Molded case circuit breaker contact bounce simulation method, device and equipment and storage medium

Abstract

The invention provides a molded case circuit breaker contact bounce simulation method, device and equipment and a storage medium, and the method comprises the steps: building a simulation model of the switching-on process of a circuit breaker operating mechanism, and carrying out the dynamic simulation of the contact bounce through setting a contact force parameter; the mechanical parameters of the operating mechanism influencing the bounce of the contact are obtained through sensitivity analysis; sample points are extracted through Latin hypercube sampling, mechanical parameters serve as independent variables, closing performance indexes obtained by substituting the mechanical parameters into dynamics simulation software serve as dependent variables, and a data set containing the independent variables and the dependent variables is fitted through a support vector machine regression model; and determining an objective function, performing multi-objective optimization through a genetic algorithm to obtain an optimal solution under a limiting condition, and inputting an independent variable parameter corresponding to the optimal solution into dynamic simulation software for simulation to measure a simulation value of the switching-on performance index. According to the method, the mechanical property and the optimization target of the mechanism can be considered more comprehensively, and the overall performance can be improved.

Description

Technical Field

[0001] The present application belongs to the technical field of circuit breaker testing, and in particular, relates to a method, device, equipment and storage medium for simulating contact bounce of a molded case circuit breaker. Background Art

[0002] Contact bounce of molded case circuit breakers refers to the phenomenon that when the circuit breaker is working, especially when opening or closing the circuit, the contacts fail to make normal and stable contact, and there is a momentary jump or rebound. Contact bounce may cause poor contact, arc sputtering, overheating and even electrical failure.

[0003] Usually, this situation is related to the following factors:

[0004] 1. The existing mechanism has a large contact bounce amplitude

[0005] 1. Unreasonable design of mechanism parameters

[0006] Influence of factors such as connecting rod length, mass, and center of mass position: In the four-bar linkage of the molded case circuit breaker, the length, mass, and center of mass position of the connecting rod have a significant impact on the motion characteristics of the moving contact. If the length of the connecting rod is not designed properly, the speed and acceleration of the moving contact will change during the movement, so that it has greater kinetic energy when it collides, which in turn leads to an increase in the amplitude of the contact bounce. For example, when the length of the connecting rod is too long, the inertia of the moving contact increases, which will generate a greater impact force at the moment of collision, causing the contact to bounce more violently.

[0007] Influence of rotational inertia: The rotational inertia of the mechanism is an important factor affecting contact bounce. If the rotational inertia of the mechanism is large, the angular acceleration of the moving contact will decrease under the same energy input, resulting in a decrease in its movement speed. However, when the contacts collide, due to the large rotational inertia, the moving contact is difficult to stop quickly and will continue to move and produce a large bounce amplitude.

[0008] 2. Improper spring parameter settings

[0009] The main spring stiffness coefficient is unreasonable: the main spring provides energy for the operating mechanism during the closing process. If the main spring stiffness coefficient is too large, the moving contact will have a higher speed and kinetic energy when it collides, resulting in an increase in the contact bounce amplitude. For example, under the condition of energy conservation, a main spring with a large stiffness coefficient will store more elastic potential energy under the same deformation amount. This energy is converted into the kinetic energy of the moving contact during the closing process, causing it to have a higher speed and more severe bounce when it collides.

[0010] The stiffness coefficient of the contact torsion spring is unreasonable: The contact torsion spring plays a buffering and resetting role during the contact bounce process. If the stiffness coefficient of the torsion spring is too small, its buffering ability is insufficient, and it cannot effectively absorb the energy generated by the collision, resulting in an increase in the contact bounce amplitude. For example, when the contact collides, the torsion spring with a small stiffness coefficient cannot provide enough reaction force to quickly stop the rebound movement of the contact, causing the contact to continue to bounce after the collision.

[0011] 3. Component manufacturing precision and assembly error

[0012] Component dimensional accuracy issues: During the manufacturing process, if the dimensional accuracy of components such as connecting rods and shafts is insufficient, it will lead to deviations between the actual parameters and the design parameters of the mechanism. For example, if the actual length of the connecting rod is slightly longer than the designed length, it will change the movement trajectory and speed of the moving contact, resulting in greater bounce during the collision.

[0013] The influence of assembly error: During the assembly process, the relative positions and connection methods between components have an important impact on the performance of the mechanism. If the assembly error is large, such as the installation position of the connecting rod shaft is offset, it will change the kinematic and dynamic characteristics of the mechanism, resulting in unstable movement of the moving contact and an increase in the contact bounce amplitude.

[0014] II. Problems with the contact bounce simulation of molded case circuit breakers

[0015] 1. Problems with the difference between the simplified simulation model and the actual situation

[0016] Insufficient accuracy caused by model simplification: When performing the contact bounce simulation of molded case circuit breakers, in order to reduce the computational complexity and improve the simulation efficiency, the actual mechanical structure is usually simplified to a certain extent. However, this simplification may ignore some key details and factors, resulting in differences between the simulation model and the actual situation and affecting the accuracy of the simulation results.

[0017] It is difficult to fully restore the complexity of the actual structure: The actual internal structure of molded case circuit breakers is complex, including numerous mechanical components and connection relationships. In the simulation model, it is difficult to fully restore these complex structures and interactions, especially in terms of the geometric shape, material properties, and contact relationships at some key positions, which further exacerbates the difference between the simulation and the actual situation.

[0018] 2. Problems with insufficient verification and calibration of simulation results

[0019] Lack of effective experimental data support: The accuracy of the contact bounce simulation results needs to be verified and calibrated through experimental data. However, in the actual experimental process, it is difficult to obtain accurate contact bounce data. The contact bounce process is very fast and complex, requiring high-precision measurement equipment and methods, such as high-speed cameras and laser displacement sensors. These devices are often expensive and complex to operate, restricting the acquisition of experimental data.

[0020] Inconsistency between experimental and simulation conditions: Even if experimental data can be obtained, it is difficult to ensure that the experimental conditions are exactly the same as the simulation conditions. For example, in experiments, external factors such as ambient temperature, humidity, and power supply voltage will all affect contact bounce, while in simulations, these factors are usually idealized or ignored, resulting in a certain deviation between the simulation results and the experimental results.

[0021] 3. Limitations of simulation software and algorithms

[0022] Limitations of software functions: Currently commonly used simulation software may have some functional limitations when dealing with complex problems such as contact bounce of molded case circuit breakers. For example, some software has limited capabilities in handling multi-body dynamics, contact collisions, and non-linear material properties, and cannot accurately simulate various physical phenomena and mechanical behaviors during the contact bounce process.

[0023] Balance between algorithm accuracy and efficiency: During the simulation process, a balance needs to be struck between the accuracy and efficiency of the algorithm. High-precision algorithms often involve large amounts of calculations and long execution times, while low-precision algorithms are fast but may lack reliability in their results. For problems such as contact bounce simulation that require a large number of iterative calculations, how to select an appropriate algorithm to obtain accurate results within a reasonable time is a challenge.

[0024] III. Difficulty in optimization design

[0025] 1. Influence of mechanism integrity on parameters

[0026] Coupling relationship between parameters: The four-bar mechanism is an integral mechanism, and there are complex coupling relationships between the parameters of each component. For example, changing the length of the connecting rod will affect parameters such as the moment of inertia, motion trajectory, and speed of the mechanism; and the change in the moment of inertia will in turn affect the force and motion characteristics of the connecting rod. This mutual influence makes it difficult to adjust a single parameter alone during the optimization design without affecting other parameters, increasing the complexity of optimization.

[0027] Trade-off problem in multi-objective optimization: During the optimization design process, multiple performance indicators need to be considered simultaneously, such as contact bounce amplitude, bounce time, closing speed, mechanism overtravel, and contact final pressure. These indicators often restrict each other, and improving one indicator may cause other indicators to deteriorate. For example, reducing the contact bounce amplitude may require increasing the moment of inertia of the mechanism, but this will in turn reduce the closing speed. Therefore, during the optimization design, multi-objective trade-offs need to be made to find the best balance point between the indicators, which further increases the difficulty of optimization design.

[0028] 2. Lack of effective optimization methods and tools

[0029] Limitations of traditional optimization methods: Traditional optimization methods, such as the analytical method, graphical method, etc., have certain limitations when dealing with complex multi-parameter and multi-objective optimization problems. These methods often require a large amount of manual calculation and trial and error, with low efficiency, and it is difficult to ensure finding the global optimal solution.

[0030] Deficiencies of simulation and analysis tools: Although there are currently some simulation software that can be used for the dynamic analysis and optimization design of mechanisms, when dealing with complex mechanisms such as molded case circuit breakers, there are still some deficiencies. For example, the accuracy of the simulation model may not be high enough to fully and accurately reflect the performance of the actual mechanism; or the operation of the simulation software is complex, requiring high professional knowledge and skills, which limits its wide application in optimization design.

[0031] 3. Limitations of designers' experience and knowledge

[0032] Lack of experience: Optimization design requires designers to have rich experience and profound professional knowledge. If designers lack experience in the design of molded case circuit breaker mechanisms, they may not be able to comprehensively consider the impact of various parameters on performance and it is difficult to propose effective optimization solutions. Summary of the Invention

[0033] In view of this, the present application aims to propose a method, device, equipment and storage medium for simulating the contact bounce of a molded case circuit breaker to solve at least one of the above problems.

[0034] To achieve the above object, the technical solution of the present application is realized as follows:

[0035] In a first aspect, the present application provides a method for simulating the contact bounce of a molded case circuit breaker, including:

[0036] Establish a simulation model of the closing process of the operating mechanism of the molded case circuit breaker in a dynamic simulation software, and perform dynamic simulation on the contact bounce by setting the contact force parameters of the moving and static contacts of the circuit breaker;

[0037] Obtain the mechanical parameters of the operating mechanism that affect the contact bounce through sensitivity analysis and determine them as the main influencing factors, where the main influencing factors at least include the position of the connecting rod shaft of the operating mechanism, the shape of the operating mechanism rod, and the spring parameters of the operating mechanism;

[0038] Use Latin hypercube sampling to extract a number of sample points, and use the mechanical parameters as independent variables and the closing performance index obtained by simulating them in the dynamic simulation software as the dependent variable, and fit the data set containing the independent variable and the dependent variable through a support vector machine regression model to verify the regression effect;

[0039] Determine the objective function, perform multi-objective optimization through the genetic algorithm to obtain the optimal solution under the constraints, and input the independent variable parameters corresponding to the optimal solution into the dynamic simulation software to measure the simulated value of the closing performance index.

[0040] In a second aspect, based on the same inventive concept, the present application further provides a molded case circuit breaker contact bounce simulation device, including:

[0041] A simulation module configured to establish a simulation model of the closing process of the operating mechanism of the molded case circuit breaker in dynamic simulation software, and perform dynamic simulation on the contact bounce by setting the contact force parameters of the moving and static contacts of the circuit breaker;

[0042] A sensitivity analysis module configured to obtain the mechanical parameters of the operating mechanism that affect the contact bounce through sensitivity analysis and determine them as the main influencing factors, where the main influencing factors at least include the position of the connecting rod shaft of the operating mechanism, the shape of the operating mechanism rod, and the spring parameters of the operating mechanism;

[0043] A sampling module configured to use Latin hypercube sampling to extract a number of sample points, use the mechanical parameters as independent variables, and use the closing performance index obtained by simulating them in the dynamic simulation software as the dependent variable, and fit the data set including the independent variable and the dependent variable through a support vector machine regression model to verify the regression effect;

[0044] An optimization module configured to determine the objective function, perform multi-objective optimization through the genetic algorithm to obtain the optimal solution under the constraints, and input the independent variable parameters corresponding to the optimal solution into the dynamic simulation software to measure the simulated value of the closing performance index.

[0045] In a third aspect, based on the same inventive concept, the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, it implements the method described in the first aspect.

[0046] In a fourth aspect, based on the same inventive concept, the present application further provides a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the method described in the first aspect.

[0047] Compared with the prior art, the molded case circuit breaker contact bounce simulation method, device, equipment, and storage medium of the present application have the following beneficial effects:

[0048] (1) This method uses Latin hypercube sampling to obtain sample points, fits the relationship between closing performance indicators and mechanical parameters through the SVM algorithm, and then globally searches for the optimal parameter combination using the genetic algorithm. While optimizing the contact bounce amplitude, it also takes into account performance indicators such as closing speed, mechanism overtravel, and contact final pressure to achieve overall performance improvement.

[0049] (2) When simulating and modeling, this method attempts to restore actual structural details as much as possible, and validates and calibrates the simulation results through experimental data to ensure the accuracy and reliability of the simulation model. At the same time, it makes full use of the powerful functions of the dynamics simulation software to accurately simulate various physical phenomena and mechanical behaviors during the contact bounce process, providing an intuitive and accurate basis for the optimization design. The optimized mechanism performs excellently in both simulation and actual tests, verifying the effectiveness of this method in solving the problem of differences between simulation and reality.

[0050] (3) Through the organic combination of Latin hypercube sampling, SVM regression model, and genetic algorithm, this method efficiently processes multi-variable and multi-objective optimization problems, breaking through the optimization difficulties brought about by the integrity of the mechanism and the parameter coupling relationship. During the optimization process, it fully considers the mutual restraint relationship between various performance indicators, and finds the best balance point through multi-objective trade-off to achieve overall performance improvement. Compared with traditional optimization methods, this method has higher efficiency and accuracy, can more comprehensively consider the mechanical properties and optimization objectives of the mechanism, and provides a more effective solution for the optimization design of the molded case circuit breaker operating mechanism. Description of the Drawings

[0051] The drawings forming a part of this application are used to provide a further understanding of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0052] Figure 1 is a flowchart of a method for simulating contact bounce of a molded case circuit breaker according to an embodiment of this application;

[0053] Figure 2 is a schematic diagram of the principle of contact bounce test according to an embodiment of this application;

[0054] Figure 3 is an analysis simulation diagram of contact bounce according to an embodiment of this application;

[0055] Figure 4 is a schematic structural diagram of a device for simulating contact bounce of a molded case circuit breaker according to an embodiment of this application;

[0056] Figure 5 is a schematic diagram of the hardware structure of an electronic device according to an embodiment of this application. Detailed Embodiments

[0057] To make the objectives, technical solutions, and advantages of this application more clear and understandable, the following further elaborates on this application in detail with reference to specific embodiments and the accompanying drawings.

[0058] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the embodiments of this application should have the ordinary meaning understood by those with ordinary skills in the field to which this application belongs. The "first", "second", and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are only used to distinguish different components. Words such as "include" or "comprise" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. Words such as "connect" or "be connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right", etc. are only used to represent relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0059] The embodiments of this application are described in detail below with reference to the accompanying drawings.

[0060] Please refer to Figure 1 As shown, this embodiment provides a method for simulating the contact bounce of a molded case circuit breaker, which specifically includes the following steps:

[0061] Step S101: Establish a simulation model of the closing process of the operating mechanism of the molded case circuit breaker in the dynamic simulation software, and perform dynamic simulation of the contact bounce by setting the contact force parameters of the moving and static contacts of the circuit breaker.

[0062] Step S102: Obtain the mechanical parameters of the operating mechanism that affect the contact bounce through sensitivity analysis and determine them as the main influencing factors. Among them, the main influencing factors at least include the position of the connecting rod shaft of the operating mechanism, the shape of the operating mechanism rod, and the spring parameters of the operating mechanism.

[0063] Step S103: Use Latin hypercube sampling to extract several sample points, and use the mechanical parameters as independent variables and the closing performance index obtained by simulating them in the dynamic simulation software as the dependent variable. Fit the data set containing the independent variable and the dependent variable through a support vector machine regression model to verify the regression effect.

[0064] Step S104: Determine the objective function, perform multi-objective optimization through the genetic algorithm to obtain the optimal solution under the restricted conditions, and input the independent variable parameters corresponding to the optimal solution into the dynamic simulation software to measure the simulation value of the closing performance index.

[0065] The method for simulating the contact bounce of a molded case circuit breaker described in this embodiment can more comprehensively consider the mechanical properties and optimization objectives of the mechanism, significantly improve the contact bounce performance and overall operating performance of the molded case circuit breaker, and has significant technical effects and practical application values.

[0066] In some embodiments, a contact bounce test is performed on the circuit breaker. By setting penalty parameters and regression coefficients, and calculating the contact force based on the contact penalty function defined in the dynamic simulation software, the contact bounce time is simulated according to the contact force.

[0067] Specifically, in this embodiment, a simulation model of the closing process of the operating mechanism of the molded case circuit breaker is established in the dynamic simulation software (ADAMS software is used in this embodiment). Drives and constraints are applied between the components, and the physical quantity parameters of the relevant components are set (including the size, shape, and material of the components, the stiffness coefficient and preload of the spring, etc.). The contact parameters of the moving and static contacts are set to simulate the contact bounce.

[0068] Since the contact bounce occurs in a very short time (millisecond level), when the overall closing process simulation time is too long (second level), it is difficult to accurately simulate in the extreme time when the contact bounce occurs, which will greatly increase the simulation error and the accuracy of the obtained results is relatively low. Therefore, in the simulation, the closing process is changed from slow closing (second level) to fast closing (millisecond level). Due to the closing characteristics of the molded case circuit breaker itself, when the mechanism runs to a certain closing position, the action of the mechanism is only driven by the main spring and the action speed increases rapidly. Therefore, as long as the simulated value of the action speed of the operating handle after this position is not greater than the action speed of the mechanism, fast closing simulation can be carried out while ensuring the correctness of the simulation. After analysis, it is considered that setting the closing time to 20 ms can meet the above requirements. Under this total duration, the contact bounce process that only appears for a few milliseconds can be simulated with high precision without making the overall simulation running duration too long. This measure can effectively improve the simulation accuracy without increasing the simulation running duration.

[0069] In the dynamic simulation software, the definition of the contact penalty function involves a restitution-based contact algorithm. This algorithm calculates the contact force through the set penalty parameters and regression coefficients. The following are the key parameters and definitions of the contact penalty function:

[0070] Penalty Parameter: The penalty parameter imposes a unilateral constraint to limit the penetration between two contacting objects. This parameter is similar to the stiffness in the contact algorithm of the collision function, but it has no association with the contact geometric material. The default value of the penalty value is 10000, and the unit is the damping unit N / (mm / sec).

[0071] Restitution Coefficient: The restitution coefficient determines the energy loss during contact. It is a dimensionless coefficient with a value range between 0 and 1. When the value is 1, it represents a perfectly elastic contact, i.e., no energy loss; when the value is 0, it represents a perfectly inelastic contact, i.e., all contact energy is converted into other forms of energy, such as heat energy or deformation energy.

[0072] To obtain the actual values of relevant parameters, it is necessary to measure the contact bounce. The schematic diagram of the actual measurement is as Figure 2 shown. Using the above circuit for testing, switch on and off 10 times, and take the average value of the measured contact bounce time to obtain the actual contact bounce time. The measured contact bounce time of this mechanism is 1.875 ms. The restitution coefficient of silver tungsten alloy is about 0.1 - 0.6. During the simulation process, the selection of the contact penalty function needs to cooperate with the restitution coefficient. An overly small contact penalty function will cause the moving contact to not obtain sufficient bounce initial velocity, and the actual calculated restitution coefficient is much smaller than the simulation setting value. An overly large contact penalty function will cause abnormal bounce of the contact and unable to close stably. Therefore, it is necessary to analyze and select an appropriate value.

[0073] According to the actual measurement results of the contact bounce time, when the restitution coefficient is taken as 0.3, the simulated contact bounce time is relatively close to the measured time. In order to cooperate with the restitution coefficient, after analysis, it is considered that the contact penalty function of 7000 is more appropriate. The subsequent simulation will use this value for contact bounce analysis and optimization.

[0074] Figure 3 For the contact bounce simulation diagram using the restitution coefficient and contact penalty function values obtained from the analysis, read the abscissa of the minimum value point. The contact moment of the moving and static contacts is 21.005 ms, the end moment of the first contact bounce is 22.49 ms, the end moment of the second contact bounce and the moment when the mechanism closes stably is 22.935 ms. The total time difference of the contact bounce is 1.93 ms, the duration of the first contact bounce is 1.485 ms, and the duration of the second contact bounce is 0.445 ms. At the same time, read the ordinates of relevant points and substitute them into the formula to calculate that the maximum contact bounce amplitude is 0.8940 mm.

[0075] In some embodiments, the sensitivity analysis formula is:

[0076]

[0077] In the formula, O represents the target value, V represents the design parameter value, i represents the iteration number, and s represents the sensitivity;

[0078] Based on the sensitivity analysis results, a single-factor analysis is carried out on the mechanical parameters affecting the contact bounce action, and the function curves of the severity of contact bounce and the closing performance index changing with the mechanical parameters are obtained. Furthermore, the variation law between the closing performance index and the mechanical parameters of each mechanism is obtained.

[0079] Study the influence degree of the change of a single design variable on the objective function. Set the value range of the design variable, and take several values within this range. After a series of simulation analyses, an analysis report is obtained. From the analysis results, the sensitivity of the design parameter change to the objective function can be known, and its value can be expressed by the above formula. It can be seen from the formula that the sensitivity of the i-th design reference value is the average value of the influence degrees of the i-1-th design reference value and the i+1-th design reference value on the target value, S i The positive and negative of which respectively indicate that the target value O gradually increases and gradually decreases during the increase of the iteration number i, and S i The larger the absolute value, the greater the influence degree of the design parameter on the target value.

[0080] First, the main influencing factors that have a greater impact on contact bounce are obtained through sensitivity analysis. Then, a single-factor analysis is carried out on these main influencing factors, and the function curves of the severity of contact bounce and the main closing performance indexes changing with these mechanical parameters are obtained, and the variation law between the closing performance index and the mechanical parameters of each mechanism is obtained.

[0081] The main influencing factors include:

[0082] 1. The influence of the change of the position of the connecting rod shaft of the operating mechanism on the contact bounce during the closing process

[0083] The four-bar linkage is an integral mechanism. The length, start and end positions of each rod, the position form of each rod, and the rotation angle of each rod at the just-closed position are closely related to each other. It is difficult to change a certain parameter alone while keeping other parameters unchanged. Therefore, for the analysis of the above parameters, under a specific mechanism, the influence of the change of the above parameters on the contact bounce during the closing process of the mechanism can be equivalently simulated by changing the initial position coordinates of each connecting rod shaft. This is because the change of each of the above parameters can be characterized by the change of the spatial position of each connecting rod shaft at the initial position. Among them, the equivalent length of each rod of the four-bar linkage and the change of the position form of each rod during the closing process can be regarded as the change of the initial position coordinates of each connecting rod shaft and the position coordinates of the relevant moving shafts during the movement of the four-bar linkage. Therefore, by changing the coordinates of the relevant connecting rod shaft at the opening position, the influence of the change of the equivalent length of each rod and the change of the spatial position form on the contact bounce during the closing process of the operating mechanism is simulated.

[0084] In the four-bar linkage mechanism, the main rotating shafts include the operating handle rotating shaft (lever shaft), the rotating shaft connecting the upper link and the jumping buckle (upper link shaft), the rotating shaft connecting the upper link and the lower link (link shaft), the rotating shaft connecting the lower link and the moving guide rod (lower link shaft), and the moving guide rod rotating shaft. To explore the influence of the change in the position of the four-bar rotating shaft on the contact bounce during the closing process, the coordinate values of the rotating shaft at the initial position (open position) can be changed in the simulation software, and the influence on the contact bounce during the closing process can be observed. Since the mechanism is symmetric about the XOY plane in the simulation software, only the coordinates of the rotating shaft on the X and Y axes need to be changed to analyze the position of the four-bar rotating shaft. Due to the excessive number of variables involved, the factors with greater influence on the results can be obtained through sensitivity analysis first, and then detailed analysis can be carried out on them.

[0085] By fixing the X and Y coordinates of the moving guide rod shaft and analyzing the influence of the remaining variables on the contact bounce, the influence of the change in the position of the four-bar rotating shaft on the contact bounce during the closing process of the mechanism can be analyzed.

[0086] Table 1 Simulation research results of the position design of the link shaft

[0087]

[0088]

[0089] In the table: Axis A represents the lower link shaft; Axis B represents the link shaft; Axis C represents the upper link shaft.

[0090] The results of the sensitivity analysis are shown in the above table. Through analysis, it can be found that the x coordinate of the link shaft, the y coordinate of the link shaft, and the x coordinate of the upper link shaft have a greater influence on the maximum contact bounce amplitude. And when the x coordinate of the link shaft increases, and the y coordinate of the link shaft and the x coordinate of the upper link shaft decrease, the contact bounce will be suppressed.

[0091] 2. Influence of the change in the shape of the operating mechanism rods on the contact bounce during the closing process

[0092] Next, the mass and centroid position of each rod of the operating mechanism are analyzed and studied to obtain the influence of the change in their values on the contact bounce during the closing process. Through the design study function in parametric analysis, the sensitivity values reflecting the influence degree of the change in the values of each variable on the contact bounce value can be obtained, and subsequent analysis and optimization can be carried out on the variables with greater sensitivity.

[0093] Table 2 Simulation research results of the rod mass design

[0094]

[0095] Table 3 Simulation results of the design study on the position of the mass center of the rod

[0096]

[0097] It can be seen from the results of the design study in the table that:

[0098] ① The shape of the moving conducting rod has the greatest influence on the contact bounce during the closing process. Without affecting the operation of the mechanism, appropriately reducing the mass of the moving conducting rod can reduce the contact bounce. By changing its shape to increase the x-coordinate and decrease the y-coordinate of the mass center position, the contact bounce can be effectively reduced.

[0099] ② The shape of the upper connecting rod also has a certain influence on the contact bounce during the closing process. Appropriately increasing the mass of the upper connecting rod can reduce the contact bounce to a certain extent, but the influence of its centroid position on the contact bounce is relatively small.

[0100] ③ The shape of the lower connecting rod has a relatively small influence on the contact bounce during the closing process, and its influence can be ignored during the actual optimization process.

[0101] ④ The shape of the main shaft has almost no influence on the contact bounce during the closing process because the centroid position of the main shaft almost overlaps with the position of the rotating shaft and its moment of inertia is almost zero, so its influence on the contact bounce can be ignored.

[0102] 3. Influence of the change of the spring parameters of the operating mechanism on the contact bounce during the closing process

[0103] The spring system of the molded case circuit breaker consists of two parts: the main spring of the mechanism and the contact torsion spring. Among them, the main spring provides energy for the operation of the operating mechanism during the closing process, and the magnitude of its parameters will determine the speed of the moving contact at the moment when the moving and static contacts are just closed. The parameters of the contact torsion spring affect the bounce degree of the moving contact during the contact bounce process. Therefore, it is of great significance to analyze the influence of the change of the above spring parameters on the contact bounce during the mechanism closing process.

[0104] Table 4 Results of the simulation study on the spring stiffness design

[0105]

[0106] Through sensitivity analysis, it is found that both the main spring stiffness coefficient and the contact torsion spring stiffness coefficient have a greater influence on the contact bounce. When the main spring stiffness coefficient decreases and the contact torsion spring stiffness coefficient increases, the contact bounce will be suppressed.

[0107] In some embodiments, a dataset containing multiple independent variables and multiple dependent variables is fitted by a support vector machine regression model. The cvpartition function is used to divide the dataset into a training set and a validation set. Among them, the parameter grid search method is used to find the optimal SVM parameters. By traversing all possible combinations of kernel functions and box constraints, the fitrsvm function is used to train the SVM model. In this SVM model, the mean square error and the coefficient of determination are calculated using the validation set.

[0108] Specifically, in this embodiment, since the relational expressions between the closing performance indicators and the mechanical parameters of each operating mechanism are relatively complex and it is difficult to obtain an analytical expression in the actual model, an approximate solution is considered through the SVM (Support Vector Machine) algorithm. First, sample points are obtained through LHS (Latin Hypercube Sampling), then approximately fitted through the SVM algorithm, and finally the genetic algorithm is used for multi-variable optimization to finally obtain the minimum value of the objective function under certain constraint conditions.

[0109] Latin Hypercube Sampling (LHS) is an advanced stratified sampling technique. It ensures the uniform distribution of samples in the multi-dimensional parameter space by equally probabilistically stratifying the value range of each dimension and randomly sampling sample points within each layer. The core of this method lies in that it not only considers the distribution of samples in each dimension, but also ensures that the sample points between different dimensions do not repeat through the Latinization process, thus avoiding unnecessary correlations between samples.

[0110] Specifically, LHS first determines the required number of samples, and then divides the value range of each dimension into several sub-intervals equal to the number of samples. Within each sub-interval, a sample point is randomly selected, and these points are unique in each dimension, ensuring the diversity of samples. Finally, through randomization, the order of the sample points is further disrupted to reduce any possible ordered correlations. This sampling strategy is particularly suitable for complex system simulations and optimization problems that require effective exploration in high-dimensional spaces.

[0111] In this embodiment, the following variables are selected as independent variables: the x coordinate of the connecting rod shaft, the y coordinate of the connecting rod shaft, the x coordinate of the upper connecting rod shaft, the mass and centroid position coordinates of the upper connecting rod shaft, the mass and centroid position coordinates of the moving guide rod, the stiffness coefficient of the main spring, and the stiffness coefficient of the contact torsion spring.

[0112] The numerical values of the following performance indicator parameters are selected as dependent variables: the maximum contact bounce amplitude, the contact bounce time, the closing time, the angular velocity of the moving guide rod at the moment of contact closure, the mechanism over-travel, and the final contact pressure.

[0113] 100 sample points are extracted using the Latin Hypercube Sampling method and simulated in ADAMS to obtain 100 data points containing 8 independent variables and 6 dependent variables.

[0114] Next, write a program in MATLAB to fit a dataset with 8 independent variables and 6 dependent variables using a Support Vector Machine (SVM) regression model. Use the cvpartition function to divide the dataset into a training set and a validation set, with 20% of the data used as the validation set. Use the parameter grid search method to find the best SVM parameters by traversing all possible combinations of kernel functions and box constraints, and use the fitrsvm function to train the SVM model. For each model, calculate the mean squared error (MSE) and coefficient of determination (R2) using the validation set, and record and save the model with the best MSE and R2 values.

[0115] The following are the output results:

[0116] The best mean squared error (MSE) for the 1st dependent variable: 0.0013;

[0117] The best coefficient of determination (R 2 ) for the 1st dependent variable: 0.9748;

[0118] The best mean squared error (MSE) for the 2nd dependent variable: 0.0074;

[0119] The best coefficient of determination (R 2 ) for the 2nd dependent variable: 0.9606;

[0120] The best mean squared error (MSE) for the 3rd dependent variable: 0.0191;

[0121] The best coefficient of determination (R 2 ) for the 3rd dependent variable: 0.9861;

[0122] The best mean squared error (MSE) for the 4th dependent variable: 66578.0845;

[0123] The best coefficient of determination (R 2 ) for the 4th dependent variable: 0.9513;

[0124] The best mean squared error (MSE) for the 5th dependent variable: 0.0118;

[0125] The best coefficient of determination (R 2 ) for the 5th dependent variable: 0.9928;

[0126] The best mean squared error (MSE) for the 6th dependent variable: 0.0468;

[0127] The best coefficient of determination (R 2 ) for the 6th dependent variable: 0.9962.

[0128] The determination coefficients of the above six dependent variables after fitting are all greater than 0.95, the fitting effect is very good, the fitting curve has high accuracy, and can be used for subsequent optimization design.

[0129] In some embodiments, according to the fitting results of the support vector machine regression model, the maximum bounce amplitude of the contact is used as the objective function, based on the set constraints, and a multi-objective optimization is performed through a genetic algorithm to obtain the optimal solution. The independent variable parameters corresponding to the optimal solution are input into the ADAMS software, and the simulation value of the closing performance index is measured and measured. The optimized value of each dependent variable is compared with the simulation value to verify whether the optimization effect is good.

[0130] Specifically, in this embodiment, since the four-bar linkage is an integral mechanism, changes in the mechanical parameters of any component may affect the overall performance of the mechanism. Therefore, various performance indicators need to be considered during the comprehensive optimization design process.

[0131] For the comprehensive optimization design of the operating mechanism, the main performance parameter indicators are as follows:

[0132] 1) The degree of bounce of the closing contacts of the mechanism should be as small as possible.

[0133] 2) The overtravel of the mechanism should be controlled within a certain range. When the overtravel is too large, it will affect the breaking performance of the circuit breaker in the case of a short circuit; when the overtravel is too small, when the contacts are worn to a certain extent, the mechanism may not be able to close stably.

[0134] 3) The final contact pressure should also be controlled within a certain range. When the final contact pressure is too large, it may cause deformation or damage to related mechanical parts and changes in contact resistance; when the final contact pressure is too small, the circuit breaker may be repelled by the electromotive force when it is subjected to short-circuit current, resulting in adverse effects.

[0135] 4) The closing speed of the mechanism should also be controlled within a certain range. When the closing speed of the mechanism is too slow, the opening time will also be longer due to the similarity of closing and opening, resulting in longer arcing time and accelerated electrical wear of the contacts; when the closing speed of the mechanism is too fast, on the one hand, it will aggravate the contact bounce, and on the other hand, it will also cause a greater impact on the mechanical parts of the circuit breaker, affecting the mechanical life.

[0136] In the process of comprehensive optimization, it is necessary to comprehensively consider various performance indicators and select the objective function to ensure that the remaining performance indicators still meet the requirements while ensuring the realization of the optimization goal.

[0137] The current expected result is that when the performance parameter indicators such as the over-travel of the mechanism, the final contact pressure, and the closing speed change within an acceptable range (±10%, it can be considered that the original parameters of the mechanism are already relatively optimal values, and small fluctuations within a small range have little impact on the performance of the mechanism), the severity of contact bounce is minimized, and finally, the optimal design of the molded case circuit breaker operating mechanism is achieved with the goal of reducing contact bounce.

[0138] According to the above analysis, continue to supplement the program to optimize the multi-variable multi-objective function. First, determine the constraint conditions, as shown in Table 5:

[0139]

[0140] After that, select the objective function:

[0141] For the selection of the objective function, it can be found from the theoretical formula calculation and the analysis of various influencing factors that there is a corresponding relationship between the maximum contact bounce amplitude and the contact bounce time, that is, the maximum contact bounce amplitude and the contact bounce time increase and decrease simultaneously. Therefore, in the subsequent optimization, one of them can be selected to characterize the severity of contact bounce. By analyzing the fitting results of the above support vector machine regression model, it is found that the maximum contact bounce amplitude has a better fitting effect than the contact bounce time, with higher numerical accuracy and is more suitable for the next optimization process. Therefore, it is proposed to use the minimum maximum contact bounce amplitude as the objective function to characterize the severity of contact bounce and conduct multi-variable multi-objective comprehensive optimization design.

[0142] After determining the objective function and constraint conditions (closing time, angular velocity of the moving conductor at the moment of contact, over-travel of the mechanism, final contact pressure), now conduct comprehensive optimization design and propose to use the genetic algorithm for optimization.

[0143] The genetic algorithm (GA) is an optimization algorithm inspired by natural selection and evolution. It gradually optimizes the solution of the objective function (fitness function) by simulating mechanisms such as selection, crossover, and mutation in the biological evolution process. Based on the above analysis, the genetic algorithm is used for the comprehensive optimization design of the operating mechanism.

[0144] In this problem, the fitness function needs to meet the following conditions:

[0145] The first dependent variable takes the minimum value: The fitness value should directly reflect the value of the first dependent variable (the maximum contact bounce amplitude) because it is necessary to minimize the contact bounce amplitude.

[0146] Value ranges of other dependent variables: For the third (closing time), fourth (angular velocity of the moving contact rod at the moment of contact), fifth (mechanism overtravel), and sixth (final contact pressure) dependent variables, the fitness function needs to ensure that their values are within the specified ranges. If they exceed the ranges, the fitness value will increase to penalize individuals that do not meet the conditions.

[0147] By plotting the fitness convergence curve of the genetic algorithm, it can be observed that after 100 iterations, the optimal fitness function value has converged to a relatively low value and the convergence is good, which also proves the effectiveness of the algorithm.

[0148] After 100 genetic iterations, the independent and dependent variable values that meet the constraint conditions are obtained, as shown in Table 6:

[0149]

[0150]

[0151] Comparison of the closing performance indicators of the mechanism before and after optimization, as shown in Table 7:

[0152] Closing performance indicators of the mechanism Before optimization After optimization Relative difference Maximum bounce amplitude of the contact / mm 0.8940 0.4707 47.35％ Contact bounce time / ms 1.93 1.2003 37.81％ Closing time / ms 21.74 22.8979 5.33％ Angular velocity of the moving guide rod at the moment of contact closure / (deg / s) 12896.0892 11607.2493 9.99％ Over-travel of the mechanism / mm 5.03 5.0071 0.46％ Final contact pressure / N 19.01 20.9095 9.99％

[0153] Based on the above table, the specific analysis is as follows:

[0154] 1) The maximum bounce amplitude and bounce time of the contact after optimization are significantly reduced, effectively suppressing the contact bounce phenomenon.

[0155] 2) The closing time of the mechanism increases slightly, and the impact on the opening and closing action performance of the mechanism is not significant and is within an acceptable range.

[0156] 3) The angular velocity of the moving contact rod at the moment of contact decreases, which helps to reduce the impact stress borne by each component during closing and improve the mechanical life of the operating mechanism.

[0157] 4) The mechanism overtravel basically remains unchanged, which enables the new mechanism to have the same good ability of rapid opening and coping with contact wear as the original mechanism.

[0158] 5) The final contact pressure increases, which enables the mechanism to have a certain degree of improvement in the ability to withstand the electro-dynamic repulsion force of short-circuit current, and will not cause the mechanism to be damaged due to excessive static and dynamic contact forces.

[0159] Substitute the above parameters into ADAMS for simulation, and the simulation values of the relevant closing performance indicators are shown in Table 8:

[0160] Closing performance indicators of the mechanism Simulation value Calculated value Relative difference Maximum bounce amplitude of the contact / mm 0.4826 0.4707 2.46％ Contact bounce time / ms 1.25 1.2003 3.98％ Closing time / ms 23.2863 22.8979 1.67％ Angular velocity of the moving guide rod at the moment of contact closure / (deg / s) 11157.6665 11607.2493 4.03％ Over-travel of the mechanism / mm 4.8257 5.0071 3.76％ Final contact pressure / N 21.436 20.9095 2.46％

[0161] Through comparison, it is found that the relative error between the simulation value and the calculated value does not exceed 5%. The error is within a small range. Thus, the correctness of the optimization algorithm results can be proved. This method has high precision and can be used in the actual optimization design of the closing contact bounce of the mechanism. This analysis method can be used not only for this type of molded case circuit breaker, but also for the analysis and optimization of the contact bounce of molded case circuit breakers in general.

[0162] It should be noted that some embodiments of the present application have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order from those in the above embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0163] Based on the same inventive concept, corresponding to the method of any of the above embodiments, an embodiment of the present application also provides a simulation device for the contact bounce of a molded case circuit breaker.

[0164] As Figure 4 shown, the simulation device for the contact bounce of a molded case circuit breaker includes:

[0165] A simulation module 11, configured to establish a simulation model of the closing process of the operating mechanism of a molded case circuit breaker in dynamic simulation software, and perform dynamic simulation of the contact bounce by setting the contact force parameters of the moving and static contacts of the circuit breaker;

[0166] A sensitivity analysis module 12, configured to obtain the mechanical parameters of the operating mechanism that affect the contact bounce through sensitivity analysis and determine them as the main influencing factors. Among them, the main influencing factors at least include the position of the connecting rod shaft of the operating mechanism, the shape of the operating mechanism rod, and the spring parameters of the operating mechanism;

[0167] A sampling module 13, configured to extract a number of sample points by Latin hypercube sampling, use the mechanical parameters as independent variables, and use the closing performance index obtained by simulation in the dynamic simulation software as the dependent variable, and fit the data set containing the independent and dependent variables through a support vector machine regression model to verify the regression effect;

[0168] An optimization module 14, configured to determine the objective function, perform multi-objective optimization through a genetic algorithm to obtain the optimal solution under the constraints, and input the independent variable parameters corresponding to the optimal solution into the dynamic simulation software to measure the simulation value of the closing performance index.

[0169] For convenience of description, when describing the above device, various modules are described separately according to their functions. Of course, when implementing the embodiments of the present application, the functions of each module can be implemented in one or more software and / or hardware.

[0170] The device in the above embodiment is used to implement the corresponding method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated herein.

[0171] Based on the same inventive concept, corresponding to the method in any of the above embodiments, an embodiment of the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the method described in any of the above embodiments.

[0172] Figure 5 FIG. shows a more specific schematic diagram of the hardware structure of the electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. Among them, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other inside the device through the bus 1050.

[0173] The processor 1010 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0174] The memory 1020 can be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1020 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0175] The input / output interface 1030 is used to connect to the input / output module to achieve information input and output. The input / output module can be configured as a component in the device (not shown in the figure), or can be externally connected to the device to provide corresponding functions. The input devices can include keyboards, mice, touchscreens, microphones, various sensors, etc., and the output devices can include displays, speakers, vibrators, indicator lights, etc.

[0176] The communication interface 1040 is used to connect to the communication module (not shown in the figure) to achieve communication interaction between this device and other devices. The communication module can achieve communication through wired means (such as USB, network cable, etc.), or can also achieve communication through wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0177] The bus 1050 includes a path to transmit information between various components of the device (such as the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0178] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device may also only include the components necessary to implement the solution of the embodiments of this specification, and do not necessarily include all the components shown in the figure.

[0179] The electronic device of the above embodiment is used to implement the corresponding method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0180] Based on the same inventive concept, corresponding to the method in any of the above embodiments, the present application also provides a non-transitory computer-readable storage medium. The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to make the computer execute the method described in any of the above embodiments.

[0181] The computer-readable medium of this embodiment includes both permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device.

[0182] The computer instructions stored in the storage medium of the above embodiment are used to cause the computer to execute the method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0183] Those of ordinary skill in the art should understand that: the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the present application (including the claims) is limited to these examples; under the concept of the present application, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the embodiments of the present application as described above, and they are not provided in detail for the sake of brevity.

[0184] In addition, for the sake of simplicity of explanation and discussion, and in order not to make the embodiments of the present application difficult to understand, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the devices may be shown in block diagram form to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present application are to be implemented (i.e., these details should be fully within the understanding of those skilled in the art). In the case where specific details (such as circuits) are set forth to describe the exemplary embodiments of the present application, it will be apparent to those skilled in the art that the embodiments of the present application can be implemented without these specific details or with variations of these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0185] Although the present application has been described in connection with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0186] Embodiments of the present application are intended to cover all such alternatives, modifications, and variations that fall within the broad scope of the appended claims. Accordingly, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the embodiments of the present application shall be included within the protection scope of the present application.

Claims

1. A simulation method for contact bounce of a molded case circuit breaker, characterized in that, Including: Establish a simulation model of the closing process of the operating mechanism of the plastic case circuit breaker in the dynamic simulation software, and conduct dynamic simulation of the contact bounce by setting the contact force parameters of the moving and static contacts of the circuit breaker; Obtain the mechanical parameters of the operating mechanism that affect the contact bounce through sensitivity analysis and determine them as the main influencing factors. Among them, the main influencing factors at least include the position of the connecting rod shaft of the operating mechanism, the shape of the rod of the operating mechanism, and the spring parameters of the operating mechanism; Use Latin hypercube sampling to extract several sample points, use the mechanical parameters as independent variables, and use the closing performance index obtained by simulation in the dynamic simulation software as the dependent variable. Fit the data set containing the independent variable and the dependent variable through a support vector machine regression model to verify the regression effect; Determine the objective function, perform multi-objective optimization through the genetic algorithm to obtain the optimal solution under the constraints, and input the independent variable parameters corresponding to the optimal solution into the dynamic simulation software to measure the simulation value of the closing performance index.

2. The method according to claim 1, wherein: Conduct a contact bounce test on the circuit breaker. By setting the penalty parameter and the regression coefficient, and calculating the contact force based on the contact penalty function defined in the dynamic simulation software, the contact bounce time is simulated according to the contact force.

3. The method according to claim 1, characterized in that, The sensitivity analysis formula is: In the formula, O represents the target value, V represents the design parameter value, i represents the iteration number, and s represents the sensitivity.

4. The method according to claim 3, characterized in that It also includes: Based on the sensitivity analysis results, conduct a single-factor analysis of the mechanical parameters that affect the contact bounce action, obtain the function curves of the severity of the contact bounce and the closing performance index changing with the mechanical parameters, and further obtain the change rules between the closing performance index and the mechanical parameters of each mechanism.

5. The method according to claim 1, wherein: The closing performance index at least includes the maximum contact bounce amplitude, contact bounce time, closing time, initial closing angular velocity of the moving conductor rod, mechanism overtravel, and final contact pressure.

6. The method according to claim 1, wherein: Fit the data set containing multiple independent variables and multiple dependent variables through a support vector machine regression model. Use the cvpartition function to divide the data set into a training set and a validation set. Among them, use the parameter grid search method to find the best SVM parameters, traverse all possible combinations of kernel functions and box constraints, use the fitrsvm function to train the SVM model, and calculate the mean square error and the coefficient of determination using the validation set in this SVM model.

7. The method according to claim 1, wherein: According to the fitting result of the support vector machine regression model, use the maximum contact bounce amplitude of the contact as the objective function, based on the set constraints, and perform multi-objective optimization through the genetic algorithm to obtain the optimal solution. Input the independent variable parameters corresponding to the optimal solution into the dynamic simulation software, simulate and measure the simulation value of the closing performance index, and compare the optimized values and simulation values of each dependent variable to verify whether the optimization effect is good.

8. A contact bounce simulation device for a molded case circuit breaker, characterized in that, Including: A simulation module, configured to establish a simulation model of the closing process of the operating mechanism of a molded case circuit breaker in dynamic simulation software, and perform dynamic simulation on contact bounce by setting the contact force parameters of the moving and static contacts of the circuit breaker; A sensitivity analysis module, configured to obtain the mechanical parameters of the operating mechanism that affect contact bounce through sensitivity analysis and determine them as the main influencing factors. Among them, the main influencing factors at least include the position of the connecting rod shaft of the operating mechanism, the shape of the operating mechanism rod, and the spring parameters of the operating mechanism; A sampling module, configured to extract a number of sample points using Latin hypercube sampling, use the mechanical parameters as independent variables, and use the closing performance index obtained by simulation in the dynamic simulation software as the dependent variable, and fit the data set containing the independent variable and the dependent variable through a support vector machine regression model to verify the regression effect; An optimization module, configured to determine an objective function, perform multi-objective optimization through a genetic algorithm to obtain an optimal solution under restricted conditions, and input the independent variable parameters corresponding to the optimal solution into the dynamic simulation software to measure the simulation value of the closing performance index; 9. An electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method described in any one of claims 1-7 is implemented.

10. A non-transitory computer-readable storage medium, characterized in that, Wherein, The non-transitory computer-readable storage medium stores computer instructions for causing a computer to execute the method described in any one of claims 1-7.

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