High-voltage switch operating mechanism cam optimization method and system based on genetic algorithm

Abstract

The invention discloses a high-voltage switch operating mechanism cam optimization method and system based on a genetic algorithm, and relates to the technical field of high voltage, and the method comprises the steps: building an operating mechanism three-dimensional simulation model based on an ADAMS virtual prototype technology; performing parameter setting and boundary condition setting on the operating mechanism simulation model based on an ADAMS virtual prototype technology; performing dynamic simulation on the simulation model based on an ADAMS virtual prototype technology to obtain a stroke and speed curve of the connecting pull rod; determining a design variable, a target function and a constraint condition of a genetic algorithm based on a high-degree polynomial to obtain an optimized mathematical model of the cam mechanism; and outputting the optimal structure parameters of the cam according to the genetic algorithm iteration step. In order to meet the kinematics and dynamics performance of the cam mechanism at the same time, the optimization problem of the cam curve is described by using the genetic algorithm, and the optimization model of the cam curve is solved.

Description

Technical Field

[0001] The invention relates to the technical field of high voltage, and in particular to a method and system for optimizing a cam of a high voltage switch operating mechanism based on a genetic algorithm. Background Art

[0002] As a key electrical equipment responsible for control and protection in the primary equipment of the power system, the high-voltage circuit breaker is the most important and complex type of high-voltage switchgear. The operating mechanism is an important supporting equipment of the high-voltage circuit breaker. The two must work together to ensure the stability of the line operation. Therefore, the working reliability of the circuit breaker depends to a large extent on the action reliability of the operating mechanism. The operating mechanism is a part independent of the circuit breaker body. The quality of its working performance has an extremely important influence on the working performance and reliability of the high-voltage circuit breaker. The operating mechanism determines whether the circuit breaker can complete the opening and closing actions. The operating mechanism is the core component of the circuit breaker, with a complex structure. Whether the stroke and opening speed of the circuit breaker moving contact can meet the requirements depends on the output characteristics of the operating mechanism.

[0003] The design of the cam profile curve of the spring operating mechanism determines whether the output force characteristics of the operating mechanism can better match the load characteristics. At present, empirical design is mostly used in the production process, and the design cycle is long. Therefore, finding a simple and accurate cam profile curve design method has important practical value and realistic significance for the optimal design of the operating mechanism. Therefore, the present invention attempts to optimize the cam profile curve of the spring operating mechanism based on genetic algorithm analysis and MATLAB programming calculation method. Summary of the invention

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

[0005] Therefore, the problem to be solved by the present invention is that the existing methods mostly adopt empirical design in the production process, and the design cycle is relatively long.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a high-voltage switch operating mechanism cam optimization method based on genetic algorithm, which includes: establishing a three-dimensional simulation model of the operating mechanism based on ADAMS virtual prototype technology; setting parameters and boundary conditions of the operating mechanism simulation model based on ADAMS virtual prototype technology; performing dynamic simulation of the simulation model based on ADAMS virtual prototype technology to obtain the connecting rod stroke and speed curve; determining the design variables, objective functions 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 solution of the cam optimization method of the high-voltage switch operating mechanism based on genetic algorithm described in the present invention, the boundary condition setting includes energy storage stage, closing stage and opening stage; the energy storage stage includes, by means of energy storage motor or manual energy storage, driving the ratchet to rotate and compressing the closing spring at the same time, until the roller on the ratchet is buckled on the arc surface of the energy storage holding latch; the closing stage includes, when receiving the closing command, the closing electromagnet pushes the closing latch to rotate counterclockwise to make way for the energy storage holding latch, and the energy storage The ratchet wheel can be pushed away by the roller on the ratchet to disengage the ratchet, thereby releasing the energy of the closing spring to close the circuit; after closing the circuit, the latching pin on the large crank arm is latched onto the closing latch, and the closing latch has a counterclockwise rotation trend, thereby causing the roller on the closing latch to latch onto the opening latch; the opening stage includes that when receiving the opening command, the opening electromagnet pushes the opening latch to rotate counterclockwise to make way for the closing latch, the closing latch is pushed away by the roller on the large crank arm and disengaged, and the opening spring energy is released to open the circuit.

[0008] As a preferred solution of the cam optimization method of the high-voltage switch operating mechanism based on genetic algorithm described in the present invention, when the simulation model is subjected to dynamic simulation, 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 solution of the cam optimization method for the high-voltage switch operating mechanism based on genetic algorithm described in the present invention, wherein: the high-order polynomial is expressed as:

[0010]

[0011] in, Represented as the closing process, C0,C p ,C q ,C r ,C s ...are coefficients to be determined, and the superscripts p, q, y, and s are power indices; θ is In the descending section Expressed as the cam angle, Expressed as the half wrap angle of the cam's basic working section.

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

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

[0014] Wherein, n=3, 4, 5..., m=1, 2, 3...; The design variables, objective function and constraint conditions of the genetic algorithm are determined by taking n and m as design variables, taking the maximum fullness coefficient ξ as the objective function, and taking the maximum acceleration is the maximum permissible acceleration, negative acceleration To allow the minimum acceleration, the minimum curvature radius R of the cam profile min ≥[R min ],[R min ] is the minimum allowed curvature radius, and 3≤n≤20, 1≤m≤20 are constraints.

[0015] As a preferred solution of the cam optimization method of the high-voltage switch operating mechanism based on genetic algorithm of the present invention, the output of the optimal structural parameters of the cam according to the iterative step of the genetic algorithm includes encoding and initializing the population, and the encoding method adopts real number encoding, that is, using N segments of uniform B-spline curves with an order of 5 to describe the cam curve, and each chromosome X is represented by,

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

[0017] Where d represents B-spline; for the real-coded cam curve genetic algorithm optimization problem, the crossover operation uses arithmetic crossover, that is, the i-th gene x of chromosome X i The crossover between two parent individuals A and B is expressed as follows:

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

[0019] Among them, r i is a random number in the interval (0,1). Parent individuals A and B are selected from the current population through a two-person random league selection method. The individual X after crossover is then mutated. The formula is:

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

[0021] Among them, r 1,i and r 2,iare still two independent random numbers in the interval (0,1), and [u,l] is the value range of the control vertex; the fast non-dominated sorting algorithm is used to perform Pareto optimal individual sorting on all individuals in the population, including setting the initial sequence number r←1, finding the Pareto optimal subset in the population, and defining the individual sequence numbers in the subset to be r, removing the Pareto optimal subset from the population, and changing the sequence number r←r+1 until all individuals are sorted.

[0022] As a preferred solution of the cam optimization method of the high-voltage switch operating mechanism based on genetic algorithm described in the present invention, wherein: the output of the optimal structural parameters of the cam according to the iterative steps of the genetic algorithm also includes adopting an elite retention strategy so that excellent individuals will not be destroyed due to crossover mutation, specifically including randomly generating a parent population P0, the population size is M and the number of generations t=0, performing crossover and mutation operations on the parent population P0 to generate a descendant population Q0, and if the termination condition is met, returning the population P t , otherwise, let F(X) = F max -Φ(X,r (k) ), for R i Sort the Pareto optimal individuals and generate the Pareto optimal frontier F1, F2, ..., F n , where F(X) is the fitness function, Φ(X,r (k) ) is the penalty function, R i is the parent population individual; the penalty function Φ(X,r (k) ) is expressed as,

[0023]

[0024] in, Expressed as a penalty term associated with the inequality constraint, r (k) It is expressed as penalty factor;

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

[0026] F(X)=F max -Φ(X,r (k) )

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

[0028] Another object of the present invention is to provide a high-voltage switch operating mechanism cam optimization system based on genetic algorithm, which can design the high-voltage switch operating mechanism cam.

[0029] To solve the above technical problems, the present invention provides the following technical solutions: 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 builds a three-dimensional simulation model of the operating mechanism based on the ADAMS virtual prototype technology; the simulation module sets parameters and boundary conditions for the simulation model of the operating mechanism based on the ADAMS virtual prototype technology, and performs dynamic simulation of the simulation model based on the ADAMS virtual prototype technology to obtain the stroke and speed curve of the connecting rod; the optimization module determines the design variables, objective functions and constraints of the genetic algorithm based on high-order polynomials, obtains an 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 comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned genetic algorithm-based high-voltage switch operating mechanism cam optimization method when executing the computer program.

[0031] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned genetic algorithm-based high-voltage switch operating mechanism cam optimization method.

[0032] The beneficial effects of the present invention are as follows: in order to solve the problem that the existing methods mostly adopt empirical design in the manufacturing process and the design cycle is long, the present invention uses a quintic uniform B-spline curve to construct a cam curve, and transforms the design of the cam curve into the calculation of the control vertex of the B-spline curve. In order to simultaneously meet the kinematic and dynamic performance of the cam mechanism, it is proposed to use a genetic algorithm to describe the optimization problem of the cam curve and solve the optimization model of the cam curve. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0034] Figure 1 Schematic diagram of the flow of the cam optimization method for the high-voltage switch operating mechanism based on genetic algorithm in Example 1.

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

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

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

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

[0039] Figure 6 This is a module structure diagram of the high-voltage switch operating mechanism cam optimization system based on genetic algorithm in Example 3. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0041] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

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

[0043] Step 101, build a 3D simulation model of the CT26 operating mechanism based on ADAMS virtual prototype technology. To study the stress of key components of the spring operating mechanism during the closing process, in order to control the calculation scale and save time, the mechanical characteristics of the key structure should be highlighted during the modeling process. In the modeling process, the entire mechanism is simplified as necessary, and the key components in the mechanism are extracted as research objects to build a 3D model.

[0044] Step 102, set parameters and boundary conditions for the simulation model of the operating mechanism based on ADAMS virtual prototype technology. In ADAMS simulation software, mechanical loads can be applied to replace the solid modeling of spring parts. The action process of the CT26 spring operating mechanism is divided into the following three stages: energy storage stage, closing stage and opening stage. In order to realize the above movement process, it is necessary to add ideal constraint pairs in the simulation model. Ideal constraints are usually constraint pairs with physical meaning, such as revolute joints, translational joints, gear joints, etc.

[0045] Step 103: Perform dynamic simulation on the simulation model based on ADAMS virtual prototype technology to obtain the connecting rod stroke and speed curve. According to the dynamic simulation analysis of the above simulation model, the CT26 spring operating mechanism is powered by the opening and closing springs, so only the opening and closing spring parameters need to be adjusted during the simulation process, and no external load needs to be applied.

[0046] Step 104 determines the design variables, objective functions and constraints of the genetic algorithm based on the high-order polynomial, and obtains the optimized mathematical model of the cam mechanism. This paper adopts the optimization method of the genetic algorithm to establish the corresponding constraint equations, and then solves the basic dimensions of the mechanism. During the design, the number of terms and powers of the polynomial can be selected arbitrarily. This paper uses the pentaminar to meet the design requirements of the cam mechanism, and expresses the cam curve with a quintic uniform B-spline curve.

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

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

[0049] Furthermore, in step 102, the ADAMS simulation software can apply mechanical loads to replace the entity modeling of the spring part, according to the spring stiffness coefficient calculation formula:

[0050]

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

[0052] Hooke's Law:

[0053] F=KΔx

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

[0055] When the boundary conditions are set, the energy storage stage: with the help of the energy storage motor or manual energy storage, the ratchet is driven to rotate and the closing spring is compressed at the same time, until the roller on the ratchet is buckled on the arc surface of the energy storage holding catch. In order to avoid the dead point of the circle, it should be rotated 185°. Closing stage: when the closing command is received, the closing electromagnet pushes the closing catch to rotate counterclockwise to let the energy storage holding catch open. The energy storage holding catch is pushed away by the roller on the ratchet and the ratchet is disengaged, so that the energy of the closing spring is released to close. Opening stage: after closing, the buckle pin on the large crank arm is buckled on the closing holding catch, and the closing holding catch has a counterclockwise rotation trend, so that the roller on the closing holding catch is buckled on the opening catch. When the opening command is received, the opening electromagnet pushes the opening catch to rotate counterclockwise to let the closing holding catch open. The closing holding catch is pushed away by the roller on the large crank arm and is disengaged, and the opening spring energy is released to open.

[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] Further, 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, Represented as the closing process, C0,C p ,C q ,C r ,C s ...are coefficients to be determined, and the superscripts p, q, y, and s are power indices; θ is In the descending section Expressed as the cam angle, It is expressed as the half wrap angle of the basic working section of the cam. When designing, the number of terms and powers of the polynomial can be selected arbitrarily, but generally the 5-term polynomial can meet the design requirements of the cam mechanism. The power index of each term can be selected according to the following formula:

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

[0061] Among them, n = 3, 4, 5 ..., m = 1, 2, 3 ...; usually, the value of n, m within 20 can meet the design requirements. In order to obtain high intake and exhaust efficiency, the maximum fullness coefficient ξ is often taken as the goal in engineering. Take n, m as design variables, ξ as the objective function, and the maximum acceleration is the maximum permissible acceleration, negative acceleration To allow the minimum acceleration, the minimum curvature radius R of the cam profile min ≥[R min ],[R min ] is the minimum allowed curvature radius, and 3≤n≤20, 1≤m≤20 are constraints.

[0062] Furthermore, in step 105, the following steps are included:

[0063] Step 105.1, population coding and initialization. The control vertex vector of the cam curve is a multidimensional optimization variable, so its coding adopts real number coding, that is, N segments of uniform B-spline curves with an order of 5 are used to describe the cam curve. Each chromosome X is expressed as,

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

[0065] Where d represents B-spline.

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

[0067] Step 105.3, genetic operation. Genetic operation includes selection operation, crossover operation and mutation operation, which is the core of GA's powerful search ability. For the real-coded CAM curve GA optimization problem, the crossover operation adopts arithmetic crossover, that is, the i-th gene x of chromosome X is i The crossover between two parent individuals A and B is expressed as follows:

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

[0069] Among them, r i is a random number in the interval (0,1). Parent individuals A and B are selected from the current population through a two-person random league selection method. The individual X after crossover is then mutated. The formula is:

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

[0071] Among them, r 1,i and r 2,i They are still two independent random numbers in the interval (0,1), and [u,l] is the value range of the control vertex.

[0072] Step 105.4, sorting of Pareto optimal individuals. This paper adopts the fast non-dominated sorting algorithm in NSGA-II to sort all individuals in the population into Pareto optimal individuals: Step 1, set the initial sequence number r←1. Step 2, find the Pareto optimal subsets in the population, and define the individual sequence numbers in these subsets as r. Step 3, remove the Pareto optimal subsets from the population, and change the sequence number r←r+1. Step 4, go to step 2 until all individuals are sorted.

[0073] Step 105.5, elite strategy. In order to ensure that excellent individuals will not be destroyed due to crossover mutation, the elite retention strategy is adopted. The specific algorithm is as follows: Step 1, randomly generate the parent population P0, the population size is M and the number of generations t = 0. Step 2, perform crossover and mutation operations on the parent population P0 to generate the offspring population Q0. Step 3, if the termination condition is met, return to the population P t , otherwise go to step 4. Step 4, set F(X) = F max -Φ(X,r (k) ), for R i Sort the Pareto optimal individuals and generate the Pareto optimal frontier F1, F2, ..., F n , where F(X) is the fitness function, Φ(X,r (k) ) is the penalty function, R i The parent population individual.

[0074] Step 105.6, constraint processing and objective function range limitation. Due to the elite strategy, R i The optimal number of individuals in exceeds the specified population size M. The present invention selects these Pareto individuals by realizing variable constraints and limiting the value range of the objective function to form a new generation population of the specified size.

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

[0076]

[0077] in, is the penalty term associated with the inequality constraint, r (k) is the penalty factor. Since the objective function of the optimization design is to find the minimum value, the following fitness function is constructed:

[0078] F(X)=F max -Φ(X,r (k) )

[0079] Among them, F max 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 forms a one-to-one correspondence with the fitness of the genetic algorithm.

[0080] Example 2, reference Figure 2-Figure 5 , which is the second embodiment of the present invention, and is different from the first embodiment in that: the high-voltage switch operating mechanism cam optimization method and system based on genetic algorithm also includes, in order to verify and illustrate the technical effects adopted in this method, this embodiment adopts the traditional technical solution and the method of the present invention for comparative testing, and compares the test results by means of scientific demonstration to verify the real effect of this method.

[0081] Based on ADAMS virtual prototype technology, a three-dimensional simulation model of the CT26 operating mechanism is established. The ADAMS virtual prototype technology is used to establish simulation models of key components of the operating mechanism. The simulation model is built using ADAMS software. Figure 2 As shown in the figure. To study the stress of the key components of the spring operating mechanism during the closing process, the model has many parts in the operating mechanism and the coordination between the parts is complex, so the simulation model is optimized to reduce the calculation time. In this model, the energy storage motor, the closing and opening coil and other parts that do not participate in the mechanical movement process of the operating mechanism are deleted, and the pre-processing function of the ADAMS software is used to realize the functions of these parts.

[0082] The parameters of the operating mechanism simulation model are set based on the ADAMS virtual prototype technology. In the ADAMS simulation software, mechanical loads can be applied to replace the entity modeling of spring parts. The stiffness coefficient and preload parameters of the CT26-110kV spring operating mechanism are calculated by combining the parameters of the closing and closing springs as shown in Table 1. The parameters in the table are added to the simulation model to complete the construction of the dynamic simulation model.

[0083] Table 1 Opening and closing spring parameters

[0084] Stiffness coefficient (N / m) Preload(N) Closing spring 80623 20204 Opening spring 69281 4469

[0085] Ideal constraints are usually constraints with physical meaning, such as revolute joint, translational joint, gear joint, etc. The main constraints added to this simulation model are shown in Table 2:

[0086] Table 2 Main constraints

[0087]

[0088]

[0089] Based on the ADAMS virtual prototype technology, the simulation model is dynamically simulated to obtain the connecting rod stroke and speed curve. According to the above simulation model, the CT26 spring operating mechanism is powered by the opening and closing springs, so in the simulation process, only the opening and closing spring parameters need to be adjusted without applying external loads. 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 The simulation analysis shows the connecting rod stroke and speed curve as shown in Figure 4 As shown, the overstroke displacement of the connecting rod is 0.43 cm, the maximum displacement of the connecting rod is 7.88 cm, and the closing time meets the mechanism action standard.

[0090] Based on high-order polynomials, the design variables, objective functions and constraints of the genetic algorithm are determined to obtain the optimal mathematical model of the cam mechanism. This paper adopts the optimization method of the genetic algorithm to establish the corresponding constraint equations, and then solves the basic dimensions of the mechanism. During the design, the number of terms and powers of the polynomial can be selected arbitrarily. This paper uses pentaminars to meet the design requirements of the cam mechanism, and expresses the cam curve with a quintic uniform B-spline curve. The B-spline curve is a segmented curve defined by control vertices. Generally, a high-order polynomial curve can be expressed as,

[0091]

[0092] Each power index can be selected as follows:

[0093] p=2, q=2n, y=2n+2m, s=2n+4m

[0094] Usually, the values ​​of n and m within 20 can meet the design requirements. In order to obtain high intake and exhaust efficiency, the maximum fullness coefficient ξ is often taken as the goal in engineering. Take n and m as design variables, ξ as the objective function, and the maximum acceleration is the maximum permissible acceleration, negative acceleration To allow the minimum acceleration, the minimum curvature radius R of the cam profile min ≥[R min ],[R min ] is the minimum allowed curvature radius, and 3≤n≤20, 1≤m≤20 are the constraints. The composite method and genetic algorithm are used to solve them respectively. The results are shown in Table 3.

[0095] Table 3 Comparison results

[0096] m n ξ <![CDATA[a min (m / s 2 )]]> <![CDATA[a max (m / s 2 )]]> <![CDATA[R min (mm)]]> Genetic Algorithms 9 6 0.611 294 1457.93 7.11 Composite method 8 8 0.610 296 1473.67 6.99

[0097] According to the iterative steps of the genetic algorithm, the optimal structural parameters of the cam are output, with the optimization goal of minimizing the maximum pressure angle of the cam mechanism. In order to verify the accuracy of the software calculation, this paper uses Solid-Works to make a virtual prototype of the conjugate cam mechanism. The virtual prototype is imported into Adams, and the comparison diagram of the design requirement curve and the simulation curve of the motion law can be obtained, as shown in the figure below. Figure 5 The figure shows the comparison between the design requirement curve and the simulation curve.

[0098] Example 3, reference Figure 6 , which is the third embodiment of the present invention, and is different from the previous two embodiments in that: a system of a high-voltage switch operating mechanism cam optimization method 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 builds a three-dimensional simulation model of the operating mechanism based on the ADAMS virtual prototype technology; the simulation module 200 sets parameters and boundary conditions for the operating mechanism simulation model based on the ADAMS virtual prototype technology, and performs dynamic simulation on the simulation model based on the ADAMS virtual prototype technology to obtain the connecting rod stroke and speed curve; the optimization module 300 determines the design variables, objective functions 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.

[0099] If the functions are implemented in the form of 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 the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0100] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0101] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0102] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0103] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A cam optimization method for a high-voltage switch operating mechanism based on a genetic algorithm, characterized in that: include, Establish a three-dimensional simulation model of the operating mechanism based on ADAMS virtual prototype technology; Parameters and boundary conditions of the operating mechanism simulation model are set based on ADAMS virtual prototype technology; Based on ADAMS virtual prototype technology, dynamic simulation of the simulation model is carried out to obtain the stroke and speed curve of the connecting rod; Based on high-order polynomials, the design variables, objective functions and constraints of the genetic algorithm are determined, and the optimal mathematical model of the cam mechanism is obtained; The optimal structural parameters of the cam are output according to the iterative steps of the genetic algorithm.

2. The method for optimizing the cam of a high-voltage switch operating mechanism based on a genetic algorithm according to claim 1, characterized in that: The boundary condition setting includes energy storage stage, closing stage and opening stage; The energy storage stage includes driving the ratchet wheel to rotate and compressing the closing spring by means of an energy storage motor or manual energy storage, until the roller on the ratchet wheel buckles on the arc surface of the energy storage retaining latch; The closing stage includes that when a closing command is received, the closing electromagnet pushes the closing latch to rotate counterclockwise to allow the energy storage holding latch to make way for the ratchet, and the energy storage holding latch is pushed away by the roller on the ratchet to release the ratchet, thereby releasing the energy of the closing spring to close the circuit; After closing, the buckle pin on the large crank arm buckles onto the closing holding latch, causing the closing holding latch to rotate counterclockwise, so that the roller on the closing holding latch buckles onto the opening latch; The opening stage includes that when receiving the opening command, the opening electromagnet pushes the opening latch to rotate counterclockwise to make way for the closing holding latch, the closing holding latch is pushed open by the roller on the large crank arm and disengaged, and the opening spring energy is released to open the circuit.

3. The method for optimizing the cam of a high-voltage switch operating mechanism based on a genetic algorithm according to claim 2, characterized in that: When the dynamics simulation is performed 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 method for optimizing the cam of a high-voltage switch operating mechanism based on a genetic algorithm according to claim 3, characterized in that: The high-order polynomial is expressed as, in, Represented as the closing process, C0,C p ,C q ,C r ,C s ...are coefficients to be determined, and the superscripts p, q, y, and s are power indices; θ is In the descending section Expressed as the cam angle, Expressed as the half wrap angle of the cam's basic working section.

5. The method for optimizing the cam of a high-voltage switch operating mechanism based on a genetic algorithm according to claim 4, characterized in that: The selection method of the power index is expressed as follows: p=2, q=2n, y=2n+2m, s=2n+4m Wherein, n=3, 4, 5…, m=1, 2, 3…; The method for determining the design variables, objective function and constraint conditions of the genetic algorithm includes taking n and m as design variables, taking the maximum fullness coefficient ξ as the objective function, and taking the maximum acceleration a max ≤[a max ],[a max ] is the maximum permissible acceleration, negative acceleration a min ≥[a min ],[a min ] is the minimum permissible acceleration, and the minimum curvature radius R of the cam profile min ≥[R min ],[R min ] is the minimum allowed curvature radius, and 3≤n≤20, 1≤m≤20 are constraints.

6. The method for optimizing the cam of a high-voltage switch operating mechanism based on a genetic algorithm according to claim 5, characterized in that: The output of the optimal structural parameters of the cam according to the iterative steps of the genetic algorithm includes encoding and initializing the population, and the encoding method adopts real number encoding, that is, using N segments of uniform B-spline curves with an order of 5 to describe the cam curve, and each chromosome X is represented by: X=[d0,d1,...,d N+4 ] Where, d represents B-spline; For the real-coded CAM curve genetic algorithm optimization problem, the crossover operation uses arithmetic crossover, that is, the i-th gene x of chromosome X i The crossover between two parent individuals A and B is expressed as follows: x i =r i a i +(1-r i )b i ,i=0,...,N+4 Among them, r i is a random number in the interval (0,1). Parent individuals A and B are selected from the current population through a two-person random league selection method. The individual X after crossover is then mutated. The formula is: x i =r 1,i x i +(1-r 1,i )[r 2,i (u-l)+l],i=0,...,N+4 Among them, r 1,i and r 2,i They are still two independent random numbers in the interval (0,1), and [u,l] is the value range of the control vertex; The fast non-dominated sorting algorithm is used to sort all individuals in the group into Pareto optimal individuals, including setting the initial sequence number r←1, finding the Pareto optimal subset in the group, and defining the individual sequence numbers in the subset to be r, removing the Pareto optimal subset from the group, and changing the sequence number r←r+1 until all individuals are sorted.

7. The method for optimizing the cam of a high-voltage switch operating mechanism based on a genetic algorithm according to claim 6, characterized in that: The method of outputting the optimal structural parameters of the cam according to the iterative steps of the genetic algorithm also includes adopting an elite retention strategy so that excellent individuals will not be destroyed due to crossover mutation, specifically including 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 descendant 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 Sort the Pareto optimal individuals and generate the Pareto optimal frontier F1, F2, ..., F n , where F(X) is the fitness function, Φ(X,r (k) ) is the penalty function, R i is the parent population individual; The penalty function Φ(X,r (k) ) is expressed as, in, Expressed as a penalty term associated with the inequality constraint, r (k) It is expressed as penalty factor; The fitness function F(X) is expressed as, F(X)=F max -Φ(X,r (k) ) Among them, F max It is expressed as a sufficiently large number to ensure that the fitness value is always greater than 0.

8. A system using the high-voltage switch operating mechanism cam optimization method based on genetic algorithm as claimed in any one of claims 1 to 7, 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 prototype technology; The simulation module (200) performs parameter setting and boundary condition setting on the operating mechanism simulation model based on the ADAMS virtual prototype technology, performs dynamic simulation on the simulation model based on the ADAMS virtual prototype technology, and obtains the connecting rod stroke and speed curve; The optimization module (300) determines the design variables, objective functions and constraint conditions of the genetic algorithm based on high-order polynomials, obtains an 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.

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

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the high-voltage switch operating mechanism cam optimization method based on a genetic algorithm as described in any one of claims 1 to 7 are implemented.

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