CN114580306A - Flyback transformer design method based on improved PSO algorithm - Google Patents
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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
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 functionCarrying out representation;
;(ii) a Wherein n represents the number of member items, and n is more than or equal to 1;an objective function representing the member of the nth term,represents the nth member;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 particlesCorresponding objective functionAnd the value of the fitness function, and further obtaining the local optimal solution of each memberAnd global optimal solution(ii) a Punishment is carried out if the particles do not meet the constraint condition;
s320: updating the position of the particle toSolving 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 resultAnd global optimal solutionUpdating is carried out;
the position update formula of the particle may be:(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);the historical optimal position of the particle i in the d dimension is taken as the optimal position;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:
(ii) a Wherein the content of the first and second substances,in order to be the initial inertial weight,in order to terminate the inertial weight(s),is the maximum number of iterations.
Preferably, the fitness function comprises a local fitness functionAnd global fitness function(ii) a Thus, in step S330, the local fitness function is comparedAndvalue of (d) and global fitness functionAndaccording to the result of the comparison, a locally optimal solution for the particleAnd global optimal solutionUpdating is carried out;
;wherein, in the step (A),representing an objective functionThe maximum value of (a) is,representing an objective functionAverage value of (d);represents the weight corresponding to the nth member, and。
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 particlesAnd 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 toThe secondary process is divided into a plurality of parts;
s302: at the beginning of each part, a local fitness function corresponding to a single particleValue of (d) and global fitness functionComparing the values of (A);
s303: for local fitness functionIs better than the global fitness functionThe iterative process of step S300 is performed in a loop until the end of each part;
s304: for local fitness functionIs different from the global fitness functionSearching 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 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 resultAnd global optimal solutionUpdating is carried out;
wherein the content of the first and second substances,,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 and the device give up speed updating in the iterative process, only keep position updating, and improve the speed updating, thereby accelerating the running speed of the algorithm to a certain extent.
(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-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 described below or between the technical features 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 functionAnd (4) performing representation.
;(ii) a Wherein n represents the number of member items, and n is more than or equal to 1;an objective function representing the member of the nth term,represents the nth member;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 will be appreciated that the target parameter is primarily influenced by the optimum variable, and soThe target function corresponding to the target parameterThe solving process of (2) is regarded as an objective function corresponding to each optimal variable memberThe solving process of (1). So that the optimal solution of the objective function corresponding to each member can be obtainedAndthe 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 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 may be defined as specification 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 particlesCorresponding objective functionAnd the value of the fitness function, and further obtaining the local optimal solution of each memberAnd global optimal solution(ii) a And if the particles do not meet the constraint condition, punishing.
S320: updating the position of the particle toAnd 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 resultAnd global optimal solutionAnd (6) updating.
The position update formula of the particle may be:(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);searching the optimal position of the particle i in the d dimension;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 functionMinimum value of (2)(ii) a Thereby the global optimal solution of the population corresponding to each member is an objective functionMinimum value of (2)。
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 total particle search space of 6 dimensions 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 memberBy solving locally optimal solutionsThe global optimal solution of the whole population can be obtained by integration。
The inertial weight w describes how much the previous generation velocity of the particle affects the current generation velocity. The calculation of the inertia 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:
(ii) a Wherein the content of the first and second substances,in order to be the original inertial weight,in order to obtain the inertial weight of the pressure shaft,is the maximum number of iterations.
It can be appreciated that the original inertial weightsAnd the dead weightThe value range of (1) is (0); original inertial weightAnd the inertia weight of the pressing shaftPreferred values of (b) are 0.9 and 0.4, respectively.
In this embodiment, the fitness function includes a local fitness functionAnd global fitness function(ii) a Thus, in step S330, the local fitness function is comparedAndvalue of (d) and global fitness functionAndaccording to the result of the comparison, a locally optimal solution for the particleAnd global optimal solutionAnd (6) updating.
;Wherein, in the step (A),representing an objective functionThe maximum value of (a) is,representing an objective functionAverage value of (d);represents the weight corresponding to the nth member, and。
it will be appreciated that the local fitness functionPartial groups corresponding to each member, global fitness functionCorresponding to a population containing all members.
Meanwhile, although each optimal variable member in the application is related to the target parameter, the influence degree of each optimal variable member on the target parameter is different, so that the overall fitness function value of the population can be calculatedSetting the weight corresponding to each member according to the influence degree of each optimal variable member on the target parameterThe 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 obtainedAndand obtaining partial groups corresponding to each memberSo as to obtain the local fitness function value of each memberAnd global fitness function values for the entire population。
Then, the positions of the particles are updated, and the solution is obtained according to the updated positions of the particles、Andso as to obtain the local fitness function value of each member at the updated positionAnd global fitness function values for the entire population。
Then, the comparison is madeAnda value of, ifIs greater thanIf so, the local optimal solution corresponding to each part group is the local optimal solution after the particle position is updated; if it isIs less thanThe local optimal solution corresponding to each partial group is the local optimal solution before the particle position is updated. Also, ifIs greater thanIf so, the global optimal solution corresponding to the population is the global optimal solution after the particle position is updated; if it isIs less thanIf so, 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 appearIs less thanAnd/orIs less thanThe value of (c); 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 particlesAnd global optimal solution(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, the method may enter into the update loop in step S320 and step S330 together with the particles that have not been punished and satisfy the constraint condition.
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 toThe secondary process is divided into multiple parts.
S302: at the beginning of each part, a local fitness function corresponding to a single particleValue of (d) and global fitness functionThe values of (a) are compared.
S303: for local fitness functionIs better than the global fitness functionUntil the end of each part, the iterative process of step S300 is repeated.
S304: for local fitness functionIs different from the global fitness functionThe 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 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 resultAnd global optimal solutionAnd (6) updating.
Wherein the content of the first and second substances,,is (0, 1), and j is more than or equal to 1.
It can be understood that by setting the control factor a, the particles can be prevented from falling into a locally 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,]、(,]、(,]、……、(,]a total of j +1 moieties; the number j of the control factors a can be set according to actual needs, andthe value of (b) is an integer. To be explainedIs prepared from (A) and (B),]For example, the iteration process corresponding to the part is shown as the second of the whole iteration processesThen toNext, the process is repeated.
At the beginning of each part, the local fitness function of the particle is calculatedIs better than the global fitness functionThe 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 particleIs different from the global fitness functionThe 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 local searchingThe process is optimized.
The foregoing has described the general principles, essential 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 (10)
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 functionCarrying out representation;
2. The flyback transformer design method based on the improved PSO algorithm of claim 1, wherein: 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 particlesCorresponding objective functionAnd the value of the fitness function, and further obtaining the local optimal solution of each memberAnd global optimal solution(ii) a If the particles do not meet the constraint conditions, punishing;
s320: updating the position of the particle toSolving the value of the fitness function of the particle position update;
s330: comparing the value of the fitness function before and after particle location updateLocal optimal solution to particles based on the comparisonAnd global optimal solutionUpdating is carried out;
the position update formula of the particle may be:(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);the historical optimal position of the particle i in the d dimension is taken as the optimal position;is the location vector of the d-th dimension of particle i in the k-th iteration.
3. The flyback transformer design method based on the improved PSO algorithm of claim 2, wherein: the inertia weight w is linearly reduced along with the increase of the iteration times, and the specific calculation formula is as follows:
4. The flyback transformer design method based on the improved PSO algorithm of claim 2, wherein: the fitness function includes a local fitness functionAnd global fitness function(ii) a Thus, in step S330, the local fitness function is comparedAndvalue of (d) and global fitness functionAndaccording to the result of the comparison, a locally optimal solution for the particleAnd global optimal solutionUpdating is carried out;
5. the method of claim 2, 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;
6. The flyback transformer design method based on the improved PSO algorithm of claim 3, wherein: 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 toThe secondary process is divided into a plurality of parts;
s302: at the beginning of each part, a local fitness function corresponding to a single particleValue of (d) and global fitness functionComparing the values of (A);
s303: for local fitness functionIs better than the global fitness functionThe iterative process of step S300 is performed in a loop until the end of each part;
s304: for local fitness functionIs different from the global fitness functionSearching 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: step S303 and step at the end of each partThe fitness functions of the particles in step 304 are compared, and the locally optimal solution is determined according to the comparison resultAnd global optimal solutionUpdating is carried out;
7. The flyback transformer design method based on the improved PSO algorithm of any of claims 1-6, 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.
8. The flyback transformer design method based on the improved PSO algorithm of claim 7, 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.
9. The method for designing a flyback transformer based on the improved PSO algorithm as claimed in claim 1, wherein the member items of the optimal variables comprise: core type, core material, core size, window area, winding wire diameter, and leakage inductance.
10. 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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