A method and device for optimizing design of a power collection system and a storage medium

Abstract

This invention relates to the field of new energy power system engineering design technology, and discloses a method, device, and storage medium for optimizing the design of a power collection system. The invention relies on standardized multi-source data to build a parametric simulation model, and combines a multi-objective collaborative optimization algorithm to achieve integrated collaborative design of topology, loop paths, and conductor cross-sections. Through adaptive hierarchical conductor selection, it considers both line safety margin and full-cycle losses. Simultaneously, it relies on simulation to complete comprehensive verification of power flow, short circuit, and relay protection, effectively solving the problems of inconsistent quality and single optimization dimensions in traditional experience-based design schemes. This can reduce the initial investment and long-term power loss of wind farm power collection systems, improve power supply reliability and grid connection stability, shorten the design iteration cycle, and achieve optimal comprehensive benefits throughout the entire life cycle of the power collection system.

Description

Technical Field

[0001] This invention relates to the field of new energy power system engineering design technology, specifically to a method, device and storage medium for optimizing the design of a power collection system. Background Technology

[0002] With the rapid development of the new energy industry, large-scale wind farms of 300MW and above have become the mainstream form of wind power development. The industry is characterized by a continuous expansion of installed capacity and a constant upgrading of single-unit wind turbine capacity, with 5MW and above large-capacity wind turbines becoming widely used. At the same time, the layout of wind farms is becoming increasingly widespread, and grid connection standards are becoming increasingly stringent, placing higher demands on the overall power supply stability, grid connection security, and operational economy of wind farms. This also presents new challenges to the design and performance optimization of the core supporting systems of wind farms. The collection system is the core hub of a wind farm, mainly responsible for collecting and transmitting the electrical energy from all wind turbines to the substation and connecting it to the public power grid. Its design directly determines the investment cost, operating losses, power supply reliability, and relay protection matching effect of the wind farm, affecting the safety and efficiency of the wind farm throughout its entire life cycle.

[0003] However, the industry currently predominantly uses traditional power collection system designs, which are mostly suitable for small and medium-sized wind turbines and small wind farms. Facing the current application scenarios of large-capacity wind turbines and large-scale wind farms, existing technologies have significant shortcomings: traditional power collection systems often employ a single radial or single-ring network topology, which has poor adaptability to large-scale wind farms. This is especially true in desert and Gobi environments where wind turbine locations are dispersed and single-circuit transmission distances are long, easily leading to a series of safety hazards such as voltage drops and expanded fault impact areas. Summary of the Invention

[0004] This invention provides a method, device, and storage medium for optimizing the design of a power collection system, in order to solve the problem that traditional power collection systems mostly adopt a single radial or single ring network topology, which has poor adaptability in large-scale wind farms. In particular, in desert and Gobi environments where wind turbines are scattered and single-circuit transmission distances are long, a series of safety hazards such as voltage drops and expanded fault impact ranges are easily caused.

[0005] In a first aspect, the present invention provides a method for optimizing the design of a current collector system, the method comprising: Based on multi-source data from wind farms, a parametric simulation model of the power collection system is constructed. Under preset hard constraints, a multi-objective collaborative optimization model is constructed with the goal of minimizing the comprehensive cost of the entire life cycle of the power collection system. The multi-objective collaborative optimization model is iteratively optimized, and the optimal topology scheme is determined based on the optimization results and input into the parameterized simulation model of the power collection system. The number of collector circuits, line laying paths, and electrical parameters of the collector system are optimized collaboratively using the parametric simulation model of the collector system to obtain the collector circuit and path planning scheme and line length parameters. Based on the preset hierarchical adaptive optimization principle of collector conductor cross-section, and combined with the collector circuit and path planning scheme and the line length parameters, the conductor cross-section configuration of each collector line is determined. The optimal topology scheme and the conductor cross-section configuration of each collector line are simulated and verified, and the optimal collector system design scheme is determined according to the simulation verification results.

[0006] This invention utilizes standardized multi-source data to build a parametric simulation model, and combines a multi-objective collaborative optimization algorithm to achieve integrated collaborative design of topology, loop paths, and conductor cross-sections. Through adaptive hierarchical conductor selection, it balances line safety margins with full-cycle losses. Simultaneously, it leverages simulation to perform comprehensive verification of power flow, short circuits, and relay protection, effectively addressing the problems of inconsistent quality and limited optimization dimensions in traditional experience-based design schemes. This can reduce initial investment and long-term energy losses in wind farm collection systems, improve power supply reliability and grid connection stability, shorten design iteration cycles, and achieve optimal comprehensive benefits throughout the entire lifecycle of the collection system.

[0007] In one optional implementation, the step of constructing a parametric simulation model of the power collection system based on multi-source data from the wind farm includes: Collect core wind farm parameters, wind turbine layout parameters, substation parameters, electrical constraint parameters, conductor and cable parameters, core equipment parameters, environmental and terrain parameters, and auxiliary parameters of the wind farm to generate multi-source data; The multi-source data is subjected to data validity verification, abnormal data tracing and correction, and parameter standardization and reduction processing, and the processed multi-source data is used to construct a multi-source database. Based on the aforementioned multi-source database, a parameterized simulation model of the power collection system is constructed.

[0008] This invention unifies the collection of various engineering parameters from wind farms, eliminates data deviations through validity verification and anomaly correction, and then standardizes and calculates these parameters to form a standardized database, thereby building a parametric simulation model. This solves the problems of messy original engineering data, inconsistent benchmarks, and distorted parameters, ensuring the accuracy and traceability of data in simulation modeling and optimization calculations. The unified per-unit benchmark eliminates differences in parameter dimensions across different equipment, significantly reducing errors in subsequent power flow and short-circuit simulation calculations.

[0009] In one optional implementation, the step of constructing a parametric simulation model of the power collection system based on the multi-source database includes: Based on the multi-source database, a wind turbine cluster module area, a power collection line module area, a booster station module area, a fault simulation module area, and a real-time monitoring module area are constructed. The wind turbine cluster module area, power collection line module area, booster station module area, fault simulation module area and real-time monitoring module area are connected by electrical nodes to generate a power collection system topology network; Based on the wind turbine layout parameters and corresponding configuration parameters from the multi-source database, a wind turbine simulation module is built. The pre-defined dual-winding transformer module and the corresponding configuration parameters in the multi-source database are used to construct the transformer substation simulation module; The preset PI-type equivalent circuit module, the conductor and cable parameters and corresponding configuration parameters in the multi-source database are called to construct the collector circuit simulation module; The simulation module for the step-up substation is constructed by calling the 35kV busbar module, the main transformer module, the receiving-end power grid equivalent module, and the corresponding configuration parameters in the multi-source database. By combining the power collection system topology network, the wind turbine simulation module, the transformer substation simulation module, the power collection line simulation module, the booster station simulation module, and the corresponding line core parameters in the multi-source database, a parameterized simulation model of the power collection system is constructed. Steady-state verification and fault transient verification are performed on the parameterized simulation model of the current collector system.

[0010] This invention employs a modular, partitioned simulation model, with independent modules for wind turbines, transformer substations, collector lines, and step-up substations, all connected to a unified topology network, and integrated with fault simulation and real-time monitoring modules. All model parameters are derived from a standardized multi-source database, ensuring convenient parameter modification and high reusability. Dual verification of steady-state and transient states guarantees the model's computational accuracy, enabling precise simulation of power flow and short-circuit fault conditions.

[0011] In one optional implementation, under preset hard constraints, a multi-objective collaborative optimization model is constructed with the goal of minimizing the overall cost of the power collection system throughout its entire lifecycle. This includes: Based on preset voltage constraints, current carrying capacity constraints, short-circuit current constraints, protection coordination constraints, and engineering constraints, hard constraints are determined. Calculate the initial investment indicators, annual loss cost indicators, and reliability indicators of the power collection system; The initial investment index, annual loss cost index and reliability index of the power collection system are weighted and integrated to determine the comprehensive cost of the power collection system throughout its entire life cycle. Under pre-defined hard constraints, a multi-objective collaborative optimization model is constructed with the goal of minimizing the overall cost throughout the entire life cycle.

[0012] This invention integrates multiple hard constraints, including voltage, current carrying capacity, short-circuit current, protection coordination, and engineering requirements. It also considers three core indicators—investment, losses, and reliability—and weights and fuses them to obtain a comprehensive life-cycle cost, thereby establishing a multi-objective collaborative optimization model. This breaks away from the limitations of traditional single-objective optimization that only controls initial investment, simultaneously balancing construction costs, long-term operating losses, and power supply safety levels. The constraints cover all dimensions of engineering design requirements, effectively avoiding problems such as voltage exceeding limits, line overload, and protection mismatch. This achieves optimal synergy among multiple indicators of the power collection system, improving the overall economic and safety benefits of the solution.

[0013] In one optional implementation, the iterative optimization of the multi-objective collaborative optimization model, determining the optimal topology scheme based on the optimization results, and inputting it into the parameterized simulation model of the power collection system includes: Multiple optimization variables are set, and a single chromosome is set as the current collection system design scheme. The multi-objective collaborative optimization model is iteratively optimized based on an improved genetic algorithm. A preset number of chromosomes are selected for each iteration, and crossover operations are performed with a high crossover probability in the early stage of the iteration and a low crossover probability in the later stage of the iteration to obtain updated chromosomes; A phased adaptive mutation probability is used to perform mutation operations on each of the updated chromosomes, and the optimal topology solution sets for radial networks, single-ring networks, and hybrid topologies are output respectively. Radial topology schemes, single-ring network topology schemes, and hybrid topology schemes are extracted from the radial optimal topology solution set, the single-ring network optimal topology solution set, and the hybrid topology optimal topology solution set, respectively. A multi-objective decision matrix is ​​constructed using the radial topology scheme, the single-ring network topology scheme, and the hybrid topology scheme as matrix rows, and the initial investment index, annual loss cost index, and reliability index of the power collection system as matrix columns. Based on the multi-objective decision matrix, the comprehensive weights of the initial investment index, the annual loss cost index, and the reliability index are calculated, and the fit of the radial topology scheme, the single-ring network topology scheme, and the hybrid topology scheme are calculated respectively. Based on the comprehensive weights and fitting degrees of each, the optimal topology scheme is determined and input into the parameterized simulation model of the power collection system.

[0014] This invention employs an improved genetic algorithm, combined with adaptive crossover and mutation probability iterative optimization, to simultaneously output three types of topological solution sets. By constructing a multi-objective decision matrix, the similarity between indicator weights and solutions is calculated, quantifying and comparing different topological structures. Compared to fixed-parameter genetic algorithms, adaptive operators improve population search efficiency and avoid local optima; multi-dimensional weighted decision-making can consider investment, losses, and reliability, objectively selecting the optimal topology without requiring subjective human evaluation, resulting in higher accuracy in topology optimization.

[0015] In one optional implementation, the step of collaboratively optimizing the number of collector circuits, line laying paths, and electrical parameters of the collector system through the parametric simulation model of the collector system to obtain the collector circuit and path planning scheme and line length parameters includes: Based on the total installed capacity of the wind farm, the incoming line interval parameters of the booster station, and the preset transmission capacity constraints, calculate the total number of collection circuits; Using the coordinates of each wind turbine location as the clustering feature, a clustering algorithm is employed to divide the wind turbines into their respective loops; A digital geographic network for wind farms is constructed, with the substation power distribution device of the wind farm digital geographic network as the starting point and each wind turbine location as a network node. The optimal broken line path length of each collection line is solved by introducing path comprehensive weight. Based on the optimal broken path length of each collection line and the line tortuosity coefficient corresponding to different terrains, the total calculated length of each collection line is calculated. The total number of collector circuits, the wind turbine circuit division results, the optimal broken line path length of each collector line, and the total calculated length of each collector line are all input into the parameterized simulation model of the collector system and the multi-objective collaborative optimization model for verification. If the verification does not meet the preset constraints, the number of collector circuits, the division of wind turbine circuits, and the laying path of the line are adjusted in reverse iteration, and all electrical parameters are recalculated. If the verification meets all the preset constraints, then the collector circuit planning scheme, the line path planning scheme, and the line length parameters of each circuit are determined.

[0016] This invention determines the circuit size by combining installed capacity, bay size, and capacity constraints, groups wind turbines using clustering algorithms, introduces terrain weights to solve for the optimal route, and calculates the total calculated route length using tortuosity coefficients. Circuit and route parameters are simultaneously fed into a dual-model verification process; if constraints are not met, iterative adjustments and recalculation of electrical parameters are performed, achieving coordinated optimization of the number of circuits, laying paths, and electrical parameters. It takes into account both geographical construction conditions and electrical operation requirements, avoiding problems such as unreasonable manual circuit routing and excessive line losses, effectively reducing the total route length, and simultaneously ensuring compliance with various electrical indicators.

[0017] In one optional implementation, the step of determining the conductor cross-section configuration of each collector line based on a preset hierarchical adaptive optimization principle for collector conductor cross-sections, combined with the collector circuit and path planning scheme and the line length parameters, includes: From the current collection circuit and path planning scheme, the line transmission distance, the line length parameters of each circuit, the annual maximum utilization hours of the wind farm and the ambient temperature are extracted from the line length parameters to calculate the adaptive economic current density. Based on the load transmitted by each circuit and the adaptive economic current density, the theoretical conductor cross-section corresponding to each segment of the line is calculated. Based on the preset adaptive optimization principle of collector conductor cross-section classification, the theoretical conductor cross-sections corresponding to each segment of the line are initially selected in a hierarchical manner. The theoretical conductor cross-section, loop load, and loop line length parameters obtained from the initial selection are input into the parameterized simulation model of the power collection system for multi-dimensional compliance verification. Based on the compliance verification results, the conductor cross-section configuration of each power collection line is determined.

[0018] This invention calculates adaptive economic current density by combining load, line length, annual utilization hours, and environmental parameters. It initially selects conductor cross-sections according to main trunk and branch levels, and verifies them through multi-dimensional simulation. If the cross-sections do not meet the standards, it iteratively calculates and upgrades the cross-sections. Taking into account hard constraints such as current carrying capacity and voltage loss, it compares the life-cycle costs of various specifications to determine the optimal conductor configuration. This overcomes the shortcomings of traditional uniform cross-section selection, which often results in high losses or redundant investment. It balances construction investment and long-term line losses while ensuring electrical safety, achieving optimal synergy between safety and economy in line selection.

[0019] In one optional implementation, the step of simulating and verifying the optimal topology scheme and the conductor cross-section configuration of each of the collector lines, and determining the optimal collector system design scheme according to the simulation and verification results, includes: The optimal topology scheme and the conductor cross-section configuration of each collector line are imported into the parameterized simulation model of the collector system for fault simulation, and the short-circuit current of each fault node is calculated. Based on the short-circuit current calculation results of each fault node, the setting values ​​and level difference verification of the three-level relay protection of the high-voltage side of the transformer substation, the collector line and the incoming line of the step-up substation are carried out through the parameterized simulation model of the collector system. The final optimal collector system design scheme is determined according to the verification results.

[0020] This invention utilizes a simulation model to calculate fault short-circuit currents and completes the setting and differential verification of three-level relay protection. If the verification fails, the process can be traced back to the model building stage to re-optimize the topology, loops, or conductor cross-sections. It achieves integrated closed-loop verification of the power collection system topology, line selection, and relay protection, solving the problems of protection coordination mismatch and insufficient short-circuit safety margin in traditional step-by-step designs. It balances operational safety and scheme economy, ensuring that all electrical and engineering constraints of the entire design meet standards, and improving the operational reliability of wind farm power collection systems.

[0021] Secondly, the present invention provides a current collector system optimization design apparatus, the apparatus comprising: The first construction module is used to build a parameterized simulation model of the power collection system based on multi-source data from the wind farm; The second construction module is used to construct a multi-objective collaborative optimization model under preset hard constraints, with the goal of minimizing the comprehensive cost of the entire life cycle of the power collection system. The iterative optimization module is used to iteratively optimize the multi-objective collaborative optimization model, determine the optimal topology scheme based on the optimization results, and input it into the parameterized simulation model of the power collection system. The planning module is used to perform collaborative optimization of the number of collector circuits, line laying paths, and electrical parameters of the collector system through the parametric simulation model of the collector system, so as to obtain the collector circuit and path planning scheme and line length parameters. The module is used to determine the conductor cross-section configuration of each collector line based on the preset hierarchical adaptive optimization principle of the collector conductor cross-section, combined with the collector circuit and path planning scheme and the line length parameters. The simulation verification module is used to perform simulation verification on the optimal topology scheme and the conductor cross-section configuration of each of the collector lines, and to determine the optimal collector system design scheme according to the simulation verification results.

[0022] Thirdly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the current collector system optimization design method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0023] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the first type of power collection system optimization design method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the second process of the current collector system optimization design method according to an embodiment of the present invention; Figure 3 This is a structural block diagram of a current collector system optimization design device according to an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0027] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0028] This invention provides a method for optimizing the design of a power collection system. It utilizes standardized multi-source data to build a parametric simulation model and combines it with a multi-objective collaborative optimization algorithm to achieve integrated collaborative design of topology, loop paths, and conductor cross-sections. Through adaptive hierarchical conductor selection, it balances line safety margins with full-cycle losses. Simultaneously, it uses simulation to perform comprehensive verification of power flow, short circuits, and relay protection, effectively solving the problems of inconsistent quality and limited optimization dimensions in traditional experience-based design schemes. This method can reduce initial investment and long-term energy losses in wind farm power collection systems, improve power supply reliability and grid connection stability, shorten design iteration cycles, and achieve optimal comprehensive benefits throughout the power collection system's life cycle.

[0029] This embodiment provides a method for optimizing the design of a current collection system. Figure 1 This is a flowchart of a current collector system optimization design method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S101: Construct a parameterized simulation model of the power collection system based on multi-source data from the wind farm.

[0030] It should be noted that multi-source data for wind farms refers to a standardized set of engineering parameters that integrates multiple dimensions and types, including wind turbines, substations, terrain, electrical equipment, environment, and engineering constraints; and parametric simulation models of power collection systems refer to simulation models of wind farm power collection lines that are built modularly and whose parameters can be customized.

[0031] In this embodiment of the invention, various engineering parameters of the wind farm are collected to generate multi-source data. The multi-source data is verified, corrected, standardized, and stored in a multi-source database. Based on the database, simulation modules such as wind turbines, transformer substations, collection lines, and step-up substations are built in different regions and connected by electrical nodes to form a topology network. All modules are integrated with the core parameters of the lines to build a complete parameterized simulation model of the collection system. The model is then subjected to steady-state and fault transient dual verification to ensure calculation accuracy.

[0032] Step S102: Under the preset hard constraints, a multi-objective collaborative optimization model is constructed with the goal of minimizing the comprehensive cost of the power collection system throughout its entire life cycle.

[0033] It should be noted that hard constraints refer to mandatory boundary requirements that must be met during the design phase, including voltage, current carrying capacity, short-circuit current, relay protection coordination, and construction engineering constraints; failure to meet these requirements renders the scheme invalid. The total life-cycle cost refers to the total cost of the power collection system from construction and commissioning to decommissioning, including weighted comprehensive indicators such as initial construction investment, annual line loss costs, and operation and maintenance replacement costs. The multi-objective collaborative optimization model refers to a mathematical optimization model that simultaneously considers multiple optimization indicators such as investment, loss, and reliability, and aims to minimize the overall cost under all hard constraints.

[0034] In this embodiment of the invention, voltage, current carrying capacity, short-circuit current, protection coordination and various engineering conditions are integrated to define hard constraints. Then, the initial investment, annual loss cost and operational reliability of the power collection system are calculated separately. The three indicators are weighted and fused to obtain the comprehensive cost of the entire life cycle. The minimum comprehensive cost is used as the optimization objective and a corresponding multi-objective collaborative optimization model is built in combination with the aforementioned hard constraints.

[0035] Step S103: Iteratively optimize the multi-objective collaborative optimization model, determine the optimal topology scheme based on the optimization results, and input it into the parameterized simulation model of the power collection system.

[0036] It should be noted that iterative optimization refers to the numerical solution process of continuously updating the design scheme population by relying on optimization algorithms to approach the optimal solution of comprehensive indicators; the optimal topology scheme refers to the wind farm grid layout form that has the best performance in terms of comprehensive investment, loss and reliability after multi-objective decision screening, and is divided into radial, single-ring and hybrid topologies.

[0037] In this embodiment of the invention, optimization variables related to loop and line layout are set, and a single chromosome represents a set of power collection system design schemes. An improved genetic algorithm with adaptive crossover and mutation probability is used to iteratively optimize the multi-objective collaborative optimization model, and outputs three types of topology solution sets: radial, single-ring network, and hybrid. A multi-objective decision matrix is ​​constructed to calculate the index weights and the closeness of the schemes. The topology scheme with the best comprehensive performance is selected and imported into the completed parameterized simulation model of the power collection system for subsequent optimization calculations.

[0038] Step S104: The number of collector circuits, line laying paths, and electrical parameters of the collector system are optimized collaboratively through the parameterized simulation model of the collector system to obtain the collector circuit and path planning scheme and line length parameters.

[0039] It should be noted that the number of collector circuits refers to the total number of collector line branches leading from the 35kV busbar of the wind farm's booster station, which is constrained by installed capacity, incoming line bay, and line transmission capacity; the line laying path refers to the geographical route of the wind turbines connected in series within each collector circuit, with the optimal broken line trajectory determined by comprehensively considering terrain, land occupation, and crossing weights; electrical parameters refer to parameters reflecting the electrical characteristics of the collector lines, including line impedance, current carrying capacity, voltage drop, and short-circuit current; the collector circuit and path planning scheme refers to the complete wind turbine network layout scheme that includes wind turbine grouping, circuit division, the laying path of each circuit line, and the total line length; and the line length parameter refers to the actual total engineering laying length of each collector line calculated by combining the geographical path and terrain tortuosity coefficient.

[0040] In this embodiment of the invention, based on the parameterized simulation model of the power collection system with the optimal topology scheme already imported, the total number of power collection circuits is determined by combining the wind farm installed capacity, substation interval parameters and transmission capacity constraints. The wind turbine circuits are assigned through clustering algorithms. The optimal line laying path is solved by combining digital geographic networks and improved Dijkstra's algorithm. The actual line length is calculated based on the terrain tortuosity coefficient. After dual-model constraint verification and iterative adjustment, a compliant and reliable power collection circuit and path planning scheme and corresponding line length parameters are output.

[0041] Step S105: Based on the preset hierarchical adaptive optimization principle of collector conductor cross-section, and combined with the collector circuit and path planning scheme and line length parameters, determine the conductor cross-section configuration of each collector line.

[0042] It should be noted that the principle of adaptive optimization of conductor cross-section classification refers to the selection criteria of adaptively matching conductor specifications based on the classification rule that the cross-section of the main line is not less than that of the branch line, combined with the load and economic current density; conductor cross-section configuration refers to the combination scheme of cable / conductor cross-sectional area specifications selected for each section of the collector line, which must take into account both economic efficiency and electrical safety constraints.

[0043] In this embodiment of the invention, the transmission distance, line length, annual maximum utilization hours of the wind farm, and ambient temperature parameters are extracted from the collection circuit and path planning scheme and line length parameters, and the adaptive economic current density is calculated. Based on this, the theoretical conductor cross-section of each segment line is solved. According to the hierarchical adaptive optimization principle that the cross-section of the main line is not less than that of the branch line, the initial selection of conductor cross-section is completed. After multi-dimensional constraint iteration verification by the simulation model, the conductor model with the best life cycle cost is selected and finally the conductor cross-section configuration of each collection line is determined.

[0044] Step S106: Perform simulation verification on the optimal topology scheme and the conductor cross-section configuration of each collector line, and determine the optimal collector system design scheme according to the simulation verification results.

[0045] It should be noted that simulation verification refers to inputting topology and conductor parameters into a parameterized simulation model to conduct steady-state power flow, fault short circuit, and relay protection checks to verify whether all hard constraints are met. The optimal power collection system design scheme refers to the final design scheme of the complete wind farm power collection system that meets all indicators and has the lowest comprehensive cost throughout its entire life cycle after going through the entire process of topology optimization, loop path planning, conductor selection, and simulation verification.

[0046] In this embodiment of the invention, the optimal topology scheme and the conductor cross-section configuration of each collector line are synchronously imported into the parameterized simulation model of the collector system to carry out fault simulation calculations. Short-circuit current data of each fault node are obtained, and the setting values ​​and differential verification of the three-level relay protection of the high-voltage side of the transformer substation, the collector line, and the incoming line of the step-up substation are completed. If the hard constraints are not met, the topology structure, circuit division, or conductor specifications are iteratively optimized until all indicators are compliant. Finally, a safe, reliable, and economically optimal overall design scheme for the collector system is selected.

[0047] This embodiment provides a method for optimizing the design of a current collection system. Figure 2 This is a flowchart of a current collector system optimization design method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Construct a parameterized simulation model of the power collection system based on multi-source data from the wind farm.

[0048] Specifically, step S201 includes: Step S2011: Collect the core parameters of the wind farm, wind turbine layout parameters, substation parameters, electrical constraint parameters, conductor and cable parameters, core equipment parameters, environmental and terrain parameters, and auxiliary parameters to generate multi-source data.

[0049] In this embodiment of the invention, core basic data of wind farms are comprehensively collected, a standardized database is established, and abnormal data is eliminated through data screening, verification, and normalization to ensure data accuracy, completeness, and standardization. This provides reliable basic support for subsequent modeling and optimization calculations. Simultaneously, a data traceability mechanism is established to facilitate subsequent scheme verification and iteration. Specifically, comprehensive basic data is collected: In accordance with engineering design specifications, eight categories of core data will be collected. All data must be matched with the engineering survey report and equipment manufacturer information. The specific collection content and requirements are as follows: 1) Core parameters of wind farm: total installed capacity (300MW~1000MW), single wind turbine capacity (5MW and above), number of wind turbines, wind turbine model, rated voltage, rated current, power factor range. The data comes from the wind turbine manufacturer's formal technical agreement or selection sample. 2) Wind turbine layout parameters: relative coordinates of each wind turbine location, wind turbine altitude, and wind turbine spacing. The data are from the project's micro-site selection report and topographic survey report. 3) Parameters of the substation: coordinates of the low-voltage side (35kV) distribution equipment, number of low-voltage side incoming line bays, number of main transformers, capacity of a single main transformer, transformation ratio, short-circuit impedance, no-load loss, load loss, and bus connection method. The data are from the preliminary design documents of the substation. 4) Electrical constraint parameters: Voltage deviation limit at wind turbine grid connection point ≤ ±5% (in accordance with GB / T 19963-2021 "Technical Regulations for Wind Farm Access to Power System"), 35kV bus short-circuit current limit ≤ 31.5kA (matching the rated breaking current of mainstream switchgear), long-term allowable current carrying capacity margin of conductor ≥ 1.1 times, relay protection time difference Δt = 0.3~0.5s; 5) Conductor and cable parameters: resistance, reactance, capacitance, allowable current carrying capacity, and cost per unit length for different types of overhead conductors and power cables, covering JL / G1A-150 / 25, JL / G1A-240 / 30, JL / G1A-300 / 40, and JL / G1A-400 / 50 steel-cored aluminum stranded wires, and ZC-YJLY23 and ZC-YJLV22 series 35kV power cables. Data are sourced from cable manufacturer samples and quotations from qualified market suppliers. 6) Core parameters of the equipment: rated parameters, impedance parameters, and protection configuration parameters of the prefabricated substation (rated capacity matching the single unit capacity of the wind turbine, such as a 6.25MW wind turbine matched with a 6900kVA prefabricated substation), 35kV switchgear, and reactive power compensation device (SVG). The data are from the technical data of the equipment manufacturer. 7) Environmental and topographic parameters: wind field topography type (plain / mountain / sand barren / coastal), altitude range, annual average temperature, extreme maximum / minimum temperature, pollution level, line corridor constraints, crossing conditions (highway / railway / power line), distribution of adverse geological areas. Data are from the project geological survey report and environmental impact assessment documents. 8) Auxiliary parameters: annual utilization hours of wind turbines, on-grid electricity price including tax, equipment depreciation period (25 years), benchmark rate of return, unit cost of line construction, unit cost of iron tower, unit time and cost of fault repair. The data are from the project feasibility study report and local engineering quotas.

[0050] Step S2012 involves performing data validity verification, abnormal data tracing and correction, and parameter standardization and reduction on the multi-source data, and then constructing a multi-source database from the processed multi-source data.

[0051] In this embodiment of the invention, data validity verification and abnormal data tracing and correction are performed as follows: 1) Multi-dimensional rationality verification and anomaly identification: Four special verifications are carried out for eight categories of basic data, and the rules for judging abnormal data are clarified: ① Rated range verification: Equipment parameters deviating from the manufacturer's rated value or industry standard limit by more than ±5% are marked as abnormal; ② Logical consistency verification: Logical contradictions in strongly correlated parameters and deviations between calculated values ​​and nominal values ​​exceeding 5% are marked as abnormal; ③ Spatial topology verification: The coordinates of the turbine location exceed the project boundary line, the turbine spacing does not meet the safe distance, and the elevation deviates from the survey report by more than 50m, all of which are marked as abnormal; ④ Standard compliance verification: Parameters that do not meet the minimum compliance requirements for wind power engineering design are marked as abnormal.

[0052] 2) Abnormal data tracing and deviation correction: Trace and verify the marked abnormal data one by one according to the path of "data source - original data - responsible party": For human error in data entry, correct it directly by referring to the original data; for ambiguous or contradictory items in the original data, send a letter to the issuing unit for clarification and use the parameters in the official reply as the standard; for items that do not meet compliance standards, complete the correction in accordance with the current specifications and grid connection requirements, and mark the source and responsible party for all corrections.

[0053] 3) Invalid data standardization and integrity supplementation: Only redundant invalid data that cannot be traced after multiple rounds of review, lacks valid supporting documents, and does not affect the core design logic will be removed. Before removal, the reason must be clearly stated in the ledger and the whole process must be recorded. If core design parameters are missing, industry-standard experience values ​​of the same model of equipment and similar projects will be used to supplement them and the source will be marked. If there are no standard experience values, they will be supplemented by on-site surveys and technical consultation with manufacturers to ensure that no core parameters are missing.

[0054] 4) Verification result solidification and traceability management: After the verification and correction are completed, a standardized and valid dataset is formed. At the same time, a "Basic Data Verification Traceability Ledger" is established to fully record the original parameter values, verification results, reasons for correction / removal, supporting documents, reviewers and review time, so as to realize the traceability and reviewability of basic data throughout the design process.

[0055] Parameter standardization reduction: 1) Set unified benchmark values: benchmark capacity SB=100MVA, benchmark voltage UB=37kV, to ensure that the dimensions of calculation parameters are consistent throughout the entire process; 2) Impedance parameter reduction: The impedance parameters of all equipment and line impedance parameters are uniformly reduced to per-unit values ​​under a 37kV base voltage and a 100MVA base capacity. The reduction formula is as follows: Z =Z×SB / UB 2 In the formula, Z Z represents the impedance per unit value, Z represents the impedance nominal value (unit: Ω), SB represents the reference capacitance, and UB represents the reference voltage. 3) Standardized data storage: All verified and recalculated data are entered into a standardized database and stored in categories such as "wind turbine parameters - line parameters - equipment parameters - constraints - economic parameters". Each data item is labeled with its source, verification time, and verifier, enabling full-process traceability.

[0056] Step S2013: Construct a parameterized simulation model of the power collection system based on a multi-source database.

[0057] In some optional implementations, step S2013 above includes: Step a1: Based on a multi-source database, construct the wind turbine cluster module area, power collection line module area, substation module area, fault simulation module area, and real-time monitoring module area.

[0058] In this embodiment of the invention, a new model is created in Simulink software and named "WindFarm_Collector". The simulation type is set to "Power System Simulation". The solver is ode23t (rigid trapezoidal method, adapted for electromagnetic transient simulation of power system). The simulation step size is set to 1e-3s. The simulation duration can be adjusted according to the requirements (10s for steady-state simulation and 2s for transient simulation). The core framework of the model is built and divided into 5 functional areas: wind turbine cluster module area, power collection line module area, substation module area, fault simulation module area and real-time monitoring module area.

[0059] Step a2: Connect the wind turbine cluster module area, power collection line module area, booster station module area, fault simulation module area and real-time monitoring module area through electrical nodes to generate the power collection system topology network.

[0060] In this embodiment of the invention, functional areas such as the wind turbine cluster module area, the power collection line module area, the booster station module area, the fault simulation module area, and the real-time monitoring module area are connected by electrical nodes to form a complete power collection system topology network.

[0061] Step a3: Based on the wind turbine layout parameters and corresponding configuration parameters from the multi-source database, build a wind turbine simulation module.

[0062] In this embodiment of the invention, the core equipment module is built and its parameters are configured. Among them, the wind turbine simulation module calls the doubly fed induction wind turbine generator module from the Simscape Electrical library, and configures the single unit capacity, rated voltage, rated power, power factor, and stator / rotor impedance parameters according to the wind turbine parameters standardized in step S2012. Each wind turbine is equipped with an independent terminal measurement module to collect terminal voltage, current, and active / reactive power data.

[0063] Step a4: Call the preset dual-winding transformer module and the corresponding configuration parameters in the multi-source database to build the transformer substation simulation module.

[0064] In this embodiment of the invention, the core equipment module is built and its parameters are configured. Among them, the transformer simulation module calls the dual-winding transformer module and configures the rated capacity (matching the single unit capacity of the wind turbine), the turns ratio (0.69kV / 35kV), the short-circuit impedance, the no-load loss, and the load loss parameters. The high-voltage side is configured with circuit breakers and current transformers to provide data support for subsequent protection verification.

[0065] Step a5: Call the preset PI-type equivalent circuit module, the conductor and cable parameters and corresponding configuration parameters in the multi-source database to construct the collector circuit simulation module.

[0066] In this embodiment of the invention, the core equipment module is built and its parameters are configured. Among them, the collector line simulation module: The collector line module calls the PI type equivalent line module, which can flexibly support the configuration of overhead line and cable parameters. According to the line parameters in step S2012, the unit length resistance, reactance, capacitance and line length parameters are configured. It supports one-click modification of line length and conductor type, and adapts to the iterative calculation of subsequent path optimization and conductor selection.

[0067] Step a6: Call the configuration parameters of the 35kV busbar module, main transformer module, receiving-end power grid equivalent module and multi-source database to build the substation simulation module.

[0068] In this embodiment of the invention, the core equipment module is built and its parameters are configured. Among them, the substation simulation module calls the 35kV bus module, the main transformer module, and the receiving-end power grid equivalent module to configure the rated capacity, transformation ratio, and short-circuit impedance parameters of the main transformer. The receiving-end power grid adopts the Thevenin equivalent model and configures the system equivalent impedance and reference voltage parameters to simulate the actual power grid environment of the wind farm grid connection.

[0069] Step a7: Combine the power collection system topology network, wind turbine simulation module, transformer substation simulation module, power collection line simulation module, step-up substation simulation module, and the corresponding line core parameters in the multi-source database to construct a parametric simulation model of the power collection system.

[0070] In this embodiment of the invention, based on the topology of the power collection system, the existing wind turbine simulation module, transformer substation simulation module, power collection line simulation module, and substation simulation module are integrated. The matching core parameters of the lines in the multi-source database are retrieved to complete the parameter assignment and coupling association of each module, thereby realizing the interconnection and precise parameterized modeling of each electrical unit. In this way, a complete parameterized simulation model of the power collection system is assembled and integrated to ensure that the model can accurately simulate the actual operating conditions of the wind farm power collection system.

[0071] Specifically, the formulas for calculating the core parameters of the line are defined as follows: The core calculation formulas for collector line length and impedance are defined to provide a unified mathematical basis for subsequent path optimization, loss calculation and power flow simulation. All formulas comply with the engineering design requirements of the "Design Code for 66kV and Below Overhead Power Lines" GB 50061-2010.

[0072] 1) Formula for calculating the total length of a single-circuit collector line The formula used to calculate the actual total length of a single-circuit collector line is the basis for calculating line impedance and loss. The formula is as follows:

[0073] In the formula, L 总 L represents the total calculated length of a single-circuit collector line, in km; n represents the total number of path segments of a single-circuit line, determined according to the wiring sequence of the series-connected fans; i Let be the length of the optimal polyline path for the i-th segment, in km, which is solved by the improved Dijkstra algorithm. K This is the tortuosity coefficient of the engineering route, used to compensate for the turning angle, sag, and construction margin of the line. It is 1.03~1.05 for flat terrain, 1.05~1.15 for mountainous / hilly terrain, and 1.04~1.06 for sandy / barren terrain.

[0074] 2) Formula for calculating line resistance

[0075] In the formula, R is the total resistance of a single-circuit collector line (unit: Ω); r0 is the resistance per unit length of the conductor (unit: Ω / km), which is determined by the selected conductor type and can be directly retrieved from the standardized conductor parameter library in step S2012.

[0076] 3) Line reactance calculation formula

[0077] In the formula, X is the total reactance of a single-circuit collector line (unit: Ω); x0 is the reactance per unit length of the conductor (unit: Ω / km), which is determined by the selected conductor type.

[0078] 4) Line capacitance calculation formula

[0079] In the formula, C is the total capacitance of a single-circuit collector line (unit: μF); c0 is the capacitance per unit length of the conductor (unit: μF / km), which is determined by the selected conductor type.

[0080] Step a8: Perform steady-state verification and fault transient verification on the parameterized simulation model of the collector system.

[0081] In this embodiment of the invention, a fault simulation and real-time monitoring module is built in the parameterized simulation model of the power collection system. The fault simulation module includes three-phase short circuit, single-phase ground fault, and two-phase short circuit fault modules set at the wind turbine grid connection point, the beginning / end of the power collection line, and the 35kV busbar. It allows setting the fault occurrence time, fault duration, and transition resistance parameters to simulate common fault scenarios in engineering. Real-time monitoring module: Voltage, current, power, and loss monitoring points are set at each wind turbine grid connection point, the beginning / end of each collector line, and the 35kV bus. Through the Scope module and the To Workspace module, real-time data display and data export are realized, providing data support for subsequent optimization calculations and protection verification.

[0082] Verify the validity of the parameterized simulation model of the current collector system: 1) Steady-state verification: Start the steady-state simulation of the model, compare the simulation output of the wind turbine terminal voltage, line current, and bus voltage with the theoretical calculation values. If the deviation is ≤2%, the model is considered to be in steady state. 2) Fault transient verification: Set a single-phase ground fault. If the deviation between the short-circuit current value output by the simulation and the theoretical calculation value by the symmetrical component method is ≤5%, the model is considered transiently valid. Once the verification is successful, the model is solidified into a standardized template, which can be adapted to wind farm projects with different installed capacities and terrains by modifying parameters, thus achieving model reusability.

[0083] Step S202: Under the preset hard constraints, a multi-objective collaborative optimization model is constructed with the goal of minimizing the comprehensive cost of the entire life cycle of the power collection system.

[0084] Specifically, step S202 includes: Step S2021: Based on the preset voltage constraints, current carrying capacity constraints, short-circuit current constraints, protection coordination constraints, and engineering constraints, determine the hard constraints.

[0085] In this embodiment of the invention, the hard constraint conditions are set as follows: 1) Voltage constraint: The voltage deviation at all wind turbine grid connection points and at the end of the collector lines shall be ≤ ±5% of the rated voltage; 2) Current carrying capacity constraint: The long-term operating current of all collector lines shall be ≤ 90% of the allowable current carrying capacity of the conductors, with a 10% margin reserved. 3) Short-circuit current constraint: The short-circuit current at each point of the 35kV busbar and collector line is ≤31.5kA and does not exceed the rated breaking current of the switchgear; 4) Protection coordination constraints: The time difference Δt between the three levels of protection for the high-voltage side of the transformer substation, the collector line, and the low-voltage side of the step-up substation is 0.3~0.5s, which meets the selective coordination requirements; 5) Engineering constraints: The number of wind turbines connected in series on a single collection line shall be ≤12, the total length of a single line shall be ≤20km, and the transmission capacity of a single line shall be ≤40MW. Ensure that the impact range of a single line fault is controllable and that the line voltage loss meets the specification requirement of ≤5% of the rated voltage.

[0086] Step S2022: Calculate the initial investment index, annual loss cost index, and reliability index of the power collection system.

[0087] In this embodiment of the invention, the core indicators are quantitatively calculated, that is, the initial investment C1, annual depreciation cost C2, and reliability index R are calculated accurately respectively: 1) Initial Investment C1: The calculation process strictly follows the "Specification for Quantity List Pricing of Power Construction Projects" GB50500-2013, with a calculation error of ≤3%. The calculation formula is as follows:

[0088] In the formula, The cost of purchasing conductors / cables is calculated based on the total length of the line and the cost per unit length. The procurement cost of prefabricated substations is calculated based on the number of wind turbines and the cost of a single prefabricated substation. For the procurement costs of 35kV switchgear, circuit breakers and other switchgear equipment; Costs for purchasing towers and insulator fittings; This covers installation and construction costs such as line construction and earthwork.

[0089] 2) Annual loss cost C2: Considering seasonal load fluctuation correction, with a correction error ≤2%, the calculation formula is as follows:

[0090]

[0091] In the formula, ΔP is the total active power loss of the collector system (unit: kW); M is the total number of collector circuits; I i R is the calculated operating current (in A) of the i-th collector line; i T represents the total resistance of the i-th collector circuit (in Ω). max ρ represents the maximum annual utilization hours of the wind turbine (unit: h); ρ represents the on-grid electricity price including tax (unit: yuan / kWh); (P / A,r,n) represents the present value annuity factor, r represents the benchmark rate of return, and n represents the depreciation period of 25 years.

[0092] 3) Reliability index R: Calculated based on line fault probability and repair time, with reference to relevant statistical data (average annual fault rate of 35kV overhead lines λ1 = 0.15 times / 100km). The annual average failure rate of 35kV power cables is λ² = 0.04 times per 100 kilometers. (year), the calculation formula is as follows:

[0093] In the formula, λ i The average annual failure probability of the i-th collector line (unit: times / 100 km) (Year); L i t is the length of the i-th collector line (in 100 km); i The average repair time after a fault on the i-th line (unit: h / time) is given. Under the radial architecture, the average outage time for overhead lines is 24h / time, and the average outage time for cable lines is 36h / time (due to the long fault location cycle). Under the ring network architecture, both types of lines can be quickly restored to power supply through ring network switching, with an average outage time of 8h / time.

[0094] Step S2023 involves weighted fusion of the initial investment indicators, annual loss cost indicators, and reliability indicators of the power collection system to determine the comprehensive cost of the power collection system throughout its entire life cycle.

[0095] In this embodiment of the invention, based on the initial investment index, annual loss cost index and reliability index, and combined with the degree of influence of each index on the operation of the wind farm power collection system, corresponding weights are assigned. The three types of indexes are quantitatively integrated through a weighted fusion algorithm. By comprehensively balancing construction investment, operating losses and power supply safety performance, the comprehensive cost of the entire life cycle that can fully reflect the overall benefits of the power collection system is accurately calculated.

[0096] Step S2024: Under the preset hard constraints, construct a multi-objective collaborative optimization model with the goal of minimizing the comprehensive cost throughout the entire life cycle.

[0097] In this embodiment of the invention, under preset hard constraints, the objective function is constructed with the goal of minimizing the overall cost over the entire life cycle:

[0098] In the formula, F is the comprehensive objective function value, and the smaller the value, the better the comprehensive benefits; w1, w2, and w3 are weight coefficients, with default values ​​of w1=0.4, w2=0.35, and w3=0.25, which can be flexibly adjusted according to the actual needs of the project, the grid connection requirements, and the terrain conditions to achieve adaptive matching of weights; C1 is the initial investment of the power collection system (ten thousand yuan), C2 is the equivalent annual loss cost of the power collection system throughout its entire life cycle (ten thousand yuan), and R is the power supply reliability index of the power collection system (dimensionless, with a value of 0~1, and the closer to 1, the higher the reliability).

[0099] It is worth mentioning that an innovative weighted adaptive multi-objective collaborative optimization function is constructed. With the optimal comprehensive cost of the power collection system throughout its entire life cycle as the core objective, it takes into account three key indicators: initial investment, operating losses, and power supply reliability. By completing the function construction, indicator calculation, and constraint setting, it achieves multi-objective collaborative optimization, which is different from the existing fixed-weight optimization method.

[0100] Step S203: Iteratively optimize the multi-objective collaborative optimization model, determine the optimal topology scheme based on the optimization results, and input it into the parameterized simulation model of the power collection system.

[0101] Specifically, step S203 includes: Step S2031: Set multiple optimization variables and set a single chromosome as the current collection system design scheme. Iteratively optimize the multi-objective collaborative optimization model based on the improved genetic algorithm.

[0102] It should be noted that the improved genetic algorithm (which introduces adaptive crossover mutation probability and elite retention strategy) is adopted. By completing the traversal calculation and comprehensive optimization of three topology schemes, it is different from the existing traditional genetic algorithm, effectively improving the convergence speed and calculation accuracy of the algorithm and avoiding the occurrence of local optima.

[0103] In this embodiment of the invention, the encoding method and population initialization are as follows: 1) Using real number encoding, the topology type, number of loops, line length, and conductor type are used as optimization variables, and each chromosome corresponds to a complete current collection system design scheme; 2) Initialize the population, with a population size of 50. The optimization variables for each chromosome (i.e. the design scheme) are randomly generated within the constraints to ensure the diversity of the initial population.

[0104] Based on the constraints and optimization objectives of the multi-objective collaborative optimization model, the division of the collector circuit, the line laying path, and the topology are selected as the core optimization variables. Each variable is combined to form a single chromosome corresponding to a complete collector system design scheme. An improved genetic algorithm with adaptive crossover and mutation mechanisms is used to continuously iterate and optimize the multi-objective collaborative optimization model and constantly update the population scheme.

[0105] Step S2032: Select a preset number of chromosomes for each iteration, and perform crossover operation with a high crossover probability in the early stage of the iteration and a low crossover probability in the later stage of the iteration to obtain updated chromosomes.

[0106] It should be noted that the preset number is the top 5 chromosomes with the best objective function values ​​in each generation of the population.

[0107] In this embodiment of the invention, the adaptive genetic operator is configured as follows: 1) Elite preservation strategy: The top 5 chromosomes with the best objective function values ​​in each generation are directly retained to the next generation to avoid losing the optimal solution and improve the convergence of the algorithm; 2) Adaptive crossover probability: In the early stage of iteration (first 50 generations), the crossover probability is set to 0.9 to expand the search range; in the later stage of iteration (last 50 generations), the crossover probability is reduced to 0.7 to accelerate the convergence speed. 3) Adaptive mutation probability: The mutation probability is set to 0.07 in the early stage of iteration to increase population diversity; the mutation probability is reduced to 0.03 in the later stage of iteration to avoid destroying the optimal solution.

[0108] The crossover probability is dynamically adjusted according to the iteration process. In the early stage of the iteration, a high crossover probability is used to expand the population search range and improve the diversity of solutions. In the later stage of the iteration, a low crossover probability is used to retain high-quality individuals and avoid the loss of the optimal solution. Chromosomal gene recombination is completed through staged crossover operations, thereby obtaining a new chromosome with better performance.

[0109] Step S2033: Perform mutation operation on each updated chromosome using phased adaptive mutation probability, and output the optimal topology solution set for radial network, single-ring network, and hybrid topology respectively.

[0110] In this embodiment of the invention, fitness function calculation and population iteration are performed as follows: 1) The reciprocal of the objective function F constructed in step S202 is used as the fitness function, i.e. Fit=1 / F. The higher the fitness value, the better the overall benefit of the solution. 2) For each chromosome in the population, calculate the fitness value and check whether it meets the hard constraints of step S202. Set the fitness value of chromosomes that do not meet the constraints to 0. 3) Using the roulette wheel selection method, chromosomes with high fitness values ​​are selected for crossover and mutation operations to generate the next generation population; 4) Set the maximum number of iterations to 100. If the optimal fitness value remains unchanged for 20 consecutive generations, terminate the iteration early and output the optimal solution set. The optimal solution set includes the radial optimal topology solution set, the single-ring network optimal topology solution set, and the hybrid topology optimal topology solution set.

[0111] Step S2034: Extract the corresponding radial topology scheme, single-ring network topology scheme, and hybrid topology scheme from the radial optimal topology solution set, single-ring network optimal topology solution set, and hybrid topology optimal topology solution set.

[0112] In this embodiment of the invention, a comprehensive evaluation of multiple schemes is based on the entropy weight method: After the iteration terminates, the optimal solutions for the three schemes—radial, single-ring, and hybrid topologies—are extracted, namely, the radial topology scheme, the single-ring topology scheme, and the hybrid topology scheme.

[0113] Step S2035: Construct a multi-objective decision matrix with the radial topology scheme, single-ring network topology scheme, and hybrid topology scheme as matrix rows, and the initial investment index, annual loss cost index, and reliability index of the power collection system as matrix columns.

[0114] In this embodiment of the invention, after the iteration terminates, the optimal solutions of three schemes—radial, single-ring, and hybrid topologies—are extracted, and a multi-objective decision matrix is ​​constructed. The rows of the matrix represent the topology schemes, and the columns represent three evaluation indicators: initial investment C1, annual loss cost C2, and reliability index R.

[0115] Step S2036: Based on the multi-objective decision matrix, calculate the comprehensive weights of the initial investment index, annual loss cost index, and reliability index, and calculate the fit of the radial topology scheme, the single-ring network topology scheme, and the hybrid topology scheme respectively.

[0116] In this embodiment of the invention, based on the multi-objective decision matrix, the objective weights of the three indicators—initial investment indicator, annual loss cost indicator, and reliability indicator—are calculated using the entropy weight method. Combined with the subjective weights in step S202, the comprehensive weights are calculated. The TOPSIS method is used to calculate the proximity of three schemes: radial, single-ring, and hybrid topologies. The scheme with the highest proximity is the topology with the best overall benefits.

[0117] Step S2037: Determine the optimal topology scheme based on each comprehensive weight and each fit degree, and input it into the parameterized simulation model of the collector system.

[0118] In this embodiment of the invention, the selected optimal topology scheme is imported into the Simulink simulation model (i.e., the parameterized simulation model of the collector system) built in step S201 to verify whether the steady-state voltage, line loss, and short-circuit current meet the constraints. After the verification is passed, the topology scheme is used as the core boundary condition for subsequent loop division and path optimization to form an optimization closed loop.

[0119] Step S204: The number of collector circuits, line laying paths, and electrical parameters of the collector system are optimized collaboratively through the parameterized simulation model of the collector system to obtain the collector circuit and path planning scheme and line length parameters.

[0120] Specifically, step S204 includes: Step S2041: Calculate the total number of collector circuits based on the total installed capacity of the wind farm, the incoming line interval parameters of the booster station, and the preset transmission capacity constraints.

[0121] It should be noted that, by combining the optimal topology, and by completing the calculation of the number of collector circuits, wind turbine clustering, and line path optimization, a collaborative optimization method of "clustering algorithm + improved Dijkstra algorithm" is adopted to achieve the coordinated linkage of collector circuit division, path optimization, and electrical parameter calculation.

[0122] In this embodiment of the invention, the number of collector circuits is accurately calculated: Taking into account the maximum allowable transmission capacity of a single circuit, the number of low-voltage side incoming line bays of the booster station, and the constraints on the grid's impact range on faults, the total number of collector circuits M is calculated using the following formula:

[0123] In the formula, P 总 P represents the total installed capacity of the wind farm. 单回路 The design transmission capacity for a single circuit of the collector line can be flexibly determined based on actual project constraints. The design transmission capacity of a single collector line should not exceed 40MW, and the number of series-connected wind turbines on a single circuit should not exceed 12. The calculation results must satisfy: M ≤ the number of 35kV incoming line bays at the substation, with two spare bays reserved.

[0124] Step S2042: Using the coordinates of each wind turbine location as the clustering feature, a clustering algorithm is used to divide the wind turbine belonging loops.

[0125] In this embodiment of the invention, the wind turbine belonging loop is determined based on the K-means clustering algorithm: 1) Using the wind turbine location coordinates as the clustering feature, the number of clusters is set to the total number of collector circuits M calculated in step S2041; 2) The K-means clustering algorithm is used to divide wind turbines that are geographically close into the same collection circuit, ensuring that the maximum spacing between wind turbines in the same circuit is ≤5km, thereby reducing line crossings and redundant lengths. 3) Circuit division balance verification: The total capacity deviation of the fans connected to each circuit is ≤10% to avoid overloading of a single circuit. At the same time, the number of fans connected in series in each circuit is ≤12 to meet the engineering constraints.

[0126] Step S2043: Construct a digital geographic network for the wind farm. Starting from the substation power distribution device of the wind farm digital geographic network and with each wind turbine location as a network node, the optimal broken line path length of each collection line is solved by introducing path comprehensive weight.

[0127] In this embodiment of the invention, route optimization is based on the improved Dijkstra algorithm: 1) Construct a digital map of the wind farm, starting from the location of the 35kV power distribution device of the substation, with each wind turbine location as a node, and mark obstacles, adverse geological areas, and crossing points as impassable areas; 2) An improvement to the traditional Dijkstra algorithm is made by introducing a comprehensive weight coefficient. The path weight calculation formula is as follows: W = L × (k1 + k2 + k3) In the formula: W is the comprehensive weight of the path; L is the straight length of the path; k1 is the terrain construction difficulty coefficient, which is 1.0 for flat terrain, 1.2~1.5 for mountainous / hilly terrain, and 1.1~1.3 for sandy / gobi wasteland; k2 is the corridor cost coefficient, which is 2.0~3.0 for basic farmland and ecological protection areas, and 1.0 for general wasteland; k3 is the crossing coefficient, which is 1.3 for crossing highways / railways, 1.2 for crossing power lines, and 1.0 for no crossing. 3) Following the series connection sequence of "substation → first wind turbine → last wind turbine", the optimal broken-line path length L of each collector line segment is calculated using an improved Dijkstra algorithm. i It automatically avoids impassable areas; Step S2044: Calculate the total calculated length of each collector line by combining the optimal broken path length of each collector line and the line tortuosity coefficient corresponding to different terrains.

[0128] In this embodiment of the invention, the total calculated length of a single-circuit collector line is calculated according to the formula for the total calculated length of a single-circuit collector line in step a7, taking into account the tortuosity coefficient of the engineering path. The coefficient is 1.03~1.05 for flat terrain and 1.05~1.15 for mountainous / hilly terrain.

[0129] Step S2045: Input the total number of collector circuits, the results of wind turbine circuit division, the optimal broken line path length of each collector line, and the total calculated length of each collector line into the parameterized simulation model and multi-objective collaborative optimization model of the collector system for verification.

[0130] In this embodiment of the invention, the path optimization results (i.e., the total number of collector circuits, the wind turbine circuit division results, the optimal broken line path length of each collector line, and the total calculated length of each collector line) are fed back in real time to the simulation model in step S201 and the optimization function in step S2024 to calculate the line impedance, power loss, and initial investment, and to verify whether the hard constraints are met. After the verification is passed, the final collector circuit division scheme, line path scheme, and line length parameters are output for subsequent conductor cross-section optimization.

[0131] In step S2046, if the verification does not meet the preset constraints, the number of collector circuits, the division of wind turbine circuits, and the laying path of the line are adjusted in reverse iteration, and all electrical parameters are recalculated.

[0132] In this embodiment of the invention, when the planning scheme of the collector circuit and the line path parameters do not meet the preset electrical and engineering constraints after verification by the simulation model, the total number of collector circuits, the classification method of wind turbine circuits and the overall line laying broken line path are adjusted in reverse iteration. At the same time, all key electrical parameters such as line impedance, current carrying capacity and voltage loss are recalculated and then sent to the simulation model for verification again until all indicators meet the preset constraints.

[0133] Step S2047: If the verification meets all the preset constraints, then determine the collector circuit planning scheme, the line path planning scheme, and the line length parameters of each circuit.

[0134] In this embodiment of the invention, when all parameters of the power collection system fully meet all preset hard constraints after simulation verification, it is determined that the current wind turbine circuit division and wiring layout meet the requirements of electrical operation and engineering construction. Then, the final power collection circuit planning scheme, the optimal laying path planning scheme of the line, and the actual line length parameters corresponding to each circuit are solidified and determined, providing accurate basic parameter support for subsequent conductor cross-section optimization and protection setting verification.

[0135] Step S205: Based on the preset hierarchical adaptive optimization principle of collector conductor cross-section, and combined with the collector circuit and path planning scheme and line length parameters, determine the conductor cross-section configuration of each collector line.

[0136] Specifically, step S205 includes: Step S2051: Extract the transmission distance of the line, the line length parameters of each circuit, the annual maximum utilization hours of the wind farm, and the ambient temperature from the collection circuit and path planning scheme and the line length parameters, and calculate the adaptive economic current density.

[0137] It should be noted that, strictly adhering to the principles of "main line cross-section ≥ branch line cross-section" and "reasonable current carrying capacity redundancy and optimal economic loss," an innovative adaptive economic current density algorithm is introduced by completing hierarchical adaptive optimization and multi-condition verification of conductor cross-sections, which differs from the existing fixed economic current density selection method. The preset hierarchical adaptive optimization principle for collector conductor cross-sections is "main line cross-section ≥ branch line cross-section" and "reasonable current carrying capacity redundancy and optimal economic loss."

[0138] In this embodiment of the invention, the transmission distance of the line, the length parameters of the sub-circuit line, the maximum annual utilization hours of the wind farm and the ambient temperature are extracted from the collection circuit and path planning scheme and the line length parameters. The conventional economic current density calculation formula is modified by combining the above-mentioned multi-dimensional influencing parameters. The numerical solution is completed by comprehensively considering the line heat loss, environmental heat dissipation conditions and annual load operating time, so as to obtain the adaptive economic current density that is suitable for the actual operating conditions of each segment of the line, and provides core calculation parameters for subsequent theoretical conductor cross-section calculation.

[0139] Specifically, the adaptive economic current density calculation involves dynamically adjusting the economic current density value based on factors such as transmission distance, maximum annual utilization hours, and ambient temperature. The calculation formula is as follows:

[0140] In the formula, J is the adaptive economic current density (unit: A / mm²); J0 is the national standard recommended benchmark value for economic current density, which is 1.15 A / mm² for annual utilization hours above 3000h and 1.65 A / mm² for 2000~3000h; k t This is the ambient temperature correction factor, set to 1.0 at an ambient temperature of 25℃, decreasing by 0.05 for every 5℃ increase; k L The transmission distance correction factor is 1.0 for transmission distances ≤5km, 0.9 for 5~10km, and 0.8 for 10~20km. For long-distance lines, the economic current density is reduced, the conductor cross-section is increased, and the line loss is reduced.

[0141] Step S2052: Based on the load transmitted by each circuit and the adaptive economic current density, calculate the theoretical conductor cross-section corresponding to each segment of the line.

[0142] In this embodiment of the invention, optimization is performed in a hierarchical manner according to the "main line - branch line" model: the main line connecting multiple collection lines has its cross-section calculated based on the total load; the branch line of a single collection line has its cross-section calculated based on the load of that single line; and the nominal cross-section of the conductor (i.e., the theoretical conductor cross-section) is calculated based on the adaptive economic current density.

[0143] In the formula: S is the nominal cross-sectional area of ​​the conductor (unit: mm²); I c Calculate the current for the line (unit: A), based on the total rated current of the wind turbine connected to the line, with a margin of 1.1 times.

[0144] Step S2053: Based on the preset adaptive optimization principle of collector conductor cross-section classification, the theoretical conductor cross-section corresponding to each segment of the line is initially selected in a graded manner.

[0145] In this embodiment of the invention, based on the calculation results of the theoretical conductor cross-section, the standard conductor cross-section closest to the next adjacent level is selected, with priority given to the three mainstream models JL / G1A-240 / 30, JL / G1A-300 / 40, and JL / G1A-400 / 50, to ensure that the selection conforms to the conventional design habits of the project.

[0146] Step S2054: Input the theoretical conductor cross-section, loop load, and loop line length parameters obtained from the initial selection into the parameterized simulation model of the power collection system for multi-dimensional compliance verification, and determine the conductor cross-section configuration of each power collection line based on the compliance verification results.

[0147] In this embodiment of the invention, the multi-condition compliance verification of the conductor cross-section is performed as follows: For the initially selected conductor cross-section, five compliance checks are conducted. If any check fails, the conductor cross-section is upgraded until all checks pass. 1) Current carrying capacity verification: Correct the conductor's allowable current carrying capacity according to the highest ambient temperature and altitude to ensure that the long-term operating current of the line is ≤ 90% of the corrected allowable current carrying capacity; 2) Voltage loss verification: The voltage loss of a single collection line is ≤2% of the rated voltage, ensuring that the voltage deviation at the wind turbine grid connection point meets the constraint requirement of ≤±5%. 3) Corona test: Under the voltage level of 35kV, the conductor cross-section is ≥150mm² to avoid corona loss in sunny weather. The test is based on the "Design Code for 110kV~750kV Overhead Transmission Lines" GB 50545-2010. 4) Mechanical strength verification: For lines in mountainous or windy areas, the conductor cross-section must be ≥240mm², and the maximum usable stress of the conductor must not exceed 40% of its tensile strength. 5) Economic verification: Calculate the total life cycle cost (initial investment + 25 years of loss cost) of conductors with different cross-sections, and select the cross-section model with the lowest total life cycle cost.

[0148] After the verification is passed, the conductor type, cross-sectional parameters, and impedance parameters per unit length of each collector line are output and synchronously updated to the simulation model in step S201 and the optimization function in step S2024, thus completing the collaborative closed loop of conductor cross-section and loop division and path optimization.

[0149] Step S206: Perform simulation verification on the optimal topology scheme and the conductor cross-section configuration of each collector line, and determine the optimal collector system design scheme according to the simulation verification results.

[0150] Specifically, step S206 includes: Step S2061: Import the optimal topology scheme and the conductor cross-section configuration of each collector line into the parameterized simulation model of the collector system for fault simulation, and calculate the short-circuit current of each fault node.

[0151] It should be noted that, based on the optimal topology and conductor cross-section configuration, by completing short-circuit current calculation, protection setting adjustment, differential coordination verification, and simulation verification, the selectivity, speed, and reliability of the protection action under fault conditions are ensured.

[0152] In this embodiment of the invention, the short-circuit current is calculated: The symmetrical component method is used to calculate the three-phase short-circuit, single-phase ground fault, and two-phase short-circuit current values ​​of each fault node under the maximum operating mode of the system. The calculation scope covers: the high-voltage side of each wind turbine box transformer, the beginning / end of each collector line, and the 35kV busbar. The short-circuit current of all fault nodes is verified to be ≤31.5kA, which does not exceed the rated breaking current of the switchgear and meets the constraint requirements.

[0153] Step S2062: Based on the short-circuit current calculation results of each fault node, the setting values ​​and level difference verification of the three-level relay protection of the high-voltage side of the transformer substation, the collector line and the incoming line of the step-up substation are carried out through the parameterized simulation model of the collector system. The final optimal collector system design scheme is determined according to the verification results.

[0154] In this embodiment of the invention, the setting value of the three-level relay protection is as follows: Referring to the "Technical Specification for Relay Protection and Safety Automatic Devices" (GB / T 14285), the "Technical Specification for Relay Protection Configuration and Setting of Grid-Connected Wind Farms" (DL / T 1631), and the "Operation and Setting Procedure for Relay Protection Devices of 3kV~110kV Power Grids" (DL / T584), the setting values ​​for the three-level protection of the high-voltage side of the transformer substation, the collector line, and the low-voltage side incoming line of the step-up substation were completed. The specific setting rules are as follows: 1) High-voltage side protection of the transformer substation: Equipped with instantaneous overcurrent protection and overcurrent protection. The instantaneous overcurrent protection is set to avoid the three-phase short-circuit current on the low-voltage side of the transformer substation, with an operating time of 0s; the overcurrent protection is set to avoid the rated current of the transformer substation, with an operating time of 0.3s. 2) Protection of the collection line: Equipped with fiber optic differential protection, overcurrent protection and zero-sequence protection. The overcurrent protection is set to avoid the maximum load current of the line, with an operating time of 0.6s and a protection level difference of ≥0.3s with the transformer substation. 3) 35kV incoming line protection for the substation: Overcurrent protection and zero-sequence protection are configured. The overcurrent protection is set to avoid the maximum load current of the busbar, with an operating time of 1.0s and a difference of ≥0.3s between the protection level and the protection level of the collector line.

[0155] Protection level fit simulation verification: 1) Input the adjusted protection settings into the Simulink protection simulation module built in step S201, build the protection action logic, and realize the functions of fault identification, action time limit control, and fault isolation. 2) Set up multiple fault scenarios: short circuit on the low-voltage side of the transformer, short circuit in the middle section of the collector line, and short circuit on the 35kV bus. Simulate metallic short circuit and short circuit through a 100Ω transition resistor, respectively. The simulation time is 2s and the fault occurrence time is set to 0.5s. 3) Collect the action time and sequence of each level of protection after the fault occurs, and verify whether the time limit difference of the three levels of protection is within the range of 0.3~0.5s to ensure that the near backup protection takes priority and the far backup protection provides reliable backup in the event of a fault, without any failure to operate or false operation.

[0156] After verification, the system outputs a list of three-level protection settings, action time limit configuration, and level coordination instructions, completing the entire process optimization of the collector system design.

[0157] Output the optimal collector system design scheme and perform closed-loop verification: After completing all the above optimization and verification steps, output a standardized and directly implementable optimal collector system design scheme. The scheme is comprehensive and standardized, and can be directly used for engineering design, construction drawing preparation and project site implementation. At the same time, all design parameters and optimization results can be imported into the Simulink simulation model to realize the simulation verification and iterative optimization of the scheme, forming a closed-loop system of "design-optimization-verification-testing" to improve the reliability and verifiability of the scheme.

[0158] The present invention has the following beneficial effects: 1) Innovative design pattern, breaking through the limitations of traditional experience-driven approaches: The design pattern of the collector system has been upgraded from experience-driven to vectorized optimization and automated closed-loop, establishing a standardized design process and precise calculation methods. The innovative introduction of data preprocessing and traceability mechanisms effectively standardizes design behavior, improves design quality and solution consistency, reduces the differences in solutions among different design teams, and lowers design management costs. Engineering verification shows that the design cycle of the collector system can be shortened from the traditional 1 month to less than 10 days, significantly improving design efficiency.

[0159] 2) Optimization strategy innovation to achieve multi-objective collaborative optimization: The weighted adaptive multi-objective optimization function is adopted, combined with the improved genetic algorithm and entropy weight method, taking into account multiple factors such as initial investment, operating loss, power supply reliability, short-circuit current control, and relay protection coordination. This effectively solves the pain point of the single design objective in traditional design, and is different from the existing optimization schemes with fixed weights and single algorithms. According to engineering cases, the method of this invention can reduce the annual loss of the power collection system by 8% to 12%, reduce the initial investment by 3% to 5%, and improve the power supply reliability by 10% to 15%, significantly improving the comprehensive benefits of the power collection system throughout its entire life cycle.

[0160] 3) Innovative technical solutions to form an integrated closed-loop system: An innovative integrated design system is constructed, consisting of "parametric modeling - multi-objective collaborative optimization - intelligent topology selection - hierarchical precise configuration - closed-loop simulation verification". This system achieves coordinated linkage between topology selection, loop division, conductor cross-section optimization, and protection verification. It also introduces modular design and adaptive algorithms, which is different from existing single-link optimization and closed-loop verification technologies. It has stronger adaptability and can be widely applied to large-scale wind farms of 300MW to 1000MW. It is suitable for engineering scenarios with different terrains such as plains, mountains, deserts, and nearshore areas, as well as different wind turbine layouts. It has significant advantages, especially for special scenarios of distributed wind turbine layout and long-distance power transmission, and has a wide range of applications.

[0161] 4) Improved efficiency and practicality, adapting to the needs of rapid project advancement: It can be seamlessly integrated into the existing engineering design process. Through parametric modeling and automatic optimization, it significantly reduces the number of design iterations, greatly improves design efficiency, and meets the needs of rapid advancement of large-scale wind power projects. At the same time, the optimized solution is in line with the actual engineering situation and can be directly implemented. It effectively avoids common problems in traditional design such as voltage deviation exceeding limits, protection malfunction / failure to operate, and high line loss. It significantly reduces the total life cycle cost of wind farms, improves the safety and stability level of grid-connected operation, and has strong engineering practicality.

[0162] 5) Rigorous and traceable data, and highly verifiable solutions: All basic data, calculation processes, and optimization results are standardized and quantitatively expressed, data errors are controlled within a reasonable range, a data traceability mechanism is established, and closed-loop verification is achieved through Simulink simulation to ensure the scientific validity, reliability, and feasibility of the solution.

[0163] This embodiment also provides a current collector system optimization design apparatus, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0164] This embodiment provides a current collector system optimization design device, such as... Figure 3 As shown, it includes: The first construction module 301 is used to construct a parameterized simulation model of the power collection system based on multi-source data from the wind farm. The second construction module 302 is used to construct a multi-objective collaborative optimization model under preset hard constraints, with the optimization objective of minimizing the comprehensive cost of the entire life cycle of the power collection system; The iterative optimization module 303 is used to iteratively optimize the multi-objective collaborative optimization model, determine the optimal topology scheme based on the optimization results, and input it into the parameterized simulation model of the collector system. Planning module 304 is used to perform collaborative optimization of the number of collector circuits, line laying path, and electrical parameters of the collector system through a parametric simulation model of the collector system, so as to obtain the collector circuit and path planning scheme and line length parameters. Combined with module 305, it is used to determine the conductor cross-section configuration of each collector line based on the preset hierarchical adaptive optimization principle of collector conductor cross-section, combined with the collector circuit and path planning scheme and line length parameters. The simulation verification module 306 is used to perform simulation verification of the optimal topology scheme and the conductor cross-section configuration of each collector line, and to determine the optimal collector system design scheme according to the simulation verification results.

[0165] In some alternative implementations, the first building module 301 includes: The data acquisition unit is used to collect core wind farm parameters, wind turbine layout parameters, substation parameters, electrical constraint parameters, conductor and cable parameters, core equipment parameters, environmental and terrain parameters, and auxiliary parameters of the wind farm, and generate multi-source data. The processing unit is used to perform data validity verification, abnormal data tracing and correction, and parameter standardization and reduction on multi-source data, and to build a multi-source database from the processed multi-source data. The model building unit is used to construct a parametric simulation model of a current collector system based on a multi-source database.

[0166] In some alternative implementations, the model building unit includes: The module area is a sub-unit used to construct the wind turbine cluster module area, power collection line module area, substation module area, fault simulation module area and real-time monitoring module area based on a multi-source database; The connecting subunit is used to connect the wind turbine cluster module area, the power collection line module area, the booster station module area, the fault simulation module area, and the real-time monitoring module area through electrical nodes to generate the power collection system topology network. The wind turbine simulation module construction subunit is used to build the wind turbine simulation module based on the wind turbine layout parameters and corresponding configuration parameters from a multi-source database. The transformer substation simulation module construction subunit is used to call the preset dual-winding transformer module and the corresponding configuration parameters in the multi-source database to construct the transformer substation simulation module; The collector line simulation module construction subunit is used to call the preset PI type equivalent line module, the conductor and cable parameters and corresponding configuration parameters in the multi-source database to construct the collector line simulation module; The substation simulation module construction subunit is used to call the configuration parameters of the 35kV bus module, main transformer module, receiving-end power grid equivalent module and multi-source database to construct the substation simulation module. Combined with sub-units, it is used to construct a parametric simulation model of the power collection system by combining the power collection system topology network, wind turbine simulation module, box-type substation simulation module, power collection line simulation module, step-up substation simulation module and corresponding line core parameters in multi-source database; The steady-state verification subunit is used to perform steady-state verification and fault transient verification on the parameterized simulation model of the collector system.

[0167] In some alternative implementations, the second building module 302 includes: The hard constraint condition unit is used to determine hard constraint conditions based on preset voltage constraint conditions, current carrying capacity constraint conditions, short circuit current constraint conditions, protection coordination constraint conditions and engineering constraint conditions; The calculation unit is used to calculate the initial investment indicators, annual loss cost indicators, and reliability indicators of the power collection system. The weighted fusion processing unit is used to perform weighted fusion processing on the initial investment indicators, annual loss cost indicators and reliability indicators of the power collection system to determine the comprehensive cost of the power collection system throughout its entire life cycle. The optimization model building unit is used to construct a multi-objective collaborative optimization model under preset hard constraints, with the goal of minimizing the comprehensive cost throughout the entire life cycle.

[0168] In some alternative implementations, the iterative optimization module 303 includes: The setting unit is used to set multiple optimization variables and set a single chromosome as the current collection system design scheme. The multi-objective collaborative optimization model is iteratively optimized based on the improved genetic algorithm. The selection unit is used to select a preset number of chromosomes for each iteration, and performs crossover operations with a high crossover probability in the early stage of the iteration and a low crossover probability in the later stage of the iteration to obtain updated chromosomes; The mutation operation unit is used to perform mutation operations on each updated chromosome using a phased adaptive mutation probability, and outputs the radial optimal topology solution set, the single-ring network optimal topology solution set, and the hybrid topology optimal topology solution set, respectively. The scheme extraction unit is used to extract radial topology schemes, single-ring network topology schemes, and hybrid topology schemes from the radial optimal topology solution set, single-ring network optimal topology solution set, and hybrid topology optimal topology solution set, respectively. The matrix construction unit is used to construct a multi-objective decision matrix with radial topology schemes, single-ring network topology schemes and hybrid topology schemes as matrix rows, and the initial investment index, annual loss cost index and reliability index of the power collection system as matrix columns. The fit calculation unit is used to calculate the comprehensive weights of the initial investment index, annual loss cost index and reliability index based on the multi-objective decision matrix, and to calculate the fit of the radial topology scheme, the single ring network topology scheme and the hybrid topology scheme respectively. The input unit is used to determine the optimal topology scheme based on each comprehensive weight and each fit degree and input it into the parameterized simulation model of the collector system.

[0169] In some alternative implementations, the planning module 304 includes: The total quantity calculation unit is used to calculate the total number of collection circuits based on the total installed capacity of the wind farm, the incoming line interval parameters of the booster station, and the preset transmission capacity constraints. Clustering units are used to divide the wind turbines into their respective loops using clustering algorithms, with the coordinates of each wind turbine's location as the clustering feature. The solution unit is used to construct a digital geographic network for wind farms. Starting from the substation power distribution device of the wind farm digital geographic network and with each wind turbine location as a network node, the optimal broken line path length of each collection line is solved by introducing path comprehensive weight. The total calculation length unit is used to calculate the total calculation length of each power collection line by combining the optimal broken path length of each power collection line and the line tortuosity coefficient corresponding to different terrains. The verification unit is used to input the total number of collector circuits, the wind turbine circuit division results, the optimal broken line path length of each collector line, and the total calculated length of each collector line into the parameterized simulation model and multi-objective collaborative optimization model of the collector system for verification. The calculation unit is used to iteratively adjust the number of collector circuits, the division of wind turbine circuits, and the laying path of lines if the verification does not meet the preset constraints, and recalculate all electrical parameters. The verification unit is used to determine the collector circuit planning scheme, the line path planning scheme, and the line length parameters of each circuit if the verification meets all the preset constraints.

[0170] In some alternative implementations, the incorporation module 305 includes: The adaptive economic current density calculation unit is used to extract the transmission distance of the line, the line length parameters of each circuit, the annual maximum utilization hours of the wind farm, and the ambient temperature from the collection circuit and path planning scheme and the line length parameters, and to calculate the adaptive economic current density. Solving conductor cross-section elements is used to solve for the theoretical conductor cross-section corresponding to each segment of the line based on the load transmitted in each circuit and the adaptive economic current density. The initial selection unit is used to perform a preliminary selection of the theoretical conductor cross-sections corresponding to each segment of the line based on a preset adaptive optimization principle for conductor cross-section classification. The compliance verification unit is used to input the initially selected theoretical conductor cross-section, loop transmission load, and loop line length parameters into the parameterized simulation model of the power collection system for multi-dimensional compliance verification, and to determine the conductor cross-section configuration of each power collection line based on the compliance verification results.

[0171] In some alternative implementations, the simulation verification module 306 includes: The fault simulation unit is used to import the optimal topology scheme and the conductor cross-section configuration of each collector line into the parameterized simulation model of the collector system for fault simulation, and to calculate the short-circuit current of each fault node. The final design scheme determination unit is used to perform setting and differential verification of the three-level relay protection of the high-voltage side of the transformer substation, the collector line and the incoming line of the step-up substation based on the short-circuit current calculation results of each fault node and through the parameterized simulation model of the collector system. Based on the verification results, the final optimal collector system design scheme is determined.

[0172] The current collector system optimization design apparatus provided in this embodiment of the invention can execute the current collector system optimization design method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0173] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the power supply system optimization design method shown in the above embodiments is implemented.

[0174] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for optimizing the design of a current collector system, characterized in that, The method includes: Based on multi-source data from wind farms, a parametric simulation model of the power collection system is constructed. Under preset hard constraints, a multi-objective collaborative optimization model is constructed with the goal of minimizing the comprehensive cost of the entire life cycle of the power collection system. The multi-objective collaborative optimization model is iteratively optimized, and the optimal topology scheme is determined based on the optimization results and input into the parameterized simulation model of the power collection system. The number of collector circuits, line laying paths, and electrical parameters of the collector system are optimized collaboratively using the parametric simulation model of the collector system to obtain the collector circuit and path planning scheme and line length parameters. Based on the preset hierarchical adaptive optimization principle of collector conductor cross-section, and combined with the collector circuit and path planning scheme and the line length parameters, the conductor cross-section configuration of each collector line is determined. The optimal topology scheme and the conductor cross-section configuration of each collector line are simulated and verified, and the optimal collector system design scheme is determined according to the simulation verification results.

2. The method according to claim 1, characterized in that, The construction of a parametric simulation model of the power collection system based on multi-source data from the wind farm includes: Collect core wind farm parameters, wind turbine layout parameters, substation parameters, electrical constraint parameters, conductor and cable parameters, core equipment parameters, environmental and terrain parameters, and auxiliary parameters of the wind farm to generate multi-source data; The multi-source data is subjected to data validity verification, abnormal data tracing and correction, and parameter standardization and reduction processing, and the processed multi-source data is used to construct a multi-source database; Based on the aforementioned multi-source database, a parameterized simulation model of the power collection system is constructed.

3. The method according to claim 2, characterized in that, The construction of a parameterized simulation model of the power collection system based on the multi-source database includes: Based on the multi-source database, a wind turbine cluster module area, a power collection line module area, a booster station module area, a fault simulation module area, and a real-time monitoring module area are constructed. The wind turbine cluster module area, power collection line module area, booster station module area, fault simulation module area and real-time monitoring module area are connected by electrical nodes to generate a power collection system topology network; Based on the wind turbine layout parameters and corresponding configuration parameters from the multi-source database, a wind turbine simulation module is built. The pre-defined dual-winding transformer module and the corresponding configuration parameters in the multi-source database are used to construct the transformer substation simulation module; The preset PI-type equivalent circuit module, the conductor and cable parameters and corresponding configuration parameters in the multi-source database are called to construct the collector circuit simulation module; The simulation module for the step-up substation is constructed by calling the 35kV busbar module, the main transformer module, the receiving-end power grid equivalent module, and the corresponding configuration parameters in the multi-source database. By combining the power collection system topology network, the wind turbine simulation module, the transformer substation simulation module, the power collection line simulation module, the booster station simulation module, and the corresponding line core parameters in the multi-source database, a parameterized simulation model of the power collection system is constructed. Steady-state verification and fault transient verification are performed on the parameterized simulation model of the current collector system.

4. The method according to claim 1, characterized in that, Under preset hard constraints, a multi-objective collaborative optimization model is constructed with the goal of minimizing the comprehensive cost of the entire life cycle of the power collection system. This model includes: Based on preset voltage constraints, current carrying capacity constraints, short-circuit current constraints, protection coordination constraints, and engineering constraints, hard constraints are determined. Calculate the initial investment indicators, annual loss cost indicators, and reliability indicators of the power collection system; The initial investment index, annual loss cost index and reliability index of the power collection system are weighted and integrated to determine the comprehensive cost of the power collection system throughout its entire life cycle. Under pre-defined hard constraints, a multi-objective collaborative optimization model is constructed with the goal of minimizing the overall cost throughout the entire life cycle.

5. The method according to claim 4, characterized in that, The iterative optimization of the multi-objective collaborative optimization model, determining the optimal topology scheme based on the optimization results and inputting it into the parameterized simulation model of the power collection system, includes: Multiple optimization variables are set, and a single chromosome is set as the current collection system design scheme. The multi-objective collaborative optimization model is iteratively optimized based on an improved genetic algorithm. A preset number of chromosomes are selected for each iteration, and crossover operations are performed with a high crossover probability in the early stage of the iteration and a low crossover probability in the later stage of the iteration to obtain updated chromosomes; A phased adaptive mutation probability is used to perform mutation operations on each of the updated chromosomes, and the optimal topology solution sets for radial networks, single-ring networks, and hybrid topologies are output respectively. Radial topology schemes, single-ring network topology schemes, and hybrid topology schemes are extracted from the radial optimal topology solution set, the single-ring network optimal topology solution set, and the hybrid topology optimal topology solution set, respectively. A multi-objective decision matrix is ​​constructed using the radial topology scheme, the single-ring network topology scheme, and the hybrid topology scheme as matrix rows, and the initial investment index, annual loss cost index, and reliability index of the power collection system as matrix columns. Based on the multi-objective decision matrix, the comprehensive weights of the initial investment index, the annual loss cost index, and the reliability index are calculated, and the fit of the radial topology scheme, the single-ring network topology scheme, and the hybrid topology scheme are calculated respectively. Based on the comprehensive weights and fitting degrees of each, the optimal topology scheme is determined and input into the parameterized simulation model of the power collection system.

6. The method according to claim 3, characterized in that, The method involves collaboratively optimizing the number of collector circuits, line laying paths, and electrical parameters of the collector system using a parametric simulation model, resulting in collector circuit and path planning schemes and line length parameters, including: Based on the total installed capacity of the wind farm, the incoming line interval parameters of the booster station, and the preset transmission capacity constraints, calculate the total number of collection circuits; Using the coordinates of each wind turbine location as the clustering feature, a clustering algorithm is employed to divide the wind turbines into their respective loops; A digital geographic network for wind farms is constructed, with the substation power distribution device of the wind farm digital geographic network as the starting point and each wind turbine location as a network node. The optimal broken line path length of each collection line is solved by introducing path comprehensive weight. Based on the optimal broken path length of each collection line and the line tortuosity coefficient corresponding to different terrains, the total calculated length of each collection line is calculated. The total number of collector circuits, the wind turbine circuit division results, the optimal broken line path length of each collector line, and the total calculated length of each collector line are all input into the parameterized simulation model of the collector system and the multi-objective collaborative optimization model for verification. If the verification does not meet the preset constraints, the number of collector circuits, the division of wind turbine circuits, and the laying path of the line are adjusted in reverse iteration, and all electrical parameters are recalculated. If the verification meets all the preset constraints, then the collector circuit planning scheme, the line path planning scheme, and the line length parameters of each circuit are determined.

7. The method according to claim 6, characterized in that, The method, based on a preset hierarchical adaptive optimization principle for collector conductor cross-sections, and in conjunction with the collector circuit and path planning scheme and the line length parameters, determines the conductor cross-section configuration for each collector line, including: From the current collection circuit and path planning scheme, the line transmission distance, the line length parameters of each circuit, the annual maximum utilization hours of the wind farm and the ambient temperature are extracted from the line length parameters to calculate the adaptive economic current density. Based on the load transmitted by each circuit and the adaptive economic current density, the theoretical conductor cross-section corresponding to each segment of the line is calculated. Based on the preset adaptive optimization principle of collector conductor cross-section classification, the theoretical conductor cross-sections corresponding to each segment of the line are initially selected in a hierarchical manner. The theoretical conductor cross-section, loop load, and loop line length parameters obtained from the initial selection are input into the parameterized simulation model of the power collection system for multi-dimensional compliance verification. Based on the compliance verification results, the conductor cross-section configuration of each power collection line is determined.

8. The method according to claim 7, characterized in that, The process of simulating and verifying the optimal topology scheme and the conductor cross-section configuration of each collector line, and determining the optimal collector system design scheme based on the simulation and verification results, includes: The optimal topology scheme and the conductor cross-section configuration of each collector line are imported into the parameterized simulation model of the collector system for fault simulation, and the short-circuit current of each fault node is calculated. Based on the short-circuit current calculation results of each fault node, the setting values ​​and level difference verification of the three-level relay protection of the high-voltage side of the transformer substation, the collector line and the incoming line of the step-up substation are carried out through the parameterized simulation model of the collector system. The final optimal collector system design scheme is determined according to the verification results.

9. A current collector system optimization design device, characterized in that, The device includes: The first construction module is used to build a parameterized simulation model of the power collection system based on multi-source data from the wind farm; The second construction module is used to construct a multi-objective collaborative optimization model under preset hard constraints, with the goal of minimizing the comprehensive cost of the entire life cycle of the power collection system. The iterative optimization module is used to iteratively optimize the multi-objective collaborative optimization model, determine the optimal topology scheme based on the optimization results, and input it into the parameterized simulation model of the power collection system. The planning module is used to perform collaborative optimization of the number of collector circuits, line laying paths, and electrical parameters of the collector system through the parametric simulation model of the collector system, so as to obtain the collector circuit and path planning scheme and line length parameters. The module is used to determine the conductor cross-section configuration of each collector line based on the preset hierarchical adaptive optimization principle of the collector conductor cross-section, combined with the collector circuit and path planning scheme and the line length parameters. The simulation verification module is used to perform simulation verification on the optimal topology scheme and the conductor cross-section configuration of each of the collector lines, and to determine the optimal collector system design scheme according to the simulation verification results.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the current collector system optimization design method according to any one of claims 1 to 8.

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