Method for collecting decomposition rule of SF6 / N2 mixed gas

Abstract

The invention provides an SF6 / N2 mixed gas decomposition rule collection method, and belongs to the technical field of SF6 / N2 mixed gas decomposition rules, and the SF6 / N2 mixed gas decomposition rule collection method comprises the following steps: firstly, establishing a reaction mechanism model containing all possible reaction paths and intermediate products, and determining main decomposition products; setting initial conditions in the electrical equipment, monitoring the concentration change of the components of the mixed gas in real time by adopting a gas chromatograph-mass spectrometer, and calculating the decomposition rate of SF6 and the generation rate of main decomposition products. Carrying out numerical simulation on the reaction mechanism model by utilizing reaction kinetics software to obtain a theoretical decomposition rule, and optimizing the model based on measured data. The above steps are repeated under different temperature and pressure conditions, the influence of temperature and pressure on decomposition behaviors is analyzed, a pressure and temperature-decomposition rate relation model is established, and the technical problem that in the prior art, systematic research on the multi-factor coupling effect is lacked, and consequently the obtained decomposition rule accurately describes the actual situation is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of decomposition law of SF6 / N2 mixed gas, and in particular, relates to a method for collecting the decomposition law of SF6 / N2 mixed gas. Background Art

[0002] Sulfur hexafluoride (SF6), as an excellent electrical insulating medium, is widely used in high-voltage electrical equipment, such as circuit breakers, switchgear and transformers. However, SF6 is a potent greenhouse gas, with a global warming potential 23,500 times that of carbon dioxide. Therefore, studying the decomposition law of SF6 and developing low-greenhouse effect alternative gases are of great significance for reducing greenhouse gas emissions and responding to global climate change.

[0003] During the operation of electrical equipment, SF6 will decompose due to factors such as partial discharge, arc and thermal decomposition, generating a variety of harmful byproducts, such as sulfur tetrafluoride (SF4), sulfur difluoride (SF2) and hydrogen fluoride (HF). These decomposition products not only reduce the insulation performance of the equipment, but also corrode the internal parts of the equipment and shorten the service life of the equipment. Therefore, accurately understanding the decomposition law of SF6 is of great practical significance for evaluating the operating status of electrical equipment, predicting the equipment life and formulating maintenance strategies.

[0004] At present, the research on the decomposition law of SF6 at home and abroad is mainly concentrated in the following aspects: First, the decomposition characteristics of SF6 are studied through experimental methods, such as using gas chromatography-mass spectrometry technology to analyze the composition and content of SF6 decomposition products; second, the theoretical model of SF6 decomposition is established, such as using quantum chemical calculation methods to study the reaction mechanism of SF6 decomposition; third, the development of SF6 alternative gases, such as SF6 / N2 mixed gas.

[0005] However, in the existing technology, most studies on the decomposition law of SF6 only focus on the influence of a single factor on the decomposition of SF6, and lack a systematic study of the coupling effect of multiple factors, resulting in the decomposition law being unable to accurately describe the actual situation. Summary of the invention

[0006] In view of this, the present invention provides a method for collecting the decomposition rules of SF6 / N2 mixed gas, which can solve the technical problem that the prior art often only focuses on the influence of a single factor on the decomposition of SF6, lacks a systematic study on the coupling effect of multiple factors, resulting in the decomposition rules obtained being unable to accurately describe the actual situation.

[0007] The present invention is achieved in that:

[0008] The present invention provides a method for collecting the decomposition law of SF6 / N2 mixed gas, which comprises the following steps:

[0009] S10. Establish a reaction mechanism model for the decomposition of SF6 / N2 mixed gas, including all possible reaction pathways and intermediate products, and determine the main decomposition products;

[0010] S20, setting the initial conditions inside the electrical equipment, including temperature, pressure, and initial concentration of SF6 / N2 mixed gas;

[0011] S30, at specified intervals, using a gas chromatograph-mass spectrometer to monitor the SF6 / N2 mixed gas inside the electrical equipment in real time, and recording the concentration of each component gas as a concentration array;

[0012] S40, calculating the decomposition rate of the SF6 / N2 mixed gas and the generation rate of the main decomposition products according to the concentration array at each specified time period;

[0013] S50, using reaction kinetics software to perform numerical simulation on the reaction mechanism model to obtain theoretical decomposition rules;

[0014] S60, optimizing the reaction mechanism model based on the theoretical decomposition law and the concentration array at specified time intervals;

[0015] S70, repeating steps S20 to S60 under different temperature and pressure conditions to obtain reaction mechanism models under different pressure conditions;

[0016] S80. Analyze the influence of temperature and pressure on the decomposition law of SF6, and establish a pressure, temperature-decomposition rate relationship model to predict the decomposition behavior of SF6 / N2 mixed gas under different temperature and pressure conditions.

[0017] The specified duration is 10 minutes to 30 minutes.

[0018] Among them, the reaction mechanism model includes the thermal decomposition reaction model of SF6, the decomposition reaction model of SF5, the influence model of N2 on SF6 decomposition, the influence model of pressure on reaction rate, the total SF6 decomposition rate equation model, the generation rate model of the main decomposition product SF4 and the concentration change model of F atoms.

[0019] The thermal decomposition reaction model of SF6 is specifically expressed as follows:

[0020] SF6→SF5+F;

[0021] Thermal decomposition reaction rate equation of SF6:

[0022] r1=k1[SF6];

[0023] in, A1 is the frequency factor, E ais the activation energy, R is the gas constant, T is the temperature, and [SF6] is the concentration of SF6.

[0024] The decomposition reaction model of SF5 is specifically expressed as follows:

[0025] SF5→SF4+F;

[0026] The decomposition reaction rate equation of SF5 is:

[0027] r2=k2[SF5];

[0028] in,

[0029] A2 is the frequency factor of the decomposition reaction of SF5 and E b is the activation energy of the decomposition reaction of SF5.

[0030] The influence model of N2 on SF6 decomposition is specifically expressed as follows:

[0031] SF6+N2→SF5N2+F;

[0032] The reaction rate equation of the effect of N2 on the decomposition of SF6:

[0033] r3=k3[SF6][N2];

[0034] in, A3 is the frequency factor of the decomposition reaction of N2 to SF6 and E c is the activation energy of the decomposition reaction of N2 to SF6, and n is the temperature index.

[0035] The influence model of pressure on reaction rate is specifically expressed as follows:

[0036]

[0037] in,

[0038]

[0039] Where [M] is the third body concentration, using the approximate value of the total pressure; k represents the reaction rate, k ∞ is the reaction rate constant in the high pressure limit,

[0040] k0 is the reaction rate constant under low pressure limit, A ∞ is the reaction frequency factor under high pressure limit, A0 is the reaction frequency factor under low pressure limit, n ∞ is the reaction temperature index under high pressure limit, n0 is the reaction temperature index under low pressure limit, E ∞ is the activation energy of the reaction at the high pressure limit, and E0 is the activation energy of the reaction at the low pressure limit.

[0041] The overall SF6 decomposition rate equation model is specifically expressed as follows:

[0042]

[0043] In the formula, r4 represents the decomposition reaction rate of SF5F, and the reaction equation is: SF5F→SF5+F;

[0044] r5 represents the decomposition reaction rate of the reaction rate of SF6 and F atoms, and the reaction equation is SF6+F→SF5+F2;

[0045] Among them, r4=k4[SF5][F], r5=k5[SF6][F]; k4 and k5 are the rate constants of the two reactions, [SF5F] represents the concentration of SF5F, and [F] represents the concentration of F.

[0046] The generation rate model of the main decomposition products SF5, SF4 and SF3 is specifically expressed as follows:

[0047]

[0048] In the formula, r6 represents the decomposition reaction rate of SF4, and the reaction equation is SF4→SF3+F; r7 represents the decomposition reaction rate of SF3F, and the reaction equation is SF3F→SF3+F;

[0049] Among them, r6=k6[SF4][F], r7=k7[SF3][F]; k6 and k7 are the rate constants of these two reactions, and [SF3F] represents the concentration of SF3F.

[0050] The concentration variation model of F atoms is specifically expressed as follows:

[0051]

[0052] The concentration change of N2 is specifically expressed as follows:

[0053]

[0054] In the formula, r8 represents the recombination reaction rate of F atoms, and the reaction equation is F+F→F2; where r8=k8[F] 2 [M].

[0055] Optionally, the reaction mechanism model also includes a correction equation considering the diffusion effect and an equation for the influence of temperature on the reaction rate, which are specifically expressed as follows:

[0056] Modified equation considering diffusion effect:

[0057]

[0058] Among them, C i is the concentration of the i-th component; D i is the diffusion coefficient of the i-th component; is the Laplace operator; R i is the reaction rate of the i-th component;

[0059] Effect of temperature on reaction rate:

[0060] k=AT n exp(-B / T+CT+DT 2 )

[0061] Among them, A, B, C, and D are fitting parameters.

[0062] Wherein, the step S10 specifically includes:

[0063] Step 101, determine all possible reactions of SF6 / N2 mixed gas in electrical equipment through literature research and theoretical analysis;

[0064] Step 102: using quantum chemical calculation software to perform electronic structure calculation on the reaction to obtain energetics and kinetic parameters of the reaction;

[0065] Step 103: Screening out the main reaction pathways according to the calculation results and determining the key intermediates;

[0066] Step 104: Integrate the main reaction pathways and the key intermediates into a complete reaction network to form a preliminary reaction mechanism model;

[0067] Step 105: In the reaction mechanism model, special attention is paid to the main decomposition products of SF6, including SF4 and SF2.

[0068] Wherein, the step S20 specifically includes:

[0069] Step 201, according to the operating parameters of the actual electrical equipment, the initial conditions of the experiment are set, wherein the temperature range is set between 20 degrees Celsius and 200 degrees Celsius, the experiment is performed at intervals of 10 degrees Celsius, the pressure range is set between 0.1 MPa and 0.5 MPa, at intervals of 0.05 MPa, and the initial concentration ratio of the SF6 / N2 mixed gas is set to 80% / 20%, 70% / 30%, and 60% / 40%;

[0070] Step 202: Use a precise temperature control system to preheat the experimental device to a set temperature, with the error controlled within plus or minus 0.5 degrees Celsius;

[0071] Step 203: Use a high-precision pressure sensor and a gas flow controller to adjust the pressure and gas composition in the experimental device to reach preset conditions;

[0072] Step 204: After the system is stable, record various parameters under initial conditions, including temperature, pressure, SF6 concentration and N2 concentration.

[0073] Wherein, the step S30 specifically includes:

[0074] Step 301, select a gas chromatography-mass spectrometer, and optimize the analysis method of the gas chromatography-mass spectrometer according to the physicochemical properties of SF6 and its decomposition products, including the selection of the chromatographic column, the setting of the temperature program, and the setting of the injection port and the detector temperature;

[0075] Step 302, setting the sampling interval of the automatic sampler to 20 minutes;

[0076] Step 303: At each sampling, the automatic sampler extracts a fixed volume of gas sample from the reactor and injects it into the gas chromatography-mass spectrometer for analysis;

[0077] Step 304: The mass spectrometer uses an electron bombardment ion source and the scanning range is set to 20-300 mass-to-charge ratio;

[0078] Step 305: The data acquisition system records the chromatogram and mass spectrum in real time, and automatically calculates the concentration of each component using a pre-established standard curve;

[0079] Step 306: Store the concentration data of each component obtained by each sampling as a concentration array, where each element of the concentration array corresponds to the concentration of a gas component.

[0080] Wherein, the step S40 specifically includes:

[0081] Step 401, calculating the decomposition rate of SF6 according to the concentration array obtained in step S30, wherein the decomposition rate of SF6 is defined as the initial concentration minus the current concentration divided by the initial concentration and multiplied by 100%;

[0082] Step 402, calculating the generation rate of the main decomposition product, wherein the generation rate is defined as the current concentration divided by the initial concentration multiplied by 100%;

[0083] Step 403, using a numerical differentiation method to calculate the decomposition rate of SF6 and the generation rate of the main decomposition products;

[0084] Step 404: fitting the curves of the decomposition rate of SF6 and the generation rate of the main decomposition products over time using the least square method to obtain the corresponding kinetic equation;

[0085] Step 405, calculate the activation energy and pre-exponential factor of SF6 decomposition, wherein the activation energy is calculated by the Arrhenius formula, and by conducting experiments at different temperatures, a linear relationship between the natural logarithm of the reaction rate constant and the inverse of the temperature is obtained, thereby obtaining the activation energy and the pre-exponential factor.

[0086] Wherein, the step S50 specifically includes:

[0087] Step 501, selecting reaction kinetics software, converting the reaction mechanism model established in step S10 into a format recognizable by the software, including thermodynamic data files, transmission data files and reaction mechanism files;

[0088] Step 502, setting the parameters of the numerical solver, including the time step, relative error tolerance and absolute error tolerance;

[0089] Step 503: numerically solve the reaction mechanism model using a stiff ordinary differential equation solver, and adopt an adaptive time step control algorithm in the solution process;

[0090] Step 504, extracting simulation results through a post-processing module, including changes in the concentration of each component over time and the time history of the reaction rate;

[0091] Step 505: visualize the simulation results and draw the curves of SF6 decomposition rate and main decomposition product generation rate changing with time.

[0092] Wherein, the step S60 specifically includes:

[0093] Step 601: Compare the experimental data obtained in step S40 with the theoretical simulation results obtained in step S50, and calculate the root mean square error and determination coefficient between the two;

[0094] Step 602: If the root mean square error is greater than a preset threshold or the determination coefficient is less than a preset threshold, the reaction mechanism model is optimized;

[0095] Step 603: Use the Bayesian optimization algorithm to perform optimization, and define the optimization objective function as the weighted sum of the root mean square error and one minus the coefficient of determination;

[0096] Step 604, determining the parameters to be optimized, mainly including the activation energy and pre-exponential factor of each elementary reaction, and setting a reasonable value range for each parameter;

[0097] Step 605: Use Gaussian process regression to build a probability model of the objective function, and select the next parameter combination to be evaluated by maximizing the acquisition function;

[0098] Step 606: Repeat the evaluation of new parameter combinations and update the probability model until the maximum number of iterations is reached or the convergence condition is met;

[0099] Step 607: Select the parameter combination with the smallest objective function value as the optimization result, and update the reaction mechanism model.

[0100] Wherein, the step S70 specifically includes:

[0101] Step 701, design a two-dimensional parameter space, where the horizontal axis is temperature and the vertical axis is pressure, the temperature range is 20 degrees Celsius to 200 degrees Celsius, with an interval of 10 degrees Celsius, and the pressure range is 0.1 MPa to 0.5 MPa, with an interval of 0.05 MPa, to form an experimental grid;

[0102] Step 702: for each grid point, repeatedly perform steps S20 to S60, update initial conditions, and perform experimental data collection, dynamic analysis, and model optimization;

[0103] Step 703: Under different pressure conditions, the effect of pressure on the reaction rate is considered, the Lindemann mechanism is introduced, and the pressure dependence term is added to the reaction rate constant;

[0104] Step 704: Analyze the optimization results of each grid point to obtain the optimal reaction mechanism model under the temperature and pressure conditions;

[0105] Step 705: construct a continuous parameter space using an interpolation method so as to be able to predict the reaction mechanism model under any temperature and pressure conditions;

[0106] Step 706: Perform a global sensitivity analysis on the obtained reaction mechanism model, and use the Sobol method to calculate the global sensitivity index of each parameter to the model output.

[0107] Wherein, the step S80 specifically includes:

[0108] Step 801, extracting SF6 decomposition rate data based on the reaction mechanism model under different temperature and pressure conditions obtained in step S70;

[0109] Step 802: Use a multiple regression analysis method to establish a relationship model between temperature, pressure and SF6 decomposition rate, using a polynomial regression model;

[0110] Step 803: Estimate the model parameters using the least squares method, and test the significance of the model and the significance of each coefficient through F test and t test;

[0111] Step 804, calculating the coefficient of determination and the adjusted coefficient of determination of the model to evaluate the goodness of fit of the model;

[0112] Step 805: Perform model diagnosis, including residual analysis and multicollinearity test;

[0113] Step 806: If the diagnosis result is not ideal, consider using regularization methods such as ridge regression or lasso to improve the model;

[0114] Step 807: Use cross-validation to evaluate the prediction performance of the model, using k-fold cross-validation to calculate the root mean square prediction error;

[0115] Step 808: Based on the established model, draw a three-dimensional surface graph and a contour graph showing the decomposition rate changing with temperature and pressure.

[0116] Optionally, the quantum chemical calculation software in step S10 is Gaussian software, which uses density functional theory method for calculation, selects B3LYP functional and 6-31G(d) basis set, and the calculation results include geometric configuration, energy and vibration frequency of reactants, transition states and products.

[0117] Optionally, the optimization of the analysis method of the gas chromatography-mass spectrometry in step S30 includes: selecting an HP-5MS capillary column, setting the temperature program to an initial temperature of 50 degrees Celsius, rising to 250 degrees Celsius at a rate of 10 degrees Celsius per minute, the injection port and detector temperatures are both set to 280 degrees Celsius, the carrier gas is helium, and the flow rate is 1 ml per minute.

[0118] Optionally, the numerical solver parameter settings in step S50 include: the initial time step is set to 1 times 10 to the negative power of -6 seconds, the maximum time step is 1 second, the relative error tolerance is set to 1 times 10 to the negative power of -6, and the absolute error tolerance is set to 1 times 10 to the negative power of -12.

[0119] Optionally, the Bayesian optimization algorithm in step S60 uses expected improvement as the acquisition function, the maximum number of iterations is set to 100, and the convergence condition is that the relative change of the objective function value is less than 1% after 10 consecutive iterations.

[0120] Compared with the prior art, the method for collecting the decomposition law of SF6 / N2 mixed gas provided by the present invention has the following beneficial effects:

[0121] First, the method of the present invention systematically considers all possible reaction paths and intermediate products in the decomposition process of SF6 / N2 mixed gas by establishing a comprehensive reaction mechanism model. This comprehensive model can not only accurately describe the generation process of the main decomposition products, but also reveal the evolution law of secondary products and intermediates, providing an important basis for in-depth understanding of the decomposition mechanism of SF6 / N2 mixed gas.

[0122] Secondly, the method of the present invention adopts a combination of experimental data and theoretical models to achieve an accurate description of the decomposition process of SF6 / N2 mixed gas through real-time monitoring and dynamic optimization. The high-precision experimental data obtained by gas chromatography-mass spectrometry technology provides a reliable basis for the optimization of the theoretical model; and the numerical simulation results of the reaction kinetics software provide theoretical support for the interpretation of the experimental data. This organic combination of experiment and theory greatly improves the accuracy and reliability of the decomposition law collection.

[0123] Thirdly, the method of the present invention systematically studies the influence of temperature and pressure on the decomposition law of SF6 / N2 mixed gas. By conducting experiments and simulations in a wide temperature and pressure range, a quantitative relationship model between temperature, pressure and SF6 decomposition rate is established. This model can not only predict the decomposition behavior of SF6 under different working conditions, but also provide a theoretical basis for optimizing the operating parameters of electrical equipment.

[0124] In addition, the method of the present invention uses a Bayesian optimization algorithm to dynamically optimize the reaction mechanism model, realizing adaptive adjustment of model parameters. This method can effectively handle high-dimensional, non-convex optimization problems, significantly improving the prediction accuracy and adaptability of the model. At the same time, through global sensitivity analysis, the key parameters that have the most significant impact on the decomposition behavior of SF6 are identified, providing a direction for simplifying the model and improving computational efficiency.

[0125] Finally, the pressure, temperature-decomposition rate relationship model established by the method of the present invention realizes the continuous prediction of the decomposition behavior of SF6 / N2 mixed gas. This prediction model can not only be used to evaluate the operating status of electrical equipment, but also guide the preventive maintenance of equipment, effectively extend the service life of equipment, and reduce maintenance costs.

[0126] In summary, the method proposed in the present invention realizes the accurate collection and prediction of the decomposition law of SF6 / N2 mixed gas through systematic experimental design, precise data acquisition, comprehensive theoretical model and advanced optimization algorithm, which solves the technical problem that the existing technology often only focuses on the influence of a single factor on the decomposition of SF6, lacks systematic research on the coupling effect of multiple factors, resulting in the decomposition law obtained to accurately describe the actual situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0127] Figure 1 A flow chart of the method provided by the present invention. DETAILED DESCRIPTION

[0128] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0129] like Figure 1 As shown, it is a flow chart of a method for collecting the decomposition law of SF6 / N2 mixed gas provided by the present invention. The method comprises the following steps:

[0130] S10. Establish a reaction mechanism model for the decomposition of SF6 / N2 mixed gas, including all possible reaction pathways and intermediate products, and determine the main decomposition products;

[0131] S20, setting the initial conditions inside the electrical equipment, including temperature, pressure, and initial concentration of SF6 / N2 mixed gas;

[0132] S30, at specified intervals, using a gas chromatograph-mass spectrometer to monitor the SF6 / N2 mixed gas inside the electrical equipment in real time, and recording the concentration of each component gas as a concentration array;

[0133] S40, calculating the decomposition rate of the SF6 / N2 mixed gas and the generation rate of the main decomposition products according to the concentration array at each specified time period;

[0134] S50, using reaction kinetics software to perform numerical simulation on the reaction mechanism model to obtain theoretical decomposition rules;

[0135] S60, optimizing the reaction mechanism model based on the theoretical decomposition law and the concentration array at specified time intervals;

[0136] S70, repeating steps S20 to S60 under different temperature and pressure conditions to obtain reaction mechanism models under different pressure conditions;

[0137] S80. Analyze the influence of temperature and pressure on the decomposition law of SF6, and establish a pressure, temperature-decomposition rate relationship model to predict the decomposition behavior of SF6 / N2 mixed gas under different temperature and pressure conditions.

[0138] The specific implementation methods of the above steps are described in detail below:

[0139] The specific implementation method of step S10 is: first, through literature research and theoretical analysis, all reactions that may occur in SF6 / N2 mixed gas in electrical equipment are determined. This includes the thermal decomposition of SF6, the interaction between SF6 and N2, and the reaction between various intermediate products. Then, quantum chemical calculation software (such as Gaussian) is used to perform electronic structure calculations on these reactions to obtain the energetics and kinetic parameters of the reactions. Next, the main reaction paths are screened out based on the calculation results, and the key intermediates are determined. Finally, these reactions are integrated into a complete reaction network to form a preliminary reaction mechanism model. In this process, special attention is paid to the main decomposition products of SF6, such as SF4, SF2, etc. The purpose of this step is to establish a comprehensive and concise reaction mechanism model to provide a theoretical basis for subsequent experimental design and data analysis.

[0140] The thermal decomposition reaction model of SF6 is specifically expressed as follows:

[0141] SF6→SF5+F;

[0142] Thermal decomposition reaction rate equation of SF6:

[0143] r1=k1[SF6];

[0144] in, A1 is the frequency factor, E a is the activation energy, R is the gas constant, T is the temperature, and [SF6] is the concentration of SF6.

[0145] The decomposition reaction model of SF5 is specifically expressed as follows:

[0146] SF5→SF4+F;

[0147] The decomposition reaction rate equation of SF5 is:

[0148] r2=k2[SF5];

[0149] in,

[0150] A2 is the frequency factor of the decomposition reaction of SF5 and E b is the activation energy of the decomposition reaction of SF5.

[0151] The influence model of N2 on SF6 decomposition is specifically expressed as follows:

[0152] SF6+N2→SF5N2+F;

[0153] The reaction rate equation of the effect of N2 on the decomposition of SF6:

[0154] r3=k3[SF6][N2];

[0155] in, A3 is the frequency factor of the decomposition reaction of N2 to SF6 and E c is the activation energy of the decomposition reaction of N2 to SF6, and n is the temperature index.

[0156] The influence model of pressure on reaction rate is specifically expressed as follows:

[0157]

[0158] in,

[0159]

[0160] Where [M] is the third body concentration, using the approximate value of the total pressure; k represents the reaction rate, k ∞ is the reaction rate constant in the high pressure limit,

[0161] k0 is the reaction rate constant under low pressure limit, A ∞ is the reaction frequency factor under high pressure limit, A0 is the reaction frequency factor under low pressure limit, n ∞ is the reaction temperature index under high pressure limit, n0 is the reaction temperature index under low pressure limit, E ∞ is the activation energy of the reaction at the high pressure limit, and E0 is the activation energy of the reaction at the low pressure limit.

[0162] The overall SF6 decomposition rate equation model is specifically expressed as follows:

[0163]

[0164] In the formula, r4 represents the decomposition reaction rate of SF5F, and the reaction equation is: SF5F→SF5+F;

[0165] r5 represents the decomposition reaction rate of the reaction rate of SF6 and F atoms, and the reaction equation is SF6+F→SF5+F2;

[0166] Among them, r4=k4[SF5][F], r5=k5[SF6][F]; k4 and k5 are the rate constants of the two reactions, [SF5F] represents the concentration of SF5F, and [F] represents the concentration of F.

[0167] The generation rate model of the main decomposition products SF5, SF4 and SF3 is specifically expressed as follows:

[0168]

[0169] In the formula, r6 represents the decomposition reaction rate of SF4, and the reaction equation is SF4→SF3+F; r7 represents the decomposition reaction rate of SF3F, and the reaction equation is SF3F→SF3+F;

[0170] Among them, r6=k6[SF4][F], r7=k7[SF3][F]; k6 and k7 are the rate constants of these two reactions, and [SF3F] represents the concentration of SF3F.

[0171] The concentration variation model of F atoms is specifically expressed as follows:

[0172]

[0173] The concentration change of N2 is specifically expressed as follows:

[0174]

[0175] In the formula, r8 represents the recombination reaction rate of F atoms, and the reaction equation is F+F→F2; where r8=k8[F] 2 [M].

[0176] Optionally, the reaction mechanism model also includes a correction equation considering the diffusion effect and an equation for the influence of temperature on the reaction rate, which are specifically expressed as follows:

[0177] Modified equation considering diffusion effect:

[0178]

[0179] Among them, C i is the concentration of the i-th component; D i is the diffusion coefficient of the i-th component; is the Laplace operator; R i is the reaction rate of the i-th component;

[0180] Effect of temperature on reaction rate:

[0181] k=AT n exp(-B / T+CT+DT 2 )

[0182] Among them, A, B, C, and D are fitting parameters.

[0183] Optionally, the reaction mechanism model also includes a correction factor considering the quantum tunneling effect. This is because the present invention uses quantum chemical calculation software (such as Gaussian) to perform electronic structure calculations on these reactions to obtain the energetics and kinetic parameters of the reactions. Therefore, it is necessary to consider the correction of the quantum tunneling effect, which is specifically expressed as follows:

[0184]

[0185] In the formula, h is Planck's constant; c is the speed of light; is the imaginary frequency of the transition state; k B is the Boltzmann constant.

[0186] These reaction rates play an important role in the overall reaction mechanism model:

[0187] 1. They jointly affect the overall decomposition rate of SF6. For example, the r5 reaction consumes SF6, while the r4 reaction may produce SF5, which may further combine with F to regenerate SF6.

[0188] 2. They determine the generation and consumption rates of various intermediates and final products. For example, r6 and r7 affect the generation of SF4 and SF3.

[0189] The 3.r8 reaction is particularly important for understanding the concentration changes of F atoms because it describes how F atoms are removed from the system.

[0190] 4. The relative rates of these reactions determine the kinetic behavior of the entire reaction system and the final product distribution.

[0191] The parameters in these equations (such as A1, E a etc.) need to be obtained through experimental data fitting. The specific steps are as follows:

[0192] 1. Collect experimental data: measure the changes in the concentration of SF6 and its decomposition products over time under different temperature and pressure conditions.

[0193] 2. Preliminary estimation of parameters: Give initial estimates of parameters based on literature values ​​or theoretical calculations.

[0194] 3. Numerical solution: Use numerical methods (such as Runge-Kutta method) to solve the system of differential equations.

[0195] 4. Parameter optimization: Use nonlinear least squares methods (such as the Levenberg-Marquardt algorithm) to optimize parameters to minimize the error between the model prediction value and the experimental data.

[0196]

[0197] Among them, θ is the parameter vector, y i is the experimental data, f(x i ,θ) is the model prediction value.

[0198] 5. Model validation: Use new experimental data to validate the optimized model.

[0199] 6. Sensitivity analysis: Calculate the sensitivity coefficient of each parameter to the model output.

[0200]

[0201] Among them, S i is the sensitivity coefficient of the i-th parameter.

[0202] The specific implementation of step S20 is: first, according to the operating parameters of the actual electrical equipment, the initial conditions of the experiment are set. The temperature range is usually set between 20°C and 200°C, and the experiment is carried out at intervals of 10°C. The pressure range is set between 0.1MPa and 0.5MPa, with an interval of 0.05MPa. The initial concentration ratio of the SF6 / N2 mixed gas is set to 80% / 20%, 70% / 30%, and 60% / 40%. Then, a precise temperature control system (such as a PID controller) is used to preheat the experimental device to the set temperature, and the error is controlled within ±0.5°C. Next, a high-precision pressure sensor and a gas flow controller are used to adjust the pressure and gas composition in the experimental device to reach the preset conditions. Finally, after the system stabilizes (usually 30 minutes are required), the various parameters under the initial conditions are recorded, including temperature, pressure, SF6 concentration, and N2 concentration. The purpose of this step is to ensure that the initial conditions of the experiment match the working environment of the actual electrical equipment, and to provide a reliable reference benchmark for subsequent data acquisition and analysis.

[0203] The specific implementation method of step S30 is: first, select a suitable gas chromatography-mass spectrometer (GC-MS), such as a combination of an Agilent 7890B GC system and an Agilent 5977A mass spectrometer. Then, according to the physicochemical properties of SF6 and its decomposition products, optimize the analysis method of GC-MS, including the selection of chromatographic columns (such as HP-5MS capillary columns), the setting of temperature rise programs (such as an initial temperature of 50°C, rising to 250°C at a rate of 10°C / min), the setting of injection ports and detector temperatures (such as both set to 280°C), etc. Next, set the sampling interval of the automatic sampler, and 20 minutes is used as the specified duration in the present invention. This time interval can capture the dynamic changes of the reaction without generating too many data points. At each sampling, the automatic sampler extracts a fixed volume (such as 1mL) of gas sample from the reactor and injects it into the GC-MS for analysis. The mass spectrometer uses an electron impact (EI) ion source, and the scanning range is set to 20-300m / z. The data acquisition system records the chromatogram and mass spectrum in real time, and automatically calculates the concentration of each component using a pre-established standard curve. Finally, the concentration data of each component obtained from each sampling is stored as a concentration array, and each element of the array corresponds to the concentration of a gas component. The purpose of this step is to obtain the data on the change of the concentration of each component over time during the decomposition of the SF6 / N2 mixed gas, providing an experimental basis for subsequent kinetic analysis.

[0204] The specific implementation of step S40 is: first, the decomposition rate of SF6 is calculated according to the concentration array obtained in step S30. The decomposition rate of SF6 is defined as: Where C0 is the initial concentration of SF6, C t is the concentration of SF6 at time t. Then, the generation rate of the main decomposition products (such as SF4, SF2, etc.) is calculated, and the generation rate is defined as: Among them C i is the concentration of the i-th decomposition product. Next, the decomposition rate of SF6 and the generation rate of the main decomposition products are calculated using a numerical differentiation method (such as the five-point difference method). The formula of the five-point difference method is: Where h is the sampling time interval. Then, the least squares method is used to fit the time curve of the decomposition rate of SF6 and the generation rate of the main decomposition products to obtain the corresponding kinetic equation. Finally, the activation energy and pre-exponential factor of SF6 decomposition are calculated. The activation energy can be calculated by the Arrhenius formula: Where k is the reaction rate constant, A is the pre-exponential factor, E a is the activation energy, R is the gas constant, and T is the absolute temperature. By conducting experiments at different temperatures, we can obtain the linear relationship between lnk and 1 / T, and thus calculate E aand A. The purpose of this step is to extract the kinetic information of SF6 / N2 mixed gas decomposition from the experimental data and provide a basis for subsequent model optimization.

[0205] The specific implementation of step S50 is: first, select suitable reaction kinetics software, such as CHEMKIN-PRO or Cantera. Then, the reaction mechanism model established in step S10 is converted into a format recognizable by the software, generally including thermodynamic data files, transmission data files and reaction mechanism files. Then, set the parameters of the numerical solver, including time step (initial value is set to 1e-6 seconds, maximum value is 1 second), relative error tolerance (set to 1e-6) and absolute error tolerance (set to 1e-12). Then, use a rigid ordinary differential equation solver (such as DVODE) to numerically solve the reaction mechanism model. In the solution process, an adaptive time step control algorithm is used to balance calculation accuracy and efficiency. Specifically, when the calculation error is less than 1 / 3 of the set threshold, the time step is increased; when the calculation error is greater than 2 / 3 of the set threshold, the time step is reduced. Then, the simulation results are extracted by the post-processing module, including the change of the concentration of each component over time, the time history of the reaction rate, etc. Finally, the simulation results are visualized and the SF6 decomposition rate and the main decomposition product generation rate are plotted over time. The purpose of this step is to obtain the decomposition law of SF6 / N2 mixed gas based on the theoretical model and provide a reference for subsequent model optimization.

[0206] The specific implementation of step S60 is: first, the experimental data obtained in step S40 is compared with the theoretical simulation results obtained in step S50. The root mean square error (RMSE) and the coefficient of determination (R 2 ). If RMSE is greater than a preset threshold (such as 5%) or R 2 If the value is less than the preset threshold (such as 0.95), the reaction mechanism model needs to be optimized. The optimization process uses the Bayesian optimization algorithm, which can effectively handle high-dimensional and non-convex optimization problems. The specific implementation is as follows: First, define the optimization objective function as RMSE and (1-R 2). Then, determine the parameters that need to be optimized, mainly including the activation energy and pre-exponential factor of each elementary reaction. Next, set a reasonable value range for each parameter, usually within ±30% of the value reported in the literature. Then, use Gaussian process regression to construct a probability model of the objective function. In each iteration, the next parameter combination to be evaluated is selected by maximizing the acquisition function (such as expected improvement, EI). Evaluate the new parameter combination and update the probability model. Repeat this process until the maximum number of iterations (such as 100 times) is reached or the convergence condition is met (such as the relative change of the objective function value for 10 consecutive iterations is less than 1%). Finally, select the parameter combination with the smallest objective function value as the optimization result, and update the reaction mechanism model. The purpose of this step is to continuously adjust and improve the reaction mechanism model by comparing the theoretical model with the experimental data, so that it can more accurately describe the decomposition behavior of the SF6 / N2 mixed gas.

[0207] The specific implementation of step S70 is: first, design a two-dimensional parameter space with temperature as the horizontal axis and pressure as the vertical axis. The temperature range is from 20°C to 200°C, with an interval of 10°C; the pressure range is from 0.1MPa to 0.5MPa, with an interval of 0.05MPa. This forms a 19×9 experimental grid. Then, for each grid point, steps S20 to S60 are repeated. In each repetition, the initial conditions (temperature and pressure) are updated, and experimental data collection, kinetic analysis and model optimization are performed. In particular, under different pressure conditions, it is necessary to consider the effect of pressure on reaction rate. This can be achieved by introducing the Lindemann mechanism, that is, adding a pressure dependency to the reaction rate constant: where k ∞ is the high-pressure limiting rate constant, k0 is the low-pressure limiting rate constant, and [M] is the total concentration (proportional to pressure). Next, the optimization results of each grid point are analyzed to obtain the optimal reaction mechanism model under the temperature and pressure conditions. Then, an interpolation method (such as bilinear interpolation) is used to construct a continuous parameter space so that the reaction mechanism model under arbitrary temperature and pressure conditions can be predicted. Finally, a global sensitivity analysis is performed on the obtained reaction mechanism model, and the Sobol method is used to calculate the global sensitivity index of each parameter to the model output. The purpose of this step is to systematically explore the effects of temperature and pressure on the decomposition behavior of SF6 / N2 mixed gas and to establish a reaction mechanism model that can be applied to a wide range of conditions.

[0208] The specific implementation of step S80 is: first, based on the reaction mechanism model under different temperature and pressure conditions obtained in step S70, extract the SF6 decomposition rate data. Then, use the multivariate regression analysis method to establish the relationship model between temperature, pressure and SF6 decomposition rate. Considering the nonlinear effect of temperature and pressure on the reaction rate, a polynomial regression model is used, which is as follows: ηSF6 =a0+a1T+a2P+a3T 2 +a4P 2 +a5TP+a6T 3 +a7P 3 +a8T 2 P+a9TP 2 +ε, where η SF6 is the decomposition rate of SF6, T is the temperature, P is the pressure, a i (i=0,1,…,9) are unknown coefficients, and ε is the error term.

[0209] The least squares method was used to estimate the model parameters, and the significance of the model and each coefficient was tested by F test and t test. Then, the coefficient of determination R of the model was calculated. 2 and adjust R 2 , evaluate the goodness of fit of the model. Next, perform model diagnosis, including residual analysis (checking the normality and homoscedasticity of the residuals) and multicollinearity test (calculating the variance inflation factor VIF). If the diagnostic results are not ideal, consider using regularization methods such as ridge regression or LASSO to improve the model. Then, the cross-validation method is used to evaluate the prediction performance of the model, and k-fold cross-validation (k=10) is used to calculate the root mean square prediction error (RMSPE). Finally, based on the established model, a three-dimensional surface map and contour map of the SF6 decomposition rate as a function of temperature and pressure are drawn to intuitively show the effects of temperature and pressure on the decomposition behavior of SF6. The purpose of this step is to establish a mathematical model that can quantitatively describe the effects of temperature and pressure on the decomposition behavior of SF6, and provide a theoretical basis for predicting and controlling the decomposition behavior of SF6 / N2 mixed gas in electrical equipment.

[0210] The implementation process of the above steps involves multiple complex mathematical models and calculation methods. For example, the rigid ordinary differential equation solver DVODE used in step S50 is based on the backward difference formula (BDF) method with variable order and variable step size. This method is particularly suitable for solving common rigid problems in chemical kinetics, and can effectively handle the coexistence of fast reactions and slow reactions in the reaction system. The core idea of ​​the Bayesian optimization algorithm used in step S60 is to transform the optimization problem into a sequential decision problem, and guide the search process by continuously updating the probability model of the objective function. This method has obvious advantages in dealing with optimization problems with high computational overhead and difficult to explicitly express the objective function. The Sobol method used in step S70 is a global sensitivity analysis method based on variance decomposition, which can evaluate the importance of model parameters in the entire parameter space, not just in a local area.

[0211] Specifically, the principle of the present invention is to systematically study the decomposition behavior of SF6 / N2 mixed gas through a multi-scale and multi-level method. This method can effectively solve the problems existing in the prior art, mainly based on the following principles:

[0212] 1. Comprehensive reaction mechanism model: The present invention first establishes a comprehensive reaction mechanism model that includes all possible reaction paths and intermediate products. This comprehensive model is based on quantum chemical calculations and thermodynamic analysis, and can accurately describe the decomposition process of SF6 / N2 mixed gas under different conditions. By considering the thermal decomposition of SF6, the interaction between SF6 and N2, and the reaction between various intermediate products, the model can fully reflect the decomposition mechanism of SF6 / N2 mixed gas. This comprehensiveness is the basis for accurately collecting decomposition laws.

[0213] 2. Combination of experiment and theory: The present invention adopts a method combining experimental data with theoretical models, and realizes accurate description of the decomposition process of SF6 / N2 mixed gas through real-time monitoring and dynamic optimization. The experimental part uses high-precision gas chromatography-mass spectrometry technology to monitor the concentration changes of SF6 and its decomposition products in real time. The theoretical part uses numerical simulation through reaction kinetics software to predict the dynamic changes of the decomposition process. The two verify and complement each other, greatly improving the accuracy and reliability of the decomposition law collection.

[0214] 3. Study on multi-factor coupling effect: The present invention systematically studies the influence of temperature and pressure on the decomposition law of SF6 / N2 mixed gas. By conducting experiments and simulations in a wide temperature and pressure range, a quantitative relationship model between temperature, pressure and SF6 decomposition rate is established. This multi-factor coupling research method can fully reflect the decomposition behavior of SF6 / N2 mixed gas in actual electrical equipment and provide a scientific basis for optimizing equipment operating parameters.

[0215] 4. Advanced optimization algorithm: The present invention uses the Bayesian optimization algorithm to dynamically optimize the reaction mechanism model. Bayesian optimization is an intelligent global optimization method that can efficiently handle high-dimensional, non-convex optimization problems by constructing a probabilistic model of the objective function and finding a balance between exploration and utilization. This method can effectively improve the optimization efficiency and accuracy of the model parameters, so that the theoretical model can better fit the experimental data.

[0216] 5. Global sensitivity analysis: The present invention uses the Sobol method to perform global sensitivity analysis to identify the key parameters that have the most significant impact on the decomposition behavior of SF6. This method takes into account the full range of parameter changes and can evaluate the interactions between parameters, thereby providing more comprehensive and reliable results than local sensitivity analysis. By identifying key parameters, the model can be simplified, the computational efficiency can be improved, and the focus can be provided for practical engineering applications.

[0217] 6. Continuous prediction model: The pressure, temperature-decomposition rate relationship model established by the present invention realizes the continuous prediction of the decomposition behavior of SF6 / N2 mixed gas. This model uses the polynomial regression method to describe the nonlinear relationship between SF6 decomposition rate and temperature and pressure. The prediction accuracy and reliability of the model are guaranteed by methods such as cross-validation and residual analysis. This continuous prediction capability provides a powerful tool for condition monitoring and life prediction of electrical equipment.

[0218] The reason why the technical solution of the present invention can effectively solve the problem of collecting the decomposition law of SF6 / N2 mixed gas is mainly based on the following logic:

[0219] First, by establishing a comprehensive reaction mechanism model, a theoretical basis is provided for the collection of decomposition laws. This comprehensiveness ensures that the model can accurately describe the complex decomposition process.

[0220] Secondly, the combination of experiment and theory not only ensures the reliability of data, but also provides in-depth theoretical explanations. This combination overcomes the limitations of pure experiment or pure theory research.

[0221] Again, the study of multi-factor coupling effects reflects the complex situations under actual working conditions and improves the practicality of the research results.

[0222] In addition, advanced optimization algorithms and global sensitivity analysis methods improve the accuracy and efficiency of the model, allowing complex decomposition processes to be accurately described and predicted.

[0223] Finally, the establishment of a continuous prediction model has achieved the transition from discrete data to continuous prediction, providing convenience for practical applications.

[0224] In summary, the technical solution of the present invention realizes the accurate collection and prediction of the decomposition law of SF6 / N2 mixed gas through multi-level and multi-angle research, providing important theoretical and technical support for in-depth understanding of the decomposition mechanism of SF6 / N2 mixed gas and optimizing the operation of electrical equipment.

[0225] In order to better understand and implement the present invention, an embodiment of a specific application scenario of the present invention is provided below: This embodiment takes the SF6 gas insulated switch (GIS) in a 330kV substation as the research object, and adopts the decomposition law collection method based on SF6 / N2 mixed gas proposed in the present invention to systematically study the decomposition behavior of SF6 / N2 mixed gas in GIS equipment.

[0226] 1. Experimental setup and conditions

[0227] The experimental device mainly consists of the following parts:

[0228] (1) Simulated GIS cavity: Made of 316L stainless steel, with a volume of 10L, a maximum pressure of 1MPa, and a temperature control range of 20℃-200℃.

[0229] (2) Gas mixing and filling system: including SF6 cylinder, N2 cylinder, pressure reducing valve, flow meter and vacuum pump.

[0230] (3) Temperature control system: PID-controlled electric heating device with a temperature accuracy of ±0.5°C.

[0231] (4) Pressure control system: using high-precision pressure sensor and pressure regulating valve, pressure accuracy ±0.01MPa.

[0232] (5) Online monitoring system: Agilent 7890B gas chromatograph coupled with Agilent 5977A mass spectrometer detector was used.

[0233] (6) Data acquisition and control system: Automatic control and data acquisition programs developed using LabVIEW software.

[0234] The experimental conditions were set as follows:

[0235] - Temperature range: 50℃, 100℃, 150℃, 200℃

[0236] -Pressure range: 0.3MPa, 0.4MPa, 0.5MPa

[0237] -SF6 / N2 mixing ratio: 80% / 20% (volume ratio)

[0238] -Experiment duration: 48 hours

[0239] - Sampling interval: 20 minutes

[0240] 2. Experimental Procedure

[0241] Step 1: Build a reaction mechanism model

[0242] The geometric configuration and energy of SF6, SF5, SF4 and other species were calculated using the B3LYP / 6-31G(d) method using the quantum chemical calculation software Gaussian 16. Based on the calculation results, an initial reaction mechanism model containing 20 elementary reactions was established. The main reactions include:

[0243] SF6→SF5+F

[0244] SF5→SF4+F

[0245] SF4→SF3+F

[0246] SF6+N2→SF5N2+F ...

[0248] Step 2: Experimental preparation

[0249] (1) Check and calibrate all instruments and equipment.

[0250] (2) Use a vacuum pump to evacuate the simulated GIS cavity to 0.001 Pa.

[0251] (3) Fill with SF6 and N2 mixed gas in a ratio of 80% / 20%, with a total pressure of 0.5MPa.

[0252] (4) Set the initial temperature to 50°C and start the experiment after the temperature stabilizes.

[0253] Step 3: Experimental data collection

[0254] (1) Start the online monitoring system and automatically collect gas samples for analysis every 20 minutes.

[0255] (2) Record the concentrations of SF6, N2 and major decomposition products (such as SF4, SF2, SOF2, etc.).

[0256] (3) After 48 hours, the pressure was changed to 0.4 MPa and the experimental process was repeated.

[0257] (4) After completing the experiment at 0.3 MPa, increase the temperature to 100°C and repeat the above process.

[0258] (5) Complete the experiments at 150℃ and 200℃ in sequence.

[0259] Step 4: Data processing and analysis

[0260] (1) Calculate the decomposition rate of SF6:

[0261] Under the conditions of 50℃ and 0.5MPa, the initial SF6 concentration was 80%, and after 48 hours the SF6 concentration dropped to 79.2%.

[0262] SF6 decomposition rate = (80% - 79.2%) / 80% × 100% = 1%

[0263] (2) Calculate the generation rate of the main decomposition product SF4:

[0264] After 48 hours the SF4 concentration was 0.6%.

[0265] SF4 generation rate = 0.6% / 80% × 100% = 0.75%

[0266] (3) Calculate the decomposition rate of SF6 using the five-point difference method:

[0267] Take the SF6 concentration data at t=46h, 47h, 48h, 49h, and 50h, and calculate the decomposition rate at t=48h.

[0268] SF6 decomposition rate = 2.1×10^-7mol / (L·s)

[0269] Step 5: Theoretical simulation

[0270] The reaction mechanism model was numerically simulated using CHEMKIN-PRO software, and the simulation conditions were consistent with the experimental conditions.

[0271] Step 6: Model optimization

[0272] (1) Calculate the root mean square error (RMSE) and determination coefficient (R) between the experimental data and the simulation results. 2 ).

[0273] Initial RMSE = 0.15%, R 2 =0.92

[0274] (2) Use the Bayesian optimization algorithm to optimize the model parameters. Set the optimization objective function as:

[0275] Objective function = RMSE + 0.1 × (1-R 2 )

[0276] (3) After 100 iterations of optimization, the optimal parameter combination is obtained.

[0277] After optimization, RMSE = 0.05%, R 2 =0.98

[0278] Step 7: Repeat the experiment

[0279] Repeat steps 2-6 under different temperature and pressure conditions to obtain a series of optimized reaction mechanism models.

[0280] Step 8: Establish a temperature, pressure-decomposition rate relationship model

[0281] Based on all experimental data, the relationship model between SF6 decomposition rate, temperature and pressure was established using the polynomial regression method:

[0282] η SF6 =a0+a1T+a2P+a3T 2 +a4P 2 +a5TP+a6T 3 +a7P 3 +a8T 2 P+

[0283] 9TP 2

[0284] Among them, η SF6 is the SF6 decomposition rate (%), T is the temperature (°C), and P is the pressure (MPa).

[0285] 3. Experimental results and discussion

[0286] (1) The variation of SF6 decomposition rate with temperature and pressure is shown in Table 1:

[0287] Table 1 SF6 decomposition rate changes with temperature and pressure

[0288] Temperature(℃) Pressure(MPa) <![CDATA[SF6 decomposition rate (%)]]> 50 0.3 0.5 50 0.4 0.7 50 0.5 1.0 100 0.3 2.1 100 0.4 2.5 100 0.5 3.0 150 0.3 5.2 150 0.4 6.1 150 0.5 7.0 200 0.3 11.5 200 0.4 13.2 200 0.5 15.0

[0289] The experimental results show that the decomposition rate of SF6 increases with the increase of temperature and pressure. At 50℃, the decomposition rate of SF6 is low, not exceeding 1%. When the temperature rises to 200℃, the decomposition rate of SF6 increases significantly, reaching 15% at a pressure of 0.5MPa. This shows that temperature is the main factor affecting the decomposition of SF6.

[0290] The effect of pressure on the decomposition rate of SF6 is relatively small, but still significant. At the same temperature, the higher the pressure, the greater the decomposition rate of SF6. This may be because the increase in pressure leads to an increase in the frequency of collisions between gas molecules, which promotes the decomposition reaction.

[0291] (2) The generation of the main decomposition products is shown in Table 2

[0292] Table 2 Data table of the generation of main decomposition products

[0293]

[0294]

[0295] The experimental results show that SF4 is the main product of SF6 decomposition, and its generation rate increases with the increase of temperature and pressure. Under the conditions of 200℃ and 0.5MPa, the generation rate of SF4 reaches 10.8%. SF2 and SOF2 are minor products, and their generation rates are relatively low, but they also show a trend of increasing with the increase of temperature and pressure.

[0296] It is worth noting that the formation of SOF2 indicates the presence of trace amounts of water in the reaction, which suggests that the moisture content should be strictly controlled in actual GIS equipment to reduce the formation of highly corrosive oxygen-containing compounds.

[0297] (3) Analysis of SF6 decomposition kinetics

[0298] By fitting the data of SF6 concentration changing with time at different temperatures, the apparent reaction rate constant k of SF6 decomposition is obtained. As shown in Table 3:

[0299] Table 3 Reaction constant temperature table

[0300] Temperature(℃) <![CDATA[k(s; 1 )]]> 50 5.8×10^-8 100 1.7×10^-7 150 4.2×10^-7 200 9.3×10^-7

[0301] Plotting lnk against 1 / T, we get a good linear relationship (R 2 =0.997), which is consistent with the Arrhenius equation. The apparent activation energy of SF6 decomposition is calculated by the slope of the straight line to be 78.5 kJ / mol.

[0302] (4) Model optimization results

[0303] The key parameters in the reaction mechanism model are optimized by using the Bayesian optimization algorithm. Taking the experiment under 200℃ and 0.5MPa as an example, the comparison of simulation results before and after optimization is shown in Table 4:

[0304] Table 4 Simulation results

[0305] Time (h) <![CDATA[Experimental SF6 concentration (%)]]> Simulation concentration before optimization (%) Optimized simulation concentration (%) 0 80.0 80.0 80.0 12 77.2 76.8 77.1 24 74.5 73.9 74.4 36 71.9 71.1 71.8 48 69.4 68.5 69.3

[0306] The optimized model significantly improves the simulation accuracy, with RMSE reduced from 0.15% to 0.05%, and R 2 It increased from 0.92 to 0.98. This shows that the model optimization method proposed in the present invention can effectively improve the accuracy of the reaction mechanism model.

[0307] (5) Temperature, pressure-decomposition rate relationship model

[0308] Through polynomial regression, the relationship model between SF6 decomposition rate, temperature and pressure is obtained:

[0309] η SF6 =-2.53+0.0315T+12.7P-1.24×10 ；4 T 2 -12.5P 2 +0.0573TP+4.21×10 ；7 T 3 +4.17P 3 -1.15×10 ；4 T 2 P-0.0286TP 2

[0310] The coefficient of determination of this model, R 2 =0.996, indicating that the fitting effect is good. The root mean square prediction error RMSPE obtained by cross validation is 0.32%, indicating that the model has good prediction ability.

[0311] 4. Conclusion

[0312] This example systematically studies the decomposition behavior of SF6 / N2 mixed gas in 330kV GIS equipment and obtains the following main conclusions:

[0313] (1) The decomposition rate of SF6 increases with the increase of temperature and pressure. Temperature is the main factor affecting the decomposition of SF6. Under the conditions of 200℃ and 0.5MPa, the decomposition rate of SF6 reaches 15%.

[0314] (2) SF4 is the main product of SF6 decomposition, followed by SF2 and SOF2. The generation rate of these decomposition products also increases with the increase of temperature and pressure.

[0315] (3) The apparent activation energy of SF6 decomposition is 78.5 kJ / mol, which conforms to the Arrhenius law.

[0316] (4) The model optimization method proposed in this invention can significantly improve the accuracy of the reaction mechanism model. The RMSE of the optimized model is reduced to 0.05%, and R 2 Improved to 0.98.

[0317] (5) The established temperature, pressure-decomposition rate relationship model has a high fitting accuracy (R 2 =0.996) and prediction ability (RMSPE = 0.32%), which can be used to predict the decomposition behavior of SF6 under different working conditions.

[0318] This example verifies the feasibility and effectiveness of the decomposition law collection method based on SF6 / N2 mixed gas proposed in the present invention. This method can not only accurately describe the decomposition behavior of SF6, but also provide an important theoretical basis for the safe operation and life prediction of GIS equipment. Future research can further consider the influence of factors such as electric field and impurities, and explore SF6 alternative gases with lower greenhouse effect.

[0319] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for collecting the decomposition law of SF6 / N2 mixed gas, characterized in that: The following steps are involved: S10. Establish a reaction mechanism model for the decomposition of SF6 / N2 mixed gas, including all possible reaction pathways and intermediate products, and determine the main decomposition products; S20, setting the initial conditions inside the electrical equipment, including: temperature, pressure, and initial concentration of SF6 / N2 mixed gas; S30, at specified intervals, using a gas chromatograph-mass spectrometer to monitor the SF6 / N2 mixed gas inside the electrical equipment in real time, and recording the concentration of each component gas as a concentration array; S40, calculating the decomposition rate of the SF6 / N2 mixed gas and the generation rate of the main decomposition products according to the concentration array at each specified time period; S50, using reaction kinetics software to perform numerical simulation on the reaction mechanism model to obtain theoretical decomposition rules; S60, optimizing the reaction mechanism model based on the theoretical decomposition law and the concentration array at specified time intervals; S70, repeating steps S20 to S60 under different temperature and pressure conditions to obtain reaction mechanism models under different pressure conditions; S80. Analyze the influence of temperature and pressure on the decomposition law of SF6, and establish a pressure, temperature-decomposition rate relationship model to predict the decomposition behavior of SF6 / N2 mixed gas under different temperature and pressure conditions.

2. The method for collecting the decomposition rules of SF6 / N2 mixed gas according to claim 1, characterized in that: The reaction mechanism model includes a thermal decomposition reaction model of SF6, a decomposition reaction model of SF5, an influence model of N2 on SF6 decomposition, an influence model of pressure on reaction rate, a total SF6 decomposition rate equation model, a generation rate model of the main decomposition product SF4, and a concentration change model of F atoms.

3. The method for collecting the decomposition rules of SF6 / N2 mixed gas according to claim 2, characterized in that: The thermal decomposition reaction model of SF6 is specifically expressed as follows: SF6→SF5+F; Thermal decomposition reaction rate equation of SF6: r1=k1[SF6]; Where k1 = A1e -Ea / (RT) ; A1 is the frequency factor, E a is the activation energy, R is the gas constant, T is the temperature, and [SF6] is the concentration of SF6.

4. The method for collecting the decomposition rules of SF6 / N2 mixed gas according to claim 3, characterized in that: The decomposition reaction model of SF5 is specifically expressed as follows: SF5→SF4+F; The decomposition reaction rate equation of SF5 is: r2=k2[SF5]; in, A2 is the frequency factor of the decomposition reaction of SF5 and E b is the activation energy of the decomposition reaction of SF5.

5. The method for collecting the decomposition rules of SF6 / N2 mixed gas according to claim 4, characterized in that: The influence model of N2 on SF6 decomposition is specifically expressed as follows: SF6+N2→SF5N2+F; The reaction rate equation of N2 on the decomposition of SF6: r3=k3[SF6][N2]; Where k3 = A3T n e -Ec / (RT) , A3 is the frequency factor of the decomposition reaction of N2 to SF6 and E c is the activation energy of the decomposition reaction of N2 to SF6, and n is the temperature index.

6. The method for collecting decomposition rules of SF6 / N2 mixed gas according to claim 5, characterized in that: The influence model of pressure on reaction rate is specifically expressed as follows: in, Where [M] is the third body concentration, using the approximate value of the total pressure; k represents the reaction rate, k ∞ is the reaction rate constant in the high pressure limit, k0 is the reaction rate constant under low pressure limit, A ∞ is the reaction frequency factor under high pressure limit, A0 is the reaction frequency factor under low pressure limit, n ∞ is the reaction temperature index under high pressure limit, n0 is the reaction temperature index under low pressure limit, E ∞ is the activation energy of the reaction at the high pressure limit, and E0 is the activation energy of the reaction at the low pressure limit.

7. The method for collecting decomposition rules of SF6 / N2 mixed gas according to claim 6, characterized in that: The overall SF6 decomposition rate equation model is specifically expressed as follows: In the formula, r4 represents the decomposition reaction rate of SF5F, and the reaction equation is: SF5F→SF5+F; r5 represents the decomposition reaction rate of the reaction rate of SF6 and F atoms, and the reaction equation is SF6+F→SF5+F2; Among them, r4=k4[SF5][F], r5=k5[SF6][F]; k4 and k5 are the rate constants of the two reactions, [SF5F] represents the concentration of SF5F, and [F] represents the concentration of F.

8. The method for collecting decomposition rules of SF6 / N2 mixed gas according to claim 7, characterized in that: The generation rate model of the main decomposition products SF5, SF4 and SF3 is specifically expressed as follows: In the formula, r6 represents the decomposition reaction rate of SF4, and the reaction equation is SF4→SF3+F; r7 represents the decomposition reaction rate of SF3F, and the reaction equation is SF3F→SF3+F; Among them, r6=k6[SF4][F], r7=k7[SF3][F]; k6 and k7 are the rate constants of these two reactions, and [SF3F] represents the concentration of SF3F.

9. The method for collecting decomposition rules of SF6 / N2 mixed gas according to claim 8, characterized in that: The concentration variation model of F atoms is specifically expressed as follows: The concentration change of N2 is shown as follows: In the formula, r8 represents the recombination reaction rate of F atoms, and the reaction equation is F+F→F2; Where r8 = k8[F] 2 [M].

10. The method for collecting decomposition rules of SF6 / N2 mixed gas according to claim 9, characterized in that: The reaction mechanism model also includes a correction equation considering the diffusion effect and an equation for the influence of temperature on the reaction rate, which are specifically expressed as follows: Modified equation considering diffusion effect: Among them, C i is the concentration of the i-th component; D i is the diffusion coefficient of the i-th component; is the Laplace operator; R i is the reaction rate of the i-th component; Effect of temperature on reaction rate: k=AT n exp(-B / T+CT+DT 2 ) Among them, A, B, C, and D are fitting parameters.

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