Method, system and equipment for calculating diffusion characteristics of characteristic gas under partial discharge defect

Abstract

The embodiment of the invention relates to the technical field of power equipment state monitoring and fault diagnosis, in particular to a method, a system and equipment for calculating diffusion characteristics of characteristic gas under a partial discharge defect, and the method comprises the steps: building a diffusion verification platform for gas dissolved in transformer oil; establishing a bubble calculation model considering a sudden fault, tracking a gas-liquid two-phase free surface by adopting an improved VOF model, representing the percentage of each phase by introducing a volume fraction parameter, and simulating the movement and deformation behaviors of bubbles in transformer oil; according to the method, a mathematical model of the diffusion and balance process of the dissolved gas in the oil is constructed, the non-steady state diffusion process is described based on the Fick second law, the diffusion coefficient is corrected according to the Arrhenius relation, and the time required for the dissolved gas to diffuse to balance at different flow speeds is calculated. Accurate simulation and quantitative analysis of the whole process of partial discharge gas production, dissolution and diffusion in the converter transformer are realized.

Description

Technical Field

[0001] This application relates to the field of power equipment condition monitoring and fault diagnosis technology, and in particular to a method, system and device for calculating characteristic gas diffusion characteristics under partial discharge defects. Background Technology

[0002] The advancement of digital power grid applications urgently requires focusing on converter transformers as the primary research object, enabling visualization of their internal multi-physics spatiotemporal evolution to support daily operation and maintenance and fault tracing. Background information for researching digital twin and dynamic simulation methods for converter transformer discharge faults involves power equipment monitoring, fault detection, and prediction. By establishing digital twin models and combining them with dynamic simulation methods, the operating status of converter transformers can be monitored in real time, discharge faults can be detected, and potential problems can be predicted, thereby improving the reliability and stability of the power system. This research helps power engineers and operators better manage power equipment, reduce downtime, and improve power transmission efficiency.

[0003] However, existing technologies still have the following problems:

[0004] Relying on static concentration thresholds cannot accurately describe the dynamic diffusion process of gas after a fault produces gas, and lacks quantitative analysis of the influence of key factors such as oil flow velocity and temperature on diffusion characteristics, resulting in insufficient early warning and accurate location capabilities for partial discharge defects. Summary of the Invention

[0005] To address this, the present invention provides a method, system, and device for calculating the diffusion characteristics of characteristic gases under partial discharge defects, in order to overcome the problems of existing technologies that rely on static concentration threshold judgments, cannot accurately describe the dynamic diffusion process of gases after fault gas generation, and lack quantitative analysis of the influence of key factors such as oil flow velocity and temperature on diffusion characteristics, resulting in insufficient early warning and accurate location capabilities for partial discharge defects.

[0006] To achieve the above objectives, the present invention provides a method for calculating the diffusion characteristics of characteristic gases under partial discharge defects, comprising:

[0007] Step S1: Build a platform for verifying the diffusion of dissolved gases in transformer oil. Use the platform to simulate gas generation during electrical faults in transformer oil, observe the gas kinetics process, collect oil samples, and record experimental parameters and measured data. The experimental parameters include transformer oil density, dynamic viscosity, and gas pressure. The measured data include bubble behavior data and concentration data. The platform includes a sealed oil tank, sampling valve, gas injection valve, pressure monitoring device, and gas collection device.

[0008] Step S2: Based on the experimental parameters recorded in step S1, establish a bubble calculation model considering sudden faults. Use an improved VOF model to track the free surface of the gas-liquid two phases. Introduce volume fraction parameters to characterize the percentage of each phase. Simulate the movement and deformation behavior of bubbles in transformer oil and output the initial dissolved gas concentration field, oil flow field state and flow field velocity distribution data after bubble dissolution.

[0009] Step S3: Based on the initial dissolved gas concentration field, oil flow field state, and velocity distribution data output in Step S2, construct a mathematical model of the diffusion and equilibrium process of dissolved gas in the oil. Describe the unsteady diffusion process based on Fick's second law, and correct the diffusion coefficient according to the Arrhenius relation. Calculate the time required for dissolved gas to diffuse to equilibrium at different flow rates. Compare the calculated diffusion characteristic data with the measured data recorded by the platform in Step S1 to calculate the deviation. If the deviation exceeds a preset threshold, reverse the parameters of the bubble calculation model in Step S2.

[0010] Further, in step S1, the construction of the dissolved gas diffusion verification platform in transformer oil includes:

[0011] The verification platform includes an oil tank made of sealed plexiglass. The top of the oil tank is equipped with a one-way pressure relief valve, a pressure gauge, a gas collection bag, and a three-way valve. Several three-way valves are installed on the oil tank wall for taking oil samples. The bottom of the oil tank is equipped with an oil drain valve and a three-way valve for injecting oil and gas.

[0012] Furthermore, in step S1, the construction of the dissolved gas diffusion verification platform in transformer oil also includes:

[0013] Oil samples were taken using a glass syringe, and degassing was performed using either the dissolution equilibrium method or the vacuum method.

[0014] Further, in step S2, establishing the bubble calculation model considering sudden failures includes:

[0015] Based on the principles of fluid mechanics, the Reynolds coefficient is used to determine the fluid motion state.

[0016] When the Reynolds coefficient is less than the preset Reynolds coefficient, the fluid motion state is determined to be laminar flow;

[0017] The Reynolds coefficient is calculated based on transformer oil density, flow velocity, characteristic length, and dynamic viscosity.

[0018] Furthermore, in step S2, establishing the bubble calculation model that considers sudden failures also includes:

[0019] The VOF model is used to represent the percentage of each phase within the computational cell using volume fraction values.

[0020] When the volume fraction of a certain phase is zero, it means that the calculation unit does not contain this phase.

[0021] When the volume fraction of a phase is between zero and one, it indicates that there is an interface between this phase and other phases in the calculation unit.

[0022] When the volume fraction of a certain phase is one, it means that the calculation unit is completely filled with this phase.

[0023] Furthermore, in step S2, establishing the bubble calculation model that considers sudden failures also includes:

[0024] The dissolution time of bubbles in oil is calculated based on the initial radius of the bubbles, the diffusion coefficient of the gas in the oil, and the solubility, where the solubility is calculated based on the ASTM D2779 standard method.

[0025] Further, in step S2, the calculation of the initial dissolved gas concentration field includes:

[0026] The amount of gas dissolved per unit volume of oil is calculated based on the liquid's molar mass, temperature, insulating oil density, gas molar volume, and the Bunsen coefficient, wherein the Bunsen coefficient is calculated based on gas pressure, liquid saturated vapor pressure, and the Ostwald constant.

[0027] Further, in step S3, the mathematical model for constructing the diffusion and equilibrium process of dissolved gases in oil includes:

[0028] The diffusion of dissolved gases in oil is described by Fick's second law as an unsteady diffusion process, in which the diffusion flux varies with time and distance.

[0029] Furthermore, in step S3, the mathematical model for constructing the diffusion and equilibrium process of dissolved gases in oil also includes:

[0030] The diffusion coefficient is calculated based on the Arrhenius relation and varies with temperature and solubility. The diffusion coefficient is equal to the reference diffusion coefficient multiplied by an exponential term, wherein the exponential term is calculated based on temperature, reference temperature, saturated solubility, and reference solubility.

[0031] Furthermore, in step S3, the mathematical model for constructing the diffusion and equilibrium process of dissolved gases in oil also includes:

[0032] A two-dimensional geometric model of a scaled-down transformer was constructed, meshed, and the internal flow field was calculated using simulation software.

[0033] Furthermore, in step S3, the mathematical model for constructing the diffusion and equilibrium process of dissolved gases in oil also includes:

[0034] The oil flow rate is controlled within a preset range by adjusting the pressure parameters at the fan boundary, and the time required for dissolved gas to diffuse to equilibrium is calculated.

[0035] A system for calculating the diffusion characteristics of characteristic gases under partial discharge defects includes:

[0036] The verification platform module is used to build a verification platform for the diffusion of dissolved gases in transformer oil, including an oil tank simulation unit, a sampling control unit, and a gas injection control unit.

[0037] The bubble calculation module, which is connected to the verification platform module, is used to establish a bubble calculation model that considers sudden failures, including a fluid state analysis unit, a VOF model calculation unit, and a solubility calculation unit.

[0038] The diffusion analysis module, which is connected to the verification platform module and the bubble calculation module respectively, is used to construct a mathematical model of the diffusion and equilibrium process of dissolved gases in oil, including a diffusion equation solving unit, a diffusion coefficient correction unit, and an equilibrium time calculation unit.

[0039] Furthermore, the verification platform module includes:

[0040] The fuel tank simulation unit is used to build a fuel tank model made of plexiglass seal, including simulations of the top one-way pressure relief valve, pressure gauge, air sump, and three-way valve;

[0041] A sampling control unit, which is connected to the oil tank simulation unit, is used to control several three-way valves installed on the oil tank wall to perform oil sampling operations;

[0042] The air injection control unit, which is connected to the fuel tank simulation unit, is used to perform fuel injection and air injection operations through the three-way valve at the bottom of the fuel tank.

[0043] Furthermore, the bubble calculation module includes:

[0044] The fluid state analysis unit is used to determine the fluid motion state based on the Reynolds coefficient.

[0045] The VOF model calculation unit, which is connected to the fluid state analysis unit, is used to track the free surface of the gas-liquid two-phase system through volume fraction parameters and simulate bubble motion and deformation behavior.

[0046] The solubility calculation unit, which is connected to the VOF model calculation unit, is used to calculate the solubility of gas in oil based on the ASTM D2779 standard method.

[0047] Furthermore, the diffusion analysis module includes:

[0048] A diffusion equation solving unit, used to establish unsteady diffusion equations based on Fick's second law;

[0049] A diffusion coefficient correction unit, which is connected to the diffusion equation solving unit, is used to perform temperature correction on the diffusion coefficient according to the Arrhenius relation;

[0050] The equilibrium time calculation unit, which is connected to the diffusion coefficient correction unit, is used to calculate the time required for dissolved gas to diffuse to equilibrium at different flow rates by adjusting the flow field boundary conditions.

[0051] An apparatus for calculating the diffusion characteristics of characteristic gases under partial discharge defects includes:

[0052] The sealed oil tank is made of plexiglass material, with several sampling valves on the walls, an oil injection valve and an air injection valve at the bottom, and a one-way pressure relief valve, a pressure gauge and an air collection bag at the top;

[0053] A gas input device, which is connected to a gas injection valve, is used to inject gas at a constant speed and in a fixed quantity;

[0054] A vacuum pump, connected to a three-way valve on the top of the oil tank, is used for vacuuming operations.

[0055] Compared with existing technologies, the advantages of this invention are as follows: The oil tank is made of 4cm thick plexiglass, which, while meeting the requirements for container mechanical strength and ensuring experimental safety, enables visualized observation of the entire process of bubble dynamics in the oil. Furthermore, the low adsorption characteristics of plexiglass effectively reduce the loss of gas concentration due to adsorption on the container walls, significantly improving the accuracy of experimental measurements. The integrated one-way pressure relief valve and pressure gauge enable safe monitoring of internal pressure and overpressure protection. The design of the gas collection bag and the conical polished top cover synergistically achieves efficient collection and gathering of free gas. The configuration of multi-port valves allows for flexible operation of various processes such as vacuuming, oil injection, gas injection, and sampling in a closed environment, avoiding contact contamination between the oil and air, and ensuring the safety and convenience of the operation process. The platform features multiple sampling valves on the tank wall with their spacing designed to meet GB7252 standards, supporting compliant and comparable oil sample collection in different spatial locations. By combining sampling with a glass syringe and degassing using the dissolution equilibrium method or vacuum method according to national standards, a standardized and repeatable oil sample collection and pretreatment process has been established, providing reliable samples for subsequent chromatographic analysis and ensuring the accuracy and authority of experimental data from the source. This platform can not only be used to directly observe bubble movement and measure gas concentration, but the experimental data obtained (such as bubble morphology, rising velocity, and spatiotemporal distribution of gas concentration) can also be directly used to verify and calibrate the bubble calculation model and gas diffusion mathematical model established in steps S2 and S3, greatly enhancing the persuasiveness and reliability of the entire calculation method.

[0056] Furthermore, this invention introduces a preset Reynolds coefficient as a criterion for laminar and turbulent flow, enabling the model to intelligently identify fluid states under different flow velocities and viscosities. When the Reynolds coefficient is less than 2000, a laminar flow model is used for efficient calculation; when it is greater than or equal to 2000, a turbulent flow model such as k-ε or k-ω is automatically introduced to accurately capture random fluctuations. This discrimination mechanism ensures that the model can adapt to various flow conditions that may occur inside the transformer, significantly broadening the applicability of the method and ensuring calculation accuracy. An improved VOF model is used, and the distribution and interface of the gas-liquid two phases are accurately characterized by the continuous change of volume fraction, allowing the model to clearly track the entire process of bubbles rising, deforming, oscillating, and even breaking up in oil. This accurate description of the phase interface lays a solid foundation for subsequent analysis of the dissolution rate and initial concentration field distribution of bubbles, overcoming the errors caused by traditional assumptions such as simplifying bubbles as rigid spheres. The model is strictly based on the fluid dynamics control equations and implemented using mature commercial simulation software such as AnsysFluent, ensuring the scientific nature of the calculation process and the repeatability of the results. This transforms complex physical processes into quantifiable and computable numerical models, providing a powerful tool for understanding the initial behavior of fault-generated gas in oil and achieving a key leap from theoretical analysis to engineering simulation. The bubble trajectory, dissolution process, and initial dissolved gas concentration field ultimately formed in the oil obtained by the bubble model simulation are crucial input conditions for diffusion calculations in step S3. At the same time, the flow field environment (whether laminar or turbulent) it calculates directly determines the convective transport intensity of the dissolved gas, greatly improving the realism and reliability of the final gas diffusion characteristic calculation results.

[0057] Furthermore, this invention standardizes and normalizes the calculation process of solubility parameters by applying the ASTM D2779 standard method, ensuring the scientific validity and credibility of the calculation results. This standard method has been widely validated, and its application significantly improves the acceptance and reliability of the entire bubble calculation model in the professional field, providing a solid theoretical and practical basis for the model. The calculation formula systematically considers multiple key physical parameters such as liquid molar mass, insulating oil density, temperature, gas pressure, and liquid saturated vapor pressure. This multi-parameter coupled calculation framework enables the model to accurately quantify the specific impact of temperature changes and pressure fluctuations on gas solubility, thereby accurately reflecting the dynamic changes in the dissolution equilibrium state under different transformer operating conditions, greatly enhancing the applicability and accuracy of the model. By introducing the Ostwald constant, which is closely related to the type of gas, and utilizing mineral oil reference data provided by IEC and IEEE standards, this model can effectively distinguish and calculate the inherent differences in the solubility of different fault characteristic gases such as hydrogen, carbon monoxide, and methane in oil. This makes the model output solubility parameters and dissolution time predictions specific to different gases, and the calculation results are closer to physical reality. Accurate solubility is one of the core variables for calculating the dissolution time of bubbles in oil. The standardized solubility calculation results provided by this method, together with the initial bubble radius and diffusion coefficient, jointly determine the life cycle of the bubble from generation to complete dissolution. This provides accurate spatiotemporal boundary conditions for the initial concentration field of the dissolved gas diffusion simulation in step S3, ensuring the data coherence and calculation accuracy of the entire technical chain from bubble dynamics to global diffusion simulation.

[0058] Furthermore, this invention constructs a mathematical model based on Fick's second law, which can accurately describe the dynamic process of dissolved gas concentration changing with time and space. By introducing the Arrhenius relation, the diffusion coefficient is corrected for temperature and solubility, enabling the model to quantify the significant impact of temperature, a key operating parameter, on the diffusion rate. This more realistically reflects the internal physical processes of the transformer under different operating conditions, significantly improving the model's prediction accuracy and engineering applicability. By obtaining the baseline diffusion coefficient at a specific temperature and extrapolating it using the verified Arrhenius relation, this method successfully extends the data obtained under limited experimental conditions to the ability to calculate the diffusion coefficient at any temperature. This method solves the problem of time-consuming and laborious direct measurement of diffusion coefficients at various temperatures, providing an economical, efficient, and reliable parameter acquisition method, greatly enhancing the practicality and accessibility of the model. By constructing a scaled-down transformer flow field model and adjusting the flow velocity at the Fan boundary, this method, for the first time, clearly reveals the decisive influence of flow velocity on the diffusion path and equilibrium time of dissolved gas in simulation. The model successfully reproduces the phenomenon of high-concentration gas center circulating with oil flow and the peaks and troughs in concentration at monitoring points. This explains the mechanistic reasons for the fluctuations in oil sample detection data in actual transformers, providing a crucial theoretical basis for the correct interpretation of DGA data. Ultimately, this method can calculate the specific time required for dissolved gas to diffuse to equilibrium at different flow velocities. This output result has direct engineering guiding significance, providing key quantitative indicators for judging the time scale of fault development, optimizing the sampling cycle and timing of oil sample detection, and evaluating the effectiveness of fault location technology. This represents a significant step forward in fault gas analysis from condition diagnosis to process prediction, laying the core model foundation for realizing the dynamic simulation function of converter transformer digital twins. Attached Figure Description

[0059] Figure 1 This is a flowchart of the calculation method for characteristic gas diffusion characteristics under partial discharge defects according to the present invention.

[0060] Figure 2 This is a schematic diagram of the structure of the characteristic gas diffusion characteristics calculation system under partial discharge defects of the present invention;

[0061] Figure 3 This is a schematic diagram of the verification platform module in the calculation system for characteristic gas diffusion characteristics under partial discharge defects of the present invention.

[0062] Figure 4 This is a schematic diagram of the bubble calculation module in the partial discharge defect characteristic gas diffusion characteristic calculation system of the present invention;

[0063] Figure 5 This is a schematic diagram of the diffusion analysis module in the calculation system for characteristic gas diffusion characteristics under partial discharge defects of the present invention. Detailed Implementation

[0064] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0065] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0066] Please see Figures 1-5 As shown, Figure 1 This is a flowchart of the calculation method for characteristic gas diffusion characteristics under partial discharge defects according to the present invention. Figure 2 This is a schematic diagram of the structure of the characteristic gas diffusion characteristics calculation system under partial discharge defects of the present invention; Figure 3 This is a schematic diagram of the verification platform module in the calculation system for characteristic gas diffusion characteristics under partial discharge defects of the present invention. Figure 4 This is a schematic diagram of the bubble calculation module in the partial discharge defect characteristic gas diffusion characteristic calculation system of the present invention; Figure 5 This is a schematic diagram of the diffusion analysis module in the calculation system for characteristic gas diffusion characteristics under partial discharge defects of the present invention.

[0067] The present invention provides a method for calculating the characteristic gas diffusion characteristics under partial discharge defects, comprising:

[0068] Step S1: Build a platform for verifying the diffusion of dissolved gases in transformer oil. Use the platform to simulate gas generation during electrical faults in transformer oil, observe the gas kinetics process, collect oil samples, and record experimental parameters and measured data. The experimental parameters include transformer oil density, dynamic viscosity, and gas pressure. The measured data include bubble behavior data and concentration data. The platform includes a sealed oil tank, sampling valve, gas injection valve, pressure monitoring device, and gas collection device.

[0069] Step S2: Based on the experimental parameters recorded in step S1, establish a bubble calculation model considering sudden faults. Use an improved VOF model to track the free surface of the gas-liquid two phases. Introduce volume fraction parameters to characterize the percentage of each phase. Simulate the movement and deformation behavior of bubbles in transformer oil and output the initial dissolved gas concentration field, oil flow field state and flow field velocity distribution data after bubble dissolution.

[0070] Step S3: Based on the initial dissolved gas concentration field, oil flow field state, and velocity distribution data output in Step S2, construct a mathematical model of the diffusion and equilibrium process of dissolved gas in the oil. Describe the unsteady diffusion process based on Fick's second law, and correct the diffusion coefficient according to the Arrhenius relation. Calculate the time required for dissolved gas to diffuse to equilibrium at different flow rates. Compare the calculated diffusion characteristic data with the measured data recorded by the platform in Step S1 to calculate the deviation. If the deviation exceeds a preset threshold, reverse the parameters of the bubble calculation model in Step S2.

[0071] Specifically, in this embodiment, the gas generation of transformer oil during electrical faults is simulated. The gas injected by the gas input device is a mixture of gases with typical characteristics of partial discharge, and its composition and proportion refer to the fault gas generation characteristics specified in GB / T 7252-2001. The gas injection rate (0.1-1 mL / min) is adjusted by the flow controller of the gas input device to simulate the gas generation process of different fault intensities (such as slight partial discharge and severe partial discharge). The correspondence between the gas injection rate and the fault current density is calibrated with reference to the IEC60599 standard.

[0072] Specifically, in this embodiment, the bubble dynamics process includes bubble morphology and rising velocity; the bubble behavior data specifically includes bubble morphology parameters and rising velocity. The bubble morphology parameters can be obtained by capturing the bubble movement process with a high-speed camera and analyzing it with image processing software. The rising velocity can be obtained by extracting the spatial coordinates of the bubble center at different times, calculating the ratio of the displacement difference to the time difference between adjacent times, and obtaining the instantaneous velocity and average rising velocity throughout the entire process. The measurement range is the entire trajectory of the bubble from its generation to its complete dissolution.

[0073] Specifically, in this embodiment, the experimental parameters also include the real-time temperature of the transformer oil in the tank, which is measured by a platinum resistance temperature sensor embedded in the middle of the tank, with a sampling interval of 10 seconds. The tank is wrapped with a constant temperature heating jacket to control the oil temperature at 20-80℃ (simulating the oil temperature range under different loads of the transformer), with a temperature control accuracy of ±0.5℃.

[0074] Specifically, in this embodiment, the improved VOF model is mainly reflected in the introduction of a volume fraction to represent the percentage of the q-th phase, resulting in an improved VOF model that can achieve tracking calculation of the free surface of the gas-liquid two phases.

[0075] Specifically, in this embodiment, the oil flow field state includes the determination result of laminar or turbulent flow.

[0076] Specifically, in this embodiment, the diffusion characteristic data includes: diffusion equilibrium time and concentration distribution curve.

[0077] Specifically, in this embodiment, in step S3, the preset threshold is determined according to the data type: based on the superposition of the measurement error of chromatographic analysis time ±5% and the flow field simulation error ±3%, the deviation threshold of diffusion equilibrium time is determined to be 8%. Referring to the allowable error of gas concentration measurement in oil in GB / T17623-1998, the deviation threshold of the concentration distribution curve is determined to be 10% of the maximum concentration point.

[0078] Specifically, in step S1, the construction of the dissolved gas diffusion verification platform in transformer oil includes:

[0079] The verification platform includes an oil tank made of sealed plexiglass. The top of the oil tank is equipped with a one-way pressure relief valve, a pressure gauge, a gas collection bag, and a three-way valve. Several three-way valves are installed on the oil tank wall for taking oil samples. The bottom of the oil tank is equipped with an oil drain valve and a three-way valve for injecting oil and gas.

[0080] In this embodiment of the invention, the entire oil tank is sealed with 4cm thick plexiglass. This design ensures sufficient mechanical strength while facilitating observation of the movement of air bubbles in the oil during experiments. Furthermore, the use of plexiglass reduces gas adsorption, improving experimental accuracy. The top of the oil tank is equipped with a one-way pressure relief valve, a pressure gauge, a gas collection bag, and a three-way valve. The one-way pressure relief valve prevents damage to the tank due to excessive internal pressure and is equipped with a pressure gauge for real-time monitoring of the tank's internal pressure. The gas collection bag collects free gas and maintains pressure balance within the tank. To facilitate gas collection, the top of the tank is conically polished at the connection point to the gas collection bag, facilitating gas delivery to the bag. A three-way valve and a two-way valve are located at the connection between the gas collection bag and the tank. The two-way valve, located on the gas collection bag, isolates the gas in the bag from the gas in the tank and ensures a tight seal when the bag is removed. The three-way valve connects to a vacuum pump for evacuating the tank. The oil tank wall is equipped with six three-way valves spaced 10cm apart for connecting glass syringes to collect oil samples. The design of the three-way valves complies with the sampling requirements of GB7252. At the bottom of the oil tank, there is an oil drain valve and a three-way valve. The oil drain valve is used to remove waste oil from the tank; the three-way valve is used for injecting new oil. A vacuum pump is connected to the top three-way valve for vacuuming, and the bottom three-way valve is connected to the oil storage tank, using atmospheric pressure to force new oil into the test oil tank. In addition, a gas input device connected to the bottom three-way valve enables constant-rate and quantitative gas injection.

[0081] Specifically, in step S1, the construction of the transformer oil dissolved gas diffusion verification platform further includes:

[0082] Oil samples were taken using a glass syringe, and degassing was performed using either the dissolution equilibrium method or the vacuum method.

[0083] In this embodiment of the invention, oil sampling includes taking 40 ml of oil from an aged or faulty oil sample using a 100 ml glass syringe, sealing it with a rubber stopper, and removing any air bubbles from the sample. Degassing involves two common methods for removing dissolved gases from oil, as described in GB / T17623-1998: the dissolution equilibrium method and the vacuum method. The dissolution equilibrium method involves fixing the oil sample in a degassing chamber and using mechanical vibration to achieve a distribution equilibrium between the gas and liquid phases. The concentrations of various gases in the gas phase are then measured, and the concentration of dissolved gases in the oil can be calculated based on the equilibrium principle. After degassing, the volume of gas removed from the oil sample needs to be recorded. Injection analysis includes inputting the volume of removed gas into the chromatographic analysis software and rapidly injecting 1 ml of the removed gas into the chromatograph using a glass syringe. To ensure measurement accuracy, a glass syringe with good airtightness should be selected.

[0084] This invention utilizes a 4cm thick acrylic glass tank, ensuring both mechanical strength and experimental safety while enabling visualized observation of the entire gas bubble dynamics process within the oil. Furthermore, the low gas adsorption characteristics of acrylic glass effectively reduce gas concentration loss due to adsorption on the tank walls, significantly improving the accuracy of experimental measurements. The integrated one-way pressure relief valve and pressure gauge provide safe monitoring and overpressure protection of the internal pressure. The design of the gas collection bag and the conical polished top cover synergistically achieves efficient collection and gathering of free gas. The multi-port valve configuration allows for flexible operation of various processes such as vacuuming, oil injection, gas injection, and sampling in a closed environment, avoiding contact contamination between the oil and air while ensuring safety and convenience during operation. Multiple valves are installed on the tank walls... The design of the sampling valves and their spacing conforms to the requirements of GB7252 standard, supporting compliant and comparable oil sample collection in different spatial locations. By combining sampling with glass syringes and degassing using the dissolution equilibrium method or vacuum method according to national standards, a standardized and repeatable oil sample collection and pretreatment process has been established, providing reliable samples for subsequent chromatographic analysis and ensuring the accuracy and authority of experimental data from the source. This platform can not only be used to directly observe bubble movement and measure gas concentration, but the experimental data obtained (such as bubble morphology, rising speed, and spatiotemporal distribution of gas concentration) can also be directly used to verify and calibrate the bubble calculation model and gas diffusion mathematical model established in steps S2 and S3, greatly improving the persuasiveness and reliability of the entire calculation method.

[0085] Specifically, in step S2, establishing the bubble calculation model that considers sudden failures includes:

[0086] Based on the principles of fluid mechanics, the Reynolds coefficient is used to determine the fluid motion state.

[0087] When the Reynolds coefficient is less than the preset Reynolds coefficient, the fluid motion state is determined to be laminar flow;

[0088] The Reynolds coefficient is calculated based on transformer oil density, flow velocity, characteristic length, and dynamic viscosity.

[0089] In this embodiment of the invention, the preset Reynolds coefficient is 2000, and the formula for calculating the Reynolds coefficient is as follows:

[0090]

[0091] in, Indicates the characteristic length. Indicates the density of transformer oil. Indicates dynamic viscosity. The velocity represents the characteristic parameter of the fluid in the oil passage. When the Reynolds coefficient is greater than or equal to the preset Reynolds coefficient, the fluid motion state is determined to be turbulent. In this case, a turbulence model must be introduced to simulate the random, high-frequency fluctuations in the flow field. For example, the k-ε (turbulent kinetic energy-dissipation rate) model or the k-ω (turbulent kinetic energy-specific dissipation rate) model might be used. These models calculate the time-averaged flow field more accurately by adding additional equations and variables characterizing turbulence to the governing equations. From the law of viscosity (laminar flow), we can obtain:

[0092]

[0093] Combining these, we can obtain the fluid motion control equations:

[0094]

[0095] In the formula, t represents time; ρ represents the density of transformer oil; µ represents the dynamic viscosity of the fluid; p represents pressure; u represents the velocity vector of the fluid, which has three components in three-dimensional space, representing the velocity of the fluid at various points in space; The time partial derivative of the velocity vector represents the rate of change of velocity with time, i.e., acceleration. This represents the convective acceleration term, indicating the acceleration of the fluid due to changes in its spatial position. This is a nonlinear term that reflects the fluid's inertial effect; F st τ represents surface tension; g represents the gravitational acceleration vector; τ represents the viscous shear stress tensor; I represents the unit tensor.

[0096] Specifically, in step S2, establishing the bubble calculation model that considers sudden failures further includes:

[0097] The VOF model is used to represent the percentage of each phase within the computational cell using volume fraction values.

[0098] When the volume fraction of a certain phase is zero, it means that the calculation unit does not contain this phase.

[0099] When the volume fraction of a phase is between zero and one, it indicates that there is an interface between this phase and other phases in the calculation unit.

[0100] When the volume fraction of a certain phase is one, it means that the calculation unit is completely filled with this phase.

[0101] In this embodiment of the invention, the VOF model can track the free surface of the gas-liquid two phases. A volume fraction aq is introduced to represent the percentage of the q-th phase. aq=0 indicates that the q-th phase is empty within the unit; 0<aq<1 indicates that the q-th phase has an interface with other phases within the unit; and aq=1 indicates that the unit is filled with the q-th phase. The bubble rising process is simulated using Ansys Fluent finite volume commercial simulation software.

[0102]

[0103]

[0104] In the formula, a q The volume fraction of phase q is a dimensionless quantity; q = 1 and 2 represent two different fluid phases; t represents time in seconds; (term) The volume fraction represents the rate of change with time; u and v represent the components of the fluid velocity vector in the x and y directions, respectively, which are the velocity fields solved in the momentum equation. This transport equation shows that the interface is driven by the overall motion of the fluid.

[0105] This invention introduces a preset Reynolds coefficient as a criterion for laminar and turbulent flow, enabling the model to intelligently identify fluid states under different flow velocities and viscosities. When the Reynolds coefficient is less than 2000, a laminar flow model is used for efficient calculation; when it is greater than or equal to 2000, a turbulent flow model such as k-ε or k-ω is automatically introduced to accurately capture random fluctuations. This discrimination mechanism ensures that the model can adapt to various flow conditions that may occur inside the transformer, significantly broadening the applicability of the method and ensuring calculation accuracy. An improved VOF model is used, and the distribution and interface of the gas-liquid two phases are accurately characterized by continuous changes in volume fraction, allowing the model to clearly track the entire process of bubbles rising, deforming, oscillating, and even breaking up in oil. This precise description of the phase interface lays a solid foundation for subsequent analysis of bubble dissolution rates and initial concentration field distribution, overcoming errors caused by traditional assumptions such as simplifying bubbles as rigid spheres. The model is strictly based on fluid dynamics control equations and implemented using mature commercial simulation software such as Ansys Fluent, ensuring the scientific nature of the calculation process and the repeatability of the results. This transforms complex physical processes into quantifiable and computable numerical models, providing a powerful tool for understanding the initial behavior of fault-generated gas in oil and achieving a key leap from theoretical analysis to engineering simulation. The bubble trajectory, dissolution process, and initial dissolved gas concentration field ultimately formed in the oil obtained by the bubble model simulation are crucial input conditions for diffusion calculations in step S3. At the same time, the flow field environment (whether laminar or turbulent) it calculates directly determines the convective transport intensity of the dissolved gas, greatly improving the realism and reliability of the final gas diffusion characteristic calculation results.

[0106] Specifically, in step S2, establishing the bubble calculation model that considers sudden failures further includes:

[0107] The dissolution time of bubbles in oil is calculated based on the initial radius of the bubbles, the diffusion coefficient of the gas in the oil, and the solubility, where the solubility is calculated based on the ASTM D2779 standard method.

[0108] Specifically, in this embodiment, the initial radius of the bubble is half of the instantaneous diameter of the bubble generated by the high-speed camera in step S1, and the average value of the measurements of 30 bubbles is taken as the model input; if simulating a specific fault type, the initial radius can be calibrated according to the typical bubble size of the fault type.

[0109] Specifically, in step S2, the calculation of the initial dissolved gas concentration field includes:

[0110] The amount of gas dissolved per unit volume of oil is calculated based on the liquid's molar mass, temperature, insulating oil density, gas molar volume, and the Bunsen coefficient, wherein the Bunsen coefficient is calculated based on gas pressure, liquid saturated vapor pressure, and the Ostwald constant.

[0111] The ASTM D2779 standard method in this embodiment of the invention is a standard method for estimating the solubility of gases in petroleum liquids. The formula for calculating the solubility is as follows:

[0112]

[0113] Where M is the molar mass of the liquid; ρ is the temperature; ρ is the density of the insulating oil, with a value of [value missing]. The molar volume of the gas at 273 K and 101.3 kPa. 1.21 and ρ1 are empirical constants for correcting ρ1 to a specific t; B is the Bunsen coefficient, representing the volume of dissolved gas per unit volume of oil under standard conditions, as shown in the formula:

[0114]

[0115] Where P(MPa) and P1(MPa) represent gas pressure and liquid saturated vapor pressure, respectively; Ostwald constant under standard conditions is dimensionless and depends on the type of gas. The Ostwald constant for mineral oil is published in IEC and IEEE standards, and the solubility of various gases in oil depends on the Ostwald constant.

[0116] This invention standardizes and normalizes the calculation process of solubility parameters by applying the ASTM D2779 standard method, ensuring the scientific validity and credibility of the calculation results. This standard method has been widely verified, and its application significantly improves the acceptance and reliability of the entire bubble calculation model in the professional field, providing a solid theoretical and practical basis for the model. The calculation formula systematically considers multiple key physical parameters such as liquid molar mass, insulating oil density, temperature, gas pressure, and liquid saturated vapor pressure. This multi-parameter coupled calculation framework enables the model to accurately quantify the specific impact of temperature changes and pressure fluctuations on gas solubility, thereby accurately reflecting the dynamic changes in the dissolution equilibrium state under different transformer operating conditions, greatly enhancing the applicability and accuracy of the model. By introducing the Ostwald constant, which is closely related to the type of gas, and utilizing mineral oil reference data provided by IEC and IEEE standards, this model can effectively distinguish and calculate the inherent differences in the solubility of different fault characteristic gases such as hydrogen, carbon monoxide, and methane in oil. This makes the model output solubility parameters and dissolution time predictions specific to different gases, and the calculation results are closer to physical reality. Accurate solubility is one of the core variables for calculating the dissolution time of bubbles in oil. The standardized solubility calculation results provided by this method, together with the initial bubble radius and diffusion coefficient, jointly determine the life cycle of the bubble from generation to complete dissolution. This provides accurate spatiotemporal boundary conditions for the initial concentration field of the dissolved gas diffusion simulation in step S3, ensuring the data coherence and calculation accuracy of the entire technical chain from bubble dynamics to global diffusion simulation.

[0117] Specifically, in step S3, the mathematical model for constructing the diffusion and equilibrium process of dissolved gases in oil includes:

[0118] The diffusion of dissolved gases in oil is described by Fick's second law as an unsteady diffusion process, in which the diffusion flux varies with time and distance.

[0119] In this embodiment of the invention, regarding the diffusion of dissolved gases in oil, due to the existence of a concentration gradient, gaseous components spontaneously transfer from high-concentration regions to low-concentration regions to reduce the impact of concentration imbalance. This molecular diffusion is theoretically based on Fick's second law, used to describe the unsteady-state diffusion process where the concentration of components at different locations within a diffusion system changes over time, and its diffusion flux changes with time and direction. Extensive research has been conducted on diffusion theory and diffusion characteristics, demonstrating theoretical feasibility. In the unsteady-state diffusion process, at a distance x from the diffusion source, the rate of change of concentration with time is equal to the negative of the rate of change of diffusion flux with distance at that location, described by Fick's second law:

[0120]

[0121] Where W is the concentration of the gas in the medium; D is the diffusion coefficient; t is the diffusion time; and x is the diffusion distance. It can be seen that the diffusion coefficient determines the diffusion rate of dissolved gas. The average diffusion coefficient Doil of gas in oil and Dpaper of gas in insulating paper were obtained by experiment at 23℃ and 70℃. The change of temperature will cause the solubility of various gases in oil and the viscosity of oil to change, and the diffusion coefficient will also change accordingly. The diffusion of gas in solid-liquid media is described by the Arrhenius relation.

[0122] Specifically, in step S3, the mathematical model for constructing the diffusion and equilibrium process of dissolved gases in oil further includes:

[0123] The diffusion coefficient is calculated based on the Arrhenius relation and varies with temperature and solubility. The diffusion coefficient is equal to the reference diffusion coefficient multiplied by an exponential term, wherein the exponential term is calculated based on temperature, reference temperature, saturated solubility, and reference solubility.

[0124] Based on diffusion coefficient and temperature and The average value of each ( ) is used as the reference diffusion coefficient. and reference temperature Extrapolating this data to other temperatures and solubilities using the Arrhenius relation, the overall equation for the diffusion coefficient D(T) as a function of temperature and solubility is shown below:

[0125]

[0126] in, For temperature, At temperature The reference diffusion coefficient, C(T), and the measured values ​​are as follows: The saturated solubility at temperature T and temperature T are respectively. The baseline solubility at that time can be obtained using the ASTM D2779 calculation method. It is a dimensionless constant, equal to β is a constant of 3464K.

[0127] Specifically, in step S3, the mathematical model for constructing the diffusion and equilibrium process of dissolved gases in oil further includes:

[0128] A two-dimensional geometric model of a scaled-down transformer was constructed, meshed, and the internal flow field was calculated using simulation software.

[0129] Specifically, in step S3, the mathematical model for constructing the diffusion and equilibrium process of dissolved gases in oil further includes:

[0130] The oil flow rate is controlled within a preset range by adjusting the pressure parameters at the fan boundary, and the time required for dissolved gas to diffuse to equilibrium is calculated.

[0131] Specifically, in this embodiment, the oil flow velocity range is extended to 0.05-0.3 m / s, referring to DL / T1573-2016, covering the typical oil flow velocities of 100-500MVA transformers; where 0.05-0.1 m / s corresponds to light load conditions, 0.1-0.2 m / s corresponds to rated load, and 0.2-0.3 m / s corresponds to overload conditions. The flow velocity is adjusted by the pressure gradient (0-50 Pa / m) at the fan boundary, and the correspondence between pressure and flow velocity is calibrated through previous no-field experiments.

[0132] In this embodiment of the invention, a two-dimensional geometric model is constructed for the scaled-down transformer model. A circulating oil channel is constructed to allow the oil flow in the tank to circulate. By defining the fan boundary, the flow velocity at the outlet of the pressure regulating oil channel is set between 0.1 m / s and 0.2 m / s. Based on this, the geometric structure is meshed. By adjusting the pressure parameters of the fan boundary, the oil flow velocity is controlled between 0.1 m / s and 0.2 m / s. The dissolved gas diffusion process is calculated, and the time required for the dissolved gas to diffuse to equilibrium under different flow velocities can be obtained.

[0133] In this embodiment of the invention, in the main oil channel of the transformer, because the diffusion rate of dissolved gas is much lower than the oil flow rate, the dissolved gas will be dispersed into several relatively high-concentration gas centers under the influence of the transformer oil flow field. These high-concentration dissolved gas centers will circulate several times with the oil flow until complete diffusion. During this circulation, they will sequentially pass through various gas concentration detection points in the main oil channel. Therefore, the concentration at the monitoring points will exhibit several peaks and troughs in the early stage of gas diffusion. After complete diffusion, the dissolved gas concentration values ​​at each monitoring point will be basically consistent, reaching a state of equilibrium in gas diffusion. For the fluid region near the upper and lower boundaries of the core, the flow velocity is one order of magnitude lower than that of the main oil channel. The gas will require several circulations to diffuse to this area, and the fluctuation of gas concentration in this region is not significant.

[0134] This invention constructs a mathematical model based on Fick's second law, which can accurately describe the dynamic process of dissolved gas concentration changing with time and space. By introducing the Arrhenius relation, the diffusion coefficient is corrected for temperature and solubility, enabling the model to quantify the significant impact of temperature, a key operating parameter, on the diffusion rate. This more realistically reflects the internal physical processes of transformers under different operating conditions, significantly improving the model's prediction accuracy and engineering applicability. By obtaining the baseline diffusion coefficient at a specific temperature and extrapolating it using the verified Arrhenius relation, this method successfully extends data obtained under limited experimental conditions to the ability to calculate the diffusion coefficient at arbitrary temperatures. This method solves the problem of time-consuming and laborious direct measurement of diffusion coefficients at various temperatures, providing an economical, efficient, and reliable parameter acquisition method, greatly enhancing the practicality and accessibility of the model. By constructing a scaled-down transformer flow field model and adjusting the flow velocity at the Fan boundary, this method, for the first time, clearly reveals the decisive influence of flow velocity on the diffusion path and equilibrium time of dissolved gas in simulation. The model successfully reproduces the phenomenon of high-concentration gas center circulating with oil flow and the peaks and troughs in concentration at monitoring points. This explains the mechanistic reasons for the fluctuations in oil sample detection data in actual transformers, providing a crucial theoretical basis for the correct interpretation of DGA data. Ultimately, this method can calculate the specific time required for dissolved gas to diffuse to equilibrium at different flow velocities. This output result has direct engineering guiding significance, providing key quantitative indicators for judging the time scale of fault development, optimizing the sampling cycle and timing of oil sample detection, and evaluating the effectiveness of fault location technology. This represents a significant step forward in fault gas analysis from condition diagnosis to process prediction, laying the core model foundation for realizing the dynamic simulation function of converter transformer digital twins.

[0135] A system for calculating the diffusion characteristics of characteristic gases under partial discharge defects includes:

[0136] The verification platform module is used to build a verification platform for the diffusion of dissolved gases in transformer oil, including an oil tank simulation unit, a sampling control unit, and a gas injection control unit.

[0137] The bubble calculation module, which is connected to the verification platform module, is used to establish a bubble calculation model that considers sudden failures, including a fluid state analysis unit, a VOF model calculation unit, and a solubility calculation unit.

[0138] The diffusion analysis module, which is connected to the verification platform module and the bubble calculation module respectively, is used to construct a mathematical model of the diffusion and equilibrium process of dissolved gases in oil, including a diffusion equation solving unit, a diffusion coefficient correction unit, and an equilibrium time calculation unit.

[0139] Specifically, the verification platform module includes:

[0140] The fuel tank simulation unit is used to build a fuel tank model made of plexiglass seal, including simulations of the top one-way pressure relief valve, pressure gauge, air sump, and three-way valve;

[0141] Specifically, in this embodiment, the oil tank simulation unit is a physical simulation device with the same equipment structure as the sealed oil tank. It is used to reproduce the sealing environment, oil flow field and fault gas generation scenario of the transformer oil tank in the experiment. Its size, valve configuration and material parameters are completely matched with the verification platform in step S1.

[0142] A sampling control unit, which is connected to the oil tank simulation unit, is used to control several three-way valves installed on the oil tank wall to perform oil sampling operations;

[0143] The air injection control unit, which is connected to the fuel tank simulation unit, is used to perform fuel injection and air injection operations through the three-way valve at the bottom of the fuel tank.

[0144] Specifically, the bubble calculation module includes:

[0145] The fluid state analysis unit is used to determine the fluid motion state based on the Reynolds coefficient.

[0146] The VOF model calculation unit, which is connected to the fluid state analysis unit, is used to track the free surface of the gas-liquid two-phase system through volume fraction parameters and simulate bubble motion and deformation behavior.

[0147] The solubility calculation unit, which is connected to the VOF model calculation unit, is used to calculate the solubility of gas in oil based on the ASTM D2779 standard method.

[0148] Specifically, the diffusion analysis module includes:

[0149] A diffusion equation solving unit, used to establish unsteady diffusion equations based on Fick's second law;

[0150] A diffusion coefficient correction unit, which is connected to the diffusion equation solving unit, is used to perform temperature correction on the diffusion coefficient according to the Arrhenius relation;

[0151] The equilibrium time calculation unit, which is connected to the diffusion coefficient correction unit, is used to calculate the time required for dissolved gas to diffuse to equilibrium at different flow rates by adjusting the flow field boundary conditions.

[0152] An apparatus for calculating the diffusion characteristics of characteristic gases under partial discharge defects includes:

[0153] The sealed oil tank is made of plexiglass material, with several sampling valves on the walls, an oil injection valve and an air injection valve at the bottom, and a one-way pressure relief valve, a pressure gauge and an air collection bag at the top;

[0154] A gas input device, which is connected to a gas injection valve, is used to inject gas at a constant speed and in a fixed quantity;

[0155] A vacuum pump, connected to a three-way valve on the top of the oil tank, is used for vacuuming operations.

[0156] The above embodiments are merely illustrative examples and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this application.

Claims

1. A method for calculating the diffusion characteristics of characteristic gases under partial discharge defects, characterized in that, include: Step S1: Build a platform for verifying the diffusion of dissolved gases in transformer oil. Use the platform to simulate gas generation during electrical faults in transformer oil, observe the gas kinetics process, collect oil samples, and record experimental parameters and measured data. The experimental parameters include transformer oil density, dynamic viscosity, and gas pressure. The measured data include bubble behavior data and concentration data. The platform includes a sealed oil tank, sampling valve, gas injection valve, pressure monitoring device, and gas collection device. Step S2: Based on the experimental parameters recorded in step S1, establish a bubble calculation model considering sudden faults. Use an improved VOF model to track the free surface of the gas-liquid two phases. Introduce volume fraction parameters to characterize the percentage of each phase. Simulate the movement and deformation behavior of bubbles in transformer oil and output the initial dissolved gas concentration field, oil flow field state and flow field velocity distribution data after bubble dissolution. Step S3: Based on the initial dissolved gas concentration field, oil flow field state, and velocity distribution data output in Step S2, construct a mathematical model of the diffusion and equilibrium process of dissolved gas in the oil. Describe the unsteady diffusion process based on Fick's second law, and correct the diffusion coefficient according to the Arrhenius relation. Calculate the time required for dissolved gas to diffuse to equilibrium at different flow rates. Compare the calculated diffusion characteristic data with the measured data recorded by the platform in Step S1 to calculate the deviation. If the deviation exceeds a preset threshold, reverse the parameters of the bubble calculation model in Step S2.

2. The method for calculating the characteristic gas diffusion characteristics under partial discharge defects according to claim 1, characterized in that, In step S1, the construction of the dissolved gas diffusion verification platform in transformer oil includes: The verification platform includes an oil tank made of sealed plexiglass. The top of the oil tank is equipped with a one-way pressure relief valve, a pressure gauge, a gas collection bag, and a three-way valve. Several three-way valves are installed on the oil tank wall for taking oil samples. The bottom of the oil tank is equipped with an oil drain valve and a three-way valve for injecting oil and gas.

3. The method for calculating the characteristic gas diffusion characteristics under partial discharge defects according to claim 2, characterized in that, In step S1, the construction of the dissolved gas diffusion verification platform in transformer oil further includes: Oil samples were taken using a glass syringe, and degassing was performed using either the dissolution equilibrium method or the vacuum method.

4. The method for calculating the characteristic gas diffusion characteristics under partial discharge defects according to claim 1, characterized in that, In step S2, establishing the bubble calculation model that considers sudden failures includes: Based on the principles of fluid mechanics, the Reynolds coefficient is used to determine the fluid motion state. When the Reynolds coefficient is less than the preset Reynolds coefficient, the fluid motion state is determined to be laminar flow; The Reynolds coefficient is calculated based on transformer oil density, flow velocity, characteristic length, and dynamic viscosity.

5. The method for calculating the characteristic gas diffusion characteristics under partial discharge defects according to claim 4, characterized in that, In step S2, establishing the bubble calculation model that considers sudden failures further includes: The VOF model is used to represent the percentage of each phase within the computational cell using volume fraction values. When the volume fraction of a certain phase is zero, it means that the calculation unit does not contain this phase. When the volume fraction of a phase is between zero and one, it indicates that there is an interface between this phase and other phases in the calculation unit. When the volume fraction of a certain phase is one, it means that the calculation unit is completely filled with this phase.

6. The method for calculating the characteristic gas diffusion characteristics under partial discharge defects according to claim 1, characterized in that, In step S2, establishing the bubble calculation model that considers sudden failures further includes: The dissolution time of bubbles in oil is calculated based on the initial radius of the bubbles, the diffusion coefficient of the gas in the oil, and the solubility, where the solubility is calculated based on the ASTM D2779 standard method.

7. The method for calculating the characteristic gas diffusion characteristics under partial discharge defects according to claim 6, characterized in that, In step S2, the calculation of the initial dissolved gas concentration field includes: The amount of gas dissolved per unit volume of oil is calculated based on the liquid's molar mass, temperature, insulating oil density, gas molar volume, and the Bunsen coefficient, wherein the Bunsen coefficient is calculated based on gas pressure, liquid saturated vapor pressure, and the Ostwald constant.

8. The method according to claim 1, characterized in that, In step S3, the mathematical model for constructing the diffusion and equilibrium process of dissolved gases in oil includes: The diffusion of dissolved gases in oil is described by Fick's second law as an unsteady diffusion process, in which the diffusion flux varies with time and distance.

9. The method for calculating the characteristic gas diffusion characteristics under partial discharge defects according to claim 8, characterized in that, In step S3, the mathematical model for constructing the diffusion and equilibrium process of dissolved gases in oil further includes: The diffusion coefficient is calculated based on the Arrhenius relation and varies with temperature and solubility. The diffusion coefficient is equal to the reference diffusion coefficient multiplied by an exponential term, wherein the exponential term is calculated based on temperature, reference temperature, saturated solubility, and reference solubility.

10. The method for calculating characteristic gas diffusion characteristics under partial discharge defects according to claim 1, characterized in that, In step S3, the mathematical model for constructing the diffusion and equilibrium process of dissolved gases in oil further includes: A two-dimensional geometric model of a scaled-down transformer was constructed, meshed, and the internal flow field was calculated using simulation software.

11. The method for calculating characteristic gas diffusion characteristics under partial discharge defects according to claim 10, characterized in that, In step S3, the mathematical model for constructing the diffusion and equilibrium process of dissolved gases in oil further includes: The oil flow rate is controlled within a preset range by adjusting the pressure parameters at the fan boundary, and the time required for dissolved gas to diffuse to equilibrium is calculated.

12. A system for calculating the diffusion characteristics of characteristic gases under partial discharge defects, characterized in that, include: The verification platform module is used to build a verification platform for the diffusion of dissolved gases in transformer oil, including an oil tank simulation unit, a sampling control unit, and a gas injection control unit. The bubble calculation module, which is connected to the verification platform module, is used to establish a bubble calculation model that considers sudden failures, including a fluid state analysis unit, a VOF model calculation unit, and a solubility calculation unit. The diffusion analysis module, which is connected to the verification platform module and the bubble calculation module respectively, is used to construct a mathematical model of the diffusion and equilibrium process of dissolved gases in oil, including a diffusion equation solving unit, a diffusion coefficient correction unit, and an equilibrium time calculation unit.

13. The calculation system for characteristic gas diffusion characteristics under partial discharge defects according to claim 12, characterized in that, The verification platform module includes: The fuel tank simulation unit is used to build a fuel tank model made of plexiglass seal, including simulations of the top one-way pressure relief valve, pressure gauge, air sump, and three-way valve; A sampling control unit, which is connected to the oil tank simulation unit, is used to control several three-way valves installed on the oil tank wall to perform oil sampling operations; The air injection control unit, which is connected to the fuel tank simulation unit, is used to perform fuel injection and air injection operations through the three-way valve at the bottom of the fuel tank.

14. The calculation system for characteristic gas diffusion characteristics under partial discharge defects according to claim 12, characterized in that, The bubble calculation module includes: The fluid state analysis unit is used to determine the fluid motion state based on the Reynolds coefficient. The VOF model calculation unit, which is connected to the fluid state analysis unit, is used to track the free surface of the gas-liquid two-phase system through volume fraction parameters and simulate bubble motion and deformation behavior. The solubility calculation unit, which is connected to the VOF model calculation unit, is used to calculate the solubility of gas in oil based on the ASTM D2779 standard method.

15. The calculation system for characteristic gas diffusion characteristics under partial discharge defects according to claim 12, characterized in that, The diffusion analysis module includes: A diffusion equation solving unit, used to establish unsteady diffusion equations based on Fick's second law; A diffusion coefficient correction unit, which is connected to the diffusion equation solving unit, is used to perform temperature correction on the diffusion coefficient according to the Arrhenius relation; The equilibrium time calculation unit, which is connected to the diffusion coefficient correction unit, is used to calculate the time required for dissolved gas to diffuse to equilibrium at different flow rates by adjusting the flow field boundary conditions.

16. A device for calculating the diffusion characteristics of characteristic gases under partial discharge defects, characterized in that, include: The sealed oil tank is made of plexiglass material, with several sampling valves on the walls, an oil injection valve and an air injection valve at the bottom, and a one-way pressure relief valve, a pressure gauge and an air collection bag at the top. A gas input device, which is connected to a gas injection valve, is used to inject gas at a constant speed and in a fixed quantity; A vacuum pump, connected to a three-way valve on the top of the oil tank, is used for vacuuming operations.

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