Optimization design method and equipment for multi-winding regulation and control transformer for flexible loop closing

Abstract

The invention relates to the field of transformer optimization design, in particular to an optimization design method and device for a multi-winding regulation transformer for flexible loop closing, and the method comprises the steps: building an optimization design model of the multi-winding regulation transformer for flexible loop closing based on a mathematical model of the transformer; a genetic optimization algorithm is used for optimizing the design model for solving, a global optimal solution of transformer design is obtained, and a fitness function in the genetic optimization algorithm is in negative correlation with an optimization objective function in the optimization design model. Compared with the prior art, the method has the advantages of reducing design cost, improving design precision and efficiency and the like.

Description

Technical Field

[0001] The present invention relates to the field of transformer optimization design, and in particular to a method and device for optimizing the design of a flexible loop-closing multi-winding control transformer. Background Art

[0002] Oil-immersed transformers, as key power grid equipment and core components of flexible control systems, are widely used and face enormous market demand. Traditionally, manual operation is the primary method for transformer design. However, this approach lengthens the design cycle, significantly reduces efficiency, and creates an extremely cumbersome process, making it difficult to meet the high-performance design requirements of specialized transformers. Currently, low design efficiency and uneven product quality pose urgent challenges that need to be addressed.

[0003] In the field of transformer optimization design, the exhaustive method, regular polyhedron method, branch and bound method, Powell method and orthogonal experimental method are commonly used optimization design algorithms. Hai Li, "Optimization design of electromagnetic parameters of industrial DC transformers based on genetic algorithm", Hunan: University of South China, 2020, disclosed that the electric furnace transformer factory used the "exhaustive method" to complete the design of 330kV transformers with double-coil structure. Ding Fanlin et al., "A new power transformer optimization design method-improved orthogonal experimental method", Transformer, 1993 (10): 2-6, proposed an improved orthogonal experimental method to solve the defects of the optimization method in transformer design. While improving the operation speed, it also solved the problem of discrete variable optimization. The existing algorithm is used in the actual operation process of multi-winding transformers, but it is not easy to obtain the global optimal solution.

[0004] Genetic algorithms, proposed by American research professor Holland, mimic the reproduction, hybridization, and mutation of biological populations. They select and eliminate candidates based on the principle of "survival of the fittest." Genetic algorithms generally include three genetic operations: selection, crossover, and mutation. Their operational processes are simple and easy to understand, leading to their widespread application. Numerous researchers in various fields, both domestically and internationally, have conducted in-depth research on them, and numerous practical applications have been discovered. Furthermore, due to their relatively simple mechanism and high portability, genetic algorithms have been successfully applied in the design of multi-winding control transformers for flexible closed-loop systems.

[0005] After searching, Chinese invention patent application publication number CN119598834A discloses a method for optimizing the design of a multi-winding transformer for flexible interconnection. The method comprises the following steps: 1. Based on a single-objective optimization design mathematical model, an optimization objective function is constructed to minimize the sum of the material cost and the ten-year operating cost of the multi-winding transformer; 2. Based on the basic electrical parameters of the multi-winding transformer for flexible interconnection, the required constraints are determined; under the obtained optimization objective function, a penalty function is used to penalize trends that violate the constraints, gradually moving the unconstrained optimization closer to the feasible region, thereby converting the constrained optimization problem of the distribution transformer into an unconstrained optimization problem, and obtaining an optimized design model for a multi-winding transformer for flexible interconnection with amplitude and phase regulation; 3. A particle swarm optimization algorithm is used to solve the optimized design model for the multi-winding transformer for flexible interconnection obtained in step 2. Although this existing patent application converges quickly, it suffers from poor adaptability to complex transformer designs and difficulty in obtaining a global optimal solution.

[0006] How to achieve efficient and high-quality optimized design of multi-winding control transformer for flexible loop has become a technical problem that needs to be solved. Summary of the Invention

[0007] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a method and device for optimizing the design of a multi-winding control transformer for flexible closed-loop use.

[0008] The purpose of the present invention can be achieved by the following technical solutions: According to one aspect of the present invention, a method for optimizing the design of a multi-winding control transformer for flexible closed-loop applications is provided, the method comprising: Based on the mathematical model of the transformer, an optimization design model for a multi-winding control transformer for flexible loop closing is constructed; The optimization design model is solved using a genetic optimization algorithm to obtain the global optimal solution for the transformer design. The fitness function in the genetic optimization algorithm is negatively correlated with the optimization objective function in the optimization design model, namely: , in, represents the fitness function, Design objective function for transformer optimization.

[0009] Preferably, the process of using a genetic optimization algorithm to optimize the design model for solving includes: Step 3.1: Set the basic parameters in the population and perform real number encoding operations on the optimization variables; Step 3.2: Calculate and compare the fitness of each individual in the population and construct a fitness function; Step 3.3, select the population, and prioritize individuals that meet both the core diameter and core window ratio and have a high fitness ranking to construct a new population; Step 3.4: Perform a directed crossover operation on the population to generate new individuals. Step 3.5: Perform mutation operations on the individuals in the population to generate individuals with new genes, thereby producing a population of offspring; Step 3.6: Repeat steps 3.3 to 3.5 until the convergence conditions are met and the global optimal solution is obtained.

[0010] More preferably, in the directional crossover operation, the proportional relationship between the core diameter and the core window height is forced to be maintained, and the proportional correction formula is used to ensure that the offspring always meets the engineering constraints.

[0011] More preferably, the ratio of the core diameter to the core window height is forcibly maintained as follows: When the parent core diameter D and iron core window height H When parameters are crossed, the following mathematical process is used to enforce the maintenance of The proportional relationship: , in, is the mixing coefficient, which controls the fusion ratio of the parent parameters; k is the preset core structure proportional constant; Represents two parents respectively p 、 q The core diameter; and are the core diameter and core window height of the offspring respectively.

[0012] More preferably, performing real number encoding on the optimization variables includes: performing real number encoding on the core diameter to ensure compliance with the punching process; using integer encoding for the number of turns of the low-voltage coil; and selecting valid parameters for the wire size directly from the industry specification table; The optimization variables include the core diameter, the number of coil turns on the low-voltage side, the low-voltage wire size, and the high-voltage wire size.

[0013] More preferably, a differentiated variation strategy is implemented in step 3.5, that is, the core diameter changes suddenly according to a specified real number step; the wire size jumps to adjacent specifications in the industry standard table.

[0014] Preferably, the optimization objective function of the optimization design model is to minimize the material cost of the multi-winding transformer, specifically: , Where, represents the optimization variable; Indicates the unit price of silicon steel sheet; Indicates the total weight of the core; Indicates the unit price of high voltage conductor; Indicates the total weight of the high-voltage conductor; Indicates the unit price of low voltage conductor; Indicates the total weight of the low-voltage conductor.

[0015] Preferably, the method further includes setting constraints that need to be followed in the transformer optimization design process based on basic electrical parameter characteristics of a multi-winding control transformer in a flexible loop scenario.

[0016] More preferably, the constraints include performance constraints, material constraints and process constraints; The performance constraints include: load loss and short-circuit loss should not be greater than 1.02 times the standard value; The specified range of impedance voltage is (1±0.5%) times the standard value; the short-circuit impedance deviation is ≤ (±7.5%); the no-load current should not be greater than 1.05 times the standard value.

[0017] According to another aspect of the present invention, an electronic device is provided, comprising a memory and a processor, wherein a computer program is stored in the memory, and the method described above is implemented when the processor executes the program.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1) The present invention applies the global search strategy of genetic algorithms to optimize the design of multi-winding transformers in flexible closed-loop scenarios. By manipulating different optimization variables, this method enables dynamic exploration within the feasible domain at their own unique speeds and directions, thereby locking in the optimal electrical parameter combination suitable for flexible closed-loop amplitude-phase regulation multi-winding transformers with a higher probability. This not only effectively reduces the design cost, but also improves the accuracy and efficiency of multi-winding transformer design with its highly flexible parameter adjustment mechanism and precise optimization capability.

[0019] 2) The genetic algorithm of this application has made adaptive improvements for the optimization design of multi-winding control transformers in flexible loop application scenarios. Real number encoding, construction of fitness function, and priority retention of individuals that meet the set ratio of core diameter to core window height and have high fitness rankings have significantly improved the accuracy and efficiency of multi-winding transformer design, achieved material cost reduction in the optimization process, and ultimately achieved a significant improvement in the overall economic efficiency of the transformer.

[0020] 3) This application uses a directed crossover operation in the genetic algorithm to enforce the proportional relationship between the core diameter and the core window height. The proportional correction formula ensures that the offspring always meets the engineering constraints, avoids structural imbalance, reduces invalid searches, and speeds up the algorithm's convergence and enhances population stability, thereby improving transformer quality and physical feasibility.

[0021] 4) This application proposes to implement a differentiated mutation strategy to change the gene sequence of individuals to enrich the diversity of the population, thereby enriching the diversity of transformer design, expanding the algorithm solution space, and helping to obtain the optimal solution.

[0022] 5) The optimization objective function of the transformer design in this application only considers material cost, discarding the uncertainty and high design complexity of the ten-year operating cost, thereby improving the rationality, efficiency and accuracy of the design. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 Schematic diagram of the overall process of optimizing the design of a multi-winding control transformer for flexible loop closing in the present invention; Figure 2 The figure is a flow chart of using the genetic optimization algorithm to solve the optimization design model in the present invention. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0025] Example 1 This embodiment relates to a method for optimizing the design of a multi-winding control transformer for flexible loop closing, such as Figure 1 , the method comprises the following steps: Step 1: Based on the mathematical model theory of single-objective optimization design, given that material costs dominate the design of multi-winding transformers, the optimal design of multi-winding transformers achieves maximum overall cost-effectiveness through multi-parameter joint optimization and technical and economic balancing. Including the ten-year operating cost factor in the objective function requires considering more variables, such as losses during operation, including iron and copper losses. This increases the number of design variables and the complexity of the design process. It also makes prediction more difficult. The ten-year operating cost involves future forecasts, including future electricity market price fluctuations, equipment aging rates, and developments in maintenance technology. When the ten-year operating cost factor is included, choosing a more complex cooling system or higher-quality insulation materials to improve long-term energy efficiency may increase construction time and initial costs, hindering the achievement of short-term goals. Measures to reduce the ten-year operating cost may conflict with short-term design objectives, such as meeting current electricity demand and ensuring rapid construction and commissioning. These factors are highly uncertain and difficult to accurately predict, which can lead to inaccurate objective function calculations and, in turn, compromise the rationality of the design.

[0026] Therefore, when designing the optimization objective function, this application only considers the material cost, and the design focuses on factors directly related to the material, such as material selection and dosage. Construct an optimization objective function with the goal of minimizing the material cost of a multi-winding transformer : (1) Where, represents the optimization variable; Indicates the unit price of silicon steel sheet; Indicates the total weight of the core; Indicates the unit price of high voltage conductor; Indicates the total weight of the high-voltage conductor; Indicates the unit price of low voltage conductor; Indicates the total weight of the low-voltage conductor.

[0027] In step 1, the single-objective optimization model can be expressed in mathematical form, as shown in formula (2); (2) Where, f( x ) represents the optimization goal of transformer design; x represents the optimization variable, Represents constraints, which mainly include three types of constraints: (1) Performance constraints Load loss and short-circuit loss should not be greater than 1.02 times the standard value; The error between the actual voltage ratio between the primary winding and the secondary winding and the theoretical design ratio should not be greater than ±0.5%; Short-circuit impedance deviation ≤ ±7.5%; The no-load current should not be greater than 1.05 times the standard value.

[0028] (2) Material constraints The coil temperature rise should not exceed the standard value; The magnetic flux density of the core should be within the specified range; The current density of the conductor should be within the specified range.

[0029] (3) Process constraints The core diameter, core window height and core center distance should be multiples of 5; The number of coil turns should be an integer; The wire specifications of the winding should be selected according to the specified table.

[0030] When selecting optimization variables in step 1, you must strictly follow the relevant selection criteria for optimization variables. Specifically, you should focus on variables that are closely related to the objective function and constraints. At the same time, you must ensure that the selected optimization variables are independent of each other and do not interfere with each other.

[0031] The selection of transformer optimization variables plays a decisive role in design efficiency and cycle time. Misselection can lead to numerous problems, including reduced efficiency and irrational design solutions. As the number of design variables increases, the computational complexity rises exponentially, undoubtedly posing a significant design challenge. Conversely, as the number of design variables decreases, finding a design solution that meets expectations becomes even more challenging. Therefore, when selecting design variables, it is crucial to fully consider their correlation with the objective function and constraints. Only those variables that are closely related to the objective function and constraints are considered appropriate design variables.

[0032] Variables often selected for optimization during transformer optimization design include core diameter, current density, magnetic flux density, and conductor size. Variable selection is then completed by adding other settings such as core structure and core cross-section.

[0033] According to the selection principle, the optimization variables for minimizing the material cost of the phase-adjustable multi-winding transformer for flexible closed-loop use should include the core diameter, the number of coil turns on the low-voltage side, the low-voltage wire size, and the high-voltage wire size.

[0034] Step 2: Based on the basic electrical parameter characteristics of the multi-winding control transformer in the flexible loop closing scenario, the set of constraints that must be followed in the design process is first clarified. Then, using the mathematical model of the transformer, an optimized design model for the multi-winding control transformer for flexible loop closing applications is finally constructed: (3) In formula (2), x represents the optimization variable; Indicates the unit price of silicon steel sheet; Indicates the total weight of the core; Indicates the unit price of high voltage conductor; Indicates the total weight of the high-voltage conductor; Indicates the unit price of low voltage conductor; Indicates the total weight of the low-voltage conductor; Indicates constraints.

[0035] In step 2, the constraints include performance constraints, material constraints, and process constraints. Only when all three constraints are met can the transformer design be considered to meet national standards and meet user needs.

[0036] The performance constraints include that the load loss and short-circuit loss should not be greater than 1.02 times the standard value, and the specified range of impedance voltage is ( 0.5%) times the standard value, short-circuit impedance deviation ≤ ±7.5%, no-load current should not be greater than 1.05 times the standard value; Material constraints include that the coil temperature rise should not exceed the standard value, the core magnetic flux density should be within the specified range, and the conductor current density should be within the specified range; The process constraints include that the core diameter, core window height and core center distance should be multiples of 5, the number of coil turns should be an integer, and the wire specifications of the winding should be selected according to the specified table.

[0037] Step 3: Use the genetic optimization algorithm to solve the optimal design model of the flexible closed-loop multi-winding control transformer obtained in step 2, as shown in Figure 2 , including the following steps: Step 3.1: First, set the basic parameters in the population. That is, perform encoding operations on the variables. The core diameter is encoded with real numbers to ensure the compliance of the punching process. The number of turns of the low-voltage winding is encoded with integers. The wire size is directly selected from the industry specification table. Preferably, in step 3.1, in the genetic algorithm, encoding is the basis of the genetic algorithm, and the object of application is often the gene string of an individual. In order to improve the efficiency of the operation, it is necessary to select an appropriate encoding method for the gene string. The following mainly introduces three commonly used encoding methods: a) Binary encoding Binary encoding, which uses 0s and 1s to perform encoding operations, is the most widely used method. Binary strings are used to represent the genetic sequences of individuals in a population. This representation is simple, uncomplicated, and easy to manipulate.

[0038] However, binary encoding also has its drawbacks. First, the binary encoding method, while simple, significantly increases the decoding workload and complicates the process. Furthermore, binary encoding exponentially increases the amount of computation required, significantly slowing down the algorithm's speed.

[0039] b) Gray code Gray code, also known as reflection code, is a derivative of binary encoding and also uses binary. However, compared to binary encoding, Gray code can improve the algorithm's solving capabilities to a certain extent.

[0040] c) Real number encoding Real number coding refers to the use of real numbers to encode individual gene sequences. Since real numbers have a sufficient number of choices, real number coding can simplify the algorithm operation, thereby improving operating efficiency and better handling the optimization design problem of transformers.

[0041] Through the comparison of the above three encoding methods, real number encoding can make the algorithm operation simpler, thereby improving the operating efficiency and better handling the optimization design problem of the transformer. Therefore, in the optimization design of the transformer, real number encoding is more suitable.

[0042] Step 3.2: Calculate and compare the fitness of each individual in the population and construct a fitness function that satisfies performance constraints and material constraints. Prioritize individuals with a core diameter to core window height ratio close to 1:4 (i.e., the ratio is allowed to fluctuate within the set range of 1:4) and high fitness rankings. In step 3.2, the fitness function serves as a primary indicator for evaluating performance. It is always non-negative, so a genetic algorithm is often used to find its maximum value. However, in transformer optimization, it is necessary to find the minimum value of the transformer optimization objective function. Therefore, the problem of finding the minimum value of the transformer optimization objective should be transformed into a problem of finding the maximum value. Furthermore, the objective function and fitness function can usually be represented by the same type of function, requiring only some transformation operations.

[0043] in, Represents the fitness function, which is negatively correlated with the transformer optimization design objective function.

[0044] Step 3.3, the selection operation, selects the best individuals from the population and uses the remaining individuals to form a new population. During this selection process, the parent population remains; no new offspring population is generated; the initial population's quality is simply improved. The selection operation is an unrestricted operator in the genetic algorithm, and different encoding methods do not affect the selection operation.

[0045] Step 3.4: Through the crossover operation, the gene sequence of the individual is replaced to generate a new population individual. In this process, a directed crossover is performed to force the core parameters to maintain the proportional relationship between the core diameter and the core window height to avoid structural imbalance.

[0046] In step 3.4, the crossover operation involves exchanging genetic fragments from individuals in the previous generation to generate new individuals, increasing population diversity and thus improving the genetic algorithm's solving ability. Generally speaking, there are three main forms of crossover: single-point crossover, multi-point crossover, and cyclic crossover.

[0047] 1) Single-point crossover like Figure 2 As shown in Figure 1, a single-point crossover is to randomly select a crossover point in the coding strings of two different individuals and then exchange the chromatids on the left and right sides of this crossover point. Single-point crossover is one of the most basic crossover operations in genetic algorithms due to its simplicity.

[0048] 2) Multi-point crossover Multi-point crossover refers to randomly selecting multiple crossover points on the gene sequence of an individual. When performing a crossover operation, two individuals complete the crossover operation at these crossover points to obtain new individuals.

[0049] 3) Cyclic Crossover Circular crossover involves randomly selecting different crossover points, cyclically replacing the chromatids in the gene strings of two different individuals to generate new individuals. Due to its unique randomness, this crossover method has a unique advantage in generating individuals with new gene sequences.

[0050] Directed crossover is not a common crossover method and is often used in complex engineering optimization scenarios.

[0051] In order to solve the complex engineering optimization problems in transformer optimization design, this application adopts directed crossover and designs a crossover strategy. Through the proportional correction formula, it ensures that the offspring always meets the engineering constraints and avoids the existence of invalid solutions. At the same time, it integrates the design rules of the transformer to improve the transformer quality and physical feasibility. More importantly, it reduces invalid searches, thereby improving the convergence speed of the algorithm and enhancing the population stability.

[0052] The core mechanism of the directed crossover operation is: When the parent core diameter D and iron core window height H When parameters are crossed, the following mathematical process is used to enforce the maintenance of The proportional relationship:

[0053] in, is the mixing coefficient, which controls the fusion ratio of the parent parameters; k is the preset core structure proportional constant; Represents two parents respectively p 、 q The core diameter.

[0054] This operation ensures that the core diameter of the offspring is and iron core window height Always satisfied , thereby avoiding the structural imbalance problem caused by the imbalance between the core diameter and the window height. It is a constrained crossover strategy designed in combination with domain knowledge in the genetic algorithm.

[0055] Step 3.5: To increase population diversity, individuals often mutate to generate individuals with new genes, thereby producing offspring. This application implements a differentiated mutation strategy, where the core diameter is mutated in specified real-number steps; the wire size is jumped to adjacent specifications within the industry standard table.

[0056] In step 3.5, the genetic algorithm performs a mutation operation to alter the genetic sequence of an individual, thereby enriching the diversity of the population. Mutation involves selecting a mutation point in the genetic sequence of an individual and altering that point. The resulting code is closely related to the encoding method. For example, if the algorithm uses binary encoding, a mutation operation will convert a 0 to a 1 or a 1 to a 0, resulting in a new individual with minimal changes. Mutation, building on the crossover operation, further increases the diversity of the population and expands the algorithm's solution space, which is crucial for obtaining the optimal solution.

[0057] Step 3.6: Repeat the above selection, crossover, and mutation steps many times, continuously eliminating the best and eliminating the worst, until the convergence conditions are met and the global optimal solution is obtained.

[0058] The multi-winding transformer designed based on this application is a flexibly controllable transformer. Its flexible loop closing device offers significant advantages in both structural rationality and performance. The transformer uses transformer oil as the insulating medium, effectively isolating oxygen to slow oxidation of the insulating cardboard and optimizing heat transfer efficiency. This significantly extends the transformer's service life while improving equipment stability and simplifying the manufacturing process.

[0059] The multi-winding transformer designed in this application uses the directional crossover operation in the genetic algorithm to ensure that the optimization process always meets the electromagnetic parameter matching requirements required for voltage regulation, thereby achieving precise control of amplitude and phase angle, which is particularly suitable for complex application scenarios such as flexible loop operation and power grid reconstruction.

[0060] The technical solution of this application focuses on the optimized design of multi-winding transformers, aiming to comprehensively improve their production efficiency and scenario adaptability.

[0061] Example 2 This embodiment also relates to an optimization design method for a multi-winding control transformer for flexible loop closing. An example optimization design is performed using a multi-winding control transformer for flexible loop closing (with four low-voltage windings). The main parameters of the multi-winding transformer are shown in Table 1.

[0062] Table 1

[0063] Based on the genetic algorithm framework, an objective function with the weighted sum of material cost and ten-year operation and maintenance cost as the optimization target is constructed, and a simulation experiment on the optimization design of multi-winding transformers is carried out.

[0064] Through an iterative optimization process, we obtain the optimal solution set for the transformer's structural parameters, performance parameters, and economic indicators. The optimization results are compared and analyzed with the manufacturer's original design data to produce a comprehensive comparison report covering multiple dimensions, including electromagnetic parameters, temperature rise characteristics, loss distribution, and life cycle costs.

[0065] The structural parameter data of the multi-winding transformer are shown in Table 2.

[0066] Table 2

[0067] The performance parameter data of the multi-winding transformer are shown in Table 3.

[0068] Table 3

[0069] The economic indicators of multi-winding transformers are shown in Table 4.

[0070] Table 4

[0071] Analyzing the above comparative data, Table 2 shows the structural parameters of the transformers. Since the core diameter of transformers is limited to a 5mm spacing between core diameters of adjacent grades, the core diameter variations in the manufacturer's proposal and the optimized design are not significantly different. The core diameter in the original design is 150mm, and the core diameter in the optimized design is also 150mm, the same value. Similarly, to limit the magnetic flux density, the transformer's low-voltage turns are also the same. As shown in the data in Table 2, the manufacturer's proposal has 41 low-voltage turns, and the optimized design has the same value. In Table 2, which compares the structural parameters, round conductors are used for the high-voltage conductors in both the manufacturer's and optimized designs, and the diameter variations of the high-voltage conductors are also the same.

[0072] The performance index comparison results are presented in Table 3. From the data in Table 3, it can be seen that as the cross-sectional area of ​​the low-voltage conductor in Table 2 decreases after optimization, the load loss of the transformer increases accordingly.

[0073] The data in Table 4 demonstrates that the optimized material cost has been reduced from 12,541.88 yuan to 11,781 yuan, a 6.07% decrease, or a full 760.88 yuan. Therefore, from a production economics perspective, the optimized solution outperforms the original solution and is more suitable for power system development.

[0074] Example 3 The electronic device of the present invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0075] Many components in a device are connected to the I / O interface, including: input units, such as a keyboard and mouse; output units, such as various types of displays and speakers; storage units, such as magnetic disks and optical disks; and communication units, such as network cards, modems, and wireless communication transceivers. The communication unit allows the device to exchange information / data with other devices via computer networks such as the Internet and / or various telecommunication networks.

[0076] The processing unit performs the various methods and processes described above. For example, in some embodiments, the method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on the device via a ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the method described above can be performed. Alternatively, in other embodiments, the CPU can be configured to execute the method in any other appropriate manner (e.g., by means of firmware).

[0077] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0078] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0079] In the context of the present invention, a machine-readable medium can be a tangible medium that can contain or store a program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0080] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A method for optimizing the design of a multi-winding control transformer for flexible loop closing, characterized in that: The method includes: Based on the mathematical model of the transformer, an optimization design model for a multi-winding control transformer for flexible loop closing is constructed; The optimization design model is solved using a genetic optimization algorithm to obtain the global optimal solution for the transformer design. The fitness function in the genetic optimization algorithm is negatively correlated with the optimization objective function in the optimization design model, namely: ， in, represents the fitness function, Design objective function for transformer optimization.

2. The method for optimizing the design of a multi-winding control transformer for flexible closed-loop according to claim 1, characterized in that: The process of solving the optimization design model using the genetic optimization algorithm includes: Step 3.1: Set the basic parameters in the population and perform real number encoding operations on the optimization variables; Step 3.2: Calculate and compare the fitness of each individual in the population and construct a fitness function; Step 3.3, select the population, and prioritize individuals that meet both the core diameter and core window ratio and have a high fitness ranking to construct a new population; Step 3.4: Perform a directed crossover operation on the population to generate new individuals. Step 3.5: Perform mutation operations on the individuals in the population to generate individuals with new genes, thereby producing a population of offspring; Step 3.6: Repeat steps 3.3 to 3.5 until the convergence conditions are met and the global optimal solution is obtained.

3. The method for optimizing the design of a multi-winding control transformer for flexible closed-loop according to claim 2, characterized in that: In the directional crossover operation, the proportional relationship between the core diameter and the core window height is forced to be maintained, and the proportional correction formula is used to ensure that the offspring always meets the engineering constraints.

4. The method for optimizing the design of a multi-winding control transformer for flexible closed-loop according to claim 3, characterized in that: The specific ratio between the core diameter and the core window height is as follows: When the parent core diameter D and iron core window height H When parameters are crossed, the following mathematical process is used to enforce the maintenance of The proportional relationship: ， in, is the mixing coefficient, which controls the fusion ratio of the parent parameters; k is the preset core structure proportional constant; Represents two parents respectively p 、 q The core diameter; and are the core diameter and core window height of the offspring respectively.

5. The method for optimizing the design of a multi-winding control transformer for flexible closed-loop according to claim 2, characterized in that: The real number encoding operation for the optimization variables includes: the real number encoding of the core diameter to ensure the compliance of the punching process; the integer encoding of the number of turns of the low-voltage coil; the effective parameters of the wire size are directly selected from the industry specification table; The optimization variables include the core diameter, the number of coil turns on the low-voltage side, the low-voltage wire size, and the high-voltage wire size.

6. The method for optimizing the design of a multi-winding control transformer for flexible closed-loop according to claim 2, characterized in that: In step 3.5, a differentiated variation strategy is implemented, that is, the core diameter is suddenly changed according to a specified real number step; the wire size jumps to adjacent specifications in the industry standard table.

7. The method for optimizing the design of a multi-winding control transformer for flexible closed-loop according to claim 1, characterized in that: The optimization objective function of the optimization design model is to minimize the material cost of the multi-winding transformer, specifically: ， Where, represents the optimization variable; Indicates the unit price of silicon steel sheet; Indicates the total weight of the core; Indicates the unit price of high voltage conductor; Indicates the total weight of the high-voltage conductor; Indicates the unit price of low voltage conductor; Indicates the total weight of the low-voltage conductor.

8. The method for optimizing the design of a multi-winding control transformer for flexible closed-loop according to claim 1, characterized in that: The method also includes setting constraints that need to be followed in the transformer optimization design process based on basic electrical parameter characteristics of the multi-winding control transformer in the flexible loop scenario.

9. The method for optimizing the design of a multi-winding control transformer for flexible closed-loop according to claim 8, characterized in that: The constraints include performance constraints, material constraints and process constraints; The performance constraints include: load loss and short-circuit loss should not be greater than 1.02 times the standard value; The specified range of impedance voltage is (1±0.5%) times the standard value; the short-circuit impedance deviation is ≤ (±7.5%); the no-load current should not be greater than 1.05 times the standard value.

10. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the program, the method according to any one of claims 1 to 9 is implemented.

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