A transformer operation state diagnosis method based on multi-physical field coupling gas component determination

Abstract

The application discloses a transformer operation state diagnosis method based on multi-physical field coupling gas component determination, relates to the technical field of transformer state monitoring, and extracts and optimizes the grid subdivision density through a fluid calculation domain, reasonably controls the network scale, and reduces the computing power cost of the multi-field simulation of the transformer operation state. The energy splitting method is adopted to greatly improve the local temperature gradient distortion problem and improve the authenticity of the thermal flow field steady-state solution. The flow field freezing method is adopted to effectively balance the relationship between the simulation accuracy and the diagnosis speed, and enhance the engineering practicability of the three-dimensional gas component simulation. The model credibility is checked through the working condition measured values of the fixed monitoring point temperature and the gas component concentration, and the state parameters of the credible model are output, multi-source information collaborative diagnosis is realized, and the accuracy of the transformer operation state monitoring is improved.

Description

Technical Field

[0001] This application relates to the field of transformer condition monitoring technology, specifically to a method for diagnosing transformer operating conditions based on multi-physics field coupled gas component determination. Background Technology

[0002] Oil-immersed transformers are core equipment in power systems. When the internal insulating oil and solid insulation materials age or fail under the influence of electrical and thermal factors, they produce characteristic fault gases such as hydrogen, methane, ethylene, and acetylene. The composition, concentration, and variation patterns of these gases differ depending on the fault type. Therefore, analyzing dissolved gases in the oil to diagnose internal transformer faults such as overheating and discharge has become an important method for monitoring transformer operating conditions. Due to the large size of the transformer and the high insulation performance requirements, oil gas detection relies on a limited number of sampling ports on the oil tank, and the internal insulation condition is analyzed using limited detection data. Therefore, multiphysics simulation plays a crucial inference role in the transformer insulation condition analysis process.

[0003] Existing computational fluid dynamics-based transformer simulation methods typically require simplification of complex structures such as winding coils, core laminations, oil channels, and clamps. The simplified model may deviate from the actual structure in terms of effective heat dissipation area and heat transfer paths. If the winding copper losses and core iron losses are still concentrated on the simplified solid surface, it can easily lead to excessively large local temperature gradients, further affecting the thermal buoyancy force, oil flow distribution, and the migration and diffusion of fault-characteristic gases. Furthermore, the computational burden of directly coupling the temperature field, flow field, and multi-component gas field is substantial, making it difficult to balance simulation accuracy and computational efficiency.

[0004] In the field of transformer fault diagnosis technology, existing technologies have focused on the detection, data analysis, and fault type identification of dissolved gases in oil. Chinese patent CN107228913B discloses a transformer fault type condition diagnosis system that identifies fault types by detecting the components and concentrations of dissolved gases in insulating oil and using a sample retrieval algorithm based on Euclidean distance. Chinese patent CN118688401B discloses a transformer condition diagnosis method, device, and electronic equipment that determines the transformer condition based on the ratio of fault characteristic gas concentrations at multiple time points, total gas production, and fault diagnosis results. While these solutions improve transformer condition diagnosis from aspects such as fault characteristic gas detection, fault sample matching, and time-series data analysis, their diagnostic basis mainly comes from gas concentration, concentration ratio, sample data, or time-varying trends, without fully incorporating the internal temperature field, flow field, and spatial distribution characteristics of multi-component fault characteristic gases within the transformer for diagnostic correction.

[0005] In summary, existing transformer fault diagnosis schemes still suffer from problems such as insufficient realism in the simplified model's heat source loading, high computational overhead in multiphysics calculations, and inadequate integration of oil chromatography data with internal temperature, flow, and gas spatial distribution information. Therefore, improving the simulation realism of the transformer's internal heat flow field and fault-characteristic gas distribution while reasonably controlling computational load, and achieving accurate fault diagnosis for oil-immersed transformers, is a pressing technical problem to be solved in this field. Summary of the Invention

[0006] In order to solve the above-mentioned technical problems, this application proposes the following technical solution: In a first aspect, embodiments of this application provide a method for diagnosing the operating status of a transformer based on multi-physics field coupled gas composition determination, including: A three-dimensional model is constructed based on the basic parameters of the transformer, the fluid computational domain is extracted, and an unstructured network is partitioned. The final partition density is selected based on the deviation of the evaluation index. The property parameters of transformer oil, windings, core, and fault characteristic gases are set in the three-dimensional model; The heat generated by copper and iron losses is decomposed into surface heat flux and volume release by using the energy decomposition method. The optimal decomposition ratio is determined by temperature deviation verification to obtain the steady-state solution of the heat flow field. Using the flow field freezing method, with the steady-state solution of the thermal flow field as the background field, a gas mass source term is added at a preset fault location to obtain the three-dimensional concentration distribution of fault characteristic gas inside the transformer. The simulated values ​​of temperature and gas concentration at fixed monitoring points are checked against the corresponding measured values. Based on the state parameters of the reliable model, the operating status of the transformer is analyzed and the diagnostic results are output. In one possible implementation, the process of constructing a three-dimensional model based on the transformer's basic parameters, extracting the fluid computational domain, performing unstructured network partitioning, and selecting the final partitioning density based on the evaluation index deviation includes: Input the basic parameters of the target transformer, and construct a scaled 3D model of the transformer in 3D modeling software based on the actual internal structure of the target transformer. The basic structure of the winding coil of the three-dimensional transformer model is retained, the number of coil layers is simplified, and structural components with little impact on the thermal flow field are eliminated. The internal fluid region is extracted from the simplified 3D model of the transformer and used as the computational domain for subsequent mesh generation and simulation. A cylindrical annular fluid domain is defined outside the winding coil as the application area for the volume release of the winding side; at the same time, a cylindrical annular fluid domain is defined at the interface where the core column and side column of the iron core are in direct contact with the flow field as the application area for the volume release of the iron core side. Based on the constructed three-dimensional geometric model, the following parameters were measured and recorded: the total area of ​​the solid surface of the winding, the total area of ​​the solid surface of the iron core, the total volume of the fluid domain of the outer cylindrical ring of the winding, and the total volume of the fluid domain of the outer cylindrical ring of the iron core. The fluid computational domain is meshed into an unstructured grid, and the final mesh density is selected by choosing a grid scheme in which the relative deviation of all evaluation indicators is less than a preset percentage value.

[0007] In one possible implementation, the step of performing unstructured mesh generation on the fluid computational domain and selecting a mesh scheme in which the relative deviation of all evaluation indicators is less than a preset percentage value as the final mesh density includes: The Meshing module is used to perform unstructured mesh generation on the fluid computation domain, and local meshing is performed on the narrow oil passages of the winding disc, the near wall of the iron core, the oil guide pipe, and the location of the preset fault point. Three different mesh schemes with different densities were set up: coarse mesh, medium mesh, and fine mesh. The coarse mesh was only used for preliminary testing. Using the oil outlet velocity, winding coil velocity, oil outlet velocity, winding wall temperature, core wall temperature, and near-wall oil temperature as evaluation indicators, the relative deviations of each evaluation indicator between medium and fine mesh grids are calculated. The calculation formula is as follows: in: These are the values ​​of the evaluation index under a medium grid. The values ​​of the same evaluation index under a fine grid; The final meshing density is selected when the relative deviation of all evaluation indicators is less than the preset percentage value.

[0008] In one possible implementation, setting the property parameters of transformer oil, windings, core, and fault characteristic gases in the three-dimensional model includes: Establish the density model and temperature-dependent physical property parameters of transformer oil; Set the copper winding material parameters and the core material parameters to constant values; Based on the needs of transformer condition diagnosis, hydrogen gas is used. methane ethane ethylene Acetylene Carbon monoxide (CO) and carbon dioxide Select one or more fault characteristic gases and set the attribute parameters of the selected fault characteristic gases.

[0009] In one possible implementation, setting the density model and temperature-related physical properties of the transformer oil includes: The Boussinesq assumption is used to characterize the thermal buoyancy effect, and a density model for transformer oil is established: in: This represents the transformer oil temperature at the corresponding calculation location within the transformer fluid computational domain. For temperature The density of the transformer oil below The reference temperature for the transformer oil density model. Reference temperature The following is the reference density of transformer oil. The coefficient of thermal expansion for the transformer oil density model; The specific heat, thermal conductivity, and dynamic viscosity of transformer oil are defined as piecewise linear functions of temperature, and the physical property parameters are calibrated at selected temperature points.

[0010] In one possible implementation, the energy decomposition method is used to decompose the heat generated by copper and iron losses into surface heat flux and volumetric release. The optimal decomposition ratio is determined by temperature deviation verification to obtain the steady-state solution of the heat flow field, including: Calculate the heat generated by the copper loss in the windings separately. Heat generated by iron core loss ; Define the first proportionality coefficient The second proportionality coefficient is ,in: This indicates the proportion of surface heat flux applied to the solid surface. ; This indicates the proportion of the volumetric fluid released into the adjacent cylindrical annular fluid domain; The heat generated by copper loss and iron loss are respectively calculated as follows: and The ratio is split into surface heat flux and volume release, which are applied to the solid surface and its adjacent cylindrical annular fluid domain to obtain the simulated temperature of the winding wall and the simulated temperature of the core wall. The simulated winding wall temperature and the simulated core wall temperature are compared and verified with the corresponding winding temperature verification benchmark values ​​and core temperature verification benchmark values, respectively. The first proportional coefficient is adjusted according to the temperature deviation. This continues until the temperature deviation meets the preset tolerance threshold.

[0011] Determine the optimal split ratio and output the steady-state solution of the heat flow field under the optimal split ratio.

[0012] In one possible implementation, the heat generated by copper loss and iron loss is respectively... and The ratio is split into surface heat flux and volumetric release, which are applied to the solid surface and its adjacent cylindrical annular fluid domain, respectively, including: The heat generated by copper loss and iron loss are respectively calculated as follows: and Divide into proportions; The heat generated by copper loss, after being split, is first proportionally applied to the solid surface of the winding in the form of surface heat flux, and second proportionally applied to the outer cylindrical annular fluid domain of the winding in the form of volume release. The heat generated by iron loss, after being decomposed, is first proportionally applied to the solid surface of the iron core in the form of surface heat flux, and second proportionally applied to the outer cylindrical annular fluid domain of the iron core in the form of volume release. in: It is the heat applied to the surface of the winding solid in the form of surface heat flux. It is the heat applied to the fluid domain of the outer cylindrical annulus of the winding in the form of volume release. This refers to the heat generated by the copper losses in the windings under rated operating conditions. It is the heat applied to the solid surface of the iron core in the form of surface heat flux. It is the heat applied to the fluid domain of the outer cylindrical ring of the iron core in the form of volume release. This refers to the heat generated due to iron loss in the iron core under rated operating conditions. This represents the total area of ​​the solid surface of the winding. This represents the total surface area of ​​the iron core solid. The total volume of the fluid domain in the outer cylindrical annulus of the winding. It represents the total volume of the fluid domain in the cylindrical annulus outside the iron core.

[0013] In one possible implementation, the flow field freezing method is employed, using the steady-state solution of the thermal flow field as the background field, and a gas mass source term is added at a preset fault location to obtain the three-dimensional concentration distribution of the fault characteristic gas inside the transformer, including: Freeze the steady-state solution of the thermal flow field, shut down the energy equation and turbulence model, and keep the velocity field and temperature field unchanged, using them as the background field for the migration and diffusion of gas components; Add one or more fault characteristic gas mass source terms to the grid cells at the preset fault gas generation location, and solve the three-dimensional spatial concentration distribution of the fault characteristic gas under the driving force of the freezing thermal flow field. The transient solution is applied up to a preset physical time, and the three-dimensional spatial concentration distribution of each fault characteristic gas at the same moment is output for subsequent transformer condition diagnosis. After the transient solution is completed, the simulated temperature values ​​corresponding to the locations of the temperature monitoring points in the transformer's fixed monitoring points are extracted, and the result is recorded as follows: The simulated temperature at each temperature monitoring point is: ,in , The total number of temperature monitoring points participating in the verification; Based on the spatial coordinates corresponding to the gas component monitoring points in the transformer's fixed monitoring points, the simulated value of the fault characteristic gas concentration at the aforementioned spatial coordinates is calculated from the simulation grid node values ​​using the inverse distance weighted interpolation method. This is denoted as the... The simulated concentrations at each gas component monitoring point are: ,in , This represents the total number of gas monitoring points participating in the verification.

[0014] In one possible implementation, adding one or more fault characteristic gas mass source terms to the grid cells at the preset fault gas generation location, and solving for the three-dimensional spatial concentration distribution of the fault characteristic gas driven by the freezing thermal flow field, includes: When performing targeted diagnosis on a transformer, one or more fault characteristic gases are selected according to the diagnostic requirements, and corresponding mass source terms are set for each gas. The three-dimensional spatial distribution of the selected fault characteristic gases under the driving force of the freezing thermal flow field is then solved. When performing a comprehensive diagnosis of a transformer, the selected fault characteristic gas should include at least hydrogen. methane ethane ethylene Acetylene And set up the mass source terms corresponding to the above-mentioned fault characteristic gases respectively, and solve the three-dimensional spatial distribution of each fault characteristic gas driven by the freezing thermal flow field. Among them, the The expression for the mass source term of the fault characteristic gas is: in: For the mass source term of the i-th fault characteristic gas, For the fault gas production rate, This represents the volume of the grid cell corresponding to the location of the gas generation failure. By initiating the component transport equations, the three-dimensional spatial concentration distribution of one or more fault-characteristic gases driven by the freezing thermal flow field is solved. The component transport equations are as follows: in: The density of the gas mixture dissolved in transformer oil. For the frozen flow velocity vector, Let i be the mass fraction of the i-th fault characteristic gas. Let be the diffusion mass flux of the i-th fault characteristic gas. Let i be the mass source term of the i-th fault characteristic gas.

[0015] In one possible implementation, the step of verifying the simulated temperature and gas concentration values ​​at fixed monitoring points with their corresponding measured values, analyzing the transformer's operating status based on reliable model state parameters, and outputting diagnostic results includes: Input the measured temperature values ​​of the temperature monitoring points of the target transformer under actual energized operating conditions. Fault characteristics of gas concentration at gas component monitoring points (measured values) ; The formulas for calculating the average absolute percentage error of temperature, the average absolute percentage error of fault characteristic gas concentration, and the coefficient of determination of fault characteristic gas concentration are as follows: in: ; The reliability of the simulation model is verified based on the average absolute percentage error of temperature, the average absolute percentage error of characteristic gas concentration, and the coefficient of determination of characteristic gas concentration. The simulation results of the global temperature field, flow field, and fault characteristic gas component concentration distribution of the transformer are output by a reliable model, and the simulation results are defined as the state parameters of the reliable model. Based on the state parameters of the reliable model, the operating status of the transformer is analyzed, and targeted or comprehensive diagnosis is performed to output the corresponding diagnostic results.

[0016] In this embodiment, addressing the issues of high computational overhead in existing multiphysics coupling simulations, distortion of heat source loading in transformer simulations, and insufficient fusion of fault diagnosis information, this application achieves reduced computational overhead by extracting the fluid computational domain and optimizing mesh density. This is achieved by rationally controlling the mesh size while ensuring computational accuracy. An energy decomposition method is employed to separate the heat generated by copper and iron losses into surface heat flux and volumetric release. The optimal decomposition ratio is determined through temperature deviation verification, significantly improving the distortion of local temperature gradients caused by concentrated heat sources and providing accurate steady-state solutions for the diffusion of fault-specific gases. A flow field freezing method is used, employing the steady-state solution of the heat flow field as a decoupled calculation method for loading gas mass source terms in the background field. This effectively balances the relationship between simulation accuracy and diagnostic speed, enhancing the engineering practicality of three-dimensional gas component simulation. Finally, the model's reliability is verified using measured values ​​of temperature and gas component concentration at fixed monitoring points, and reliable model state parameters are output, enabling collaborative diagnosis of multi-source information and improving the accuracy of transformer operating status monitoring. Attached Figure Description

[0017] Figure 1 A flowchart illustrating a transformer operating status diagnosis method based on multi-physics field coupled gas component determination, provided for an embodiment of this application; Figure 2 This is a schematic diagram of the three-dimensional model structure of the transformer body provided in the embodiments of this application; Figure 3 A schematic diagram of mesh partitioning provided for an embodiment of this application; Figure 4 The calculated temperature field profile cloud map provided for the embodiments of this application; Figure 5 The calculated velocity field profile cloud map provided for the embodiments of this application; Figure 6 The calculated acetylene gas mass fraction profile cloud map provided for the embodiments of this application. Detailed Implementation

[0018] The present solution will now be described in conjunction with the accompanying drawings and specific embodiments.

[0019] See Figure 1 The transformer operating condition diagnosis method based on multi-physics field coupled gas component determination provided in this embodiment includes: S101: A three-dimensional model is constructed based on the basic parameters of the transformer, the fluid computation domain is extracted, an unstructured network is partitioned, and the final partition density is selected based on the deviation of the evaluation index.

[0020] The target oil-immersed transformer in this embodiment can be a converter transformer, a step-up transformer, a step-down transformer, or other oil-immersed transformers. A converter transformer with forced oil circulation and air cooling is used as an example. The converter transformer is equipped with an external cooler and a submersible oil pump. The transformer body forms a closed oil circulation loop with the external cooler through the oil inlet and outlet. An oil guide pipe is installed at the bottom of the transformer body. The transformer oil, cooled by the external cooler, enters the transformer body under the drive of the submersible oil pump and is transported to the winding and core areas through the oil guide pipe. It then flows out through the outlet and returns to the external cooler, thus forming forced oil circulation. The converter transformer body includes an oil tank, windings, core, oil and oil channels, oil guiding structure, cooling structure interface, and fixed monitoring points, among other structures. The fixed monitoring points are the locations of monitoring sensors or online monitoring devices that are fixedly deployed on the converter transformer at the time of manufacture, including gas composition monitoring points and temperature monitoring points. The gas composition monitoring points are used to obtain the measured values ​​of fault characteristic gas concentrations in the transformer oil at the corresponding locations. The measured gas concentrations include the gas concentrations in the upper, middle, and bottom parts of the transformer body and other preset locations. The temperature monitoring points are used to obtain the measured values ​​of temperature at the corresponding locations. The measured temperature values ​​include the top oil temperature, bottom oil temperature, winding temperature, and temperatures in other preset locations. Each fixed monitoring point has unique spatial coordinates corresponding to the three-dimensional model.

[0021] This embodiment is not limited to a specific model, size, voltage level, capacity level, or cooling parameter. Instead, it establishes a collaborative analysis method for the flow field, temperature field, and gas composition field corresponding to the actual structural parameters, operating parameters, and fixed monitoring point layout of the target oil-immersed transformer. This method analyzes and verifies the fault characteristic gas composition and temperature, analyzes their spatial distribution, and further completes the diagnosis of the transformer's operating status.

[0022] Input the basic parameters of the target oil-immersed transformer, including the transformer nameplate parameters, cooling system parameters, internal structural geometry, and factory test data. The nameplate parameters include rated capacity, rated voltage, and cooling method; the cooling system parameters include oil pump flow rate, oil pipe layout, and cooler interface information; and the factory test data includes rated copper loss, rated iron loss, and temperature rise test data. Based on the actual internal structure of the target oil-immersed transformer, a scaled-down 3D model of the transformer was constructed in Ansys SpaceClaim. The basic structure of the winding coils was retained, and the number of coil layers in both the grid-side and valve-side windings was simplified to 20 layers. Small structural components with minimal impact on the thermal flow field, such as fasteners and tie plates, were removed. Figure 2 As shown.

[0023] The internal fluid region was extracted from the simplified 3D model and used as the computational domain for subsequent mesh generation and simulation. A 2 cm thick cylindrical annular fluid domain was defined outside the winding disc as the application area for the volume release on the winding side. Simultaneously, at the interfaces where the core column and side column of the iron core directly contact the flow field, a 2 cm thick cylindrical annular fluid domain was defined as the application area for the volume release on the iron core side. The following parameters were measured and recorded: total area of ​​the solid surface of the winding. Total surface area of ​​the iron core solid Total volume of the outer cylindrical annular fluid domain of the winding Total volume of the cylindrical annular fluid domain outside the iron core .

[0024] The Meshing module was used to perform unstructured mesh generation on the fluid computational domain, with localized mesh refinement applied to narrow oil passages in the winding coil, near-wall areas of the core, oil guide pipes, and locations of preset fault points. Three mesh schemes were set up: coarse (2.75 million elements), medium (4.3 million elements), and fine (9.35 million elements), with the coarse mesh used only for preliminary testing. The relative deviations of each evaluation index between the medium and fine meshes were calculated, using the oil guide pipe outlet velocity, winding coil velocity, oil outlet velocity, winding wall temperature, core wall temperature, and near-wall oil temperature as evaluation indicators. in: These are the values ​​of the evaluation index under a medium grid. This refers to the value of the same evaluation index under a fine grid.

[0025] In this embodiment, the relative deviations of all evaluation metrics for the medium-sized network are less than 2.2%, and the medium-sized grid is selected as the final mesh density. Figure 3 As shown.

[0026] S102, set the property parameters of transformer oil, winding, core and fault characteristic gas in the three-dimensional model.

[0027] The transformer oil density is set. In this implementation, the Boussinesq assumption is used to characterize the thermal buoyancy effect. The transformer oil density is: in: This represents the transformer oil temperature at the corresponding calculation location within the transformer fluid computational domain. For temperature The density of the transformer oil below The reference temperature for the transformer oil density model. Reference temperature The following is the reference density of transformer oil. The coefficient of thermal expansion is the density model of transformer oil; in this embodiment... kg / m³, , K.

[0028] The temperature-related physical properties of transformer oil are set. In this embodiment, the specific heat, thermal conductivity, and dynamic viscosity of transformer oil are defined as piecewise linear functions of temperature. They are calibrated at four temperature points: 26.85℃, 49.85℃, 76.85℃, and 99.85℃, with the following values: specific heat 1900, 1960, 2040, 2100 J / (kg·K); thermal conductivity 0.12, 0.118, 0.115, 0.110 W / (m·K); dynamic viscosity 0.0200, 0.0080, 0.0040, 0.0025 kg / (m·s).

[0029] The parameters of the copper winding material and the iron core material are set to constant values. In this embodiment, the parameters of the copper winding material are: density 8978 kg / m³, specific heat 381 J / (kg·K), and thermal conductivity 387.6 W / (m·K). The parameters of the iron core material are: density 7650 kg / m³, specific heat 467 J / (kg·K), and thermal conductivity 35.0 W / (m·K).

[0030] Based on the needs of transformer condition diagnosis, hydrogen gas is used. methane ethane ethylene Acetylene Carbon monoxide (CO) and carbon dioxide Select one or more fault characteristic gases and set the attribute parameters of the selected fault characteristic gases. Specifically, when performing targeted diagnosis, select one or more required fault characteristic gases; when performing comprehensive diagnosis, the selected fault characteristic gases must include at least hydrogen. methane ethane ethylene Acetylene .

[0031] S103 uses the energy decomposition method to decompose the heat generated by copper loss and iron loss into surface heat flux and volume release. The optimal decomposition ratio is determined by temperature deviation verification to obtain the steady-state solution of the heat flow field.

[0032] In this implementation, the heat generated due to winding copper losses Heat generated by iron core loss Under rated operating conditions, the heat generated is determined by the transformer's factory test data; under non-rated operating conditions, the heat generated due to copper losses is calculated by the square of the load rate, and the heat generated due to iron losses is approximated by the rated value or corrected by the no-load loss characteristic curve.

[0033] Define the first proportionality coefficient The second proportionality coefficient is ,in . This indicates the proportion of surface heat flux applied to the solid surface. This represents the proportion of the volumetric fluid release applied to the adjacent cylindrical annular fluid domain. Initial calculations use... Set adjustment step size (Preferred value: 0.05–0.1) and temperature tolerance threshold (Preferably, the absolute error should not exceed 2℃ or the relative error should not exceed 5%).

[0034] The heat generated by copper loss and iron loss are respectively calculated as follows: and The heat generated by copper loss is divided into two proportions. The first proportion of heat is applied to the solid surface of the winding in the form of surface heat flux, and the second proportion of heat is applied to the outer cylindrical annular fluid domain of the winding in the form of volume release. The heat generated by iron loss, after being decomposed, is first proportionally applied to the solid surface of the iron core in the form of surface heat flux, and second proportionally applied to the outer cylindrical annular fluid domain of the iron core in the form of volume release. in: It is the heat applied to the surface of the winding solid in the form of surface heat flux. It is the heat applied to the fluid domain of the outer cylindrical annulus of the winding in the form of volume release. This refers to the heat generated by the copper losses in the windings under rated operating conditions. This refers to the heat generated by iron loss in the iron core under rated operating conditions. It is the heat applied to the solid surface of the iron core in the form of surface heat flux. It is the heat applied to the fluid domain of the outer cylindrical ring of the iron core in the form of volume release. This represents the total area of ​​the solid surface of the winding. This represents the total surface area of ​​the iron core solid. The total volume of the fluid domain in the outer cylindrical annulus of the winding. It represents the total volume of the fluid domain in the cylindrical annulus outside the iron core.

[0035] A pressure-based transient solver was used, with the energy equation and turbulence model enabled (Realizable k-ε turbulence model preferred). The gravitational acceleration was set to -9.8 m / s², the inner wall of the tank was adiabatic with no slippage, and the SIMPLE algorithm was employed. An external pipeline power source term was included, and the cooler temperature was fixed. After convergence, the simulated winding wall temperature was obtained. Simulated temperature of iron core wall .

[0036] Obtain the winding temperature verification reference value corresponding to the current simulation conditions. Core temperature verification benchmark value The winding temperature verification reference value is mentioned above. The core temperature is determined based on temperature rise test data under the corresponding operating conditions of the target transformer, oil temperature data near the winding obtained from temperature monitoring points in fixed monitoring points, or theoretical calculation results verified by test data, and is used to verify the simulated temperature of the winding wall; the core temperature verification benchmark value is used to verify the simulated temperature of the winding wall. The temperature is determined based on the temperature rise test data of the target transformer under the corresponding operating conditions or the theoretical calculation results verified by the test data, and is used to verify the simulated temperature of the iron core wall.

[0037] The simulated temperature of the winding wall Simulated temperature of iron core wall Each was checked against the corresponding winding temperature reference value. Core temperature verification benchmark value Compare and calculate the maximum temperature deviation: like Furthermore, if the simulation temperature is too high, then reduce... ( );like Furthermore, if the simulation temperature is too low, it will increase. ( Return to reapply the heat source and repeat the simulation until the temperature accuracy requirements are met. Then the current This is the optimal splitting ratio. In this embodiment, when... At that time, the simulation yielded ℃, ℃, maximum deviation ℃ ≤ 2℃, which meets the deviation accuracy requirements.

[0038] Determine the optimal split ratio and output the heat flow field, recording the final result that meets the deviation accuracy requirements. Value, denoted as Simultaneously, the steady-state solution of the thermal flow field (including the velocity field) under this optimal split ratio is obtained. and temperature field Output for later use.

[0039] Figure 4 For the temperature field simulation profile cloud map, by Figure 4 It can be seen that, with the help of the energy decomposition method, the simulation model successfully constructed a reasonable temperature field of the transformer body. The heat generated by copper loss and iron loss caused the temperature of the winding and core wall to rise, and the heat was dissipated to the outside through the oil flow near the wall. Figure 5This is a velocity field profile contour map, from Figure 5 It can be seen that, under the combined action of thermal buoyancy and forced oil circulation, transformer oil enters the transformer body from the lower oil guide pipe and flows towards the area where the two winding columns are located. In the winding area, it flows upward along the oil channels around the winding columns and exhibits a clear chimney effect. Then it gathers in the area below the top cover and flows towards the oil outlet.

[0040] S104. Using the flow field freezing method, with the steady-state solution of the thermal flow field as the background field, a gas mass source term is added at the preset fault location to obtain the three-dimensional concentration distribution of the fault characteristic gas inside the transformer.

[0041] Close the energy equation and turbulence model, freeze the steady-state solution of the thermal flow field, and maintain the velocity field. and temperature field constant; When performing targeted diagnosis on a transformer, one or more fault characteristic gases are selected according to the diagnostic requirements, and corresponding mass source terms are set for each gas. The three-dimensional spatial distribution of the selected fault characteristic gases under the driving force of the freezing thermal flow field is then solved. When performing a comprehensive diagnosis of a transformer, the selected fault characteristic gas should include at least hydrogen. methane ethane ethylene Acetylene And set up the mass source terms corresponding to the above-mentioned fault characteristic gases respectively, and solve the three-dimensional spatial distribution of each fault characteristic gas driven by the freezing thermal flow field. Among them, the The expression for the mass source term of the fault characteristic gas is: in: For the mass source term of the i-th fault characteristic gas, For the fault gas production rate, This represents the volume of the grid cell corresponding to the location of the faulty gas generation. In this embodiment, the gas generation location is preset to the bottom end of the valve-side sleeve, and the gas generation rate is 185.6 μL / s, which is converted to a mass source term. kg / (m³·s).

[0042] By initiating the component transport equations, the three-dimensional spatial concentration distribution of one or more fault characteristic gases driven by a freezing thermal flow field is solved. When solving for multiple fault characteristic gases, each fault characteristic gas has a corresponding mass fraction, mass diffusion flux, and mass source term. The component transport equations are as follows: in The density of the gas mixture dissolved in transformer oil. For the frozen flow velocity vector, Let i be the mass fraction of the i-th fault characteristic gas. Let be the diffusion mass flux of the i-th fault characteristic gas. For the i-th type of fault characteristic gas, the mass source term is provided. The transient solution is performed up to a preset physical time, preferably 60 minutes, with a time step of 1 second. Within each time step, the solution is iterated 20 times to output the three-dimensional spatial concentration distribution of each fault characteristic gas at the same moment, which is used for subsequent transformer condition diagnosis. Figure 6 To obtain a transient profile of the acetylene mass fraction after 60 minutes. Figure 6 This indicates that, through the aforementioned flow field freezing method, the simulation model constructs a reasonable spatial distribution field of C2H2 gas components. The C2H2 components diffuse from the gas source point to the surrounding areas and exhibit obvious directional movement characteristics under the drive of the transformer oil hot flow field.

[0043] After the transient solution is completed, the simulated temperature values ​​corresponding to the locations of the temperature monitoring points in the transformer's fixed monitoring points are extracted, and the result is recorded as follows: The simulated temperature at each temperature monitoring point is: ,in , The total number of temperature monitoring points participating in the verification; Based on the spatial coordinates corresponding to the gas component monitoring points in the transformer's fixed monitoring points, the simulated value of the fault characteristic gas concentration at the aforementioned spatial coordinates is calculated from the simulation grid node values ​​using the inverse distance weighted interpolation method. This is denoted as the... The simulated concentrations at each gas component monitoring point are: ,in , This represents the total number of gas monitoring points participating in the verification.

[0044] S105 verifies the simulated temperature and gas concentration values ​​at fixed monitoring points against the corresponding measured values. Based on the state parameters of the reliable model, it analyzes the transformer's operating status and outputs diagnostic results.

[0045] Input the measured temperature values ​​of the temperature monitoring points of the target transformer under actual energized operating conditions. Fault characteristics of gas concentration at gas component monitoring points (measured values) The types and quantities of fault characteristic gases used in the verification are consistent with those selected in the aforementioned gas component simulation; the spatial coordinates of the temperature monitoring point and the gas component monitoring point in the target transformer are recorded; The formulas for calculating the average absolute percentage error of temperature, the average absolute percentage error of fault characteristic gas concentration, and the coefficient of determination of fault characteristic gas concentration are as follows: in: .

[0046] The model confidence condition is set to the average absolute percentage error of temperature. The average absolute percentage error of the fault characteristic gas concentration Fault characteristic gas concentration determination coefficient When all three conditions are met, the constructed simulation model is deemed reliable, and the corresponding reliable model state parameters are output. These reliable model state parameters include the simulation results of the global temperature field, flow field, and fault characteristic gas component concentration distribution of the transformer. If any condition is not met, the simulation returns to adjust the attribute parameters or adjust the mesh density, and the simulation and verification are repeated until all three conditions for model reliability are met.

[0047] When conducting targeted diagnostics, based on the state parameters of the reliable model, the gas concentration data corresponding to the target gas type is directly output and compared with the preset concentration thresholds of the key gases (such as hydrogen, acetylene, etc.) or the total amount of gases (such as total hydrocarbons, etc.) to obtain the corresponding targeted diagnostic conclusions.

[0048] During comprehensive diagnosis, based on the state parameters of the reliable model, global gas concentration data and temperature data are output, and a comprehensive analysis is performed using multi-source data. The multi-source data includes measured data from monitoring points and the state parameters of the reliable model. The comprehensive analysis involves calculating the first, second, and third-level observations and comparing them with the corresponding first, second, and third-level criteria to obtain the first, second, and third-level diagnostic conclusions, thereby achieving a comprehensive diagnosis of the transformer's operating status.

[0049] Based on the state parameters of the reliable model, all gas concentration data are extracted. When the gas content or increment does not exceed the attention value, the first-level diagnostic conclusion is set to 0, denoted as J1=0, and the calculation and analysis of the first-level observation value and the first-level criterion are no longer performed. When the gas content or increment exceeds the attention value, methane is calculated. With hydrogen concentration ratio Acetylene With ethylene concentration ratio ethylene With ethane Yes, the concentration ratio ,Will , , As the first-level observation value; the attention value adopts the attention value of the three-ratio method; the first-level observation value is encoded according to the three-ratio method rules, and the three-ratio method criterion is used as the first-level criterion. The encoded values ​​are compared and analyzed to obtain the preliminary judgment result; the preliminary judgment result is specifically the fault type obtained by using the three-ratio method; based on the preliminary judgment result, the first-level diagnostic conclusion is classified and assigned a value. When the initial judgment result is an overheating fault, the first-level diagnostic conclusion is 1, recorded as J1=1. When the initial judgment result is a discharge fault, the first-level diagnostic conclusion is 2, recorded as J1=2. When the initial judgment result is a discharge and overheating fault, the first-level diagnostic conclusion is 3, recorded as J1=3. The area where the gas content or increase exceeds the attention value is called the high gas concentration area.

[0050] Based on the state parameters of the reliable model, all temperature data are extracted. Two types of temperature features are calculated: the first type is hotspot features, including local hotspot temperature values, local hotspot temperature rise gradients, and the regions where local hotspot temperatures are located; the second type is statistical features, including regions exceeding preset temperature thresholds and their volume percentage. The local hotspot temperature values, local hotspot temperature rise gradients, and the volume percentage of regions exceeding preset temperature thresholds are used as second-layer observations. Criteria are established for the second-layer observations. For the local hotspot temperature values ​​and local hotspot temperature rise gradients in the second-layer observations, the criterion is that either value exceeds the highest normal temperature value of the corresponding operating condition. If this condition is met, the first diagnostic conclusion of the second layer is 1, denoted as J. 2-1 =1, otherwise, denoted as J 2-1 =0; The percentage of the volume of the region exceeding the preset temperature threshold in the second layer of observations is determined by the percentage exceeding the normal range of the corresponding operating condition. If this condition is met, the second diagnostic conclusion of the second layer is 1, denoted as J. 2-2 =1, otherwise, denoted as J 2-2 =0. The analysis of the first and second diagnostic conclusions in the second layer is conducted in parallel, without interference or sequential relationship. The area where the local hotspot temperature is located and the area exceeding the preset temperature threshold are collectively referred to as the temperature hotspot zone.

[0051] Based on the state parameters of the reliable model, the spatial consistency between the high gas concentration region and the temperature hotspot region is calculated. This spatial consistency is calculated using the distance between the spatial centers of the high gas concentration region and the temperature hotspot region, or the degree of overlap between them, and these distances or overlaps are used as the third-layer observation values. If the third-layer observation value is less than a preset distance threshold or greater than a preset overlap threshold, a third-layer diagnostic conclusion is given. If the third criterion is met, the third-layer diagnostic conclusion is 1, denoted as J3=1; otherwise, the third-layer diagnostic conclusion is 0, denoted as J3=0.

[0052] A comprehensive diagnosis is made based on the diagnostic conclusions of the first, second, and third levels.

[0053] When J1=0, if J 2-1 J 2-2 If there is no value of 1, the overall diagnostic result is "normal", and the value of J3 is no longer analyzed; if J 2-1 J 2-2 If the value is 1, the overall diagnostic conclusion is "Attention, there is a temperature anomaly". The spatial range of the temperature hotspot area is output simultaneously to achieve rapid spatial location of the temperature anomaly point. The value of J3 is no longer analyzed.

[0054] When J1=1, if J 2-1 J 2-2 If the value of 1 is not present, the overall diagnostic result is "Note: Only the three-ratio method indicates an overheating fault." The analysis of the J3 value is no longer necessary; the investigation continues according to the overheating fault handling procedure of the three-ratio method. If J... 2-1 J 2-2 If the value is 1, the analysis of J3 continues. If J3=1, the comprehensive diagnosis result is "overheating fault, accompanied by abnormal temperature in the same location". The spatial range of the high gas concentration area and the spatial range of the temperature hot spot area are output simultaneously. The sum of the two spatial ranges is defined as the suspected overheating fault area, realizing rapid spatial location of the overheating fault point. If J3=0, the comprehensive diagnosis result is "overheating fault, accompanied by abnormal temperature in the opposite location". The spatial range of the high gas concentration area and the spatial range of the temperature hot spot area are output simultaneously. The spatial range of the high gas concentration area is defined as the first suspected overheating fault area, and the spatial range of the temperature hot spot area is defined as the second suspected overheating fault area. The investigation and processing are carried out in the order of first suspected area and then second suspected area, realizing rapid spatial location of the overheating fault point.

[0055] When J1=2, if J 2-1 J 2-2 If the value of 1 is not present, the comprehensive diagnostic result is "discharge fault, no temperature abnormality," and the spatial range of the high gas concentration area is simultaneously output and defined as the suspected discharge fault area, achieving rapid spatial location of the discharge fault point. The value of J3 is no longer analyzed. If J... 2-1 J 2-2If the value is 1, the analysis of J3 continues. If J3=1, the comprehensive diagnosis result is "discharge fault, accompanied by abnormal temperature in the same location". The spatial range of the high gas concentration area and the spatial range of the temperature hotspot area are output simultaneously, and the sum of the two spatial ranges is defined as the suspected discharge fault area, realizing rapid spatial location of the discharge fault point. If J3=0, the comprehensive diagnosis result is "discharge fault, accompanied by abnormal temperature in the opposite location". The spatial range of the high gas concentration area and the spatial range of the temperature hotspot area are output simultaneously, and the spatial range of the high gas concentration area is defined as the first suspected discharge fault area, and the spatial range of the temperature hotspot area is defined as the second suspected discharge fault area. The investigation and processing are carried out in the order of first suspected area and then second suspected area, realizing rapid spatial location of the discharge fault point.

[0056] When J1=3, if J 2-1 J 2-2 If the value of 1 is not present, the overall diagnostic result is "Note: Only the three-ratio method indicates a discharge and overheating fault." The J3 value will not be analyzed further; the troubleshooting will continue according to the three-ratio method's procedure for handling discharge and overheating faults. If J... 2-1 J 2-2 If the value is 1, the analysis of J3 continues. If J3=1, the comprehensive diagnosis result is "discharge and overheating fault, discharge and overheating are in the same location," and the spatial range of the high gas concentration area and the spatial range of the temperature hotspot area are output simultaneously. The sum of the two spatial ranges is defined as the suspected area of ​​discharge and overheating fault, realizing rapid spatial location of the discharge and overheating fault point. If J3=0, the comprehensive diagnosis result is "discharge and overheating fault, discharge and overheating may be out of place," and the spatial range of the high gas concentration area and the spatial range of the temperature hotspot area are output simultaneously. The spatial range of the high gas concentration area is defined as the first suspected area of ​​discharge and overheating fault, and the spatial range of the temperature hotspot area is defined as the second suspected area of ​​discharge and overheating fault. The investigation and processing are carried out in the order of first suspected area and then second suspected area to realize rapid spatial location of discharge and overheating fault point.

[0057] Output a comprehensive diagnostic result. This comprehensive diagnostic result can be one of the following: "Normal," "Caution: Temperature Anomaly Present," "Caution: Overheating Fault Indicated Only by Three-Ratio Method," "Overheating Fault, Accompanied by In-situ Temperature Anomaly," "Overheating Fault, Accompanied by Out-of-situ Temperature Anomaly," "Discharge Fault, No Temperature Anomaly," "Discharge Fault, Accompanied by In-situ Temperature Anomaly," "Discharge Fault, Accompanied by Out-of-situ Temperature Anomaly," "Caution: Discharge and Overheating Fault Indicated Only by Three-Ratio Method," "Discharge and Overheating Fault, Discharge and Overheating in the Same Location," or "Discharge and Overheating Fault, Discharge and Overheating May Be Out-of-Situ." If the comprehensive diagnostic result is not "Normal," then one or more supplementary data points, such as the spatial range of the high gas concentration area and the spatial range of the temperature hotspot area, are simultaneously output.

[0058] It should be noted that this embodiment uses oil chromatography as the detection method for collecting gas component data at a fixed monitoring point, but it is not limited to this method. Gas chromatography, spectroscopy, photoacoustic, electrochemical, semiconductor gas sensing, or other gas detection methods can also be used. For different detection methods, the optical, electrical, acoustic, or other response signals output by the detection device can be converted into gas concentration or equivalent concentration characterization values ​​through calibration relationships, and the transformer condition diagnosis can be performed using the method described in this application. Data collected by multiple different detection methods can be used simultaneously, and it is not limited to single oil chromatography monitoring.

[0059] It should be noted that this embodiment uses the three-ratio method as the first-level criterion, but it is not limited to this method. Improved or modified three-ratio methods or other methods may also be used.

[0060] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects have an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, and c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0061] The above description is merely a specific embodiment of this application. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application. The protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A method for diagnosing the operating status of a transformer based on multi-physics field coupled gas composition determination, characterized in that, include: A three-dimensional model is constructed based on the basic parameters of the transformer, the fluid computational domain is extracted, and an unstructured network is partitioned. The final partition density is selected based on the deviation of the evaluation index. The property parameters of transformer oil, windings, core, and fault characteristic gases are set in the three-dimensional model; The heat generated by copper and iron losses is decomposed into surface heat flux and volume release by using the energy decomposition method. The optimal decomposition ratio is determined by temperature deviation verification to obtain the steady-state solution of the heat flow field. Using the flow field freezing method, with the steady-state solution of the thermal flow field as the background field, a gas mass source term is added at a preset fault location to obtain the three-dimensional concentration distribution of fault characteristic gas inside the transformer. The simulated values ​​of temperature and gas concentration at fixed monitoring points are verified against the corresponding measured values. Based on the state parameters of the reliable model, the operating status of the transformer is analyzed and diagnostic results are output.

2. The transformer operating condition diagnosis method based on multi-physics field coupled gas component determination according to claim 1, characterized in that, The process of constructing a three-dimensional model based on the transformer's basic parameters, extracting the fluid computational domain, performing unstructured network partitioning, and selecting the final partitioning density based on the evaluation index deviation includes: Input the basic parameters of the target transformer, and construct a scaled 3D model of the transformer in 3D modeling software based on the actual internal structure of the target transformer. The basic structure of the winding coil of the three-dimensional transformer model is retained, the number of coil layers is simplified, and structural components with little impact on the thermal flow field are eliminated. The internal fluid region is extracted from the simplified 3D model of the transformer and used as the computational domain for subsequent mesh generation and simulation. A cylindrical annular fluid domain is defined outside the winding coil as the application area for the volume release of the winding side; at the same time, a cylindrical annular fluid domain is defined at the interface where the core column and side column of the iron core are in direct contact with the flow field as the application area for the volume release of the iron core side. Based on the constructed three-dimensional geometric model, the following parameters were measured and recorded: the total area of ​​the solid surface of the winding, the total area of ​​the solid surface of the iron core, the total volume of the fluid domain of the outer cylindrical ring of the winding, and the total volume of the fluid domain of the outer cylindrical ring of the iron core. The fluid computational domain is meshed into an unstructured grid, and the final mesh density is selected by choosing a grid scheme in which the relative deviation of all evaluation indicators is less than a preset percentage value.

3. The transformer operating condition diagnosis method based on multi-physics field coupled gas component determination according to claim 2, characterized in that, The process of performing unstructured mesh generation on the fluid computational domain, and selecting mesh schemes where the relative deviation of all evaluation indicators is less than a preset percentage value as the final mesh density, includes: The Meshing module is used to perform unstructured mesh generation on the fluid computation domain, and local meshing is performed on the narrow oil passages of the winding disc, the near wall of the iron core, the oil guide pipe, and the location of the preset fault point. Three different mesh schemes with different densities were set up: coarse mesh, medium mesh, and fine mesh. The coarse mesh was only used for preliminary testing. Using the oil outlet velocity, winding coil velocity, oil outlet velocity, winding wall temperature, core wall temperature, and near-wall oil temperature as evaluation indicators, the relative deviations of each evaluation indicator between medium and fine mesh grids are calculated. The calculation formula is as follows: in: These are the values ​​of the evaluation index under a medium grid. The values ​​of the same evaluation index under a fine grid; The final meshing density is selected when the relative deviation of all evaluation indicators is less than the preset percentage value.

4. The transformer operating condition diagnosis method based on multi-physics field coupled gas component determination according to claim 1, characterized in that, The method of setting property parameters for transformer oil, windings, core, and fault characteristic gases in the three-dimensional model includes: Establish the density model and temperature-related physical properties of transformer oil; Set the copper winding material parameters and the iron core material parameters to constant values; Based on the needs of transformer condition diagnosis, hydrogen gas is used. methane ethane ethylene Acetylene Carbon monoxide (CO) and carbon dioxide Select one or more fault characteristic gases and set the attribute parameters of the selected fault characteristic gases.

5. The transformer operating condition diagnosis method based on multi-physics field coupled gas component determination according to claim 4, characterized in that, The density model and temperature-related physical properties of the transformer oil are defined, including: The Boussinesq assumption is used to characterize the thermal buoyancy effect, and a density model for transformer oil is established: in: This represents the transformer oil temperature at the corresponding calculation location within the transformer fluid computational domain. For temperature The density of the transformer oil below The reference temperature for the transformer oil density model. Reference temperature The following is the reference density of transformer oil. The coefficient of thermal expansion for the transformer oil density model; The specific heat, thermal conductivity, and dynamic viscosity of transformer oil are defined as piecewise linear functions of temperature, and the physical property parameters are calibrated at selected temperature points.

6. The transformer operating condition diagnosis method based on multi-physics field coupled gas component determination according to claim 1, characterized in that, The method employs energy decomposition to separate the heat generated by copper and iron losses into surface heat flux and volumetric heat release. The optimal decomposition ratio is determined through temperature deviation verification to obtain the steady-state solution of the heat flow field, including: Calculate the heat generated by the copper loss in the windings separately. Heat generated by iron core loss ; Define the first proportionality coefficient The second proportionality coefficient is ,in: This indicates the proportion of surface heat flux applied to the solid surface. ; This indicates the proportion of the volumetric fluid released into the adjacent cylindrical annular fluid domain; The heat generated by copper loss and iron loss are respectively calculated as follows: and The ratio is split into surface heat flux and volume release, which are applied to the solid surface and its adjacent cylindrical annular fluid domain to obtain the simulated temperature of the winding wall and the simulated temperature of the core wall. The simulated winding wall temperature and the simulated core wall temperature are compared and verified with the corresponding winding temperature verification benchmark values ​​and core temperature verification benchmark values, respectively. The first proportional coefficient is adjusted according to the temperature deviation. Until the temperature deviation meets the preset tolerance threshold; Determine the optimal split ratio and output the steady-state solution of the heat flow field under the optimal split ratio.

7. The transformer operating condition diagnosis method based on multi-physics field coupled gas component determination according to claim 6, characterized in that, The heat generated by copper loss and iron loss is respectively divided into... and The ratio is split into surface heat flux and volumetric release, which are applied to the solid surface and its adjacent cylindrical annular fluid domain, respectively, including: The heat generated by copper loss and iron loss are respectively calculated as follows: and Divide into proportions; The heat generated by copper loss, after being split, is first proportionally applied to the solid surface of the winding in the form of surface heat flux, and second proportionally applied to the outer cylindrical annular fluid domain of the winding in the form of volume release. The heat generated by iron loss, after being decomposed, is first proportionally applied to the solid surface of the iron core in the form of surface heat flux, and second proportionally applied to the outer cylindrical annular fluid domain of the iron core in the form of volume release. in: It is the heat applied to the surface of the winding solid in the form of surface heat flux. It is the heat applied to the fluid domain of the outer cylindrical annulus of the winding in the form of volume release. This refers to the heat generated by the copper losses in the windings under rated operating conditions. It is the heat applied to the solid surface of the iron core in the form of surface heat flux. It is the heat applied to the fluid domain of the outer cylindrical ring of the iron core in the form of volume release. This refers to the heat generated due to iron loss in the iron core under rated operating conditions. This represents the total area of ​​the solid surface of the winding. This represents the total surface area of ​​the iron core solid. The total volume of the fluid domain in the outer cylindrical annulus of the winding. It represents the total volume of the fluid domain in the cylindrical annulus outside the iron core.

8. The method for diagnosing transformer operating status based on multi-physics field coupled gas component determination according to claim 1, characterized in that, The flow field freezing method, using the steady-state solution of the thermal flow field as the background field, adds a gas mass source term at a preset fault location to obtain the three-dimensional concentration distribution of the fault characteristic gas inside the transformer, including: Freeze the steady-state solution of the thermal flow field, shut down the energy equation and turbulence model, and keep the velocity field and temperature field unchanged, using them as the background field for the migration and diffusion of gas components; Add one or more fault characteristic gas mass source terms to the grid cells at the preset fault gas generation location, and solve the three-dimensional spatial concentration distribution of the fault characteristic gas under the driving force of the freezing thermal flow field. The transient solution is applied up to a preset physical time, and the three-dimensional spatial concentration distribution of each fault characteristic gas at the same moment is output for subsequent transformer condition diagnosis. After the transient solution is completed, the simulated temperature values ​​corresponding to the locations of the temperature monitoring points in the transformer's fixed monitoring points are extracted, and the result is recorded as follows: The simulated temperature at each temperature monitoring point is: ,in , The total number of temperature monitoring points participating in the verification; Based on the spatial coordinates corresponding to the gas component monitoring points in the transformer's fixed monitoring points, the simulated value of the fault characteristic gas concentration at the aforementioned spatial coordinates is calculated from the simulation grid node values ​​using the inverse distance weighted interpolation method. This is denoted as the... The simulated concentrations at each gas component monitoring point are: ,in , This represents the total number of gas monitoring points participating in the verification.

9. The transformer operating condition diagnosis method based on multi-physics field coupled gas component determination according to claim 8, characterized in that, The step of adding one or more fault characteristic gas mass source terms to the grid cells at the preset fault gas generation location, and solving the three-dimensional spatial concentration distribution of the fault characteristic gas under the driving force of the freezing thermal flow field, includes: When performing targeted diagnosis on a transformer, one or more fault characteristic gases are selected according to the diagnostic requirements, and corresponding mass source terms are set for each gas. The three-dimensional spatial distribution of the selected fault characteristic gases under the driving force of the freezing thermal flow field is then solved. When performing a comprehensive diagnosis of a transformer, the selected fault characteristic gas should include at least hydrogen. methane ethane ethylene Acetylene And set up the mass source terms corresponding to the above-mentioned fault characteristic gases respectively, and solve the three-dimensional spatial distribution of each fault characteristic gas driven by the freezing thermal flow field. Among them, the The expression for the mass source term of the fault characteristic gas is: in: For the mass source term of the i-th fault characteristic gas, For the fault gas production rate, This represents the volume of the grid cell corresponding to the location of the gas generation failure. By initiating the component transport equations, the three-dimensional spatial concentration distribution of one or more fault-characteristic gases driven by the freezing thermal flow field is solved. The component transport equations are as follows: in: The density of the gas mixture dissolved in transformer oil. For the frozen flow velocity vector, Let i be the mass fraction of the i-th fault characteristic gas. Let be the diffusion mass flux of the i-th fault characteristic gas. Let i be the mass source term of the i-th fault characteristic gas.

10. The transformer operating condition diagnosis method based on multi-physics field coupled gas component determination according to claim 8, characterized in that, The process involves verifying the simulated temperature and gas concentration values ​​at fixed monitoring points against their corresponding measured values. Based on the reliable model's state parameters, the transformer's operating status is analyzed, and diagnostic results are output, including: Input the measured temperature values ​​of the temperature monitoring points of the target transformer under actual energized operating conditions. Fault characteristics of gas concentration at gas component monitoring points (measured values) ; The formulas for calculating the average absolute percentage error of temperature, the average absolute percentage error of fault characteristic gas concentration, and the coefficient of determination of fault characteristic gas concentration are as follows: in: ; The reliability of the simulation model is verified based on the average absolute percentage error of temperature, the average absolute percentage error of characteristic gas concentration, and the coefficient of determination of characteristic gas concentration. The simulation results of the global temperature field, flow field, and fault characteristic gas component concentration distribution of the transformer are output by a reliable model, and the simulation results are defined as the state parameters of the reliable model. Based on the state parameters of the reliable model, the operating status of the transformer is analyzed, and targeted or comprehensive diagnosis is performed to output the corresponding diagnostic results.

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