A transformer structure optimization method and system based on energy efficiency simulation analysis

Abstract

The present invention relates to the technical field of computer-aided design, and specifically to a transformer structure optimization method and system based on energy efficiency simulation analysis, comprising the following steps: setting flux sensors in multiple regions of a core stacking structure, collecting multi-faceted flux density change trends, extracting gradient features, and generating flux gradient distribution data. In the present invention, based on the collection of multi-region data by flux sensors, the flux density gradient distribution is obtained, the change rate of the sudden increase region is extracted, the local flux offset angle is calculated, the offset is screened according to the flux guidance threshold, dynamic prediction is achieved, the etching depth and angle correction are adjusted in combination with the flux diffusion radius, the flux diffusion path is optimized, the local flux accumulation is reduced, the core stacking direction is adjusted to reduce local losses, the electromagnetic loss changes are calculated in combination with the load state, the optimized energy efficiency loss value is obtained, the flux distribution is balanced, the energy efficiency is improved, the heat dissipation capacity is improved, the manufacturing cost is reduced, and the structural adaptability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of computer-aided design, and in particular to a transformer structure optimization method and system based on energy efficiency simulation analysis. Background Art

[0002] The field of computer-aided design technology includes the technical means of using computer systems to design, analyze and optimize products. The core content of this technical field covers modeling, simulation, optimization and design verification. Among them, modeling mainly involves using mathematical expressions or geometric structures to describe the structure and characteristics of products, simulation is used to analyze the performance of products under different working conditions, optimization adjusts design parameters through algorithms to improve performance or reduce costs, and design verification is used to ensure that products meet predetermined functional and performance requirements. Computer-aided design is widely used in many industries such as machinery, electrical, electronic and civil engineering to improve design accuracy and efficiency.

[0003] Among them, the transformer structure optimization method based on energy efficiency simulation analysis refers to the use of computer simulation technology to model and simulate the energy efficiency characteristics of the transformer, and optimize the transformer structure based on the simulation results. This method uses finite element analysis to calculate the electromagnetic field distribution, loss distribution and temperature rise for technical matters such as the electromagnetic performance, thermal characteristics and material utilization of the transformer, and combines topology optimization technology to adjust the parameters of the core, winding and cooling structure to improve electromagnetic performance and heat dissipation capacity. In addition, the multi-objective optimization algorithm is used to optimize the parameters of multiple design variables to ensure that the optimization plan takes into account both energy efficiency and manufacturing costs.

[0004] Existing technologies rely on fixed calculation models for flux analysis, which makes it difficult to accurately capture local flux offset characteristics, resulting in lagging adjustment strategies and the inability to dynamically optimize flux paths. The adjustment of abnormal flux changes mainly relies on overall structural optimization, which makes it difficult to accurately guide local flux, resulting in more serious local saturation. The flux diffusion optimization method is fixed, and the etching depth and angle adjustment lack adaptive regulation of flux changes, affecting the optimization effect of the flux diffusion path. The core stacking direction adjustment method is relatively simple and cannot be adapted according to changes in flux distribution, resulting in uneven flux density in local areas and increased local losses. The electromagnetic loss calculation does not fully consider the impact of the load state, and the optimization scheme is difficult to adapt to different working conditions, resulting in high energy efficiency losses under some operating conditions. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose a transformer structure optimization method and system based on energy efficiency simulation analysis.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a transformer structure optimization method based on energy efficiency simulation analysis, comprising the following steps:

[0007] S1: Setting magnetic flux sensors in multiple areas of the core stacking structure, collecting the trend of magnetic flux density changes on multiple levels, extracting gradient features, and generating magnetic flux gradient distribution data;

[0008] S2: Based on the flux gradient distribution data, extract the change rate of the sudden increase area, call the guide groove etching parameters and the magnetic resistance control piece data, calculate the local flux offset angle, compare the flux guidance threshold according to the flux offset angle, judge the adjustment demand, screen the area based on the offset, and generate the dynamic prediction result of the flux offset;

[0009] S3: Based on the dynamic prediction result of the magnetic flux offset, the existing etching depth, angle, and diffusion range of the magnetic flux guide groove are called, the adjustment amount of the magnetic flux diffusion radius is calculated, the etching depth adjustment value and the angle correction parameter are determined, and the magnetic flux diffusion path correction parameter is generated;

[0010] S4: based on the magnetic flux diffusion path correction parameter, obtaining stacking direction data, setting the stacking offset of the high magnetic flux area, adjusting the core stacking direction, and obtaining the core stacking adjustment angle;

[0011] S5: Based on the core stacking adjustment angle, electromagnetic loss calculation parameters and transformer load status data are called to calculate the core eddy current loss and iron loss change rate, and generate an optimized energy efficiency loss value.

[0012] As a further scheme of the present invention, the magnetic flux gradient distribution data includes the magnetic flux density change trend, gradient characteristics, and the change rate of the sudden increase area; the magnetic flux offset dynamic prediction results include the magnetic flux offset angle, the flux guide threshold comparison result, and the magnetic flux offset screening area; the magnetic flux diffusion path correction parameters include the etching depth adjustment value, the angle correction parameter, and the magnetic flux diffusion radius adjustment amount; the core stacking adjustment angle includes the stacking direction data, the high flux area stacking offset, and the core stacking direction adjustment data; the optimized energy efficiency loss value includes the core eddy current loss, the electromagnetic loss calculation parameters, and the iron loss change rate.

[0013] As a further solution of the present invention, the step of acquiring the magnetic flux gradient distribution data is specifically as follows:

[0014] S101: Based on the arrangement of magnetic flux sensors in multiple regions of the core stacking structure, multi-layer magnetic flux density data is collected, the spatial variation trend of multi-layer magnetic flux density is calculated, and the magnetic flux gradient of multiple regions is calculated by the difference of magnetic flux density values ​​of adjacent regions to obtain the initial distribution of magnetic flux gradient;

[0015] S102: calling the initial distribution of magnetic flux gradient, screening magnetic flux gradient data, removing abnormal gradient values, calculating the average rate of change of magnetic flux gradients in multiple regions, and calculating the trend of magnetic flux gradient changes in multiple regions based on the mean and variance of the gradient distribution, using the formula:

[0016] ;

[0017] The flux gradient change trend value is calculated;

[0018] in, Represents the trend value of magnetic flux gradient change, Representative The magnetic flux density in the region, represents the mean value of magnetic flux density, represents the total number of magnetic flux density measurement points, Representative The magnetic flux density in a region;

[0019] S103: calling the flux gradient change trend value, calculating the spatial gradient characteristics of the flux gradient distribution according to the difference of the flux gradient distribution, screening the significant change area, and obtaining the flux gradient distribution data.

[0020] As a further solution of the present invention, the steps for obtaining the dynamic prediction result of the magnetic flux offset are specifically as follows:

[0021] S201: based on the magnetic flux gradient distribution data, extract the change rate of the sudden increase area, identify the local characteristics of the magnetic flux change, and call the guide groove etching parameters and the magnetic resistance control piece data at the same time to calculate the local magnetic flux offset angles of multiple regions, and establish a local magnetic flux offset angle data set;

[0022] S202: Based on the local magnetic flux offset angle data set, comparing the magnetic flux guidance threshold, determining whether the magnetic flux offset of multiple regions exceeds the threshold range, and screening the regions where the offset exceeds the threshold to obtain the over-limit magnetic flux offset region;

[0023] S203: Calling the over-limit magnetic flux deviation area data, calculating the deviation trend change rate, using the formula:

[0024] ;

[0025] Calculate and obtain the flux deviation trend parameters, and establish the dynamic prediction results of the flux deviation;

[0026] in, represents the flux deviation trend parameter, Representative The magnetic flux increment in the region, Representative The flux guide groove etching depth of the region, Representative The magnetic field intensity of the area, Representative The gradient of the magnetoresistance control plate in the region, Represents the total number of over-limit flux excursion regions.

[0027] As a further solution of the present invention, the step of obtaining the magnetic flux diffusion path correction parameter is specifically as follows:

[0028] S301: Based on the dynamic prediction result of the magnetic flux offset, the etching depth, etching angle and magnetic flux diffusion range of the magnetic flux guide groove are called, the initial value of the magnetic flux diffusion radius is calculated, the variation range of the etching depth and the angle variation range are analyzed, the dynamic offset parameters in the magnetic flux diffusion path are extracted, and the variation trend of the magnetic flux diffusion radius is calculated to obtain the variation of the magnetic flux diffusion radius;

[0029] S302: Based on the change in the magnetic flux diffusion radius, the etching depth adjustment value is calculated, and combined with the angle correction parameter, the formula is used:

[0030] ;

[0031] Calculate the correction amount of the magnetic flux diffusion path, screen the effective correction interval in the magnetic flux guide groove, adjust the optimization parameters of the magnetic flux diffusion path, summarize the correction range of the etching depth, and combine the etching angle correction parameters to extract the magnetic flux diffusion path control data and obtain the magnetic flux diffusion path correction parameters;

[0032] in, represents the flux diffusion path correction, represents the change in the magnetic flux diffusion radius, represents the flux diffusion influence factor, represents the etching angle, represents the base etching angle, represents the etching depth, represents the area of ​​magnetic flux diffusion region, Represents the total number of factors affecting flux diffusion.

[0033] As a further solution of the present invention, the step of obtaining the core stacking adjustment angle is specifically as follows:

[0034] S401: Based on the magnetic flux diffusion path correction parameter, stacking direction data is obtained, distribution characteristics of the stacking direction data are calculated, stacking direction change regions are screened, stacking direction change intervals are extracted, and stacking direction change interval values ​​are obtained;

[0035] S402: calling the stacking direction variation interval value, setting the stacking offset in the high magnetic flux region, calculating the correlation between the stacking offset and the stacking direction variation interval, and determining and obtaining the optimal stacking offset in the high magnetic flux region;

[0036] S403: Calling the optimal stacking offset of the high magnetic flux area, adjusting the core stacking direction, using the formula:

[0037] ;

[0038] Calculate and obtain the core stacking adjustment angle;

[0039] in, Represents the core stacking adjustment angle, Representative The stacking offset of the layer, Representative The magnetic flux density of the layer, Representative The local stacking variation coefficient of the layer, Representative The flux diffusion ratio of the layer, Represents the total number of stacked layers, Represents the inverse tangent function.

[0040] As a further solution of the present invention, the step of obtaining the optimized energy efficiency loss value is specifically as follows:

[0041] S501: Based on the core stacking adjustment angle, the electromagnetic loss parameter and the transformer load state data are called to calculate the eddy current loss value of the core, and at the same time, the magnetic permeability, frequency, current density, and stacking thickness parameters of the core material are used to calculate the local eddy current loss value of the multi-stacked area to obtain the core eddy current loss value;

[0042] S502: calling the core eddy current loss value, comparing the eddy current loss value before adjustment based on the adjusted core stacking angle, and calculating the change amplitude, and at the same time calculating the average loss change rate based on the iron loss change amplitude before and after adjustment, and calculating the change ratio of the multi-layer iron loss in combination with the correction coefficient of the electromagnetic field distribution in the core area, and obtaining the iron loss change rate after summarization;

[0043] S503: calling the iron loss change rate, and based on the optimized core stacking angle and the transformer load state data, using the formula:

[0044] ;

[0045] Calculate and obtain the optimized energy efficiency loss value;

[0046] in, Represents the energy efficiency loss value after optimization, Represents the core eddy current loss value, Represents the load loss value, represents the transformer load efficiency, represents the loss change corresponding to the iron loss change rate, Represents the iron loss value before adjustment.

[0047] A transformer structure optimization system based on energy efficiency simulation analysis, the transformer structure optimization system based on energy efficiency simulation analysis is used to execute the above-mentioned transformer structure optimization method based on energy efficiency simulation analysis, the system comprises:

[0048] The magnetic flux distribution monitoring module obtains the magnetic flux sensor data of multiple areas in the core stacking structure, monitors the magnetic flux density of multiple layers, calculates the change trend of the magnetic flux density, extracts the magnetic flux gradient characteristics, analyzes the magnetic flux change rate of multiple areas, screens the sudden increase area, and obtains the magnetic flux gradient distribution data;

[0049] The magnetic flux offset calculation module extracts the change rate of the sudden increase area based on the magnetic flux gradient distribution data, calls the guide groove etching parameters and the magnetic resistance control piece data, calculates the local magnetic flux offset angles of multiple areas, compares the offset angles of multiple areas according to the magnetic flux guidance threshold, selects the areas that need to be adjusted, and establishes the dynamic prediction results of magnetic flux offset;

[0050] The magnetic flux diffusion correction module, based on the dynamic prediction result of the magnetic flux offset, calls the magnetic flux guide groove etching depth, the magnetic flux guide groove etching angle and the magnetic flux guide groove diffusion range, calculates the change amount of the magnetic flux diffusion radius of multiple regions, screens the magnetic flux diffusion path offset region, calculates the magnetic flux diffusion radius adjustment amount of multiple regions, calculates the correction path of the magnetic flux diffusion according to the adjustment amount, extracts the multi-region etching depth adjustment value and the angle correction parameter, and generates the magnetic flux diffusion path correction parameter;

[0051] The core stacking optimization module obtains stacking direction data based on the flux diffusion path correction parameter, extracts the stacking offset in the high flux area, calculates the stacking adjustment angle, calls the core stacking direction adjustment strategy, and obtains the core stacking adjustment angle;

[0052] The energy efficiency loss assessment module is based on the core stacking adjustment angle, calls the electromagnetic loss calculation parameters and transformer load status data, calculates the core eddy current loss and iron loss change rate, and establishes the optimized energy efficiency loss value.

[0053] Compared with the prior art, the advantages and positive effects of the present invention are:

[0054] In the present invention, based on multi-region data collection of magnetic flux sensors, the gradient distribution of magnetic flux density is obtained, the rate of change of the sudden increase area is extracted, and the local magnetic flux offset angle is accurately calculated. According to the magnetic flux guidance threshold, the offset is screened to realize the dynamic prediction of local magnetic flux offset. Combined with the calculation of the magnetic flux diffusion radius, the etching depth and angle correction parameters are adjusted to optimize the magnetic flux diffusion path and reduce local magnetic flux accumulation. The core stacking direction is adjusted to make the stacking offset in the high magnetic flux area more targeted, improve the magnetic flux distribution, and reduce local losses. The electromagnetic loss change is calculated in combination with the load state, and the optimized energy efficiency loss value is accurately obtained. Measures such as magnetic flux path adjustment, diffusion radius optimization, and core stacking direction control make the magnetic flux distribution more balanced, reduce local magnetic flux concentration, improve energy efficiency, and improve the heat dissipation capacity of the core, while reducing manufacturing costs and improving structural adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0056] Figure 2 A flow chart of the steps for obtaining magnetic flux gradient distribution data of the present invention;

[0057] Figure 3 A flow chart of the steps for obtaining the dynamic prediction results of magnetic flux offset according to the present invention;

[0058] Figure 4 A flow chart of the steps for obtaining the correction parameters of the magnetic flux diffusion path of the present invention;

[0059] Figure 5 A flow chart of the steps for obtaining the core stacking adjustment angle of the present invention;

[0060] Figure 6 This is a flow chart of the steps for obtaining the energy efficiency loss value after optimization of the present invention. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0062] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0063] Embodiment 1

[0064] See also Figure 1 The present invention provides a technical solution: a transformer structure optimization method based on energy efficiency simulation analysis, comprising the following steps:

[0065] S1: Setting magnetic flux sensors in multiple areas of the core stacking structure, collecting the trend of magnetic flux density changes on multiple levels, extracting gradient features, and generating magnetic flux gradient distribution data;

[0066] S2: Based on the flux gradient distribution data, the change rate of the sudden increase area is extracted, the guide groove etching parameters and the magnetic resistance control piece data are called, the local flux offset angle is calculated, and the flux guidance threshold is compared with the flux offset angle to determine the adjustment needs, and the area is screened based on the offset to generate the dynamic prediction result of the flux offset;

[0067] S3: Based on the dynamic prediction result of magnetic flux offset, the existing etching depth, angle and diffusion range of the magnetic flux guide groove are called, the adjustment amount of the magnetic flux diffusion radius is calculated, the etching depth adjustment value and the angle correction parameter are determined, and the magnetic flux diffusion path correction parameter is generated;

[0068] S4: Based on the magnetic flux diffusion path correction parameter, the stacking direction data is obtained, the stacking offset of the high magnetic flux area is set, the core stacking direction is adjusted, and the core stacking adjustment angle is obtained;

[0069] S5: Based on the core stacking adjustment angle, the electromagnetic loss calculation parameters and transformer load status data are called to calculate the core eddy current loss and iron loss change rate, and generate the optimized energy efficiency loss value.

[0070] The flux gradient distribution data includes the flux density change trend, gradient characteristics, and change rate of the sudden increase area. The dynamic prediction results of flux offset include the flux offset angle, flux guidance threshold comparison results, and flux offset screening area. The flux diffusion path correction parameters include etching depth adjustment value, angle correction parameters, and flux diffusion radius adjustment amount. The core stacking adjustment angle includes stacking direction data, high flux area stacking offset, and core stacking direction adjustment data. The energy efficiency loss value after optimization includes core eddy current loss, electromagnetic loss calculation parameters, and iron loss change rate.

[0071] See also Figure 2 , the specific steps for obtaining the magnetic flux gradient distribution data are:

[0072] S101: Based on the arrangement of magnetic flux sensors in multiple regions of the core stacking structure, multi-layer magnetic flux density data is collected, the spatial variation trend of multi-layer magnetic flux density is calculated, and the magnetic flux gradient of multiple regions is calculated by the difference of magnetic flux density values ​​of adjacent regions to obtain the initial distribution of magnetic flux gradient;

[0073] It is necessary to clarify the installation position of the flux sensor. According to the symmetry of the core structure and the law of magnetic flux distribution, the flux sensor is arranged in key areas, such as the center area, corners and edge areas of the core. At least two sensors are arranged in each area to improve the measurement accuracy. In specific implementation, high-precision flux sensors, such as sensors with an accuracy of 0.1mT, can be selected, and evenly spaced arrangements can be adopted to ensure the spatial uniformity of the measurement data. When collecting multi-faceted magnetic flux density data, a fixed sampling frequency is set, such as 100Hz, to ensure the temporal integrity of the magnetic flux density data. Subsequently, the magnetic flux density value of each measuring point is recorded. For example, if the magnetic flux density of a certain area is measured to be 1.2mT and the magnetic flux density of another adjacent area is 1.0mT, the difference in the magnetic flux density values ​​of the adjacent areas is calculated. The specific calculation method is: , as mentioned above, mT, differential calculations are performed on all measurement points in turn to obtain the initial distribution of multi-region magnetic flux gradients and form preliminary magnetic flux density gradient field data. The results show that between multiple measurement points of the core stacking structure, the spatial variation trend of the magnetic flux density can be intuitively reflected by the initial distribution data of the magnetic flux gradient, which provides basic data support for the subsequent calculation of the flux gradient variation trend.

[0074] S102: Call the initial distribution of magnetic flux gradient, screen the magnetic flux gradient data, remove abnormal gradient values, calculate the average change rate of magnetic flux gradients in multiple regions, and calculate the change trend of magnetic flux gradients in multiple regions based on the mean and variance of the gradient distribution, using the formula:

[0075] ;

[0076] The flux gradient change trend value is calculated;

[0077] in, Represents the trend value of magnetic flux gradient change, Representative The magnetic flux density in the region, represents the mean value of magnetic flux density, represents the total number of magnetic flux density measurement points, Representative The magnetic flux density in a region;

[0078] formula:

[0079] ;

[0080] For example, setting mT, if a certain area If the value exceeds this range, it is judged as an abnormal gradient value and the data of the measurement point is eliminated. The filtered gradient data is used to calculate the average change rate of the magnetic flux gradient in multiple regions. The calculation method is , for example, to set the total number of measurement points The magnetic flux density at each measuring point is mT, the average rate of change is calculated as follows:

[0081] ;

[0082] ;

[0083] Then, the mean value of the magnetic flux density is calculated :

[0084] ;

[0085] Calculate the magnetic flux density variance:

[0086] ;

[0087] ;

[0088] Calculate the standard deviation:

[0089] ;

[0090] Finally, the flux gradient change trend value is calculated:

[0091] ;

[0092] The results show that the change trend value of the magnetic flux gradient is 0.5047, which represents the comprehensive change level of the magnetic flux gradient in multiple regions. The larger the value, the more drastic the spatial change of the magnetic flux gradient, and the smaller the value, the more uniform the distribution of the magnetic flux gradient. This numerical result is directly related to the target result of the previous step, indicating the overall change of the magnetic flux gradient. This result can be further used to judge the degree of change of the magnetic flux gradient in different regions and provide a basis for screening areas with significant changes.

[0093] S103: calling the flux gradient change trend value, calculating the spatial gradient characteristics of the flux gradient distribution according to the difference of the flux gradient distribution, screening the significant change area, and obtaining the flux gradient distribution data.

[0094] The specific method is to classify the magnetic flux gradient data and set the gradient range. For example, a gradient less than 0.2mT / cm is classified as a low gradient area, between 0.2-0.5mT / cm is classified as a medium gradient area, and greater than 0.5mT / cm is classified as a high gradient area. Assume that there are the following five areas of magnetic flux gradient change trend values:

[0095] Table 1 Classification table of magnetic flux gradient change trend values

[0096] area Magnetic flux gradient change trend value (mT / cm) A 0.15 B 0.25 C 0.45 D 0.55 E 0.70

[0097] As shown in Table 1, according to the classification standard, A belongs to the low gradient area, B and C belong to the medium gradient area, and D and E belong to the high gradient area. When screening the significant change area, the significant change threshold can be set to above 0.5 mT / cm. Therefore, D and E are selected as the significant change areas. After obtaining the magnetic flux gradient distribution data, the gradient change trend diagram is drawn in combination with the regional distribution, and finally the spatial characteristic data of the magnetic flux gradient distribution is obtained.

[0098] See also Figure 3 , the specific steps for obtaining the dynamic prediction results of magnetic flux offset are:

[0099] S201: based on the magnetic flux gradient distribution data, extract the change rate of the sudden increase area, identify the local characteristics of the magnetic flux change, and call the guide groove etching parameters and the magnetic resistance control piece data at the same time to calculate the local magnetic flux offset angle of multiple regions and establish a local magnetic flux offset angle data set;

[0100] First, the magnetic flux gradient in each region needs to be discretely sampled. Assume that in the region Selected in sampling points, and the flux gradient change value of each sampling point is recorded as , and then calculate the change rate of the sudden increase area, and use the finite difference method to calculate the gradient change rate of adjacent points, that is, , if a region Exceeding the set threshold , then the area is marked as a sudden increase area. On this basis, the guide groove etching parameters and magnetoresistive control sheet data are called, where the guide groove etching parameters mainly involve the groove depth and slot width The data of the magnetoresistance control piece includes its magnetic permeability change gradient , in order to calculate the local flux deviation angle, it is necessary to calculate the flux increment in each area With magnetic induction intensity Specifically, the flux deviation angle It can be expressed as:

[0101] ;

[0102] Assume that the flux increment in a certain area T, magnetic induction intensity T, then we can get:

[0103] ;

[0104] The results show that in this area, since the flux increment is small relative to the magnetic induction intensity, the flux deviation angle is relatively small, indicating that the flux change in this area is relatively stable. If the value is much larger than a certain set threshold, there may be an abnormal flux change trend in this area, which requires further screening before entering the next step of analysis.

[0105] S202: Based on the local magnetic flux offset angle data set, compare the magnetic flux guidance threshold, determine whether the magnetic flux offset of multiple regions exceeds the threshold range, and filter the regions where the offset exceeds the threshold to obtain the over-limit magnetic flux offset region;

[0106] Based on the above data set, the flux guidance threshold is compared To filter, set the threshold , to determine whether all calculated local flux offset angles exceed the threshold. Specifically, for all Perform traversal judgment, if , it is considered that there is an over-limit flux offset in the area, and it is marked as an over-limit area. The number of the over-limit area and its corresponding offset angle are further screened. For example, the flux offset angles calculated in a certain area are , then the out-of-limit areas obtained after screening are numbered as area 2, area 4 and area 5, and their offset angles are recorded respectively , and finally formed an over-limit flux offset area data set. The result shows that there is a significant flux offset phenomenon in the screened areas. These areas may be areas with strong magnetic field disturbances during system operation. It is necessary to further calculate their offset trend to determine whether there is a continuous growth trend in the flux offset, so as to judge whether the system parameters need to be optimized.

[0107] S203: Call the over-limit flux offset area data and calculate the offset trend change rate using the formula:

[0108] ;

[0109] Calculate and obtain the flux deviation trend parameters, and establish the dynamic prediction results of the flux deviation;

[0110] in, represents the flux deviation trend parameter, Representative The magnetic flux increment in the region, Representative The flux guide groove etching depth of the region, Representative The magnetic field intensity of the area, Representative The gradient of the magnetoresistance control plate in the region, Represents the total number of over-limit flux excursion regions.

[0111] formula:

[0112] ;

[0113] Assume that there are 3 over-limit areas, and their data are shown in the following table:

[0114] Table 2 Magnetic flux offset calculation parameters

[0115] Area No. Magnetic flux increment #timg#(T) Guide groove etching depth #timg#(mm) Magnetic induction intensity #timg#(T) Magnetoresistive control gradient#timg# 2 0.25 2.0 1.6 0.3 4 0.30 2.5 1.4 0.4 5 0.35 3.0 1.2 0.5

[0116] Calculate based on the above data:

[0117] ;

[0118] Calculate item by item:

[0119] ;

[0120] ;

[0121] ;

[0122] final:

[0123] ;

[0124] The result shows that the flux offset trend parameter is 1.298, which is a large value, indicating that the flux offset trend in multiple over-limit areas is more obvious, and under the combined effect of the guide groove etching depth and the magnetic resistance control gradient, the flux offset trend is at a high level. If the value exceeds the stability threshold set by the system (for example, the threshold is set to 1.2), it indicates that the flux offset may continue to grow in a short period of time. The system needs to optimize the flux guiding structure, adjust the etching depth or the distribution of the magnetic resistance control sheet to reduce the flux offset trend, thereby reducing the instability of the subsequent system. This result is directly related to the goal of this step, that is, to use the flux offset trend parameter to measure the future trend of the system's flux offset and provide a quantitative basis for the formulation of subsequent optimization measures.

[0125] See also Figure 4 , the specific steps for obtaining the flux diffusion path correction parameters are as follows:

[0126] S301: Based on the dynamic prediction result of magnetic flux offset, the etching depth, etching angle and magnetic flux diffusion range of the magnetic flux guide groove are called, the initial value of the magnetic flux diffusion radius is calculated, the variation range of the etching depth and the angle variation range are analyzed, the dynamic offset parameters in the magnetic flux diffusion path are extracted, and the variation trend of the magnetic flux diffusion radius is calculated to obtain the variation of the magnetic flux diffusion radius;

[0127] First, the etching depth, etching angle and flux diffusion range of the flux guide groove are called, and the initial value of the flux diffusion radius is calculated to determine the diffusion trend of the magnetic flux in the guide groove. Specifically, the calling of the etching depth requires the extraction of the initial thickness of the groove material, and the setting of etching layers of different depths in combination with the process parameters of the processing equipment. For example, for a high magnetic permeability alloy groove, the initial thickness can be set to 2.5mm, and the conventional etching depth range is between 0.2mm and 1.5mm. The calling of the etching angle needs to be set according to the shape of the groove. For example, the etching angle of a rectangular groove can be set to 0°, and the etching angle of a trapezoidal groove is usually between 5° and 20°. In order to calculate the initial value of the flux diffusion radius, it is necessary to combine the magnetic permeability and magnetic flux density of the material. Assuming that the magnetic permeability is 800H / m and the initial value of the magnetic flux density is 1.2T, the magnetic induction equation can be used. The initial value of the calculated flux diffusion radius is about 1.5mm. Then the variation range of the etching depth is analyzed. The incremental analysis method is used to calculate the influence of depth variation on flux diffusion in 0.1mm steps. For example, in the process of increasing the depth from 0.5mm to 1.0mm, the flux diffusion radius increases from 1.5mm to 2.3mm. The angle variation range is analyzed by angle step scanning. Assuming that the angle variation step is 2°, when the angle increases from 10° to 14°, the flux diffusion radius increment can be increased from 0.2mm to 0.35mm. The dynamic offset in the flux diffusion path For parameter extraction, judgment is made based on the directional change of the magnetic flux density, and the threshold is set to 0.05T. If the offset value exceeds the threshold, the diffusion path is recalculated and the change trend of the diffusion radius is recorded. The increment of the magnetic flux diffusion radius is calculated at different etching depths. For example, at an etching depth of 0.8mm, the change in the magnetic flux diffusion radius is calculated to be 0.6mm. This result shows that the increase in etching depth will directly affect the range of magnetic flux diffusion. The dynamic change parameters of the magnetic flux can be used to correct the etching strategy of the magnetic flux guide groove, thereby optimizing the magnetic flux diffusion path and making the final magnetic flux guiding effect more accurate.

[0128] S302: Based on the change in the magnetic flux diffusion radius, the etching depth adjustment value is calculated, and combined with the angle correction parameter, the formula is used:

[0129] ;

[0130] Calculate the correction amount of the magnetic flux diffusion path, screen the effective correction interval in the magnetic flux guide groove, adjust the optimization parameters of the magnetic flux diffusion path, summarize the correction range of the etching depth, and combine the etching angle correction parameters to extract the magnetic flux diffusion path control data and obtain the magnetic flux diffusion path correction parameters;

[0131] in, represents the flux diffusion path correction, represents the change in the magnetic flux diffusion radius, represents the flux diffusion influence factor, represents the etching angle, represents the base etching angle, represents the etching depth, represents the area of ​​magnetic flux diffusion region, Represents the total number of factors affecting flux diffusion.

[0132] formula:

[0133] ;

[0134] First, determine the parameters required for flux diffusion path correction, including the change in flux diffusion radius , Magnetic flux diffusion influence factor , Etching Angle , reference etching angle , Etching Depth And the area of ​​magnetic flux diffusion region The value of the flux diffusion influencing factor is set according to the material properties, etching process parameters and environmental factors. For example, for high permeability materials, the influencing factor It can be between 0.05 and 0.15, and for low permeability materials, it can be between 0.1 and 0.25. 10°, the reference etching angle When the angle is 8°, the angle correction parameter The calculated etching depth is 2°. Take 1.0mm, the area of ​​magnetic flux diffusion region According to the area formula, if the slot width is 3mm and the length is 10mm, then , put it into the formula:

[0135] ;

[0136] Assumptions , the impact factors are 0.08, 0.12, and 0.1 respectively, then the sum is , and finally calculate:

[0137] ;

[0138] Then, the flux diffusion path correction amount Calculate the etching depth adjustment value and make incremental adjustments based on the original etching depth. For example, if the original depth is 0.8 mm, the adjusted depth is At the same time, combined with the etching angle correction parameters, the correction range is set, the effective correction interval in the flux guide groove is screened through depth optimization, the flux diffusion path parameters are optimized, the flux diffusion path control data is extracted, and finally the flux diffusion path correction parameters are obtained. The results show that the corrected etching depth is optimized on the original basis, taking into account the dynamic characteristics of flux diffusion. By adjusting the etching depth and angle, the flux diffusion path is more in line with the target flux guide requirements, thereby effectively improving the flux control accuracy.

[0139] Table 3 Magnetic flux diffusion path correction parameter table

[0140] Etching depth(mm) Change in magnetic flux diffusion radius (mm) Angle correction parameter (°) Total impact factor Calculate the correction amount #timg#(mm) 0.8 0.6 2 0.3 0.278 1.0 0.8 3 0.4 0.320 1.2 1.0 4 0.5 0.365

[0141] As shown in Table 3, when the etching depth changes, the relationship between the adjustment amount of the magnetic flux diffusion radius and the angle correction parameter is quantitatively calculated, and finally the magnetic flux diffusion path correction parameter is summarized. The result shows that under different etching depth conditions, the correction parameter of the magnetic flux diffusion path shows a gradual increase trend. When the etching depth increases, the change of the magnetic flux diffusion radius increases accordingly, and the sum of the influencing factors also changes, resulting in the correction amount. Different degrees of adjustment are generated, and these data can be used to further optimize the design parameters of the flux guide slots to ensure the optimal control strategy for the flux diffusion path.

[0142] See also Figure 5 , the steps for obtaining the core stacking adjustment angle are as follows:

[0143] S401: based on the magnetic flux diffusion path correction parameter, obtaining stacking direction data, calculating the distribution characteristics of the stacking direction data, screening the stacking direction change area, extracting the stacking direction change interval, and obtaining the stacking direction change interval value;

[0144] Assuming that a core area is divided into 10 layers, the magnetic flux density data is as follows:

[0145] Table 4 Magnetic flux density monitoring data table

[0146] Number of layers#timg# Magnetic flux density #timg#(T) 1 1.2 2 1.15 3 1.18 4 1.1 5 1.05 6 1.0 7 0.98 8 0.95 9 0.92 10 0.9

[0147] As shown in Table 4, the magnetic flux density data of different layers vary to a certain extent. When calculating the distribution characteristics of the stacking direction data, it is necessary to first determine the area where the stacking direction changes, and by comparing the flux density change rate of adjacent layers, screen out the stacking direction change interval where the flux density changes significantly. For example, set the change rate threshold to 5% and calculate the flux density change rate between adjacent layers:

[0148] ;

[0149] :Rate of change = (not exceeding threshold);

[0150] :Rate of change = (not exceeding threshold);

[0151] :Rate of change = (surpassing the threshold, record this layer);

[0152] :Rate of change = (not exceeding threshold);

[0153] Thus, the stacking direction change interval is screened out, namely the third layer (1.18T→1.1T). The numerical range of this change interval can be further extracted, and its stacking direction change interval value can be defined as T, this result indicates that the stacking direction of this flux density variation range may need to be further adjusted to optimize the flux distribution.

[0154] S402: calling the stacking direction variation interval value, setting the stacking offset in the high magnetic flux area, calculating the correlation between the stacking offset and the stacking direction variation interval, and determining and obtaining the optimal stacking offset in the high magnetic flux area;

[0155] Assuming that the stack offset setting range is 0-0.5mm, the initial value of the stack offset of each layer is as follows:

[0156] ;

[0157] Set the calculation formula:

[0158] ;

[0159] Multiply the flux density by the stack offset to calculate the weighted sum of the offsets of different layers:

[0160] ;

[0161] After calculation, the optimal stacking offset The value is 0.18 mm. The result shows that in the high flux area, the appropriate stacking offset can optimize the flux distribution, make the flux density more uniform, and improve the rationality of the stacking direction.

[0162] S403: Call the optimal stacking offset in the high flux area and adjust the core stacking direction using the formula:

[0163] ;

[0164] Calculate and obtain the core stacking adjustment angle;

[0165] in, Represents the core stacking adjustment angle, Representative The stacking offset of the layer, Representative The magnetic flux density of the layer, Representative The local stacking variation coefficient of the layer, Representative The flux diffusion ratio of the layer, Represents the total number of stacked layers, Represents the inverse tangent function.

[0166] formula:

[0167] ;

[0168] Among them, the local stacking variation coefficient Set to 0.05, the flux diffusion ratio Set it to 0.02 and calculate the denominator:

[0169] ;

[0170] Calculate the adjustment angle:

[0171] ;

[0172] The results show that the final adjustment angle after adjusting the core stacking direction is 10.4°, which reflects the flux diffusion effect after the current stacking direction is optimized. and local stacking variation coefficient The angle value is the optimal adjustment value for the current magnetic flux distribution characteristics. The adjustment of this angle helps to reduce the unevenness of local magnetic flux density, make the magnetic flux distribution of the entire core more stable, and reduce magnetic flux loss.

[0173] See also Figure 6 , the specific steps for obtaining the optimized energy efficiency loss value are:

[0174] S501: Based on the core stacking adjustment angle, the electromagnetic loss parameter and the transformer load state data are called to calculate the eddy current loss value of the core. At the same time, the magnetic permeability, frequency, current density, and stacking thickness parameters of the core material are used to calculate the local eddy current loss value of the multi-stacked area to obtain the core eddy current loss value;

[0175] First, it is necessary to call the electromagnetic loss parameters and transformer load status data, where the electromagnetic loss parameters include the magnetic permeability (μ), frequency (f), current density (J), and stacking thickness (d) of the core material. These parameters can be obtained through experimental testing. For example, the magnetic permeability μ of the core material can be measured using the hysteresis loop method. The frequency f depends on the power supply frequency of the power grid (such as 50Hz or 60Hz). The current density J can be calculated by measuring the current flowing through the core and combining it with the cross-sectional area of ​​the core. The stacking thickness d is generally determined by the production process, such as 0.23mm, 0.27mm, 0.30mm and other common specifications. Then, for the core as a whole, calculate its eddy current loss value P_ec. The eddy current loss can be calculated according to the formula:

[0176] ;

[0177] Calculate, where B is the magnetic induction intensity of the core, k is the empirical coefficient, which depends on the characteristics of the core material. Assuming that the magnetic induction intensity of a core material is B=1.5T, the frequency is f=50Hz, the stacking thickness is d=0.27mm, and the empirical coefficient is k=0.002, the eddy current loss is calculated as follows:

[0178] ;

[0179] In addition, in the multi-stack area, due to the uneven distribution of the local magnetic field in the core, the eddy current loss calculation needs to be processed by region, considering the local electromagnetic field parameter correction coefficient , assuming its value is 1.2, the local eddy current loss can be expressed as:

[0180] ;

[0181] Finally, the core eddy current loss value is obtained by summing up the loss values ​​of multiple regions. For example, considering multiple local regions, assuming that the total loss value is 8.5W after calculation, this value will be used as the input parameter for the next calculation. This result shows that after optimizing the adjustment angle, the overall eddy current loss of the core has been calculated. This data will be used to further calculate the change of iron loss before and after adjustment to determine whether the overall loss of the transformer is reduced after optimization.

[0182] S502: calling the eddy current loss value of the core, comparing the eddy current loss value before adjustment based on the adjusted core stacking angle, and calculating the change amplitude, and at the same time calculating the average loss change rate based on the iron loss change amplitude before and after adjustment, and calculating the change ratio of the multi-layer iron loss in combination with the correction coefficient of the electromagnetic field distribution in the core area, and obtaining the iron loss change rate after summarization;

[0183] The specific operation is to calculate the iron loss value before adjustment and adjusted iron loss values The difference is used to obtain the change , the formula is as follows:

[0184] ;

[0185] Assume the iron loss value before adjustment , the iron loss value measured after adjusting the angle , then:

[0186] ;

[0187] Next, calculate the average loss change rate , which is defined as:

[0188] ;

[0189] That is, the loss is reduced by 8%. At this time, it is necessary to combine the electromagnetic field distribution correction factor in the core area , assuming it is 1.1, the multi-layer iron loss change ratio can be calculated as:

[0190] ;

[0191] That is, the final calculated iron loss change rate is -8.8%, which means that the iron loss is reduced by 8.8% compared with before the adjustment. This result shows that after the stacking angle is optimized, the iron loss of the core is significantly reduced. This data will be used to adjust the calculation formula in the subsequent calculation of the energy efficiency loss value after optimization, so that the final energy efficiency loss reflects the impact of the iron loss optimization.

[0192] S503: Call the iron loss change rate, and based on the optimized core stacking angle and the transformer load status data, use the formula:

[0193] ;

[0194] Calculate and obtain the optimized energy efficiency loss value;

[0195] in, Represents the energy efficiency loss value after optimization, Represents the core eddy current loss value, Represents the load loss value, represents the transformer load efficiency, represents the loss change corresponding to the iron loss change rate, Represents the iron loss value before adjustment.

[0196] formula:

[0197] ;

[0198] in: (The eddy current loss value of the core obtained by the above calculation), (assuming load loss value), (Transformer load efficiency), -

[0199] First calculate the energy efficiency loss value without loss correction:

[0200] ;

[0201] Then calculate the loss correction factor:

[0202] ;

[0203] The final calculated optimized energy efficiency loss value is:

[0204] ;

[0205] The results show that by optimizing the core stacking angle, the total energy efficiency loss of the transformer reached 146.08W, which is higher than that before optimization (135.26W). This means that although the optimized stacking angle reduces the iron loss, it may have little effect on other losses of the overall transformer or bring a certain degree of negative impact. Therefore, this result needs to be further analyzed in combination with other factors to determine whether it is necessary to adjust the optimization strategy, or to further improve the overall efficiency of the transformer based on the optimization of other parameters.

[0206] Table 5: Summary of calculation parameters

[0207] Parameter name symbol Numeric unit Calculation source Core eddy current loss value #timg# 8.5 W Calculated Load loss value #timg# 120 W Settings Transformer load efficiency #timg# 0.95 - Settings Iron loss value before adjustment #timg# 50 W Measurements Adjusted iron loss #timg# 46 W Measurements Iron loss change #timg# -4 W Calculated Iron loss change rate #timg# -0.08 - Calculated Corrected iron loss change rate #timg# -0.088 - Calculated Energy efficiency loss value after optimization #timg# 146.08 W Calculated

[0208] As shown in Table 5, the calculation results show that the impact of the adjusted iron loss optimization has been reflected in the final energy efficiency loss value. This result can be used to evaluate the overall loss level of the transformer and determine whether further optimization and adjustment parameters are needed.

[0209] A transformer structure optimization system based on energy efficiency simulation analysis, the transformer structure optimization system based on energy efficiency simulation analysis is used to execute the above-mentioned transformer structure optimization method based on energy efficiency simulation analysis, the system comprises:

[0210] The magnetic flux distribution monitoring module obtains the magnetic flux sensor data of multiple areas in the core stacking structure, monitors the magnetic flux density of multiple layers, calculates the change trend of the magnetic flux density, extracts the magnetic flux gradient characteristics, analyzes the magnetic flux change rate of multiple areas, screens the sudden increase area, and obtains the magnetic flux gradient distribution data;

[0211] The flux offset calculation module extracts the change rate of the sudden increase area based on the flux gradient distribution data, calls the guide groove etching parameters and the magnetic resistance control piece data, calculates the local flux offset angles of multiple areas, compares the offset angles of multiple areas according to the flux guidance threshold, selects the areas that need to be adjusted, and establishes the dynamic prediction results of the flux offset;

[0212] The magnetic flux diffusion correction module, based on the dynamic prediction result of magnetic flux offset, calls the etching depth, etching angle and diffusion range of the magnetic flux guide groove, calculates the change of magnetic flux diffusion radius in multiple regions, screens the offset region of magnetic flux diffusion path, calculates the adjustment amount of magnetic flux diffusion radius in multiple regions, calculates the correction path of magnetic flux diffusion according to the adjustment amount, extracts the adjustment value of etching depth and angle correction parameters in multiple regions, and generates magnetic flux diffusion path correction parameters;

[0213] The core stacking optimization module obtains stacking direction data based on the flux diffusion path correction parameters, extracts the stacking offset in the high flux area, calculates the stacking adjustment angle, calls the core stacking direction adjustment strategy, and obtains the core stacking adjustment angle;

[0214] The energy efficiency loss assessment module is based on the core stacking adjustment angle, calls the electromagnetic loss calculation parameters and transformer load status data, calculates the core eddy current loss and iron loss change rate, and establishes the optimized energy efficiency loss value.

[0215] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A transformer structure optimization method based on energy efficiency simulation analysis, characterized in that: The following steps are involved: S1: Setting magnetic flux sensors in multiple areas of the core stacking structure, collecting the trend of magnetic flux density changes on multiple levels, extracting gradient features, and generating magnetic flux gradient distribution data; S2: Based on the flux gradient distribution data, extract the change rate of the sudden increase area, call the guide groove etching parameters and the magnetic resistance control piece data, calculate the local flux offset angle, compare the flux guidance threshold according to the flux offset angle, judge the adjustment demand, screen the area based on the offset, and generate the dynamic prediction result of the flux offset; S3: Based on the dynamic prediction result of the magnetic flux offset, the existing etching depth, angle, and diffusion range of the magnetic flux guide groove are called, the adjustment amount of the magnetic flux diffusion radius is calculated, the etching depth adjustment value and the angle correction parameter are determined, and the magnetic flux diffusion path correction parameter is generated; S4: based on the magnetic flux diffusion path correction parameter, obtaining stacking direction data, setting the stacking offset of the high magnetic flux area, adjusting the core stacking direction, and obtaining the core stacking adjustment angle; S5: Based on the core stacking adjustment angle, electromagnetic loss calculation parameters and transformer load status data are called to calculate the core eddy current loss and iron loss change rate, and generate an optimized energy efficiency loss value.

2. The transformer structure optimization method based on energy efficiency simulation analysis according to claim 1 is characterized in that: The magnetic flux gradient distribution data includes the magnetic flux density change trend, gradient characteristics, and change rate of the sudden increase area. The dynamic prediction results of the magnetic flux offset include the magnetic flux offset angle, the magnetic flux guidance threshold comparison result, and the magnetic flux offset screening area. The magnetic flux diffusion path correction parameters include the etching depth adjustment value, the angle correction parameter, and the magnetic flux diffusion radius adjustment amount. The core stacking adjustment angle includes the stacking direction data, the high flux area stacking offset, and the core stacking direction adjustment data. The optimized energy efficiency loss value includes the core eddy current loss, the electromagnetic loss calculation parameter, and the iron loss change rate.

3. The transformer structure optimization method based on energy efficiency simulation analysis according to claim 2 is characterized in that: The steps for acquiring the magnetic flux gradient distribution data are specifically as follows: S101: Based on the arrangement of magnetic flux sensors in multiple regions of the core stacking structure, multi-layer magnetic flux density data is collected, the spatial variation trend of multi-layer magnetic flux density is calculated, and the magnetic flux gradient of multiple regions is calculated by the difference of magnetic flux density values ​​of adjacent regions to obtain the initial distribution of magnetic flux gradient; S102: calling the initial distribution of magnetic flux gradient, screening magnetic flux gradient data, removing abnormal gradient values, calculating the average rate of change of magnetic flux gradients in multiple regions, and calculating the trend of magnetic flux gradient changes in multiple regions based on the mean and variance of the gradient distribution, using the formula: ； The flux gradient change trend value is calculated; in, Represents the trend value of magnetic flux gradient change, Representative The magnetic flux density in the region, represents the mean value of magnetic flux density, represents the total number of magnetic flux density measurement points, Representative The magnetic flux density in a region; S103: calling the flux gradient change trend value, calculating the spatial gradient characteristics of the flux gradient distribution according to the difference of the flux gradient distribution, screening the significant change area, and obtaining the flux gradient distribution data.

4. The transformer structure optimization method based on energy efficiency simulation analysis according to claim 3 is characterized in that: The steps for obtaining the dynamic prediction result of magnetic flux offset are specifically as follows: S201: based on the magnetic flux gradient distribution data, extract the change rate of the sudden increase area, identify the local characteristics of the magnetic flux change, and call the guide groove etching parameters and the magnetic resistance control piece data at the same time to calculate the local magnetic flux offset angles of multiple regions, and establish a local magnetic flux offset angle data set; S202: Based on the local magnetic flux offset angle data set, comparing the magnetic flux guidance threshold, determining whether the magnetic flux offset of multiple regions exceeds the threshold range, and screening the regions where the offset exceeds the threshold to obtain the over-limit magnetic flux offset region; S203: Calling the over-limit magnetic flux deviation area data, calculating the deviation trend change rate, using the formula: ； Calculate and obtain the flux deviation trend parameters, and establish the dynamic prediction results of the flux deviation; in, represents the flux deviation trend parameter, Representative The magnetic flux increment in the region, Representative The flux guide groove etching depth of the region, Representative The magnetic field intensity of the area, Representative The gradient of the magnetoresistance control plate in the region, Represents the total number of over-limit flux excursion regions.

5. The transformer structure optimization method based on energy efficiency simulation analysis according to claim 4 is characterized in that: The steps for obtaining the magnetic flux diffusion path correction parameter are specifically as follows: S301: Based on the dynamic prediction result of the magnetic flux offset, the etching depth, etching angle and magnetic flux diffusion range of the magnetic flux guide groove are called, the initial value of the magnetic flux diffusion radius is calculated, the variation range of the etching depth and the angle variation range are analyzed, the dynamic offset parameters in the magnetic flux diffusion path are extracted, and the variation trend of the magnetic flux diffusion radius is calculated to obtain the variation of the magnetic flux diffusion radius; S302: Based on the change in the magnetic flux diffusion radius, the etching depth adjustment value is calculated, and combined with the angle correction parameter, the formula is used: ； Calculate the correction amount of the magnetic flux diffusion path, screen the effective correction interval in the magnetic flux guide groove, adjust the optimization parameters of the magnetic flux diffusion path, summarize the correction range of the etching depth, and combine the etching angle correction parameters to extract the magnetic flux diffusion path control data and obtain the magnetic flux diffusion path correction parameters; in, represents the flux diffusion path correction, represents the change in the magnetic flux diffusion radius, represents the flux diffusion influence factor, represents the etching angle, represents the base etching angle, represents the etching depth, represents the area of ​​magnetic flux diffusion region, Represents the total number of factors affecting flux diffusion.

6. The transformer structure optimization method based on energy efficiency simulation analysis according to claim 5 is characterized in that: The steps for obtaining the core stacking adjustment angle are specifically as follows: S401: Based on the magnetic flux diffusion path correction parameter, stacking direction data is obtained, distribution characteristics of the stacking direction data are calculated, stacking direction change regions are screened, stacking direction change intervals are extracted, and stacking direction change interval values ​​are obtained; S402: calling the stacking direction variation interval value, setting the stacking offset in the high magnetic flux region, calculating the correlation between the stacking offset and the stacking direction variation interval, and determining and obtaining the optimal stacking offset in the high magnetic flux region; S403: Calling the optimal stacking offset of the high magnetic flux area, adjusting the core stacking direction, using the formula: ； Calculate and obtain the core stacking adjustment angle; in, Represents the core stacking adjustment angle, Representative The stacking offset of the layer, Representative The magnetic flux density of the layer, Representative The local stacking variation coefficient of the layer, Representative The flux diffusion ratio of the layer, Represents the total number of stacked layers, Represents the inverse tangent function.

7. The transformer structure optimization method based on energy efficiency simulation analysis according to claim 6 is characterized in that: The steps for obtaining the optimized energy efficiency loss value are specifically as follows: S501: Based on the core stacking adjustment angle, the electromagnetic loss parameter and the transformer load state data are called to calculate the eddy current loss value of the core, and at the same time, the magnetic permeability, frequency, current density, and stacking thickness parameters of the core material are used to calculate the local eddy current loss value of the multi-stacked area to obtain the core eddy current loss value; S502: calling the core eddy current loss value, comparing the eddy current loss value before adjustment based on the adjusted core stacking angle, and calculating the change amplitude, and at the same time calculating the average loss change rate based on the iron loss change amplitude before and after adjustment, and calculating the change ratio of the multi-layer iron loss in combination with the correction coefficient of the electromagnetic field distribution in the core area, and obtaining the iron loss change rate after summarization; S503: calling the iron loss change rate, and based on the optimized core stacking angle and the transformer load state data, using the formula: ； Calculate and obtain the optimized energy efficiency loss value; in, Represents the energy efficiency loss value after optimization, Represents the core eddy current loss value, Represents the load loss value, represents the transformer load efficiency, represents the loss change corresponding to the iron loss change rate, Represents the iron loss value before adjustment.

8. A transformer structure optimization system based on energy efficiency simulation analysis, characterized in that: According to the transformer structure optimization method based on energy efficiency simulation analysis according to any one of claims 1 to 7, the system comprises: The magnetic flux distribution monitoring module obtains the magnetic flux sensor data of multiple areas in the core stacking structure, monitors the magnetic flux density of multiple layers, calculates the change trend of the magnetic flux density, extracts the magnetic flux gradient characteristics, analyzes the magnetic flux change rate of multiple areas, screens the sudden increase area, and obtains the magnetic flux gradient distribution data; The magnetic flux offset calculation module extracts the change rate of the sudden increase area based on the magnetic flux gradient distribution data, calls the guide groove etching parameters and the magnetic resistance control piece data, calculates the local magnetic flux offset angles of multiple areas, compares the offset angles of multiple areas according to the magnetic flux guidance threshold, selects the areas that need to be adjusted, and establishes the dynamic prediction results of magnetic flux offset; The magnetic flux diffusion correction module, based on the dynamic prediction result of the magnetic flux offset, calls the magnetic flux guide groove etching depth, the magnetic flux guide groove etching angle and the magnetic flux guide groove diffusion range, calculates the change amount of the magnetic flux diffusion radius of multiple regions, screens the magnetic flux diffusion path offset region, calculates the magnetic flux diffusion radius adjustment amount of multiple regions, calculates the correction path of the magnetic flux diffusion according to the adjustment amount, extracts the multi-region etching depth adjustment value and the angle correction parameter, and generates the magnetic flux diffusion path correction parameter; The core stacking optimization module obtains stacking direction data based on the flux diffusion path correction parameter, extracts the stacking offset in the high flux area, calculates the stacking adjustment angle, calls the core stacking direction adjustment strategy, and obtains the core stacking adjustment angle; The energy efficiency loss assessment module is based on the core stacking adjustment angle, calls the electromagnetic loss calculation parameters and transformer load status data, calculates the core eddy current loss and iron loss change rate, and establishes the optimized energy efficiency loss value.

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