Simulation analysis method and system for detecting SF6 gas decomposition components based on MOFs material

Abstract

The invention discloses a simulation analysis method and system for detecting SF6 gas decomposition components based on an MOFs material. The method comprises the following steps: constructing a corresponding structure model based on an MOFs cluster structure and an SF6 gas decomposition product; selecting an exchange association functional and a base group of a density functional theory; performing iterative optimization on the structure model based on the exchange association functional and the base group to obtain a minimum energy state structure model; and carrying out adsorption simulation based on the minimum energy state structure model, and evaluating the adsorption capacity of the MOFs on the SF6 gas decomposition products. Simulation calculation is carried out based on the density functional theory, and high precision of the result is ensured; the adsorption capacity of the MOFs on the SF6 gas decomposition product can be comprehensively reflected, and a theoretical basis is provided for judging whether the partial discharge phenomenon exists in the GIS equipment or not.

Description

Technical Field

[0001] The present invention relates to the technical field of online monitoring and fault diagnosis of electric power equipment, and in particular to a simulation analysis method and system for detecting decomposition components of SF6 gas based on MOFs materials. Background Art

[0002] Although the gas-insulated metal-enclosed switchgear (GIS) equipment has high operating reliability, some insulation defects may exist inside the equipment during the production and assembly process and after long-term operation, such as metal protrusion defects, free conductive particle defects, insulator surface contamination, and insulator air gap defects. These insulation defects cause electric field distortion inside the GIS equipment, or form poor contact, or cause new insulation defects, resulting in partial discharge inside the GIS equipment. When partial discharge occurs in the GIS equipment, SF6 and its impurities will react chemically to produce new gases. By detecting the content and changes of these characteristic gases, it can be determined whether there is partial discharge inside the GIS equipment and the severity of the discharge. If the potential defects inside the GIS equipment cannot be discovered in time, it may cause insulation breakdown or even spark discharge and arc discharge inside the GIS equipment, resulting in irreversible damage to the GIS equipment and affecting the safety and stability of the power grid. Therefore, the detection of SF6 gas decomposition components is of great practical significance.

[0003] Metal-organic frameworks (MOFs) are inorganic-organic hybrid crystalline porous materials with periodic network structures formed by metal ions or metal atom clusters as nodes and connected to organic ligands through coordination bonds. MOFs materials have great potential in detecting SF6 discharge decomposition products due to their superior properties. However, due to the large number of MOFs and their derivative materials, it is inefficient to screen MOFs materials that meet the requirements of SF6 discharge decomposition product detection by experimental methods.

[0004] Therefore, it is necessary to propose a new simulation analysis method to determine whether a certain MOFs material is suitable for SF6 discharge decomposition product detection. Summary of the invention

[0005] The purpose of the present invention is to determine the adsorption capacity of MOFs materials on SF6 gas decomposition components, so as to efficiently complete material screening and provide a reference for the subsequent application of gas sensors. In order to achieve the above purpose, the present invention provides a simulation analysis method and system for detecting SF6 gas decomposition components based on MOFs materials.

[0006] In a first aspect, an embodiment of the present invention provides a simulation analysis method for detecting decomposition components of SF6 gas based on MOFs materials, comprising:

[0007] Construct corresponding structural models based on MOFs cluster structure and SF6 gas decomposition products;

[0008] Select the exchange-correlation functional and basis set for density functional theory;

[0009] Iteratively optimizing the structural model based on the exchange-correlation functional and the basis set to obtain a minimum energy state structural model;

[0010] Adsorption simulation was performed based on the minimum energy state structural model to evaluate the adsorption capacity of MOFs for SF6 gas decomposition products.

[0011] Preferably, the corresponding structural model is constructed based on the MOFs cluster structure and the SF6 gas decomposition product, including:

[0012] Based on the MOFs cluster structure and SF6 gas decomposition products, the corresponding structural model was constructed using material calculation software.

[0013] Preferably, the exchange-correlation functional and basis set of the selected density functional theory include:

[0014] Based on the simulation computing hardware configuration and the period of the metal elements in the MOFs material, the exchange-correlation functional of the density functional theory is selected;

[0015] With the goal of saving simulation calculation time, multiple density functional theory basis functions are selected, and all of the basis functions are combined to obtain a basis set.

[0016] Preferably, the exchange-correlation functional comprises a PBE0 functional.

[0017] Preferably, the method aims to save simulation calculation time, selects multiple basis functions of density functional theory, and combines all the basis functions to obtain a basis set, including:

[0018] Based on the period of the metal elements in the MOFs material, a first basis function of density functional theory is selected, wherein the first basis function is a basis function including pseudopotential;

[0019] Based on the simulation calculation being in a geometric structure optimization stage, selecting a second basis function of density functional theory, wherein the second basis function is a basis function having an accuracy less than a first threshold;

[0020] Based on the simulation calculation of the single point energy stage, selecting a third basis function of the density functional theory, wherein the third basis function is a basis function with an accuracy higher than a second threshold;

[0021] The first basis function, the second basis function and the third basis function are combined to obtain a basis set of density functional theory.

[0022] Preferably, the first basis function comprises a def2 basis function, the second basis function comprises a 2-zeta basis function, and the third basis function comprises a 3-zeta basis function.

[0023] Preferably, the iterative optimization of the structural model based on the exchange-correlation functional and the basis set to obtain the minimum energy state structural model includes:

[0024] The structural model is iteratively optimized based on the exchange-correlation functional and the basis set until a convergence criterion is reached to obtain a minimum energy state structural model, wherein the convergence criterion is that the energy change of adjacent iteration steps is within a preset range.

[0025] Preferably, the iterative optimization of the structural model based on the exchange-correlation functional and the basis set until a convergence criterion is reached to obtain a minimum energy state structural model comprises:

[0026] The structural model is rapidly iteratively optimized based on the exchange-correlation functional and the basis set until a convergence criterion is reached to obtain a minimum energy state structural model.

[0027] Preferably, the adsorption simulation based on the minimum energy state structure model to evaluate the adsorption capacity of MOFs for SF6 gas decomposition products includes:

[0028] Adsorption simulation is performed based on the minimum energy state structure model to evaluate the adsorption capacity of MOFs for SF6 gas decomposition products from multiple dimensions, wherein the multiple dimensions include the adsorption energy of MOFs for SF6 gas decomposition products, the adsorption distance between MOFs and SF6 gas decomposition products, the charge transfer amount of SF6 gas decomposition products during the adsorption process, the changes in bond length and bond angle of SF6 gas decomposition products before and after adsorption, and the changes in orbital occupancy before and after adsorption.

[0029] In a second aspect, an embodiment of the present invention provides a simulation analysis system for detecting decomposition components of SF6 gas based on MOFs materials, comprising:

[0030] Structural model building module, used to build corresponding structural models based on MOFs cluster structure and SF6 gas decomposition products;

[0031] Functional basis set selection module, used to select exchange-correlation functionals and basis sets for density functional theory;

[0032] An iterative optimization module, used for iteratively optimizing the structural model based on the exchange-correlation functional and the basis set to obtain a minimum energy state structural model;

[0033] The adsorption simulation module is used to perform adsorption simulation based on the minimum energy state structure model to evaluate the adsorption capacity of MOFs for SF6 gas decomposition products.

[0034] Compared with the prior art, the simulation analysis method and system for detecting SF6 gas decomposition components based on MOFs materials in the embodiment of the present invention have the following beneficial effects: the simulation calculation is based on density functional theory to ensure the high accuracy of the result; the exchange correlation functional with excellent accuracy is adopted to further improve the reliability of the calculation; the basis function that can effectively reduce the time consumption of double electron integral calculation is selected to significantly improve the calculation efficiency; the changes of SF6 gas decomposition products and MOFs clusters before and after adsorption simulation are described from multiple dimensions, which more comprehensively reflects the adsorption capacity of MOFs for SF6 gas decomposition products, and provides a theoretical basis for judging whether there is local discharge phenomenon inside GIS equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flow chart of a simulation analysis method for detecting decomposition components of SF6 gas based on MOFs materials according to an embodiment of the present invention;

[0036] Figure 2 is a schematic diagram of a flow chart of selecting an exchange-correlation functional and a basis set according to an embodiment of the present invention;

[0037] Figure 3 is a schematic diagram of a process for obtaining a basis set according to an embodiment of the present invention;

[0038] Figure 4 It is a schematic diagram of the orbital occupancy of the Mg-MOF-74 material after adsorbing SO2 gas molecules in an embodiment of the present invention;

[0039] Figure 5 is another schematic diagram of the orbital occupancy of the Mg-MOF-74 material after adsorbing SO2 gas molecules in an embodiment of the present invention;

[0040] Figure 6 It is a structural schematic diagram of a simulation analysis system for detecting decomposition components of SF6 gas based on MOFs materials according to an embodiment of the present invention;

[0041] Figure 4 Energy, energy; DOS, density of states; Total DOS, total density of states;

[0042] Figure 5 In Chinese, Energy, energy; DOS, density of states. DETAILED DESCRIPTION

[0043] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0044] In the description of the present invention, it should be understood that the terms "first" and "second" etc. are used in the present invention to distinguish different objects rather than to describe a specific order.

[0045] like Figure 1 As shown, the embodiment of the present invention provides a simulation analysis method for detecting SF6 gas decomposition components based on MOFs materials, comprising the steps of:

[0046] S1. Construct the corresponding structural model based on MOFs cluster structure and SF6 gas decomposition products;

[0047] Based on the MOFs cluster structure and SF6 gas decomposition products, the corresponding structural model is constructed through material calculation software. Specifically, first, the specific structure of MOFs clusters is understood, including the coordination number of metal ions or metal clusters, the configuration of organic ligands, and the connection between them; secondly, the decomposition products of SF6 gas under specific conditions (such as high temperature, arc discharge, etc.) are understood, which include CF4, CS2, H2S, SO2, SO2F2, SOF2, and HF; finally, the MOFs cluster structure and SF6 gas decomposition products are combined to construct the corresponding structural model in the software based on density functional theory. At the same time, the atoms in the SF6 gas decomposition products are numbered so that they can be distinguished when measuring changes in bond lengths and bond angles.

[0048] S2, select the exchange-correlation functional and basis set of density functional theory;

[0049] Specifically, Figure 2 As shown, step S2 includes:

[0050] S201. Based on the simulation computing hardware configuration and the period of the metal elements in the MOFs material, select the exchange-correlation functional of the density functional theory;

[0051] The simulation of the present invention is based on density functional theory, and the core task of density functional theory calculation is to solve the eigenvalue and eigenfunction of the Hermite matrix to obtain the ground state energy level and wave function. Due to the total energy regulation of the Kohn-Sham system, under the premise of a given external potential, the electron kinetic energy can be solved by a monomer wave function, and the electron Coulomb repulsion energy is only related to the electron density and can be solved by the electron density, so solving the electron exchange energy term and the electron correlation energy term is the key point to solve the Kohn-Sham equation, and is also the core factor affecting the accuracy of the simulation calculation.

[0052] In the solution process, the exchange-correlation functional needs to be introduced to solve the exchange-correlation problem of electrons. The exchange-correlation functional is a method to establish a set of methods that satisfy as many physical laws as possible and systematically improve the accuracy of density functional theory. It is a method to parameterize the real exchange-correlation energy that is currently difficult to solve. It is based on some existing chemical images. After a function form is given, the function is continuously optimized to make it more accurately describe the system. Therefore, the selection of a suitable exchange-correlation functional is of great significance for simulation calculations.

[0053] Specifically, based on the simulation computing hardware configuration, local density approximation (LDA), generalized gradient approximation (GGA, such as BLYP, BP86, PW91, PBE), meta-GGA (such as mPWB95, TPSS), hybrid-GGA, hybrid-meta-GGA (such as B3LYP, X3LYP, PBE0) can be selected. When selecting the exchange-correlation functional, it is necessary to consider the period in which the metal elements in the MOFs material are located. Specifically, when the metal central atom of MOFs is a metal element in the third period and later, or a transition metal or a lanthanide actinide complex, an exchange-correlation functional with excellent accuracy (such as PBE0) is selected. It can be understood that the exchange-correlation functional of the present invention includes the PBE0 functional.

[0054] S202, with the goal of saving simulation calculation time, select multiple density functional theory basis functions, and combine all basis functions to obtain a basis set.

[0055] Most of the time spent on density functional theory calculations is spent on double-electron integral calculations, so choosing a basis function that can effectively reduce the time spent on double-electron integral calculations can significantly improve the efficiency of the calculations. Since the double-electron integral is proportional to the fourth power of the number of Gaussian basis functions, if one of the basis sets can use fewer Gaussian basis functions to describe the molecular orbitals through the fragment contraction technique when the two basis sets contain the same number of basis functions, then this basis set is a better choice, which can reduce the amount of calculation while maintaining the accuracy of the calculation.

[0056] Specifically, Figure 3 As shown, step S202 includes:

[0057] S202-A, based on the period of metal elements in MOFs materials, select the first basis function of density functional theory;

[0058] If the metal central atom of MOFs is a metal element of the 4th period or later, a transition metal, or a lanthanide or actinide, a first basis function including pseudopotential is selected. Specifically, the first basis function includes a def2 basis function.

[0059] S202-B, based on the simulation calculation in the geometric structure optimization stage, selecting the second basis function of the density functional theory;

[0060] In order to further save simulation time, a second basis function with an accuracy less than the first threshold is selected in the geometric structure optimization stage which takes the longest time. Specifically, the second basis function includes a 2-zeta basis function. It is understandable that the accuracy of the 2-zeta basis function is lower.

[0061] S202-C, based on the simulation calculation of the single point energy stage, selecting the third basis function of the density functional theory;

[0062] When simulating and calculating the single point energy, a third basis function with an accuracy higher than the second threshold is selected. Specifically, the third basis function includes a 3-zeta basis function. It can be understood that the 3-zeta basis function has a higher accuracy.

[0063] S202-D. Combine the first basis function, the second basis function and the third basis function to obtain a basis set of density functional theory.

[0064] S3, iteratively optimize the structural model based on the exchange-correlation functional and basis set to obtain the minimum energy state structural model;

[0065] Specifically, the structural model is iteratively optimized based on the exchange-correlation functional and the basis set until the convergence criterion is reached to obtain the minimum energy state structural model. Furthermore, the convergence criterion is that the energy change of adjacent iteration steps is within a preset range.

[0066] It should be noted that the process of iterative optimization of the structural model based on the exchange-correlation functional and basis set includes iteratively solving the Kohn-Sham equation. In each iteration, the Kohn-Sham equation needs to be re-solved to update the electron wave function and electron density. These updated electron wave functions and electron density are then used to calculate the new total energy and possible gradients, that is, the derivatives of energy with respect to molecular structural parameters, where the gradient information is crucial for guiding how to adjust the molecular structure to reduce energy. Through the iterative process, the structure with the minimum energy, that is, the minimum energy state structural model, is found.

[0067] In order to accelerate the convergence of structural optimization, the structural model is rapidly iterated and optimized based on the exchange-correlation functional and the basis set until the convergence criterion is reached to obtain the minimum energy state structural model. Specifically, the present embodiment adopts the GDIIS algorithm for rapid iterative optimization. It is understandable that the GDIIS algorithm is an effective method for transition state search, with the characteristics of iterativeness, subspace construction, direct inversion, etc. It has a wide range of application prospects in chemical calculations, and helps to gain a deeper understanding of the mechanism and path of chemical reactions.

[0068] S4. Adsorption simulation was performed based on the minimum energy state structure model to evaluate the adsorption capacity of MOFs for SF6 gas decomposition products.

[0069] Specifically, adsorption simulation was performed based on the minimum energy state structure model to evaluate the adsorption capacity of MOFs for SF6 gas decomposition products from multiple dimensions. Further, the multiple dimensions include the adsorption energy of MOFs for SF6 gas decomposition products, the adsorption distance between MOFs and SF6 gas decomposition products, the charge transfer amount of SF6 gas decomposition products during adsorption, the bond length and bond angle changes of SF6 gas decomposition products before and after adsorption, and the orbital occupancy changes before and after adsorption.

[0070] The following is a detailed description of the adsorption simulation process from multiple dimensions:

[0071] 1) Adsorption energy of MOFs on SF6 gas decomposition products

[0072] To express the total energy change of gas molecules when adsorbed on MOFs, the adsorption energy is defined as:

[0073] E binding =E MOF-gas -E MOF -E gas

[0074] Among them, E binding represents the adsorption energy of MOFs materials to gas molecules, E MOF-gas represents the total energy of the MOFs material system after adsorption, E MOF represents the total energy of MOFs material clusters before adsorption, E gas Represents the total energy of gas molecules before adsorption.

[0075] In a specific embodiment, the MOFs material is Mg-MOF-74, and its adsorption energy for seven gas molecules including SF6 and SF6 gas decomposition products (CF4, CS2, H2S, SO2, SO2F2, SOF2) is shown in Table 1.

[0076] Table 1 Adsorption energy of Mg-MOF-74 for gas molecules

[0077]

[0078] Among them, the unit au of gas molecule energy, cluster energy before adsorption, and system energy after adsorption is Hartree unit, and the unit eV of adsorption energy is converted from Hartree. According to the definition formula of adsorption energy, the adsorption energy of Mg-MOF-74 material for each gas molecule is less than 0, indicating that the system reaches a lower energy stable state after heat release during the adsorption process. The relationship between the adsorption capacity of Mg-MOF-74 material for each gas is: H2S>SO2>SOF2>SO2F2>CS2>SF6>CF4.

[0079] 2) Adsorption distance between MOFs and SF6 gas decomposition products

[0080] In a specific embodiment, the MOFs material is Mg-MOF-74, and the adsorption distances between seven gas molecules including SF6 and SF6 gas decomposition products (CF4, CS2, H2S, SO2, SO2F2, SOF2) and Mg atoms on the Mg-MOF-74 material are shown in Table 2.

[0081] Table 2 Adsorption distance between gas molecules and Mg atoms on Mg-MOF-74 material

[0082]

[0083] It can be seen from Table 2 that the adsorption distance between each gas molecule and Mg atom is smaller than the van der Waals radius and larger than the covalent radius. Therefore, based on the adsorption distance, it can be inferred that the strength of the chemical bond formed by the adsorption of Mg atoms on the seven gas molecules on the Mg-MOF-74 material is greater than the van der Waals interaction, but smaller than the covalent bond strength, and belongs to a weaker type of chemical interaction.

[0084] 3) Charge transfer amount of SF6 gas decomposition products during adsorption

[0085] The charge transfer amount between the gas molecule and the adsorption substrate is defined as:

[0086] ΔQ=Q1-Q2

[0087] Among them, ΔQ represents the charge transfer amount, Q1 represents the charge of the gas molecules after adsorption, and Q2 represents the charge of the gas molecules before adsorption.

[0088] In a specific embodiment, the MOFs material is Mg-MOF-74, and the charge transfer amounts of seven gas molecules including SF6 and SF6 gas decomposition products (CF4, CS2, H2S, SO2, SO2F2, SOF2) when adsorbed by Mg-MOF-74 are shown in Table 3.

[0089] Table 3 Charge transfer of gas molecules during Mg-MOF-74 adsorption

[0090]

[0091] From the definition formula of the charge transfer amount and Table 3, it can be seen that the gas molecules lose electrons during the adsorption process, and the Mg-MOF-74 material gains electrons. This shows that there is a charge transfer phenomenon in the process of Mg-MOF-74 material adsorbing gas molecules.

[0092] 4) Changes in bond length and angle of SF6 gas decomposition products before and after adsorption

[0093] In a specific embodiment, the Mg-MOF-74 material adsorbs SO2F2 gas molecules, and the bond length changes before and after the adsorption of SO2F2 gas molecules are shown in Table 4, and the bond angle changes are shown in Table 5.

[0094] Table 4 Changes in bond length before and after adsorption of SO2F2 gas molecules

[0095]

[0096] Table 5 Changes in bond angles before and after adsorption of SO2F2 gas molecules

[0097] Bond Angle Bond angle before adsorption (°) Bond angle after adsorption (°) Change(°) F(1)-S(1)-F(2) 94.8349 95.3059 0.4710 F(1)-S(1)-O(1) 107.9328 108.0827 0.1499 F(1)-S(1)-O(2) 107.9394 105.7348 -2.2046 F(2)-S(1)-O(1) 107.9328 108.6352 0.7024 F(2)-S(1)-O(2) 107.9394 107.7750 -0.1644 O(1)-S(1)-O(2) 125.8551 126.7594 0.9043

[0098] It can be seen from Tables 4 and 5 that the bond lengths of SO2F2 gas molecules have slight changes, and the more obvious change in the bond angle is that F(1)-S(1)-O(2) decreases by 2.2°, indicating that SO2F2 gas molecules are affected by the adsorption of Mg-MOF-74 material.

[0099] 5) Changes in bond length and angle of SF6 gas decomposition products before and after adsorption and changes in orbital occupancy before and after adsorption

[0100] The orbital occupancy calculation results of the simulation are extracted and imported into the analysis software to obtain the orbital occupancy information at different energies. The orbital occupancy information is broadened with a Gaussian function to obtain a continuous state density diagram, which can clearly and intuitively analyze the orbital occupancy changes.

[0101] In a specific embodiment, the MOFs material is Mg-MOF-74. According to the electron configuration outside the Mg nucleus, the 3p orbital of the Mg atom is not filled with electrons, and the Mg atom forms Mg by losing two electrons in the 3s orbital. 2+ Therefore, the bonding interaction during adsorption was analyzed by the overlap of the 3s orbital curve and 3p orbital curve of the Mg atom on the Mg-MOF-74 material and the outer orbital curves of the atoms at the action point of SF6, CF4, CS2, H2S, SO2, SO2F2, and SOF2 gas molecules.

[0102] Figure 4 The figure shows the orbital occupancy of Mg-MOF-74 material after adsorbing SO2 gas molecules. Figure 4It can be seen that after Mg-MOF-74 adsorbs SO2 gas molecules, a new orbital occupancy appears near -3.56eV, and the other energy positions do not change significantly. Compared with the total orbital occupancy, it can be seen that the orbital occupancy changes near the energy positions of -3.56eV, -10.35eV, and 1.38eV after the adsorption of SO2 are contributed by SO2.

[0103] Figure 5 The figure shows another orbital occupancy of Mg-MOF-74 material after adsorbing SO2 gas molecules. Figure 5 It can be seen that by analyzing the 3s orbital occupancy and 3p orbital occupancy of the adsorbed substrate Mg atoms and the 2p orbital occupancy of the O atoms in the SO2 gas molecules, it can be intuitively found that the 2p orbital occupancy broadening curve of the O atoms and the 3p orbital occupancy broadening curve of the Mg atoms overlap near the energy position of -3.56eV, indicating that the Mg-MOF-74 material has a bonding adsorption effect on the SO2 gas molecules.

[0104] The embodiment of the present invention provides a simulation analysis method for detecting SF6 gas decomposition components based on MOFs materials. The simulation calculation is performed based on density functional theory to ensure the high accuracy of the results. The exchange-correlation functional with excellent accuracy is adopted to further improve the reliability of the calculation. The basis function that can effectively reduce the time consumption of double-electron integral calculation is selected to significantly improve the calculation efficiency. The changes of SF6 gas decomposition products and MOFs clusters before and after adsorption simulation are described from multiple dimensions, which more comprehensively reflects the adsorption capacity of MOFs for SF6 gas decomposition products, and provides a theoretical basis for judging whether there is local discharge phenomenon inside GIS equipment.

[0105] Based on the above simulation analysis method for detecting SF6 gas decomposition components based on MOFs materials, Figure 6 As shown, an embodiment of the present invention provides a simulation analysis system for detecting decomposition components of SF6 gas based on MOFs materials, including:

[0106] Structural model building module 01, used to build the corresponding structural model based on the MOFs cluster structure and SF6 gas decomposition products;

[0107] Functional basis set selection module 02, used to select the exchange-correlation functional and basis set of density functional theory;

[0108] Iterative optimization module 03, used for iteratively optimizing the structural model based on the exchange-correlation functional and the basis set to obtain the minimum energy state structural model;

[0109] Adsorption simulation module 04 is used to perform adsorption simulation based on the minimum energy state structure model to evaluate the adsorption capacity of MOFs for SF6 gas decomposition products.

[0110] It should be noted that each unit in the above-mentioned simulation analysis system for detecting the decomposition components of SF6 gas based on MOFs materials can be fully or partially implemented by software, hardware and a combination thereof. The above-mentioned units can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. For the specific definition of a simulation analysis system for detecting the decomposition components of SF6 gas based on MOFs materials, please refer to the definition of a simulation analysis method for detecting the decomposition components of SF6 gas based on MOFs materials above. The two have the same functions and effects, which will not be repeated here.

[0111] In summary, the embodiment of the present invention is a simulation analysis method and system for detecting SF6 gas decomposition components based on MOFs materials. The simulation calculation is based on density functional theory to ensure the high accuracy of the results. The exchange correlation functional with excellent accuracy is adopted to further improve the reliability of the calculation. The basis function that can effectively reduce the time consumption of double-electron integral calculation is selected to significantly improve the calculation efficiency. The changes of SF6 gas decomposition products and MOFs clusters before and after adsorption simulation are described from multiple dimensions, which more comprehensively reflects the adsorption capacity of MOFs for SF6 gas decomposition products, and provides a theoretical basis for judging whether there is local discharge phenomenon inside GIS equipment.

[0112] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0113] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention. These improvements and substitutions should also be regarded as the scope of protection of the present invention.

Claims

1. A simulation analysis method for detecting SF6 gas decomposition components based on MOFs materials, characterized in that: include: Construct corresponding structural models based on MOFs cluster structure and SF6 gas decomposition products; Select the exchange-correlation functional and basis set for density functional theory; Iteratively optimizing the structural model based on the exchange-correlation functional and the basis set to obtain a minimum energy state structural model; Adsorption simulation was performed based on the minimum energy state structural model to evaluate the adsorption capacity of MOFs for SF6 gas decomposition products.

2. The simulation analysis method according to claim 1, characterized in that: The corresponding structural model is constructed based on the MOFs cluster structure and the SF6 gas decomposition product, including: Based on the MOFs cluster structure and SF6 gas decomposition products, the corresponding structural model was constructed using material calculation software.

3. The simulation analysis method according to claim 1, characterized in that: The exchange-correlation functional and basis set of the selected density functional theory include: Based on the simulation computing hardware configuration and the period of the metal elements in the MOFs material, the exchange-correlation functional of the density functional theory is selected; With the goal of saving simulation calculation time, multiple density functional theory basis functions are selected, and all of the basis functions are combined to obtain a basis set.

4. The simulation analysis method according to claim 1 or 3, characterized in that: The exchange-correlation functional includes a PBE0 functional.

5. The simulation analysis method according to claim 3, characterized in that: The method aims to save simulation calculation time, selects multiple basis functions of density functional theory, and combines all the basis functions to obtain a basis set, including: Based on the period of the metal elements in the MOFs material, a first basis function of density functional theory is selected, wherein the first basis function is a basis function including pseudopotential; Based on the simulation calculation being in a geometric structure optimization stage, selecting a second basis function of density functional theory, wherein the second basis function is a basis function having an accuracy less than a first threshold; Based on the simulation calculation of the single point energy stage, selecting a third basis function of the density functional theory, wherein the third basis function is a basis function with an accuracy higher than a second threshold; The first basis function, the second basis function and the third basis function are combined to obtain a basis set of density functional theory.

6. The simulation analysis method according to claim 5, characterized in that: The first basis function includes a def2 basis function, the second basis function includes a 2-zeta basis function, and the third basis function includes a 3-zeta basis function.

7. The simulation analysis method according to claim 1, characterized in that: The iterative optimization of the structural model based on the exchange-correlation functional and the basis set to obtain the minimum energy state structural model includes: The structural model is iteratively optimized based on the exchange-correlation functional and the basis set until a convergence criterion is reached to obtain a minimum energy state structural model, wherein the convergence criterion is that the energy change of adjacent iteration steps is within a preset range.

8. The simulation analysis method according to claim 7, characterized in that: The iterative optimization of the structural model based on the exchange-correlation functional and the basis set until a convergence criterion is reached to obtain a minimum energy state structural model includes: The structural model is rapidly iteratively optimized based on the exchange-correlation functional and the basis set until a convergence criterion is reached to obtain a minimum energy state structural model.

9. The simulation analysis method according to claim 1, characterized in that: The adsorption simulation is performed based on the minimum energy state structure model to evaluate the adsorption capacity of MOFs for SF6 gas decomposition products. include: Adsorption simulation is performed based on the minimum energy state structure model to evaluate the adsorption capacity of MOFs for SF6 gas decomposition products from multiple dimensions, wherein the multiple dimensions include the adsorption energy of MOFs for SF6 gas decomposition products, the adsorption distance between MOFs and SF6 gas decomposition products, the charge transfer amount of SF6 gas decomposition products during the adsorption process, the changes in bond length and bond angle of SF6 gas decomposition products before and after adsorption, and the changes in orbital occupancy before and after adsorption.

10. A simulation analysis system for detecting SF6 gas decomposition components based on MOFs materials, characterized in that: include: Structural model building module, used to build corresponding structural models based on MOFs cluster structure and SF6 gas decomposition products; Functional basis set selection module, used to select exchange-correlation functionals and basis sets for density functional theory; An iterative optimization module, used for iteratively optimizing the structural model based on the exchange-correlation functional and the basis set to obtain a minimum energy state structural model; The adsorption simulation module is used to perform adsorption simulation based on the minimum energy state structure model to evaluate the adsorption capacity of MOFs for SF6 gas decomposition products.

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