A state diagnosis method of a transformer and related equipment

Abstract

This invention relates to a condition diagnosis method for a current transformer, comprising the following steps: monitoring the current transformer using an optical fiber sensor to obtain monitoring data; wherein the monitoring data includes temperature changes and mechanical stress distribution of the current transformer; obtaining the internal magnetic field distribution of the current transformer based on the temperature changes and mechanical stress distribution using a fluxgate sensor; constructing a three-dimensional magnetic field distribution map based on the magnetic field distribution using a preset inverse problem algorithm; evaluating the characteristics of the current transformer based on the three-dimensional magnetic field distribution map to obtain an evaluation result; if the evaluation result indicates that the characteristics of the current transformer are abnormal, analyzing and diagnosing the three-dimensional magnetic field distribution map based on the evaluation result to obtain a diagnostic report; this method solves the technical problem of difficulty in capturing subtle changes in the operating state of a current transformer in real time and accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of mutual inductors, and in particular to a state diagnosis method of a mutual inductor and related equipment. Background Art

[0002] Traditional transformer detection methods mostly rely on manual periodic inspections and offline testing, which is not only time-consuming and labor-intensive, but also difficult to capture subtle changes in the transformer's operating state in real time and accurately, especially the monitoring of its internal magnetic field distribution and mechanical stress state. As a key device in the power system, the stability and reliability of the transformer's performance directly affect the safe operation of the power system. With the improvement of the level of intelligence of the power system, higher requirements are placed on the status monitoring of the transformer, and a new technology that can realize online, fast and comprehensive diagnosis of the transformer status is urgently needed. Summary of the invention

[0003] The main purpose of the present invention is to provide a state diagnosis method of a transformer and related equipment, which solves the technical problem that it is difficult to capture subtle changes of the transformer in the operating state in real time and accurately.

[0004] To achieve the above object, the present invention provides a method for diagnosing the state of a transformer, wherein an optical fiber sensor and a fluxgate sensor are provided on the transformer, and the method comprises the following steps:

[0005] The mutual inductor is monitored by the optical fiber sensor to obtain monitoring data; wherein the monitoring data includes temperature change and mechanical stress distribution of the mutual inductor;

[0006] The magnetic field distribution inside the mutual inductor is obtained based on the temperature change and the mechanical stress distribution by the fluxgate sensor;

[0007] Using a preset inverse problem algorithm, construct a three-dimensional magnetic field distribution map based on the magnetic field distribution;

[0008] Evaluate the characteristics of the mutual inductor based on the three-dimensional magnetic field distribution diagram to obtain an evaluation result;

[0009] If the evaluation result shows that the characteristics of the mutual inductor are abnormal, the three-dimensional magnetic field distribution diagram is analyzed and diagnosed based on the evaluation result to obtain a diagnosis report.

[0010] Further, the mutual inductor is monitored by the optical fiber sensor to obtain monitoring data, including:

[0011] Injecting laser pulses of a specific wavelength into the optical fiber sensor for activation through a preset light source system;

[0012] Using the injected activation pulse to reflect the grating inside the optical fiber of the mutual inductor to obtain a reflected laser pulse;

[0013] Performing spectral analysis on the reflected laser pulse to obtain wavelength change of the laser pulse;

[0014] Based on the specific wavelength and wavelength variation of the laser pulse, a wavelength shift of the laser pulse is obtained;

[0015] Using the Bragg equation, the temperature of the mutual inductor is calculated based on the wavelength offset to obtain a temperature change of the mutual inductor;

[0016] Performing frequency domain analysis on the reflected laser pulse by using a preset distributed optical fiber sensing technology to obtain the transmission time and attenuation characteristics of the reflected laser pulse;

[0017] The mechanical stress distribution along the length of the optical fiber is calculated based on the transmission time and the attenuation characteristics.

[0018] Furthermore, the magnetic field distribution includes that the magnetic field strength and magnetic field direction are affected by temperature changes and mechanical stress distribution, and the magnetic field distribution inside the mutual inductor is obtained based on the temperature changes and mechanical stress distribution through a preset fluxgate sensor, including:

[0019] The magnetic field inside the sensor is collected by a preset fluxgate sensor to obtain preliminary magnetic field strength information;

[0020] Performing vector conversion on the temperature change to obtain a temperature conversion vector;

[0021] Performing vector transformation on the mechanical stress distribution to obtain a mechanical distribution vector;

[0022] Taking the temperature conversion vector as a row vector and the mechanical distribution vector as a column vector;

[0023] Multiply the row vector and the column vector to obtain a multiplication matrix; wherein the multiplication matrix is ​​a multiplication matrix of m rows and n columns;

[0024] Performing singular value decomposition on the multiplication matrix to obtain a first orthogonal matrix, a diagonal matrix, and a second orthogonal matrix; wherein the first orthogonal matrix is ​​an orthogonal matrix with m rows and m columns, the diagonal matrix is ​​a diagonal matrix with m rows and n columns, and the second orthogonal matrix is ​​a multiplication matrix with n rows and n columns;

[0025] Calculate the traces of the first orthogonal matrix, the diagonal matrix, and the second orthogonal matrix respectively, and obtain the first trace, the second trace, and the third trace accordingly;

[0026] Arrange and combine the first trace, the second trace and the third trace to obtain an arrangement vector; wherein the arrangement vector has six vectors;

[0027] constructing a permutation matrix based on the permutation vector;

[0028] The arrangement matrix is ​​input into a preset boundary element algorithm to perform magnetic field distribution analysis to obtain the magnetic field distribution inside the mutual inductor.

[0029] Furthermore, a three-dimensional magnetic field distribution map is constructed based on the magnetic field distribution through a preset inverse problem algorithm, including:

[0030] Inserting magnetic field data at different positions of the mutual inductor through a preset magnetic field sensor to obtain preliminary magnetic field strength data;

[0031] Preprocessing the preliminary magnetic field strength data to obtain preprocessed data; wherein the preprocessing includes noise filtering and smoothing;

[0032] Acquire the physical configuration characteristics of the mutual inductor, and construct an initial three-dimensional model based on the physical configuration characteristics; wherein the physical configuration characteristics include a physical structure, a relative position relationship of the physical structure, and material properties; the physical structure includes a coil, an iron core, and an air gap; and the material properties include magnetic permeability and electrical conductivity;

[0033] Using a preset finite element method, the magnetic field distribution is implanted into the initial three-dimensional model to obtain a three-dimensional magnetic field distribution diagram with magnetic field lines;

[0034] Color-coding the magnetic field lines with different colors to obtain color-coded magnetic field lines; wherein the different colors represent different levels of magnetic field strength;

[0035] Inserting vector arrows into the color-coded magnetic field lines to obtain standard magnetic field lines;

[0036] The standard magnetic field lines are aligned with the spatial coordinate system in the initial three-dimensional model to obtain a three-dimensional magnetic field distribution map.

[0037] Furthermore, the magnetic field distribution is implanted into the initial three-dimensional model to obtain a three-dimensional magnetic field distribution diagram with magnetic field lines, including:

[0038] Segmenting the magnetic field distribution along a specified direction to obtain magnetic field distribution images on multiple planes; wherein the specified direction is an X-axis direction, a Y-axis direction, or a Z-axis direction;

[0039] By means of a preset fast Fourier transform, each of the magnetic field distribution images is converted from the spatial domain to the frequency domain to obtain frequency domain magnetic field data;

[0040] Performing spatial transformation and reconstruction on the frequency domain magnetic field data to obtain a reconstructed magnetic field distribution;

[0041] The reconstructed magnetic field distribution is implanted into the initial three-dimensional model to obtain a three-dimensional magnetic field distribution map with magnetic field lines.

[0042] Further, the characteristics of the mutual inductor are evaluated based on the three-dimensional magnetic field distribution diagram to obtain an evaluation result, including:

[0043] Mapping the magnetic field strength and magnetic field direction in the three-dimensional magnetic field distribution diagram onto the physical structure to obtain the electromagnetic load distribution in the coil and core area;

[0044] Obtaining a current density of the transformer based on the electromagnetic load distribution;

[0045] Using Ampere's circuit law and Biot-Savart's law, the magnetic resistance, magnetic flux and mutual inductance of each electromagnetic unit are calculated based on the physical configuration characteristics and current density;

[0046] Using a preset electromagnetic-circuit conversion algorithm, the magnetic resistance, magnetic flux and mutual inductance coefficient are converted into resistance, inductance and mutual inductance coefficient in the circuit respectively;

[0047] Constructing an equivalent circuit based on the resistance, inductance, mutual inductance, physical structure and relative position relationship;

[0048] Performing performance analysis on the equivalent circuit to obtain electrical performance indicators;

[0049] The characteristics of the sensor are evaluated based on the electrical performance index to obtain an evaluation result.

[0050] Further, the three-dimensional magnetic field distribution diagram is analyzed and diagnosed based on the evaluation result to obtain a diagnosis report, including:

[0051] Performing fault classification on the evaluation result to obtain a fault classification result;

[0052] Inputting the fault classification result into a preset fault factor algorithm for analysis to obtain a fault factor;

[0053] Performing fault location mapping on the three-dimensional magnetic field distribution diagram based on the fault factor to obtain fault factor location;

[0054] The three-dimensional magnetic field distribution diagram is analyzed and diagnosed based on the fault factor location to obtain a diagnosis report.

[0055] The present invention also provides a state diagnosis device for a mutual inductor, wherein the mutual inductor is provided with an optical fiber sensor and a fluxgate sensor, comprising:

[0056] A monitoring module, used to monitor the mutual inductor through the optical fiber sensor to obtain monitoring data; wherein the monitoring data includes temperature change and mechanical stress distribution of the mutual inductor;

[0057] A distribution module, used to obtain the magnetic field distribution inside the mutual inductor based on the temperature change and mechanical stress distribution through the fluxgate sensor;

[0058] A construction module, used to construct a three-dimensional magnetic field distribution map based on the magnetic field distribution through a preset inverse problem algorithm;

[0059] An evaluation module, used to evaluate the characteristics of the mutual inductor based on the three-dimensional magnetic field distribution diagram to obtain an evaluation result;

[0060] The detection module is used to analyze and diagnose the three-dimensional magnetic field distribution diagram based on the evaluation result to obtain a diagnosis report if the evaluation result shows that the characteristics of the mutual inductor are abnormal.

[0061] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0062] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0063] The state diagnosis method of a mutual inductor provided by the present invention comprises the following steps: monitoring the mutual inductor by means of the optical fiber sensor to obtain monitoring data; wherein the monitoring data comprises the temperature change and mechanical stress distribution of the mutual inductor; obtaining the magnetic field distribution inside the mutual inductor based on the temperature change and mechanical stress distribution by means of the fluxgate sensor; constructing a three-dimensional magnetic field distribution diagram based on the magnetic field distribution by means of a preset inverse problem algorithm; evaluating the characteristics of the mutual inductor based on the three-dimensional magnetic field distribution diagram to obtain an evaluation result; if the evaluation result is that the characteristics of the mutual inductor are abnormal, analyzing and diagnosing the three-dimensional magnetic field distribution diagram based on the evaluation result to obtain a diagnosis report; the above steps solve the technical problem of being difficult to capture the subtle changes of the mutual inductor in real time and accurately under the operating state, realize real-time grasp of the state of the mutual inductor, improve the overall operation safety and stability of the power system, and ensure the continuity and reliability of power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 is a schematic diagram of the steps of a method for diagnosing a state of a transformer in one embodiment of the present invention;

[0065] Figure 2 is a structural block diagram of a state diagnosis device for a transformer in one embodiment of the present invention;

[0066] Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0067] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0069] like Figure 1 As shown, Figure 1 is a schematic diagram of the steps of a method for diagnosing a state of a transformer in one embodiment of the present invention;

[0070] In one embodiment of the present invention, a method for diagnosing the state of a mutual inductor is provided. The mutual inductor is provided with an optical fiber sensor and a fluxgate sensor, and the method comprises the following steps:

[0071] Step S1, monitoring the mutual inductor by means of the optical fiber sensor to obtain monitoring data; wherein the monitoring data includes temperature change and mechanical stress distribution of the mutual inductor.

[0072] Specifically, this step uses modern sensing technology, especially fiber optic sensors, to achieve precise monitoring of the operating status of the transformer. Fiber optic sensors are installed at key positions of the transformer due to their strong anti-electromagnetic interference, high sensitivity and small size. Their working principle is based on the propagation characteristics of light. When the temperature of the transformer changes or is subjected to mechanical stress, the optical signal inside the optical fiber will change accordingly, such as phase shift, light intensity attenuation, etc. Temperature change monitoring: The transformer will generate heat due to the passage of current or the temperature fluctuation of the surrounding environment during operation, causing its own temperature to change. Fiber optic sensors can sense these tiny temperature changes, measure the characteristic changes of optical signals (such as spectral shift), and convert them into electrical signals, so as to monitor the temperature distribution on the surface and even inside of the transformer in real time. This is crucial for evaluating the thermal stability of the transformer and preventing overheating failures. Mechanical stress distribution monitoring: When the transformer is subjected to factors such as power system load, external vibration or internal material aging, mechanical stress will be generated on the structure. Fiber optic sensors can also sensitively capture the tiny deformations caused by these stresses, even very weak tensile or compressive changes. By analyzing the transmission time and phase difference of the optical signal in the optical fiber, the distribution of stress can be inferred. This helps to detect structural damage in a timely manner, prevent mechanical failure, and ensure the safe operation of the transformer. Therefore, monitoring the transformer through optical fiber sensors can not only continuously track the key parameter of temperature, but also gain an in-depth understanding of the mechanical stress state of the transformer, providing valuable data support for subsequent fault prediction, life assessment, and safe operation.

[0073] Step S2, obtaining the magnetic field distribution inside the mutual inductor based on the temperature change and mechanical stress distribution through the fluxgate sensor.

[0074] Specifically, this step utilizes the unique performance of the fluxgate sensor, combined with the temperature change and mechanical stress distribution information previously monitored by the optical fiber sensor, to accurately measure the magnetic field distribution inside the transformer in a non-contact manner. The fluxgate sensor is a highly sensitive magnetic induction element, whose working principle is based on the magnetic saturation phenomenon and can respond to changes in the external magnetic field. Based on temperature changes: The temperature change inside the transformer will affect the magnetic permeability of the material, thereby indirectly changing the magnetic field distribution. This is because the magnetic saturation characteristics of ferromagnetic materials will change at different temperatures, thereby affecting the path and intensity of the magnetic field. With the assistance of temperature change data, the fluxgate sensor can more accurately correct the slight changes in the magnetic field distribution caused by temperature to ensure the accuracy of the measurement results. Combined with mechanical stress distribution: The distribution of mechanical stress also affects the magnetic field characteristics of the transformer, especially in the core and coil structure. Changes in stress will cause slight deformations in the material microstructure, which will in turn change the magnetostrictive effect of the material and further affect the magnetic field distribution. When analyzing, the fluxgate sensor will take into account these magnetic field fine-tuning caused by mechanical stress, making the depiction of the internal magnetic field more delicate and realistic. In summary, by combining the fluxgate sensor with the temperature change and mechanical stress distribution information collected in the early stage, this step can comprehensively consider the impact of environmental and structural changes on the magnetic field, and efficiently and accurately depict the complex magnetic field distribution inside the transformer. This process is of great significance for in-depth understanding of the working status of the transformer, prediction of potential failures, and optimization of design.

[0075] Step S3, constructing a three-dimensional magnetic field distribution map based on the magnetic field distribution through a preset inverse problem algorithm.

[0076] Specifically, the inverse problem algorithm plays a core role here. Its task is to reversely calculate the three-dimensional magnetic field distribution map inside the transformer from the known magnetic field distribution. The specific implementation steps are as follows: Data collection and processing: First, the magnetic field data obtained by the fluxgate sensor in the previous step is used. These data reflect the magnetic field strength and direction of the transformer at different positions. These raw data are filtered and calibrated to remove noise and ensure the accuracy of the data. Inverse problem construction: Next, the preset inverse problem algorithm is used. This is a mathematical model that takes the magnetic field measurement value as input and attempts to solve the source distribution of these magnetic fields. Inverse problems are usually more complicated than forward calculations because it involves the process of tracing the cause from the effect, which is to reverse the magnetic source distribution from the magnetic field. Three-dimensional reconstruction technology: Under the guidance of the inverse problem algorithm, combined with the physical structure information of the transformer (such as size, material properties, etc.), the real magnetic field source distribution is gradually approached through iterative calculation and optimization methods. This process involves complex numerical simulations, such as finite element method (FEM), boundary element method (BEM), etc., to reconstruct the magnetic field distribution map in three-dimensional space. Result verification and optimization: The generated three-dimensional magnetic field distribution map needs to be compared and verified with the actual measured magnetic field data to evaluate the accuracy of the inverse problem solution. If there is a deviation, the algorithm parameters need to be adjusted or the model needs to be optimized, and iterated repeatedly until the model prediction is in good agreement with the measured data. Through this series of operations, not only can the magnetic field state inside the transformer be visualized, but also the magnetic field characteristics can be deeply analyzed, providing an intuitive and accurate information basis for performance evaluation, fault diagnosis and optimal design of the equipment.

[0077] Step S4, evaluating the characteristics of the mutual inductor based on the three-dimensional magnetic field distribution diagram to obtain an evaluation result.

[0078] Specifically, based on the constructed three-dimensional magnetic field distribution map, evaluating the characteristics of the transformer is a key step to ensure that its performance meets the design requirements and safety standards. This process mainly includes the following aspects: Magnetic field uniformity evaluation: Analyze the three-dimensional magnetic field distribution map and calculate the uniformity of the magnetic field strength in the working area of ​​the transformer. Uniform magnetic field distribution is essential for accurate measurement of the transformer. Inhomogeneity may cause measurement errors, so it is necessary to identify any areas where the local field strength is too high or too low. Leakage magnetic field evaluation: Check whether there is magnetic field leakage to the outside of the transformer or unexpected areas in the three-dimensional map. Leakage magnetic field not only affects the measurement accuracy, but also may cause energy loss and electromagnetic interference problems. Through evaluation, determine whether measures need to be taken to reduce leakage magnetic field, such as optimizing shielding design. Saturation analysis: Evaluate the magnetic flux density distribution of the transformer core at the maximum expected current to determine whether there is saturation. The magnetic field distribution map can help identify areas where the core may reach saturation, which will affect the linearity and dynamic range of the transformer. Consideration of temperature effects: Although the three-dimensional magnetic field diagram directly reflects the magnetic field distribution, by combining the knowledge that the magnetic permeability of the material changes with temperature, the impact of temperature changes on the magnetic field distribution can be indirectly evaluated to ensure the stable performance of the transformer at different operating temperatures. Stress and deformation effects: Analyze the relationship between the magnetic field distribution and the transformer structure, and evaluate how mechanical stress or structural deformation changes the magnetic field characteristics, especially under long-term operation or extreme conditions. Calculation of comprehensive performance indicators: Based on the above analyses, calculate the key performance indicators of the transformer, such as accuracy level, load capacity, frequency response characteristics, etc., to comprehensively evaluate the comprehensive performance of the transformer. Ultimately, based on these detailed evaluation results, targeted improvement suggestions can be made or the transformer design can be confirmed to meet established standards, thereby ensuring its reliable operation in the power system.

[0079] Step S5: If the evaluation result shows that the characteristics of the mutual inductor are abnormal, the three-dimensional magnetic field distribution diagram is analyzed and diagnosed based on the evaluation result to obtain a diagnosis report.

[0080] Specifically, if the evaluation results show that the transformer characteristics are abnormal, the next step is to deeply analyze the three-dimensional magnetic field distribution map to identify the specific cause of the abnormality and develop a corresponding diagnostic report. This process involves the following key links: Abnormal area location: First, accurately locate the magnetic field abnormal area in the three-dimensional magnetic field distribution map. These areas may show abnormally high or low magnetic field intensity, uneven distribution or abnormal magnetic flux paths. By comparing the expected magnetic field distribution under normal working conditions, the deviation from the normal pattern is identified. Cause tracing: Combine the structural information, material properties and working conditions of the transformer to analyze the possible causes of magnetic field abnormalities. Including material degradation, structural damage, design defects, overload use, etc. Use reverse reasoning to reverse the potential physical problems from the magnetic field abnormality. Impact assessment: Evaluate the specific impact of these anomalies on the performance of the transformer, such as whether it affects the measurement accuracy, whether it will increase energy consumption, whether it may cause local overheating or accelerated aging, etc. At the same time, consider whether these anomalies will affect the normal operation of other components of the power system. Solution proposal: Based on the diagnostic results, propose repair or improvement measures. Including replacement of components, structural adjustment, material upgrade, optimization of operating parameters, etc. Each suggestion should be accompanied by an assessment of the expected effect and difficulty of implementation. Write a diagnostic report: Organize the above analysis, diagnostic process and suggestions into a formal diagnostic report. The report should record the abnormal phenomenon, analysis process, diagnostic conclusions and recommended measures in detail, and include graphics, data tables and explanations of calculation models when necessary, so that technicians and managers can understand and take action accordingly. Through this series of analysis and diagnostic steps, a scientific basis can be provided for the maintenance, repair or improvement of transformers to ensure the stable and safe operation of the power system.

[0081] In a specific embodiment, the mutual inductor is monitored by the optical fiber sensor to obtain monitoring data, including:

[0082] Injecting laser pulses of a specific wavelength into the optical fiber sensor for activation through a preset light source system;

[0083] Using the injected activation pulse to reflect the grating inside the optical fiber of the mutual inductor to obtain a reflected laser pulse;

[0084] Performing spectral analysis on the reflected laser pulse to obtain wavelength change of the laser pulse;

[0085] Based on the specific wavelength and wavelength variation of the laser pulse, a wavelength shift of the laser pulse is obtained;

[0086] Using the Bragg equation, the temperature of the mutual inductor is calculated based on the wavelength offset to obtain a temperature change of the mutual inductor;

[0087] Performing frequency domain analysis on the reflected laser pulse by using a preset distributed optical fiber sensing technology to obtain the transmission time and attenuation characteristics of the reflected laser pulse;

[0088] The mechanical stress distribution along the length of the optical fiber is calculated based on the transmission time and the attenuation characteristics.

[0089] Specifically, the solution details how to use fiber optic sensors to monitor mutual inductors with high precision, with a particular focus on measuring temperature changes and mechanical stress distribution. The following is a detailed explanation of each step: Light source activation: First, a preset light source system is used to send laser pulses of a specific wavelength to the fiber optic sensor. This activation process is completed by a precisely controlled laser, providing the necessary energy input for the subsequent grating reflection measurement. Grating reflection and laser pulse capture: The injected activation pulse is reflected at the grating inside the fiber. These gratings can be inherent to the fiber itself or deliberately implanted in the fiber, and they reflect light of a specific wavelength. The reflected laser pulse is captured by the system. Spectral analysis and wavelength shift measurement: The reflected laser pulse is sent to a spectrum analyzer for analysis. By analyzing the spectrum of these pulses, the change in the laser wavelength relative to the original injection wavelength, that is, the wavelength shift, can be accurately measured. This change is caused by changes in physical parameters inside the fiber, such as temperature changes. Temperature calculation: The wavelength shift can be converted into a temperature change value using the Bragg equation. The Bragg equation is a physical law that describes the relationship between wavelength shift and refractive index change, which is closely related to temperature. Therefore, the temperature distribution of the transformer can be accurately calculated by the wavelength offset. Application of distributed fiber optic sensing technology: For the measurement of mechanical stress, distributed fiber optic sensing technology is used, which is a method that can detect strain or pressure changes along the entire optical fiber. Through the frequency domain analysis of the reflected laser pulse, the time delay and signal attenuation information of the light pulse transmitted in the optical fiber can be obtained. Stress distribution calculation: The changes in the transmission time and attenuation characteristics directly reflect the strain along the optical fiber, that is, the distribution of mechanical stress. Through complex signal processing algorithms, the mechanical stress distribution diagram along the length of the optical fiber can be calculated from these data, thereby reflecting the stress state of the transformer structure. The entire process is highly dependent on the high sensitivity and spatial resolution of fiber optic sensing technology, which can non-invasively and in real time monitor the temperature and mechanical stress of the transformer, providing important data support for the status assessment and fault warning of the transformer.

[0090] In a specific embodiment, the magnetic field distribution includes that the magnetic field strength and magnetic field direction are affected by temperature changes and mechanical stress distribution, and the magnetic field distribution inside the mutual inductor is obtained based on the temperature changes and mechanical stress distribution through a preset fluxgate sensor, including:

[0091] The magnetic field inside the sensor is collected by a preset fluxgate sensor to obtain preliminary magnetic field strength information;

[0092] Performing vector conversion on the temperature change to obtain a temperature conversion vector;

[0093] Performing vector transformation on the mechanical stress distribution to obtain a mechanical distribution vector;

[0094] Taking the temperature conversion vector as a row vector and the mechanical distribution vector as a column vector;

[0095] Multiply the row vector and the column vector to obtain a multiplication matrix; wherein the multiplication matrix is ​​a multiplication matrix of m rows and n columns;

[0096] Performing singular value decomposition on the multiplication matrix to obtain a first orthogonal matrix, a diagonal matrix, and a second orthogonal matrix; wherein the first orthogonal matrix is ​​an orthogonal matrix with m rows and m columns, the diagonal matrix is ​​a diagonal matrix with m rows and n columns, and the second orthogonal matrix is ​​a multiplication matrix with n rows and n columns;

[0097] Calculate the traces of the first orthogonal matrix, the diagonal matrix, and the second orthogonal matrix respectively, and obtain the first trace, the second trace, and the third trace accordingly;

[0098] Arrange and combine the first trace, the second trace and the third trace to obtain an arrangement vector; wherein the arrangement vector has six vectors;

[0099] constructing a permutation matrix based on the permutation vector;

[0100] The arrangement matrix is ​​input into a preset boundary element algorithm to perform magnetic field distribution analysis to obtain the magnetic field distribution inside the mutual inductor.

[0101] Specifically, this step describes a method for analyzing the magnetic field distribution inside the transformer by combining temperature and mechanical stress information with fluxgate sensors, using matrix operations and singular value decomposition techniques. The following is a detailed explanation: Magnetic field acquisition: First, use a preset fluxgate sensor to measure the magnetic field inside the transformer and record preliminary magnetic field strength information. The fluxgate sensor can detect the magnetic field strength with high sensitivity, providing basic data for subsequent analysis. Vector conversion: Convert the temperature change and mechanical stress distribution information obtained from the fiber optic sensor into vector form. The temperature change is converted into a "temperature conversion vector" to reflect the temperature change at different points; the mechanical stress distribution is converted into a "mechanical distribution vector" to show the distribution pattern of stress in the transformer structure. Matrix construction and multiplication: The temperature conversion vector is used as a row vector and the mechanical distribution vector is used as a column vector. The two are multiplied to obtain an m-row and n-column multiplication matrix. This process aims to quantify the combined effect of temperature and stress on the magnetic field distribution. Singular value decomposition: Perform singular value decomposition on the multiplication matrix, which is a powerful matrix decomposition technique that can decompose complex matrices into parts that are easier to understand and process. The decomposition results include three matrices: the first orthogonal matrix, the diagonal matrix, and the second orthogonal matrix, each representing information of different dimensions. Calculate traces and construct permutation vectors: Calculate the traces of the above three matrices (i.e., the sum of the diagonal elements) to obtain the first trace, the second trace, and the third trace. These three traces contain important characteristics of the magnetic field affected by temperature and stress. Afterwards, the three traces are combined into a six-dimensional permutation vector, and each dimension represents the weight of the influence of different physical parameters on the magnetic field. Construct permutation matrix: Construct a matrix based on the permutation vector, which is used to further describe and organize the relationship between these influencing factors in preparation for subsequent analysis. Boundary element algorithm analysis: Finally, the permutation matrix is ​​input into the preset boundary element algorithm. Boundary element method is a numerical analysis method that is particularly suitable for solving electromagnetic field problems with complex boundary conditions. In this way, the magnetic field distribution inside the transformer can be accurately calculated, taking into account the influence of temperature changes and mechanical stress distribution. The whole process combines the quantification of physical quantities, the efficient processing capabilities of matrix operations, and the powerful analytical capabilities of the boundary element algorithm, thereby achieving a comprehensive analysis of the complex magnetic field distribution inside the transformer, which helps to deeply understand the working status and potential faults of the transformer.

[0102] In a specific embodiment, a three-dimensional magnetic field distribution map is constructed based on the magnetic field distribution through a preset inverse problem algorithm, including:

[0103] Inserting magnetic field data at different positions of the mutual inductor through a preset magnetic field sensor to obtain preliminary magnetic field strength data;

[0104] Preprocessing the preliminary magnetic field strength data to obtain preprocessed data; wherein the preprocessing includes noise filtering and smoothing;

[0105] Acquire the physical configuration characteristics of the mutual inductor, and construct an initial three-dimensional model based on the physical configuration characteristics; wherein the physical configuration characteristics include a physical structure, a relative position relationship of the physical structure, and material properties; the physical structure includes a coil, an iron core, and an air gap; and the material properties include magnetic permeability and electrical conductivity;

[0106] Using a preset finite element method, the magnetic field distribution is implanted into the initial three-dimensional model to obtain a three-dimensional magnetic field distribution diagram with magnetic field lines;

[0107] Color-coding the magnetic field lines with different colors to obtain color-coded magnetic field lines; wherein the different colors represent different levels of magnetic field strength;

[0108] Inserting vector arrows into the color-coded magnetic field lines to obtain standard magnetic field lines;

[0109] The standard magnetic field lines are aligned with the spatial coordinate system in the initial three-dimensional model to obtain a three-dimensional magnetic field distribution map.

[0110] Specifically, the scheme details the whole process of constructing the three-dimensional magnetic field distribution map of the transformer through the inverse problem algorithm. The specific steps are as follows: Magnetic field data acquisition: First, the preset magnetic field sensor is used to measure at different positions of the transformer to collect preliminary magnetic field strength data. These data directly reflect the magnetic field state inside and around the transformer. Data preprocessing: The collected raw data usually contains noise and needs to be preprocessed to improve the accuracy of subsequent analysis. This step includes noise filtering and data smoothing to remove irrelevant interference and make the magnetic field strength data purer and continuous. Three-dimensional model construction: Based on the physical structure information of the transformer, such as the layout of the coil, core, and air gap, and the material properties of these components (magnetic permeability, electrical conductivity, etc.), an initial three-dimensional model is constructed. This step is the basic framework for subsequent magnetic field distribution mapping. Finite element method application: The pre-processed magnetic field data is implanted into the above three-dimensional model using the preset finite element method (FEM). The finite element method is a numerical calculation method that can decompose complex physical problems into smaller and more manageable sub-problems, thereby effectively simulating the distribution of magnetic fields inside and around the transformer. Generate magnetic field line diagram: Through the calculation of finite element method, the obtained model not only contains three-dimensional structure, but also shows the distribution of magnetic field lines, each of which represents the direction of magnetic field. Through color coding, different colors are used to represent magnetic fields of different intensity levels, making the visual effect of magnetic field distribution more intuitive. Vector arrow annotation: In order to more clearly indicate the direction of magnetic field, vector arrows are added to color-coded magnetic field lines. This step further enhances the indicativeness of magnetic field distribution diagram and helps to intuitively understand the directional characteristics of magnetic field. Coordinate system alignment: Finally, ensure that the generated standard magnetic field lines are accurately aligned with the spatial coordinate system of the three-dimensional model. This step ensures the accuracy and readability of magnetic field distribution diagram, which is convenient for subsequent analysis, evaluation and communication. In summary, this solution realizes accurate visualization of the magnetic field distribution of transformer through precise data acquisition, rigorous data processing, meticulous model construction and advanced numerical simulation technology, providing a powerful tool for performance evaluation, fault diagnosis and design optimization.

[0111] In a specific embodiment, the magnetic field distribution is implanted into the initial three-dimensional model to obtain a three-dimensional magnetic field distribution diagram with magnetic field lines, including:

[0112] Segmenting the magnetic field distribution along a specified direction to obtain magnetic field distribution images on multiple planes; wherein the specified direction is an X-axis direction, a Y-axis direction, or a Z-axis direction;

[0113] By means of a preset fast Fourier transform, each of the magnetic field distribution images is converted from the spatial domain to the frequency domain to obtain frequency domain magnetic field data;

[0114] Performing spatial transformation and reconstruction on the frequency domain magnetic field data to obtain a reconstructed magnetic field distribution;

[0115] The reconstructed magnetic field distribution is implanted into the initial three-dimensional model to obtain a three-dimensional magnetic field distribution map with magnetic field lines.

[0116] Specifically, this process is a further refinement of the method of constructing a three-dimensional magnetic field distribution map, especially the use of advanced signal processing technology in processing and analyzing magnetic field data. The following is a detailed explanation: Magnetic field distribution segmentation: First, in order to facilitate analysis and processing, the overall magnetic field distribution is segmented along a specific direction (X-axis, Y-axis or Z-axis). This means that the magnetic field data in three-dimensional space is cut into a series of snapshots of two-dimensional planes, each of which represents the magnetic field intensity of all points at a certain position in this specific direction. This approach helps to better understand and manipulate local magnetic field characteristics in subsequent processing. Fast Fourier Transform (FFT): Then, the fast Fourier transform is applied to the magnetic field distribution image of each two-dimensional plane. Fast Fourier transform is a method of efficiently converting time domain (or spatial domain) signals to frequency domain. In this application scenario, it converts the magnetic field intensity distribution of each plane from the actual spatial distribution to the distribution of frequency components, which helps to reveal the periodic characteristics and potential regular structures in the magnetic field distribution, and is also conducive to filtering out high-frequency noise. Spatial transformation reconstruction: After filtering or enhancing the data in the frequency domain, the processed frequency domain magnetic field data is converted back to the spatial domain through the inverse fast Fourier transform (IFFT), that is, spatial transformation reconstruction is performed. This process is equivalent to recombining the analyzed and processed information into a magnetic field distribution image, but the noise has been removed or certain features have been enhanced, so as to obtain a clearer or more targeted reconstructed magnetic field distribution. Embedding a three-dimensional model: Finally, the reconstructed magnetic field distribution obtained after the above processing is embedded into the initially constructed three-dimensional model. In this way, it is possible to intuitively see how the magnetic field lines are distributed in the three-dimensional structure of the transformer, which not only shows the direction of the magnetic field, but also shows the changes in the magnetic field intensity through color coding or other visual aids. Such a three-dimensional magnetic field distribution map is crucial for a deep understanding of the working principle of the transformer, optimizing the design, and predicting and diagnosing potential magnetic field-related problems. In summary, this method combines image segmentation, frequency domain analysis and processing, and three-dimensional modeling technology, providing a complete process from raw data to high-quality visualization results, greatly improving the efficiency and accuracy of magnetic field distribution analysis.

[0117] In a specific embodiment, the characteristics of the mutual inductor are evaluated based on the three-dimensional magnetic field distribution diagram to obtain an evaluation result, including:

[0118] Mapping the magnetic field strength and magnetic field direction in the three-dimensional magnetic field distribution diagram onto the physical structure to obtain the electromagnetic load distribution in the coil and core area;

[0119] Obtaining a current density of the transformer based on the electromagnetic load distribution;

[0120] Using Ampere's circuit law and Biot-Savart's law, the magnetic resistance, magnetic flux and mutual inductance of each electromagnetic unit are calculated based on the physical configuration characteristics and current density;

[0121] Using a preset electromagnetic-circuit conversion algorithm, the magnetic resistance, magnetic flux and mutual inductance coefficient are converted into resistance, inductance and mutual inductance coefficient in the circuit respectively;

[0122] Constructing an equivalent circuit based on the resistance, inductance, mutual inductance, physical structure and relative position relationship;

[0123] Performing performance analysis on the equivalent circuit to obtain electrical performance indicators;

[0124] The characteristics of the sensor are evaluated based on the electrical performance index to obtain an evaluation result.

[0125] Specifically, the scheme describes in detail how to comprehensively evaluate the characteristics of the transformer based on the three-dimensional magnetic field distribution map, and ensures the accuracy and comprehensiveness of the evaluation results through a series of rigorous calculation and analysis steps. The specific steps are as follows: Mapping electromagnetic load distribution: First, the magnetic field strength and direction information in the three-dimensional magnetic field distribution map is mapped to the physical structure of the transformer, especially the coil and core area. This process reveals the distribution of electromagnetic energy on the key components of the transformer, laying the foundation for subsequent analysis. Calculation of current density: The electromagnetic load distribution obtained by mapping is used to further calculate the current density distribution inside the transformer. Current density is a key parameter for evaluating the degree of current concentration in a conductor and is crucial to understanding the working state of the transformer. Electromagnetic parameter calculation: Based on Ampere's loop law and Biot-Savart's law, combined with the physical structure characteristics of the transformer and the calculated current density, the magnetic resistance, magnetic flux and mutual inductance coefficient of each electromagnetic unit are calculated. These parameters are quantitative descriptions of electromagnetic field behavior and are crucial to understanding the electromagnetic characteristics of the transformer. Equivalent circuit construction: Through the preset algorithm, electromagnetic parameters such as magnetic resistance, magnetic flux and mutual inductance are converted into resistance, inductance and mutual inductance in circuit theory. This conversion builds a bridge between electromagnetic phenomena and circuit theory, so that complex electromagnetic problems can be analyzed through circuit models. Equivalent circuit performance analysis: After the equivalent circuit is constructed, the performance analysis is performed, including the calculation of electrical performance indicators such as voltage ratio, current ratio, power factor, and loss. These indicators directly reflect the effectiveness and efficiency of the transformer in actual work. Characteristic evaluation: Finally, based on the above electrical performance indicators, the overall characteristics of the transformer are evaluated. This includes judging whether the transformer meets the design requirements, whether the operation is stable, and whether there are potential performance bottlenecks or hidden faults. The evaluation results provide a scientific basis for the optimal design, fault diagnosis and maintenance plan of the transformer. The whole process starts with the intuitive visualization of physical phenomena and gradually goes deep into theoretical analysis and performance evaluation, forming a comprehensive evaluation system from phenomenon to essence and from macro to micro, ensuring the systematic and scientific evaluation of transformer characteristics.

[0126] In a specific embodiment, the three-dimensional magnetic field distribution map is analyzed and diagnosed based on the evaluation result to obtain a diagnosis report, including:

[0127] Performing fault classification on the evaluation result to obtain a fault classification result;

[0128] Inputting the fault classification result into a preset fault factor algorithm for analysis to obtain a fault factor;

[0129] Performing fault location mapping on the three-dimensional magnetic field distribution diagram based on the fault factor to obtain fault factor location;

[0130] The three-dimensional magnetic field distribution diagram is analyzed and diagnosed based on the fault factor location to obtain a diagnosis report.

[0131] Specifically, this step mainly revolves around the fault diagnosis of the transformer, through a series of rigorous analysis processes, from evaluation results to generating diagnostic reports, the specific explanation is as follows: Fault classification: First, based on the previous evaluation results of the transformer characteristics, classify the possible problems. This step aims to classify the anomalies found in the evaluation into different fault types, such as electrical performance degradation, structural damage, insulation fault, etc., to lay the foundation for subsequent in-depth analysis. Fault factor algorithm analysis: The obtained fault classification results are used as input and the preset fault factor algorithm is applied for analysis. This algorithm is usually based on historical fault cases, theoretical knowledge and expert experience, and can screen out several key factors that are most relevant to a specific fault type from a large number of parameters. These factors are considered to be the main causes of the fault. Fault factor location mapping: Based on the determined fault factors, the three-dimensional magnetic field distribution map is fault-located. This means identifying the areas where the fault factors have the most significant impact on the three-dimensional model, and intuitively displaying the fault location and its impact range through color marking, arrow pointing or highlighting, etc., to provide intuitive geographical information for diagnosis. Analysis and diagnosis: Finally, combined with the fault factor location information, the three-dimensional magnetic field distribution map is analyzed again in depth to discuss in detail how each fault factor affects the magnetic field distribution of the transformer and how this influence leads to abnormal transformer performance. By comprehensively analyzing this information, a diagnostic report is written, which includes the fault type, specific location, degree of impact, possible cause analysis, and recommended countermeasures or maintenance plans. The whole process is a diagnostic process from macro to micro, from theory to practice. Through systematic analysis and positioning, the accuracy and pertinence of the diagnostic results are ensured, providing scientific guidance for the maintenance and repair of transformers.

[0132] The above describes the state diagnosis method of the transformer in the embodiment of the present invention. The following describes the state diagnosis device of the transformer in the embodiment of the present invention. Figure 2 , an embodiment of the state diagnosis system of the transformer in the embodiment of the present invention includes:

[0133] A monitoring module 21, used to monitor the mutual inductor through the optical fiber sensor to obtain monitoring data; wherein the monitoring data includes the temperature change and mechanical stress distribution of the mutual inductor;

[0134] A distribution module 22, configured to obtain the magnetic field distribution inside the transformer based on the temperature change and the mechanical stress distribution through the fluxgate sensor;

[0135] A construction module 23 is used to construct a three-dimensional magnetic field distribution map based on the magnetic field distribution through a preset inverse problem algorithm;

[0136] An evaluation module 24, configured to evaluate the characteristics of the mutual inductor based on the three-dimensional magnetic field distribution diagram to obtain an evaluation result;

[0137] The detection module 25 is used to analyze and diagnose the three-dimensional magnetic field distribution diagram based on the evaluation result to obtain a diagnosis report if the evaluation result shows that the characteristics of the mutual inductor are abnormal.

[0138] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to the above method embodiment, which will not be repeated here.

[0139] Reference Figure 3 The present invention also provides a computer device in an embodiment, wherein the internal structure of the computer device can be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0140] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0141] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0142] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0143] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0144] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for diagnosing the state of a transformer, characterized in that: The mutual inductor is provided with an optical fiber sensor and a fluxgate sensor, and the steps include: The mutual inductor is monitored by the optical fiber sensor to obtain monitoring data; wherein the monitoring data includes temperature change and mechanical stress distribution of the mutual inductor; The magnetic field distribution inside the mutual inductor is obtained based on the temperature change and the mechanical stress distribution by the fluxgate sensor; Using a preset inverse problem algorithm, construct a three-dimensional magnetic field distribution map based on the magnetic field distribution; Evaluate the characteristics of the mutual inductor based on the three-dimensional magnetic field distribution diagram to obtain an evaluation result; If the evaluation result shows that the characteristic of the mutual inductor is abnormal, analyzing and diagnosing the three-dimensional magnetic field distribution diagram based on the evaluation result to obtain a diagnosis report; The magnetic field distribution includes the magnetic field intensity and magnetic field direction being affected by temperature changes and mechanical stress distribution. The magnetic field distribution inside the transformer is obtained based on the temperature changes and mechanical stress distribution through a preset fluxgate sensor, including: The magnetic field inside the sensor is collected by a preset fluxgate sensor to obtain preliminary magnetic field strength information; Performing vector conversion on the temperature change to obtain a temperature conversion vector; Performing vector transformation on the mechanical stress distribution to obtain a mechanical distribution vector; Taking the temperature conversion vector as a row vector and the mechanical distribution vector as a column vector; Multiply the row vector and the column vector to obtain a multiplication matrix; wherein the multiplication matrix is ​​a multiplication matrix of m rows and n columns; Performing singular value decomposition on the multiplication matrix to obtain a first orthogonal matrix, a diagonal matrix, and a second orthogonal matrix; wherein the first orthogonal matrix is ​​an orthogonal matrix with m rows and m columns, the diagonal matrix is ​​a diagonal matrix with m rows and n columns, and the second orthogonal matrix is ​​a multiplication matrix with n rows and n columns; Calculate the traces of the first orthogonal matrix, the diagonal matrix, and the second orthogonal matrix respectively, and obtain the first trace, the second trace, and the third trace accordingly; Arrange and combine the first trace, the second trace and the third trace to obtain an arrangement vector; wherein the arrangement vector has six vectors; constructing a permutation matrix based on the permutation vector; The arrangement matrix is ​​input into a preset boundary element algorithm to perform magnetic field distribution analysis to obtain the magnetic field distribution inside the mutual inductor.

2. The state diagnosis method of a mutual inductor according to claim 1, characterized in that: The mutual inductor is monitored by the optical fiber sensor to obtain monitoring data, including: Injecting laser pulses of a specific wavelength into the optical fiber sensor for activation through a preset light source system; Using the injected activation pulse to reflect the grating inside the optical fiber of the mutual inductor to obtain a reflected laser pulse; Performing spectral analysis on the reflected laser pulse to obtain wavelength change of the laser pulse; Based on the specific wavelength and wavelength variation of the laser pulse, a wavelength shift of the laser pulse is obtained; Using the Bragg equation, the temperature of the mutual inductor is calculated based on the wavelength offset to obtain the temperature change of the mutual inductor; Performing frequency domain analysis on the reflected laser pulse by using a preset distributed optical fiber sensing technology to obtain the transmission time and attenuation characteristics of the reflected laser pulse; The mechanical stress distribution along the length of the optical fiber is calculated based on the transmission time and the attenuation characteristics.

3. The state diagnosis method of a mutual inductor according to claim 1, characterized in that: A three-dimensional magnetic field distribution map is constructed based on the magnetic field distribution through a preset inverse problem algorithm, including: Inserting magnetic field data at different positions of the mutual inductor through a preset magnetic field sensor to obtain preliminary magnetic field strength data; Preprocessing the preliminary magnetic field strength data to obtain preprocessed data; wherein the preprocessing includes noise filtering and smoothing; Acquire the physical configuration characteristics of the mutual inductor, and construct an initial three-dimensional model based on the physical configuration characteristics; wherein the physical configuration characteristics include a physical structure, a relative position relationship of the physical structure, and material properties; the physical structure includes a coil, an iron core, and an air gap; and the material properties include magnetic permeability and electrical conductivity; Using a preset finite element method, the magnetic field distribution is implanted into the initial three-dimensional model to obtain a three-dimensional magnetic field distribution diagram with magnetic field lines; Color-coding the magnetic field lines with different colors to obtain color-coded magnetic field lines; wherein the different colors represent different levels of magnetic field strength; Inserting vector arrows into the color-coded magnetic field lines to obtain standard magnetic field lines; The standard magnetic field lines are aligned with the spatial coordinate system in the initial three-dimensional model to obtain a three-dimensional magnetic field distribution map.

4. The method for diagnosing the state of a mutual inductor according to claim 3, characterized in that: The magnetic field distribution is implanted into the initial three-dimensional model to obtain a three-dimensional magnetic field distribution diagram with magnetic field lines, including: Segmenting the magnetic field distribution along a specified direction to obtain magnetic field distribution images on multiple planes; wherein the specified direction is an X-axis direction, a Y-axis direction, or a Z-axis direction; By means of a preset fast Fourier transform, each of the magnetic field distribution images is converted from the spatial domain to the frequency domain to obtain frequency domain magnetic field data; Performing spatial transformation and reconstruction on the frequency domain magnetic field data to obtain a reconstructed magnetic field distribution; The reconstructed magnetic field distribution is implanted into the initial three-dimensional model to obtain a three-dimensional magnetic field distribution map with magnetic field lines.

5. The method for diagnosing the state of a mutual inductor according to claim 4, characterized in that: The characteristics of the mutual inductor are evaluated based on the three-dimensional magnetic field distribution diagram to obtain an evaluation result, including: Mapping the magnetic field strength and magnetic field direction in the three-dimensional magnetic field distribution diagram onto the physical structure to obtain the electromagnetic load distribution in the coil and core area; Obtaining a current density of the transformer based on the electromagnetic load distribution; Using Ampere's circuit law and Biot-Savart's law, the magnetic resistance, magnetic flux and mutual inductance of each electromagnetic unit are calculated based on the physical configuration characteristics and current density; Using a preset electromagnetic-circuit conversion algorithm, the magnetic resistance, magnetic flux and mutual inductance coefficient are converted into resistance, inductance and mutual inductance coefficient in the circuit respectively; Constructing an equivalent circuit based on the resistance, inductance, mutual inductance, physical structure and relative position relationship; Performing performance analysis on the equivalent circuit to obtain electrical performance indicators; The characteristics of the sensor are evaluated based on the electrical performance index to obtain an evaluation result.

6. The method for diagnosing the state of a mutual inductor according to claim 1, characterized in that: The three-dimensional magnetic field distribution diagram is analyzed and diagnosed based on the evaluation result to obtain a diagnosis report, including: Performing fault classification on the evaluation result to obtain a fault classification result; Inputting the fault classification result into a preset fault factor algorithm for analysis to obtain a fault factor; Performing fault location mapping on the three-dimensional magnetic field distribution diagram based on the fault factor to obtain fault factor location; The three-dimensional magnetic field distribution diagram is analyzed and diagnosed based on the fault factor location to obtain a diagnosis report.

7. A state diagnosis device for a transformer, characterized in that: The mutual inductor is provided with an optical fiber sensor and a fluxgate sensor, including: A monitoring module, used to monitor the mutual inductor through the optical fiber sensor to obtain monitoring data; wherein the monitoring data includes temperature change and mechanical stress distribution of the mutual inductor; A distribution module, used to obtain the magnetic field distribution inside the mutual inductor based on the temperature change and mechanical stress distribution through the fluxgate sensor; A construction module, used to construct a three-dimensional magnetic field distribution map based on the magnetic field distribution through a preset inverse problem algorithm; An evaluation module, used to evaluate the characteristics of the mutual inductor based on the three-dimensional magnetic field distribution diagram to obtain an evaluation result; a detection module, configured to analyze and diagnose the three-dimensional magnetic field distribution diagram based on the evaluation result to obtain a diagnosis report if the evaluation result shows that the characteristic of the mutual inductor is abnormal; The magnetic field distribution includes the magnetic field intensity and magnetic field direction being affected by temperature changes and mechanical stress distribution. The magnetic field distribution inside the transformer is obtained based on the temperature changes and mechanical stress distribution through a preset fluxgate sensor, including: The magnetic field inside the sensor is collected by a preset fluxgate sensor to obtain preliminary magnetic field strength information; Performing vector conversion on the temperature change to obtain a temperature conversion vector; Performing vector transformation on the mechanical stress distribution to obtain a mechanical distribution vector; Taking the temperature conversion vector as a row vector and the mechanical distribution vector as a column vector; Multiply the row vector and the column vector to obtain a multiplication matrix; wherein the multiplication matrix is ​​a multiplication matrix of m rows and n columns; Performing singular value decomposition on the multiplication matrix to obtain a first orthogonal matrix, a diagonal matrix, and a second orthogonal matrix; wherein the first orthogonal matrix is ​​an orthogonal matrix with m rows and m columns, the diagonal matrix is ​​a diagonal matrix with m rows and n columns, and the second orthogonal matrix is ​​a multiplication matrix with n rows and n columns; Calculate the traces of the first orthogonal matrix, the diagonal matrix, and the second orthogonal matrix respectively, and obtain the first trace, the second trace, and the third trace accordingly; Arrange and combine the first trace, the second trace and the third trace to obtain an arrangement vector; wherein the arrangement vector has six vectors; constructing a permutation matrix based on the permutation vector; The arrangement matrix is ​​input into a preset boundary element algorithm to perform magnetic field distribution analysis to obtain the magnetic field distribution inside the mutual inductor.

8. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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