Miniature transformer structure and parameter optimization method suitable for micro-motion mold physical simulation, storage medium and electronic equipment

Abstract

The invention discloses a miniature transformer structure and parameter optimization method suitable for micro-motion model physical simulation, a storage medium and electronic equipment, and the method comprises the following steps: quantifying the size relation between magnetic resistance and a transformer for a three-phase three-column structure; a micro-motion model physical simulation platform is constructed, and a full-working-condition verification environment is provided; optimizing variables and establishing a magnetic circuit-circuit coupling total loss model; a standard snake optimization algorithm is improved; and a rectangular magnetic cross section is optimized into a circular arc transition type, so that collaborative optimization of loss and volume is realized. According to the method, the real characteristics of physical simulation are reserved, the problems that digital simulation depends on model parameters and the network operation risk is high are solved, the full working conditions of the electric power system can be safely reproduced, and protection device verification and personnel training are adapted. It can be comprehensively confirmed from electrical and electromagnetic field levels that the miniature transformer is equivalent to a traditional transformer and has no magnetic saturation risk.

Description

Technical Field

[0001] This invention relates to the field of dynamic simulation technology for power systems, and in particular to a method for optimizing the structure and parameters of a micro-transformer, a storage medium, and an electronic device suitable for micro-dynamic physical simulation. Background Technology

[0002] Currently, the rapid development of new power systems, with the large-scale integration of new energy sources such as wind power and photovoltaics into the grid, has led to significant differences in the fault characteristics of power systems compared to traditional systems. This has created an increasingly urgent need for verification of protection and control equipment. However, traditional dynamic simulation systems suffer from high construction costs, inconvenient maintenance, and limited quantity. Furthermore, existing micro-transformers lack adaptability in low-voltage scenarios, and their loss and volume optimization are limited, making it difficult to meet the needs of large-scale verification and personnel training for new power systems. This invention constructs a physical simulation platform for power system micro-dynamic simulations, designs a dedicated micro-simulation transformer, and introduces an improved snake optimization algorithm (ISO) to optimize the total loss model of the micro-transformer. Simultaneously, it optimizes the core column structure, achieving coordinated optimization of micro-transformer losses and volume, thus resolving the contradiction between magnetic circuit uniformity and compact size in traditional structures.

[0003] Physical simulation verification in power system protection device verification relies on similarity theory to construct models. In recent years, related research has focused on the electronic and digital improvement of the system. One approach is to equate the dynamic simulation system to an electronic dynamic model system, integrating physical and numerical methods with digital simulation as the core and hardware as the presentation carrier (Ma Linlin. Research on Dynamic Simulation of Electronic Power Systems [D]. Shandong University, 2016). Another approach is to simplify the experimental device of the conventional dynamic model system using a PLC controller (Zhou Kunpeng, Liao Liqing, Yang Yong. Design of PLC-based Dynamic Simulation Experimental Device for Power Systems [J]. Guangdong Electric Power, 2010). However, these improvements have not fundamentally solved the core problems of limited simulation verification scale and difficult parameter adjustment in traditional dynamic model systems. Therefore, domestic and foreign research has gradually carried out research on the miniaturization of power system dynamic simulation systems. For example, Florida International University has successfully built an AC / DC hybrid micro-simulation grid and completed the development of real-time monitoring software. However, existing research lacks targeted optimization in the design of micro transformers. The traditional transformer model is not adaptable to the working conditions of the micro-dynamic model. The magnetic circuit of the traditional rectangular iron core column is uneven and has high loss, which cannot meet the requirements of the micro-dynamic model platform. The micro-transformer optimization design method based on the micro-motion model physical simulation platform researched in this invention patent can significantly reduce losses, effectively shrink the size, and stabilize the electrical characteristics of the micro-transformer in low-voltage micro-motion model scenarios. In other words, it fulfills the design requirements of the new power system micro-motion model simulation platform for the core components of "low loss, small size, and high adaptability". It gives full play to the advantages of snake optimization algorithm and arc transition iron core column, ensuring that the electrical characteristics of the micro-transformer are equivalent to those of traditional high-voltage transformers, and can stably adapt to the power system micro-motion model physical simulation platform. Summary of the Invention

[0004] The purpose of this invention is to provide a method for optimizing the structure and parameters of a micro-transformer, a storage medium, and an electronic device suitable for physical simulation of micro-moving models. This addresses the shortcomings of traditional moving model systems and existing micro-transformers in low-voltage scenarios, limitations in transformer loss and volume optimization, low efficiency in optimizing core column dimensions, and uneven magnetic circuit distribution caused by the core column structure.

[0005] The technical solution adopted in this invention is as follows:

[0006] A method for optimizing the structure and parameters of a micro-transformer suitable for physical simulation of micro-motion models includes the following steps:

[0007] Step A: For a three-phase three-column structure, based on Ohm's law for magnetic circuits, the magnetic reluctance of the main magnetic circuit is decomposed into the sum of the magnetic reluctance of the core column, the yoke, and the hidden air gap, and the relationship between magnetic reluctance and transformer size is quantified.

[0008] Step B: Construct a micro-motion model physical simulation platform with the triple equivalent of "magnetic circuit-circuit-structure" as the core. Fault reproduction is achieved through voltage regulation and cascade design. The compact cabinet layout is adapted to new energy components and provides a full-condition verification environment.

[0009] Step C: Introduce a dynamic correlation mechanism between the geometric parameters of the core column and the magnetic properties of the material, linking the winding structure and the core column dimensions to accurately quantify losses, and using the core column rectangular cross-section length 'a', core column rectangular cross-section width 'b', and core column height 'l' as parameters. c A total loss model for magnetic circuit-electrical circuit coupling was established for the core optimization variables;

[0010] Step D: Improve the standard snake optimization algorithm, take minimizing the total transformer loss as the objective function, optimize the core size variable to be optimized, set the parameters and use MATLAB to solve the optimal combination of core column size and minimum loss;

[0011] Step E: Based on the traditional rectangular iron core column size optimization, the rectangular magnetic cross section is optimized into a circular arc transition type. The parameter matching and correlation are measured under the premise of "stable main magnetic flux + no increase in loss". The quantitative requirements that the improved iron core column size parameters need to meet are clarified through correlation, thereby achieving coordinated optimization of loss and volume.

[0012] Step A specifically includes the following steps:

[0013] The expression for the magnetic reluctance of each phase of the main magnetic circuit of a three-phase three-limb transformer is shown in equation (1):

[0014]

[0015] In the formula, Rcore For the reluctance of the iron core column; R yoke For the iron yoke reluctance; R g It is a hidden air gap magnetoresistive; l c The effective magnetic circuit length of the iron core column; l y μ is the effective magnetic path length of a single-sided yoke. r δ is the relative permeability of the core column material; δ is the equivalent total air gap length; A c Let A be the cross-sectional area of ​​the iron core column. c = a × b, where a is the length of the rectangular cross-section of the iron core column; b is the width of the rectangular cross-section of the iron core column; A y This is the effective cross-sectional area of ​​the iron yoke;

[0016] According to Ohm's law for magnetic circuits, the expression for the main magnetic flux is shown in equation (2):

[0017]

[0018] In the formula, I0 is the no-load current; from equation (2), we can see that the magnetic reluctance of the main magnetic circuit affects the actual distribution of the main magnetic flux. Theoretically, when the magnetomotive force is fixed, the magnetic reluctance R of the main magnetic circuit can be controlled. main To control the main magnetic flux Φ m .

[0019] Step C involves custom designing the core column and windings of the micro-transformer to establish a total loss model coupled with magnetic circuit and electrical parameters. This model aims to accurately quantify the sources of loss and target key variables. By associating parameters through constraints, the total loss model is made to be related only to the size of the core variable to be optimized. Specifically, this includes the following steps:

[0020] C1, Volume of the iron core cylinder V c =3(A c l c +2A y l y ) = 3(abl c +3.154abl y In the core column loss, a dynamic correlation mechanism between the geometric parameters of the core column and the magnetic properties of the material is introduced. The key parameters of B23R080 silicon steel sheet are embedded into the improved Bertotti model. Based on the improved Bertotti model formula, the transformer core column loss expression is approximately calculated as shown in equation (3):

[0021]

[0022] In the formula, P h For the hysteresis loss of the iron core column; P e For eddy current losses in the iron core column; k h P is the hysteresis coefficient; r B represents the residual loss of the core column; α and β are the core column loss coefficients;m k is the magnetic flux density. e d is the eddy current coefficient; d is the lamination thickness; k r This is the residual loss coefficient;

[0023] C2, the primary side of the micro transformer winding is star-connected with a line voltage of 100V, and the secondary side is delta-connected with a line voltage of 100V. Taps are provided to achieve voltage regulation. The rated capacity is 300VA. The winding uses round solid wires and is arranged concentrically, with the low-voltage coil on the inside and the high-voltage coil on the outside.

[0024] C3, the total winding loss consists of DC resistance loss and additional loss, as shown in equation (4):

[0025]

[0026] In the formula, P dc For DC resistance loss; P ac As additional losses, AC additional losses at power frequency are mainly caused by the skin effect and proximity effect, and are therefore simplified as P in engineering. ac ≈0.05P dc R1 and R2 are the DC resistances of the high-voltage and low-voltage windings, respectively. The calculation formula for the DC resistance of the windings is as follows: ρ is the resistivity of copper; l w1 and l w2 These are the average turn lengths of the primary and secondary windings, respectively; A w1 and A w2 Let I be the cross-sectional area of ​​the primary and secondary winding conductors, respectively. According to the relationship between current and cross-sectional area, I = J·A w and have to C4. In summary, the total loss of the micro-transformer in the micro-motion platform is the core column loss P. c With winding loss P w The sum, expressed as in equation (5):

[0027]

[0028] According to equation (5), the key influencing parameters of the total loss model are the length a of the rectangular cross-section of the core column, the width b of the rectangular cross-section of the core column, and the height l of the core column. c , length of iron yoke l y Rated magnetic flux density B m and the average turn length l of the primary and secondary windings w1 l w2 Therefore, the transformer structure has 8 variables to be designed, X = [a, b, B]. m ,l c ,l y ,l w1 ,lw2 To establish the relationship between the transformer parameters to be designed and the dimensional variables to be designed, constraint conditions are used to constrain the variables to be constrained. After constraining the variables to be constrained by the above constraint conditions, substituting them into equation (5) yields a result that is only related to a, b, l c Related transformer total loss model:

[0029]

[0030] This achieves the size optimization requirement of minimizing the total loss of the micro transformer, using a, b, l c For the core variable to be optimized, an optimization model based on the improved snake optimization algorithm (ISO) is constructed.

[0031] The specific constraints in step C4 are as follows:

[0032] C41: According to the law of electromagnetic induction, U1 = 4.44fN1B m A c Constraints:

[0033]

[0034] As can be seen from equation (6), theoretically, the cross-sectional area A of the iron core column can be controlled. c To control B m ;

[0035] C42: Based on the total copper cross-sectional area of ​​the winding not exceeding the window area S of the core column. w Constraints:

[0036] N1A w1 +N2A w2 ≤S w (7)

[0037] In the formula, S w =k w ×l c ×w, k w Let w be the window utilization factor, and w be the window width, satisfying w = l y -a, combined with the ratio and A w1 A w2 The expression is further simplified based on transformer power balance. To fully utilize window space and avoid size redundancy in the optimized design, the values ​​of N1 and N2 are:

[0038] (8)

[0039]

[0040] C43: Combining the spatial constraint relationship between the high and low voltage coils under the above concentric arrangement, a functional relationship is established between the average turn length of the winding and the cross-sectional parameters of the core column. Figure 3 It can be seen that the low-voltage side winding is located on the inner side, and the average turn length is:

[0041] l w2 =2(a+b+d²) (10)

[0042] The high-voltage side winding is located outside the low-voltage side winding, and the average turn length is:

[0043] l w1 =2(a+b+d1+2d2) (11)

[0044] In the formula, d1 is the radial thickness of the primary winding; d2 is the radial thickness of the secondary winding.

[0045] C44: Engineering experience constraint, yoke width a y =a, height b y =b, length of the yoke l y =1.2a.

[0046] Step D specifically includes the following steps:

[0047] D1: Improved population initialization, calculated as x k+1 =cos(kcos -1 (x k ), where k is the order. When k≥2, regardless of whether the initial values ​​are similar, the iterated sequences are uncorrelated; the improved population initialization formula is:

[0048]

[0049] In the formula, r is a random number between [0,1]; X p It is the initial position of the p-th individual in the population; X max and X min These represent the upper and lower bounds of the value range for the optimization problem. The population is divided into two subpopulations, female and male, each with a 50% population size. The best individual is found in each subpopulation, resulting in the best male and female individuals (i.e., the optimal individuals in both subpopulations), along with the food location, temperature, and food quantity:

[0050]

[0051] In the formula, t is the current iteration number; T is the maximum iteration number; c1 is a constant; the temperature decreases continuously with each iteration, realizing the transition of the population from global search to local exploration;

[0052] D2: Improved Exploration Phase. In the exploration phase, a spiral sinusoidal perturbation mechanism is introduced to perturb the snake optimization algorithm during the exploration phase. The calculation formula is as follows: When a = 2.3 and x0 = 0.7, the calculation formula simplifies to x k+1 =sin(πx) k A spiral coefficient z is introduced, with a value between [-1, 1]. When Q < 0.25, the snake searches for food by selecting any random location and updates its position accordingly. The position update formulas for male and female groups are as follows:

[0053]

[0054] In the formula, X p,w X represents the position of the p-th male snake. r,w The location of a randomly selected male snake; X p,u X represents the position of the p-th female snake. r,u The location of a randomly selected female snake; rand is a random number within [0,1]; A w The ability of male snakes to find food is calculated using the following formula: f r,w For X r,w fitness, f p,w For X p,w fitness; A u The ability to find food for female snakes. f r,u For X r,u fitness, f p,u For X p,u The fitness of;

[0055] D3: In the improvement and development phase, the accuracy and efficiency of local search are improved by incorporating Piecewise chaotic mapping. The calculation formula is as follows:

[0056]

[0057] In the formula, i takes the value of 0.4; x(1) is rand; when Q>0.25, the population expands its search range; when the temperature>0.60, the environment is in a hot state, and the population only looks for food; the updated formula for the snake population location update in the development stage is:

[0058]

[0059] In the formula, X p,q This represents the location of an individual (male or female) snake; when the temperature is <0.60, the environment is cold, and the population is in either fighting or mating mode. The formula for calculating the location update in fighting mode is:

[0060] Xp,w (t+1)=X p,w (t)±2×FW×rand×(Q×X best,u -X p,w (t)) (20)

[0061] X p,u (t+1)=X p,u (t)±2×FU×rand×(Q×X best,w -X p,u (t)) (21)

[0062] In the formula, X best,w X best,u These represent the optimal positions for the female and male snake groups, respectively; FW represents the combat ability of the male snake, and FU represents the combat ability of the female snake. The calculation formula is as follows: f best,u The fitness of the best individuals in the female snake group; f best,w The fitness of the best individuals in the male snake group; f p The fitness of individual p, and the mating pattern calculation formula is:

[0063] X p,u (t+1)=X p,u (t)±c3×M u ×rand×(Q×X p,w -X p,u (t)) (22)

[0064] In the formula, M w and M u These represent the mating abilities of the male and female, respectively, and are calculated using the following formula: f p,w f p,u Let be the fitness of the p-th male and female individuals, respectively. The improved snake algorithm retains the standard snake algorithm's formula for finding the position of the worst individual when the new individual's fitness is better than the worst individual in the mating mode:

[0065] X worst,w =X min +rand×(X max -X min ) (twenty three)

[0066] X worst,u =X min +rand×(X max -X min ) (twenty four)

[0067] Among them, X worst,w X worst,uThese are the worst individuals in the male and female snake groups, respectively. Finally, the worst-performing individual in the population is replaced with a new one, completing the improvement. Based on the core logic of the improved snake algorithm, fitness is mapped to the total loss of a miniature transformer.

[0068] Step E specifically includes the following steps:

[0069] The rectangular magnetic cross-section of the transformer core column is then optimized into a circular arc transition type. The optimized core column structure satisfies the prerequisite E1. The optimization process establishes the dimension correlation equation through the principle of equivalent cross-sectional area conservation.

[0070] A c =a·b=A′ c =a·t+πr 2 (25)

[0071] In the formula, t is the thickness of the straight section of the improved iron core column; r is the radius of the arc; at the same time, in order to ensure that the total width of the iron core column is consistent with the original design, it is necessary to satisfy t+2r=b, so that the arc protrusion compensates for the reduction in the thickness of the straight section. Under the condition of ensuring that the effective cross-sectional area of ​​the iron core column remains unchanged, the rectangular section is optimized into a "straight section + arc transition" structure. Substituting into formula (25), the improved iron core column size expression is derived:

[0072]

[0073] Furthermore, based on the constraints, the change in core column volume is used to measure the change in core column loss. The original core column volume was V. c =3·a·b·l c +2·a y ·b·l y The volume of the improved iron core column is V′ c =3·(a·t+πr) 2 )·l c +2·a′ y ·t·l y From equations (25) and (27), we can see that the volume of the iron core column is equal to that of the original design and t = b - 2r < b. And the length of the iron yoke l y The volume of the improved iron core column remains unchanged:

[0074]

[0075] Depend on Therefore V c ′<V c The volume of the improved core column structure is smaller than that of the original core column structure, which means that the improved core column structure reduces the transformer core column loss.

[0076] In step B, during the establishment of the micro-motion model physical simulation platform, the selection and scaling of components are not simply reduced proportionally. Instead, simulated components that are completely consistent with the physical characteristics of the prototype system are selected, and the per-unit values ​​of parameters are strictly controlled to be equal to those of the prototype. While retaining the core characteristics of the physical components of the real power system, the size of the device is precisely reduced to the size of desktop experimental equipment. At the same time, a compact cabinet layout of 1800mm long × 1200mm wide × 950mm high is adopted, which is composed of hardware and software collaboration.

[0077] In terms of core functional module design, a cascaded architecture is adopted to adapt to low voltage requirements: the power supply side uses an AC-DC-AC frequency converter as a key intermediate power conversion device, connected in series between the power grid and the system. It converts 220V three-phase AC power to 600V DC power through AC-DC rectification, and after energy storage and filtering, it outputs 380V AC power through DC-AC inverter, realizing precise control of input and output voltage and providing customized power supply for low voltage scenarios; the isolation transformer T1 is integrated synchronously to achieve electrical isolation and avoid interference caused by direct current flow. At the same time, a three-phase micro-mode transformer T2 is specially designed to adapt to low voltage conditions to ensure the adaptability of power conversion.

[0078] The transmission line section uses a π-type equivalent system model, which more accurately simulates coupling characteristics than the conventional lumped parameter model.

[0079] In the testing and monitoring phase, the protection device can be directly connected to the system without the need for additional conversion equipment or complex debugging, breaking through the limitation of traditional micro-physical simulation platforms that require customized adaptation devices;

[0080] The substation monitoring system integrates core modules such as data acquisition, status monitoring, and remote control. It can track and analyze fault components and waveforms in real time. Moreover, the platform parameter design strictly follows the per-unit value specifications for different voltage levels to ensure that the characteristics of various faults are consistent with the actual power system, thus solving the problem of fault reproduction distortion in low-voltage scenarios.

[0081] The platform reserves standardized interfaces to adapt to the access of new energy components, taking into account both current verification needs and future scenario expansion.

[0082] A computer-readable storage medium storing a computer program thereon, wherein when the computer program is executed by a processor, the device on which the computer-readable storage medium is located performs the aforementioned method for optimizing the structure and parameters of a micro-transformer suitable for micro-motion model physical simulation.

[0083] An electronic device includes a memory and a processor, wherein the memory stores a program executable on the processor, and the processor executes the program to implement the aforementioned method for optimizing the structure and parameters of a micro-transformer suitable for physical simulation of micro-motion models.

[0084] This invention provides a dedicated environment for the design optimization and verification of micro-transformers through the architecture of a micro-mode physical simulation platform for low-voltage scenarios. It overcomes the limitations of traditional loss models that independently model core and winding losses. By utilizing constraints to construct a total loss model coupled with magnetic circuit and circuit parameters, it achieves precise quantification of loss sources and targeted location of key variables. Furthermore, by associating parameters through constraints, the total loss model is only related to the size of the core variable to be optimized. Further improvements are made to the snake optimization algorithm (ISO) and the use of a circular arc transition core column magnetic cross-section, resulting in a more significant reduction in total loss, a more uniform magnetic field distribution, and a more pronounced improvement in magnetic flux transmission efficiency compared to other algorithms and traditional rectangular core columns. Compared to other verification methods, this method retains the realistic characteristics of physical simulation while avoiding the problems of reliance on model parameters and high risks associated with grid-connected operation in digital simulation. It can safely reproduce the full operating conditions of the power system and is suitable for protection device verification and personnel training. Based on a dual-dimensional verification system, compared to single simulation verification methods, this method can comprehensively demonstrate the equivalence of micro-transformers to traditional transformers from electrical and electromagnetic field perspectives, without the risk of magnetic saturation. Attached Figure Description

[0085] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0086] Figure 1 This is a diagram showing the overall architecture of the micro-motion model physical simulation platform of the present invention;

[0087] Figure 2 The equivalent magnetic circuit model diagram of the miniature transformer of this invention;

[0088] Figure 3 Dimensional diagram of the three-phase three-limb transformer of the present invention;

[0089] Figure 4 Schematic diagram of the coil winding of the present invention;

[0090] Figure 5 A comparison chart of the convergence speed of the optimization algorithm of this invention;

[0091] Figure 6 Voltage and current diagrams of the high and low voltage sides during normal operation of this invention;

[0092] Figure 7 The present invention provides voltage and current diagrams of the high and low voltage sides during a single-phase inter-turn short-circuit fault on the high-voltage side.

[0093] Figure 8 The present invention provides voltage and current diagrams of the high and low voltage sides during a single-phase inter-turn short-circuit fault on the low voltage side.

[0094] Figure 9 The present invention provides a current diagram for the no-load closing (closing angles of 0, 30, and 90 degrees) of the miniature transformer.

[0095] Figure 10 The diagram of no-load closing voltage and current (closing angle 30 degrees) under inter-turn short circuit on the high and low voltage sides of this invention;

[0096] Figure 11 The three-dimensional model and magnetic field distribution diagram of the transformer structure before and after optimization at multiple time points according to the present invention;

[0097] Figure 12 A flowchart of the present invention. Detailed Implementation

[0098] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0099] like Figure 12 As shown, the present invention includes the following steps:

[0100] Step A: For a three-phase three-column structure, based on Ohm's law for magnetic circuits, the magnetic reluctance of the main magnetic circuit is decomposed into the sum of the magnetic reluctance of the core column, the yoke, and the hidden air gap, and the relationship between magnetic reluctance and transformer size is quantified.

[0101] Step B: Construct a micro-motion model physical simulation platform with the triple equivalent of "magnetic circuit-circuit-structure" as the core. Fault reproduction is achieved through voltage regulation and cascade design. The compact cabinet layout is adapted to new energy components and provides a full-condition verification environment.

[0102] Step C: Introduce a dynamic correlation mechanism between the geometric parameters of the core column and the magnetic properties of the material, linking the winding structure and the core column dimensions to accurately quantify losses, and using the core column rectangular cross-section length 'a', core column rectangular cross-section width 'b', and core column height 'l' as parameters. c A total loss model for magnetic circuit-electrical circuit coupling was established for the core optimization variables;

[0103] Step D: Improve the standard snake optimization algorithm, take minimizing the total transformer loss as the objective function, optimize the core size variable to be optimized, set the parameters and use MATLAB to solve the optimal combination of core column size and minimum loss;

[0104] Step E: Based on the traditional rectangular iron core column size optimization, the rectangular magnetic cross section is optimized into a circular arc transition type. The parameter matching and correlation are measured under the premise of "stable main magnetic flux + no increase in loss". The quantitative requirements that the improved iron core column size parameters need to meet are clarified through correlation, thereby achieving coordinated optimization of loss and volume.

[0105] The structural innovation of this invention reduces the perimeter of the core column cross-section, thereby lowering the average turn length and DC resistance of the winding. This means the optimized core column structure results in lower winding losses compared to the original structure, while also reducing local magnetic reluctance abrupt changes. Ultimately, this achieves synergistic optimization of loss and volume, resolving the contradiction between magnetic circuit uniformity and compact size in traditional structures. The following detailed explanation of the method uses specific examples, including the following steps:

[0106] Step 1: The micro-motion model physical simulation platform designed in this invention patent is based on similarity theory, breaking through the limitations of traditional scaled-down models that only focus on parameter matching, and realizing physical equivalence in low-voltage scenarios. The overall architecture diagram is as follows. Figure 1 As shown. In terms of component selection and scaling, it is not a simple matter of reducing the size proportionally. Instead, simulated components that are completely consistent with the physical characteristics of the prototype system are selected, and the per-unit values ​​of the parameters are strictly controlled to be equal to those of the prototype. While retaining the core characteristics of the physical components of the real power system, the size of the device is precisely reduced to the size of desktop experimental equipment. At the same time, a compact cabinet layout of 1800mm (length) × 1200mm (width) × 950mm (height) is adopted, which is composed of hardware and software. In the design of core functional modules, a cascaded architecture is adopted to adapt to low-voltage requirements: At the power supply end, an AC-DC-AC frequency converter is used as a key intermediate power conversion device, connected in series between the power grid and the system. Through AC-DC rectification, 220V three-phase AC power is converted to 600V DC power. After energy storage and filtering, it is then output as 380V AC power by DC-AC inverter, achieving precise control of input and output voltage and providing customized power for low-voltage scenarios. A synchronously integrated isolation transformer T1 achieves electrical isolation, avoiding interference caused by direct current flow. A specially designed three-phase micro-mode transformer T2 is used to adapt to low-voltage conditions, rather than modifying a traditional high-voltage transformer, ensuring the adaptability of power conversion. The transmission line section uses a π-type equivalent system model, which more accurately simulates coupling characteristics than the conventional lumped parameter model.

[0107] In the testing and monitoring phases, protection devices can be directly connected to the system without additional conversion equipment or complex debugging, overcoming the limitations of traditional micro-physical simulation platforms that require customized adapters. The substation monitoring system integrates core modules such as data acquisition, status monitoring, and remote control, enabling real-time tracking and analysis of fault components and waveforms. Furthermore, the platform's parameter design strictly adheres to per-unit specifications for different voltage levels, ensuring that the characteristics of various faults are consistent with the actual power system, thus solving the problem of distortion in fault reproduction under low-voltage scenarios. In addition, the platform reserves standardized interfaces to adapt to the access of new energy components, balancing current verification needs with future scenario expansion. Through these innovative designs, a micro-motion model physical simulation platform capable of accurately reproducing full-condition characteristics under low-voltage environments is ultimately constructed, providing a dedicated environment for the design optimization and verification of micro-transformers.

[0108] Step 2: Based on the equivalent magnetic circuit model of a three-phase, three-limb transformer, quantify the relationship between magnetic reluctance and transformer dimensions. The equivalent magnetic circuit model of the transformer core limbs is as follows: Figure 2 As shown, Φ m Represents the main magnetic flux, Φ 1δ Φ 2δ F represents leakage flux, Φ0 represents zero-sequence flux, and F L F H R represents the low-pressure magnetomotive force and high-pressure magnetomotive force of each phase, R0 represents the zero-sequence magnetic reluctance corresponding to the zero-sequence magnetic flux, and r1 and r2 represent the leakage magnetic reluctance. core R yoke These represent the magnetic reluctance of the core column and the yoke, respectively. The expression for the magnetic reluctance of each phase of the main magnetic circuit of a three-phase three-column transformer is shown in equation (1):

[0109]

[0110] In the formula, R core For the reluctance of the iron core column; R yoke For the iron yoke reluctance; R g It is a hidden air gap magnetoresistive; l c The effective magnetic circuit length of the iron core column; l y μ is the effective magnetic path length of a single-sided yoke. r δ is the relative permeability of the core column material; δ is the equivalent total air gap length; A c Let A be the cross-sectional area of ​​the iron core column. c = a × b, where a is the length of the rectangular cross-section of the iron core column; b is the width of the rectangular cross-section of the iron core column; A y Let A be the effective cross-sectional area of ​​the yoke. Based on engineering experience constraints, A is usually chosen. y =1.577A c .

[0111] According to Ohm's law for magnetic circuits, the expression for the main magnetic flux is shown in equation (2):

[0112]

[0113] In the formula, I0 is the no-load current. From equation (2), we can see that the magnetic reluctance of the main magnetic circuit affects the actual distribution of the main magnetic flux. Theoretically, when the magnetomotive force is fixed, the magnetic reluctance R of the main magnetic circuit can be controlled. main To control the main magnetic flux Φ m .

[0114] This invention patent features a customized design for the core columns and windings of a micro-transformer. It overcomes the limitations of traditional loss models that independently model core column and winding losses, establishing a total loss model coupled with magnetic circuit and electrical parameters. This allows for precise quantification of loss sources and targeted location of key variables. Furthermore, by associating parameters through constraints, the total loss model is made solely dependent on the size of the core variable to be optimized. The core column material is B23R080 silicon steel sheet, which meets the core design requirements for core column materials in micro-simulated transformers. A schematic diagram of the three-phase, three-column transformer is shown below. Figure 3 As shown, by Figure 3 The volume V of the iron core column can be obtained. c =3(A c l c +2A y l y ) = 3(abl c +3.154abl y In the core column loss, a dynamic correlation mechanism between the geometric parameters of the core column and the magnetic properties of the material is introduced. The key parameters of B23R080 silicon steel sheet are embedded into the improved Bertotti model. Based on the improved Bertotti model formula, the transformer core column loss expression is approximately calculated as shown in equation (3):

[0115]

[0116] In the formula, P h For the hysteresis loss of the iron core column; P e For eddy current losses in the iron core column; k h P is the hysteresis coefficient; r B represents the residual loss of the core column; α and β are the core column loss coefficients; m k is the magnetic flux density. e d is the eddy current coefficient; d is the lamination thickness; k r This is the residual loss coefficient;

[0117] The miniature transformer has a primary side star connection with a line voltage of 100V and a secondary side delta connection with an output line voltage of 100V. Taps are provided for voltage regulation. The rated capacity is 300VA. The windings use round solid conductors arranged concentrically, with the low-voltage coil on the inner side and the high-voltage coil on the outer side. Cross-sectional views of the core and coils are shown below. Figure 4As shown. The simplified assumption in the traditional model that the winding resistance is only related to the wire material is corrected. It is known that the total winding loss consists of DC resistance loss and additional loss, as shown in equation (4):

[0118]

[0119] In the formula, P dc For DC resistance loss; P ac As additional losses, AC additional losses at power frequency are mainly caused by the skin effect and proximity effect, and are therefore simplified as P in engineering. ac ≈0.05P dc R1 and R2 are the DC resistances of the high-voltage and low-voltage windings, respectively. The calculation formula for the DC resistance of the windings is as follows: ρ is the resistivity of copper; l w1 and l w2 These are the average turn lengths of the primary and secondary windings, respectively; A w1 and A w2 Let I be the cross-sectional area of ​​the primary and secondary winding conductors, respectively. According to the relationship between current and cross-sectional area, I = J·A w and have to

[0120] In summary, the total loss of the micro-transformer in the micro-motion platform is the core column loss P. c With winding loss P w The sum, expressed as in equation (5):

[0121]

[0122] According to equation (5), the key influencing parameters of the total loss model are the length a of the rectangular cross-section of the core column, the width b of the rectangular cross-section of the core column, and the height l of the core column. c , length of iron yoke l y Rated magnetic flux density B m and the average turn length l of the primary and secondary windings w1 l w2 Therefore, the transformer structure has 8 variables to be designed, X = [a, b, B]. m ,l c ,l y ,l w1 ,l w2 To establish the relationship between the parameters of the transformer to be designed and the dimensional variables to be designed, constraint conditions are used to constrain the variables to be constrained:

[0123] a. According to the law of electromagnetic induction, U1 = 4.44fN1B m A c Constraints:

[0124]

[0125] As can be seen from equation (6), theoretically, the cross-sectional area A of the iron core column can be controlled. c To control B m .

[0126] b. Based on the fact that the total copper cross-sectional area of ​​the winding does not exceed the window area S of the core column. w Constraints:

[0127] N1A w1 +N2A w2 ≤S w (7)

[0128] In the formula, S w =k w ×l c ×w, k w Let w be the window utilization factor, and w be the window width, satisfying w = l y -a, combined with the ratio and A w1 A w2 The expression is further simplified based on transformer power balance. To fully utilize window space and avoid size redundancy in the optimized design, the values ​​of N1 and N2 are:

[0129]

[0130]

[0131] c. Based on the spatial constraint relationship between the high and low voltage coils under the above concentric arrangement, establish a functional relationship between the average turn length of the winding and the cross-sectional parameters of the core column. Figure 3 It can be seen that the low-voltage side winding is located on the inner side, and the average turn length is:

[0132] l w2 =2(a+b+d²) (10)

[0133] The high-voltage side winding is located outside the low-voltage side winding, and the average turn length is:

[0134] l w1 =2(a+b+d1+2d2) (11)

[0135] In the formula, d1 is the radial thickness of the primary winding; d2 is the radial thickness of the secondary winding.

[0136] d. Engineering experience constraints, yoke width a y =a, height b y =b, length of the yoke l y =1.2a.

[0137] After constraining the variables to be constrained by the above constraints, substituting them into equation (5) yields a result that is only related to a, b, and l. c Related transformer total loss model:

[0138]

[0139] To address the size optimization requirement of minimizing the total loss of a micro transformer, with a, b, l c For the core variable to be optimized, an optimization model based on the improved snake optimization algorithm (ISO) is constructed.

[0140] Step 3: Addressing the shortcomings of the standard snake optimization algorithm (SO) in transformer parameter optimization, namely slow convergence and susceptibility to local optima, an improved snake algorithm (ISO) is used to optimize the total loss model of a micro-transformer. The standard snake optimization algorithm consists of two core phases: global exploration and local exploitation. In the global exploration phase, when there is no food nearby, the snake will search for food by moving closer to or away from other individuals. Once enough food is obtained, the algorithm enters the local exploitation phase, which is divided into several transitional phases to improve optimization efficiency. In this phase, when food is found and the temperature is high, the snake will be closer to the global optimal fitness. However, if food is found but the temperature is low, the snake may enter a fighting or mating mode with a certain probability. Because the basic snake algorithm suffers from slow convergence and susceptibility to local optima when solving optimization problems, it is improved. The specific contents of the improved snake algorithm include:

[0141] a. Improved population initialization. This invention patent utilizes Chebyshev chaotic mapping to reconstruct the population initialization process, solving the blind spot problem caused by traditional random initialization. The calculation formula is x. k+1 =cos(kcos -1 (x k Where k is the order, and when k≥2, the iterated sequences are uncorrelated regardless of the similarity of the initial values. The improved population initialization formula is:

[0142]

[0143] In the formula, r is a random number between [0,1]; X p It is the initial position of the p-th individual in the population; X max and X min These represent the upper and lower bounds of the value range for the optimization problem. The population is divided into two subpopulations, female and male, each with a 50% population size. The best individual is found in each subpopulation, resulting in the best male and female individuals (i.e., the optimal individuals in both subpopulations), along with the food location, temperature, and food quantity:

[0144]

[0145] In the formula, t is the current iteration number; T is the maximum iteration number; and c1 is a constant, taken as 0.5. The temperature decreases continuously with each iteration, realizing the transition of the population from global search to local exploration.

[0146] b. Improved Exploration Phase. During the exploration phase, the standard snake algorithm is prone to getting trapped in local optima during iteration. Therefore, this invention introduces a spiral sinusoidal perturbation mechanism to perturb the exploration phase of the snake optimization algorithm. The calculation formula is as follows: When a = 2.3 and x0 = 0.7, the calculation formula simplifies to x k+1 =sin(πx) k A spiral coefficient z is introduced, with a value between [-1, 1]. When Q < 0.25, the snake searches for food by selecting any random location and updates its position accordingly. The position update formulas for male and female groups are as follows:

[0147]

[0148] In the formula, X p,w X represents the position of the p-th male snake. r,w The location of a randomly selected male snake; X p,u X represents the position of the p-th female snake. r,u The location of a randomly selected female snake; rand is a random number within [0,1]; A w The ability of male snakes to find food is calculated using the following formula: f r,w For X r,w fitness, f p,w For X p,w fitness; A u The ability to find food for female snakes. f r,u For X r,u fitness, f p,u For X p,u The degree of adaptability.

[0149] c. Improved Development Phase. This invention proposes incorporating Piecewise chaotic mapping into the development phase. Piecewise chaotic mapping enables the population to maintain good search capabilities during iterative position updates and avoids the population getting stuck in a dead loop within a region. It also optimizes individual update strategies in mating and combat modes, improving the accuracy and efficiency of local searches. Its calculation formula is as follows:

[0150]

[0151] In the formula, i takes the value of 0.4; x(1) is rand. When Q > 0.25, the population expands its search range; when the temperature > 0.60, the environment is in a hot state, and the population only looks for food. The updated formula for the snake population location update in the development stage is:

[0152]

[0153] In the formula, X p,q This represents the location of an individual (male or female) snake; when the temperature is <0.60, the environment is cold, and the population is in either fighting or mating mode. The formula for calculating the location update in fighting mode is:

[0154] X p,w (t+1)=X p,w (t)±2×FW×rand×(Q×X best,u -X p,w (t)) (20)

[0155] X p,u (t+1)=X p,u (t)±2×FU×rand×(Q×X best,w -X p,u (t)) (21)

[0156] In the formula, X best,w X best,u These represent the optimal positions for the female and male snake groups, respectively; FW represents the combat ability of the male snake, and FU represents the combat ability of the female snake. The calculation formula is as follows: f best,u The fitness of the best individuals in the female snake group; f best,w The fitness of the best individuals in the male snake group; f p The fitness of individual p, and the mating pattern calculation formula is:

[0157] X p,u (t+1)=X p,u (t)±c3×M u ×rand×(Q×X p,w -X p,u (t)) (22)

[0158] In the formula, M w and M u These represent the mating abilities of the male and female, respectively, and are calculated using the following formula: f p,w f p,u Let be the fitness of the p-th male and female individuals, respectively. The improved snake algorithm retains the standard snake algorithm's formula for replacing the worst individual in the mating mode when the new individual's fitness is better than the worst individual:

[0159] X worst,w =X min +rand×(X max -X min ) (twenty three)

[0160] X worst,u =X min +rand×(X max -X min ) (twenty four)

[0161] Among them, X worst,w X worst,u These are the worst individuals in the male and female snake groups, respectively. Finally, the worst-performing individual in the population is replaced with a new one, completing the improvement. Based on the core logic of the improved snake algorithm, fitness is mapped to the total loss of a miniature transformer.

[0162] Step 4: Based on the optimization of the core column size in Step 3, the rectangular magnetic cross section is improved into a circular arc transition type. Combined with the correlation between parameters established by the constraint conditions, the quantitative requirements that the improved core column size parameters must meet are clarified, and the transformer volume and loss are further reduced. Before the core column structure is optimized, the dual constraint premise of "stable main magnetic flux + no increase in loss" must be met: a. Ensure that the main magnetic flux is stable and the rated magnetic flux density is safe after the core column is improved. According to equations (1)(2)(6), the main magnetic flux and rated magnetic flux density are affected by the magnetic reluctance of the main magnetic circuit and the cross-sectional area of ​​the core column. Therefore, it is necessary to ensure that the magnetic reluctance of the main magnetic circuit and the cross-sectional area of ​​the core column remain unchanged. That is, it is only necessary to make the effective cross-sectional areas of the core column and the yoke before and after the improvement equal. b. The change of the core column structure should avoid a significant sacrifice of efficiency. This condition can be transformed into a loss problem: under the same working conditions and when condition a is met, the loss per unit volume of core column remains unchanged. Therefore, the total loss of the core column can be measured by the core column volume. According to Joule's law and equation (5), the winding loss is measured by the average winding turn length. The rectangular magnetic cross-section of the transformer core column is then optimized into a circular arc transition type. The optimized core column structure satisfies prerequisite a. The optimization process establishes the dimension correlation equation through the principle of equivalent cross-sectional area conservation.

[0163] A c =a·b=A′ c =a·t+πr 2 (25)

[0164] In the formula, t is the thickness of the straight section of the improved iron core column; r is the radius of the arc. At the same time, in order to ensure that the total width of the iron core column is consistent with the original design, it is necessary to satisfy t+2r=b, so that the arc protrusion compensates for the reduction in the thickness of the straight section. Under the condition of ensuring that the effective cross-sectional area of ​​the iron core column remains unchanged, the rectangular section is optimized into a "straight section + arc transition" structure. Substituting into formula (25), the improved iron core column size expression is derived:

[0165]

[0166] Furthermore, according to condition b, the change in core column volume can be used to measure the change in core column loss. The volume of the core column before the improvement is V. c =3·a·b·l c +2·a y ·b·l y The volume of the improved iron core column is V′ c =3·(a·t+πr) 2 )·l c +2·a′ y ·t·l y From equations (25) and (27), we can see that the volume of the iron core column is equal to that of the original design and t = b - 2r < b. And the length of the iron yoke l y The volume of the improved iron core column remains unchanged:

[0167]

[0168] Depend on Therefore V c ′<V c The volume of the improved core column structure is smaller than that of the original structure, meaning the improved core column structure reduces transformer core column losses. Similarly, the average winding turn length is determined by the perimeter of the core column cross-section. Therefore, transformer winding losses can be transformed into a comparison of the perimeter of the core column cross-section before and after optimization. The perimeter before improvement is 2(a+b), and the perimeter after improvement is 2a+2t+πr. Therefore, when the radius r of the arc satisfies... At that time, the perimeter after the improvement was smaller than the perimeter before the improvement.

[0169] To further demonstrate the advantages of the present invention, this application provides further explanation and illustration through the following two experimental examples:

[0170] Example a: A comparative analysis of the optimization performance of different algorithms on the total loss model of a micro transformer is conducted. The parameters are set as follows: population size N = 30, maximum number of iterations T = 30. Simulation experiments are performed using MATLAB R2023a. The optimization results of each algorithm are compared below. Figure 5 As shown. The total transformer loss is minimized to P after optimization using the improved snake algorithm. t=1.02W, and through comparison, it was found that the improved snake optimization algorithm has advantages over the other two algorithms in both convergence speed and optimization effect. The total loss is reduced by 18% compared with the traditional snake algorithm and by 31% compared with SSA. When achieving the same loss accuracy, the number of iterations is reduced by 30% to 40%, and the algorithm performance is significantly improved. The optimization results show that the improved snake algorithm can find the optimal core column size combination and minimum loss more quickly and effectively when solving the transformer loss optimization problem.

[0171] Example b. Verifies the effectiveness of the parameter design and the rationality of the structural optimization of the miniature simulated transformer at the levels of electrical and electromagnetic characteristics. At the level of electrical characteristic verification, relying on the micro-simulation platform, the electrical response of the optimized and improved miniature transformer under four operating conditions—normal operation, inter-turn short-circuit fault, no-load closing transient, and no-load closing under inter-turn short circuit—is reproduced. Figures 6-10 As shown, this verifies that the micro transformer and the traditional high-voltage transformer are highly equivalent in core electrical characteristics. Regarding electromagnetic field characteristics, based on the optimized core column structure, three-dimensional models of the transformer structure before and after optimization were built using COMSOL finite element software at different times. Simulation calculations were then used to obtain data on the magnetic field distribution and magnetic flux density modulus. The simulation results of the transformer model at times of 0.0125s and 0.025s before and after the improvement are shown below. Figure 11 As shown, comparative analysis proves that the concentration phenomenon of the magnetic field density in the optimized design is significantly reduced, which helps to reduce the risk of core saturation and improve the efficiency and stability of the transformer. The maximum magnetic field strength before optimization was 0.531T, and after optimization it was 0.578T, an increase of 8.9%. The maximum magnetic field strength before optimization was 0.492T, and after optimization it was 0.57T, an increase of 15.9%. Therefore, the maximum magnetic field strength is improved after optimization, reflecting that the optimized design has improved the magnetic flux transmission efficiency. The overall analysis demonstrates the structural rationality and parameter reliability of the designed micro transformer.

[0172] Based on the characteristics of low loss and small size of micro transformers, this invention constructs a micro-motion model physical simulation platform based on the synergistic equivalence of multi-dimensional physical characteristics using similarity theory. It establishes a total loss model of micro transformers by combining constraints and optimizes the total loss model using an improved snake algorithm. Based on the electromagnetic characteristics, it optimizes the magnetic cross-section structure of the iron core column to complete the optimized design of micro transformers.

[0173] A computer-readable storage medium stores a computer program thereon. When executed by a processor, the computer program causes the device containing the computer-readable storage medium to perform the micro-transformer structure and parameter optimization method described above, applicable to micro-motion model physical simulation. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), random access memory, and other memories.

[0174] An electronic device includes a memory and a processor, wherein the memory stores a program executable on the processor, and the processor executes the program to implement the micro-transformer structure and parameter optimization method described above for micro-motion model physical simulation.

[0175] If the modules / units integrated in the electronic device described in this application are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can also be implemented by a computer program instructing related hardware devices. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above.

[0176] Furthermore, the computer-readable storage medium may primarily include a stored program area and a stored data area, wherein the stored program area may store the operating system, an application program required for at least one function, etc.; and the stored data area may store data created based on the use of blockchain nodes, etc.

[0177] The computer-readable storage medium stores computer-readable instructions, which are executed by a processor in an electronic device to implement the micro-transformer structure and parameter optimization method for micro-motion model physical simulation as described in any of the above embodiments.

[0178] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0179] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0180] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

[0181] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification and claims of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0182] Note that the above description is merely a preferred embodiment and application of the technical principles of the present invention. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the specific embodiments described herein, and may include many other effective embodiments without departing from the concept of the present invention. The scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for optimizing the structure and parameters of a micro-transformer suitable for physical simulation of micro-motion models, characterized in that: Includes the following steps: Step A: For a three-phase three-column structure, based on Ohm's law for magnetic circuits, the magnetic reluctance of the main magnetic circuit is decomposed into the sum of the magnetic reluctance of the core column, the yoke, and the hidden air gap, and the relationship between magnetic reluctance and transformer size is quantified. Step B: Construct a micro-motion model physical simulation platform with "magnetic circuit-circuit-structure" triple equivalence as its core. Fault reproduction is achieved through voltage regulation and cascade design. The compact cabinet layout is adapted to new energy components, providing a full-condition verification environment. Step C: Introduce a dynamic correlation mechanism between the geometric parameters of the iron core column and the magnetic properties of the material. This correlates the winding structure with the iron core column dimensions, accurately quantifies losses, and uses the length 'a', width 'b', and height 'l' of the iron core column's rectangular cross-section as parameters. c A total loss model for magnetic circuit-electrical circuit coupling was established for the core optimization variables; Step D: Improve the standard snake optimization algorithm, take minimizing the total transformer loss as the objective function, optimize the core size variable to be optimized, set the parameters and use MATLAB to solve the optimal combination of core column size and minimum loss; Step E: Based on the traditional rectangular iron core column size optimization, the rectangular magnetic cross section is optimized into a circular arc transition type. The parameter matching and correlation are measured with the premise of "stable main magnetic flux + no increase in loss". The quantitative requirements that the improved iron core column size parameters need to meet are clarified through correlation, so as to achieve synergistic optimization of loss and volume.

2. The method for optimizing the structure and parameters of a micro-transformer suitable for physical simulation of a micro-motion model according to claim 1, characterized in that: Step A specifically includes the following steps: The expression for the magnetic reluctance of each phase of the main magnetic circuit of a three-phase three-limb transformer is shown in equation (1): In the formula, R core For the reluctance of the iron core column; R yoke For the iron yoke reluctance; R g It is a hidden air gap magnetoresistive; l c The effective magnetic circuit length of the iron core column; l y μ is the effective magnetic path length of a single-sided yoke. r δ is the relative permeability of the core column material; δ is the equivalent total air gap length; A c Let A be the cross-sectional area of ​​the iron core column. c = a × b, where a is the length of the rectangular cross-section of the iron core column; b is the width of the rectangular cross-section of the iron core column; A y This is the effective cross-sectional area of ​​the iron yoke; According to Ohm's law for magnetic circuits, the expression for the main magnetic flux is shown in equation (2): In the formula, I0 is the no-load current; from equation (2), we can see that the magnetic reluctance of the main magnetic circuit affects the actual distribution of the main magnetic flux. Theoretically, when the magnetomotive force is fixed, the magnetic reluctance R of the main magnetic circuit can be controlled. main To control the main magnetic flux Φ m .

3. The method for optimizing the structure and parameters of a micro-transformer suitable for physical simulation of micro-motion models according to claim 1, characterized in that: Step C involves custom designing the core column and windings of the micro-transformer to establish a total loss model coupled with magnetic circuit and electrical parameters. This model aims to accurately quantify the sources of loss and target key variables. By associating parameters through constraints, the total loss model is made to be related only to the size of the core variable to be optimized. Specifically, this includes the following steps: C1, Volume of the iron core cylinder V c =3(A c l c +2A y l y ) = 3(abl c +3.154abl y In the core column loss, a dynamic correlation mechanism between the geometric parameters of the core column and the magnetic properties of the material is introduced. The key parameters of B23R080 silicon steel sheet are embedded into the improved Bertotti model. Based on the improved Bertotti model formula, the transformer core column loss expression is approximately calculated as shown in equation (3): In the formula, P h For the hysteresis loss of the iron core column; P e For eddy current losses in the iron core column; k h P is the hysteresis coefficient; r B represents the residual loss of the core column; α and β are the core column loss coefficients; m k is the magnetic flux density. e d is the eddy current coefficient; d is the lamination thickness; k r This is the residual loss coefficient; C2, the primary side of the micro transformer winding is star-connected with a line voltage of 100V, and the secondary side is delta-connected with a line voltage of 100V. Taps are provided to achieve voltage regulation. The rated capacity is 300VA. The winding uses round solid wires and is arranged concentrically, with the low-voltage coil on the inside and the high-voltage coil on the outside. C3, the total winding loss consists of DC resistance loss and additional loss, as shown in equation (4): In the formula, P dc For DC resistance loss; P ac As additional losses, AC additional losses at power frequency are mainly caused by the skin effect and proximity effect, and are therefore simplified as P in engineering. ac ≈0.05P dc R1 and R2 are the DC resistances of the high-voltage and low-voltage windings, respectively. The calculation formula for the DC resistance of the windings is as follows: ρ is the resistivity of copper; l w1 and l w2 These are the average turn lengths of the primary and secondary windings, respectively; A w1 and A w2 Let I be the cross-sectional area of ​​the primary and secondary winding conductors, respectively. According to the relationship between current and cross-sectional area, I = J·A w and have to C4. In summary, the total loss of the micro-transformer in the micro-motion platform is the core column loss P. c With winding loss P w The sum, expressed as in equation (5): According to equation (5), the key influencing parameters of the total loss model are the length a of the rectangular cross-section of the core column, the width b of the rectangular cross-section of the core column, and the height l of the core column. c , length of iron yoke l y Rated magnetic flux density B m and the average turn length l of the primary and secondary windings w1 l w2 Therefore, the transformer structure has 8 variables to be designed, X = [a, b, B]. m ,l c ,l y ,l w1 ,l w2 To establish the relationship between the transformer parameters to be designed and the dimensional variables to be designed, constraint conditions are used to constrain the variables to be constrained. After constraining the variables to be constrained by the above constraint conditions, substituting them into equation (5) yields a result that is only related to a, b, l c Related transformer total loss model: This achieves the size optimization requirement of minimizing the total loss of the micro transformer, using a, b, l c For the core variable to be optimized, an optimization model based on the improved snake optimization algorithm (ISO) is constructed.

4. The method for optimizing the structure and parameters of a micro-transformer suitable for physical simulation of a micro-motion model according to claim 3, characterized in that: The specific constraints in step C4 are as follows: C41: According to the law of electromagnetic induction, U1 = 4.44fN1B m A c Constraints: As can be seen from equation (6), theoretically, the cross-sectional area A of the iron core column can be controlled. c To control B m ; C42: Based on the total copper cross-sectional area of ​​the winding not exceeding the window area S of the core column. w Constraints: N1A w1 +N2A w2 ≤S w (7) In the formula, S w =k w ×l c ×w, k w Let w be the window utilization factor, and w be the window width, satisfying w = l y -a, combined with the ratio and A w1 A w2 The expression is further simplified based on transformer power balance. To fully utilize window space and avoid size redundancy in the optimized design, the values ​​of N1 and N2 are: C43: Based on the spatial constraint relationship between the high and low voltage coils under the above concentric arrangement, a functional relationship between the average turn length of the winding and the cross-sectional parameters of the core column is established. As shown in Figure 3, the low voltage side winding is located on the inner side, and the average turn length is: l w2 =2(a + b + d 2) (10) The high-voltage side winding is located outside the low-voltage side winding, and the average turn length is: l w1 =2(a + b + d 1+ 2d 2) (11) In the formula, d1 is the radial thickness of the primary winding; d2 is the radial thickness of the secondary winding. C44: Engineering experience constraint, yoke width a y =a, height b y =b, length of the yoke l y =1.2a.

5. The method for optimizing the structure and parameters of a micro-transformer suitable for physical simulation of micro-motion models according to claim 1, characterized in that: Step D specifically includes the following steps: D1: Improved population initialization, calculated as x k+1 =cos(kcos -1 (x k ), where k is the order. When k≥2, regardless of whether the initial values ​​are similar, the iterated sequences are uncorrelated; the improved population initialization formula is: In the formula, r is a random number between [0,1]; X p It is the initial position of the p-th individual in the population; X max and X min These represent the upper and lower bounds of the value range for the optimization problem. The population is divided into two subpopulations, female and male, each with a 50% population size. The best individual is found in each subpopulation, resulting in the best male and female individuals (i.e., the optimal individuals in both subpopulations), along with the food location, temperature, and food quantity: In the formula, t is the current iteration number; T is the maximum iteration number; c1 is a constant; the temperature decreases continuously with each iteration, realizing the transition of the population from global search to local exploration; D2: Improved Exploration Phase. In the exploration phase, a spiral sinusoidal perturbation mechanism is introduced to perturb the snake optimization algorithm during the exploration phase. The calculation formula is as follows: When a = 2.3 and x0 = 0.7, the calculation formula simplifies to x k+1 =sin(πx) k A spiral coefficient z is introduced, with a value between [-1, 1]. When Q < 0.25, the snake searches for food by selecting any random location and updates its position accordingly. The position update formulas for male and female groups are as follows: In the formula, X p,w X represents the position of the p-th male snake; r,w The location of a randomly selected male snake; X p,u X represents the position of the p-th female snake. r,u The location of a randomly selected female snake; rand is a random number within [0,1]; A w The ability of male snakes to find food is calculated using the following formula: f r,w For X r,w fitness, f p,w For X p,w fitness; A u The ability to find food for female snakes. f r,u For X r,u fitness, f p,u For X p,u The fitness of; D3: In the improvement and development phase, the accuracy and efficiency of local search are improved by incorporating Piecewise chaotic mapping. The calculation formula is as follows: In the formula, i takes the value of 0.4; x(1) is rand; when Q>0.25, the population expands its search range; when the temperature>0.60, the environment is in a hot state, and the population only looks for food. The updated formula for calculating the snake population location during the development phase is as follows: In the formula, X p,q This represents the location of an individual (male or female) snake; when the temperature is <0.60, the environment is cold, and the population is in either fighting or mating mode. The formula for calculating the location update in fighting mode is: X p,w (t+1)=X p,w (t)±2×FW×rand×(Q×X best,u -X p,w (t)) (20) X p,u (t+1)=X p,u (t)±2×FU×rand×(Q×X best,w -X p,u (t)) (21) In the formula, X best,w X best,u These represent the optimal positions for the female and male snake groups, respectively; FW represents the combat ability of the male snake, and FU represents the combat ability of the female snake. The calculation formula is as follows: f best,u The fitness of the best individuals in the female snake group; f best,w The fitness of the best individuals in the male snake group; f p The fitness of individual p, and the mating pattern calculation formula is: X p,u (t+1)=X p,u (t)±c3×M u ×rand×(Q×X p,w -X p,u (t)) (22) In the formula, M w and M u These represent the mating abilities of the male and female, respectively, and are calculated using the following formula: f p,w f p,u Let be the fitness of the p-th male and female individuals, respectively. The improved snake algorithm retains the standard snake algorithm's formula for replacing the worst individual when the new individual's fitness is better than the worst individual in the mating pattern: X worst,w =X min +rand×(X max -X min ) (23) X worst,u =X min +rand×(X max -X min ) (24) Among them, X worst,w X worst,u These are the worst individuals in the male and female snake groups, respectively. Finally, the worst-performing individual in the population is replaced with a new one, completing the improvement. Based on the core logic of the improved snake algorithm, fitness is mapped to the total loss of a miniature transformer.

6. The method for optimizing the structure and parameters of a micro-transformer suitable for physical simulation of a micro-motion model according to claim 4, characterized in that: Step E specifically includes the following steps: The rectangular magnetic cross-section of the transformer core column is then optimized into a circular arc transition type. The optimized core column structure satisfies the prerequisite E1. The optimization process establishes the dimension correlation equation through the principle of equivalent cross-sectional area conservation. A c =a·b=A′ c =a·t+πr 2 (25) In the formula, t is the thickness of the straight section of the improved iron core column; r is the radius of the arc; at the same time, in order to ensure that the total width of the iron core column is consistent with the original design, it is necessary to satisfy t+2r=b, so that the arc protrusion compensates for the reduction in the thickness of the straight section. Under the condition of ensuring that the effective cross-sectional area of ​​the iron core column remains unchanged, the rectangular section is optimized into a "straight section + arc transition" structure. Substituting into formula (25), the improved iron core column size expression is derived: Furthermore, based on the constraints, the change in core column volume is used to measure the change in core column loss. The original core column volume was V. c =3·a·b·l c +2·a y ·b·l y The volume of the improved iron core column is V. c ′=3·(a·t+πr 2 )·l c +2·a′ y ·t·l y From equations (25) and (27), we can see that the volume of the iron core column is equal to that of the original design and t = b - 2r < b. And the length of the iron yoke l y The volume of the improved iron core column remains unchanged: Depend on Therefore V′ c <V c The volume of the improved core column structure is smaller than that of the original core column structure, which means that the improved core column structure reduces the transformer core column loss.

7. The method for optimizing the structure and parameters of a micro-transformer suitable for physical simulation of micro-motion models according to claim 1, characterized in that: In step B, during the establishment of the micro-motion model physical simulation platform, the selection and scaling of components are not simply reduced proportionally. Instead, simulated components that are completely consistent with the physical characteristics of the prototype system are selected, and the per-unit values ​​of parameters are strictly controlled to be equal to those of the prototype. While retaining the core characteristics of the physical components of the real power system, the size of the device is precisely reduced to the size of desktop experimental equipment. At the same time, a compact cabinet layout of 1800mm long × 1200mm wide × 950mm high is adopted, which is composed of hardware and software collaboration. In terms of core functional module design, a cascaded architecture is adopted to adapt to low voltage requirements: the power supply side uses an AC-DC-AC frequency converter as a key intermediate power conversion device, connected in series between the power grid and the system. It converts 220V three-phase AC power to 600V DC power through AC-DC rectification, and after energy storage and filtering, it outputs 380V AC power through DC-AC inverter, realizing precise control of input and output voltage and providing customized power supply for low voltage scenarios; the isolation transformer T1 is integrated synchronously to achieve electrical isolation and avoid interference caused by direct current flow. At the same time, a three-phase micro-mode transformer T2 is specially designed to adapt to low voltage conditions to ensure the adaptability of power conversion. The transmission line section uses a π-type equivalent system model, which more accurately simulates coupling characteristics than the conventional lumped parameter model. In the testing and monitoring phase, the protection device can be directly connected to the system without the need for additional conversion equipment or complex debugging, breaking through the limitation of traditional micro-physical simulation platforms that require customized adaptation devices; The substation monitoring system integrates core modules such as data acquisition, status monitoring, and remote control. It can track and analyze fault components and waveforms in real time. Moreover, the platform parameter design strictly follows the per-unit value specifications for different voltage levels to ensure that the characteristics of various faults are consistent with the actual power system, thus solving the problem of fault reproduction distortion in low-voltage scenarios. The platform reserves standardized interfaces to adapt to the access of new energy components, taking into account both current verification needs and future scenario expansion.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it causes the device containing the computer-readable storage medium to perform the micro-transformer structure and parameter optimization method for micro-motion model physical simulation as described in any one of claims 1-7.

9. An electronic device, characterized in that, include: A memory and a processor, wherein the memory stores a program that can run on the processor, and the processor executes the program to implement the method for optimizing the structure and parameters of a micro-transformer suitable for physical simulation of a micro-motion model as described in any one of claims 1-7.

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