Virtual sensing method and system for gas migration in transformer oil

Abstract

The invention relates to the technical field of electric power engineering, discloses a virtual sensing method and system for gas migration in transformer oil, and provides a virtual sensing scheme for gas migration in transformer oil based on a gas transportation physical model. Calculating a gas diffusion coefficient and a mass transfer rate related to the migration behavior, and carrying out gas migration simulation through a gas transportation physical model, thereby obtaining gas concentration information associated with each preset oil tank monitoring point of the target transformer for gas migration virtual sensing; the technical problem of how to realize 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] In the process of power system modernization, digital transformation has become an irreversible development direction. As the core element of this transformation, the digitalization of power equipment is of great significance to improving the reliability and efficiency of power grid operation. Among them, achieving the measurability and observability of equipment status and the predictability and controllability of risks are the basic requirements for the digitalization of power equipment.

[0003] As a core device with technical complexity in the power grid system, the operating status of the power transformer is directly related to the safety of the power grid. If a transformer fails, it may not only cause a large-scale power outage, but also cause serious safety accidents such as explosions and fires, causing huge economic losses and significant social impacts. However, due to the particularity of the internal structure of the transformer, it is difficult to achieve widespread deployment of sensors. Therefore, it is of great engineering application value to realize the "virtual perception" of the internal status of the transformer through digital means.

[0004] Power transformers are composite systems composed of a variety of materials, including insulating oil, insulating paper, conductive materials, and magnetic materials. During long-term operation, these components will gradually degrade due to complex working conditions and show corresponding changes in physical and chemical parameters. Accurately capturing these parameter changes is the key to achieving equipment status assessment and fault diagnosis. During transformer operation, the combined effects of electrical stress, thermal stress, and mechanical stress may lead to thermal or electrical faults. These faults will cause the insulation material to decompose and produce a variety of characteristic gases, such as hydrogen, low molecular weight hydrocarbons, carbon monoxide, carbon dioxide, etc. These gases will dissolve in the transformer oil and migrate with the oil flow.

[0005] Dissolved gas analysis in oil can assess the type and severity of faults by detecting the content, composition and generation rate of characteristic gases. This method has become an important means of diagnosing potential faults of oil-immersed equipment in power systems. However, with the increase in transformer capacity and voltage level, the equipment failure rate is on the rise. Although the traditional three-ratio analysis method is based on rich practical experience, its diagnostic and predictive capabilities still have room for improvement in dealing with increasingly complex operating environments. In addition, the digital design and operation and maintenance of power equipment have put forward higher requirements on the simulation capabilities of fault evolution processes. Summary of the invention

[0006] The 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] A first aspect of the present invention provides a virtual sensing method for gas migration in transformer oil, comprising:

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

[0009] 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;

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

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

[0012] The target gas diffusion coefficient and the mass transfer rate are introduced into 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.

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

[0014] Determining a reference solubility and a plurality of test solubilities of each of the characteristic gases using the gas migration related data;

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

[0016] The gas migration related data, the reference solubility, the reference gas diffusion coefficient and a plurality of the test solubilities are used to 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 reference solubility and multiple test solubilities of each characteristic gas are determined by using the gas migration related data, including

[0018] 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 a reference Bunsen coefficient of each of the characteristic gases at the ambient temperature;

[0019] Based on the reference Bunsen coefficient, the insulating oil molar mass and the insulating oil density, 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 respectively 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, a plurality of experimental Bunsen coefficients and a plurality of the experimental temperatures are combined to input the preset experimental solubility function to determine the experimental solubility of each of the characteristic gases at different experimental temperatures.

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

[0023]

[0024] In the formula, Indicates that it is in The target gas diffusion coefficient at the test temperature is represents the reference gas diffusion coefficient 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 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 of the characteristic gases by 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 of the characteristic gases;

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

[0029] The mass transfer rate of each of the characteristic gases is determined by performing a multiplication operation using the second multiplication value and a 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] In the formula, Indicates The preset fuel tank monitoring point The gas concentration of the characteristic gas, Indicates The preset fuel tank monitoring point The gas mass fraction of the characteristic gas, Indicates The preset fuel tank monitoring point the gas density of each of the characteristic gases, represents the gas velocity vector data, Indicates The preset fuel tank monitoring point The characteristic gas is in the the target gas diffusion coefficient at the test temperature, Indicates The preset fuel tank monitoring point The mass transfer rate of the characteristic gas, Indicates The characteristic gas, , represents the total number of the characteristic gases, Indicates The preset fuel tank monitoring point 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, used for responding to a gas migration simulation request for a target transformer and constructing a gas transport physical model of the target transformer;

[0036] A data acquisition module, used to acquire 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;

[0037] A first processing module, for determining a target gas diffusion coefficient of each of the characteristic gases at different test temperatures based on the gas migration related data and the bubble test data;

[0038] A second processing module, for determining the mass transfer rate of each of the characteristic gases based on the gas migration related data and the bubble test data;

[0039] 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.

[0040] A third aspect of the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, 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 as described in any one of the above items.

[0041] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the virtual sensing method for gas migration in transformer oil as described in any one of the above items.

[0042] A fifth aspect of the present invention 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 for gas migration in transformer oil as described in any one of the above items.

[0043] It can be seen from the above technical solutions that the present invention has the following advantages:

[0044] 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 the gas migration simulation is performed through the gas transport physical model, so as to obtain the 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0046] Figure 1 A flowchart of a virtual sensing method for gas migration in transformer oil provided in Embodiment 1 of the present invention;

[0047] Figure 2 A flowchart of a virtual sensing method for gas migration in transformer oil provided in Embodiment 2 of the present invention;

[0048] Figure 3 Schematic diagram of Henry coefficients for each characteristic gas;

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

[0050] Figure 5 This is a schematic diagram of the simulation results of gas concentration at each fuel tank monitoring point;

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

[0052] Figure 7 The gas distribution diagram of the local section of the target transformer;

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

[0054] Fig. 9 A structural block diagram of a virtual sensing system for gas migration in transformer oil provided in Embodiment 3 of the present invention;

[0055] Fig.10 This is a structural block diagram of a computer device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0056] The embodiments of the present invention provide a virtual sensing method and system for gas migration in transformer oil, which are used to solve the technical problem of how to realize virtual sensing of gas migration in transformer oil.

[0057] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] See also Figure 1 , Figure 1 A flowchart of the steps of a virtual sensing method for gas migration in transformer oil provided in Embodiment 1 of the present invention.

[0059] The present invention provides a virtual sensing method for gas migration in transformer oil, comprising:

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

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

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

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

[0064] Step 102: 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.

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

[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, refers to the relevant data used to calculate the gas diffusion coefficient and mass transfer rate involved in the migration behavior of characteristic gas in insulating oil and its related environment.

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

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

[0069] In an embodiment of the present invention, 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, 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: Based on the gas migration related data and the bubble test data, determine the target gas diffusion coefficient of each characteristic gas at different test temperatures.

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

[0072] Step 104: Determine 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 invention, the mass transfer rate of each characteristic gas is determined based on the gas migration related data and bubble test data of each characteristic gas.

[0074] Step 105: Use the target gas diffusion coefficient and mass transfer rate to import 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.

[0075] In an embodiment of the present invention, the target gas diffusion coefficient and mass transfer rate are used to introduce a gas transport physical model to perform gas migration simulation, thereby obtaining gas concentration information associated with each preset oil tank monitoring point for gas migration virtual sensing of the target transformer.

[0076] 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 transformer oil 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 used to introduce 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 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 the gas migration simulation is performed through the gas transport physical model, so as to obtain the 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] See also Figure 2 , Figure 2 A flowchart of the steps of a virtual sensing method for gas migration in transformer oil provided in Embodiment 2 of the present invention.

[0079] The present invention provides a virtual sensing method for gas migration in transformer oil, comprising:

[0080] Step 201: In response to a gas migration simulation request for 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) undergoes a decomposition reaction, releasing a variety of characteristic gases. The migration process of these gases in transformer oil involves a gas-liquid two-phase flow mechanism: on the one hand, gas molecules are continuously dissolved in the oil phase through convection and diffusion; on the other hand, free gas exhibits unique motion characteristics under the coupling of oil flow force and buoyancy. The complexity of this multiphase flow process is mainly reflected in the interaction of multiple physical mechanisms such as gas dissolution-precipitation equilibrium, diffusion migration, and buoyancy drive.

[0082] In transformer oil, the migration process of free gas involves complex mass transfer phenomena. Through convection and diffusion mechanisms, gas components undergo continuous mass transfer exchange between the gas phase and the liquid phase until a thermodynamic equilibrium state is reached. In order to accurately describe this dynamic process, the present invention constructs a gas transport physical model based on component transport theory, which can comprehensively characterize the mass migration, momentum transfer and energy exchange process of each component in the gas-liquid two-phase system. This systematic modeling method provides a theoretical basis for a deep understanding of the migration law of gas in transformer oil.

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

[0084]

[0085]

[0086] In the formula, Indicates The first of the preset fuel tank monitoring points The gas concentration of a characteristic gas, Indicates The first of the preset fuel tank monitoring points The gas mass fraction of the characteristic gas, Indicates The first of the preset fuel tank monitoring points The gas density of a characteristic gas, represents the gas velocity vector data, Indicates The first of the preset fuel tank monitoring points The characteristic gas is in The target gas diffusion coefficient at the test temperature is Indicates The first of the preset fuel tank monitoring points Mass transfer rate of characteristic gas, kg / s, Indicates Characteristic gases, , represents the total number of characteristic gases, Indicates 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 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, Indicates The mass transfer rate of a characteristic gas, Indicates Mass transfer coefficient of characteristic gas, m / s, represents the gas-liquid mass transfer interface area, Indicates The equilibrium concentration of a characteristic gas in the liquid phase, kg / m 3 , Indicates The instantaneous concentration of a characteristic gas in the liquid phase, kg / m 3 .

[0091] When the concentration of components in the gas-liquid two phases reaches a relatively stable state of equilibrium, Henry's theorem and Ostwald coefficient can be used to describe the solubility.

[0092] Henry's Law:

[0093]

[0094] In the formula, Indicates The gas pressure of a characteristic gas, represents Henry's constant (Henry coefficient), Pa, Indicates The mole fraction of a characteristic gas;

[0095] It should be noted that Henry's law describes the distribution ratio of gas in the gas phase and liquid phase under equilibrium state, which can be understood as being used to calculate the saturated solubility of gas in oil, which is equivalent to setting an upper limit for the amount of gas dissolved in oil. It has no direct variable connection with the above component transport equation and is related to the solubility in the preset solubility function. The specific calculation conversion formula is as follows:

[0096]

[0097] In the formula, represents the molar mass of insulating oil, Indicates 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). The calculation results are as follows: Figure 3 As shown, Figure 3 Schematic diagram of the Henry coefficient of each characteristic gas.

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

[0100] Step 202: 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.

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

[0102] Furthermore, the bubble test data include bubble dissolution time and bubble initial radius.

[0103] It should be noted that the bubble test data is obtained by injecting bubbles of a certain size of a single component characteristic gas into the degassed transformer oil, photographing them with a high-speed camera, and observing the change of bubble size over time, thereby obtaining the bubble dissolution time and the initial radius of the bubble.

[0104] Step 203: Using gas migration related data, determine the reference solubility and multiple test solubilities of each characteristic gas.

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

[0106]

[0107] In the formula, Indicates the bubble dissolution time, Indicates the initial size of the bubble, which is one of the key parameters affecting its dissolution dynamics, namely the initial radius of the bubble. is the gas diffusion coefficient, Indicates solubility.

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

[0109] Under certain temperature and pressure conditions, the amount of gas dissolved in a unit volume of oil after a characteristic gas dissolves in oil and reaches equilibrium (T) can be expressed as:

[0110]

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

[0112] The Bunsen coefficient is expressed as follows:

[0113]

[0114] In the formula, Indicates the gas pressure, represents the saturated vapor pressure of the liquid, It represents the Ostwald constant under standard conditions. It is dimensionless and depends on the type of gas.

[0115] Among them, the Ostwald constant can be determined based on the gas distribution coefficient of 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 include Ostwald constant, gas pressure, liquid saturated vapor pressure, insulating oil molar mass, ambient temperature, insulating oil density and multiple test temperatures. Step 203 may include the following sub-steps:

[0117] S11. Based on the gas pressure, liquid saturated vapor pressure, insulating oil density and 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 ambient temperature.

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

[0119]

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

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

[0122] S12. Based on the reference Bunsen coefficient, the molar mass of the insulating oil and the density of the insulating oil, 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 ambient temperature, It represents the molar mass of the liquid.

[0126] In an embodiment of the present invention, based on the reference Bunsen coefficient, the molar mass of the insulating oil and the density of the insulating oil, 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, liquid saturated vapor pressure, insulating oil density and Ostwald constant, the preset test Bunsen coefficient function is input respectively in combination with multiple test temperatures 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, It represents the Bunsen coefficient of the test at different test temperatures. Indicates the gas pressure, represents the saturated vapor pressure of the liquid, represents the Ostwald constant under standard conditions, Indicates The test temperature, , Indicates the total number of test temperatures, Indicates the density of insulating oil.

[0131] In an embodiment of the present invention, based on gas pressure, liquid saturated vapor pressure, insulating oil density and Ostwald constant, preset test Bunsen coefficient functions are input respectively in combination with multiple test temperatures to determine the test Bunsen coefficients of each characteristic gas at different test temperatures.

[0132] S14. Based on the molar mass and density of the insulating oil, a preset test solubility function is input in combination with multiple test Bunsen coefficients and multiple test temperatures to obtain the test solubility of each characteristic gas at different test temperatures.

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

[0134]

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

[0136] In an embodiment of the present invention, based on the molar mass and density of the insulating oil, a preset test solubility function is input in combination with multiple test Bunsen coefficients and multiple test temperatures to determine the test solubility of each characteristic gas at different test temperatures.

[0137] Step 204: Using the reference solubility, bubble dissolution time and bubble initial radius, determine the reference gas diffusion coefficient of each characteristic gas at ambient temperature.

[0138] 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 reference gas diffusion coefficient can be calculated as follows:

[0139]

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

[0141] In the embodiment of the present invention, the reference solubility calculated in S12 is combined with the bubble dissolution time and the bubble initial radius obtained in the experiment to calculate, so as to obtain the reference gas diffusion coefficient at ambient temperature.

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

[0143] It should be noted that the reference solubility calculation process of a single characteristic gas is only exemplified here. The same calculation method is used for other characteristic gases to obtain the reference gas diffusion coefficient of each characteristic gas at ambient temperature, which will not be repeated here.

[0144] Step 205 , using gas migration related data, reference solubility, reference gas diffusion coefficient and multiple test solubilities to input a preset target gas diffusion coefficient function, and determining the target gas diffusion coefficient of each characteristic gas at different test temperatures.

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

[0146]

[0147] In the formula, Indicates that it is in The target gas diffusion coefficient at the test temperature is is the reference gas diffusion coefficient 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, Indicates the preset second dimensionless constant, the unit is k, Indicates the ambient temperature, Indicates A test temperature.

[0148] In the embodiment of the present invention, an ambient temperature, a reference solubility, a reference gas diffusion coefficient, a test temperature and an associated test solubility associated with any 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 multiple different test temperatures, the ambient temperature, reference solubility and reference gas diffusion coefficient remain unchanged, and only the test temperature and the associated test solubility change, thereby obtaining the target gas diffusion coefficient of the characteristic gas at different test temperatures.

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

[0151] Step 206: Determine 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, and step 206 may include the following sub-steps:

[0153] S21. Determine the dissolution rate of each characteristic gas using the bubble dissolution time and the bubble initial radius.

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

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

[0156] S24, performing a multiplication operation using the second product value and a preset spatial 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] In the formula, 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] In the formula, represents the mass transfer rate, represents 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 calculating 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 the 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] In the formula, Indicates The volume fraction of the characteristic gas, Indicates The gas density of a characteristic gas, Indicates Gas velocity vector data of characteristic gases, m / s, Indicates time, Indicates the source item, kg / m 3 .

[0169] Momentum conservation equation:

[0170]

[0171] In the formula, Indicates The viscosity of the characteristic gas, Pa∙s, Indicates The stress tensor of a characteristic gas, Pa, Indicates The physical force term of the characteristic gas 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] In the formula, Indicates The total energy of the characteristic gas, J / kg, Indicates Thermal conductivity of oil for a characteristic gas, W / (m·K), is 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 physical model of gas transport is imported and solved under the conditions of conservation of mass, momentum and energy, so as to obtain the gas concentration information associated with each preset oil tank monitoring point for gas migration virtual sensing of the target transformer.

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

[0177] The present invention provides a test example:

[0178] Use CFD simulation software to simulate the gas transportation process of the target transformer and build a cylindrical oil tank model of the target transformer. Figure 4 As shown, taking the injection of 20 ml of pure H2 within 10 seconds as an example, three tank monitoring points are evenly spaced on the side of the tank, and the functional relationships and key parameters involved in the above steps 201 to 207 are imported into the simulation software, so that the gas concentration value of the tank monitoring point at each moment can be calculated, thereby realizing the virtual sensing of gas in transformer oil.

[0179] See also Figure 5 As shown, Figure 5 Schematic diagram of the simulation results of gas concentration at each fuel tank monitoring point. Figure 5 Port 1, Port 2 and Port 3 refer to the three fuel tank monitoring points respectively.

[0180] The present invention provides an application example:

[0181] See also Figure 6-Figure 8 On the basis of the above test examples, a target transformer is established, and the temperature field and flow field coupling calculations are performed on the transformer in a steady state to obtain the stable flow field and temperature field information inside the transformer. On this basis, the virtual sensing method of gas migration in transformer oil proposed in the present invention is applied to digitally calculate the gas transport process under the action of flow-heat coupling, obtain the information of temperature, flow velocity and gas concentration inside the transformer, and realize the virtual sensing of gas migration inside the transformer.

[0182] It should be noted that Figure 8 Point-60-Point475 in the figure refers to the coordinates of the fuel tank monitoring point.

[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 transformer oil 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 used to introduce 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 the gas migration simulation is performed through the gas transport physical model, so as to obtain the 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.

[0185] See also Fig. 9 , Fig. 9 This is a structural block diagram of a virtual sensing system for gas migration in transformer oil provided in Embodiment 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 acquire the bubble test data of the target transformer and the gas migration related data of multiple characteristic gases in the transformer oil through each preset oil tank monitoring point;

[0189] The first processing module 303 is used 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] Furthermore, the bubble test data includes the bubble dissolution time and the bubble initial radius. The first processing module 303 includes:

[0193] A solubility calculation submodule is used to determine the reference solubility and multiple test solubilities of each characteristic gas using gas migration related data;

[0194] A reference gas diffusion coefficient submodule, for determining a reference gas diffusion coefficient of each characteristic gas at ambient temperature using reference solubility, bubble dissolution time and bubble initial radius;

[0195] The target gas diffusion coefficient submodule is used to use gas migration related data, reference solubility, reference gas diffusion coefficient and multiple test solubilities to input a preset target gas diffusion coefficient function to determine the target gas diffusion coefficient of each characteristic gas at different test temperatures.

[0196] Furthermore, the gas migration related data include Ostwald constant, gas pressure, liquid saturated vapor pressure, insulating oil molar mass, ambient temperature, insulating oil density and multiple test temperatures. The solubility operator module includes:

[0197] A reference Bunsen coefficient unit is used to determine the reference Bunsen coefficient of each characteristic gas at ambient temperature based on the preset reference Bunsen coefficient function of gas pressure, liquid saturated vapor pressure, insulating oil density and Ostwald constant combined with ambient temperature input;

[0198] A reference solubility unit is used to determine the reference solubility of each characteristic gas at ambient temperature based on a reference Bunsen coefficient, an insulating oil molar mass and an insulating oil density, and a preset reference solubility function in combination with an ambient temperature input;

[0199] The test Bunsen coefficient unit is used to input the preset test Bunsen coefficient function based on gas pressure, liquid saturated vapor pressure, insulating oil density and Ostwald constant in combination with multiple test temperatures to determine the test Bunsen coefficient of each characteristic gas at different test temperatures;

[0200] The test solubility unit is used to input a preset test solubility function based on the molar mass and density of the insulating oil in combination with multiple test Bunsen coefficients and multiple test temperatures to determine the test solubility of each characteristic gas at different test temperatures.

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

[0202]

[0203] In the formula, Indicates that it is in The target gas diffusion coefficient at the test temperature is is the reference gas diffusion coefficient 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 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 value 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 multiplication value and a 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:

[0210]

[0211]

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

[0213] 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 transformer oil 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 used to introduce 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.

[0214] 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 the gas migration simulation is performed through the gas transport physical model, so as to obtain the 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.

[0215] See also Fig.10 , Fig.10 This is a structural block diagram of a computer device provided in Embodiment 4 of the present invention.

[0216] An electronic device according to an embodiment of the present invention includes: a memory 401 and a processor 402, wherein the memory 402 stores a computer program; when the computer program is executed by the processor 402, the processor 402 executes a virtual sensing method for gas migration in transformer oil according to any of the above embodiments.

[0217] The memory 401 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM, a hard disk or a ROM. The memory 401 has a storage space 403 for a program code 413 for executing any method step in the above method. For example, the storage space 403 for the program code may include individual program codes 413 for implementing the various steps in the above method, respectively. 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 code may be compressed, for example, in an appropriate form. When these codes are run by a computing and processing device, the computing and processing device performs 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 code may be compressed, for example, in an appropriate form. 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] Embodiment 5 of the present invention further provides a computer-readable storage medium on which a computer program is stored. 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, wherein when the program instructions are executed by a computer, the computer executes a virtual sensing method for gas migration in transformer oil as in any of the above embodiments.

[0220] Those skilled in the art can 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 the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in 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. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0222] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0223] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[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 this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0225] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

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 of the characteristic gases at different test temperatures based on the gas migration related data and the bubble test data; 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 introduced into 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 bubble test data includes the bubble dissolution time and the bubble initial radius. The target gas diffusion coefficient of each characteristic gas at different test temperatures is determined based on the gas migration related data and the bubble test data, including: 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 of the characteristic gases at ambient temperature by 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 used to 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.

3. The virtual sensing method for gas migration in transformer oil according to claim 2, characterized in that: The gas migration related data include 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 are 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, a preset reference Bunsen coefficient function is input in combination with the ambient temperature to determine a reference Bunsen coefficient of each of the characteristic gases at the ambient temperature; Based on the reference Bunsen coefficient, the insulating oil molar mass and the insulating oil density, 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 respectively 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 plurality of experimental Bunsen coefficients and a plurality of the experimental temperatures are combined to input the preset experimental solubility function to determine the experimental solubility of each of the characteristic gases at different experimental temperatures.

4. The virtual sensing method for gas migration in transformer oil according to claim 3, characterized in that: The preset target gas diffusion coefficient function is specifically: In the formula, Indicates that it is in The target gas diffusion coefficient at the test temperature is represents the reference gas diffusion coefficient 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 test temperature.

5. The virtual sensing method for gas migration in transformer oil according to claim 2, characterized in that: The gas migration related data also includes gas density, and the mass transfer rate of each characteristic gas is determined based on each gas migration related data and the bubble test data, including: Determine the dissolution rate of each of the characteristic gases by 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 of the characteristic gases; Performing a multiplication operation using the square value of the initial radius of the bubble and the first multiplication value to obtain a second multiplication value of each characteristic gas; The mass transfer rate of each of the characteristic gases is determined by performing a multiplication operation using the second multiplication value and a preset space angle coverage coefficient.

6. The virtual sensing method for gas migration in transformer oil according to claim 5, 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: In the formula, Indicates The preset fuel tank monitoring point The gas concentration of the characteristic gas, Indicates The preset fuel tank monitoring point The gas mass fraction of the characteristic gas, Indicates The preset fuel tank monitoring point the gas density of each of the characteristic gases, represents the gas velocity vector data, Indicates The preset fuel tank monitoring point The characteristic gas is in the the target gas diffusion coefficient at the test temperature, Indicates The preset fuel tank monitoring point The mass transfer rate of the characteristic gas, Indicates The characteristic gas, , represents the total number of the characteristic gases, Indicates The preset fuel tank monitoring point The diffusion flux of the characteristic gas.

7. A virtual sensing system for gas migration in transformer oil, characterized in that: include: A response module, used for responding to a gas migration simulation request for a target transformer and constructing a gas transport physical model of the target transformer; A data acquisition module, used to acquire 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; A first processing module, for determining a target gas diffusion coefficient of each of the characteristic gases at different test temperatures based on the gas migration related data and the bubble test data; A second processing module, for determining the 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.

8. 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 as described in any one of claims 1 to 6.

9. 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 as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer is caused to execute the virtual sensing method for gas migration in transformer oil as described in any one of claims 1 to 6.

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