Risk quantitative analysis method and system for transformer failure mode and related device
Through a hybrid evaluation framework of multi-physics field coupling simulation and deep learning, the problems of data fragmentation and model separation in transformer failure risk assessment are solved, dynamic collaborative quantification of transformer failure risk is achieved, and the evaluation accuracy and adaptability are improved.
Patent Information
- Application Number
- CN202510895392.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-30
- Publication Date
- 2025-09-26
AI Technical Summary
Existing transformer failure risk assessment methods have problems such as data limitations, model uniformity, and insufficient quantification of random failure risks, which lead to inaccurate assessment results and difficulty in coping with the dynamic changes of transformers.
A hybrid evaluation framework of multi-physics field coupling simulation and deep learning is adopted. By constructing a transformer electromagnetic-thermal-environmental coupling simulation model and an LSTM failure prediction model, risk assessment is performed in combination with real-time data to achieve dynamic collaborative quantification.
The accuracy of transformer failure risk assessment has been improved by more than 40%, and risk probability distribution maps can be generated in real time to support the full life cycle management of transformers.
Smart Images
Figure FT_1
Abstract
Description
Technical Field
[0001] The present invention relates to the field of transformer risk assessment, and in particular to a method, system and related devices for quantitative risk analysis in transformer failure modes. Background Art
[0002] Transformers are core equipment for energy conversion and transmission in power systems, and their operational reliability is directly related to the safety and stability of the power grid. However, over long-term operation, transformers are subject to multiple influences, including electromagnetic vibration, mechanical wear, chemical corrosion, and environmental factors. This can cause them to gradually deteriorate in health and increase their risk of failure.
[0003] Existing transformer failure risk assessment methods are mainly divided into two categories: model-driven methods and data-driven methods. However, these methods still have significant defects in practical applications, such as: (1) Data limitations Existing methods mostly rely on average values to quantify risks, ignoring the temporal and spatial differences of dynamic factors such as load behavior and meteorological conditions, resulting in assessment results that are out of touch with reality.
[0004] (2) Model singularity Insufficient overload risk assessment: Traditional methods usually only focus on a single indicator of temperature rise or aging, without comprehensively considering the synergistic effects of top oil temperature, winding hot spot temperature and insulation life loss.
[0005] Ignoring the risk of random failure: The impact of meteorological conditions (such as thunderstorms and typhoons) on random failure of transformers has not been fully quantified.
[0006] Current transformer failure risk assessment faces three major challenges: data fragmentation, model fragmentation, and poor dynamic adaptability. A hybrid assessment framework that integrates component-level health status, multi-physics coupling (electromagnetic, thermal, and environmental), and real-time data is urgently needed to advance risk quantification from "static segmentation" to "dynamic collaboration." Summary of the Invention
[0007] The purpose of the present invention is to provide a method, system and related devices for quantitative risk analysis under transformer failure modes, which solves the defect of inaccurate results in existing transformer failure risk assessment.
[0008] In order to achieve the above object, the technical solution adopted in the present invention is: In a first aspect, the present invention provides a method for quantitative risk analysis under transformer failure modes, comprising the following steps: The operating state parameters and environmental parameters of the transformer under test are used as inputs to the constructed electromagnetic-thermal-environmental coupled simulation model to simulate the dynamic stress distribution during transformer operation and obtain simulation parameters. The obtained operating state parameters, environmental parameters and simulation parameters are used as inputs of the pre-built transformer failure prediction model to predict the probability of failure of the transformer under test in the future. The risk of the transformer under test is evaluated based on the predicted probability of failure of the transformer under test in the future.
[0009] Preferably, the operating state parameters include winding current density, core magnetic permeability, winding temperature, partial discharge amplitude, transformer oil viscosity, and dissolved gas concentration in the oil; and the environmental parameters include ambient temperature and ambient humidity.
[0010] Preferably, a transformer electromagnetic-thermal-environmental coupling simulation model is constructed based on COMSOL.
[0011] Preferably, a transformer electromagnetic-thermal-environmental coupling simulation model is constructed based on COMSOL, and the specific method is: Establish a three-dimensional model of the transformer to be tested and import the three-dimensional model of the transformer into COMSOL; Determine the material properties required for transformer electromagnetic-thermal-environmental coupled simulation based on the transformer material; Add the physical fields required for transformer electromagnetic-thermal-environmental coupled simulation analysis, and set the boundary conditions of each physical field and couple them according to the actual conditions and the parameters required for transformer electromagnetic-thermal-environmental coupled simulation analysis; Set the mesh accuracy to mesh the 3D model and set the solver parameters to accelerate the simulation convergence, implement the electromagnetic-thermal-environmental coupled finite element simulation of the transformer, and obtain the simulation results; The obtained simulation results are compared with the operating state parameters, and Kalman filtering is used to correct the model parameters to obtain the optimal transformer electromagnetic-thermal-environmental coupling simulation model.
[0012] Preferably, the method for constructing the pre-built transformer failure prediction model includes: Build a transformer failure prediction model based on LSTM; The transformer failure prediction model is optimized using the historical fault data and simulation parameters of the transformer to be tested, and a pre-built transformer failure prediction model is obtained.
[0013] In a second aspect, the present invention provides a risk quantitative analysis system for transformer failure modes, comprising: The parameter acquisition unit uses the operating state parameters and environmental parameters of the transformer to be tested as inputs to the constructed electromagnetic-thermal-environmental coupling simulation model to simulate the dynamic stress distribution during transformer operation and obtain simulation parameters; A probability prediction unit is used to use the obtained operating state parameters, environmental parameters and simulation parameters as inputs of a pre-built transformer failure prediction model to predict the probability of failure of the transformer under test in the future; The risk assessment unit is used to assess the risk of the transformer under test based on the predicted probability of failure of the transformer under test in the future.
[0014] In a third aspect, the present invention provides an electronic device comprising a processor and a memory, wherein the memory stores computer instructions, and when the computer instructions are executed by the processor, the electronic device executes the method described.
[0015] In a fourth aspect, the present invention provides a computing device cluster, comprising at least one computing device, each computing device comprising a processor and a memory; The processor of the at least one computing device is configured to execute instructions stored in the memory of the at least one computing device, so that the computing device cluster performs the method.
[0016] In a fifth aspect, the present invention provides a computer program product, which includes computer-executable instructions, and the computer-executable instructions implement the method when executed.
[0017] In a sixth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions implement the described method when executed by a processor.
[0018] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a method for quantitative risk analysis under transformer failure modes. This method uses transformer simulation parameters, operating state parameters, and environmental parameters as inputs to a transformer failure prediction model, effectively resolving data fragmentation issues. Furthermore, traditional risk assessment relies on static segmentation thresholds (such as an alarm when the oil temperature exceeds 85°C), making it difficult to cope with dynamic changes in complex operating conditions. This application uses LSTM to construct a transformer failure prediction model, using operating state parameters, environmental parameters, and simulation parameters as inputs. This method generates a risk probability distribution map in real time, achieving a transition from "static segmentation" to "dynamic collaboration" and improving risk assessment accuracy by over 40%. This method eliminates information silos through data fusion, captures risk evolution patterns with a dynamic collaborative network, and improves assessment accuracy through the combination of multi-physics field simulation and deep learning, providing technical support for transformer lifecycle management.
[0019] Furthermore, by fusing real-time monitoring data with the Kalman filter and dynamically correcting simulation parameters, model adaptive optimization can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] Figure 1 Schematic diagram of a flow chart of an embodiment of the present invention. DETAILED DESCRIPTION
[0021] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.
[0022] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.
[0023] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
[0024] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.
[0025] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.
[0026] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.
[0027] Example 1 This embodiment provides a method for quantitative risk analysis under transformer failure modes. This method addresses the current problems of data fragmentation, model fragmentation, and poor dynamic adaptability in transformer risk assessment. This method proposes a hybrid assessment framework based on multi-physics field coupled dynamic simulation, real-time data fusion, and neural network deep learning. This framework achieves a transition from "static segmentation" to "dynamic collaboration" in risk quantification through the following steps: Step 1: Obtain operating state parameters, environmental parameters, and simulation parameters corresponding to the transformer to be tested respectively; Step 2: Using the obtained operating state parameters, environmental parameters, and simulation parameters as inputs to a pre-built transformer failure prediction model to predict the probability of failure of the transformer under test in the future. Step 3: Evaluate the risk of the transformer under test based on the predicted probability of failure of the transformer under test in the future.
[0028] This method achieves dynamic collaborative quantitative analysis of transformer failure mode risks through multi-physics field coupling simulation, real-time data fusion and neural network deep learning, solves the three major challenges in current assessments, and provides strong support for the safe and stable operation of power systems.
[0029] Example 2 Based on Example 1, this embodiment provides a method for quantitative risk analysis under transformer failure modes. In step 1, the operating status parameters include winding current density, core magnetic permeability, winding temperature, partial discharge amplitude, transformer oil viscosity, and dissolved gas concentration in the oil; and the environmental parameters include ambient temperature and ambient humidity.
[0030] The method to obtain simulation parameters is: The transformer electromagnetic-thermal-environmental coupling simulation model was constructed based on COMSOL; The operating state parameters and environmental parameters are used as inputs of the transformer electromagnetic-thermal-environmental coupling simulation model to simulate the stress distribution of the transformer under different working conditions and obtain simulation parameters.
[0031] Example 3 Based on Example 2, this embodiment provides a method for quantitative risk analysis under transformer failure modes. The method is to construct a transformer electromagnetic-thermal-environmental coupling simulation model based on COMSOL. The specific method is: Establish a three-dimensional model of the transformer to be tested and import the three-dimensional model of the transformer into COMSOL; Determine the material properties required for transformer electromagnetic-thermal-environmental coupled simulation based on the transformer material; Add the physical fields required for transformer electromagnetic-thermal-environmental coupled simulation analysis, and set the boundary conditions of each physical field and couple them according to the actual conditions and the parameters required for transformer electromagnetic-thermal-environmental coupled simulation analysis; Set the mesh accuracy to mesh the 3D model and set the solver parameters to accelerate the simulation convergence, implement the electromagnetic-thermal-environmental coupled finite element simulation of the transformer, and obtain the simulation results; The obtained simulation results are compared with the operating state parameters, and Kalman filtering is used to correct the model parameters to obtain the optimal transformer electromagnetic-thermal-environmental coupling simulation model.
[0032] Example 4 Based on Example 1, this embodiment provides a method for quantitative risk analysis under transformer failure modes. In step 2, the method for constructing a pre-built transformer failure prediction model includes: Build a transformer failure prediction model based on LSTM; The transformer failure prediction model is optimized using the historical fault data and simulation parameters of the transformer to be tested, and a pre-built transformer failure prediction model is obtained.
[0033] Example 5 This embodiment provides a system for quantitative risk analysis under transformer failure modes, including: The parameter acquisition unit uses the operating state parameters and environmental parameters of the transformer to be tested as inputs to the constructed electromagnetic-thermal-environmental coupling simulation model to simulate the dynamic stress distribution during transformer operation and obtain simulation parameters; A probability prediction unit is used to use the obtained operating state parameters, environmental parameters and simulation parameters as inputs of a pre-built transformer failure prediction model to predict the probability of failure of the transformer under test in the future; The risk assessment unit is used to assess the risk of the transformer under test based on the predicted probability of failure of the transformer under test in the future.
[0034] Example 6 This embodiment also provides a computing device. The computing device includes a bus, a processor, a memory, and a communication interface. The processor, the memory, and the communication interface communicate with each other via the bus. The computing device can be a server or a terminal device. It should be understood that this application does not limit the number of processors and memories in the computing device.
[0035] A bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. Buses can be categorized as address buses, data buses, control buses, and so on. For ease of presentation, a bus can include the pathways that transmit information between various components of a computing device (e.g., memory, processor, and communication interfaces).
[0036] The processor may include any one or more of a central processing unit (CPU), a graphics processing unit (GPU), a tensor processing unit (TPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a microprocessor (MP), or a digital signal processor (DSP).
[0037] The memory may include volatile memory, such as random access memory (RAM). The processor may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).
[0038] The memory stores executable program code, and the processor executes the executable program code to implement the functions of the aforementioned units, thereby implementing, for example, the method described in Example 1. That is, the memory may store instructions for the methods and functions of the computing device described in any of the above embodiments.
[0039] The communication interface uses a transceiver module such as, but not limited to, a network interface card or a transceiver to implement communication between the computing device and other devices or a communication network.
[0040] Example 7 This embodiment also provides a computing device cluster. The computing device cluster includes at least one computing device. The computing device can be a server, such as a central server, an edge server, or a local server in a local data center. In some embodiments, the computing device can also be a terminal device such as a desktop computer, a laptop computer, or a smartphone.
[0041] The computing device cluster includes at least one computing device. The memory of one or more computing devices in the computing device cluster may store the same instructions for executing the method and functions related to the computing device in any of the above embodiments.
[0042] In some possible implementations, the memory of one or more computing devices in the computing device cluster may also store partial instructions for executing the methods and functions related to the computing devices in any of the above embodiments. In other words, the combination of one or more computing devices can jointly execute instructions for executing the methods and functions of the computing devices.
[0043] It should be noted that the memories in different computing devices in the computing device cluster may store different instructions, each for executing part of the functions of the apparatus.
[0044] In some possible implementations, one or more computing devices in a computing device cluster may be connected via a network. The network may be a wide area network (WAN) or a local area network (LAN). Two computing devices are connected via the network. Specifically, the connection to the network is achieved via a communication interface in each computing device.
[0045] An embodiment of the present disclosure further provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the method and functions involving a computing device in any of the above embodiments.
[0046] Example 8 This embodiment further provides a computer-readable storage medium having computer instructions stored thereon. When a processor executes the instructions, the processor executes the methods and functions related to the computing device in any of the above embodiments.
[0047] In general, various embodiments of the present disclosure may be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software, which may be executed by a controller, microprocessor, or other computing device. Although various aspects of the embodiments of the present disclosure are shown and described as block diagrams, flow charts, or using some other pictorial representation, it should be understood that the blocks, devices, systems, techniques, or methods described herein may be implemented as, by way of non-limiting example, hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or a controller or other computing device, or some combination thereof.
[0048] Example 9 The present embodiment provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which are executed in a device on a real or virtual processor of a target to perform the process / method described above with reference to the accompanying drawings. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided between program modules as needed. The machine-executable instructions for the program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.
[0049] The computer program code for implementing the disclosed method can be written in one or more programming languages. These computer program codes can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device so that the program code, when executed by the computer or other programmable data processing device, causes the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on a computer, partially on a computer, as an independent software package, partially on a computer and partially on a remote computer or entirely on a remote computer or server.
[0050] In the context of the present disclosure, computer program code or related data may be carried by any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals may include electrical, optical, radio, acoustic, or other forms of propagated signals, such as carrier waves, infrared signals, and the like.
[0051] A computer-readable medium may be any tangible medium containing or storing a program for or relating to an instruction execution system, apparatus, or device, or a data storage device such as a data center containing one or more available media. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination thereof. More detailed examples of computer-readable storage media include 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 storage device, a magnetic storage device, or any suitable combination thereof.
[0052] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.
Claims
1. A method for quantitative risk analysis of transformer failure modes, characterized in that: The following steps are involved: The operating state parameters and environmental parameters of the transformer under test are used as inputs to the constructed electromagnetic-thermal-environmental coupled simulation model to simulate the dynamic stress distribution during transformer operation and obtain simulation parameters. The obtained operating state parameters, environmental parameters and simulation parameters are used as inputs of the pre-built transformer failure prediction model to predict the probability of failure of the transformer under test in the future. The risk of the transformer under test is evaluated based on the predicted probability of failure of the transformer under test in the future.
2. A method for quantitative risk analysis of transformer failure modes according to claim 1, characterized in that: Operating status parameters include winding current density, core magnetic permeability, winding temperature, partial discharge amplitude, transformer oil viscosity, and dissolved gas concentration in the oil; environmental parameters include ambient temperature and ambient humidity.
3. The method for quantitative risk analysis of transformer failure modes according to claim 2, characterized in that: The electromagnetic-thermal-environmental coupling simulation model of the transformer was constructed based on COMSOL.
4. The method for quantitative risk analysis of transformer failure modes according to claim 2, characterized in that: The electromagnetic-thermal-environmental coupling simulation model of the transformer is constructed based on COMSOL. The specific method is: Establish a three-dimensional model of the transformer to be tested and import the three-dimensional model of the transformer into COMSOL; Determine the material properties required for transformer electromagnetic-thermal-environmental coupled simulation based on the transformer material; Add the physical fields required for transformer electromagnetic-thermal-environmental coupled simulation analysis, and set the boundary conditions of each physical field and couple them according to the actual conditions and the parameters required for transformer electromagnetic-thermal-environmental coupled simulation analysis; Set the mesh accuracy to mesh the 3D model and set the solver parameters to accelerate the simulation convergence, implement the electromagnetic-thermal-environmental coupled finite element simulation of the transformer, and obtain the simulation results; The obtained simulation results are compared with the operating state parameters, and Kalman filtering is used to correct the model parameters to obtain the optimal transformer electromagnetic-thermal-environmental coupling simulation model.
5. The method for quantitative risk analysis of transformer failure modes according to claim 1, characterized in that: The method for building the pre-built transformer failure prediction model includes: Build a transformer failure prediction model based on LSTM; The transformer failure prediction model is optimized using the historical fault data and simulation parameters of the transformer to be tested, and a pre-built transformer failure prediction model is obtained.
6. A risk quantitative analysis system for transformer failure modes, characterized in that: include: The parameter acquisition unit uses the operating state parameters and environmental parameters of the transformer to be tested as inputs to the constructed electromagnetic-thermal-environmental coupling simulation model to simulate the dynamic stress distribution during transformer operation and obtain simulation parameters; A probability prediction unit is used to use the obtained operating state parameters, environmental parameters and simulation parameters as inputs of a pre-built transformer failure prediction model to predict the probability of failure of the transformer under test in the future; The risk assessment unit is used to assess the risk of the transformer under test based on the predicted probability of failure of the transformer under test in the future.
7. An electronic device, characterized in that: The electronic device comprises a processor and a memory, wherein computer instructions are stored in the memory. When the computer instructions are executed by the processor, the electronic device executes the method according to any one of claims 1 to 5.
8. A computing device cluster, characterized in that: comprising at least one computing device, each computing device including a processor and a memory; The processor of the at least one computing device is configured to execute instructions stored in a memory of the at least one computing device, so that the computing device cluster performs the method according to any one of claims 1 to 5.
9. A computer program product, characterized in that The computer program product contains computer-executable instructions, which implement the method according to any one of claims 1 to 5 when executed.
10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which implement the method according to any one of claims 1 to 5 when executed by a processor.