Performance optimization method and system for oil-impregnated insulating paper

Abstract

The present invention relates to a performance optimization method and system for oil-impregnated insulating paper. By means of simulation, the size (size), volume fraction (concentration), type and shape of filling particles added to the insulating paper are determined in sequence, and the highest breakdown field strength of the insulating paper under different filling conditions is found. The size (size), volume fraction (concentration), type and shape of the filling particles corresponding to the highest breakdown field strength are taken as the optimal result, which can provide support for improving the performance of oil-impregnated paper insulation.

Description

Technical Field

[0001] The invention relates to the technical field of material engineering, and in particular to a performance optimization method and system for oil-impregnated insulating paper. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] In converter transformers, the rheological valve side winding usually adopts oil-impregnated paper insulation structure. As the transformer runs, the performance of oil-impregnated paper insulation will gradually deteriorate, resulting in its breakdown strength being easily reduced. In order to improve the breakdown strength of oil-impregnated paper insulation structure, the content of moisture and other impurities in transformer oil is reduced, or the formula and manufacturing process of transformer oil and insulation paper are improved. However, such methods are costly and have limited effects. It takes multiple theoretical simulations and actual tests to improve the performance of oil-impregnated paper insulation. Summary of the invention

[0004] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a performance optimization method and system for oil-impregnated insulating paper. By means of simulation, nanoparticles of different sizes, concentrations and shapes are doped into the insulating paper to improve the breakdown strength of the oil-impregnated paper insulation, which can provide a reference basis for actual conditions.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] A first aspect of the present invention provides a method for optimizing the performance of oil-impregnated insulating paper, comprising the following steps:

[0007] According to the set shape, volume fraction and type of filling particles, the maximum breakdown voltage corresponding to filling particles of different sizes is selected to determine the optimal size of the filling particles;

[0008] According to the same set shape, the same set type and the best size of the filling particles, the maximum breakdown voltage corresponding to different volume fractions of the filling particles is selected to determine the best volume fraction of the filling particles;

[0009] According to the same set shape, optimal size and optimal volume fraction of the filling particles, the maximum breakdown voltage corresponding to different types of filling particles is selected to determine the optimal type of filling particles;

[0010] According to the filling particles under the optimal size, optimal volume fraction and optimal type, by changing the set shape of the filling particles, the maximum breakdown voltage corresponding to the filling particles under different shapes is obtained, and the shape, size, volume fraction and type corresponding to the maximum breakdown voltage are the optimal values ​​obtained.

[0011] Furthermore, the optimal size of the filling particles is determined as follows:

[0012] Establish a research area based on the simulation software, add filling particles of set shape, volume fraction and size to the research area; add type parameters corresponding to the set dielectric constant to the research area;

[0013] Set the external voltage and field strength in the study area, and import the set damage evolution equation;

[0014] The breakdown time and breakdown field strength are obtained as single simulation results according to the set transient parameters;

[0015] By adjusting the parameters to obtain multiple simulation results, the maximum breakdown voltage corresponding to filling particles of different sizes is selected to determine the optimal size of the filling particles.

[0016] Furthermore, the damage evolution equation is shown as follows:

[0017]

[0018]

[0019]

[0020]

[0021] L0 is the kinetic coefficient related to the interface mobility, f th is the critical energy density that causes insulation damage, H(f ele +f joule -f th ) is the unit step function, f sep is the free energy density, the middle term represents the gradient energy density, f ele is the electric field energy density, f joule is the thermal energy density, α is a positive coefficient defining the phase separation energy barrier, ε0 is the relative permittivity of vacuum, and ε r is the space- and time-dependent relative dielectric constant, and E is solved by solving the Poisson equation: f is the external voltage frequency, T is the temperature, tanδ0 is the dielectric loss at 0℃, f ε (η(P,t)) is the load transfer function.

[0022] Furthermore, filler particles of set shape, volume fraction and size are added to the study area; specifically:

[0023] Setting the size, shape, quantity and position of the added filler particles;

[0024] Set the array and displacement of the added fill particles.

[0025] Furthermore, in the process of determining the optimal volume fraction of the filling particles by simulation software, the ratio of the product of the volume of a single filling particle and the number of filling particles to the entire reaction area is the volume fraction.

[0026] Furthermore, the volume fraction simulation is achieved by adjusting the array and number of filling particles added in the study area.

[0027] Furthermore, the maximum breakdown voltage corresponding to different types of filling particles is simulated by changing the dielectric constant of the filling particles.

[0028] A second aspect of the present invention provides a performance optimization system for oil-impregnated insulating paper, comprising:

[0029] The size optimization module is configured to: select the maximum breakdown voltage corresponding to the filling particles of different sizes according to the set shape, volume fraction and type of the filling particles, and determine the optimal size of the filling particles;

[0030] The concentration optimization module is configured to: select the maximum breakdown voltage corresponding to different volume fractions of the filling particles under the same set shape, the same set type and the optimal size of the filling particles, and determine the optimal volume fraction of the filling particles;

[0031] The type optimization module is configured to: select the maximum breakdown voltage corresponding to different types of filling particles under the same set shape, optimal size and optimal volume fraction of the filling particles, and determine the optimal type of the filling particles;

[0032] The shape optimization module is configured to obtain the maximum breakdown voltage corresponding to the filling particles under different shapes by changing the set shape of the filling particles according to the filling particles under the optimal size, optimal volume fraction and optimal type. The shape, size, volume fraction and type corresponding to the maximum breakdown voltage are the optimal values ​​obtained.

[0033] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-mentioned method for optimizing the performance of oil-impregnated insulating paper.

[0034] A fourth aspect of the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the above-mentioned method for optimizing the performance of oil-impregnated insulating paper when executing the program.

[0035] Compared with the prior art, one or more of the above technical solutions have the following beneficial effects:

[0036] 1. Through simulation, the size (size), volume fraction (concentration), type and shape of the filling particles added to the insulating paper are determined in turn, and the highest breakdown field strength of the insulating paper under different filling conditions is found. The filling particle size (size), volume fraction (concentration), type and shape corresponding to the highest breakdown field strength are taken as the optimal result, which can provide support for improving the performance of oil-impregnated paper insulation and at a lower cost.

[0037] 2. Based on the phase field method, by introducing continuous damage variables to describe the damage state of the material, it can more accurately simulate the entire process of insulating paper from micro crack initiation to macro crack propagation. Based on the influence of the complex microstructure inside the material on the damage evolution, it provides a damage change simulation that is closer to the actual situation. There is no need to preset the crack path, and these complex behaviors can be captured naturally. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0039] Figure 1 is a schematic diagram of a process for determining the optimal size of spherical particles provided by one or more embodiments of the present invention;

[0040] Figure 2 is a schematic diagram of a process for determining an optimal volume fraction of spherical particles provided by one or more embodiments of the present invention;

[0041] Figure 3 is a schematic diagram of a process for determining the optimal size of group i1 cylindrical particles provided by one or more embodiments of the present invention;

[0042] Figure 4 is a schematic diagram of a process for determining the optimal size of the i2 group of cylindrical particles provided by one or more embodiments of the present invention;

[0043] Figure 5 is a schematic diagram of a process for determining an optimal volume fraction of cylindrical particles provided by one or more embodiments of the present invention;

[0044] Figure 6 is a schematic diagram of a process for determining the optimal size of i3 group cubic particles provided by one or more embodiments of the present invention;

[0045] Figure 7 is a schematic diagram of a process for determining the optimal size of i4 group cubic particles provided by one or more embodiments of the present invention;

[0046] Figure 8 It is a schematic diagram of a process for determining the optimal volume fraction of cubic particles provided by one or more embodiments of the present invention. DETAILED DESCRIPTION

[0047] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0048] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0049] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0050] As introduced in the background technology, in order to improve the breakdown strength of the oil-impregnated paper insulation structure, the current method is to reduce the content of moisture and other impurities in the transformer oil, or improve the formula and manufacturing process of the transformer oil and insulation paper. However, such methods are costly and have limited effects, and it takes many theoretical simulations and actual tests to improve the performance of the oil-impregnated paper insulation.

[0051] Therefore, the following embodiments provide a performance optimization method and system for oil-impregnated insulating paper. By means of simulation, nanoparticles of different sizes, concentrations, and shapes are doped into the insulating paper to improve the breakdown strength of the oil-impregnated paper insulation, which can provide a reference for actual situations.

[0052] The "insulating paper" or "paper" referred to in the embodiments below refers to the fiber paper impregnated with insulating oil in the oil-immersed insulation structure.

[0053] Embodiment 1:

[0054] like Figure 1-Figure 8 As shown, this embodiment provides a method for optimizing the performance of oil-impregnated insulating paper, comprising the following steps:

[0055] According to the set shape, volume fraction and type of filling particles, the maximum breakdown voltage corresponding to filling particles of different sizes is selected to determine the optimal size of the filling particles;

[0056] According to the filling particles having the same set shape, the same set type and the best size, the maximum breakdown voltage corresponding to different volume fractions of the filling particles is selected to determine the best volume fraction of the filling particles;

[0057] According to the same set shape, optimal size and optimal volume fraction of the filling particles, the maximum breakdown voltage corresponding to different types of filling particles is selected to determine the optimal type of filling particles;

[0058] According to the filling particles under the optimal size, optimal volume fraction and optimal type, by changing the set shape of the filling particles, the maximum breakdown voltage corresponding to the filling particles under different shapes is obtained, and the shape, size, volume fraction and type corresponding to the maximum breakdown voltage are the optimal values ​​obtained.

[0059] Adding nanoparticles to insulating paper can improve the insulation performance. In this embodiment, the size, shape, volume fraction and type of nanoparticles are compared to simulate the insulating paper filled with nanomaterials, and the highest breakdown field strength of the paper under different filling conditions is found as the optimization target, thereby determining the shape, size, volume fraction and type of the corresponding optimal filling material.

[0060] This embodiment applies the Allen-Cahn equation (an equation describing the phase change behavior of a material), as shown below:

[0061]

[0062] η(P,t) is regarded as a phase field variable that varies with time and space, and F represents the total free energy in the system. The variational method δF / δη(P,t) is used to study the effect of the change of F on η(P,t).

[0063] The growth of electrical trees in insulating paper is driven by the free energy in the system and takes into account the combined effects of temperature and field strength on the paper, as shown in the following equation:

[0064]

[0065] Among them, f sep represents the free energy density, the middle term represents the gradient energy density, f ele represents the electric field energy density, f joule Represents thermal energy density.

[0066] f sep (P, t) = αη(P, t) 2 (1-η(P,t)) 2

[0067]

[0068] f joule (P,t)=-2πfε r tanδ(P,T)E(P,t)·E(P,t)

[0069] The free energy density is usually expressed using a one-dimensional finite depth potential well, where α is a positive coefficient defining the phase separation energy barrier, ε0 is the relative permittivity of vacuum, and ε r is the space- and time-dependent relative permittivity, and E is solved by solving the Poisson equation. f is the external voltage frequency and T is the temperature.

[0070] The following formula is used to express the spatial variation of dielectric constant and impedance tangent during the evolution of microstructure:

[0071] ε r =f ε (η(P,t))ε D +[1-f ε (η(P,t))]ε I

[0072] tanδ=f ε (η(P,t))tanδ D +[1-f ε (η(P,t))]tanδ I

[0073] f ε (η(P,t))=(4η(P,t) 3 -3η(P,t) 4 )

[0074] tanδ p =tanδ0e aT

[0075] In the case where the operating frequency of power equipment is usually 50 or 60 Hz, the tanδ of most polymers increases rapidly at high temperatures, so an exponential function is used to describe their relationship.

[0076] Tanδ0 is the dielectric loss at 0°C, a is the temperature coefficient, and the dielectric constant of most insulating materials is relatively stable at different temperatures, so the influence of the dielectric constant is not considered.

[0077] f ε (η(P,t)) is the load transfer function, which characterizes the variation law of the relative dielectric constant and impedance tangent of the material. The domain and range are between 0 and 1 and are strictly monotonically increasing. When the independent variable is 0 and 1, the first-order derivative of the function is 0.

[0078] The evolution of the damage stage of oil-impregnated insulation paper is realized using the following equation:

[0079]

[0080]

[0081]

[0082]

[0083] Where L0 is the kinetic coefficient related to the interface mobility, f th is the critical energy density that causes insulation damage, which is related to the location of the interface region in the material. ele +f joule -f th ) is the unit step function, let

[0084] The damage and crack propagation of insulating paper are often very complex and may include the interaction of multiple damage mechanisms, such as crack branching and merging. Based on the phase field method, by introducing continuous damage variables to describe the damage state of the material, this method can more accurately simulate the entire process of insulating paper from microscopic crack initiation to macroscopic crack propagation. The phase field method can take into account the influence of the complex microstructure inside the material on the damage evolution, thereby providing a damage change simulation that is closer to the actual situation. It does not require a preset crack path and can naturally capture these complex behaviors, which is difficult to achieve with traditional crack tracking methods.

[0085] The model parameters in the phase field method can be adjusted according to experimental data to ensure that the simulation results match the actual material behavior. This parameterization process allows the model to better adapt to different types of insulating paper materials. At the same time, the phase field method can not only simulate known damage behaviors, but also has the ability to predict damage evolution under unknown conditions. This is of great significance for the design improvement and life prediction of insulating paper.

[0086] In this embodiment, the diameter or side size of the nanoparticles is defined as ranging from 10 nm to 100 nm, the shapes are selected as sphere, cylinder and cube, and the types are selected as four materials.

[0087] First, only the size of the filling particles is considered, while other factors remain unchanged; then the volume fraction is considered, while other factors remain unchanged; then the type is considered, while other factors remain unchanged; finally, the shape is considered, while other factors remain unchanged.

[0088] 1. Particle size is generally considered to be the fundamental factor affecting the performance of composite materials, which is directly related to the microstructure and interface characteristics of the material. In composite materials, particle size affects the distance and distribution between particles, thereby affecting the mechanical properties, thermal properties and electrical properties of the material. Therefore, the particle size is considered first in order to establish a benchmark on which to further study the influence of other factors.

[0089] 2. After determining the optimal particle size, volume fraction is another key factor, which determines the content of filler material in the matrix and directly affects the overall performance of the composite material. Changes in volume fraction will change the material's properties such as density, modulus and conductivity, so it is reasonable to consider volume fraction after the particle size is determined.

[0090] 3. After the particle size and volume fraction are determined, the type of filler material can be considered to further optimize the performance of the material. Different types of fillers have different physical and chemical properties, which will affect the overall performance of the composite material. Therefore, the choice of type is a further optimization after the basic structural parameters have been determined.

[0091] 4. The last consideration is shape because it affects the arrangement and interaction of particles in the matrix, thus affecting the anisotropy and interface properties of the material. The influence of shape is usually a microscopic effect. When the particle size, volume fraction and type have been determined, it can be used as the final adjustment to further refine the material properties.

[0092] This step-by-step approach allows the effect of each factor on breakdown strength to be systematically investigated while avoiding the complexity caused by interactions between factors. This approach helps to gradually optimize the properties of the composite material and ultimately find the best filler material configuration.

[0093] The specific steps are as follows:

[0094] Part I: Determine the optimal volume fraction, size and type of filler particles when the particle shape is spherical.

[0095] Step 1: Determine the optimal size of the nanomaterial, the volume fraction of the filled nanoparticles is 0.5%, the particle type is TiO2, and the shape is sphere. Figure 1 As shown, the following steps are included:

[0096] 1. Measure the breakdown voltage of insulating paper filled with spherical particles with a diameter of 10 and 20 nm, and obtain E b1 、E b2 .

[0097] 2. If E b1 >E b2 , the optimal size of spherical particles was determined to be 10 nm.

[0098] 3. If E b1 <E b2 , continue to measure the breakdown voltage E of paper filled with particles with a diameter of 30nm b3 , further judge E b3 and E b2 size.

[0099] 4. If Eb3 <E b2 , the optimal size of spherical particles was determined to be 20nm.

[0100] 5. If E b3 >E b2 , continue to measure E b4 , repeat the operation until E b(1+i) >E b(2+i) , and determined that the optimal size of spherical particles is 10(1+i)nm.

[0101] This embodiment uses Comsol software to implement the above process, including the following steps:

[0102] (1) A cuboid with a width and length of 1 μm and a height of 2 μm was established as the research area. This area was extracted from actual paper and had a volume of 2 μm. 3 .

[0103] a. In the Geometry section of the Model Builder component, select μm as the length unit.

[0104] b. Set the width, depth and height of the cuboid to 1, 1 and 2 respectively, the position reference to the angle, and x, y and z to 0;

[0105] c. The line segment is specified as the starting point (0.5, 0.5, 2) and the end point (0.5, 0.5, 1.9) as the triggering area.

[0106] (2) Add spheres (as spherical filling particles, with an initial volume fraction of 0.5%). The diameter of the filling spherical particles is 10 nm. According to (volume of a single filling particle*number) / (entire reaction area)=0.005, the number of filling particles is 19098.

[0107] a. In the sphere (c1) size and shape options, select a radius of 0.005 and a sector angle of 360; the position base is centered, and x, y, and z are all 0.01.

[0108] b. Add an array, input the object as (c1), set the array type to rectangular in the size option, set the sizes of x, y and z to 21, 21 and 43 respectively, and set x, y and z to 0.04, 0.04 and 0.04 in the displacement option.

[0109] (3) Add materials in the software.

[0110] a. Add the Plastic coated paper material, select the rectangle as the geometric entity in this material, fill the relative dielectric constant into the material in the basic properties under the material properties, and set the relative dielectric constant to 3.7 in the material property details.

[0111] b. Add TiO2, select all the filled spheres in the first step as geometric entities, fill the relative dielectric constant into the material in the basic properties under material properties, and set the relative dielectric constant to 100 in the material property details.

[0112] (4) Add an electrostatic module and give external conditions. The applied external voltage condition is selected as an AC voltage at 50 Hz, and the field strength is increased at a rate of 1 kV / mm*s.

[0113] In ModelBuilder, in Global Definitions, do the following:

[0114] a. Create a waveform function, select the name as wv, select the type as sine, the period as 6.5pi, the phase and baseline as 0, and the size as 1.

[0115] b. Construct an analytical function A with the expression wv(x)*x / 1000*5.12. The variable is x. The unit option has the function unit of s and the unit of x of ms. The lower and upper time limits of x are 0 and 20s.

[0116] c. Add an electrostatic module to the component. In the electrostatic module, select the relative dielectric constant in the charge conservation option of the dielectric model. The user defines it as the above ε r The potential is selected as the top boundary and the ground as the bottom boundary. The initial value is 0.

[0117] (5) Add weak form partial differential equations and bring in mathematical equations.

[0118] The following formula:

[0119] Convert to weak form and substitute the mathematical equation:

[0120]

[0121] The meaning of each physical quantity in the above formula has been mentioned above. In the weak form, a point-by-point constraint is given as the starting point of the electrical tree.

[0122] (6) Add transient calculations to the study. After the run is completed, the time taken for breakdown t(s) and the corresponding breakdown field strength t(kV / mm) are obtained.

[0123] (7) Repeat steps (1) to (6) and adjust the parameters. In the second step, the diameter of the spherical particles is changed to 20 nm and the number of particles to be filled is 2387. In step (2) of the second step, the sizes of x, y, and z in the array are 11, 11, and 20 respectively, and the x, y, and z in the displacement options are 0.055, 0.055, and 0.055. The other operations remain unchanged.

[0124] Step 2: Determine the optimal volume fraction of the nanomaterial to be filled, the particle type is TiO2, the size is the size determined in the first step, and the shape is sphere. Figure 2 As shown, the specific steps are:

[0125] 1. Measure the paper breakdown voltage of spherical particles with a filling volume fraction of 0.5% and 1%, and obtain E c0.5 、E c1 .

[0126] 2. If E c0.5 >E c1 , the optimal volume fraction of spherical particles was determined to be 0.5%.

[0127] 3. If E c0.5 <E c1 , and then measure the breakdown voltage E of the paper with a volume fraction of 1.5% filled with particles. c1.5 , further judge E c1 and E c1.5 size.

[0128] 4. If E c1.5 <E c1 , the optimal volume fraction of spherical particles was determined to be 1%.

[0129] 5. If E c1.5 >E c1 , continue to measure E c2 , repeat the operation until E c(0.5+n) >E c(1+n) , and determined that the optimal filling volume fraction of spherical particles is (0.5+n)%.

[0130] In this step, the steps implemented by the comsol software are similar to the above. In the first step, the optimal diameter of the filling particles is known, and only the filling quantity corresponding to different volume contents needs to be calculated. Further, the array displacement and quantity are changed in b in the above (2).

[0131] Step 3: Determine the best type of filling nanoparticles, the size and volume fraction of the particles are the size and volume fraction determined in the first and second steps, and the shape is spheres.

[0132] Detailed steps in the third step: Taking the following four materials as examples: Al2O3, SiO2, Fe2O3, Fe3O4, the breakdown voltage of paper filled with nano-spherical particles of the four materials is measured to be E d1 、E d2 、E d3 、E d4, judge the high and low breakdown voltages of the four materials. The material corresponding to the high breakdown voltage is the best type among the four materials. This can also be achieved through Comsol Runajin. It is only necessary to adjust the filling material in b in the above (3) to obtain the corresponding breakdown field strength in the first part (6).

[0133] Part 2: When the particle shape is cylindrical, determine the optimal volume fraction, size, and type of filling particles.

[0134] Step 1: Determine the optimal size of the nanomaterial, the volume fraction of the filled nanoparticles is 0.5%, the particle type is TiO2, and the shape is cylindrical. The specific steps are:

[0135] 1. Keep the cylinder diameter unchanged and measure the optimal cylinder height.

[0136] (1) Determine that the diameter of the cylinder is 10 nm, and measure the breakdown voltage of the paper when the height of the cylinder is 10 nm and 20 nm. 1 b1 、E 1 b2 ,like Figure 3 shown.

[0137] (2) If E 1 b1 >E 1 b2 , and determined that the optimal cylinder height of the cylinder is 10nm.

[0138] (3) If E 1 b1 <E 1 b2 , continue to measure the breakdown voltage E of paper filled with cylindrical particles with a height of 30nm 1 b3 , further judge E 1 b3 and E 1 b2 size.

[0139] (4) If E 1 b3 <E 1 b2 , the optimal height size of cylindrical particles was determined to be 20nm.

[0140] (5) If E 1 b3 >E 1 b2 , continue to measure E 1 b4 , repeat the operation until The optimal column height of the particles was determined to be 10(1+i1)nm.

[0141] 2. Keeping the height of the cylinder unchanged, measure the diameter of the cylinder.

[0142] (1) Determine the height of the cylinder as the optimal cylinder height in step 1 of the second part of the detailed steps of the first step, and measure the paper breakdown voltage E when the cylinder diameter is 10nm and 20nm 2 b1 、E 2 b2 ,like Figure 4 shown.

[0143] (2) If E 2 b1 >E 2 b2 , the optimal diameter of the cylinder was determined to be 10 nm.

[0144] (3) If E 2 b1 <E 2 b2 , continue to measure the breakdown voltage E of paper filled with cylindrical particles with a diameter of 30nm 2 b3 , further judge E 2 b3 and E 2 b2 size.

[0145] (4) If E 2 b3 <E 2 b2 , the optimal diameter of cylindrical particles was determined to be 20nm.

[0146] (5) If E 2 b3 >E 2 b2 , continue to measure E 2 b4 , repeat the operation until The optimal diameter size of cylindrical particles was determined to be 10(1+i2)nm.

[0147] The above process is also implemented using Comsol software. The specific steps are:

[0148] (1) A cuboid with a width and length of 1 μm and a height of 2 μm is established as the study area, which is extracted from actual paper. 3 .

[0149] a. In the Geometry options of the component under the Model Builder, select μm as the unit of length.

[0150] b. The width, depth and height of the cuboid are 1, 1 and 2, the reference in the position is the angle, and x, y and z are 0;

[0151] c. The line segment is specified as the starting point (0.5, 0.5, 2) and the end point (0.5, 0.5, 1.9) as the triggering area.

[0152] (2) Add cylinders (as cylindrical filling particles, with a volume fraction of 0.5%). The diameter of the filling cylindrical particles is 10 nm and the height is 10 nm. According to (volume of a single filling particle * number) / (entire reaction area) = 0.005, the number of fillings is 12732.

[0153] a. In the cylinder (c2) size and shape options, select the radius and height as 0.005 and 0.01 respectively, the position x, y and z as 0.02, the axis as the z axis, and the rotation angle as 30 degrees.

[0154] b. Add the array, input the object as (c2), set the array type to rectangular in the size option, and set the sizes of x, y and z to 18, 18 and 39 respectively. Set the displacement options to 0.03, 0.03 and 0.03 for x, y and z.

[0155] (3) Add materials in the software.

[0156] a. Add the Plastic coated paper material, select the geometry entity as a rectangle in this material, fill the relative dielectric constant into the material in the basic properties under the material properties, and set the relative dielectric constant to 3.7 in the material property details.

[0157] b. Add TiO2, select all the filled cylinders in the first step as geometric entities, fill the relative dielectric constant into the material in the basic properties under material properties, and set the relative dielectric constant to 100 in the material property details.

[0158] (4) Add an electrostatic module and give external conditions. The applied external voltage condition is selected as an AC voltage at 50 Hz, and the potential is increased at a rate of 1 kV / mm*s.

[0159] In the ModelBuilder on the far left of the interface, in Global Definitions

[0160] a. Create a waveform function, select the name as wv, select the type as sine, the period as 6.5pi, the phase and baseline as 0, and the size as 1.

[0161] b. Construct an analytical function A with the expression wv(x)*x / 1000*5.12. The variable is x. The unit option has the function unit of s and the unit of x of ms. The lower and upper time limits of x are 0 and 20s.

[0162] c. Add an electrostatic module to the component. In the electrostatic module, select the relative dielectric constant in the charge conservation option of the dielectric model. The user defines it as the above ε r The potential is selected as the top boundary and the ground as the bottom boundary. The initial value is 0.

[0163] (5) Add weak form partial differential equations and bring in mathematical equations.

[0164] (6) Add transient state for calculation. After the operation is completed, the time taken for breakdown t(s) and the corresponding breakdown field strength t(kV / mm) are obtained.

[0165] (7) Repeat steps (1) to (6) and adjust the parameters. In the second step, the height of the cylindrical particles is changed to 20 nm and the number of particles to be filled is 6366. In step (2) of the second step, the sizes of x, y, and z in the array are 14, 14, and 32 respectively, and the x, y, and z in the displacement options are 0.04, 0.04, and 0.03. Other operations remain unchanged.

[0166] In the case of keeping the height of the cylinder unchanged and measuring the diameter of the cylinder, the implementation process is basically the same as the comsol implementation process above, so I will not go into details.

[0167] Step 2: Determine the optimal volume fraction of the nanomaterial to be filled, the particle type is TiO2, the size is the size determined in the first step, and the shape is cylindrical. The specific steps are as follows: Figure 5 shown.

[0168] 1. Measure the paper breakdown voltage of cylindrical particles with a filling volume fraction of 0.5% and 1%, and obtain E 1 c1 、E 1 c2 .

[0169] 2. If E 1 c0.5 >E 1 c1 , the optimal volume fraction of cylindrical particles was determined to be 0.5%.

[0170] 3. If E 1 c0.5 <E 1 c1 , and then measure the breakdown voltage E of the paper with a volume fraction of 1.5% filled with particles. 1 c1.5 , further judge E 1 c1 and E 1 c1.5 size.

[0171] 4. If E 1c1.5 <E 1 c1 , the optimal volume fraction of cylindrical particles was determined to be 1%.

[0172] 5. If E 1 c1.5 >E 1 c1 , continue to measure E 1 c2 , repeat the operation until The optimal filling volume fraction of cylindrical particles is determined to be (0.5+n1)%.

[0173] In this step, the comsol implementation steps are similar to the above. In the first step, the optimal height and radius of the filled cylindrical particles are known. It is only necessary to calculate the number of fillings corresponding to different volume contents through (volume of a single filled particle * number) / (entire reaction area) = volume fraction. Further, the array displacement and number are changed in b in the above (2).

[0174] Step 3: Determine the best type of nanoparticles for filling. The size and volume fraction of the particles are the size and volume fraction determined in the first and second steps, and the shape is cylindrical.

[0175] Take the following four materials as examples: Al2O3, SiO2, Fe2O3, and Fe3O4.

[0176] The breakdown voltage of paper filled with four types of nano-cylindrical particles was measured to be E 1 d1 、E 1 d2 、E 1 d3 、E 1 d4 , judge the four kinds of breakdown voltages, and the material with high breakdown voltage is the best type among the four materials. When implemented by comsol, it is only necessary to adjust the filling material in b in the above (3) and obtain the corresponding breakdown field strength in the second part (6).

[0177] Part 3: Determine the optimal volume fraction, size, and type of filling particles when the particle shape is cubic.

[0178] Step 1: Determine the optimal size of the nanomaterial, the volume fraction of the filled nanoparticles is 0.5%, the particle type is TiO2, and the shape is a cube. The specific steps are:

[0179] 1. Keeping the base area of ​​the cube constant, measure the optimal cube height.

[0180] (1) Determine that the length of the sides of the cube base is 10 nm. Measure the breakdown voltage of paper when the height of the cube is 10 nm and 20 nm. 3 b1 、E 3 b2 ,like Figure 6 shown.

[0181] (2) If E 3 b1 >E 3 b2 , and determined that the optimal height of cubic particles is 10nm.

[0182] (3) If E 3 b1 <E 3 b2 , and then measure the breakdown voltage E of paper filled with cubic particles with a height of 30 nm. 3 b3 , further judge E 3 b3 and E 3 b2 size.

[0183] (4) If E 3 b3 <E 3 b2 , the optimal height of cubic particles was determined to be 20nm.

[0184] (5) If E 3 b3 >E 3 b2 , continue to measure E 3 b4 , repeat the operation until The optimal cube height was determined to be 10(1+i3)nm.

[0185] 2. Keeping the height of the cube unchanged, measure the optimal cube base area.

[0186] (1) Determine the height of the cube as the optimal cube height in step 1 of the first step of Part 3. Set the length and width of the bottom of the cube to be the same. Measure the paper breakdown voltage E when the filling particles have a bottom side length of 10nm and 20nm. 2 b1 、E 2 b2 ,like Figure 7 shown.

[0187] (2) If E 2 b1 >E 2 b2, determining that the optimal base length of the packed cube is 10nm.

[0188] (3) If E 2 b1 <E 2 b2 , continue to measure the breakdown voltage E of the paper filled with cubes with a base length of 30nm 2 b3 , further judge E 2 b3 and E 2 b2 size.

[0189] (4) If E 2 b3 <E 2 b2 , it is determined that the optimal base length of the filled cube is 20nm.

[0190] (5) If E 2 b3 >E 2 b2 , continue to measure E 2 b4 , repeat the operation until The optimal base length of cubic particles was determined to be 10(1+i4)nm.

[0191] The above steps are implemented using comsol software. The specific steps are as follows:

[0192] (1) A cuboid with a width and length of 1 μm and a height of 2 μm is established as the study area, which is extracted from actual paper. 3 .

[0193] a. In the Geometry options of the component under the Model Builder, select μm as the unit of length.

[0194] b. The width, depth and height of the cuboid are 1, 1 and 2, the reference in the position is the angle, and x, y and z are 0;

[0195] c. The line segment is specified as the starting point (0.5, 0.5, 2) and the end point (0.5, 0.5, 1.9) as the triggering area.

[0196] (2) Add cubes (as cubic filling particles, with a volume fraction of 0.5%). The bottom side length of the filling cubic particles is 10 nm and the height is 10 nm. According to (volume of a single filling particle * number) / (entire reaction area) = 0.005, it can be known that the number of fillings is 10,000.

[0197] a. In the cube (c3) size and shape options, select width, depth, and height as 0.01, position x, y, and z as 0.02, axis as z, and rotation angle as 30 degrees.

[0198] b. Add the array, input the object as (c3), set the array type to rectangular in the size option, and set the sizes of x, y and z to 17, 17 and 34 respectively. Set the displacement options to x, y and z as 0.03 and 0.03.

[0199] (3) Add materials in the software.

[0200] a. Add the Plastic coated paper material, select the geometry entity as a rectangle in this material, fill the relative dielectric constant into the material in the basic properties under the material properties, and set the relative dielectric constant to 3.7 in the material property details.

[0201] b. Add TiO 2. Select all the filled cubes in the first step as geometric entities. In the basic properties under material properties, fill the relative dielectric constant into the material. In the material property details, set the relative dielectric constant to 100.

[0202] (4) Add an electrostatic module and give external conditions. The applied external voltage condition is selected as an AC voltage at 50 Hz, and the potential is increased at a rate of 1 kV / mm*s.

[0203] In ModelBuilder, in Global Definitions, do the following:

[0204] a. Create a waveform function, select the name as wv, select the type as sine, the period as 6.5pi, the phase and baseline as 0, and the size as 1.

[0205] b. Construct an analytical function A with the expression wv(x)*x / 1000*5.12. The variable is x. The unit option has the function unit of s and the unit of x of ms. The lower and upper time limits of x are 0 and 20s.

[0206] c. Add an electrostatic module to the component. In the electrostatic module, select the relative dielectric constant in the charge conservation option of the dielectric model. The user defines it as the above ε r The potential is selected as the top boundary and the ground as the bottom boundary. The initial value is 0.

[0207] (5) Add weak form partial differential equations and bring in mathematical equations.

[0208] (6) Add transient state for calculation. After the operation is completed, the time taken for breakdown t(s) and the corresponding breakdown field strength t(kV / mm) are obtained.

[0209] (7) Repeat steps (1) to (6) and adjust the parameters. In the second step, the height of the cube particles is changed to 20 nm and the number of fillers is 5000. In step (2) of the second step, the sizes of x, y, and z in the array are 13, 14, and 28 respectively, and the x, y, and z in the displacement options are 0.045, 0.04, and 0.04. Other operations remain unchanged.

[0210] In the case of keeping the height of the cube unchanged and measuring the bottom area of ​​the cube, the implementation process is basically the same as the comsol implementation process above, so I will not go into details.

[0211] Step 2: Determine the optimal volume fraction of the nanomaterial to be filled, the particle type is TiO2, the size is the size determined in the first step, and the shape is a cube. The specific steps are as follows Figure 8 As shown:

[0212] 1. Measure the paper breakdown voltage of cubic particles with a filling volume fraction of 0.5% and 1%, and obtain E 2 c1 、E 2 c2 .

[0213] 2. If E 2 c0.5 >E 2 c1 , the optimal volume fraction of cubic particles was determined to be 0.5%.

[0214] 3. If E 2 c0.5 <E 2 c1 , and then measure the breakdown voltage E of the paper with a volume fraction of 1.5% filled with particles. 2 c1.5 , further judge E 2 c1 and E 2 c1.5 size.

[0215] 4. If E 2 c1.5 <E 2 c1 , the optimal volume fraction of cubic particles was determined to be 1%.

[0216] 5. If E 2 c1.5 >E 2 c1 , continue to measure E 2 c2 , repeat the operation until The optimal filling volume fraction of cubic particles was determined to be 0.5(1+n1)%.

[0217] In this step, the comsol implementation steps are similar to the above. The optimal height and bottom area of ​​the filled cubic particles are known in the first step. It is only necessary to calculate the number of fillings corresponding to different volume contents by (volume of a single filled particle * number) / (entire reaction area) = volume fraction. Further, the array displacement and number are changed in b in the above (2).

[0218] Step 3: Determine the best type of filling nanoparticles. The volume fraction and size of the particles are the same as those determined in steps 1 and 2, and the shape is a cube.

[0219] Taking the following four materials as examples: Al2O3, SiO2, Fe2O3, Fe3O4, the breakdown voltage of paper filled with nanocubic particles of the four materials was measured to be E 2 d1 、E 2 d2 、E 2 d3 、E 2 d4 , judge the four breakdown voltages, get the highest breakdown voltage, and the material with the highest breakdown voltage is the best type among the four materials. When implemented by comsol, it is only necessary to adjust the filling material in b in the above (3) and get the corresponding breakdown field strength in the third part (6).

[0220] Part 4: Comparison of the three highest breakdown voltages E obtained in step 3 of the above three parts d 、E 1 d 、E 2 d , determine the maximum breakdown voltage, and its corresponding particle condition is the optimal choice.

[0221] Through the above optimization process, the optimal concentration (volume fraction), size, type and shape of the nanoparticles filled in the insulating paper are determined to maximize the breakdown strength of the insulating paper. Through simulation, it can provide reference for actual conditions and reduce economic losses.

[0222] Embodiment 2:

[0223] Performance optimization system for oil-impregnated insulation paper, including:

[0224] The size optimization module is configured to: select the maximum breakdown voltage corresponding to the filling particles of different sizes according to the set shape, volume fraction and type of the filling particles, and determine the optimal size of the filling particles;

[0225] The concentration optimization module is configured to: select the maximum breakdown voltage corresponding to different volume fractions of the filling particles under the same set shape, the same set type and the optimal size of the filling particles, and determine the optimal volume fraction of the filling particles;

[0226] The type optimization module is configured to: select the maximum breakdown voltage corresponding to different types of filling particles under the same set shape, optimal size and optimal volume fraction of the filling particles, and determine the optimal type of the filling particles;

[0227] The shape optimization module is configured to obtain the maximum breakdown voltage corresponding to the filling particles under different shapes by changing the set shape of the filling particles according to the filling particles under the optimal size, optimal volume fraction and optimal type. The shape, size, volume fraction and type corresponding to the maximum breakdown voltage are the optimal values ​​obtained.

[0228] Embodiment three:

[0229] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the above-mentioned method for optimizing the performance of oil-impregnated insulating paper.

[0230] Embodiment 4:

[0231] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the above-mentioned method for optimizing the performance of oil-impregnated insulating paper are implemented.

[0232] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for optimizing the performance of oil-impregnated insulating paper, characterized in that: The following steps are involved: According to the set shape, volume fraction and type of filling particles, the maximum breakdown voltage corresponding to filling particles of different sizes is selected to determine the optimal size of the filling particles; According to the same set shape, the same set type and the best size of the filling particles, the maximum breakdown voltage corresponding to different volume fractions of the filling particles is selected to determine the best volume fraction of the filling particles; According to the same set shape, optimal size and optimal volume fraction of the filling particles, the maximum breakdown voltage corresponding to different types of filling particles is selected to determine the optimal type of filling particles; According to the filling particles under the optimum size, optimum volume fraction and optimum type, by changing the set shape of the filling particles, the maximum breakdown voltage corresponding to the filling particles under different shapes is obtained, and the shape, size, volume fraction and type corresponding to the maximum breakdown voltage are the obtained optimal values; Determine the optimal size of the filler particles, specifically: Establish a research area based on the simulation software, add filling particles of set shape, volume fraction and size to the research area; add type parameters corresponding to the set dielectric constant to the research area; Set the external voltage and field strength in the study area, and import the set damage evolution equation; The breakdown time and breakdown field strength are obtained as single simulation results according to the set transient parameters; By adjusting the parameters to obtain multiple simulation results, the maximum breakdown voltage corresponding to the filling particles of different sizes is selected to determine the optimal size of the filling particles; The damage evolution equation is shown as follows: Where L0 is the kinetic coefficient related to the interface mobility, f th is the critical energy density that causes insulation damage, H(f ele +f joule -f th ) is the unit step function, f sep is the free energy density, the middle term represents the gradient energy density, f ele is the electric field energy density, f joule is the thermal energy density, α is a positive coefficient defining the phase separation energy barrier, ε0 is the relative permittivity of vacuum, and ε r is the space- and time-dependent relative dielectric constant, and E is solved by solving the Poisson equation: f is the external voltage frequency, tanδ0 is the dielectric loss at 0℃, f ε (η(P,t)) is the load transfer function.

2. The performance optimization method of oil-impregnated insulating paper according to claim 1, characterized in that: Add filler particles of specified shape, volume fraction and size to the study area; specifically: Setting the size, shape, quantity and position of the added filler particles; Set the array and displacement of the added fill particles.

3. The performance optimization method of oil-impregnated insulating paper according to claim 1, characterized in that: In the process of determining the optimal volume fraction of filling particles by simulation software, the ratio between the product of the volume of a single filling particle and the number of filling particles and the entire reaction area is the volume fraction.

4. The performance optimization method of oil-impregnated insulating paper according to claim 1, characterized in that: The simulation of volume fraction is achieved by the array and number of filler particles added in the study area.

5. The performance optimization method of oil-impregnated insulating paper according to claim 1, characterized in that: The maximum breakdown voltage corresponding to different types of filling particles is simulated by changing the dielectric constant of the filling particles.

6. A system for optimizing the performance of oil-impregnated insulating paper, based on the method for optimizing the performance of oil-impregnated insulating paper according to claims 1-5, characterized in that: include: The size optimization module is configured to: select the maximum breakdown voltage corresponding to the filling particles of different sizes according to the set shape, volume fraction and type of the filling particles, and determine the optimal size of the filling particles; The concentration optimization module is configured to: select the maximum breakdown voltage corresponding to different volume fractions of filling particles under the same set shape, the same set type and the optimal size of the filling particles, and determine the optimal volume fraction of the filling particles; The type optimization module is configured to: select the maximum breakdown voltage corresponding to different types of filling particles under the same set shape, optimal size and optimal volume fraction of the filling particles, and determine the optimal type of the filling particles; The shape optimization module is configured to obtain the maximum breakdown voltage corresponding to the filling particles under different shapes by changing the set shape of the filling particles according to the filling particles under the optimal size, optimal volume fraction and optimal type. The shape, size, volume fraction and type corresponding to the maximum breakdown voltage are the optimal values ​​obtained.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the performance optimization method of oil-impregnated insulating paper as described in any one of claims 1 to 5 are implemented.

8. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for optimizing the performance of oil-impregnated insulating paper as claimed in any one of claims 1 to 5 are implemented.

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