CN114580306B - Flyback transformer design method based on improved PSO algorithm - Google Patents

Flyback transformer design method based on improved PSO algorithm Download PDF

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CN114580306B
CN114580306B CN202210452322.4A CN202210452322A CN114580306B CN 114580306 B CN114580306 B CN 114580306B CN 202210452322 A CN202210452322 A CN 202210452322A CN 114580306 B CN114580306 B CN 114580306B
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CN114580306A (en
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王一鸣
许颇
夏鲲
张凌翔
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Abstract

The application discloses a flyback transformer design method based on an improved PSO algorithm, which comprises the following design steps: s100: setting target parameters and constraint conditions of the flyback transformer, and selecting member items of optimal variables; s200: randomly searching in respective value ranges through each member to form a multi-particle population; s300: iterating each particle, and solving the local optimum and the global optimum of each member according to the positions of the particles in the partial group in the iteration process; s400: judging the iteration times of the particles; if the iteration times are less than the set maximum iteration times, the particle reenters the step S300 to perform the next iteration, and if the iteration times reach the maximum iteration times, the optimal solution of each member is output. The members of different optimal variables are searched in a global range in different directions, so that the optimal value or the approximate optimal value of the target parameter of the flyback transformer design can be obtained with higher probability.

Description

Flyback transformer design method based on improved PSO algorithm
Technical Field
The application relates to the technical field of transformer optimization design, in particular to a design method of a flyback transformer.
Background
The flyback transformer has the advantages of simple structure, small volume, low cost, no filter inductance, capability of realizing isolated output and the like, and is widely applied to the low-power direct-current application occasions below 100W, such as the fields of medical electronics, mobile communication, industrial control and the like.
When the flyback converter works as a switching power supply, the design of the transformer plays an important role in the working performance of the circuit. In the field of transformer optimization design, some common algorithms are: the Monte-Karlo method, the effective constraint direct method, the Powell method, the improved complex shape method, the circulation pass method, the positive variation experiment method and the like, but the algorithms are not ideal for the design of the transformer; or the calculation amount is too large, and the solving efficiency is low; or the algorithm itself cannot ensure global optimization; or the algorithm has poor universality, and the program cannot be conveniently transplanted; or the algorithm can not be applied to a complex mixed discrete variable optimization problem, and the like. There are many limitations to these approaches for solving the nonlinear programming problem.
Disclosure of Invention
One of the objectives of the present application is to provide a method for designing a flyback transformer capable of global optimization.
In order to achieve the purpose, the technical scheme adopted by the application is as follows: a flyback transformer design method based on an improved PSO algorithm comprises the following design steps:
s100: setting target parameters and constraint conditions of the flyback transformer, and selecting member items of optimal variables;
s200: randomly searching in respective value ranges through each member to form a multi-particle population;
s300: iterating each particle, and solving the local optimum and the global optimum of each member according to the positions of the particles in the partial group in the iteration process;
s400: judging the iteration times of the particles; if the iteration times are less than the set maximum iteration times, the particle reenters the step S300 to perform the next iteration, and if the iteration times reach the maximum iteration times, the optimal solution of each member is output.
Preferably, the target parameter of the flyback transformer in step S100 is at least one, and may be determined by an objective function
Figure 869188DEST_PATH_IMAGE001
Carrying out representation;
Figure 798223DEST_PATH_IMAGE002
；
Figure 594141DEST_PATH_IMAGE003
(ii) a Wherein n represents the number of member items, and n is more than or equal to 1;
Figure 347334DEST_PATH_IMAGE004
an objective function representing the member of the nth term,
Figure 228702DEST_PATH_IMAGE005
represents the nth member;
Figure 459963DEST_PATH_IMAGE006
and representing the member function corresponding to the nth member.
Preferably, the iterative process of the particles in step S300 includes the following steps:
s310: judging whether the particles in the step S200 meet the constraint condition, if so, solving the current position of the particles
Figure 110387DEST_PATH_IMAGE007
Corresponding objective function
Figure 768902DEST_PATH_IMAGE008
And the value of the fitness function, and further obtaining the local optimal solution of each member
Figure 636101DEST_PATH_IMAGE009
And global optimal solution
Figure 671053DEST_PATH_IMAGE010
(ii) a Punishment is carried out if the particles do not meet the constraint condition;
s320: updating the position of the particle to
Figure 175984DEST_PATH_IMAGE011
Solving the value of the fitness function corresponding to the updated particle position;
s330: comparing the value of the fitness function before and after the updating of the particle position, and obtaining the local optimal solution of the particle according to the comparison result
Figure 5400DEST_PATH_IMAGE009
And global optimal solution
Figure 861360DEST_PATH_IMAGE012
Updating is carried out;
the position update formula of the particle may be:
Figure 434424DEST_PATH_IMAGE013
(ii) a Wherein k represents the iteration times of the particles, and k is more than or equal to 1; w is the inertial weight; c is a learning factor; r is [0, 1 ]]The random number of (1);
Figure 59441DEST_PATH_IMAGE014
the historical optimal position of the particle i in the d dimension is taken as the optimal position;
Figure 561222DEST_PATH_IMAGE015
is the location vector of the d-th dimension of particle i in the k-th iteration.
Preferably, the inertia weight w decreases linearly with the increase of the number of iterations, and the specific calculation formula is as follows:
Figure 638900DEST_PATH_IMAGE016
(ii) a Wherein,
Figure 281234DEST_PATH_IMAGE017
is the weight of the initial inertia, and,
Figure 760757DEST_PATH_IMAGE018
in order to terminate the inertial weight(s),
Figure 197554DEST_PATH_IMAGE019
is the maximum number of iterations.
Preferably, the fitness function comprises a local fitness function
Figure 496949DEST_PATH_IMAGE020
And global fitness function
Figure 411815DEST_PATH_IMAGE021
(ii) a Thus, in step S330, the local fitness function is compared
Figure 11424DEST_PATH_IMAGE020
And
Figure 117658DEST_PATH_IMAGE022
value of (d) and global fitness function
Figure 904348DEST_PATH_IMAGE021
And
Figure 622905DEST_PATH_IMAGE023
according to the result of the comparison, a locally optimal solution for the particle
Figure 77020DEST_PATH_IMAGE024
And global optimal solution
Figure 855621DEST_PATH_IMAGE010
Updating is carried out;
Figure 395186DEST_PATH_IMAGE025
；
Figure 917434DEST_PATH_IMAGE026
wherein
Figure 473660DEST_PATH_IMAGE027
representing an objective function
Figure 423162DEST_PATH_IMAGE004
The maximum value of (a) is,
Figure 184444DEST_PATH_IMAGE028
representing an objective function
Figure 510384DEST_PATH_IMAGE004
Average value of (d);
Figure 673512DEST_PATH_IMAGE029
represents the weight corresponding to the nth member, and
Figure 528335DEST_PATH_IMAGE030
。
preferably, the penalty for the particles not meeting the constraint condition in step S310 includes the following steps:
s311: randomly searching the particles which do not meet the constraint condition again in the value range to obtain a new position;
s312: judging whether the particles meet the constraint condition again;
s313: if the particles meet the constraint conditions, solving the corresponding objective function according to the current positions of the particles
Figure 42493DEST_PATH_IMAGE001
And the value of the fitness function; and if the particles still do not meet the constraint condition, removing the particles which do not meet the constraint condition.
Preferably, the constraints of the flyback transformer in step S100 include performance index constraints, structural and technical constraints, and form size and weight constraints.
Preferably, the performance indicator constraints include: the upper limit of voltage ratio error, the upper limit of magnetic flux density, the electric intensity margin of main insulation and longitudinal insulation of a winding, the upper limit of impedance voltage error, the limit of winding temperature rise, the upper limit of iron core temperature rise, the upper limit of oil temperature rise and the lower limit of efficiency; structural and technical condition constraints include specification constraints; form size and weight constraints include an upper limit on winding high error.
Preferably, the member items of the optimal variables include: core type, core material, core size, window area, winding wire diameter, and leakage inductance.
Preferably, the target parameter of the flyback transformer in step S100 includes a high-precision output voltage.
Preferably, in the process of iterating the particle, in order to avoid the particle from falling into the local optimum, the particle needs to be corrected, and the specific correction includes the following steps:
s301: setting the control factor a such that the particles are iterated to
Figure 405079DEST_PATH_IMAGE019
The secondary process is divided into a plurality of parts;
s302: at the beginning of each part, a local fitness function corresponding to a single particle
Figure 157134DEST_PATH_IMAGE031
Value of (d) and global fitness function
Figure 714018DEST_PATH_IMAGE021
Comparing the values of (A);
s303: for local fitness function
Figure 449893DEST_PATH_IMAGE020
Is better than the global fitness function
Figure 852055DEST_PATH_IMAGE032
The iterative process of step S300 is performed in a loop until the end of each part;
s304: for local fitness function
Figure 724196DEST_PATH_IMAGE020
Is different from the global fitness function
Figure 451981DEST_PATH_IMAGE021
Searching the position of the particle in the feasible region again, and then circularly performing the iterative process of the step S300 until the end of each part;
s305: comparing the fitness function of the particles in the step S303 and the step 304 at the end of each part, and carrying out local optimal solution according to the comparison result
Figure 911037DEST_PATH_IMAGE009
And global optimal solution
Figure 382470DEST_PATH_IMAGE010
Updating is carried out;
wherein,
Figure 109117DEST_PATH_IMAGE033
，
Figure 742224DEST_PATH_IMAGE034
is (0, 1), and j is more than or equal to 1.
Compared with the prior art, the beneficial effect of this application lies in:
(1) according to the method and the device, searching can be performed in a global range in different directions through members of different optimal variables, so that the optimal value or the approximate optimal value of the key parameter of the flyback transformer design can be obtained with high probability.
(2) The method gives up the speed updating in the iteration process, only keeps the position updating, and accelerates the running speed of the algorithm to a certain extent by removing the improvement of the speed updating.
(3) The method improves the problems of encoding, initial population, functions and the like of the traditional algorithm, and greatly improves the overall performance of the algorithm.
(4) Compared with the existing design method, the method can be suitable for the optimal solution of a plurality of target parameters, has intrinsic parallelism, and enables the design of the flyback transformer with multi-path output to achieve higher solution precision.
(5) Compared with the prior art, the method and the device have the advantages that the probability search technology is used, and the flexibility is higher.
Drawings
FIG. 1 is a schematic overall flow chart of the present invention.
Fig. 2 is a flow chart of an iterative process in the present invention.
FIG. 3 is a flow chart of the penalty process in the present invention.
Detailed Description
The present application is further described below with reference to specific embodiments, and it should be noted that, without conflict, any combination between the embodiments or technical features described below may form a new embodiment.
In the description of the present application, it should be noted that, for the terms of orientation, such as "central", "lateral", "longitudinal", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc., it indicates that the orientation and positional relationship shown in the drawings are based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, but does not indicate or imply that the device or element referred to must have a specific orientation, be constructed in a specific orientation, and be construed as limiting the specific scope of protection of the present application.
It is noted that the terms first, second and the like in the description and in the claims of the present application are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
In one preferred embodiment of the present application, as shown in fig. 1 to 2, a method for designing a flyback transformer based on an improved PSO algorithm includes the following steps:
s100: and setting target parameters and constraint conditions of the flyback transformer, and selecting member items of the optimal variables.
S200: and randomly searching in respective value ranges through each member to form a multi-particle population.
S300: and iterating each particle, and solving the local optimum and the global optimum of each member according to the positions of the particles in the partial group in the iteration process.
S400: judging the iteration times of the particles; if the iteration times are less than the set maximum iteration times, the particle reenters the step S300 to perform the next iteration, and if the iteration times reach the maximum iteration times, the optimal solution of each member is output.
It is understood that the basic idea of the PSO algorithm stems from the foraging behavior of a flock of birds. Conventional PSO algorithms assign two attributes to a particle, velocity and position, when the particle is formed. The velocity represents the direction and distance that the particle will move in the next iteration, and the position represents a solution to the problem to be solved, so that the velocity and position of the particle need to be updated during each iteration.
In the improved PSO algorithm, only the positions of the particles are updated in each iteration process, and the updating of the speed is abandoned. The speed is constant value in each iteration process, so that the calculated amount of particles in updating can be reduced to a certain extent, and the solving speed of the algorithm is improved.
It will also be appreciated that each item member corresponds to a multi-particle partial population, such that the plurality of partial populations together form a multi-particle population. In each iteration process, the members are independent from each other in the iteration process, so that the optimal solution of the particles in the partial group of each member can be obtained according to the iteration process of each member, and the optimal solution is a local optimal solution; by integrating the local optimal solutions of all the members, the global optimal solution of the whole population can be obtained.
In this embodiment, the target parameter of the flyback transformer in step S100 is at least one, and may be determined by an objective function
Figure 452691DEST_PATH_IMAGE008
And (4) performing representation.
Figure 196656DEST_PATH_IMAGE002
；
Figure 43389DEST_PATH_IMAGE003
(ii) a Wherein n represents the number of member items, and n is more than or equal to 1;
Figure 611512DEST_PATH_IMAGE004
an objective function representing the member of the nth term,
Figure 543696DEST_PATH_IMAGE005
represents the nth member;
Figure 356931DEST_PATH_IMAGE035
and representing the member function corresponding to the nth member.
It can be understood that, when the flyback transformer is designed, the target parameters can be divided into two cases, namely single target parameters and multi-target parameters. For the calculation of multi-target parameters, a common method is to convert multiple targets into a single target through mathematical transformation to solve the problem. The specific mathematical transformation process has a plurality of modes, which are common knowledge in the field, and the skilled person can select the mathematical transformation process according to actual needs; so for convenience of description, the present application will be described with a single target parameter.
In this embodiment, under the condition that the performance of the flyback transformer is satisfied, the high accuracy of the output voltage can be selected as the objective function.
In the embodiment, the design process of the flyback transformer is carried out, and the selection of the optimal variable determines the calculated amount of the algorithm; the more optimal variable choices, the better the optimal result, but the calculation amount will also increase; the less the optimal variable selection is, the less the calculation amount is, but the worse the optimal result is. Therefore, when selecting the optimal variable, the optimal variable that is independent and has a large influence on the target parameter and the constraint condition is the best choice. Meanwhile, the structural size that must meet the relevant standards and design specifications should not be selected as an optimization variable for the design of the flyback transformer.
It can be understood that, since the target parameter is mainly affected by the optimal variable, the target function corresponding to the target parameter can be obtained
Figure 58171DEST_PATH_IMAGE001
The solving process of (2) is regarded as an objective function corresponding to each optimal variable member
Figure 33080DEST_PATH_IMAGE036
The solving process of (1). So that the optimal solution of the objective function corresponding to each member can be obtained
Figure 718139DEST_PATH_IMAGE001
And
Figure 69486DEST_PATH_IMAGE036
the optimal solution of the target parameter is obtained by the relational expression of (A).
In this embodiment, the items for selecting the optimal variables according to the above principle include: magnetic core type, magnetic core material, magnetic core size, window area, winding wire diameter and leakage inductance; thus, the number n of the members in this embodiment takes the value of 6.
There are many constraints in the design process of the flyback transformer, the most important of which is three constraints. The first aspect is a performance indicator constraint; values such as impedance voltage, no-load losses, no-load current and temperature rise cannot exceed the limits. The second aspect is structural and technical constraints; for example, for constraints of mechanical clamping force, the minimum patch width of the core cannot be too narrow; the number of parallel turns of continuous winding cannot be more than 6 due to the limitation of winding process conditions. The third aspect is form size and weight limitations; the external dimensions and weight of the transformer must be limited to a certain extent in view of the transport and installation space. When the constraint condition is defined, the more the constraint condition is defined, the fewer the number of particles meeting the constraint condition, and the calculation amount of the algorithm is reduced, but the influence of the particles by irrelevant factors is increased, and the optimal result of the particles is shifted from the actually required result. The less the constraint, the greater the number of particles that satisfy the constraint, and the more the algorithm is computationally intensive, the less the optimal result of the particles will be used. Therefore, the constraint condition needs to be defined in relation to the target parameter.
According to the above principle; in the embodiment, the performance index constraint can limit the upper limit of voltage ratio error, the upper limit of magnetic flux density, the electric strength margin of main insulation and longitudinal insulation of the winding, the upper limit of impedance voltage error, the upper limit of winding temperature rise, the upper limit of iron core temperature rise, the upper limit of oil temperature rise and the lower limit of efficiency. Structural and technical constraints can be defined as convention constraints. Form size and weight constraints may be defined as upper limits of winding height error.
In one preferred embodiment of the present application, as shown in fig. 2, the iterative process of the particles in step S300 includes the following steps:
s310: judging whether the particles in the step S200 meet the constraint condition, if so, solving the current position of the particles
Figure 126697DEST_PATH_IMAGE007
Corresponding objective function
Figure 272508DEST_PATH_IMAGE008
And the value of the fitness function, and further obtaining the local optimal solution of each member
Figure 913705DEST_PATH_IMAGE009
And global optimal solution
Figure 68743DEST_PATH_IMAGE010
(ii) a And if the particles do not meet the constraint condition, punishing.
S320: updating the position of the particle to
Figure 744574DEST_PATH_IMAGE011
And solving the value of the fitness function corresponding to the updated particle position.
S330: comparing the value of the fitness function before and after the updating of the particle position, and obtaining the local optimal solution of the particle according to the comparison result
Figure 326866DEST_PATH_IMAGE009
And global optimal solution
Figure 455359DEST_PATH_IMAGE010
And (4) updating.
The position update formula of the particle may be:
Figure 647043DEST_PATH_IMAGE037
(ii) a Wherein k represents the iteration times of the particles, and k is more than or equal to 1; w is the inertial weight; c is a learning factor; r is [0, 1 ]]The random number of (1);
Figure 442961DEST_PATH_IMAGE014
searching the optimal position of the particle i in the d dimension;
Figure 196153DEST_PATH_IMAGE038
is the location vector of the d-th dimension of particle i in the k-th iteration.
It is understood that the multi-particle fraction populations corresponding to each member are iterated independently of each other. Meanwhile, the iterative solving process of the particles is the convergence process of the objective function, so that the optimal solution of the particles is the limiting value of the objective function to be converged. That is, in this embodiment, the local optimal solution corresponding to each part of the group of particles is the objective function
Figure 546363DEST_PATH_IMAGE036
Minimum value of (2)
Figure 43204DEST_PATH_IMAGE024
(ii) a Thereby the global optimal solution of the population corresponding to each member is an objective function
Figure 693628DEST_PATH_IMAGE008
Minimum value of (2)
Figure 352142DEST_PATH_IMAGE012
。
In this embodiment, the number of iterations of the particle may be set according to actual needs. Meanwhile, according to the value of the member number n of the optimal variable, the particles can form the dimension D of the search space with the corresponding number, so that D =1, 2, … …, D; d = n = 6.
It can be understood that the multi-particle part group corresponding to each optimal variable member corresponds to a search space of one dimension. Thus, assuming that the number of best variable members is 6, a particle search space of 6 dimensions in total can be formed. The particles corresponding to each member are searched in the search space of respective dimension to solve the local optimal solution of each member
Figure 956692DEST_PATH_IMAGE024
By solving locally optimal solutions
Figure 991644DEST_PATH_IMAGE009
The global optimal solution of the whole population can be obtained by integration
Figure 496575DEST_PATH_IMAGE010
。
The inertial weight w describes how much the previous generation velocity of the particle affects the current generation velocity. The calculation of the inertial weight w may be linear or non-linear; the selection can be made according to practical problems. In the embodiment, global search can be firstly adopted for the target parameters, so that the search space is rapidly converged in a certain region, and then high-precision solution is obtained by adopting local fine search; therefore, an inertia weight w which can be linearly reduced along with the increase of the iteration number is provided, and a specific calculation formula is as follows:
Figure 591570DEST_PATH_IMAGE016
(ii) a Wherein,
Figure 181951DEST_PATH_IMAGE017
in order to be the original inertial weight,
Figure 755015DEST_PATH_IMAGE039
in order to obtain the inertial weight of the pressure shaft,
Figure 380032DEST_PATH_IMAGE019
is the maximum number of iterations.
It can be appreciated that the original inertial weights
Figure 144463DEST_PATH_IMAGE017
And the dead weight
Figure 956561DEST_PATH_IMAGE018
The value range of (1) is (0); original inertial weight
Figure 333316DEST_PATH_IMAGE017
And the dead weight
Figure 812839DEST_PATH_IMAGE018
Preferred values of (b) are 0.9 and 0.4, respectively.
In this embodiment, the fitness function includes a local fitness function
Figure 249636DEST_PATH_IMAGE020
And global fitness function
Figure 814610DEST_PATH_IMAGE032
(ii) a Thus, in step S330, the local fitness function is compared
Figure 729476DEST_PATH_IMAGE020
And
Figure 818831DEST_PATH_IMAGE022
value of (d) and global fitness function
Figure 426530DEST_PATH_IMAGE021
And
Figure 213220DEST_PATH_IMAGE023
according to the result of the comparison, a locally optimal solution for the particle
Figure 197357DEST_PATH_IMAGE009
And global optimal solution
Figure 651472DEST_PATH_IMAGE010
And (6) updating.
Figure 430072DEST_PATH_IMAGE025
；
Figure 438479DEST_PATH_IMAGE026
Wherein
Figure 960727DEST_PATH_IMAGE027
representing an objective function
Figure 767884DEST_PATH_IMAGE004
The maximum value of (a) is,
Figure 717386DEST_PATH_IMAGE028
representing an objective function
Figure 744247DEST_PATH_IMAGE004
Average value of (d);
Figure 804607DEST_PATH_IMAGE029
represents the weight corresponding to the nth member, and
Figure 967735DEST_PATH_IMAGE040
。
it will be appreciated that the local fitness function
Figure 88138DEST_PATH_IMAGE020
Partial groups corresponding to each member, global fitness function
Figure 336717DEST_PATH_IMAGE021
Corresponding to a population containing all members.
Meanwhile, although each optimal variable member is related to the target parameter in the application, the influence degree of each optimal variable member on the target parameter is different, so that when the global fitness function value of the population is calculated, the weight corresponding to each optimal variable member can be set according to the influence degree of each optimal variable member on the target parameter
Figure 702233DEST_PATH_IMAGE029
The value of (c).
The specific updating process is as follows:
first, the objective function value of each particle at the position is solved according to the position of each particle in the partial group corresponding to the member.
Then, comparing the value of the objective function corresponding to each particle, the partial group corresponding to each member can be obtained
Figure 719867DEST_PATH_IMAGE009
And
Figure 11171DEST_PATH_IMAGE027
and obtaining partial groups corresponding to each member
Figure 747046DEST_PATH_IMAGE028
So as to obtain the local fitness function value of each member
Figure 414788DEST_PATH_IMAGE020
And global fitness function values for the entire population
Figure 552508DEST_PATH_IMAGE021
。
Then, the positions of the particles are updated, and the solution is obtained according to the updated positions of the particles
Figure 14713DEST_PATH_IMAGE009
、
Figure 470840DEST_PATH_IMAGE027
And
Figure 942273DEST_PATH_IMAGE028
so as to obtain the local fitness function value of each member at the updated position
Figure 934500DEST_PATH_IMAGE022
And global fitness function values for the entire population
Figure 567606DEST_PATH_IMAGE023
。
Then, the comparison is made
Figure 278073DEST_PATH_IMAGE020
And
Figure 22038DEST_PATH_IMAGE022
a value of, if
Figure 868772DEST_PATH_IMAGE020
Is greater than
Figure 938359DEST_PATH_IMAGE022
If so, the local optimal solution corresponding to each part group is the local optimal solution after the particle position is updated; if it is
Figure 106428DEST_PATH_IMAGE020
Is less than
Figure 919664DEST_PATH_IMAGE022
The local optimal solution corresponding to each partial group is the local optimal solution before the particle position is updated. Also, if
Figure 620903DEST_PATH_IMAGE021
Is greater than
Figure 595813DEST_PATH_IMAGE023
If so, the global optimal solution corresponding to the population is the global optimal solution after the particle position is updated; if it is
Figure 15293DEST_PATH_IMAGE021
Is less than
Figure 366640DEST_PATH_IMAGE023
The global optimal solution corresponding to the population is the global optimal solution before the particle position is updated.
It will be appreciated that when the particles are first renewed, they appear
Figure 420921DEST_PATH_IMAGE020
Is less than
Figure 832311DEST_PATH_IMAGE022
And/or
Figure 739087DEST_PATH_IMAGE021
Is less than
Figure 894125DEST_PATH_IMAGE023
Value of (A)The case (1); the local optimal solution corresponding to each part of the clusters is a historical local optimal solution, and the global optimal solution corresponding to the clusters is a historical global optimal solution. The historical local optimal solution and the global optimal solution may be obtained through experiments or experience.
In one embodiment of the present application, as shown in fig. 3, the penalty for the particles that do not satisfy the constraint condition in step S310 includes the following steps:
s311: and carrying out random search again on the particles which do not meet the constraint condition in the value range to obtain a new position.
S312: and judging whether the particles meet the constraint condition again.
S313: if the particles meet the constraint condition, solving a local optimal solution according to the positions of the particles
Figure 569957DEST_PATH_IMAGE009
And global optimal solution
Figure 152248DEST_PATH_IMAGE010
(ii) a And if the particles still do not meet the constraint condition, removing the particles which do not meet the constraint condition.
It can be understood that the value range of the particles is the dimensional space corresponding to the particles. Meanwhile, for the particles which satisfy the constraint condition after the penalty, the method can enter the step S320 and the step S330 together with the particles which are not penalized to perform the update loop.
In one preferred embodiment of the present application, in the process of iterating the particle, the particle may be corrected, and the specific correction process includes the following steps:
s301: setting the control factor a such that the particles are iterated to
Figure 546320DEST_PATH_IMAGE019
The secondary process is divided into multiple parts.
S302: at the beginning of each part, a local fitness function corresponding to a single particle
Figure 239470DEST_PATH_IMAGE020
Value of (A) andlocal fitness function
Figure 271273DEST_PATH_IMAGE021
The values of (a) are compared.
S303: for local fitness function
Figure 758886DEST_PATH_IMAGE020
Is better than the global fitness function
Figure 640254DEST_PATH_IMAGE021
Until the end of each part, the iterative process of step S300 is repeated.
S304: for local fitness function
Figure 137095DEST_PATH_IMAGE020
Is different from the global fitness function
Figure 787519DEST_PATH_IMAGE021
The position of the particle is searched again in the feasible region, and the iterative process of step S300 is performed in a loop until the end of each part.
S305: comparing the fitness functions of the particles in the step S303 and the step 304 at the end of each part, and carrying out local optimal solution according to the comparison result
Figure 446033DEST_PATH_IMAGE009
And global optimal solution
Figure 549119DEST_PATH_IMAGE010
And (6) updating.
Wherein,
Figure 82606DEST_PATH_IMAGE033
，
Figure 853116DEST_PATH_IMAGE034
is (0, 1), and j is more than or equal to 1.
It will be appreciated that, by setting the control factor a,the particles can be prevented from being trapped in a local optimal state, so that the global optimal result does not meet the use requirement. The number j of control factors a may divide the overall iterative process for each particle into 1,
Figure 948111DEST_PATH_IMAGE041
]、（
Figure 538492DEST_PATH_IMAGE041
，
Figure 377135DEST_PATH_IMAGE042
]、（
Figure 2151DEST_PATH_IMAGE042
，
Figure 2468DEST_PATH_IMAGE043
]、……、（
Figure 80146DEST_PATH_IMAGE044
，
Figure 782584DEST_PATH_IMAGE019
]a total of j +1 moieties; the number j of the control factors a can be set according to actual needs, and
Figure 262107DEST_PATH_IMAGE044
the value of (b) is an integer. In addition, the following components are added
Figure 698905DEST_PATH_IMAGE042
，
Figure 998299DEST_PATH_IMAGE043
]For example, the iteration process corresponding to the part is shown as the second of the whole iteration processes
Figure 178745DEST_PATH_IMAGE045
Next to
Figure 512774DEST_PATH_IMAGE043
Next, the process is carried out.
At the beginning of each part, the local fitness function of the particle is calculated
Figure 854894DEST_PATH_IMAGE020
Is better than the global fitness function
Figure 405698DEST_PATH_IMAGE021
The value of (b) indicates that the particle is more suitable for local search, so that the current inertia weight w of the particle is not changed, and the value of the inertia weight w of the particle in the part is linearly changed in the corresponding iteration process of the whole part, so as to realize local optimization of the particle. Local fitness function of particle
Figure 389835DEST_PATH_IMAGE020
Is different from the global fitness function
Figure 578371DEST_PATH_IMAGE021
The value of (2) indicates that the optimizing capability of the particle at the current position is poor; the fully feasible region of the part of particles in the corresponding dimensional space can be searched again to expect that the part of particles can find a better solution value in the subsequent loop process of the iterative part. Finally, at the end of each iteration part, the solution values of the particles of the two parts are compared to obtain the final optimal solution, so that the particles can be prevented from being trapped in the local optimization process.
The foregoing has described the principles, principal features, and advantages of the application. It will be understood by those skilled in the art that the present application is not limited to the embodiments described above, which are merely illustrative of the principles of the application, but that various changes and modifications may be made without departing from the spirit and scope of the application, and these changes and modifications are intended to be within the scope of the application as claimed. The scope of protection claimed by this application is defined by the following claims and their equivalents.

Claims (8)

1. A flyback transformer design method based on an improved PSO algorithm is characterized by comprising the following design steps:
s100: setting target parameters and constraint conditions of the flyback transformer, and selecting member items of optimal variables;
s200: randomly searching in respective value ranges through each member to form a multi-particle population;
s300: iterating each particle, and solving the local optimum and the global optimum of each member according to the position of the particle in the population in the iteration process;
s400: judging the iteration times of the particles; if the iteration times are less than the set maximum iteration times, the particles reenter the step S300 to carry out the next iteration, and if the iteration times reach the maximum iteration times, the optimal solution of each member is output;
wherein, the target parameter is at least one and can pass through the target function
Figure DEST_PATH_IMAGE001
Carrying out representation;
Figure DEST_PATH_IMAGE002
；
Figure DEST_PATH_IMAGE003
(ii) a Wherein n represents the number of member items, and n is more than or equal to 1;
Figure DEST_PATH_IMAGE004
an objective function representing the member of the nth term,
Figure DEST_PATH_IMAGE005
represents the nth member;
Figure DEST_PATH_IMAGE006
representing member functions corresponding to the nth member;
the iterative process of the particles in step S300 includes the following steps:
s310: determining particles in step S200Whether the child meets the constraint condition or not, if the particle meets the constraint condition, the current position of the particle is solved
Figure DEST_PATH_IMAGE007
Corresponding objective function
Figure 949221DEST_PATH_IMAGE001
And the value of the fitness function, and further obtaining the local optimal solution of each member
Figure DEST_PATH_IMAGE008
And global optimal solution
Figure DEST_PATH_IMAGE009
(ii) a Punishment is carried out if the particles do not meet the constraint condition;
s320: updating the position of the particle to
Figure DEST_PATH_IMAGE010
Solving the value of the fitness function of the particle position update;
s330: comparing the value of the fitness function before and after the updating of the particle position, and obtaining the local optimal solution of the particle according to the comparison result
Figure 919713DEST_PATH_IMAGE008
And global optimal solution
Figure 948849DEST_PATH_IMAGE009
Updating is carried out;
the position update formula of the particle may be:
Figure DEST_PATH_IMAGE012
(ii) a Wherein k represents the iteration times of the particles, and k is more than or equal to 1; w is the inertial weight; c is a learning factor; r is [0, 1 ]]The random number of (1);
Figure DEST_PATH_IMAGE013
is particle i ind-dimensional historical best position;
Figure DEST_PATH_IMAGE014
a position vector of a d-th dimension of the particle i in the k-th iteration is obtained;
in the process of iterating the particle, in order to avoid the particle from falling into a local optimum, the particle needs to be corrected, and the specific correction includes the following steps:
s301: setting the control factor a such that the particles are iterated to
Figure DEST_PATH_IMAGE015
The secondary process is divided into a plurality of parts;
s302: at the beginning of each part, a local fitness function corresponding to a single particle
Figure DEST_PATH_IMAGE016
Value of (d) and global fitness function
Figure DEST_PATH_IMAGE017
Comparing the values of (A);
s303: for local fitness function
Figure 937140DEST_PATH_IMAGE016
Is better than the global fitness function
Figure 418937DEST_PATH_IMAGE017
The iterative process of step S300 is performed in a loop until the end of each part;
s304: for local fitness function
Figure 567022DEST_PATH_IMAGE016
Is different from the global fitness function
Figure 450664DEST_PATH_IMAGE017
The position of the particle is searched again in the feasible regionThen, the iterative process of step S300 is performed in a loop until the end of each part;
s305: comparing the fitness function of the particles in the step S303 and the step 304 at the end of each part, and carrying out local optimal solution according to the comparison result
Figure 924371DEST_PATH_IMAGE008
And global optimal solution
Figure 893464DEST_PATH_IMAGE009
Updating is carried out;
wherein,
Figure DEST_PATH_IMAGE018
，
Figure DEST_PATH_IMAGE019
is (0, 1), and j is more than or equal to 1.
2. The flyback transformer design method based on the improved PSO algorithm of claim 1, wherein: the inertia weight w is linearly reduced along with the increase of the iteration times, and the specific calculation formula is as follows:
Figure DEST_PATH_IMAGE020
(ii) a Wherein,
Figure DEST_PATH_IMAGE021
in order to be the original inertial weight,
Figure DEST_PATH_IMAGE022
in order to obtain the inertial weight of the pressure shaft,
Figure DEST_PATH_IMAGE023
is the maximum number of iterations.
3. Flyback transformer based on improved PSO algorithm according to claim 1The design method is characterized in that: the fitness function comprises a local fitness function
Figure DEST_PATH_IMAGE024
And global fitness function
Figure DEST_PATH_IMAGE025
(ii) a Thus, in step S330, the local fitness function is compared
Figure 64813DEST_PATH_IMAGE024
And
Figure DEST_PATH_IMAGE026
value of (d) and global fitness function
Figure 599700DEST_PATH_IMAGE025
And
Figure DEST_PATH_IMAGE027
according to the result of the comparison, a locally optimal solution for the particle
Figure DEST_PATH_IMAGE028
And global optimal solution
Figure DEST_PATH_IMAGE029
Updating is carried out;
Figure DEST_PATH_IMAGE030
；
Figure DEST_PATH_IMAGE031
wherein
Figure DEST_PATH_IMAGE032
representing an objective function
Figure 133056DEST_PATH_IMAGE004
The maximum value of (a) is,
Figure DEST_PATH_IMAGE033
representing an objective function
Figure 120604DEST_PATH_IMAGE004
Average value of (d);
Figure DEST_PATH_IMAGE034
represents the weight corresponding to the nth member, and
Figure DEST_PATH_IMAGE035
。
4. the method of claim 1, wherein the punishment of the particles not meeting the constraint condition in step S310 comprises the following steps:
s311: randomly searching the particles which do not meet the constraint condition again in the value range to obtain a new position;
s312: judging whether the particles meet the constraint condition again;
s313: if the particles meet the constraint condition, solving a local optimal solution according to the positions of the particles
Figure DEST_PATH_IMAGE036
And global optimal solution
Figure DEST_PATH_IMAGE037
(ii) a And if the particles still do not meet the constraint condition, removing the particles which do not meet the constraint condition.
5. The flyback transformer design method based on the improved PSO algorithm of any of claims 1-4, wherein: the constraint conditions of the flyback transformer in step S100 include performance index constraints, structural and technical condition constraints, and form size and weight constraints.
6. The flyback transformer design method based on the improved PSO algorithm of claim 5, wherein:
the performance index constraints include: the upper limit of voltage ratio error, the upper limit of magnetic flux density, the electric intensity margin of main insulation and longitudinal insulation of a winding, the upper limit of impedance voltage error, the limit of winding temperature rise, the upper limit of iron core temperature rise, the upper limit of oil temperature rise and the lower limit of efficiency;
structural and technical constraints include: constraint of the specification;
form size and weight constraints include: high upper error limit of winding.
7. The flyback transformer design method based on the improved PSO algorithm of claim 1, wherein the member items of the optimal variables include: core type, core material, core size, window area, winding wire diameter, and leakage inductance.
8. The flyback transformer design method based on the improved PSO algorithm of claim 1, wherein: the target parameter of the flyback transformer in step S100 includes a high-precision output voltage.
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