Modeling method, device and electronic equipment of transformer

Abstract

The application discloses a transformer modeling method and device and electronic equipment. The method comprises the following steps: obtaining external point cloud data corresponding to a target transformer and internal structure parameters, wherein the target transformer comprises multiple components, the multiple components comprise an oil tank and internal devices, the oil tank is provided with a sleeve, the internal devices comprise an iron core and a winding, the winding is connected with the sleeve, and the external point cloud data comprises sleeve point cloud data; determining geometric boundary constraints corresponding to the oil tank according to the external point cloud data; determining an initial transformer model corresponding to the internal devices according to the internal structure parameters; determining a corrected transformer model according to the geometric boundary constraints and the initial transformer model; and registering internal device models in the corrected transformer model by taking the sleeve point cloud data as anchor point data to obtain a registered transformer model. The application solves the technical problem of inaccurate modeling of the transformer in the related art.

Description

Technical Field

[0001] This invention relates to the field of power equipment modeling, and more specifically, to a transformer modeling method, apparatus, and electronic device. Background Technology

[0002] Large power transformers are core equipment in the power grid, and their insulation condition directly affects the safe and stable operation of the grid. Partial discharge (PD) is a major manifestation of transformer insulation degradation. Currently, time difference of arrival (TDoA) based on ultra-high frequency (UHF) or ultrasonic waves is the main method for detecting and locating PD sources.

[0003] However, when partial discharge signals propagate inside a transformer, they are obstructed, refracted, and reflected by complex structures such as the core, windings, and insulating paperboard. Furthermore, the propagation speed of the signal varies significantly among transformer oil, metal, and insulating materials. Therefore, constructing a high-precision 3D model of the transformer's internal structure is a prerequisite for simulating partial discharge signal paths, correcting wave velocity, and accurately locating the signal. However, current transformer modeling techniques suffer from inaccurate modeling issues.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a transformer modeling method, apparatus, and electronic device to at least solve the technical problem of inaccurate modeling when modeling transformers in related technologies.

[0006] According to one aspect of the present invention, a transformer modeling method is provided, comprising: acquiring external point cloud data and internal structural parameters corresponding to a target transformer, wherein the target transformer includes multiple components, the multiple components include an oil tank and internal devices, the oil tank is provided with a bushing, the internal devices include an iron core and windings, the windings are connected to the bushings, and the external point cloud data includes bushing point cloud data; determining geometric boundary constraints corresponding to the oil tank based on the external point cloud data; determining an initial transformer model corresponding to the internal devices based on the internal structural parameters; determining a modified transformer model based on the geometric boundary constraints and the initial transformer model; and registering the internal device models inside the modified transformer model with the bushing point cloud data as anchor point data to obtain a registered transformer model.

[0007] Optionally, determining the geometric boundary constraints corresponding to the oil tank based on the external point cloud data includes: constructing a standardized coordinate system with the physical center of the transformer as the reference based on the external point cloud data; correcting the external point cloud data in the standardized coordinate system to obtain corrected point cloud data; determining the outer surface dimension parameters corresponding to the oil tank based on the corrected point cloud data; determining the inner wall dimension parameters corresponding to the oil tank based on the outer surface dimension parameters and wall thickness compensation parameters; and determining the geometric boundary constraints corresponding to the oil tank based on the inner wall dimension parameters.

[0008] Optionally, determining the initial transformer model corresponding to the internal components based on the internal structural parameters includes: determining the core geometric parameters and the tap changer modeling radius based on the internal structural parameters; determining the winding parameters based on the core geometric parameters; determining the initial internal component model and the assembly pose corresponding to the initial internal component model based on the core geometric parameters and the winding parameters, wherein the initial internal component model includes a core model, and the core model is a multi-stage stepped core model; determining the tap changer model and the switch pose corresponding to the tap changer model based on the tap changer modeling radius, tap changer modeling height, and tap changer modeling position; and determining the initial transformer model based on the initial internal component model and the assembly pose corresponding to the initial internal component model, as well as the tap changer model and the switch pose corresponding to the tap changer model.

[0009] Optionally, determining a modified transformer model based on the geometric boundary constraints and the initial transformer model includes: determining the total limit dimensions of the internal components corresponding to the internal components based on the initial transformer model; determining the actual insulation clearance between the internal components and the tank based on the geometric boundary constraints and the total limit dimensions of the components; determining the margin deviation value corresponding to the initial transformer model based on the actual insulation clearance; and iteratively modifying the initial transformer model if the margin deviation value is less than a predetermined threshold until a predetermined condition is met to obtain the modified transformer model, wherein the predetermined condition includes at least one of the following: obtaining a transformer model with a corresponding deviation margin value greater than or equal to the predetermined threshold, and the number of iterations reaching a predetermined number.

[0010] Optionally, using the bushing point cloud data as anchor point data, the internal component models within the modified transformer model are registered to obtain a registered transformer model. This includes: determining the set of bushing anchor point coordinates in a standardized coordinate system based on the bushing point cloud data; determining the set of winding anchor point coordinates corresponding to the winding in the internal component model in the standardized coordinate system based on the modified transformer model; and registering the internal component models within the modified transformer model based on the set of bushing anchor point coordinates and the set of winding anchor point coordinates to obtain a registered transformer model.

[0011] Optionally, using the bushing point cloud data as anchor point data, the internal component models within the modified transformer model are registered to obtain the registered transformer model. The process further includes: acquiring geometric structure data and physical attribute parameters corresponding to the registered transformer model, wherein the geometric structure data includes the three-dimensional contour information corresponding to each of the multiple components, and the physical attribute parameters include the physical field propagation parameters corresponding to each of the multiple components; based on the geometric structure data and the physical attribute parameters, the registered transformer model is discretized using voxelization to obtain discrete voxel units; and based on the discrete voxel units, a voxel mesh corresponding to the registered transformer model is generated.

[0012] Optionally, using the bushing point cloud data as anchor point data, the internal component models inside the modified transformer model are registered to obtain the registered transformer model. Then, the following steps are taken: setting a geometric constraint controller corresponding to the registered transformer model, wherein the geometric constraint controller includes degree-of-freedom constraints, which are used to lock the movement degrees of freedom of the multiple components.

[0013] According to one aspect of the present invention, a transformer modeling apparatus is provided, comprising: an acquisition module, configured to acquire external point cloud data and internal structural parameters corresponding to a target transformer, wherein the target transformer includes multiple components, the multiple components include an oil tank and internal devices, the oil tank is provided with a bushing, the internal devices include a core and windings, the windings are connected to the bushings, and the external point cloud data includes bushing point cloud data; a first determination module, configured to determine geometric boundary constraints corresponding to the oil tank based on the external point cloud data; a second determination module, configured to determine an initial transformer model corresponding to the internal devices based on the internal structural parameters; a third determination module, configured to determine a modified transformer model based on the geometric boundary constraints and the initial transformer model; and a registration module, configured to register the internal device models within the modified transformer model using the bushing point cloud data as anchor point data, to obtain a registered transformer model.

[0014] According to one aspect of the present invention, an electronic device is provided, comprising: a device processor; and a device memory for storing executable instructions of the device processor; wherein the device processor is configured to execute the instructions to implement the transformer modeling method described in any of the preceding claims.

[0015] According to one aspect of the present invention, a computer-readable storage medium is provided, wherein when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the transformer modeling method described in any of the preceding claims.

[0016] In this embodiment of the invention, external point cloud data and internal structural parameters corresponding to the target transformer are acquired. The target transformer includes multiple components, including an oil tank and internal devices. The oil tank is equipped with bushings, and the internal devices include a core and windings connected to the bushings. The external point cloud data includes bushing point cloud data. Based on the external point cloud data, geometric boundary constraints corresponding to the oil tank are determined. Based on the internal structural parameters, an initial transformer model corresponding to the internal devices is determined. Based on the geometric boundary constraints and the initial transformer model, a modified transformer model is determined. Using the bushing point cloud data as anchor point data, the internal device models within the modified transformer model are registered to obtain a registered transformer model. By combining joint modeling of internal and external structures with bushing anchor point registration, the transformer body model is generated by constraining the oil tank boundary and internal parameters through external point cloud data, and precise alignment of internal and external spaces is achieved using bushings. This improves the accuracy and structural consistency of the transformer's 3D modeling, thereby achieving a high degree of matching between the transformer model and the real physical structure and eliminating spatial offset between internal and external models. This solves the technical problem of inaccurate modeling when modeling transformers in related technologies. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0018] Figure 1 This is a flowchart of a transformer modeling method according to an embodiment of the present invention;

[0019] Figure 2 This is a schematic diagram of the structure of an optional transformer provided by an optional embodiment of the present invention;

[0020] Figure 3 This is a schematic diagram of transformer point cloud data provided by an optional embodiment of the present invention;

[0021] Figure 4 This is a structural block diagram of a transformer modeling device according to an embodiment of the present invention;

[0022] Figure 5 This is a structural block diagram of a computing device according to an embodiment of this application;

[0023] Figure 6 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of 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 skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] Example 1

[0027] According to an embodiment of the present invention, an embodiment of a transformer modeling method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0028] Figure 1 This is a flowchart of a transformer modeling method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0029] Step S102: Obtain the external point cloud data and internal structural parameters corresponding to the target transformer. The target transformer includes multiple components, including an oil tank and internal devices. The oil tank is equipped with a bushing. The internal devices include an iron core and windings. The windings are connected to the bushings. The external point cloud data includes bushing point cloud data.

[0030] Among them, the target transformer refers to the transformer to be modeled in 3D. It can clearly define the specific object to be modeled and provide guidance for all subsequent modeling steps, such as an oil-immersed transformer used for power transmission in a power system.

[0031] Among them, external point cloud data, such as the three-dimensional point set of the target transformer's external structure obtained through SLAM scanning technology, can accurately present the actual geometric shape of the transformer's exterior, providing measured data support for determining the tank boundary, such as the three-dimensional point cloud data of the tank surface and bushing obtained by laser scanning.

[0032] Among them, internal structural parameters refer to the design dimensions, specifications and other parameters of the internal components of the target transformer. These parameters can reflect the actual structural characteristics of the internal components and provide an accurate basis for constructing the internal model. Examples include parameters such as the diameter of the iron core, the number of turns of the winding, and the width of the oil passage.

[0033] Among them, multiple components refer to the constituent parts of the target transformer, which can clearly divide the structural units of the transformer, making it easier to carry out targeted modeling work and ensuring that the modeling process proceeds in an orderly manner.

[0034] The oil tank, referring to the external casing of the target transformer, is the core of the transformer's external structure. It houses the internal components and provides protection and heat dissipation.

[0035] Among them, internal components refer to the core functional parts inside the oil tank, which can realize the power conversion function of the transformer. They are the core objects of modeling, and their structural accuracy directly affects the modeling quality.

[0036] Among them, bushing refers to the insulating component installed on the oil tank, which can lead out the leads of the transformer's internal windings and also play an insulating protection role. Its position is fixed and can be clearly identified by external point clouds, such as the high-voltage bushing and low-voltage bushing on the top of the oil tank.

[0037] Among them, the iron core refers to the core magnetic circuit component of the internal devices. It can guide the magnetic field and reduce magnetic loss. It is the key to the transformer to achieve electromagnetic induction, and its size parameters directly determine the electromagnetic performance of the transformer.

[0038] Among them, the winding refers to the conductive coil wound around the iron core, which can generate a magnetic field through current to realize the conversion and transmission of electrical energy. Its parameters such as the number of turns and the winding method affect the voltage level of the transformer.

[0039] Among them, the casing point cloud data refers to the set of three-dimensional points corresponding to the casing in the external point cloud data, which can accurately reflect the actual spatial position of the casing and provide a positioning benchmark for subsequent registration of internal and external models.

[0040] In this step, the external point cloud data of the target transformer is acquired, along with the internal structural design parameters. This clarifies the external morphology and internal structural composition of the target transformer, and defines the relationships between its components. This step achieves comprehensive acquisition of foundational modeling data, providing both accurate external geometric information and precise internal design parameters. It solves the problem of incomplete modeling data caused by relying solely on internal parameters or external data, providing a complete and accurate data source for subsequent modeling steps. This avoids errors in subsequent modeling due to missing or biased data, laying the foundation for accurate modeling.

[0041] Step S104: Determine the geometric boundary constraints corresponding to the fuel tank based on the external point cloud data;

[0042] Among them, geometric boundary constraints refer to the three-dimensional contour constraints of the fuel tank extracted based on external point cloud data. These constraints can clearly define the spatial range, shape, and size of the fuel tank, providing spatial boundary references for modeling internal components, such as the length, width, and height of the fuel tank, the inner wall contour, and the boundary constraints corresponding to the installation position of the sleeve.

[0043] In this step, the acquired external point cloud data is processed to extract the 3D contour information corresponding to the fuel tank, determine the geometric boundary constraints of the fuel tank, and clarify the spatial range for modeling internal components. This step transforms the externally measured fuel tank shape into boundary conditions suitable for modeling, avoiding the problem of internal component models exceeding the actual fuel tank range or interfering with the inner wall of the fuel tank due to blurred fuel tank boundaries. This ensures that the subsequently constructed internal model can adapt to the spatial dimensions of the actual fuel tank, improving the spatial rationality and accuracy of the modeling.

[0044] Step S106: Determine the initial transformer model corresponding to the internal components based on the internal structural parameters;

[0045] The initial transformer model refers to a three-dimensional model that reflects the structure of internal components, built based on internal structural parameters. It can initially present the geometric shape and relative position of internal components, providing a basic prototype for subsequent model correction. For example, it is an internal three-dimensional model built based on the size parameters of the core and windings.

[0046] In this step, based on the acquired internal structural parameters, a parametric modeling method is used to construct an initial transformer model corresponding to internal components such as the core and windings, clarifying the initial shape and relative positional relationships of the internal components. This step transforms the abstract internal structural parameters into a concrete 3D model, providing a basic prototype for subsequent model correction based on the tank boundary. It avoids problems such as structural confusion and dimensional deviations in internal components caused by a lack of benchmarks in direct modeling, thus improving the efficiency and standardization of modeling.

[0047] Step S108: Determine the modified transformer model based on the geometric boundary constraints and the initial transformer model;

[0048] Among them, the modified transformer model refers to the three-dimensional model obtained by matching and adjusting the initial transformer model with the geometric boundary constraints of the oil tank. This enables the internal component model to adapt to the spatial boundary of the oil tank. For example, adjusting the size and position of the initial model ensures that the internal components do not exceed the oil tank boundary and maintain a reasonable gap with the inner wall of the oil tank.

[0049] In this step, the initial transformer model is compared with the tank geometric boundary constraints determined in step S104. The size and position of the initial model are adjusted and calibrated to eliminate the mismatch between the initial model and the tank boundary, resulting in a corrected transformer model. This step solves the problem of model deviation from the actual structure caused by the initial transformer model only considering internal parameters and not incorporating the external real tank boundary. The corrected model conforms to the design parameters of the internal components and fits the actual spatial shape of the tank, further improving the accuracy of modeling.

[0050] Step S110: Using the bushing point cloud data as anchor point data, register the internal component models inside the modified transformer model to obtain the registered transformer model.

[0051] Anchor point data refers to the reference data used for positioning and alignment, which in this case is the bushing point cloud data. It can provide a precise reference for aligning the inner and outer models by utilizing the fixed connection relationship between the bushing and the internal winding, ensuring that the spatial position of the internal device model is consistent with that of the external fuel tank point cloud.

[0052] Among them, the internal component model refers to the three-dimensional model of the corresponding iron core and winding in the modified transformer model. It is the core object of registration, and the accuracy of its spatial position directly determines the accuracy of the entire transformer model.

[0053] Among them, the registered transformer model refers to the three-dimensional model of a transformer obtained by anchor point registration, which has precise alignment of internal and external structures. It can completely and accurately reflect the real internal and external structure of the target transformer, such as a three-dimensional model in which the spatial positions of internal components, tank and bushing are completely matched.

[0054] In this step, using the bushing point cloud data as an anchor point reference, and leveraging the fixed connection between the bushing and the internal windings, the internal component models in the modified transformer model are spatially positioned and adjusted. This achieves precise alignment between the internal component models and the external tank point cloud, resulting in a registered transformer model. This step resolves the potential spatial misalignment between internal components and the external tank in the modified transformer model. By utilizing the clearly identifiable bushing as an anchor point, rigid registration of the internal and external models is achieved, ensuring that the registered model closely matches the internal and external structure of the real transformer. This completely solves the technical problem of inaccurate transformer modeling in related technologies, providing precise model support for subsequent applications such as UHF and ultrasonic partial discharge localization simulation.

[0055] Through steps S102-S110 above, external point cloud data and internal structural parameters corresponding to the target transformer are obtained. The target transformer includes multiple components, including an oil tank and internal devices. The oil tank has bushings, and the internal devices include a core and windings connected to the bushings. The external point cloud data includes bushing point cloud data. Based on the external point cloud data, geometric boundary constraints corresponding to the oil tank are determined. Based on the internal structural parameters, an initial transformer model corresponding to the internal devices is determined. Based on the geometric boundary constraints and the initial transformer model, a modified transformer model is determined. Using the bushing point cloud data as anchor point data, the internal device models within the modified transformer model are registered to obtain a registered transformer model. This method combines joint modeling of internal and external structures with bushing anchor point registration. By constraining the oil tank boundary with external point cloud data and generating the transformer body model using internal parameters, and utilizing bushings to achieve precise alignment of internal and external spaces, the accuracy and structural consistency of the transformer's 3D modeling are improved. This achieves a high degree of matching between the transformer model and the actual physical structure, eliminating spatial offset between internal and external models, and thus solving the technical problem of inaccurate modeling when modeling transformers in related technologies.

[0056] As an optional embodiment, the geometric boundary constraints corresponding to the oil tank are determined based on external point cloud data, including: constructing a standardized coordinate system with the physical center of the transformer as the reference based on the external point cloud data; correcting the external point cloud data in the standardized coordinate system to obtain corrected point cloud data; determining the outer surface dimension parameters corresponding to the oil tank based on the corrected point cloud data; determining the inner wall dimension parameters corresponding to the oil tank based on the outer surface dimension parameters and wall thickness compensation parameters; and determining the geometric boundary constraints corresponding to the oil tank based on the inner wall dimension parameters.

[0057] Among them, the physical center of the transformer refers to the geometric center of the transformer entity, which can serve as a reference for the coordinate system, ensuring consistent spatial positioning and avoiding coordinate offset.

[0058] The standardized coordinate system refers to a unified spatial coordinate system established with the physical center of the transformer as the origin. It can standardize the spatial reference of all models and eliminate coordinate confusion.

[0059] Among them, calibrated point cloud data refers to point cloud data after coordinate system calibration, which can eliminate point cloud offset and distortion errors and improve data accuracy.

[0060] Among them, the external surface dimension parameters refer to the length, width, height, curvature, and other dimensional data of the external outline of the fuel tank, which can intuitively reflect the actual size of the external surface of the fuel tank.

[0061] Among them, the wall thickness compensation parameter refers to the thickness value of the fuel tank shell, which can convert the outer surface size into the inner wall size to match the actual physical structure.

[0062] Among them, the inner wall dimension parameter refers to the size data of the cavity inside the oil tank, which can provide realistic spatial constraints for modeling internal components.

[0063] In this embodiment, a standardized coordinate system is first established with the physical center of the transformer, and the external point cloud is corrected. Then, the outer surface dimensions of the oil tank are extracted in sequence, and the inner wall dimensions are obtained by combining the wall thickness compensation. Finally, the geometric boundary constraints are determined.

[0064] This approach eliminates spatial distortions and positioning errors that may occur during point cloud acquisition, coordinate transformation, and model stitching by relying on a unified standardized coordinate system. This ensures that all subsequent modeling steps are performed within the same spatial reference system, completely avoiding systematic errors such as model misalignment and spatial misalignment caused by inconsistent coordinates from multiple data sources. Simultaneously, by using wall thickness compensation to accurately convert the outer surface dimensions into inner wall dimensions, the actual physical thickness of the tank's metal shell is fully considered, rather than directly using the outer contour as the modeling boundary. This fundamentally avoids problems such as internal model space planning errors, physical interference with the tank's inner wall, and distortion in insulation distance calculations caused by ignoring shell thickness. This ensures that the geometric boundary constraints perfectly match the transformer's actual internal physical space, guaranteeing both the physical authenticity and engineering rationality of the boundary constraints. It also provides a rigorous and accurate spatial benchmark for subsequent internal component modeling, dimensional verification, and insulation margin checks, significantly improving the overall modeling accuracy and reliability, and laying a solid geometric foundation for subsequent high-precision simulation analysis.

[0065] As an optional embodiment, the initial transformer model corresponding to the internal components is determined based on the internal structural parameters, including: determining the core geometric parameters and the tap changer modeling radius based on the internal structural parameters; determining the winding parameters based on the core geometric parameters; determining the initial internal component model and the assembly pose corresponding to the initial internal component model based on the core geometric parameters and the winding parameters, wherein the initial internal component model includes a core model, and the core model is a multi-level stepped core model; determining the tap changer model and the switch pose corresponding to the tap changer model based on the tap changer modeling radius, tap changer modeling height, and tap changer modeling position; and determining the initial transformer model based on the initial internal component model and the assembly pose corresponding to the initial internal component model, as well as the tap changer model and the switch pose corresponding to the tap changer model.

[0066] Among them, the core geometric parameters, namely the core diameter, lamination thickness, and window height, determine the basic shape of the core model and are the core parameters for electromagnetic structure design.

[0067] Among them, the tap changer modeling radius refers to the radial dimension parameter of the tap changer, which can determine the lateral size of the tap changer model.

[0068] Among them, winding parameters refer to the number of turns, wire diameter, and winding radius of the winding, which can match the core parameters to construct a winding model that meets electromagnetic performance requirements.

[0069] The initial internal device model refers to the core internal model constructed from the iron core and windings, which can represent the structure of the core electromagnetic components of the transformer.

[0070] Among them, assembly posture refers to the installation angle and relative position of internal components in the oil tank, which can restore the actual assembly relationship.

[0071] Among them, the iron core model refers to the three-dimensional model that simulates the iron core, and the multi-level stepped iron core model refers to the iron core modeling form that adopts a multi-level stepped structure, which conforms to the actual laminated stepped structure of the iron core.

[0072] Among them, the tap changer modeling height refers to the axial dimension of the tap changer, and the tap changer modeling position refers to the installation coordinates of the tap changer inside the oil tank.

[0073] Among them, the tap changer model refers to the three-dimensional model simulating the tap changer, and the switch pose refers to the installation posture and spatial position of the tap changer.

[0074] The initial transformer model refers to a preliminary three-dimensional model that integrates internal components and tap changers, and can fully represent the overall internal structure of the transformer.

[0075] In this embodiment, the basic dimensions of the core and tap changer are first determined based on the internal structural parameters, then the winding parameters are derived, and the internal device model and tap changer model are constructed and matched with the corresponding poses, and finally integrated into the initial transformer model. This approach employs a parametric, interconnected modeling logic, ensuring that the dimensions of the core and windings are strictly matched to electromagnetic design principles. This guarantees that the initial model not only possesses geometric form but also conforms to the fundamental laws of transformer electromagnetic conversion, thus possessing practical engineering significance. The multi-level, stepped core model highly replicates the lamination structure and shape characteristics of the actual core, avoiding structural distortion and dimensional deviations caused by oversimplification, making the model closer to the real product form. Simultaneously, the assembly poses of internal components and the switching poses of the tap changer are predefined, solidifying the spatial relationships, assembly angles, and installation positions of each core component. This eliminates problems such as positional offsets, angle misalignments, and component interference that may occur during subsequent model assembly. This not only significantly improves the efficiency and standardization of initial modeling but also ensures that the initial model closely matches the real transformer in terms of structural form, electromagnetic matching, and assembly relationships. This lays a solid structural foundation for subsequent model correction, spatial registration, and simulation calculations, effectively reducing the workload of subsequent model adjustments.

[0076] As an optional embodiment, determining a modified transformer model based on geometric boundary constraints and an initial transformer model includes: determining the total limit dimensions of the internal components based on the initial transformer model; determining the actual insulation clearance between the internal components and the tank based on the geometric boundary constraints and the total limit dimensions of the components; determining the margin deviation value corresponding to the initial transformer model based on the actual insulation clearance; and iteratively modifying the initial transformer model if the margin deviation value is less than a predetermined threshold until a predetermined condition is met to obtain a modified transformer model, wherein the predetermined condition includes at least one of the following: obtaining a transformer model with a corresponding deviation margin value greater than or equal to a predetermined threshold, and the number of iterations reaching a predetermined number.

[0077] The total limit dimension of the device refers to the maximum outer contour dimension of the internal device model, which reflects the overall space occupied by the internal model.

[0078] Among them, the actual insulation clearance refers to the real spatial distance between internal components and the inner wall of the oil tank, which is the core indicator of transformer insulation safety.

[0079] Among them, the margin deviation value refers to the difference between the actual insulation clearance and the design standard, which can quantify whether the insulation margin of the model meets the standard.

[0080] Among them, the predetermined threshold refers to the minimum safe standard value of the insulation clearance, which can be used as the basis for judging model correction.

[0081] Iterative correction refers to the operation of repeatedly adjusting the size and position of the model, which can gradually reduce the deviation until the target is met.

[0082] Among them, the predetermined conditions refer to the termination rules of the iterative correction, including the margin being met or the number of iterations being reached.

[0083] The number of iterations refers to the number of cycles in which the model is corrected. This can control the correction efficiency and avoid infinite iteration.

[0084] Among them, the modified transformer model refers to the model whose insulation margin meets the standard after iterative adjustment.

[0085] In this embodiment, the total limit dimensions of the internal components are first calculated, and then the actual insulation clearance and margin deviation are calculated in combination with the geometric boundary constraints. If the deviation does not meet the standard, the initial model is iteratively corrected until the predetermined conditions are met to obtain the corrected model. This approach uses the crucial insulation clearance, a key safety indicator in transformer operation, as the core basis for model correction, rather than simply adjusting geometric dimensions. This ensures the corrected model strictly meets power equipment insulation safety specifications, fundamentally eliminating safety hazards such as partial discharge, insulation breakdown, and short circuits caused by insufficient insulation clearance, significantly improving the model's electrical safety and reliability. By dynamically optimizing the model's size, spatial location, and layout through iterative correction, it gradually approaches the optimal structural arrangement, ensuring sufficient and reasonable insulation margins between internal components and the tank, and between individual internal components. This makes the model conform to power equipment design standards and on-site operational requirements. Simultaneously, setting an upper limit on the number of iterations effectively avoids the waste of computational resources and low modeling efficiency caused by infinite iterations while maintaining correction accuracy. This achieves a balance between modeling accuracy and computational efficiency, resulting in a corrected model that combines strict electrical safety performance, a reasonable physical structure layout, and high engineering practicality. This provides a safe, compliant, and dimensionally accurate model foundation for subsequent model registration and simulation analysis.

[0086] As an optional embodiment, using bushing point cloud data as anchor point data, the internal component models within the modified transformer model are registered to obtain a registered transformer model. This includes: determining the set of bushing anchor point coordinates in a standardized coordinate system based on the bushing point cloud data; determining the set of winding anchor point coordinates corresponding to the windings in the internal component models in a standardized coordinate system based on the modified transformer model; and registering the internal component models within the modified transformer model based on the bushing anchor point coordinate set and the winding anchor point coordinate set to obtain a registered transformer model.

[0087] Among them, the casing anchor point coordinate set refers to the key positioning coordinates of the casing point cloud under the standardized coordinate system, which can serve as a precise reference point in the external space.

[0088] Among them, the set of winding anchor point coordinates refers to the coordinates of the connection end between the winding and the bushing in the internal device model, which are the corresponding positioning points of the internal model.

[0089] In this embodiment, the anchor point coordinates of the bushing point cloud and the anchor point coordinates of the internal winding are extracted, and the internal device model is registered based on the two sets of coordinates to obtain the registered transformer model. This method fully utilizes the fixed physical connection between bushings and windings as the core basis for registration. It selects externally measured high-precision bushing anchor points and internal model winding anchor points to construct a one-to-one spatial matching relationship. Relying on a unified standardized coordinate system, it achieves rigid and precise registration of the internal and external models, completely resolving issues such as spatial offset and relative position distortion between internal components and external tanks / bushings that still exist in the corrected model. Bushings, as components with clear external contours, fixed positions, and easy high-precision scanning and identification, possess extremely high measured accuracy and reliability in their point cloud coordinates. Using these as a benchmark to calibrate the internal model can eliminate accumulated spatial positioning errors throughout the modeling process to the greatest extent possible. This ensures that the spatial positions and assembly postures of internal components completely reproduce the physical assembly state of the real transformer, making the relative positions of the internal and external structures of the overall model accurate and error-free. This not only elevates the overall modeling accuracy to the high-precision level required for engineering applications but also provides a model carrier that perfectly fits the actual physical space for subsequent high-precision simulation analysis scenarios such as UHF and ultrasonic partial discharge localization. This guarantees that subsequent simulation calculation results are true, reliable, and reproducible, directly supporting practical engineering applications of transformer fault detection and condition assessment.

[0090] As an optional embodiment, using bushing point cloud data as anchor data, the internal component models inside the modified transformer model are registered. After obtaining the registered transformer model, the method further includes: acquiring the geometric structure data and physical property parameters corresponding to the registered transformer model, wherein the geometric structure data includes the three-dimensional contour information corresponding to multiple components, and the physical property parameters include the physical field propagation parameters corresponding to multiple components; based on the geometric structure data and physical property parameters, the registered transformer model is discretized into voxel elements to obtain discrete voxel units; and based on the discrete voxel units, a voxel mesh corresponding to the registered transformer model is generated.

[0091] Among them, geometric structure data refers to the three-dimensional contour dimensions of each component of the transformer, which can fully present the geometric shape of the model.

[0092] Among them, physical property parameters refer to the acoustic, electromagnetic and other physical characteristics of the component, while physical field propagation parameters refer to the propagation speed and attenuation coefficient of the signal within the component, which can provide physical property support for simulation.

[0093] Among them, voxelization discretization refers to the operation of splitting a continuous three-dimensional model into regular solid units, which can transform the model into a mesh form that can be recognized by simulation.

[0094] Discrete voxel units refer to the smallest three-dimensional units after voxelization and are the basis for constructing voxel meshes.

[0095] Among them, voxel mesh refers to a three-dimensional mesh model composed of discrete voxel units with physical properties, which can support the simulation of partial discharge signal propagation.

[0096] In this embodiment, based on the geometric structure and physical field propagation parameters of the registration model, the model is discretized into voxels to generate attributed voxel meshes. This method transforms a continuous, smooth geometric model into uniform, regular discrete voxel units. It fully preserves the overall geometric structure, component dimensions, and spatial layout characteristics of the transformer while precisely assigning acoustic and electromagnetic physical field propagation parameters to each voxel unit corresponding to the component. This upgrades a simple geometric model into a dedicated simulation model with complete physical properties. The voxel mesh can clearly distinguish different medium regions such as transformer oil, core, windings, and tank shell. It provides a quantifiable and calculable refined carrier for tracking the propagation path, calculating propagation speed, attenuation, and accurately measuring the time difference of attack (TDoA) of ultra-high frequency electromagnetic waves and ultrasonic waves in different media. This completely solves the technical problems of traditional geometric models, such as their inability to support physical field propagation simulation, insufficient accuracy in signal propagation calculation, and ambiguous medium distinction. Furthermore, the voxel-based discretization format is highly compatible with mainstream simulation algorithms and numerical calculation programs, improving the computational efficiency of subsequent partial discharge localization simulations while ensuring the accuracy of signal propagation simulation and localization calculations. This allows the 3D modeling results to directly and efficiently serve practical engineering scenarios such as transformer partial discharge detection, fault source localization, and condition monitoring, expanding the model's application value.

[0097] As an optional embodiment, the internal component models inside the modified transformer model are registered using bushing point cloud data as anchor data. After obtaining the registered transformer model, the following steps are taken: setting a geometric constraint controller corresponding to the registered transformer model, wherein the geometric constraint controller includes degree-of-freedom constraints, which are used to lock the movement degrees of freedom of multiple components.

[0098] The geometric constraint controller refers to the built-in model interaction control module, which can manage the manual adjustment of the model.

[0099] Among them, the degree of freedom constraint refers to the control rules that restrict the direction of model movement, and the degree of movement freedom refers to the model's ability to move along the spatial coordinate axes.

[0100] In this embodiment, a geometric constraint controller with degree-of-freedom constraints is set for the registered transformer model to lock the movement degrees of freedom of the components. This method, during the manual optimization of the model through human-computer interaction, strictly limits the movement direction, range, and posture of each component through degree-of-freedom constraints. This effectively prevents problems caused by human error, such as components going beyond the tank boundary, internal components colliding and interfering with each other, the destruction of preset insulation spacing, and incorrect assembly posture. The geometric constraint controller has built-in spatial restriction rules that conform to the physical structure and electrical safety specifications of the transformer. This ensures that all manual adjustment operations are restricted to a reasonable, compliant, and safe effective topological space. It retains the flexibility of human-computer interaction in fine-tuning the model and optimizing details, meeting the actual needs of engineers to make personalized adjustments to the model. At the same time, the rigid constraint mechanism ensures the physical rationality, structural integrity, and insulation safety performance of the model, avoiding the destruction of the accurate spatial relationships, safety layout, and electromagnetic matching relationships formed in the early modeling by manual modifications. It also avoids the situation where the model fails due to operational errors and needs to be remodeled, greatly improving the efficiency and stability of model optimization and adjustment. This ensures that subsequent model modifications are always carried out within a safe, standardized, and accurate framework, guaranteeing that the model can still meet the requirements of high-precision modeling and simulation analysis after interactive optimization.

[0101] Based on the above embodiments and optional embodiments, an optional implementation method is provided, which is described in detail below.

[0102] Among the relevant technologies, existing technologies include: Technology 1: Manual modeling based on 2D design drawings: Relying on 2D CAD drawings or paper archives of the transformer at the factory, the core, windings, and tank models are manually drawn using software such as SolidWorks and AutoCAD through manual drawing interpretation. Technology 2: External reconstruction based on 3D laser scanning (LiDAR / SLAM): Using handheld SLAM devices or stationary LiDAR to scan the transformer's exterior, obtaining high-precision point cloud data, and reconstructing 3D models of the transformer tank shell, radiator, and bushings. Technology 3: Parametric modeling based on empirical formulas for rated parameters: According to classic empirical formulas, based on parameters such as rated voltage and capacity on the transformer nameplate, the core diameter, number of winding turns, and window height are calculated to generate a theoretical internal transformer body model.

[0103] Analysis reveals at least the following defects and shortcomings in existing technologies: First, existing technology is flawed because in-service transformers often have long operating lives (10-20 years), and paper drawings are easily lost or damaged, resulting in a lack of available diagrams. Furthermore, manual modeling is extremely time-consuming and cannot meet the needs of rapid on-site testing. Second, existing technology is flawed because laser and vision sensors cannot penetrate the metal tank, resulting in models that only have an outer shell and are hollow inside. Partial discharge localization requires calculating the signal propagation path in the winding gap (oil channel), and a model with only an outer shell cannot simulate the signal obstruction effect of internal obstacles, leading to significant positioning errors. Third, existing technology is flawed because the calculated internal dimensions may be larger than the actual tank size of the transformer on-site (causing model penetration), or the calculated insulation distance may be much larger than the distance in a compact design. It can only generate the geometry of internal components but cannot determine their specific positions within the tank space (such as the accurate coordinates of the tap changer relative to the tank wall, and the alignment of the core within the tank), causing the model to be unable to align with the actual sensor positions.

[0104] To address the shortcomings of existing technologies, such as reliance on drawings for transformer internal structure modeling, inability to obtain internal information through external scanning, mismatch between theoretically calculated model dimensions and actual shell dimensions, and inability to accurately align internal and external spatial coordinates, this invention provides a transformer modeling method in optional embodiments, which can also be described as a digital twin modeling method for transformer internal structure that integrates 3D scanning and parameter inversion. Figure 2 This is a schematic diagram of the structure of an optional transformer provided by an optional embodiment of the present invention. Figure 3 This is a schematic diagram of transformer point cloud data provided by an optional embodiment of the present invention, such as... Figure 2-3 As shown, it will be introduced below:

[0105] The specific technical problems solved by the optional embodiments of the present invention are as follows:

[0106] 1. Solve the problem of modeling the invisible internal structure of transformers when there are no drawings or the packaging is not opened;

[0107] In existing technologies, for old transformers with missing drawings, it is necessary to remove the power and open the casing to obtain internal structural information, which is costly and risky; or only a hollow model can be obtained through external scanning, which cannot meet the needs of internal physical field simulation.

[0108] The first technical problem solved by this invention is: how to reconstruct the geometric model of invisible internal components such as core, windings, and tap changers using only externally visible nameplate parameters (voltage, capacity, etc.) and appearance point cloud data, without lifting the cover, opening the box, or requiring the original design drawings, and using an inversion algorithm.

[0109] 2. Resolve the problem of mismatch between theoretical calculation models and actual physical boundaries (clipping or abnormal gaps);

[0110] In existing technologies, the internal transformer body model generated based on empirical formulas often deviates from the actual transformer tank dimensions. For example, the theoretically calculated core diameter may be too large, causing the generated body model to collide (through the mold) when placed into the actual scanned tank model, or the calculated insulation distance may be much smaller than the high-voltage insulation safety specifications, resulting in model distortion.

[0111] The second technical problem solved by this invention is to establish a boundary constraint mechanism for external measured data, using the actual dimensions of the inner wall of the oil tank obtained by SLAM scanning as a constraint condition, and theoretical design parameters such as core diameter coefficient and oil passage width, to ensure that the generated internal model can be perfectly adapted to and accommodated in the actual oil tank space in terms of geometric dimensions, and conforms to the insulation design specifications.

[0112] 3. Resolve the issue of inaccurate alignment between the spatial coordinate systems of the internal and external models;

[0113] In existing technologies, internal models are typically built based on the geometric center, while external SLAM scanning models are built based on the scanner's starting position, with their coordinate systems being independent. Relying solely on manual alignment makes it difficult to determine the accurate position of the internal windings relative to the external bushing and tank wall, leading to inaccurate calculations of the relative positions of the sensor (located externally) and the discharge power source (located internally) during subsequent partial discharge localization.

[0114] The third technical problem solved by this invention is: using the identifiable bushing features in the external scanned point cloud as spatial anchor points, and automatically calculating and executing rigid body transformation based on the physical correspondence of the transformer lead structure, to achieve automatic and accurate registration from the internal transformer body model coordinate system to the external tank model coordinate system.

[0115] 4. Solve the problem of large partial discharge positioning errors caused by the lack of physical propagation medium properties in the general geometric model;

[0116] Existing technologies typically generate general-purpose 3D models that only contain surface mesh information and lack acoustic or electromagnetic medium properties. Partial discharge localization algorithms can usually only simplify the inside of a transformer to a single homogeneous medium (oil), ignoring the refraction of signals and changes in sound velocity caused by metal windings and insulating paperboard.

[0117] The fourth technical problem solved by this invention is to achieve synchronous mapping of structural discretization and physical properties during the modeling process, automatically divide the generated model into a solid domain (iron core, copper wire) and a fluid domain (transformer oil), and assign corresponding acoustic impedance and dielectric constant parameters, so as to provide a heterogeneous model basis with physical properties for the simulation of the propagation path of UHF / ultrasonic partial discharge signals.

[0118] The methods involved in the optional embodiments of the present invention are described below:

[0119] 1. High-precision acquisition and structured preprocessing of multi-source basic data;

[0120] The first step in implementing this invention is to construct the geometric and physical parameter dataset required for the digital twin of the transformer. This process is not simply equivalent to taking photos and copying nameplates, but rather a rigorous process involving multi-sensor data fusion and unstructured text structuring. The specific implementation method is as follows:

[0121] First, at the geometric data acquisition level, a handheld or stationary SLAM (Simultaneous Localization and Mapping) scanning device integrating a multi-line LiDAR and an inertial measurement unit (IMU) is used to collect data from the target transformer from all directions. To ensure the integrity of the reconstructed model and eliminate scanning blind spots, the operator must follow a specific closed-loop scanning path strategy, that is, starting from one side of the transformer, performing a 360-degree circumferential scan around the transformer body, radiator array, and oil conservator area, and finally returning to the starting point to achieve loop closure detection, thereby eliminating the cumulative drift error caused by long-term scanning. During the scanning process, the LiDAR emits laser pulses at a rate of no less than 300,000 points / second, measures the time of flight or phase difference from the center of the device to the object surface, and calculates and outputs raw three-dimensional point cloud data containing tens of millions of discrete points in real time (same as the external point cloud data mentioned above). For the high-voltage and low-voltage bushing areas on the top of the transformer, due to the obstructed view, it is necessary to perform overhead scanning by extending the scanning rod or using a drone equipped with a lidar to ensure that the geometric features of the bushing top terminals and base flanges are completely preserved. The final output raw point cloud data must meet the high density requirement of surface point spacing of less than 5mm to ensure the robustness of subsequent plane fitting and feature extraction algorithms.

[0122] Secondly, at the level of acquiring physical constraint parameters, the system needs to manually input key parameters (same as the internal structure parameters mentioned above) from the transformer's nameplate that determine the design of the internal insulation structure. Specifically, the extracted parameters include rated voltage, capacity, and core structure. This forms a parameter vector. , This represents the core structure type index (corresponding to three-phase three-column, three-phase five-column, or single-phase two-column type). This parameter directly determines the number of core windows and the magnetic circuit topology during subsequent modeling. Among them:

[0123] .

[0124] .

[0125] .

[0126] 2. Extraction of external boundaries and initialization of coordinate system based on point cloud (same as above, determining the geometric boundary constraints corresponding to the fuel tank based on external point cloud data).

[0127] This step aims to process the raw unstructured point cloud data acquired in S1 using algorithms. Through denoising, feature plane fitting, and principal component analysis (PCA), a standard local coordinate system for the transformer body is constructed, and the effective internal space dimensions of the tank are accurately calculated, providing accurate geometric boundary constraints for the subsequent placement of internal components. The specific implementation process consists of the following three sub-steps:

[0128] 2.1 Point cloud preprocessing and noise reduction based on statistical filtering;

[0129] The original point cloud often contains flying points (outliers) caused by laser scattering and background points that are not part of the transformer itself (such as ground weeds and fences). First, the point cloud is cleaned using the Statistical Outlier Removal (SOR) algorithm to obtain a clean point cloud set.

[0130] 2.2 Adaptive construction of the standard local coordinate system for transformers;

[0131] Since the initial coordinate system of SLAM scanning is random (depending on the device startup position), a standardized coordinate system based on the physical center of the transformer must be established (similar to the above-mentioned standardized coordinate system based on the physical center of the transformer constructed based on external point cloud data).

[0132] The specific steps are as follows:

[0133] 1) Z-axis calibration;

[0134] Extract the maximum plane from the downsampled point cloud. Set a distance threshold. (e.g., 10mm), iteratively fit the plane equation. The plane with the most points and a normal vector that is close to vertically upward is determined to be the ground or base plane. The plane equation is fitted using the RANSAC (Random Sample Consensus) algorithm: The normal vector of this plane Defined as the Z-axis direction of the new coordinate system, the entire point cloud is corrected to horizontal using a rotation matrix (similar to the correction of external point cloud data in the standardized coordinate system to obtain corrected point cloud data).

[0135] 2) PCA-based alignment of the principal X and Y axes;

[0136] Ground points and top casing points are removed, retaining only the point cloud of the tank sidewall. The sidewall point cloud is projected onto the XY plane (horizontal plane) to construct a 2D covariance matrix S: ,in, For the projected points, Let S be the centroid of the point cloud. Perform eigenvalue decomposition on S to obtain the eigenvectors. and Among them, the largest eigenvalue corresponding feature vector The direction representing the most discrete data distribution is the major axis of the transformer tank (defined as the X-axis); the second largest eigenvalue. corresponding feature vector That is, the direction of the minor axis (defined as the Y-axis).

[0137] 3) Origin normalization;

[0138] Calculate the extreme values ​​of the point cloud on the side wall of the fuel tank in the X, Y, and Z directions after correction: Define a new coordinate origin. for: .

[0139] Here, the midpoint between X and Y is taken as the center, and the minimum value of Z is taken as the bottom reference. This is achieved through a translation transformation matrix. Move the center of the point cloud to (0, 0, 0). At this point, the transformer model is in a standard pose: the long side is along the X-axis, the short side is along the Y-axis, and the bottom surface is located in the Z=0 plane.

[0140] 2.3 Solution of effective internal geometric boundaries;

[0141] After establishing a standard coordinate system, the effective internal space needs to be calculated by working backward from the outer dimensions, which is the maximum space that the core and windings can occupy.

[0142] 1) Extraction of outer dimensions (same as above, based on the corrected point cloud data, determine the outer surface dimension parameters corresponding to the fuel tank):

[0143] Based on the aligned point cloud, the axis alignment bounding box parameters of the outer surface of the fuel tank are directly extracted, including the outer length: External width: External height: ,in: ; ; ;

[0144] 2) Wall thickness compensation and internal space calculation (same as above, based on the external surface dimension parameters and wall thickness compensation parameters, determine the internal wall dimension parameters corresponding to the oil tank).

[0145] Since the lidar can only scan the outer wall of the oil tank, the system needs to incorporate a wall thickness compensation parameter. This parameter can be set to a fixed empirical value (e.g., 10mm-14mm for medium-sized transformers) or entered using actual measurements from an ultrasonic thickness gauge. Setting the sidewall thickness parameter. and top and bottom wall thickness ;

[0146] Calculate the effective dimensions of the tank's inner wall: Inner cavity length: Inner cavity width: Inner cavity height: .in:

[0147] .

[0148] .

[0149] 3) Output the set of boundary constraints (same as above, determine the geometric boundary constraints corresponding to the oil tank based on the inner wall dimension parameters);

[0150] The final output is a set of boundary constraints. This set will serve as the benchmark for adaptive back-correction of parameters in subsequent step S4, and its total size must be strictly smaller than the spatial range defined by this set. (Hard constraint set) Defined as:

[0151]

[0152] 3: Initial parametric modeling of the internal structure based on standard design principles (similar to determining the initial transformer model corresponding to the internal components based on the internal structural parameters mentioned above).

[0153] This step aims to establish an initial theoretical model of the transformer's internal structure. The system incorporates a parametric generation engine based on the electromagnetic calculation principles of transformers. Using the structured nameplate data entered in step 1 as input, all geometric dimensions of the core and windings are derived through analytical formulas. At this point, the generated model is in an ideal design state and has not yet undergone constraint correction from external scan data. The specific implementation process includes the following three steps:

[0154] 3.1 Derivation and modeling of the principal geometric parameters of the iron core;

[0155] The core is the magnetic circuit framework of the transformer, and its diameter D is the most critical variable determining the overall size of the transformer. Based on the core structure type (default is three-phase three-limb or three-phase five-limb), the system performs the following calculations:

[0156] 1) Calculation of core diameter D: Three phases are Single-phase , .

[0157] in, , ;

[0158] 2) Calculation of the height and center distance of the iron core window;

[0159] Based on the calculated diameter D, the longitudinal and transverse dimensions are derived according to the proportional relationships of a standard transformer: Core window height: Center distance of iron core column

[0160] 3) Discretization of the core cross-section;

[0161] To approximate a realistic circular cross-section and optimize the multiphysics simulation mesh, a multi-level stepped rectangular fitting method is used instead of a simple cylinder. The system automatically generates a 12-level concentric rectangular stack model based on the diameter D. The width and stack thickness of each level of rectangle satisfy the circumcircle constraint, thereby constructing a three-dimensional solid model of the core column and yoke.

[0162] 3.2 Calculation of winding turns and radial dimensions;

[0163] Geometric modeling of the windings requires not only determining the shape but also calculating the number of turns for subsequent inductance parameter and partial discharge signal propagation path analysis. The system follows an inside-out principle, calculating the low-voltage winding (LV), medium-voltage winding (MV), and high-voltage winding (HV) sequentially.

[0164] 1) Calculation of winding height and number of turns:

[0165] Winding height: ;

[0166] Number of turns: In the formula, Phase voltage (V); Frequency (50Hz);

[0167] Design magnetic flux density (using the standard value of 1.7 Tesla); : Geometric cross-sectional area of ​​the iron core; Stacking factor (taken as 0.96). The calculated result N is rounded up.

[0168] 2) Winding radial thickness W;

[0169] In the formula, ; ;

[0170] in:

[0171] ;

[0172] J: represents current density;

[0173] W: Represents the radial thickness of the winding;

[0174] N: Represents the number of turns in the winding;

[0175] Hcoil: Represents the height of the winding (coil);

[0176] Kfill: Represents the fill factor or space utilization rate.

[0177] 3) Insulation spacing ;

[0178] The width of the main insulating oil channel between each winding is determined based on the voltage level U, using an empirical formula for linear fitting: .

[0179] 3.3 Topological assembly of the initial vessel body model;

[0180] After obtaining all the above scalar parameters, the system assembles the three-dimensional topology in memory from bottom to top, with the origin as the reference point:

[0181] 1) Positioning reference: Place the geometric center of the iron core bottom yoke at the origin of the coordinate system.

[0182] 2) Concentric assembly: With the central axis of the iron core column as the axis, the low-voltage winding cylinder, the main insulating oil channel, and the high-voltage winding cylinder are generated sequentially from the inside to the outside.

[0183] Low-voltage winding inner diameter: Low-voltage winding outer diameter: ;

[0184] High voltage winding inner diameter: High-voltage winding outer diameter: ;

[0185] in:

[0186] .

[0187] .

[0188] .

[0189] .

[0190] .

[0191] Where H: high pressure, L: low pressure, in: inner diameter, out: outer diameter.

[0192] 3) For a three-phase transformer, along the X-axis, the above single-phase components are arranged in the array according to the calculated center distance A of the core columns: A phase center: (-A, 0, 0) B phase center: (0, 0, 0) C phase center: (A, 0, 0).

[0193] The three-phase magnetic circuit is closed by the upper and lower iron yokes to form a complete initial body model.

[0194] The output model at this time has an internal structure that fully complies with the electromagnetic design principle. It is necessary to calculate the total width of its body for subsequent verification: . The system will judge this total dimension whether it can be placed within the oil tank boundary calculated in step S2 ( ). If , it means that the design is too large and parameter adaptive correction needs to be triggered in the next step.

[0195] Among them, ;

[0196] 3.4 Parametric modeling and initial layout of the tap-changer;

[0197] The system constructs a cylindrical geometric model of the tap-changer according to the input rated voltage level and oil tank height parameters:

[0198] 1) Looking up geometric dimensions: Establish a standard dimension database of the tap-changer based on the voltage level and refer to the recommended modeling radius in the industry.

[0199] If UN ≤ 35 kV, set the radius Rswitch = 200 mm;

[0200] If 35 kV < UN ≤ 110 kV, set the radius Rswitch = 300 mm;

[0201] If 110 kV < UN ≤ 220 kV, set the radius Rswitch = 360 mm;

[0202] If UN > 220 kV, set the radius Rswitch = 450 mm.

[0203] 2) Height: Based on the inner cavity height of the oil tank calculated in step 2 , combined with the bottom suspended insulation distance , calculate the height of the tap-changer cylinder , among which, .

[0204] 3) Initial pose presetting;

[0205] Since the tap-changer is usually installed near the short-axis side wall of the oil tank, the system sets the initial theoretical position. Establish a coordinate system with the geometric center of the oil tank as the origin and preset the tap-changer in the corner area of the high-voltage side of the oil tank:

[0206] ;

[0207] ;

[0208] ;

[0209] in:

[0210] SW is an abbreviation for switch.

[0211] .

[0212] .

[0213] .

[0214] Y_sw: The coordinates of the tap changer center point on the Y-axis (short side direction);

[0215] W_tank_in: is the total internal width of the fuel tank calculated in step 2.3;

[0216] R_switch: Represents the radius of the cylinder containing the tap changer;

[0217] δswitch_wall: Represents the safety insulation gap / physical distance that must be reserved between the tap changer surface and the inner wall of the tank.

[0218] 4. Parameter adaptive reverse correction based on boundary constraints (same as above, based on geometric boundary constraints and initial transformer model, determine the corrected transformer model).

[0219] This step constructs a closed-loop feedback control system to resolve potential dimensional conflicts between the initial theoretical model generated in step 3 and the actual physical boundaries calculated in step S2. The system uses the geometric dimensions of the tank's inner wall as a hard constraint and the insulation specifications corresponding to the voltage level as a soft constraint. It iteratively adjusts the core design coefficient K to achieve adaptive fine-tuning of the internal structure. The specific implementation process includes the following three sub-steps:

[0220] 4.1 Virtual assembly and insulation margin calculation;

[0221] The system first virtually places the initial vessel model generated in step 3 at the center of the internal space of the fuel tank calculated in step 2. To quantify whether the model complies with regulations, the system performs the following geometric calculations:

[0222] 1) Calculation of the overall dimensions of the device body (same as the overall limit dimensions of the above-mentioned components);

[0223] Based on the calculation results in step 3, the limiting physical dimensions of the three-phase transformer body model in the X and Y axis directions are determined:

[0224] long: ,in, This refers to the outer radius of the high-voltage winding.

[0225] Width: ;

[0226] high: .

[0227] 2) Calculation of actual clearance (same as above for actual insulation clearance);

[0228] Using the tank inner wall dimensions output in step 2 Calculate the current actual insulation distance:

[0229] Sidewall insulation distance: ;

[0230] Phase-to-phase / end insulation distance: ;

[0231] Top insulation distance: .

[0232] in:

[0233] ;

[0234] ;

[0235] ;

[0236] ;

[0237] ;

[0238] ;

[0239] ;

[0240] .

[0241] 4.2 Construction of insulation constraint criteria;

[0242] To determine whether the current geometric clearance meets electrical safety requirements, this invention constructs a multi-level insulation threshold database based on voltage levels. The system extracts the highest voltage level from the input parameters. Automatically indexed set of standard minimum insulation distance thresholds The specific judgment logic is as follows:

[0243] 1) Establish the difference function: Define the margin deviation between the vessel body and the tank wall. (Same as the above margin deviation value):

[0244] ;

[0245] .

[0246] in:

[0247] .

[0248] .

[0249] .

[0250] .

[0251] .

[0252] .

[0253] .

[0254] .

[0255] .

[0256] .

[0257] 2) Decision logic:

[0258] like and If the condition is met, the model is considered compliant and no corrections are needed; the model can be output directly.

[0259] like or If the condition is not met, it is considered a violation (i.e., there is mold penetration or insufficient insulation distance), and the reverse iterative correction process in step 4.3 must be triggered.

[0260] 4.3 Adaptive Iterative Correction of Core Diameter Coefficient K (similar to the above iterative correction of the initial transformer model until the predetermined conditions are met, resulting in the corrected transformer model).

[0261] When a violation is detected, it indicates that the transformer core size calculated based on the standard coefficient (K=57) is too large. Since the transformer voltage level and capacity are fixed values ​​and cannot be modified, this system selects to adjust the empirical coefficient K (usually ranging from 45 to 60) to compress the core and winding dimensions.

[0262] Fast optimization can be achieved using binary search or gradient descent.

[0263] 1) Initialize iteration variables: Set initial coefficients Step length Number of iterations ;

[0264] 2) Perform iterative calculations: when When, execute Utilizing new Repeat steps 3.1 and 3.2 to generate a new core diameter. New window height and new winding radial thickness .

[0265] 3) Termination conditions:

[0266] Condition A (convergence): and ( =5mm), which means it fits perfectly without wasting space.

[0267] Condition B (Exceeding Limits): Less than the critical value (45) At this point, the iteration stops and an error message is output indicating that the tank size is seriously inconsistent with the nameplate parameters.

[0268] 4) Final Model Solidification: After the iteration is completed, the model at convergence time will be finalized. The corresponding geometric parameters are locked to generate the final adaptive corrector body model.

[0269] 5. Accurate registration of internal and external coordinate systems based on visual feature anchor points (similar to the above, using bushing point cloud data as anchor point data to register the internal component models inside the modified transformer model to obtain the registered transformer model).

[0270] After step 4, a correctly sized internal model is obtained, but it is still located at the origin (0, 0, 0) by default. In an actual transformer, the transformer body may be offset within the tank. This step uses the clearly identifiable bushings in the SLAM point cloud as anchor points to achieve rigid registration of the internal and external coordinate systems.

[0271] 5.1 External casing feature extraction and center localization (same as above, based on casing point cloud data, determine the set of casing anchor point coordinates in the standardized coordinate system).

[0272] In the point cloud data processed in step 2, cluster analysis was performed on the point cloud clusters above the fuel tank top cover. A clustering tolerance of 50 mm was set, dividing the data into several independent point cloud clusters. RANSAC cylinder fitting was performed on each point cloud cluster to identify the cylinder models belonging to the high-pressure bushing (thin and tall type) and the low-pressure bushing (thick and short type). The projected coordinates of the center of the fitted cylinder's base on the XY plane were calculated and denoted as the external anchor point set. This enables coordinate extraction.

[0273] in:

[0274] .

[0275] .

[0276] 5.2 Calculation of theoretical coordinates of internal lead nodes (based on the modified transformer model mentioned above, determine the set of winding anchor point coordinates corresponding to the winding in the internal device model under the standardized coordinate system).

[0277] In the internal transformer body model generated in step 3, based on the transformer lead design specifications (the high-voltage winding leads are usually located at the center of the upper end of the winding or in the tangential direction), the theoretical coordinates of the high-voltage winding lead-out terminals of the corresponding phases are calculated and denoted as the internal anchor point set:

[0278]

[0279] The A-phase take-off point is usually located at (-A, 0, ...). )nearby.

[0280] in:

[0281] .

[0282] .

[0283] 5.3 Rigid body transformation registration based on ICP algorithm (similar to the above, based on the bushing anchor point coordinate set and the winding anchor point coordinate set, the internal device model inside the modified transformer model is registered to obtain the registered transformer model).

[0284] To align the internal model with the external sleeve, the translation vector is calculated. .

[0285] 1) Horizontal alignment (XY translation);

[0286] ;

[0287] ;

[0288] 2) Vertical alignment (Z-axis positioning);

[0289] ;

[0290] in:

[0291] ;

[0292] ;

[0293] .

[0294] 3) Transformation execution: Apply transformation matrix M to the entire modified internal body model to position it in the final SLAM coordinate system.

[0295] 6. Physical property mapping and mesh discretization for partial discharge simulation (similar to obtaining the geometric structure data and physical property parameters corresponding to the registered transformer model, where the geometric structure data includes the three-dimensional contour information corresponding to multiple components, and the physical property parameters include the physical field propagation parameters corresponding to multiple components; based on the geometric structure data and physical property parameters, the registered transformer model is discretized into discrete voxel elements; based on the discrete voxel elements, a voxel mesh corresponding to the registered transformer model is generated).

[0296] To meet the requirements of UHF and ultrasonic partial discharge localization simulations, this step transforms the generated geometric model into a voxel mesh with acoustic / electromagnetic properties. This is a crucial preprocessing step for achieving high-precision TDoA localization.

[0297] 6.1 Component-level semantic segmentation and attribute tagging;

[0298] While generating the geometric solid, the system automatically assigns a unique material ID to each Mesh object:

[0299] ID=1 corresponds to the iron core and yoke model. Associated attribute: speed of sound. ;

[0300] ID=2 corresponds to the physical portion of the winding coil. Associated attribute: Equivalent composite speed of sound. ;

[0301] ID=3 corresponds to all unoccupied gaps and oil passages between windings. Associated attribute: speed of sound. ;

[0302] ID=4, corresponding tap switch, associated attribute: speed of sound ;

[0303] 6.2 Refined discretization of winding structure;

[0304] Existing technologies often simplify the winding into a solid cylinder, ignoring the tiny oil gaps between the coils, leading to distortion in the acoustic wave transmission simulation. This invention employs a layered slicing technique, based on the number of turns N and winding height calculated in step 3.2. According to the actual process structure, the winding cylinder is cut along the Z-axis into sections. A circular ring (a thread).

[0305] ;

[0306] in:

[0307] .

[0308] .

[0309] .

[0310] .

[0311] Between every two copper wire discs, a layer of thickness is forcibly inserted. An oil layer of 4 mm.

[0312] 6.3 Generation of heterogeneous voxel fields;

[0313] A 3D mesh with a resolution of 5mm is defined, using the entire area of ​​the fuel tank as the boundary. The mesh is then iterated through the center point of each voxel. If the point is inside the iron core mesh, the assignment matrix V[i, j, k] = 5200; if the point is inside the winding mesh, the assignment matrix V[i, j, k] = 3800; otherwise, the assignment matrix V[i, j, k] = 1400 (oil).

[0314] 7. Human-computer interactive model optimization based on physical constraints (same as the geometric constraint controller for the registered transformer model set above, wherein the geometric constraint controller includes degree-of-freedom constraints, which are used to lock the movement degrees of freedom of multiple components).

[0315] 7.1 Multi-dimensional information fusion rendering;

[0316] The point cloud data of the inner wall of the oil tank obtained by SLAM scanning was rendered using X-Ray semi-transparent material as a visual reference. The parametrically generated internal body model was rendered with solid material and overlaid on the point cloud layer.

[0317] 7.2 Constrained Interaction Logic Based on Geometric Constraints;

[0318] To prevent human error from causing the model to violate physical laws, the system incorporates a geometric constraint controller. When a movement command is received for a specific component (such as a tap changer or lead wire), the system automatically locks the component's degrees of freedom in non-permitted directions (e.g., locking the Z-axis height, allowing movement only in the XY plane). The system monitors the collision status of the component's bounding box with the point cloud of the tank's inner wall in real time. When the user drags a component attempting to pass through the tank wall, the system automatically calculates the contact point normal vector and applies a reverse virtual repulsive force, forcibly confining the component's coordinates within the effective topological space inside the tank. When the user manually adjusts key geometric parameters such as the core diameter, the system automatically triggers the parametric engine in step 3, updating the winding turns, window height, and oil channel width in real time to ensure that the model still meets the basic constraints of electromagnetic and insulation design after manual modifications, avoiding data silos.

[0319] The above optional implementation methods can achieve at least the following beneficial effects:

[0320] (1) This invention solves the problem of mismatch between theoretical models and actual space, and achieves high-precision virtual-real geometric fusion. Compared with the existing technology, the internal model calculated solely by the nameplate formula is often idealized and often cannot be placed in the actual tank due to manufacturing tolerances or compact design (through mold) or excessive insulation gap. The optional implementation of this invention introduces the point cloud of SLAM scanning as a geometric hard constraint and establishes a set of parameter adaptive reverse correction mechanism. The system can automatically detect and fine-tune design parameters such as the core diameter coefficient to ensure that the generated internal body model can perfectly fit the measured tank space in terms of geometric dimensions. This makes the model reconstructed without drawings no longer just theoretical, but a precise twin that conforms to the actual size constraints of the site.

[0321] (2) This invention solves the problem of separating the internal and external coordinate systems, significantly improving the reference accuracy of partial discharge source localization. Compared with existing technologies that typically rely on manual estimation to place the internal model inside the tank, lacking an alignment reference, resulting in a relative position error of tens of centimeters between the sensor (located externally) and the winding (located internally), this invention innovatively utilizes an externally visible bushing as a spatial anchor point connecting the internal and external systems. By automatically calculating the topological correspondence between the bushing and the winding leads through an algorithm, automatic rigid registration of the internal and external coordinate systems is achieved. This design ensures that the relative position accuracy between the sensor and the internal structure in the model reaches the millimeter level, providing an accurate spatial reference for subsequent calculation of the time delay difference (TDoA) of the partial discharge signal;

[0322] (3) It breaks through the bottleneck that general geometric models cannot be used for physical simulation and significantly corrects the partial discharge localization error. It solves the problem that the models generated by existing technologies are mostly pure geometric appearances, and the partial discharge algorithm is forced to simplify the transformer into a single uniform medium (all oil), ignoring the blocking and refraction of signals by metal and insulating paperboard, resulting in large localization deviations.

[0323] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0324] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0325] Example 2

[0326] According to an embodiment of the present invention, an apparatus for implementing the above-described transformer modeling method is also provided. Figure 4 This is a structural block diagram of a transformer modeling device according to an embodiment of the present invention, such as... Figure 4 As shown, the device includes: an acquisition module 402, a first determination module 404, a second determination module 406, and a registration module 410. The device will be described in detail below.

[0327] The acquisition module 402 is used to acquire external point cloud data and internal structural parameters corresponding to the target transformer. The target transformer includes multiple components, including an oil tank and internal devices. The oil tank is equipped with bushings, and the internal devices include an iron core and windings connected to the bushings. The external point cloud data includes bushing point cloud data. The first determination module 404, connected to the acquisition module 402, is used to determine the geometric boundary constraints corresponding to the oil tank based on the external point cloud data. The second determination module 406, connected to the first determination module 404, is used to determine the initial transformer model corresponding to the internal devices based on the internal structural parameters. The third determination module 408, connected to the second determination module 406, is used to determine the modified transformer model based on the geometric boundary constraints and the initial transformer model. The registration module 410, connected to the second determination module 406, is used to register the internal device models inside the modified transformer model with the bushing point cloud data as anchor point data to obtain the registered transformer model.

[0328] It should be noted here that the above-mentioned acquisition module 402, first determination module 404, second determination module 406, and registration module 410 correspond to steps S102 to S110 in the transformer modeling method. The multiple modules and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiment 1.

[0329] Example 3

[0330] Embodiments of this application may provide a computing device. Figure 5 This is a structural block diagram of a computing device according to an embodiment of this application. Figure 5 As shown, the computing device may include one or more (one shown in the figure) processors, memory, memory controllers, and peripheral interfaces.

[0331] The aforementioned computing device can be understood as an integrated intelligent terminal, including but not limited to servers, desktop computers, PCs (Personal Computers), and all-in-one model machines. Furthermore, the computing device may pre-install the model described in the above embodiments of this application.

[0332] Example 4

[0333] According to another aspect of the present invention, an electronic device is also provided. Figure 6 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 6 As shown, it includes: a device processor; and a device memory for storing executable instructions of the device processor, wherein the device processor is configured to execute instructions to implement the transformer modeling method of any of the above.

[0334] Example 5

[0335] According to another aspect of the present invention, a computer-readable storage medium is also provided, which, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the transformer modeling method described above.

[0336] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0337] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0338] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0339] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0340] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0341] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0342] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for modeling a transformer, characterized in that, include: Acquire external point cloud data and internal structural parameters corresponding to the target transformer. The target transformer includes multiple components, including an oil tank and internal devices. The oil tank is equipped with a bushing. The internal devices include an iron core and windings. The windings are connected to the bushings. The external point cloud data includes bushing point cloud data. Based on the external point cloud data, determine the geometric boundary constraints corresponding to the fuel tank; Based on the internal structural parameters, determine the initial transformer model corresponding to the internal components; Based on the geometric boundary constraints and the initial transformer model, the modified transformer model is determined; Using the bushing point cloud data as anchor point data, the internal component models inside the modified transformer model are registered to obtain the registered transformer model.

2. The method according to claim 1, characterized in that, Based on the external point cloud data, determine the geometric boundary constraints corresponding to the fuel tank, including: Based on the external point cloud data, a standardized coordinate system is constructed with the physical center of the transformer as the reference. In the standardized coordinate system, the external point cloud data is corrected to obtain corrected point cloud data; Based on the corrected point cloud data, determine the outer surface dimension parameters corresponding to the fuel tank; Based on the outer surface dimension parameters and wall thickness compensation parameters, determine the inner wall dimension parameters corresponding to the oil tank; Based on the inner wall dimension parameters, determine the geometric boundary constraints corresponding to the oil tank.

3. The method according to claim 1, characterized in that, Based on the internal structural parameters, determine the initial transformer model corresponding to the internal components, including: Based on the internal structural parameters, determine the core geometric parameters and the tap changer modeling radius; Based on the core geometry parameters, determine the winding parameters; Based on the core geometric parameters and the winding parameters, an initial internal device model corresponding to the internal device and an assembly pose corresponding to the initial internal device model are determined. The initial internal device model includes a core model, which is a multi-level stepped core model. Based on the tap switch modeling radius, tap switch modeling height, and tap switch modeling position, determine the tap switch model and the switch pose corresponding to the tap switch model; The initial transformer model is determined based on the initial internal device model and the assembly pose corresponding to the initial internal device model, as well as the tap changer model and the switch pose corresponding to the tap changer model.

4. The method according to claim 1, characterized in that, Based on the geometric boundary constraints and the initial transformer model, the modified transformer model is determined, including: Based on the initial transformer model, determine the total limit dimensions of the devices corresponding to the internal components; Based on the geometric boundary constraints and the total limit dimensions of the device, the actual net insulation distance between the internal device and the oil tank is determined; Based on the actual insulation clearance, determine the margin deviation value corresponding to the initial transformer model; If the margin deviation value is less than a predetermined threshold, the initial transformer model is iteratively corrected until a predetermined condition is met to obtain the corrected transformer model. The predetermined condition includes at least one of the following: obtaining a transformer model with a corresponding deviation margin value greater than or equal to the predetermined threshold, and the number of iterations reaching a predetermined number.

5. The method according to claim 1, characterized in that, Using the bushing point cloud data as anchor point data, the internal component models within the modified transformer model are registered to obtain a registered transformer model, including: Based on the casing point cloud data, determine the set of casing anchor point coordinates in the standardized coordinate system; Based on the modified transformer model, determine the set of winding anchor point coordinates corresponding to the winding in the internal device model under the standardized coordinate system; Based on the set of bushing anchor points and the set of winding anchor points, the internal component models inside the modified transformer model are registered to obtain the registered transformer model.

6. The method according to claim 1, characterized in that, Using the bushing point cloud data as anchor point data, the internal component models within the modified transformer model are registered. After obtaining the registered transformer model, the process further includes: Obtain the geometric structure data and physical property parameters corresponding to the registered transformer model, wherein the geometric structure data includes the three-dimensional contour information corresponding to the plurality of components respectively, and the physical property parameters include the physical field propagation parameters corresponding to the plurality of components respectively; Based on the geometric structure data and the physical property parameters, the registered transformer model is discretized into discrete voxel units. Based on the discrete voxel units, a voxel mesh corresponding to the registration transformer model is generated.

7. The method according to any one of claims 1 to 6, characterized in that, Using the bushing point cloud data as anchor point data, the internal component models within the modified transformer model are registered to obtain the registered transformer model, which includes: A geometric constraint controller is set up corresponding to the registered transformer model, wherein the geometric constraint controller includes degree-of-freedom constraints, which are used to lock the movement degrees of freedom of the plurality of components.

8. A transformer modeling device, characterized in that, include: The acquisition module is used to acquire external point cloud data and internal structural parameters corresponding to the target transformer. The target transformer includes multiple components, including an oil tank and internal devices. The oil tank is equipped with a bushing. The internal devices include an iron core and windings. The windings are connected to the bushings. The external point cloud data includes bushing point cloud data. The first determining module is used to determine the geometric boundary constraints corresponding to the fuel tank based on the external point cloud data. The second determining module is used to determine the initial transformer model corresponding to the internal device based on the internal structural parameters. The third determining module is used to determine the modified transformer model based on the geometric boundary constraints and the initial transformer model; The registration module is used to register the internal component models inside the modified transformer model with the bushing point cloud data as anchor point data, so as to obtain the registered transformer model.

9. An electronic device, characterized in that, include: Device processor; Device memory used to store executable instructions of the device processor; The device processor is configured to execute the instructions to implement the transformer modeling method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform the transformer modeling method as described in any one of claims 1 to 7.

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