CN115659764A - 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
- Publication number
- CN115659764A CN115659764A CN202211576900.1A CN202211576900A CN115659764A CN 115659764 A CN115659764 A CN 115659764A CN 202211576900 A CN202211576900 A CN 202211576900A CN 115659764 A CN115659764 A CN 115659764A
- Authority
- CN
- China
- Prior art keywords
- motor
- value
- optimal
- variable
- population
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Landscapes
- Control Of Electric Motors In General (AREA)
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 proxy 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 being concerned by a plurality of automobile enterprises, so that intensive research on the aspects of structural design, structural optimization, torque characteristic analysis and the like of the permanent magnet synchronous motor is needed.
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 rate, 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 global optimum, the problems that the diversity of the group is reduced and local optimum is easy to be caused still occur.
Therefore, it is necessary to propose 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 of the selected design variable, 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 proxy model is represented as follows:
wherein the content of the first and second substances,
represents the predicted value corresponding to any sample point,
an approximation function representing a constant is used,
expressed as mean 0 and variance
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 step S56, and if not, returning to execute 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 at one
In a dimensional search space, there is
Stand for excellenceChange the parameters to
A variable is in
The position in the dimensional search space is,
，
，
is shown as
A variable is in
The position of the dimension(s) is (are),
the dimension of the variable representing the problem to be optimized,
then is the number of parameters to be optimized.
Preferably, initializing the population and setting various parameters in the motor kriging agent model by using Tent chaotic mapping comprises the following steps:
initializing population by Tent chaotic mapping and generating chaotic variables
The expression formula is as follows:
wherein the content of the first and second substances,
the number of particles in the chaotic sequence is,
is composed of
A random number in between;
bringing the chaotic variable carrier to a solution space for solving a problem to obtain
Wherein, the first and the second end of the pipe are connected with each other,
the information on the position of the current location is shown,
and
respectively represent the first
Dimension variables
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;
wherein the content of the first and second substances,
which represents the number of the current iteration numbers,
，
the maximum number of iterations is indicated,
denotes the first
A sparrow is on the first place
Information on the position in the dimension(s),
is a random number that is a function of the number,
and
respectively represent the early warning value and the safety value,
a random number that follows a normal distribution is represented,
represents one
When the early warning value is smaller than the safety value, a finder can widely perform searching; 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 the content of the first and second substances,
denotes the first
The global worst location for the second iteration,
denotes the first
The sub-iteration finds the optimal position of the user,
is composed of
And the elements randomly assign a matrix value of 1 or-1, an
When it comes to
When the position of the user is too low, the user is in a hunger state, and the user needs to fly to other places to forage; when in use
When the fitness is high, the participants move to the vicinity of the discoverer to fight for food under the condition of high fitness;
according to the formula
Updating the position of the early-warning person;
wherein the content of the first and second substances,
which represents the current global optimum position of the mobile terminal,
representing the current global worst-case position of the mobile terminal,
the control parameter, which represents the step size, is a normally distributed random number with a mean value of 0 and a variance of 1,
is a random number that is a function of the number,
the value of the fitness of the individual is represented,
the value of the best fitness value is represented,
the value of the worst-case fitness value is represented,
represents a very small constant, and prevents the denominator from being zero when
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 in use
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.
Preferably, a cosine weight factor is added when the positions of the discoverers in the sparrow population are updated, and the expression of the cosine weight factor is as follows:
wherein the content of the first and second substances,
、
respectively representing the maximum value and the minimum value of the weight;
the formula of the improved finder position is:
wherein the content of the first and second substances,
which represents the number of the current iteration numbers,
，
the maximum number of iterations is indicated,
is shown as
A sparrow is on the first place
Information on the position in the dimension(s),
is a random number that is a function of the number,
and
respectively represent the early warning value and the safety value,
a random number that follows a normal distribution is represented,
represents one
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.
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:
by calculation, the new solution after the dimension-by-dimension variation is:
the calculation formula is expressed as follows:
wherein, the first and the second end of the pipe are connected with each other,
the number of times of the current iteration is,
is a degree of freedom of
Is/are as follows
-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 proxy model building unit is used for building a motor kriging proxy 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 is used for judging whether the motor kriging proxy model reaches preset precision, if so, executing the step S5, and if not, adding sampling points and returning to execute the step S2;
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-to-be-optimized 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.
Drawings
In order to illustrate embodiments of the present invention or technical solutions in the prior art more clearly, the drawings which are needed in the embodiments will be briefly described below, so that the features and advantages of the present invention can be understood more clearly 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 effort. 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 design variables of the motor;
s2: sampling according to the value of the selected design variable and the corresponding variation range of the selected design variable, 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.
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 dimension-by-dimension 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 optimum 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, an optimization target 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 target is performance parameters of the permanent magnet synchronous motor and comprises output torque, back electromotive force at the maximum rotating speed, tooth socket torque and peak power, and the constraint conditions are the variation range of the design variables and the constraint extreme value of the optimization target.
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:
wherein the content of the first and second substances,
represents the predicted value corresponding to any sample point,
an approximation function representing a constant is used,
expressed as mean 0 and variance
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 at one
In dimension search space, exist
A parameter to be optimized is then
A variable is in
The position in the dimensional search space is,
，
，
is shown as
A variable is in
The position of the dimension(s) is,
the dimension of the variable representing the problem to be optimized,
then is the number of 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.
Initializing a population by using Tent chaotic mapping and setting various parameters in a motor kriging agent model, comprising the following steps of:
initializing population by Tent chaotic mapping and generating chaotic variable
The expression formula is as follows:
wherein the content of the first and second substances,
the number of particles in the chaotic sequence,
is composed of
A random number in between;
bringing the chaotic variable carrier to a solution space for solving a problem to obtain
Wherein the content of the first and second substances,
the information on the position of the current location is shown,
and
respectively represent
Dimension variable
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 discoverer;
wherein the content of the first and second substances,
which represents the number of the current iteration numbers,
，
the maximum number of iterations is indicated,
is shown as
A sparrow is at the second place
Information on the position in the dimension(s),
is a random number that is a function of the number,
and
respectively represent the early warning value and the safety value,
a random number that follows a normal distribution is represented,
represents one
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 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 the content of the first and second substances,
is shown as
The global worst location of the sub-iteration,
denotes the first
The sub-iteration finds the optimal position of the user,
is composed of
And the elements randomly assign a matrix value of 1 or-1, an
When is coming into contact with
When the user is in a state of hunger, the user needs to fly to other places to forage; when in use
When the food is eaten, the energy value of the joining person is higher, and the joining person moves to the vicinity of the finding person to fight for the food under the condition of better fitness;
according to the formula
Updating the position of the early-warning person;
wherein, the first and the second end of the pipe are connected with each other,
which represents the current global optimum position, is,
representing the current global worst-case position of the mobile terminal,
the control parameter, which represents the step size, is a normally distributed random number with a mean value of 0 and a variance of 1,
is a random number that is a function of the number,
the value of the fitness of the individual is represented,
the value of the best fitness value is represented,
the value of the worst fitness value is represented,
represents a very small constant, prevents the denominator from being zero when
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 in use
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 the content of the first and second substances,
which represents the number of current iterations,
，
the maximum number of iterations is indicated,
is shown as
A sparrow is at the second place
Information on the position in the dimension(s),
is oneThe number of the random numbers is determined,
and
respectively represent the early warning value and the safety value,
a random number that follows a normal distribution is represented,
represents one
When the early warning value is smaller than the safety value, a finder can widely perform searching; 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:
by calculation, the new solution after the dimension-by-dimension variation is:
the calculation formula is expressed as follows:
wherein the content of the first and second substances,
the number of times of the current iteration is,
is a degree of freedom of
Is/are as follows
-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 proxy model building unit 300, configured to build 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;
the proxy model precision judging unit 400 is used for judging whether the motor kriging proxy model reaches the preset precision, if so, executing the step S5, and if not, adding sampling points and returning to execute the step S2;
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 in order to avoid redundancy, the system is not described again.
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. Other variations and modifications will be apparent to persons skilled in the art in light of the above 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 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 permanent magnet synchronous motor according to claim 1, wherein in step S1, a finite element simulation model of the motor is constructed to further determine an optimization target and constraint conditions, 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, cogging 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 proxy model is represented as follows:
wherein the content of the first and second substances,
represents the predicted value corresponding to any sample point,
an approximation function representing a constant is used,
expressed as mean 0 and variance
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 at one
In a dimensional search space, there is
A parameter to be optimized is then
A variable is in
The position in the dimensional search space is,
，
，
is shown as
A variable is in
The position of the dimension(s) is,
represents the dimensions of the variables of the problem to be optimized,
then is the number of 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 and generating chaotic variable
The expression formula is:
wherein the content of the first and second substances,
the number of particles in the chaotic sequence,
is composed of
A random number in between;
bringing the chaotic variable carrier to a solution space for solving a problem to obtain
Wherein, the first and the second end of the pipe are connected with each other,
the information on the position of the current location is shown,
and
respectively represent
Dimension variable
Minimum and maximum values of.
7. The improved sparrow search algorithm-based permanent magnet synchronous motor optimization method according to claim 4, wherein the specific process of updating the positions of discoverers, followers and forewarners in the sparrow population is as follows:
according to the formula
Updating the position of the finder;
wherein, the first and the second end of the pipe are connected with each other,
which represents the number of current iterations,
，
the maximum number of iterations is indicated,
is shown as
A sparrow is at the second place
Information on the position in the dimension(s),
is a random number that is a function of the number,
and
respectively represent the early warning value and the safety value,
a random number that follows a normal distribution is represented,
represents one
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 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 the content of the first and second substances,
is shown as
The global worst location of the sub-iteration,
denotes the first
The sub-iteration finds the optimal position of the user,
is composed of
And the elements randomly assign a matrix value of 1 or-1, an
When is coming into contact with
When the user is in a state of hunger, the user needs to fly to other places to forage; when in use
When the food is eaten, the energy value of the joining person is higher, and the joining person moves to the vicinity of the finding person to fight for the 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,
which represents the current global optimum position of the mobile terminal,
indicating the current global worst position of the image,
the control parameter, which represents the step size, is a normally distributed random number with a mean value of 0 and a variance of 1,
is a random number that is a function of,
a value representing the fitness of the individual is indicated,
the value of the best fitness value is represented,
the value of the worst fitness value is represented,
represents a very small constant, and prevents the denominator from being zero when
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 in use
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 improved sparrow search algorithm-based permanent magnet synchronous motor optimization method according to claim 7, wherein cosine weight factors are added when positions of discoverers in a sparrow population are updated, and the cosine weight factor expression is as follows:
wherein, the first and the second end of the pipe are connected with each other,
、
respectively representing the maximum value and the minimum value of the weight;
the formula of the improved finder position is:
wherein, the first and the second end of the pipe are connected with each other,
which represents the number of current iterations,
，
the maximum number of iterations is indicated,
is shown as
A sparrow is on the first place
Information on the position in the dimension(s),
is a random number that is a function of the number,
and
respectively represent the early warning value and the safety value,
a random number that follows a normal distribution is represented,
represents one
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 dimensional-to-dimensional variation on the global optimal variable comprises the following steps:
if the search space is d-dimensional, the current global optimal solution is:
by calculation, the new solution after the dimension-by-dimension variation is:
the calculation formula is expressed as follows:
wherein, the first and the second end of the pipe are connected with each other,
for the current number of iterations,
is a degree of freedom of
Is
-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 proxy model building unit is used for building a motor kriging proxy 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 is used for judging whether the motor kriging proxy model reaches preset precision, if so, executing the step S5, and if not, adding sampling points and returning to execute the step S2;
the agent model training unit is used for carrying out iterative training on the motor kriging agent 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.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211576900.1A CN115659764B (en) | 2022-12-09 | 2022-12-09 | Permanent magnet synchronous motor optimization method and system based on improved sparrow search algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211576900.1A CN115659764B (en) | 2022-12-09 | 2022-12-09 | Permanent magnet synchronous motor optimization method and system based on improved sparrow search algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115659764A true CN115659764A (en) | 2023-01-31 |
| CN115659764B CN115659764B (en) | 2023-04-07 |
Family
ID=85019467
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211576900.1A Active CN115659764B (en) | 2022-12-09 | 2022-12-09 | Permanent magnet synchronous motor optimization method and system based on improved sparrow search algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115659764B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117494442A (en) * | 2023-11-09 | 2024-02-02 | 浙江博旭新能源科技有限公司 | Multi-physical-field design optimization method and system for high-speed motor |
| CN117709167A (en) * | 2024-02-02 | 2024-03-15 | 山西省机电设计研究院有限公司 | Finite element model-based motor design optimization method, storage medium and equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114819349A (en) * | 2022-04-28 | 2022-07-29 | 河北工业大学 | Energy hierarchical management optimization method for active power distribution network of multi-microgrid system |
| CN115100864A (en) * | 2022-06-24 | 2022-09-23 | 北京联合大学 | Traffic signal control optimization method based on improved sparrow search algorithm |
| CN115374689A (en) * | 2022-01-17 | 2022-11-22 | 浙江科技学院 | Air quality index prediction method based on improved sparrow search algorithm optimization |
-
2022
- 2022-12-09 CN CN202211576900.1A patent/CN115659764B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115374689A (en) * | 2022-01-17 | 2022-11-22 | 浙江科技学院 | Air quality index prediction method based on improved sparrow search algorithm optimization |
| CN114819349A (en) * | 2022-04-28 | 2022-07-29 | 河北工业大学 | Energy hierarchical management optimization method for active power distribution network of multi-microgrid system |
| CN115100864A (en) * | 2022-06-24 | 2022-09-23 | 北京联合大学 | Traffic signal control optimization method based on improved sparrow search algorithm |
Non-Patent Citations (1)
| Title |
|---|
| 王鹏: "基于改进麻雀搜索算法的微电网群优化调度研究" * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117494442A (en) * | 2023-11-09 | 2024-02-02 | 浙江博旭新能源科技有限公司 | Multi-physical-field design optimization method and system for high-speed motor |
| CN117709167A (en) * | 2024-02-02 | 2024-03-15 | 山西省机电设计研究院有限公司 | Finite element model-based motor design optimization method, storage medium and equipment |
| CN117709167B (en) * | 2024-02-02 | 2024-04-19 | 山西省机电设计研究院有限公司 | Finite element model-based motor design optimization method, storage medium and equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115659764B (en) | 2023-04-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115659764A (en) | Permanent magnet synchronous motor optimization method and system based on improved sparrow search algorithm | |
| Seo et al. | Multimodal function optimization based on particle swarm optimization | |
| CN111950728B (en) | Image feature extraction model construction method, image retrieval method and storage medium | |
| CN110619082B (en) | Project recommendation method based on repeated search mechanism | |
| CN110996333A (en) | Wireless sensor network node positioning method based on whale algorithm | |
| CN113222096A (en) | Improved particle swarm algorithm for cloud computing task scheduling | |
| CN116859903A (en) | Robot smooth path planning method based on improved Harris eagle optimization algorithm | |
| CN113434958B (en) | Magnetic suspension flywheel motor multi-objective optimization method based on heterogeneous integrated proxy model | |
| CN112131089B (en) | Software defect prediction method, classifier, computer device and storage medium | |
| CN116796597A (en) | Multi-objective optimization method suitable for multi-parameter coupling permanent magnet vernier rim propulsion motor | |
| CN117013890A (en) | Method and system for optimizing electromagnetic vibration noise of permanent magnet synchronous motor by improving mayday algorithm | |
| CN115759979B (en) | Intelligent process processing method and system based on RPA and process mining | |
| Verma et al. | A case study on the application of a genetic algorithm for optimization of engine parameters | |
| CN115526267A (en) | Power distribution network operation scene extraction method and device | |
| CN114839940A (en) | Cross-domain collaborative workshop dynamic scheduling method based on balance index adaptive evolution | |
| CN115131605A (en) | Structure perception graph comparison learning method based on self-adaptive sub-graph | |
| Agah et al. | A genetic algorithm-based controller for decentralized multi-agent robotic systems | |
| CN113191486A (en) | Graph data and parameter data mixed partitioning method based on parameter server architecture | |
| CN113408160A (en) | Motor parameter design method based on multi-objective optimization | |
| CN112995987A (en) | Self-adaptive road network semantic position privacy protection method based on multi-objective optimization problem | |
| CN116403077B (en) | Abnormality detection model training method, abnormality detection device and electronic equipment | |
| CN115238613B (en) | Fan blade shape optimization method and system, storage medium and equipment | |
| Wang et al. | Scaled IoT Intrusion Detection Model based on Improved PSO Algorithm Optimization | |
| Shin et al. | A surrogate model-based genetic algorithm for the optimal policy in cart-pole balancing environments | |
| CN112488231B (en) | Cosine measurement supervision depth hash algorithm with balanced similarity |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |