Simulation model generation method and device of transformer, readable medium and electronic equipment

Abstract

The invention discloses a simulation model generation method and device of a transformer, a readable medium and electronic equipment, and relates to the field of power equipment. The simulation model generation method of the transformer comprises the following steps: obtaining structure information of a converter transformer, wherein the structure information comprises a plurality of substructures; determining a hierarchical type of the sub-structure, and obtaining a sub-model of the sub-structure according to the hierarchical type of the sub-structure; actual parameters of the converter transformer are obtained, and the sub-models are adjusted according to the actual parameters; and carrying out registration on the adjusted sub-models, and combining the registered sub-models to obtain a simulation model of the converter transformer. The method can improve the accuracy of model construction.

Description

Technical Field

[0001] The present application relates to the field of power equipment, and in particular to a method, device, readable medium and electronic device for generating a simulation model of a transformer. Background Art

[0002] Digital twin is the process of modeling, simulating and analyzing physical entities, processes or systems in the physical world through digital technology to achieve the ability to monitor, optimize and predict them. Digital twin is to build a virtual model of an object or system and update the model according to the real-time data of the real object. Registration is an important link in digital twin modeling. Registration aligns images, maps, models or other information from multiple or multiple data sources to the same coordinate system or reference frame to improve the accuracy, consistency and availability of data and provide reliable basic data and information support for various applications. Digital twin can help optimize and predict electrical systems. By analyzing and simulating the digital model of the system, it can optimize and predict the system performance, efficiency and stability. The converter transformer has a complex structure, a changeable operating environment, and a complex multi-physical field coupling relationship. It is difficult to support the accuracy of the simulation model by relying on traditional model registration methods. Summary of the invention

[0003] The present application provides a method, device, readable medium and electronic device for generating a simulation model of a transformer, which can improve the accuracy of constructing the transformer simulation model.

[0004] In a first aspect, the present application provides a method for generating a simulation model of a transformer, comprising:

[0005] Acquire structural information of a converter transformer, wherein the structural information includes a plurality of substructures;

[0006] Determine the hierarchical type of the substructure, and obtain the submodel of the substructure according to the hierarchical type of the substructure;

[0007] Acquiring actual parameters of the converter transformer, and adjusting the sub-model according to the actual parameters;

[0008] The adjusted sub-models are aligned, and the aligned sub-models are merged to obtain a simulation model of the converter transformer.

[0009] According to the simulation model generation method of the transformer in this embodiment, according to the structural information of the converter transformer, the converter transformer is divided into multiple parts, each part is a substructure, and then different substructures are modeled in different ways, which can make the modeling method more suitable for each part, thereby improving the efficiency and quality of modeling as a whole. After the submodel corresponding to each substructure is obtained by modeling, the submodel is adjusted by comparing it with the actual parameters of the converter transformer, further improving the consistency between the model and the real converter transformer and improving the accuracy of the model. Then, the submodels are aligned and merged to obtain the overall simulation model of the converter transformer, and the model scales of each part can be unified. In addition, the simulation model includes the details of each part, which is conducive to the simulation control of the converter transformer.

[0010] In an exemplary implementation, generating the model of the substructure according to the hierarchical type corresponding to the substructure includes:

[0011] When the hierarchical type corresponding to the substructure is an overall layer, acquiring point cloud data of the substructure, and generating a point cloud geometric model based on the point cloud data;

[0012] When the hierarchical type corresponding to the substructure is a component layer, parameter information of the substructure is obtained, and parameterized modeling is performed on the substructure based on the parameter information to obtain a parameterized model.

[0013] In an exemplary implementation, adjusting the sub-model according to the actual parameter includes:

[0014] Inputting the actual parameters into a preset decision tree, and obtaining the optimization type of the actual parameters through the preset decision tree;

[0015] An optimization element corresponding to the optimization type is obtained, and a size of the optimization element in the sub-model is adjusted.

[0016] In an exemplary embodiment, adjusting the size of the optimization element in the sub-model includes:

[0017] Obtaining the actual size of the converter transformer from the actual parameters;

[0018] The particle swarm optimization algorithm is used to optimize the size of the optimization element in the sub-model until the difference between the sub-model and the actual size is less than a preset value, thereby obtaining an optimized sub-model.

[0019] In an exemplary implementation, the registering the adjusted sub-models includes:

[0020] Acquiring a real image of the converter transformer;

[0021] Detecting feature points in the real image and each of the sub-models;

[0022] Matching the feature points to determine the transformation relationship between the feature points in the real image and the feature points in each of the sub-models;

[0023] Each of the sub-models is transformed based on the transformation relationship to obtain a registered sub-model.

[0024] In an exemplary implementation, the registering the adjusted sub-models includes:

[0025] Using a scale-invariant feature transformation algorithm to determine the transformation relationship between each of the sub-models;

[0026] Each of the sub-models is transformed based on the transformation relationship to obtain a registered sub-model.

[0027] In an exemplary implementation, the obtaining actual parameters of the converter transformer and adjusting the sub-model according to the actual parameters includes:

[0028] It is detected whether the actual parameters of the converter transformer are updated. If the actual parameters are updated, the sub-model is readjusted using the updated actual parameters.

[0029] In a second aspect, the present application provides a device for generating a simulation model of a transformer, comprising:

[0030] A structure acquisition module, used to acquire the structure information of the converter transformer, wherein the structure information includes a plurality of substructures;

[0031] A hierarchical modeling module, used to determine the hierarchical type of the substructure and obtain a submodel of the substructure according to the hierarchical type of the substructure;

[0032] A model adjustment module, used for obtaining actual parameters of the converter transformer and adjusting the sub-model according to the actual parameters;

[0033] The model registration module is used to register the adjusted sub-models and merge the registered sub-models to obtain the simulation model of the converter transformer.

[0034] In a third aspect, the present application provides an electronic device, the electronic device comprising a memory and one or more processors. The memory stores one or more computer programs, the computer programs comprising instructions, and when the instructions are executed by the processor, the electronic device can execute the method for generating a simulation model of a transformer as in the first aspect.

[0035] In a fourth aspect, the present application provides a computer-readable storage medium having instructions stored therein. When the instructions are executed on an electronic device, the electronic device executes the method for generating a simulation model of a transformer as in the first aspect.

[0036] In a fifth aspect, the present application provides a computer program product. When the computer program product is run on an electronic device, the electronic device executes the method for generating a simulation model of a transformer as described in the first aspect.

[0037] It can be understood that the beneficial effects that can be achieved by the transformer simulation model generation device, electronic device, computer-readable storage medium, and computer program product provided above can refer to the beneficial effects in the first aspect and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic diagram of a flow chart of a method for generating a simulation model of a transformer provided in an embodiment of the present application;

[0039] Figure 2 A schematic diagram of the structure of a device for generating a simulation model of a transformer provided in an embodiment of the present application;

[0040] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to facilitate the clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, the words "first", "second" and the like are used to distinguish the same items or similar items with substantially the same functions and effects. For example, the first chip and the second chip are only used to distinguish different chips, and their order is not limited. Those skilled in the art can understand that the words "first", "second" and the like do not limit the quantity and execution order, and the words "first", "second" and the like do not necessarily limit the difference. It should be noted that in the embodiments of the present application, the words "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or design solutions. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way. In the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more.

[0042] It should be noted that the “at…” in the embodiments of the present application may be the instant when a certain situation occurs, or may be a period of time after the occurrence of a certain situation, and the embodiments of the present application do not specifically limit this.

[0043] The implementation of this embodiment will be described in detail below with reference to the accompanying drawings.

[0044] The present embodiment provides a method for generating a simulation model of a transformer. By way of example, the method for generating a simulation model of a transformer can be applied to various electronic devices such as computers (PCs), tablet computers, virtual reality / augmented reality devices, wearable devices, industrial computers, and vehicle computers; it can also be applied to servers, cloud computing, server clusters, etc., and the present embodiment does not make any special limitation on this.

[0045] Figure 1 A schematic flow chart of a method for generating a simulation model of a transformer provided in an embodiment of the present application is shown.

[0046] like Figure 1 As shown, the method for generating a simulation model of the transformer may include the following steps:

[0047] Step 101: Acquire structural information of a converter transformer, wherein the structural information includes multiple substructures.

[0048] The method of this embodiment can be applied to converter transformers. Converter transformer refers to an electric power transformer connected between a converter bridge and an AC system. A converter transformer is used to connect the converter bridge to the AC bus and provide a three-phase commutation voltage with an ungrounded neutral point for the converter bridge. The structural information of the converter transformer refers to the real structural level of the converter transformer, and the real structure of the converter transformer can be divided into multiple different levels in advance. In this exemplary embodiment, the real structure of the converter transformer is divided into three levels, namely: overall level, component level, and detail level. Each level type may include different component information, each component may be a substructure, and a substructure may also include multiple components of the same level type. For example, the structural information may be: overall level, specifically the overall outline, structure, size, etc. of the transformer; component level, specifically the core, winding, bushing, tap changer, oil pillow, etc.; detail level, specifically the conductor layout, insulation thickness, etc. of the winding.

[0049] Step 102: Determine the hierarchical type of the substructure, and obtain the submodel of the substructure according to the hierarchical type of the substructure.

[0050] Exemplarily, the hierarchy types may include an overall layer, a component layer, and a detail layer. Different hierarchy types adopt different modeling methods, specifically including: when the hierarchy type corresponding to the substructure is an overall layer, obtaining the point cloud data of the substructure, and generating a point cloud geometric model based on the point cloud data; when the hierarchy type corresponding to the substructure is a component layer, obtaining parameter information of the substructure, and performing parametric modeling on the substructure based on the parameter information to obtain a parametric model.

[0051] For the substructure of the overall layer, this type of substructure can characterize the structural contour of the converter transformer. Therefore, the point cloud data of the substructure of the overall layer can be collected by laser, camera, etc., and then the point cloud data is used to construct a point cloud geometric model of the substructure of the overall layer by three-dimensional reconstruction. For example, a laser scanning device is used for on-site scanning to obtain the contour data of the converter transformer, and these data are converted into point cloud data of three-dimensional coordinates. Then, the point cloud data is filtered and other preliminary processing is performed to obtain the geometric shape and structure of the overall layer of the converter transformer. Then, the three-dimensional modeling software is used to correct the point cloud data after preliminary processing, and the corrected point cloud data is used to generate a submodel of the substructure of the overall layer.

[0052] For the substructures of the component layer and the detail layer, the parameters of the core, winding, etc. are obtained through the description information and drawing data of the converter transformer, such as CAD data, and then the parametric modeling interface is used to parametrically model the substructures of the component layer and the detail layer to obtain the submodel of the corresponding substructure.

[0053] For each substructure of the converter transformer, a submodel of each substructure is constructed according to the hierarchical type of the substructure.

[0054] Step 103: Acquire actual parameters of the converter transformer, and adjust the sub-model according to the actual parameters.

[0055] The actual parameters of the converter transformer may include the actual size of the converter transformer, and may also include the actual operating conditions, temperature, electric field, magnetic field and other data of the converter transformer. The actual parameters include the actual size of the converter transformer. According to the actual size and the size of each sub-model, it is possible to calculate and model whether the size of the simulation model matches the actual size. If the difference between the two is too large, the sub-model can be adjusted to reduce the difference between the actual size and the size of the simulation model and improve the accuracy of the simulation model.

[0056] Adjusting the sub-model according to the actual parameters specifically includes: inputting the actual parameters into a preset decision tree, obtaining the optimization type of the actual parameters through the preset decision tree; obtaining the optimization element corresponding to the optimization type, and adjusting the size of the optimization element in the sub-model.

[0057] The preset decision tree is a pre-trained model used to classify the current optimization type of the converter transformer according to the current actual parameters of the converter transformer. The optimization types may include temperature, electric field, and magnetic field. The operating conditions, temperature, electric field, and magnetic field data of the converter transformer are detected in real time to obtain the current actual parameters. The current actual parameters are then input into the preset decision tree to obtain the optimization type of its output. Each optimization type corresponds to one or more elements to be optimized, i.e., optimization elements. The optimization element that currently needs to be optimized can be determined based on the optimization type corresponding to the current actual parameters. The size of the optimization element in the sub-model is then adjusted. Each optimization element may include multiple size parameters to be optimized, as shown in the following table:

[0058]

[0059] Exemplarily, the adjusting the size of the optimized element in the sub-model includes: obtaining the actual size of the sub-structure from the actual parameters; optimizing the target part in the sub-model using a particle swarm optimization algorithm until the difference between the actual size of the sub-model and the corresponding sub-structure is less than a preset value, thereby obtaining an optimized sub-model.

[0060] The particle swarm optimization algorithm (PSO) is used to search for the optimal solution under multiple size parameters to meet the preset constraints. Specifically, PSO can be expressed as:

[0061]

[0062] Among them, v i t is the velocity update speed of particle i at time t, x i t is the position of particle i at time t, pbest{i} is the individual optimal solution of the particle, gbest is the global optimal solution, w is the inertia weight, c 1 and c 2 is the learning factor, and rand() is the random number generation function. The PSO algorithm is used to dynamically adjust the size of converter transformer components and establish a sub-model of the global optimal solution. Under different external conditions and set goals, the comparison error between the model and the actual object is ≤4%, thus achieving the establishment of a high-fidelity converter transformer model.

[0063] The comparison error R between the model and the real object is calculated by the following formula:

[0064]

[0065] In the formula, R i is the relative error of the i-th size parameter, Mi is the value of the i-th geometric parameter in the model, A i is the actual measured value of the ith dimension parameter. n is the number of dimension parameters.

[0066] The optimal size parameters of each optimization element are determined through the PSO optimization algorithm, so that the constructed sub-model is close to the actual size of the converter transformer, improving the consistency between the model construction and the converter transformer.

[0067] Step 104: aligning the adjusted sub-models, merging the aligned sub-models, and obtaining a simulation model of the converter transformer.

[0068] The sub-models corresponding to different sub-structures are constructed using different modeling methods, and it is necessary to unify the sub-models to the same scale. Specifically, the adjusted sub-models are registered, including the following: obtaining a real image of the converter transformer; detecting feature points in the real image and each sub-model; matching the feature points to determine the transformation relationship between the feature points in the real image and the feature points in each sub-model; transforming each sub-model based on the transformation relationship to obtain the registered sub-model.

[0069] The real image of the converter transformer is obtained through a camera or other shooting equipment, and the data or images of sub-models at different levels can also be obtained. The feature points in the real image and the feature points in each sub-model are obtained through feature point detection, and then the feature points are matched to determine the correspondence between the feature points of each sub-model and the feature points in the real image. The correspondence is used to indicate the feature points in the sub-model corresponding to the feature points in the real image. The transformation relationship of each sub-model is calculated using the feature point pairs with corresponding relationships, and the coordinates of each sub-model are transformed so that they are aligned or corresponded in the same coordinate system to achieve model registration. After obtaining the registered sub-models, all sub-models are merged to obtain the overall simulation model of the converter transformer.

[0070] The simulation model of the converter transformer can be used to construct a digital twin system corresponding to the converter transformer, and the operation of the converter transformer can also be simulated and calculated based on the simulation model to achieve prediction and control of the operating state of the converter transformer.

[0071] In this embodiment, the Scale Invariant Feature Transform (SIFT) algorithm is used to determine the transformation relationship of each sub-model; each sub-model is transformed based on the transformation relationship to obtain the registered sub-model. Multiple data sets or images of sub-models at different levels of the converter transformer are associated so that they are aligned or corresponded in the same coordinate system, achieving high-fidelity hierarchical registration, enhancing the consistency of the model with the real converter transformer, and the accuracy of the simulation.

[0072] By using the scale-invariant feature of the SIFT algorithm, Gaussian filters at different scales are used to construct Gaussian pyramids, so that local extreme points in the image are detected at different levels as candidate feature points to achieve feature point (also called key point) detection. Gaussian pyramid formula:

[0073] D(x,y,σ)=(G(x,y,k·σ)-G(x,y,σ))*I(x,y) (3)

[0074] Among them, G(x,y,σ) is the Gaussian function, x and y are the pixel coordinates in the image, σ is the scale parameter in space, which indicates the degree of Gaussian blur at different scale layers. k is the scale factor, and I(x,y) is the real image.

[0075] Use the following conditions to find extreme points, i.e. key points, in the scale space:

[0076]

[0077] By accurately locating the extreme points in the scale space, detecting the local extreme points in the image in different scale spaces as candidate feature points, determining the key points with stable features, and eliminating the points with low contrast and weak edge response to achieve key point positioning, as shown below:

[0078]

[0079] x is the key point position, δx is the offset of the position, is the gradient, is the Hessian matrix.

[0080] Then assign a main direction to each key point so that the descriptor is rotation invariant. The details are as follows:

[0081]

[0082] Where: m(x,y) is the gradient magnitude, θ(x,y) is the gradient direction, and a main direction is assigned to each key point so that the descriptor is rotationally invariant.

[0083] Based on the gradient information around the key points, a descriptor vector is constructed to describe the local features of the area around the key points. SIFT uses 16×16 pixel blocks, each of which is divided into 4×4 small blocks. The gradient amplitude and direction are calculated for each small block, and a feature vector containing 128 elements is constructed. The distance or similarity between descriptors is used to match feature points to obtain mutually matching feature point pairs, which are used to determine the transformation relationship of the sub-model, thereby achieving image registration, etc.

[0084] The SIFT algorithm can detect the same feature points at different scales, so that the feature points can still be accurately detected after the image is enlarged or reduced. The same feature points can still be detected when the image is rotated, and feature descriptors that are independent of rotation can be extracted. It has good stability and robustness to the influence of image translation, scaling, rotation, lighting changes, etc.

[0085] This implementation method also includes: detecting whether the actual parameters of the converter transformer are updated, and if the actual parameters are updated, using the updated actual parameters to readjust the sub-model.

[0086] By performing real-time detection on the converter transformer, the actual parameters can be updated in real time. When the actual parameters change, the changed actual parameters are used to readjust each sub-model.

[0087] Furthermore, this embodiment also provides a transformer simulation model generation device, which can be used to execute the transformer simulation model generation method. Figure 2 As shown, the simulation model generating device 200 of the transformer specifically includes: a structure acquisition module 201, used for acquiring the structural information of the converter transformer, wherein the structural information includes a plurality of substructures; a hierarchical modeling module 202, used for determining the hierarchical type of the substructure, and acquiring the submodel of the substructure according to the hierarchical type of the substructure; a model adjustment module 203, used for acquiring the actual parameters of the converter transformer, and adjusting the submodel according to the actual parameters; a model alignment module 204, used for aligning the adjusted submodels, merging the aligned submodels, and obtaining the simulation model of the converter transformer.

[0088] In an exemplary embodiment, the hierarchical modeling module 202 is specifically used to: when the hierarchical type corresponding to the substructure is the overall layer, obtain the point cloud data of the substructure, and generate a point cloud geometric model based on the point cloud data; when the hierarchical type corresponding to the substructure is the component layer, obtain the parameter information of the substructure, and parametrically model the substructure based on the parameter information to obtain a parametric model.

[0089] In an exemplary embodiment, the model adjustment module 203 specifically includes a parameter optimization module, which is used to input the actual parameters into a preset decision tree, obtain the optimization type of the actual parameters through the preset decision tree; obtain the optimization element corresponding to the optimization type, and adjust the size of the optimization element in the sub-model.

[0090] In an exemplary embodiment, the parameter optimization module is also used to: obtain the actual size of the converter transformer from the actual parameters; optimize the size of the optimization element in the sub-model using a particle swarm optimization algorithm until the difference between the sub-model and the actual size is less than a preset value, thereby obtaining an optimized sub-model.

[0091] In an exemplary embodiment, the model registration module 204 is specifically used to: obtain a real image of the converter transformer; detect feature points in the real image and each of the sub-models; match the feature points to determine the transformation relationship between the feature points in the real image and the feature points in each of the sub-models; transform each of the sub-models based on the transformation relationship to obtain a registered sub-model.

[0092] In an exemplary embodiment, the model registration module 204 is further specifically configured to determine the transformation relationship between each of the sub-models using a scale-invariant feature transformation algorithm; and transform each of the sub-models based on the transformation relationship to obtain the registered sub-models.

[0093] In an exemplary embodiment, the transformer simulation model generation device further includes an updating module, which is used to detect whether actual parameters of the converter transformer are updated, and if the actual parameters are updated, readjust the sub-model using the updated actual parameters.

[0094] The specific details of each module or unit in the above-mentioned transformer simulation model generation device have been described in detail in the corresponding transformer simulation model generation method, so they will not be repeated here.

[0095] The embodiment of the present application also provides an electronic device, Figure 3 A schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure is shown. Figure 3 The electronic device 600 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0096] like Figure 3As shown, electronic device 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 into a random access memory (RAM) 603. In RAM 603, various programs and data required for system operation are also stored. CPU 601, ROM 602 and RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to bus 604.

[0097] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage section 608 as needed.

[0098] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, the above-mentioned functions defined in the embodiment of the present application are executed.

[0099] For example, when the computer program is executed by the central processing unit (CPU) 601, it can be executed as follows: obtaining structural information of the converter transformer, the structural information including multiple substructures; determining the hierarchical type of the substructure, and obtaining a submodel of the substructure according to the hierarchical type of the substructure; obtaining actual parameters of the converter transformer, and adjusting the submodel according to the actual parameters; aligning the adjusted submodels, merging the aligned submodels, and obtaining a simulation model of the converter transformer.

[0100] It should be noted that the computer-readable medium shown in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0101] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0102] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, and the units described may also be arranged in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0103] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and the one or more programs include instructions, and when the instructions are executed by the electronic device, the electronic device implements the method described in the above embodiment.

[0104] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0105] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application shall be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for generating a simulation model of a transformer, characterized in that: include: Acquire structural information of a converter transformer, wherein the structural information includes a plurality of substructures; Determine the hierarchical type of the substructure, and obtain the submodel of the substructure according to the hierarchical type of the substructure; Acquiring actual parameters of the converter transformer, and adjusting the sub-model according to the actual parameters; The adjusted sub-models are aligned, and the aligned sub-models are merged to obtain a simulation model of the converter transformer.

2. The method for generating a simulation model of a transformer according to claim 1, characterized in that: The step of generating a model of the substructure according to the hierarchical type corresponding to the substructure includes: When the hierarchical type corresponding to the substructure is an overall layer, acquiring point cloud data of the substructure, and generating a point cloud geometric model based on the point cloud data; When the hierarchical type corresponding to the substructure is a component layer, parameter information of the substructure is obtained, and parameterized modeling is performed on the substructure based on the parameter information to obtain a parameterized model.

3. The method for generating a simulation model of a transformer according to claim 1, characterized in that: The adjusting the sub-model according to the actual parameter comprises: Inputting the actual parameters into a preset decision tree, and obtaining the optimization type of the actual parameters through the preset decision tree; An optimization element corresponding to the optimization type is obtained, and a size of the optimization element in the sub-model is adjusted.

4. The method for generating a simulation model of a transformer according to claim 3, characterized in that: The adjusting the size of the optimization element in the sub-model includes: Obtaining the actual size of the converter transformer from the actual parameters; The particle swarm optimization algorithm is used to optimize the size of the optimization element in the sub-model until the difference between the sub-model and the actual size is less than a preset value, thereby obtaining an optimized sub-model.

5. The method for generating a simulation model of a transformer according to claim 1, characterized in that: The registering the adjusted sub-models comprises: Acquiring a real image of the converter transformer; Detecting feature points in the real image and each of the sub-models; Matching the feature points to determine the transformation relationship between the feature points in the real image and the feature points in each of the sub-models; Each of the sub-models is transformed based on the transformation relationship to obtain a registered sub-model.

6. The method for generating a simulation model of a transformer according to claim 1, characterized in that: The registering the adjusted sub-models comprises: Using a scale-invariant feature transformation algorithm to determine the transformation relationship between each of the sub-models; Each of the sub-models is transformed based on the transformation relationship to obtain a registered sub-model.

7. The method for generating a simulation model of a transformer according to claim 1, characterized in that: The obtaining actual parameters of the converter transformer and adjusting the sub-model according to the actual parameters includes: It is detected whether the actual parameters of the converter transformer are updated. If the actual parameters are updated, the sub-model is readjusted using the updated actual parameters.

8. A device for generating a simulation model of a transformer, characterized in that: include: A structure acquisition module, used to acquire the structure information of the converter transformer, wherein the structure information includes a plurality of substructures; A hierarchical modeling module, used to determine the hierarchical type of the substructure and obtain a submodel of the substructure according to the hierarchical type of the substructure; A model adjustment module, used for obtaining actual parameters of the converter transformer and adjusting the sub-model according to the actual parameters; The model registration module is used to register the adjusted sub-models and merge the registered sub-models to obtain the simulation model of the converter transformer.

9. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the method for generating a simulation model of a transformer according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The electronic device comprises a processor and a memory, wherein one or more computer programs are stored in the memory, and the one or more computer programs include instructions. When the instructions are executed by the electronic device, the electronic device executes the method for generating a simulation model of a transformer according to any one of claims 1 to 7.

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