A method, system, device, and medium for analyzing corona discharge characteristic products
By combining zero-dimensional simulation with molecular dynamics models, cross-scale simulation of the long-term corrosion process of corona discharge was achieved, which solved the shortcomings of existing technologies in simulating time scale and reaction input, and provided assessment of the insulation status of electrical equipment and fault early warning.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SICHUAN UNIV
- Filing Date
- 2026-01-30
- Publication Date
- 2026-06-12
Smart Images
Figure CN122201466A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of power equipment insulation monitoring technology, and in particular relates to a method, system, equipment and medium for analyzing corona discharge characteristic products. Background Technology
[0002] In high-voltage electrical equipment, partial corona discharge often occurs at points with weak insulation. Short-term corona discharge has little impact on equipment operation, but long-term effects can lead to electrochemical corrosion of insulating materials or metal electrodes, potentially causing the following problems: Firstly, the chemical substances generated during the discharge react with the equipment surface, producing new impurity gases and reducing the insulation strength of the gaseous insulating medium. Secondly, corona corrosion creates micro-grooves on the equipment surface, further exacerbating local electric field distortion and leading to more severe insulation degradation. Therefore, monitoring the degree of corrosion caused by corona discharge is crucial for assessing the insulation condition of the equipment, and this can be achieved by analyzing changes in the internal gas composition of the equipment.
[0003] Since corona corrosion is a long-term cumulative result, direct experimental observation is difficult, time-consuming, and costly. Numerical simulation methods for analyzing gas composition changes offer significant economic advantages and timeliness, and can reveal the gas evolution process at the mechanistic level. Currently, commonly used numerical simulation methods in this field mainly fall into three categories, each with its own characteristics and limitations: 1. Molecular dynamics simulation: Simulating gas-solid interface chemical reactions at the atomic / molecular scale can effectively elucidate reaction mechanisms, but its modeling is complex and its computational scale is limited. It can usually only simulate picosecond to nanosecond durations and is difficult to couple with the physical processes of gas discharge.
[0004] 2. Two-dimensional fluid model: It can combine the physical and chemical phenomena in the discharge process and take into account the actual equipment geometry. It is a commonly used method for simulating gas insulation. However, its time scale is mostly limited to nanoseconds to microseconds, and it cannot realize the simulation of long-term processes.
[0005] 3. Zero-dimensional model (global model): Ignoring spatial geometric details and focusing on the change of average species concentration over time, this model significantly reduces the computational dimensionality and scale, thus enabling simulations lasting minutes or even longer with conventional computing resources. However, this model relies on pre-known complete chemical reaction pathways, making it difficult to directly and accurately simulate actual corrosion processes when the surface chemical reactions involved in corona discharge corrosion are not yet fully understood.
[0006] In summary, existing methods are insufficient to independently achieve the entire process analysis from mechanism elucidation to long-scale simulation: molecular dynamics can reveal mechanisms but is limited by time scale; zero-dimensional models are suitable for long-term simulations but lack accurate reaction inputs. Therefore, a new method that can balance the realism of reaction mechanisms with the timeliness of simulations is urgently needed. Summary of the Invention
[0007] The purpose of this invention is to provide a method, system, device, and medium for analyzing characteristic products of corona discharge, so as to solve the problems existing in the prior art.
[0008] In a first aspect, to achieve the above objectives, the present invention provides a method for analyzing characteristic products of corona discharge, comprising: S1: Construct a zero-dimensional simulation model of the gas space inside the electrical equipment; S2: Simulate gas phase discharge based on the zero-dimensional simulation model to obtain gas composition data; S3: Based on the gas composition data, perform molecular dynamics simulation on the surface of the electrical equipment to obtain molecular dynamics results; S4: Fit the surface reaction kinetic equations based on the molecular dynamics results; S5: Feed the surface reaction kinetic equation back to the zero-dimensional simulation model to simulate the chemical reaction of corona discharge gas and the electrochemical corrosion process on the surface of electrical equipment, and dynamically predict the evolution trajectory of gas components.
[0009] Optionally, step S2 specifically includes: Obtain input parameters, which are the initial environmental parameters of the electrical equipment and the corona discharge excitation conditions; Based on the input parameters, the physical and chemical processes occurring in the gas phase during corona discharge of electrical equipment are simulated by solving a set of coupled equations to obtain gas composition data; wherein, the set of coupled equations includes electron energy equation, species mass conservation equation and reaction kinetic equation.
[0010] Optionally, step S3 specifically includes: A molecular dynamics simulation model corresponding to the surface material of electrical equipment is constructed. The gas composition data is introduced into the molecular dynamics simulation model to run molecular dynamics simulation, track the interaction process between gas molecules and surface atoms, and obtain molecular dynamics results.
[0011] Optionally, step S4 specifically includes: Statistical analysis and thermodynamic / kinetic calculations were performed on the molecular dynamics results to obtain the atomic-scale reaction process; By abstracting and quantifying atomic-scale reaction processes into continuous mathematical expressions that can be used in macroscopic fluid models, surface reaction kinetic equations are obtained.
[0012] Optionally, step S5 specifically includes: The surface reaction kinetic equations are fed back to the zero-dimensional simulation model to obtain an extended zero-dimensional simulation model. Based on the extended zero-dimensional simulation model, the chemical reaction of corona discharge gas and the electrochemical corrosion process of the equipment surface are simulated, and the evolution trajectory of gas components is predicted.
[0013] Optionally, after step S5, an iterative optimization process is included, specifically including: repeatedly executing steps S2 to S5 for iterative optimization, stopping the iteration when the preset number of iterations or the preset termination condition is met, and outputting the evolution trajectory of the gas components under long-term action.
[0014] Secondly, to achieve the above objectives, the present invention provides a corona discharge characteristic product analysis system, comprising: The zero-dimensional simulation module is used to construct a zero-dimensional simulation model of the gas space inside electrical equipment; based on the zero-dimensional simulation model, gas phase discharge is simulated to obtain gas composition data; The molecular dynamics simulation module is used to perform molecular dynamics simulations on the surface of electrical equipment based on the gas composition data, and obtain molecular dynamics results. The coupled calculation module is used to fit the surface reaction kinetic equation based on the molecular dynamics results; feed the surface reaction kinetic equation back to the zero-dimensional simulation model to simulate the chemical reaction of corona discharge gas and the electrochemical corrosion process of electrical equipment surface, and dynamically predict the evolution trajectory of gas components.
[0015] Thirdly, to achieve the above objectives, the present invention provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the corona discharge characteristic product analysis method described in the first aspect.
[0016] Fourthly, to achieve the above objectives, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a method for analyzing corona discharge characteristic products as described in the first aspect.
[0017] The technical effects of this invention are as follows: This invention proposes a novel method that couples molecular dynamics models with zero-dimensional models. It aims to combine the advantages of molecular dynamics models in analyzing microscopic reaction mechanisms with the efficiency of zero-dimensional models in long-term simulations, thereby achieving accurate analysis of gas composition changes during the long-term interaction between corona discharge and equipment surfaces. This provides a theoretical basis for monitoring the insulation status and providing early warning of faults in electrical equipment. This invention is applicable to evaluating the changes in gas composition caused by corona discharge inside large electrical equipment during long-term operation and their impact on insulation performance. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments 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.
[0019] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart illustrating the implementation of an embodiment of the present invention. Detailed Implementation
[0020] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.
[0021] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Every smaller range between any stated value or intermediate value within a stated range, and any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.
[0022] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be obvious to those skilled in the art. This application specification and embodiments are merely exemplary.
[0023] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.
[0024] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments. Example
[0025] like Figure 1 As shown, this embodiment provides a method for analyzing characteristic products of corona discharge, including: S1: Construct a zero-dimensional simulation model of the gas space inside the electrical equipment; S2: Simulate gas phase discharge based on the zero-dimensional simulation model to obtain gas composition data; S3: Based on the gas composition data, perform molecular dynamics simulation on the surface of the electrical equipment to obtain molecular dynamics results; S4: Fit the surface reaction kinetic equations based on the molecular dynamics results; S5: Feed the surface reaction kinetic equation back to the zero-dimensional simulation model to simulate the chemical reaction of corona discharge gas and the electrochemical corrosion process on the surface of electrical equipment, and dynamically predict the evolution trajectory of gas components.
[0026] Repeat steps S2 to S5 for iterative optimization. Stop iterating when the preset number of iterations or the preset termination condition is met, and output the evolution trajectory of the gas components under long-term action.
[0027] This embodiment provides a method for analyzing the types of local corona products based on the coupling of a zero-dimensional model and a molecular dynamics model. It aims to address the prominent problem of existing numerical simulation methods struggling to balance the accuracy of reaction mechanisms with long-term computational efficiency in analyzing long-term corona discharge corrosion. This method constructs a cross-scale, iterative feedback coupled simulation framework, systematically integrating the efficiency advantages of zero-dimensional models in macroscopic long-term evolution simulation with the accuracy advantages of molecular dynamics in the analysis of microscopic gas-solid interface reaction mechanisms. This enables a complete and highly reliable simulation of the changes in internal gas composition and surface corrosion processes of equipment under corona discharge.
[0028] The specific implementation process of this embodiment includes: First, a zero-dimensional model, also known as a global model, describing the internal gas environment of the equipment is established. This model ignores specific geometric details and focuses on simulating a series of physicochemical processes occurring in the gas phase during corona discharge, including ionization, excitation, dissociation, and ion-molecule reactions, under given initial gas composition, pressure, temperature, and external excitation conditions, by solving a set of coupled equations, including the electron energy equation, the matter continuity equation, and the reaction kinetic equations. The equations involved include: (1) Equation of continuity of matter:
[0029] The first term on the right side of the equation represents the increase or loss of substance i caused by gas-phase reaction j. aij(R) and aij(L) refer to the quantities of substance i on the left and right sides of the equation for reaction j, respectively. The second term represents the increase or loss of gas-phase substances due to gas flow, where τ... flow n is the average residence time of the gas in the reaction vessel. io It is the number density of substances flowing into the reaction vessel.
[0030] (2) Electron temperature:
[0031] Where, n e It is the electron density, k b This is Boltzmann's constant. The first term on the right-hand side of the equation represents the power consumed by the plasma discharge, corresponding to the input energy of the external circuit. Under atmospheric pressure, due to the slow velocity and low kinetic energy of the ions, it can be assumed that all the energy from the external circuit is initially stored in electrons. The current density j and electric field E are obtained through user settings, derived from the voltage setting or pulse power setting of the circuit module. The second term on the right-hand side represents the change in electron energy due to inelastic collisions, where Δε i The electron and particle density is n i The energy change that occurs after matter collides, k i This represents the rate coefficient of the collision reaction. The third term on the right represents the gaseous substance n. i and m i The increase in electron energy density following a collision (e.g., Penning ionization). The last term represents the magnitude of momentum transferred by electrons to neutral atoms and molecules during elastic collisions, where... This refers to the frequency at which these momentum transfer collisions occur. and These are the masses of electrons and neutral particles, respectively. This indicates the temperature of a neutral gas.
[0032] This stage of calculation yields the average concentrations of various neutral particles, ions, and active groups in the space after the discharge reaches a quasi-steady state, as well as their macroscopic evolution trends over time. This step provides crucial input for subsequent surface reaction analysis, clarifying the key gaseous species and their proportions that may participate in surface reactions under specific discharge conditions, thus constituting the first layer of information transmission from the macroscopic discharge environment to the microscopic interface reaction.
[0033] Based on the information obtained about representative gaseous components, this embodiment moves on to the stage of revealing the microscopic reaction mechanism. For the surface material of the device under study, a molecular dynamics simulation model at the atomic / molecular scale is constructed, and a corresponding simulation environment is set according to the key gas components and environmental conditions output by the zero-dimensional model. By introducing gaseous components into the vicinity of the material surface in a certain proportion and with energy, the entire process of interaction between gas molecules and surface atoms is dynamically tracked using molecular dynamics simulation based on first principles or empirical force fields. This simulation can intuitively reveal the adsorption sites, adsorption configurations, reaction energy barriers, intermediate transition states, final stable products, and possible structural damage morphologies of gas molecules on the surface, thereby accurately elucidating the specific chemical reaction pathways, rate-determining steps, and microscopic generation mechanisms of corrosion products at the gas-solid interface at the atomic level. This step is the core mechanism analysis stage of this embodiment, transforming the "gas inventory" output by the macroscopic zero-dimensional model into a profound understanding of the microscopic surface chemical reaction mechanism.
[0034] Next, to achieve feedback from microscopic mechanisms to macroscopic models, it is necessary to reasonably simplify and refine the complex reaction networks revealed by molecular dynamics simulations. Through statistical analysis of reaction events, calculation of reaction free energy barriers, and fitting of reaction probabilities, the observed atomic-scale reaction processes are abstracted and quantified into surface reaction kinetic equations suitable for macroscopic fluid or chemical kinetic models. O3 +W s →O s + O2, k r =0.01 O3 + O s →O 2s + O s , k r =0.01 Among them, W s The symbol represents the surface, and the subscript 's' indicates that the substance is adsorbed on the surface active sites. These two reaction equations represent the process by which O3 reacts with the surface to produce oxygen atoms and molecules adsorbed on the surface. These active substances adsorbed on the surface further participate in subsequent surface reactions, which are also coupled into the model using similar equations. These equations are usually expressed in Arrhenius form or generalized collisional reaction form, containing key parameters such as reaction formulas, reaction rate constants, and activation energies, thus describing the consumption or generation rate of specific gaseous species per unit surface area. This refinement process is crucial; it establishes a mathematical bridge from microscopic simulation to macroscopic modeling, enabling atomic-level insights to be identified and utilized by macroscopic computational frameworks.
[0035] After refining the surface reaction equations, this embodiment enters the coupled iterative calculation stage. The obtained surface reaction kinetic equations are integrated into the initial zero-dimensional model as a new reaction module. At this point, the reaction network of the zero-dimensional model is no longer limited to pure gas-phase reactions, but extends to a complete system including both "gas-phase-gas-phase reactions" and "gas-phase-surface reactions." Specifically, an additional term needs to be added to the material continuity equation:
[0036] The newly added third term on the right side of the equation includes the substance i produced by the surface reaction occurring on the container surface, and the density of the substance i lost on the surface made of material m due to surface diffusion. Where f m D is the area fraction of the container wall made of material m. i This is the diffusion coefficient of substance i. The viscosity coefficient S km It is a coefficient multiplied by the diffusion flux, used to represent the portion of the diffusion flux that disappears at the container wall, i, while g ikm This represents the proportion of substance k consumed in the surface reaction that is used to generate substance i; Λ is the diffusion length of the plasma, and its expression varies depending on the reaction vessel.
[0037] In the coupled model, gaseous species participate in both volumetric reactions with each other and, depending on their concentration and kinetic parameters, react with surfaces at virtual boundaries, leading to their own consumption and potentially generating new gaseous products. Based on this, long-term calculations of the zero-dimensional model are restarted. With the introduction of the surface reaction module, the model can now simultaneously simulate the chemical reactions of corona discharge gases and the electrochemical corrosion process on the device surface, thereby dynamically predicting the evolution trajectory of gas components and the cumulative degree of surface corrosion over long periods. If necessary, the changed gas environment obtained from the new round of macroscopic simulations can be input back into the molecular dynamics model for fine-tuning, forming iterative optimization and further improving the accuracy of the simulation.
[0038] This embodiment breaks down the barriers between models of different scales, achieving cross-scale integrated simulation of the complex physicochemical process of long-term corona discharge corrosion through a closed-loop research path of "macroscopic gas composition determination → microscopic interface reaction analysis → macroscopic reaction parameter feedback." This method not only overcomes the limitations of extremely short timescales in molecular dynamics simulations but also compensates for the prediction distortion caused by the lack of realistic surface reaction mechanisms in zero-dimensional models. Ultimately, this embodiment provides a more robust, reliable, and efficient theoretical analysis tool and numerical simulation basis for evaluating the aging rate of electrical equipment insulation materials under long-term corona exposure, predicting the evolution trend of internal gas insulation performance, and formulating fault early warning strategies based on gas composition analysis. Example
[0039] This embodiment provides a cross-scale, phased coupled computing system with an information feedback loop, including: (1) Model building and initialization calculation module: First, a zero-dimensional simulation module representing the internal gas space of the electrical equipment is established. Structurally, this module is a solver for a system of equations containing a complex network of chemical reactions. Its inputs are the initial environmental parameters of the equipment (such as gas pressure, temperature, and background gas composition) and the corona discharge excitation conditions (such as pulse voltage waveform and average electric field strength). The core function of this module is to efficiently calculate the macroscopic laws governing the changes in the average concentrations of electrons, ions, neutral reactive groups, and stable products over time in the discharged plasma, while neglecting the specific geometry and surface chemical effects of the equipment. After calculation by this module, the system outputs a characteristic gas composition spectrum that reaches dynamic equilibrium under specific discharge conditions. This spectrum clearly identifies which highly reactive species (such as O atoms, O3, NOx, excited-state molecules, etc.) may pose the main chemical threat to the equipment surface, as well as their relative abundance. This step provides precise targets for subsequent microscopic analysis, avoiding the enormous computational resource consumption caused by blindly screening reactants in molecular dynamics simulations.
[0040] The equations involved in this module include: (1) Equation of continuity of matter:
[0041] The first term on the right side of the equation represents the increase or loss of substance i caused by gas-phase reaction j. aij(R) and aij(L) refer to the quantities of substance i on the left and right sides of the equation for reaction j, respectively. The second term represents the increase or loss of gas-phase substances due to gas flow, where τ... flow n is the average residence time of the gas in the reaction vessel. io It is the number density of substances flowing into the reaction vessel.
[0042] (2) Electron temperature: The formula for calculating electron temperature is:
[0043] Where, n e It is the electron density, k b This is Boltzmann's constant. The first term on the right-hand side of the equation represents the power consumed by the plasma discharge, corresponding to the input energy of the external circuit. Under atmospheric pressure, due to the slow velocity and low kinetic energy of the ions, it can be assumed that all the energy from the external circuit is initially stored in electrons. The current density j and electric field E are obtained through user settings, derived from the voltage setting or pulse power setting of the circuit module. The second term on the right-hand side represents the change in electron energy due to inelastic collisions, where Δεi The electron and particle density is n i The energy change that occurs after matter collides, k i This represents the rate coefficient of the collision reaction. The third term on the right represents the gaseous substance n. i and m i The increase in electron energy density following a collision (e.g., Penning ionization). The last term represents the magnitude of momentum transferred by electrons to neutral atoms and molecules during elastic collisions, where... This refers to the frequency at which these momentum transfer collisions occur. and These are the masses of electrons and neutral particles, respectively. This indicates the temperature of a neutral gas.
[0044] (2) Microscopic interface reaction mechanism analysis module: The system then transitions to the microscopic simulation module. At its core is a molecular dynamics model built for the device's surface materials (such as silicone rubber, epoxy resin, or metal oxide layers). The system uses the characteristic gas components and their proportions output from the previous zero-dimensional module as environmental input parameters to dynamically construct a simulation system encompassing gaseous reactants and the solid surface. Under set energy conditions (the particle energy range simulating corona discharge), this module runs molecular dynamics simulations, atomically tracking the entire process of collisions, adsorption, migration, bond breaking, and bonding between gas particles and surface atoms. Its core function is to directly observe and record the actual chemical reaction pathways, intermediates, final products, and physical damage to the surface structure (such as etching and group grafting) occurring in the gaseous environment predicted by the macroscopic model. This step is one of the key innovations in the "structure" of this embodiment. It does not rely on isolated theoretical assumptions or literature reviews to speculate on reactions, but rather uses first-principles calculations to "experimentally" reveal the specific surface chemical mechanisms of a particular system at the atomic scale.
[0045] (3) Refinement and Formatting Module for Reaction Kinetic Equations: After acquiring the reaction trajectories at the atomic scale, the system enters a mechanism transformation and formatting module. This module performs statistical analysis and thermodynamic / kinetic calculations on massive amounts of molecular dynamics trajectory data, abstracting and simplifying the observed discrete reaction events into continuous mathematical expressions usable in macroscopic fluid models. Specifically, it identifies the main reaction pathways, calculates reaction barriers and trial frequencies, fits the surface reaction rate constant expressed in the form of the Arrhenius equation, and organizes each reaction into a standard chemical reaction formula. Ultimately, the output of this module is a set of well-formatted and parameterized "surface reaction kinetic equations." This step acts as a "translator" connecting the microscopic and macroscopic worlds, transforming the intuitive results of atomic simulations into mathematical language that zero-dimensional models can recognize and utilize, thus forming a bridge for information feedback.
[0046] Surface reaction kinetic equations: O3 +W s →O s + O2, k r =0.01 O3 + O s →O 2s + O s , k r =0.01 In the formula, W s The symbol represents the surface, and the subscript 's' indicates that the substance is adsorbed on the surface active sites. These two reaction equations represent the process by which O3 reacts with the surface to produce oxygen atoms and molecules adsorbed on the surface. These active substances adsorbed on the surface will further participate in subsequent surface reactions, which are also coupled into the model using similar equations.
[0047] (4) Cross-scale coupling and long-term evolution simulation module: Finally, the system performs the core coupled calculations. The initial zero-dimensional model is upgraded to an extended zero-dimensional model at this stage. The system's coupler seamlessly integrates the surface reaction kinetic equations generated in the third step as a new "surface chemistry" plugin into the original pure gas-phase chemical reaction network of the zero-dimensional model. Structurally, this is equivalent to adding a subroutine specifically for handling gas-solid interactions to the macroscopic simulation module. Specifically, an additional term needs to be added to the matter continuity equation:
[0048] In the formula, the newly added third term on the right-hand side includes the density of substance i produced by the surface reaction occurring on the container surface, and the density of substance i lost on the surface made of material m due to surface diffusion. Where f mD is the area fraction of the container wall made of material m. i This is the diffusion coefficient of substance i. The viscosity coefficient S km It is a coefficient multiplied by the diffusion flux, used to represent the portion of the diffusion flux that disappears at the container wall, i, while g ikm This represents the proportion of substance k consumed in the surface reaction that is used to generate substance i; Λ is the diffusion length of the plasma, and its expression varies depending on the reaction vessel.
[0049] Building upon this foundation, the system undergoes a new long-term simulation. During this simulation, both gas-phase and surface reaction equations are solved simultaneously: reactive species generated in the gas phase are consumed or transformed on the surface according to new kinetic parameters; surface reactions may also release new gaseous products back into the volume. Through this bidirectional coupling, the system can ultimately simulate the evolution of the gas composition inside the device over hours or even longer periods, and simultaneously assess the accumulation of surface corrosion products or the degree of surface structure degradation. This iterative closed-loop structure—that is, using the coupled calculation results to further refine the input environment of the microscopic simulation—gives the entire analysis system the ability to self-verify and optimize.
[0050] In summary, this embodiment creates a mechanism-driven positive R&D closed loop through a structured process of "macroscopic orientation → microscopic analysis → parameter feedback → macroscopic verification," which significantly improves the theoretical credibility and prediction accuracy of simulation results and solves the problem that macroscopic models and microscopic analysis are often disconnected in traditional methods.
[0051] This embodiment strictly limits the computationally intensive molecular dynamics simulations to the most critical reaction environments selected by the macroscopic model, avoiding resource waste on irrelevant or secondary reaction pathways. Simultaneously, the long-term evolution task after elucidating the mechanism is entrusted to an efficient zero-dimensional model, making it possible to complete the entire process analysis "from mechanism to lifetime" with limited computational resources.
[0052] The various modules of the system provided in this embodiment (zero-dimensional, molecular dynamics, equation extraction, and coupler) are relatively independent. It can be quickly adapted to different equipment materials (requiring only changes to the force field and initial structure of the molecular dynamics model) or different insulating gases (requiring only updates to the gas-phase reaction library of the zero-dimensional model), making this method widely applicable to insulation condition assessment of various electrical equipment such as GIS, transformers, and cable accessories.
[0053] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A method for analyzing characteristic products of corona discharge, characterized in that, include: S1: Construct a zero-dimensional simulation model of the gas space inside the electrical equipment; S2: Simulate gas phase discharge based on the zero-dimensional simulation model to obtain gas composition data; S3: Based on the gas composition data, perform molecular dynamics simulation on the surface of the electrical equipment to obtain molecular dynamics results; S4: Fit the surface reaction kinetic equations based on the molecular dynamics results; S5: Feed the surface reaction kinetic equation back to the zero-dimensional simulation model to simulate the chemical reaction of corona discharge gas and the electrochemical corrosion process on the surface of electrical equipment, and dynamically predict the evolution trajectory of gas components.
2. The method for analyzing characteristic products of corona discharge according to claim 1, characterized in that, Step S2 specifically includes: Obtain input parameters, which are the initial environmental parameters of the electrical equipment and the corona discharge excitation conditions; Based on the input parameters, the physical and chemical processes occurring in the gas phase during corona discharge of electrical equipment are simulated by solving a set of coupled equations to obtain gas composition data; wherein, the set of coupled equations includes electron energy equation, species mass conservation equation and reaction kinetic equation.
3. The method for analyzing characteristic products of corona discharge according to claim 1, characterized in that, Step S3 specifically includes: A molecular dynamics simulation model corresponding to the surface material of electrical equipment is constructed. The gas composition data is introduced into the molecular dynamics simulation model to run molecular dynamics simulation, track the interaction process between gas molecules and surface atoms, and obtain molecular dynamics results.
4. The method for analyzing characteristic products of corona discharge according to claim 3, characterized in that, Step S4 specifically includes: Statistical analysis and thermodynamic / kinetic calculations were performed on the molecular dynamics results to obtain the atomic-scale reaction process; By abstracting and quantifying atomic-scale reaction processes into continuous mathematical expressions that can be used in macroscopic fluid models, surface reaction kinetic equations are obtained.
5. The method for analyzing characteristic products of corona discharge according to claim 1, characterized in that, Step S5 specifically includes: The surface reaction kinetic equations are fed back to the zero-dimensional simulation model to obtain an extended zero-dimensional simulation model. Based on the extended zero-dimensional simulation model, the chemical reaction of corona discharge gas and the electrochemical corrosion process of the equipment surface are simulated, and the evolution trajectory of gas components is predicted.
6. The method for analyzing characteristic products of corona discharge according to claim 1, characterized in that, The process following step S5 includes an iterative optimization process, specifically: repeating steps S2 to S5 for iterative optimization. When the preset number of iterations or the preset termination condition is met, the iteration is stopped, and the evolution trajectory of the gas components under long-term action is output.
7. A system for analyzing characteristic products of corona discharge, characterized in that, include: The zero-dimensional simulation module is used to construct a zero-dimensional simulation model of the gas space inside electrical equipment; based on the zero-dimensional simulation model, gas phase discharge is simulated to obtain gas composition data; The molecular dynamics simulation module is used to perform molecular dynamics simulations on the surface of electrical equipment based on the gas composition data, and obtain molecular dynamics results. The coupled calculation module is used to fit the surface reaction kinetic equation based on the molecular dynamics results; feed the surface reaction kinetic equation back to the zero-dimensional simulation model to simulate the chemical reaction of corona discharge gas and the electrochemical corrosion process of electrical equipment surface, and dynamically predict the evolution trajectory of gas components.
8. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to enable the electronic device to perform a method for analyzing corona discharge characteristic products according to any one of claims 1-6.
9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements a method for analyzing corona discharge characteristic products as described in any one of claims 1-6.