A Cam Optimization Method and System for High-Voltage Switch Operating Mechanism Based on Genetic Algorithm

Abstract

This invention discloses a cam optimization method and system for high-voltage switch operating mechanisms based on genetic algorithms, relating to the field of high-voltage technology. The method includes: establishing a three-dimensional simulation model of the operating mechanism using ADAMS virtual prototyping technology; setting parameters and boundary conditions for the simulation model using ADAMS virtual prototyping technology; performing dynamic simulation on the simulation model using ADAMS virtual prototyping technology to obtain the stroke and velocity curves of the connecting rod; determining the design variables, objective function, and constraints of the genetic algorithm based on high-order polynomials to obtain the optimized mathematical model of the cam mechanism; and outputting the optimal structural parameters of the cam according to the iterative steps of the genetic algorithm. To simultaneously satisfy the kinematic and dynamic performance of the cam mechanism, this invention proposes using a genetic algorithm to describe the optimization problem of the cam curve and solve the optimized model of the cam curve.

Description

Technical Field

[0001] This invention relates to the field of high voltage technology, and in particular to a cam optimization method and system for high voltage switch operating mechanisms based on genetic algorithms. Background Technology

[0002] High-voltage circuit breakers, as key electrical equipment responsible for control and protection in primary power systems, are among the most important and complex types of high-voltage switchgear. The operating mechanism is a crucial supporting component of the high-voltage circuit breaker; the two must work together to ensure the stability of line operation. Therefore, the operational reliability of the circuit breaker largely depends on the operational reliability of the operating mechanism. The operating mechanism is an independent part of the circuit breaker body, and its performance has a significant impact on the operational performance and reliability of the high-voltage circuit breaker, determining whether the circuit breaker can complete opening and closing actions. The operating mechanism is the core component of the circuit breaker, with a complex structure; whether the travel of the circuit breaker's moving contact and the opening speed meet the requirements depends on the output characteristics of the operating mechanism.

[0003] The design of the cam profile curve of a spring-operated mechanism determines whether the output force characteristics of the mechanism can better match the load characteristics. Currently, however, manufacturing processes often rely on experience-based design, resulting in long design cycles. Therefore, finding a simple and accurate cam profile curve design method is of significant practical value and importance for the optimized design of operating mechanisms. This invention attempts to optimize the design of the cam profile curve of a spring-operated mechanism based on genetic algorithm analysis and MATLAB programming calculations. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the problem that this invention aims to solve is that existing methods often rely on experience-based design during the manufacturing process, resulting in a long design cycle.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a cam optimization method for a high-voltage switch operating mechanism based on a genetic algorithm, comprising: establishing a three-dimensional simulation model of the operating mechanism based on ADAMS virtual prototyping technology; setting parameters and boundary conditions for the simulation model of the operating mechanism based on ADAMS virtual prototyping technology; performing dynamic simulation on the simulation model based on ADAMS virtual prototyping technology to obtain the stroke and speed curves of the connecting rod; determining the design variables, objective function, and constraints of the genetic algorithm based on high-order polynomials to obtain an optimized mathematical model of the cam mechanism; and outputting the optimal structural parameters of the cam according to the iterative steps of the genetic algorithm.

[0007] As a preferred embodiment of the cam optimization method for high-voltage switch operating mechanism based on genetic algorithm described in this invention, the boundary condition setting includes an energy storage stage, a closing stage, and a opening stage; the energy storage stage includes, by means of an energy storage motor or manual energy storage, driving the ratchet to rotate while compressing the closing spring, until the roller on the ratchet engages with the arc surface of the energy storage holding stop; the closing stage includes, when a closing command is received, the closing electromagnet pushes the closing stop to rotate counterclockwise to allow the energy storage holding stop to move out of position, storing... The mechanism allows the holding stop to be pushed away by the rollers on the ratchet, disengaging the ratchet and releasing the energy of the closing spring to close the circuit. After closing, the latching pin on the large crank arm engages with the closing holding stop and causes it to rotate counterclockwise, thus engaging the rollers on the closing holding stop with the opening stop. The opening stage includes the following steps: upon receiving an opening command, the opening electromagnet pushes the opening stop to rotate counterclockwise to allow the closing holding stop to move aside. The closing holding stop is then pushed away by the rollers on the large crank arm and disengaged, releasing the energy of the opening spring to open the circuit.

[0008] As a preferred embodiment of the cam optimization method for high-voltage switch operating mechanism based on genetic algorithm described in this invention, wherein: when performing dynamic simulation on the simulation model, a Mark point is selected on the operating mechanism to reflect the kinematic characteristics, and a point is selected on the connecting rod as the Mark point.

[0009] As a preferred embodiment of the cam optimization method for high-voltage switch operating mechanism based on genetic algorithm described in this invention, the higher-order polynomial is expressed as follows:

[0010]

[0011] in, This is represented as the closing process, C0, C p C q C r C s ... represents undetermined coefficients, and the superscripts p, q, r, and s represent the power exponents; θ in the rising segment is... In the descent segment This is represented as the cam rotation angle. This is represented as the half-wrap angle of the basic working segment of the cam.

[0012] As a preferred embodiment of the cam optimization method for high-voltage switch operating mechanism based on genetic algorithm described in this invention, the selection method of the power exponent is expressed as follows:

[0013] p=2, q=2n, r=2n+2m, s=2n+4m

[0014] Where n = 3, 4, 5…, m = 1, 2, 3…; the determination of the design variables, objective function, and constraints of the genetic algorithm includes taking n and m as design variables, using the maximum fullness coefficient ξ as the objective function, and the maximum acceleration… For the maximum permissible acceleration, negative acceleration The minimum radius of curvature R of the cam profile curve is required to achieve the minimum permissible acceleration. min ≥[R min ], [R min ] represents the minimum permissible radius of curvature, and 3≤n≤20 and 1≤m≤20 are constraints.

[0015] As a preferred embodiment of the cam optimization method for high-voltage switch operating mechanism based on genetic algorithm described in this invention, the step of outputting the optimal cam structural parameters according to the iterative steps of the genetic algorithm includes encoding and initializing the population. The encoding method adopts real number encoding, that is, using N segments of uniform B-spline curves of order 5 to describe the cam curve, and each chromosome X is represented as...

[0016] X = [d0, d1, ..., d N+4 ]

[0017] Where d represents a B-spline; for the genetic algorithm optimization problem of cam curves with real-number encoding, the crossover operation adopts arithmetic crossover, that is, the i-th gene x of chromosome X is crossed. i The following representation is obtained by crossing two parent individuals A and B.

[0018] x i =r i a i +(1-r i )b i i = 0, ..., N+4

[0019] Where, r i It is a random number in the interval (0,1). Parent individuals A and B were selected from the current population using a two-person random league selection method. The crossover individual X then undergoes a mutation operation, expressed by the formula:

[0020] x i =r 1,i x i +(1-r 1,i )[r 2,i [ul)+l],i=0,...,N+4

[0021] Where, r 1,i and r 2,iThe two independent random numbers are still in the interval (0,1), and [l,u] is the range of values ​​for the control vertex. The Pareto optimal individual sorting of all individuals in the population is performed using the fast non-dominated sorting algorithm, which includes setting the initial index r←1, finding the Pareto optimal subset in the population and defining the index of each individual in the subset as r, removing the Pareto optimal subset from the population and changing the index r←r+1, until all individuals are sorted.

[0022] As a preferred embodiment of the cam optimization method for high-voltage switch operating mechanism based on genetic algorithm described in this invention, the step of outputting the optimal cam structural parameters according to the iterative steps of the genetic algorithm further includes adopting an elite retention strategy to prevent excellent individuals from being destroyed by crossover and mutation. Specifically, this includes randomly generating a parent population P0 with a population size of M and a generation number t=0, performing crossover and mutation operations on the parent population P0 to generate a offspring population Q0, and returning the population P0 if the termination condition is met. t Otherwise, let F(X) = F max -Φ(X,r (k) ), for R i Perform Pareto optimal individual ranking to generate the Pareto optimal frontier F1, F2, ..., F n Where F(X) is the fitness function, Φ(X,r) (k) Let R be the penalty function. i Individuals in the parent population; the penalty function Φ(X,r) (k) ) is represented as,

[0023]

[0024] in, Represented as a penalty term related to inequality constraints, r (k) Represented as a penalty factor;

[0025] The fitness function F(X) is expressed as follows:

[0026] F(X)=F max -Φ(X,r (k) )

[0027] Among them, F max It is represented as a sufficiently large number to ensure that the fitness value is always greater than 0.

[0028] Another objective of this invention is to provide a cam optimization system for high-voltage switch operating mechanisms based on genetic algorithms, which can design cams for high-voltage switch operating mechanisms.

[0029] To address the aforementioned technical problems, this invention provides the following technical solution: a system for optimizing the cam of a high-voltage switch operating mechanism based on a genetic algorithm, comprising: a model building module, a simulation module, and an optimization module; the model building module establishes a three-dimensional simulation model of the operating mechanism based on ADAMS virtual prototyping technology; the simulation module sets parameters and boundary conditions for the simulation model of the operating mechanism based on ADAMS virtual prototyping technology, and performs dynamic simulation on the simulation model based on ADAMS virtual prototyping technology to obtain the stroke and speed curves of the connecting rod; the optimization module determines the design variables, objective function, and constraints of the genetic algorithm based on high-order polynomials, obtains the optimized mathematical model of the cam mechanism, and outputs the optimal structural parameters of the cam according to the iterative steps of the genetic algorithm.

[0030] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the high-voltage switch operating mechanism cam optimization method based on genetic algorithm as described above.

[0031] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the cam optimization method for a high-voltage switch operating mechanism based on a genetic algorithm as described above.

[0032] The beneficial effects of this invention are as follows: To address the problem that existing methods often rely on empirical design during the manufacturing process, resulting in long design cycles, this invention utilizes a five-dimensional uniform B-spline curve to construct the cam curve, transforming the cam curve design into the calculation of the B-spline curve control vertices. To simultaneously satisfy the kinematic and dynamic performance requirements of the cam mechanism, a genetic algorithm is proposed to describe the cam curve optimization problem and solve the optimization model of the cam curve. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0034] Figure 1 This is a flowchart illustrating the cam optimization method for the high-voltage switch operating mechanism based on genetic algorithm in Example 1.

[0035] Figure 2 The diagram shows the ADAMS simulation model of the cam optimization method for the high-voltage switch operating mechanism based on genetic algorithm in Example 2.

[0036] Figure 3This is a diagram of the connecting rod in Example 2, which describes the cam optimization method for the high-voltage switch operating mechanism based on a genetic algorithm.

[0037] Figure 4 The diagram shows the closing displacement curve of the connecting rod in the high-voltage switch operating mechanism cam optimization method based on genetic algorithm in Example 2.

[0038] Figure 5 This is a comparison chart of the requirement curve and the simulation curve of the cam optimization method for the high-voltage switch operating mechanism based on genetic algorithm in Example 2.

[0039] Figure 6 This is a block diagram of the cam optimization system for the high-voltage switch operating mechanism based on genetic algorithm in Example 3. Detailed Implementation

[0040] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0041] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0042] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a cam optimization method for a high-voltage switch operating mechanism based on a genetic algorithm, including, for example... Figure 1 As shown:

[0043] Step 101: Establish a 3D simulation model of the CT26 operating mechanism based on ADAMS virtual prototyping technology. This is to study the stress on key components of the spring operating mechanism during the closing process. To control the computational scale and save time, the mechanical characteristics of key structures should be emphasized during modeling. The entire mechanism was simplified as necessary during the modeling process, and key components were extracted as the research objects to establish a 3D model.

[0044] Step 102: Set parameters and boundary conditions for the simulation model of the operating mechanism based on ADAMS virtual prototyping technology. In ADAMS simulation software, mechanical loads can be applied to replace solid modeling of the spring components. The operation process of the CT26 spring operating mechanism consists of three stages: energy storage stage, closing stage, and opening stage. To realize these motion processes, ideal constraint pairs need to be added to the simulation model. Ideal constraints are usually physically meaningful constraint pairs, such as revolute joints, translational joints, and gear joints.

[0045] Step 103: Perform dynamic simulation on the simulation model based on ADAMS virtual prototyping technology to obtain the stroke and speed curves of the connecting rod. Based on the above simulation model, the CT26 spring operating mechanism is powered by the cooperation of opening and closing springs, thus requiring only adjustment of the opening and closing spring parameters during the simulation process; no external load needs to be applied.

[0046] Step 104 determines the design variables, objective function, and constraints of the genetic algorithm based on high-order polynomials, obtaining the optimized mathematical model of the cam mechanism. This paper uses the genetic algorithm optimization method to establish the corresponding constraint equations and then solve for the basic dimensions of the mechanism. During the design, the number of terms and the power of the polynomial can be arbitrarily selected. This paper uses a five-term polynomial, which can meet the design requirements of the cam mechanism, and the cam curve is expressed by a fifth-order uniform B-spline curve.

[0047] Step 105: Output the optimal structural parameters of the cam according to the iteration steps of the genetic algorithm (GA), with minimizing the maximum pressure angle of the cam mechanism as the optimization objective.

[0048] Furthermore, in step 101, since the CT26 spring operating mechanism has many parts and the coordination between them is complex, the simulation model is optimized to reduce computation time. In this model, components that do not participate in the mechanical movement of the operating mechanism, such as the energy storage motor and the opening and closing coils, are removed. Instead, the pre-processing function of ADAMS software is used to realize the functions of these components.

[0049] Furthermore, in step 102, the ADAMS simulation software can apply mechanical loads to replace the solid modeling of the spring parts, based on the spring stiffness coefficient calculation formula:

[0050]

[0051] Where G is the material shear modulus, d is the spring wire diameter, d2 is the spring major diameter, and n is the effective number of spring coils.

[0052] Hooke's Law:

[0053] F=KΔx

[0054] Where K is the spring stiffness coefficient and Δx is the spring compression.

[0055] When setting boundary conditions, during the energy storage phase: Using an energy storage motor or manual energy storage, the ratchet rotates while compressing the closing spring until the roller on the ratchet engages on the arc surface of the energy storage holding stop. To avoid dead spots in the circular motion, it should rotate 185°. During the closing phase: Upon receiving a closing command, the closing electromagnet pushes the closing stop counterclockwise to allow the energy storage holding stop to move aside. The energy storage holding stop is then pushed away by the roller on the ratchet, disengaging the ratchet and releasing the energy of the closing spring to close the circuit. During the opening phase: After closing, the latching pin on the large crank arm engages with the closing holding stop, causing the closing holding stop to rotate counterclockwise, thus engaging the roller on the closing holding stop with the opening stop. Upon receiving a opening command, the opening electromagnet pushes the opening stop counterclockwise to allow the closing holding stop to move aside. The closing holding stop is then pushed away by the roller on the large crank arm, disengaging the closing spring and releasing the energy to open the circuit.

[0056] Furthermore, in step 103, when simulating the closing process of the spring operating mechanism, a Mark point should be selected on the operating mechanism to reflect its kinematic characteristics, and a point should be selected on the connecting rod as the Mark point.

[0057] Furthermore, in step 104, the B-spline curve is a piecewise curve defined by control vertices; generally, a high-order polynomial curve can be expressed as...

[0058]

[0059] in, This is represented as the closing process, C0, C p C q C r C s ... represents undetermined coefficients, and the superscripts p, q, r, and s represent the power exponents; θ in the rising segment is... In the descent segment This is represented as the cam rotation angle. This represents the half-wrap angle of the basic working section of the cam. During design, the number of terms and the power of the polynomial can be arbitrarily chosen, but a 5-term polynomial is generally sufficient to meet the design requirements of the cam mechanism. The power exponents can be selected using the following formula:

[0060] p=2, q=2n, r=2n+2m, s=2n+4m

[0061] Where n = 3, 4, 5…, m = 1, 2, 3…; typically, values ​​of n and m within 20 are sufficient to meet design requirements. To achieve high intake and exhaust efficiency, the maximum fill factor ξ is often the objective in engineering. Taking n and m as design variables, ξ as the objective function, and the maximum acceleration… For the maximum permissible acceleration, negative acceleration The minimum radius of curvature R of the cam profile curve is required to achieve the minimum permissible acceleration. min ≥[R min ], [R min ] represents the minimum permissible radius of curvature, and 3≤n≤20 and 1≤m≤20 are constraints.

[0062] Furthermore, step 105 includes the following steps:

[0063] Step 105.1, Population Encoding and Initialization. The control vertex vector of the cam curve is a multidimensional optimization variable, so its encoding adopts a real number encoding method, that is, the cam curve is described by N uniform B-spline curves of order 5, and each chromosome X is represented as,

[0064] X = [d0, d1, ..., d N+4 ]

[0065] Where d represents a B-spline.

[0066] Step 105.2, Algorithm Parameters and Initialization: The most important parameters for GA include: population size M and crossover probability p. c Mutation probability p m And the maximum algebra T.

[0067] Step 105.3, Genetic Operations. Genetic operations include selection, crossover, and mutation, which are the core of GA's powerful search capabilities. For the GA optimization problem of real-number encoded cam curves, the crossover operation uses arithmetic crossover, that is, the i-th gene x on chromosome X is selected by the i-th gene x. i The following representation is obtained by crossing two parent individuals A and B.

[0068] x i =r i a i +(1-r i )b i i = 0, ..., N+4

[0069] Where, r i It is a random number in the interval (0,1). Parent individuals A and B were selected from the current population using a two-person random league selection method. The crossover individual X then undergoes a mutation operation, expressed by the formula:

[0070] x i =r 1,i x i +(1-r 1,i )[r 2,i [ul)+l],i=0,...,N+4

[0071] Where, r 1,i and r 2,i These are still two independent random numbers in the interval (0,1), and [l,u] is the range of values ​​for the control vertex.

[0072] Step 105.4: Sort Pareto Optimal Individuals. This paper uses the fast non-dominated sorting algorithm in NSGA-II to sort all individuals in the population for Pareto optimal individuals: First, set the initial index r←1. Second, find the Pareto optimal subsets in the population and define the index of individuals in these subsets as r. Third, remove the Pareto optimal subsets from the population and change the index r←r+1. Fourth, return to step two until all individuals are sorted.

[0073] Step 105.5, Elite Preservation Strategy. To ensure that superior individuals are not destroyed due to crossover and mutation, an elite preservation strategy is adopted. The specific algorithm is as follows: First, randomly generate a parent population P0 with a population size of M and generation t = 0. Second, perform crossover and mutation operations on the parent population P0 to generate a offspring population Q0. Third, if the termination condition is met, return the population P0. t Otherwise, proceed to step four. Step four: Let F(X) = F... max -Φ(X,r (k) ), for R i Perform Pareto optimal individual ranking to generate Pareto optimal frontiers F1, F2, ..., F n Where F(X) is the fitness function, Φ(X,r) (k) Let R be the penalty function. i Individuals from the parent population.

[0074] Step 105.6: Constraint handling and objective function range limitation. Due to the adoption of an elite strategy, R... i If the number of optimal individuals exceeds the prescribed population size M, this invention selects these Pareto individuals by implementing variable constraints and limiting the range of the objective function, in order to form a new generation of population of the prescribed size.

[0075] Step 105.7: Since the goal of function design is to find a design solution that satisfies the constraints and minimizes the required value of the corresponding objective function, a new objective function, namely the penalty function, is introduced based on the described optimization problem:

[0076]

[0077] in, As a penalty term related to inequality constraints, r (k) Let be the penalty factor. Since the objective function of the optimization design is to find the minimum value, the fitness function is constructed as follows:

[0078] F(X)=F max -Φ(X,r (k) )

[0079] Among them, F max It is a sufficiently large number to ensure that the fitness value is always greater than 0. In this way, the fullness coefficient of the cam is in a one-to-one correspondence with the fitness of the genetic algorithm.

[0080] Example 2, refer to Figures 2-5 This is the second embodiment of the present invention, which differs from the first embodiment in that: the high-voltage switch operating mechanism cam optimization method and system based on genetic algorithm further includes, in order to verify and explain the technical effect adopted in this method, a comparative test is conducted between the traditional technical solution and the method of the present invention, and the test results are compared by means of scientific demonstration to verify the real effect of the method.

[0081] A 3D simulation model of the CT26 type operating mechanism was established based on ADAMS virtual prototyping technology. The model was then used to build simulation models of key components of the operating mechanism, and ADAMS software was employed to construct the simulation model. Figure 2 As shown, this study investigates the force conditions of key components of the spring operating mechanism during the closing process. Due to the large number of parts in the operating mechanism and the complex inter-part coordination, the simulation model was optimized to reduce computation time. In this model, components that do not participate in the mechanical motion of the operating mechanism, such as the energy storage motor and the opening / closing coils, were removed. Instead, the pre-processing function of ADAMS software was used to implement the functions of these components.

[0082] The parameters of the operating mechanism simulation model are set based on ADAMS virtual prototyping technology. In the ADAMS simulation software, mechanical loads can be applied, thus replacing the solid modeling of spring parts. Combined with...

[0083] The stiffness coefficient and preload parameters of the opening and closing springs of the CT26-110kV type spring operating mechanism are calculated and shown in Table 1. The parameters in the table are added to the simulation model to complete the construction of the dynamic simulation model.

[0084] Table 1. Parameters of opening and closing springs

[0085] Stiffness coefficient (N / m) Preload (N) Closing spring 80623 20204 Opening spring 69281 4469

[0086] Ideal constraints are typically physical constraint pairs, such as revolute joints, translational joints, and gear joints. The main constraint pairs added to this simulation model are shown in Table 2.

[0087] Table 2 Main Constraint Pairs

[0088]

[0089]

[0090] Dynamic simulations were performed on the simulation model using ADAMS virtual prototyping technology to obtain the stroke and velocity curves of the connecting rod. Based on the simulation model, dynamic simulation analysis was conducted. The CT26 type spring operating mechanism is powered by the interaction of opening and closing springs; therefore, only the parameters of the opening and closing springs need to be adjusted during the simulation, without applying any external load. When simulating the closing process of the spring operating mechanism, a Mark point should be selected on the operating mechanism to reflect its kinematic characteristics, such as... Figure 3 As shown in the figure. The simulation analysis yielded the stroke and speed curves of the connecting rod as follows. Figure 4 As shown, the overstroke displacement of the connecting rod is 0.43cm, the maximum displacement of the connecting rod is 7.88cm, and the closing time meets the mechanism action standard.

[0091] Based on high-order polynomials, the design variables, objective function, and constraints of the genetic algorithm are determined, resulting in an optimized mathematical model of the cam mechanism. This paper employs the genetic algorithm optimization method to establish corresponding constraint equations and then solve for the basic dimensions of the mechanism. During design, the number of terms and the power of the polynomial can be arbitrarily chosen; this paper uses a five-term polynomial, which satisfies the design requirements of the cam mechanism. The cam curve is expressed using a fifth-order uniform B-spline curve. The B-spline curve is a piecewise curve defined by the control vertices; generally, a high-order polynomial curve can be expressed as...

[0092]

[0093] The power exponents can be selected using the following formula:

[0094] p=2, q=2n, r=2n+2m, s=2n+4m

[0095] Typically, values ​​of n and m below 20 are sufficient to meet design requirements. To achieve high intake and exhaust efficiency, the maximum fill factor ξ is often the objective in engineering. Taking n and m as design variables, ξ as the objective function, and the maximum acceleration as the target... For the maximum permissible acceleration, negative acceleration The minimum radius of curvature of the cam profile curve to achieve the minimum permissible acceleration.

[0096] R min ≥[R min ], [R min [ ] is the minimum permissible radius of curvature, and 3≤n≤20, 1≤m≤20 are constraints. The solutions are obtained using the composite method and the genetic algorithm, respectively. The results are shown in Table 3.

[0097] Table 3 Comparison Results

[0098] m n ξ <![CDATA[a min (m / s 2 )]]> <![CDATA[a max (m / s 2 )]]> <![CDATA[R min (mm)]]> Genetic Algorithm 9 6 0.611 294 1457.93 7.11 Composite method 8 8 0.610 296 1473.67 6.99

[0099] The optimal structural parameters of the cam are output based on the iterative steps of the genetic algorithm, with minimizing the maximum pressure angle of the cam mechanism as the optimization objective. To verify the accuracy of the software calculations, a virtual prototype of the conjugate cam mechanism is created using SolidWorks. Importing the virtual prototype into Adams yields a comparison graph of the design requirement curve and the simulation curve of the motion law, as shown below. Figure 5 The diagram shows a comparison between the design requirement curve and the simulation curve.

[0100] Example 3, referring to Figure 6 This is the third embodiment of the present invention, which differs from the previous two embodiments in that: a system for optimizing the cam of a high-voltage switch operating mechanism based on a genetic algorithm includes a model building module 100, a simulation module 200, and an optimization module 300; the model building module 100 establishes a three-dimensional simulation model of the operating mechanism based on ADAMS virtual prototyping technology; the simulation module 200 sets parameters and boundary conditions for the simulation model of the operating mechanism based on ADAMS virtual prototyping technology, and performs dynamic simulation on the simulation model based on ADAMS virtual prototyping technology to obtain the stroke and speed curves of the connecting rod; the optimization module 300 determines the design variables, objective function, and constraints of the genetic algorithm based on high-order polynomials to obtain the optimized mathematical model of the cam mechanism, and outputs the optimal structural parameters of the cam according to the iteration steps of the genetic algorithm.

[0101] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0102] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0103] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0104] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0105] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A cam optimization method for high-voltage switch operating mechanisms based on genetic algorithms, characterized in that: include, A three-dimensional simulation model of the operating mechanism was established based on ADAMS virtual prototyping technology; Parameter and boundary condition settings are performed on the simulation model of the operating mechanism based on ADAMS virtual prototyping technology; Dynamic simulation of the simulation model was performed based on ADAMS virtual prototyping technology to obtain the stroke and speed curves of the connecting rod. The design variables, objective function, and constraints of the genetic algorithm are determined based on high-order polynomials, and an optimized mathematical model of the cam mechanism is obtained. The optimal structural parameters of the cam are output based on the iterative steps of the genetic algorithm. The higher-order polynomial is represented as follows: in, This is represented as the closing process, C0, C p C q C r C s ... represents undetermined coefficients, and the superscripts p, q, r, and s represent the power exponents; θ in the rising segment is... In the descent segment This is represented as the cam rotation angle. This is represented as the half-wrap angle of the basic working segment of the cam; The step of outputting the optimal cam structure parameters according to the iterative steps of the genetic algorithm includes encoding and initializing the population. The encoding method uses real number encoding, that is, using N uniform B-spline curves of order 5 to describe the cam curve, and each chromosome X is represented as... X=[d0,d1,...,d N+4 ] Where d represents a B-spline; For the genetic algorithm optimization problem of cam curves with real-number encoding, the crossover operation adopts arithmetic crossover, that is, the i-th gene x of chromosome X... i The following representation is obtained by crossing two parent individuals A and B. x i =r i a i +(1-r i )b i ,i=0,...,N+4 Where, r i It is a random number in the interval (0,1). Parent individuals A and B were selected from the current population using a two-person random league selection method. The crossover individual X then undergoes a mutation operation, expressed by the formula: x i =r 1,i x i +(1-r 1,i )[r 2,i (u-l)+l],i=0,...,N+4 Where, r 1,i and r 2,i These are still two independent random numbers in the interval (0,1), and [l,u] is the range of values ​​for the control vertex; The fast non-dominated sorting algorithm is used to sort all individuals in a population to Pareto optimal individuals. This process includes setting an initial index r←1, finding the Pareto optimal subset in the population and defining the index of each individual in the subset as r, removing the Pareto optimal subset from the population and changing the index r←r+1, until all individuals are sorted.

2. The cam optimization method for high-voltage switch operating mechanism based on genetic algorithm as described in claim 1, characterized in that: The boundary condition settings include an energy storage phase, a closing phase, and a opening phase; The energy storage stage includes using an energy storage motor or manual energy storage to drive the ratchet to rotate while compressing the closing spring until the roller on the ratchet is engaged with the arc surface of the energy storage holding stop. The closing stage includes the following steps: when a closing command is received, the closing electromagnet pushes the closing stop to rotate counterclockwise to allow the energy storage and holding stop to move out of position. The energy storage and holding stop is pushed away by the roller on the ratchet and the ratchet is disengaged, thereby releasing the energy of the closing spring to close the circuit. After closing, the latching pin on the large crank arm engages with the closing holding stop, causing the closing holding stop to rotate counterclockwise, which in turn causes the roller on the closing holding stop to engage with the opening stop. The tripping stage includes the following steps: when a tripping command is received, the tripping electromagnet pushes the tripping lever to rotate counterclockwise to give way to the closing holding lever. The closing holding lever is then pushed away by the roller on the large crank arm and disengaged, releasing the energy of the tripping spring to trip the circuit.

3. The cam optimization method for high-voltage switch operating mechanism based on genetic algorithm as described in claim 2, characterized in that: When performing dynamic simulation on the simulation model, a Mark point is selected on the operating mechanism to reflect the kinematic characteristics, and a point is selected on the connecting rod as the Mark point.

4. The cam optimization method for high-voltage switch operating mechanism based on genetic algorithm as described in claim 3, characterized in that: The method for selecting the power exponent is expressed as follows: p=2, q=2n, r=2n+2m, s=2n+4m Where n = 3, 4, 5…, m = 1, 2, 3…; The process of determining the design variables, objective function, and constraints of the genetic algorithm includes taking n and m as design variables, using the maximum fullness coefficient ξ as the objective function, and the maximum acceleration a as the objective function. max ≤[a max ], [a max [This refers to] the maximum permissible acceleration, and the negative acceleration a. min ≥[a min ], [a min The minimum radius of curvature R of the cam profile curve is the minimum permissible acceleration. min ≥[R min ], [R min ] represents the minimum permissible radius of curvature, and 3≤n≤20 and 1≤m≤20 are constraints.

5. The cam optimization method for high-voltage switch operating mechanism based on genetic algorithm as described in claim 4, characterized in that: The step of outputting the optimal cam structure parameters according to the iterative steps of the genetic algorithm also includes adopting an elite preservation strategy to prevent superior individuals from being destroyed by crossover and mutation. Specifically, this includes randomly generating a parent population P0 with a population size of M and a generation number t=0, performing crossover and mutation operations on the parent population P0 to generate a offspring population Q0, and returning the population P0 if the termination condition is met. t Otherwise, let F(X) = F max -Φ(X,r (k) ), for R i Perform Pareto optimal individual ranking to generate the Pareto optimal frontier F1, F2, ..., F n Where F(X) is the fitness function, Φ(X,r) (k) Let R be the penalty function. i Individuals from the parent population; The penalty function Φ(X,r) (k) ) is represented as, in, Represented as a penalty term related to inequality constraints, r (k) Represented as a penalty factor; The fitness function F(X) is expressed as follows: F(X)=F max -Φ(X,r (k) ) Among them, F max It is represented as a sufficiently large number to ensure that the fitness value is always greater than 0.

6. A system employing the cam optimization method for high-voltage switch operating mechanisms based on genetic algorithms as described in any one of claims 1 to 5, characterized in that: It includes a model building module (100), a simulation module (200), and an optimization module (300); The model building module (100) builds a three-dimensional simulation model of the operating mechanism based on ADAMS virtual prototyping technology; The simulation module (200) sets parameters and boundary conditions for the simulation model of the operating mechanism based on ADAMS virtual prototyping technology, and performs dynamic simulation of the simulation model based on ADAMS virtual prototyping technology to obtain the stroke and speed curves of the connecting rod; The optimization module (300) determines the design variables, objective function and constraints of the genetic algorithm based on high-order polynomials, obtains the optimized mathematical model of the cam mechanism, and outputs the optimal structural parameters of the cam according to the iteration steps of the genetic algorithm.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the high-voltage switch operating mechanism cam optimization method based on genetic algorithm as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the high-voltage switch operating mechanism cam optimization method based on genetic algorithm as described in any one of claims 1 to 5.

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