CN115659764B - Permanent magnet synchronous motor optimization method and system based on improved sparrow search algorithm - Google Patents
Permanent magnet synchronous motor optimization method and system based on improved sparrow search algorithm Download PDFInfo
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Abstract
The embodiment of the invention provides a permanent magnet synchronous motor optimization method and system based on an improved sparrow search algorithm, wherein the method comprises the steps of constructing a finite element simulation model of a motor and determining design variables of the motor; sampling according to the values of the design variables and the corresponding variation ranges thereof, and calculating the response values of the sample points of the design variables in all the groups; constructing a motor kriging agent model between a parameter to be optimized and a target function; judging whether the kriging proxy model of the motor reaches the preset precision, if so, executing the step S5, and if not, adding sampling points and returning to execute the step S2; performing iterative training on the motor kriging agent model, and searching a motor optimal treatment optimization variable; and substituting the optimal motor treatment optimization variable into a motor Kriging proxy model meeting the precision requirement to obtain the optimal motor performance parameter. The invention improves the optimization effect of the variable to be optimized of the motor and enhances the performance of the motor.
Description
Technical Field
The invention relates to the technical field of motor optimization, in particular to a permanent magnet synchronous motor optimization method and system based on an improved sparrow search algorithm.
Background
In recent years, with the increase of the world energy crisis, new energy and clean energy automobiles are popularized and used, green transportation motorcades are strengthened, and the promotion of public fields and logistics distribution is accelerated and the promotion of new energy automobiles is important. Compared with other types of motors, the permanent magnet synchronous motor is more suitable as a power source of a new energy automobile by taking the advantages of high efficiency, strong overload capacity, high power density, wide speed regulation range, low vibration noise and the like as well as is concerned by a plurality of automobile enterprises, so that the structural design, the structural optimization, the torque characteristic analysis and other aspects of the permanent magnet synchronous motor need to be deeply researched.
Because the design problem of the permanent magnet synchronous motor has the characteristics of multiple targets, multiple variables, nonlinearity and the like, the traditional optimization algorithm is difficult to complete the global optimization design. The central idea of the group intelligent optimization algorithm proposed in recent years is to search the optimal solution of a solution space distributed in a certain range by simulating the motion and behavior rules of some things or organisms in the nature. Compared with other group intelligent optimization algorithms, the sparrow search algorithm has the characteristics of high search precision, high convergence speed, good stability, strong robustness and the like. However, the sparrow search algorithm is the same as other group intelligent optimization algorithms, and when the search is close to the global optimum, the problems that the diversity of the group is reduced and the local optimum is easy to fall still occur.
Therefore, it is necessary to provide a new optimization method to solve the above problems.
Disclosure of Invention
Therefore, the invention provides a permanent magnet synchronous motor optimization method and system based on an improved sparrow search algorithm, which are used for solving the problems that the permanent magnet synchronous motor optimization method in the prior art is reduced in population diversity and is easy to fall into local optimization.
In order to solve the technical problem, an embodiment of the present invention provides a permanent magnet synchronous motor optimization method based on an improved sparrow search algorithm, including:
s1: constructing a finite element simulation model of the motor, and determining the design variable of the motor;
s2: sampling according to the value of the selected design variable and the corresponding variation range thereof, and calculating the response values of the sample points of the design variables in all the groups;
s3: constructing a motor kriging proxy model between the parameter to be optimized and the objective function according to the sample points and the corresponding response values;
s4: judging whether the kriging proxy model of the motor reaches the preset precision, if so, executing the step S5, and if not, adding sampling points and returning to execute the step S2;
s5: performing iterative training on the motor kriging agent model, and searching a motor optimal treatment optimization variable;
s6: and substituting the optimal motor treatment optimization variable into a motor Kriging proxy model meeting the precision requirement to obtain the optimal motor performance parameter.
Preferably, in step S1, an optimization target and a constraint condition may also be determined by constructing a finite element simulation model of the motor, where the design variables include a magnetic steel thickness, a magnetic steel included angle, an auxiliary slot size, a magnetic bridge width, a slot opening width, and a stator tooth width, the optimization target includes an output torque, a back electromotive force at a maximum rotation speed, a cogging torque, and a peak power, and the constraint condition is a variation range of the design variables and a constraint extreme value of the optimization target.
Preferably, in step S3, the motor kriging agent model is represented as follows:
y(x)＝β+Z(x)
wherein y (x) represents a predicted value corresponding to an arbitrary sample point, β represents an approximation function of a constant, Z (x) represents a mean value of 0, and a variance σ represents 2 A random process of (a).
Preferably, in step S5, the method for iteratively training the motor kriging agent model to find the optimal motor parameter includes the following steps:
s51: initializing a population by using Tent chaotic mapping, and setting each parameter in a motor kriging agent model constructed by Latin hypercube sampling;
s52: calculating and sequencing fitness values of sparrow individuals, namely variables to be optimized, and finding out an optimal fitness value, a worst fitness value and a position corresponding to the optimal fitness value and the worst fitness value;
s53: updating the positions of discoverers, followers and early-warning persons in the sparrow population;
s54: calculating the fitness of the updated whole sparrow population, finding out a global optimal variable, and carrying out dimension-by-dimension variation on the global optimal variable;
s55: judging whether a preset convergence condition is reached, if so, executing the step S56, and if not, returning to execute the step S52;
s56: and outputting the optimal variable to be optimized of the motor.
Preferably, each parameter in the motor kriging proxy model is:
the fitness values of all the parameters to be optimized can be formulated as:
wherein, in a D-dimension search space, n parameters to be optimized exist, and the position of the nth variable in the D-dimension search space is X i ＝[x i1 ，…，x id ，…x iD ]，i＝1,2,…,n，x id And d represents the position of the ith variable in the d-dimension, d represents the dimension of the variable of the problem to be optimized, and n is the number of the parameters to be optimized.
Preferably, initializing the population and setting each parameter in the motor kriging agent model by using Tent chaotic mapping comprises the following steps:
initializing the population by Tent chaotic mapping and generating a chaotic variable Z d And, its expression formula is:
wherein N is T The number of particles in the chaotic sequence, rand (0, 1) is [0,1 ]]A random number in between;
bringing the chaotic variable carrier to a solution space for solving a problem to obtain
X new d ＝d min +(d max -d min )Z d
Wherein, X new d Indicating the present position information, d min And d max Respectively represent d-dimension variables X new d Minimum and maximum values of.
Preferably, the specific process of updating the positions of the discoverers, the followers and the early-warning persons in the sparrow population is as follows:
according to the formula
Updating the position of the discoverer;
where t represents the current number of iterations, j =1,2,3 max Denotes the maximum number of iterations, X i，j Indicates the ith hempPosition information of a sparrow in the j dimension, alpha ∈ [0,1 ]]Is a random number, R (R is equal to 0,1]) And ST (ST ∈ [0.5,1)]) Respectively representing an early warning value and a safety value, Q represents a random number which obeys normal distribution, L represents a matrix of 1 multiplied by d, wherein each element in the matrix is 1, and when the early warning value is smaller than the safety value, a finder can widely perform search; when the early warning value is larger than the safety value, the early warning person finds the danger, and the population individuals including the found person need to be transferred to avoid the danger;
according to the formula
Updating the position of the follower;
wherein, X worst Represents the global worst position, X, of the t-th iteration p t+1 Representing the optimal position of the discoverer in the t +1 th iteration, A is a matrix with 1 x d and randomly assigned values of 1 or-1, and A + ＝A T (AA T ) -1 When i is larger than n/2, the joiner at the worse position is in a state of full hunger, and needs to fly to other places for foraging; when i is less than or equal to n/2, the energy value of the subscriber is higher, and the subscriber moves to the vicinity of the finder to contend for food under the condition of better fitness;
according to the formula
Updating the position of the early-warning person;
wherein, X best t Representing the current global optimum position, X worst t Represents the current global worst position, beta represents a step size control parameter, is a normal distribution random number with the mean value of 0 and the variance of 1, and belongs to ∈ [ -1,1]Is a random number, f i Representing the fitness value of the individual, f g Represents the best fitness value, f w Represents the worst fitness value, epsilon represents a minimum constant, and prevents the occurrence of a denominator of zero when f i ≠f g When the early-warning person finds danger at the optimal position of the population, the early-warning person can move to other individual positions in the population; when f is i ＝f g When the early-warning person is positioned at the edge of the population, the early-warning person finds danger and will turn toAnd moving the optimal position of the former population to escape.
Preferably, a cosine weight factor is added when the positions of discoverers in the sparrow population are updated, and the cosine weight factor is expressed as:
wherein, ω is max 、ω min Respectively representing the maximum value and the minimum value of the weight;
the formula of the improved finder position is:
where t represents the current iteration number, j =1,2,3, \ 8230;, d, iter max Denotes the maximum number of iterations, X i，j Represents the position information of the ith sparrow in the jth dimension, and is within the scope of [0,1 ]]Is a random number, R (R is equal to 0,1]) And ST (ST ∈ [0.5,1)]) Respectively representing an early warning value and a safety value, Q represents a random number obeying normal distribution, L represents a 1 x d matrix, wherein all elements in the matrix are 1, and a finder can widely perform searching when the early warning value is smaller than the safety value; when the early warning value is larger than the safety value, the early warning person finds the danger, and the population individuals including the finder need to transfer to avoid the danger.
Preferably, finding the global optimal variable, performing the dimension-by-dimension variation on the global optimal variable comprises the following steps:
if the search space is d-dimensional, the current global optimal solution is:
X best ＝X best 1 ,X best 2 ,...,X best d
by calculation, the new solution after the dimension-by-dimension variation is:
X new ＝X new 1 ,X new 2 ,···,X new d
the calculation formula is expressed as follows:
wherein iter is the current iteration number,
is with a degree of freedom of->
In:>
-distribution.
The embodiment of the invention provides a permanent magnet synchronous motor optimization system based on an improved sparrow search algorithm, which comprises:
the simulation model building unit is used for building a finite element simulation model of the motor and determining the design variable of the motor;
initializing a sample point and a response unit thereof, sampling according to the value of the selected design variable and the corresponding variation range thereof, and calculating the response values of the sample points of the design variables in all groups;
the system comprises a proxy model building unit, a target function and a parameter optimizing unit, wherein the proxy model building unit is used for building a motor kriging proxy model between a parameter to be optimized and the target function according to sample points and corresponding response values;
the proxy model precision judging unit is used for judging whether the motor kriging proxy model reaches preset precision, if so, executing the proxy model training unit, and if not, adding sampling points and returning to execute the initialization sample points and the response units thereof;
the proxy model training unit is used for carrying out iterative training on the motor kriging proxy model and searching a motor optimal treatment optimization variable;
and the motor performance parameter output unit is used for substituting the motor optimal treatment optimization variable into a motor kriging agent model meeting the precision requirement to obtain the optimal motor performance parameter.
Compared with the prior art, the technical scheme of the invention has the following advantages:
the embodiment of the invention provides a method and a system for optimizing a permanent magnet synchronous motor based on an improved sparrow search algorithm.
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In order to illustrate embodiments of the present invention or the technical solutions in the prior art more clearly, the drawings which are needed in the embodiments will be briefly introduced, the features and advantages of the present invention will be more clearly understood by referring to the drawings which are schematic and should not be understood as limiting the present invention in any way, and for those skilled in the art, other drawings can be obtained on the basis of these drawings without inventive efforts. Wherein:
fig. 1 is a flowchart of a permanent magnet synchronous motor optimization method based on an improved sparrow search algorithm provided in an embodiment;
fig. 2 is a block diagram of a permanent magnet synchronous motor optimization system based on an improved sparrow search algorithm provided in an embodiment.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
Referring to fig. 1, an embodiment of the present invention provides a permanent magnet synchronous motor optimization method based on an improved sparrow search algorithm, where the method includes:
s1: constructing a finite element simulation model of the motor, and determining the design variable of the motor;
s2: sampling according to the value of the selected design variable and the corresponding variation range thereof, and calculating the response values of the sample points of the design variables in all the groups;
s3: constructing a motor kriging proxy model between the parameter to be optimized and the objective function according to the sample points and the corresponding response values;
s4: judging whether the kriging proxy model of the motor reaches the preset precision, if so, executing the step S5, and if not, adding sampling points and returning to execute the step S2;
s5: performing iterative training on the motor kriging agent model, and searching a motor optimal treatment optimization variable;
s6: and substituting the optimal motor optimization variables into a motor kriging agent model meeting the precision requirement to obtain optimal motor performance parameters.
The invention provides a permanent magnet synchronous motor optimization method based on an improved sparrow search algorithm, which comprises the steps of constructing a motor performance function proxy model according to sampling of initial sample points of a motor and response values of the initial sample points; the Tent chaotic mapping, the cosine weight factor and the one-dimensional variation method are introduced into a sparrow search algorithm, the optimization calculation is carried out on the motor performance function proxy model according to the improved sparrow search algorithm, the population diversity is enriched, the quality of an initial solution is improved, the global search capability of the algorithm is enhanced, the problem that the algorithm is easy to fall into local optimization is overcome, the algorithm jumps out to limit continuous search is promoted, the algorithm search precision is improved, the optimization effect of variables to be optimized of the permanent magnet synchronous motor is improved, and the motor performance is enhanced.
Further, in step S1:
the method comprises the steps of constructing a finite element simulation model of the motor, determining design variables, optimization targets and constraint conditions of the motor, wherein the design variables are main structural parameters of the permanent magnet synchronous motor and comprise magnetic steel thickness, magnetic steel included angle, auxiliary groove size, magnetic bridge width, notch width and stator tooth width, the optimization targets are performance parameters of the permanent magnet synchronous motor and comprise output torque, back electromotive force at maximum rotating speed, tooth space torque and peak power, and the constraint conditions are the variation range of the design variables and the constraint extreme value of the optimization targets.
Further, in step S2:
and performing Latin hypercube sampling according to the value of the selected design variable and the corresponding variation range, and calculating the response values of the sample points of the design variables in all groups by using a finite element method.
Further, in step S3:
according to the sample points and the corresponding response values thereof, a motor kriging proxy model between the parameters to be optimized and the objective function is constructed, wherein the motor kriging proxy model is expressed as follows:
y(x)＝β+Z(x)
wherein y (x) represents a predicted value corresponding to an arbitrary sample point, β represents an approximation function of a constant, Z (x) represents a mean value of 0, and a variance σ represents 2 A random process of (a).
Further, in step S4:
and judging whether the motor kriging proxy model reaches the preset precision, if so, executing the next step, and if not, increasing sampling points and returning to the step S2 to obtain the motor kriging proxy model meeting the preset precision.
Further, in step S5:
the method for iteratively training the motor kriging agent model to find the optimal motor parameter specifically comprises the following steps:
s51: initializing a population by using Tent chaotic mapping, and setting each parameter in a motor kriging agent model constructed by Latin hypercube sampling;
s52: calculating and sequencing fitness values of sparrow individuals, namely variables to be optimized, and finding out an optimal fitness value, a worst fitness value and a position corresponding to the optimal fitness value and the worst fitness value;
s53: updating the positions of discoverers, followers and early-warning persons in the sparrow population;
s54: calculating the fitness of the updated whole sparrow population, finding out a global optimal variable, and carrying out dimension-by-dimension variation on the global optimal variable;
s55: judging whether a preset convergence condition is reached, if so, executing the step S56, and if not, returning to execute the step S52;
s56: and outputting the optimal variable to be optimized of the motor.
Each parameter in the motor kriging proxy model is as follows:
the fitness values of all the parameters to be optimized can be formulated as:
wherein, in a D-dimension search space, n parameters to be optimized exist, and the position of the nth variable in the D-dimension search space is X i ＝[x i1 ，…，x id ，…x iD ]，i＝1,2,…,n，x id The position of the ith variable in the d-dimension is represented, d represents the dimension of the variable of the problem to be optimized, and n is the number of the parameters to be optimized.
The sparrow search algorithm is improved by introducing Tent chaotic mapping, cosine weight factors and a one-dimensional variation method, the quality of an initial solution is improved, the global search capability of the algorithm is enhanced, the problem that the algorithm is easy to fall into local optimum is overcome, the algorithm is promoted to jump out to limit continuous search, and the algorithm search precision is improved.
The method for initializing the population and setting various parameters in the motor kriging agent model by using Tent chaotic mapping comprises the following steps of:
initializing the population by Tent chaotic mapping and generating a chaotic variable Z d And, its expression formula is:
wherein N is T The number of particles in the chaotic sequence, rand (0, 1) is [0,1 ]]A random number in between;
bringing the chaotic variable carrier to a solution space for solving a problem to obtain
X new d ＝d min +(d max -d min )Z d
Wherein, X new d Indicating the present bitSetting information, d min And d max Respectively represent d-dimension variables X new d Minimum and maximum values of.
The specific process of updating the positions of the discoverers, the followers and the early-warning persons in the sparrow population is as follows:
according to the formula
Updating the position of the finder;
where t represents the current number of iterations, j =1,2,3 max Denotes the maximum number of iterations, X i，j Represents the position information of the ith sparrow in the jth dimension, and is within the scope of [0,1 ]]Is a random number, R (R is equal to 0,1]) And ST (ST ∈ [0.5,1)]) Respectively representing an early warning value and a safety value, Q represents a random number obeying normal distribution, L represents a 1 x d matrix, wherein all elements in the matrix are 1, and a finder can widely perform searching when the early warning value is smaller than the safety value; when the early warning value is larger than the safety value, the early warning person finds a danger, and population individuals including the found person need to transfer to avoid the danger;
according to the formula
Updating the position of the follower;
wherein, X worst Represents the global worst position, X, of the t-th iteration p t+1 Representing the optimal position of the discoverer in the t +1 th iteration, A is a matrix with 1 x d and randomly assigned values of 1 or-1, and A + ＝A T (AA T ) -1 When i is larger than n/2, the joiner at the worse position is in a state of full hunger, and needs to fly to other places for foraging; when i is less than or equal to n/2, the energy value of the subscriber is higher, and the subscriber moves to the vicinity of the finder to contend for food under the condition of better fitness;
according to the formula
Updating the position of the early-warning person;
wherein the content of the first and second substances,X best t representing the current global optimum position, X worst t Represents the current global worst position, beta represents a step size control parameter, is a normal distribution random number with the mean value of 0 and the variance of 1, and K belongs to [ -1,1]Is a random number, f i Representing the fitness value of the individual, f g Represents the best fitness value, f w Represents the worst fitness value, epsilon represents a minimum constant, and prevents the occurrence of a denominator of zero when f i ≠f g When the early-warning person finds danger at the optimal position of the population, the early-warning person can move to other individual positions in the population; when f is i ＝f g And when the early-warning person is positioned at the edge of the population and finds danger, the early-warning person moves to the optimal position of the current population to escape.
In order to better improve the optimization solving capability of the algorithm, a cosine weight factor is added when the position of a finder is updated, namely
Wherein, ω is max 、ω min Respectively representing the maximum value and the minimum value of the weight;
the formula of the improved finder position is:
where t represents the current iteration number, j =1,2,3, \ 8230;, d, iter max Denotes the maximum number of iterations, X i，j Indicating the position information of the ith sparrow in the jth dimension, and belongs to [0,1 ]]Is a random number, R (R is E [0,1 ]]) And ST (ST ∈ [0.5,1)]) Respectively representing an early warning value and a safety value, Q represents a random number which obeys normal distribution, L represents a matrix of 1 multiplied by d, wherein each element in the matrix is 1, and when the early warning value is smaller than the safety value, a finder can widely perform search; when the early warning value is larger than the safety value, the early warning person finds dangers, and population individuals including the found person need to be transferred to avoid the dangers.
The individual is interfered by a mutation operation to increase the diversity of the population, the local optimum is jumped out, the optimum individual is mutated by a self-adaptive t-distribution mutation operator, and the specific implementation mode of the one-dimensional mutation strategy is as follows:
if the search space is d-dimensional, the current global optimal solution is:
X best ＝X best 1 ,X best 2 ,...,X best d
by calculation, the new solution after the dimension-by-dimension variation is:
X new ＝X new 1 ,X new 2 ,···,X new d
the calculation formula is expressed as follows:
wherein iter is the current iteration number,
is with a degree of freedom of->
Is/are>
-distribution.
The mutation operation increases interference information on the basis of the current optimal solution, and is beneficial to the algorithm to jump out of local optimization.
As shown in fig. 2, an embodiment of the present invention provides a permanent magnet synchronous motor optimization system based on an improved sparrow search algorithm, where the system includes:
a simulation model building unit 100 for building a finite element simulation model of the motor and determining the design variables of the motor;
initializing a sample point and a response unit 200 thereof, sampling according to the value of the selected design variable and the variation range corresponding to the value, and calculating the response values of the sample points of the design variables in all groups;
a constructing agent model unit 300, configured to construct a motor kriging agent model between the parameter to be optimized and the target function according to the sample points and the corresponding response values;
the proxy model precision judging unit 400 is used for judging whether the motor kriging proxy model reaches preset precision, if so, executing the proxy model training unit, and if not, adding sampling points and returning to execute the initialization sample points and the response units thereof;
the agent model training unit 500 is used for performing iterative training on the motor kriging agent model and searching a motor optimal treatment optimization variable;
and a motor performance parameter output unit 600, configured to bring the motor optimal treatment optimization variable into a motor kriging proxy model meeting the precision requirement to obtain an optimal motor performance parameter.
The system improves the optimization effect of the variable to be optimized of the motor and enhances the performance of the motor. The method solves the problems that the existing optimization algorithm has reduced population diversity and is easy to fall into local optimization.
The system is used for realizing the permanent magnet synchronous motor optimization method based on the improved sparrow search algorithm, and is not described herein again in order to avoid redundancy.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Various other modifications and alterations will occur to those skilled in the art upon reading the foregoing description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of the invention may be made without departing from the spirit or scope of the invention.
Claims (10)
1. A permanent magnet synchronous motor optimization method based on an improved sparrow search algorithm is characterized by comprising the following steps:
s1: constructing a finite element simulation model of the motor, and determining design variables of the motor;
s2: sampling according to the value of the selected design variable and the corresponding variation range thereof, and calculating the response values of the sample points of the design variables in all the groups;
s3: constructing a motor kriging proxy model between the parameter to be optimized and the objective function according to the sample points and the corresponding response values;
s4: judging whether the kriging proxy model of the motor reaches the preset precision, if so, executing the step S5, and if not, adding sampling points and returning to execute the step S2;
s5: performing iterative training on the motor kriging agent model, and searching a motor optimal treatment optimization variable;
s6: and substituting the optimal motor treatment optimization variable into a motor Kriging proxy model meeting the precision requirement to obtain the optimal motor performance parameter.
2. The improved sparrow search algorithm-based optimization method for the PMSM according to claim 1, wherein in step S1, a finite element simulation model of the motor is constructed, an optimization target and constraint conditions can be further determined, wherein the design variables include magnetic steel thickness, magnetic steel included angle, auxiliary slot size, magnetic bridge width, slot opening width and stator tooth width, the optimization target includes output torque, back electromotive force at maximum rotation speed, tooth slot torque and peak power, and the constraint conditions are a variation range of the design variables and a constraint extreme value of the optimization target.
3. The improved sparrow search algorithm-based optimization method for the permanent magnet synchronous motor according to claim 1, wherein in step S3, the motor kriging agent model is expressed as follows:
y(x)＝β+Z(x)
wherein y (x) represents a predicted value corresponding to an arbitrary sample point, β represents an approximation function of a constant, Z (x) represents a mean value of 0, and a variance σ represents 2 A random process of (a).
4. The improved sparrow search algorithm-based permanent magnet synchronous motor optimization method according to claim 1, wherein in step S5, the method for iteratively training the motor kriging agent model to find optimal motor parameters comprises the following steps:
s51: initializing a population by using Tent chaotic mapping, and setting each parameter in a motor kriging agent model constructed by Latin hypercube sampling;
s52: calculating and sequencing fitness values of sparrow individuals, namely variables to be optimized, and finding out an optimal fitness value, a worst fitness value and a position corresponding to the optimal fitness value and the worst fitness value;
s53: updating the positions of discoverers, followers and early-warning persons in the sparrow population;
s54: calculating the fitness of the updated whole sparrow population, finding out a global optimal variable, and carrying out dimension-by-dimension variation on the global optimal variable;
s55: judging whether a preset convergence condition is reached, if so, executing the step S56, and if not, returning to execute the step S52;
s56: and outputting the optimal variable to be optimized of the motor.
5. The improved sparrow search algorithm-based permanent magnet synchronous motor optimization method according to claim 4, wherein the parameters in the motor kriging proxy model are as follows:
the fitness values of all the parameters to be optimized can be formulated as:
wherein, in a D-dimension search space, n parameters to be optimized exist, and the position of the nth variable in the D-dimension search space is X i ＝[x i1 ，…，x id ，…x iD ]，i＝1,2,…,n，x id The position of the ith variable in the d-dimension is represented, d represents the dimension of the variable of the problem to be optimized, and n is the number of the parameters to be optimized.
6. The permanent magnet synchronous motor optimization method based on the improved sparrow search algorithm according to claim 4 or 5, wherein the step of initializing a population by using Tent chaotic mapping and setting various parameters in a motor Kriging proxy model comprises the following steps:
initializing population by Tent chaotic mapping to generate chaotic variable Z d And, its expression formula is:
wherein N is T The number of particles in the chaotic sequence, rand (0, 1) is [0,1 ]]A random number in between;
bringing the chaotic variable carrier to a solution space for solving a problem to obtain
X new d ＝d min +(d max -d min )Z d
Wherein, X new d Indicating the present position information, d min And d max Respectively represent d-dimension variables X new d Minimum and maximum values of.
7. The permanent magnet synchronous motor optimization method based on the improved sparrow search algorithm is characterized in that the specific process of updating the positions of discoverers, followers and forewarners in a sparrow population is as follows:
according to the formula
Updating the position of the finder;
where t represents the current iteration number, j =1,2,3 max Denotes the maximum number of iterations, X i，j Indicating the position information of the ith sparrow in the jth dimension, and belongs to [0,1 ]]Is a random number, R (R is equal to 0,1]) And ST (ST ∈ [0.5,1 ]]) Respectively representing an early warning value and a safety value, Q represents a random number obeying normal distribution, L represents a matrix of 1 multiplied by d, wherein each element in the matrix is 1, and when the early warning value is smallAt the time of the security value, the finder can perform the search extensively; when the early warning value is larger than the safety value, the early warning person finds the danger, and the population individuals including the found person need to be transferred to avoid the danger;
according to the formula
Updating the position of the follower;
wherein X worst Represents the global worst position, X, of the t-th iteration p t+1 Represents the optimal position of the discoverer in the (t + 1) th iteration, A is a matrix of 1 xd and the random assignment value of the elements is 1 or-1, and A + ＝A T (AA T ) -1 When i is larger than n/2, the subscriber with poor position is in a state of full hunger, and needs to fly to other places for foraging; when i is less than or equal to n/2, the energy value of the subscriber is higher, and the subscriber moves to the vicinity of the finder to contend for food under the condition of better fitness;
according to the formula
Updating the position of the early-warning person; />
Wherein, X best t Representing the current global optimum position, X worst t Represents the current global worst position, beta represents a step size control parameter, is a normal distribution random number with the mean value of 0 and the variance of 1, and belongs to ∈ [ -1,1]Is a random number, f i Representing the fitness value of the individual, f g Represents the best fitness value, f w Represents the worst fitness value, epsilon represents a minimum constant, and prevents the occurrence of a denominator of zero when f i ≠f g When the early-warning person finds danger at the optimal position of the population, the early-warning person can move to other individual positions in the population; when f is i ＝f g And when the early-warning person is positioned at the edge of the population and finds danger, the early-warning person moves to the optimal position of the current population to escape.
8. The permanent magnet synchronous motor optimization method based on the improved sparrow search algorithm as claimed in claim 7, wherein a cosine weight factor is added when the position of a finder in a sparrow population is updated, and the cosine weight factor expression is as follows:
wherein, ω is max 、ω min Respectively representing the maximum value and the minimum value of the weight;
the formula of the improved finder position is:
where t represents the current iteration number, j =1,2,3, \ 8230;, d, iter max Denotes the maximum number of iterations, X i，j Indicating the position information of the ith sparrow in the jth dimension, and belongs to [0,1 ]]Is a random number, R (R is equal to 0,1]) And ST (ST ∈ [0.5,1)]) Respectively representing an early warning value and a safety value, Q represents a random number which obeys normal distribution, L represents a matrix of 1 multiplied by d, wherein each element in the matrix is 1, and when the early warning value is smaller than the safety value, a finder can widely perform search; when the early warning value is larger than the safety value, the early warning person finds the danger, and the population individuals including the finder need to transfer to avoid the danger.
9. The improved sparrow search algorithm-based permanent magnet synchronous motor optimization method according to claim 4, wherein the step of finding the global optimal variable and performing the dimension-by-dimension variation on the global optimal variable comprises the following steps of:
if the search space is d-dimensional, the current global optimal solution is:
X best ＝X best 1 ,X best 2 ,...,X best d
by calculation, the new solution after the dimension-by-dimension variation is:
X new ＝X new 1 ,X new 2 ,···,X new d
the calculation formula is expressed as follows:
wherein iter is the current iteration number,
is with a degree of freedom of->
In:>
-distribution.
10. A permanent magnet synchronous motor optimization system based on an improved sparrow search algorithm is characterized by comprising:
the simulation model building unit is used for building a finite element simulation model of the motor and determining the design variables of the motor;
initializing a sample point and a response unit thereof, sampling according to the value of the selected design variable and the corresponding variation range thereof, and calculating the response values of the sample points of the design variables in all groups;
the system comprises a proxy model building unit, a target function and a parameter optimizing unit, wherein the proxy model building unit is used for building a motor kriging proxy model between a parameter to be optimized and the target function according to sample points and corresponding response values;
the proxy model precision judging unit is used for judging whether the motor kriging proxy model reaches preset precision, if so, executing the proxy model training unit, and if not, adding sampling points and returning to execute the initialization sample points and the response units thereof;
the proxy model training unit is used for carrying out iterative training on the motor kriging proxy model and searching a motor optimal treatment optimization variable;
and the motor performance parameter output unit is used for substituting the motor optimal treatment optimization variable into a motor kriging agent model meeting the precision requirement to obtain the optimal motor performance parameter.
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