Risk assessment method and device for transformer insulation system

Abstract

The invention discloses a risk assessment method and a risk assessment device for a transformer insulation system, which are used for solving the technical problems of insufficient space-time continuity, microscopic evolution and multi-field coupling simulation precision due to the fact that the dynamic evolution process of the transformer insulation material system cannot be well simulated in the prior art. The method comprises the following steps: acquiring material performance parameters of an insulating material in a transformer insulating system; performing multi-physical field coupling simulation according to the material performance parameters to obtain thermal field distribution and electric field distribution of the insulating material; performing thermal aging state distribution prediction based on the thermal field distribution to obtain a polymerization degree index so as to perform thermal aging risk assessment on the insulating material; and performing discharge risk prediction based on the electric field distribution to obtain an insulation margin index so as to perform discharge risk assessment on the insulating material.

Description

Technical Field

[0001] This invention relates to the field of transformer performance analysis technology, and in particular to a risk assessment method for a transformer insulation system, a risk assessment device for a transformer insulation system, an electronic device, and a storage medium. Background Technology

[0002] Currently, power systems are rapidly evolving towards higher reliability and intelligence, placing unprecedented demands on the health management and operational safety of critical power equipment. Against this backdrop, power equipment health status assessment and risk prediction technologies have become one of the core supports for ensuring the safe, stable, and economical operation of the power grid. As a crucial core piece of equipment in the power system, the operating status of transformers directly affects the reliability and lifespan of the entire power grid. Among these, the performance of the transformer insulation system is the cornerstone of safe equipment operation. Its aging, deterioration, or failure is a major cause of transformer failure and even major accidents.

[0003] The condition of the insulation system directly affects the operational safety of equipment and profoundly impacts its service life. Therefore, accurately assessing the health status of transformer insulation systems and predicting potential risks (such as the likelihood, time window, and severity of faults like insulation breakdown, overheating, and discharge) is of great significance and engineering value for enabling condition-based maintenance of transformers, optimizing operation and maintenance strategies, preventing major accidents, extending equipment lifespan, and ultimately ensuring the safe and reliable operation of the smart grid. Transformer risk prediction technology is a key intelligent tool developed in response to this need.

[0004] Transformer performance prediction and condition simulation rely heavily on accurate acquisition of material parameters. Currently, parameter acquisition primarily depends on the traditional method combining online and offline testing. Online testing continuously collects indirect operational data reflecting the overall macroscopic state of the equipment through sensors such as DGA (Dissolved Gas Analysis), partial discharge, and temperature monitoring. Offline testing, on the other hand, requires sampling key insulating materials (oil and paper) during equipment shutdown or dismantling for standardized laboratory testing to obtain a "precise" parameter snapshot at a specific time point.

[0005] The traditional approach combining online and offline testing has the following limitations: First, the destructive and discrete nature (sparse time points) of offline testing completely severs the continuous evolution trajectory of material parameters over time. While online data possesses temporal continuity, it remains only at the macroscopic system level and cannot address microscopic material behavior. Second, the highly idealized laboratory offline testing environment cannot simulate the complex coupling effects of real transformer operation, leading to parameter distortion. Furthermore, macroscopic indicators monitored online (such as characteristic gases) cannot directly and accurately reflect the microscopic transient response and evolution of the material's internal physicochemical parameters. In addition, both methods struggle to effectively correlate and quantify the personalized damage accumulation caused to the material by the unique historical loads, special operating conditions, and environmental spectra of a single device. Therefore, traditional approaches cannot construct an accurate database reflecting the cross-scale continuous spatiotemporal evolution of material parameters under real complex operating conditions, severely hindering the development of high-precision transformer risk prediction models. Summary of the Invention

[0006] This invention provides a risk assessment method for transformer insulation systems, a risk assessment device for transformer insulation systems, an electronic device, and a storage medium, which are used to solve or partially solve the technical problems that current related technologies cannot well simulate the dynamic evolution process of transformer insulation material systems, thus resulting in deficiencies in spatiotemporal continuity, microscopic evolution, and multi-field coupling simulation accuracy.

[0007] This invention provides a risk assessment method for a transformer insulation system, comprising:

[0008] Obtain the material performance parameters of the insulating materials in the transformer insulation system;

[0009] Based on the material performance parameters, multiphysics coupling simulation was performed to obtain the thermal and electric field distributions of the insulating material.

[0010] Based on the thermal field distribution, the thermal aging state distribution is predicted to obtain a polymerization degree index, which is used to assess the thermal aging risk of the insulating material.

[0011] Discharge risk is predicted based on the electric field distribution to obtain an insulation margin index, which is used to assess the discharge risk of the insulating material.

[0012] Optionally, the material performance parameters include space charge density, fluid density, dielectric constant, and dynamic viscosity; the step of performing multiphysics coupling simulation based on the material performance parameters to obtain the thermal and electric field distributions of the insulating material includes:

[0013] Based on the space charge density and the dielectric constant, the electric field control equation is constructed;

[0014] Based on the fluid density and the dynamic viscosity, a magneto-thermal-fluid coupling equation is constructed.

[0015] Based on the electric field control equation and the magneto-thermal-fluid coupling equation, a multiphysics coupling model is constructed.

[0016] By performing multiphysics coupling simulation using the aforementioned multiphysics coupling model, the thermal and electric field distributions of the insulating material can be obtained.

[0017] Optionally, the step of predicting the thermal aging state distribution based on the thermal field distribution to obtain the polymerization degree index includes:

[0018] Based on the thermal field distribution, the actual operating temperature of the insulating material is determined;

[0019] Obtain the reference operating temperature, actual moisture content, and apparent activation energy of the insulating material;

[0020] Simultaneously considering the influence coefficient of water content and the general gas constant, the thermal aging state distribution is predicted and calculated based on the actual operating temperature, the reference operating temperature, the actual water content, and the apparent activation energy to obtain the polymerization degree index of the insulating material.

[0021] Optionally, the simultaneous consideration of the moisture content influence coefficient and the universal gas constant, and the calculation of the thermal aging state distribution prediction based on the actual operating temperature, the reference operating temperature, the actual moisture content, and the apparent activation energy to obtain the polymerization degree index of the insulating material, includes:

[0022] Considering the universal gas constant, the temperature shift factor is calculated based on the actual operating temperature, the reference operating temperature, and the apparent activation energy.

[0023] Simultaneously considering the water content influence coefficient and the general gas constant, the water content shift factor is calculated based on the actual operating temperature and the actual water content.

[0024] The combined shift factor is determined based on the temperature shift factor and the water content shift factor.

[0025] Considering the degree of aggregation loss score threshold, the degree of aggregation loss score is calculated based on the joint shift factor;

[0026] Based on the degree of polymerization loss fraction, the target degree of polymerization is calculated in combination with the initial degree of polymerization, and the target degree of polymerization is used as the degree of polymerization index of the insulating material.

[0027] Optionally, the step of predicting discharge risk based on the electric field distribution to obtain an insulation margin index includes:

[0028] Based on the electric field distribution, determine the path vector and electric field strength of each electric field line segment in the insulating material;

[0029] For each of the power line segments, the voltage value and effective length of the power line segment are calculated based on the path vector and electric field strength.

[0030] Based on the voltage value and the effective length, a discharge risk prediction calculation considering electric field non-uniformity is performed to obtain the insulation margin of the power line segment;

[0031] The insulation margins of all the power line segments are integrated to obtain the insulation margin distribution, which serves as an insulation margin index for the insulating material.

[0032] Optionally, the step of calculating the discharge risk prediction based on the voltage value and the effective length, taking into account electric field non-uniformity, to obtain the insulation margin of the power line segment includes:

[0033] Calculate the average electric field strength of the electric field line segment based on the voltage value and the effective length;

[0034] The electric field non-uniformity coefficient is calculated based on the average electric field strength and the maximum electric field strength.

[0035] Based on the electric field non-uniformity coefficient and the effective length, the allowable electric field value is calculated;

[0036] The insulation margin of the electric field segment is calculated based on the allowable electric field value and the average electric field strength.

[0037] Optionally, the method further includes:

[0038] Molecular modeling is performed on the insulating material of the transformer insulation system, and the material performance parameters of the insulating material are obtained through molecular simulation calculations.

[0039] By calling cross-scale parameters, the material performance parameters are transmitted to the multiphysics simulation platform, so that the multiphysics simulation platform can automatically call, dynamically assign, and couple the material properties of the insulating material based on the material performance parameters when performing simulation actions.

[0040] The present invention also provides a risk assessment device for a transformer insulation system, comprising:

[0041] The parameter acquisition unit is used to acquire the material performance parameters of the insulating materials in the transformer insulation system.

[0042] The coupled simulation unit is used to perform multiphysics field coupled simulation based on the material performance parameters to obtain the thermal field distribution and electric field distribution of the insulating material.

[0043] A thermal aging prediction unit is used to predict the thermal aging state distribution based on the thermal field distribution and obtain a polymerization degree index, which is used to assess the thermal aging risk of the insulating material.

[0044] The discharge risk prediction unit is used to predict the discharge risk based on the electric field distribution and obtain an insulation margin index, which is used to assess the discharge risk of the insulating material.

[0045] The present invention also provides an electronic device, the device comprising a processor and a memory:

[0046] The memory is used to store program code and transmit the program code to the processor;

[0047] The processor is used to execute the risk assessment method for the transformer insulation system as described above, according to the instructions in the program code.

[0048] The present invention also provides a computer-readable storage medium for storing program code for performing a risk assessment method for a transformer insulation system as described in any of the preceding claims.

[0049] As can be seen from the above technical solutions, the present invention has the following advantages:

[0050] This paper presents a risk assessment method for transformer insulation systems. The method involves obtaining the material performance parameters of the insulating materials in the transformer insulation system; performing multiphysics coupled simulation based on these parameters to obtain the thermal and electric field distributions of the insulating materials; predicting the thermal aging state distribution based on the thermal field distribution to obtain a polymerization index for assessing the thermal aging risk of the insulating materials; and predicting the discharge risk based on the electric field distribution to obtain an insulation margin index for assessing the discharge risk of the insulating materials. By directly introducing molecular simulation calculation parameters into multiphysics simulation, a cross-scale joint simulation process of molecular simulation and multiphysics coupled simulation is established. This allows simulation to replace experiments in calculating insulation system parameters, enabling better simulation of the dynamic evolution process of the transformer insulation material system. This method overcomes the problems of spatiotemporal fragmentation and scale disconnect in traditional "online + offline" methods, and overcomes the shortcomings of current technologies in spatiotemporal continuity, microscopic evolution, and multi-field coupled simulation accuracy, achieving more accurate dynamic prediction and assessment of transformer thermal aging state and discharge risk. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart illustrating the steps of a risk assessment method for a transformer insulation system;

[0053] Figure 2 A schematic diagram of the overall process for a risk assessment method for a transformer insulation system;

[0054] Figure 3 Example diagrams of cellulose with DP values ​​of 4, 6, and 8;

[0055] Figure 4 An example diagram of density changes in molecular dynamics;

[0056] Figure 5 This is a structural block diagram of a risk assessment device for a transformer insulation system. Detailed Implementation

[0057] This invention provides a risk assessment method for transformer insulation systems, a risk assessment device for transformer insulation systems, an electronic device, and a storage medium, which are used to solve or partially solve the technical problems that current related technologies cannot well simulate the dynamic evolution process of transformer insulation material systems, thus resulting in deficiencies in spatiotemporal continuity, microscopic evolution, and multi-field coupling simulation accuracy.

[0058] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0059] As an example, transformer performance prediction and condition simulation rely heavily on the accurate acquisition of material parameters. Currently, parameter acquisition mainly depends on the traditional method of combining "online testing + offline testing." Online testing continuously collects indirect operating data reflecting the overall macroscopic state of the equipment through sensors (such as DGA, partial discharge, and temperature monitoring). Offline testing, on the other hand, requires sampling key insulating materials (oil, paper) during equipment shutdown or dismantling for standardized laboratory testing to obtain a "precise" parameter snapshot at a specific time point.

[0060] The traditional approach combining online and offline testing has the following limitations: First, the destructive and discrete nature (sparse time points) of offline testing completely severs the continuous evolution trajectory of material parameters over time. While online data possesses temporal continuity, it remains only at the macroscopic system level and cannot address microscopic material behavior. Second, the highly idealized laboratory offline testing environment cannot simulate the complex coupling effects of real transformer operation, leading to parameter distortion. Furthermore, macroscopic indicators monitored online (such as characteristic gases) cannot directly and accurately reflect the microscopic transient response and evolution of the material's internal physicochemical parameters. In addition, both methods struggle to effectively correlate and quantify the personalized damage accumulation caused to the material by the unique historical loads, special operating conditions, and environmental spectra of a single device. Therefore, traditional approaches cannot construct an accurate database reflecting the cross-scale continuous spatiotemporal evolution of material parameters under real complex operating conditions, severely hindering the development of high-precision transformer risk prediction models.

[0061] Further analysis reveals that molecular simulation is an advanced method for studying material behavior at the atomic level. By constructing molecular models of insulating materials and combining reactive force fields with molecular dynamics methods, it is possible to simulate the dynamic evolution of material systems while adhering to fundamental physicochemical principles, and to reveal the microstructural changes and performance degradation mechanisms under thermal, electrical, and chemical environments. The core advantage of molecular simulation lies in its ability to non-destructively, dynamically, and quantitatively reveal the evolution laws and degradation mechanisms of the microstructure and performance parameters of insulating materials under complex multi-physics coupling environments at the atomic scale. This fundamentally overcomes the shortcomings of traditional experimental methods in terms of spatiotemporal continuity, microscopic resolution, accuracy of multi-field coupling simulations, and depth of understanding of mechanisms.

[0062] Therefore, one of the core inventive points of this invention is to propose a cross-scale coupling approach to address the shortcomings of current technologies. Specifically, molecular simulation calculation parameters are directly introduced into multiphysics simulation. By establishing a cross-scale joint simulation process of molecular simulation and multiphysics coupling simulation, simulation replaces experimentation to calculate the parameters of the oil-paper insulation system. This allows for a better simulation of the dynamic evolution of the transformer insulation material system, overcoming the problems of spatiotemporal fragmentation and scale disconnect in traditional "online + offline" methods, and overcoming the shortcomings of current technologies in terms of spatiotemporal continuity, microscopic evolution, and the accuracy of multi-field coupling simulation.

[0063] Reference Figure 1 The diagram illustrates a flowchart of a risk assessment method for a transformer insulation system provided by an embodiment of the present invention, which may specifically include the following steps:

[0064] Step 101: Obtain the material performance parameters of the insulating materials in the transformer insulation system;

[0065] In a specific implementation, the embodiments of the present invention first perform molecular modeling on the insulating material of the transformer insulation system, and obtain the material performance parameters of the insulating material through molecular simulation calculation; then, through cross-scale parameter calling, the material performance parameters are transmitted to the multiphysics simulation platform, so that the multiphysics simulation platform can automatically call, dynamically assign and couple the material properties of the insulating material based on the material performance parameters when performing simulation actions.

[0066] Specifically, the molecular structures of insulating oil and insulating paper are first constructed. Insulating oil commonly uses alkanes, cycloalkanes, or aromatic hydrocarbons as representative molecules, such as isodecane or toluene. Insulating paper uses cellulose as the main component, constructing β-D-glucose units and polymerizing them to form long-chain molecules. The constructed molecular structures are then randomly filled to form a three-dimensional amorphous structure with periodic boundaries.

[0067] Next, the electrical, thermal, and mechanical parameters of the transformer's internal insulation material are calculated. The density parameter can be obtained through long-term simulations under NPT (Number of Particles, Pressure, Temperature, an ensemble in statistical mechanics describing a system with constant particle number, pressure, and temperature). Recording the volume and mass of the system in equilibrium allows for the calculation of the average density value. The dielectric constant can be obtained through molecular dynamics simulations under NVT (Number of Particles, Volume, Temperature, another ensemble in statistical mechanics describing a system with constant particle number, volume, and temperature), recording the dipole moment fluctuations of the system. This simulation time can be set to 2000 ps or more. Using linear response theory, the dielectric constant of the system can be calculated using the mean square fluctuations of the dipole moment, i.e.:

[0068] ;

[0069] in, Represents the static permittivity (relative permittivity) of the system; It represents the total dipole moment vector of the system at a certain moment, that is, the vector sum of the dipole moments of all molecules; This represents the square of the magnitude of the total dipole moment vector. ; This represents the statistical results obtained from the entire molecular dynamics trajectory (or ensemble). The ensemble average; It represents the ensemble average of the total dipole moment vector (usually close to 0 for isotropic systems in the absence of an external field). express The square of the modulus; This represents the volume of the simulation box. Represents the Boltzmann constant; Indicates the absolute temperature of the system; It represents the vacuum permittivity.

[0070] The dynamic viscosity (viscosity) of insulating oil molecules is obtained by calculating the self-diffusion coefficient. This provides microscopic input parameters for subsequent multiphysics simulations. At the molecular scale, the dynamic viscosity of a fluid reflects the internal friction between fluid molecules when subjected to external forces. The dynamic viscosity and diffusion coefficient satisfy the following relationship:

[0071] ;

[0072] in, This represents the diffusion coefficient, used to describe the ability of particles to diffuse in a medium; This represents dynamic viscosity (viscosity), which is the viscosity coefficient of a medium. The effective size parameter representing a particle or solute molecule is often taken as the particle diameter or its equivalent hydrodynamic size. This represents dimensionless empirical coefficients / boundary condition coefficients.

[0073] The diffusion coefficient is one-sixth of the slope of the mean square displacement (MSD) growth curve over time, i.e.:

[0074] ;

[0075] in, This represents the number of oil molecules in the entire model.

[0076] After completing the molecular simulation calculations, the obtained material property parameters are exported in .csv or .txt format for subsequent cross-scale parameter calls.

[0077] In this embodiment of the invention, a cross-scale parameter calling platform is set up to realize the transfer of microscopic material parameters extracted from molecular simulation software to the multiphysics simulation platform after standardization, format conversion and structural encapsulation, and realize the automatic calling, dynamic assignment and coupling input of material properties in the simulation process.

[0078] Specifically, relevant material performance parameters calculated from molecular simulation software are exported, such as material density (including space charge density at the electrical level and fluid density at the fluid level), dynamic viscosity, and dielectric constant calculated in the aforementioned steps. By writing parameter extraction scripts and combining regular expressions with data structure parsing methods, the target material performance parameters can be automatically extracted. All extracted values ​​must be uniformly converted to International Standard Units (SI, the globally accepted system of measurement units).

[0079] Considering that material properties vary with temperature in practical applications ( ),frequency( The complexity of the changes necessitates function fitting of the extracted parameter points to construct... , , This step can be implemented using methods such as linear fitting, polynomial fitting, or the Arrhenius model (a model used in chemistry to describe the relationship between reaction rate and temperature).

[0080] To support cross-platform calls, parameters need to be encapsulated in a structured data format to construct a parameter dictionary structure. Script template generation and interface calls are achieved by rendering script templates through code to generate input scripts for the multiphysics simulation software, enabling automatic material assignment within the software.

[0081] After executing the script or importing parameters, the material nodes in the multiphysics simulation software automatically acquire and assign values, including modules such as current and electrothermal. If the parameter is a function parameter, a variable expression (such as temperature) needs to be bound. This enables dynamic value assignment.

[0082] Step 102: Perform multiphysics coupling simulation based on the material performance parameters to obtain the thermal field distribution and electric field distribution of the insulating material;

[0083] This step mainly involves constructing a multiphysics coupling model to achieve multiphysics coupling simulation of the insulation materials in the transformer insulation system.

[0084] This invention proposes a method for calculating the interface charge distribution in large-size structures. Specifically, a bipolar carrier model is employed, and the interface charge behavior is characterized using the Maxwell-Wagner model (a model used in electrochemistry to describe the behavior of double-layer capacitance) and the Poole-Frenkel model (a model explaining charge migration and breakdown phenomena under strong electric fields). A multilayer "liquid-solid" oil-paper insulation space / interface charge calculation model is established. Based on the space charge distribution model, a transformer electro-magnetic-thermal-fluid coupled field model considering space charge is established to obtain more accurate electric field and magneto-thermal-fluid coupled field equations.

[0085] As discussed above, the main material properties of insulating materials can include space charge density, fluid density, dielectric constant, and dynamic viscosity. In some embodiments, the process of obtaining the thermal and electric field distributions of insulating materials through multiphysics coupling simulation based on material properties can include: constructing electric field control equations based on space charge density and dielectric constant; constructing magneto-thermal-fluid coupling equations based on fluid density and dynamic viscosity; constructing a multiphysics coupling model based on the electric field control equations and the magneto-thermal-fluid coupling equations; and performing multiphysics coupling simulation through the multiphysics coupling model to obtain the thermal and electric field distributions of the insulating material.

[0086] Specifically, the governing equations of the electric field, which describe the influence of space charge distribution on the electric field and the transport behavior of charges under the influence of electric and flow fields, can be composed of the potential equation and the charge transport equation as shown below:

[0087] ;

[0088] in, Represents electric potential; Represents the dielectric constant, which can be spatially dependent. This reflects the polarization response of the medium to the electric field; Here, the total space charge density is expressed in C / m³, contributed by various types of charged particles; express The charge flux / current density vector of charge carriers (such as ions or charged particles of a specific polarity), in A / m². express Space charge density of charge carriers, expressed in C / m³; express The mobility of charge carriers characterizes their drift capability under a unit electric field. It represents the velocity field vector of a fluid, such as the velocity of an oil flow or gas flow, and is used to describe the convective transport of electric charge. express The source and sink terms for charge carriers, measured in C / (m³·s), include the net effects of charge injection, generation, recombination, or loss. Represents the gradient operator. Then it represents the divergence operator.

[0089] The magneto-thermal-fluid coupling equations used to describe fluid motion (velocity, pressure) and its coupling effects with electric and temperature fields can be composed of the continuity equation, the Navier-Stokes equations (fundamental equations in fluid mechanics that describe fluid motion and forces), and the temperature field governing equations, as shown below:

[0090] ;

[0091] in, Indicates the axial coordinate; Represents radial coordinates; , Represents the partial derivatives with respect to the axial and radial directions; This refers to the fluid density, expressed in kg / m³. This indicates the fluid in the axial direction. The velocity component in the direction, in m / s; Indicates the radial direction of the fluid The velocity component in the direction, in m / s; This represents fluid pressure, with the unit being Pa. Dynamic viscosity (viscosity) is expressed in Pa·s and can be determined by... Electric fields, etc.; Represents the temperature field, with units of K; This represents the specific heat capacity at constant pressure, expressed in J / (kg·K). Thermal conductivity is expressed as W / (m·K). , Indicates the mass flux components (axial and radial). , The convective transport term represents momentum; , The convective transport term representing energy (enthalpy); This represents the axial volumetric force density source term, with units of N / m³. This represents the volumetric heat source term, with units of W / m³. Represents the divergence / diffusion term in axisymmetric cylindrical coordinates; , This represents the viscous diffusion term in the momentum equation; , This represents the heat conduction and diffusion term in the energy equation.

[0092] Based on the above equations, a multiphysics coupled simulation model can be constructed. Multiphysics coupled simulation calculations based on this model can yield the thermal and electric field distributions inside the transformer. The calculated thermal and electric field distributions can be used for subsequent aging condition and discharge risk prediction.

[0093] Step 103: Based on the thermal field distribution, predict the thermal aging state distribution to obtain the degree of polymerization index, which is used to assess the thermal aging risk of the insulating material.

[0094] This step mainly establishes a predictive model for the distribution of thermal aging conditions of transformers, and calculates a degree of aggregation index that can be used to assess the thermal aging risk of insulation materials.

[0095] In some embodiments, the process of predicting the thermal aging state distribution based on the thermal field distribution to obtain the degree of polymerization index may specifically include: determining the actual operating temperature of the insulating material according to the thermal field distribution; obtaining the reference operating temperature, actual moisture content, and apparent activation energy of the insulating material; and simultaneously considering the moisture content influence coefficient and the universal gas constant, performing thermal aging state distribution prediction calculation based on the actual operating temperature, reference operating temperature, actual moisture content, and apparent activation energy to obtain the degree of polymerization index of the insulating material.

[0096] Furthermore, considering both the moisture content influence coefficient and the universal gas constant, and based on the actual operating temperature, reference operating temperature, actual moisture content, and apparent activation energy, a process for predicting the thermal aging state distribution and obtaining the polymerization degree index of the insulating material is implemented. Specifically, this process may include: considering the universal gas constant, calculating the temperature shift factor based on the actual operating temperature, reference operating temperature, and apparent activation energy; simultaneously considering the moisture content influence coefficient and the universal gas constant, calculating the moisture content shift factor based on the actual operating temperature and actual moisture content; determining the joint shift factor based on the temperature shift factor and the moisture content shift factor; considering the polymerization degree loss fraction threshold, calculating the polymerization degree loss fraction based on the joint shift factor; and calculating the target polymerization degree based on the polymerization degree loss fraction and the initial polymerization degree, using the target polymerization degree as the polymerization degree index of the insulating material.

[0097] Specifically, this step can be understood as constructing a dynamic numerical model that considers the temperature-moisture thermal aging state distribution.

[0098] By employing a time-temperature-moisture superposition method, the degree of polymerization can be reduced to a certain extent under different temperatures and moisture conditions. The deduction process is as follows:

[0099] The temperature shift factor is calculated using the following formula:

[0100] ;

[0101] The water displacement factor is calculated using the following formula:

[0102] ;

[0103] The joint shift factor is calculated using the following formula:

[0104] ;

[0105] The degree of aggregation loss fraction is calculated using the following formula:

[0106] ;

[0107] The final degree of aggregation, i.e., the target degree of aggregation, is calculated using the following formula:

[0108] ;

[0109] in, Indicates the temperature shift factor; Indicates the water-bearing shift factor; Indicates the temperature-moisture joint shift factor; Indicates the initial degree of aggregation; express The degree of aggregation at any given moment; Indicates the degree of polymerization loss fraction (degree of degradation); This represents the loss fraction of the limiting degree of aggregation (empirical parameter / fit parameter); This represents the constant of the degree of polymerization decay rate under reference conditions; This indicates the cumulative aging time, i.e., the equipment under current operating conditions. Effective operating or testing time under the following conditions; This refers to the actual temperature, i.e., the operating temperature (K) of the transformer windings / solid insulation, which affects the aging reaction rate; Indicates the reference operating temperature, used for definition The reference temperature (K) for the shift factor can typically be selected as either a typical test temperature or a standard temperature; The actual moisture content is indicated here. The moisture level in the paper insulation or system (such as mass fraction, ppm, etc., as defined by the selected method) has a significant accelerating effect on the degradation rate. Represents the apparent activation energy, an energy barrier parameter (J / mol) describing the temperature sensitivity of cellulose thermal degradation, and appears in the Arrhenius-type temperature factor; The water content influence coefficient is an empirical fitting parameter used to characterize water content. Sensitivity to increased degradation rate; This represents the universal gas constant.

[0110] The degree of polymerization, as a key indicator of the molecular chain of cellulose in transformer insulation paper, can accurately reflect the degree of thermal aging of the insulation material. In practical applications, by periodically detecting the decline in the degree of polymerization of cellulose, the thermal aging process inside the transformer caused by long-term temperature rise, overload, or local overheating can be quantitatively assessed.

[0111] Step 104: Based on the electric field distribution, perform discharge risk prediction to obtain an insulation margin index, which is used to assess the discharge risk of the insulating material.

[0112] This step mainly establishes a transformer discharge risk prediction model and calculates an insulation margin index that can be used to assess the discharge risk of insulation materials.

[0113] In some embodiments, the process of predicting discharge risk based on electric field distribution to obtain an insulation margin index may specifically include: for each power line segment, calculating the voltage value and effective length of the power line segment according to the path vector and electric field strength; performing discharge risk prediction calculation considering electric field non-uniformity based on the voltage value and effective length to obtain the insulation margin of the power line segment; integrating the insulation margins of all power line segments to obtain the insulation margin distribution, which serves as the insulation margin index of the insulating material.

[0114] Furthermore, the implementation process for calculating the insulation margin of the power line segment by considering the discharge risk prediction due to electric field non-uniformity based on the voltage value and effective length can specifically include: calculating the average electric field strength of the power line segment based on the voltage value and effective length; calculating the electric field non-uniformity coefficient based on the average electric field strength and the maximum electric field strength; calculating the allowable electric field value based on the electric field non-uniformity coefficient and the effective length; and calculating the insulation margin of the power line segment based on the allowable electric field value and the average electric field strength.

[0115] Specifically, this step can be understood as constructing a discharge risk prediction model that takes into account the inhomogeneity of the electric field.

[0116] To address the interaction between material conductivity and electric field distribution, a Newton-Rafaelson iterative algorithm is employed to solve for the electric field distribution, and complete electric field lines are plotted within the field domain. Electric field intensity is then applied along the path of each electric field line, on each segment of the electric field line between oil gaps. With distance By integrating, the voltage values ​​of the electric field line segments are obtained respectively. and distance value As shown in the following formula:

[0117]

[0118] ;

[0119] in, , Indicates the start and end points of the same power line; Indicates the point along the power line Time The voltage (potential difference); This represents the electric field intensity vector, with units of V / m. When integrating, its component in the tangential direction of the electric field lines is taken. Represents a tiny path vector (length element) along the direction of the electric field line, in meters; This indicates that the power line is moved from... arrive The number of line segments obtained after discretization; Indicates the first Numbering of discrete segments of power lines; Indicates the first The representative electric field intensity on each line segment (usually the midpoint or average electric field of that segment, taking the tangential component). Indicates the first The path vector of each line segment (its size is the length of the segment, and its direction is along the electric field line); Indicates the first The voltage drop on each line segment (the contribution of that small segment to the total potential difference). Indicates from arrive The total geometric distance or effective length of the oil gap can be determined by all The summation yields the value used to characterize the oil gap thickness or the average local field strength, etc.

[0120] The voltage value obtained by the aforementioned steps and distance value The ratio of these values ​​represents the average electric field strength within that oil gap section. :

[0121] ;

[0122] After the above steps, the average electric field strength and electric field line distribution in different oil gaps can be obtained.

[0123] To describe the degree of non-uniformity in various electric fields, embodiments of the present invention introduce an electric field non-uniformity coefficient. It is represented as:

[0124] ;

[0125] In the formula, The maximum electric field strength between the two points is given.

[0126] when At that time, the allowable electric field value (allowable field strength) for each oil gap. for:

[0127] ;

[0128] when At this time, the allowable value for each oil gap is reduced by 10%. The allowable electric field value under this condition is... for:

[0129] ;

[0130] The insulation margin can be determined by using the ratio of the allowable electric field value to the average electric field strength of the oil gap at this oil gap size. for:

[0131] ;

[0132] After calculating the insulation margin for each power line segment, the distribution of the insulation margin, i.e., the distribution of discharge risk, can be obtained. Insulation margin, as a key parameter characterizing the difference between the withstand voltage capability and the operating field strength of a transformer insulation system, directly reflects the actual safety threshold of the internal insulation material. When the margin decreases, it indicates that the electric field stress borne by the insulation material approaches its breakdown strength. Therefore, the risk level of partial discharge or insulation breakdown in the transformer can be quantitatively characterized. This allows for a cross-scale prediction scheme, from microscopic molecular simulation to macroscopic risk prediction.

[0133] It should be noted that the method proposed in this embodiment of the invention can be used not only for risk assessment of traditional kraft paper insulating paper and mineral oil, but also for different types of insulating paper and insulating oil. The difference lies in the initial oil and paper modeling and data correction, but the overall processing approach is consistent. It is understood that this invention does not impose any limitations on this.

[0134] In this embodiment of the invention, a risk assessment method for transformer insulation systems based on cross-scale coupling is proposed. Molecular simulation calculation parameters are directly introduced into multiphysics simulation. By establishing a cross-scale joint simulation process of molecular simulation and multiphysics coupling simulation, simulation replaces experimentation to calculate the parameters of the oil-paper insulation system. This method can better simulate the dynamic evolution process of the transformer insulation material system, overcoming the problems of spatiotemporal fragmentation and scale disconnect in traditional "online + offline" methods, and overcoming the shortcomings of current technology in spatiotemporal continuity, microscopic evolution, and the accuracy of multi-field coupling simulation.

[0135] This invention employs a molecular simulation-multi-physics cross-scale co-simulation approach to achieve more accurate dynamic prediction and assessment of transformer thermal aging status and discharge risk. It overcomes the limitations of traditional "online testing + offline testing" methods, such as poor real-time performance, high experimental dependence, and fragmented data. It can directly and rapidly obtain key parameters of insulating oil paper materials based on molecular simulation. Furthermore, compared to traditional methods with limited experimental conditions, this invention can obtain reliable parameters under extreme conditions such as high temperature and strong electric fields, thus expanding the applicability of parameter acquisition. In addition, this invention constructs an automated data extraction, unit standardization, and cross-platform interface generation process, achieving efficient processing and dynamic retrieval of material parameters. This significantly reduces manual operation costs and improves the accuracy and efficiency of parameter management and simulation input.

[0136] For better illustration, refer to Figure 2 This diagram illustrates the overall flow of a risk assessment method for a transformer insulation system according to an embodiment of the present invention. It should be noted that this embodiment only provides a brief overview of the general flow of risk assessment for a transformer insulation system. The specific implementation process of each step can be understood by referring to the relevant content in the foregoing embodiments, and will not be elaborated upon here. It is understood that the present invention does not impose any limitations on this.

[0137] Step 201: Perform molecular modeling on the insulation material of the transformer insulation system, and obtain the material performance parameters of the insulation material through molecular simulation calculations;

[0138] Step 202: Transfer material property parameters to the multiphysics simulation platform through cross-scale parameter calling;

[0139] Step 203: Construct the electric field control equation and the magnetic-thermal-fluid coupling equation according to the material performance parameters, and construct a multiphysics coupling model based on the electric field control equation and the magnetic-thermal-fluid coupling equation;

[0140] Step 204: Perform multiphysics coupling simulation based on the multiphysics coupling model to obtain the thermal and electric field distributions of the insulating material;

[0141] Step 205: Based on the thermal field distribution, predict the thermal aging state distribution considering temperature and moisture, obtain the degree of polymerization index, and conduct a thermal aging risk assessment of the insulating material based on the degree of polymerization index.

[0142] Step 206: Based on the electric field distribution, perform discharge risk prediction considering electric field non-uniformity, obtain insulation margin index, and conduct discharge risk assessment on insulation materials based on the insulation margin index.

[0143] Traditional online monitoring methods only provide macroscopic-level state information (such as DGA characteristic gases, partial discharges, and temperature), lacking analytical capabilities for the microscopic behavior of insulating materials. Offline testing is highly destructive and produces discrete data, reflecting only a snapshot of parameters at a specific moment and unable to dynamically track material evolution. This invention, however, obtains the microscopic parameters of materials under different temperatures, moisture levels, and electric fields through molecular simulation, ensuring data continuity and integrity. Implementing the technical solution provided by this invention, through combined molecular simulation calculations and multiphysics coupled simulations, achieves cross-scale dynamic prediction from micro to macro, avoiding spatiotemporal fragmentation and scale disconnect, thus overcoming the fragmentation and limitations of "online + offline" testing.

[0144] Traditional solutions struggle to account for the historical operation of individual transformers and the impact of special operating conditions on insulation aging. This invention, however, utilizes molecular simulation for targeted molecular modeling and cross-scale parameter fitting and functionalization. This allows for the incorporation of individualized evolution patterns under different oil-paper types, load conditions, and environmental spectra into predictions, enabling equipment-level customized risk assessment with enhanced individualized and differentiated adaptability.

[0145] To enable those skilled in the art to better understand the technical solutions of the present invention, the following specific example is used to illustrate the embodiments of the present invention.

[0146] Step 1: Molecular Modeling

[0147] The main component composition ratios of mineral oil are shown in Table 1:

[0148] Table 1: Composition and Proportion of Mineral Oil

[0149]

[0150] Each group of oil molecules contains 1 hydrocarbon molecule, 2 monocyclic alkane molecules, 3 dicyclic alkane molecules, 2 tricyclic alkane molecules, and 1 tetracyclic alkane molecule, for a total of 10 groups. The cellulose model selects groups with DP (Dielectric Permittivity) values ​​of 4, 6, and 8. Figure 3 As shown, according to the moisture content obtained from the operating parameter extractor, a corresponding amount of water molecules were added to the insulating paper, and a periodic transformer oil-paper model was established, with an initial density set at 0.8 g / cm³. 3 The Compass II force field was then used to optimize the system structure until convergence. Before performing reaction molecular dynamics simulations, the constructed model needs to be relaxed and optimized for equilibrium to bring the system to a stable state. In this example, the cutoff radius was set to 12.5 Å, and the Smart algorithm was selected.

[0151] Step 2: Molecular Dynamics Simulation

[0152] The system was optimized using reactive molecular dynamics. Compass II was selected as the force field, and the NPT ensemble was initially set. The simulation time was 2000 ps, ​​with one frame output every 1000 steps. Charge was set to Force field assigned, and the simulation temperature was adjusted according to actual conditions. Nose was selected as the temperature control method to ensure that the density reached a stable state. Then, a 2000 ps molecular dynamics simulation was performed under the NVT ensemble to ensure the convergence of the system's thermodynamic properties and to obtain reliable correlation coefficients.

[0153] Step 3: Calculation of material property parameters

[0154] The electrical, thermal, and mechanical parameters of the internal materials of the transformer are calculated. The density can be obtained through long-term simulation under NPT conditions, and the density values ​​of the system at different temperatures and pressures in equilibrium are recorded using a script. Figure 4 The average density under equilibrium conditions is calculated using a script.

[0155] The main methods for calculating the static dielectric constant using molecular dynamics include the wave method and the external field method. Cellulose, due to its large number of hydroxyl groups, possesses a permanent dipole moment. Therefore, by applying a small electric field to simulate small external perturbations, the relationship between the total dipole moment and the dielectric constant can be obtained. The dielectric constant can be calculated using molecular dynamics simulations under NVT conditions, recording the dipole moment fluctuations of the system. This simulation time can be set to 2000 ps or more. Using linear response theory, the dielectric constant of the system can be calculated using the mean square fluctuations of the dipole moment.

[0156] The mean square displacement of insulating oil molecules is obtained through molecular simulation calculations, thereby yielding the diffusion coefficient and subsequently calculating its dynamic viscosity (viscosity). This provides microscopic input parameters for subsequent multiphysics simulations.

[0157] The diffusion coefficient is one-sixth of the slope of the mean square displacement (MSD) versus time curve. Specifically, by linearly fitting the MSD versus time curve to the form y = ax + b through molecular simulations, the diffusion coefficient is one-sixth of the curve's slope. At the molecular scale, the dynamic viscosity of a fluid reflects the internal friction that occurs between fluid molecules when subjected to external forces.

[0158] Step 4: Parameter Export

[0159] After completing the molecular simulation calculations, export the obtained material property parameters, such as density, dielectric constant, diffusion coefficient, and dynamic viscosity, into .csv or .txt format. Maintain the data structure for easy subsequent retrieval and batch processing. The exported file should include the calculation conditions (temperature, pressure, system scale) to ensure result traceability.

[0160] Step 5: Parameter Calling and Conversion

[0161] By writing a parameter extraction script that combines regular expressions and data structure parsing methods, this system can read .csv / .txt files and extract specified parameters. Regular expressions are used to identify parameter names and values ​​in the file, avoiding errors from manual searching.

[0162] All extracted values ​​are converted to International Standard Units (SI) through unit standardization, such as density (kg / m³), dynamic viscosity (Pa·s), and dielectric constant (dimensionless). Multiple simulation condition files can be processed simultaneously, improving efficiency.

[0163] Material parameters vary with temperature ( ),frequency( The discrete data points are fitted into a functional form using the Arrhenius model, resulting in the following: , , Continuous function expressions are provided for dynamic calling in multiphysics simulations.

[0164] Parameters are stored as key-value pairs, such as {“density”: 890, “epsilon”: “ε(T)”}. To support cross-platform calls, the parameters need to be encapsulated in a structured data format to construct a parameter dictionary structure. Script template generation and interface calls are achieved by rendering script templates through code to generate input scripts for the multiphysics simulation software, enabling automatic material assignment within the multiphysics simulation software.

[0165] After executing the script or importing parameters, the material nodes in the multiphysics simulation software automatically acquire and assign values, including modules such as current and electrothermal. If the parameter is a function parameter, a variable expression (such as temperature) needs to be bound. This enables dynamic value assignment.

[0166] Step 6: Automatic Calling and Dynamic Assignment in Multiphysics Software

[0167] After executing the script or importing parameters, the material nodes in the multiphysics simulation software automatically acquire and assign values, including modules such as current and electrothermal. If the parameter is a function parameter, a variable expression (such as temperature) needs to be bound. This enables dynamic value assignment.

[0168] Step 7: Multiphysics Coupled Simulation

[0169] Building upon the preceding steps, a multiphysics coupling model is constructed to achieve multiphysics coupling simulation of the insulating materials in the transformer insulation system. The specific implementation process for this step can be found in the aforementioned embodiments and will not be repeated here.

[0170] Step 8: Transformer thermal aging condition and discharge risk prediction

[0171] On the one hand, a transformer thermal aging state distribution prediction model is established, and a cohesion index that can be used for thermal aging risk assessment of insulation materials is obtained through calculation. On the other hand, a transformer discharge risk prediction model is established, and an insulation margin index that can be used for discharge risk assessment of insulation materials is obtained through calculation. The specific implementation process of this step can be referred to the aforementioned embodiments, and will not be repeated here.

[0172] By periodically monitoring the decline in the degree of polymerization of cellulose, the thermal aging process inside a transformer caused by long-term temperature rise, overload, or localized overheating can be quantitatively assessed. By solving for the insulation margin index, the risk level of partial discharge or insulation breakdown in the transformer can be quantitatively characterized. This allows for a cross-scale prediction scheme, from microscopic molecular simulation to macroscopic risk prediction.

[0173] Reference Figure 5 The diagram illustrates a structural block diagram of a risk assessment device for a transformer insulation system provided in an embodiment of the present invention, which may specifically include:

[0174] The parameter acquisition unit 501 is used to acquire the material performance parameters of the insulating material in the transformer insulation system.

[0175] The coupled simulation unit 502 is used to perform multiphysics field coupled simulation based on the material performance parameters to obtain the thermal field distribution and electric field distribution of the insulating material.

[0176] The thermal aging prediction unit 503 is used to predict the thermal aging state distribution based on the thermal field distribution and obtain the degree of polymerization index. The degree of polymerization index is used to assess the thermal aging risk of the insulating material.

[0177] The discharge risk prediction unit 504 is used to predict the discharge risk based on the electric field distribution and obtain an insulation margin index, which is used to assess the discharge risk of the insulating material.

[0178] In one optional embodiment, the material performance parameters include space charge density, fluid density, dielectric constant, and dynamic viscosity; the coupled simulation unit 502 includes:

[0179] An electric field control equation construction unit is used to construct electric field control equations based on the space charge density and the dielectric constant.

[0180] A magneto-thermal-fluid coupling equation construction unit is used to construct a magneto-thermal-fluid coupling equation based on the fluid density and the dynamic viscosity;

[0181] A multiphysics coupling model construction unit is used to construct a multiphysics coupling model based on the electric field control equation and the magneto-thermal-fluid coupling equation.

[0182] The coupled simulation execution unit is used to perform multiphysics coupling simulation through the multiphysics coupling model to obtain the thermal field distribution and electric field distribution of the insulating material.

[0183] In one optional embodiment, the thermal aging prediction unit 503 includes:

[0184] The actual operating temperature determination unit is used to determine the actual operating temperature of the insulating material based on the thermal field distribution.

[0185] The data acquisition unit is used to acquire the reference operating temperature, actual moisture content, and apparent activation energy of the insulating material.

[0186] The thermal aging state distribution prediction calculation unit is used to simultaneously consider the water content influence coefficient and the general gas constant, and to perform thermal aging state distribution prediction calculation based on the actual operating temperature, the reference operating temperature, the actual water content and the apparent activation energy to obtain the degree of polymerization index of the insulating material.

[0187] In one optional embodiment, the thermal aging state distribution prediction calculation unit includes:

[0188] The temperature shift factor calculation unit is used to calculate the temperature shift factor based on the actual operating temperature, the reference operating temperature, and the apparent activation energy, taking into account the universal gas constant.

[0189] A water displacement factor calculation unit is used to simultaneously consider the water influence coefficient and the general gas constant, and calculate the water displacement factor based on the actual operating temperature and the actual water content.

[0190] A joint shift factor calculation unit is used to determine a joint shift factor based on the temperature shift factor and the water content shift factor.

[0191] The degree of aggregation loss score calculation unit is used to calculate the degree of aggregation loss score based on the joint shift factor, taking into account the degree of aggregation loss score threshold.

[0192] The target degree of polymerization calculation unit is used to calculate the target degree of polymerization based on the degree of polymerization loss fraction and the initial degree of polymerization, and to use the target degree of polymerization as the degree of polymerization index of the insulating material.

[0193] In one optional embodiment, the discharge risk prediction unit 504 includes:

[0194] An electric field strength determination unit is used to determine the path vector and electric field strength of each electric field line segment in the insulating material based on the electric field distribution.

[0195] A voltage calculation unit is used to calculate the voltage value and effective length of each power line segment based on the path vector and electric field strength.

[0196] The discharge risk prediction calculation unit is used to perform discharge risk prediction calculation considering electric field non-uniformity based on the voltage value and the effective length, and to obtain the insulation margin of the power line segment.

[0197] An insulation margin integration unit is used to integrate the insulation margins of all the power line segments to obtain the insulation margin distribution, which serves as an insulation margin index for the insulating material.

[0198] In one optional embodiment, the discharge risk prediction calculation unit includes:

[0199] An average electric field strength calculation unit is used to calculate the average electric field strength of the electric field line segment based on the voltage value and the effective length.

[0200] The electric field non-uniformity coefficient calculation unit is used to calculate the electric field non-uniformity coefficient based on the average electric field strength and the maximum electric field strength.

[0201] The electric field allowable value calculation unit is used to calculate the electric field allowable value based on the electric field non-uniformity coefficient and the effective length.

[0202] An insulation margin calculation unit is used to calculate the insulation margin of the power line segment based on the allowable electric field value and the average electric field strength.

[0203] In one alternative embodiment, the device further includes:

[0204] The molecular simulation calculation unit is used to perform molecular modeling of the insulating material of the transformer insulation system and obtain the material performance parameters of the insulating material through molecular simulation calculation.

[0205] The cross-scale parameter calling unit is used to transmit the material performance parameters to the multiphysics simulation platform through cross-scale parameter calling, so that the multiphysics simulation platform can automatically call, dynamically assign, and couple the material properties of the insulating material based on the material performance parameters when performing simulation actions.

[0206] As the device embodiment is basically similar to the method embodiment, it is described in a relatively simple way. For relevant details, please refer to the description of the method embodiment above.

[0207] This invention also provides an electronic device, which includes a processor and a memory:

[0208] The memory is used to store program code and transfer the program code to the processor;

[0209] The processor is used to execute the risk assessment method for the transformer insulation system of any embodiment of the present invention according to the instructions in the program code.

[0210] This invention also provides a computer-readable storage medium for storing program code for executing the risk assessment method for a transformer insulation system according to any embodiment of this invention.

[0211] 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 units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0212] 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 invention are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0213] In the embodiments provided by this invention, 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 units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units 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 an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0214] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0215] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0216] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0217] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A risk assessment method for a transformer insulation system, characterized in that, include: Obtain the material performance parameters of the insulating materials in the transformer insulation system; Based on the material performance parameters, multiphysics coupling simulation was performed to obtain the thermal and electric field distributions of the insulating material. Based on the thermal field distribution, the thermal aging state distribution is predicted to obtain a polymerization degree index, which is used to assess the thermal aging risk of the insulating material. Discharge risk is predicted based on the electric field distribution to obtain an insulation margin index, which is used to assess the discharge risk of the insulating material.

2. The risk assessment method for transformer insulation systems according to claim 1, characterized in that, The material performance parameters include space charge density, fluid density, dielectric constant, and dynamic viscosity; the multiphysics coupling simulation based on the material performance parameters to obtain the thermal and electric field distributions of the insulating material includes: Based on the space charge density and the dielectric constant, the electric field control equation is constructed; Based on the fluid density and the dynamic viscosity, a magneto-thermal-fluid coupling equation is constructed. Based on the electric field control equation and the magneto-thermal-fluid coupling equation, a multiphysics coupling model is constructed. By performing multiphysics coupling simulation using the aforementioned multiphysics coupling model, the thermal and electric field distributions of the insulating material can be obtained.

3. The risk assessment method for transformer insulation systems according to claim 1, characterized in that, The prediction of thermal aging state distribution based on the thermal field distribution to obtain the polymerization degree index includes: Based on the thermal field distribution, the actual operating temperature of the insulating material is determined; Obtain the reference operating temperature, actual moisture content, and apparent activation energy of the insulating material; Simultaneously considering the influence coefficient of water content and the general gas constant, the thermal aging state distribution is predicted and calculated based on the actual operating temperature, the reference operating temperature, the actual water content, and the apparent activation energy to obtain the polymerization degree index of the insulating material.

4. The risk assessment method for a transformer insulation system according to claim 3, characterized in that, The method simultaneously considers the influence coefficient of water content and the universal gas constant, and performs thermal aging state distribution prediction calculations based on the actual operating temperature, the reference operating temperature, the actual water content, and the apparent activation energy to obtain the polymerization degree index of the insulating material, including: Considering the universal gas constant, the temperature shift factor is calculated based on the actual operating temperature, the reference operating temperature, and the apparent activation energy. Simultaneously considering the water content influence coefficient and the general gas constant, the water content shift factor is calculated based on the actual operating temperature and the actual water content. The combined shift factor is determined based on the temperature shift factor and the water content shift factor. Considering the degree of aggregation loss score threshold, the degree of aggregation loss score is calculated based on the joint shift factor; Based on the degree of polymerization loss fraction, the target degree of polymerization is calculated in combination with the initial degree of polymerization, and the target degree of polymerization is used as the degree of polymerization index of the insulating material.

5. The risk assessment method for a transformer insulation system according to claim 1, characterized in that, The process of predicting discharge risk based on the electric field distribution to obtain an insulation margin index includes: Based on the electric field distribution, determine the path vector and electric field strength of each electric field line segment in the insulating material; For each of the power line segments, the voltage value and effective length of the power line segment are calculated based on the path vector and electric field strength. Based on the voltage value and the effective length, a discharge risk prediction calculation considering electric field non-uniformity is performed to obtain the insulation margin of the power line segment; The insulation margins of all the power line segments are integrated to obtain the insulation margin distribution, which serves as an insulation margin index for the insulating material.

6. The risk assessment method for a transformer insulation system according to claim 5, characterized in that, The step of calculating the discharge risk prediction based on the voltage value and the effective length, taking into account electric field inhomogeneity, to obtain the insulation margin of the power line segment includes: Calculate the average electric field strength of the electric field line segment based on the voltage value and the effective length; The electric field non-uniformity coefficient is calculated based on the average electric field strength and the maximum electric field strength. Based on the electric field non-uniformity coefficient and the effective length, the allowable electric field value is calculated; The insulation margin of the electric field segment is calculated based on the allowable electric field value and the average electric field strength.

7. The risk assessment method for a transformer insulation system according to any one of claims 1 to 6, characterized in that, Also includes: Molecular modeling is performed on the insulating material of the transformer insulation system, and the material performance parameters of the insulating material are obtained through molecular simulation calculations. By calling cross-scale parameters, the material performance parameters are transmitted to the multiphysics simulation platform, so that the multiphysics simulation platform can automatically call, dynamically assign, and couple the material properties of the insulating material based on the material performance parameters when performing simulation actions.

8. A risk assessment device for a transformer insulation system, characterized in that, include: The parameter acquisition unit is used to acquire the material performance parameters of the insulating materials in the transformer insulation system. The coupled simulation unit is used to perform multiphysics field coupled simulation based on the material performance parameters to obtain the thermal field distribution and electric field distribution of the insulating material. A thermal aging prediction unit is used to predict the thermal aging state distribution based on the thermal field distribution and obtain a polymerization degree index, which is used to assess the thermal aging risk of the insulating material. The discharge risk prediction unit is used to predict the discharge risk based on the electric field distribution and obtain an insulation margin index, which is used to assess the discharge risk of the insulating material.

9. An electronic device, characterized in that, The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the risk assessment method for the transformer insulation system according to any one of claims 1-7, based on the instructions in the program code.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code for executing the risk assessment method for the transformer insulation system according to any one of claims 1-7.

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