A method for optimizing design of high-voltage coil segmented axial oil channel of 10kV transformer

Abstract

The present application relates to the technical field of transformer, especially to a kind of 10kV transformer high voltage coil segmented axial oil channel optimization design method.The technical scheme is as follows:A kind of 10kV transformer high voltage coil segmented axial oil channel optimization design method, comprising the following steps:S1: establish three-dimensional multi-physical field coupling simulation model including high voltage coil, insulating oil channel and cooling oil;Wherein, three-dimensional multi-physical field coupling simulation model is parameterized model, its adjustable structure parameter includes coil axial segmentation height, the axial oil channel width and quantity of each line pie, and radial oil gap size;S2: electromagnetic-fluid-thermal coupling simulation is carried out to simulation model, obtains the loss density distribution of high voltage coil, insulating oil flow field distribution and steady temperature field distribution.The present application solves the problem of uneven oil flow distribution and cooling resource mismatch by establishing parameterized multi-physical field coupling simulation model.

Description

Technical Field

[0001] This invention relates to the field of transformer technology, and in particular to an optimized design method for the segmented axial oil passages of a 10kV transformer high-voltage coil. Background Technology

[0002] Oil-immersed transformers are core equipment in power systems for power transmission and distribution, and their operational reliability directly affects the safety and stability of the power grid. During transformer operation, winding losses generate a large amount of heat. If this heat cannot be dissipated effectively and in a timely manner, it will lead to accelerated aging of the insulation material and even serious faults such as insulation breakdown. As one of the main heat sources, the quality of the heat dissipation design of the high-voltage coil is particularly critical.

[0003] Currently, the cooling structure design of high-voltage coils in oil-immersed transformers of 10kV and above largely relies on traditional experience. A common practice is to install several uniformly wide oil channels along the coil axis, relying on the natural or forced circulation of insulating oil to remove heat. This design method has the following main problems: First, the oil channel design is crude and uniform. Because the loss distribution of the coil itself is not uniform in the axial and radial directions—for example, high losses due to end effects and concentrated leakage magnetic flux on the inner diameter side—traditional uniform oil channels cannot specifically respond to this uneven thermal load, resulting in an unreasonable distribution of cooling oil flow. This often leads to "overcooling" in low-loss areas and insufficient flow in high-loss areas. Second, there is a lack of precise prediction and control methods for local hot spots. Traditional designs rely on simplified formulas or steady-state average temperature rise calculations, making it difficult to accurately capture "local hot spots" in the three-dimensional space inside the coil, and even more so, lacking effective methods for actively suppressing them during the structural design stage. Excessively high hot spot temperatures are a primary cause of insulation aging. Finally, it is difficult to optimize cooling efficiency and mechanical strength in a coordinated manner. Simply increasing the width or number of oil channels to improve heat dissipation may weaken the mechanical support structure of the coil and reduce its ability to withstand short-circuit electrodynamic forces; conversely, adopting a conservative design to ensure strength will restrict heat dissipation performance. Existing technology lacks a systematic method for integrated and coordinated simulation and optimization of multiple physical fields such as electromagnetics, fluids, heat, and force in the early stages of design, which often leads to passive trade-offs between heat dissipation performance and operational reliability in transformer design.

[0004] Therefore, there is an urgent need in this field for an innovative optimization design method for the segmented axial oil passages of the high-voltage coil of a 10kV transformer, in order to achieve a leap from experience-based design to precise and controllable design, thereby improving heat dissipation efficiency while ensuring and even enhancing the structural reliability of the equipment. Summary of the Invention

[0005] This invention proposes an optimized design method for segmented axial oil channels in the high-voltage coil of a 10kV transformer. This method solves the problems in the traditional cooling design of the high-voltage coil of a 10kV transformer, such as the mismatch between the uniform layout of the oil channels and the actual non-uniform heat load distribution of the coil, as well as the difficulty in coordinating the improvement of cooling efficiency with the guarantee of mechanical structural strength.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for optimizing the axial oil passage design of a 10kV transformer high-voltage coil segment includes the following steps: S1: Establishing a three-dimensional multiphysics coupled simulation model containing the high-voltage coil, insulating oil passages, and cooling oil; wherein, the three-dimensional multiphysics coupled simulation model is a parametric model, and its adjustable structural parameters include the axial segment height of the coil, the width and number of axial oil passages in each coil, and the radial oil gap size; S2: Performing electromagnetic-fluid-thermal coupled simulation on the simulation model to obtain the loss density distribution, insulating oil flow field distribution, and steady-state temperature field distribution of the high-voltage coil; S3: Dividing the high-voltage coil into multiple cooling blocks with different heat flow characteristics along the axial and radial directions according to the loss density distribution, flow field distribution, and temperature field distribution; S4: Formulating and implementing differentiated axial oil passage structure optimization strategies for different types of cooling blocks; S5: Adjusting the structural parameters of the simulation model based on the optimization strategies, and performing iterative simulation optimization until the preset cooling performance indicators are met; S6: Outputting the final oil passage structure design parameters to guide the winding and assembly of the high-voltage coil.

[0007] Furthermore, the electromagnetic-fluid-thermal coupling simulation of the simulation model described in step S2 is specifically performed through the following sub-steps using a sequential bidirectional coupling method: S2.1: Electromagnetic field simulation, calculate the eddy current loss and resistive loss of the high-voltage coil under rated operating conditions and preset overload conditions, and obtain the volume loss density distribution cloud map that varies with spatial position. S2.2: Computational fluid dynamics simulation, using the flow channel geometry defined by the parameterized model established in step S1 as the computational domain, calculates the velocity and pressure field distribution of insulating oil under preset inlet boundary conditions, and identifies the low-speed zone, the return zone and the main flow path. S2.3: Conjugate heat transfer simulation. The volume loss density distribution obtained in S2.1 is used as a volume heat source and applied to the solid region of the coil. The velocity field calculated in S2.2 is used as the boundary condition for forced convection heat transfer. Fluid-structure conjugate heat transfer calculation is performed to obtain the three-dimensional steady-state temperature field distribution of the high-voltage coil conductor, insulation and insulating oil. S2.4: Feature Extraction. Based on the simulation results from S2.1 to S2.3, key physical field features are extracted to guide partitioning and optimization, including: Based on the temperature field distribution, the hottest location, axial and radial temperature gradients, and high-temperature regions exceeding the set percentage of average temperature rise are extracted. Based on the flow field distribution, the average oil flow velocity, flow resistance coefficient and flow uniformity index of each axial segment are extracted. Based on the spatial correspondence between loss density and temperature field, contradictory regions such as high loss-high temperature, high loss-low temperature, and low loss-high temperature are identified.

[0008] Furthermore, in step S3, based on the key physical field features extracted in step S2.4, the cooling block is divided into: High temperature and high resistance zone: a region with high loss density, high temperature, and average oil flow velocity lower than the overall average velocity. High-temperature flow zone: A region with high loss density and high temperature, but where the average oil flow velocity is higher than or equal to the overall average velocity. Low-temperature weak flow region: The loss density and temperature rise are lower than the average, but the average oil flow velocity is significantly lower than the overall average velocity, and there is a flow dead zone in the region; Low-temperature high-flow zone: a region where loss density, temperature rise, and oil flow velocity are all at benign levels.

[0009] Furthermore, in step S4, the differentiated strategy implemented for the high-temperature, high-resistivity region is a current-increasing and capacity-expanding strategy, specifically including: Calculate the local flow resistance and thermal resistance based on the loss density, temperature rise, and current flow field data of the region. Based on the calculation results, and prioritizing the reduction of flow resistance, the following operations are selectively performed: increasing the axial oil passage width of the corresponding coil in this region, inserting auxiliary pads of a preset thickness between adjacent coils to form additional axial oil passages, and splitting the single large-size oil passage in this region into multiple parallel small-size oil passages to increase the heat dissipation surface area.

[0010] Furthermore, in step S4, the differentiated strategy implemented for the high-temperature, easily flowing zone is a flow-guiding and efficiency-enhancing strategy, specifically including: At the end of the coil or the inlet of the axial oil passage corresponding to this region, a guide baffle with a specific inclination angle or curvature is provided; the inclination angle or curvature of the guide baffle is designed based on the local streamline direction determined by flow field simulation, and is used to guide the main oil flow to the heating surface of the coil. Alternatively, when assembling wire coils, a wedge-shaped pad with a gradually varying thickness on one side can be used instead of a standard rectangular pad to create a wedge-shaped oil gap with a tendency to converge or expand between the wire coils. This generates a controllable lateral velocity component in the oil channel, enhancing the scouring effect of the oil flow on the surface of the wire.

[0011] Furthermore, in step S5, the iterative simulation optimization is achieved through a multi-level, phased automatic feedback optimization process, specifically including the following sub-steps: S5.1: Hot spot suppression and flow resistance control. Based on the differentiated strategy formulated in S4, the structural parameters of the high-temperature and high-resistance region and the high-temperature and easy-flow region of the simulation model are adjusted. The adjustment is based on the efficiency index of "temperature drop per unit voltage drop increment". The optimization strategy that performs better in this index is given priority. After each adjustment, the coupled simulation is run until the temperature drop of the hottest spot of the high-voltage coil drops below the preset limit, and the total voltage drop P1 of the insulating oil at this time is recorded. S5.2: Uniformity optimization and fine voltage drop adjustment: Based on the achievement of the hot spot temperature rise target by the first-level optimization, the second-level optimization is initiated; under the constraint that the increase in total voltage drop P1 does not exceed the set percentage A, the uniformity of temperature rise in the axial and radial directions of the coil is optimized. S5.3: Structural strength co-verification and final balancing. The oil passage structure model determined in step S5.2 is subjected to short-circuit strength simulation calculation. If the mechanical strength is insufficient, the oil passage structure parameters are reversely fine-tuned in the weak strength area, and the process is returned to step S5.1 for verification to ensure that the cooling performance index is still met under the premise that the strength meets the standard. S5.4: Optimize Convergence and Output The iterative optimization is considered to have converged when the design simultaneously meets the following conditions: the temperature rise of the hottest spot meets the standard, the temperature rise uniformity is better than the set threshold, the total voltage drop is within the allowable range, and the short-circuit strength simulation is passed.

[0012] Furthermore, in step S5.2, the optimization of the axial and radial temperature rise uniformity of the coil is specifically performed as follows: Analyze the current temperature field to identify the main high-temperature and low-temperature regions, excluding the hottest spots; For the main high-temperature areas, a flow diversion and efficiency enhancement strategy is adopted for fine-tuning; For the low-temperature high-flow region, the oil passage size is slightly narrowed without affecting the temperature rise; After each fine-tuning simulation, the standard deviation or range of temperature rise at each point of the coil is used as the uniformity evaluation index to seek the optimal solution for uniformity under voltage drop constraints.

[0013] Furthermore, it also includes step S7: A high-voltage coil prototype is manufactured according to the design parameters output in step S6, and a temperature rise test is conducted. During the winding of the coil, distributed optical fiber temperature sensors are pre-embedded between the coils in the high-temperature and high-resistivity region and the high-temperature and easy-flow region predicted by simulation. The accuracy of the multiphysics coupling simulation model is calibrated and verified by comparing the temperature distribution measured by the experiment with the temperature field distribution predicted by the simulation in steps S2 and S5.

[0014] Furthermore, it also includes step S8: Establish a segmented axial oil passage design knowledge base, and link and store the verified combinations of different optimization strategies, corresponding simulation model parameters, and the final performance indicators. The knowledge base is used to recommend matching oilway optimization strategy combinations and initial model parameters based on the target product's capacity, size, and initial loss values ​​in new transformer design tasks, serving as the starting point for iterative simulation optimization.

[0015] Furthermore, the differentiated optimization strategy formulated in step S4 is quantified into adjustable design variables and constraints during the iterative optimization process in step S5 and input into the optimization algorithm. The optimization algorithm automatically seeks optimization based on the parameterized model, with the goal of achieving the optimal comprehensive evaluation value of cooling performance index and structural strength index, and outputs a set of oil passage structure parameters that satisfy all constraints.

[0016] The positive effects of this invention are: This approach achieves precise and intelligent cooling design, fundamentally improving oil flow distribution. By establishing a parameterized multiphysics coupled simulation model, the interaction between non-uniform electromagnetic loss distribution, complex oil flow paths, and heat transfer processes is precisely incorporated into the design for the first time. Based on simulation results, the coil is finely partitioned into "high-temperature and high-resistance zones" and "high-temperature and easy-flow zones," and differentiated strategies are implemented for different regions, such as increasing flow capacity and improving flow efficiency. This completely changes the blindness of traditional uniform oil channel design, enabling the cooling oil flow to be actively guided to the most needed areas, thus solving the problems of uneven oil flow distribution and misallocation of cooling resources at the source.

[0017] This invention effectively suppresses localized overheating, significantly improving heat dissipation uniformity and equipment lifespan. Through a closed-loop process of "simulation-based hotspot location - precise structural measures," it can directly target simulated and predicted localized overheating areas with enhanced cooling design. Whether by directly increasing the flow rate of the cooling medium in the hotspot area through a flow-enhancing strategy or by strengthening local heat transfer intensity through a flow-directing efficiency strategy, it effectively reduces the temperature rise of the hottest spot and narrows the temperature difference between the axial and radial sides of the coil. A more uniform temperature distribution significantly reduces the overall thermal stress of the insulation material, thereby effectively delaying insulation aging and extending the transformer's service life and operational reliability.

[0018] A three-stage iterative optimization process was proposed, encompassing both thermal-fluid performance optimization and mechanical verification. This process prioritizes hot spot suppression and uniformity optimization, then mandates short-circuit strength simulation verification, triggering a coordinated rebalancing of design parameters if the strength is insufficient. This mechanism ensures that the final optimized solution is not only thermodynamically and hydrodynamically optimal but also mechanically safe and reliable. For the first time, it achieves integrated and coordinated optimization of heat dissipation performance and mechanical strength at the methodological level, thereby improving the overall reliability of the product. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the process for optimizing the axial oil passage design of the high-voltage coil segment of a 10kV transformer according to the present invention. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention. Example

[0021] Combination Figure 1 As shown, a method for optimizing the axial oil passages of a 10kV transformer high-voltage coil segment includes the following steps: S1: Establishing a three-dimensional multiphysics coupled simulation model containing the high-voltage coil, insulating oil passages, and cooling oil; wherein, the three-dimensional multiphysics coupled simulation model is a parametric model, and its adjustable structural parameters include the axial segment height of the coil, the width and number of axial oil passages for each coil, and the radial oil gap size; S2: Performing electromagnetic-fluid-thermal coupled simulation on the simulation model to obtain the loss density distribution, insulating oil flow field distribution, and steady-state temperature field distribution of the high-voltage coil; S3: Dividing the high-voltage coil into multiple cooling blocks with different heat flow characteristics along the axial and radial directions according to the loss density distribution, flow field distribution, and temperature field distribution; S4: Formulating and implementing differentiated axial oil passage structure optimization strategies for different types of cooling blocks; S5: Adjusting the structural parameters of the simulation model based on the optimization strategies, and performing iterative simulation optimization until the preset cooling performance indicators are met; S6: Outputting the final oil passage structure design parameters to guide the winding and assembly of the high-voltage coil.

[0022] This method follows a complete process from virtual simulation to physical implementation, aiming to achieve quantification and precision in oil passage design. The specific steps are as follows: S1: Establish a three-dimensional multiphysics coupling simulation model that includes high-voltage coils, insulating oil channels, and cooling oil.

[0023] Model building software: Use simulation platforms such as ANSYS Workbench, COMSOL Multiphysics, or similar platforms with multiphysics coupling capabilities.

[0024] Geometric Modeling: Based on the transformer design drawings, a 1:1 axisymmetric or 3D model is created in the software. The model must include: the high-voltage coil (including conductors and turn insulation), the low-voltage coil, the core magnetic circuit, the main insulation between coils, the axial oil channels and radial oil gaps inside the coils, support bars, pads, and all other key structures affecting the electromagnetic, fluid, and thermal fields. To simplify calculations, a model containing one phase or one core column can be created, and periodic or symmetrical boundary conditions can be applied.

[0025] Parametric settings: The high-voltage coil is divided into several calculation segments along the axial direction (e.g., every 2-4 coil discs per segment). For each segment, its axial oil channel width (W_channel), the number of axial oil channels within that segment (N_channel), and the corresponding radial oil gap distance (D_gap) are set as independent input parameters (variables). In the software, these parameters are associated with the geometric dimensions; modifying the parameter values ​​automatically updates the model geometry. Simultaneously, the axial segment height of the coil (H_segment) is set as a parameter to accommodate different coil disc heights.

[0026] Material property definition: Precisely set the density, specific heat capacity, thermal conductivity, resistivity, relative permeability, and other material properties of copper (wire), insulating paperboard, and insulating oil (such as 25# or 45# transformer oil) as they change with temperature.

[0027] S2: Perform electromagnetic-fluid-thermal coupling simulation on the simulation model.

[0028] Electromagnetic field simulation settings: In the low-frequency electromagnetic field module, apply a 50Hz power frequency excitation current. Select "Eddy Current Field" or "Frequency Domain" as the calculation type to accurately calculate the coil's resistive loss (I²R) and eddy current loss. After simulation, export the volumetric loss density (unit: W / m³) distribution cloud map of the entire high-voltage coil in three-dimensional space. This distribution is non-uniform, especially higher at the ends of the line segment and on the inner diameter side.

[0029] Flow field (CFD) simulation settings: The oil passage and oil gap space are extracted as a fluid domain. In the CFD module, a turbulence model suitable for internal flow, such as k-ε or k-ω SST, is selected. Boundary conditions are set as follows: the inlet (usually the bottom of the coil) is a mass flow rate inlet (corresponding to the actual oil pump flow rate), and the outlet is a pressure outlet. Stable velocity and pressure fields are calculated, focusing on the low-velocity region with flow velocities below 0.05 m / s, the backflow region with obvious vortices, and the main flow channel location with the highest flow velocity.

[0030] Temperature field (conjugate heat transfer) simulation settings: The volumetric loss density distribution obtained from electromagnetic simulation is used as a heat source and applied to the solid domain (conductor) of the high-voltage coil. The steady-state flow field (velocity, pressure) obtained from CFD calculation is used as the initial and boundary conditions of the fluid domain. Appropriate convective heat transfer wall conditions are set, and the energy equation of the entire computational domain (solid + fluid) is solved to obtain a three-dimensional temperature distribution cloud map under steady state, accurately obtaining the temperature and location of the hottest spot, as well as the overall temperature gradient.

[0031] S3: Divide the cooling blocks according to the simulation results.

[0032] Data extraction: Extract the average data for each axial segment (or custom grid region) from the simulation results of S2: average volumetric loss density (P_avg), average oil flow velocity (V_avg), and average temperature rise (ΔT_avg).

[0033] Threshold setting and classification: Set a classification threshold (e.g., use ±20% of the overall average as a boundary). For a specific region i: If P_i > 1.2P_avg and V_i < 0.8V_avg, then it is determined to be a high temperature and high resistance region.

[0034] If P_i > 1.2P_avg and V_i >= 0.8V_avg, then it is determined to be a high-temperature, easily flowing region.

[0035] If P_i <= 1.2P_avg and V_i < 0.5V_avg, it is determined to be a low-temperature weak flow region (the flow velocity threshold is more stringent).

[0036] If P_i <= 1.2P_avg and V_i >= 0.8V_avg, then it is determined to be a low-temperature high-flow region.

[0037] Visual annotation: These four types of areas are clearly marked on the 3D model using different colors (such as red, orange, yellow, and green) to form a "heat-flow diagnostic map".

[0038] S4: Develop and implement differentiated axial oil passage structure optimization strategies.

[0039] Strategy library establishment: Different structural modification operation instructions are preset for four types of regions (see details in claims 4-5). For example, for the high temperature and high resistance region, the operation instruction is "increase W_channel by 2mm" or "add one oil channel in N_channel".

[0040] Parametric Adjustment: Based on the S3 map, in the parametric model, apply the corresponding modification commands to parameters such as W_channel, N_channel, and D_gap for different regions. For example, change the W_channel of the high-temperature, high-resistivity section marked in red from 6mm to 8mm.

[0041] S5: Iterative simulation optimization.

[0042] Automation scripts: Write scripts (such as using Python / Javascript to control simulation software) to automate the process of applying S4 strategies, updating models, performing simulation calculations, reading and judging results (whether the S8 criteria are met).

[0043] Looping Operation: The script automatically runs a loop of "modify parameters -> run coupled simulation -> read results -> judge". After each loop, the modification strategy for the next round is dynamically adjusted based on the results (such as the ratio of the temperature rise and fall of the hottest spot to the increase in flow resistance). For example, if widening the oil passage has no effect, the number of oil passages will be increased instead.

[0044] Convergence criterion: When the temperature rise of the hottest spot is less than 0.5K after several consecutive iterations (e.g., 3 times) and all indicators meet the requirements, the optimization is considered to have converged.

[0045] S6: Output the final design parameters.

[0046] Generate manufacturing drawings: From the converged parametric model, export the final W_channel, N_channel, and D_gap value tables for all axial segments, as well as the corresponding coil winding dimension diagrams.

[0047] Generate process documents: Based on the optimized structure, output special process requirements, such as "use 8mm wide pads in sections 15-18" and "install 30° inclined guide plates at the end of the disc in section 22".

[0048] This method achieves a transformation from "experience-based design" to "intelligent design based on precise physical field control" for oil passage structures through a closed loop of "parametric modeling - multi-field coupled simulation - quantitative partitioning - strategic adjustment - automatic iteration", systematically solving the problem of coordinated design of heat dissipation and mechanical strength.

[0049] The electromagnetic-fluid-thermal coupling simulation of the simulation model described in step S2 is specifically performed through the following sub-steps via sequential bidirectional coupling: S2.1: Electromagnetic field simulation, calculating the eddy current loss and resistive loss of the high-voltage coil under rated operating conditions and preset overload conditions, obtaining the volume loss density distribution cloud map that varies with spatial position; S2.2: Computational fluid dynamics simulation, using the flow channel geometry defined by the parameterized model established in step S1 as the computational domain, calculating the velocity field and pressure field distribution of the insulating oil under preset inlet boundary conditions, identifying the low-speed region, the return region, and the main flow path; S2.3: Conjugate heat transfer simulation, using the volume loss density distribution obtained in S2.1 as a volume heat source loaded onto the solid region of the coil, using the velocity field calculated in S2.2 as the boundary condition for forced convection heat transfer, performing fluid-solid conjugate heat transfer calculations, obtaining the three-dimensional steady-state temperature field distribution of the high-voltage coil conductor, insulation, and insulating oil; S2.4: Feature Extraction. Based on the simulation results from S2.1 to S2.3, key physical field features are extracted to guide partitioning and optimization, including: Based on the temperature field distribution, the hottest location, axial and radial temperature gradients, and high-temperature regions exceeding the set percentage of average temperature rise are extracted. Based on the flow field distribution, the average oil flow velocity, flow resistance coefficient and flow uniformity index of each axial segment are extracted. Based on the spatial correspondence between loss density and temperature field, contradictory regions such as high loss-high temperature, high loss-low temperature, and low loss-high temperature are identified.

[0050] This step is fundamental to obtaining accurate physical field data, and its specific operation is as follows: Electromagnetic field simulation: Procedure: In ANSYS Maxwell or similar software, create a 2D axisymmetric or 3D model of the coil. To reduce computational load, a single-phase model can be created, and master-slave boundary conditions can be set to simulate periodicity. Specify a 50Hz rated current excitation for each turn of the conductor in the "High Voltage Coil" (or use a homogenized model for each coil). In the "Analysis Settings," check "Calculate Eddy Current Effects." After solving, create an "Ohmic Loss Density" field plot in the "Results." Using the "Field Calculator," export the field data to a format that can be read by subsequent CFD software (such as a CSV file containing the coordinates and loss values ​​for each grid cell).

[0051] Computational Fluid Dynamics (CFD) Simulation: Operation: In ANSYS Fluent or STAR-CCM+, import or create the fluid domain (oil channel) geometry in S1. Use the "CutCell" or "Poly-Hexcore" meshing technique to generate 3-5 boundary layer meshes near the solid wall to accurately capture the velocity gradient. Select the "Realizable k-epsilon" turbulence model and enable "Enhanced Wall Treatment". Boundary conditions: Set the inlet to "Mass Flow Inlet" (the value is estimated based on the total oil flow rate of the transformer and the cross-sectional area of ​​the oil channel), and the outlet to "Pressure Outlet". After initialization, iterate until the residual curve converges and the velocity and pressure values ​​at the monitoring points stabilize. In post-processing, use velocity vector diagrams and streamline diagrams to identify the low-velocity region (blue area in the velocity contour map) and the recirculation region (streamlines form closed loops).

[0052] Conjugate heat transfer simulation: Procedure: In the conjugate heat transfer module of ANSYS Steady-State Thermal or Fluent, import the solid and fluid domains. Load the volumetric loss density file exported in step 1 onto the corresponding high-voltage coil solid region mesh using interpolation or mapping, as the volumetric heat source. Use the converged flow field (.dat or .cas file) calculated in step 2 as the initial flow field for the entire fluid domain. The software will automatically handle convective heat transfer at all solid-liquid interfaces. Set the transformer oil inlet temperature as the initial temperature, and set appropriate convective or adiabatic boundaries on the outer wall. After solving, a three-dimensional temperature field containing the oil temperature and coil temperature will be obtained.

[0053] Feature extraction: Operation: In post-processing software (such as CFD-Post), write scripts or utilize built-in functions. For example, create an "Expression" to calculate the volumetric average flow rate and average temperature rise. Use the "Isosurface" function to plot a specific isothermal surface (e.g., the surface with the highest temperature of -2K). Place probes at specific locations using "Create Point" and "Create Line" functions to record precise values. Finally, generate a report listing: a) the coordinates and temperature values ​​of the hottest spots; b) the average temperature profile every 50 mm along the axial height of the coil (from bottom to top); c) the temperature distribution profile at mid-height along the radial direction of the coil (from inner diameter to outer diameter); d) a list of average flow rates for each major oil passage.

[0054] By using sequentially coupled simulation, complex multiphysics problems are decomposed and solved, improving computational efficiency while maintaining accuracy. The feature extraction step transforms massive amounts of field data into intuitive key indicators that guide design, serving as a bridge between simulation and design decisions.

[0055] In step S3, based on the key physical field features extracted in step S2.4, the cooling block is divided into: High temperature and high resistance zone: a region with high loss density, high temperature, and average oil flow velocity lower than the overall average velocity. High-temperature flow zone: A region with high loss density and high temperature, but where the average oil flow velocity is higher than or equal to the overall average velocity. Low-temperature weak flow region: The loss density and temperature rise are lower than the average, but the average oil flow velocity is significantly lower than the overall average velocity, and there is a flow dead zone in the region; Low-temperature high-flow zone: a region where loss density, temperature rise, and oil flow velocity are all at benign levels.

[0056] This step provides a quantitative basis for the "divide and conquer" design philosophy.

[0057] Specific operations: From the feature report of S2.4, obtain the average loss density P_avg, average oil flow velocity V_avg, and average temperature rise ΔT_avg of the entire high-voltage coil.

[0058] In the three-dimensional model, the coil is divided into multiple small computational units along the axial direction (Z direction) with each coil or every two coils as the basic unit, and along the radial direction (R direction) with the inner, middle and outer regions.

[0059] For each unit, extract its average loss density P_i, average oil flow velocity V_i (usually the normal velocity component perpendicular to the flow channel cross-section) and average temperature rise ΔT_i.

[0060] Application judgment rules: High temperature and high resistance region: satisfying (P_i>1.2*P_avg)&&(ΔT_i>1.2*ΔT_avg)&&(V_i<0.5*V_avg). This is the weakest link in cooling, characterized by high heat generation and extremely poor heat dissipation conditions.

[0061] High-temperature, high-flow region: satisfies (P_i>1.2*P_avg)&&(ΔT_i>1.2*ΔT_avg)&&(V_i>= 0.8*V_avg). Heat is not effectively removed, resulting in high heat generation and sufficient oil flow, but low heat exchange efficiency.

[0062] Low-temperature weak flow region: satisfies (P_i<0.8*P_avg)&&(ΔT_i<0.8*ΔT_avg)&&(V_i<0.3*V_avg). This is a flow dead zone risk point; although it generates little heat, the oil flow is almost stagnant, and long-term operation may lead to heat accumulation or impurity deposition.

[0063] Low-temperature high-flow region: satisfies (P_i<1.0*P_avg)&&(ΔT_i<1.0*ΔT_avg)&&(V_i>= 1.0*V_avg). This region has abundant cooling resources and can be used as a source for "borrowing" cooling resources during structural adjustments.

[0064] In the simulation software, the "User Defined Field Function (CUF)" or color marking function is used to automatically color each grid cell according to the above rules, generating a visual partition map.

[0065] By setting explicit, relative ratio-based quantization thresholds, continuous physical fields are discretized into a finite number of typical states. This allows designers to quickly pinpoint the root cause of problems and match a pre-defined library of optimization strategies to each "problem state," thus achieving standardization and efficiency in design decisions.

[0066] In step S4, the differentiated strategy implemented for the high-temperature, high-resistivity region is a current-increasing and capacity-expanding strategy, specifically including: Calculate the local flow resistance and thermal resistance based on the loss density, temperature rise, and current flow field data of the region. Based on the calculation results, and prioritizing the reduction of flow resistance, the following operations are selectively performed: increasing the axial oil passage width of the corresponding coil in this region, inserting auxiliary pads of a preset thickness between adjacent coils to form additional axial oil passages, and splitting the single large-size oil passage in this region into multiple parallel small-size oil passages to increase the heat dissipation surface area.

[0067] This strategy aims to maximize the flow of cooling oil to hot spots with minimal structural modifications.

[0068] Specific operations: Calculate local flow resistance and thermal resistance: Flow resistance R_f: Estimated according to the Darcy-Weisbach formula, R_f = f * (L / D_h) * (ρV² / 2). Where f is the friction coefficient (related to the Reynolds number Re and the surface roughness of the oil passage), L is the length of the oil passage, D_h is the hydraulic diameter of the oil passage, ρ is the oil density, and V is the flow velocity. Through CFD post-processing, the pressure difference ΔP and flow rate Q at the inlet and outlet of the oil passage in this region can be directly read, then the flow resistance R_f = ΔP / Q.

[0069] Thermal resistance R_th: R_th = ΔT / Q, where ΔT is the difference between the average temperature of the coil and the oil inlet temperature in this region, and Q is the total loss generated in this region (obtained from electromagnetic simulation).

[0070] Implementation of the capacity expansion strategy: Increase the channel width: In the parametric model, increase the W_channel parameter value corresponding to this region. For example, increase it from the standard 6mm to 8mm. This directly increases the hydraulic diameter D_h and significantly reduces the flow resistance R_f (R_f ∝ 1 / D_h^5 for laminar flow, R_f ∝ 1 / D_h^4; for turbulent flow, the relationship is complex but the trend is consistent).

[0071] Inserting auxiliary pads to form additional oil channels: During coil winding, several rectangular insulating pads with a thickness of h (e.g., 4 mm) are placed between two adjacent coils in the high-temperature, high-resistance zone. These pads are the same height as the coils and are equidistant along the circumference. Their function is to "separate" an existing wide oil channel into an additional narrow flow channel, or to directly create new axial oil channels between the coils. This is equivalent to increasing the number of parallel flow channels, significantly reducing the overall flow resistance.

[0072] The large oil channel is split into multiple smaller channels: The original large oil channel with a width of W is transformed into two parallel smaller channels with a width of W / 2 by adding a support bar between the channels. With the total cross-sectional area remaining unchanged, the total heat dissipation perimeter increases, resulting in a larger effective heat transfer area. Simultaneously, the smaller oil channels may more easily generate turbulence at low flow rates, enhancing heat transfer.

[0073] Addressing the core contradiction of "insufficient flow due to high flow resistance" in high-temperature and high-resistance regions, this strategy starts from the basic formulas of fluid mechanics and directly and efficiently reduces the flow resistance in this region by increasing the flow cross-sectional area (methods 1 and 2) or adding parallel flow paths (methods 2 and 3). This "guides" more cooling oil to the hot spot area, which is the most fundamental and effective structural measure to solve local overheating.

[0074] In step S4, the differentiated strategy implemented for the high-temperature, easily flowing zone is a flow-guiding and efficiency-enhancing strategy, specifically including: At the end of the coil or the inlet of the axial oil passage corresponding to this region, a guide baffle with a specific inclination angle or curvature is provided; the inclination angle or curvature of the guide baffle is designed based on the local streamline direction determined by flow field simulation, and is used to guide the main oil flow to the heating surface of the coil. Alternatively, when assembling wire coils, a wedge-shaped pad with a gradually varying thickness on one side can be used instead of a standard rectangular pad to create a wedge-shaped oil gap with a tendency to converge or expand between the wire coils. This generates a controllable lateral velocity component in the oil channel, enhancing the scouring effect of the oil flow on the surface of the wire.

[0075] This strategy aims to enhance convective heat transfer intensity by improving flow patterns without significantly altering flow resistance.

[0076] Specific operations: Install a flow deflector: Design: Extract the streamline diagram at the axial oil passage inlet in the high-temperature, easily flowing region from the flow field results of S2. Analyze the mainstream direction. Design a thin sheet-like nylon or laminated wood guide vane using CAD software. Its mounting surface is fixed to the struts or filaments, and the guide surface forms an angle α (typically 15°-45°) with the mainstream direction. Its shape can be a simple straight plate or an airfoil with curvature.

[0077] Installation: When winding into this area, the guide plate is secured to the inlet side of the axial oil passage with insulating tape. Its working principle is to change the direction of the incoming flow, transforming it from parallel to the conductor to impacting the conductor surface at an angle, thereby breaking the thermal boundary layer on the conductor surface and guiding the core low-temperature oil flow to the high-temperature surface.

[0078] Wedge-shaped pads are used: Design: Replace the standard rectangular pads (with uniform thickness, such as 3mm) used in this area with right-angled trapezoidal pads that are thicker on one side and thinner on the other. For example, the pads could be 2mm thick at one end and 4mm thick at the other end, forming a wedge angle β.

[0079] Installation: During coil winding, the thicker end of the spacer should face the inner diameter of the coil (or the side with more severe heat generation), and the thinner end should face the outer diameter. When cooling oil flows through the wedge-shaped oil gap formed by the wedge-shaped spacer, due to the gradual change in the flow cross-section, according to the law of conservation of mass and Bernoulli's principle, the oil flow will generate a transverse velocity component from the high-pressure side (thicker end) to the low-pressure side (thinner end). This transverse flow can effectively scour the sides of the conductor, enhancing heat transfer. A converging oil gap (large inlet, small outlet) can accelerate the oil flow; an expanding gap may generate eddies to enhance mixing.

[0080] To address the issue of "high flow rate but low heat transfer" in high-temperature, easily flowing regions, this strategy does not aim to increase the total flow rate. Instead, it introduces flow field control structures (guide plates, wedge-shaped channels) to actively generate lateral flow or secondary flow within the oil passages, disrupting the laminar sublayer and directly enhancing the convective heat transfer process near the wall. This is a highly efficient and sophisticated "active cooling" enhancement technology.

[0081] In step S5, the iterative simulation optimization is achieved through a multi-level, phased automatic feedback optimization process, specifically including the following sub-steps: S5.1: Hot spot suppression and flow resistance control. Based on the differentiated strategy formulated in S4, the structural parameters of the high-temperature and high-resistance region and the high-temperature and easy-flow region of the simulation model are adjusted. The adjustment is based on the efficiency index of "temperature drop per unit voltage drop increment". The optimization strategy that performs better in this index is given priority. After each adjustment, the coupled simulation is run until the temperature drop of the hottest spot of the high-voltage coil drops below the preset limit, and the total voltage drop P1 of the insulating oil at this time is recorded. S5.2: Uniformity Optimization and Fine Pressure Drop Regulation. On the basis that the first-level optimization achieves the hotspot temperature rise target, start the second-level optimization; under the constraint that the increase in the total pressure drop P1 does not exceed the set percentage A, optimize the temperature rise uniformity in the axial and radial directions of the coil. S5.3: Structural Strength Coordination Verification and Final Balance. Perform short-circuit strength simulation calculations on the oil duct structure model determined in step S5.2. If the mechanical strength is insufficient, reverse fine-tune the oil duct structure parameters in the weak strength area and return to step S5.1 for review to ensure that the cooling performance indicators are still met on the premise of meeting the strength standard. S5.4: Optimization Convergence and Output When the design simultaneously meets the following conditions: the hottest spot temperature rise meets the standard, the temperature rise uniformity is better than the set threshold, the total pressure drop is within the allowable range, and the short-circuit strength simulation passes, it is determined that the iterative optimization converges.

[0082] This process is the core of the intelligent decision-making for multi-objective automatic optimization.

[0083] The specific operations are as follows: S5.1 First-level Optimization (Hotspot Suppression): Set the goal: T_hotspot < T_limit (e.g., 98 °C).

[0084] Optimization variables: W_channel and N_channel in the high-temperature high-resistance area and high-temperature easy-flow area.

[0085] Optimization algorithm: Adopt the gradient descent method or the response surface method. Define an evaluation function F1 = (ΔT_reduction) / (ΔP_increase), that is, "temperature reduction amplitude / flow resistance increase amplitude". In each iteration, make a small perturbation (e.g., ± 0.5 mm) to the adjustable variables, run a simulation once, and calculate F1. Select the adjustment direction that maximizes the F1 value (for example, if widening the oil duct in area A makes F1 = 2.1 K / kPa and increasing the oil duct in area B makes F1 = 1.8 K / kPa, then select to widen the oil duct in area A in this iteration).

[0086] Execute the loop until T_hotspot meets the standard. Record the total pump power (or the inlet and outlet pressure drop P1) at this time.

[0087] S5.2 Second-level Optimization (Uniformity Optimization): Set the constraint: the increase in the total flow resistance < 10% * P1.

[0088] Optimization goal: Minimize the standard deviation σ of the overall temperature distribution of the coil.

[0089] Operation: Under constraints, a slight reduction in the oil passage in the low-temperature, high-flow region is permitted (e.g., reducing W_channel from 6mm to 5.5mm). The "saved" flow resistance "quota" is then redistributed to the sub-high-temperature region, which still requires improvement, for implementing strategies such as flow diversion efficiency enhancement. This is a resource reallocation process under a fixed "flow budget".

[0090] Evaluation: Calculate a new σ after each adjustment until σ can no longer be reduced or reaches the upper limit of the flow resistance constraint.

[0091] S5.3 Level 3 Optimization (Strength Verification and Balancing): Strength simulation: Import the optimized geometric model into structural mechanics software (such as ANSYS Mechanical). Apply the short-circuit electrodynamic force specified by the standard (such as IEC 60076-5) (calculated based on the transformer short-circuit impedance). Perform transient dynamic analysis to check the stress on the coil and pads, as well as the radial and axial deformation of the coil.

[0092] Judgment and feedback: If the compressive stress of the pad exceeds the allowable value, or the coil deformation is too large, the weak area with excessive stress is located.

[0093] Cooperative rebalancing: In this weak area, fine-tune previous modifications made for heat dissipation that might have weakened the strength. For example, if widening the oil passages reduces the support area of ​​the pads in this area, slightly reduce the width of the oil passages or increase the number of pads. Then, rerun a fast coupled simulation of S5.1 and S5.2 (with a simplified model) only for this modification to verify whether the temperature rise is still within the limit after the strength requirement is met.

[0094] S5.4 Convergence: The optimization loop terminates when the design passes both thermal performance (T_hotspot, σ) and mechanical performance (stress, deformation) tests.

[0095] This process decomposes complex multi-objective optimization into sequential stages with clear priorities (safety > performance > cost / strength). Level 1 optimization quickly suppresses safety limits (hotspots), Level 2 optimization pursues optimal performance (uniformity) under cost (flow resistance) constraints, and Level 3 optimization ensures reliability (strength). This "layered-feedback" mechanism ensures that the design achieves an optimal balance for engineering use across multiple dimensions of heat, flow, and force.

[0096] In step S5.2, the optimization of the axial and radial temperature rise uniformity of the coil is specifically performed as follows: Analyze the current temperature field to identify the main high-temperature and low-temperature regions, excluding the hottest spots; For the main high-temperature areas, a flow diversion and efficiency enhancement strategy is adopted for fine-tuning; For the low-temperature high-flow region, the oil passage size is slightly narrowed without affecting the temperature rise; After each fine-tuning simulation, the standard deviation or range of temperature rise at each point of the coil is used as the uniformity evaluation index to seek the optimal solution for uniformity under voltage drop constraints.

[0097] This step is the specific execution logic of the secondary optimization, which enables fine-tuning of cooling resources.

[0098] Specific steps: Identify regions: From the current temperature field, identify the "second-highest temperature regions" (ranked 2nd to 5th) and the 2-3 "lowest temperature regions" (ranked 2nd to 3rd). Record their locations and current temperature rise values.

[0099] Perform "minimally invasive surgery" on the sub-high temperature zone: These areas have high temperatures, but directly widening the oil passages may no longer be economical (due to the high cost of flow resistance). In such cases, the "flow-guiding efficiency enhancement" strategy of claim 5 is employed. For example, a 20° micro-guide vane is installed at the oil passage inlet of a sub-high-temperature coil. This operation has minimal impact on flow resistance (verifiable by Fluent simulations; the pressure drop increase is typically less than 0.5%), but it can potentially reduce the temperature rise at that point by 2-3 K by improving the local flow field.

[0100] "Resource recovery" in low-temperature zones: These areas are overcooled. While maintaining their own temperature rise safety margin (e.g., below the limit of 15K), slightly reduce their cooling resources. For example, narrow their oil passage width from 6mm to 5.5mm. This operation increases the flow resistance in this area, reduces the oil flow through it, and thus "releases" a portion of the system's total flow resistance (pump work) quota.

[0101] Global evaluation and iteration: Use the flow resistance quota "released" in step 3 to support the "minimally invasive surgery" in step 2. Rerun the simulation.

[0102] Evaluation metric: Calculate the standard deviation σ of the temperature at all monitoring points across the entire coil. σ = sqrt[ Σ(T_i - T_avg)² / (n-1) ]. The smaller σ is, the better the uniformity.

[0103] Compare the σ values ​​before and after this adjustment. If σ decreases, it indicates that resource allocation is effective, and the modification is accepted. Based on the new changes, repeat steps 1-3 to find the next optimizable "secondary high temperature - low temperature" pair.

[0104] If σ no longer decreases, or has reached the upper limit of the flow resistance constraint, then stop. At this point, the system reaches the state of "most uniform temperature distribution" under the current flow resistance constraint.

[0105] This is a refined control method of "peak shaving and valley filling." It doesn't simply "add" resources to the high-temperature zone, but simultaneously "subtracts" resources from the low-temperature zone (recovers redundant resources) and precisely allocates the recovered resources to the more efficient secondary high-temperature zone. Through this dynamic balance, the overall temperature field is homogenized while the total cooling resources (pump power) remain essentially unchanged, thereby extending the overall lifespan of the insulation system.

[0106] It also includes step S7: A high-voltage coil prototype is manufactured according to the design parameters output in step S6, and a temperature rise test is conducted. During the winding of the coil, distributed optical fiber temperature sensors are pre-embedded between the coils in the high-temperature and high-resistivity region and the high-temperature and easy-flow region predicted by simulation. The accuracy of the multiphysics coupling simulation model is calibrated and verified by comparing the temperature distribution measured by the experiment with the temperature field distribution predicted by the simulation in steps S2 and S5.

[0107] This step serves as a bridge between the virtual design and the physical world, used to verify and refine the simulation model.

[0108] Specific operation: Sensor pre-embedding: Based on the coordinates of the high-temperature, high-resistivity region and the high-temperature, high-flow region predicted by S5 optimization simulation, the sensing fiber (approximately 0.5mm in diameter) of the distributed fiber optic temperature sensor (DTS) is pre-placed between the insulating paper or spacers between the coil coils during coil winding. Typically, 2-3 measuring points are arranged radially in each hot spot area, with a total of 10-20 measuring points arranged axially. The fiber is led out from inside the coil and connected to the demodulator.

[0109] Prototype Temperature Rise Test: The coils manufactured using the optimized scheme are assembled into a transformer prototype. A temperature rise test is conducted according to national standards (such as GB 1094.2). Under rated current, heating is continued until the oil surface temperature stabilizes (usually requiring more than 12 hours).

[0110] Data Comparison: After the experiment stabilized, the stable temperature values ​​T_test_i of all fiber optic measurement points were recorded. Simultaneously, under identical load and boundary conditions, the final simulation model was run, and the temperature values ​​T_sim_i of the corresponding coordinate points in the simulation were read.

[0111] Model calibration: Calculate the absolute error ΔT_i = T_test_i - T_sim_i and the relative error for each measurement point. If a systematic bias exists (e.g., simulation values ​​for all measurement points are 3-5K lower), analyze the possible causes: Contact thermal resistance: The solid-to-solid contact thermal resistance set in the simulation may be too small. Appropriately increase the contact thermal resistance values ​​between the coils and between the coil and the pad in the simulation model.

[0112] Material properties: The actual thermal conductivity of insulating paper or transformer oil may differ from the default values ​​used in the simulation. Material property parameters should be corrected in reverse based on experimental results.

[0113] Boundary conditions: Actual heat dissipation conditions (such as enclosure heat dissipation) may be more severe than simulation assumptions. Adjust the environmental heat transfer coefficient in the simulation.

[0114] Iterative correction: Rerun the simulation with the corrected parameters until the error between the simulation results and the experimental data is within an acceptable range (e.g., within ±2K). This calibrated high-confidence model can then be used for the accurate design of subsequent similar products.

[0115] By using a closed loop of "design-manufacturing-testing-calibration" to "calibrate" the boundary conditions and physical property parameters of the simulation model with physical test data, the prediction accuracy and reliability of the simulation model are greatly improved, making the simulation-based optimization design results more authoritative and reducing the reliance on expensive prototype tests.

[0116] It also includes step S8: Establish a segmented axial oil passage design knowledge base, and link and store the verified combinations of different optimization strategies, corresponding simulation model parameters, and the final performance indicators. The knowledge base is used to recommend matching oilway optimization strategy combinations and initial model parameters based on the target product's capacity, size, and initial loss values ​​in new transformer design tasks, serving as the starting point for iterative simulation optimization.

[0117] This step aims to transform successful case design experience into reusable corporate knowledge assets.

[0118] Specific steps: Knowledge base data structure: Establish a relational database (such as MySQL) or a NoSQL database. Each record (a success case) contains the following fields: Product features: Rated capacity (kVA), voltage level (kV), short-circuit impedance (%), coil type (continuous, spiral).

[0119] Initial heat flux problem: the highest temperature rise, the maximum temperature difference, and the lowest flow rate obtained from the initial simulation.

[0120] Application strategy: A structured field that records which regions (indicated by coordinates or pie chart numbers) applied which one or more of the combined strategies of claims 4-5 (e.g., “Area A: widened oil passage + guide vane”; “Area B: wedge pad”).

[0121] Final parameters: Optimized values ​​for W_channel, N_channel, and D_gap for each segment.

[0122] Performance results: Optimized maximum temperature rise, average temperature rise, temperature standard deviation, and total flow resistance.

[0123] New design task matching: When a new transformer (product X) needs to be designed, input its key characteristics (such as capacity, size, and preliminary calculated loss values).

[0124] Intelligent Recommendation: The system runs a similarity matching algorithm on the knowledge base. For example, it calculates the Euclidean distance or cosine similarity between product X and the feature vector of each case in the database. It then finds the top K (e.g., K=3) most similar cases.

[0125] Output Initial Design: The system recommends the optimization strategy combination adopted by these K similar cases, and uses their final oil passage parameters as high-quality initial conjecture values ​​for iterative optimization of product X, directly importing them into the parameterized model of S1. For example, the system prompts: "This design has an 85% similarity to case #A023 (10MVA / 10kV) in the library. It is recommended to adopt its strategy combination of 'widening the oil passage in the high-temperature zone at the upper end and adding a guide vane in the middle flow section'. The initial oil passage width setting is referenced in Appendix 1." Implicit design knowledge, which relies on expert experience, is transformed into structured, queryable, and matchable explicit data. Through case-based reasoning and similarity matching, a validated, near-optimal starting point is provided for new designs, avoiding the need to optimize "from scratch" every time. This significantly shortens the design cycle and ensures the continuity and consistency of design quality.

[0126] The differentiated optimization strategy formulated in step S4 is quantified into adjustable design variables and constraints during the iterative optimization process in step S5 and input into the optimization algorithm. The optimization algorithm automatically seeks optimization based on the parameterized model, with the goal of achieving the optimal comprehensive evaluation value of cooling performance index and structural strength index, and outputs a set of oil passage structure parameters that satisfy all constraints.

[0127] This step takes the aforementioned strategies and optimization processes to a fully automated advanced stage.

[0128] Specific operation: Define design variables: Define the adjustable structural parameters in claims 4 and 5, such as the oil passage width W_i of each segment, the guide vane angle α_i, the wedge pad inclination angle β_i, etc., as a design variable vector X = [x1, x2, ..., xn]. Each variable has its upper and lower limits (e.g., W_i is between 4mm and 10mm).

[0129] Define the objective function and constraints: Objective 1 (Minimize): F1(X) = T_hotspot(X) (highest temperature) Objective 2 (Minimization): F2(X) = σ(X) (Standard deviation of temperature) Objective 3 (Minimize): F3(X) = P_loss(X) (total flow resistance pressure drop, representing pump power) Constraint 1: Stress_max(X) < [σ] (maximum mechanical stress is less than the allowable stress) Constraint 2: Deformation_max(X) < D_limit (maximum deformation is less than the limit value) Selection and operation of optimization algorithm: Adopt the multi-objective genetic algorithm (MOGA) or the non-dominated sorting genetic algorithm (NSGA-II). Set the population size (e.g., 50) and the number of generations of evolution (e.g., 100).

[0130] Algorithm initialization: Randomly generate a set of design variable vectors X (population).

[0131] Automated process: For each individual X in the population, automatically execute: update the parametric model -> run the coupled simulation of S2 -> run the strength simulation of S5.3 -> calculate its objective function values F1, F2, F, and the degree of constraint violation.

[0132] Core of the algorithm: According to the "Pareto optimality" principle, in each generation, select those "non-dominated solutions" that are not comprehensively surpassed by other individuals in terms of F1, F2, and F3. Generate a new generation of population through selection, crossover, and mutation. Repeat this process.

[0133] Output and decision-making: After 100 generations, the algorithm will output a "Pareto front" - a set of design solutions that achieve different optimal trade-offs among the three objectives. For example, Solution A: T_hotspot = 95°C, σ = 5K, P_loss = 50 kPa; Solution B: T_hotspot = 93°C, σ = 6K, P_loss = 55 kPa. Designers can select the final solution from this optimal solution set according to the specific emphasis of the product (the lowest extreme temperature rise, or the most uniform, or the most energy-efficient).

[0134] By completely mathematizing the design problem into a multi-objective constrained optimization problem and using intelligent optimization algorithms to perform global search in the vast design space, it is possible to discover more superior design solutions that go beyond artificial experience and intuition. It realizes the "fully automated optimal design" in the true sense, which is the core embodiment of the method of this invention from computer-aided design to artificial intelligence design.

[0135] The above-described embodiments are detailed and specific, illustrating preferred embodiments of the present invention. They are only used to illustrate the technical ideas and features of the present invention, with the aim of enabling those skilled in the art to understand the content of the present invention and implement it accordingly. However, they are not limited to the present invention, and the patent scope of the present invention cannot be limited by this embodiment alone. That is, any equivalent changes or modifications made to the spirit disclosed in the present invention, without departing from the structure of the present invention, such as local improvements within the system and modifications or transformations between subsystems, are still within the patent scope of the present invention.

Claims

1. A method for optimizing the segmented axial oil passage design of a 10kV transformer high-voltage coil, characterized in that, Includes the following steps: S1: Establish a three-dimensional multiphysics coupling simulation model including a high-voltage coil, insulating oil channels, and cooling oil; wherein, the three-dimensional multiphysics coupling simulation model is a parametric model, and its adjustable structural parameters include the axial segment height of the coil, the axial oil channel width and number of each coil, and the radial oil gap size; S2: Perform electromagnetic-fluid-thermal coupling simulation on the simulation model to obtain the loss density distribution, insulating oil flow field distribution and steady-state temperature field distribution of the high-voltage coil; S3: Based on the loss density distribution, flow field distribution and temperature field distribution, the high-voltage coil is divided into multiple cooling blocks with different heat flow characteristics along the axial and radial directions; S4: Develop and implement differentiated axial oil passage structure optimization strategies for different types of cooling blocks; S5: Adjust the structural parameters of the simulation model based on the optimization strategy, and perform iterative simulation optimization until the preset cooling performance index is met; S6: Outputs the final oil passage structure design parameters to guide the winding and assembly of the high-voltage coil.

2. The method for optimizing the segmented axial oil passage design of a 10kV transformer high-voltage coil according to claim 1, characterized in that, The electromagnetic-fluid-thermal coupling simulation of the simulation model described in step S2 is specifically performed through the following sub-steps using a sequential bidirectional coupling method: S2.1: Electromagnetic field simulation, calculate the eddy current loss and resistive loss of the high-voltage coil under rated operating conditions and preset overload conditions, and obtain the volume loss density distribution cloud map that varies with spatial position. S2.2: Computational fluid dynamics simulation, using the flow channel geometry defined by the parameterized model established in step S1 as the computational domain, calculates the velocity and pressure field distribution of insulating oil under preset inlet boundary conditions, and identifies the low-speed zone, the return zone and the main flow path. S2.3: Conjugate heat transfer simulation. The volume loss density distribution obtained in S2.1 is used as a volume heat source and applied to the solid region of the coil. The velocity field calculated in S2.2 is used as the boundary condition for forced convection heat transfer. Fluid-structure conjugate heat transfer calculation is performed to obtain the three-dimensional steady-state temperature field distribution of the high-voltage coil conductor, insulation and insulating oil. S2.4: Feature Extraction. Based on the simulation results from S2.1 to S2.3, key physical field features are extracted to guide partitioning and optimization, including: Based on the temperature field distribution, the hottest location, axial and radial temperature gradients, and high-temperature regions exceeding the set percentage of average temperature rise are extracted. Based on the flow field distribution, the average oil flow velocity, flow resistance coefficient and flow uniformity index of each axial segment are extracted. Based on the spatial correspondence between loss density and temperature field, contradictory regions such as high loss-high temperature, high loss-low temperature, and low loss-high temperature are identified.

3. The method for optimizing the segmented axial oil passage design of a 10kV transformer high-voltage coil according to claim 2, characterized in that, In step S3, based on the key physical field features extracted in step S2.4, the cooling block is divided into: High temperature and high resistance zone: a region with high loss density, high temperature, and average oil flow velocity lower than the overall average velocity. High-temperature flow zone: A region with high loss density and high temperature, but where the average oil flow velocity is higher than or equal to the overall average velocity. Low-temperature weak flow region: The loss density and temperature rise are lower than the average, but the average oil flow velocity is significantly lower than the overall average velocity, and there is a flow dead zone in the region; Low-temperature high-flow zone: a region where loss density, temperature rise, and oil flow velocity are all at benign levels.

4. The method for optimizing the axial oil passage design of a 10kV transformer high-voltage coil segment according to claim 3, characterized in that, In step S4, the differentiated strategy implemented for the high-temperature, high-resistivity region is a current-increasing and capacity-expanding strategy, specifically including: Calculate the local flow resistance and thermal resistance based on the loss density, temperature rise, and current flow field data of the region. Based on the calculation results, and prioritizing the reduction of flow resistance, the following operations are selectively performed: increasing the axial oil passage width of the corresponding coil in this region, inserting auxiliary pads of a preset thickness between adjacent coils to form additional axial oil passages, and splitting the single large-size oil passage in this region into multiple parallel small-size oil passages to increase the heat dissipation surface area.

5. The method for optimizing the axial oil passage design of a 10kV transformer high-voltage coil segment according to claim 3, characterized in that, In step S4, the differentiated strategy implemented for the high-temperature, easily flowing zone is a flow-guiding and efficiency-enhancing strategy, specifically including: At the end of the coil or the inlet of the axial oil passage corresponding to this region, a guide baffle with a specific inclination angle or curvature is provided; the inclination angle or curvature of the guide baffle is designed based on the local streamline direction determined by flow field simulation, and is used to guide the main oil flow to the heating surface of the coil. Alternatively, when assembling wire coils, a wedge-shaped pad with a gradually varying thickness on one side can be used instead of a standard rectangular pad to create a wedge-shaped oil gap with a tendency to converge or expand between the wire coils. This generates a controllable lateral velocity component in the oil channel, enhancing the scouring effect of the oil flow on the surface of the wire.

6. The method for optimizing the axial oil passage design of a 10kV transformer high-voltage coil segment as described in claim 1, characterized in that, In step S5, the iterative simulation optimization is achieved through a multi-level, phased automatic feedback optimization process, specifically including the following sub-steps: S5.1: Hot spot suppression and flow resistance control. Based on the differentiated strategy formulated in S4, the structural parameters of the high temperature and high resistance region and the high temperature and easy flow region of the simulation model are adjusted. The adjustment basis is: the "temperature drop obtained by unit pressure drop increment" is used as the efficiency index, and the optimization strategy that performs better in this index is given priority. After each adjustment, run the coupling simulation until the temperature rise of the hottest spot of the high-voltage coil drops below the preset limit, and record the total voltage drop P1 of the insulating oil at this time; S5.2: Uniformity optimization and fine voltage drop adjustment: Based on the achievement of the hot spot temperature rise target by the first-level optimization, the second-level optimization is initiated; under the constraint that the increase in total voltage drop P1 does not exceed the set percentage A, the uniformity of temperature rise in the axial and radial directions of the coil is optimized. S5.3: Structural strength co-verification and final balancing: The oil passage structure model determined in step S5.2 is subjected to short-circuit strength simulation calculation; If the mechanical strength is insufficient, the oil passage structure parameters are finely adjusted in reverse in the weak area, and the process is returned to step S5.1 for verification to ensure that the cooling performance indicators are still met under the premise that the strength meets the standard. S5.4: Optimizing Convergence and Output The iterative optimization is considered to have converged when the design simultaneously meets the following conditions: the temperature rise of the hottest spot meets the standard, the temperature rise uniformity is better than the set threshold, the total voltage drop is within the allowable range, and the short-circuit strength simulation is passed.

7. The method for optimizing the segmented axial oil passage design of a 10kV transformer high-voltage coil according to claim 6, characterized in that, In step S5.2, the optimization of the axial and radial temperature rise uniformity of the coil is specifically performed as follows: Analyze the current temperature field to identify the main high-temperature and low-temperature regions, excluding the hottest spots; For the main high-temperature areas, a flow diversion and efficiency enhancement strategy is adopted for fine-tuning; For the low-temperature high-flow region, the oil passage size is slightly narrowed without affecting the temperature rise; After each fine-tuning simulation, the standard deviation or range of temperature rise at each point of the coil is used as the uniformity evaluation index to seek the optimal solution for uniformity under voltage drop constraints.

8. The method for optimizing the axial oil passage design of a 10kV transformer high-voltage coil segment according to claim 1, characterized in that, It also includes step S7: A high-voltage coil prototype is manufactured according to the design parameters output in step S6, and a temperature rise test is conducted. During the winding of the coil, distributed optical fiber temperature sensors are pre-embedded between the coils in the high-temperature and high-resistivity region and the high-temperature and easy-flow region predicted by simulation. The accuracy of the multiphysics coupling simulation model is calibrated and verified by comparing the temperature distribution measured by the experiment with the temperature field distribution predicted by the simulation in steps S2 and S5.

9. The method for optimizing the segmented axial oil passage design of a 10kV transformer high-voltage coil according to claim 1, characterized in that, It also includes step S8: Establish a segmented axial oil passage design knowledge base, and link and store the verified combinations of different optimization strategies, corresponding simulation model parameters, and the final performance indicators. The knowledge base is used to recommend matching oilway optimization strategy combinations and initial model parameters based on the target product's capacity, size, and initial loss values ​​in new transformer design tasks, serving as the starting point for iterative simulation optimization.

10. The method for optimizing the axial oil passage design of a 10kV transformer high-voltage coil segment according to claim 5, characterized in that, The differentiated optimization strategy formulated in step S4 is quantified into adjustable design variables and constraints during the iterative optimization process in step S5 and input into the optimization algorithm. The optimization algorithm automatically seeks optimization based on the parameterized model, with the goal of achieving the optimal comprehensive evaluation value of cooling performance index and structural strength index, and outputs a set of oil passage structure parameters that satisfy all constraints.

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