A virtual sensing method and system for gas migration in transformer oil

Abstract

The present application relates to the technical field of electric power engineering, and discloses a virtual sensing method and system for gas migration in transformer oil, and proposes a virtual sensing scheme for gas migration in transformer oil based on a gas transport physical model, according to the obtained gas migration related data and bubble test data, the gas diffusion coefficient and mass transfer rate involved in the migration behavior are calculated, and the gas migration simulation is carried out through the gas transport physical model, so as to obtain the gas concentration information of each preset oil tank monitoring point associated with the virtual sensing of gas migration of the target transformer, and the technical problem of how to realize the virtual sensing of gas migration in transformer oil is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power engineering, and in particular to a virtual sensing method and system for gas migration in transformer oil. Background Art

[0002] Digital transformation has become an irreversible trend in the modernization of power systems. As a core element of this transformation, the digitization of power equipment is crucial for improving the reliability and efficiency of grid operations. The fundamental requirements for digitalization are the measurability and observability of equipment status, as well as the predictability and controllability of risks.

[0003] As a core component of technological complexity within power grid systems, the operating status of power transformers is directly related to grid security. A transformer failure can not only trigger a large-scale power outage but also lead to serious safety incidents such as explosions and fires, resulting in substantial economic losses and significant social impact. However, the unique internal structure of transformers makes widespread deployment of sensors difficult. Therefore, achieving "virtual perception" of transformer internal conditions through digital means has important engineering applications.

[0004] Power transformers are composite systems composed of multiple materials, including insulating oil, insulating paper, conductive materials, and magnetic materials. Over long-term operation, these components are subject to complex operating conditions, gradually degrading and exhibiting corresponding changes in physical and chemical parameters. Accurately capturing these changes is crucial for equipment condition assessment and fault diagnosis. During transformer operation, the combined effects of electrical, thermal, and mechanical stresses can lead to thermal or electrical faults. These faults can cause the insulation to decompose, generating a variety of characteristic gases, such as hydrogen, low-molecular-weight hydrocarbons, carbon monoxide, and carbon dioxide. These gases dissolve in the transformer oil and migrate with the oil flow.

[0005] Dissolved gas analysis (DGA) technology assesses the type and severity of faults by detecting the content, composition, and generation rate of characteristic gases. This method has become an important tool for diagnosing potential faults in oil-immersed equipment in power systems. However, with the increase in transformer capacity and voltage levels, equipment failure rates are on the rise. While the traditional three-ratio analysis method is based on extensive practical experience, its diagnostic and predictive capabilities still need to be improved in the face of increasingly complex operating environments. Furthermore, the digital design and operation and maintenance of power equipment place higher demands on the simulation capabilities of fault evolution processes. Summary of the Invention

[0006] The present invention provides a virtual sensing method and system for gas migration in transformer oil, which solves the technical problem of how to realize virtual sensing of gas migration in transformer oil.

[0007] The first aspect of the present application provides a virtual sensing method for gas migration in transformer oil, comprising:

[0008] In response to a request for gas migration simulation of a target transformer, a gas transport physical model of the target transformer is constructed;

[0009] Obtain bubble test data of the target transformer, and obtain gas migration related data of multiple characteristic gases in transformer oil at each preset oil tank monitoring point;

[0010] Based on each of the gas migration related data and the bubble test data, determine the target gas diffusion coefficient of each of the characteristic gases at different test temperatures;

[0011] Based on each of the gas migration related data and the bubble test data, determine the mass transfer rate of each of the characteristic gases;

[0012] The target gas diffusion coefficient and the mass transfer rate are introduced into the gas transport physical model to obtain the gas concentration information associated with each of the preset oil tank monitoring points for gas migration virtual sensing of the target transformer.

[0013] Optionally, the bubble test data includes bubble dissolution time and bubble initial radius, and the determination of the target gas diffusion coefficient of each of the characteristic gases at different test temperatures based on each of the gas migration related data and the bubble test data comprises:

[0014] Using the gas migration related data, determine the reference solubility and multiple test solubilities of each of the characteristic gases;

[0015] Using the reference solubility, the bubble dissolution time and the bubble initial radius, determine the reference gas diffusion coefficient of each of the characteristic gases at ambient temperature;

[0016] Using the gas migration related data, the reference solubility, the reference gas diffusion coefficient and multiple test solubilities, input a preset target gas diffusion coefficient function to determine the target gas diffusion coefficient of each of the characteristic gases at different test temperatures.

[0017] Optionally, the gas migration related data includes Ostwald constant, gas pressure, liquid saturated vapor pressure, insulating oil molar mass, ambient temperature, insulating oil density and multiple test temperatures, and the determination of the reference solubility and multiple test solubilities of each of the characteristic gases using the gas migration related data comprises

[0018] Based on the gas pressure, the liquid saturated vapor pressure, the insulating oil density and the Ostwald constant, and in combination with the ambient temperature, a preset reference Bunsen coefficient function is input to determine a reference Bunsen coefficient of each characteristic gas at the ambient temperature;

[0019] Based on the reference Bunsen coefficient, the molar mass of the insulating oil, and the density of the insulating oil, and in combination with the ambient temperature, a preset reference solubility function is input to determine the reference solubility of each of the characteristic gases at the ambient temperature;

[0020] Based on the gas pressure, the liquid saturated vapor pressure, the insulating oil density and the Ostwald constant, a preset test Bunsen coefficient function is inputted in combination with a plurality of test temperatures to determine the test Bunsen coefficient of each characteristic gas at different test temperatures;

[0021] Based on the molar mass and density of the insulating oil, combined with a plurality of experimental Bunsen coefficients and a plurality of the experimental temperatures, the preset experimental solubility function is input to determine the experimental solubility of each characteristic gas at different experimental temperatures.

[0022] Optionally, the preset target gas diffusion coefficient function is specifically:

[0023]

[0024] Where, Indicates that it is in The target gas diffusion coefficient at the test temperature is represents the diffusion coefficient of the reference gas at the ambient temperature, represents the preset first dimensionless constant, Indicates that it is in The test solubility at the test temperature is represents the reference solubility at the stated ambient temperature, represents the preset second dimensionless constant, represents the ambient temperature, Indicates the The test temperature.

[0025] Optionally, the gas migration related data further includes gas density, and determining the mass transfer rate of each characteristic gas based on each gas migration related data and the bubble test data includes:

[0026] Determine the dissolution rate of each characteristic gas using the bubble dissolution time and the bubble initial radius;

[0027] Performing a multiplication operation using the gas density and the dissolution rate to obtain a first multiplication value of each characteristic gas;

[0028] Performing a multiplication operation on the square of the initial bubble radius and the first multiplication value to obtain a second multiplication value of each characteristic gas;

[0029] The mass transfer rate of each characteristic gas is determined by performing a multiplication operation on the second multiplication value and the preset space angle coverage coefficient.

[0030] Optionally, the gas migration related data further includes gas mass fraction and gas velocity vector data, and the gas transport physical model is specifically:

[0031]

[0032]

[0033] Where, Indicates the The first of the preset fuel tank monitoring points The gas concentration of each of the characteristic gases, Indicates the The first of the preset fuel tank monitoring points The gas mass fraction of the characteristic gas, Indicates the The first of the preset fuel tank monitoring points the gas density of each of the characteristic gases, represents the gas velocity vector data, Indicates the The first of the preset fuel tank monitoring points The characteristic gas is in the The target gas diffusion coefficient at the test temperature, Indicates the The first of the preset fuel tank monitoring points The mass transfer rate of the characteristic gas, Indicates the The characteristic gas, , represents the total number of the characteristic gases, Indicates the The first of the preset fuel tank monitoring points The diffusion flux of the characteristic gas.

[0034] A second aspect of the present invention provides a virtual sensing system for gas migration in transformer oil, comprising:

[0035] A response module is configured to, in response to a request for gas migration simulation of a target transformer, construct a gas transport physical model of the target transformer;

[0036] A data acquisition module is configured to acquire bubble test data of the target transformer and gas migration related data of a plurality of characteristic gases in transformer oil of each preset oil tank monitoring point;

[0037] A first processing module is configured to determine a target gas diffusion coefficient of each characteristic gas at different test temperatures based on each gas migration related data and the bubble test data;

[0038] A second processing module is configured to determine a mass transfer rate of each characteristic gas based on each gas migration related data and the bubble test data;

[0039] A gas migration simulation module is configured to import the gas transport physical model by using the target gas diffusion coefficient and the mass transfer rate, and acquire gas concentration information associated with each preset oil tank monitoring point for gas migration virtual sensing of the target transformer.

[0040] The third aspect of the present application provides an electronic device, including a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor, so that the processor executes the steps of the virtual sensing method of gas migration in transformer oil according to any one of the above aspects.

[0041] The fourth aspect of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed to realize the virtual sensing method of gas migration in transformer oil according to any one of the above aspects.

[0042] The fifth aspect of the present application provides a computer program product, which includes a computer program stored on a non-transitory computer readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer executes the virtual sensing method of gas migration in transformer oil according to any one of the above aspects.

[0043] From the above technical solutions, the present application has the following advantages:

[0044] The present application provides a virtual sensing scheme for gas migration in transformer oil based on a gas transport physical model, calculates the gas diffusion coefficient and mass transfer rate involved in the migration behavior according to the obtained gas migration related data and bubble test data, and performs gas migration simulation through the gas transport physical model, so as to obtain the gas concentration information of each preset oil tank monitoring point associated with the virtual sensing of gas migration in the target transformer, and solve the technical problem of how to realize the virtual sensing of gas migration in transformer oil. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0046] Figure 1 A step flow chart of a virtual sensing method for gas migration in transformer oil provided by the first embodiment of the present application;

[0047] Figure 2 A step flow chart of a virtual sensing method for gas migration in transformer oil provided by the second embodiment of the present application;

[0048] Figure 3 A schematic diagram of Henry coefficient for each characteristic gas;

[0049] Figure 4 A schematic diagram of a cylindrical oil tank model;

[0050] Figure 5 A schematic diagram of gas concentration simulation results of each oil tank monitoring point;

[0051] Figure 6 A schematic diagram of flow field distribution of a local position section of the target transformer;

[0052] Figure 7 A schematic diagram of gas distribution of a local position section of the target transformer;

[0053] Figure 8 A schematic diagram of virtual sensing gas concentration value of the target transformer;

[0054] Figure 9 A structural block diagram of a virtual sensing system for gas migration in transformer oil provided by the third embodiment of the present application;

[0055] Figure 10 A structural block diagram of a computer device provided by the fourth embodiment of the present application. DETAILED DESCRIPTION

[0056] The embodiment of the present application provides a virtual sensing method and system for gas migration in transformer oil, and aims to solve the technical problem of how to realize virtual sensing of gas migration in transformer oil.

[0057] In order to make the inventive purposes, features and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the following described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0058] Please refer to Figure 1 , Figure 1 A step flow chart of a virtual sensing method for gas migration in transformer oil provided by the embodiment one of the present application.

[0059] The virtual sensing method for gas migration in transformer oil provided by the present application comprises the following steps.

[0060] Step 101, in response to a request for simulation of gas migration in a target transformer, a gas transport physical model of the target transformer is constructed.

[0061] The request for simulation of gas migration is request information for simulating the migration process of gases in transformer oil in the target transformer under the action of different physical fields.

[0062] The gas transport physical model refers to a mathematical model constructed based on component transport theory, and is used for describing the mass migration, momentum transfer and energy exchange process of each component in a gas-liquid two-phase system.

[0063] In the embodiment of the present application, in response to the received request information for simulating the migration process of gases in transformer oil in the target transformer under the action of different physical fields, the gas transport physical model of the target transformer is constructed.

[0064] Step 102, bubble test data of the target transformer are obtained, and gas migration related data of multiple characteristic gases in transformer oil through each preset oil tank monitoring point are obtained.

[0065] The preset oil tank monitoring point refers to a monitoring point position determined on the transformer oil tank of the target transformer, and is used for capturing the concentration change of gases in the migration process according to the gas migration related data of multiple characteristic gases in transformer oil through each preset oil tank monitoring point and the bubble test data.

[0066] Gas migration related data, including but not limited to Ostwald constant, gas pressure, liquid saturated vapor pressure, insulating oil molar mass, ambient temperature, insulating oil density, multiple test temperatures, gas density, gas mass fraction and gas velocity vector data, refer to related data for calculating gas diffusion coefficient and mass transfer rate involved in the migration behavior of the characteristic gas in the insulating oil and its related environment.

[0067] Bubble test data, including but not limited to bubble dissolution time and bubble initial radius, refer to related data for calculating gas diffusion coefficient and mass transfer rate involved in the migration behavior of the characteristic gas in the insulating oil and its related environment.

[0068] It is worth mentioning that the bubble test data is obtained by performing relevant tests on the transformer oil inside the target transformer.

[0069] In the embodiment of the present application, the bubble test data of the target transformer is obtained by performing relevant tests on the transformer oil inside the target transformer, and at the same time, the gas migration related data of multiple characteristic gases in the transformer oil of the target transformer passing through each preset oil tank monitoring point is obtained.

[0070] Step 103, determining the target gas diffusion coefficient of each characteristic gas at different test temperatures based on the gas migration related data and the bubble test data of each characteristic gas.

[0071] In the embodiment of the present application, the target gas diffusion coefficient of each characteristic gas at different test temperatures is determined according to the gas migration related data and the bubble test data of each characteristic gas.

[0072] Step 104, determining the mass transfer rate of each characteristic gas based on the gas migration related data and the bubble test data.

[0073] In the embodiment of the present application, the mass transfer rate of each characteristic gas is determined according to the gas migration related data and the bubble test data of each characteristic gas.

[0074] Step 105, importing the target gas diffusion coefficient and the mass transfer rate into a gas transport physical model to obtain the gas concentration information of each preset oil tank monitoring point associated with the gas migration virtual sensing of the target transformer.

[0075] In the embodiment of the present application, the target gas diffusion coefficient and the mass transfer rate are imported into the gas transport physical model for gas migration simulation, so as to obtain the gas concentration information of each preset oil tank monitoring point associated with the gas migration virtual sensing of the target transformer.

[0076] In the present application, in response to a request for gas migration simulation of a target transformer, a gas transport physical model of the target transformer is constructed; gas bubble test data of the target transformer is obtained, and gas migration related data of a plurality of characteristic gases in the transformer oil of each preset oil tank monitoring point is obtained; based on the gas migration related data and the gas bubble test data, target gas diffusion coefficients of each characteristic gas at different test temperatures are determined; based on the gas migration related data and the gas bubble test data, mass transfer rates of each characteristic gas are determined; the target gas diffusion coefficients and the mass transfer rates are introduced into the gas transport physical model to obtain gas concentration information associated with each preset oil tank monitoring point of the target transformer for gas migration virtual sensing.

[0077] The present application provides a virtual sensing scheme for gas migration in transformer oil based on a gas transport physical model. According to the obtained gas migration related data and gas bubble test data, gas diffusion coefficients and mass transfer rates involved in the migration behavior are calculated, and gas migration simulation is performed through the gas transport physical model, so as to obtain gas concentration information associated with each preset oil tank monitoring point of the target transformer for gas migration virtual sensing, thereby solving the technical problem of how to realize virtual sensing of gas migration in transformer oil.

[0078] Please refer to Figure 2 , Figure 2 A step flow chart of a virtual sensing method for gas migration in transformer oil according to Embodiment Two of the present application is shown in the figure.

[0079] The virtual sensing method for gas migration in transformer oil provided by the present application comprises the following steps:

[0080] Step 201, in response to a request for gas migration simulation of a target transformer, a gas transport physical model of the target transformer is constructed.

[0081] It should be noted that when an electrical or thermal fault occurs inside the transformer, a series of complex physical and chemical processes will be triggered. Specifically, the insulation system (including insulating oil and insulating paper) decomposes and releases a plurality of characteristic gases. The migration process of these gases in the transformer oil involves gas-liquid two-phase flow mechanism: on the one hand, gas molecules are constantly dissolved in the oil phase through convection and diffusion; on the other hand, free gas shows unique movement characteristics under the coupling action of oil flow force and buoyancy. The complexity of this multiphase flow process mainly lies in the interaction of a plurality of physical mechanisms such as gas dissolution-precipitation balance, diffusion migration and buoyancy driving.

[0082] The migration of free gases in transformer oil involves complex mass transfer phenomena. Through convection and diffusion, gas components continuously exchange mass between the gas and liquid phases until thermodynamic equilibrium is reached. To accurately describe this dynamic process, this paper constructs a gas transport physics model based on component transport theory. This model comprehensively characterizes the mass transfer, momentum transfer, and energy exchange of each component in the gas-liquid two-phase system. This systematic modeling approach provides a theoretical foundation for a deeper understanding of the migration of gases in transformer oil.

[0083] Furthermore, the gas migration related data also includes gas mass fraction and gas velocity vector data. The component transport equation of the gas transport physical model is specifically:

[0084]

[0085]

[0086] Where, Indicates the The first of the preset fuel tank monitoring points The gas concentration of a characteristic gas, Indicates the The first of the preset fuel tank monitoring points The gas mass fraction of the characteristic gas, Indicates the The first of the preset fuel tank monitoring points The gas density of a characteristic gas, represents the gas velocity vector data, Indicates the The first of the preset fuel tank monitoring points The characteristic gas is in the The target gas diffusion coefficient at the test temperature is Indicates the The first of the preset fuel tank monitoring points Mass transfer rate of characteristic gas, kg / s, Indicates the characteristic gases, , represents the total number of characteristic gases, Indicates the The first of the preset fuel tank monitoring points The diffusion flux of a characteristic gas, represents the convection term, which is used to describe the flow of gas components along with the fluid. represents the diffusion term, which describes the migration of components due to gradient concentration and is usually expressed according to Fick's law as:

[0087]

[0088] It should be noted that according to the mass transfer theorem:

[0089]

[0090] In the formula, represents the mass transfer rate of the th characteristic gas, represents the mass transfer coefficient of the th characteristic gas, m / s, represents the gas-liquid mass transfer interface area, represents the equilibrium concentration of the th characteristic gas in the liquid phase, kg / m 3 . represents the instantaneous concentration of the th characteristic gas in the liquid phase, kg / m 3 .

[0091] When the relative stable state of component concentration in gas-liquid two-phase reaches equilibrium, Henry's law and Ostwald's coefficient can be used to describe the amount of dissolution.

[0092] Henry's law:

[0093]

[0094] In the formula, represents the gas pressure of the th characteristic gas, represents the Henry constant (Henry coefficient), Pa, represents the mole fraction of the th characteristic gas;

[0095] It should be noted that Henry's law describes the distribution ratio of gas in gas and liquid phases in equilibrium state, which can be understood as calculating the saturated solubility of gas in oil, which is equivalent to setting an upper limit value for the dissolution of gas in oil. There is no direct variable relationship with the above component transport equation, and the solubility related to the solubility function in the preset solubility function, the specific calculation conversion formula is as follows:

[0096]

[0097] In the formula, represents the molar mass of the insulating oil, represents the molar mass of the characteristic gas.

[0098] It is worth mentioning that the Henry coefficient can be calculated by ASTM D2779 (Standard Test Method for Vapor-Liquid Ratio Temperature Determination of Fuels) and the calculation result is as shown in Figure 3 , Figure 3 is a schematic diagram of the Henry coefficient of each characteristic gas.

[0099] In the embodiment of the present application, the specific implementation process of step 201 is similar to that of step 101, which will not be repeated here.

[0100] In step 202, the bubble test data of the target transformer is obtained, and the gas migration related data of the multiple characteristic gases in the transformer oil of each preset oil tank monitoring point is obtained.

[0101] In the embodiment of the present application, the specific implementation process of step 202 is similar to that of step 102, which will not be repeated here.

[0102] Further, the bubble test data includes bubble dissolution time and bubble initial radius.

[0103] It should be noted that the bubble test data is obtained by injecting a single-component characteristic gas bubble of a certain size into the degassed transformer oil, using a high-speed camera to shoot it, observing the change of the bubble size with time, and then obtaining the bubble dissolution time, and at the same time, the bubble initial radius can be obtained.

[0104] In step 203, the reference solubility and the multiple test solubilities of each characteristic gas are determined by using the gas migration related data.

[0105] It should be noted that according to the Epstein model, the dissolution time of the single-component characteristic gas in the oil can be obtained as follows:

[0106]

[0107] In the formula, represents the bubble dissolution time, represents the initial size of the bubble, which is one of the key parameters affecting the dissolution kinetics, that is, the bubble initial radius, represents the gas diffusion coefficient, represents the solubility.

[0108] In addition, the dissolution process of the bubble is also significantly affected by two temperature-dependent parameters: one is the diffusion coefficient of the gas in the oil phase , and the other is the saturation solubility .

[0109] The amount of gas dissolved in unit volume of oil under certain temperature and pressure conditions (T) can be expressed as:

[0110]

[0111] In the formula, represents the Bunsen coefficient, and represents the volume of the gas dissolved in unit volume of oil under standard conditions, represents the molar mass of the liquid, represents the temperature, represents the density of the insulating oil;

[0112] The Bunsen coefficient expression is as follows:

[0113]

[0114] In the formula, represents the gas pressure, represents the liquid saturated vapor pressure, represents the Ostwald constant under standard conditions, which is dimensionless and related to the type of gas.

[0115] Among them, the Ostwald constant can be determined according to the gas distribution coefficient of the power transformer insulating oil given in DL / T 722-2014 (Guidelines for Analysis and Judgment of Dissolved Gases in Transformer Oil) and the empirical value proposed in ASTM D2779.

[0116] Further, the gas migration related data includes the Ostwald constant, the gas pressure, the liquid saturated vapor pressure, the insulating oil molar mass, the environmental temperature, the insulating oil density and multiple test temperatures, and step 203 can include the following sub-steps:

[0117] S11, based on the gas pressure, the liquid saturated vapor pressure, the insulating oil density and the Ostwald constant, inputting a preset reference Bunsen coefficient function combined with the environmental temperature to determine the reference Bunsen coefficient of each characteristic gas under the environmental temperature.

[0118] The preset reference Bunsen coefficient function is as follows:

[0119]

[0120] In the formula, represents the reference Bunsen coefficient under the environmental temperature, represents the gas pressure, represents the liquid saturated vapor pressure, represents an Ostwald constant at standard conditions, represents an ambient temperature, represents an insulating oil density.

[0121] In the embodiment of the present application, based on the gas pressure, the liquid saturated vapor pressure, the insulating oil density and the Ostwald constant, a preset reference Bunsen coefficient function is input in combination with the ambient temperature to determine the reference Bunsen coefficient of each characteristic gas at the ambient temperature.

[0122] S12, based on the reference Bunsen coefficient, the insulating oil molar mass and the insulating oil density, a preset reference solubility function is input in combination with the ambient temperature to determine the reference solubility of each characteristic gas at the ambient temperature.

[0123] The preset reference solubility function is as follows:

[0124]

[0125] In the formula, represents the reference solubility at the ambient temperature, represents the molar mass of the liquid.

[0126] In the embodiment of the present application, based on the reference Bunsen coefficient, the insulating oil molar mass and the insulating oil density, a preset reference solubility function is input in combination with the ambient temperature to determine the reference solubility of each characteristic gas at the ambient temperature.

[0127] S13, based on the gas pressure, the liquid saturated vapor pressure, the insulating oil density and the Ostwald constant, a preset test Bunsen coefficient function is input in combination with a plurality of test temperatures respectively to determine the test Bunsen coefficient of each characteristic gas at different test temperatures.

[0128] The preset test Bunsen coefficient function is as follows:

[0129]

[0130] In the formula, represents the test Bunsen coefficient at different test temperatures, represents the gas pressure, represents the liquid saturated vapor pressure, represents an Ostwald constant at standard conditions, represents the i th test temperature, , represents the total number of test temperatures, represents the insulating oil density.

[0131] In the embodiment of the present application, based on the gas pressure, the liquid saturated vapor pressure, the insulating oil density and the Ostwald constant, the preset test Bunsen coefficient function is inputted respectively in combination with multiple test temperatures, so as to determine the test Bunsen coefficient of each characteristic gas under different test temperatures.

[0132] S14, based on the insulating oil molar mass and the insulating oil density, in combination with multiple test Bunsen coefficients and multiple test temperatures, the preset test solubility function is inputted, so as to obtain the test solubility of each characteristic gas under different test temperatures.

[0133] The preset test solubility function is as follows:

[0134]

[0135] In the formula, represents the test solubility under different test temperatures, represents the molar mass of the liquid.

[0136] In the embodiment of the present application, based on the insulating oil molar mass and the insulating oil density, in combination with multiple test Bunsen coefficients and multiple test temperatures, the preset test solubility function is inputted, so as to obtain the test solubility of each characteristic gas under different test temperatures.

[0137] Step 204, using the reference solubility, the bubble dissolution time and the bubble initial radius, the reference gas diffusion coefficient of each characteristic gas under the ambient temperature is determined.

[0138] In the specific implementation, for the convenience of the implementation of the method, the above process can be converted into the form of formula encapsulation, wherein the calculation method of the reference gas diffusion coefficient can be as follows:

[0139]

[0140] In the formula, represents the reference gas diffusion coefficient under the ambient temperature, represents the bubble initial radius, represents the bubble dissolution time, represents the reference solubility.

[0141] In the embodiment of the present application, the reference solubility calculated according to S12 is combined with the bubble dissolution time and the bubble initial radius obtained by the test, so as to obtain the reference gas diffusion coefficient under the ambient temperature.

[0142] It should be noted that the bubble initial radius, the bubble dissolution time and the reference solubility associated with any one characteristic gas are used as a group of calculation data, and through the above calculation method, the reference gas diffusion coefficient of the characteristic gas under the ambient temperature is obtained.

[0143] It should be noted that the reference solubility calculation process of the single characteristic gas is only taken as an example, and the same calculation method is used for other characteristic gases to obtain the reference gas diffusion coefficient of each characteristic gas at the ambient temperature, which will not be described here.

[0144] Step 205: inputting the gas migration related data, the reference solubility, the reference gas diffusion coefficient and the plurality of test solubilities into a preset target gas diffusion coefficient function to determine the target gas diffusion coefficient of each characteristic gas at different test temperatures.

[0145] Further, the preset target gas diffusion coefficient function is specifically:

[0146]

[0147] In the formula, Dti represents the target gas diffusion coefficient at the i-th test temperature, Dti represents the target gas diffusion coefficient at the i-th test temperature, D0 represents the reference gas diffusion coefficient at the ambient temperature, C1 represents a preset first dimensionless constant, Si represents the test solubility at the i-th test temperature, Si represents the test solubility at the i-th test temperature, S0 represents the reference solubility at the ambient temperature, C2 represents a preset second dimensionless constant, and k represents a unit, T0 represents the ambient temperature, Ti represents the i-th test temperature. Ti represents the i-th test temperature.

[0148] In the embodiment of the present application, an ambient temperature, a reference solubility, a reference gas diffusion coefficient, a test temperature and a test solubility associated with any one characteristic gas are used to obtain the target gas diffusion coefficient of the characteristic gas at the current test temperature;

[0149] Since each characteristic gas involves a plurality of different test temperatures, the ambient temperature, the reference solubility and the reference gas diffusion coefficient are constant, and only the test temperature and the associated test solubility change, so as to obtain the target gas diffusion coefficient of the characteristic gas at different test temperatures.

[0150] Further, through the above process, the target gas diffusion coefficients of a plurality of characteristic gases at different test temperatures can be obtained.

[0151] Step 206: determining the mass transfer rate of each characteristic gas based on the gas migration related data and the bubble test data.

[0152] Furthermore, the gas migration related data also includes gas density. Step 206 may include the following sub-steps:

[0153] S21. Use the bubble dissolution time and the bubble initial radius to determine the dissolution rate of each characteristic gas.

[0154] S22. Perform a multiplication operation using the gas density and the dissolution rate to obtain a first multiplication value of each characteristic gas.

[0155] S23. Perform a multiplication operation on the square value of the initial bubble radius and the first multiplication value to obtain a second multiplication value of each characteristic gas.

[0156] S24. Perform a multiplication operation using the second product value and a preset space angle coverage coefficient to determine the mass transfer rate of each characteristic gas.

[0157] It should be noted that, based on the change in bubble radius over time (i.e., dissolution rate) obtained in the experiment, the following function can be obtained:

[0158]

[0159] Where, represents the mass transfer rate, Indicates the gas quality, represents the initial radius of the bubble, represents the gas density, Indicates the bubble dissolution time, Indicates the dissolution rate.

[0160] In a specific implementation, in order to facilitate the implementation of the method, the above process can be converted into a formula encapsulation form, wherein the mass transfer rate can be calculated as follows:

[0161]

[0162] Where, represents the mass transfer rate, Indicates the preset spatial angle coverage factor, represents the initial radius of the bubble, represents the gas density, represents the dissolution rate, Indicates the bubble dissolution time.

[0163] In the embodiment of the present invention, the mass transfer rate of each characteristic gas is obtained by calculation based on the gas migration related data and bubble test data associated with each characteristic gas.

[0164] Step 207: Use the target gas diffusion coefficient and mass transfer rate to import the gas transport physical model to obtain gas concentration information associated with each preset oil tank monitoring point for gas migration virtual sensing of the target transformer.

[0165] It is worth mentioning that when the target gas diffusion coefficient and mass transfer rate are introduced into the gas transport physical model for solution, the physical conservation laws are satisfied, including conservation of mass, conservation of momentum and conservation of energy.

[0166] The mass conservation equation:

[0167]

[0168] Where, Indicates the The volume fraction of the characteristic gas, Indicates the The gas density of a characteristic gas, Indicates the Gas velocity vector data of characteristic gases, m / s, Indicates time, Indicates the data source term, kg / m 3 .

[0169] Momentum conservation equation:

[0170]

[0171] Where, Indicates the The viscosity of the characteristic gas, Pa∙s, Indicates the The stress tensor of a characteristic gas, Pa, Indicates the The physical force term of the characteristic gas, which represents the force acting on the External forces of characteristic gases, such as gravity, buoyancy, etc., N / m 3 , represents the surface tension, represents the identity matrix, Indicates gas pressure.

[0172] Energy conservation equation:

[0173]

[0174] Where, Indicates the The total energy of the characteristic gas, J / kg, Indicates the Thermal conductivity of oil for a characteristic gas, W / (m·K), represents the flux source term, W / m³.

[0175] In the embodiment of the present invention, the target gas diffusion coefficient of each characteristic gas calculated in step 205 is and the mass transfer rate of each characteristic gas calculated in step 206 The gas transport physical model is introduced, and under the conditions of satisfying mass conservation, momentum conservation and energy conservation, the gas concentration information associated with each preset oil tank monitoring point for gas migration virtual sensing of the target transformer is obtained.

[0176] The gas concentration information here refers to the gas concentration of each characteristic gas when the gas migration virtual sensing passes through each preset oil tank monitoring point .

[0177] The present application provides a test example:

[0178] The CFD simulation software is used to simulate the gas transport process of the target transformer, and a cylindrical oil tank model of the target transformer is built, please refer to Figure 4 , 20ml of pure H2 is injected in 10s, for example, 3 oil tank monitoring points are distributed at equal intervals on the side of the oil tank, and the function relationship and key parameters involved in the above steps 201-207 are introduced into the simulation software, that is, the gas concentration value of the oil tank monitoring point at each time can be calculated, and the virtual sensing of the gas in the transformer oil is realized.

[0179] Please refer to Figure 5 , as shown in Figure 5 , which is a simulation result diagram of the gas concentration of each oil tank monitoring point. Figure 5 The first port, the second port and the third port in

[0180] The present application provides an application example:

[0181] Please refer to Figure 6-Figure 8 , on the basis of the above test example, a target transformer is established, the temperature field and flow field coupling calculation of the transformer is carried out under steady state, the stable flow field and temperature field information in the transformer is obtained, on the basis of which, the virtual sensing method of gas migration in the transformer oil proposed by the present application is applied, the transport process of the gas under the action of flow-heat coupling is digitally calculated, the information of temperature, flow rate and gas concentration in the transformer is obtained, and the virtual sensing of the gas migration in the transformer is realized.

[0182] It should be noted that Figure 8 Point-60-Point475 in

[0183] In the present invention, in response to a gas migration simulation request for a target transformer, a gas transport physical model of the target transformer is constructed; bubble test data of the target transformer and gas migration-related data of multiple characteristic gases in the transformer oil passing through each preset oil tank monitoring point are obtained; based on each gas migration-related data and bubble test data, the target gas diffusion coefficient of each characteristic gas at different test temperatures is determined; based on each gas migration-related data and bubble test data, the mass transfer rate of each characteristic gas is determined; the target gas diffusion coefficient and mass transfer rate are introduced into the gas transport physical model to obtain gas concentration information associated with each preset oil tank monitoring point of the target transformer for gas migration virtual sensing.

[0184] The present invention proposes a virtual sensing scheme for gas migration in transformer oil based on a gas transport physical model. According to the acquired gas migration-related data and bubble test data, the gas diffusion coefficient and mass transfer rate involved in the migration behavior are calculated, and gas migration simulation is performed through the gas transport physical model to obtain the gas concentration information associated with each preset oil tank monitoring point for gas migration virtual sensing of the target transformer, thereby solving the technical problem of how to realize virtual sensing of gas migration in transformer oil.

[0185] See also Figure 9 , Figure 9 This is a structural block diagram of a virtual sensing system for gas migration in transformer oil provided in Example 3 of the present invention.

[0186] The present invention provides a virtual sensing system for gas migration in transformer oil, comprising:

[0187] A response module 301 is used to respond to a gas migration simulation request for a target transformer and construct a gas transport physical model for the target transformer;

[0188] The data acquisition module 302 is used to obtain the bubble test data of the target transformer and the gas migration related data of multiple characteristic gases in the transformer oil passing through each preset oil tank monitoring point;

[0189] A first processing module 303 is configured to determine the target gas diffusion coefficient of each characteristic gas at different test temperatures based on the gas migration related data and the bubble test data;

[0190] The second processing module 304 is used to determine the mass transfer rate of each characteristic gas based on the gas migration related data and the bubble test data;

[0191] The gas migration simulation module 305 is used to introduce the gas transport physical model using the target gas diffusion coefficient and mass transfer rate to obtain the gas concentration information associated with each preset oil tank monitoring point for gas migration virtual sensing of the target transformer.

[0192] Further, the bubble test data comprises bubble dissolution time and bubble initial radius, and the first processing module 303 comprises:

[0193] a solubility operator module, configured to determine reference solubility and a plurality of test solubilities of each characteristic gas by using the gas migration related data;

[0194] a reference gas diffusion coefficient submodule, configured to determine the reference gas diffusion coefficient of each characteristic gas at the ambient temperature by using the reference solubility, the bubble dissolution time and the bubble initial radius;

[0195] a target gas diffusion coefficient submodule, configured to input a preset target gas diffusion coefficient function by using the gas migration related data, the reference solubility, the reference gas diffusion coefficient and the plurality of test solubilities, and determine the target gas diffusion coefficient of each characteristic gas at different test temperatures.

[0196] Further, the gas migration related data comprises Ostwald constant, gas pressure, liquid saturated vapor pressure, insulating oil molar mass, ambient temperature, insulating oil density and a plurality of test temperatures, and the solubility operator module comprises:

[0197] a reference Bunsen coefficient unit, configured to input a preset reference Bunsen coefficient function by using the gas pressure, the liquid saturated vapor pressure, the insulating oil density and the Ostwald constant in combination with the ambient temperature, and determine the reference Bunsen coefficient of each characteristic gas at the ambient temperature;

[0198] a reference solubility unit, configured to input a preset reference solubility function by using the reference Bunsen coefficient, the insulating oil molar mass and the insulating oil density in combination with the ambient temperature, and determine the reference solubility of each characteristic gas at the ambient temperature;

[0199] a test Bunsen coefficient unit, configured to input a preset test Bunsen coefficient function by using the gas pressure, the liquid saturated vapor pressure, the insulating oil density and the Ostwald constant in combination with the plurality of test temperatures respectively, and determine the test Bunsen coefficient of each characteristic gas at different test temperatures;

[0200] a test solubility unit, configured to input a preset test solubility function by using the insulating oil molar mass and the insulating oil density in combination with the plurality of test Bunsen coefficients and the plurality of test temperatures, and obtain the test solubility of each characteristic gas at different test temperatures.

[0201] Further, the preset target gas diffusion coefficient function is specifically:

[0202]

[0203] wherein, represents the target gas diffusion coefficient of each characteristic gas at the i th test temperature. The target gas diffusion coefficient at the test temperature is represents the diffusion coefficient of the reference gas at ambient temperature, represents the preset first dimensionless constant, Indicates that it is in The test solubility at the test temperature is represents the reference solubility at ambient temperature, represents the preset second dimensionless constant, Indicates the ambient temperature, Indicates the A test temperature.

[0204] Furthermore, the gas migration related data also includes gas density, and the second processing module 304 includes:

[0205] The dissolution rate submodule is used to determine the dissolution rate of each characteristic gas using the bubble dissolution time and the bubble initial radius;

[0206] A first multiplication submodule is used to perform a multiplication operation using the gas density and the dissolution rate to obtain a first multiplication value of each characteristic gas;

[0207] A second multiplication submodule is used to perform a multiplication operation on the square of the initial radius of the bubble and the first multiplication value to obtain a second multiplication value of each characteristic gas;

[0208] The mass transfer rate submodule is used to perform a multiplication operation using the second product value and the preset space angle coverage coefficient to determine the mass transfer rate of each characteristic gas.

[0209] Furthermore, the gas migration related data also includes gas mass fraction and gas velocity vector data. The gas transport physical model is specifically as follows:

[0210]

[0211]

[0212] Where, Indicates the The first of the preset fuel tank monitoring points The gas concentration of a characteristic gas, Indicates the The first of the preset fuel tank monitoring points The gas mass fraction of the characteristic gas, Indicates the The first of the preset fuel tank monitoring points The gas density of a characteristic gas, represents the gas velocity vector data, Indicates the The first of the preset fuel tank monitoring points a target gas diffusion coefficient of the characteristic gas at the a mass transfer rate of the characteristic gas in the a characteristic gas, , a total number of characteristic gases, a target gas diffusion coefficient of the characteristic gas at the a diffusion flux of the characteristic gas in the

[0213] In the present application, in response to a request for gas migration simulation of a target transformer, a gas transport physical model of the target transformer is constructed; bubble test data of the target transformer is obtained, and gas migration related data of multiple characteristic gases in transformer oil of each preset tank monitoring point is obtained; based on the gas migration related data and the bubble test data, target gas diffusion coefficients of each characteristic gas at different test temperatures are determined; based on the gas migration related data and the bubble test data, mass transfer rates of each characteristic gas are determined; the target gas diffusion coefficients and the mass transfer rates are introduced into the gas transport physical model to obtain gas concentration information of each preset tank monitoring point associated with gas migration virtual sensing of the target transformer.

[0214] The present application provides a virtual sensing scheme for gas migration in transformer oil based on a gas transport physical model, calculates gas diffusion coefficients and mass transfer rates involved in migration behavior according to obtained gas migration related data and bubble test data, and performs gas migration simulation through the gas transport physical model to obtain gas concentration information of each preset tank monitoring point associated with gas migration virtual sensing of the target transformer, thereby solving the technical problem of how to realize virtual sensing of gas migration in transformer oil.

[0215] Please refer to Figure 10 , Figure 10 A structural block diagram of a computer device provided for the fourth embodiment of the present application.

[0216] The electronic device of the embodiment of the present application comprises a memory 401 and a processor 402, and the memory 402 stores a computer program; when the computer program is executed by the processor 402, the processor 402 executes the virtual sensing method of gas migration in transformer oil according to any one of the above embodiments.

[0217] ​​​​​Memory 401 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 401 has storage space 403 for program code 413 for executing any of the method steps described above. For example, storage space 403 for program code may include individual program codes 413 for implementing various steps in the method described above. These program codes may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards, or floppy disks. The program codes may be compressed, for example, in a suitable format. When executed by a processing device, these codes cause the processing device to execute the various steps in the method described above. These program codes may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards, or floppy disks. The program codes may be compressed, for example, in a suitable format. When these codes are executed by a computing and processing device, they cause the computing and processing device to perform the steps of the virtual sensing method for gas migration in transformer oil described above.

[0218] The fifth embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the virtual sensing method for gas migration in transformer oil as described in any of the above embodiments is implemented.

[0219] Embodiment 6 of the present invention further provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the virtual sensing method for gas migration in transformer oil as described in any of the above embodiments.

[0220] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0221] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the division of the units is only a logical function division, and there can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0222] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments of the present application.

[0223] In addition, each functional unit in the various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically as a separate unit, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware, or in the form of a software functional unit.

[0224] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such an understanding, the technical solutions of the present application essentially or substantially, or all or part of the technical solutions, can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of the present application. The foregoing storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, and various media that can store program codes.

[0225] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A virtual sensing method for gas migration in transformer oil, characterized in that: include: In response to a gas migration simulation request for a target transformer, a gas transport physical model of the target transformer is constructed; Acquire bubble test data of the target transformer and gas migration related data of multiple characteristic gases in transformer oil passing through each preset oil tank monitoring point; Determining the target gas diffusion coefficient of each characteristic gas at different test temperatures based on the gas migration related data and the bubble test data; The bubble test data includes the bubble dissolution time and the bubble initial radius. The determining of the target gas diffusion coefficient of each characteristic gas at different test temperatures based on the gas migration related data and the bubble test data includes: Determining a reference solubility and a plurality of test solubilities of each of the characteristic gases using the gas migration related data; Determine a reference gas diffusion coefficient of each characteristic gas at ambient temperature using the reference solubility, the bubble dissolution time, and the bubble initial radius; The gas migration related data, the reference solubility, the reference gas diffusion coefficient, and a plurality of the test solubilities are input into a preset target gas diffusion coefficient function to determine the target gas diffusion coefficient of each of the characteristic gases at different test temperatures; The gas migration related data includes Ostwald constant, gas pressure, liquid saturated vapor pressure, insulating oil molar mass, ambient temperature, insulating oil density and multiple test temperatures. The gas migration related data is used to determine the reference solubility and multiple test solubilities of each characteristic gas, including Based on the gas pressure, the liquid saturated vapor pressure, the insulating oil density and the Ostwald constant, and in combination with the ambient temperature, a preset reference Bunsen coefficient function is input to determine a reference Bunsen coefficient of each characteristic gas at the ambient temperature; Based on the reference Bunsen coefficient, the molar mass of the insulating oil, and the density of the insulating oil, and in combination with the ambient temperature, a preset reference solubility function is input to determine the reference solubility of each of the characteristic gases at the ambient temperature; Based on the gas pressure, the liquid saturated vapor pressure, the insulating oil density and the Ostwald constant, a preset test Bunsen coefficient function is inputted in combination with a plurality of test temperatures to determine the test Bunsen coefficient of each characteristic gas at different test temperatures; Based on the molar mass and density of the insulating oil, a preset experimental solubility function is input in combination with a plurality of experimental Bunsen coefficients and a plurality of the experimental temperatures to determine the experimental solubility of each characteristic gas at different experimental temperatures; Determining the mass transfer rate of each of the characteristic gases based on the gas migration related data and the bubble test data; The target gas diffusion coefficient and the mass transfer rate are used to import the gas transport physical model to obtain gas concentration information associated with each of the preset oil tank monitoring points used for gas migration virtual sensing of the target transformer.

2. The virtual sensing method for gas migration in transformer oil according to claim 1, characterized in that: The preset target gas diffusion coefficient function is specifically: ； Where, Indicates that it is in The target gas diffusion coefficient at the test temperature is represents the diffusion coefficient of the reference gas at the ambient temperature, represents the preset first dimensionless constant, Indicates that it is in The test solubility at the test temperature is represents the reference solubility at the stated ambient temperature, represents the preset second dimensionless constant, represents the ambient temperature, Indicates the The test temperature.

3. The virtual sensing method for gas migration in transformer oil according to claim 1, characterized in that: The gas migration related data also includes gas density. Determining the mass transfer rate of each characteristic gas based on each gas migration related data and the bubble test data includes: Determine the dissolution rate of each characteristic gas using the bubble dissolution time and the bubble initial radius; Performing a multiplication operation using the gas density and the dissolution rate to obtain a first multiplication value of each characteristic gas; Performing a multiplication operation on the square of the initial bubble radius and the first multiplication value to obtain a second multiplication value of each characteristic gas; The mass transfer rate of each characteristic gas is determined by performing a multiplication operation on the second multiplication value and the preset space angle coverage coefficient.

4. The virtual sensing method for gas migration in transformer oil according to claim 3, characterized in that: The gas migration related data also includes gas mass fraction and gas velocity vector data. The gas transport physical model is specifically: ； ； Where, Indicates the The first of the preset fuel tank monitoring points The gas concentration of each of the characteristic gases, Indicates the The first of the preset fuel tank monitoring points The gas mass fraction of the characteristic gas, Indicates the The first of the preset fuel tank monitoring points the gas density of each of the characteristic gases, represents the gas velocity vector data, Indicates the The first of the preset fuel tank monitoring points The characteristic gas is in the The target gas diffusion coefficient at the test temperature, Indicates the The first of the preset fuel tank monitoring points The mass transfer rate of the characteristic gas, Indicates the The characteristic gas, , represents the total number of the characteristic gases, Indicates the The first of the preset fuel tank monitoring points The diffusion flux of the characteristic gas.

5. A virtual sensing system for gas migration in transformer oil, characterized in that: The virtual sensing system for gas migration in transformer oil is used to implement the virtual sensing method for gas migration in transformer oil according to any one of claims 1 to 4, and the virtual sensing system for gas migration in transformer oil comprises: A response module, configured to respond to a gas migration simulation request for a target transformer and construct a gas transport physical model for the target transformer; A data acquisition module is used to obtain bubble test data of the target transformer and gas migration related data of multiple characteristic gases in the transformer oil passing through each preset oil tank monitoring point; A first processing module is configured to determine a target gas diffusion coefficient of each characteristic gas at different test temperatures based on the gas migration related data and the bubble test data; a second processing module, configured to determine a mass transfer rate of each of the characteristic gases based on the gas migration related data and the bubble test data; The gas migration simulation module is used to use the target gas diffusion coefficient and the mass transfer rate to import the gas transport physical model to obtain the gas concentration information associated with each of the preset oil tank monitoring points used for gas migration virtual sensing of the target transformer.

6. An electronic device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the virtual sensing method for gas migration in transformer oil according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the virtual sensing method for gas migration in transformer oil according to any one of claims 1 to 4 is implemented.

8. A computer program product, characterized in that The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer is caused to perform the virtual sensing method for gas migration in transformer oil according to any one of claims 1 to 4.

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