Transformer optimization design method and system based on bionic swarm intelligence algorithm

Abstract

The invention relates to the technical field of transformer optimization, and provides a transformer optimization design method and system based on a bionic swarm intelligence algorithm, and the method comprises the steps: determining an optimization target and a constraint condition of a transformer, the optimization target comprises the minimization of the total life cycle cost, and the constraint condition comprises the performance index limitation; based on the optimization target and the constraint condition, a parameterized transformer analysis model is constructed, and the parameterized transformer analysis model is used for calculating the total cost and performance indexes of the whole life cycle according to design variables; performing optimization solution on the parameterized transformer analysis model by applying a bionic swarm intelligence algorithm to search a global optimal design parameter combination on the premise of meeting the constraint conditions, the bionic swarm intelligence algorithm simulating biological swarm behaviors to perform global exploration and local development; and verifying the global optimal design parameter combination, and judging whether the global optimal design parameter combination meets all performance standards or not.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of transformer optimization, and in particular to a transformer optimization design method and system based on a bionic swarm intelligence algorithm. BACKGROUND

[0002] In the prior art, transformer design is a key link in the manufacturing of power equipment. The traditional method mainly relies on manual experience and the assistance of software tools for optimization. On the one hand, the early design method relies on the experience of designers for manual calculation and parameter adjustment. Although the operation is intuitive, the efficiency is low, the design period is long, and it is seriously dependent on the experience level of the designer, and it is difficult to achieve global optimization. On the other hand, the modern method uses computer-aided design (CAD) and finite element analysis (FEA) software for simulation and iterative optimization. Although the calculation accuracy is improved, manual intervention is still required, which limits the number of design schemes explored and easily falls into a local optimal solution, and cannot fully tap the potential of cost reduction and efficiency improvement. In the prior art, optimization algorithms (such as genetic algorithms or particle swarm algorithms) attempt to achieve automatic optimization, but generally have slow convergence speed, complex parameter setting, and early convergence problems. When dealing with high-dimensional and complex constraint transformer design problems, it is difficult to find a global optimal solution.

[0003] Therefore, under the premise of meeting performance indicators, the prior art is difficult to achieve efficient optimization of the total life cycle cost, which limits the design quality and economic benefits. SUMMARY

[0004] In view of the above defects, the purpose of the present application is to propose a transformer optimization design method and system based on a bionic swarm intelligence algorithm, which aims to overcome the technical bottlenecks of traditional transformer design methods, such as low efficiency, easy to fall into local optimum, and inability to achieve comprehensive optimization of the total life cycle cost. By integrating the bionic swarm intelligence algorithm with the transformer design model, efficient and automated global optimal design is achieved, which significantly improves the design quality and reduces the comprehensive cost.

[0005] To achieve this purpose, the present application adopts the following technical solutions: A transformer optimization design method based on a bionic swarm intelligence algorithm, the transformer optimization design method comprising the following steps: S1: determining the optimization target and the constraint condition of the transformer, the optimization target including minimizing the total life cycle cost, and the constraint condition including the performance indicator limit; S2: based on the optimization target and the constraint condition, constructing a parameterized transformer analysis model, the parameterized transformer analysis model being used to calculate the total life cycle cost and the performance indicator according to the design variable; S3: applying a bionic swarm intelligence algorithm to the parameterized transformer analysis model to search for a globally optimal design parameter combination under the premise of meeting the constraint conditions, wherein the bionic swarm intelligence algorithm simulates biological swarm behavior to perform global exploration and local development; S4: verifying the globally optimal design parameter combination to determine whether it meets all performance standards.

[0006] Preferably, the objective function of minimizing the total cost of the whole life cycle , meets the relationship: ; Wherein, the material cost , represents the weight of silicon steel sheet, represents the unit price of silicon steel sheet, represents the weight of copper wire, represents the unit price of copper wire, represents the cost of insulating material, represents the cost of the remaining materials; The operating cost , represents the capitalization coefficient of no-load loss, represents the no-load loss, represents the capitalization coefficient of load loss, represents the load loss, represents the design variable vector including at least one of core diameter, magnetic flux density, current density and winding turns.

[0007] Preferably, the performance index limit includes an upper limit of no-load loss, an upper limit of load loss, an upper limit of temperature rise and a short-circuit impedance range; The performance index limit is introduced into the objective function by the penalty function method to construct a modified objective function: ; Wherein, represents the violation degree of the th constraint condition, represents the penalty factor for controlling the punishment intensity of constraint violation.

[0008] Preferably, step S3 includes: Initializing the honey source population, wherein each honey source represents a design variable vector; Calculating the fitness value of each honey source, the fitness value meets the relationship: , wherein represents the modified objective function; Performing local search by hiring bees to generate new honey source parameters , which meets the relationship: wherein, represents the i-th parameter of the current food source, represents the i-th parameter of another food source selected randomly, represents a random number, represents the i-th individual of the food source population; by observing that the bees select a food source for exploitation based on a selection probability, satisfies the relationship: ; by observing that the bees select a food source for exploitation based on a selection probability, satisfies the relationship: ; by observing that the bees select a food source for exploitation based on a selection probability, wherein, represents a lower limit of the design variable, represents an upper limit of the design variable, represents a random number; iteratively performing the search process until a convergence condition is satisfied, and outputting a globally optimal design parameter combination .

[0009] Preferably, the parameterized transformer analysis model comprises an electromagnetic calculation model, which comprises: a model for calculating the effective cross-sectional area of the core, which satisfies the relationship: ; wherein, represents the core lamination factor, represents the core diameter; based on the effective cross-sectional area of the core calculates the amplitude of the core magnetic flux density , which satisfies the relationship: ; wherein, represents the voltage per turn, represents the power supply frequency, represents the number of turns of the winding; calculates the winding resistance , which satisfies the relationship: wherein, represents the electrical resistivity of the conductor, represents the average length of turns of the winding, represents the cross-sectional area of the wire; calculates the no-load loss , which satisfies the relationship: wherein, represents the iron loss coefficient, represents the Steinmetz index, represents the core weight; calculates the load loss , satisfies the relationship: , and wherein represents a vortex loss coefficient, represents a winding phase current, represents a winding resistance; calculating the short-circuit impedance , satisfies the relationship: ; wherein, represents a leakage magnetic area, represents a Rogowski coefficient, represents a process coefficient, represents a winding reactance height, represents a transformer capacity.

[0010] Preferably, the parameterized transformer analysis model comprises a temperature rise calculation model, the temperature rise calculation model comprising: calculating the top layer oil temperature rise , satisfies the relationship: ; wherein, represents a rated top layer oil temperature rise, represents a total loss of the current scheme, represents a no-load loss of the current scheme, represents a rated total loss, represents a heat dissipation index; calculating the winding hot spot temperature rise , satisfies the relationship: wherein, represents a rated winding hot spot temperature rise, represents a load loss of the current scheme, represents a rated load loss, represents a winding heat dissipation index; calculating the winding hot spot absolute temperature , satisfies the relationship: wherein, represents an ambient temperature.

[0011] A transformer optimization design system based on a bionic swarm intelligence algorithm, the transformer optimization design system being applied to the transformer optimization design method based on the bionic swarm intelligence algorithm as described above, the transformer optimization design system based on the bionic swarm intelligence algorithm comprising: a target setting module, configured to determine an optimization target and a constraint condition of the transformer, the optimization target comprising minimizing a total cost of a whole life cycle, and the constraint condition comprising a performance index limit; A model construction module is configured to construct a parameterized transformer analysis model based on the optimization target and the constraint conditions, the parameterized transformer analysis model being used to calculate the total life cycle cost and the performance index according to the design variables; An optimization solution module is configured to apply a bionic swarm intelligence algorithm to perform optimization solution on the parameterized transformer analysis model to search for a globally optimal design parameter combination under the premise of meeting the constraint conditions, wherein the bionic swarm intelligence algorithm simulates the behavior of biological groups to perform global exploration and local development; A verification module is configured to verify the globally optimal design parameter combination to determine whether it meets all performance standards.

[0012] Preferably, in the target setting module, the target function of minimizing the total life cycle cost , meets the relationship: ; Wherein, the material cost , represents the weight of silicon steel sheet, represents the unit price of silicon steel sheet, represents the weight of copper wire, represents the unit price of copper wire, represents the cost of insulation material, represents the cost of remaining materials; The operating cost , represents the capitalization coefficient of no-load loss, represents the no-load loss, represents the capitalization coefficient of load loss, represents the load loss, represents the design variable vector including at least one of core diameter, magnetic flux density, current density and winding turns; The performance index limit includes the upper limit of no-load loss, the upper limit of load loss, the upper limit of temperature rise and the short-circuit impedance range; The performance index limit is introduced into the target function by the penalty function method to construct a modified target function: ; Wherein, represents the violation degree of the th constraint condition, represents a penalty factor for controlling the punishment intensity of constraint violation; In the optimization solution module, it includes: Initialize the honey source population, wherein each honey source represents a design variable vector; Calculate the fitness value of each honey source, the fitness value meets the relationship: , wherein This represents the modified objective function; New nectar source parameters are generated by employing bees to conduct local searches. The following relation is satisfied: ,in, Indicates the current honey source's number One parameter, This represents the first nectar source randomly selected from another nectar source. One parameter, Represents a random number. The first nectar source population Individual; By observing bees selecting nectar sources based on selection probability, we can understand the selection probability... Satisfying the relation: ; New nectar sources are generated by replacing unimproved nectar sources with scout bees. ,in Indicates the lower limit of the design variable. Indicates the upper limit of design variables. Represents a random number; Iteratively execute the search process until the convergence condition is met, and output the globally optimal combination of design parameters. .

[0013] Preferably, in the model building module, the parametric transformer analysis model includes an electromagnetic calculation model, which includes: Model for calculating the effective cross-sectional area of ​​the iron core, effective cross-sectional area of ​​the iron core Satisfying the relation: ; in, Indicates the lamination factor of the iron core. Indicates the diameter of the iron core; Based on the effective cross-sectional area of ​​the iron core Calculate the amplitude of the magnetic flux density in the iron core The following relation is satisfied: ; in, This indicates the voltage per turn. Indicates the power supply frequency. Indicates the number of turns in the winding; Calculate winding resistance The following relation is satisfied: ,in Represents the resistivity of a conductor. This indicates the average turn length of the winding. Indicates the cross-sectional area of ​​the conductor; Calculate no-load loss The following relation is satisfied: ,in, denotes the iron loss coefficient, denotes the Steinmetz index, denotes the core weight; calculating the load loss satisfying the relationship: , and wherein denotes the eddy current loss coefficient, denotes the winding phase current, denotes the winding resistance; calculating the short-circuit impedance satisfying the relationship: ; wherein, denotes the leakage magnetic area, denotes the Rogowski coefficient, denotes the process coefficient, denotes the winding reactance height, denotes the transformer capacity.

[0014] Preferably, in the model construction module, the parameterized transformer analysis model comprises a temperature rise calculation model, and the temperature rise calculation model comprises: calculating the top layer oil temperature rise satisfying the relationship: ; wherein, denotes the rated top layer oil temperature rise, denotes the total loss of the current scheme, denotes the no-load loss of the current scheme, denotes the rated total loss, denotes the heat dissipation index; calculating the winding hot spot temperature rise satisfying the relationship: wherein, denotes the rated winding hot spot temperature rise, denotes the load loss of the current scheme, denotes the rated load loss, denotes the winding heat dissipation index; calculating the winding hot spot absolute temperature satisfying the relationship: wherein, denotes the ambient temperature.

[0015] One of the above technical solutions has the following advantages or beneficial effects: The present application can overcome the defects of traditional trial-and-error method, such as fuzzy target and dependence on experience, by taking the minimization of total life cycle cost as an explicit optimization target and setting key performance constraints; the traditional manual calculation or CAD / FEA iteration process depending on artificial intervention can be replaced by constructing a parameterized transformer analysis model to realize the fast automatic mapping between design variables and performance and cost indicators; the problem that traditional methods and conventional optimization algorithms are prone to fall into local optimum can be effectively avoided by introducing a bionic swarm intelligence algorithm with global exploration and local development capabilities to systematically search for a global optimal solution satisfying the constraints in a complex design space; finally, the verification step ensures that the design scheme meets all performance standards in theory and practice, and in general, through the above-mentioned automation and intelligentization of the whole process, the paradigm shift from 'experience-driven' to'model and algorithm-driven' is realized in transformer design, which significantly improves the design efficiency and the economy of the scheme while ensuring the design quality and reliability. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, brief introductions will be given to the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute the embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of the provided drawings.

[0017] Figure 1 is a flowchart of the transformer optimization design method based on the bionic swarm intelligence algorithm provided by the embodiments of the present application; Figure 2 is a structural schematic diagram of the transformer optimization design system based on the bionic swarm intelligence algorithm provided by the embodiments of the present application. DETAILED DESCRIPTION

[0018] The embodiments of the present application will be described in detail below, and examples of the embodiments are shown in the drawings, wherein the same or similar reference signs represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are only used to explain the present application, and cannot be understood as a limitation of the present application.

[0019] In the present application, the term "comprising" or "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article or device including the element.

[0020] A transformer optimization design method based on a biomimetic swarm intelligence algorithm, such as Figure 1 As shown, the transformer optimization design method includes the following steps: S1: Determine the optimization objective and constraints of the transformer. The optimization objective includes minimizing the total cost over the entire life cycle, and the constraints include performance index limitations. It should be noted that the optimization objective refers to the core goal to be achieved in transformer design, such as minimizing the total life-cycle cost, including material and operating costs. Constraints refer to the performance limits that must be met during transformer design, such as upper limits for no-load loss, load loss, temperature rise, and short-circuit impedance. In step S1, the optimization objective and constraints determine the direction and boundaries of the design, providing clear objectives and constraints for the subsequent optimization process.

[0021] Understandably, the purpose of step S1 is to clarify the optimization direction and constraints of the design. By defining the optimization objective (such as minimizing the total lifecycle cost), a clear optimization direction can be provided for subsequent optimization algorithms. At the same time, by defining constraints (such as performance index limits), it can be ensured that the optimization results meet the actual engineering requirements. This clear definition can effectively avoid the blindness of the optimization process and improve design efficiency.

[0022] S2: Based on the optimization objective and constraints, a parameterized transformer analysis model is constructed. The parameterized transformer analysis model is used to calculate the total life cycle cost and performance indicators according to the design variables. It should be noted that the parametric transformer analysis model refers to a model that uses mathematical formulas and physical models to link the transformer's design variables (such as core diameter, magnetic flux density, number of winding turns, etc.) with optimization objectives (such as total life cycle cost) and constraints (such as performance indicators). The role of the parametric transformer analysis model is to quickly calculate the corresponding total life cycle cost and performance indicators based on the input design variables, providing a computational basis for subsequent optimization algorithms.

[0023] Understandably, the purpose of step S2 is to transform the physical process of transformer design into a computable mathematical model. Through parametric modeling, the complex transformer design problem can be decomposed into a series of quantifiable and optimizable mathematical expressions, thereby providing a foundation for the optimization solution of biomimetic swarm intelligence algorithms. This parametric model can not only improve computational efficiency, but also ensure the accuracy and reliability of the optimization process.

[0024] S3: applying a bionic swarm intelligence algorithm to the parameterized transformer analysis model for optimization solving to search for a globally optimal design parameter combination under the premise of meeting the constraint conditions, wherein the bionic swarm intelligence algorithm simulates the behavior of biological groups for global exploration and local development; It should be noted that the bionic swarm intelligence algorithm is an optimization algorithm that simulates the behavior of biological groups in nature (such as honey bees collecting nectar and birds foraging), and its core idea is to achieve a balance between global exploration and local development through the cooperation and information sharing of the group. The role of the bionic swarm intelligence algorithm is to search for a globally optimal design parameter combination that meets the constraint conditions according to the parameterized transformer analysis model.

[0025] It can be understood that the purpose of step S3 is to use the global search ability and local development ability of the bionic swarm intelligence algorithm to solve the high-dimensional, nonlinear, and multi-constraint optimization problem in transformer design. By simulating the behavior of biological groups (such as the behavior of honey bees collecting nectar), the bionic swarm intelligence algorithm can efficiently explore the design space and find the optimal design scheme that meets the performance indicators and has the lowest cost. The advantage of this algorithm is that it can jump out of the local optimal solution and find the global optimal solution.

[0026] S4: verifying the globally optimal design parameter combination to determine whether it meets all performance standards.

[0027] It should be noted that verification refers to high-precision checking of the globally optimal design parameter combination obtained through optimization using professional finite element analysis (FEA) software or other verification tools to ensure that its performance indicators fully meet the actual engineering requirements. Performance standards refer to industry standards or user requirements that the transformer design must meet, such as no-load loss standards, temperature rise standards, etc.

[0028] It can be understood that the purpose of step S4 is to ensure the feasibility and reliability of the optimization result. Since the optimization process of the bionic swarm intelligence algorithm is based on the parameterized model, which may have certain simplifications and approximations, it is necessary to verify the optimization result through high-precision finite element analysis or other verification means to effectively avoid design problems caused by model errors and ensure that the final scheme meets all performance standards.

[0029] Preferably, the objective function of minimizing the total life cycle cost , satisfies the relationship: ; Wherein, the material cost , represents the weight of silicon steel sheet, represents the unit price of silicon steel sheet, represents the weight of copper wire, represents the unit price of copper wire, represents the insulation material cost, represents the remaining material cost; operating cost , represents the no-load loss capitalization coefficient, represents the no-load loss, represents the load loss capitalization coefficient, represents the load loss, represents the design variable vector including at least one of core diameter, magnetic flux density, current density, and winding turns.

[0030] It should be noted that the objective function refers to a mathematical expression used to measure the pros and cons of the design scheme in transformer optimization design, and the purpose is to minimize the total life cycle cost. Material cost includes the cost of silicon steel sheet, copper wire, insulation material and other materials required for transformer manufacturing; operating cost refers to the long-term operating cost due to loss (no-load loss and load loss) during the operation of the transformer. Design variable vector refers to the key parameters that affect the performance and cost of the transformer, such as core diameter, winding turns, magnetic flux density, etc. These parameters are adjusted in the optimization process to minimize the objective function .

[0031] It can be understood that by mathematical modeling, the total life cycle cost of the transformer is divided into material cost and operating cost, and they are integrated into the objective function This decomposition and integration can clearly quantify the cost composition of transformer design, thereby providing a clear optimization direction for subsequent optimization algorithms. By minimizing the objective function , the comprehensive optimization of material and operating cost can be achieved to achieve the design goal of reducing cost and increasing efficiency.

[0032] Exemplarily, in the optimization design of a certain distribution transformer, the construction and calculation of the objective function are as follows: assuming that the weight of silicon steel sheet is 50 kg, the unit price is 5 yuan / kg, then the cost of silicon steel sheet is 250 yuan; the weight of copper wire is 10 kg, the unit price is 60 yuan / kg, then the cost of copper wire is 600 yuan; the cost of insulation material is 200 yuan, the cost of other materials is 150 yuan. The total material cost is 1200 yuan. In terms of operating cost, the no-load loss is 150 W, the capitalization coefficient is 0.1, then the no-load loss capitalization cost is 15 yuan; the load loss is 500 W, the capitalization coefficient is 0.05, then the load loss capitalization cost is 25 yuan, the total operating cost is 40 yuan, and finally the total life cycle cost is 1240 yuan. By adjusting the design variables (such as core diameter, winding turns, etc.), the objective function can be further optimized to find a lower-cost design scheme.

[0033] Preferably, the performance index limits include an upper limit of no-load loss, an upper limit of load loss, an upper limit of temperature rise, and a short-circuit impedance range; The performance index limits are introduced into the objective function by a penalty function method to construct a modified objective function: ; wherein, represents the violation degree of the i-th constraint condition, represents a penalty factor for controlling the penalty intensity of constraint violation.

[0034] It should be noted that the constraint conditions refer to performance index limits that must be met in the transformer design process, such as no-load loss, load loss, temperature rise, short-circuit impedance, etc. These constraint conditions ensure the performance and safety of the transformer in actual operation. The modified objective function is constructed based on the original objective function by introducing a penalty function method, which is used to penalize design schemes that violate constraint conditions during optimization. The penalty factor is a positive number that controls the penalty intensity of constraint violation, ensuring that the optimization algorithm prioritizes finding feasible solutions that meet the constraint conditions. The constraint violation degree represents the violation degree of the i-th constraint condition by the design scheme, and if the constraint condition is met, .

[0035] It can be understood that by integrating the constraint conditions into the objective function through the penalty function method, the optimization process can simultaneously consider cost minimization and performance index limits. In transformer design, performance index limits such as the upper limit of no-load loss and the upper limit of temperature rise are red lines that cannot be exceeded. Any design scheme must meet these basic requirements. By introducing the penalty function method into the objective function, design schemes that violate constraint conditions will be penalized, and their objective function values will significantly increase, thus being automatically excluded by the optimization algorithm. This method can effectively guide the optimization algorithm to search within the feasible solution space, avoiding the generation of design schemes that do not meet actual engineering requirements. Meanwhile, the introduction of the penalty factor can adjust the penalty intensity, ensuring the stability and convergence of the optimization process.

[0036] Illustratively, in a certain transformer optimization design project, the design requirements are as follows: the upper limit of no-load loss is 150 W, the upper limit of load loss is 500 W, the upper limit of temperature rise is 55°C, and the short-circuit impedance range is 4%-6%. Assuming the penalty factor , for a certain design scheme, its no-load loss is 160 W (exceeding the upper limit by 10 W), load loss is 480 W (meeting the upper limit), temperature rise is 54°C (meeting the upper limit), and short-circuit impedance is 5.5% (meeting the range). At this time, the constraint violation degree​​ (Exceeding the no-load loss limit), all other constraints are met, therefore The penalty term is 100,000. Original objective function value. The objective function value is adjusted to 1240 yuan (including material and operating costs). Due to the impact of the penalty term, the objective function value of the design scheme increases significantly. The optimization algorithm will prioritize eliminating such schemes that do not meet the constraints, thereby ensuring that the final optimal design scheme satisfies both performance requirements and has a lower cost.

[0037] Preferably, step S3 includes: Initialize the nectar source population, where each nectar source represents one of the design variable vectors; Calculate the fitness value for each nectar source, the fitness value Satisfying the relation: ,in This represents the modified objective function; New nectar source parameters are generated by employing bees to conduct local searches. The following relation is satisfied: ,in, Indicates the current honey source's number One parameter, This represents the first nectar source randomly selected from another nectar source. One parameter, Represents a random number. The first nectar source population Individual; By observing bees selecting nectar sources based on selection probability, we can understand the selection probability... Satisfying the relation: ; New nectar sources are generated by replacing unimproved nectar sources with scout bees. ,in Indicates the lower limit of the design variable. Indicates the upper limit of design variables. Represents a random number; Iteratively execute the search process until the convergence condition is met, and output the globally optimal combination of design parameters. .

[0038] It should be noted that initializing the bee population means generating a set of random design variable vectors at the beginning of the optimization algorithm, each vector representing a potential design scheme. The fitness value represents the degree of excellence of each design scheme, which is calculated by the modified objective function, and is used to evaluate whether the scheme meets the performance indicators and cost requirements. The hired bee represents an agent that performs local search on the current design scheme, generates new design scheme parameters through a specific formula, explores the solution space, and finds better solutions. The observation bee selects the honey source according to the selection probability, which is related to the fitness value, and the higher the fitness value, the greater the probability of being selected. The scout bee is responsible for replacing the scheme that has not been improved for a long time, introducing a new random scheme, increasing the diversity of the population, and the iteration process continues until the preset number of iterations is reached or the convergence condition is met, and finally the global optimal design parameter combination is output.

[0039] It can be understood that through each step of the artificial bee colony algorithm, the global optimization of the transformer design parameters is realized. The artificial bee colony algorithm simulates the honey collecting behavior of bees, utilizes the different roles of hired bees, observation bees and scout bees, and cooperatively completes the exploration and development of the design space. Initializing the bee population provides a diverse initial solution for the algorithm, and the fitness value calculation can quantify the quality of each solution. The local search of the hired bee and the selection mechanism of the observation bee can efficiently find better solutions in the solution space, while the random replacement of the scout bee avoids the algorithm from falling into local optimum. Through iterative optimization, the algorithm can gradually approach the global optimal solution, and finally output the design scheme that meets the performance constraints and has the lowest total life cycle cost.

[0040] For example, in a certain transformer optimization design project, it is assumed that the initial population size is 50 honey sources, each honey source represents a design scheme, and the design variables include core diameter, winding turns, etc. The fitness value of each scheme is calculated, for example, the modified objective function value of a certain scheme is 101240, and its fitness value is 1 / (1+101240)= The hired bee performs local search on the current scheme, generates new parameters, for example, adjusts the core diameter from 0.5m to 0.52m. The observation bee selects the honey source according to the selection probability, and the scheme with higher probability is more likely to be selected. If a certain scheme has not been improved for 20 consecutive iterations, the scout bee replaces it with a new random scheme, for example, adjusts the winding turns from 1200 turns to 1100 turns. After multiple iterations, the global optimal design parameter combination is finally obtained, which has the lowest total life cycle cost and meets all performance constraints.

[0041] Preferably, the parametric transformer analysis model includes an electromagnetic calculation model, and the electromagnetic calculation model includes: a model for calculating the effective cross-sectional area of the core, the effective cross-sectional area of the core satisfying the relationship: ; wherein, represents a core lamination coefficient, represents a core diameter; based on the core effective cross-sectional area calculates a core magnetic flux density amplitude satisfies the relationship: ; wherein, represents a voltage per turn, represents a power supply frequency, represents a winding number of turns; calculates a winding resistance satisfies the relationship: wherein represents a conductor resistivity, represents an average length of turns of the winding, represents a wire cross-sectional area; calculates a no-load loss satisfies the relationship: wherein, represents an iron loss coefficient, represents a Steinmetz index, represents a core weight; calculates a load loss satisfies the relationship: , and wherein represents an eddy current loss coefficient, represents a winding phase current, represents a winding resistance; calculates a short-circuit impedance satisfies the relationship: ; wherein, represents a leakage magnetic area, represents a Rogowski coefficient, represents a process coefficient, represents a winding reactance height, represents a transformer capacity.

[0042] It should be noted that the electromagnetic calculation model is an important part of the parameterized transformer analysis model, and is used to calculate key electromagnetic performance indicators of the transformer. The core effective cross-sectional area refers to the effective area of the core actually used for the magnetic flux path, which takes into account the lamination structure of the core. The core magnetic flux density amplitude refers to the maximum value of the magnetic flux density in the core, which directly affects the no-load loss. The winding resistance refers to the direct current resistance of the winding, which affects the load loss. The no-load loss Mainly composed of core hysteresis and eddy current loss, is an important part of the transformer operating cost. Load loss Mainly caused by winding resistance and current, reflects the transformer's loss when running under load. Short circuit impedance Refers to the impedance voltage of the transformer under short circuit conditions, affecting the load capacity and voltage stability of the transformer. These parameters are interrelated through electromagnetic calculation models, which together determine the performance and cost of the transformer.

[0043] It can be understood that the key electromagnetic performance indicators of the transformer are accurately calculated through the electromagnetic calculation model, providing accurate data support for optimal design. The electromagnetic calculation model is based on the basic physical principles of the transformer, relating design variables (such as core diameter, winding turns, etc.) to performance indicators (such as no-load loss, load loss, short circuit impedance, etc.). By accurately calculating these performance indicators, designers can evaluate the pros and cons of different design schemes, thereby minimizing the total life cycle cost while meeting performance requirements. The accuracy of the electromagnetic calculation model directly determines the reliability and economy of the optimization results.

[0044] Exemplarily, in a certain transformer optimization design project, the design requirements are as follows: core lamination coefficient , core diameter , voltage per turn , power frequency , winding turns , conductor resistivity , average length of winding turns , wire cross-sectional area , iron loss coefficient , Steinmetz index , core weight , eddy current loss coefficient , winding phase current , leakage magnetic area , Rogowski coefficient , process coefficient , winding reactance height , load loss , transformer capacity . According to the above parameters, the effective cross-sectional area of the core , core magnetic flux density amplitude , winding resistance , no-load loss , load loss , short circuit impedance The accurate performance evaluation provided for the optimization algorithm helps the designer to find the design scheme with the lowest cost under the premise of meeting the performance requirements.

[0045] Preferably, the parameterized transformer analysis model comprises a temperature rise calculation model, the temperature rise calculation model comprising: calculating the top oil temperature rise , satisfying the relationship: ; wherein, represents the rated top oil temperature rise, represents the total loss of the current scheme, represents the no-load loss of the current scheme, represents the rated total loss, represents the heat dissipation index; calculating the winding hot spot temperature rise , satisfying the relationship: wherein, represents the rated winding hot spot temperature rise, represents the load loss of the current scheme, represents the rated load loss, represents the winding heat dissipation index; calculating the winding hot spot absolute temperature , satisfying the relationship: wherein, represents the ambient temperature.

[0046] It should be noted that the temperature rise calculation model is an important part of the parameterized transformer analysis model, which is used to evaluate the temperature change of the transformer during operation. The top oil temperature rise refers to the temperature difference between the top oil temperature of the transformer and the ambient temperature, reflecting the overall heat dissipation performance of the transformer. The winding hot spot temperature rise refers to the temperature difference between the hottest point in the winding and the top oil temperature, reflecting the heat distribution in the winding. The winding hot spot absolute temperature refers to the actual temperature of the hottest point in the winding, which is an important indicator for evaluating the insulation performance and operational safety of the transformer. These parameters are interrelated through the temperature rise calculation model, and together determine the thermal performance and operational reliability of the transformer.

[0047] It can be understood that the thermal performance of the transformer is accurately evaluated by the temperature rise calculation model, and accurate temperature data support is provided for optimization design. The temperature rise calculation model is based on the heat conduction and heat dissipation principle of the transformer, and associates the design variables (such as winding loss, heat dissipation structure, etc.) with temperature change. By accurately calculating the top oil temperature rise and winding hot spot temperature rise, the designer can evaluate the thermal performance of different design schemes, so as to minimize the total cost of the whole life cycle under the premise of meeting the thermal performance requirements. The accuracy of the temperature rise calculation model directly determines the reliability and safety of the optimization results.

[0048] Exemplarily, in a certain transformer optimization design project, the design requirements are as follows: the rated top oil temperature rise is 55°C, the rated total loss is 650W, the no-load loss is 150W, the heat dissipation index is 0.8; the rated winding hot spot temperature rise is 65°C, the rated load loss is 500W, the winding heat dissipation index is 0.9; the ambient temperature is 40°C. According to the current design scheme, the total loss is 600W, the load loss is 450W, the calculated top oil temperature rise is 52.3°C; the winding hot spot temperature rise is 58.7°C; the winding hot spot absolute temperature is 40+52.3+58.7=151°C, which provides accurate thermal performance evaluation for the optimization algorithm, helping the designer to find the design scheme with the lowest cost under the premise of meeting the thermal performance requirements.

[0049] A transformer optimization design system based on a bionic swarm intelligence algorithm, as shown in Figure 2 , the transformer optimization design system is applied to the transformer optimization design method based on the bionic swarm intelligence algorithm as described above, and the transformer optimization design system based on the bionic swarm intelligence algorithm comprises: a target setting module for determining the optimization target and the constraint condition of the transformer, the optimization target comprising minimizing the total cost of the whole life cycle, and the constraint condition comprising performance index limitation; a model construction module for constructing a parameterized transformer analysis model based on the optimization target and the constraint condition, the parameterized transformer analysis model being used to calculate the total cost of the whole life cycle and the performance index according to the design variables; The optimization and solution module is used to apply a biomimetic swarm intelligence algorithm to optimize and solve the parameterized transformer analysis model, so as to search for the globally optimal combination of design parameters under the premise of satisfying the constraints. The biomimetic swarm intelligence algorithm simulates the behavior of biological groups to conduct global exploration and local development. The verification module is used to verify the globally optimal design parameter combination and determine whether it meets all performance standards.

[0050] Preferably, in the target setting module, the objective function is to minimize the total lifecycle cost. The following relation is satisfied: ; Among them, material costs , Indicates the weight of silicon steel sheets. This indicates the unit price of silicon steel sheets. Indicates the weight of the copper wire. This indicates the unit price of copper wire. Indicates the cost of insulation materials. Indicates the cost of the remaining materials; Operating costs , This represents the capitalization factor for no-load losses. Indicates no-load loss. This represents the capitalization factor for load losses. Indicates load loss. This represents a vector of design variables including at least one of the following: core diameter, magnetic flux density, current density, and number of winding turns. The performance limits include the upper limit of no-load loss, the upper limit of load loss, the upper limit of temperature rise, and the short-circuit impedance range; By incorporating performance metric constraints into the objective function using the penalty function method, a modified objective function is constructed: ; in, Indicates the first The degree of violation of each constraint, The penalty factor represents the intensity of punishment used to control the violation of constraints; The optimization solution module includes: Initialize the nectar source population, where each nectar source represents one of the design variable vectors; Calculate the fitness value for each nectar source, the fitness value Satisfying the relation: ,in This represents the modified objective function; New nectar source parameters are generated by employing bees to conduct local searches. The following relation is satisfied: wherein, represents the i-th parameter of the current food source, represents the i-th parameter of another food source selected randomly, represents a random number, represents the i-th individual of the food source population; The selection probability of the bees to select the food source for exploitation is observed, and the selection probability satisfies the relationship: ; The scout bees replace the unimproved food source to generate a new food source wherein represents the lower limit of the design variable, represents the upper limit of the design variable, represents a random number; The search process is iteratively performed until the convergence condition is met, and the global optimal design parameter combination is output .

[0051] Preferably, in the model construction module, the parameterized transformer analysis model comprises an electromagnetic calculation model, and the electromagnetic calculation model comprises: a model for calculating the effective cross-sectional area of the core, the effective cross-sectional area of the core satisfies the relationship: ; wherein, represents the core lamination coefficient, represents the core diameter; Based on the effective cross-sectional area of the core the core magnetic flux density amplitude is calculated, which satisfies the relationship: ; wherein, represents the voltage per turn, represents the power frequency, represents the number of turns of the winding; The winding resistance is calculated, which satisfies the relationship: wherein represents the electrical resistivity of the conductor, represents the average length of turns of the winding, represents the cross-sectional area of the wire; The no-load loss is calculated, which satisfies the relationship: wherein, represents the iron loss coefficient, represents the Steinmetz index, represents the core weight; The load loss ​​​, satisfies the relationship: , and wherein represents the eddy current loss coefficient, represents the winding phase current, represents the winding resistance; calculating the short-circuit impedance , satisfies the relationship: ; wherein, represents the leakage magnetic area, represents the Rogowski coefficient, represents the process coefficient, represents the winding reactance height, represents the transformer capacity.

[0052] Preferably, in the model construction module, the parameterized transformer analysis model comprises a temperature rise calculation model, and the temperature rise calculation model comprises: calculating the top layer oil temperature rise , satisfies the relationship: ; wherein, represents the rated top layer oil temperature rise, represents the total loss of the current scheme, represents the no-load loss of the current scheme, represents the rated total loss, represents the heat dissipation index; calculating the winding hot spot temperature rise , satisfies the relationship: wherein, represents the rated winding hot spot temperature rise, represents the load loss of the current scheme, represents the rated load loss, represents the winding heat dissipation index; calculating the winding hot spot absolute temperature , satisfies the relationship: wherein, represents the ambient temperature.

[0053] In the description of the present specification, the description referring to the terms "one embodiment", "some embodiments", "exemplary embodiment", "example", "specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the exemplary description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0054] While embodiments of the application have been shown and described, it is to be understood that the application is not limited to the details of the embodiments described, since the scope of the application will be defined with respect to the claims and their equivalents.

Claims

1. A transformer optimization design method based on a biomimetic swarm intelligence algorithm, characterized in that, The transformer optimization design method includes the following steps: S1: Determine the optimization objective and constraints of the transformer. The optimization objective includes minimizing the total cost over the entire life cycle, and the constraints include performance index limitations. S2: Based on the optimization objective and constraints, a parameterized transformer analysis model is constructed. The parameterized transformer analysis model is used to calculate the total life cycle cost and performance indicators according to the design variables. S3: Apply a biomimetic swarm intelligence algorithm to optimize and solve the parameterized transformer analysis model, so as to search for the globally optimal combination of design parameters under the premise of satisfying the constraints, wherein the biomimetic swarm intelligence algorithm simulates the behavior of biological groups to perform global exploration and local development; S4: Verify the globally optimal design parameter combination to determine whether it meets all performance standards.

2. The transformer optimization design method based on biomimetic swarm intelligence algorithm according to claim 1, characterized in that, The objective function to minimize the total lifecycle cost The following relation is satisfied: ; Among them, material costs , Indicates the weight of silicon steel sheets. This indicates the unit price of silicon steel sheets. Indicates the weight of the copper wire. This indicates the unit price of copper wire. Indicates the cost of insulation materials. Indicates the cost of the remaining materials; Operating costs , This represents the capitalization factor for no-load losses. Indicates no-load loss. This represents the capitalization factor for load losses. Indicates load loss. This represents a vector of design variables including at least one of the following: core diameter, magnetic flux density, current density, and number of winding turns.

3. The transformer optimization design method based on biomimetic swarm intelligence algorithm according to claim 2, characterized in that, The performance limits include the upper limit of no-load loss, the upper limit of load loss, the upper limit of temperature rise, and the short-circuit impedance range; By incorporating performance metric constraints into the objective function using the penalty function method, a modified objective function is constructed: ； in, Indicates the first The degree of violation of each constraint, The penalty factor represents the intensity of punishment used to control the violation of constraints.

4. The transformer optimization design method based on biomimetic swarm intelligence algorithm according to claim 3, characterized in that, Step S3 includes: Initialize the nectar source population, where each nectar source represents one of the design variable vectors; Calculate the fitness value for each nectar source, the fitness value Satisfying the relation: ,in This represents the modified objective function; New nectar source parameters are generated by employing bees to conduct local searches. The following relation is satisfied: ,in, Indicates the current honey source's number One parameter, This represents the first nectar source randomly selected from another nectar source. One parameter, Represents a random number. The first nectar source population Individual; By observing bees selecting nectar sources based on selection probability, we can understand the selection probability... Satisfying the relation: ; New nectar sources are generated by replacing unimproved nectar sources with scout bees. ,in Indicates the lower limit of the design variable. Indicates the upper limit of design variables. Represents a random number; Iteratively execute the search process until the convergence condition is met, and output the globally optimal combination of design parameters. .

5. The transformer optimization design method based on biomimetic swarm intelligence algorithm according to claim 1, characterized in that, The parameterized transformer analysis model includes an electromagnetic calculation model, which includes: Model for calculating the effective cross-sectional area of ​​the iron core, effective cross-sectional area of ​​the iron core Satisfying the relation: ; in, Indicates the lamination factor of the iron core. Indicates the diameter of the iron core; Based on the effective cross-sectional area of ​​the iron core Calculate the amplitude of the magnetic flux density in the iron core The following relation is satisfied: ; in, This indicates the voltage per turn. Indicates the power supply frequency. Indicates the number of turns in the winding; Calculate winding resistance The following relation is satisfied: ,in Represents the resistivity of a conductor. This indicates the average turn length of the winding. Indicates the cross-sectional area of ​​the conductor; Calculate no-load loss The following relation is satisfied: ,in, Indicates the iron loss coefficient. The Steinmetz index represents... Indicates the weight of the iron core; Calculate load loss The following relation is satisfied: ,and ,in This represents the eddy current loss coefficient. Indicates the winding phase current. Indicates the winding resistance; Calculate short-circuit impedance The following relation is satisfied: ； in, Indicates the leakage magnetic area. Represents the Logovsky coefficient. Indicates the process coefficient, Indicates the winding reactance height. This indicates the transformer capacity.

6. The transformer optimization design method based on biomimetic swarm intelligence algorithm according to claim 1, characterized in that, The parameterized transformer analysis model includes a temperature rise calculation model, which includes: Calculate the temperature rise of the top oil layer The following relation is satisfied: ； in, Indicates the rated top oil temperature rise. This represents the total loss of the current plan. This indicates the no-load loss of the current scheme. Indicates the rated total loss. Indicates the heat dissipation index; Calculate the temperature rise of the winding hot spot The following relation is satisfied: ,in, This indicates the rated winding hot spot temperature rise. This indicates the load loss of the current solution. Indicates the rated load loss. Indicates the heat dissipation index of the winding; Calculate the absolute temperature of the winding hot spot The following relation is satisfied: ,in, Indicates ambient temperature.

7. A transformer optimization design system based on a biomimetic swarm intelligence algorithm, characterized in that, The transformer optimization design system is applied to the transformer optimization design method based on the biomimetic swarm intelligence algorithm as described in any one of claims 1-6, wherein the transformer optimization design system based on the biomimetic swarm intelligence algorithm comprises: The objective setting module is used to determine the optimization objective and constraints of the transformer. The optimization objective includes minimizing the total cost over the entire life cycle, and the constraints include performance index limitations. The model building module is used to build a parametric transformer analysis model based on the optimization objectives and constraints. The parametric transformer analysis model is used to calculate the total life cycle cost and performance indicators based on the design variables. The optimization and solution module is used to apply a biomimetic swarm intelligence algorithm to optimize and solve the parameterized transformer analysis model, so as to search for the globally optimal combination of design parameters under the premise of satisfying the constraints. The biomimetic swarm intelligence algorithm simulates the behavior of biological groups to conduct global exploration and local development. The verification module is used to verify the globally optimal design parameter combination and determine whether it meets all performance standards.

8. The transformer optimization design system based on biomimetic swarm intelligence algorithm according to claim 7, characterized in that, In the objective setting module, the objective function is to minimize the total lifecycle cost. The following relation is satisfied: ; Among them, material costs , Indicates the weight of silicon steel sheets. This indicates the unit price of silicon steel sheets. Indicates the weight of the copper wire. This indicates the unit price of copper wire. Indicates the cost of insulation materials. Indicates the cost of the remaining materials; Operating costs , This represents the capitalization factor for no-load losses. Indicates no-load loss. This represents the capitalization factor for load losses. Indicates load loss. This represents a vector of design variables including at least one of the following: core diameter, magnetic flux density, current density, and number of winding turns. The performance limits include the upper limit of no-load loss, the upper limit of load loss, the upper limit of temperature rise, and the short-circuit impedance range; By incorporating performance metric constraints into the objective function using the penalty function method, a modified objective function is constructed: ； in, Indicates the first The degree of violation of each constraint, The penalty factor represents the intensity of punishment used to control the violation of constraints; The optimization solution module includes: Initialize the nectar source population, where each nectar source represents one of the design variable vectors; Calculate the fitness value for each nectar source, the fitness value Satisfying the relation: ,in This represents the modified objective function; New nectar source parameters are generated by employing bees to conduct local searches. The following relation is satisfied: ,in, Indicates the current honey source's number One parameter, This represents the first nectar source randomly selected from another nectar source. One parameter, Represents a random number. The first nectar source population Individual; By observing bees selecting nectar sources based on selection probability, we can understand the selection probability... Satisfying the relation: ; New nectar sources are generated by replacing unimproved nectar sources with scout bees. ,in Indicates the lower limit of the design variable. Indicates the upper limit of design variables. Represents a random number; Iteratively execute the search process until the convergence condition is met, and output the globally optimal combination of design parameters. .

9. The transformer optimization design system based on biomimetic swarm intelligence algorithm according to claim 7, characterized in that, In the model building module, the parametric transformer analysis model includes an electromagnetic calculation model, which includes: Model for calculating the effective cross-sectional area of ​​the iron core, effective cross-sectional area of ​​the iron core Satisfying the relation: ; in, Indicates the lamination factor of the iron core. Indicates the diameter of the iron core; Based on the effective cross-sectional area of ​​the iron core Calculate the amplitude of the magnetic flux density in the iron core The following relation is satisfied: ; in, This indicates the voltage per turn. Indicates the power supply frequency. Indicates the number of turns in the winding; Calculate winding resistance The following relation is satisfied: ,in Represents the resistivity of a conductor. This indicates the average turn length of the winding. Indicates the cross-sectional area of ​​the conductor; Calculate no-load loss The following relation is satisfied: ,in, Indicates the iron loss coefficient. The Steinmetz index represents... Indicates the weight of the iron core; Calculate load loss The following relation is satisfied: ,and ,in This represents the eddy current loss coefficient. Indicates the winding phase current. Indicates the winding resistance; Calculate short-circuit impedance The following relation is satisfied: ； in, Indicates the leakage magnetic area. Represents the Logovsky coefficient. Indicates the process coefficient, Indicates the winding reactance height. This indicates the transformer capacity.

10. The transformer optimization design system based on biomimetic swarm intelligence algorithm according to claim 7, characterized in that, In the model building module, the parametric transformer analysis model includes a temperature rise calculation model, which includes: Calculate the temperature rise of the top oil layer The following relation is satisfied: ； in, Indicates the rated top oil temperature rise. This represents the total loss of the current plan. This indicates the no-load loss of the current scheme. Indicates the rated total loss. Indicates the heat dissipation index; Calculate the temperature rise of the winding hot spot The following relation is satisfied: ,in, This indicates the rated winding hot spot temperature rise. This indicates the load loss of the current solution. Indicates the rated load loss. Indicates the heat dissipation index of the winding; Calculate the absolute temperature of the winding hot spot The following relation is satisfied: ,in, Indicates ambient temperature.

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