputting the membership degree of the wind turbine to the loop within each substation interval obtained by the improved fuzzy clustering algorithm;

[0110] S108, calculate the coupling degree between any i-th wind turbine in the substation section and the two loops with the highest membership, divide the second highest membership by the two highest memberships as the coupling degree between the i-th wind turbine and the loop with the highest membership; if the coupling degree exceeds the preset value, it is determined that the i-th wind turbine is a static wind turbine of the loop with the highest membership, and the i-th wind turbine is classified into the set of static wind turbines of the loop with the highest membership; otherwise, it is determined that the i-th wind turbine is a dynamic wind turbine of the loop with the highest membership, and the i-th wind turbine is classified into the set of dynamic wind turbines of the loop with the highest membership.

[0111] After all wind turbines are divided into substation sections, a fuzzy clustering model based on an improved genetic algorithm is used to precode the wind turbines within each substation section. This model uses improved fuzzy clustering to group the wind turbines. After clustering, a single-loop path optimization algorithm is used to connect the wind turbines in each cluster into a loop. The preset first objective function Obj1 is constructed with the goal of optimizing the investment and operating costs of medium-voltage cables in the power collection system. The genetic algorithm is used to iteratively optimize the weight coefficients and the number of clusters. Specifically, the functional expression of the preset first objective function Obj1 is:

[0112] ,

[0113] ,

[0114] in, Cost of medium voltage cables used for route planning, is the total service life of the cable in years, is the cable power loss cost for one year, is the annual inflation rate of energy prices, t is the time, is the total number of hours in a year, is the medium voltage cable power loss, is the set of wind turbine nodes, For medium voltage cable assembly, For wind turbine nodes The cost of connecting the two rooms via type A cable, For wind turbine nodes The cable power loss cost per year in this example is Value , is the annual inflation rate of energy prices, , Indicates the total service life of the cable in years. , is the total number of hours in a year, taking the value .

[0115] In step S104 of this embodiment, the function expression for iteratively calculating the membership degree of the wind turbine to each cluster center is:

[0116] ,

[0117] ,

[0118] in, For the Typhoon The membership degree of the cluster centers, is the set of cluster centers in the substation interval, is the coordinate of the i-th wind turbine To Cluster centers The comprehensive distance, is the coordinate of the i-th wind turbine To the kth cluster center The comprehensive distance, is the exponent of the membership matrix, is the index of the cluster center, is the collection of all wind turbine nodes in a large-scale offshore wind farm. for The collection size of For the 1st Typhoon The membership degree of cluster centers of q times the coordinates of the i-th wind turbine The accumulation of For the 1st Typhoon The membership degree of cluster centers of q The accumulation of powers, for The collection size.

[0119] In step 2) of this embodiment, a two-layer dynamic decoupling model for static and dynamic wind turbines in the loop is established, which includes an outer layer model for decoupling the wind turbines from the substation section and an inner layer model for decoupling the wind turbines from the ring network cluster. The function expression of the outer layer model is:

[0120] ,

[0121] ,

[0122] ,

[0123] ,

[0124] ,

[0125] in, 、 and Connected to the substation section Cables for all fans, static and dynamic A binary variable, and Connected to the substation section Cables for all fans and static fans A binary variable, is the union, 、 、 and Substation section The set of all nodes, substation nodes, static wind turbine nodes and dynamic wind turbine nodes, is the set of substation intervals, and are the upper and lower limits, and Connected to the substation section All wind turbine cables and A binary variable, Substation area The number of loops within Substation area The number of fans, is the capacity of a single fan, is the maximum allowable capacity of the cable; the function expression of the inner layer model is:

[0126] ,

[0127] ,

[0128] ,

[0129] ,

[0130] ,

[0131] in, 、 and Connected to the substation section and loop Cables for all fans, static and dynamic A binary variable, 、 and Connected to the substation section and loop Wind turbine nodes for all wind turbines, static wind turbines and dynamic wind turbines The binary variable of the cable between Substation area A collection of Substation area The loop set, 、 and Substation section and loop The set of all wind turbine nodes, static wind turbine nodes and dynamic wind turbine nodes, and Substation section Loop Wind turbine node i and wind turbine nodes j voltage.

[0132] In this embodiment, a multi-cable planning model under different operating conditions is constructed to plan medium voltage and high voltage cables according to different operating conditions. Figure 3 Figure (a) shows a medium-voltage collection system with multiple cables. The cable capacity ratings must meet redundancy requirements for the two most severe fault conditions. The dashed lines indicate the energy flow direction under these conditions. When cable Br1 fails and is isolated, the power flow is indicated by the green dashed line, with energy flowing from the feeder end to the substation. Br2 through Br8 each carry energy from one to seven wind turbines. When cable Br8 fails and is isolated, the power flow reverses, as indicated by the red dashed line. In either case, the cable capacity rating must not be less than the maximum energy. Figure 3(b) shows the flow in each cable under normal conditions, and the solid line represents the ideal direction of energy flow during normal operation. By ignoring the influence of flow rate and cable impedance parameters on power distribution, a symmetrical power flow distribution model is adopted to reduce computational complexity. Cables Br1 to Br4 carry energy equivalent to 3.5, 2.5, 1.5, and 0.5 wind turbines, respectively, and energy flows from the middle of the loop to the substation. Accordingly, the multi-cable planning model under different operating conditions constructed in step 2) of this embodiment includes a medium-voltage multi-cable model and a high-voltage multi-cable model. The functional expression of the medium-voltage multi-cable model is:

[0133] ,

[0134] ,

[0135] ,

[0136] ,

[0137] ,

[0138] in, For medium voltage cable assembly, Substation area and loop The set of all wind turbine nodes, and Have positive and negative sort positions respectively Wind turbine node Binary variables for medium voltage cables between and Have positive and negative sort positions respectively Wind turbine node Binary variables for medium voltage cables between is the capacity of a single fan, The maximum allowed capacity of the cable. is the set of sorted positions of the medium voltage cables, and Wind turbine nodes The maximum sort position between and the optimal sort position The power flow energy of the cable under normal working conditions, is the index in the sorted position set of the medium-voltage cable; in the boosted multi-cable high-voltage transmission system, power flow is unidirectional and the high-voltage cables are not redundant. Therefore, a modeling method similar to the radial network can be used. The functional expression of the high-voltage multi-cable model is:

[0139] ,

[0140] ,

[0141]

[0142] in, Substation area The total energy of all fans in the 、 Substation section The collection of all wind turbine nodes and substation nodes, Substation area The energy of each fan, Substation area A collection of is the sorted position set of high-voltage cables, Substation area Has positive sort position The binary variable of the high voltage cable, Substation area Has positive sort position The maximum capacity allowed for high voltage cable operation.

[0143] In step 2) of this embodiment, the objective function of the topology optimization problem of constructing a bilateral ring network power collection system with minimum total investment and operating costs is expressed as follows:

[0144] ,

[0145] ,

[0146] ,

[0147] ,

[0148] ,

[0149] ,

[0150] ,

[0151] ,

[0152] in, The objective function of the topology optimization problem of bilateral ring network collection system is to minimize the total investment and operating costs. The cost of key equipment, For high and medium voltage cable costs, is the total service life of the cable in years, is the cable power loss cost for one year, is the annual inflation rate of energy prices, t is the time, is the total number of hours in a year, is the power loss of high voltage and medium voltage cables; and They are respectively a collection of high voltage and medium voltage cables, is the sorted position set of high-voltage cables, Substation area Has positive sort position The binary variable of the high voltage cable, and are the linear cost coefficients of offshore substation and platform respectively, 、 Substation section Has positive sort position The rated power of the offshore substation and the platform to which the cable is connected, and are the constant terms of offshore substation and platform respectively, is the cost of high voltage cables, is the cost of medium voltage cables, and Substation section Has positive sort position The cost of high-voltage cables, wind turbine nodes The cost of medium voltage cables between and Substation section The length of the high-voltage cable and the wind turbine node The length of the medium voltage cable between and Substation section Has positive sort position The resistance of the high-voltage cable and the wind turbine node There is a positive sort position between The resistance of the medium voltage cable, For wind turbine nodes There is a positive sort position between Binary variable for medium voltage cables, For wind turbine nodes There is a negative sort position between Binary variable for medium voltage cables, For wind turbine nodes Take the positive sort position between , negative sort position The cost of the medium voltage cable in the maximum value, is the high voltage cable power loss, is the medium voltage cable power loss, Substation area The total energy of all fans in the is the capacity of a single fan, For wind turbine nodes Take the positive sort position between , negative sort position The resistance of the cable with the maximum value in this embodiment is , , , .

[0153] In step 2) of this embodiment, when constructing the objective function of the bilateral ring network collection system topology optimization problem with the minimum total investment and operating cost and adding constraints, the constraints added are:

[0154] ,

[0155] in, For wind turbine nodes The cables between For wind turbine nodes The cables between For wind turbine nodes The cables between For wind turbine nodes The cables between A collection of crossover cable pairs.

[0156] Define a new binary variable , The positive sequence position is Cable A binary variable, The negative sequence position is Cable The binary variable, the medium voltage cable cost in the above objective function is calculated using the following equation and medium voltage cable power losses Linearization:

[0157] ,

[0158] in, A cable that describes the product of the positive and negative order positions A binary variable, is a collection of cable types, For all binary variables Therefore, in step 3) of this embodiment, when the bilateral ring network collection system topology optimization problem is converted into an integer linear programming problem, the medium voltage cable cost is and medium voltage cable power losses Linearization, and medium voltage cable costs and medium voltage cable power losses The linearized function expression is:

[0159] ,

[0160] ,

[0161] in, A collection of medium voltage cables, is the set of sorted positions of the medium voltage cables, For wind turbine nodes A binary variable describing the product of the positive and negative sort positions of the medium voltage cable, For wind turbine nodes Take the positive sort position between , negative sort position The cost of the medium voltage cable in the maximum value, For wind turbine nodes The length of the medium voltage cable between For all A collection of For wind turbine nodes After linearizing the objective function of the large-scale offshore wind farm power system planning, a commercial solver is used to obtain the optimal solution for the large-scale offshore wind farm power system planning based on the above constraints. The optimal set of sorted positions of medium-voltage cables is obtained. and the sorting position set of high voltage cables , which includes the connection sequence of wind turbines, is the solution result required by the dynamic decoupling planning method for the large-scale offshore wind farm power system in this embodiment.

[0162] To verify the dynamic decoupling planning method for large-scale offshore wind farm power systems in this embodiment, a simulation was performed. Two single-substation test systems were constructed: an offshore wind farm system consisting of 30 5MW wind turbines and an offshore wind farm system consisting of 42 6.25MW wind turbines. One multi-substation system was also constructed: an offshore wind farm system consisting of 91 5MW wind turbines and two substations. Table 1 shows the specifications of the 33kV medium-voltage and 132kV high-voltage aluminum cross-linked polyethylene cables used in these offshore wind farms, including cross-sectional area, rated capacity, resistance per unit length (referred to as resistance), inductance, and cost.

[0163] Table 1: Specifications of three (single) core aluminum XLPE cables

[0164]

[0165] In Table 1, MVA refers to the cable capacity in megavolt-amperes (million volt-amperes). MV stands for medium voltage cable, and HV stands for high voltage cable.

[0166] Figure 4 、 Figure 5 and Figure 6 The topology design results of the bilateral ring network collection system using the method of this embodiment and other methods were compared for offshore wind farms with 30 wind turbines, 42 wind turbines, and 91 wind turbines, respectively. All methods considered precise cable ratings and network loss requirements. Other methods include: the global optimal solution obtained by the genetic algorithm-modified fuzzy clustering method in the first phase of this embodiment, and the mathematical programming method. In the first phase of this embodiment, the population size was 30, and the crossover and mutation rates were set to 0.7. The mathematical programming method used a mixed integer quadratic programming model to minimize cable cost and total power loss, finding the global minimum of the total cable investment cost and total power loss over the entire life cycle of the offshore wind farm. The mathematical programming method was implemented using the Gurobi solver. The solution gap in the mathematical programming method refers to the relative difference between the upper bound (the target value of the current best feasible solution) and the lower bound (the target value of the relaxed problem) at the end of the solution process. The scanning method uses a scanning algorithm to divide all wind turbines into several clusters counterclockwise from the direction of the high-voltage cable. The genetic algorithm is then used to plan the wind farm collection system. The improved genetic algorithm scanning method randomly determines the number of wind turbines in each group through a genetic algorithm, adopts a scanning algorithm to divide the wind turbines contained in each group counterclockwise from the direction of the high-voltage cable, uses a genetic algorithm to plan the path, and continuously updates the population to obtain the optimal solution with the goal of minimizing the total cost. In the improved genetic algorithm scanning method, the population size is set to 30, and the crossover rate and mutation rate are 0.7. The simulation and optimization of all methods in this embodiment are carried out in Gurobi and MATLAB R2019a software on a PC with a 2.10 GHz CPU and 32-GB RAM environment. The economic results of the design of the bilateral ring network collection system of the offshore wind farm using 30 / 42 / 91 wind turbines are shown in Table 2.

[0167] Table 2: Comparison of economic performance of offshore wind farms using 30, 42, and 91 turbines

[0168]

[0169] As shown in Table 2, compared to the first phase of this embodiment, the total cost of this embodiment's method for wind farms with 30 and 42 turbines was reduced by 1.46% and 1.82%, respectively, and the investment cost was reduced by 1.48% and 2.29%, respectively. This indicates that in the second phase, repartitioning the loop based on the degree of coupling between each turbine can further approach the optimal solution by employing an inter-layer decoupling method. Compared to the mathematical programming method, this embodiment's method significantly reduced the computation time by 0.06% in the 42-turbine system, significantly reducing the total cost. It can be seen that minimizing cable costs and total network losses using the mathematical programming method results in a solution gap of 4.72% and 7.21% in the 30-turbine and 42-turbine systems, respectively, indicating that even after a significant amount of solution time, the global optimal solution may not be reached. Compared to the scanning method, this embodiment's method reduced the total cost and investment cost by 0.54% and 0.62%, respectively, in the 91-turbine wind farm system. Compared with the genetic algorithm improved scanning method, in a wind farm system with 91 wind turbines, the total cost and investment cost of the method in this embodiment were saved by 0.23% and 0.27% respectively. Figure 6 From (a), (b) and (c), we can see that wind turbine No. 53 has changed the substation range to which it belongs. The wind farm planning effect of the method in this embodiment is better.

[0170] In summary, due to the high reliability of the bilateral ring network topology, it is widely used in the collection system of offshore wind farms. The current study considers how to ensure the high reliability of the offshore wind farm collection system, overcome the combinatorial explosion problem caused by the surge in the number of units, reduce the modeling complexity of cable capacity and tidal current under normal and fault conditions, and coordinate the strong coupling of substation capacity and cable layout to achieve the global minimization of the investment cost and operating loss of the collection system. This has become a key technical problem that needs to be solved urgently. The large-scale offshore wind farm power system dynamic decoupling planning model constructed based on the acquired wind farm data in this embodiment is a two-stage dynamic decoupling planning model. In the first stage, the wind turbines are divided into static and dynamic categories according to the distance between the wind turbines and the substation, and a hybrid clustering model improved by the genetic algorithm is constructed to pre-code the candidate wind turbines in each substation interval, which significantly improves the rationality of the decoupling process. In the second stage, a two-layer dynamic decoupling model and a multi-cable planning model under different operating conditions are established. Finally, based on the above model, the objective function is constructed with the minimum total investment and operating cost, and constraints are added to convert it into an integer linear programming problem and solve it. This invention, through a two-stage dynamic decoupling planning approach, effectively addresses the combinatorial explosion, high modeling complexity, and strong coupling optimization challenges in offshore wind farm collection system planning, achieving global optimization goals. This invention dynamically decouples the relationship between wind turbines and substations using a fuzzy clustering model improved by a genetic algorithm. Combined with redundancy design and linearized modeling of tidal constraints, this significantly improves computational efficiency and cost-effectiveness, balancing investment costs with minimizing operating losses while maintaining high reliability.

[0171] In addition, this embodiment also provides a large-scale offshore wind farm power system dynamic decoupling planning system, including an interconnected microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the large-scale offshore wind farm power system dynamic decoupling planning method.

[0172] In addition, this embodiment also provides a computer-readable storage medium, which stores a computer program or instructions. The computer program or instructions are programmed or configured to execute the large-scale offshore wind farm power system dynamic decoupling planning method through a processor.

[0173] In addition, this embodiment also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the large-scale offshore wind farm power system dynamic decoupling planning method through a processor.

[0174] Those skilled in the art should understand that the technical solution provided by the present invention may be in the form of a method, a system, or a computer program product. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the functions described in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including the instruction device, which implements the function specified in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0175] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A dynamic decoupling planning method for a large-scale offshore wind farm power system, characterized in that: The steps include: 1) Obtain wind farm data for offshore wind farms; 2) A two-stage dynamic decoupling planning model is constructed based on wind farm data. In the first stage, wind turbines within the substation area are divided into static and dynamic categories based on their distance from the substation. A fuzzy clustering algorithm modified by a genetic algorithm is used to precode all wind turbines to classify the wind turbines in the loop into static and dynamic categories. In the second stage, a two-layer dynamic decoupling model for static and dynamic wind turbines in the loop is established, and a multi-cable planning model is constructed under different operating conditions. The objective function of the bilateral ring network collection system topology optimization problem is constructed with the minimum total investment and operating costs, and constraints are added. 3) Convert the bilateral ring network power collection system topology optimization problem into an integer linear programming problem and solve the bilateral ring network power collection system topology and total cost, and output the optimal bilateral ring network power collection system topology and minimum total cost; In step 2), the function expression for dividing the wind turbines in the substation area into static and dynamic categories according to the distance between the wind turbines and the substation is: ， ， ， In the above formula, and are the collections of all wind turbine nodes and substation nodes in large-scale offshore wind farms, and Respectively The set of static wind turbine nodes and dynamic wind turbine nodes in each substation interval, and Respectively The distance between the typhoon generator and the nearest substation and the second nearest substation. For the Typhoon and The coupling degree between substations, is the critical value of coupling degree.

2. The large-scale offshore wind farm power system dynamic decoupling planning method according to claim 1 is characterized in that: In step 2), a fuzzy clustering algorithm improved by a genetic algorithm is used to precode all wind turbines to classify the wind turbines in the loop into static and dynamic categories, including: S101, initializing and setting the two-dimensional population number and value of the genetic algorithm, where the two dimensions are the number of clusters and weights; S102, initializing and setting the number of clusters, the weights between azimuth angle and distance, and the number of iterations of the fuzzy clustering algorithm; S103, initializing the membership matrix; S104, iteratively calculating the membership degree of the wind turbine to each cluster center; S105, determining the preset clustering objective function Convergence: If not, jump to step S104 to continue iteration; otherwise, output the clustering situation and jump to step S106; wherein, the preset clustering objective function The function expression is: ， ， ， ， ， in, is the collection of all wind turbine nodes in a large-scale offshore wind farm. is the set of cluster centers in the substation interval, For the Typhoon The membership degree of the cluster centers, is the exponent of the membership matrix, is the coordinate of the i-th wind turbine To the cth cluster center The comprehensive distance, is the comprehensive distance coefficient, is the weight between azimuth and distance, For the Typhoon azimuth and the azimuth of the c-th cluster center The difference, is the absolute distance between the i-th wind turbine and the j-th cluster center, is the distance constant, Substation area Inner Ring Road The number of blowers, is the capacity of a single fan, The maximum allowed capacity of the cable. and are the comprehensive distance coefficients at time t and time t-1 respectively; S106, connecting the wind turbines in each cluster into a loop using a single-loop path optimization algorithm according to the clustering situation; S107, determine whether the preset first objective function Obj1 has converged. If it has not converged, perform crossover and mutation operations on the two-dimensional population, and jump to step S102 to continue iteration using the genetic algorithm; otherwise, output the membership degree of the wind turbine to the loop within each substation interval obtained by the improved fuzzy clustering algorithm; the function expression of the preset first objective function Obj1 is: ， ， in, Cost of medium voltage cables used for route planning, is the total service life of the cable in years, is the cable power loss cost for one year, is the annual inflation rate of energy prices, t is the time, is the total number of hours in a year, is the medium voltage cable power loss, is the set of wind turbine nodes, For medium voltage cable assembly, For wind turbine nodes The cost of connecting the two rooms via type A cable, For wind turbine nodes Length of medium voltage cables; S108, calculate the coupling degree between any i-th wind turbine in the substation section and the two loops with the highest membership, divide the second highest membership by the two highest memberships as the coupling degree between the i-th wind turbine and the loop with the highest membership; if the coupling degree exceeds the preset value, it is determined that the i-th wind turbine is a static wind turbine of the loop with the highest membership, and the i-th wind turbine is classified into the set of static wind turbines of the loop with the highest membership; otherwise, it is determined that the i-th wind turbine is a dynamic wind turbine of the loop with the highest membership, and the i-th wind turbine is classified into the set of dynamic wind turbines of the loop with the highest membership.

3. The large-scale offshore wind farm power system dynamic decoupling planning method according to claim 2 is characterized in that: The function expression for iteratively calculating the membership degree of the wind turbine to each cluster center in step S104 is: ， ， in, For the Typhoon The membership degree of the cluster centers, is the set of cluster centers in the substation interval, is the coordinate of the i-th wind turbine To Cluster centers The comprehensive distance, is the coordinate of the i-th wind turbine To the kth cluster center The comprehensive distance, is the exponent of the membership matrix, is the index of the cluster center, is the collection of all wind turbine nodes in a large-scale offshore wind farm. for The collection size of For the 1st Typhoon The membership degree of cluster centers of q times the coordinates of the i-th wind turbine The accumulation of For the 1st Typhoon The membership degree of cluster centers of q The accumulation of powers, for The collection size.

4. The large-scale offshore wind farm power system dynamic decoupling planning method according to claim 1 is characterized in that: In step 2), a two-layer dynamic decoupling model for static and dynamic wind turbines in the loop is established, which includes an outer layer model for decoupling the wind turbines to the substation interval and an inner layer model for decoupling the wind turbines to the ring network cluster. The function expression of the outer layer model is: ， ， ， ， ， in, 、 and Connected to the substation section Wind turbine nodes for all wind turbines, static wind turbines and dynamic wind turbines The binary variable of the cable between and Connected to the substation section All wind turbines and static wind turbine nodes The binary variable of the cable between is the union, 、 、 and Substation section The set of all wind turbine nodes, substation nodes, static wind turbine nodes and dynamic wind turbine nodes, is the set of substation intervals, and are the upper and lower limits, and Connected to the substation section The binary variables of the cable between the substation node s and the wind turbine node i, and the binary variables of the cable between the wind turbine node j and the substation node s, Substation area The number of loops within Substation area The number of fans, is the capacity of a single fan, is the maximum allowable capacity of the cable; the function expression of the inner layer model is: ， ， ， ， ， in, 、 and Connected to the substation section and loop Cables for all fans, static and dynamic A binary variable, 、 and Connected to the substation section and loop Wind turbine nodes for all wind turbines, static wind turbines and dynamic wind turbines The binary variable of the cable between Substation area A collection of Substation area The loop set, 、 and Substation section and loop The set of all wind turbine nodes, static wind turbine nodes and dynamic wind turbine nodes, and Substation section Loop Wind turbine node i and wind turbine nodes j voltage.

5. The large-scale offshore wind farm power system dynamic decoupling planning method according to claim 1 is characterized in that: The multi-cable planning model under different operating conditions constructed in step 2) includes a medium voltage multi-cable model and a high voltage multi-cable model. The function expression of the medium voltage multi-cable model is: ， ， ， ， ， in, For medium voltage cable assembly, Substation area and loop The set of all wind turbine nodes, and Have positive and negative sort positions respectively Wind turbine node Binary variables for medium voltage cables between and Have positive and negative sort positions respectively Wind turbine node Binary variables for medium voltage cables between is the capacity of a single fan, The maximum allowed capacity of the cable. is the set of sorted positions of the medium voltage cables, and Wind turbine nodes The maximum sort position between and the optimal sort position The power flow energy of the cable under normal working conditions, is the index in the set of sorted positions of the medium voltage cable; The functional expression of the high-voltage multi-cable model is: ， ， in, Substation area The total energy of all fans in the 、 Substation section The collection of all wind turbine nodes and substation wind turbine nodes, Substation area The energy of each fan, Substation area A collection of is the sorted position set of high-voltage cables, Substation area Has positive sort position The binary variable of the high voltage cable, Substation area Has positive sort position The maximum capacity allowed for high voltage cable operation.

6. The large-scale offshore wind farm power system dynamic decoupling planning method according to claim 1 is characterized in that: The functional expression of the objective function of the bilateral ring network collection system topology optimization problem in step 2) with the minimum total investment and operating cost is: ， ， ， ， ， ， ， ， in, The objective function of the topology optimization problem of bilateral ring network collection system is to minimize the total investment and operating costs. The cost of key equipment, For high and medium voltage cable costs, is the total service life of the cable in years, is the cable power loss cost for one year, is the annual inflation rate of energy prices, t is the time, is the total number of hours in a year, is the power loss of high voltage and medium voltage cables; and They are respectively a collection of high voltage and medium voltage cables, is the sorted position set of high-voltage cables, Substation area Has positive sort position The binary variable of the high voltage cable, and are the linear cost coefficients of offshore substation and platform respectively, 、 Substation section Has positive sort position The rated power of the offshore substation and the platform to which the cable is connected, and are the constant terms of offshore substation and platform respectively, is the cost of high voltage cables, is the cost of medium voltage cables, and Substation section Has positive sort position The cost of high-voltage cables, wind turbine nodes The cost of medium voltage cables between and Substation section The length of the high-voltage cable and the wind turbine node The length of the medium voltage cable between and Substation section Has positive sort position The resistance of the high-voltage cable and the wind turbine node There is a positive sort position between The resistance of the medium voltage cable, For wind turbine nodes There is a positive sort position between Binary variable for medium voltage cables, For wind turbine nodes There is a negative sort position between Binary variable for medium voltage cables, For wind turbine nodes Take the positive sort position between , negative sort position The cost of the medium voltage cable in the maximum value, is the high voltage cable power loss, is the medium voltage cable power loss, Substation area The total energy of all fans in the is the capacity of a single fan, For wind turbine nodes Take the positive sort position between , negative sort position The resistance of the cable with the maximum value in step 2); when constructing the objective function of the bilateral ring network collection system topology optimization problem with the minimum total investment and operating cost and adding constraints, the constraints added are: ， in, For wind turbine nodes The cables between For wind turbine nodes The cables between For wind turbine nodes The cables between For wind turbine nodes The cables between is a collection of cross-cable pairs; when the bilateral ring network collection system topology optimization problem is converted into an integer linear programming problem in step 3), the medium voltage cable cost is and medium voltage cable power losses Linearization, and medium voltage cable costs and medium voltage cable power losses The linearized function expression is: ， ， in, A collection of medium voltage cables, is the set of sorted positions of the medium voltage cables, For wind turbine nodes A binary variable describing the product of the positive and negative sort positions of the medium voltage cable, For wind turbine nodes Take the positive sort position between , negative sort position The cost of the medium voltage cable in the maximum value, For wind turbine nodes The length of the medium voltage cable between For all A collection of For wind turbine nodes Active power on the cable between them.

7. A large-scale offshore wind farm power system dynamic decoupling planning system, comprising an interconnected microprocessor and a memory, characterized in that: The microprocessor is programmed or configured to execute the large-scale offshore wind farm power system dynamic decoupling planning method according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program or instruction stored therein, characterized in that: The computer program or instruction is programmed or configured to execute the large-scale offshore wind farm power system dynamic decoupling planning method according to any one of claims 1 to 6 through a processor.

9. A computer program product comprising a computer program or instructions, characterized in that The computer program or instruction is programmed or configured to execute the large-scale offshore wind farm power system dynamic decoupling planning method according to any one of claims 1 to 6 through a processor.

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