Method for establishing scaling model of gas insulation equipment and electronic equipment

Abstract

The invention discloses a method for establishing a scaling model of gas insulation equipment and electronic equipment, and belongs to the technical field of scaling experiments of the gas insulation equipment. According to the method for establishing the scaling model of the gas insulation equipment, the problem that an existing model is insufficient in prediction precision is solved through component independent scaling, a modal frequency constraint equation and a multi-objective optimization framework, specifically, scaling coefficients are independently defined according to the unique geometric and material characteristics of all components, and the model prediction precision is improved; modal distortion caused by overall scaling is avoided, and a high-precision model foundation is laid; through deep analysis of dynamic characteristics, a frequency constraint equation is established, the relation between a scaling coefficient and inherent frequency is accurately described, the model theory preciseness is enhanced, and the optimization direction is guided; the optimal scaling combination is solved by adopting a multi-objective genetic algorithm, the interaction among components is comprehensively balanced, and the global modal consistency is ensured, so that the model prediction precision and accuracy are improved, and powerful technical support is provided for design optimization and performance evaluation of the gas insulation equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of scaled-down experiments of gas-insulated equipment, and particularly relates to a method for establishing a scaled-down model of gas-insulated equipment and an electronic device. Background Art

[0002] As a core component of modern power systems, gas-insulated switchgear (GIS) has been widely used in power systems due to its compact layout, excellent reliability, and convenient maintenance characteristics. However, with the gradual increase in the voltage level and continuous expansion of the capacity of power systems, the requirements for the performance of GIS are also increasing. In order to deeply explore the mechanical performance of GIS, scaled-down model tests have emerged as an indispensable research tool. With the help of scaled-down models, researchers can accurately simulate the actual operating state of GIS in a laboratory environment, and then conduct comprehensive tests and analyses on it, providing a solid theoretical basis and technical support for practical engineering applications.

[0003] However, traditional scaled-down modeling methods often adopt the principle of overall equal-proportion scaling when dealing with components with complex geometric shapes and material properties, which proves ineffective in practical applications. Especially for the busbar components in GIS, including reducing-diameter cylinders, conductive rods, and insulators, their geometric shapes and material properties vary significantly, resulting in the distortion of modal frequencies. This not only affects the accuracy of the scaled-down model but also greatly limits its application value in the study of GIS performance.

[0004] Specifically, in existing similar studies on scaled-down models of GIS busbar components, the distortion factors considered are often relatively single, only focusing on the changes of one or a few parameters, while ignoring the mutual coupling effects between multiple factors. The actual operating environment of GIS is extremely complex, and there are intricate relationships between multiple physical quantities. Therefore, the study of a single distortion factor cannot comprehensively reflect the actual situation, resulting in a large difference between the scaled-down model and the actual prototype, and unable to accurately simulate the performance of the actual equipment.

[0005] Due to the incomplete consideration of distortion factors, the scaled-down models established by existing methods have serious deficiencies in predicting the various performances of GIS busbars, especially the natural frequencies. However, in practical applications, the natural frequency is crucial for evaluating the mechanical stability and reliability of GIS busbars. There is a large error between the natural frequencies predicted by traditional methods and the actual values, which makes the analysis and design based on these models carry great risks, unable to accurately simulate the performance of actual equipment, and unable to meet the high-precision requirements of modern power systems for equipment.

[0006] Therefore, how to consider the coupling effect of multiple factors, design and optimize the scaled model, and thus improve the prediction accuracy and precision of the scaled model has become a technical problem that needs to be urgently solved by those skilled in the art in the current field. Summary of the Invention

[0007] The purpose of the present invention is to provide a method and an electronic device for establishing a scaled model of a gas-insulated equipment, aiming to solve the problem of insufficient prediction accuracy of the scaled model due to incomplete consideration of distortion factors in the design process of the scaled model in the prior art.

[0008] The present invention solves the above technical problems through the following technical solutions: A method for establishing a scaled model of a gas-insulated equipment includes the following steps: S1. Based on the independent scaling principle of each component of the scaled model of the gas-insulated equipment, establish a set of scaling coefficients, where each component includes a reduced-diameter bushing, a conducting rod, and an insulating basin; S2. Based on the set of scaling coefficients, combine the dynamic characteristics of each component to establish a set of frequency constraint equations; based on the frequency relationship between the prototype and the scaled model, construct a multi-objective optimization function for the reduced-diameter bushing, the conducting rod, and the insulating basin; S3. Obtain the geometric parameters and material parameters of the prototype of the gas-insulated equipment and the material parameters of the scaled model of the gas-insulated equipment. Combine the set of frequency constraint equations, solve the optimal solution of the multi-objective optimization function through a multi-objective genetic algorithm, and calculate the geometric parameters of the scaled model of the gas-insulated equipment based on the optimal solution.

[0009] A further improvement of the present invention is that the set of scaling coefficients specifically includes a length scaling coefficient , a reduced-diameter bushing diameter scaling coefficient , a reduced-diameter bushing wall thickness scaling coefficient , a conducting rod diameter scaling coefficient , a material elastic modulus scaling coefficient , and an insulating basin thickness scaling coefficient .

[0010] A further improvement of the present invention is that the length scaling coefficient is specifically:

[0011] where is the length of the scaled model; is the length of the prototype; The reduced-diameter bushing diameter scaling coefficient is specifically:

[0012] where is the diameter of the variable-diameter busbar cylinder of the scaled model; is the diameter of the variable-diameter busbar cylinder of the prototype; Wall thickness scaling coefficient Specifically:

[0013] Among them, is the wall thickness of the variable-diameter busbar cylinder of the scaled model; is the wall thickness of the variable-diameter busbar cylinder of the prototype; Conductive rod diameter scaling coefficient Specifically:

[0014] Among them, is the diameter of the conductive rod of the scaled model; is the diameter of the conductive rod of the prototype; And, the conductive rod diameter scaling coefficient Satisfies the following relationship:

[0015] Among them, is the cross-sectional moment of inertia correction relationship; is the cross-sectional moment of inertia of the scaled model; is the cross-sectional moment of inertia of the prototype; Material elastic modulus scaling coefficient Specifically:

[0016] Among them, is the material elastic modulus of the scaled model; is the material elastic modulus of the prototype; Insulating basin thickness scaling coefficient Specifically:

[0017] Among them, is the thickness of the insulating basin of the scaled model; is the thickness of the insulating basin of the prototype.

[0018] A further improvement of the present invention is that: the set of frequency constraint equations specifically includes the modal frequency constraint equation of the variable-diameter busbar cylinder, the modal frequency constraint equation of the conductive rod, and the modal frequency constraint equation of the insulating basin.

[0019] A further improvement of the present invention is that: establishing the set of frequency constraint equations specifically includes the following steps: S21. Based on the length scaling coefficient the variable-diameter busbar cylinder diameter scaling coefficient and the wall thickness scaling coefficient , establish the modal frequency constraint equation of the stepped busbar cylinder, specifically as follows:

[0020] Among them, is the modal frequency of the stepped busbar cylinder of the scaled model; is the modal frequency of the stepped busbar cylinder of the prototype; is the elastic modulus of the stepped busbar cylinder of the scaled model; is the elastic modulus of the stepped busbar cylinder of the prototype; is the density of the stepped busbar cylinder of the scaled model; is the density of the stepped busbar cylinder of the prototype; S22. Based on the length scaling coefficient and the diameter scaling coefficient of the conductive rod, establish the modal frequency constraint equation of the conductive rod, specifically as follows:

[0021] Among them, is the modal frequency of the conductive rod of the scaled model; is the modal frequency of the conductive rod of the prototype; is the elastic modulus of the conductive rod of the scaled model; is the elastic modulus of the conductive rod of the prototype; is the density of the conductive rod of the scaled model; is the density of the conductive rod of the prototype; S23. Let the diameter of the insulating basin be equal to the diameter of the stepped busbar cylinder. Based on the diameter scaling coefficient of the stepped busbar cylinder and the thickness scaling coefficient of the insulating basin, establish the modal frequency constraint equation of the insulating basin, specifically as follows:

[0022] Among them, is the modal frequency of the insulating basin of the scaled model; is the modal frequency of the insulating basin of the prototype; is the elastic modulus of the insulating basin of the scaled model; is the elastic modulus of the insulating basin of the prototype; is the density of the insulating basin of the scaled model; is the density of the insulating basin of the prototype.

[0023] A further improvement of the present invention lies in: the multi-objective optimization function Specifically as follows:

[0024] Among them, is the minimum value function.

[0025] A further improvement of the present invention lies in that the multi-objective genetic algorithm is one of NPGA, NSGA, NSGA-II, PAES, SPEA or VEGA.

[0026] A further improvement of the present invention lies in that it further includes S4. Based on the geometric parameters of the scaled model, a real scaled model of the gas insulated equipment is fabricated, and the modal frequency error between the real scaled model of the gas insulated equipment and the prototype of the gas insulated equipment is verified through modal testing.

[0027] A further improvement of the present invention lies in that when fabricating the real scaled model of the gas insulated equipment, when the material similarity criterion is met, the material of the scaled model can be replaced with a material that is inconsistent with the prototype of the gas insulated equipment.

[0028] The present invention also provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method for establishing the scaled model of the gas insulated equipment as described above are implemented.

[0029] Compared with the prior art, the positive and progressive effects of the present invention are as follows: The method for establishing the scaled model of the gas insulated equipment provided by the present invention solves the problem of insufficient prediction accuracy of the existing model through component-independent scaling, modal frequency constraint equations and a multi-objective optimization framework. Specifically: for the unique geometric and material characteristics of each component, the scaling coefficient is independently defined to avoid modal distortion caused by overall scaling and lay the foundation for a high-precision model; by deeply analyzing the dynamic characteristics, a frequency constraint equation is established to accurately describe the relationship between the scaling coefficient and the natural frequency, enhancing the theoretical rigor of the model and guiding the optimization direction; a multi-objective genetic algorithm is used to solve the optimal scaling combination, comprehensively weighing the interaction between components to ensure global modal consistency, thereby improving the prediction accuracy and accuracy of the model and providing strong technical support for the design optimization and performance evaluation of the gas insulated equipment.

[0030] Furthermore, the multi-objective genetic algorithm is one of NPGA, NSGA, NSGA-II, PAES, SPEA or VEGA; these algorithms have powerful global search capabilities and robustness, can avoid falling into local optimal solutions, thereby improving the prediction accuracy and reliability of the scaled model, can more accurately reflect the performance characteristics of the actual equipment, and provide a more reliable basis for design optimization and performance evaluation.

[0031] Furthermore, by actually fabricating a scaled-down model and conducting modal tests, the consistency between the scaled-down model and the prototype in terms of frequency characteristics can be visually verified. This is not only a practical test of the predictive ability of the theoretical model but also a crucial step in ensuring that the scaled-down model can accurately reflect the performance of the prototype device. By comparing the test data, the predictive accuracy of the scaled-down model can be quantitatively evaluated, providing valuable feedback for subsequent model optimization and practical applications.

[0032] Furthermore, on the premise of meeting the material similarity criterion, the flexibility of material replacement broadens the material selection range for fabricating the scaled-down model. By selecting alternative materials that meet the material similarity criterion, the production cost and time can be effectively reduced; at the same time, it can ensure that the scaled-down model is consistent with the prototype device in terms of key performance indicators, thus more accurately reflecting the performance characteristics of the prototype device. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings of the specification are used to provide a further understanding of the present invention and form a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention.

[0034] Figure 1 It is a schematic flow diagram of the method for establishing a scaled-down model of a gas-insulated equipment according to the present invention; Figure 2 It is a schematic diagram of the prototype of Embodiment 1 of the present invention. Among them, Fig. (a) is a schematic diagram of the prototype of the variable-diameter busbar cylinder, Fig. (b) is a schematic diagram of the prototype of the conductive rod, and Fig. (c) is a schematic diagram of the prototype of the insulating basin; Figure 3 It is a simulation schematic diagram of the variable-diameter busbar cylinder of Embodiment 1 of the present invention. Among them, Fig. (a) is a first-order simulation schematic diagram of the variable-diameter busbar cylinder, and Fig. (b) is a second-order simulation schematic diagram of the variable-diameter busbar cylinder; Figure 4 It is a simulation schematic diagram of the conductive rod of Embodiment 1 of the present invention. Among them, Fig. (a) is a first-order simulation schematic diagram of the conductive rod, and Fig. (b) is a second-order simulation schematic diagram of the conductive rod; Figure 5 It is a simulation schematic diagram of the insulating basin of Embodiment 1 of the present invention. Among them, Fig. (a) is a first-order simulation schematic diagram of the insulating basin, and Fig. (b) is a second-order simulation schematic diagram of the insulating basin; Figure 6 It is a distortion schematic diagram of the variable-diameter busbar cylinder of the actually fabricated scaled-down model of the gas-insulated equipment in Embodiment 1 of the present invention; among them, Fig. (a) is a first-order schematic diagram of the actual variable-diameter busbar cylinder; Fig. (b) is a second-order schematic diagram of the actual variable-diameter busbar cylinder; Fig. (c) is a third-order schematic diagram of the actual variable-diameter busbar cylinder; Fig. (d) is a fourth-order schematic diagram of the actual variable-diameter busbar cylinder. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and shown in the accompanying drawings here can be arranged and designed in various different configurations.

[0036] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0037] The following further detailed description of the present invention with reference to the accompanying drawings and specific embodiments is an explanation of the present invention rather than a limitation.

[0038] See Figure 1 , a method for establishing a scaled-down model of a gas-insulated equipment, comprising the following steps: S1. Based on the independent scaling principle of each component of the scaled-down model of the gas-insulated equipment, establish a set of scaling factors, and the components include a reduced-diameter busbar cylinder, a conductive rod, and an insulating basin; S2. Based on the set of scaling factors, combined with the dynamic characteristics of each component, establish a set of frequency constraint equations; based on the frequency relationship between the prototype and the scaled-down model, construct a multi-objective optimization function for the reduced-diameter busbar cylinder, the conductive rod, and the insulating basin; S3. Obtain the geometric parameters and material parameters of the prototype of the gas-insulated equipment and the material parameters of the scaled-down model of the gas-insulated equipment. Combine the set of frequency constraint equations, solve the optimal solution of the multi-objective optimization function through a multi-objective genetic algorithm, and calculate the geometric parameters of the scaled-down model of the gas-insulated equipment based on the optimal solution.

[0039] The method for establishing a scaled-down model of a gas-insulated equipment provided by the present invention effectively solves the problem of insufficient prediction accuracy caused by incomplete consideration of distortion factors in existing scaled-down models through component-independent scaling, establishment of modal frequency constraint equations, and application of a multi-objective optimization framework, improving the accuracy of the scaled-down model in simulating the performance of actual equipment and providing strong technical support for the design optimization and performance evaluation of gas-insulated equipment. Specifically: By adopting a component-independent scaling strategy, the unique geometric shapes and material properties of each component are fully considered, avoiding the problem of modal frequency distortion caused by overall scaling and laying a solid foundation for constructing a high-precision scaled-down model; By deeply analyzing the dynamic characteristics of each component, a set of frequency constraint equations is established to accurately describe the mathematical relationship between the scaling coefficient and the natural frequency of the component, not only enhancing the theoretical rigor of the model but also providing a clear guiding direction for the subsequent optimization process and ensuring that the scaled-down model is highly consistent with the prototype in terms of frequency characteristics; Combining the frequency constraint equations, a multi-objective genetic algorithm is used to solve the optimal scaling combination, which not only considers the performance optimization of a single component but also can comprehensively balance the interactions between multiple components to find the optimal solution that satisfies the global modal consistency, thereby improving the prediction accuracy and precision of the scaled-down model, especially when dealing with GIS busbar components with complex coupling relationships.

[0040] Specifically, the set of scaling coefficients specifically includes the length scaling coefficient , the diameter scaling coefficient of the stepped busbar cylinder , the wall thickness scaling coefficient of the stepped busbar cylinder , the diameter scaling coefficient of the conducting rod , the scaling coefficient of the material elastic modulus , and the thickness scaling coefficient of the insulating basin .

[0041] Specifically, the length scaling coefficient is specifically:

[0042] wherein, is the length of the scaled-down model; is the length of the prototype; The diameter scaling coefficient of the stepped busbar cylinder is specifically:

[0043] wherein, is the diameter of the stepped busbar cylinder of the scaled-down model; is the diameter of the stepped busbar cylinder of the prototype; The wall thickness scaling coefficient is specifically:

[0044] Among them, is the wall thickness of the variable-diameter busbar cylinder of the scaled model; is the wall thickness of the variable-diameter busbar cylinder of the prototype; Scaling coefficient of the diameter of the conductive rod Specifically:

[0045] Among them, is the diameter of the conductive rod of the scaled model; is the diameter of the conductive rod of the prototype; And, the scaling coefficient of the diameter of the conductive rod satisfies the following relationship:

[0046] Among them, is the correction relationship of the moment of inertia of the cross-section; is the moment of inertia of the cross-section of the scaled model; is the moment of inertia of the cross-section of the prototype; Scaling coefficient of the elastic modulus of the material Specifically:

[0047] Among them, is the elastic modulus of the material of the scaled model; is the elastic modulus of the material of the prototype; Scaling coefficient of the thickness of the insulating basin Specifically:

[0048] Among them, is the thickness of the insulating basin of the scaled model; is the thickness of the insulating basin of the prototype.

[0049] Specifically, the set of frequency constraint equations specifically includes the modal frequency constraint equation of the variable-diameter busbar cylinder, the modal frequency constraint equation of the conductive rod, and the modal frequency constraint equation of the insulating basin.

[0050] Specifically, establishing the set of frequency constraint equations specifically includes the following steps: S21. Based on the length scaling coefficient the diameter scaling coefficient of the variable-diameter busbar cylinder and the wall thickness scaling coefficient , establish the modal frequency constraint equation of the variable-diameter busbar cylinder, specifically:

[0051] Among them, is the modal frequency of the variable-diameter busbar cylinder of the scaled model; The modal frequency of the reducing busbar cylinder with [prototype] as the prototype; The elastic modulus of the reducing busbar cylinder of the scaled-down model; The elastic modulus of the reducing busbar cylinder of the prototype; The density of the reducing busbar cylinder of the scaled-down model; The density of the reducing busbar cylinder of the prototype; S22. Based on the length scale factor and the diameter scale factor of the conducting rod , establish the modal frequency constraint equation of the conducting rod, specifically:

[0052] Among them, The modal frequency of the conducting rod of the scaled-down model; The modal frequency of the conducting rod of the prototype; The elastic modulus of the conducting rod of the scaled-down model; The elastic modulus of the conducting rod of the prototype; The density of the conducting rod of the scaled-down model; The density of the conducting rod of the prototype; S23. Let the diameter of the insulating basin be equal to the diameter of the reducing busbar cylinder. Based on the diameter scale factor of the reducing busbar cylinder and the thickness scale factor of the insulating basin , establish the modal frequency constraint equation of the insulating basin, specifically:

[0053] Among them, The modal frequency of the insulating basin of the scaled-down model; The modal frequency of the insulating basin of the prototype; The elastic modulus of the insulating basin of the scaled-down model; The elastic modulus of the insulating basin of the prototype; The density of the insulating basin of the scaled-down model; The density of the insulating basin of the prototype.

[0054] Specifically, the multi-objective optimization function Specifically:

[0055] Among them, Is the minimum value function.

[0056] Specifically, the multi-objective genetic algorithm is one of NPGA (Niched Pareto Genetic Algorithm), NSGA (Nondominated Sorting Genetic Algorithm), NSGA-II (Nondominated Sorting Genetic Algorithm II), PAES (Pareto Archived Evolution Strategy), SPEA (Strength Pareto Evolutionary Algorithm), or VEGA (Vector Evaluated Genetic Algorithm); these algorithms have powerful global search capabilities and robustness, can avoid falling into local optimal solutions, thereby improving the prediction accuracy and reliability of the scaled model, can more accurately reflect the performance characteristics of the actual equipment, and provide a more reliable basis for design optimization and performance evaluation.

[0057] Specifically, it also includes S4. Based on the geometric parameters of the scaled model, a real scaled model of the gas-insulated equipment is made, and the modal frequency error between the real scaled model of the gas-insulated equipment and the gas-insulated equipment prototype is verified through modal testing; by actually making the scaled model and conducting modal testing, the consistency between the scaled model and the prototype in terms of frequency characteristics can be visually verified. It is not only an actual test of the prediction ability of the theoretical model but also a key link to ensure that the scaled model can accurately reflect the performance of the prototype equipment. By comparing the test data, the prediction accuracy of the scaled model can be quantitatively evaluated, providing valuable feedback for subsequent model optimization and practical applications.

[0058] Specifically, when making a real scaled model of the gas-insulated equipment, when the material similarity criterion is met, the material of the scaled model can be replaced with a material inconsistent with the gas-insulated equipment prototype. Specifically: when the material of the reduced-diameter busbar cylinder or the conductive rod prototype is aluminum alloy, a real scaled model of the gas-insulated equipment is made using stainless steel material; when the material of the insulating basin prototype is epoxy resin, a real scaled model of the gas-insulated equipment is made using fiberglass-reinforced plastic material; on the premise of meeting the material similarity criterion, the flexibility of material replacement broadens the material selection range for making the scaled model. By selecting alternative materials that meet the material similarity criterion, the production cost and time can be effectively reduced; at the same time, it can ensure that the scaled model is consistent with the prototype equipment in key performance indicators, thus more accurately reflecting the performance characteristics of the prototype equipment.

[0059] Example 1 See Figure 2 , taking a certain GIS busbar as an example: Measure the geometric parameters and material parameters of the prototype of the gas-insulated equipment. Among them, the material parameters of the prototype of the gas-insulated equipment are: The reduced-diameter busbar cylinder is made of aluminum alloy, and the density of the reduced-diameter busbar cylinder of the prototype 3 , and the elastic modulus of the reduced-diameter busbar cylinder of the prototype = 71 GPa; the conducting rod is made of copper, and the density of the conducting rod of the prototype 3 , and the elastic modulus of the conducting rod of the prototype = 103 GPa; the insulating basin is made of epoxy resin, and the density of the insulating basin of the prototype / m 3 , and the elastic modulus of the insulating basin of the prototype = 1 GPa.

[0060] For the geometric parameters of the prototype of the gas-insulated equipment, see Table 1: Table 1 Geometric Parameters of the Prototype of the Gas-Insulated Equipment

[0061] Among them, the large-end radius D1 and the small-end radius D2 of the reduced diameter both follow the diameter reduction ratio coefficient of the reduced-diameter busbar cylinder .

[0062] Select the material of the reduced-diameter busbar cylinder of the scaled model to be stainless steel: the density of the reduced-diameter busbar cylinder of the scaled model / m 3 , and the elastic modulus of the reduced-diameter busbar cylinder of the scaled model = 195 GPa. The material of the conducting rod remains copper, and the material of the insulating basin remains epoxy resin.

[0063] According to the parameters of the actual existing materials, solve the optimal solution of the multi-objective optimization function through the multi-objective genetic algorithm to obtain the following reduction ratios: Length reduction ratio = 0.5; Diameter reduction ratio of the reduced-diameter busbar cylinder = 0.5; Wall thickness reduction ratio of the reduced-diameter busbar cylinder = 0.25; Diameter reduction ratio of the conducting rod = 0.7; Thickness reduction ratio of the insulating basin = 0.25; Elastic modulus reduction ratio of the material of the reduced-diameter busbar cylinder Specifically:

[0064] Density reduction ratio coefficient of the reduced-diameter busbar cylinder Specifically:

[0065] Since the materials of the guide rod and the insulating basin remain unchanged, that is, the elastic modulus reduction ratio coefficient of the guide rod material , the density reduction ratio coefficient of the guide rod ; the elastic modulus reduction ratio coefficient of the insulating basin material , the density reduction ratio coefficient of the insulating basin .

[0066] See Figure 3 , Figure 4 and Figure 5 , combined with the reduction ratio coefficient, using finite element for simulation, the simulation data is obtained as shown in Table 2. By analyzing the modal frequency relationship between the prototype and the reduced-scale model, the first-order bending modal error ≤ 1% can be obtained.

[0067] Table 2 Simulation data

[0068] To verify the effectiveness of this method, based on the optimal solution, the geometric parameters of the reduced-scale model of the gas-insulated equipment are calculated, and a real reduced-scale model of the gas-insulated equipment is fabricated. The modal frequency error between the real reduced-scale model of the gas-insulated equipment and the prototype of the gas-insulated equipment is verified through modal testing.

[0069] The geometric parameters of the reduced-scale model of the gas-insulated equipment are shown in Table 3.

[0070] Table 3 Geometric parameters of the reduced-scale model of the gas-insulated equipment

[0071] See Figure 6 , in this embodiment, only the reduced-diameter busbar cylinder is fabricated using aluminum alloy material. An impulse excitation is applied to the fabricated reduced-diameter busbar cylinder specimen using an impact hammer (5800B3, DYTRAN). 128 measuring points are arranged on the surface of the reduced-diameter busbar cylinder specimen, and the free boundary condition is realized by an elastic rope. The test data is recorded by a data analyzer (m+p VibPilot) and modal analysis is performed. The first four-order bending modes and modal frequencies obtained from the test are shown in Table 4. It can be obtained that the vibration modes obtained from the test are basically the same as those obtained from the simulation calculation, and the error of the modal frequencies of the first four-order bending modes is controlled within , which fully demonstrates the performance reduction ability of the method for establishing the reduced-scale model of the gas-insulated equipment proposed by the present invention for the physical prototype.

[0072] Table 4 Test results

[0073] This method includes: 1. Independent scaling of components: defining the scaling factor sets for the variable-diameter busbar cylinder, the conductive rod, and the insulator respectively; 2. Modal frequency constraint equation: establishing the mathematical relationship between the scaling factors of each component and the natural frequency; 3. Multi-objective optimization framework: solving the optimal scaling combination that satisfies the global modal consistency through a multi-objective genetic algorithm. The method of the present invention comprehensively considers these coupling relationships, is closer to the actual situation, can effectively improve the accuracy of the scaled model in simulating the performance of the actual equipment, and improve the prediction accuracy. Because the problem of single distortion factor is successfully solved and the coupling effect of each factor is comprehensively considered, the established scaled model is more accurate. For the prediction of the modal natural frequency, large errors will no longer occur due to ignoring the key coupling relationships. Accurate prediction of the bending modal natural frequency is crucial for evaluating the mechanical stability and reliability of the GIS busbar, which greatly reduces the analysis and design risks based on the model of the present invention and can meet the high-precision requirements of modern power systems for equipment.

[0074] When solving the optimal scaling factor combination that satisfies the global modal consistency through a multi-objective optimization algorithm causes the size of a certain component to exceed the machining accuracy range, the stiffness loss is compensated by adding stiffeners or adjusting the wall thickness distribution, and at the same time, the geometric parameters in the constraint equation are updated. At the same time, this method can be extended and applied to the scaled modeling of gas-insulated switchgear (GIS), transformer bushings or DC wall bushings, and cross-equipment transplantation is achieved by adjusting the scaling factor sets and constraint equation parameters of each component.

[0075] Based on the same inventive concept, the present invention also provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method for establishing the scaled model of the gas-insulated equipment are implemented; wherein, the memory may include a memory, such as a high-speed random access memory, and may also include a non-volatile memory, for example, at least one disk memory, etc.; the processor, the network interface, and the memory are interconnected through an internal bus, and this internal bus can be an Industry Standard Architecture bus, a Peripheral Component Interconnect standard bus, an Extended Industry Standard Architecture bus, etc., and the bus can be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the program may include program codes, and the program codes include computer operation instructions. The memory may include a memory and a non-volatile memory, and provide instructions and data to the processor.

[0076] Finally, it should be noted that the above-listed embodiments exist merely as one or more specific manifestations of the technical solution of the present invention. Their purpose is to clearly elaborate the concept, principle, and application method of the present invention through specific examples, rather than intending to limit the protection scope of the present invention to these specific embodiments. In fact, the true value of the present invention lies in the proposed technical idea and innovation point, rather than its manifestation form or implementation means.

[0077] For those of ordinary skill in the art, after deeply reading and understanding the technical solution of the present invention, they are fully capable of making various forms of changes, modifications, or equivalent substitutions to the specific implementation manners of the invention based on their own professional knowledge and skills. These changes may include, but are not limited to: adjusting the value range of technical parameters, optimizing the algorithm process to improve efficiency, replacing some technical components to achieve better compatibility or reduce costs, etc. As long as the changed technical solution still substantially maintains the technical features required to be protected by the original invention, that is, still can achieve the core functions and effects of the present invention, then these changes should be regarded as falling within the protection scope of the pending claims of the present invention.

[0078] In addition, with the continuous progress and development of technology, new technical means and methods are constantly emerging, which also provides a broad space for the further improvement and perfection of the present invention. Therefore, the protection scope of the present invention should also include those reasonably foreseeable improvements and expansions based on the existing technology, as long as these improvements and expansions do not depart from the basic principle and core concept of the present invention, they should be regarded as equivalents of the present invention and are equally protected by the patent right.

Claims

1. A method for establishing a scaled-down model of a gas-insulated equipment, characterized in that The method includes the following steps: S1. Based on the independent scaling principle of each component of the gas-insulated equipment scaled model, a set of scaling factors is established, and the components include a reduced-diameter busbar cylinder, a conductive rod, and an insulating basin; S2. Based on the set of scaling factors and combined with the dynamic characteristics of each component, a set of frequency constraint equations is established; based on the frequency relationship between the prototype and the scaled model, a multi-objective optimization function regarding the reduced-diameter busbar cylinder, the conductive rod, and the insulating basin is constructed; S3. Geometric parameters of the gas-insulated equipment prototype, material parameters, and material parameters of the gas-insulated equipment scaled model are obtained. Combining with the set of frequency constraint equations, the optimal solution of the multi-objective optimization function is solved by a multi-objective genetic algorithm. Based on the optimal solution, the geometric parameters of the gas-insulated equipment scaled model are calculated.

2. A method for establishing a scaled-down model of a gas-insulated equipment according to claim 1, characterized in that The set of scale factors specifically includes the length scale factor , the diameter scale factor of the variable-diameter bushing , the wall thickness scale factor of the variable-diameter bushing , the diameter scale factor of the conductive rod , the scale factor of the elastic modulus of the material , and the scale factor of the thickness of the insulating basin .

3. A method for establishing a scaled-down model of a gas-insulated equipment according to claim 2, characterized in that, Length reduction ratio coefficient Specifically: Wherein, is the length of the scaled model; is the length of the prototype; Diameter reduction ratio coefficient of variable-diameter busbar cylinder Specifically: Among them, is the diameter of the variable-diameter busbar cylinder of the scaled model; is the diameter of the variable-diameter busbar cylinder of the prototype; Wall thickness reduction ratio coefficient Specifically: Among them, is the wall thickness of the reduced-scale model's variable-diameter busbar cylinder; is the wall thickness of the prototype's variable-diameter busbar cylinder; Conductive rod diameter reduction ratio coefficient Specifically: Among them, is the diameter of the conductive rod of the scaled model; is the diameter of the conductive rod of the prototype. Moreover, the diameter reduction ratio coefficient of the conductive rod satisfies the following relationship: Among them, is the correction relationship of the section moment of inertia; is the section moment of inertia of the scaled model; is the section moment of inertia of the prototype; Scaling coefficient of material elastic modulus Specifically: Among them, is the elastic modulus of the material of the scaled model; is the elastic modulus of the material of the prototype; Insulation basin thickness reduction ratio coefficient Specifically: Among them, is the thickness of the insulating basin of the scaled model; is the thickness of the insulating basin of the prototype.

4. A method for establishing a scaled-down model of a gas-insulated equipment according to claim 3, characterized in that, The set of frequency constraint equations specifically includes the modal frequency constraint equation of the reduced-diameter busbar cylinder, the modal frequency constraint equation of the conductive rod, and the modal frequency constraint equation of the insulating basin.

5. A method for establishing a scaled-down model of a gas-insulated equipment according to claim 4, characterized in that, The establishment of the set of frequency constraint equations specifically includes the following steps: S21. Based on the length scaling factor , the diameter scaling factor of the stepped busbar cylinder and the wall thickness scaling factor , establish the modal frequency constraint equation of the stepped busbar cylinder, specifically: Among them, is the modal frequency of the reduced-scale model's stepped busbar cylinder; is the modal frequency of the prototype's stepped busbar cylinder; is the elastic modulus of the reduced-scale model's stepped busbar cylinder; is the elastic modulus of the prototype's stepped busbar cylinder; is the density of the reduced-scale model's stepped busbar cylinder; is the density of the prototype's stepped busbar cylinder; S22. Based on the length scaling factor and the diameter scaling factor of the conductive rod , establish the modal frequency constraint equation of the conductive rod, specifically as follows: Among them, is the modal frequency of the conductive rod of the scaled model; is the modal frequency of the conductive rod of the prototype; is the elastic modulus of the conductive rod of the scaled model; is the elastic modulus of the conductive rod of the prototype; is the density of the conductive rod of the scaled model; is the density of the conductive rod of the prototype; S23. Let the diameter of the insulating basin be equal to the diameter of the reduced-diameter busbar cylinder, and based on the diameter reduction ratio coefficient of the reduced-diameter busbar cylinder and the thickness reduction ratio coefficient of the insulating basin , establish the modal frequency constraint equation of the insulating basin, specifically: Among them, is the modal frequency of the insulating basin of the scaled model; is the modal frequency of the insulating basin of the prototype; is the elastic modulus of the insulating basin of the scaled model; is the elastic modulus of the insulating basin of the prototype; is the density of the insulating basin of the scaled model; is the density of the insulating basin of the prototype.

6. A method for establishing a scaled-down model of a gas-insulated equipment according to claim 5, characterized in that, Multi-objective optimization function Specifically: Among them, is the minimum value function.

7. A method for establishing a scaled-down model of a gas-insulated equipment according to claim 1, characterized in that The multi-objective genetic algorithm is one of NPGA, NSGA, NSGA-II, PAES, SPEA, or VEGA.

8. A method for establishing a scaled-down model of a gas-insulated equipment according to claim 1, characterized in that It further includes S4. Based on the geometric parameters of the scaled model, a real gas-insulated equipment scaled model is manufactured, and the modal frequency error between the real gas-insulated equipment scaled model and the gas-insulated equipment prototype is verified through modal testing.

9. A method for establishing a scaled-down model of a gas-insulated equipment according to claim 8, characterized in that, When manufacturing the real gas-insulated equipment scaled model, when the material similarity criterion is met, the material of the scaled model can be replaced with a material inconsistent with that of the gas-insulated equipment prototype.

10. An electronic device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method for establishing the gas-insulated equipment scaled model according to any one of claims 1 to 9 are implemented.

Images