LCC-MMC hybrid converter topological structure optimization method

Abstract

The invention discloses a topological structure optimization method for an LCC-MMC (Liquid Crystal Converter-Multi Media Card) hybrid converter. The method comprises the following steps of: establishing a multi-dimensional optimization model containing a core variable and defining a constraint boundary on the basis of topological composition, element parameters and operation conditions of the converter; constructing a four-target weighted superposition function of submodule volume, total loss and the like, and determining a dynamic weight through fusion of an analytic hierarchy process and an entropy weight method; then solving an optimal parameter combination by adopting a multi-target particle swarm algorithm containing improved strategies such as adaptive inertia weight and the like; and finally, verifying a scheme result from three aspects through simulation and a reduced scale prototype test. According to the method, a multi-dimensional optimization variable model is established; a weighted superposition optimization function is constructed, collaborative optimization of subjective requirements and objective data is realized by adopting a method for determining dynamic weights by fusing an analytic hierarchy process and an entropy weight method, the compactness, economical efficiency and stability of the converter are remarkably improved, and reliable support is provided for engineering design and operation optimization.

Description

Technical Field

[0001] This invention relates to the field of power electronics technology, and in particular to a method for optimizing the topology of an LCC-MMC hybrid converter. Background Technology

[0002] With the widespread application of high-voltage direct current (HVDC) transmission technology in inter-regional energy transmission and grid interconnection, the LCC-MMC hybrid converter, leveraging the large capacity and low cost advantages of LCC (Line Commutated Converter) converters and the low harmonics and flexible control characteristics of MMC (Modular Multilevel Converter) converters, has become one of the core devices in HVDC transmission systems. However, the existing topology design of LCC-MMC hybrid converters still suffers from numerous technical challenges, hindering their economic efficiency and reliability in engineering applications. 1. The topology optimization objective is singular, ignoring the coupling relationship between submodule volume, total loss, fault repair cost, and local temperature difference; 2. Existing methods lack rationality in weight allocation, often employing fixed subjective weights or a single objective weight, resulting in poor adaptability of optimization results; 3. Traditional multi-objective optimization suffers from slow convergence speed and other problems, making it difficult to quickly find the globally optimal combination of topological parameters under complex constraints.

[0003] Therefore, there is an urgent need to develop a multi-objective collaborative optimization method for LCC-MMC hybrid converter topology optimization, which features scientific weight allocation, efficient algorithms, and thorough verification, to address the shortcomings of existing technologies and improve the overall performance and engineering applicability of converters. Summary of the Invention

[0004] This invention aims to at least solve one of the technical problems existing in related technologies. To this end, this invention provides a method for optimizing the topology of an LCC-MMC hybrid converter, verifying the effectiveness of the optimization results from multiple dimensions, and providing a basis for converter engineering design and operational optimization.

[0005] This invention provides a method for optimizing the topology of an LCC-MMC hybrid converter, comprising: S1: Based on the converter topology, component parameters and operating conditions, establish a multi-dimensional optimization variable model and clarify the voltage constraint boundary and current constraint boundary; S2: Based on the multi-dimensional optimization variable model, voltage constraint boundary, and current constraint boundary, a four-item objective weighted optimization function is constructed, and the dynamic weights are determined by the analytic hierarchy process and the entropy weight method. S3: Based on the dynamic weights combined with topological constraints and the objective function, the optimal combination of topological parameters is solved by an improved multi-objective particle swarm optimization algorithm; S4: The optimal combination of topology parameters is verified through simulation testing and experiments. The optimization results are evaluated from three aspects to provide a basis for converter engineering design and operation optimization.

[0006] According to the present invention, an LCC-MMC hybrid converter topology optimization method is provided, step S1 including: The LCC-MMC topology consists of an LCC converter bridge, an MMC converter valve group, sub-module units, connecting reactors, and filtering devices; the component parameters include sub-module capacitance values, IGBT rated current and voltage, reactor inductance values, and converter transformer turns ratio; the operating conditions include rated transmission power, voltage level, load fluctuation range, and probability of failure scenarios. The core variables of the multi-dimensional optimization variable model include: submodule type selection, number of submodules connected in series, inductance value of MMC bridge arm reactor, firing angle of LCC converter bridge, and capacitance value of submodule. The specific constraints are: voltage constraint, current constraint, space constraint, thermal constraint, and fault constraint. Among them, the voltage constraint is that the fluctuation range of the converter output line voltage is less than or equal to ±5% of the rated voltage; the current constraint is that the component operating current is less than or equal to 1.2 times the rated current; the space constraint is that the total installation volume of the submodule is less than or equal to a preset threshold; the thermal constraint is that the maximum operating temperature of the component is less than or equal to 125℃; and the fault constraint is that the converter can still maintain greater than or equal to 80% of the rated power output when a single module fails.

[0007] According to the present invention, a method for optimizing the topology of an LCC-MMC hybrid converter is provided, wherein step S2 includes: S21: Optimize the objective function The expression is: in, To comprehensively optimize the target value, The weight of the submodule volume. The weight of the total loss of the submodule. Weighting of submodule failures and maintenance costs. The weights of the temperature differences between submodules satisfy the following conditions: And 0 < <1, 0< <1, 0< <1, 0< <1; For the size of the submodule, Total loss, For failure and repair costs, For temperature difference; S22: Analytic Hierarchy Process (AHP) for Determining Subjective Weights: Constructing a hierarchical structure of target layer and criterion layer, building a judgment matrix through expert scoring, and calculating the subjective weights of the sub-module volumes of each target. Subjective weight of total loss of submodule Subjective weighting of submodule failures and maintenance costs Subjective weight of temperature difference in sub-modules ; S23: Construct a decision matrix based on m sets of historical operating data or simulation data. Standardization process yields the standard decision matrix. : in, for No. Line number The data in the column, for No. Line number The data in the column, for No. The data in the column, It is a minimum value function. It is a function with maximum value. The row number, The column ordinal number; Calculate the information entropy of each target ,pass To obtain objective weights : in, The weight ordinal number; S24: Dynamic weight fusion to obtain the final weight. : in, This is the weighting balance coefficient.

[0008] According to the LCC-MMC hybrid converter topology optimization method provided by the present invention, the objective functions in step S21 are defined as follows: Where N is the number of submodules, For the volume of a single capacitor, For the volume of a single IGBT module, For the volume of a single heat dissipation structure; in, For converter conduction losses, For switching losses, For reactor losses, Transformer losses; and The calculation formula is as follows: in, The on-resistance of the component. This is the operating current. For the number of IGBT switching operations, Single switching loss; in, For failure costs, For maintenance costs; in, This represents the highest temperature of the submodule within the bridge arm. This is the lowest temperature of the submodule within the bridge arm.

[0009] This invention provides a method for optimizing the topology of an LCC-MMC hybrid converter. The value range is 0.3≤ ≤0.7.

[0010] According to the LCC-MMC hybrid converter topology optimization method provided by the present invention, step S3 includes: S31: Abstract each set of topological structure parameters into a particle. The dimension of the particle corresponds exactly to the number of optimization variables. Based on the preset constraint boundary, the initial particle population is randomly generated. S32: Substitute the topological parameters corresponding to each particle into the weighted superposition optimization objective function to calculate the initial fitness value; at the same time, preset constraints and make a feasibility judgment on the particles; for infeasible solutions that violate the constraints, increase their fitness value by assigning an additional penalty term, and the penalty intensity is positively correlated with the severity of the constraint violation. S33: Each particle dynamically adjusts its speed and position based on three types of optimal solutions: the individual optimal solution is the fitness-optimal solution found by the particle in the historical iterations, the global optimal solution is the optimal solution of the entire particle population up to the current iteration, and the neighborhood optimal solution is the optimal solution of other particles within the particle's surrounding range; after the position is updated, constraint verification is performed. If the updated parameters exceed the constraint boundary, they are automatically adjusted to the boundary value to ensure that the particle is always in the feasible solution space. S34: Set dual convergence conditions: first, the number of iterations reaches a preset maximum value; second, in 10 consecutive iterations, the change in the fitness value of the global optimal solution is less than a threshold. If either condition is met, the iteration stops. S35: After the iteration terminates, extract all non-dominated solutions from the elite storage pool, and select the optimal combination of topology parameters from the set of non-dominated solutions that takes into account the core requirements and the performance of other objectives, based on the priority requirements set in the actual project.

[0011] According to the LCC-MMC hybrid converter topology optimization method provided by the present invention, the simulation test in step S4 is based on the construction of an LCC-MMC hybrid converter simulation model using PSCAD (Power Systems Computer Aided Design), inputting the optimal topology parameters, and simulating three typical operating conditions: rated power, load change, and single module failure. Topology rationality: Submodule layout compactness is greater than or equal to 0.85, and component stress distribution uniformity is less than or equal to 1.2; Target optimization results: Compared with before optimization, the submodule volume reduction rate is greater than or equal to 15%, the total loss reduction rate is greater than or equal to 10%, the failure and maintenance cost reduction rate is greater than or equal to 8%, and the temperature difference reduction rate is greater than or equal to 20%; Operational stability: DC voltage ripple rate less than or equal to 2%, converter trigger delay time less than or equal to 5ms, and voltage recovery time under fault conditions less than or equal to 0.1s; The experiment uses a 1:10 scale prototype to test the target parameters and stability indicators in actual operation. If the error between the simulation and the experimental results is less than or equal to 5%, the optimization is confirmed.

[0012] An LCC-MMC hybrid converter topology optimization device includes: Optimization variable modeling module: used to establish a multi-dimensional optimization variable model based on converter topology, component parameters and operating conditions, and to define voltage constraint boundaries and current constraint boundaries; Objective function and weight construction module: used to construct a four-item objective weighted optimization function based on the multi-dimensional optimization variable model, voltage constraint boundary and current constraint boundary, and to determine the dynamic weights using the analytic hierarchy process and entropy weight method; The optimization solution module is used to solve for the optimal combination of topological structure parameters by combining the dynamic weights with topological constraints and objective functions through an improved multi-objective particle swarm optimization algorithm. The results evaluation module is used to evaluate the optimization results from three aspects after the optimal combination of topology parameters has been verified by simulation testing and experiments, providing a basis for converter engineering design and operation optimization.

[0013] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the LCC-MMC hybrid converter topology optimization method as described above.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the LCC-MMC hybrid converter topology optimization method as described above.

[0015] The beneficial effects of the technical solution provided by this invention are: 1. Multi-objective collaborative optimization: For the first time, sub-module volume, total loss, fault repair cost, and temperature difference are incorporated into a unified optimization framework, which solves the limitations of single-objective optimization and achieves optimal overall performance; 2. Scientific weight allocation: The dynamic weighting method combines the analytic hierarchy process (AHP) and the entropy weighting method, taking into account both engineering requirements and data characteristics, and adapting to different application scenarios. 3. Improved efficiency and accuracy: The improved multi-objective particle swarm optimization algorithm enhances convergence speed and optimal solution quality through strategies such as adaptive inertia weighting and crowding sorting.

[0016] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a topology diagram of an LCC-MMC hybrid converter.

[0019] Figure 2 This is a flowchart of a method for optimizing the topology of an LCC-MMC hybrid converter.

[0020] Figure 3 This is a block diagram of a system for optimizing the topology of an LCC-MMC hybrid converter.

[0021] Figure 4 A schematic diagram of the structure of the electronic device provided by the present invention.

[0022] Figure Labels Figure label: 101. Optimization variable modeling module; 102. Objective function and weight construction module; 103. Optimization solution module; 104. Result evaluation module; 810. Processor; 820. Communication interface; 830. Memory; 840. Communication bus. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The following embodiments are used to illustrate this invention but cannot be used to limit the scope of this invention.

[0024] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0025] The following is combined Figures 1 to 4 This invention is described.

[0026] Example Specifically, such as Figure 1 As shown, Figure 1 This is a topology diagram of an LCC-MMC hybrid converter.

[0027] Specifically, such as Figure 2 As shown, an LCC-MMC hybrid converter topology optimization method includes the following steps: S1: Based on the converter topology, component parameters and operating conditions, establish a multi-dimensional optimization variable model and clarify the voltage constraint boundary and current constraint boundary; S2: Based on the multi-dimensional optimization variable model, voltage constraint boundary, and current constraint boundary, a four-item objective weighted optimization function is constructed, and the dynamic weights are determined by the analytic hierarchy process and the entropy weight method. S3: Based on the dynamic weights combined with topological constraints and the objective function, the optimal combination of topological parameters is solved by an improved multi-objective particle swarm optimization algorithm; S4: The optimal combination of topology parameters is verified through simulation testing and experiments. The optimization results are evaluated from three aspects to provide a basis for converter engineering design and operation optimization.

[0028] Specifically, step S1 includes: The converter topology consists of an LCC converter bridge, an MMC converter valve group, sub-module units, connecting reactors, and filtering devices; Component parameters include submodule capacitance, IGBT rated current and voltage, reactor inductance, and converter transformer turns ratio; Operating conditions include rated transmission power, voltage level, load fluctuation range, and probability of failure scenarios; The core variables of the multi-dimensional optimization variable model include: submodule type selection, number of submodules connected in series, inductance value of MMC bridge arm reactor, firing angle of LCC converter bridge, and capacitance value of submodule. The specific constraints are: voltage constraint, current constraint, space constraint, thermal constraint, and fault constraint. Among them, the voltage constraint is that the fluctuation range of the converter output line voltage is less than or equal to ±5% of the rated voltage; the current constraint is that the component operating current is less than or equal to 1.2 times the rated current; the space constraint is that the total installation volume of the submodule is less than or equal to a preset threshold; the thermal constraint is that the maximum operating temperature of the component is less than or equal to 125℃; and the fault constraint is that the converter can still maintain greater than or equal to 80% of the rated power output when a single module fails.

[0029] Based on the converter topology, component parameters and operating conditions, a multi-dimensional optimization variable model is established and the constraint boundaries such as voltage and current are clearly defined. This embodiment focuses on a ±800kV high-voltage direct current (HVDC) transmission project for inter-regional energy transmission. The LCC-MMC hybrid converter is the core converter device of the system, undertaking the long-distance transmission of 5000MW rated power. The operating environment is outdoor, under normal climate conditions, requiring the converter to balance compact layout, low energy consumption, high reliability, and long lifespan. Specific initial parameters for each component are shown in Table 1. Table 1 Initial parameters of each component

[0030] Based on the converter topology characteristics and engineering feasibility, five core optimization variables were determined, with their values ​​strictly following equipment selection and operational constraints: when the submodule type combination is zero, 0 represents a half-bridge submodule; when the submodule type combination is 1, it represents a full-bridge submodule. Each phase adopts a mixed configuration of half-bridge and full-bridge submodules, with the number of full-bridge submodules accounting for 10%-30%. The number of submodules connected in series, N, ∈ [8, 20], must meet the converter output voltage level requirements; the inductance value L of the MMC bridge arm reactor, L ∈ [15mH, 45mH], balances the filtering effect and loss; the LCC converter bridge firing angle α, α ∈ [20°, 40°], avoids commutation failure and excessive loss; the submodule capacitance value C, C ∈ [800μF, 1800μF], balances voltage support and size control.

[0031] Specific quantification of constraints: Voltage constraint: The converter output line voltage must be between 760kV and 840kV, i.e., fluctuation less than 5%, to ensure matching with the system voltage level; Current constraint: The operating current of components such as IGBTs (Insulated Gate Bipolar Transistors) and reactors must be less than or equal to 6kA, i.e., 1.2 times the rated current, to avoid overcurrent damage to components; Space constraint: Based on the upper limit of engineering installation space, the total installation volume of the submodule must be less than or equal to 8m². 3 Thermal constraint: To prevent accelerated thermal aging, the maximum operating temperature T of all power electronic components is limited. max ≤125℃; Fault constraint: In the event of a single module failure, the converter output power shall be greater than or equal to 4000MW (80% of rated power) to ensure power supply continuity.

[0032] A weighted optimization function for four objectives, including submodule volume and total loss, is constructed, and the dynamic weights are determined by the analytic hierarchy process and the entropy weight method. The construction of the objective function is based on actual engineering needs. The calculation of each objective is based on mature engineering formulas and measured data to ensure quantitative accuracy.

[0033] Specifically, step S2 includes: S21: Optimize the objective function The expression is: in, To comprehensively optimize the target value; The weight of the submodule volume; The weight of the total loss of the submodule. Weighting of submodule failures and maintenance costs. The weights of the temperature differences between submodules satisfy the following conditions: And 0 < <1, 0< <1, 0< <1, 0< <1; For the size of the submodule, Total loss, For failure and repair costs, For temperature difference; Specifically, Where N is the number of submodules, For the volume of a single capacitor, For the volume of a single IGBT module, For the volume of a single heat dissipation structure; in, For converter conduction losses, For switching losses, For reactor losses, Transformer losses; and The calculation formula is as follows: in, The on-resistance of the component. This is the operating current. For the number of IGBT switching operations, Single switching loss; in, For failure costs, For maintenance costs; in, This represents the highest temperature of the submodule within the bridge arm. This is the lowest temperature of the submodule within the bridge arm.

[0034] S22: Analytic Hierarchy Process (AHP) for Determining Subjective Weights: Constructing a hierarchical structure of target layer and criterion layer, building a judgment matrix through expert scoring, and calculating the subjective weights of the sub-module volumes of each target. Subjective weight of total submodule loss Subjective weighting of submodule failures and maintenance costs Subjective weight of temperature difference in sub-modules ; S23: Construct a decision matrix based on m sets of historical operating data or simulation data. Standardization process yields the standard decision matrix. : in, for No. Line number The data in the column, for No. Line number The data in the column, for No. The data in the column, It is a minimum value function. It is a function with maximum value. The row number, The column ordinal number; Calculate the information entropy of each target ,pass To obtain objective weights : in, The weight ordinal number; S24: Dynamic weight fusion to obtain the final weight. : in, This is the weighting balance coefficient.

[0035] Specifically, , ; Converter conduction loss and switching losses Based on the formula, with a rated current of 5kA, the average daily switching frequency of the IGBT is 1.728 × 10⁻⁶. 6 Based on a single-switch loss of 0.001J, the annual average conduction loss is approximately 1.2MW, and the annual average switching loss is approximately 0.63MW. Reactor losses ; Transformer losses ; Initial total loss It is 3.73MW; Failure and repair costs 10,000 yuan / year; Temperature difference Based on the heat conduction model, the highest temperature of the submodule inside the bridge arm under initial operating conditions is 85℃ and the lowest temperature is 71℃. initial temperature difference =14℃.

[0036] Five technical professionals were invited to conduct a analytic hierarchy process (AHP) to determine subjective weights. A 1-9 scale was used to perform pairwise comparisons and scores on the four objectives, constructing a judgment matrix A and calculating the largest eigenvalue λ. max=4.012, Consistency index CI=(4.012-4) / (4-1)=0.004; Average random consistency index RI=0.90, Consistency ratio CR=CI / RI=0.004 / 0.90<0.1, Judgment matrix is ​​valid; After normalization, we get [ , , , = [0.38, 0.28, 0.20, 0.14]; Historical data covering 10 typical operating scenarios with different loads and fault conditions were collected. The specific data is shown in Table 2. Table 2 Historical Data for Typical Scenarios

[0037] Construct a decision matrix, apply range normalization to each column of data to obtain a standardized matrix; calculate the information entropy. H2≈0.92, H3≈0.93, H4≈0.90; Calculate the objective weights to obtain [ , , , = [0.24, 0.27, 0.22, 0.27]; Taking the weight balancing coefficient λ=0.5, the final weights are calculated as follows: =0.5×0.38+0.5×0.24=0.31; =0.5×0.28+0.5×0.27=0.275; =0.5×0.20+0.5×0.22=0.21; =0.5×0.14+0.5×0.27=0.205; satisfy + + + =1.

[0038] Specifically, step S3 includes: S31: Abstract each set of topological structure parameters into a particle. The dimension of the particle corresponds exactly to the number of optimization variables. Based on the preset constraint boundary, the initial particle population is randomly generated. S32: Substitute the topological parameters corresponding to each particle into the weighted superposition optimization objective function to calculate the initial fitness value; at the same time, preset constraints and make a feasibility judgment on the particles; for infeasible solutions that violate the constraints, increase their fitness value by assigning an additional penalty term, and the penalty intensity is positively correlated with the severity of the constraint violation. S33: Each particle dynamically adjusts its speed and position based on three types of optimal solutions: the individual optimal solution is the fitness-optimal solution found by the particle in the historical iterations, the global optimal solution is the optimal solution of the entire particle population up to the current iteration, and the neighborhood optimal solution is the optimal solution of other particles within the particle's surrounding range; after the position is updated, constraint verification is performed. If the updated parameters exceed the constraint boundary, they are automatically adjusted to the boundary value to ensure that the particle is always in the feasible solution space. S34: Set dual convergence conditions: first, the number of iterations reaches a preset maximum value; second, in 10 consecutive iterations, the change in the fitness value of the global optimal solution is less than a threshold. If either condition is met, the iteration stops. S35: After the iteration terminates, extract all non-dominated solutions from the elite storage pool, and select the optimal combination of topology parameters from the set of non-dominated solutions that takes into account the core requirements and the performance of other objectives, based on the priority requirements set in the actual project.

[0039] In this embodiment, the adaptive inertia weight decreases linearly from the initial 0.9 to 0.4 with each iteration. In the early stage of the iteration, a wide range of searches are guaranteed, while in the later stage, local fine-grained optimization is focused. After each iteration, the crowding degree of non-dominated solutions is calculated, and sparsely distributed solutions are retained first to avoid homogenization of optimization results. At the same time, an elite storage pool with a capacity of 20 is opened to continuously save high-quality non-dominated solutions in the iteration process and eliminate individuals with weak performance to prevent the loss of high-quality solutions. First, particle swarm initialization is performed, abstracting the five core optimization variables of the converter into five dimensions of particles, generating 50 initial particles. The parameters of each particle are randomly generated within the constraint boundary and are automatically verified by the system to ensure that they are in the feasible solution space. Then, the fitness value is calculated. Feasible solutions directly enter the next round of iteration, while infeasible solutions are given corresponding penalty terms according to the severity of the constraint violation, and their fitness values ​​are increased to gradually eliminate them. During the particle update phase, each particle adjusts its parameters under the collaborative guidance of its own historical best solution, the population's global best solution, and the neighborhood best solution. If the parameters exceed the constraint boundary after the update, they are automatically truncated to the boundary value, always keeping the particle within the feasible solution space. The convergence judgment adopts a dual condition: when the number of iterations reaches 200 or the fitness change of the global best solution for 10 consecutive generations is ≤0.001, the algorithm stops iterating. After the iteration terminates, from the 20 non-dominated solutions in the elite storage pool, combined with the core requirement of prioritizing the reduction of total loss in engineering, the optimal solution with the lowest total loss and all other objectives satisfying the constraints is selected. Specifically, it consists of 10 half-bridges + 4 full-bridges, 14 sub-modules, 22mH MMC bridge arm reactors, 26° LCC converter bridge trigger angle, and 1100μF sub-module capacitors, which are fully adapted to the actual engineering application scenario.

[0040] Specifically, the simulation test in step S4 is based on building an LCC-MMC hybrid converter simulation model using PSCAD, inputting the optimal topology parameters, and simulating three typical operating conditions: rated power, load change, and single module failure. Topology rationality: Submodule layout compactness is greater than or equal to 0.85, and component stress distribution uniformity is less than or equal to 1.2; Target optimization results: Compared with before optimization, the submodule volume reduction rate is greater than or equal to 15%, the total loss reduction rate is greater than or equal to 10%, the failure and maintenance cost reduction rate is greater than or equal to 8%, and the temperature difference reduction rate is greater than or equal to 20%; Operational stability: DC voltage ripple rate less than or equal to 2%, converter trigger delay time less than or equal to 5ms, and voltage recovery time under fault conditions less than or equal to 0.1s; The experiment uses a 1:10 scale prototype to test the target parameters and stability indicators in actual operation. If the error between the simulation and the experimental results is less than or equal to 5%, the optimization is confirmed.

[0041] A hybrid LCC-MMC converter model conforming to engineering design standards was built using PSCAD. The MMC converter valve adopted a detailed sub-module-level model including the electrical and thermal characteristics of components such as IGBTs and capacitors. The LCC converter bridge, reactors, and transformers were all modeled according to measured parameters. Simultaneously, a control system consistent with the engineering project was built, including voltage and current loops and constant DC voltage. Subsequently, three operating conditions were simulated: rated power of 5000MW, load sudden drop from 5000MW to 4500MW, and single-module failure. The results showed that under the rated power condition, the sub-module compactness was 1.168, the component stress uniformity was 1.14, and the four major optimization objectives were achieved. All aspects were significantly improved, the operational stability indicators met the standards, the voltage recovery time after a sudden load change was 0.07s, and the output power was 4120MW when a single module failed. To further verify the feasibility of the project, a scaled-down prototype was made according to the 1:10 similarity principle, scaling up the rated power, voltage and component parameters to restore the actual layout and heat dissipation structure. High-precision power analyzers, infrared thermal imagers, oscilloscopes and other equipment were used for testing. After measuring the actual values ​​corresponding to the target parameters, the results were compared with the simulation results. The relative errors were all within 5%. The errors were caused by the temperature fluctuations of the test environment and the measurement accuracy of the equipment, which fully verified the accuracy and engineering applicability of the optimization results.

[0042] In summary, the advantages of this stability assessment method for high-proportion new energy transmission systems via UHV transmission, which focuses on quantifying multi-band resonance risks, are as follows: 1. The method proposed in this invention incorporates submodule volume, total loss, fault repair cost, and local temperature difference into a unified optimization framework, fully considering the coupling relationship between various objectives and overcoming the limitations of single-objective optimization.

[0043] 2. Based on the dynamic weight allocation mechanism proposed in this invention, subjective engineering experience and objective data characteristics are integrated to replace fixed or single weights and improve the adaptability of optimization results.

[0044] 3. Using the improved multi-objective particle swarm optimization algorithm proposed in this invention as the solution tool, the convergence performance is optimized, and the globally optimal combination of topological parameters can be found quickly under complex constraints, thus solving the pain points of traditional algorithms.

[0045] like Figure 3 As shown, the present invention also provides an LCC-MMC hybrid converter topology optimization system, the device comprising: Optimization variable modeling module 101: Used to establish a multi-dimensional optimization variable model based on converter topology, component parameters and operating conditions, and to define voltage constraint boundaries and current constraint boundaries; Objective function and weight construction module 102: Used to construct a four-item objective weighted optimization function based on the multi-dimensional optimization variable model, voltage constraint boundary and current constraint boundary, and to determine the dynamic weights using the analytic hierarchy process and entropy weight method; Optimization and solution module 103: used to solve for the optimal combination of topological structure parameters by combining the dynamic weights with topological constraints and objective functions through an improved multi-objective particle swarm optimization algorithm; Result Evaluation Module 104: This module is used to evaluate the optimization results from three aspects after the optimal combination of topology parameters has been verified by simulation testing and experiments, providing a basis for converter engineering design and operation optimization.

[0046] The execution entities of the above modules and units can be devices with computing functions such as computers, microcontrollers, and single-chip microcomputers. In specific implementation, the embodiments of the present invention do not limit the execution entities and can select them according to the needs of actual applications.

[0047] Unless otherwise specified, the model numbers of the various devices in this embodiment of the invention are not limited, and any device that can perform the above functions is acceptable.

[0048] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0049] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0050] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other through the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute an LCC-MMC hybrid converter topology optimization method, which includes: S1: Based on the converter topology, component parameters and operating conditions, establish a multi-dimensional optimization variable model and clarify the voltage and current constraint boundaries; S2: Construct a four-objective weighted optimization function and use the analytic hierarchy process and entropy weight method to determine the dynamic weights; S3: Combining topological constraints and objective functions, the optimal combination of topological parameters is solved by improving the multi-objective particle swarm optimization algorithm; S4: Through simulation testing and experimental verification, the optimization results are evaluated from three aspects to provide a basis for converter engineering design and operation optimization.

[0051] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0052] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the aforementioned LCC-MMC hybrid converter topology optimization method, the method comprising: S1: Based on the converter topology, component parameters and operating conditions, establish a multi-dimensional optimization variable model and clarify the voltage constraint boundary and current constraint boundary; S2: Based on the multi-dimensional optimization variable model, voltage constraint boundary, and current constraint boundary, a four-item objective weighted optimization function is constructed, and the dynamic weights are determined by the analytic hierarchy process and the entropy weight method. S3: Based on the dynamic weights combined with topological constraints and the objective function, the optimal combination of topological parameters is solved by an improved multi-objective particle swarm optimization algorithm; S4: The optimal combination of topology parameters is verified through simulation testing and experiments. The optimization results are evaluated from three aspects to provide a basis for converter engineering design and operation optimization.

[0053] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0054] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0056] It should be noted that the embodiments of this disclosure can be implemented using hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a programmable memory or a data carrier such as an optical or electronic signal carrier.

[0057] Furthermore, although the operation of the methods of this disclosure is described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Rather, the steps depicted in the flowcharts may be performed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps. It should also be noted that the features and functions of two or more devices according to this disclosure may be embodied in one device. Conversely, the features and functions of one device described above may be further divided and embodied by multiple devices.

[0058] While this disclosure has been described with reference to several specific embodiments, it should be understood that this disclosure is not limited to the specific embodiments disclosed. This disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims

1. A method for optimizing the topology of an LCC-MMC hybrid converter, characterized in that, include: S1: Based on the converter topology, component parameters and operating conditions, establish a multi-dimensional optimization variable model and clarify the voltage constraint boundary and current constraint boundary; S2: Based on the multi-dimensional optimization variable model, voltage constraint boundary, and current constraint boundary, a four-item objective weighted optimization function is constructed, and the dynamic weights are determined by the analytic hierarchy process and the entropy weight method. S3: Based on the dynamic weights combined with topological constraints and the objective function, the optimal combination of topological parameters is solved by an improved multi-objective particle swarm optimization algorithm; S4: The optimal combination of topology parameters is verified through simulation testing and experiments. The optimization results are evaluated from three aspects to provide a basis for converter engineering design and operation optimization.

2. The method for optimizing the topology of an LCC-MMC hybrid converter according to claim 1, characterized in that, Step S1 includes: The converter topology consists of an LCC converter bridge, an MMC converter valve group, sub-module units, connecting reactors, and filtering devices; Component parameters include submodule capacitance, IGBT rated current and voltage, reactor inductance, and converter transformer turns ratio; Operating conditions include rated transmission power, voltage level, load fluctuation range, and probability of failure scenarios; The core variables of the multi-dimensional optimization variable model include: submodule type selection, number of submodules connected in series, inductance value of MMC bridge arm reactor, firing angle of LCC converter bridge, and capacitance value of submodule. The specific constraints are: voltage constraint, current constraint, space constraint, thermal constraint, and fault constraint. Among them, the voltage constraint is that the fluctuation range of the converter output line voltage is less than or equal to ±5% of the rated voltage; the current constraint is that the component operating current is less than or equal to 1.2 times the rated current; the space constraint is that the total installation volume of the submodule is less than or equal to a preset threshold; the thermal constraint is that the maximum operating temperature of the component is less than or equal to 125℃; and the fault constraint is that the converter can still maintain greater than or equal to 80% of the rated power output when a single module fails.

3. The method for optimizing the topology of an LCC-MMC hybrid converter according to claim 1, characterized in that, Step S2 includes: S21: Optimize the objective function The expression is: in, To comprehensively optimize the target value, The weight of the submodule volume. The weight of the total loss of the submodule. Weighting of submodule failures and maintenance costs. The weights of the temperature differences between submodules satisfy the following conditions: And 0 < <1, 0< <1, 0< <1, 0< <1; For the size of the submodule, Total loss, For failure and repair costs, For temperature difference; S22: Analytic Hierarchy Process (AHP) for Determining Subjective Weights: Constructing a hierarchical structure of target layer and criterion layer, building a judgment matrix through expert scoring, and calculating the subjective weights of the sub-module volumes of each target. Subjective weight of total loss of submodule Subjective weighting of submodule failures and maintenance costs Subjective weight of temperature difference in sub-modules ; S23: Construct a decision matrix based on m sets of historical operating data or simulation data. Standardization process yields the standard decision matrix. : in, for No. Line number The data in the column, for No. Line number The data in the column, for No. The data in the column, It is a minimum value function. It is a function with maximum value. The row number, The column ordinal number; Calculate the information entropy of each target ,pass To obtain objective weights : in, The weight ordinal number; S24: Dynamic weight fusion to obtain the final weight. : in, This is the weighting balance coefficient.

4. The method for optimizing the topology of an LCC-MMC hybrid converter according to claim 3, characterized in that, The objective functions for step S21 are defined as follows: Where N is the number of submodules, For the volume of a single capacitor, For the volume of a single IGBT module, For the volume of a single heat dissipation structure; in, For converter conduction losses, For switching losses, For reactor losses, Transformer losses; and The calculation formula is as follows: in, The on-resistance of the component. This is the operating current. For the number of IGBT switching operations, Single switching loss; in, For failure costs, For maintenance costs; in, This represents the highest temperature of the submodule within the bridge arm. This is the lowest temperature of the submodule within the bridge arm.

5. The method for optimizing the topology of an LCC-MMC hybrid converter according to claim 3, characterized in that, The value range is 0.3≤ ≤0.

7.

6. The method for optimizing the topology of an LCC-MMC hybrid converter according to claim 1, characterized in that, Step S3 includes: S31: Abstract each set of topological structure parameters into a particle. The dimension of the particle corresponds exactly to the number of optimization variables. Based on the preset constraint boundary, the initial particle population is randomly generated. S32: Substitute the topological parameters corresponding to each particle into the weighted superposition optimization objective function to calculate the initial fitness value; at the same time, preset constraints and make a feasibility judgment on the particles; for infeasible solutions that violate the constraints, increase their fitness value by assigning an additional penalty term, and the penalty intensity is positively correlated with the severity of the constraint violation. S33: Each particle dynamically adjusts its speed and position based on three types of optimal solutions: the individual optimal solution is the fitness-optimal solution found by the particle in the historical iterations, the global optimal solution is the optimal solution of the entire particle population up to the current iteration, and the neighborhood optimal solution is the optimal solution of other particles within the particle's surrounding range; after the position is updated, constraint verification is performed. If the updated parameters exceed the constraint boundary, they are automatically adjusted to the boundary value to ensure that the particle is always in the feasible solution space. S34: Set dual convergence conditions: first, the number of iterations reaches a preset maximum value; second, in 10 consecutive iterations, the change in the fitness value of the global optimal solution is less than a threshold. If either condition is met, the iteration stops. S35: After the iteration terminates, extract all non-dominated solutions from the elite storage pool, and select the optimal combination of topology parameters from the set of non-dominated solutions that takes into account the core requirements and the performance of other objectives, based on the priority requirements set in the actual project.

7. The method for optimizing the topology of an LCC-MMC hybrid converter according to claim 1, characterized in that, The simulation test in step S4 is based on the construction of an LCC-MMC hybrid converter simulation model using PSCAD. The optimal topology parameters are input to simulate three typical operating conditions: rated power, load change, and single module failure. Topology rationality: Submodule layout compactness is greater than or equal to 0.85, and component stress distribution uniformity is less than or equal to 1.2; Target optimization results: Compared with before optimization, the submodule volume reduction rate is greater than or equal to 15%, the total loss reduction rate is greater than or equal to 10%, the failure and maintenance cost reduction rate is greater than or equal to 8%, and the temperature difference reduction rate is greater than or equal to 20%; Operational stability: DC voltage ripple rate less than or equal to 2%, converter trigger delay time less than or equal to 5ms, and voltage recovery time under fault conditions less than or equal to 0.1s; The experiment uses a 1:10 scale prototype to test the target parameters and stability indicators in actual operation. If the error between the simulation and the experimental results is less than or equal to 5%, the optimization is confirmed.

8. A topology optimization device for an LCC-MMC hybrid converter, characterized in that, include: Optimization variable modeling module: used to establish a multi-dimensional optimization variable model based on converter topology, component parameters and operating conditions, and to define voltage constraint boundaries and current constraint boundaries; Objective function and weight construction module: used to construct a four-item objective weighted optimization function based on the multi-dimensional optimization variable model, voltage constraint boundary and current constraint boundary, and to determine the dynamic weights using the analytic hierarchy process and entropy weight method; The optimization solution module is used to solve for the optimal combination of topological structure parameters by combining the dynamic weights with topological constraints and objective functions through an improved multi-objective particle swarm optimization algorithm. The results evaluation module is used to evaluate the optimization results from three aspects after the optimal combination of topology parameters has been verified by simulation testing and experiments, providing a basis for converter engineering design and operation optimization.

9. An electronic device, comprising a processor, a communication interface, a memory, and a communication bus, characterized in that, When the processor executes a computer program, it implements the steps of the LCC-MMC hybrid converter topology optimization method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the LCC-MMC hybrid converter topology optimization method as described in any one of claims 1 to 7.

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