Multi-objective optimization method, device and electronic equipment for magnetically controlled transformers

Abstract

This invention discloses a multi-objective optimization method, apparatus, and electronic device for magnetically controlled transformers. The method includes: acquiring an initial simulation model of the magnetically controlled transformer and multiple objectives to be optimized; generating an initial population of the initial simulation model, wherein the initial population includes multiple individuals, each corresponding to different configuration parameters of the initial simulation model; iteratively optimizing the initial population based on the multiple objectives to obtain target individuals, wherein the target configuration parameters corresponding to the target individuals are used to achieve a balance of the magnetically controlled transformer across the multiple objectives. This invention solves the technical problem of low operating efficiency of magnetically controlled transformers in related technologies.

Description

Technical Field

[0001] This invention relates to the field of transformers, and more specifically, to a multi-objective optimization method, apparatus, and electronic device for magnetically controlled transformers. Background Technology

[0002] In modern power systems, voltage fluctuations are significantly amplified due to variations in power supply and load, leading to a sharp decline in the system's voltage support and regulation capabilities. Furthermore, the large-scale application of cables greatly increases capacitive reactive power consumption, thus necessitating the use of inductive reactive power compensation equipment.

[0003] Traditional reactive power and voltage control system solutions mainly include passive reactive power and voltage compensation systems and active reactive power and voltage generators based on power electronic devices. However, active reactive power and voltage generators based on power electronic devices suffer from problems such as low reliability, significant susceptibility to environmental factors, high operation and maintenance costs, and electromagnetic interference from high-frequency switching that significantly impacts the power grid's electromagnetic environment. Furthermore, with the increasing difficulty of new power grid construction and expansion, passive static reactive power and voltage compensation systems require a large land area, have unsatisfactory compensation effects, and are difficult to adapt to changes in new power systems.

[0004] A magnetically controlled distribution transformer (MCDT) is a comprehensive, multi-functional compensation device based on a magnetron reactor. It integrates the functions of a conventional power transformer and a magnetron reactor, offering voltage regulation and dynamic reactive power control. However, current MCDT parameter design systems are incomplete, lacking a systematic multi-objective optimization combination of reactive power parameters and intrinsic electromagnetic parameters, resulting in relatively low operating efficiency.

[0005] There is currently no effective solution to the above problems. Summary of the Invention

[0006] This invention provides a multi-objective optimization method, apparatus, and electronic device for magnetically controlled transformers, to at least solve the technical problem of low working efficiency of magnetically controlled transformers in related technologies.

[0007] According to one aspect of the present invention, a multi-objective optimization method for a magnetically controlled transformer is provided, comprising: obtaining an initial simulation model of the magnetically controlled transformer and multiple objectives to be optimized for the magnetically controlled transformer; generating an initial population of the initial simulation model, wherein the initial population includes multiple individuals, and different individuals correspond to different configuration parameters of the initial simulation model; iteratively optimizing the initial population based on the multiple objectives to obtain target individuals, wherein the target configuration parameters corresponding to the target individuals are used to achieve a balance of the magnetically controlled transformer on multiple objectives.

[0008] Furthermore, the initial population is iteratively optimized based on multiple objectives to obtain target individuals, including: determining the objective functions corresponding to multiple objectives, and adjusting the variables of different individuals in the initial population based on objective constraints to obtain a first adjustment result; determining the function fitness of multiple individuals for the objective functions based on the first adjustment result, wherein the function fitness is used to quantify the performance of multiple individuals on multiple objectives; and iteratively optimizing multiple individuals based on the function fitness to obtain target individuals.

[0009] Furthermore, multiple individuals are iteratively optimized based on the fitness function to obtain the target individuals, including: screening multiple individuals in the initial population based on the fitness function to obtain an elite population; searching multiple individuals in the initial population based on the objective function to obtain a subpopulation; iteratively searching the elite population and the subpopulation, and obtaining the target individuals contained in the final population when the number of iterations is greater than the preset number of iterations.

[0010] Furthermore, the elite population is obtained by screening multiple individuals in the initial population based on the function fitness, including: screening multiple individuals based on the function fitness to obtain multiple initial individuals; determining the crowding degree of the multiple initial individuals in the objective function space corresponding to the objective function, wherein the crowding degree is used to represent the distribution density of the multiple initial individuals in the objective function space; and screening multiple initial individuals based on the crowding degree to obtain the elite population.

[0011] Furthermore, an iterative search is performed on the elite population and subpopulations. If the number of iterations exceeds the preset number of iterations, the target individuals contained in the final population are obtained. This includes: adjusting the variables of different individuals in the elite population and subpopulations based on the target constraints to obtain a second adjustment result; performing an iterative search on the elite population and subpopulations based on the second adjustment result and multiple targets, and recording the number of iterations of the iterative search. If the number of iterations exceeds the preset number of iterations, the target individuals are obtained.

[0012] Furthermore, the method also includes: adjusting the initial simulation model based on the target configuration parameters to obtain the target simulation model; and constructing a magnetically controlled transformer based on the target simulation model.

[0013] Furthermore, several objectives include: the permeability of the magnetically controlled transformer, the volume of the magnetically controlled transformer, and the reactive power output of the magnetically controlled transformer.

[0014] Furthermore, an initial simulation model of the magnetically controlled transformer is obtained, including: constructing an initial simulation model based on the core permeability, magnetic valve length, number of winding turns, core thickness, power supply voltage, load side voltage, coil turns ratio, line inductance, and line resistance of the magnetically controlled transformer.

[0015] According to another aspect of the present invention, a multi-objective optimization device for a magnetically controlled transformer is also provided, comprising: an acquisition module for acquiring an initial simulation model of the magnetically controlled transformer and multiple objectives to be optimized for the magnetically controlled transformer; a generation module for generating an initial population of the initial simulation model using a multi-objective optimization algorithm, wherein the initial population includes multiple individuals, different individuals corresponding to different configuration parameters of the initial simulation model, and the multi-objective optimization algorithm is used to optimize multiple objectives; and an optimization module for iteratively optimizing the initial population based on multiple objectives to obtain target individuals, wherein the target configuration parameters corresponding to the target individuals are used to achieve a balance of the magnetically controlled transformer on multiple objectives.

[0016] According to another aspect of the present invention, an electronic device is also provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods of various embodiments of the present invention during runtime.

[0017] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0018] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0019] In this embodiment of the invention, an initial simulation model of the magnetically controlled transformer and multiple targets to be optimized are first obtained. Then, an initial population of the initial simulation model is generated, comprising multiple individuals, each corresponding to different configuration parameters of the initial simulation model. Finally, the initial population is iteratively optimized based on the multiple targets to obtain target individuals. The target configuration parameters corresponding to the obtained target individuals can be used to achieve the balance of the magnetically controlled transformer across multiple targets. It is noteworthy that this application achieves the goal of keeping the magnetically controlled transformer in a balanced state across multiple targets by obtaining the initial simulation model of the magnetically controlled transformer and the multiple targets to be optimized, generating an initial population of the initial simulation model, and iteratively optimizing the initial population to obtain target individuals. This realizes the optimization of the magnetically controlled transformer across multiple targets and solves the technical problem of low working efficiency of magnetically controlled transformers in related technologies. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0021] Figure 1 This is a flowchart of a multi-objective optimization method for a magnetically controlled transformer according to an embodiment of the present invention;

[0022] Figure 2 This is a structural diagram of a magnetically controlled transformer according to an embodiment of the present invention;

[0023] Figure 3 This is an equivalent circuit diagram of a magnetically controlled transformer according to an embodiment of the present invention;

[0024] Figure 4 This is a voltage waveform diagram of a magnetically controlled transformer according to an embodiment of the present invention;

[0025] Figure 5 This is a current waveform diagram of a magnetically controlled transformer according to an embodiment of the present invention;

[0026] Figure 6 This is a graph showing the change in magnetic permeability in a magnetically controlled transformer according to an embodiment of the present invention;

[0027] Figure 7 This is a diagram showing the volume change of a magnetic valve in a magnetically controlled transformer according to an embodiment of the present invention;

[0028] Figure 8 This is a graph showing the change in magnetic permeability in another magnetically controlled transformer according to an embodiment of the present invention;

[0029] Figure 9 This is a schematic diagram of a multi-objective optimization device for a magnetically controlled transformer according to an embodiment of the present invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] According to an embodiment of the present invention, an embodiment of a multi-objective optimization method for a magnetically controlled transformer is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0033] Figure 1 This is a flowchart of a multi-objective optimization method for a magnetically controlled transformer according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0034] Step S102: Obtain the initial simulation model of the magnetically controlled transformer and the multiple objectives to be optimized for the magnetically controlled transformer.

[0035] The aforementioned magnetically controlled transformer can refer to a transformer that uses a controlled magnetic field to achieve voltage conversion and power transmission. The functions of a magnetically controlled transformer include, but are not limited to, voltage conversion, power transmission, and improving the electromagnetic compatibility of the system. The types of magnetically controlled transformers include, but are not limited to, magnetically controlled tube transformers, magnetically controlled current transformers, magnetically controlled voltage transformers, and magnetically controlled power transformers. The specific type of magnetically controlled transformer can be determined according to the system requirements and is not limited here.

[0036] The aforementioned initial simulation model can refer to a basic model obtained based on theoretical analysis and calculation before designing a magnetically controlled transformer. It can be used to predict the performance of the magnetically controlled transformer and provide a reference for its design and optimization.

[0037] The aforementioned multiple objectives refer to several performance indicators that need to be considered during the design process of a magnetically controlled transformer. These objectives may include, but are not limited to, the permeability, volume, and reactive power output of the magnetically controlled transformer. The specific objectives can be determined based on the specific performance of the magnetically controlled transformer, and are not limited here.

[0038] In one alternative embodiment, Figure 2 This is a structural diagram of a magnetically controlled transformer according to an embodiment of the present invention, such as... Figure 2 As shown, the magnetically controlled transformer includes four iron core columns, one diode, two thyristors, a high-voltage coil, a low-voltage coil, and a sinusoidal AC voltage. and load voltage The four core posts include core post 1, core post 2, core post 3 and core post 4, and the two thyristors include thyristor TH1 and thyristor TH2.

[0039] exist Figure 2 The diagram shows a magnetically controlled transformer structure, consisting of a magnetically saturated reactor and a transformer connected together. An N / 2-turn coil is wound around the upper and lower portions of core pillars 1 and 2. The upper coil of core pillar 1 has a tap connected to the beginning of the lower coil via a thyristor TH1. Similarly, the lower coil of core pillar 2 also has a tap connected to the end of the upper coil via a thyristor TH2. The upper and lower coils of core pillars 1 and 2 are cross-connected, and a diode D is connected between the upper coils of core pillars 1 and 2 to ensure continuous current flow. Core pillars 1 and 2 and their corresponding coils constitute part of the magnetically saturated reactor. Core pillar 3, with an N-turn coil wound around its upper and lower portions, constitutes the transformer portion. The upper coil of core pillar 3 is a low-voltage coil, with the load connected to both ends; therefore, the voltage across the two ends is the load voltage. The lower coil is a high-voltage coil, with one end connected to the first end of the upper coil of iron core columns 1 and 2, and the other end connected to the sinusoidal AC power supply of the power grid. Therefore, the voltage between the two ends is a sinusoidal AC voltage. .

[0040] Figure 3 This is an equivalent circuit diagram of a magnetically controlled transformer according to an embodiment of the present invention, such as... Figure 3 As shown, the equivalent circuit diagram includes: power supply voltage Current Line resistance Line inductance It has 4 iron core columns, 1 diode, 2 thyristors, a high-voltage coil, and a low-voltage coil. The 4 iron core columns include iron core column 1, iron core column 2, iron core column 3 and iron core column 4, and the 2 thyristors include thyristor TH1 and thyristor TH2.

[0041] Figure 3 yes Figure 2 The equivalent circuit diagram of a magnetically controlled transformer, in Figure 3Thyristors TH1 and TH2 conduct alternately, causing core columns 1 and 2 to experience alternating increases and decreases in the magnetic field. By adjusting the thyristor conduction angle, the magnitude of the DC control current can be changed, thereby altering the magnetic saturation level of the magnetically saturated reactor and thus affecting the equivalent reactance value. This results in a change in the voltage shared across the reactor, and simultaneously, a change in the voltage across the high-voltage winding, which is part of the transformer, thus achieving continuous regulation of the output voltage.

[0042] Therefore, the magnetically saturated reactor in a magnetically controlled transformer can be considered as a variable reactance, whose reactance value changes with the conduction angle of the thyristor. The calculation formula is as follows:

[0043] ;

[0044] In the formula, It is the load-side voltage. It is the power supply voltage. It is the secondary side voltage. It is the primary side voltage. It is the voltage drop at the source end of the line. It is the number of turns on the primary side. It is the number of turns on the secondary side. It is the turns ratio.

[0045] In one optional embodiment, an initial simulation model of the magnetically controlled transformer and multiple objectives to be optimized are obtained. During the design and optimization process of the magnetically controlled transformer, the electromagnetic behavior of the magnetically controlled transformer under different operating conditions can be simulated through the initial simulation model. Based on the multiple objectives to be optimized, the performance of the magnetically controlled transformer can be comprehensively considered and optimized, thereby enabling the magnetically controlled transformer to achieve good performance.

[0046] Step S104: Generate the initial population of the initial simulation model, wherein the initial population includes multiple individuals, and different individuals correspond to different configuration parameters of the initial simulation model.

[0047] The initial population mentioned above can refer to the population defined at the start of the simulation, and can be used to represent the initial state in the simulation environment.

[0048] The aforementioned multiple individuals can refer to multiple individual entities in the initial population, each with its own corresponding characteristics.

[0049] The configuration parameters mentioned above can refer to variables used to define the characteristics of multiple individuals. Different individuals correspond to different configuration parameters of the initial simulation model. The types of configuration parameters can include, but are not limited to, the model's input variables, algorithm parameters, and environment settings. The specific types of configuration parameters can be determined according to the actual needs of the model, and are not limited here.

[0050] In one alternative embodiment, generating an initial population for the initial simulation model involves creating a group of multiple individuals, each representing a specific parameter configuration of the simulation model. After generating the initial population, the initial simulation model is simulated. During the simulation, the model's parameter configuration can be iterated based on the performance of the initial population to continuously improve the model's performance and enhance overall performance.

[0051] Step S106: Iteratively optimize the initial population based on multiple objectives to obtain target individuals. The target configuration parameters corresponding to the target individuals are used to achieve the balance of the magnetically controlled transformer on multiple objectives.

[0052] The aforementioned iterative optimization can refer to continuously improving individuals in the initial population through operations such as crossover and selection, so that individuals in the population gradually adapt to the problem objective through iterative optimization.

[0053] The aforementioned target individuals can refer to those selected during the iteration process that have better fitness and perform better in meeting the problem objectives.

[0054] The aforementioned target configuration parameters can refer to the configuration parameters corresponding to a specific target, and can be used to achieve the balance of the magnetically controlled transformer across multiple targets.

[0055] In one alternative embodiment, the initial population is iteratively optimized based on an initial simulation model to find target individuals based on multiple objectives. The target configuration parameters corresponding to the target individuals are then applied to the magnetically controlled transformer, thereby achieving a balance between the magnetically controlled transformer and multiple objectives and ensuring that the magnetically controlled transformer has good performance on each objective.

[0056] In this embodiment of the invention, an initial simulation model of the magnetically controlled transformer and multiple targets to be optimized are first obtained. Then, an initial population of the initial simulation model is generated, comprising multiple individuals, each corresponding to different configuration parameters of the initial simulation model. Finally, the initial population is iteratively optimized based on the multiple targets to obtain target individuals. The target configuration parameters corresponding to the obtained target individuals can be used to achieve the balance of the magnetically controlled transformer across multiple targets. It is noteworthy that this application achieves the goal of keeping the magnetically controlled transformer in a balanced state across multiple targets by obtaining the initial simulation model of the magnetically controlled transformer and the multiple targets to be optimized, generating an initial population of the initial simulation model, and iteratively optimizing the initial population to obtain target individuals. This realizes the optimization of the magnetically controlled transformer across multiple targets and solves the technical problem of low working efficiency of magnetically controlled transformers in related technologies.

[0057] Optionally, the initial population is iteratively optimized based on multiple objectives to obtain target individuals, including: determining the objective functions corresponding to multiple objectives, and adjusting the variables of different individuals in the initial population based on objective constraints to obtain a first adjustment result; determining the function fitness of multiple individuals for the objective functions based on the first adjustment result, wherein the function fitness is used to quantify the performance of multiple individuals on multiple objectives; and iteratively optimizing multiple individuals based on the function fitness to obtain target individuals.

[0058] The objective function mentioned above can refer to a mathematical expression used to evaluate the performance of a solution on a specific objective.

[0059] The aforementioned objective constraints can refer to the constraints that the solution must satisfy. These constraints are usually related to the actual requirements of the problem and can be determined according to the actual requirements. No specific restrictions are imposed here.

[0060] The aforementioned first adjustment result can refer to the result obtained after adjusting the variables of individuals in the initial population based on the objective constraints. The first adjustment result can be used to provide a basis for subsequent iterative optimization, and the objective function can be further optimized based on the first adjustment result.

[0061] The aforementioned function fitness can be a quantitative indicator used to measure an individual's performance on multiple objectives, and multiple individuals can be screened based on function fitness.

[0062] In one optional embodiment, multiple objective functions are determined based on multiple objectives. Then, different configuration parameters corresponding to multiple individuals in the initial population are adjusted according to the objective constraints to obtain a first adjustment result. The function fitness of multiple individuals in the first adjustment result is calculated using multiple objective functions to evaluate the performance of individuals across multiple objectives. Finally, the individuals are iteratively optimized based on the function fitness to obtain the target individual. This process, through calculating the function fitness of multiple individuals and iterative optimization, improves the performance of individuals, thereby ensuring that the configuration parameters corresponding to the target individual are the target configuration parameters.

[0063] Optionally, iterative optimization is performed on multiple individuals based on the fitness function to obtain the target individual, including: screening multiple individuals in the initial population based on the fitness function to obtain an elite population; searching multiple individuals in the initial population based on the objective function to obtain a subpopulation; iteratively searching the elite population and the subpopulation, and obtaining the target individuals contained in the final population if the number of iterations is greater than the preset number of iterations.

[0064] The aforementioned elite population can refer to a population composed of better individuals selected based on function fitness, which can be used to ensure that better solutions are preserved during the iteration process.

[0065] The aforementioned subpopulation can refer to the set of new individuals obtained from the initial population through a search using an objective function. This can be used to increase population diversity and attempt to find better solutions.

[0066] The aforementioned preset number of iterations can refer to the number of times the function performs the iterative search. The preset number of iterations can be 10, 50, 100, or 200 times, etc. The specific preset number of iterations can be determined based on specific circumstances such as the population size, and is not limited here.

[0067] In one optional embodiment, after generating an initial population containing multiple individuals, the performance of these individuals can be evaluated using a fitness function. Individuals with better performance are selected and combined into an elite population. Simultaneously, a subpopulation is obtained by searching the initial population using an objective function. The elite population and subpopulation are then iteratively searched. When the number of iterations exceeds a pre-set number, the iteration is complete, and the individual with the highest fitness from the completed populations is selected as the target individual. This process effectively explores the solution space and combines individual fitness evaluation with iterative population search, improving computational efficiency while finding a globally optimal solution.

[0068] Optionally, an elite population is obtained by screening multiple individuals in the initial population based on the function fitness, including: screening multiple individuals based on the function fitness to obtain multiple initial individuals; determining the crowding degree of the multiple initial individuals in the objective function space corresponding to the objective function, wherein the crowding degree is used to represent the distribution density of the multiple initial individuals in the objective function space; and screening multiple initial individuals based on the crowding degree to obtain the elite population.

[0069] The aforementioned selection can refer to non-dominated ranking based on the fitness of multiple individuals' objective functions. Non-dominated ranking is a commonly used ranking method in multi-objective optimization. Its principle is that if an individual is not inferior to another individual in all objective functions, and is superior to that individual in at least one objective function, then the former is non-dominated to the latter. Through this process, all individuals in the population can be ranked according to their non-dominated levels, with lower levels indicating better individual performance.

[0070] The aforementioned multiple initial individuals can refer to multiple individuals that have been screened and whose fitness for the objective function has been non-dominated and ranked.

[0071] The crowding density mentioned above refers to the distribution density of individuals in the objective function space, usually calculated by measuring the distance between individuals on each objective function. The greater the distance between individuals, the lower the crowding density, indicating that the individual is relatively isolated in the objective function space and has better diversity. Crowding density can be used to select individuals that are sparsely distributed in the objective function space to prevent the algorithm from prematurely converging to a local optimum.

[0072] In one optional embodiment, multiple individuals in the initial population are first screened using a non-dominated ranking based on the fitness of the objective function, resulting in a sorted initial population. The crowding of these initial individuals in the objective function space is then determined, and they are further screened based on this crowding, prioritizing those individuals that are sparsely distributed in the objective function space. This avoids excessive concentration of individuals in the elite population, thereby improving population diversity. Through these steps, the resulting elite population not only performs well on the objective function but also has a uniform distribution in the objective space, which helps the model explore different regions of the solution space, improving the model's global search capability and solution diversity.

[0073] Optionally, an iterative search is performed on the elite population and subpopulations. If the number of iterations is greater than a preset number of iterations, the target individuals contained in the final population are obtained. This includes: adjusting the variables of different individuals in the elite population and subpopulations based on the target constraints to obtain a second adjustment result; performing an iterative search on the elite population and subpopulations based on the second adjustment result and multiple targets, and recording the number of iterations of the iterative search. If the number of iterations is greater than a preset number of iterations, the target individuals are obtained.

[0074] The second adjustment result mentioned above can refer to the result of adjusting the variables of different individuals in the elite population and subpopulation to satisfy the constraints.

[0075] The purposes of recording the number of iterations in the iterative search are as follows: it can be used to compare the recorded number of iterations with the preset number of iterations, and if it is greater than the preset number of iterations, the target individual can be obtained; it can be used to analyze the impact of different number of iterations on the results, to obtain a better number of iterations, and then apply the better number of iterations to the model, thereby improving the overall efficiency of the model. The purpose of recording the number of iterations here is only an example, and the specific purpose can be determined according to the actual situation. It is not limited here.

[0076] In one optional embodiment, variables for different individuals in the elite population and subpopulation are adjusted based on objective constraints to obtain a second adjustment result. Then, based on the second adjustment result and multiple objectives, the elite population and subpopulation are iteratively searched until the number of iterations exceeds a preset number of iterations, thus obtaining the target individuals. In this process, each iteration aims to improve the performance of population individuals in satisfying the objective constraints, ultimately resulting in an optimized population containing multiple target individuals. This process effectively handles complex optimization problems, especially when multiple conflicting objectives exist, finding a set of solutions that provide a good trade-off between the different objectives.

[0077] Optionally, the method further includes: adjusting the initial simulation model based on the target configuration parameters to obtain the target simulation model; and constructing a magnetically controlled transformer based on the target simulation model.

[0078] The aforementioned target simulation model can refer to the model obtained by applying the target configuration parameters corresponding to the determined target individual to the initial simulation model.

[0079] In one alternative embodiment, during the design process of the magnetically controlled transformer, the initial simulation model is finely adjusted by selecting appropriate target configuration parameters to obtain a more accurate target simulation model. This model can not only more realistically simulate the working state of the magnetically controlled transformer, but also predict its performance under different targets. Constructing a target simulation model is crucial for optimizing the design, improving efficiency, and ensuring the performance of the final magnetically controlled transformer.

[0080] Optionally, multiple objectives include: the permeability of the magnetically controlled transformer, the volume of the magnetically controlled transformer, and the reactive power output of the magnetically controlled transformer.

[0081] The aforementioned permeability refers to a physical quantity that measures a material's ability to conduct magnetic fields, usually represented by the symbol μ. The higher the permeability, the stronger the material's ability to conduct magnetic fields. In magnetically controlled transformers, permeability determines the transformer's magnetic circuit characteristics, affecting its efficiency and performance. Materials with high permeability can reduce hysteresis losses and eddy current losses, thereby improving transformer efficiency.

[0082] The volume mentioned above may refer to the volume of the magnetically controlled transformer. A smaller volume can save space and reduce costs, but it may affect the transformer's performance and heat dissipation. Therefore, the volume of the magnetically controlled transformer needs to be determined based on the actual situation.

[0083] The aforementioned reactive power output refers to the power generated by the phase difference between current and voltage in an AC circuit due to the presence of inductors and capacitors. In a magnetically controlled transformer, reactive power output affects the transformer's power factor and efficiency. By controlling reactive power output, the transformer's performance can be optimized and the system's stability improved.

[0084] In one alternative embodiment, the capacity of the magnetically controlled reactor section of the magnetically controlled transformer is typically configured as a percentage of the main variable capacity, generally 10-30%. The magnetic induction intensity of 30Q130 silicon steel sheets is... When = 1.8 (T), the magnetic field strength =464 (A / m), then the permeability after saturation for:

[0085] ;

[0086] In the formula, The values ​​represent the permeability; 2.026 indicates the initial permeability of the 30Q130 silicon steel sheet in the absence of a magnetic field; 4900 indicates the initial magnetic field strength of the 30Q130 silicon steel sheet in the absence of a magnetic field; and 1.8 and 464 represent the magnetic induction intensity of the 30Q130 silicon steel sheet, respectively. When = 1.8 (T), the magnetic field strength =464 (A / m). The above parameter values ​​are for illustrative purposes only. The specific parameter values ​​can be determined according to the actual situation. No limit is set here.

[0087] The formula for calculating the total permeability of the solenoid valve region is as follows:

[0088] ;

[0089] In the formula, Represents the total permeability. Indicates the thickness of the iron core. Indicates permeability, Indicates the length of the non-magnetic valve portion. Indicates the length of the solenoid valve section. Indicates the number of solenoid valves. This indicates logarithmic calculation.

[0090] The formula for calculating the reactive power of a magnetically controlled transformer is as follows:

[0091] ;

[0092] In the formula, Indicates reactive power. Represents the system's angular frequency. This represents the voltage across the inductor. Indicates mutual intuition. Indicates the system frequency. Indicates the number of coil turns. This represents the total permeability.

[0093] In one alternative embodiment, the design of a magnetically controlled transformer typically involves considering multiple objectives, such as increasing permeability, reducing size, and controlling reactive power output. These objectives may interact and constrain each other. For example, increasing permeability may require the use of larger materials, which conflicts with the goal of reducing size. Therefore, the design process must comprehensively consider these objectives, optimizing parameter settings and selecting appropriate materials to achieve the best performance balance.

[0094] Optionally, an initial simulation model of the magnetically controlled transformer can be obtained, including: constructing an initial simulation model based on the core permeability, magnetic valve length, number of winding turns, core thickness, power supply voltage, load side voltage, coil turns ratio, line inductance, and line resistance of the magnetically controlled transformer.

[0095] The aforementioned core permeability refers to a physical quantity that measures the magnetic permeability of the core material. It is a key parameter for calculating the magnetic flux density and hysteresis loss of a magnetically controlled transformer.

[0096] The aforementioned solenoid valve length may refer to the length of the solenoid valve or magnetic core in a magnetically controlled transformer.

[0097] The number of winding turns mentioned above refers to the number of coils in the coil of a magnetically controlled transformer. The number of winding turns directly affects the transformer's turns ratio and inductance value, and is an important parameter that must be considered when designing a transformer.

[0098] The aforementioned core thickness can refer to the thickness of the magnetic core. The core thickness affects the magnetic reluctance and magnetic flux density of the magnetic circuit and can be used to calculate magnetic flux and hysteresis loss.

[0099] The aforementioned power supply voltage may refer to the voltage on the primary side of the magnetically controlled transformer.

[0100] The aforementioned load-side voltage may refer to the voltage on the secondary side of the magnetically controlled transformer.

[0101] The above-mentioned coil turns ratio can refer to the ratio of the number of turns in the primary coil to the number of turns in the secondary coil of a magnetically controlled transformer.

[0102] The aforementioned line inductance can refer to the inductance value of the coil of the magnetically controlled transformer. The line inductance affects the energy storage capacity and response to alternating current of the magnetically controlled transformer.

[0103] The aforementioned line resistance can refer to the resistance value of the coil of a magnetically controlled transformer, which can be used to calculate the heat loss and efficiency of the magnetically controlled transformer.

[0104] In one alternative embodiment, when constructing the initial simulation model of the magnetically controlled transformer, several key parameters need to be considered, including the permeability of the core material, the size of the magnetic valve, the number of turns and configuration of the coils, and the transformer's electrical characteristics such as power supply voltage, load voltage, and turns ratio. These parameters collectively determine the transformer's performance. By accurately simulating these parameters, the transformer's performance in practical applications can be predicted, thereby optimizing the design and improving the transformer's performance and reliability.

[0105] In one optional embodiment, a multi-objective optimization method for a magnetically controlled transformer includes the following steps: Step 1, inputting initial parameters of the power distribution system line; Step 2, generating an initial population based on the initial simulation model. Each individual in the population corresponds to an optimization strategy; Step 3, modifying variables until all variables satisfy the constraints; Step 4, calculating the fitness of the objective function for all individuals in the population, and classifying all individuals in the population using a fast dominance ranking method; Step 5, selecting elite individuals in the population to directly enter the new temperature based on the adaptive elite retention strategy, according to the non-dominant rank, dominance strength, and crowding distance between individuals; Step 6, performing a global or local search on the objective function of the previous generation snake population to obtain a new generation of subpopulations, and modifying the individual optimization variables of the subpopulations according to the constraints, mixing them with the elite population to generate a new temperature; Step 7, checking whether the iteration conditions are met, i.e., confirming whether the preset iteration number is met.

[0106] Figure 4 This is a voltage waveform diagram of a magnetically controlled transformer according to an embodiment of the present invention, such as... Figure 4 As shown, the horizontal axis represents time in milliseconds (ms), and the vertical axis represents the voltage waveform in volts (V). The graph illustrates two voltage types: primary voltage and secondary voltage. Figure 4 It reflects the voltage waveform changes of the primary and secondary voltages over time.

[0107] Figure 5 This is a current waveform diagram of a magnetically controlled transformer according to an embodiment of the present invention, such as... Figure 5 As shown, the horizontal axis represents time in milliseconds (ms), and the vertical axis represents the thyristor current waveform in amperes (A). The figure illustrates two types of current: the current in thyristor 1 and the current in thyristor 2. Figure 5 It reflects the changes in the current waveforms of thyristor 1 current and thyristor 2 current over time.

[0108] In one alternative embodiment, to minimize the volume and permeability of the magnetically controlled transformer, a typical multi-objective optimization problem is mathematically formulated as follows:

[0109] ;

[0110] In the formula, denoted by , h represents the length of the solenoid valve section, , n represents the number of solenoid valves (which can be 2, 3, 4, or 5; this number is just an example, and the actual number can be determined based on specific needs; it is not limited here), and b represents the core thickness. Indicates partial permeability. V represents the total permeability, and V represents the volume. This indicates the search for the minimum value of the function. The values ​​2.592 and 0.24 mentioned above are the relevant parameters of the solenoid valve determined based on the actual situation. This is only for illustrative purposes and can be determined according to the actual situation. No limitation is made here.

[0111] Based on the above mathematical formulas, a simulation model was established. After 50 iterations, the multi-objective optimization results of the magnetically controlled transformer are as follows:

[0112] Figure 6 This is a permeability variation diagram in a magnetically controlled transformer according to an embodiment of the present invention, such as... Figure 6 As shown, the horizontal axis represents time, the vertical axis represents magnetic permeability, and n represents the number of solenoid valves. Here, n takes values ​​of 2, 3, 4, and 5. Figure 6 This shows the change in permeability of the magnetically controlled transformer over time as the number of solenoid valves increases.

[0113] Since the number of solenoid valves is an integer and represents a discontinuous point, the optimized number of solenoid valves is also a discontinuous point. When the number of solenoid valves increases from 2 to 3, 4, or 5, the magnetic permeability of the solenoid valve region will significantly decrease as the volume increases.

[0114] Figure 7 This is a diagram showing the volume change of a magnetic valve in a magnetically controlled transformer according to an embodiment of the present invention, such as... Figure 7 As shown, the x-axis represents the length of the solenoid valve. The y-axis represents the distance h of the solenoid valve, and the z-axis represents the volume v of the solenoid valve. As the length of the solenoid valve... As the distance h increases, the volume v of the solenoid valve also increases.

[0115] Figure 8 This is a permeability variation diagram in another magnetically controlled transformer according to an embodiment of the present invention, such as... Figure 8 As shown, the x-axis represents the length of the solenoid valve. The y-axis represents the distance h of the solenoid valve, and the z-axis represents the permeability Q of the magnetic core.

[0116] like Figure 8 As shown, when the length of a single solenoid valve When the distance h between the solenoid valves is fixed and gradually decreases, the permeability of the magnetic core also decreases slightly. When the distance h between the solenoid valves is fixed and the length of a single solenoid valve is... As the core permeability decreases gradually, the permeability of the magnetic core decreases rapidly.

[0117] From the above multi-objective optimization results of the magnetically controlled transformer, we take any two sets of relatively optimal solutions for further analysis. Case 1 and Case 2 are any two sets of relatively optimal solutions from the multi-objective optimization results of the magnetically controlled transformer. Case 1 indicates that when n=2, When n=0.037m and h=0.08m, the volume of the magnetically controlled transformer is 2.67m³, the permeability is 1.0034×10⁻⁵H / m, and the reactive power is 39.1kVar; Case 2 indicates that when n=2, When m = 0.015m and h = 0.09m, the volume of the magnetically controlled transformer is 2.65m³, the permeability is 1.2281×10⁻⁵H / m, and the reactive power is 32.1kVar.

[0118] According to another aspect of the present invention, a multi-objective optimization device for a magnetically controlled transformer is also provided. This device can execute the multi-objective optimization method for a magnetically controlled transformer described in the above embodiments. The specific implementation method and preferred application scenarios are the same as those described in the above embodiments, and will not be repeated here.

[0119] Figure 9 This is a schematic diagram of a multi-objective optimization device for a magnetically controlled transformer according to an embodiment of the present invention. As shown in the figure, the device includes: an acquisition module 902, a generation module 904, and an optimization module 906.

[0120] The acquisition module is used to acquire the initial simulation model of the magnetically controlled transformer and the multiple objectives to be optimized for the magnetically controlled transformer;

[0121] The generation module is used to generate the initial population of the initial simulation model using a multi-objective optimization algorithm. The initial population includes multiple individuals, and different individuals correspond to different configuration parameters of the initial simulation model. The multi-objective optimization algorithm is used to optimize multiple objectives.

[0122] The optimization module is used to iteratively optimize the initial population based on multiple objectives to obtain target individuals. The target configuration parameters corresponding to the target individuals are used to achieve the balance of the magnetically controlled transformer on multiple objectives.

[0123] Optionally, the optimization module further includes: a first determining unit, which determines the objective function corresponding to multiple objectives and adjusts the variables of different individuals in the initial population based on the objective constraints to obtain a first adjustment result; a second determining unit, which determines the function fitness of multiple individuals for the objective function based on the first adjustment result, wherein the function fitness is used to quantify the performance of multiple individuals on multiple objectives; and an iterative unit, which iteratively optimizes multiple individuals based on the function fitness to obtain the target individual.

[0124] Optionally, the iterative unit includes: a screening subunit, which filters multiple individuals in the initial population based on the fitness function to obtain an elite population; a first search subunit, which searches multiple individuals in the initial population based on the objective function to obtain a subpopulation; and a second search subunit, which iteratively searches the elite population and the subpopulation, and obtains the target individuals contained in the final population if the number of iterations is greater than the preset number of iterations.

[0125] Optionally, the screening subunit includes: screening multiple individuals based on function fitness to obtain multiple initial individuals; determining the crowding degree of the multiple initial individuals in the objective function space corresponding to the objective function, wherein the crowding degree is used to represent the distribution density of the multiple initial individuals in the objective function space; and screening the multiple initial individuals based on the crowding degree to obtain an elite population.

[0126] Optionally, the second search subunit includes: adjusting the variables of different individuals in the elite population and subpopulation based on the target constraints to obtain a second adjustment result; performing an iterative search on the elite population and subpopulation based on the second adjustment result and multiple targets, and recording the number of iterations of the iterative search; and obtaining the target individual if the number of iterations is greater than the preset number of iterations.

[0127] Optionally, the device further includes: an adjustment module for adjusting the initial simulation model based on the target configuration parameters to obtain the target simulation model; and a construction module for constructing a magnetically controlled transformer based on the target simulation model.

[0128] Optionally, the acquisition module may include multiple targets, such as the permeability of the magnetically controlled transformer, the volume of the magnetically controlled transformer, and the reactive power output of the magnetically controlled transformer.

[0129] Optionally, the initial simulation model of the magnetically controlled transformer can be obtained in the acquisition module by constructing an initial simulation model based on the core permeability, magnetic valve length, number of winding turns, core thickness, power supply voltage, load side voltage, coil turns ratio, line inductance, and line resistance of the magnetically controlled transformer.

[0130] According to another aspect of the present invention, an electronic device is also provided, comprising: one or more processors; a storage device for storing one or more programs; and, when the one or more programs are executed by the one or more processors, causing the one or more processors to perform the above-described multi-objective optimization method for magnetically controlled transformers.

[0131] The storage device in the above steps can be a type of sequential logic circuit, a memory component used to store data and instructions, mainly used to store programs and data; the processor can be a functional unit that interprets and executes instructions, and it has a unique set of operation commands, which can be called the processor's instruction set, such as store, load, etc.; the storage device stores computer programs, which can be a set of instructions that a computer can recognize and execute, running on an electronic computer, and is an information tool that meets people's certain needs.

[0132] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, the above-described multi-objective optimization method for magnetically controlled transformers is executed in the processor of the device.

[0133] The computer storage medium mentioned in the above steps can be a medium used in computer memory to store certain discontinuous physical quantities. Computer storage media mainly include semiconductors, magnetic cores, magnetic drums, magnetic tapes, and laser discs. Computer-readable storage media includes stored programs, which can be a set of instructions that a computer can recognize and execute, running on an electronic computer to meet certain human needs—an information tool.

[0134] According to another aspect of the present invention, a computer program product is also provided, including a computer program that is executed by a processor using the multi-objective optimization method for the magnetically controlled transformer described above.

[0135] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0136] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0137] 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 units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0138] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0139] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or 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, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0140] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A multi-objective optimization method for a magnetically controlled transformer, characterized in that, include: An initial simulation model of the magnetically controlled transformer and multiple objectives to be optimized for the magnetically controlled transformer are obtained. The multiple objectives include: the permeability of the magnetically controlled transformer, the volume of the magnetically controlled transformer, and the reactive power output of the magnetically controlled transformer. The multiple objectives have mutual influence and constraints. An initial population for the initial simulation model is generated, wherein the initial population includes multiple individuals, and different individuals correspond to different configuration parameters of the initial simulation model; The initial population is iteratively optimized based on the multiple objectives to obtain target individuals. The target configuration parameters corresponding to the target individuals are used to achieve the balance of the magnetically controlled transformer on the multiple objectives. The target individuals are obtained by iteratively configuring the parameters of the initial simulation model based on the performance of the initial population. Obtain the initial simulation model of the magnetically controlled transformer, including: The initial simulation model is constructed based on the core permeability, magnetic valve length, number of winding turns, core thickness, power supply voltage, load side voltage, coil turns ratio, line inductance, and line resistance of the magnetically controlled transformer. The initial population is iteratively optimized based on the multiple objectives to obtain target individuals, including: Determine the objective functions corresponding to the multiple objectives, and adjust the variables of different individuals in the initial population based on the objective constraints to obtain the first adjustment result; Based on the first adjustment result, the function fitness of the plurality of individuals with respect to the objective function is determined, wherein the function fitness is used to represent the performance quantification index of the plurality of individuals on the plurality of objectives, and the function fitness is used to screen the plurality of individuals; Iterative optimization is performed on the plurality of individuals based on the function fitness to obtain the target individual; wherein, iterative optimization of the plurality of individuals based on the function fitness to obtain the target individual includes: Based on the fitness of the function, the multiple individuals in the initial population are screened to obtain an elite population, wherein the elite population is obtained by screening multiple initial individuals according to crowding, and the multiple initial individuals are obtained by screening multiple individuals according to the fitness of the function, wherein the crowding is obtained by calculating the distance between the multiple individuals on the objective function; Based on the objective function, a subpopulation is obtained by searching the multiple individuals in the initial population; An iterative search is performed on the elite population and the subpopulation. If the number of iterations is greater than the preset number of iterations, the target individuals contained in the final population are obtained. Based on the running efficiency of the initial simulation model corresponding to different number of iterations, the target number of iterations is determined and applied to the initial simulation model.

2. The multi-objective optimization method for magnetically controlled transformers according to claim 1, characterized in that, Based on the fitness function, the multiple individuals in the initial population are screened to obtain an elite population, including: Based on the fitness of the function, the multiple individuals are screened to obtain multiple initial individuals; Determine the crowding degree of the plurality of initial individuals in the objective function space corresponding to the objective function, wherein the crowding degree is used to represent the distribution density of the plurality of initial individuals in the objective function space; The elite population is obtained by screening the multiple initial individuals based on the crowding level.

3. The multi-objective optimization method for magnetically controlled transformers according to claim 1, characterized in that, An iterative search is performed on the elite population and the subpopulation. If the number of iterations exceeds a preset number of iterations, the target individuals included in the final population are obtained, including: Based on the target constraints, the variables of different individuals in the elite population and the subpopulation are adjusted to obtain the second adjustment result; Based on the second adjustment result and the multiple targets, the elite population and the subpopulation are iteratively searched, and the number of iterations of the iterative search is recorded. If the number of iterations is greater than the preset number of iterations, the target individual is obtained.

4. The multi-objective optimization method for magnetically controlled transformers according to claim 1, characterized in that, The method further includes: The initial simulation model is adjusted based on the target configuration parameters to obtain the target simulation model; The magnetically controlled transformer is constructed based on the target simulation model.

5. A multi-objective optimization device for a magnetically controlled transformer, characterized in that, include: The acquisition module is used to acquire the initial simulation model of the magnetically controlled transformer and multiple objectives to be optimized for the magnetically controlled transformer. The multiple objectives include: the permeability of the magnetically controlled transformer, the volume of the magnetically controlled transformer, and the reactive power output of the magnetically controlled transformer. The multiple objectives have mutual influence and constraint relationships. A generation module is used to generate an initial population of the initial simulation model using a multi-objective optimization algorithm, wherein the initial population includes multiple individuals, different individuals correspond to different configuration parameters of the initial simulation model, and the multi-objective optimization algorithm is used to optimize the multiple objectives; An optimization module is used to iteratively optimize the initial population based on the multiple objectives to obtain target individuals. The target configuration parameters corresponding to the target individuals are used to achieve the balance of the magnetically controlled transformer on the multiple objectives. The target individuals are obtained by iteratively configuring the parameters of the initial simulation model based on the performance of the initial population. The acquisition module is also used to construct the initial simulation model based on the core permeability, magnetic valve length, number of winding turns, core thickness, power supply voltage, load side voltage, coil turns ratio, line inductance, and line resistance of the magnetically controlled transformer. The initial population is iteratively optimized based on the multiple objectives to obtain target individuals, including: Determine the objective functions corresponding to the multiple objectives, and adjust the variables of different individuals in the initial population based on the objective constraints to obtain the first adjustment result; Based on the first adjustment result, the function fitness of the plurality of individuals with respect to the objective function is determined, wherein the function fitness is used to represent the performance quantification index of the plurality of individuals on the plurality of objectives, and the function fitness is used to screen the plurality of individuals; Based on the fitness of the function, the multiple individuals are iteratively optimized to obtain the target individual; The process of iteratively optimizing the plurality of individuals based on the functional fitness to obtain the target individual includes: Based on the fitness of the function, the multiple individuals in the initial population are screened to obtain an elite population, wherein the elite population is obtained by screening multiple initial individuals according to crowding, and the multiple initial individuals are obtained by screening multiple individuals according to the fitness of the function, wherein the crowding is obtained by calculating the distance between the multiple individuals on the objective function; Based on the objective function, a subpopulation is obtained by searching the multiple individuals in the initial population; An iterative search is performed on the elite population and the subpopulation. If the number of iterations is greater than the preset number of iterations, the target individuals contained in the final population are obtained. Based on the running efficiency of the initial simulation model corresponding to different number of iterations, the target number of iterations is determined and applied to the initial simulation model.

6. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 4.

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