A dry-type transformer temperature rise field simulation system and method

Abstract

This invention relates to the field of transformer simulation technology, and more particularly to a simulation system and method for the temperature rise field of a dry-type transformer. The method includes the following steps: collecting the geometric parameters of the core laminations, the spatial arrangement information of the windings, and the positioning relationships of the insulation components of the dry-type transformer to be simulated, forming structural design data; constructing three-dimensional geometric modeling data based on the structural design data; importing the three-dimensional geometric modeling data into a finite element electromagnetic simulation platform; setting the energizing conditions of the excitation winding; and applying rated current or voltage to obtain the local magnetic flux density distribution data at each core lamination position throughout the entire operating cycle. This invention, based on three-dimensional structural data, integrates three types of physical processes—electromagnetic field, thermal field, and fluid field—to construct a high-precision local heat source model and a dynamic temperature rise prediction model, achieving full-parameter modeling of geometry, materials, and boundaries from the entire structure of the core laminations, windings, and insulation components.

Description

Technical Field

[0001] This invention relates to the field of transformer simulation technology, and in particular to a simulation system and method for the temperature rise field of a dry-type transformer. Background Technology

[0002] Dry-type transformers are oil-free, with heat primarily generated by losses in the core and windings (copper losses and iron losses), which are dissipated to the surrounding environment through convection, radiation, and conduction. The temperature rise process can be categorized as follows: transient temperature rise: during startup or sudden load changes, the temperature rises exponentially over time; steady-state temperature rise: after reaching thermal equilibrium, the temperature rise tends to stabilize. However, during long-term operation, dry-type transformers are susceptible to factors such as localized overheating, difficulty in detecting winding hotspots, and poor ventilation due to their compact structure, leading to safety risks such as excessive temperature rise, insulation aging, and even breakdown. Therefore, accurate prediction and control of the internal temperature rise distribution of dry-type transformers is crucial for ensuring their operational reliability and lifespan.

[0003] Traditional temperature rise assessment methods are mostly based on experimental temperature measurements or empirically modified formulas, which cannot reflect the complex local heat distribution patterns in the windings, core, and insulation structure. Especially under conditions of non-uniform heat sources, local airflow blockage, or structural modifications, their accuracy and adaptability are significantly insufficient. At the same time, although some numerical simulation methods introduce electromagnetic-thermal coupling calculations, they often ignore the non-uniformity of magnetic flux density in local directions of the core, or fail to consider the real changes in heat convection and heat transfer boundaries, making it difficult to accurately reflect the dynamic temperature rise field evolution process of dry-type transformers under actual operating conditions. Summary of the Invention

[0004] Therefore, it is necessary for the present invention to provide a dry-type transformer temperature rise field simulation system and method to solve at least one of the above-mentioned technical problems.

[0005] To achieve the above objectives, a method for simulating the temperature rise field of a dry-type transformer includes the following steps:

[0006] Step S1: Collect the geometric parameters of the core laminations, the spatial arrangement information of the windings, and the positioning relationship of the insulation components of the dry-type transformer to be simulated to form structural design data;

[0007] Step S2: Construct three-dimensional geometric modeling data based on structural design data, import the three-dimensional geometric modeling data into the finite element electromagnetic simulation platform, set the energizing conditions of the excitation winding, apply rated current or voltage, and thus obtain the local magnetic flux density distribution data at each core lamination position during the entire operating cycle.

[0008] Step S3: Map the local magnetic flux density distribution data to spatial coordinates, and calculate the eddy current loss and hysteresis loss respectively by combining the directional parameters of the core material, and output the core loss distribution data containing each spatial unit.

[0009] Step S4: Use the core loss distribution data as the heat source input, establish an electromagnetic-thermal-fluid coupling model in the multiphysics simulation platform, set the functional relationship between the thermal conductivity parameters and temperature, and set the air velocity, temperature, and convective heat transfer coefficient under natural or forced air cooling as boundary conditions.

[0010] Step S5: Use the transient finite element solver to iteratively solve the electromagnetic-thermal-fluid coupling model, and obtain dynamic temperature rise field data containing the global temperature distribution at each time point through the solution.

[0011] This invention also provides a dry-type transformer temperature rise field simulation system for executing the above-described dry-type transformer temperature rise field simulation method, wherein the dry-type transformer temperature rise field simulation system includes:

[0012] The structural modeling preparation module is used to collect the geometric parameters of the core laminations, the spatial arrangement information of the windings, and the positioning relationship of the insulation components of the dry-type transformer to be simulated, so as to form structural design data.

[0013] The electromagnetic modeling and simulation module is used to construct three-dimensional geometric modeling data based on structural design data. It imports the three-dimensional geometric modeling data into the finite element electromagnetic simulation platform, sets the energizing conditions of the excitation winding, and applies rated current or voltage to obtain local magnetic flux density distribution data at each core lamination position throughout the entire operating cycle.

[0014] The loss calculation and mapping module is used to map local magnetic flux density distribution data to spatial coordinates, calculate eddy current loss and hysteresis loss respectively by combining the directional parameters of the core material, and output the core loss distribution data containing each spatial unit.

[0015] The multi-field coupling modeling module is used to take the core loss distribution data as the heat source input, establish an electromagnetic-thermal-fluid coupling model in the multi-physics simulation platform, set the functional relationship between thermal conductivity parameters and temperature, and set the air velocity, temperature, and convective heat transfer coefficient under natural or forced air cooling as boundary conditions.

[0016] The temperature rise field solution module is used to iteratively solve the electromagnetic-thermal-fluid coupling model using a transient finite element solver, and obtain dynamic temperature rise field data containing the global temperature distribution at each time point through the solution.

[0017] This invention, through the aforementioned transient simulation steps based on electromagnetic-thermal-fluid coupling modeling, can comprehensively and accurately reproduce the temperature rise evolution law of dry-type transformers during actual operation, thus providing a solid data foundation and highly consistent physical reference for thermal field analysis and structural optimization. Firstly, in the mesh generation stage, by setting a finer mesh for the core boundary, winding surface, and fluid-structure interface region, and by constraining and controlling the overall mesh skewness and aspect ratio, the geometric quality and stability of the solution element are ensured. A high-quality mesh directly affects the accuracy of capturing the thermal-fluid boundary layer and the ability to reproduce local turbulence characteristics in the fluid field, and is a key foundation for whether the entire coupled simulation can reflect the real physical process.

[0018] Secondly, properly configuring transient solution parameters helps to balance computational efficiency and solution accuracy. The simulation time and time step are determined based on the rate of change of the physical field and the mesh size. Especially when the airflow velocity is high or there are subtle heat source gradients in the structure, using a small step size can effectively capture sudden temperature rises. Setting residual limits for the energy and momentum equations ensures the convergence of the thermal-flow field solution at each time step, avoiding numerical drift from misleading the overall temperature rise trend. Furthermore, by reasonably initializing the temperature and velocity boundaries and setting initial conditions that physically conform to actual operating conditions, the simulation starting point has engineering realism, avoiding simulation results deviating from reality due to distorted assumptions.

[0019] Furthermore, when the heat source input is applied to the model, the iron losses in the core (including hysteresis losses and eddy current losses) and the copper losses in the windings are converted into spatially distributed heat sources according to different physical mechanisms, and accurately applied to their respective unit cells through mapping. This heat source loading method driven by electromagnetic field simulation data avoids the method of using equivalent heat power average distribution in previous simplified models, and can clearly reflect the spatial non-uniformity of losses. Especially when there are abnormal concentrations of loss density at winding edges, ends, and ventilation dead corners, it can more keenly reveal potential overheating risk points. In addition, by setting continuous boundary conditions for temperature and heat flow at the fluid-structure interaction interface, a real heat exchange path is established between heat conduction inside the solid and convection heat transfer outside the fluid, realizing dynamic feedback and linkage response between the thermal field and the flow field. For natural cooling or forced air cooling scenarios, buoyancy models or inlet and outlet velocity, pressure boundaries, and Reynolds number-related turbulence parameters are set respectively, so that the boundary control of the fluid field has multi-field operating condition adaptability. The introduction of the turbulence model is particularly crucial for analyzing heat exchange enhancement regions, such as high-speed turbulence areas within the duct, the return flow area at the winding ends, and the convection enhancement area in the winding air passage. This model can fully reflect phenomena such as uneven airflow and changes in heat transfer efficiency in actual equipment. Through high-fidelity settings of the aforementioned physical parameters, boundary controls, material properties, and initial states, the model can realistically present the temperature response process of various components of a dry-type transformer during long-term energized operation during iterative solutions. The output dynamic temperature rise field data not only includes the temperature distribution throughout the entire space but also retains the transient evolution characteristics during the temperature rise process. Compared to traditional steady-state thermal simulation methods, this method can achieve temperature evolution analysis at different stages before, during, and after a fault, especially demonstrating higher sensitivity and resolution in identifying temperature abrupt change points, heat accumulation areas, and abnormal heat transfer efficiency in the air passage. This high-resolution, full-process dynamic evolution simulation data provides a foundation for visualization, quantification, and traceability in subsequent temperature anomaly type identification, heat source attribution judgment, and structural heat dissipation channel optimization. In engineering practice, it can help designers predict thermal control risks during operation in the initial stage of structural design, carry out targeted structural optimization in advance, significantly reduce failure risks such as winding overheating, local core ablation, and inter-turn insulation breakdown, and provide basic model data support for intelligent equipment monitoring and fault early warning strategies, thereby realizing the leap from passive heat dissipation design to active thermal control optimization of dry-type transformers. Attached Figure Description

[0020] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0021] Figure 1 This is a flowchart illustrating the steps of a method for simulating the temperature rise field of a dry-type transformer according to the present invention.

[0022] Figure 2 for Figure 1 A detailed flowchart of step S1;

[0023] Figure 3 This is a time series line graph of magnetic flux density of a lamination unit in an embodiment of the present invention. Detailed Implementation

[0024] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0025] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0026] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0027] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a method for simulating the temperature rise field of a dry-type transformer, the method comprising the following steps:

[0028] Step S1: Collect the geometric parameters of the core laminations, the spatial arrangement information of the windings, and the positioning relationship of the insulation components of the dry-type transformer to be simulated to form structural design data;

[0029] Step S2: Construct three-dimensional geometric modeling data based on structural design data, import the three-dimensional geometric modeling data into the finite element electromagnetic simulation platform, set the energizing conditions of the excitation winding, apply rated current or voltage, and thus obtain the local magnetic flux density distribution data at each core lamination position during the entire operating cycle.

[0030] Step S3: Map the local magnetic flux density distribution data to spatial coordinates, and calculate the eddy current loss and hysteresis loss respectively by combining the directional parameters of the core material, and output the core loss distribution data containing each spatial unit.

[0031] Step S4: Use the core loss distribution data as the heat source input, establish an electromagnetic-thermal-fluid coupling model in the multiphysics simulation platform, set the functional relationship between the thermal conductivity parameters and temperature, and set the air velocity, temperature, and convective heat transfer coefficient under natural or forced air cooling as boundary conditions.

[0032] Step S5: Use the transient finite element solver to iteratively solve the electromagnetic-thermal-fluid coupling model, and obtain dynamic temperature rise field data containing the global temperature distribution at each time point through the solution.

[0033] Furthermore, the following steps are included after step S5:

[0034] The degree of local temperature abrupt change is quantified based on dynamic temperature rise field data to locate high temperature gradient regions;

[0035] In some embodiments, three-dimensional temperature distribution data at multiple time points are extracted from transient simulation results. A stage where the temperature rise tends to stabilize (e.g., after 30 minutes or 1 hour of operation) is selected as the analysis section, and the temperature field tensor corresponding to that moment is extracted. Then, the three-dimensional gradient operator (…) is used… Numerical differencing is performed on the entire temperature field to obtain the temperature change rate of each element in the X (radial), Y (tangential), and Z (axial) directions, generating a three-dimensional temperature gradient vector field. Next, the temperature gradient magnitude of each element is calculated. T|, by setting a gradient threshold (such as more than twice the average gradient), filters out regions with significant temperature abrupt changes and marks their spatial range as high-temperature gradient regions. The high-temperature gradient regions are output as a set of closed meshes according to spatial coordinates, along with information such as temperature, gradient vector, and the component (such as winding, core, insulation) of each mesh cell.

[0036] It should be noted that the judgment of gradient abrupt change should be based on the relative gradient (temperature change per unit length) rather than the absolute temperature difference; otherwise, misjudgment may occur in high-temperature but uniformly distributed areas.

[0037] The temperature gradient along the winding height direction within the high-temperature gradient region is calculated as the axial gradient, and the temperature gradient along the winding radius direction within the high-temperature gradient region is calculated as the radial gradient.

[0038] In some embodiments, the high-temperature gradient region is confined within the "winding" assembly to eliminate interference from the core and insulation structure; and a local cylindrical coordinate system is established with the winding center axis (Z-axis) as the reference. Then, in this coordinate system, the partial derivative of the temperature field in the z-direction ( As the axial gradient, the partial derivative in the r direction ( As the radial gradient, the axial and radial temperature gradient values ​​are discretized within the high-temperature gradient region using the central difference or higher-order finite difference method. For each unit or slice region, the axial and radial temperature gradient values ​​are calculated separately, and statistical averaging or weighted averaging is performed to form representative axial and radial temperature gradient indices.

[0039] It should be noted that the tangential direction ( The direction usually has little effect on temperature rise and can be ignored in this method; if the winding adopts a segmented structure, the gradient of each segment should be calculated separately and then averaged.

[0040] Calculate the ratio of the axial gradient to the radial gradient, denoted as the axial-radial gradient ratio;

[0041] In some embodiments, the average axial temperature gradient obtained in the above steps is used as a reference. and mean radial temperature gradient Substituting into the formula: Axial-Radial Gradient Ratio This ratio reflects whether the directionality of heat conduction inside the winding is balanced. The larger the value, the more the heat accumulation is mainly along the axial direction and is not easy to dissipate. The smaller the value, the more the heat accumulation is mainly in the radial direction and is not easy to diffuse.

[0042] Optionally, this ratio is also calculated for each grid cell to form an axial-radial gradient ratio field (tensor field) for spatial visualization of thermal diagnostics.

[0043] When the axial-radial gradient ratio is greater than or equal to 3, it is determined that the axial heat dissipation is obstructed due to the winding end heat dissipation obstruction or the axial ventilation channel blockage.

[0044] When the axial-radial gradient ratio is greater than 0.5 and less than 3, it is determined to be local overheating caused by uneven distribution of heat sources inside the winding.

[0045] When the axial-radial gradient ratio is less than or equal to 0.5, it is determined that the low heat dissipation efficiency of the inter-turn air passage is caused by insufficient radial convection.

[0046] In some embodiments, the heat dissipation status of the winding region can be quantitatively classified and diagnosed based on the numerical distribution of the axial-radial temperature gradient ratio. When the ratio is greater than or equal to 3, it indicates that the heat conduction in the axial direction is significantly hindered, preventing heat from diffusing smoothly along the winding height to the upper and lower ends. This often manifests as a significantly higher temperature in the central region of the winding compared to the ends. This characteristic is usually attributed to obstructed heat dissipation at the winding ends or blockage of the axial ventilation channels. Possible causes include poor end structure sealing, obstruction of ventilation channels by foreign objects, or excessively tight end insulation. When the axial-radial gradient ratio is between 0.5 and 3, heat has a certain degree of diffusion capability in both the axial and radial directions, without severe thermal hindrance in a single direction. However, due to internal electromagnetic losses or structural unevenness, localized heat accumulation may occur, forming isolated hot spots. This situation is usually identified as localized overheating caused by uneven distribution of heat sources within the winding. Potential causes include localized short-turn faults, concentrated current density, uneven winding stacking, or increased stray losses. If the axial-radial gradient ratio is less than or equal to 0.5, it indicates that heat is mainly conducted and diffused in the radial direction, which may lead to high-temperature accumulation areas at the outer diameter of the winding or between turns, reflecting insufficient radial convection capacity and low heat dissipation efficiency of the inter-turn air passages. This phenomenon is often caused by unreasonable ventilation structure design, insufficient cooling airflow, or blockage of ventilation channels after the winding is impregnated with varnish.

[0047] It should be noted that the above judgment criteria are based on the reliability of the simulated temperature rise field. In practical applications, cross-validation should be performed using actual thermometer measurement data or infrared thermograms to improve diagnostic accuracy.

[0048] Furthermore, step S1 includes the following steps:

[0049] Step S11: Measure the length, width, and thickness of the core laminations in the dry-type transformer to be simulated, and record the lamination material, the thickness of the insulation layer between the laminations, the core window size, and the core column spacing parameters to form the core geometric parameters;

[0050] In some embodiments, the geometric dimensional parameters of the core laminations in the dry-type transformer to be simulated are obtained through precision measurement methods, including the length, width, and thickness of the laminations. The measured dimensions should cover the possible geometric differences in different parts of the core (such as the core column and the yoke). Furthermore, the type and model of the core lamination material used must be identified and recorded, such as whether it is oriented silicon steel, amorphous alloy, etc., for subsequent extraction of the material's directional magnetic properties. For the insulating coating or dielectric layer between the core laminations, its average thickness should be confirmed using a microscope or thin-layer measurement tool and used as an input parameter for the insulation gap in loss calculation. Simultaneously, the overall window dimensions of the core, including the window height and width, are measured to determine the available space for winding arrangement.

[0051] It should be noted that for three-phase transformers, the center distance between each core column should also be recorded to accurately reflect the relative arrangement of the multiphase windings. All the above parameters will be compiled into a core geometric parameter dataset, which will serve as the basic input for subsequent 3D modeling and electromagnetic simulation.

[0052] Step S12: Collect the inner diameter, outer diameter, and height dimensions of the high-voltage and low-voltage windings of the dry-type transformer to be simulated; measure the axial position coordinates and radial spacing of the windings; record the number of winding turns, conductor cross-sectional area, and distance between windings to form the winding spatial arrangement parameters.

[0053] In some embodiments, it is necessary to comprehensively collect spatial arrangement information of the high-voltage and low-voltage windings in the dry-type transformer. First, the inner diameter, outer diameter, and height parameters of each winding are obtained through external shape measurement, and verified with drawings if necessary. Further, a three-dimensional coordinate measuring machine is used to determine the axial center position and radial position of the winding within the core window, clarifying the cladding relationship and spacing arrangement of the high-voltage and low-voltage windings in space. Simultaneously, the number of turns, current rating, and conductor cross-sectional area parameters of the windings are read, and the conductor cross-sectional shape (e.g., round or flat wire) is recorded; this data will be used for current density distribution calculation in electromagnetic simulation. In the winding arrangement, special attention should be paid to measuring the radial distance between windings (i.e., the insulation gap between windings) and the distance between the winding and the core to reflect the actual electrical insulation structure and heat exchange path. All parameters will be uniformly compiled into a winding spatial arrangement parameter dataset, providing a structural basis for the construction of electromagnetic, thermal, and fluid coupling models.

[0054] Step S13: Identify the material type and thickness parameters of the insulation between windings, the insulation between windings and ground, and the insulation between phases based on the geometric parameters of the iron core and the spatial arrangement parameters of the windings. Measure the spatial coordinates of each insulation component, record the relative positional relationship between the insulation component and the iron core and windings, and establish the spatial positioning data of the insulation component.

[0055] In some embodiments, the main insulating components in a dry-type transformer include insulation between high- and low-voltage windings (such as laminates and air gaps), insulation to ground (such as bottom pads and supports), and phase-to-phase insulation between multi-phase windings (such as slot plates or insulating partitions). Subsequently, the actual thickness parameters of each insulating component are obtained using standard thickness measuring tools (such as vernier calipers or precision rulers), and their material type and heat resistance rating are noted in conjunction with design data. Through physical positioning, comparison with structural drawings, or 3D measurement, the 3D spatial coordinates of each insulator relative to the core and windings are further determined, identifying its nesting, attachment, or suspension status within the overall structure. During the recording process, it is necessary to ensure that the relative positional accuracy of all insulating components is sufficient to support heat flow path analysis and thermal resistance modeling. Finally, the above material, thickness, and spatial relationships are organized into spatial positioning data for insulating components, providing input for setting heat conduction paths and boundary conditions in the electromagnetic-thermal-fluid coupling model.

[0056] It should be noted that although insulators have extremely low conductivity in electromagnetic simulations and hardly participate in the current path, they often form the dominant thermal resistance path in thermal simulations. Therefore, this step is crucial in the construction of the temperature rise field.

[0057] Step S14: By merging the geometric parameters of the core, the spatial arrangement parameters of the winding, and the spatial positioning data of the insulation components into structural design data through the physical contact relationship between the core, winding, and insulation components of the dry-type transformer to be simulated.

[0058] In some embodiments, the core geometric parameters, winding spatial arrangement parameters, and insulation component spatial positioning data obtained separately are integrated into a complete structural design dataset. First, the spatial positions of all components should be aligned and stitched together based on a three-dimensional coordinate system to ensure that the relative relationships between the core, windings, and insulation components are consistent with the actual structure. Next, the physical contact relationships between the various structures are analyzed, such as whether there is close contact between the windings and the core, or whether there is an insulating support in between. This information will be used to assign values ​​to contact thermal resistance and interface heat dissipation coefficient in subsequent modeling. The data integration process can be performed geometrically via a CAD software platform or completed using scripts in a simulation preprocessing tool. The final output structural design data should include a complete three-dimensional geometric model file (such as in STEP format) and a physical property table associated with each structural component, serving as a unified input interface for the joint solution of electromagnetic, thermal, and flow fields.

[0059] It should be noted that this structural design data is not only the basis for geometric modeling, but also serves as the core basis for parameter binding, material property mapping, and boundary setting in the coupled simulation system. Its accuracy will directly affect the physical reliability and engineering reference value of the simulation results.

[0060] Furthermore, step S2, which involves constructing 3D geometric modeling data based on the structural design data, includes:

[0061] Establish a right-handed coordinate system with the center of the iron core as the origin, the axial direction as the Z-axis, and the radial direction as the X / Y-axis.

[0062] Based on the core lamination dimensions in the structural design data, silicon steel sheets are stacked layer by layer in a stepped joint in a right-hand coordinate system to obtain the core lamination model.

[0063] Based on the inner / outer diameter dimensions of the winding in the structural design data, the winding is stretched into a cylinder, and the axial / radial air passage positions are marked to obtain the winding model;

[0064] Based on the insulation positioning coordinates in the structural design data, an insulation component is inserted between the core lamination model and the winding model, and the gap with the winding is verified to form an initial three-dimensional model.

[0065] Assign magnetic material properties, conductor material properties, and insulating material properties to the initial 3D model, and set the conductivity of the XY plane to zero, while inputting the conductivity of the Z direction according to the measured value to obtain the 3D geometric modeling data.

[0066] In some embodiments, a unified spatial coordinate system is established, with the geometric center point of the iron core as the origin. The Z-axis is set along the axial direction of the iron core column (i.e., the direction of the winding centerline), and the X-axis and Y-axis are defined as radial directions, forming a right-handed rectangular coordinate system. Under this coordinate system, the iron core is three-dimensionally modeled according to the iron core lamination dimensions and stacking method provided in the structural design data: based on the length, width, thickness of a single silicon steel sheet and the interlayer insulation thickness, the actual lamination structure of the iron core is simulated by a "stepped joint layer-by-layer stacking" method, and the overall iron core frame model is formed by splicing together according to the spatial relationship between the iron core column and the yoke. Subsequently, based on the inner diameter, outer diameter, and axial height parameters of the high-voltage winding and the low-voltage winding, a hollow cylindrical shell is generated by stretching in the iron core window area to represent the geometric volume of the winding; at the same time, based on the air duct data, the axial / radial positions of the air ducts or heat conduction plates are marked within the axial height range of the winding, and the air duct area is hollowed out or embedded in the geometric model according to the number and thickness of the air ducts, realizing the detailed modeling of the ventilation structure in the winding. Based on the positioning coordinates of the insulation components, insert them into the predetermined positions between the windings and the core, including inter-winding insulation, phase-to-phase insulation, and ground insulation, and precisely align their contact interfaces with the surrounding structures. For the assembly of the insulation components, the minimum gap value between them and the winding model should be verified in the 3D modeling platform to ensure that geometric conflicts do not affect subsequent mesh generation and solution stability. After the initial geometric arrangement of all components is completed, the entire structure is defined as the initial 3D model. Based on the initial 3D model, material properties are further assigned to each structure. Specifically, the core region is assigned ferromagnetic material properties with directional permeability and hysteresis loss coefficient; the winding region is assigned a high-conductivity conductor material (such as copper or aluminum); and the insulation part is assigned corresponding electrical insulation material properties with low thermal conductivity and zero conductivity. Simultaneously, to accurately reflect the interlayer conductivity characteristics of the core material, the conductivity of the core material in the XY plane should be set to 0 in the electromagnetic simulation, indicating no current path between the silicon steel sheets; while the conductivity in the Z direction should be input according to measured data or material parameters provided by the manufacturer to accurately assess the power frequency eddy current distribution.

[0067] It should be noted that although the iron core as a whole behaves as an electromagnetic functional body, its sheet-like structure and directional magnetic characteristics have a decisive influence on the magnetic flux distribution and loss. Therefore, the three-dimensional geometric model should not only accurately reflect the shape, but also refine the material's directional properties and layered structure to ensure the physical accuracy of the magnetic field solution in subsequent simulations.

[0068] Furthermore, in step S2, the three-dimensional geometric modeling data is imported into the finite element electromagnetic simulation platform, the energizing conditions of the excitation winding are set, and the rated current or voltage is applied, thereby obtaining the local magnetic flux density distribution data at each core lamination position throughout the entire operating cycle, including:

[0069] The 3D geometric modeling data is imported into the finite element electromagnetic simulation platform in a standard format, and the integrity of the geometry and the absence of overlap between components are verified to obtain a complete 3D model tree.

[0070] In the complete 3D model tree, assign the BH magnetization curve of silicon steel sheet to the iron core lamination, input the nonlinear parameter of permeability changing with magnetic field strength, set the resistivity and density properties of the iron core material, input the resistivity, density and cross-sectional area parameters of the conductors of the high voltage winding and low voltage winding in the complete 3D model tree, set the geometric path of the winding end connection line, define the energizing conditions of the excitation winding, apply the rated current or voltage, and collect the local magnetic flux density tensor field;

[0071] The local magnetic flux density tensor field is divided into multiple instantaneous magnetic flux density vector layers according to the simulation time step, and the spatial coordinate index is marked at each time point to obtain the spatiotemporal corresponding vector layer.

[0072] The spatiotemporal corresponding vector layer is sliced ​​in three axes, and the magnetic flux distribution characteristics on different cross sections inside each iron core lamination are extracted. The average magnetic flux density and gradient change rate of each lamination layer are calculated as local magnetic flux density distribution data.

[0073] In some embodiments, the completed 3D geometric modeling data is imported into the finite element electromagnetic simulation platform in a standard format (such as .step, .iges, or .sat geometric exchange files compatible with CAD / CAE systems). The platform's built-in geometric diagnostic tools are then used to verify the integrity of the model structure, including boundary closure checks, surface normal direction consistency checks, and verification of whether there are overlaps, intersections, or undefined connections between components. After verification, a complete 3D model tree structure is automatically generated, in which the core, windings, and insulation components are assigned as different physical region nodes, facilitating subsequent attribute assignment and boundary condition setting. Next, material properties are assigned to each lamination of the core in the model tree. To accurately reflect the magnetic saturation and hysteresis behavior of the silicon steel sheet, the BH magnetization curve of the material should be input, i.e., the nonlinear relationship between magnetic flux density B and magnetic field strength H, which can be input in the form of a vector table or function fitting. In addition, to consider power frequency eddy current losses, the resistivity of the core material needs to be set, and its density should be input for subsequent thermo-electric coupling simulation. In the high-voltage and low-voltage winding regions, the resistivity and density of the conductor materials need to be allocated, and the conductor cross-sectional area needs to be specified according to the geometry of the winding cross-section so that the simulation system can automatically calculate the current density distribution. Simultaneously, the electrical connection paths at both ends of the winding should be clearly defined, and the geometric directions of their input and output lines should be defined to ensure that the current flows accurately through the entire winding body under energized conditions. Regarding boundary condition settings, the "excitation winding" energizing condition is specified for the simulation system, i.e., applying a sinusoidal AC current or voltage source of rated amplitude to the high-voltage or low-voltage winding. The simulation frequency is typically set to the power frequency (50 Hz or 60 Hz) to simulate the equipment's operating state under rated conditions. During the simulation, the system will automatically calculate the magnetic flux density tensor field throughout the entire structure and record the three-dimensional vector distribution of the magnetic induction intensity B that varies with time in all structural units. The magnetic flux density tensor field is then divided according to the set simulation time step (e.g., 1 ms per cycle divided into 100 steps) to generate multiple instantaneous vector layers. Each layer is labeled with a corresponding timestamp and a 3D spatial index, that is, the position coordinates of each unit within the model volume and its corresponding magnetic flux density vector information at that time, thus forming a complete spatiotemporal magnetic field map. Subsequently, the magnetic flux density layer data at each time point is spatially sliced ​​in the X, Y, and Z directions to extract magnetic flux distribution images on different axial, radial, and circumferential sections. Within each sliced ​​region, the magnitude and vector direction of the magnetic flux density B are further extracted. The average magnetic flux density is calculated according to the lamination layers of the core, and the gradient change rate is estimated using the magnetic flux difference between adjacent lamination layers. This constructs the local magnetic flux density distribution data of each lamination layer throughout the entire operating cycle, providing an accurate basis for subsequent loss calculations and heat source assignment.

[0074] It should be noted that, in order to ensure that the spatial resolution of the magnetic flux density data matches the laminate structure, a finite element meshing strategy with a thickness comparable to that of the iron core laminate must be used, and appropriate local mesh refinement regions must be enabled. Otherwise, the magnetic flux information in high gradient regions may be distorted, thereby affecting the accuracy of local eddy current loss and hysteresis loss assessment.

[0075] Furthermore, defining the energizing conditions of the excitation winding and applying rated current or voltage includes: for voltage excitation, applying rated voltage amplitude and frequency parameters to the end of the high-voltage winding and setting the end of the low-voltage winding as a ground boundary; for current excitation, applying rated current density distribution in the high-voltage winding and automatically calculating the induced current distribution in the low-voltage winding through the ampere-turns relationship.

[0076] In some embodiments, depending on the simulation analysis objective, either voltage excitation or current excitation can be selected to simulate the electromagnetic behavior of a dry-type transformer under power frequency operation. Voltage excitation is typically suitable for studying the magnetic flux distribution and no-load loss of the core under normal operating voltage.

[0077] In this approach, a voltage excitation boundary is set at one end of the high-voltage winding, with the amplitude (e.g., 10kV) and frequency (e.g., 50Hz) of the rated voltage input. The other end is set to float freely or have a zero potential to ground. Simultaneously, both ends of the low-voltage winding are defined as ground boundaries or reference potentials to stabilize the electric field distribution. The simulation platform automatically solves the time-series response of the induced voltage in the low-voltage winding based on the winding electrical parameters and mutual inductance coupling characteristics, thereby reproducing the coupled magnetic field between the winding and the core of the actual transformer under no-load operation.

[0078] For current-excitation methods, this is more commonly used to evaluate iron losses, temperature rise, or short-circuit characteristics during load operation. In this method, an equivalent current density distribution needs to be applied within the physical volume of the high-voltage winding. The current density per unit cross-section can be obtained by giving the total current amplitude and the number of turns in the winding (e.g., applying a rated total current of 100A to a winding with a cross-sectional area of ​​50mm² and 500 turns corresponds to a current density of 2A / mm²). The simulation platform uniformly distributes this current density within the conductor volume. Simultaneously, based on the conservation relationship of excitation ampere-turns (i.e., the principle of magnetomotive force balance), the system automatically solves for the equivalent current density distribution induced by the change in main magnetic flux in the low-voltage winding. Even if there is no external current flowing through the low-voltage winding, the system can accurately simulate the dynamic response of its induced current and its reaction effect on the magnetic field distribution.

[0079] For example, when simulating a 10kV / 400V dry-type transformer, in voltage excitation mode, a 10kV, 50Hz sinusoidal voltage source can be applied to the high-voltage winding while the low-voltage winding is grounded. The system will then solve for the change in the main magnetic flux in the core and the voltage amplitude and phase relationship induced on the low-voltage side. If current excitation is used instead, a 100A AC current density can be input to the high-voltage winding. Using the 500-turn high-voltage winding information defined in the system, the simulation platform will automatically calculate the induced current on the low-voltage side according to the principle of magnetic flux consistency, thus closing the magnetomotive force loop in the system and completing the steady-state excitation solution.

[0080] It should be noted that in current-excitation mode, the simulation system must automatically match the induced current, which places high demands on the stability of the solver and the accuracy of the mesh. Therefore, when simulating multi-turn windings, it is recommended to use a multi-region coupled vector boundary setting to ensure accurate ampere-turn control and avoid distortion of high-frequency eddy current distribution. Furthermore, voltage excitation is suitable for iron loss and magnetic saturation studies, while current excitation is more suitable for thermal simulation and load response analysis. The actual selection should be matched according to the simulation objectives.

[0081] Furthermore, step S3 includes the following steps:

[0082] Step S31: Extract the magnetic flux density time series of each core lamination unit from the local magnetic flux density distribution data, calculate the peak value, effective value and frequency of change of the magnetic flux density, and identify the magnetic flux density components along the rolling direction and perpendicular to the rolling direction according to the rolling direction of the core material.

[0083] In some embodiments, the magnetic flux density vector value of each core lamination unit over time is extracted from the local magnetic flux density distribution data, forming a magnetic flux density time series for that unit within one power frequency operating cycle. This series typically records values ​​every millisecond or every angular step (e.g., every 5°), forming a complete time waveform curve. The peak value (i.e., maximum amplitude), effective value (root mean square RMS value), and dominant frequency component of the magnetic flux density can be calculated from this curve to determine whether there are high-frequency disturbances in the magnetic flux variation. Furthermore, combined with the silicon steel sheet rolling direction information recorded in the structural design, the three-dimensional magnetic flux density vector can be decomposed into two components, "along the rolling direction (RD)" and "perpendicular to the rolling direction (TD)," through vector projection. These components need to be processed separately for subsequent loss analysis.

[0084] Step S32: Calculate the hysteresis loss density of the core lamination unit in one operating cycle based on the BH magnetization curve of the silicon steel sheet using the magnetic flux density component, where the hysteresis loss density is equal to the product of the area of ​​the hysteresis loop and the operating frequency.

[0085] In some embodiments, based on the magnetic flux density time series in the two directions mentioned above, the corresponding BH magnetization curve of silicon steel sheet is called from the core material database to calculate the hysteresis loop within one cycle for each lamination unit. The hysteresis loss density is the area enclosed by the loop multiplied by the power supply frequency (e.g., 50 Hz). For example, if the area of ​​the hysteresis loop formed by a unit in the rolling direction is 0.6 J / m³, then the hysteresis loss density in that direction is 30 W / m³; if the area of ​​the loop perpendicular to the rolling direction is 0.3 J / m³, then it is 15 W / m³. The sum of the two is the total hysteresis loss density.

[0086] Step S33: Calculate the eddy current loss density along the rolling direction and perpendicular to the rolling direction based on the rate of change of magnetic flux density in the magnetic flux density component and the thickness and resistivity parameters of the core lamination, and then vector synthesize the eddy current loss density components in the two directions as the eddy current loss density.

[0087] In some embodiments, to calculate the eddy current loss density, it is necessary to first calculate the time derivative (i.e., rate of change) of the magnetic flux density component, and then substitute it into the classical eddy current loss formula. For a single core lamination unit, its eddy current loss density along the rolling direction (RD) and perpendicular direction (TD) can be calculated using the following formulas:

[0088]

[0089] in For the thickness of the laminate, For frequency, Peak magnetic flux density, Let be the material resistivity. The above calculations are applied to the RD and TD directions respectively to obtain the eddy current loss density in both directions. Then, the total eddy current loss density of the element is obtained through vector synthesis or energy superposition methods.

[0090] Step S34: Superimpose the hysteresis loss density and eddy current loss density of each core lamination unit to obtain the total core loss density of the unit, and multiply it by the volume of the corresponding unit to calculate the core loss power value of each spatial unit.

[0091] In some embodiments, the hysteresis loss density and eddy current loss density of each core lamination unit are added together to obtain the total loss density of the unit (in W / m³). This total loss density is then multiplied by the volume of the unit (usually a three-dimensional product of the unit mesh size) to obtain the specific core loss power value (in W). For example, for a finite element unit with a volume of 1e-6 m³, if its loss density is 5000 W / m³, then the core loss power of that unit is 5 mW.

[0092] Step S35: Arrange the core loss power values ​​of all core lamination units according to the spatial coordinate index to form a loss power distribution matrix in three-dimensional space, and output the core loss distribution data containing the coordinates of each spatial unit and the corresponding loss power value.

[0093] In some embodiments, the power loss values ​​of all core lamination units are indexed and arranged according to their corresponding spatial coordinates in the model, forming a three-dimensional power distribution matrix. Each data record includes the spatial coordinates of the unit in the model (such as the x, y, z positions of the center point) and the power loss value corresponding to that point. This dataset constitutes the core loss distribution data, which can be exported to formats such as CSV and VTK for subsequent thermal simulation input, visualization processing, or high-loss region analysis.

[0094] It should be noted that during the hysteresis loop calculation, the resolution of the BH data points must match the sampling rate of the time series; otherwise, inaccurate loop closure will result in hysteresis loss errors. Furthermore, due to the orientation of actual iron core laminations, the BH curves along the rolling direction and the perpendicular direction are usually inconsistent and must be modeled and processed separately; an equivalent mean cannot be simply used as a substitute.

[0095] refer to Figure 3The time-series line graph of the magnetic flux density of the lamination core unit is shown, which is a typical schematic diagram of the time-domain magnetic flux density response. This graph illustrates the variation trend of a specific lamination core unit along the rolling direction (B_RD), perpendicular to the rolling direction (B_TD), and the amplitude of the total magnetic flux density (|B|) within a complete operating cycle. This data is key analytical data extracted from the local magnetic flux density tensor field. In step S31, by performing a time-series expansion of the magnetic flux density tensor of this unit, component curves along the rolling direction and perpendicular to the rolling direction are obtained (the blue and pink lines in the graph). Based on this, the peak value (e.g., B_RD is approximately 1.1T, B_TD is approximately 0.9T), effective value (by integrating the root mean square), and dominant frequency (corresponding to a period of 20ms, i.e., 50Hz) of each component can be further calculated, laying the foundation for subsequent magnetic loss calculations. By fitting and integrating the magnetic flux density components in the B_RD and B_TD directions of the core material with the corresponding BH magnetization curves in the figure, the hysteresis loop area per unit volume is obtained. Multiplying this area by the periodic frequency yields the hysteresis loss density. Since the magnetic flux density differs significantly between the two directions (B_RD is consistently higher than B_TD in the figure), the hysteresis loss will exhibit significant anisotropy in actual calculations and should be calculated separately and then summed. By taking the slope of the derivative of the magnetic flux density with time in the figure, dB / dt in different directions can be calculated. Combining this with the thickness and resistivity of the core laminations, the eddy current loss density per unit volume is calculated in both the B_RD and B_TD directions according to the classical eddy current loss model. Finally, the eddy current losses in the two directions are vector-superimposed to obtain the total eddy current loss density. Subsequently, the hysteresis loss density and eddy current loss density are spatially superimposed and multiplied by the volume of the lamination unit to obtain the total core loss power corresponding to the point shown in the figure. This process not only reflects the frequency response characteristics of material loss, but also captures the loss gradient caused by the uneven distribution of local magnetic flux in the iron core laminations.

[0096] Ultimately, it will be similar Figure 3 The time-series data is extended to all units within the entire core lamination space, forming a three-dimensional loss power matrix through coordinate mapping. This provides accurate input data for the subsequent construction of the heat source distribution field. This matrix not only records the coordinate position and corresponding loss value of each unit, but also outputs dynamic power distribution information by combining the time step evolution trend.

[0097] It should be noted that, Figure 3 The total magnetic flux density amplitude |B| curve (green) is not a simple algebraic sum, but rather the magnitude of the magnetic flux density vector in three-dimensional space. It reflects the actual physical magnitude of the magnetic flux density after being superimposed in two orthogonal directions. It should be used with caution as a reference value for the final composite loss and should not be used directly for hysteresis or eddy current loss calculations. Instead, it should be processed using B_RD and B_TD direction data respectively.

[0098] Furthermore, step S4 includes the following steps:

[0099] Step S41: Import three-dimensional geometric modeling data into the multiphysics simulation platform and establish a heat transfer calculation domain. Specifically, the heat transfer calculation domain is established by setting the iron core, winding, and insulation components as solid heat transfer domains, setting the air around the transformer as a fluid heat transfer domain, and setting fluid-structure coupling boundary conditions at the interface between the solid domain and the fluid domain.

[0100] In some embodiments, the three-dimensional geometric modeling data constructed in step S2 is imported into a multiphysics simulation platform (such as COMSOL, ANSYS Fluent / CFX, etc.), and a heat transfer calculation domain is constructed based on this. Specifically, the core laminations, windings (high voltage / low voltage), and insulation components in the model are respectively set as solid domains. The heat conduction calculation of these regions needs to consider the anisotropy of the materials (e.g., the directional thermal conductivity of silicon steel sheets). At the same time, based on the air space or duct area inside the dry-type transformer casing, a fluid domain is formed to simulate the process of cooling gas (usually air) flowing and carrying away heat on the winding and core surfaces. The contact surface between the solid domain and the fluid domain (e.g., between the outer wall of the winding and the air) is defined as a conjugate heat transfer interface, where the system automatically handles the continuity conditions of temperature and heat flux to ensure that heat transfer actually occurs.

[0101] Step S42: The core loss distribution data is loaded as a volume heat source into the core lamination unit of the heat transfer calculation domain, and the copper loss of the winding is converted into a volume heat source according to the current density distribution and loaded into the corresponding winding unit to form a three-dimensional spatial heat source distribution field.

[0102] In some embodiments, the core loss distribution data calculated in the previous stage is applied as a volumetric heat source to all core lamination units in the heat transfer calculation domain. That is, each core unit applies localized heat in the form of unit volumetric heat power. Simultaneously, the resistivity of each winding conductor and the density of the flowing current in the structural design must be considered, according to the following formula:

[0103]

[0104] The copper loss (ohmic loss) of each unit in the winding is converted into a thermal power value and applied to the corresponding winding unit in the form of a volume heat source. Finally, a complete three-dimensional heat source distribution field that varies with spatial location is established in the model, including the iron loss in the core region and the copper loss in the winding region, as the driving source for solving heat conduction and heat transfer.

[0105] Step S43: Set the fluid domain boundary conditions in the electromagnetic-thermal-fluid coupling model. For natural air cooling, set the buoyancy parameters and gravity direction. For forced air cooling, set the air velocity and temperature at the air inlet and the pressure boundary at the air outlet. Calculate and set the turbulence model parameters based on the Reynolds number.

[0106] In some embodiments, boundary conditions for the fluid domain are set in the coupled model, distinguishing between two typical operating conditions: natural air cooling and forced air cooling. In natural air cooling mode, the buoyancy force model of the simulation platform needs to be enabled, and the direction of gravity and scalar gravitational acceleration need to be set to simulate the natural convection process of hot air rising from the bottom due to density differences; the initial air temperature can be set to ambient temperature (e.g., 25°C). In forced air cooling mode, the inlet velocity vector (e.g., 2 m / s), inlet temperature (e.g., 20°C), and static pressure boundary of the outlet (usually set to 0 Pa or atmospheric pressure) need to be specified on the inlet surface of the model to ensure that the airflow can continuously pass through the transformer interior. For forced cooling scenarios with high flow velocities, the Reynolds number (Re) of the system should also be calculated to determine whether the flow is in a turbulent state, and accordingly, an appropriate turbulence model (e.g., k-1000m / s) should be selected in the simulation platform. (or SST model) to improve heat exchange accuracy.

[0107] Step S44: Set convective heat transfer boundary conditions at the coupling interface between the solid domain and the fluid domain, and establish an electromagnetic-thermal-fluid coupling model that includes heat conduction, convective heat transfer, and radiative heat transfer through the three-dimensional spatial heat source distribution field and turbulence model parameters.

[0108] In some embodiments, convective heat transfer boundary conditions are set at the interface between the fluid and solid domains. The simulation platform will automatically couple and calculate the boundary heat flux from the fluid heat transfer equation and the solid heat conduction equation to satisfy heat transfer equilibrium. To further improve the accuracy of the thermally coupled model, a radiation heat transfer module (Radiation to Ambient / Surface-to-Surface) can also be enabled at the interface between high-temperature components (such as the outer surface of windings) and the fluid, by inputting the emissivity parameters of the material surface (such as copper surface). =0.1, epoxy resin surface =0.9), to cover the potentially significant radiative heat loss during natural cooling. Taking into account the heat source, conduction, convection, and radiation effects, a complete electromagnetic-thermal-fluid three-field coupled model is ultimately formed for the dynamic solution of the subsequent temperature rise process.

[0109] It should be noted that when setting convection boundary conditions, if the duct geometry has complex turns or dead zones, local recirculation or low-speed zones may be formed. It is recommended to refine the local mesh of the fluid domain and enable steady-state initial field superposition transient iteration (such as SIMPLEC+Transient) in the solver. Otherwise, it may lead to difficulties in initial convergence or distortion of temperature rise results.

[0110] Furthermore, step S5 includes:

[0111] The electromagnetic-thermal-fluid coupling model was meshed, and the mesh was refined at the core lamination boundary, winding surface and fluid-structure interaction interface to obtain the finite element mesh model, wherein the mesh skewness is less than 0.8 and the aspect ratio is less than 5.

[0112] In some embodiments, after completing geometric modeling and physics configuration, the entire three-dimensional coupled model is meshed using finite element methods. To ensure solution accuracy and thermal boundary layer capture capability, locally refined meshes (local minimum element size can be controlled within 1–2 mm) are applied to the core lamination boundaries, winding outer surfaces, winding axial ventilation channels, and fluid-structure interaction interfaces where solids and fluids meet. The remaining areas use relatively coarse volumetric meshes to balance computational resources. After meshing, the overall mesh quality is automatically evaluated to ensure that the generated mesh skewness is less than 0.8, meaning that the shape of each element is not severely tilted; simultaneously, the mesh aspect ratio is controlled to be less than 5 to prevent a decrease in solver stability due to overstretched elements. Finally, a finite element mesh model suitable for multiphysics transient solutions is obtained.

[0113] Transient solution parameters, including total simulation time, time step, and convergence criterion, are set based on the finite element mesh model. The time step is determined according to the minimum mesh size and the maximum flow velocity, and the convergence criterion is set to have an energy residual less than 1 / 3. The momentum residual is less than ;

[0114] In some embodiments, the core parameters for transient solution need to be configured on the finite element mesh model. First, set the total simulation time (e.g., 600 seconds) and the time step (e.g., 0.5 seconds), where the time step should satisfy the CFL (Courant–Friedrichs–Lewy) stability criterion, referring to the formula: ;in The minimum side length of the grid cell in the model. This represents the maximum possible velocity in the fluid domain (which can be initially estimated or determined by the forced wind speed input). Furthermore, to improve simulation accuracy and convergence efficiency, a convergence criterion is set at each time step, i.e., the residual limit for each physical field equation. Recommended setting: energy residual (thermal field) less than... The momentum residual (flow field) is less than This ensures that the temperature field and flow velocity field achieve a high-precision balance at each time step.

[0115] Initial conditions are set on the nodes of the finite element mesh model. The initial temperature of the solid domain mesh nodes is set to the ambient temperature, and the initial flow velocity of all fluid domain mesh nodes is set to zero. The initial heat source distribution of each element is set according to the mapping relationship between the mesh element and the heat source region. Temperature and heat flow continuity constraints are established on the mesh nodes of the fluid-structure interaction interface, thereby obtaining an iteratively solvable multiphysics coupling initial state model.

[0116] In some embodiments, initial conditions for transient simulation are set at the mesh node level. The temperature of all nodes in the solid domain (core, winding, insulator, etc.) is uniformly initialized to the ambient temperature (e.g., 25°C) to simulate the initial cooling state. The velocity of all nodes in the fluid domain is set to zero, indicating no airflow at the beginning. Furthermore, combined with the three-dimensional heat source distribution field defined in step S4, the local loss power corresponding to each mesh element is used as the volume heat source input through mapping relationships, forming initial heat source conditions with consistent spatial distribution and zero excitation at time zero. To ensure correct heat transfer between the solid-fluid interface, conjugate boundary conditions for temperature and heat flux are set at the mesh nodes on these coupling boundaries. This automatically balances the conduction and convection heat transfer terms on both sides of the boundary during the solution process, achieving seamless coupling. Thus, an initial state model of the electromagnetic-thermal-fluid multiphysics field is formed, which can be iteratively solved by the transient simulation solver.

[0117] Based on the iteratively solvable multiphysics coupled initial state model, the transient finite element solver is called to perform electromagnetic-thermal-fluid field coupled simulation. The temperature distribution of each grid node in the model is calculated iteratively step by step, and the global temperature field results calculated at multiple time steps are integrated into dynamic temperature rise field data according to the time series.

[0118] In some embodiments, a transient finite element solver in the platform (such as COMSOL's Time-Dependent Solver or ANSYS Fluent's Transient Solver) is invoked to iteratively solve the multi-field model. At each time step, the temperature distribution of the solid domain under the influence of the heat source is calculated first, then the heat transfer effect of the airflow field on the surface is coupled and solved, and the temperature and velocity information of all nodes are updated. Iteration continues until the total simulation time is reached, obtaining the complete dynamic temperature rise response process. Finally, the global three-dimensional temperature field results output from all time steps are compiled into a time-varying dataset, forming the so-called Dynamic Temperature Field, which is used for subsequent high-temperature region identification, temperature gradient analysis, fault location, and other processes.

[0119] It should be noted that if the simulation time span is long (e.g., ≥600s), it is recommended to use an adaptive time stepping strategy, which automatically reduces the step size when the temperature rise changes drastically and automatically widens it during the steady phase, in order to improve efficiency without sacrificing accuracy.

[0120] This invention also provides a dry-type transformer temperature rise field simulation system for executing the above-described dry-type transformer temperature rise field simulation method, wherein the dry-type transformer temperature rise field simulation system includes:

[0121] The structural modeling preparation module is used to collect the geometric parameters of the core laminations, the spatial arrangement information of the windings, and the positioning relationship of the insulation components of the dry-type transformer to be simulated, so as to form structural design data.

[0122] The electromagnetic modeling and simulation module is used to construct three-dimensional geometric modeling data based on structural design data. It imports the three-dimensional geometric modeling data into the finite element electromagnetic simulation platform, sets the energizing conditions of the excitation winding, and applies rated current or voltage to obtain local magnetic flux density distribution data at each core lamination position throughout the entire operating cycle.

[0123] The loss calculation and mapping module is used to map local magnetic flux density distribution data to spatial coordinates, calculate eddy current loss and hysteresis loss respectively by combining the directional parameters of the core material, and output the core loss distribution data containing each spatial unit.

[0124] The multi-field coupling modeling module is used to take the core loss distribution data as the heat source input, establish an electromagnetic-thermal-fluid coupling model in the multi-physics simulation platform, set the functional relationship between thermal conductivity parameters and temperature, and set the air velocity, temperature, and convective heat transfer coefficient under natural or forced air cooling as boundary conditions.

[0125] The temperature rise field solution module is used to iteratively solve the electromagnetic-thermal-fluid coupling model using a transient finite element solver, and obtain dynamic temperature rise field data containing the global temperature distribution at each time point through the solution.

[0126] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0127] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for simulating the temperature rise field of a dry-type transformer, characterized in that, Includes the following steps: Step S1: Collect the geometric parameters of the core laminations, the spatial arrangement information of the windings, and the positioning relationship of the insulation components of the dry-type transformer to be simulated to form structural design data; Step S2: Construct three-dimensional geometric modeling data based on structural design data, import the three-dimensional geometric modeling data into the finite element electromagnetic simulation platform, set the energizing conditions of the excitation winding, apply rated current or voltage, and thus obtain the local magnetic flux density distribution data at each core lamination position during the entire operating cycle. Step S3: Map the local magnetic flux density distribution data to spatial coordinates, and calculate eddy current loss and hysteresis loss respectively in combination with the directional parameters of the core material, outputting the core loss distribution data containing each spatial unit; Step S3 includes: Step S31: Extract the magnetic flux density time series of each core lamination unit from the local magnetic flux density distribution data, calculate the peak value, effective value and frequency of change of the magnetic flux density, and identify the magnetic flux density components along the rolling direction and perpendicular to the rolling direction according to the rolling direction of the core material. Step S32: Calculate the hysteresis loss density of the core lamination unit in one operating cycle based on the BH magnetization curve of the silicon steel sheet using the magnetic flux density component, where the hysteresis loss density is equal to the product of the area of ​​the hysteresis loop and the operating frequency. Step S33: Calculate the eddy current loss density along the rolling direction and perpendicular to the rolling direction based on the rate of change of magnetic flux density in the magnetic flux density component and the thickness and resistivity parameters of the core lamination, and then vector synthesize the eddy current loss density components in the two directions as the eddy current loss density. Step S34: Superimpose the hysteresis loss density and eddy current loss density of each core lamination unit to obtain the total core loss density of the unit, and multiply it by the volume of the corresponding unit to calculate the core loss power value of each spatial unit. Step S35: Arrange the core loss power values ​​of all core lamination units according to the spatial coordinate index to form a loss power distribution matrix in three-dimensional space, and output the core loss distribution data containing the coordinates of each spatial unit and the corresponding loss power value. Step S4: Use the core loss distribution data as the heat source input, establish an electromagnetic-thermal-fluid coupling model in the multiphysics simulation platform, set the functional relationship between the thermal conductivity parameters and temperature, and set the air velocity, temperature, and convective heat transfer coefficient under natural or forced air cooling as boundary conditions. Step S5: Use the transient finite element solver to iteratively solve the electromagnetic-thermal-fluid coupling model, and obtain dynamic temperature rise field data containing the global temperature distribution at each time point through the solution.

2. The method for simulating the temperature rise field of a dry-type transformer according to claim 1, characterized in that, The following steps are included after step S5: The degree of local temperature abrupt change is quantified based on dynamic temperature rise field data to locate high temperature gradient regions; The temperature gradient along the winding height direction within the high-temperature gradient region is calculated as the axial gradient, and the temperature gradient along the winding radius direction within the high-temperature gradient region is calculated as the radial gradient. Calculate the ratio of the axial gradient to the radial gradient, denoted as the axial-radial gradient ratio; When the axial-radial gradient ratio is greater than or equal to 3, it is determined that the axial heat dissipation is obstructed due to the winding end heat dissipation obstruction or the axial ventilation channel blockage. When the axial-radial gradient ratio is greater than 0.5 and less than 3, it is determined to be local overheating caused by uneven distribution of heat sources inside the winding. When the axial-radial gradient ratio is less than or equal to 0.5, it is determined that the low heat dissipation efficiency of the inter-turn air passage is caused by insufficient radial convection.

3. The method for simulating the temperature rise field of a dry-type transformer according to claim 2, characterized in that, Step S1 includes the following steps: Step S11: Measure the length, width, and thickness of the core laminations in the dry-type transformer to be simulated, and record the lamination material, the thickness of the insulation layer between the laminations, the core window size, and the core column spacing parameters to form the core geometric parameters; Step S12: Collect the inner diameter, outer diameter, and height dimensions of the high-voltage and low-voltage windings of the dry-type transformer to be simulated; measure the axial position coordinates and radial spacing of the windings; record the number of winding turns, conductor cross-sectional area, and distance between windings to form the winding spatial arrangement parameters. Step S13: Identify the material type and thickness parameters of the insulation between windings, the insulation between windings and ground, and the insulation between phases based on the geometric parameters of the iron core and the spatial arrangement parameters of the windings. Measure the spatial coordinates of each insulation component, record the relative positional relationship between the insulation component and the iron core and windings, and establish the spatial positioning data of the insulation component. Step S14: By merging the geometric parameters of the core, the spatial arrangement parameters of the winding, and the spatial positioning data of the insulation components into structural design data through the physical contact relationship between the core, winding, and insulation components of the dry-type transformer to be simulated.

4. The method for simulating the temperature rise field of a dry-type transformer according to claim 3, characterized in that, Step S2, which involves constructing 3D geometric modeling data based on structural design data, includes: Establish a right-handed coordinate system with the center of the iron core as the origin, the axial direction as the Z-axis, and the radial direction as the X / Y-axis. Based on the core lamination dimensions in the structural design data, silicon steel sheets are stacked layer by layer in a stepped joint in a right-hand coordinate system to obtain the core lamination model. Based on the inner / outer diameter dimensions of the winding in the structural design data, the winding is stretched into a cylinder, and the axial / radial air passage positions are marked to obtain the winding model; Based on the insulation positioning coordinates in the structural design data, an insulation component is inserted between the core lamination model and the winding model, and the gap with the winding is verified to form an initial three-dimensional model. Assign magnetic material properties, conductor material properties, and insulating material properties to the initial 3D model, and set the conductivity of the XY plane to zero, while inputting the conductivity of the Z direction according to the measured value to obtain the 3D geometric modeling data.

5. The method for simulating the temperature rise field of a dry-type transformer according to claim 4, characterized in that, In step S2, the three-dimensional geometric modeling data is imported into the finite element electromagnetic simulation platform, the energizing conditions of the excitation winding are set, and the rated current or voltage is applied to obtain the local magnetic flux density distribution data at each core lamination position throughout the entire operating cycle, including: The 3D geometric modeling data is imported into the finite element electromagnetic simulation platform in a standard format, and the integrity of the geometry and the absence of overlap between components are verified to obtain a complete 3D model tree. In the complete 3D model tree, assign the BH magnetization curve of silicon steel sheet to the iron core lamination, input the nonlinear parameter of permeability changing with magnetic field strength, set the resistivity and density properties of the iron core material, input the resistivity, density and cross-sectional area parameters of the conductors of the high voltage winding and low voltage winding in the complete 3D model tree, set the geometric path of the winding end connection line, define the energizing conditions of the excitation winding, apply the rated current or voltage, and collect the local magnetic flux density tensor field; The local magnetic flux density tensor field is divided into multiple instantaneous magnetic flux density vector layers according to the simulation time step, and the spatial coordinate index is marked at each time point to obtain the spatiotemporal corresponding vector layer. The spatiotemporal corresponding vector layer is sliced ​​in three axes, and the magnetic flux distribution characteristics on different cross sections inside each iron core lamination are extracted. The average magnetic flux density and gradient change rate of each lamination layer are calculated as local magnetic flux density distribution data.

6. The method for simulating the temperature rise field of a dry-type transformer according to claim 5, characterized in that, The definition of the energizing conditions for the excitation winding, including applying rated current or voltage, includes: For voltage-excited windings, the rated voltage amplitude and frequency parameters are applied to the ends of the high-voltage windings, and the ends of the low-voltage windings are set as ground boundaries. For current-excited windings, the rated current density distribution is applied to the high-voltage windings, and the induced current distribution in the low-voltage windings is automatically calculated based on the ampere-turns relationship.

7. The method for simulating the temperature rise field of a dry-type transformer according to claim 6, characterized in that, Step S4 includes the following steps: Step S41: Import three-dimensional geometric modeling data into the multiphysics simulation platform and establish a heat transfer calculation domain. Specifically, the heat transfer calculation domain is established by setting the iron core, winding, and insulation components as solid heat transfer domains, setting the air around the transformer as a fluid heat transfer domain, and setting fluid-structure coupling boundary conditions at the interface between the solid domain and the fluid domain. Step S42: The core loss distribution data is loaded as a volume heat source into the core lamination unit of the heat transfer calculation domain, and the copper loss of the winding is converted into a volume heat source according to the current density distribution and loaded into the corresponding winding unit to form a three-dimensional spatial heat source distribution field. Step S43: Set the fluid domain boundary conditions in the electromagnetic-thermal-fluid coupling model. For natural air cooling, set the buoyancy parameters and gravity direction. For forced air cooling, set the air velocity and temperature at the air inlet and the pressure boundary at the air outlet. Calculate and set the turbulence model parameters based on the Reynolds number. Step S44: Set convective heat transfer boundary conditions at the coupling interface between the solid domain and the fluid domain, and establish an electromagnetic-thermal-fluid coupling model that includes heat conduction, convective heat transfer, and radiative heat transfer through the three-dimensional spatial heat source distribution field and turbulence model parameters.

8. The method for simulating the temperature rise field of a dry-type transformer according to claim 7, characterized in that, Step S5 includes: The electromagnetic-thermal-fluid coupling model was meshed, and the mesh was refined at the core lamination boundary, winding surface and fluid-structure interaction interface to obtain the finite element mesh model, wherein the mesh skewness is less than 0.8 and the aspect ratio is less than 5. Transient solution parameters, including total simulation time, time step, and convergence criterion, are set based on the finite element mesh model. The time step is determined according to the minimum mesh size and the maximum flow velocity, and the convergence criterion is set to have an energy residual less than 1 / 3. The momentum residual is less than ; Initial conditions are set on the nodes of the finite element mesh model. The initial temperature of the solid domain mesh nodes is set to the ambient temperature, and the initial flow velocity of all fluid domain mesh nodes is set to zero. The initial heat source distribution of each element is set according to the mapping relationship between the mesh element and the heat source region. Temperature and heat flow continuity constraints are established on the mesh nodes of the fluid-structure interaction interface, thereby obtaining an iteratively solvable multiphysics coupling initial state model. Based on the iteratively solvable multiphysics coupled initial state model, the transient finite element solver is called to perform electromagnetic-thermal-fluid field coupled simulation. The temperature distribution of each grid node in the model is calculated iteratively step by step, and the global temperature field results calculated at multiple time steps are integrated into dynamic temperature rise field data according to the time series.

9. A simulation system for temperature rise field of a dry-type transformer, characterized in that, For executing the dry-type transformer temperature rise field simulation method as described in claim 1, the dry-type transformer temperature rise field simulation system includes: The structural modeling preparation module is used to collect the geometric parameters of the core laminations, the spatial arrangement information of the windings, and the positioning relationship of the insulation components of the dry-type transformer to be simulated, so as to form structural design data. The electromagnetic modeling and simulation module is used to construct three-dimensional geometric modeling data based on structural design data. It imports the three-dimensional geometric modeling data into the finite element electromagnetic simulation platform, sets the energizing conditions of the excitation winding, and applies rated current or voltage to obtain local magnetic flux density distribution data at each core lamination position throughout the entire operating cycle. The loss calculation and mapping module is used to map local magnetic flux density distribution data to spatial coordinates, calculate eddy current loss and hysteresis loss respectively by combining the directional parameters of the core material, and output the core loss distribution data containing each spatial unit. The multi-field coupling modeling module is used to take the core loss distribution data as the heat source input, establish an electromagnetic-thermal-fluid coupling model in the multi-physics simulation platform, set the functional relationship between thermal conductivity parameters and temperature, and set the air velocity, temperature, and convective heat transfer coefficient under natural or forced air cooling as boundary conditions. The temperature rise field solution module is used to iteratively solve the electromagnetic-thermal-fluid coupling model using a transient finite element solver, and obtain dynamic temperature rise field data containing the global temperature distribution at each time point through the solution.

Images