Rapid optimization design method for lightweight robustness of environment-friendly gas bus and related equipment

Abstract

The invention discloses an environment-friendly gas bus lightweight robustness rapid optimization design method and related equipment. According to the method, based on the six-sigma robustness design concept, the structure size optimization model of the replacement bus using the environment-friendly gas as the insulating medium is established, on the premise that the size of the replacement bus is not increased, the requirements for light weight and reliable performance of the replacement bus are comprehensively considered, meanwhile, the weight consistency is kept, and the reliability of the replacement bus is improved. And the performance parameters of the replacement bus are constrained, so that the reliability of bus optimization design is ensured. Moreover, the performance agent model is used for replacing complex simulation, the optimization process is accelerated, and the optimization efficiency is improved.

Description

Technical Field

[0001] This application relates to the technical field of busbar design, and particularly to a method for rapidly optimizing the lightweight robustness of an environmentally friendly gas busbar and related equipment. Background Art

[0002] SF6 (sulfur hexafluoride) is widely used in power transmission and transformation equipment due to its excellent insulation and arc extinguishing performance. However, SF6 is the gas with the highest greenhouse effect among known greenhouse gases, and the resulting greenhouse effect cannot be ignored in terms of environmental impact. In related technologies, environmentally friendly gases (such as a mixture of C4F7N (perfluoroisobutyronitrile) / CO2 (carbon dioxide), C5F 10 O (perfluoropentanone) / CO2 (carbon dioxide) mixture) are used to replace SF6 as the gas insulation medium for high-voltage busbars. The change in the gas insulation medium will affect the current-carrying and insulation performance of the high-voltage busbar. Therefore, it is necessary to optimize the geometric structure of the replaced high-voltage busbar to ensure its reliable operation after replacing SF6 with an environmentally friendly gas.

[0003] In some related technologies of high-voltage busbar structure design, the geometric structure of the replaced high-voltage busbar is locally adjusted according to experience, often with a large margin, making it difficult to obtain an optimal structure design. Moreover, it has high requirements for the knowledge and experience level of designers, affecting the final reliability of the high-voltage busbar, unable to meet the design performance requirements in all aspects, and requiring multiple modifications, resulting in low design efficiency, and it is difficult to achieve the reliability and optimization of the high-voltage busbar.

[0004] In other related technologies of high-voltage busbar structure design, a deterministic optimization method is used to design the structure for a single performance of the high-voltage busbar, without considering uncertain factors such as processing or use losses. The obtained structure design and its corresponding performance often approach the maximum limit. However, in the actual manufacturing and use process, there are many uncertain factors (such as manufacturing accuracy, material properties, wear and aging, etc.), which may lead to the deterioration of the performance of the high-voltage busbar, and even exceed the maximum limit of the performance, resulting in equipment failure and unable to ensure the reliability of the design. Summary of the Invention

[0005] In view of this, this application provides a method for rapidly optimizing the lightweight robustness of an environmentally friendly gas busbar and related equipment. Based on the six sigma robustness design concept, a structural size optimization model of the replaced busbar using an environmentally friendly gas as the insulation medium is established. Without increasing the volume of the replaced busbar, the requirements of lightweight and reliable performance of the replaced busbar are comprehensively considered, while maintaining the consistency of weight, and the performance parameters of the replaced busbar are constrained to ensure the reliability of the busbar optimization design. Moreover, a performance surrogate model is used to replace complex simulations to accelerate the optimization process and improve the optimization efficiency.

[0006] According to one aspect of the present application, a rapid optimization design method for the lightweight robustness of an environmentally friendly gas busbar is provided, including:

[0007] Replace the gas insulation medium in the high-voltage busbar to be optimized with an environmentally friendly gas to obtain a replaced busbar;

[0008] Extract the structural dimension samples of the replaced busbar within the preset dimension range of the replaced busbar, and obtain the simulation values of the performance parameters of the structural dimension samples according to the preset physical property parameters of the environmentally friendly gas;

[0009] Determine a performance surrogate model according to the simulation values of the performance parameters of the structural dimension samples, where the performance surrogate model is used to characterize the corresponding relationship between the target structural dimensions corresponding to the performance parameters in the structural dimension samples and the performance parameters;

[0010] Determine the optimization objective according to the weight of the metal material in the replaced busbar, and constrain the performance parameters to construct a structural dimension optimization model of the replaced busbar;

[0011] Based on the performance surrogate model, solve the structural dimension optimization model to obtain the optimized structural dimensions, and optimize the replaced busbar according to the optimized structural dimensions to obtain the optimized replaced busbar.

[0012] According to another aspect of the present application, an optimization design device for the lightweight robustness of an environmentally friendly gas busbar is provided, including:

[0013] A replacement module for replacing the gas insulation medium in the high-voltage busbar to be optimized with an environmentally friendly gas to obtain a replaced busbar;

[0014] A sampling module for extracting the structural dimension samples of the replaced busbar within the preset dimension range of the replaced busbar, and obtaining the simulation values of the performance parameters of the structural dimension samples according to the preset physical property parameters of the environmentally friendly gas;

[0015] A construction module for determining a performance surrogate model according to the simulation values of the performance parameters of the structural dimension samples, where the performance surrogate model is used to characterize the corresponding relationship between the target structural dimensions corresponding to the performance parameters in the structural dimension samples and the performance parameters; and,

[0016] Determine the optimization objective according to the weight of the metal material in the replaced busbar, and constrain the performance parameters to construct a structural dimension optimization model of the replaced busbar;

[0017] An optimization module for solving the structural dimension optimization model based on the performance surrogate model to obtain the optimized structural dimensions, and optimizing the replaced busbar according to the optimized structural dimensions to obtain the optimized replaced busbar.

[0018] According to another aspect of the present application, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the above-mentioned rapid optimization design method for the lightweight and robust environmental gas busbar are realized.

[0019] According to yet another aspect of the present application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the program, the steps of the above-mentioned rapid optimization design method for the lightweight and robust environmental gas busbar are realized.

[0020] By means of the above technical solutions, the present application provides a rapid optimization design method for the lightweight and robust environmental gas busbar and related devices. According to the environmental protection requirements in the actual application scenario, the gas insulation medium in the high-voltage busbar to be optimized is replaced with the required environmental protection gas to obtain a replacement busbar. Then, based on the preset physical property parameters of the environmental protection gas, through diversified size sample simulations, the influence of the structural dimensions of the replacement busbar under the environmental protection gas on its insulation and current-carrying performance is comprehensively evaluated. Moreover, a mathematical relationship between the structural dimensions and performance parameters of the replacement busbar is established to obtain a performance surrogate model to replace complex simulations and accelerate the optimization process. Then, based on the six sigma robust design concept, a structural dimension optimization model of the replacement busbar is established. Without increasing the volume of the replacement busbar, while comprehensively considering the requirements of lightweight and reliable performance of the replacement busbar, the sensitivity of the performance of the replacement busbar to uncertain factors is reduced, and the performance parameters of the replacement busbar are constrained to ensure the reliability of the busbar optimization design. Thus, based on the performance surrogate model, the structural dimension optimization model is quickly solved to obtain the optimized structural dimensions of the replacement busbar, so that the optimized replacement busbar can still meet the performance requirements for safe operation under various possible size fluctuations, and the reliability of the performance of the high-voltage busbar under actual working conditions is improved.

[0021] The above description is only an overview of the technical solutions of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specific embodiments of the present application are specifically given. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation to the present application. In the drawings:

[0023] Figure 1 A flowchart showing the rapid optimization design method for the lightweight and robust environmental gas busbar provided by the embodiment of the present application is shown;

[0024] Figure 2 shows the geometric structure of the high-voltage busbar to be optimized provided by the embodiments of the present application;

[0025] Figure 3 shows the performance fitting response surface of the maximum field strength in the gas domain provided by the embodiments of the present application;

[0026] Figure 4 shows the performance fitting response surface of the maximum tangential field strength provided by the embodiments of the present application;

[0027] Figure 5 shows the performance fitting response surface of the maximum field strength inside the insulator provided by the embodiments of the present application;

[0028] Figure 6 shows the performance fitting response surface of the maximum temperature rise of the housing provided by the embodiments of the present application;

[0029] Figure 7 shows the performance fitting response surface of the maximum temperature rise of the conductor provided by the embodiments of the present application;

[0030] Figure 8 shows the performance fitting response surface of the maximum stress inside the insulator provided by the embodiments of the present application;

[0031] Figure 9 shows the performance fitting response surface of the maximum stress at the bonding place of the conductor provided by the embodiments of the present application;

[0032] Figure 10 shows the comparison diagram of the optimized design results provided by the embodiments of the present application;

[0033] Figure 11 shows the structural block diagram of the lightweight robust optimization design device for the eco-friendly gas busbar provided by the embodiments of the present application. Detailed implementation manners

[0034] In the following, the present application will be described in detail with reference to the drawings and in conjunction with the embodiments. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0035] The embodiments of the present application are described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described by referring to the drawings below are exemplary and are only used to explain the present application and cannot be construed as a limitation to the present application.

[0036] Those skilled in the art can understand that, unless specifically stated otherwise, the singular forms "a", "an", "the", and "said" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of this application means the presence of the described features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or their groups. It should be understood that when we say an element is "connected" or "joined" to another element, it can be directly connected or joined to other elements, or there may also be intermediate elements. In addition, the "connection" or "joining" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any unit and all combinations of one or more associated listed items.

[0037] Now, exemplary embodiments according to the present application will be described in more detail with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many different forms and should not be construed as being limited only to the embodiments set forth herein. It should be understood that these embodiments are provided so that the disclosure of the present application is thorough and complete, and the concept of these exemplary embodiments is fully conveyed to those of ordinary skill in the art.

[0038] In this embodiment, a method for rapidly optimizing the lightweight robustness design of an environmentally friendly gas busbar is provided, as Figure 1 shown. The method includes:

[0039] Step 101, replacing the gas insulation medium in the high-voltage busbar to be optimized with an environmentally friendly gas to obtain a replacement busbar.

[0040] In this embodiment, an existing high-voltage busbar design can be selected as the high-voltage busbar to be optimized, and then, according to the environmental protection requirements in the actual application scenario, the gas insulation medium in the high-voltage busbar to be optimized is replaced with the required environmentally friendly gas to obtain a replacement busbar.

[0041] Here, the high-voltage busbar refers to a high-voltage and large-current transmission line. For example, a 550 kV SF6 busbar, etc. The environmentally friendly gas can be a mixed gas of 9% C4F7N / 91% CO2, or a mixed gas of 3% C5F 10 O / 97% CO2, etc.

[0042] Step 102, extracting structural dimension samples of the replacement busbar within the preset dimension range of the replacement busbar, and obtaining simulation values of the performance parameters of the structural dimension samples according to the preset physical property parameters of the environmentally friendly gas.

[0043] In this embodiment, the insulation and current-carrying performance are used as the basis for evaluating the busbar design. Among the numerous structural dimensions of the replacement busbar, typical structural dimensions that have a greater impact on the insulation and current-carrying performance are selected as the sampling structural dimensions of the replacement busbar. Thus, sampling is carried out within the preset dimension range of the replacement busbar to obtain different combinations of sampling structural dimensions (i.e., different structural dimension samples) to cover a wider design space.

[0044] Then, the physical property parameters of the environmentally friendly gas are determined in advance and used as the input for simulation calculations. Thus, the insulation performance and current-carrying capacity of the replacement busbar after being replaced by the environmentally friendly gas can be accurately evaluated by using the preset physical property parameters of the environmentally friendly gas, enabling the simulation values of the performance parameters obtained from the simulation calculations to truly reflect the interaction between the environmentally friendly gas and each structure in the replacement busbar and improving the reliability of the simulation.

[0045] Next, according to the preset physical property parameters of the environmentally friendly gas, parametric simulations are carried out on the structural dimension samples to obtain the simulation values of the performance parameters of the structural dimension samples (i.e., when the replacement busbar is in the geometric structure corresponding to the structural dimension sample, the simulation values of the performance parameters of the replacement busbar) to quantify the influence of different structural dimensions of the replacement busbar under the environmentally friendly gas on its insulation and current-carrying performance.

[0046] Here, the physical property parameters may include the density, specific heat capacity at constant pressure, viscosity, and thermal conductivity of the gas, etc. The performance parameters are parameters related to the insulation and current-carrying performance of the replacement busbar. For example, the performance parameters may be the maximum temperature rise of the housing in the replacement busbar, the maximum temperature rise of the conductor, the maximum tangential field strength, the maximum field strength in the gas domain, the maximum field strength inside the insulator, the maximum local stress of the insulator, and the maximum stress at the conductor bonding location. Among them, the maximum temperature rise of the housing is the maximum value of the temperature rise of the housing of the high-voltage busbar under operating conditions due to the heat generation of the internal conductor or the influence of the ambient temperature. The maximum temperature rise of the housing can measure the heat dissipation capacity of the housing. Taking it as a performance parameter can prevent the possible material aging, degradation of insulation performance, or even mechanical deformation or fire risk caused by excessive temperature rise. The maximum temperature rise of the conductor is the maximum value of the temperature rise of the internal conductor of the high-voltage busbar due to resistance loss during current-carrying. The maximum temperature rise of the conductor can reflect the current-carrying efficiency and safety of the conductor. Taking it as a performance parameter can prevent the increase of conductor resistance, accelerated insulation aging, or short circuit caused by excessive temperature rise. The maximum tangential field strength is the maximum value of the electric field strength along the tangent direction of the conductor surface. Taking it as a performance parameter can prevent local discharge caused by excessive tangential field strength, and long-term discharge may corrode the insulation material or cause breakdown. The maximum field strength in the gas domain is the maximum value of the electric field strength inside the gas insulation medium in the high-voltage busbar. The maximum field strength in the gas domain can measure the gas insulation performance. Taking it as a performance parameter can prevent insulation failure, arc, or short circuit caused by the field strength exceeding the gas breakdown threshold. The maximum field strength inside the insulator is the maximum value of the electric field strength inside the insulator. The maximum field strength inside the insulator can measure the electric strength resistance of the insulation medium. Taking it as a performance parameter can prevent the destruction of insulation performance caused by local discharge or breakdown due to excessive field strength. The maximum local stress of the insulator is the maximum value of the local stress concentration in the local area of the insulator caused by mechanical load, temperature gradient, or assembly pre-tightening force. Taking it as a performance parameter can prevent the cracking or fatigue failure of the insulator. The maximum stress at the conductor bonding location is the maximum value of the mechanical stress at the bonding interface between the conductor and the insulator or other components, including shear stress and peel stress. Taking it as a performance parameter can prevent the poor conductivity or mechanical looseness caused by the cracking or debonding of the adhesive layer due to excessive stress at the bonding location.

[0047] Exemplarily, a 550 kV GIL (Gas Insulated Power Line) is used as the high-voltage busbar to be optimized, and its geometric structure is as Figure 2 shown. Among them, an environmentally friendly mixed gas of 3% C5F 10 O / 97% CO2 at 0.6 MPa is used to replace SF6 in the 550 kV GIL to obtain a replacement busbar. Then, as Figure 2As shown in the figure, 15 typical structural dimensions including five parts such as the housing 1, the conductor 2, the pot insulator 3, the central insert 4, and the shielding cover 5 are selected as the sampling structural dimensions. Among them, the sampling structural dimensions include the inner diameter d1 of the housing, the outer diameter d2 of the conductor, the thickness h1 of the housing, the thickness h2 of the conductor, the arc height p1 on the side of the shielding cover adjacent to the central insert, the second arc height p2 on the side of the shielding cover far from the central insert, the first arc height p3 on the side of the shielding cover far from the central insert, the arc radius r1 on the side of the concave surface of the insulator adjacent to the central insert, the arc radius r2 on the side of the convex surface of the insulator adjacent to the central insert, the arc radius r3 on the side of the convex surface of the insulator adjacent to the sealing groove, the included angle r4 between the concave and convex surfaces of the insulator, the line segment length x1 on the side of the concave surface of the pot insulator adjacent to the central insert, the line segment length x2 on the side of the convex surface of the insulator adjacent to the central insert, the first arc height z1 of the central insert, and the second arc height z2 of the central insert. Here, Figure 2 In it, 6 is the voltage stabilizing ring and 7 is the flange.

[0048] Next, based on the influence of the housing outer diameter and the conductor on the economic performance and the volume of the replacement busbar, and the fact that the housing thickness design needs to meet the requirements of the short-term withstand current (63 kA / 3 s) and the peak withstand current (171 kA) specified in the national standard GB / T 22383-2017 "Rigid Gas Insulated Transmission Lines with Rated Voltage of 72.5 kV and Above", as well as the feasibility of the actual structure and production process of the insulator and its related components and mechanical properties and other factors, the preset size range of the replacement busbar is set as shown in Table 1.

[0049] Table 1

[0050] Sampling structure size Preset size range Sampling structure size Preset size range Sampling structure size Preset size range <![CDATA[d1]]> [320,520] <![CDATA[p2]]> [5,20] <![CDATA[r4]]> [1,10] <![CDATA[d2]]> [50,150] <![CDATA[p3]]> [2,10] <![CDATA[x1]]> [30,90] <![CDATA[h1]]> [5,20] <![CDATA[r1]]> [20,50] <![CDATA[x2]]> [10,20] <![CDATA[h2]]> [10,30] <![CDATA[r2]]> [90,120] <![CDATA[z1]]> [0.5,5] <![CDATA[p1]]> [1,5] <![CDATA[r3]]> [5,30] <![CDATA[z2]]> [1,10]

[0051] Then, 100 structural dimension samples of the replacement busbar are extracted within the preset size range of the replacement busbar by the Latin hypercube method, and each structural dimension sample includes 15 sampling structural dimensions. Here, the Latin hypercube method adopts disordered stratified sampling, which can cover the entire design space more comprehensively through a small number of sample points.

[0052] Furthermore, according to the preset physical property parameters of the environmental protection mixed gas of 3% C5F 10 O / 97% CO2 at 0.6 MPa, parametric simulation is carried out on the structural dimension samples to obtain the simulation values of the performance parameters of the structural dimension samples.

[0053] It is worth mentioning that, based on the original initial structural dimension design of the high-voltage busbar to be optimized and the preset physical property parameters of the environmental protection gas, the simulation values of the performance parameters of the replacement busbar under the initial structural dimension design can be obtained to verify whether the replacement busbar meets the safety standards of insulation and current-carrying performance under the initial structural dimension design, so as to optimize each sampling structural dimension of the replacement busbar according to the verification results. Here, the safety standards can be determined through relevant national standard documents.

[0054] Exemplarily, the inflation pressure in a conventional SF6 high-voltage busbar is usually 0.4 to 0.5 MPa, while the insulation performance of the C5F 10 O / CO2 mixed gas at 0.6 MPa is about 65% to 75% of that of SF6 at 0.4 MPa. It is difficult to directly replace the busbar structure of the same voltage level. Therefore, it is necessary to optimize the structural dimensions of the replacement busbar.

[0055] In one embodiment, the rapid optimization design method for the lightweight robustness of the environmentally friendly gas busbar further includes: establishing a coupled simulation model based on three physical fields of insulation, current-carrying capacity, and stress, inputting the preset physical property parameters of the environmentally friendly gas and the structural dimension samples into the coupled simulation model, and obtaining the simulation values of the performance parameters of the structural dimension samples to quantify the insulation and current-carrying capacity performance of the replacement busbar under different structural dimensions.

[0056] In an actual application scenario, the heat source during the current-carrying process of the high-voltage busbar comes from the power losses generated by its internal shell and conductor. The heat of the conductor is transferred to the shell through the natural convection of the internal insulating gas and the radiation on the outer surface. The shell dissipates heat through the radiation and convection of the external air. The heat generated by the high-voltage busbar under the action of current and the heat transferred to the air gradually reach a dynamic balance, and the temperature of the high-voltage busbar is constant and reaches a stable state. During this process, the temperature of the high-voltage busbar rises under the action of the power losses of the shell and the conductor, resulting in an increase in the resistivity of the shell and the conductor and an increase in the power losses. At the same time, the temperature gradient changes the flow of the gas, and the convection of the gas acts on the temperature field in turn. This is a complex non-linear problem of multi-physical field coupling of magnetic field, flow field, and temperature field.

[0057] In this embodiment, the coupled simulation model includes an insulation simulation model, a current-carrying capacity simulation model, and a stress simulation model to make the simulation calculation more in line with the actual working conditions. Specifically, the insulation simulation model is based on the symmetry of the high-voltage busbar structure and uses an axisymmetric model to perform finite element calculation of the electric field strength. The current-carrying capacity simulation model calculates the temperature rise value after the high-voltage busbar carries current through the electromagnetic field frequency domain mathematical model and the fluid field-temperature field transient mathematical model. The stress simulation model simulates the mechanical stress.

[0058] Exemplarily, when an alternating current passes through the high-voltage busbar conductor, eddy currents of equal magnitude and opposite directions will be induced in the high-voltage busbar housing. The power loss of the high-voltage busbar is generated by both the conductor current and the housing eddy currents. To simplify the calculation, the effects of space charge and displacement current are ignored, and it is assumed that the relative magnetic permeability of the housing and the conductor is a constant and the conductivity is only related to temperature. Based on the Maxwell equations, the electromagnetic field frequency-domain analysis is carried out. According to the magnetic permeability of the housing or the conductor and the change of the resistivity of the housing or the conductor with temperature, the control equations are established, and based on the control equations, the cross-sectional area of the conductor or the housing and the resistivity of the housing or the conductor at various ambient temperatures, the electromagnetic field frequency-domain mathematical model is determined.

[0059] Furthermore, when the high-voltage busbar is operating, the temperature change of the external air and the internal gas causes a density gradient, and natural convection will occur under the action of gravity. Based on the three heat exchange methods of the high-voltage busbar, namely heat conduction, heat convection and heat radiation, the heat transfer power inside the high-voltage busbar, and the mass conservation equation, momentum conservation equation and energy conservation equation during fluid convective heat transfer, a transient mathematical model of the fluid field-temperature field is constructed.

[0060] Step 103: Determine the performance surrogate model according to the simulation values of the performance parameters of the structural size samples.

[0061] Among them, the performance surrogate model is used to characterize the corresponding relationship between the target structural size corresponding to the performance parameters in the structural size samples and the performance parameters.

[0062] In this embodiment, according to the simulation values of the performance parameters of the structural size samples, a mathematical relationship between the size and the performance is established to obtain the performance surrogate model, so as to quickly predict the performance parameters of the replacement busbar under different structural sizes, without having to perform a large number of time-consuming simulation calculations during optimization, and improving the optimization efficiency.

[0063] Furthermore, as a refinement and extension of the specific implementation manner of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, determining the performance surrogate model according to the simulation values of the performance parameters of the structural size samples includes: fitting the simulation values of the performance parameters of the structural size samples to obtain a performance fitting response surface, and the performance fitting response surface is used to characterize the corresponding relationship between the sampled structural sizes and the performance parameters in the structural size samples; calculating the sensitivity index of the sampled structural sizes in the performance fitting response surface; screening out the target structural sizes corresponding to the performance parameters from the sampled structural sizes according to the sensitivity index; in the performance fitting response surface, removing the sampled structural sizes other than the target structural sizes corresponding to the performance parameters, and determining the performance surrogate model according to the performance fitting response surface after removal.

[0064] In this embodiment, a Kriging model is used to perform spatial modeling and interpolation calculation on the simulation values of the performance parameters of the structural size samples according to the covariance function, establish the mathematical relationship between the sampling structural sizes and the performance parameters in the structural size samples, obtain the performance fitting response surface, and predict the simulation values of the performance parameters at the unsampled points through the performance fitting response surface to obtain the fitting values of the performance parameters under various sampling structural size combinations.

[0065] It can be understood that each performance parameter corresponds to a performance fitting response surface to characterize the corresponding relationship between the sampling structural size and the performance parameter through the performance fitting response surface.

[0066] Then, according to the sensitivity index of the sampling structural size in the performance fitting response surface, the target structural size that has the greatest influence on the performance parameter is selected from the sampling structural sizes, and the sampling structural sizes that have less influence on the performance parameter are removed from the performance fitting response surface to reduce the dimension of the performance fitting response surface. Thus, according to the performance fitting response surface after removal, a performance surrogate model is determined to use the performance surrogate model to characterize the corresponding relationship between the target structural size corresponding to the performance parameter and the performance parameter. Furthermore, in the subsequent steps of solving the structural size optimization model, using the performance surrogate model to replace the complex simulation calculation can quickly search for the optimal solution and improve the optimization speed.

[0067] Here, the sampling structural sizes with a sensitivity index greater than or equal to 5% in the performance fitting response surface can be determined as the target structural sizes that have a greater influence on the performance parameter, and the sampling structural sizes with a sensitivity index less than 5% are filtered in the performance fitting response surface to eliminate the sampling structural sizes that have less influence on the performance fitting response surface and reduce the optimization dimension.

[0068] Exemplarily, continuing with the above embodiment using 3% C5F 10 O / 97% CO2 environmentally friendly mixed gas to replace SF6 in 550 kV GIL to obtain a replacement busbar, and taking the performance parameters as the maximum temperature rise of the shell, the maximum temperature rise of the conductor, the maximum tangential field strength, the maximum field strength in the gas domain, the maximum field strength inside the insulator, the maximum local stress of the insulator, and the maximum stress at the conductor bonding position in the replacement busbar as an example, the sensitivity indices of 15 sampling structural sizes in each performance fitting response surface are shown in Table 2.

[0069] Table 2

[0070]

[0071]

[0072] Here, some key dimensions are selected from each performance response surface in Table 2, and their sensitivity indices are calculated. CoP(d1) is the sensitivity index of the inner diameter of the shell, CoP(d2) is the sensitivity index of the outer diameter of the conductor, CoP(h1) is the sensitivity index of the thickness of the shell, CoP(h2) is the sensitivity index of the thickness of the conductor, CoP(p1) is the sensitivity index of the arc height on the side of the shielding cover adjacent to the central insert, CoP(p2) is the sensitivity index of the second arc height on the side of the shielding cover away from the central insert, CoP(p3) is the sensitivity index of the first arc height on the side of the shielding cover away from the central insert, CoP(r1) is the sensitivity index of the arc radius on the concave side of the insulator adjacent to the central insert, CoP(r2) is the sensitivity index of the arc radius on the convex side of the insulator adjacent to the central insert, CoP(r3) is the sensitivity index of the arc radius on the convex side of the insulator adjacent to the sealing groove, CoP(r4) is the sensitivity index of the included angle between the concave and convex surfaces of the insulator, CoP(x1) is the sensitivity index of the line segment length on the concave side of the pot insulator adjacent to the central insert, CoP(x2) is the sensitivity index of the line segment length on the convex side of the insulator adjacent to the central insert, CoP(z1) is the sensitivity index of the first arc height of the central insert, and CoP(z2) is the sensitivity index of the second arc height of the central insert. CoP in the last row of Table 2 represents the prediction coefficient of each performance fitting response surface.

[0073] It should be noted that the calculation methods and functions of the prediction coefficient and the sensitivity index are explained in the following embodiments and will not be elaborated here.

[0074] As can be seen from Table 2, the prediction coefficients of each performance fitting response surface are all greater than 95%, the fitting quality is good, and it has high reliability. Through the sensitivity analysis of each sampled structural dimension, it is found that within the preset dimension range, the maximum field strength in the gas domain is mainly affected by the inner diameter of the shell; the maximum field strength inside the insulator, the maximum tangential field strength, the maximum temperature rise of the conductor, and the maximum temperature rise of the shell are mainly affected by the outer diameter of the conductor; the maximum local stress of the insulator and the maximum stress at the bonding position of the conductor are mainly affected by the arc radius on the convex side of the insulator adjacent to the sealing groove and the line segment length on the concave side of the pot insulator adjacent to the insert, respectively. The sensitivities of 7 sampled structural dimensions, such as the thickness of the conductor, the arc height on the side of the shielding cover adjacent to the insert, the arc height on the side of the shielding cover away from the insert, the arc radius on the concave side of the insulator adjacent to the insert, the arc radius on the convex side of the insulator adjacent to the insert, the included angle between the concave and convex surfaces of the insulator, and the line segment length on the convex side of the insulator adjacent to the insert, to the electric field, temperature rise, and stress are less than 5% and can be filtered.

[0075] Here, in order to further reduce the dimension of the performance fitting response surface, the two sampled structural dimensions with the largest sensitivity indices in the performance fitting response surface are used as the target structural dimensions corresponding to the performance parameters, and the performance fitting response surface after removal is as Figures 3 to 9 shown. From Figures 3 to 9It can be seen that in terms of insulation performance, as the inner diameter of the housing increases, the insulation distance increases, and the field strength in the gas region and the internal field strength of the insulator decrease accordingly; increasing the outer diameter of the conductor has an optimization effect on the field strength in the gas region, the tangential and internal field strengths of the insulator. In terms of current-carrying performance, the power loss is mainly affected by the current density. Under the condition of a certain current-carrying capacity, increasing the metal cross-sectional area will result in a decrease in the temperature rise. Therefore, the current-carrying temperature rise is negatively correlated with both the outer diameter of the housing and the outer diameter of the conductor. In terms of mechanical stress, the length of the line segment on the concave side of the insulator adjacent to the insert decreases, the force arm decreases, and the stress decreases accordingly; at the same time, the local maximum stress of the insulator is positively correlated with the radius of the arc on the convex side of the insulator adjacent to the sealing groove, and the maximum stress at the conductor bonding position is negatively correlated with the height of the second arc of the insert.

[0076] Furthermore, as a refinement and extension of the specific implementation manner of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, the sensitivity index of the sampling structure size in the performance fitting response surface is calculated, including: determining the prediction coefficient of the performance fitting response surface according to the simulation value and the fitting value of the performance parameters of the structure size sample, and the fitting value of the performance parameters of the structure size sample is determined according to the performance fitting response surface; determining the sensitivity index of the sampling structure size in the performance fitting response surface according to the response variance caused by the sampling structure size in the performance fitting response surface, the total response variance of the performance fitting response surface, and the prediction coefficient of the performance fitting response surface.

[0077] In this embodiment, the prediction coefficient CoP (Coefficient of Prognosis) is used to evaluate the quality of the performance fitting response surface, measure the degree of fitting the simulation value of the performance fitting response surface, and ensure the accuracy of the performance fitting response surface. Here, the value of the prediction coefficient CoP can increase with the increase in the number of samples, without the phenomenon of false precision, ensuring the reliability of the quality assessment.

[0078] Furthermore, the prediction coefficient of the performance fitting response surface is used to calculate the sensitivity index of the sampling structure size in the performance fitting response surface, so as to quantify the influence degree of the sampling structure size on the performance parameters through the sensitivity index, ensuring the reliability of dimensionality reduction.

[0079] Exemplarily, the prediction coefficient CoP is expressed as: CoP = 1 - SS E / SS T . Among them, Y a is the simulation value of the performance parameter of the structure size sample a, is the fitting value of the performance parameter of the structure size sample a in the performance fitting response surface, It is the mean of the fitted values of the performance parameters of the structural dimension sample in the performance fitting response surface. Here, the higher the prediction coefficient CoP, the closer the fitted values of the performance fitting response surface are to the simulation values. When the prediction coefficient CoP is greater than 75%, it can be determined that the performance fitting response surface is relatively accurate.

[0080] The sensitivity index CoP(x b ) of the sampling structural dimension in the performance fitting response surface is expressed as: CoP(x b ) = CoP·S T (x b ). Among them, S T (x b ) is the percentage of the response variance caused by the sampling structural dimension x b in the total response variance of the performance fitting response surface. Here, the response variance caused by the sampling structural dimension x b in the performance fitting response surface is the variance of the fitted values of the performance parameters obtained after changing the sampling structural dimension x b in the performance fitting response surface, which can reflect the fluctuation of the fitted values of the performance parameters caused by the change of the sampling structural dimension x b . The total response variance of the performance fitting response surface is the variance of the fitted values of the performance parameters obtained after changing all sampling dimensions, which covers the total influence of the changes of all sampling structural dimensions on the performance parameter fluctuations. Therefore, the influence degree of the sampling structural dimension on the performance parameters can be quantified by the sensitivity index CoP(x b ).

[0081] Furthermore, as a refinement and extension of the specific implementation manner of the above embodiment, in order to completely illustrate the specific implementation process of this embodiment, according to the removed performance fitting response surface, a performance surrogate model is determined, including: determining an initial surrogate model according to the removed performance fitting response surface; if the prediction coefficient of the initial surrogate model is less than the first preset threshold, then re-sample the structural dimension samples of the replacement busbar within the preset size range of the replacement busbar, and increase the number of the re-sampled structural dimension samples until the prediction coefficient of the initial surrogate model determined according to the re-sampled structural dimension samples is greater than or equal to the first preset threshold; if the prediction coefficient of the initial surrogate model is greater than or equal to the first preset threshold, then determine the initial surrogate model with a prediction coefficient greater than or equal to the first preset threshold as the performance surrogate model.

[0082] In this embodiment, a response surface is fitted according to the performance after removal. First, an initial surrogate model is determined, and the prediction coefficient is used to evaluate the accuracy of the fitting result of the initial surrogate model. If the prediction coefficient of the initial surrogate model is less than the first preset threshold, it indicates that the fitting degree of the initial surrogate model is poor. Then, step 102 needs to be performed again, and the number of structure size samples redrawn in step 102 is increased to improve the fitting accuracy of the initial surrogate model until the prediction coefficient of the initial surrogate model determined according to the redrawn structure size samples is greater than or equal to the first preset threshold.

[0083] Furthermore, if the prediction coefficient of the initial surrogate model is greater than or equal to the first preset threshold, it indicates that the initial surrogate model can better fit the simulation values of the performance parameters of the structure size samples. Therefore, the initial surrogate model with a prediction coefficient greater than or equal to the first preset threshold is determined as the performance surrogate model, and the structure size optimization model is quickly solved based on the performance surrogate model to ensure the reliability of the optimization.

[0084] Exemplarily, the first preset threshold can be 75%.

[0085] Here, the calculation method of the preset coefficient of the initial surrogate model is the same as that of the performance fitting response surface, which will not be elaborated here.

[0086] It can be understood that in this embodiment, each performance parameter corresponds to a performance surrogate model to represent the corresponding relationship between the target structure size corresponding to the performance parameter and the performance parameter through the performance surrogate model.

[0087] Step 104, determine the optimization objective according to the weight of the metal material in the replacement busbar, and constrain the performance parameters to construct a structure size optimization model of the replacement busbar.

[0088] It should be noted that in the related technologies of high-voltage busbar structure design, the deterministic optimization method assumes that all variables and parameters are determined without any randomness or changing factors. However, in the actual manufacturing and use process, there are many uncertain factors (such as manufacturing accuracy, material properties, wear and aging, etc.) that make the performance of the high-voltage busbar (such as temperature rise, electric field, and stress, etc.) show a probability distribution, which may lead to the deterioration of the high-voltage busbar performance and even exceed the maximum limit, resulting in equipment failure.

[0089] In this embodiment, based on the Six Sigma (6σ) robust design concept, considering the uncertain factors (such as material variation, environmental fluctuation, etc.) during the processing or use of the replacement busbar, a structural size optimization model of the replacement busbar is constructed. Among them, on the premise of ensuring that the volume of the replacement busbar is not increased, the requirements of lightweight and reliable performance of the replacement busbar are comprehensively considered, and while maintaining the consistency of weight, the optimization objective of the structural size optimization model is set. Moreover, to reduce the sensitivity of the performance of the replacement busbar to uncertain factors, it is controlled that each performance parameter and the sampled structural size are less than the maximum limit within the range of 6 times the standard deviation, and the constraint conditions of the structural size optimization model are constructed.

[0090] Specifically, the structural size optimization model is expressed as:

[0091]

[0092] Among them, F is the objective function of the structural size optimization model, minF is the optimization objective of the structural size optimization model, μ M is the mean value of the weight of the metal material in the replacement busbar, σ M is the standard deviation of the weight of the metal material in the replacement busbar, μ j is the mean value of the performance parameter j within the range of uncertain factor perturbation, σ j is the standard deviation of the performance parameter j within the range of uncertain factor perturbation, X jmax is the preset maximum limit of the performance parameter j, d1 is the inner diameter of the shell in the replacement busbar, h1 is the thickness of the shell in the replacement busbar, L is the preset size limit, X i is the sampled structural size i in the structural size sample, X imin is the preset minimum limit of the sampled structural size i in the structural size sample, σ' Xi is the coefficient of variation of the sampled structural size i in the structural size sample, X imax is the preset maximum limit of the sampled structural size i in the structural size sample, M is the number of performance parameters, and N is the number of sampled structural sizes in the structural size sample.

[0093] It can be understood that the weight of the replacement busbar is determined according to the volume and density of the replacement busbar, and moreover, the weight of the replacement busbar mainly comes from the weight of its internal metal material. And the volume of the replacement busbar is determined based on the sampled structural size, and the change of the sampled structural size will in turn affect the insulation and current-carrying performance of the replacement busbar, which is reflected in the numerical values of the performance parameters under different combinations of sampled structural sizes. Therefore, in the structural size optimization model, the objective function can be minimized by adjusting the sampled structural size while satisfying all constraint conditions, so as to optimize the weight and ensure stable performance at the same time.

[0094] In this embodiment, the structural size optimization model minimizes the objective function to reduce the average weight of the replacement busbar, and strictly controls the fluctuation range of the weight of the replacement busbar by using the standard deviation of the metal material weight to ensure the stability of the weight of the replacement busbar. At the same time, the structural size optimization model closely combines the lightweight objective with performance reliability to ensure that the replacement busbar can still stably meet all technical requirements even in the presence of uncertain factors during the manufacturing and use processes.

[0095] It is worth mentioning that constraint X imin +6σ' Xi ≤X i ≤X imax -6σ' Xi The coefficient of variation is used to limit the value range of the sampled structural size to reflect the fluctuation of the structural size of the replacement busbar caused by uncertain factors. At the same time, the coefficient of variation of the sampled structural size also affects the solution space of the objective function, making the optimized replacement busbar not only meet the performance requirements but also have the possibility of actual manufacturing.

[0096] Constraint μ j ≤X jmax -6σ j Strictly constrains the insulation and current-carrying performance of the replacement busbar. Using the 6σ robust design concept, in the form of "μ (mean) + 6σ (six times the standard deviation) ≤ maximum limit", the fluctuation range of the performance parameters is restricted within the safety standard range to ensure that the replacement busbar can still meet the performance requirements for safe operation under various possible size fluctuations, improving the reliability of the performance of the replacement busbar under actual working conditions. The constraint d1 + 2h1 ≤ L limits the volume of the replacement busbar to ensure that the original design space is not increased after optimization and meets the space limitations of actual applications. At the same time, the constraint μ j ≤X jmax -6σ j And the constraint d1 + 2h1 ≤ L also constraints the feasible solutions of the objective function, requiring the weight of the replacement busbar to be optimized on the premise of meeting the safety standards.

[0097] Exemplarily, continuing with the above embodiment, using the environmentally friendly mixed gas of 3% C5F 10 O / 97% CO2 to replace SF6 in 550 kV GIL to obtain a replacement busbar. Selecting 15 sampled structural sizes and performance parameters such as the maximum temperature rise of the shell, the maximum temperature rise of the conductor, the maximum tangential field strength, the maximum field strength in the gas domain, the maximum field strength inside the insulator, the maximum local stress of the insulator, and the maximum stress at the conductor bonding location as examples, the structural size optimization model of the replacement busbar is expressed as:

[0098]

[0099] Among them, μE1max is the mean value of the maximum field strength in the gas region, σ E1max is the standard deviation of the maximum field strength in the gas region, μ E2max is the mean value of the maximum tangential field strength, σ E2max is the standard deviation of the maximum tangential field strength, μ E3max is the mean value of the maximum field strength inside the insulator, σ E3max is the standard deviation of the maximum field strength inside the insulator, μ T1max is the mean value of the maximum temperature rise of the housing, σ T1max is the standard deviation of the maximum temperature rise of the housing, μ T2max is the mean value of the maximum temperature rise of the conductor, σ T2max is the standard deviation of the maximum temperature rise of the conductor, μ F1max is the mean value of the maximum local stress of the insulator, σ F1max The standard deviation of the maximum local stress of the insulator, μ F2max is the mean value of the maximum stress at the conductor bonding location, σ F2max is the standard deviation of the maximum stress at the conductor bonding location.

[0100] Here, μ E1max ≤16.5 - 6σ E1max 、μ E2max ≤8.25 - 6σ E2max and μ E3max ≤21.65 - 6σ E3max are the electric field strength constraints, ensuring that under the perturbation of uncertain factors, the electric field strength will not be too high, avoiding electrical problems such as insulation breakdown caused by excessive electric field strength, and ensuring the safe and reliable electrical performance of the replacement busbar. μ T1max ≤60 - 6σ T1max and μ T2max ≤30 - 6σ T2max are the temperature rise constraints. By restricting the mean values of the maximum temperature rise of the conductor and the housing, it ensures that the temperature rise of the conductor and the housing during the operation of the replacement busbar is within a safe range, preventing the decline of material performance or potential safety hazards caused by overheating. μ F1max ≤70 - 6σ F1max and μ F2max ≤25 - 6σ F2max are the stress constraints, ensuring that the local stress of the insulator and the stress at the conductor bonding location are within a safe range under uncertain factors, avoiding structural damage problems such as insulator rupture and conductor bonding location detachment caused by excessive stress, and ensuring the stability of the structure of the replacement busbar.

[0101] It should be noted that for the preset maximum limit values of each performance parameter, they are determined according to the following embodiments:

[0102] With the rated AC voltage of the high-voltage bus being 252 kV and the rated lightning impulse withstand voltage being 1050 kV, and using an environmentally friendly mixed gas of 3% C5F 10 O / 97% CO2 with an absolute pressure of 0.6 MPa as the gas insulation medium, at this time the partial pressure of C5F 10 O in the environmentally friendly mixed gas is approximately 20 kPa. The maximum limit values of the electric field strength for different components include:

[0103] (1) Under lightning impulse voltage, the maximum electric field strength on the metal surface of the high-voltage side of the gas region should not exceed the maximum electric field strength of the gap. The maximum electric field strength of the gas gap under lightning impulse for the mixed gas with a C5F 10 O partial pressure of 20 kPa at 0.6 MPa is approximately 16.5 kV·mm -1 .

[0104] (2) Under lightning impulse voltage, the tangential electric field strength along the surface of the insulator is 8.25 kV·mm -1 .

[0105] (3) During long-term live operation, partial discharge needs to be controlled. The maximum electric field strength value of the insulator and its inserts under the rated effective value of the phase voltage should not exceed 3 - 4 kV·mm -1 , which is equivalent to the electric field strength under lightning impulse voltage not exceeding 21.65 kV·mm -1 .

[0106] Regarding the maximum limit value of the temperature rise caused by current flow, it is pointed out in the national standard GB / T 11022 - 2011 that the test current is 1.1 times the rated current, that is, 3465 A. The maximum limit values are as follows:

[0107] (1) When the ambient air temperature does not exceed 313 K, the temperature rise of the touchable components during normal operation does not exceed 30 K. Therefore, the temperature rise of the shell is limited to 30 K.

[0108] (2) The temperature rise of the bare aluminum alloy in the insulating gas does not exceed 75 K. However, according to the experience of previous bus temperature rise tests, the temperature rise of the contact finger is about 0 - 5 K higher than that of the conductor. Therefore, in this embodiment, the maximum temperature rise value of the conductor is the same as that of the contact finger material in the national standard, which is 60 K.

[0109] For the maximum limit of mechanical stress, the inflation pressure is 0.6 MPa at 293 K. According to the regulations in GB / T 11022-2011, when the ambient temperature is 313 K, the maximum temperature rise is 75 K, that is, 388 K. Through the ideal gas state equation, it can be obtained that the pressure difference on both sides of the insulator does not exceed 0.8 MPa (P = 0.6×388 / 293 = 0.79 MPa). It is pointed out in GB 7674-2008 that considering the safety margin, the failure pressure of the basin insulator should reach 3 times the design pressure, that is, 2.4 MPa. In previous work, the tensile strength of the epoxy resin material was measured. The local stress of the insulator and the stress at the conductor bonding should not exceed 75 MPa and 25 MPa.

[0110] In one embodiment, the method for rapidly optimizing the lightweight robustness of the environmentally friendly gas busbar further includes: determining the standard deviation of the sampled structural dimensions according to the design values of the sampled structural dimensions; and determining the coefficient of variation of the sampled structural dimensions according to the standard deviation of the sampled structural dimensions.

[0111] In this embodiment, the standard deviation of the sampled structural dimensions is 0.015 times its nominal dimension (i.e., the design value). Assuming that the sampled structural dimensions all follow a normal distribution and considering that the mean value of the sampled structural dimensions is approximately equal to its design dimension, the coefficient of variation (standard deviation / mean value) is 0.0025 at this time, indicating a large design margin.

[0112] Step 105: Based on the performance surrogate model, solve the structural dimension optimization model to obtain the optimized structural dimensions, and optimize the replacement busbar according to the optimized structural dimensions to obtain the optimized replacement busbar.

[0113] In this embodiment, the performance surrogate model can quickly obtain the numerical values of the performance parameters under different combinations of sampled structural dimensions within the solution space of the structural dimension optimization model. Further, with the minimization of the objective function as the optimization goal, by using the performance surrogate model in combination with the constraint conditions in the structural dimension optimization model, quickly search for the optimal solution within the solution space of the structural dimension optimization model to obtain the optimized structural dimensions, thus accelerating the optimization efficiency. Then, optimize the replacement busbar according to the optimized structural dimensions to obtain the optimized replacement busbar, so that the optimized replacement busbar can balance lightweight and performance robustness.

[0114] Furthermore, as a refinement and extension of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, based on the performance surrogate model, the structural size optimization model is solved to obtain the optimized structural size, including: based on the performance surrogate model, the genetic algorithm is used to solve the structural size optimization model to obtain the candidate optimized size; based on the performance surrogate model, the fitted value of the performance parameter under the candidate optimized size is obtained, and according to the preset physical property parameters of the environmentally friendly gas, the simulated value of the performance parameter under the candidate optimized size is obtained; according to the simulated value and the fitted value of the performance parameter under the candidate optimized size, the fitting error of the performance surrogate model is determined; if the fitting error of the performance surrogate model is greater than the second preset threshold, the structural size sample of the replacement busbar is redrawn within the preset size range of the replacement busbar, and the number of the redrawn structural size samples is increased until the fitting error of the performance surrogate model determined according to the redrawn structural size samples is less than or equal to the second preset threshold; if the fitting error of the performance surrogate model is less than or equal to the second preset threshold, the target optimized size is determined as the optimized structural size, and the target optimized size is the candidate optimized size determined according to the performance surrogate model with the fitting error less than or equal to the second preset threshold.

[0115] In this embodiment, the genetic algorithm is used to perform global search on the solution space of the structural size optimization model based on the performance surrogate model. Since the performance surrogate model is computationally fast, the genetic algorithm can explore a large number of candidate solutions in a short time, find the optimal solution that satisfies all the constraint conditions and minimizes the objective function, reduce the number of high-cost simulations, and improve the optimization efficiency while ensuring the optimization effect. The global search ability of the genetic algorithm here helps to avoid falling into local optima.

[0116] Furthermore, after obtaining the optimal solution through the genetic algorithm, the optimal solution is first used as the candidate optimized size, and parametric simulation is performed according to the preset physical property parameters of the environmentally friendly gas and the candidate optimized size to obtain the simulated value of the performance parameter under the candidate optimized size (that is, when the replacement busbar is in the geometric structure corresponding to the candidate optimized size, the simulated value of the performance parameter of the replacement busbar). Exemplarily, the preset physical property parameters of the environmentally friendly gas and the candidate optimized size can be input into the co-simulation model in the above embodiment to obtain the simulated value of the performance parameter under the candidate optimized size.

[0117] Next, the candidate optimized size is input into the performance surrogate model to obtain the fitted value predicted by the performance surrogate model for the candidate optimized size. Then, the fitted value and the simulated value of the performance parameter under the candidate optimized size are compared to obtain the fitting error of the performance surrogate model.

[0118] Exemplarily, the fitting error of the performance surrogate model can be in the form of an absolute error or a relative error. For example, the fitting error of the performance surrogate model = |simulation value of the performance parameter at the candidate optimized dimension - fitted value of the performance parameter at the candidate optimized dimension|, or the fitting error of the performance surrogate model = |(simulation value of the performance parameter at the candidate optimized dimension - fitted value of the performance parameter at the candidate optimized dimension) / simulation value of the performance parameter at the candidate optimized dimension|.

[0119] Furthermore, the fitting error of the performance surrogate model is used to perform accuracy verification on the performance surrogate model, so as to evaluate the accuracy of the performance surrogate model near the candidate optimized dimension through the fitting error, and ensure that the finally obtained optimization result is reliable in practical applications.

[0120] Exemplarily, if the fitting error of the performance surrogate model is greater than the second preset threshold, it indicates that the fitted value of the performance parameter predicted by the performance surrogate model near the candidate optimized dimension is unreliable, and the candidate optimized dimension obtained based on the performance surrogate model is also inaccurate. Then, step 102 is performed again, and the number of structure dimension samples redrawn in step 102 is increased to further improve the performance surrogate model and the candidate optimized dimension until the fitting error of the performance surrogate model is less than or equal to the second preset threshold.

[0121] If the fitting error of the performance surrogate model is less than or equal to the second preset threshold, it indicates that the candidate optimized dimension obtained based on this performance surrogate model has sufficient accuracy. At this time, the candidate optimized dimension obtained based on this performance surrogate model is used as the finally high-confidence optimized structure dimension, so as to ensure that the finally optimized structure dimension can truly reflect the optimal state of the actual physical system and avoid design mistakes caused by the error of the performance surrogate model.

[0122] Here, the second preset threshold can be 0.1.

[0123] In one embodiment, the genetic algorithm is used to perform optimization calculations on the performance surrogate model and the deterministic optimization model respectively, and the optimization design results are compared as Figure 10 shown. Among them, Figure 10 the robust optimization structure is the optimized replacement busbar obtained by using the environmentally friendly gas busbar lightweight robust fast optimization design method described in this application. The optimized structure dimensions described in this application are shown in Table 3. It can be seen that after using the environmentally friendly gas busbar lightweight robust fast optimization design method described in this application, the outer diameter of the housing, the thickness of the housing, the outer diameter of the conductor, and the thickness of the conductor are reduced by 47.96 mm, 5.00 mm, 12.27 mm, and 10.04 mm respectively.

[0124] Table 3

[0125]

[0126]

[0127] Table 4 shows the optimization design results of the lightweight robust rapid optimization design method and the deterministic optimization design method for the environmentally friendly gas busbar described in this application, as well as the σ (standard deviation) level of the performance parameters.

[0128] Table 4

[0129]

[0130] It can be seen from Table 4 that the weight of the high-voltage busbar after deterministic optimization has decreased by 53.17%, but the field strength in the gas domain, and the temperature rises of the conductor and the shell do not reach the 6σ level. In actual application, affected by the fluctuations of uncertain factors, it is very likely to exceed their respective constraint boundaries and cause equipment failures. After the optimization by the lightweight robust rapid optimization design method for the environmentally friendly gas busbar described in this application, the weight of the high-voltage busbar has decreased by 52.45%. Compared with the deterministic optimization result, the weight has increased, but all performance indicators meet the 6σ level. In long-term operation, the reliability reaches more than 99.99966%, meeting the design requirements.

[0131] It should be noted that the magnitude of the sequence numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of this application.

[0132] Furthermore, as Figure 11 shown, as a specific implementation of the lightweight robust rapid optimization design method for the environmentally friendly gas busbar, the embodiment of this application provides an environmentally friendly gas busbar lightweight robust optimization design device 1100. The environmentally friendly gas busbar lightweight robust optimization design device 200 includes: a replacement module 1101, a sampling module 1102, a construction module 1103, and an optimization module 1104.

[0133] Among them, the replacement module 1101 is used to replace the gas insulation medium in the high-voltage busbar to be optimized with an environmentally friendly gas to obtain a replacement busbar;

[0134] The sampling module 1102 is used to extract a structural dimension sample of the replacement busbar within the preset dimension range of the replacement busbar, and obtain the simulation value of the performance parameter of the structural dimension sample according to the preset physical property parameters of the environmentally friendly gas;

[0135] The construction module 1103 is used to determine a performance surrogate model according to the simulation value of the performance parameter of the structural dimension sample. The performance surrogate model is used to characterize the corresponding relationship between the target structural dimension corresponding to the performance parameter in the structural dimension sample and the performance parameter; and,

[0136] Determine the optimization objective based on the weight of the metal material in the replacement busbar, and constrain the performance parameters to construct a structural dimension optimization model for the replacement busbar;

[0137] An optimization module 1104, configured to solve the structural dimension optimization model based on the performance surrogate model to obtain the optimized structural dimensions, and optimize the replacement busbar according to the optimized structural dimensions to obtain the optimized replacement busbar.

[0138] In one embodiment, the construction module 1103 is specifically configured to fit the simulation values of the performance parameters of the structural dimension samples to obtain a performance fitting response surface, where the performance fitting response surface is used to characterize the corresponding relationship between the sampled structural dimensions in the structural dimension samples and the performance parameters; calculate the sensitivity index of the sampled structural dimensions in the performance fitting response surface; screen out the target structural dimensions corresponding to the performance parameters from the sampled structural dimensions; in the performance fitting response surface, remove the sampled structural dimensions other than the target structural dimensions corresponding to the performance parameters, and determine the performance surrogate model according to the performance fitting response surface after removal.

[0139] In one embodiment, the construction module 1103 is specifically configured to determine the prediction coefficient of the performance fitting response surface according to the simulation values and fitting values of the performance parameters of the structural dimension samples, where the fitting values of the performance parameters of the structural dimension samples are determined according to the performance fitting response surface; determine the sensitivity index of the sampled structural dimensions in the performance fitting response surface according to the response variance caused by the sampled structural dimensions in the performance fitting response surface, the total response variance of the performance fitting response surface, and the prediction coefficient of the performance fitting response surface.

[0140] In one embodiment, the construction module 1103 is specifically configured to determine an initial surrogate model according to the performance fitting response surface after removal; if the prediction coefficient of the initial surrogate model is less than the first preset threshold, re-sample the structural dimensions of the replacement busbar within the preset dimension range of the replacement busbar, and increase the number of the re-sampled structural dimension samples until the prediction coefficient of the initial surrogate model determined according to the re-sampled structural dimension samples is greater than or equal to the first preset threshold; if the prediction coefficient of the initial surrogate model is greater than or equal to the first preset threshold, determine the initial surrogate model with the prediction coefficient greater than or equal to the first preset threshold as the performance surrogate model.

[0141] In one embodiment, the construction module 1103 is specifically configured to represent the structural dimension optimization model as:

[0142]

[0143] where F is the objective function of the structural dimension optimization model, minF is the optimization objective of the structural dimension optimization model, μ M is the mean value of the weight of the metal material in the replacement busbar, σ MFor replacing the standard deviation of the weight of the metal material in the busbar, μ j For the mean value of performance parameter j, σ j For the standard deviation of performance parameter j, X jmax For the preset maximum limit value of performance parameter j, d1 is the inner diameter of the housing in the replacement busbar, h1 is the thickness of the housing in the replacement busbar, L is the preset size limit, X i For the sampling structural dimension i in the structural dimension sample, X imin For the preset minimum limit value of the sampling structural dimension i in the structural dimension sample, σ' Xi For the coefficient of variation of the sampling structural dimension i in the structural dimension sample, X imax For the preset maximum limit value of the sampling structural dimension i in the structural dimension sample, M is the number of performance parameters, and N is the number of sampling structural dimensions in the structural dimension sample.

[0144] In one embodiment, the construction module 1103 is specifically configured to determine the standard deviation of the sampling structural dimension according to the design value of the sampling structural dimension; and determine the coefficient of variation of the sampling structural dimension according to the standard deviation of the sampling structural dimension.

[0145] In one embodiment, the optimization module 1104 is specifically configured to solve the structural dimension optimization model by using a genetic algorithm based on the performance surrogate model to obtain candidate optimized dimensions; obtain the fitted values of the performance parameters under the candidate optimized dimensions based on the performance surrogate model, and determine the fitting error of the performance surrogate model according to the fitted values and the simulation values of the performance parameters under the candidate optimized dimensions; if the fitting error of the performance surrogate model is greater than the second preset threshold, re-sample the structural dimensions of the replacement busbar within the preset size range of the replacement busbar and increase the number of the re-sampled structural dimension samples until the fitting error of the performance surrogate model determined according to the re-sampled structural dimension samples is less than or equal to the second preset threshold; if the fitting error of the performance surrogate model is less than or equal to the second preset threshold, determine the target optimized dimension as the optimized structural dimension, and the target optimized dimension is the candidate optimized dimension determined according to the performance surrogate model with the fitting error less than or equal to the second preset threshold.

[0146] For the specific limitations of the environmentally friendly gas busbar lightweight robust optimization design device, reference may be made to the limitations on the environmentally friendly gas busbar lightweight robust rapid optimization design method described above, which will not be elaborated here. Each module in the above environmentally friendly gas busbar lightweight robust optimization design device can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to the above-mentioned modules.

[0147] Based on the above as Figure 1The method described above, correspondingly, an embodiment of the present application also provides a readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the above-mentioned environmental-friendly gas busbar lightweight robust rapid optimization design method as Figure 1 shown.

[0148] Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, and this software product can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in various implementation scenarios of the present application.

[0149] Based on the above-mentioned method as Figure 1 shown, and Figure 11 the virtual device embodiment shown, in order to achieve the above object, an embodiment of the present application also provides a computer device, specifically it can be a personal computer, server, network device, etc., and this computer device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to implement the above-mentioned environmental-friendly gas busbar lightweight robust rapid optimization design method as Figure 1 shown.

[0150] Optionally, the computer device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, sensors, an audio circuit, a WI-FI module, and so on. The user interface may include a display screen (Display), an input unit such as a keyboard (Keyboard), etc., and optionally the user interface may further include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a Bluetooth interface, a WI-FI interface), etc.

[0151] Those skilled in the art can understand that the structure of a computer device provided in this embodiment does not constitute a limitation on the computer device, and it may include more or fewer components, or combine certain components, or have different component arrangements.

[0152] The storage medium may also include an operating system and a network communication module. The operating system is a program for managing and storing the hardware and software resources of the computer device, and supports the operation of information processing programs and other software and / or programs. The network communication module is used to implement communication between the components inside the storage medium, and communication with other hardware and software in this entity device.

[0153] Through the description of the above embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus a necessary general hardware platform, or this application embodiment can be implemented by hardware.

[0154] Those skilled in the art can understand that the drawings are only schematic diagrams of a preferred embodiment scenario, and the modules or processes in the drawings are not necessarily essential for implementing this application. Those skilled in the art can understand that the modules in the device in the embodiment scenario can be distributed in the device in the embodiment scenario according to the description of the embodiment scenario, or can be correspondingly changed and located in one or more devices different from this embodiment scenario. The modules in the above embodiment scenario can be combined into one module, or can be further split into multiple sub-modules.

[0155] The above serial numbers of this application are only for description and do not represent the advantages or disadvantages of the embodiment scenario. The above disclosure is only several specific embodiment scenarios of this application. However, this application is not limited thereto, and any changes that can be thought of by those skilled in the art should fall within the protection scope of this application.

Claims

1. An environmentally friendly gas busbar lightweight robust and rapid optimization design method, characterized in that The method includes: Replacing the gas insulation medium in the high-voltage busbar to be optimized with an environmentally friendly gas to obtain a replaced busbar; Extracting a structural dimension sample of the replaced busbar within a preset dimension range of the replaced busbar, and obtaining a simulation value of the performance parameter of the structural dimension sample according to preset physical property parameters of the environmentally friendly gas; Determining a performance surrogate model according to the simulation value of the performance parameter of the structural dimension sample, where the performance surrogate model is used to characterize the corresponding relationship between the target structural dimension corresponding to the performance parameter in the structural dimension sample and the performance parameter; Determining an optimization target according to the weight of the metal material in the replaced busbar, and imposing constraints on the performance parameter to construct a structural dimension optimization model of the replaced busbar; Based on the performance surrogate model, solving the structural dimension optimization model to obtain an optimized structural dimension, and optimizing the replaced busbar according to the optimized structural dimension to obtain the optimized replaced busbar.

2. The rapid optimization design method for the lightweight robustness of the environmentally friendly gas busbar according to claim 1, characterized in that, The determining the performance surrogate model according to the simulation value of the performance parameter of the structural dimension sample includes: Fitting the simulation value of the performance parameter of the structural dimension sample to obtain a performance fitting response surface, where the performance fitting response surface is used to characterize the corresponding relationship between the sampled structural dimension in the structural dimension sample and the performance parameter; Calculating a sensitivity index of the sampled structural dimension in the performance fitting response surface; Screening out the target structural dimension corresponding to the performance parameter from the sampled structural dimensions according to the sensitivity index; In the performance fitting response surface, removing the sampled structural dimensions other than the target structural dimension corresponding to the performance parameter, and determining the performance surrogate model according to the performance fitting response surface after removal.

3. The rapid optimization design method for the lightweight robustness of the environmentally friendly gas busbar according to claim 2, characterized in that The calculating the sensitivity index of the sampled structural dimension in the performance fitting response surface includes: Determining a prediction coefficient of the performance fitting response surface according to the simulation value and the fitting value of the performance parameter of the structural dimension sample, where the fitting value of the performance parameter of the structural dimension sample is determined according to the performance fitting response surface; Determining the sensitivity index of the sampled structural dimension in the performance fitting response surface according to the response variance caused by the sampled structural dimension in the performance fitting response surface, the total response variance of the performance fitting response surface, and the prediction coefficient of the performance fitting response surface.

4. The rapid optimization design method for the lightweight robustness of the environmentally friendly gas busbar according to claim 2, wherein The determining the performance surrogate model according to the performance fitting response surface after removal includes: Determining an initial surrogate model according to the performance fitting response surface after removal; If the prediction coefficient of the initial surrogate model is less than a first preset threshold, re-extracting a structural dimension sample of the replaced busbar within the preset dimension range of the replaced busbar and increasing the number of the re-extracted structural dimension samples until the prediction coefficient of the initial surrogate model determined according to the re-extracted structural dimension sample is greater than or equal to the first preset threshold; If the prediction coefficient of the initial surrogate model is greater than or equal to the first preset threshold, the initial surrogate model with the prediction coefficient greater than or equal to the first preset threshold is determined as the performance surrogate model.

5. The rapid optimization design method for the lightweight robustness of the environmentally friendly gas busbar according to claim 1, characterized in that The structural size optimization model is expressed as: Among them, F is the objective function of the structural dimension optimization model, minF is the optimization objective of the structural dimension optimization model, μ M is the mean value of the weight of the metal material in the replacement busbar, σ M is the standard deviation of the weight of the metal material in the replacement busbar, μ j is the mean value of the performance parameter j, σ j is the standard deviation of the performance parameter j, X jmax is the preset maximum limit value of the performance parameter j, d1 is the inner diameter of the shell in the replacement busbar, h1 is the thickness of the shell in the replacement busbar, L is the preset dimension limit, X i is the sampled structural dimension i in the structural dimension sample, X imin is the preset minimum limit value of the sampled structural dimension i in the structural dimension sample, σ' Xi is the coefficient of variation of the sampled structural dimension i in the structural dimension sample, X imax is the preset maximum limit value of the sampled structural dimension i in the structural dimension sample, M is the number of performance parameters, and N is the number of sampled structural dimensions in the structural dimension sample.

6. The rapid optimization design method for the lightweight robustness of the environmentally friendly gas busbar according to claim 5, characterized in that, The method further includes: Determining the standard deviation of the sampling structural size according to the design value of the sampling structural size; Determining the coefficient of variation of the sampling structural size according to the standard deviation of the sampling structural size.

7. The rapid optimization design method for the lightweight robustness of the environmentally friendly gas busbar according to claim 1, characterized in that Solving the structural size optimization model based on the performance surrogate model to obtain the optimized structural size, including: Solving the structural size optimization model by using a genetic algorithm based on the performance surrogate model to obtain candidate optimized sizes; Based on the performance surrogate model, obtaining the fitted value of the performance parameter at the candidate optimized size, and determining the fitting error of the performance surrogate model according to the fitted value and the simulation value of the performance parameter at the candidate optimized size; If the fitting error of the performance surrogate model is greater than the second preset threshold, resampling the structural size of the replacement busbar within the preset size range of the replacement busbar and increasing the number of the resampled structural size samples until the fitting error of the performance surrogate model determined according to the resampled structural size samples is less than or equal to the second preset threshold; If the fitting error of the performance surrogate model is less than or equal to the second preset threshold, determining the target optimized size as the optimized structural size, where the target optimized size is the candidate optimized size determined according to the performance surrogate model with the fitting error less than or equal to the second preset threshold.

8. An environmentally friendly gas busbar lightweight robustness optimization design device, characterized in that, The device includes: A replacement module, configured to replace the gas insulation medium in the high-voltage busbar to be optimized with an environmentally friendly gas to obtain a replacement busbar; A sampling module, configured to sample the structural size of the replacement busbar within the preset size range of the replacement busbar and obtain the simulation value of the performance parameter of the structural size sample according to the preset physical property parameters of the environmentally friendly gas; A construction module, configured to determine a performance surrogate model according to the simulation value of the performance parameter of the structural size sample, where the performance surrogate model is used to characterize the corresponding relationship between the target structural size corresponding to the performance parameter in the structural size sample and the performance parameter; and Determining an optimization target according to the weight of the metal material in the replacement busbar, constraining the performance parameter, and constructing a structural size optimization model of the replacement busbar; An optimization module, configured to solve the structural size optimization model based on the performance surrogate model to obtain the optimized structural size, and optimize the replacement busbar according to the optimized structural size to obtain the optimized replacement busbar.

9. A readable storage medium, on which a program or instructions are stored, characterized in that, When the program or instruction is executed by a processor, the steps of the environmentally friendly gas busbar lightweight robustness rapid optimization design method according to any one of claims 1 to 7 are implemented.

10. A computer device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the program, the environmentally friendly gas busbar lightweight robustness rapid optimization design method according to any one of claims 1 to 7 is implemented.

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