Temperature prediction method, device and equipment for thermal risk of transformer and storage medium

Abstract

The invention relates to the technical field of power systems, and provides a transformer thermal risk temperature prediction method, device and equipment and a storage medium, and the method comprises the steps: obtaining an oil paper insulation system model of a transformer, and extracting the operation state parameters of the oil paper insulation system model; the microscopic conditions of bubble escape under the water content of the insulation paper are obtained through bubble microscopic stress analysis; the microcosmic condition of bubble escape is used for indicating the molecular density of critical gas of bubble escape; molecular simulation calculation is carried out, and when the number of simulated pyrolysis gas molecules reaches the molecular density of critical gas for bubble escape, the temperature at the corresponding moment is determined as the macroscopic bubble starting temperature under the water content of the insulation paper. According to the method, the effect of predicting the bubble generation critical temperature under any moisture based on digital simulation of a micromechanism can be achieved, the overheating risk of the transformer is accurately predicted, and technical support is provided for promoting intelligent evaluation and intelligent detection of the operation state of power equipment and digital transformation of risk early warning.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method for predicting the temperature risk of transformer thermal risks, a device for predicting the temperature risk of transformer thermal risks, a corresponding electronic device, and a corresponding computer-readable storage medium. Background Technology

[0002] As a core component of the power transmission network, the operational reliability of power transformers directly impacts the safety and stability of the power grid. Specifically, transformer failure is the primary cause of power grid accidents. Although the widely used oil-paper insulation system possesses excellent insulation and economic efficiency, it will age under long-term electrical stress. Especially when local overheating occurs, bubbles will be generated inside due to moisture vaporization and cellulose pyrolysis. These bubbles significantly degrade insulation performance, induce partial discharge and electric field distortion, and are key precursors to thermal failures and even insulation breakdown.

[0003] While current standards use 140°C as the upper limit of hot spot temperature to suppress bubble formation, the actual operating conditions of transformers are complex and variable. The critical temperature for bubble formation is dynamically affected by multiple factors, including the moisture content of the insulation paper, its aging degree, the gas composition and pressure in the oil. Traditional static temperature limits are difficult to fully adapt to all risk scenarios and may lead to protection lag or excessive conservatism. Summary of the Invention

[0004] This application provides a method, device, equipment, and storage medium for predicting the thermal risk of transformers. It can achieve the effect of predicting the critical temperature for bubble formation under any moisture content through digital simulation based on microscopic mechanisms, accurately predicting the overheating risk of transformers, and providing technical support for promoting the intelligent assessment and intelligent detection of the operating status of power equipment and the digital transformation of risk warning.

[0005] In one aspect, this application provides a temperature prediction method for transformer thermal risk, the method comprising:

[0006] Obtain a model of the oil-paper insulation system of the transformer, and extract the operating state parameters of the model; the operating state parameters include the moisture content of the insulation paper.

[0007] By analyzing the microscopic forces acting on the bubbles, the microscopic conditions for bubble escape at the moisture content of the insulating paper are obtained; the microscopic conditions for bubble escape are used to indicate the critical gas molecule density for bubble escape.

[0008] Molecular simulation calculations are performed, and when the number of simulated pyrolysis gas molecules reaches the critical gas molecule density for the escape of the bubbles, the temperature at the corresponding moment is determined as the macroscopic bubble initiation temperature under the moisture content of the insulating paper.

[0009] On the other hand, this application provides a temperature prediction device for transformer thermal risk, the device comprising:

[0010] The operating status parameter extraction module is used to obtain the oil-paper insulation system model of the transformer and extract the operating status parameters of the oil-paper insulation system model; the operating status parameters include the moisture content of the insulation paper;

[0011] By analyzing the microscopic forces acting on the bubbles, the microscopic conditions for bubble escape at the moisture content of the insulating paper are obtained; the microscopic conditions for bubble escape are used to indicate the critical gas molecule density for bubble escape.

[0012] Molecular simulation calculations are performed, and when the number of simulated pyrolysis gas molecules reaches the critical gas molecule density for the escape of the bubbles, the temperature at the corresponding moment is determined as the macroscopic bubble initiation temperature under the moisture content of the insulating paper.

[0013] In another aspect, this application also provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the temperature prediction method for transformer thermal risk as described in any one of the claims.

[0014] In another aspect, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the temperature prediction method for transformer thermal risk as described in any one of the claims.

[0015] In another aspect, this application also provides a computer program product containing instructions that, when run on a computer, cause the computer to execute the temperature prediction method for transformer thermal risk described in the above aspects.

[0016] The temperature prediction method, apparatus, equipment, and storage medium for transformer thermal risk provided in this application obtain a model of the transformer's oil-paper insulation system and extract the operating state parameters of the oil-paper insulation system model. The extracted operating state parameters may include the moisture content of the insulation paper. At this time, the microscopic conditions for bubble escape under the moisture content of the insulation paper can be obtained first through microscopic force analysis of bubbles. The microscopic conditions for bubble escape are mainly used to indicate the critical gas molecule density for bubble escape. Then, molecular simulation calculations are performed. When the number of simulated pyrolysis gas molecules reaches the critical gas molecule density for bubble escape, the temperature at the corresponding moment is determined as the macroscopic bubble initiation temperature under the moisture content of the insulation paper. By replacing traditional high-voltage testing with molecular simulation, the generation of pyrolysis gas molecules in the oil-paper insulation system over time is calculated based on molecular simulation. Specifically, molecular simulation is used to obtain the amount of simulated gas molecules generated in the oil-paper insulation system under relevant moisture content. This is then compared with the microscopic conditions for bubble escape to calculate the gas initiation temperature under different moisture contents. This enables temperature prediction of the oil-paper insulation system under any moisture content, achieving the effect of predicting the critical temperature for bubble generation under any moisture content through digital simulation based on microscopic mechanisms. This can accurately predict transformer overheating risks and promote the digital transformation of intelligent assessment and intelligent detection of power equipment operating status, as well as risk warning. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the steps of a temperature prediction method for transformer thermal risk provided in an embodiment of this application.

[0018] Figure 2 This is a flowchart illustrating the implementation of transformer thermal risk prediction based on insulating oil paper pyrolysis, as provided in the embodiments of this application.

[0019] Figure 3 This is a schematic diagram of the equivalent model of the insulating paper provided in the embodiments of this application;

[0020] Figure 4 This is a schematic diagram of cellulose provided in an embodiment of this application;

[0021] Figure 5 This is a schematic diagram of the oil paper pyrolysis model provided in the embodiments of this application;

[0022] Figure 6 This is a schematic diagram showing the changes in the content of reactants and products over time at different times, based on the content of each gas molecule at different moments, provided in the embodiments of this application.

[0023] Figure 7 This is a schematic diagram showing the bubble escape time under different moisture contents provided in the embodiments of this application;

[0024] Figure 8 This is a structural block diagram of a temperature prediction device for transformer thermal risk provided in an embodiment of this application;

[0025] Figure 9 This is a structural block diagram of an electronic device provided in an embodiment of this application;

[0026] Figure 10 This is a structural block diagram of a computer-readable storage medium provided in an embodiment of this application. Detailed Implementation

[0027] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] Current research on key parameters supporting thermal risk prediction, particularly the determination of the critical temperature for bubble formation, still primarily relies on macroscopic experiments. These methods determine the critical point by observing macroscopic phenomena (such as bubble formation), typically requiring the construction of complex experimental platforms and the preparation of specific winding samples—a time-consuming, labor-intensive, and costly process. More importantly, existing experimental methods are limited by the singularity of sample preparation, making it difficult to systematically cover operating conditions under different combinations of key risk factors such as moisture content and aging levels. This results in the inability to establish a comprehensive and universally applicable model for predicting the critical temperature for bubble formation, severely restricting the accurate assessment of transformer dynamic thermal risks—making it difficult to grasp the bubble formation patterns under the coupled effects of multiple factors, and even more difficult to reliably predict the critical temperature and its potential risk range under specific operating conditions.

[0029] Therefore, accurately predicting the critical temperature for bubble formation has become a core attribute for characterizing and controlling the thermal risks of transformers.

[0030] This application's embodiments enable temperature prediction of oil-paper insulation systems with arbitrary moisture content, saving significant costs and increasing efficiency. Furthermore, it promotes the digital transformation of power equipment operation status assessment and monitoring, replacing traditional testing with digital measurement methods. Specifically, starting from the essential characterization of thermal risk—the critical temperature for bubble formation—it determines the microscopic criteria for stable bubble escape based on the microscopic mechanisms of bubble formation (such as water vaporization and gas evolution) and mechanical equilibrium theory, thus obtaining the critical gas molecule density for bubble escape. Moreover, using molecular dynamics simulation technology, it directly calculates the microscopic temperature corresponding to the number of pyrolysis gas molecules reaching the critical density during the heating process of the oil-paper insulation system with any given moisture content. Additionally, it combines theories such as the Arrhenius equation for macroscopic conversion, effectively transforming the microscopic temperature obtained from molecular simulation to the macroscopic bubble initiation temperature. Finally, it predicts the critical temperature for bubble formation with arbitrary moisture content through digital simulation, providing an efficient and flexible theoretical basis for constructing a refined, multi-factor coupled thermal risk prediction model. In addition, thermal risk prediction models based on the real-time operating status of transformers (such as temperature, load, moisture / gas content in oil, insulation aging index, etc.) can be developed, and corresponding online monitoring and early warning systems can be built to dynamically assess insulation status, identify overheating risks in advance, optimize operating strategies, and prevent catastrophic accidents.

[0031] Reference Figure 1 The diagram illustrates a flowchart of a temperature prediction method for transformer thermal risk provided in an embodiment of this application, which may specifically include the following steps:

[0032] Step S101: Obtain the oil-paper insulation system model of the transformer and extract the operating state parameters of the oil-paper insulation system model.

[0033] The actual operating condition of a transformer will affect the lifespan of its oil-paper insulation system. Because transformers have complex structures and large volumes, and the aging and breakdown characteristics of oil-paper insulation systems are affected by multiple factors such as temperature, electric field, and moisture, this application embodiment can study the insulation performance of a transformer based on a constructed model of its actual oil-paper insulation system.

[0034] In some embodiments of this application, operating status parameters of the oil-paper insulation system model can be extracted to monitor key risk factors of thermal risks in in-service transformers in real time. Optionally, the extracted operating status parameters include the moisture content of the insulation paper in the oil-paper insulation system model. The moisture content of the insulation paper, as an evaluation index of insulation performance, can be used to accurately predict the thermal risks of transformers in real time.

[0035] For example, such as Figure 2As shown, the extraction of operating status parameters can be achieved by an operating status parameter extractor. The operating status parameter extractor may include at least extraction functions such as the moisture content of insulating paper and oil temperature. This application embodiment does not limit this.

[0036] Step S102: Through microscopic force analysis of bubbles, the microscopic conditions for bubble escape under the moisture content of insulating paper are obtained.

[0037] The microscopic conditions for bubble escape can be used to indicate the microscopic critical density, specifically the critical gas molecule density for bubble escape.

[0038] In some embodiments of this application, the critical gas molecule density can be determined by real-time monitoring of the moisture content of the insulating paper of in-service transformers and combining this with microscopic force analysis of bubbles. Specifically, starting from the essential characterization of thermal risk—the critical temperature for bubble formation—and based on the microscopic mechanisms of bubble formation (such as moisture vaporization and gas evolution) and mechanical equilibrium theory, the microscopic criteria for stable bubble escape can be determined, thus obtaining the critical gas molecule density for bubble escape. Optionally, such as... Figure 2 As shown, the critical gas molecule density can be determined by correcting experimental values, and this application does not limit this.

[0039] Step S103: Perform molecular simulation calculations. When the number of simulated pyrolysis gas molecules reaches the critical gas molecule density for bubble escape, the temperature at the corresponding moment is determined as the macroscopic bubble initiation temperature under the moisture content of the insulating paper.

[0040] The bubbles generated in oil-paper insulation systems with different moisture contents are due to the phase transition of gas molecules produced by the pyrolysis of cellulose and water molecules in cellulose.

[0041] In some embodiments of this application, the generation of pyrolysis gas molecules in the oil-paper system over time can be calculated based on molecular simulation, and the initial gas temperature under different water contents can be calculated based on the microscopic conditions of bubble escape.

[0042] Specifically, such as Figure 2 As shown, molecular simulation calculations can be performed via a molecular simulation module. These calculations rely on input parameters of the operating state (such as the moisture content of the insulating paper) and guidance from the microscopic conditions of bubble escape. Specifically, molecular dynamics simulation techniques can be used to directly calculate the microscopic temperature corresponding to the critical density of pyrolysis gas molecules in the oil-paper insulation system during heating, under any given moisture content. Furthermore, by combining theories such as the Arrhenius equation, a macroscopic transformation is performed to effectively convert the microscopic temperature obtained from the molecular simulation to the macroscopic bubble initiation temperature. The macroscopic bubble initiation temperature is the critical thermal risk temperature.

[0043] In some embodiments of this application, thermal risk prediction models based on the real-time operating status of transformers (such as temperature, load, moisture / gas content in oil, insulation aging indicators, etc.) can also be developed, and corresponding online monitoring and early warning systems can be constructed. This is of great significance for dynamically assessing insulation status, identifying overheating risks in advance, optimizing operating strategies, and preventing catastrophic accidents.

[0044] Specifically, it can issue a thermal risk warning and trigger an alarm when the temperature of the sensors inside the transformer exceeds the bubble initiation temperature, thereby achieving timely intervention, fault prevention, and condition assessment. For example, such as... Figure 2 As shown, thermal risk warning can be implemented through the output parameter execution module. The output parameter execution module can include at least long real-time and short-time temperature alarms, but this application embodiment does not limit this.

[0045] In this embodiment of the application, molecular simulation is used to replace traditional high-voltage testing, thereby enabling digital calculation of the bubble initiation temperature (i.e., critical thermal risk temperature) of the transformer oil-paper insulation system throughout its operation. This provides core support for accurately predicting transformer overheating risks and promoting the digital transformation of intelligent condition monitoring and risk early warning.

[0046] In some embodiments of this application, step S102 may specifically include the following sub-steps:

[0047] Sub-step S21: By performing force analysis on the bubble on the horizontal wall, the bubble's detachment radius is obtained; the bubble's detachment radius is used to determine the bubble's volume.

[0048] Sub-step S22: Substitute the volume of the bubble into the ideal gas law to obtain the number of gas molecules inside the bubble;

[0049] Sub-step S23: Obtain the volume of the cellulose portion in the bubble;

[0050] In sub-step S24, the critical gas molecule density is calculated by dividing the number of gas molecules inside the bubble by the volume of the cellulose portion.

[0051] The air bubble in the insulating paper can be considered equivalent to a sphere, and the surrounding liquid is incompressible. During bubble growth, the magnitude of the force acting on it changes. When the net force in the vertical direction of the bubble is greater than zero, the bubble will lose stability and detach from the paperboard surface; the radius of this detachment is called the detachment radius. This application's embodiments, through force analysis of the bubble on a horizontal wall surface, can establish a force balance equation in the vertical direction of the bubble to predict its detachment radius.

[0052] Specifically, on the solid surface of a still liquid, air bubbles are mainly subjected to buoyancy force F. b Unstable growth drag F duyContact pressure F cp and surface tension stress F sy The effect of this. For example, the force balance equation in the vertical direction (i.e., the y-direction) can be:

[0053] (Equation 1)

[0054] in:

[0055] (Equation 2)

[0056] (Equation 3)

[0057] (Equation 4)

[0058] (Equation 5)

[0059] In the formula, Density of insulating oil; Where is the gas density, which is negligible; V is the bubble volume; g is the gravitational acceleration; r is the bubble radius; and t is the bubble growth time. d is the surface tension coefficient; w θ represents the pore diameter of the cellulose microtubules; θ is the wall contact angle. The values ​​of each parameter can be represented as shown in Figure 1:

[0060] Table 1. Bubble Model Parameter Table

[0061]

[0062] To link the macroscopic and microscopic conditions of bubble escape, the number of gas molecules contained in a spherical bubble can be determined.

[0063] Specifically, after obtaining the bubble's escape radius, the bubble's volume can be calculated using this radius. Then, the number of gas molecules inside the bubble can be determined using the ideal gas law PV=nRT; where P is the absolute pressure of the gas, V is the bubble volume, n is the amount of substance of the gas, and R is the gas constant (generally 8.314 J / (m³)). (Note: Values ​​need to be converted for different unit systems), where T is the thermodynamic temperature of the gas. For example, assuming the bubble's escape radius calculated using equations 1 to 5 above is 198 μm, then the bubble volume... If the temperature is 120℃, then the number of gas molecules contained in a spherical bubble with a radius of 198µm is calculated by converting T=120℃ and... Substituting into the above ideal gas equation, we obtain Finally, we use the numerator formula: N = n × N AThe number N of gas molecules inside the bubble is calculated, where N A Here is Avogadro's constant, usually valued at 1. .

[0064] The bubbles generated in oil-paper insulation systems with different moisture contents are due to the phase transition of gas molecules produced by the pyrolysis of cellulose and water molecules in cellulose.

[0065] In some embodiments of this application, the gas in the bubble may originate from the cellulose portion. In this case, the critical density of the bubble can be obtained by dividing the number of molecules inside the bubble by the relevant portion of the cellulose below the bubble. That is, the critical gas molecule density of the bubble.

[0066] Specifically, the cellulose portion is the part of the cylindrical region equivalent to the insulating paper, excluding the pores. The radius of this cylindrical region is the detachment radius of the bubble. The total volume of the cylinder corresponding to the cylindrical region can be calculated using the detachment radius of the bubble and a preset height. However, the cellulose portion contains pores. In this case, the volume of the pores can be calculated. Specifically, the average radius of the pores is obtained, and the volume of a single pore is calculated using the average radius of the pores and a preset height. Then, based on the distribution spacing of the pores and the volume of a single pore, the total volume of the pores is calculated. Finally, the difference between the total volume of the cylinder and the total volume of the pores is taken as the volume of the cellulose portion in the bubble.

[0067] After calculating the volume of the cellulose fraction, the critical gas molecule density can be calculated by dividing the number of gas molecules inside the bubble by the volume of the cellulose fraction.

[0068] For example, suppose the gas molecules contained in the bubble originate entirely from the pyrolysis of cellulose, excluding the pores, within a cylindrical region of equivalent size (radius 198 μm, height 10 μm) in the insulating paper below. That is, the bubble with a radius of 198 μm originates from the corresponding cylinder below with a radius of 198 μm and a height of 10 μm. Figure 3 As shown in (a), cellulose has porous properties. Assuming the average pore radius is 10 μm and the average cellulose width is around 40 μm, cellulose can be equivalent to the following: Figure 3 In (b) shown, there are red dots with a radius of 10 μm, spaced 40 μm apart. The other areas are composed of cellulose and are uniformly distributed. In this case, the total molecular weight N of the gas in the bubble can be divided by (total volume of the cylinder - pore volume) to calculate that the cellulose in this region produces an average of 0.57 gas molecules per cubic nanometer, i.e., the critical gas molecule density is 0.57 molecules / nm³. In the embodiments of this application, it can be understood that the bubble initiation temperature corresponds to the temperature at which the gas molecule density reaches 0.57 molecules / nm³ due to the combined effects of cellulose pyrolysis and water molecule phase change.

[0069] In some embodiments of this application, step S103 may specifically include the following sub-steps:

[0070] Sub-step S31: Using molecular dynamics simulation, calculate the microscopic time required for the number of pyrolysis gas molecules generated by the oil-paper insulation system model to reach the critical gas molecule density under the moisture content of the insulating paper.

[0071] Sub-step S32 converts the microscopic time using the Arrhenius equation to predict the macroscopic bubble initiation temperature under the moisture content of the insulating paper.

[0072] To predict a key characteristic of transformer thermal risk, namely the bubble initiation temperature, embodiments of this application can use molecular simulation to calculate the generation of pyrolysis gas molecules in an oil-paper insulation system over time. For example, the number of pyrolysis gas molecules in oil-paper insulation systems with different moisture contents can be calculated over time at a set high temperature (2400K). When the number of simulated gas molecules reaches the microscopic critical density (determined by micromechanical analysis) required for bubble escape, the temperature at that corresponding moment is determined as the bubble initiation temperature under the current moisture content state. This temperature directly quantifies the critical risk point for thermal failure under a specific insulation state, i.e., the critical thermal risk temperature.

[0073] Molecular dynamics simulations can be implemented based on molecular models. In some embodiments of this application, molecular models with different numbers of water molecules can be established based on different moisture contents of insulating paper. Then, based on molecular models with different numbers of water molecules, the change in the number of pyrolysis gas molecules of oil-paper insulation systems with different moisture contents of insulating paper over time is calculated at a set high temperature, thus obtaining the microscopic time required for the transformer's oil-paper insulation system model to reach the critical gas molecule density.

[0074] After obtaining the microscopic time, the reaction time of the macroscopic experiment can be obtained by solving the Arrhenius equation and the microscopic time.

[0075] Since the timescale of molecular simulation is on the picosecond scale, which is much smaller than that of macroscopic experiments, the pyrolysis process can be accelerated by increasing the temperature (2400K), so that it can reach a degree of pyrolysis comparable to that of macroscopic experiments within the simulation time.

[0076] Theoretically, it can be assumed that the degree of pyrolysis at the microscopic level is the same as that in the macroscopic experiment. The degree of pyrolysis at the microscopic level can be quantified based on the product of the microscopic pyrolysis rate and the microscopic time, while the degree of pyrolysis in the macroscopic experiment can be quantified based on the product of the macroscopic pyrolysis rate and the macroscopic reaction time, thus ensuring consistency between the simulation results and macroscopic phenomena. For example, the specific formula can be as follows:

[0077] (Equation 6)

[0078] In the formula, k 微观 For the microscopic pyrolysis rate, t 微观 For microscopic time, k 宏观 For the macroscopic pyrolysis rate, t 宏观 This refers to the reaction time in a macroscopic experiment.

[0079] To establish the correspondence between microscopic simulated temperatures and macroscopic actual temperatures, the Arrhenius equation can be used for correlation. Specifically, the microscopic and macroscopic pyrolysis rates can be expressed using the Arrhenius equation, yielding an equation representing the degree of pyrolysis, as shown below:

[0080] (Equation 7)

[0081] In the formula, A is the pre-exponential factor; Ea is the activation energy; T 微观 T is the temperature for molecular simulation; T0 is the initial temperature for the macroscopic experiment; t 宏观 R represents the reaction time in the macroscopic experiment; R is the gas constant, with a general value of 8.314 J / ( Numerical values ​​need to be converted accordingly for different unit systems.

[0082] In this embodiment, the known microscopic time can be substituted into the pyrolysis degree equation shown in Equation 7 to obtain the reaction time of the macroscopic experiment. Then, based on the obtained macroscopic reaction time, the heating rate can be determined, and the initial temperature can be obtained. Using the initial temperature and heating rate, the macroscopic bubble initiation temperature at the moisture content of the insulating paper can be calculated. For example, the pyrolysis degree of a reaction at 2400 K for 100 ps in a microscopic molecular simulation can correspond to the pyrolysis degree in a macroscopic experiment where the initial temperature is 20 °C and the heating rate is 4 K / min to 160 °C.

[0083] To help those skilled in the art further understand the temperature prediction process provided in the embodiments of this application, the following explanation is given in conjunction with examples:

[0084] Optionally, in order to establish a realistic insulating oil model for molecular dynamics simulation, a mineral oil model and a cellulose amorphous region model can be used to simulate the thermal decomposition process of transformer oil.

[0085] For example, three-dimensional models of hydrocarbon molecules can be built separately and their structures optimized, with the Compass II force field selected for optimization. Specifically, based on the main component composition ratios of mineral oil shown in Table 2 below, each group of oil molecules contains 1 chain hydrocarbon molecule, 2 monocyclic alkane molecules, 3 dicyclic alkane molecules, 2 tricyclic alkane molecules, and 1 tetracyclic alkane molecule, for a total of four groups; the cellulose model is selected as follows... Figure 4 The DP values ​​shown are grouped into four sets: 4, 6, and 8.

[0086] Table 2. Composition and Proportion of Mineral Oil

[0087]

[0088] Then, according to the moisture content obtained from the operating parameter extractor, a corresponding amount of water molecules can be added to the insulating paper, and a periodic transformer oil paper model can be established with an initial density of 0.8 g / cm3. The Compass II force field is then used to optimize the system structure until convergence. Before performing reaction molecular dynamics simulations, the constructed model can be relaxed and optimized to bring the system to a stable state. Assuming the calculated cutoff radius is set to 12.5 Å, using the Smart algorithm, the system density is approximately 1 g / cm3, and the cell edge length is 30.1 Å. The oil paper pyrolysis model can be... Figure 5 As shown.

[0089] In practical applications, after establishing molecular models containing different numbers of water molecules based on different water contents, reaction molecular dynamics methods can be used to analyze, for example... Figure 5 The oil-paper pyrolysis model shown is used for high-temperature pyrolysis simulation. Optionally, the force field can be ReaxFF5.5, which can be used for bonding and breaking between atoms. During the simulation, bonds between atoms can be automatically formed or broken, directly displaying the chemical reaction. The system is set to an NVT (N is the number of atoms in the simulation system, V is the volume, and T is the temperature) canonical system; the temperature control method is Nose, the step size is 0.25 fs, the simulation time is 100 ps, ​​one frame is output every 1000 steps, the Charge is set to Force field assigned, and the simulation temperature is 2400 K. Since high temperature does not affect the pyrolysis reaction mechanism, this relatively high temperature can be selected to accelerate the pyrolysis reaction. At high temperatures, the bonds between molecules will break and recombine automatically, resulting in the generation of new gas molecules. As time increases, the bonds between molecules continuously break and recombine, thus showing the change in the number of pyrolysis gas molecules over time.

[0090] The molecular simulations in this application were conducted under ultra-high temperature conditions, where both transformer oil and insulating paper may undergo pyrolysis. However, based on the above compositional analysis of thermally induced bubbles in the oil-paper insulation system, the gases within the bubbles mainly originate from the pyrolysis products of cellulose, including water (H2O), carbon dioxide (CO2), and carbon monoxide (CO), while the contribution from the pyrolysis of transformer oil is relatively small. Therefore, when counting the number of gas molecules, calculations and analyses can be performed only on these three key gases (H2O, CO2, and CO) generated by the pyrolysis of cellulose to more accurately characterize the microscopic mechanism of bubble formation.

[0091] For example, based on the pyrolysis reaction trajectory file, the content of each gas molecule at different times is statistically analyzed, and curves showing the changes in the content of reactants and products over time under the different gas molecule contents at different times can be plotted. Figure 6 As shown.

[0092] The pyrolysis of cellulose mainly occurs in the amorphous region because its molecular chains are loosely arranged, its hydrogen bond network is weak, and its thermal stability is low. Glycosidic bonds break and volatilize at relatively low temperatures. In contrast, the crystalline region, with its tightly ordered structure, requires higher temperatures to break its hydrogen bonds and crystal lattice structure. This difference stems from the strength of the intermolecular forces between the two; the higher the crystallinity, the higher the initial pyrolysis temperature.

[0093] The molecular simulation model of the oil-paper insulation in this application only includes the pyrolysis of the amorphous region of cellulose, and does not include the long cellulose in the crystalline region. The microscopic conditions for bubble escape are for the entire cellulose, including the amorphous region and the crystalline region. At this time, the microscopic conditions for the entire cellulose can be transformed into microscopic conditions for the amorphous region by a correction coefficient. For example, assuming that the ratio of the amorphous region to the crystalline region is 0.13:0.87, the correction coefficient can be determined to be 7.73.

[0094] When transforming the microscopic conditions for gas molecules throughout the cellulose to those for the amorphous region only, the aforementioned correction factor (7.73) can be applied. Assuming the molecular simulation model is 3nm*3nm*3nm, multiplying the bubble escape microscopic conditions by 3nm*3nm*3nm yields an equivalent value of 119. Figure 7 The intersection of the black line and the curves showing the change in gas molecules over time at various water contents indicates the moment of bubble escape. The moment of bubble escape refers to the microscopic moment when the number of pyrolysis gas molecules reaches the critical gas molecule density, i.e. Figure 7 The x-axis of 119 is 100 ns; then, 100 can be used as t. 微观 Substituting into Equation 7, with other parameters known, we can then solve for t. 宏观 Then, based on the initial temperature and heating rate, the corresponding macroscopic bubble initiation temperature is obtained.

[0095] Among them, in such Figure 7 The molecular simulation-based prediction results for the critical temperature of thermal risk in oil-paper insulation show that the moisture content of the insulating paper has a significant impact on the critical temperature of thermal risk: when the moisture content is 1.8%, the predicted critical risk temperature is 161℃; when the moisture content increases to 3%, the critical risk temperature decreases to 143℃; and when the moisture content reaches 4%, the critical risk temperature further decreases to 135℃. These results can quantify the key temperature thresholds for triggering overheating faults under different insulation states (moisture content), providing a direct basis for transformer condition assessment and thermal risk early warning.

[0096] It should be noted that the temperature prediction method for transformer thermal risk provided in this application is not only applicable to the prediction of bubble initiation temperature of kraft insulating paper and mineral oil, but can also be used to predict the bubble initiation temperature of different types of insulating paper and insulating oil. The difference lies in the initial oil and paper modeling and data correction. This application does not impose any limitations on this.

[0097] In this embodiment of the application, by obtaining the oil-paper insulation system model of the transformer, the operating state parameters of the oil-paper insulation system model are extracted. The extracted operating state parameters may include the moisture content of the insulation paper. At this time, the microscopic conditions for bubble escape under the moisture content of the insulation paper can be obtained first through microscopic force analysis of bubbles. The microscopic conditions for bubble escape are mainly used to indicate the critical gas molecule density for bubble escape. Then, molecular simulation calculation is performed. When the number of simulated pyrolysis gas molecules reaches the critical gas molecule density for bubble escape, the temperature at the corresponding moment is determined as the macroscopic bubble initiation temperature under the moisture content of the insulation paper. By replacing traditional high-voltage testing with molecular simulation, the generation of pyrolysis gas molecules in the oil-paper insulation system over time is calculated based on molecular simulation. Specifically, molecular simulation is used to obtain the amount of simulated gas molecules generated in the oil-paper insulation system under relevant moisture content. This is then compared with the microscopic conditions for bubble escape to calculate the gas initiation temperature under different moisture contents. This enables temperature prediction of the oil-paper insulation system under any moisture content, achieving the effect of predicting the critical temperature for bubble generation under any moisture content through digital simulation based on microscopic mechanisms. This can accurately predict transformer overheating risks and promote the digital transformation of intelligent assessment and intelligent detection of power equipment operating status, as well as risk warning.

[0098] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of this application.

[0099] Reference Figure 8 The diagram shows a structural block diagram of a temperature prediction device for transformer thermal risk provided in an embodiment of this application, which may specifically include the following modules:

[0100] The operating status parameter extraction module 801 is used to obtain the oil-paper insulation system model of the transformer and extract the operating status parameters of the oil-paper insulation system model; the operating status parameters include the moisture content of the insulation paper;

[0101] The critical density determination module 802 is used to obtain the microscopic conditions for bubble escape under the moisture content of the insulating paper through microscopic force analysis of bubbles; the microscopic conditions for bubble escape are used to indicate the critical gas molecule density for bubble escape.

[0102] The molecular simulation calculation module 803 is used to perform molecular simulation calculations. When the number of simulated pyrolysis gas molecules reaches the critical gas molecule density for bubble escape, the temperature at the corresponding moment is determined as the macroscopic bubble initiation temperature under the moisture content of the insulating paper.

[0103] In some embodiments of this application, the critical density determination module 802 may include the following sub-modules:

[0104] The critical gas molecular density calculation submodule is used to obtain the bubble's escape radius by performing force analysis on the bubble on the horizontal wall; the bubble's escape radius is used to determine the bubble's volume; the bubble's volume is substituted into the ideal gas law to obtain the number of gas molecules inside the bubble; the volume of the cellulose portion in the bubble is obtained; and the critical gas molecular density is calculated by dividing the number of gas molecules inside the bubble by the volume of the cellulose portion.

[0105] In some embodiments of this application, the gas in the bubble originates from the cellulose portion, which is the part of the cylindrical region equivalent to insulating paper, excluding the pores, and the radius of the cylindrical region is the bubble's escape radius; the critical gas molecule density calculation submodule may include the following units:

[0106] The cellulose portion volume calculation unit is used to calculate the total volume of the cylinder corresponding to the cylindrical region using the bubble's detachment radius and preset height; obtain the average radius of the pores, and calculate the volume of a single pore using the average radius of the pores and preset height; obtain the pore distribution spacing, and calculate the total volume of the pores using the pore distribution spacing and the volume of a single pore; and take the difference between the total volume of the cylinder and the total volume of the pores as the volume of the cellulose portion in the bubble.

[0107] In some embodiments of this application, the molecular simulation calculation module 803 may include the following sub-modules:

[0108] The bubble initiation temperature prediction submodule is used to calculate the microscopic time required for the number of pyrolysis gas molecules generated by the oil-paper insulation system model to reach the critical gas molecule density under the moisture content of the insulating paper, using molecular dynamics simulation. The microscopic time is then converted using the Arrhenius equation to predict the macroscopic bubble initiation temperature under the moisture content of the insulating paper.

[0109] In some embodiments of this application, molecular dynamics simulations are implemented based on molecular models; the bubble initiation temperature prediction submodule may include the following units:

[0110] The microscopic time calculation unit is used to establish molecular models with different numbers of water molecules based on different moisture contents of insulating paper. Based on the molecular models with different numbers of water molecules, the unit calculates the change in the number of pyrolysis gas molecules of the oil-paper insulation system with different moisture contents of insulating paper over time at a set high temperature, and obtains the microscopic time required for the transformer's oil-paper insulation system model to reach the critical gas molecule density.

[0111] In some embodiments of this application, the bubble initiation temperature prediction submodule may include the following units:

[0112] The bubble initiation temperature prediction unit is used to obtain the reaction time of the macroscopic experiment by solving the Arrhenius equation and microscopic time; the reaction time of the macroscopic experiment is used to determine the heating rate; the initial temperature is obtained, and the macroscopic bubble initiation temperature under the moisture content of the insulating paper is calculated using the initial temperature and the heating rate.

[0113] In some embodiments of this application, the degree of microscopic pyrolysis is the same as that of macroscopic experiments. The degree of microscopic pyrolysis is quantified based on the product of the microscopic pyrolysis rate and the microscopic time, while the degree of macroscopic pyrolysis is quantified based on the product of the macroscopic pyrolysis rate and the macroscopic reaction time. The bubble initiation temperature prediction unit may include the following sub-units:

[0114] The reaction time solution unit for macroscopic experiments is used to express the microscopic pyrolysis rate and the macroscopic pyrolysis rate using the Arrhenius equation to obtain the equation representing the degree of pyrolysis; by substituting the microscopic time into the equation representing the degree of pyrolysis, the reaction time of the macroscopic experiment is obtained.

[0115] In this embodiment of the application, by obtaining the oil-paper insulation system model of the transformer, the operating state parameters of the oil-paper insulation system model are extracted. The extracted operating state parameters may include the moisture content of the insulation paper. At this time, the microscopic conditions for bubble escape under the moisture content of the insulation paper can be obtained first through microscopic force analysis of bubbles. The microscopic conditions for bubble escape are mainly used to indicate the critical gas molecule density for bubble escape. Then, molecular simulation calculation is performed. When the number of simulated pyrolysis gas molecules reaches the critical gas molecule density for bubble escape, the temperature at the corresponding moment is determined as the macroscopic bubble initiation temperature under the moisture content of the insulation paper. By replacing traditional high-voltage testing with molecular simulation, the generation of pyrolysis gas molecules in the oil-paper insulation system over time is calculated based on molecular simulation. Specifically, molecular simulation is used to obtain the amount of simulated gas molecules generated in the oil-paper insulation system under relevant moisture content. This is then compared with the microscopic conditions for bubble escape to calculate the gas initiation temperature under different moisture contents. This enables temperature prediction of the oil-paper insulation system under any moisture content, achieving the effect of predicting the critical temperature for bubble generation under any moisture content through digital simulation based on microscopic mechanisms. This can accurately predict transformer overheating risks and promote the digital transformation of intelligent assessment and intelligent detection of power equipment operating status, as well as risk warning.

[0116] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0117] This application also provides an electronic device, see embodiments thereof. Figure 9 The provided electronic device 900 includes a memory 910, a processor 920, and a computer program 911 stored in the memory 910 and capable of running on the processor 920. When the computer program 911 is executed by the processor, it implements the various processes of the above-described embodiment of the temperature prediction method for transformer thermal risk and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0118] This application also provides a computer-readable storage medium, see embodiments thereof. Figure 10 The computer-readable storage medium 1000 provides a computer program 911 stored on it. When the computer program 911 is executed by the processor, it implements the various processes of the above-described embodiment of the temperature prediction method for transformer thermal risk and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0119] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0120] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of the embodiments of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. The division of modules in the embodiments of this application is merely a logical division; in actual applications, there may be other division methods. For example, multiple modules may be combined into or integrated into another system, or some features may be ignored or not performed. Additionally, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interface, and the indirect coupling or communication connection between modules may be electrical or other similar forms, none of which are limited in the embodiments of this application. Furthermore, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed among multiple circuit modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of this application.

[0121] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0122] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0123] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between apparatuses or modules, and may be electrical, mechanical, or other forms.

[0124] The modules described as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0125] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0126] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0127] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0128] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0129] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes; these computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0130] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0131] Finally, it should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0132] The technical solutions provided in the embodiments of this application have been described in detail above. Specific examples have been used in the embodiments of this application to illustrate the principles and implementation methods of the embodiments of this application. The description of the above embodiments is only for the purpose of helping to understand the methods and core ideas of the embodiments of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the embodiments of this application. Therefore, the content of this specification should not be construed as a limitation on the embodiments of this application.

Claims

1. A method for predicting the thermal risk of a transformer at a specific temperature, characterized in that, The method includes: Obtain a model of the oil-paper insulation system of the transformer, and extract the operating state parameters of the model; the operating state parameters include the moisture content of the insulation paper. By analyzing the microscopic forces acting on the bubbles, the microscopic conditions for bubble escape at the moisture content of the insulating paper are obtained; the microscopic conditions for bubble escape are used to indicate the critical gas molecule density for bubble escape. Molecular simulation calculations are performed, and when the number of simulated pyrolysis gas molecules reaches the critical gas molecule density for the escape of the bubbles, the temperature at the corresponding moment is determined as the macroscopic bubble initiation temperature under the moisture content of the insulating paper.

2. The method according to claim 1, characterized in that, The microscopic conditions for bubble escape at the specified moisture content of the insulating paper, obtained through microscopic force analysis of bubbles, include: By performing force analysis on the bubble on the horizontal wall, the bubble's escape radius is obtained; the bubble's escape radius is used to determine the bubble's volume. Substituting the volume of the bubble into the ideal gas law, we can obtain the number of gas molecules inside the bubble. Obtain the volume of the cellulose portion in the bubble; The critical gas molecule density is calculated by dividing the number of gas molecules inside the bubble by the volume of the cellulose portion.

3. The method according to claim 2, characterized in that, The gas in the bubble originates from the cellulose portion, which is the portion of the cylindrical region equivalent to insulating paper, excluding the pores. The radius of the cylindrical region is the detachment radius of the bubble. Obtaining the volume of the cellulose portion in the bubble includes: The total volume of the cylinder corresponding to the cylindrical region is calculated using the bubble's detachment radius and preset height. Obtain the average radius of the pores, and calculate the volume of a single pore using the average radius of the pores and the preset height; The distribution spacing of the pores is obtained, and the total volume of the pores is calculated using the distribution spacing of the pores and the volume of a single pore; The difference between the total volume of the cylinder and the total volume of the pores is taken as the volume of the cellulose portion in the bubble.

4. The method according to claim 1, characterized in that, The molecular simulation calculation, when the number of simulated pyrolysis gas molecules reaches the critical gas molecule density for bubble escape, determines the temperature at the corresponding moment as the macroscopic bubble initiation temperature under the moisture content of the insulating paper, including: Molecular dynamics simulations were used to calculate the microscopic time required for the number of pyrolysis gas molecules generated by the oil-paper insulation system model to reach the critical gas molecule density at the moisture content of the insulating paper. The microscopic time was converted using the Arrhenius equation to predict the macroscopic bubble initiation temperature at the moisture content of the insulating paper.

5. The method according to claim 4, characterized in that, Molecular dynamics simulation is based on a molecular model; the calculation of the microscopic time required for the number of pyrolysis gas molecules generated by the oil-paper insulation system model to reach the critical gas molecule density at the given moisture content of the insulating paper using molecular dynamics simulation includes: Molecular models containing different numbers of water molecules were established based on different moisture contents of insulating paper. Based on the molecular models with different numbers of water molecules, the change in the number of pyrolysis gas molecules over time in oil-paper insulation systems with different water contents of insulating paper is calculated at a set high temperature, and the microscopic time required for the oil-paper insulation system model of the transformer to reach the critical gas molecule density is obtained.

6. The method according to claim 4, characterized in that, The step of converting the microscopic time using the Arrhenius equation to predict the macroscopic bubble initiation temperature at the moisture content of the insulating paper includes: The reaction time of the macroscopic experiment is obtained by solving the Arrhenius equation and the microscopic time; the reaction time of the macroscopic experiment is used to determine the heating rate. The initial temperature is obtained, and the macroscopic bubble initiation temperature at the moisture content of the insulating paper is calculated using the initial temperature and the heating rate.

7. The method according to claim 6, characterized in that, The degree of pyrolysis at the microscopic level is the same as that at the macroscopic level. The degree of pyrolysis at the microscopic level is quantified based on the product of the microscopic pyrolysis rate and the microscopic time. The degree of pyrolysis at the macroscopic level is quantified based on the product of the macroscopic pyrolysis rate and the reaction time of the macroscopic experiment. The process of obtaining the reaction time of the macroscopic experiment through the Arrhenius equation and the microscopic time calculation includes: The microscopic pyrolysis rate and the macroscopic pyrolysis rate are expressed using the Arrhenius equation to obtain the equation representing the degree of pyrolysis. Substituting the microscopic time into the equation representing the degree of pyrolysis, the reaction time of the macroscopic experiment is obtained.

8. A temperature prediction device for transformer thermal risk, characterized in that, The device includes: The operating status parameter extraction module is used to obtain the oil-paper insulation system model of the transformer and extract the operating status parameters of the oil-paper insulation system model; the operating status parameters include the moisture content of the insulation paper; The critical density determination module is used to obtain the microscopic conditions for bubble escape at the moisture content of the insulating paper by analyzing the microscopic forces acting on the bubbles; the microscopic conditions for bubble escape are used to indicate the critical gas molecule density for bubble escape. The molecular simulation calculation module is used to perform molecular simulation calculations. When the number of simulated pyrolysis gas molecules reaches the critical gas molecule density for the escape of the bubble, the temperature at the corresponding moment is determined as the macroscopic bubble initiation temperature under the moisture content of the insulating paper.

9. An electronic device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the temperature prediction method for transformer thermal risk as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the temperature prediction method for transformer thermal risk as described in any one of claims 1 to 7.

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