Variable pitch distance centripetal speed regulating mechanism parameter optimization design method and system

Abstract

The invention discloses a variable pitch distance centripetal speed regulating mechanism parameter optimization design method and system. The method comprises the steps that optimizable centrifugal speed regulation mechanism design parameters are determined and comprise the cam initial eccentric angle beta 0, the speed regulation spring rigidity kT, the speed regulation spring initial compression amount x0 and the amplification or reduction multiple kxy of the mass inertia product of a centrifugal block, multi-parameter optimization is conducted based on a genetic algorithm, the optimization target is to find a parameter combination XE = [beta 0, kT, x0 and kxy], and the optimal centrifugal speed regulation mechanism design parameters are obtained. Enabling a stability objective function f (XE) of the speed regulation system to be maximum; wherein during calculation, balance points (omega eq and theta eq) corresponding to each parameter combination are determined by using a numerical calculation method according to a kinetic equation of the speed regulation system and a calculation formula of each type of torque, parameters associated with each type of torque are calculated according to the (omega eq and theta eq) to establish a closed-loop control model of the speed regulation system, and a closed-loop transfer function corresponding to each group of parameters is constructed; and calculating a target function value f according to the amplitude margin and the phase angle margin. The method has a remarkable effect in the aspect of improving the stability of the variable-pitch distance centripetal speed regulation system.

Description

Technical Field

[0001] The present invention belongs to the field of structural dynamics and stability, and particularly relates to a parameter optimization design method and system for a variable pitch centrifugal speed regulating mechanism based on a closed-loop control system. Background Art

[0002] A centrifugal speed regulating mechanism is a system that uses the centrifugal force of a rotating body to adjust the speed of a machine, and can be widely applied to various mechanical devices, such as diesel engines, water turbines, wind turbines, etc. The components of a centrifugal speed regulating mechanism include: a centrifugal sensing element, an execution control element, and a balance reset element.

[0003] Figure 1 is a schematic diagram of a variable pitch centrifugal speed regulating mechanism. The centrifugal block serves as the centrifugal sensing element and adjusts the speed by changing the magnitude of the centrifugal force; the blade is the execution control element and affects the external aerodynamic force by changing the pitch angle, and the role of the aerodynamic force is to drive the rotation of the rotating main shaft; the balance reset element is a speed regulating spring, which is used to apply a reverse force to the centrifugal block when it moves quickly to keep the speed regulating system running stably. In devices such as wind turbines, the variable pitch speed regulating system has self-regulating ability, and its speed regulating process forms a negative feedback regulation, so that the speed of the rotating shaft remains stable when the load changes suddenly. The specific regulation process is as follows: when the load acts on the rotating shaft, the system speed decreases, resulting in a decrease in the centrifugal force provided by the centrifugal block, thereby causing the pitch angle of the blade to decrease, the aerodynamic load to change, and the elastic force provided by the spring to also decrease; on the other hand, the sudden change in speed changes the tip speed, directly affecting the driving torque received by the speed regulating system. By superimposing the change effects of the aerodynamic force, the torque received by the rotating shaft changes, ensuring that the speed will not decrease endlessly due to the action of the load, but is adjusted to a new equilibrium state.

[0004] The speed regulating system should provide stable power for the rear-end load device, which means that when the load is connected, the speed regulating system should have high stability to prevent failure. However, a research difficulty in the existing variable pitch centrifugal speed regulating mechanism is that design parameters such as the spring stiffness and the size of the centrifugal block will affect the stability of the speed regulating system. Although increasing the spring stiffness is a relatively effective means to enhance stability, its influence mechanism and the optimization schemes of other parameters remain to be explored. Therefore, it is necessary to establish an effective mathematical model, adopt a reasonable stability criterion, construct an accurate fitness function, and use an efficient optimization algorithm for parameter optimization design. Summary of the Invention

[0005] Object of the Invention: Aiming at the technical difficulties of neural network distributed dynamic load identification, a parameter optimization design method and system for a variable pitch centrifugal speed regulating mechanism are proposed, which improves the anti-noise ability of dynamic load identification and has quite high stability and accuracy for various types of loads.

[0006] Technical solution: To achieve the above purpose, the present invention adopts the following technical solution:

[0007] A method for optimizing the parameters of a variable pitch centroid speed regulating mechanism comprises the following steps:

[0008] Step 1: Determine the optimizable design parameters of the centrifugal speed regulating mechanism including the initial eccentric angle β of the cam 0 , speed regulating spring stiffness k T , initial compression of speed regulating spring x 0 and the magnification or reduction factor k of the mass inertia product of the centrifugal block xy , then the optimization goal is to find a parameter combination X E =[β 0 ,k T ,x 0 ,k xy ], so that the speed control system stability objective function f(X E )maximum;

[0009] Step 2: Set the search range for the optimal solution, set the population size and number of iterations of the genetic algorithm, as well as the crossover and mutation probabilities, and complete population initialization according to the algorithm settings;

[0010] Step 3: According to the dynamic equation of the speed control system and the calculation formula of various types of torque, the equilibrium point (ω) corresponding to each parameter combination is determined by numerical calculation method. eq ,θ eq ),ω eq is the speed when the speed control system is at the equilibrium point, θ eq is the blade pitch angle when the speed control system is at the equilibrium point, according to (ω eq ,θ eq ) Calculate the parameters associated with each type of torque to establish a closed-loop control model of the speed control system and construct a closed-loop transfer function corresponding to each set of parameters;

[0011] Step 4: Obtain the amplitude margin and phase margin corresponding to the speed control system under different design parameter combinations by drawing the Bode diagram, and calculate the objective function value f;

[0012] Step 5: Determine whether the algorithm termination conditions are met. If so, output the current optimal parameter combination and the optimal objective function value. If not, perform genetic operations to generate the next generation population, and repeat the cycle until the maximum number of iterations is reached to complete the parameter optimization design process.

[0013] Furthermore, the stability objective function f(X E )for:

[0014] f(X E )=f(β 0 ,kT , x 0 , k xy ) = z(h + γ)

[0015] Where z is the weight coefficient, h is the amplitude margin, and γ is the phase margin.

[0016] Furthermore, the dynamic equation of the speed control system is as follows:

[0017] Based on the mechanical relationships of the components in the speed control system, the dynamic equation of the speed control process is established by applying Newton's second law:

[0018]

[0019] In the formula, J 1 refers to the moment of inertia of the main shaft of the blade rotation, θ is the blade pitch angle, M y , M c and M t are respectively the aerodynamic pitch moment, the centrifugal moment generated by the centrifugal block, and the spring moment provided by the speed control spring, all acting on the blade rotation axis. M d is the damping moment acting on the blade pitch axis; J 2 refers to the moment of inertia of the main shaft of the speed control system rotation, ω is the rotational speed, M D is the driving torque received by the speed control system, M L is the load torque generated by the rear-end load;

[0020] The calculation formulas of various types of torques are all functions related to ω or θ:

[0021]

[0022] In the formula, C y and C D are aerodynamic torque coefficients, P d is the dynamic pressure, s is the reference area, taken as a constant; I xy (θ) is the mass moment of inertia product of the centrifugal block; k T is the spring stiffness, x 0 is the initial compression of the spring, R T is the cam rotation radius, β 0 is the initial eccentric angle of the cam, θ 0 is the initial pitch angle of the blade; B is the damping coefficient; P L is the load power.

[0023] Furthermore, the closed-loop control model of the speed control system is as follows:

[0024] Taking (ω eq , θ eq) is the balance point. Denote the moment when the load changes as the initial moment, and set the load mutation value as ΔM. L , which is used as the input signal of the speed regulation system. After a time Δt, the working state of the speed regulation system becomes (ω t , θ t ), where ω t = ω eq + Δω, θ t = θ eq + Δθ. Take Δω as the output signal of the speed regulation system. During this period, the change amounts of various types of torques are ΔM y , ΔM D , ΔM c , ΔM t and ΔM d .

[0025] Further, the construction method of the closed-loop transfer function corresponding to each group of parameters is as follows:

[0026] After linearizing the dynamic equation, we get:

[0027]

[0028] Taking the Laplace transform of both sides of the above equation under zero initial conditions, we have:

[0029]

[0030] Thus, the closed-loop transfer function of the speed regulation system is obtained:

[0031]

[0032] In the formula,

[0033]

[0034] Further, the open-loop transfer function of the speed regulation system on which the Bode plot depends is:

[0035]

[0036] Further, the genetic operation process of the genetic algorithm is as follows: Under the condition that the maximum number of iterations has not been reached, perform the selection operation. Determine the probability of an individual being selected according to its fitness value. The higher the fitness value, the greater the probability that the parameter combination X is selected. Next, perform the crossover and mutation operations. The crossover operation is to select two individuals and swap some of their elements according to the crossover probability to generate a new parameter combination X i1 ; The mutation operation is to determine the number of mutated individuals in the population according to the mutation probability and change some of the parameters to generate a new parameter combination X j1, after operations of selection, crossover, and mutation, the population completes one iteration and evolves into X 11 , X 21 , X 31 …X m1 As the first-generation population, then repeat the above process in a loop and gradually optimize it to the population X corresponding to the maximum number of iterations n 1n , X 2n , X 3n …X mn , at this time, the algorithm termination condition is satisfied, and the individual with the highest fitness value in this generation is output as the optimal parameter combination X E , and the optimal objective function value f(X E ) is output.

[0037] A parameter optimization design system for a variable pitch centrifugal speed regulating mechanism, comprising:

[0038] A parameter combination construction module, used to determine the design parameters of the centrifugal speed regulating mechanism that can be optimized, including the initial eccentric angle β of the cam 0 , the stiffness k of the speed regulating spring T , the initial compression x of the speed regulating spring 0 , and the magnification or reduction factor k of the mass moment of inertia of the centrifugal block xy , then the optimization goal is to find a parameter combination X E = [β 0 , k T , x 0 , k xy , such that the stability objective function f(X E ) of the speed regulating system is maximized;

[0039] An algorithm initialization module, used to set the search range of the optimal solution, set the population size and the number of iterations of the genetic algorithm, as well as the crossover and mutation probabilities, and complete the population initialization according to the algorithm settings;

[0040] A first calculation module, used to determine the balance point (ω eq , θ eq ) corresponding to each parameter combination according to the dynamic equation of the speed regulating system and the calculation formulas of various types of torques, using numerical calculation methods. ω eq is the rotational speed of the speed regulating system at the balance point, and θ eq is the blade pitch angle of the speed regulating system at the balance point. Calculate the parameters associated with various types of torques according to (ω eq , θ eq ) to establish a closed-loop control model of the speed regulating system and construct the closed-loop transfer function corresponding to each group of parameters;

[0041] The second calculation module is used to obtain the amplitude margin and phase margin corresponding to the speed regulation system under different combinations of design parameters by plotting a Bode diagram, and calculate the value of the objective function f;

[0042] The algorithm iteration control module is used to determine whether the algorithm termination condition is satisfied. If it is satisfied, the current optimal parameter combination and the optimal objective function value are output; if not, genetic operations are performed to generate the next generation of population, and the loop is repeated until the maximum number of iterations is reached, completing the parameter optimization design process.

[0043] The present invention also provides a computer device, including: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the program is executed by the processor, the steps of the parameter optimization design method of the variable pitch centrifugal speed regulation mechanism as described above are implemented.

[0044] The present invention also provides a computer storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the parameter optimization design method of the variable pitch centrifugal speed regulation mechanism as described above are implemented.

[0045] Beneficial effects: The parameter optimization design method of the centrifugal speed regulation mechanism proposed by the present invention first establishes a closed-loop control system according to the self-regulation process of the speed regulation mechanism, linearly approximates the nonlinear system, and reveals the influence mechanism of the structural design parameters on the stability of the speed regulation system; then an objective function for evaluating the stability of the speed regulation system is established, and a parameter optimization design process based on the genetic algorithm principle is proposed. Using the established closed-loop control model, the parameter optimization design method has a significant effect in improving the stability of the variable pitch centrifugal speed regulation system. After optimization iteration, the stability of the speed regulation system with updated structural parameters has been significantly enhanced. Description of the Drawings

[0046] Figure 1 is a schematic diagram of the variable pitch centrifugal speed regulation mechanism;

[0047] Figure 2 is a flow chart of the parameter optimization design of the centrifugal speed regulation mechanism;

[0048] Figure 3 is the parameter optimization process of the speed regulation system;

[0049] Figure 4 is the Bode diagram of the speed regulation system before optimization;

[0050] Figure 5 is the Bode diagram of the speed regulation system after optimization;

[0051] Figure 6 is a comparison chart of the speed change curves before and after parameter optimization. Detailed implementation manners

[0052] The technical solutions of the present invention will be further described below with reference to the accompanying drawings.

[0053] In view of the technical difficulty that it is difficult to directly correlate the structural parameters of the speed control element with the system stability, the present invention constructs a closed-loop control model to represent the working process of the pitch centrifugal speed control mechanism. This model takes the load torque as the input variable and the rotational speed change as the output variable, and the model parameters all correspond to the actual structural parameters. On this basis, the stability margin is extracted from the closed-loop control system as an index for evaluating the stability of the speed control system, and a multi-parameter optimization objective and process for the speed control system are further established. Given only the initial structural parameter range, the parameters that maximize the system stability can be solved through the multi-parameter optimization process proposed by the present invention, realizing the optimization of the design parameters of the speed control system.

[0054] The formation and implementation of the technical solutions include the following parts.

[0055] I. Establish the dynamic equation of the pitch centrifugal speed control system

[0056] According to the mechanical relationships of the components in the speed control system, the dynamic equation of the speed control process is established by applying Newton's second law:

[0057]

[0058] In the formula, J 1 refers to the moment of inertia of the blade rotation main shaft, θ is the blade pitch angle, M y , M c and M t are respectively the aerodynamic pitch torque, the centrifugal torque generated by the centrifugal block, and the spring torque provided by the speed control spring, all acting on the blade rotation axis, M d is the damping torque acting on the blade pitch axis; J 2 refers to the moment of inertia of the rotation main shaft of the speed control system, ω is the rotational speed, M D is the driving torque received by the speed control system, and M L is the load torque generated by the rear-end load. The calculation formulas of each type of torque are all functions related to ω or θ.

[0059]

[0060] In the formula, C y and C D are aerodynamic torque coefficients, P d is the dynamic pressure, s is the reference area, taken as a constant; I xy (θ) is the mass inertia product of the centrifugal block; k T is the spring stiffness, x 0is the initial compression of the spring, R T is the cam rotation radius, β 0 is the initial eccentric angle of the cam, θ 0 is the initial pitch angle of the blade; B is the damping coefficient; P L is the load power.

[0061] According to equations (1) and (2), the equilibrium point corresponding to each parameter combination (ω eq , θ eq ) can be determined by numerical calculation methods. Determining the equilibrium point by numerical calculation methods involves iterative calculations. This problem involves two equations and two unknowns in equation (1): the rotational speed ω and the pitch angle θ, and their initial values are 0 and θ 0 respectively. Using the fourth-order Runge-Kutta method or other numerical solution methods for ordinary differential equations, after multiple iterations, the solution of the equation with higher accuracy can be gradually approximated. During the solution process, various types of torques, as well as ω and θ, are updated in each iteration until convergence to the equilibrium point (ω eq , θ eq ).

[0062] II. Establishing the closed-loop control model of the variable pitch centrifugal speed regulation system

[0063] Using the method of linear approximation to construct the closed-loop control model of the speed regulation system can relate each design parameter to the closed-loop transfer function. Taking the equilibrium state where the speed regulation mechanism is rotating at a constant speed as the initial state, defining the equilibrium point (ω eq , θ eq ), recording the moment when the load changes as the initial moment, and setting the load mutation value as ΔM L , as the input signal of the speed regulation system. After a time Δt, the working state of the speed regulation system becomes (ω t , θ t ), where ω t = ω eq + Δω, θ t = θ eq + Δθ, taking Δω as the output signal of the speed regulation system. During this period, the change amounts of various types of torques are ΔM y , ΔM D , ΔM c , ΔM t and ΔM d , all related to Δt. Taking ΔM y as an example,

[0064]

[0065] Multiply both sides of the equation by Δt to get,

[0066]

[0067] The same applies to other torques.

[0068]

[0069] After linearizing the dynamic equations (1) and (2), we get:

[0070]

[0071] Taking the Laplace transform of both sides of the above equation under zero initial conditions, we have:

[0072]

[0073] Thus, the closed-loop transfer function of the speed control system is obtained:

[0074]

[0075] In the formula,

[0076]

[0077] III. Stability Assessment of the Pitch-Distance Centrifugal Speed Control System

[0078] According to Equation (7), the characteristic equation of the closed-loop control model of the speed control system is:

[0079] J 1 J 2 s 3 +(J 2 B - b 1 J 1 )s 2 +(J 2 k θ - b 1 B)s - (b 1 k θ + a 1 k ω ) = 0

[0080] Let

[0081]

[0082] According to the Routh criterion, the necessary and sufficient condition for the system to be stable is that it needs to satisfy:

[0083]

[0084] The open-loop transfer function of the speed control system is:

[0085]

[0086] The present invention establishes a pitch-distance centrifugal speed regulation mechanism as a closed-loop control model, and the system is a closed-loop control system based on this model. The amplitude margin and phase margin are important indicators for measuring the stability of a closed-loop system, which are determined by the open-loop transfer function. The amplitude margin reflects the tolerance of the system to gain fluctuations, and the phase margin reflects the sensitivity of the system to phase changes. By plotting the Bode diagram through the open-loop transfer function, the amplitude margin h (Gm / dB = 20lg h) and phase margin γ (°) of the system can be read, so as to judge the stability degree of the system.

[0087] IV. Parameter Optimization Design of Pitch-Distance Centrifugal Speed Regulation System

[0088] The design parameters of the centrifugal speed regulation mechanism that can be optimized include: the initial eccentric angle β of the cam 0 , the stiffness k of the speed regulation spring T , the initial compression amount x of the speed regulation spring 0 and the amplification or reduction multiple k of the mass moment of inertia of the centrifugal block, where the optimized mass moment of inertia of the centrifugal block is k xy ·I xy ·I(θ). xy (θ).

[0089] In the actual application scenario, the weights of the amplitude margin and phase margin in the stability evaluation depend on the specific problem. The present invention takes the weight coefficients of 0.5 for both the amplitude margin and phase margin as the stability index, and formulates the objective function as f(β 0 , k T , x 0 , k xy ) = 0.5(h + γ).

[0090] Use the genetic algorithm for multi-parameter optimization. The optimization goal is to find a parameter combination X E = [β 0 , k T , x 0 , k xy that maximizes the calculated objective function value f(X E ). Since the genetic algorithm is an optimization algorithm for solving the minimum value, the fitness function is taken as -f. The optimization process first involves setting the parameter range and initializing the population. The upper and lower bounds of these four parameters are selected to determine the search range of the optimal solution, and several parameter combinations X 10 , X 20 , X 30 ... X m0 are randomly taken as the initial population, X m0In the subscript m0, m represents the number of populations, and 0 represents the initial population. Subsequently, the fitness is evaluated, and the fitness value f(X) of each individual X is calculated according to the definition of the objective function. After that, since the iteration termination condition is not met: reaching the maximum number of iterations, a selection operation is performed. The probability of an individual being selected is determined based on its fitness value. The higher the fitness value, the greater the probability that the parameter combination X is selected. Next, crossover and mutation operations are carried out. The crossover operation is to select two individuals and swap some of their elements according to the crossover probability to generate a new parameter combination X i1 ; The mutation operation is to determine the number of mutated individuals in the population according to the mutation probability and change some of the parameters therein to generate a new parameter combination X j1 . After the operations of selection, crossover, and mutation, the population completes one iteration and evolves into X 11 , X 21 , X 31 …X m1 As the first-generation population, then the above process is repeated, gradually optimized to the population X corresponding to the maximum number of iterations n 1n , X 2n , X 3n …X mn . At this time, the algorithm termination condition is satisfied, and the individual with the highest fitness value in this generation is output as the optimal parameter combination X E , and the optimal objective function value f(X E ) is output. X E contains the optimized values of each structural parameter, making the stability of the speed control system optimal.

[0091] Embodiments of the present invention: According to the core logic of "kinetic equation - closed-loop control system - optimization calculation - effect verification", based on the speed regulation principle of the variable pitch centrifugal speed regulating mechanism, a closed-loop control model associated with the design parameters of the speed control system is established, an index for measuring the stability of the speed control system according to the design parameters is determined, the optimization objective is further determined, and the genetic algorithm is used to complete the parameter optimization to achieve a good optimization effect. Refer to Figure 2 , the specific steps of the technical solution are as follows:

[0092] Step 1: Set the search range of the optimal solution, set the population size, the number of iterations, and the crossover and mutation probabilities of the genetic algorithm. Complete the population initialization according to the algorithm settings.

[0093] Step 2: According to the kinetic equation (1) of the speed control system and the calculation formulas (2) of various types of torques, use the numerical calculation method to determine the equilibrium point (ω eq , θ eq ) corresponding to each parameter combination. According to (ω eq , θ eq ), calculate a using formulas (2) to (8)1 , a 2 , a 3 , a 4 , b 1 , b 2 , b 3 to establish a closed-loop control model of the speed regulation system and construct the closed-loop transfer function corresponding to each set of parameters.

[0094] Step 3: Obtain the amplitude margin and phase margin corresponding to the speed regulation system under different combinations of design parameters by plotting the Bode diagram, and calculate the objective function value f.

[0095] Step 4: Determine whether the algorithm termination condition is satisfied. If satisfied, output the current optimal parameter combination and the optimal objective function value; if not satisfied, perform genetic operations to generate the next generation of population, and loop sequentially until the maximum number of iterations is reached to complete the parameter optimization design process.

[0096] Step 5: Compare the change curves of the rotating speed of the speed regulation mechanism shaft when the load is introduced before and after parameter optimization to illustrate the optimization effect.

[0097] In one embodiment, some structural parameters of the variable pitch centrifugal speed regulation mechanism are shown in Table 1, where β 0 , k T , x 0 and k xy are the parameters to be optimized, and the other parameters remain unchanged during the optimization process.

[0098] Table 1 Structural parameters of the variable pitch centrifugal speed regulation mechanism

[0099]

[0100] The specific steps of the method are as follows:

[0101] Step 1: Set the lower bound of the search range of the optimal parameter combination to [2, 180, 10.8, 0.8], and the upper bound to [8, 220, 14.8, 1.2]. Set the population to contain 10 individuals and the number of iterations to 20; set the crossover and mutation probabilities to 0.8 and 0.001 respectively. Generate 10 random initial design parameter combinations within the preset parameter range.

[0102] Step 2: Evaluate the fitness of each individual in this generation of population. Specifically, for these 10 sets of parameters, calculate the equilibrium point (ω eq , θ eq ) and construct the transfer function to obtain the stability margin, and further calculate the objective function f.

[0103] Step 3: When the maximum number of iterations is not reached, update the information of individuals in the population through genetic operations, that is, change some elements in the parameter combination for fitness evaluation of the next generation until the maximum number of iterations is reached, and select the individual with the highest fitness value, that is, the design parameter combination that can achieve the maximum objective function value.

[0104] Step 4: The optimization process is as Figure 3 shown. The algorithm converges rapidly from the third generation to the fifth generation, and then the objective function gradually approaches the maximum value. After 20 generations of optimization of a population of size 10, the optimal design parameter combination of the speed control system finally obtained is: β 0 = 3.58, k T = 208.54, x 0 = 11.14, k xy = 0.89. Compared with the initial parameter combination, the initial eccentricity angle of the cam decreases, the stiffness of the speed control spring increases while the initial compression amount decreases, and k xy less than 1 means that the product of inertia of the centrifugal block mass is reduced. Before optimization, f(5.8, 200, 12.8, 1) = 3.29, while after optimization, f(3.58, 208.54, 11.14, 0.89) = 11.095, and the speed control stability is greatly improved.

[0105] Step 5: Draw the Bode diagrams of the system before and after optimization, as Figure 4 and Figure 5 shown. Before optimization, the amplitude margin of the system is 1.2408 (1.87 dB), and the phase margin is 5.34°; after parameter optimization, the amplitude margin of the system is 1.8821 (5.49 dB), and the phase margin is 20.3°.

[0106] Draw the speed change curves of the speed control system before and after optimization when subjected to load shocks, as Figure 6 shown. This time-domain diagram intuitively shows that the optimized speed control system can adjust the shaft speed to the balanced state more quickly and stably in the face of load mutations. The present invention has a significant optimization effect on the parameter design of the variable pitch centrifugal speed control mechanism.

[0107] Based on the same technical concept as the method embodiment, the present invention also provides a variable pitch centrifugal speed control mechanism parameter optimization design system, including:

[0108] A parameter combination construction module for determining the design parameters of the centrifugal speed control mechanism that can be optimized, including the initial eccentricity angle β of the cam 0 , the stiffness k of the speed control spring T , the initial compression amount x of the speed control spring 0 and the amplification or reduction multiple k of the product of inertia of the centrifugal block mass xy , then the optimization goal is to find a parameter combination X E= [β 0 , k T , x 0 , k xy , such that the stability objective function f(X E ) of the speed control system is maximized;

[0109] The algorithm initialization module is used to set the search range of the optimal solution, set the population size and the number of iterations of the genetic algorithm, as well as the crossover and mutation probabilities, and initialize the population according to the algorithm settings;

[0110] The first calculation module is used to determine the equilibrium point (ω eq , θ eq ) corresponding to each parameter combination by using the numerical calculation method according to the dynamic equation of the speed control system and the calculation formulas of various types of torques. ω eq is the rotational speed of the speed control system at the equilibrium point, and θ eq is the blade pitch angle of the speed control system at the equilibrium point. Establish a closed-loop control model of the speed control system by calculating the parameters associated with various types of torques according to (ω eq , θ eq ), and construct the closed-loop transfer function corresponding to each group of parameters;

[0111] The second calculation module is used to obtain the amplitude margin and phase margin corresponding to the speed control system under different design parameter combinations by plotting the Bode diagram, and calculate the objective function value f;

[0112] The algorithm iteration control module is used to determine whether the algorithm termination condition is satisfied. If it is satisfied, output the current optimal parameter combination and the optimal objective function value; if not, perform genetic operations to generate the next generation of the population, and loop in turn until the maximum number of iterations is reached to complete the parameter optimization design process.

[0113] The present invention also provides a computer device, including: one or more processors; a memory; and one or more programs, where the one or more programs are stored in the memory and are configured to be executed by the one or more processors. When the program is executed by the processor, the steps of the variable pitch centrifugal speed regulating mechanism parameter optimization design method described above are implemented.

[0114] The present invention also provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the variable pitch centrifugal speed regulating mechanism parameter optimization design method described above are implemented.

[0115] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, an apparatus, a computer device, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0116] The present invention is described with reference to the flowchart of a method according to an embodiment of the present invention. It should be understood that each process in the flowchart and the combination of processes in the flowchart can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in one Figure 1 process or multiple processes. These computer program instructions can also be stored in a computer-readable memory capable of guiding the computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in one Figure 1 process or multiple processes. These computer program instructions can also be loaded onto the computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in one Figure 1 process or multiple processes.

Claims

1. A method for optimizing the parameters of a variable pitch centroid speed control mechanism, characterized in that: The following steps are involved: Step 1: Determine the design parameters of the centrifugal speed regulating mechanism that can be optimized, including the initial eccentric angle β0 of the cam and the stiffness k of the speed regulating spring. T , the initial compression of the speed regulating spring x0 and the magnification or reduction factor k of the mass inertia product of the centrifugal block xy , then the optimization goal is to find a parameter combination X E =[β0,k T ,x0,k xy ], so that the speed control system stability objective function f(X E )maximum; Step 2: Set the search range for the optimal solution, set the population size and number of iterations of the genetic algorithm, as well as the crossover and mutation probabilities, and complete population initialization according to the algorithm settings; Step 3: According to the dynamic equation of the speed control system and the calculation formula of various types of torque, the equilibrium point (ω) corresponding to each parameter combination is determined by numerical calculation method. eq ,θ eq ),ω eq is the speed when the speed control system is at the equilibrium point, θ eq is the blade pitch angle when the speed control system is at the equilibrium point, according to (ω eq ,θ eq ) Calculate the parameters associated with each type of torque to establish a closed-loop control model of the speed control system and construct a closed-loop transfer function corresponding to each set of parameters; Step 4: Obtain the amplitude margin and phase margin corresponding to the speed control system under different design parameter combinations by drawing the Bode diagram, and calculate the objective function value f; Step 5: Determine whether the algorithm termination conditions are met. If so, output the current optimal parameter combination and the optimal objective function value. If not, perform genetic operations to generate the next generation population, and repeat the cycle until the maximum number of iterations is reached to complete the parameter optimization design process.

2. The method according to claim 1, characterized in that The stability objective function f(X E )for: f(X E )=f(β0,k T ,x0,k xy )=z(h+γ) Where z is the weight coefficient, h is the amplitude margin, and γ is the phase margin.

3. The method according to claim 1, characterized in that The dynamic equation of the speed control system is as follows: According to the mechanical relationship of each component in the speed control system, the dynamic equation of the speed control process is established using Newton's second law: Where J1 is the moment of inertia of the blade's main axis of rotation, θ is the blade pitch angle, and M is y 、M c and M t They are the aerodynamic pitch moment of the blade, the centrifugal moment generated by the centrifugal block and the spring moment provided by the speed regulating spring, all of which act on the blade rotation axis. d is the damping torque acting on the blade pitch axis; J2 refers to the moment of inertia of the main shaft of the speed control system, ω is the speed, M D M is the driving torque of the speed control system. L The load moment generated by the rear end load; The calculation formulas for each type of torque are functions related to ω or θ: Where C y and C D is the aerodynamic moment coefficient, P d is the dynamic pressure, s is the reference area, taken as a constant; I xy (θ) is the mass inertia product of the centrifugal block; k T is the spring stiffness, x0 is the initial compression of the spring, R T is the cam rotation radius, β0 is the cam initial eccentricity angle, θ0 is the blade initial pitch angle; B is the damping coefficient; P L is the load power.

4. The method according to claim 3, characterized in that: The closed-loop control model of the speed regulation system is as follows: With (ω eq ,θ eq ) is the equilibrium point, the moment when the load changes is recorded as the initial moment, and the load mutation value is set to ΔM L , as the input signal of the speed control system, after time Δt, the working state of the speed control system becomes (ω t ,θ t ), where ω t =ω eq +Δω、θ t =θ eq +Δθ, Δω is used as the output signal of the speed control system, and the changes of each type of torque during this period are ΔM y , ΔM D , ΔM c , ΔM t and ΔM d .

5. The method according to claim 4, characterized in that The closed-loop transfer function corresponding to each set of parameters is constructed as follows: After linearizing the kinetic equation, we get: Under zero initial conditions, the Laplace transform of both ends of the above equation is: The closed-loop transfer function of the speed control system is obtained as follows: In the formula, 6. The method according to claim 5, characterized in that The open-loop transfer function of the speed control system on which the Bode diagram is drawn is:

7. The method according to claim 1, characterized in that The genetic operation process of the genetic algorithm is as follows: under the condition that the maximum number of iterations has not been reached, a selection operation is performed to determine the probability of being selected based on the fitness value of the individual. The higher the fitness value, the greater the probability of the parameter combination X being selected. Next, a crossover and mutation operation is performed. The crossover operation is to select two individuals and exchange some of their elements according to the crossover probability to generate a new parameter combination X. i1 The mutation operation is to determine the number of individuals in the population that mutate according to the mutation probability, change some of the parameters, and thus generate a new parameter combination X j1 After the operations of selection, crossover, and mutation, the population has completed one iteration and evolved into X 11 , X 21 , X 31 …X m1 As the first generation population, the above process is then repeated to gradually optimize to the population X corresponding to the maximum number of iterations n. 1n , X 2n , X 3n …X mn At this time, the algorithm termination condition is met, and the individual with the highest fitness value in this generation is output as the optimal parameter combination X E , and output the optimal objective function value f(X E ).

8. A parameter optimization design system for a variable pitch centroid speed control mechanism, characterized in that: include: The parameter combination building module is used to determine the optimizable design parameters of the centrifugal speed regulating mechanism, including the initial eccentric angle β0 of the cam, the stiffness k of the speed regulating spring T , the initial compression of the speed regulating spring x0 and the magnification or reduction factor k of the mass inertia product of the centrifugal block xy , then the optimization goal is to find a parameter combination X E =[β0,k T ,x0,k xy ], so that the stability objective function f(X E )maximum; The algorithm initialization module is used to set the search range of the optimal solution, set the population size and number of iterations of the genetic algorithm, as well as the crossover and mutation probabilities, and complete the population initialization according to the algorithm settings; The first calculation module is used to determine the equilibrium point (ω) corresponding to each parameter combination by using numerical calculation method according to the dynamic equation of the speed control system and the calculation formula of various types of torque. eq ,θ eq ),ω eq is the speed when the speed control system is at the equilibrium point, θ eq is the blade pitch angle when the speed control system is at the equilibrium point, according to (ω eq ,θ eq ) Calculate the parameters associated with each type of torque to establish a closed-loop control model of the speed control system and construct a closed-loop transfer function corresponding to each set of parameters; The second calculation module is used to obtain the amplitude margin and phase margin corresponding to the speed control system under different design parameter combinations by drawing a Bode diagram, and calculate the objective function value f; The algorithm iteration control module is used to determine whether the algorithm termination conditions are met. If so, the current optimal parameter combination and the optimal objective function value are output; if not, genetic operations are performed to generate the next generation population, and the cycle is repeated until the maximum number of iterations is reached to complete the parameter optimization design process.

9. A computer device, characterized in that: include: one or more processors; Memory; And one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the steps of the variable pitch distance centroid speed control mechanism parameter optimization design method as described in any one of claims 1 to 7 are implemented.

10. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing the parameters of a variable pitch centroid speed regulating mechanism according to any one of claims 1 to 7 are implemented.

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