A collaborative optimization design method for the layout of power generation units and collection cables of a water-based photovoltaic power station collection system

Abstract

The present invention relates to a collaborative optimization design method for the layout of power generation units and collector cables of a water surface photovoltaic power station's power collection system. Compared with the prior art, the method solves the problem of difficulty in achieving a reasonable layout of power generation units and a reasonable laying of collector cables in a water surface photovoltaic power station. The method comprises the following steps: obtaining the conditions of the water area and photovoltaic devices of a water surface photovoltaic power station; optimizing the layout of power generation units of a water surface photovoltaic power station; optimizing the collector cables of a water surface photovoltaic power station; collaborative optimization of the layout of power generation units and collector cables of a water surface photovoltaic power station; and obtaining the results of laying the layout of power generation units and collector cables of a water surface photovoltaic power station. The present invention proposes optimization of the layout of power generation units of a water surface photovoltaic power station's power collection system based on the minimum power generation unit, as well as optimization of collector cables that integrates fuzzy C-means clustering, DM-MSTP algorithm, and automatic selection of collector cables. Furthermore, the particle swarm algorithm is used to collaboratively optimize the layout of power generation units and collector cables of a water surface photovoltaic power station with the total investment cost of the water surface photovoltaic power station as the target, thereby achieving the purpose of collaborative optimization design of the layout of power generation units and collector cables of a water surface photovoltaic power station's power collection system.

Description

Technical Field

[0001] The present invention relates to the technical field of water surface photovoltaic power stations, and in particular to a collaborative optimization design method for the layout of power generation units and collection cables of a power collection system of a water surface photovoltaic power station. Background Art

[0002] The cost of the power collection system of a water-based photovoltaic power station accounts for a significant proportion of the investment in the station. The optimization of the power collection system includes two parts: optimization of the layout of photovoltaic power generation units and optimization of the power collection cables.

[0003] Optimizing the layout of PV power generation units not only affects the AC-side capacity configuration and AC / DC cable engineering requirements of the PV power generation system, but also its overall efficiency. Compared to optimizing the layout of terrestrial PV power generation units, optimizing the layout of water-based PV power generation units is more complex, limited not only by the shape of the water surface but also by operational and maintenance access. These access requirements have their own impacts, including the installed capacity, capacity ratio, unit shape, and cable routing of the PV power generation units.

[0004] Existing research results on surface photovoltaic power stations focus on the design of mooring systems for surface photovoltaic power stations. Limited research has been conducted on the design and optimization of collection systems for surface photovoltaic power stations. Given that both surface photovoltaic power stations and offshore wind farms involve collection system optimization, this study draws on research reports on offshore wind farm collection system optimization. In offshore wind farm collection system optimization, heuristic algorithms and graph theory-based methods are often used to optimize the length of collection cables. Sequential optimization, combinatorial optimization, and two-layer optimization methods are used for the coordinated optimization of offshore wind farm microsite selection and collection cables. Regarding the layout of power generation units and the laying of collection cables for surface photovoltaic power station collection systems, these methods are still based on manual design or BIM software design, resulting in low automation, poor economic efficiency, and time-consuming design.

[0005] Therefore, the coordinated optimization design of the layout of power generation units and the laying of collector cables in the water surface photovoltaic power station collection system has become a key technical problem that needs to be solved urgently in the field of water surface photovoltaic power station technology. Summary of the Invention

[0006] The purpose of the present invention is to solve the defect in the existing technology that it is difficult to achieve a reasonable layout of the power generation units and the reasonable laying of the collection cables of the water surface photovoltaic power station, and to provide a collaborative optimization design method for the layout of the power generation units and the collection cables of the collection system of the water surface photovoltaic power station to solve the above problems.

[0007] In order to achieve the above object, the technical solution of the present invention is as follows:

[0008] A method for collaboratively optimizing the layout of power generation units and the design of power collection cables in a water surface photovoltaic power station power collection system comprises the following steps:

[0009] 11) Obtaining water area and photovoltaic device information: Obtain basic data on the water area for site selection of the water surface photovoltaic power station and the photovoltaic devices of the water surface photovoltaic power station. The photovoltaic devices of the water surface photovoltaic power station include inverters, photovoltaic panels, and box-type substations. Pre-calculate the basic data to obtain the number of photovoltaic modules in the minimum power generation unit and the length and width of the photovoltaic modules;

[0010] 12) Optimization of the layout of power generation units of water surface photovoltaic power station: the minimum number of power generation units N in the water surface photovoltaic power generation unit p.unit The aspect ratio K = L / W is used as the optimization variable, the investment cost of the power generation unit of the water surface photovoltaic power station is used as the objective function, and the water conditions, power generation capacity, and operation and maintenance channel constraints are used as constraints. Based on the minimum power generation unit, the particle swarm algorithm is used to optimize the layout of the power generation unit of the water surface photovoltaic power station to obtain the layout of the power generation unit of the water surface photovoltaic power station; the box-type substation is set to be arranged in the middle of the power generation unit of the water surface photovoltaic power station, and the coordinates of the box-type substation are obtained according to the layout of the power generation unit of the water surface photovoltaic power station;

[0011] 13) Optimization of collector cables for water-surface photovoltaic power stations: Based on the coordinates of box-type substations, an improved fuzzy C clustering method, DM-MSTP, and automatic selection of collector cables are integrated to optimize the cables between box-type substations in water-surface photovoltaic power stations, and the laying method of collector cables for water-surface photovoltaic power stations is obtained;

[0012] 14) Collaborative optimization of the layout of power generation units and collection cables in the power collection system of a water-surface photovoltaic power station: Calculate the total investment cost of the power collection system of a water-surface photovoltaic power station and use a particle swarm algorithm to iteratively optimize until the convergence conditions are met to determine the minimum number of power generation units N in the water-surface photovoltaic power generation unit. p.unit and the length-to-width ratio K=L / W, the optimal total investment cost of the water surface photovoltaic power station collection system, the layout of the water surface photovoltaic power station power generation units, and the overall layout of the collection cable laying are obtained.

[0013] The layout optimization of the power generation units of the water surface photovoltaic power station comprises the following steps:

[0014] 21) Determine the capacity of the water surface photovoltaic power generation unit based on the definition of the minimum power generation unit:

[0015] A photovoltaic power generation unit consisting of an inverter and n photovoltaic modules is defined as the minimum power generation unit. Using the PVsyst software, the geographical parameters of the water surface photovoltaic power station, the photovoltaic panel model, and the inverter model are input. The obtained number of photovoltaic modules in parallel is divided by the number of inverters. The quotient is the number of photovoltaic modules in the minimum power generation unit, n.

[0016] The minimum power generation unit capacity is expressed as:

[0017] P min =P INV ×λ (1)

[0018] Where: P min is the minimum power generation unit capacity, P INV is the rated power of the inverter, and λ is the capacity ratio, which is calculated based on the PVsyst software;

[0019] The power generation capacity of the water surface photovoltaic power generation unit is expressed as:

[0020] P=P min ×N p.unit (2)

[0021] Where: P is the power generation capacity of the water surface photovoltaic power generation unit, N p.unit is the minimum number of power generation units;

[0022] 22) Define the aspect ratio of the water surface photovoltaic power generation unit based on the arrangement of photovoltaic modules:

[0023] Arrange the photovoltaic modules of the power generation unit of the water surface photovoltaic power station into a rows and b columns. Suppose there are K b PV panels, each column has K a PV panels, there are:

[0024] K a ×K b =n×N p.unit (3)

[0025] The length and width of the power generation unit of the water surface photovoltaic power station are defined as:

[0026] L=L unit ×K b (4)

[0027] W=W unit ×K a (5)

[0028] Where: L and W represent the length and width of the water surface photovoltaic power generation unit respectively, L unit 、W unit Respectively represent the length and width of each photovoltaic module, and their values ​​are determined by the selected photovoltaic panel model, photovoltaic tilt angle, and spacing between panels;

[0029] 23) Establish an optimization model for the layout of power generation units of a water surface photovoltaic power station as follows:

[0030] The minimum number of power generation units N p.unit The aspect ratio K = L / W of the water surface photovoltaic power generation unit is set as the optimization variable, and the minimum investment cost of the water surface photovoltaic power generation unit is set as the optimization target. The objective function is expressed as:

[0031] min C unit (6)

[0032] C unit =C p.unit ×N p.unit ×N unit (7)

[0033] Where: C unit is the investment cost of the power generation unit, C p.unit is the minimum power generation unit cost, N unit is the number of power generation units of the water surface photovoltaic power station;

[0034] Considering that the total capacity of the power generation units of a water-surface photovoltaic power station must meet the power generation capacity constraints, all power generation units of the water-surface photovoltaic power station cannot exceed the water boundary, there is no intersection or overlap between power generation units, and the width reserved for operation and maintenance channels must meet national standards; therefore, the constraints that need to be met for the layout optimization of the power generation units of a water-surface photovoltaic power station are:

[0035]

[0036] Where: P total is the power generation capacity, x i,v 、y i,v They are the horizontal and vertical coordinates of each vertex of the water surface photovoltaic power generation unit, where i = 1, 2…m, represents the serial number of the water surface photovoltaic power generation unit, v = 1, 2, 3, 4 represents the vertex serial number of the water surface photovoltaic power generation unit, and x min 、x max 、y min 、y max Represent the boundary coordinates of the water area, L space The width of the operation and maintenance channel;

[0037] 24) In steps 21)-23), the particle swarm algorithm is used to optimize the layout of the power generation units of the water surface photovoltaic power station to obtain the initial version of the layout of the power generation units of the water surface photovoltaic power station, and the box-type substation is arranged in the middle of the power generation units of the water surface photovoltaic power station. The coordinates of the box-type substation are obtained according to the layout of the power generation units of the water surface photovoltaic power station.

[0038] The optimization of the collector cables of the water surface photovoltaic power station comprises the following steps:

[0039] 31) Calculation of the load-bearing capacity of a single collector cable:

[0040] The maximum number of box-type substations supported by a single collector cable is expressed as:

[0041]

[0042] Where: N max is the maximum number of box-type substations that a single collector cable can withstand, U is the collector system voltage, I maxis the maximum current carrying capacity of the collector cable, cosθ is the power factor, P N is the power generation power of the power generation unit;

[0043] 32) The improved fuzzy C clustering algorithm is used to partition the box-type substation. The improved fuzzy C clustering algorithm is as follows:

[0044] 321) The improved fuzzy C clustering mathematical model is established as follows:

[0045]

[0046] Constraints:

[0047]

[0048] Where u ij It represents the membership of the ith box-type substation to the jth cluster. The serial number of the box-type substation is consistent with the serial number of the water surface photovoltaic power generation unit. g is the membership factor, and p i is the coordinate of the ith box-type substation, c j is the coordinate of the jth cluster center, m is the number of box-type substations, and c is the number of cluster centers;

[0049] 322) Randomly generate the membership degree u according to the constraints of formula (11) ij ;

[0050] 323) Iterate and update the cluster center;

[0051] 324) Calculate radial distance: The coordinates of the box-type substation are represented in the form of a universal formula. The coordinate vector of the box-type substation is recorded as:

[0052]

[0053] The coordinate vector of the cluster center is recorded as:

[0054]

[0055] Calculate the angle between the box-type substation and the cluster center, and we have:

[0056]

[0057] Determine whether the angle θ is greater than 90°. If it is greater than 90°, set the distance d from the box-type substation to the cluster center vector to infinity. Otherwise, update the distance according to formula (15):

[0058]

[0059] 325) Update membership:

[0060]

[0061] 326) Determine the termination condition and jump out of the loop:

[0062] |u ij (t)-u ij (t-1)|≤σ (17)

[0063] σ is the set termination condition judgment value, and the membership degree u of the current iteration is ij If the error of the value of relative to the previous iteration does not exceed the termination judgment value, the iteration ends, otherwise it proceeds to the next iteration;

[0064] 327) Determine the minimum number of partitions and partition:

[0065] Based on the improved fuzzy C clustering algorithm, the initial number of partitions is set to 1, and it is gradually increased until it meets the equation (9). At this time, it is the minimum number of partitions that meets the current carrying capacity constraint. This number is recorded as N, and the box-type substation is partitioned with N as the number of partitions.

[0066] 33) Construct a distance matrix: After partitioning the box-type substations with the onshore booster station as the radial center, calculate the distances between the box-type substations in each partition and between the box-type substations and the onshore booster station to construct a distance matrix;

[0067] The distance calculation formula is expressed as:

[0068]

[0069] Where: L d is the element in the distance matrix of the dth partition; are the horizontal and vertical coordinates of the box-type substation in the d-th zone, where d = 1, 2...N, i = 1, 2...m;

[0070] 34) Use the DM-MSTP algorithm to optimize the cable length of each partition: The DM-MSTP algorithm collects data through the adjacency matrix, where the Min-Columns list is used to collect column minimum values, and the MST-Path list is used to archive the minimum spanning path;

[0071] 341) Find the minimum element of each column and record it in the Min-Columns list, select the maximum element, and then save the connection method corresponding to the maximum element in the adjacency matrix into the MST-path;

[0072] 342) Delete the connected elements in the adjacency matrix and mark the rows and columns of the connected elements. Find the minimum element in each column among the marked elements and record it in the Min-Columns list. Select the minimum value and save the connection method in the MST-path.

[0073] 343) If all elements are connected, go to 344), otherwise return to 342);

[0074] 344) Generate a minimum spanning tree according to the connection mode saved in the MST-path;

[0075] 35) Optimizing the selection of collector cables for water-based photovoltaic power stations based on an automatic selection algorithm for collector cables:

[0076] 351) The investment cost function of the collector cable is established:

[0077] The established collector cable investment cost function is:

[0078]

[0079] Where: C cable is the investment cost of the collector cable, N is the number of partitions of the fuzzy C cluster, m is the number of box-type substations in each partition, L C.q is the length of the qth collector cable, C C.q (φ) is the cost per kilometer of the qth collector cable, and φ is the cross-sectional area of ​​the collector cable;

[0080] The automatic selection of the collector cable must meet its capacity constraints. That is, the maximum carrying capacity of the selected collector cable model must be greater than or equal to the sum of the capacities of the downstream box-type substations. The constraints that the automatic selection of the collector cable must meet are:

[0081]

[0082] Where Q is the number of collection cables in the collection system, S q·max represents the maximum apparent power in the qth collector cable, and F represents the number of box-type substations in the qth collector cable;

[0083]

[0084] Where, I represents the current carrying capacity of the collector cable; P N is the rated power of the water surface photovoltaic power generation unit; U is the voltage level of the collection system; Power factor; when the current carrying capacity of a box-type substation carried by a collector cable exceeds its limit, it will automatically change to another larger type of collector cable;

[0085] 352) Based on the optimization results of the collector cable length, a collector cable connection table is generated for each partition. The first row in the table represents the starting node, the second row represents the ending node, and the third row represents the distance between the corresponding starting node and the ending node;

[0086] 353) Determine whether the number of node appearances is less than or equal to 2. If the number of node appearances is less than or equal to 2, the program automatically determines that the topology is a chain connection, and then performs power flow calculation according to equations (9), (20), and (21), selects the collector cable, and jumps out of the step. Otherwise, enter 354);

[0087] 354) If it is determined to be a non-chain connection topology, select a node other than the boost station on the road that appears 1 times;

[0088] In the collector cable connection table, find the connection mode of the node with the number of occurrences of 1, record it as col, and the corresponding position in the table as s. Let r = s-1. If Link(1, s) = Link(2, r), where Link(1, s) represents the element in the first row and s column of the table, and Link(2, r) represents the element in the second row and r column, it means it is a series connection mode, then s-1, r-1; otherwise r-1, continue to judge Link(1, s) = Link(2, r-1) and select according to the constraints of formulas (9), (20), and (21);

[0089] Traverse forward until the sequence number is 1, end the traversal process, record the number of box-type substations on each tree branch, and save the branch node sequence number connected to the tree branch respectively, and go to step 355);

[0090] 355) Select a node with a number of occurrences greater than or equal to 3, indicating that a branch occurs at this point; find a node with a number of occurrences greater than or equal to 3 in the collector cable connection table, record the corresponding position in the table as u, and traverse from back to front to calculate the number of downstream box-type substations loaded by this node. Add the node itself to determine the number of box-type substations loaded by its upstream collector line; the selection rules for the trunk are the same as 353). If it is in series connection, increase the number of box-type substations loaded by the record, and so on, traversing forward until the sequence number is 1;

[0091] 356) Calculate C based on the selection results and the price of the collector cable cable , and obtain the laying method of collector cables for water surface photovoltaic power stations.

[0092] The collaborative optimization of the layout of power generation units and the collection cables of the water surface photovoltaic power station collection system includes the following steps:

[0093] 41) Based on the available water area, restricted water area, and operation and maintenance access requirements, determine the area where the power generation units of the water surface photovoltaic power station can be laid. Based on the photovoltaic panel model, inverter model, and cable model, use PVsyst software to calculate the number of series modules, the number of parallel strings, and the capacity ratio parameters.

[0094] 42) Taking the minimum investment cost of power generation units in water surface photovoltaic power station as the optimization goal, the minimum number of power generation units N p.unit The initial value of the minimum number of power generation units is set to 1, and the initial value of the power generation unit aspect ratio is set to K. a =1, in order to satisfy the constraint condition of formula (8), the particle swarm algorithm is used to implement the optimization and judge whether the total capacity of the power generation unit meets the power generation capacity requirement. If it does, the power generation unit plane layout diagram is generated, and then the box-type substation is arranged in the center of the power generation unit to generate the coordinates of the box-type substation; if it does not meet the requirement, N p.unit =N p.unit +1, K=K+1 and repeat 42);

[0095] 43) Based on the constraints of the collector cable current carrying capacity and the principle of the minimum number of partitions, the improved fuzzy C clustering algorithm is first used to partition the box-type substation with the substation as the base point. Then, the DM-MSTP algorithm is used to optimize the collector cable length within each partition. Finally, the automatic cable selection algorithm is used to automatically select the cable for chain and tree topologies to achieve the optimal selection of the collector cable.

[0096] 44) Calculate the total investment cost C of the water surface photovoltaic power station collection system, C = C unit +C cable And use the particle swarm algorithm to iteratively optimize. When the fitness value reaches the convergence condition within the preset number of iterations, that is, the total investment cost of the water surface photovoltaic power station collection system is no longer significantly reduced, the overall layout diagram of the water surface photovoltaic power station collection system and the optimal total investment cost of the collection system are output; if not, then N p.unit =N p.unit +1, K=K+1 and repeat 42)-44) until the convergence condition is met.

[0097] Beneficial effects

[0098] With regard to the layout of power generation units and the laying of collector cables in the collector system of a water-surface photovoltaic power station, existing designs based on manual design or BIM software suffer from low automation, poor economic efficiency, and time-consuming design. Therefore, the collaborative optimization design problem of the layout of power generation units and the laying of collector cables in a water-surface photovoltaic power station has become a key technical issue that urgently needs to be addressed in the field of water-surface photovoltaic power station technology. In order to achieve the collaborative optimization of the layout of photovoltaic power generation units and the laying of collector cables while satisfying the constraints of water surface shape and operation and maintenance channels, a layout optimization method for power generation units in the collector system of a water-surface photovoltaic power station based on the minimum power generation unit is proposed, as well as a collector cable optimization method that integrates fuzzy C-means clustering, DM-MSTP algorithm, and automatic collector cable selection. A particle swarm algorithm is then used to collaboratively optimize the layout of power generation units and the laying of collector cables in a water-surface photovoltaic power station based on the total investment cost of the water-surface photovoltaic power station, achieving the goal of collaborative optimization design of the layout of power generation units and the laying of collector cables in the collector system of a water-surface photovoltaic power station.

[0099] The present invention does not require manual layout of power generation units. While ensuring smooth operation and maintenance channels and meeting the total power generation requirements, it can adaptively respond to different complex water conditions, fully utilize the water space, and achieve the optimal economic goal. The present invention addresses the optimization problem of collector cables in water-based photovoltaic power stations and proposes a method that integrates improved fuzzy C clustering, DM-MSTP algorithm, and automatic selection of collector cables. Compared to manual wiring and manual selection, which are time-consuming, labor-intensive, and do not guarantee economic optimization, this method achieves automatic wiring and selection of collector cables while ensuring that the collector cables do not cross and do not exceed the current-carrying capacity limit of the collector cables. This greatly improves design efficiency and effectively saves investment costs for collector cables.

[0100] The present invention proposes a collaborative optimization design method for the layout of power generation units and collector cables of a water surface photovoltaic power station collection system. Compared with the traditional design method of first arranging the power generation units of a water surface photovoltaic power station and then optimizing the length of the collector cables and manually selecting the collector cables, the proposed method first optimizes the layout of power generation units of a water surface photovoltaic power station collection system based on the minimum power generation unit, and then integrates fuzzy C-means clustering, DM-MSTP algorithm and automatic selection of collector cables to automatically optimize the length and selection of collector cables. Finally, a particle swarm algorithm is used to collaboratively optimize the layout of power generation units and collector cables of a water surface photovoltaic power station collection system with the total investment cost of the water surface photovoltaic power station as the target. This method not only overcomes the shortcomings of low automation, poor economy and time-consuming design of existing collection system designs of water surface photovoltaic power stations, but also can effectively avoid falling into local optimal solutions, thereby effectively saving economic costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] Figure 1 is a method sequence diagram of the present invention;

[0102] Figure 2This is a flow chart of the collaborative optimization scheme for the layout of power generation units and the collection cables of the water surface photovoltaic power station collection system of the present invention;

[0103] Figure 3 An optimization diagram of the length of the collector cables of a water surface photovoltaic power station generated by an embodiment of the present invention;

[0104] Figure 4 This is an optimized diagram of the power generation unit layout and collection cables of the water surface photovoltaic power station collection system generated by an embodiment of the present invention. DETAILED DESCRIPTION

[0105] In order to provide a further understanding and appreciation of the structural features and effects achieved by the present invention, a detailed description is provided with reference to preferred embodiments and accompanying drawings as follows:

[0106] like Figure 1 As shown, the present invention provides a method for collaboratively optimizing the layout of power generation units and the collection cables of a water surface photovoltaic power station collection system, comprising the following steps:

[0107] The first step is to obtain the water conditions and photovoltaic device conditions: obtain the basic data of the water area for the site selection of the water surface photovoltaic power station and the photovoltaic devices of the water surface photovoltaic power station. The photovoltaic devices of the water surface photovoltaic power station include inverters, photovoltaic panels and box-type substations. Pre-calculate the basic data to obtain the number of photovoltaic modules in the minimum power generation unit and the length and width of the photovoltaic modules.

[0108] The second step is to optimize the layout of the power generation units of the water surface photovoltaic power station: the minimum number of power generation units N in the water surface photovoltaic power generation unit p.unit The optimization variables are the length-to-width ratio K = L / W, the investment cost of the generating units of a water surface photovoltaic power station is the objective function, and the water conditions, power generation capacity, and operation and maintenance access constraints are the constraints. Based on the definition of the minimum generating unit, the capacity of the generating units of the water surface photovoltaic power station is determined. The particle swarm optimization algorithm is then used to optimize the layout of the generating units of the water surface photovoltaic power station. The box-type substation is set to be located between the generating units of the water surface photovoltaic power station, and the coordinates of the box-type substation are obtained based on the generating unit layout of the water surface photovoltaic power station. Compared with the manual generation unit layout of the water surface photovoltaic power station, this method can automatically determine the optimal generating unit capacity and length-to-width ratio, fully utilizing the water area, reducing design time, and saving economic costs.

[0109] (1) Determine the capacity of the water surface photovoltaic power generation unit based on the definition of the minimum power generation unit:

[0110] The photovoltaic power generation unit consisting of an inverter and n photovoltaic modules is defined as the minimum photovoltaic power generation unit. Based on the PVsyst software, the geographical parameters of the water surface photovoltaic power station, the photovoltaic panel model, and the inverter model are input. The obtained number of photovoltaic modules in parallel is divided by the number of inverters. The quotient is the number of photovoltaic modules in the minimum power generation unit, n.

[0111] The minimum power generation unit capacity is expressed as:

[0112] P min =P INV ×λ (1)

[0113] Where: P min is the minimum power generation unit capacity, P INV is the rated power of the inverter, and λ is the capacity ratio, which is calculated based on the PVsyst software;

[0114] The power generation capacity of the water surface photovoltaic power generation unit is expressed as:

[0115] P=P min ×N p.unit (2)

[0116] Where: P is the power generation capacity of the water surface photovoltaic power generation unit, N p.unit is the minimum number of power generation units.

[0117] (2) Define the aspect ratio of the water surface photovoltaic power generation unit based on the photovoltaic group arrangement method:

[0118] Arrange the photovoltaic modules of the power generation unit of the water surface photovoltaic power station into a rows and b columns. Suppose there are K b PV panels, each column has K a PV panels, there are:

[0119] K a ×K b =n×N p.unit (3)

[0120] The length and width of the power generation unit of the water surface photovoltaic power station are defined as:

[0121] L=L unit ×K b (4)

[0122] W=W unit ×K a (5)

[0123] Where: L and W represent the length and width of the water surface photovoltaic power generation unit respectively, L unit 、W unit Respectively represent the length and width of each photovoltaic module, and their values ​​are determined by the selected photovoltaic panel model, photovoltaic tilt angle, and spacing between panels.

[0124] (3) Establish an optimization model for the layout of power generation units of a water surface photovoltaic power station as shown below:

[0125] The minimum number of power generation units N p.unitThe aspect ratio K = L / W of the water surface photovoltaic power generation unit is set as the optimization variable, and the minimum investment cost of the water surface photovoltaic power generation unit is set as the optimization target. The objective function is expressed as:

[0126] min C unit (6)

[0127] C unit =C p.unit ×N p.unit ×N unit (7)

[0128] Where: C unit is the investment cost of the power generation unit, C p.unit is the minimum power generation unit cost, N unit is the number of power generation units of the water surface photovoltaic power station;

[0129] Considering that the total capacity of the power generation units of the water surface photovoltaic power station meets the power generation capacity constraints, all power generation units of the water surface photovoltaic power station cannot exceed the water boundary, there is no intersection or overlap between power generation units, and the width reserved for operation and maintenance channels should meet the national standards; therefore, the constraints satisfied by the layout optimization of the power generation units of the water surface photovoltaic power station are as follows:

[0130]

[0131] Where: P total is the power generation capacity, x i,v 、y i,v They are the horizontal and vertical coordinates of each vertex of the water surface photovoltaic power generation unit, where i = 1, 2…m, represents the serial number of the water surface photovoltaic power generation unit, v = 1, 2, 3, 4 represents the vertex serial number of the water surface photovoltaic power generation unit, and x min 、x max 、y min 、y max Represent the boundary coordinates of the water area, L space The width of the operation and maintenance channel;

[0132] (4) In steps (1)-(3), the particle swarm algorithm is used to optimize the layout of the power generation units of the water surface photovoltaic power station to obtain the initial version of the layout of the power generation units of the water surface photovoltaic power station, and the box-type substation is arranged in the middle of the power generation units of the water surface photovoltaic power station. The coordinates of the box-type substation are obtained according to the layout of the power generation units of the water surface photovoltaic power station.

[0133] The third step involves optimizing the collector cables for water-based photovoltaic power stations. Based on the coordinates of the box-type substations, an improved fuzzy C-clustering algorithm, DM-MSTP, and an automatic collector cable selection method are integrated to optimize the cables between the box-type substations in the water-based photovoltaic power station. This ensures that the collector cables do not cross each other and do not exceed the cable current carrying capacity. This enables automated laying of collector cables for water-based photovoltaic power stations, saving time and money.

[0134] (1) Calculation of the load-bearing capacity of a single collector cable:

[0135] The maximum number of box-type substations supported by a single collector cable is expressed as:

[0136]

[0137] Where: N max is the maximum number of box-type substations that a single collector cable can withstand, U is the collector system voltage, I max is the maximum current carrying capacity of the collector cable, cosθ is the power factor, P N is the power generation capacity of the power generation unit.

[0138] (2) The improved fuzzy C clustering algorithm is used to partition the box-type substation. The improved fuzzy C clustering algorithm is as follows:

[0139] A1) The improved fuzzy C clustering mathematical model is established as follows:

[0140]

[0141] Constraints:

[0142]

[0143] Where u ij It represents the membership of the ith box-type substation to the jth cluster. The serial number of the box-type substation is consistent with the serial number of the water surface photovoltaic power generation unit. g is the membership factor, and p i is the coordinate of the ith box-type substation, c j is the coordinate of the jth cluster center, m is the number of box-type substations, and c is the number of cluster centers;

[0144] A2) Randomly generate the membership degree u according to the constraint condition of formula (11) ij ;

[0145] A3) Iterate and update the cluster center;

[0146] A4) Calculation of radial distance: The coordinates of the box-type substation are represented by a universal formula. The coordinate vector of the box-type substation is recorded as:

[0147]

[0148] The coordinate vector of the cluster center is recorded as:

[0149]

[0150] Calculate the angle between the box-type substation and the cluster center, and we have:

[0151]

[0152] Determine whether the angle θ is greater than 90°. If it is greater than 90°, set the distance d from the box-type substation to the cluster center vector to infinity. Otherwise, update the distance according to formula (15):

[0153]

[0154] A5) Update membership:

[0155]

[0156] A6) Determine the termination condition and exit the loop:

[0157] |u ij (t)-u ij (t-1)|≤σ (17)

[0158] σ is the set termination condition judgment value, and the membership degree u of the current iteration is ij If the error of the value of relative to the previous iteration does not exceed the termination judgment value, the iteration ends, otherwise it proceeds to the next iteration;

[0159] A7) Determine the minimum number of partitions and partition them:

[0160] Based on the improved fuzzy C clustering algorithm, the initial number of partitions is set to 1 and gradually increases until it satisfies equation (9). At this point, the minimum number of partitions that meets the current carrying capacity constraint is recorded as N, and the box-type substation is partitioned with N as the number of partitions.

[0161] (3) Constructing a distance matrix: After partitioning the box-type substations with the onshore booster station as the radial center, calculate the distances between the box-type substations in each partition and between the box-type substations and the onshore booster station to construct a distance matrix;

[0162] The distance calculation formula is expressed as:

[0163]

[0164] Where: L d is the element in the distance matrix of the dth partition; are the horizontal and vertical coordinates of the box-type substation in the d-th zone, where d = 1, 2...N, i = 1, 2...m.

[0165] (4) Use the DM-MSTP algorithm to optimize the cable length of each partition: The DM-MSTP algorithm collects data through the adjacency matrix, where the Min-Columns list is used to collect column minimum values, and the MST-Path list is used to archive the minimum generation path;

[0166] B1) Find the minimum element in each column and record it in the Min-Columns list. Select the maximum element and then save the connection method corresponding to the maximum element in the adjacency matrix into the MST-path.

[0167] B2) Delete the connected elements in the adjacency matrix and mark the rows and columns of the connected elements. Find the minimum element in each column among the marked elements and record it in the Min-Columns list. Select the minimum value and save the connection method in the MST-path.

[0168] B3) If all elements are connected, proceed to 344), otherwise return to 342);

[0169] B4) Generate a minimum spanning tree according to the connection mode saved in the MST-path.

[0170] (5) Automatic selection of collector cables to optimize the selection of collector cables for water-surface photovoltaic power stations:

[0171] C1) Establishment of the investment cost function of the collector cable:

[0172] The established collector cable investment cost function is:

[0173]

[0174] Where: C cable is the investment cost of the collector cable, N is the number of partitions of the fuzzy C cluster, m is the number of box-type substations in each partition, L C.q is the length of the qth collector cable, C C.q (φ) is the cost per kilometer of the qth collector cable, and φ is the cross-sectional area of ​​the collector cable;

[0175] The automatic selection of the collector cable must meet its capacity constraints. That is, the maximum carrying capacity of the selected collector cable model must be greater than or equal to the sum of the capacities of the downstream box-type substations. The constraints that the automatic selection of the collector cable must meet are:

[0176]

[0177] Where Q is the number of collection cables in the collection system, S q·max represents the maximum apparent power in the qth collector cable, and F represents the number of box-type substations in the qth collector cable;

[0178]

[0179] Where, I represents the current carrying capacity of the collector cable; P N is the rated power of the water surface photovoltaic power generation unit; U is the voltage level of the collection system; Power factor; when the current carrying capacity of a box-type substation carried by a collector cable exceeds its limit, it will automatically change to another larger type of collector cable;

[0180] C2) Based on the optimization results of the collector cable length, a collector cable connection table is generated for each partition. The first row in the table represents the starting node, the second row represents the ending node, and the third row represents the distance between the corresponding starting node and the ending node;

[0181] C3) Determine whether the number of node appearances is less than or equal to 2. If the number of node appearances is less than or equal to 2, the program automatically determines that the topology is a chain connection, and then performs power flow calculation according to equations (9), (20), and (21), selects the collector cable, and jumps out of the step. Otherwise, it goes to C4);

[0182] C4) If it is determined to be a non-chain connection topology, select a node other than the boost station on the road that appears 1 times;

[0183] In the collector cable connection table, find the connection mode of the node with the number of occurrences of 1, record it as col, and the corresponding position in the table as s. Let r = s-1. If Link(1, s) = Link(2, r), where Link(1, s) represents the element in the first row and s column of the table, and Link(2, r) represents the element in the second row and r column, it means it is a series connection mode, then s-1, r-1; otherwise r-1, continue to judge Link(1, s) = Link(2, r-1) and select according to the constraints of formulas (9), (20), and (21);

[0184] Traverse forward until the sequence number reaches 1, end the traversal process, record the number of box-type substations on each tree branch, and save the branch node sequence number connected to the tree branch respectively, and go to step C5);

[0185] C5) Select a node with a number of occurrences greater than or equal to 3, indicating a branch at this point; Find a node with a number of occurrences greater than or equal to 3 in the collector cable connection table, record the corresponding position in the table as u, and traverse from back to front to calculate the number of downstream box-type substations loaded by this node. Add the node itself to determine the number of box-type substations loaded by its upstream collector line; The selection rules for the trunk are the same as C3). If it is a series connection, increase the number of box-type substations loaded by the record, and so on, traversing forward until the sequence number reaches 1;

[0186] C6) Calculate C based on the selection results and the price of the collector cable cable , and obtain the laying method of collector cables for water surface photovoltaic power stations.

[0187] The fourth step is to coordinate the optimization of the layout of the power generation units and the collection cables of the water surface photovoltaic power station: calculate the total investment cost of the water surface photovoltaic power station collection system, and use the particle swarm algorithm to iteratively optimize until the convergence conditions are met, and determine the minimum number of power generation units N in the water surface photovoltaic power generation unit. p.unit and the aspect ratio K = L / W, we can get the optimal total investment cost of the power generation unit and the collector cable of the water surface photovoltaic power station, the layout of the power generation unit of the water surface photovoltaic power station, and the overall layout of the collector cable. The implementation flow chart of the proposed scheme is as follows: Figure 2 shown.

[0188] (1) Based on the available water area, restricted water area, and operation and maintenance channel requirements, the area where the power generation units of the water surface photovoltaic power station can be laid is obtained; based on the photovoltaic panel model, inverter model, and cable model, the number of series components, the number of parallel strings, and the capacity ratio parameters are calculated using PVsyst software;

[0189] (2) Taking the minimum investment cost of the power generation unit of the water surface photovoltaic power station as the optimization goal, the minimum number of power generation units N p.unit The initial value of the minimum number of power generation units is set to 1, and the initial value of the power generation unit aspect ratio is set to K. a =1, in order to satisfy the constraint condition of formula (8), the particle swarm algorithm is used to implement the optimization and judge whether the total capacity of the power generation unit meets the power generation capacity requirement. If it does, the power generation unit plane layout diagram is generated, and then the box-type substation is arranged in the center of the power generation unit to generate the coordinates of the box-type substation; if it does not meet the requirement, N p.unit =N p.unit +1, K=K+1 and repeat (2);

[0190] (3) Based on the constraints of the current carrying capacity of the collector cable and the principle of the minimum number of partitions, the improved fuzzy C clustering algorithm is first used to partition the box-type substation with the substation as the base point. Then, the DM-MSTP algorithm is used to optimize the length of the collector cable in each partition. Then, the automatic cable selection algorithm is used to automatically select the cable for the chain and tree topologies to achieve the optimization of the collector cable selection.

[0191] (4) Calculate the total investment cost C of the water surface photovoltaic power station collection system, C = C unit +C cable And use the particle swarm algorithm to iteratively optimize. When the fitness value reaches the convergence condition within the preset number of iterations, that is, the total investment cost of the water surface photovoltaic power station collection system is no longer significantly reduced, the overall layout diagram of the water surface photovoltaic power station collection system and the optimal total investment cost of the collection system are output; if not, then N p.unit =N p.unit +1, K=K+1 and repeat (2)-(4) until the convergence condition is met.

[0192] To further illustrate the accuracy and reliability of the method of the present invention, a case study is conducted on the layout of power generation units and the laying of power collection cables for a water surface photovoltaic power station. The models and parameters of the relevant equipment of the water surface photovoltaic power station are shown in Table 1:

[0193] Table 1 Models and parameters of relevant equipment of water surface photovoltaic power station

[0194]

[0195]

[0196] Through the optimization of the power generation unit layout of the water surface photovoltaic power station, it was found that there were 70 power generation units in the water area, each of which contained 7 minimum power generation units, with an aspect ratio of 30:17. The box-type substation was assumed to be located at the center of the power generation unit, and the coordinates of the box-type substation were extracted, where coordinate 1 was the location of the road-level booster station. The 70 box-type substations were partitioned using the improved fuzzy C clustering method, and the 70 box-type transformers were divided into 5 zones. Figure 3 The five-pointed star in the figure represents the cluster center. The cable connection based on the DM-MSTP algorithm yields the following results: Figure 3 shown.

[0197] The specifications of the collector cables used in this water surface photovoltaic power station are shown in Table 2.

[0198] Table 2 Collector Cable Model Parameters

[0199]

[0200] When the combination of 7 minimum power generation units is used, the automatic selection table for the collector cable is shown in Table 3.

[0201] Table 3 Automatic selection table for collector cables

[0202]

[0203]

[0204] The water surface photovoltaic power station collection system studied is optimized through the layout of power generation units and collection cables. The optimization results are as follows: Figure 4 shown.

[0205] Table 4 Comparison of cable length and total investment of different methods

[0206]

[0207] Table 4 shows a comparison of cable length and total investment for different methods of collecting systems for water surface photovoltaic power stations. Compared with the manual design method, the sequential optimization method and the proposed method achieved a reduction of 3.4% and 24.2% in the length of the collecting cables, a reduction of 13.4% and 17.1% in the investment in collecting cables, and a reduction of 0.7% and 0.91% in the total investment, respectively. The proposed collaborative optimization design method for the layout of power generation units and collecting cables in the collecting system of a water surface photovoltaic power station is significantly superior to the manual design method and the sequential optimization method, and can reduce the investment cost for investors in the construction of water surface photovoltaic power stations.

[0208] Aiming at the design problem of the power collection system of a water-surface photovoltaic power station, the present invention proposes a collaborative optimization design method for the layout of power generation units and power collection cables of the power collection system of a water-surface photovoltaic power station, which integrates the minimum power generation unit, improved fuzzy C clustering, DM-MSTP algorithm, automatic selection algorithm for power collection cables, and particle swarm algorithm. While ensuring smooth operation and maintenance channels and meeting the total power generation amount, the method adaptively responds to different complex water conditions, fully utilizes the water area space, and achieves the goal of economic optimization; while ensuring that the power collection cables do not cross and do not exceed the current carrying capacity limit of the power collection cables, the method realizes automatic laying of the power collection cables while ensuring that the power collection cables do not cross and do not exceed the current carrying capacity limit of the power collection cables; the method significantly reduces the design time of the power collection system of a water-surface photovoltaic power station, effectively avoids falling into the local optimal solution, and effectively saves economic costs.

[0209] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A collaborative optimization design method for the layout of power generation units and collection cables of a water surface photovoltaic power station collection system, characterized in that: The following steps are involved: 11) Obtaining water area and photovoltaic device information: Obtain basic data on the water area for site selection of the water surface photovoltaic power station and the photovoltaic devices of the water surface photovoltaic power station. The photovoltaic devices of the water surface photovoltaic power station include inverters, photovoltaic panels, and box-type substations. Pre-calculate the basic data to obtain the number of photovoltaic modules in the minimum power generation unit and the length and width of the photovoltaic modules; 12) Optimization of the layout of power generation units of water surface photovoltaic power station: the minimum number of power generation units N in the water surface photovoltaic power generation unit p.unit The aspect ratio K = L / W is used as the optimization variable, the investment cost of the power generation unit of the water surface photovoltaic power station is used as the objective function, and the water conditions, power generation capacity, and operation and maintenance channel constraints are used as constraints. Based on the minimum power generation unit, the particle swarm algorithm is used to optimize the layout of the power generation unit of the water surface photovoltaic power station to obtain the layout of the power generation unit of the water surface photovoltaic power station; the box-type substation is set to be arranged in the middle of the power generation unit of the water surface photovoltaic power station, and the coordinates of the box-type substation are obtained according to the layout of the power generation unit of the water surface photovoltaic power station; 13) Optimization of collector cables for water-surface photovoltaic power stations: Based on the coordinates of box-type substations, an improved fuzzy C clustering method, DM-MSTP, and automatic selection of collector cables are integrated to optimize the cables between box-type substations in water-surface photovoltaic power stations, and the laying method of collector cables for water-surface photovoltaic power stations is obtained; 14) Collaborative optimization of the layout of power generation units and collection cables in the power collection system of a water-surface photovoltaic power station: Calculate the total investment cost of the power collection system of a water-surface photovoltaic power station and use a particle swarm algorithm to iteratively optimize until the convergence conditions are met to determine the minimum number of power generation units N in the water-surface photovoltaic power generation unit. p.unit and the length-to-width ratio K=L / W, the optimal total investment cost of the water surface photovoltaic power station collection system, the layout of the water surface photovoltaic power station power generation units, and the overall layout of the collection cable laying are obtained.

2. The method for collaborative optimization design of power generation unit layout and collection cables of a water surface photovoltaic power station collection system according to claim 1, characterized in that: The layout optimization of the power generation units of the water surface photovoltaic power station comprises the following steps: 21) Determine the capacity of the water surface photovoltaic power generation unit based on the definition of the minimum power generation unit: A photovoltaic power generation unit consisting of an inverter and n photovoltaic modules is defined as the minimum power generation unit. Using the PVsyst software, the geographical parameters of the water surface photovoltaic power station, the photovoltaic panel model, and the inverter model are input. The obtained number of photovoltaic modules in parallel is divided by the number of inverters. The quotient is the number of photovoltaic modules in the minimum power generation unit, n. The minimum power generation unit capacity is expressed as: P min =P INV ×λ (1) Where: P min is the minimum power generation unit capacity, P INV is the rated power of the inverter, and λ is the capacity ratio, which is calculated based on the PVsyst software; The power generation capacity of the water surface photovoltaic power generation unit is expressed as: P=P min ×N p.unit (2) Where: P is the power generation capacity of the water surface photovoltaic power generation unit, N p.unit is the minimum number of power generation units; 22) Define the aspect ratio of the water surface photovoltaic power generation unit based on the arrangement of photovoltaic modules: Arrange the photovoltaic modules of the power generation unit of the water surface photovoltaic power station into a rows and b columns. Suppose there are K b PV panels, each column has K a PV panels, there are: K a ×K b =n×N p.unit (3) The length and width of the power generation unit of the water surface photovoltaic power station are defined as: L=L unit ×K b (4) W=W unit ×K a (5) Where: L and W represent the length and width of the water surface photovoltaic power generation unit respectively, L unit 、W unit Respectively represent the length and width of each photovoltaic module, and their values ​​are determined by the selected photovoltaic panel model, photovoltaic tilt angle, and spacing between panels; 23) Establish an optimization model for the layout of power generation units of a water surface photovoltaic power station as follows: The minimum number of power generation units N p.unit The aspect ratio K = L / W of the water surface photovoltaic power generation unit is set as the optimization variable, and the minimum investment cost of the water surface photovoltaic power generation unit is set as the optimization target. The objective function is expressed as: my C unit (6) C unit =C p.unit ×N p.unit ×N unit (7) Where: C unit is the investment cost of the power generation unit, C p.unit is the minimum power generation unit cost, N unit is the number of power generation units of the water surface photovoltaic power station; Considering that the total capacity of the power generation units of a water-surface photovoltaic power station must meet the power generation capacity constraints, all power generation units of the water-surface photovoltaic power station cannot exceed the water boundary, there is no intersection or overlap between power generation units, and the width reserved for operation and maintenance channels must meet national standards; therefore, the constraints that need to be met for the layout optimization of the power generation units of a water-surface photovoltaic power station are: Where: P total is the power generation capacity, x i,v 、y i,v They are the horizontal and vertical coordinates of each vertex of the water surface photovoltaic power generation unit, where i = 1, 2…m, represents the serial number of the water surface photovoltaic power generation unit, v = 1, 2, 3, 4 represents the vertex serial number of the water surface photovoltaic power generation unit, and x min 、x max 、y min 、y max Represent the boundary coordinates of the water area, L space The width of the operation and maintenance channel; 24) In steps 21)-23), the particle swarm algorithm is used to optimize the layout of the power generation units of the water surface photovoltaic power station to obtain the initial version of the layout of the power generation units of the water surface photovoltaic power station, and the box-type substation is arranged in the middle of the power generation units of the water surface photovoltaic power station. The coordinates of the box-type substation are obtained according to the layout of the power generation units of the water surface photovoltaic power station.

3. The method for collaborative optimization design of power generation unit layout and collection cables of a water surface photovoltaic power station collection system according to claim 1, characterized in that: The optimization of the collector cables of the water surface photovoltaic power station comprises the following steps: 31) Calculation of the load-bearing capacity of a single collector cable: The maximum number of box-type substations supported by a single collector cable is expressed as: Where: N max is the maximum number of box-type substations that a single collector cable can withstand, U is the collector system voltage, I max is the maximum current carrying capacity of the collector cable, cosθ is the power factor, P N is the power generation power of the power generation unit; 32) The improved fuzzy C clustering algorithm is used to partition the box-type substation. The improved fuzzy C clustering algorithm is as follows: 321) The improved fuzzy C clustering mathematical model is established as follows: Constraints: Where u ij It represents the membership of the ith box-type substation to the jth cluster. The serial number of the box-type substation is consistent with the serial number of the water surface photovoltaic power generation unit. g is the membership factor, and p i is the coordinate of the ith box-type substation, c j is the coordinate of the jth cluster center, m is the number of box-type substations, and c is the number of cluster centers; 322) Randomly generate the membership degree u according to the constraints of formula (11) ij ; 323) Iterate and update the cluster center; 324) Calculate radial distance: The coordinates of the box-type substation are represented in the form of a universal formula. The coordinate vector of the box-type substation is recorded as: The coordinate vector of the cluster center is recorded as: Calculate the angle between the box-type substation and the cluster center, and we have: Determine whether the angle θ is greater than 90°. If it is greater than 90°, set the distance d from the box-type substation to the cluster center vector to infinity. Otherwise, update the distance according to formula (15): 325) Update membership: 326) Determine the termination condition and jump out of the loop: |u ij (t)-u ij (t-1)|≤σ (17) σ is the set termination condition judgment value, and the membership degree u of the current iteration is ij If the error of the value of relative to the previous iteration does not exceed the termination judgment value, the iteration ends, otherwise it proceeds to the next iteration; 327) Determine the minimum number of partitions and partition: Based on the improved fuzzy C clustering algorithm, the initial number of partitions is set to 1, and it is gradually increased until it meets the equation (9). At this time, it is the minimum number of partitions that meets the current carrying capacity constraint. This number is recorded as N, and the box-type substation is partitioned with N as the number of partitions. 33) Construct a distance matrix: After partitioning the box-type substations with the onshore booster station as the radial center, calculate the distances between the box-type substations in each partition and between the box-type substations and the onshore booster station to construct a distance matrix; The distance calculation formula is expressed as: Where: L d is the element in the distance matrix of the dth partition; are the horizontal and vertical coordinates of the box-type substation in the d-th zone, where d = 1, 2...N, i = 1, 2...m; 34) Use the DM-MSTP algorithm to optimize the cable length of each partition: The DM-MSTP algorithm collects data through the adjacency matrix, where the Min-Columns list is used to collect column minimum values, and the MST-Path list is used to archive the minimum spanning path; 341) Find the minimum element of each column and record it in the Min-Columns list, select the maximum element, and then save the connection method corresponding to the maximum element in the adjacency matrix into the MST-path; 342) Delete the connected elements in the adjacency matrix and mark the rows and columns of the connected elements. Find the minimum element in each column among the marked elements and record it in the Min-Columns list. Select the minimum value and save the connection method in the MST-path. 343) If all elements are connected, go to 344), otherwise return to 342); 344) Generate a minimum spanning tree according to the connection mode saved in the MST-path; 35) Optimizing the selection of collector cables for water-based photovoltaic power stations based on an automatic selection algorithm for collector cables: 351) The investment cost function of the collector cable is established: The established collector cable investment cost function is: Where: C cable is the investment cost of the collector cable, N is the number of partitions of the fuzzy C cluster, m is the number of box-type substations in each partition, L C.q is the length of the qth collector cable, C C.q (φ) is the cost per kilometer of the qth collector cable, and φ is the cross-sectional area of ​​the collector cable; The automatic selection of the collector cable must meet its capacity constraints. That is, the maximum carrying capacity of the selected collector cable model must be greater than or equal to the sum of the capacities of the downstream box-type substations. The constraints that the automatic selection of the collector cable must meet are: Where Q is the number of collection cables in the collection system, S q·max represents the maximum apparent power in the qth collector cable, and F represents the number of box-type substations in the qth collector cable; Where, I represents the current carrying capacity of the collector cable; P N is the rated power of the water surface photovoltaic power generation unit; U is the voltage level of the collection system; Power factor; when the current carrying capacity of a box-type substation carried by a collector cable exceeds its limit, it will automatically change to another larger type of collector cable; 352) Based on the optimization results of the collector cable length, a collector cable connection table is generated for each partition. The first row in the table represents the starting node, the second row represents the ending node, and the third row represents the distance between the corresponding starting node and the ending node; 353) Determine whether the number of node appearances is less than or equal to 2. If the number of node appearances is less than or equal to 2, the program automatically determines that the topology is a chain connection, and then performs power flow calculation according to equations (9), (20), and (21), selects the collector cable, and jumps out of the step. Otherwise, enter 354); 354) If it is determined to be a non-chain connection topology, select a node other than the boost station on the road that appears 1 times; In the collector cable connection table, find the connection mode of the node with the number of occurrences of 1, record it as col, and the corresponding position in the table as s. Let r = s-1. If Link(1, s) = Link(2, r), where Link(1, s) represents the element in the first row and s column of the table, and Link(2, r) represents the element in the second row and r column, it means it is a series connection mode, then s-1, r-1; otherwise r-1, continue to judge Link(1, s) = Link(2, r-1) and select according to the constraints of formulas (9), (20), and (21); Traverse forward until the sequence number is 1, end the traversal process, record the number of box-type substations on each tree branch, and save the branch node sequence number connected to the tree branch respectively, and go to step 355); 355) Select a node with a number of occurrences greater than or equal to 3, indicating that a branch occurs at this point; find a node with a number of occurrences greater than or equal to 3 in the collector cable connection table, record the corresponding position in the table as u, and traverse from back to front to calculate the number of downstream box-type substations loaded by this node. Add the node itself to determine the number of box-type substations loaded by its upstream collector line; the selection rules for the trunk are the same as 353). If it is in series connection, increase the number of box-type substations loaded by the record, and so on, traversing forward until the sequence number is 1; 356) Calculate C based on the selection results and the price of the collector cable cable , and obtain the laying method of collector cables for water surface photovoltaic power stations.

4. The method for collaborative optimization design of power generation unit layout and collection cables of a water surface photovoltaic power station collection system according to claim 2, characterized in that: The collaborative optimization of the layout of power generation units and the collection cables of the water surface photovoltaic power station collection system includes the following steps: 41) Based on the available water area, restricted water area, and operation and maintenance access requirements, determine the area where the power generation units of the water surface photovoltaic power station can be laid. Based on the photovoltaic panel model, inverter model, and cable model, use PVsyst software to calculate the number of series modules, the number of parallel strings, and the capacity ratio parameters. 42) Taking the minimum investment cost of power generation units in water surface photovoltaic power station as the optimization goal, the minimum number of power generation units N p.unit The initial value of the minimum number of power generation units is set to 1, and the initial value of the power generation unit aspect ratio is set to K. a =1, in order to satisfy the constraint condition of formula (8), the particle swarm algorithm is used to implement the optimization and judge whether the total capacity of the power generation unit meets the power generation capacity requirement. If it does, the power generation unit plane layout diagram is generated, and then the box-type substation is arranged in the center of the power generation unit to generate the coordinates of the box-type substation; if it does not meet the requirement, N p.unit =N p.unit +1, K=K+1 and repeat 42); 43) Based on the constraints of the collector cable current carrying capacity and the principle of the minimum number of partitions, the improved fuzzy C clustering algorithm is first used to partition the box-type substation with the substation as the base point. Then, the DM-MSTP algorithm is used to optimize the collector cable length within each partition. Finally, the automatic cable selection algorithm is used to automatically select the cable for chain and tree topologies to achieve the optimal selection of the collector cable. 44) Calculate the total investment cost C of the water surface photovoltaic power station collection system, C = C unit +C cable And use the particle swarm algorithm to iteratively optimize. When the fitness value reaches the convergence condition within the preset number of iterations, that is, the total investment cost of the water surface photovoltaic power station collection system is no longer significantly reduced, the overall layout diagram of the water surface photovoltaic power station collection system and the optimal total investment cost of the collection system are output; if not, then N p.unit =N p.unit +1, K=K+1 and repeat 42)-44) until the convergence condition is met.

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