CN117291004A - Transformer parameter optimization design method based on MSPBO algorithm - Google Patents
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Abstract
The invention discloses a transformer parameter optimization design method based on an MSPBO algorithm; the invention obtains the electromagnetic parameters of the dry-type transformer with low loss and low manufacturing cost by applying an algorithm after the improvement of the SPBO, namely an MSPBO algorithm to obtain the optimal solution; the automation degree is high, and the design period is short; the method can obtain the design scheme of the dry-type transformer with low manufacturing cost meeting the requirement of performance parameters in a short time, can reduce the production cost of the dry-type transformer, improves the loss performance of the dry-type transformer, and has important significance for the practical production application of the energy-saving dry-type transformer.
Description
Technical Field
The invention belongs to the technical field of transformer design, and particularly relates to a transformer parameter optimization design method based on an MSPBO algorithm.
Background
Transformers are expensive power equipment in a power system and play a vital role in power transmission and distribution of the power system. With the development of the transformer industry, the requirement for transformers is increasingly urgent. Compared with the traditional silicon steel sheet iron core transformer, the energy-saving dry type transformer has excellent no-load performance and occupies a great amount of demand in the distribution transformer market. In recent years, as the prices of raw materials such as iron cores, wires, accessories, clamping pieces and the like required for manufacturing the transformer rise, the price of the transformer rises, and thus, some impediments are generated to popularization and application of the transformer; on the premise of ensuring the performance of the transformer, the transformer is optimally designed, so that the production cost of the transformer is particularly important to be reduced; the optimal design of the transformer is a multi-variable, multi-constraint condition, discrete and nonlinear programming design problem, when manual calculation is adopted, the calculated optimization degree is influenced by too many human and environmental factors, so that the waste of production materials and the deficiency of product quality stability are caused, the traditional transformer design method is low in automation degree, long in design period and difficult to obtain an optimal transformer design scheme, and along with the development of computer technology and artificial intelligence, a plurality of solving algorithms of the optimal design model of the transformer are proposed at present, such as a genetic algorithm, a hybrid frog-leaping algorithm, a bacterial foraging algorithm, a differential evolution algorithm, an SPBO algorithm, a particle swarm algorithm and the like, and the algorithms are applied to the optimal design of transformer parameters, so that the transformer design period can be effectively shortened and the design efficiency can be improved. In practical application, the search speed of the solution space is high due to the solution space search mode of the SPBO algorithm, but the global search of the solution space is deficient and is easy to fall into local optimum; therefore, there is a need to design a transformer parameter optimization design method based on the MSPBO algorithm to solve the above-mentioned problems.
Disclosure of Invention
The invention aims to solve the technical problem of providing a transformer parameter optimization design method based on an MSPBO algorithm, and the method aims to solve the problem that the SPBO algorithm is insufficient in global searching capacity in the actual optimization design process, and solves the transformer design scheme meeting various constraint conditions by utilizing the improved MSPBO algorithm, so that the main material cost of the transformer can be reduced to a greater extent.
In order to solve the technical problems, the invention adopts the following technical scheme:
a transformer parameter optimization design method based on an MSPBO algorithm comprises the following steps:
s1, determining performance parameters according to the requirements of users or standards of the dry type transformer, and setting constraint conditions and optimizing the value range of parameter variables;
s2, generating codes of all the dimension optimization variables and corresponding actual value arrays, then randomly generating the positions of the first generation population, and evaluating the fitness of the population;
s3, dividing the population in the step S2 into four types of individuals, and carrying out update iteration according to an SPBO algorithm;
s4, after updating iteration of four species of individuals in the population, performing fitness evaluation and Metropolis criterion execution in a simulated annealing algorithm, and returning new species of individuals to an initial judgment condition;
s5, stopping the algorithm when the algorithm reaches the set maximum iteration times, and outputting the optimal design scheme in the population; if the output condition is not met, carrying out individual iterative updating again; and sequencing the cost prices of the main materials of the final population from low to high, and outputting the transformer design scheme with the lowest cost of the main materials meeting the performance standard to generate a calculation sheet.
Preferably, in step S3, the specific implementation method of the SPBO algorithm is as follows:
four types of individuals with the group division correspond to four levels of the SPBO algorithm respectively: the optimal students, good students, common students and randomly fluctuation score students update iteration according to the following corresponding iteration modes when t+1 exams are carried out:
s301, the highest total score of the examination is listed as the optimal student, and the iteration formula is as follows:
if rand()＞rand
else
wherein a=2·a monotonically decreases,taking max generation=100;
j is examination subject, k is 1 or 2 at random; i students are any students except the optimal students; random takes any value in [0,1 ];
s302, the students with excellent performance for each subject are listed as good students, and an iteration formula is as follows:
wherein b=2·a·rand () -a; in the formula, for good students, two numbers r are required to be taken from (0, 1) before an iterative formula is selected 1 And r 2 When r is 1 ＞r 2 If so, taking the formula (4), otherwise, iterating according to the formula (5),class average performance for subject j at t exams;
s303, the students with learning performance close to the average class level are listed as ordinary students, and an iterative formula is as follows:
s304, the students with random states of all course subjects to be treated are listed as the students with random fluctuation of the results, and the iterative formula is as follows:
wherein X is min,j And X max,j The lowest score and the highest score of subject j at the t-th examination, respectively.
Preferably, in step S4, the simulated annealing is performedThe algorithm is divided into an inner loop and an outer loop, the temperature of the outer loop is continuously reduced, the inner loop is in different states generated under the condition that the temperature of the outer loop is continuously disturbed, when the temperature of the outer loop is higher, the probability of disturbance is also higher, when the temperature of the outer loop is reduced to be in a stable region, the probability of disturbance is also reduced, and finally the inner loop also tends to be in a stable value, wherein the inner loop is executed according to a Metropolis criterion: initial state X of the relative position characterization of the given particle old The energy of the state is E old The method comprises the steps of carrying out a first treatment on the surface of the Then, the randomly selected particles are randomly changed in a tiny way through a perturbation device, so that the particles acquire a new state X new The energy value of the new state is E new ；
From state X in system old Change to X new The probability of acceptance of (c) is:
wherein T is the temperature, ifWhen the random number is greater than the interval [0,1], the state X is still accepted new As the current state.
Preferably, in step S5, the optimization objective of the optimal design scheme includes economic indicators, where the economic indicators include main material cost, core cost, low-voltage winding cost, high-voltage winding cost, and clip cost of the dry-type transformer, and the corresponding objective function expression is:
wherein f (X) is the cost of the main material of the transformer, W Fe Is the weight of the iron core, V Fe Is iron core unit price, W Low For low voltage wire weight, V Low Is the unit price of low-voltage wire, W High For the weight of the high-voltage wire, V High Is the unit price of high-voltage wires, f am Costs for accessories and clips; x is X 1 Is iron core laminated thickness X 2 For the number of layers, X, of low-voltage windings 3 Is the width of the low-voltage wire, X 4 Is the thickness X of the low-voltage wire 5 Is the width of high-voltage wire, X 6 The thickness of the high-voltage wire is equal to that of the high-voltage wire; the smaller the objective function f (X) is, the better the design is.
Preferably, in step S5, the optimization targets of the optimal design scheme include technical performance indexes and materials and process technologies, and constraint conditions are as follows:
(1) No-load loss: p (P) 0 (X)≤P 0r ；
(2) Load loss: p (P) k (X)≤P kr ；
(3) No-load current: i 0 (X)≤I 0r ；
(4) Impedance voltage:
(5) Low voltage winding temperature rise: t (T) l (X)≤T l max ；
(6) Temperature rise of the high-voltage winding:
(7) Low voltage winding wire current density:
(8) High voltage winding wire current density:
(9) Magnetic flux density:
(10) Efficiency is that: eta (X) is not less than eta r ；
Wherein P is 0 (X)、P 0r The actual value and the rated value of the no-load loss are respectively;
P k (X)、P kr respectively the actual value and the sum of the load lossSetting a value;
I 0 (X)、I 0r the actual value and the rated value of the no-load current are respectively;
U k (X) is the actual value of the short-circuit impedance,respectively a minimum value and a maximum value of the short-circuit impedance;
T l (X) is the actual temperature rise value of the low-voltage winding, T l max The maximum limit value of the temperature rise of the low-voltage winding is set;
T h (X) is the actual temperature rise value of the high-voltage winding,the maximum limit value of the temperature rise of the high-voltage winding is set;
J 1 (X)、J h (X) is the actual value of the current density of the low and high voltage wires, the current density of the low-voltage wire and the high-voltage wire are respectively the maximum limiting values;
B m (X) is the actual value of the magnetic flux density of the iron core,and->Respectively the minimum value and the maximum value of the magnetic flux density of the iron core;
η(X)、η r the actual efficiency and the nominal efficiency, respectively.
Preferably, the transformer parameter optimization design system based on the MSPBO algorithm comprises a dry-type transformer parameter setting module, an MSPBO algorithm and optimization variable upper and lower limit setting module, an MSPBO algorithm engine module and an optimization result output module.
Preferably, the dry-type transformer parameter setting module is used for setting and optimizing the rated capacity, iron core materials, iron core forms and insulation grade parameters of the dry-type transformer, and allowing parameter adjustment to be performed manually to meet actual product requirements;
preferably, the SPBO algorithm and optimization variable upper and lower limit setting module is used for setting population scale and maximum iteration number parameters of the MSPBO algorithm, and setting upper and lower limits of core lamination thickness, upper and lower limits of low voltage layer number, upper and lower limits of high voltage winding line width or line thickness, and upper and lower limits of low voltage winding line width or line thickness;
preferably, the MSPBO algorithm engine module is used for performing MSPBO algorithm-related calculation, including random encoding and generating an initial variable population, calculating an objective function value and an fitness value of population individuals of the generated initial population, updating the population position, finally generating a new offspring population, and performing iterative calculation until convergence conditions are met, and stopping calculation;
preferably, the optimization result output module is used for outputting a plurality of dry-type transformer parameter optimization schemes which are acquired by the MSPBO algorithm and meet the requirements of clients or performance parameter standards, and selecting the optimal transformer parameter optimization design scheme according to the low manufacturing cost and the low loss requirements.
The beneficial effects of the invention are as follows:
according to the invention, the core lamination thickness, the low-voltage winding layer number, the high-voltage winding wire width, the high-voltage winding wire thickness, the low-voltage winding wire width and the low-voltage winding wire thickness are selected as optimization variables, the dry-type transformer is optimized by adopting an improved SPBO algorithm under constraint conditions, the cost of main materials including the cost of the core, the low-voltage wire, the high-voltage wire, the clamp and other accessories are used as objective functions, a plurality of dry-type transformer optimization design schemes can be obtained under the condition of meeting performance standards, the design scheme with low main material cost is selected from the design schemes, and the design scheme can be output in a form of a calculation list.
Drawings
FIG. 1 is a flow chart of an algorithm of the present invention;
FIG. 2 is a flow chart of a dry-type transformer design according to the present invention;
FIG. 3 is a chart showing the convergence comparison of the MSPBO algorithm and the SPBO algorithm of the present invention;
fig. 4 is a structural design diagram of the transformer parameter optimization system based on the SPBO algorithm of the present invention.
Detailed Description
Embodiment one:
as shown in fig. 1 to 4, the transformer parameter optimization design method based on the MSPBO algorithm comprises the following steps:
s1, determining performance parameters according to the requirements of users or standards of the dry type transformer, and setting constraint conditions and optimizing the value range of parameter variables;
s2, generating codes of all the dimension optimization variables and corresponding actual value arrays, then randomly generating the positions of the first generation population, and evaluating the fitness of the population;
s3, dividing the population in the step S2 into four types of individuals, and carrying out update iteration according to an SPBO algorithm;
s4, after updating iteration of four species of individuals in the population, performing fitness evaluation and Metropolis criterion execution in a simulated annealing algorithm, and returning new species of individuals to an initial judgment condition;
s5, stopping the algorithm when the algorithm reaches the set maximum iteration times, and outputting the optimal design scheme in the population; if the output condition is not met, carrying out individual iterative updating again; and sequencing the cost prices of the main materials of the final population from low to high, and outputting the transformer design scheme with the lowest cost of the main materials meeting the performance standard to generate a calculation sheet.
Preferably, in step S3, the specific implementation method of the SPBO algorithm is as follows:
four types of individuals with the group division correspond to four levels of the SPBO algorithm respectively: the optimal students, good students, common students and randomly fluctuation score students update iteration according to the following corresponding iteration modes when t+1 exams are carried out:
s301, the highest total score of the examination is listed as the optimal student, and the iteration formula is as follows:
if rand()＞rand
else
wherein a=2·a monotonically decreases,taking max generation=100;
j is examination subject, k is 1 or 2 at random; i students are any students except the optimal students; random takes any value in [0,1 ];
s302, the students with excellent performance for each subject are listed as good students, and an iteration formula is as follows:
wherein b=2·a·rand () -a; in the formula, for good students, two numbers r are required to be taken from (0, 1) before an iterative formula is selected 1 And r 2 When r is 1 ＞r 2 If so, taking the formula (4), otherwise, iterating according to the formula (5),class average performance for subject j at t exams;
s303, the students with learning performance close to the average class level are listed as ordinary students, and an iterative formula is as follows:
s304, the students with random states of all course subjects to be treated are listed as the students with random fluctuation of the results, and the iterative formula is as follows:
wherein X is min,j And X max,j The lowest score and the highest score of subject j at the t-th examination, respectively.
Preferably, in step S4, the simulated annealing algorithm is divided into an inner loop and an outer loop, the temperature of the outer loop is continuously reduced, the inner loop is in different states generated under the condition that the temperature of the outer loop is continuously disturbed, when the temperature of the outer loop is higher, the probability of disturbance is also higher, when the temperature of the outer loop is reduced to be in a stable region, the probability of disturbance is also reduced, and finally the inner loop also tends to be in a stable value, wherein the inner loop is executed according to the metapolis criterion: initial state X of the relative position characterization of the given particle old The energy of the state is E old The method comprises the steps of carrying out a first treatment on the surface of the Then, the randomly selected particles are randomly changed in a tiny way through a perturbation device, so that the particles acquire a new state X new The energy value of the new state is E new ；
From state X in system old Change to X new The probability of acceptance of (c) is:
wherein T is the temperature, ifWhen the random number is greater than the interval [0,1], the state X is still accepted new As the current state.
Preferably, in step S5, the optimization objective of the optimal design scheme includes economic indicators, where the economic indicators include main material cost, core cost, low-voltage winding cost, high-voltage winding cost, and clip cost of the dry-type transformer, and the corresponding objective function expression is:
wherein f (X) is the cost of the main material of the transformer, W Fe Is the weight of the iron core, V Fe Is iron core unit price, W Low For low voltage wire weight, V Low Is the unit price of low-voltage wire, W High For the weight of the high-voltage wire, V High Is the unit price of high-voltage wires, f am Costs for accessories and clips; x is X 1 Is iron core laminated thickness X 2 For the number of layers, X, of low-voltage windings 3 Is the width of the low-voltage wire, X 4 Is the thickness X of the low-voltage wire 5 Is the width of high-voltage wire, X 6 The thickness of the high-voltage wire is equal to that of the high-voltage wire; the smaller the objective function f (X) is, the better the design is.
Preferably, in step S5, the optimization targets of the optimal design scheme include technical performance indexes and materials and process technologies, and constraint conditions are as follows:
(1) No-load loss: p (P) 0 (X)≤P 0r ；
(2) Load loss: p (P) k (X)≤P kr ；
(3) No-load current: i 0 (X)≤I 0r ；
(4) Impedance voltage:
(5) Low voltage winding temperature rise: t (T) l (X)≤T l max ；
(6) Temperature rise of the high-voltage winding:
(7) Low voltage winding wire current density:
(8) High voltage winding wire current density:
(9) Magnetic flux density:
(10) Efficiency is that: eta (X) is not less than eta r ；
Wherein P is 0 (X)、P 0r The actual value and the rated value of the no-load loss are respectively;
P k (X)、P kr load loss actual and rated values, respectively;
I 0 (X)、I 0r the actual value and the rated value of the no-load current are respectively;
U k (X) is the actual value of the short-circuit impedance,respectively a minimum value and a maximum value of the short-circuit impedance;
T l (X) is the actual temperature rise value of the low-voltage winding, T l max The maximum limit value of the temperature rise of the low-voltage winding is set;
T h (X) is the actual temperature rise value of the high-voltage winding,the maximum limit value of the temperature rise of the high-voltage winding is set;
J 1 (X)、J h (X) is the actual value of the current density of the low and high voltage wires,the current density of the low-voltage wire and the high-voltage wire are respectively the maximum limiting values;
B m (X) is the actual value of the magnetic flux density of the iron core,and->Respectively the minimum value and the maximum value of the magnetic flux density of the iron core;
η(X)、η r the actual efficiency and the nominal efficiency, respectively.
Preferably, the transformer parameter optimization design system based on the MSPBO algorithm comprises a dry-type transformer parameter setting module, an MSPBO algorithm and optimization variable upper and lower limit setting module, an MSPBO algorithm engine module and an optimization result output module.
Preferably, the dry-type transformer parameter setting module is used for setting and optimizing the rated capacity, iron core materials, iron core forms and insulation grade parameters of the dry-type transformer, and allowing parameter adjustment to be performed manually to meet actual product requirements;
preferably, the SPBO algorithm and optimization variable upper and lower limit setting module is used for setting population scale and maximum iteration number parameters of the MSPBO algorithm, and setting upper and lower limits of core lamination thickness, upper and lower limits of low voltage layer number, upper and lower limits of high voltage winding line width or line thickness, and upper and lower limits of low voltage winding line width or line thickness;
preferably, the MSPBO algorithm engine module is used for performing MSPBO algorithm-related calculation, including random encoding and generating an initial variable population, calculating an objective function value and an fitness value of population individuals of the generated initial population, updating the population position, finally generating a new offspring population, and performing iterative calculation until convergence conditions are met, and stopping calculation;
preferably, the optimization result output module is used for outputting a plurality of dry-type transformer parameter optimization schemes which are acquired by the MSPBO algorithm and meet the requirements of clients or performance parameter standards, and selecting the optimal transformer parameter optimization design scheme according to the low manufacturing cost and the low loss requirements.
Embodiment two:
in the step S1, the parameters of the optimization object which need to be determined before the system is optimized include rated capacity, iron core form, connection group and insulation grade; for the design scheme of the integral dry-type transformer, the selected optimized parameters include core stack thickness, low-voltage winding layer number, low-voltage winding line width, low-voltage winding line thickness, high-voltage winding line width and high-voltage winding line thickness.
In step S2, a first generation population consisting of six-dimensional random vectors is generated through a random process, and the upper and lower limit values of the values of each dimension random variable are determined by a line gauge table in the system.
The calculation and inspection are carried out according to the transformer calculation process shown in fig. 2 for each particle in the population, and the specific steps are as follows, according to the selected transformer model and the iron core structural form, the calculation is carried out according to the following formula:
the core diameter is estimated, and core limb and yoke cross sections, turn potentials, and turns are designed.
Wherein D is the estimated core diameter; k is the iron core coefficient;is rated capacity; a is that j To estimate the net cross-sectional area of the core; e, e t To estimate the turn potential; f is frequency of 50Hz; b (B) m To estimate the core flux density, 1.25T was taken.
Calculated from the following formula:
the number of turns of the high-voltage winding and the low-voltage winding is calculated and integrated into an integer.
Wherein N is d Turns for the low voltage winding; u (U) dφ Is a low-voltage phase voltage; u (U) gφ Is a high-voltage phase voltage;
according to the winding structure form, the specification of the wire is determined by the formula:
the axial and radial dimensions of the windings are calculated.
Wherein Z is gyzx Is the axial dimension of the high-voltage winding; w (W) mdk Each section of the high-voltage winding is wide; l (L) zbdk 、L djdk Respectively in high-voltage windingsThe part is filled with air and the section is filled with air; h gyfx Is the radial dimension of the high-voltage winding; h dxjy The insulation thickness of the high-voltage wire is high; h is a gyjy The interlayer insulation thickness of the high-voltage winding is; x is x gydx The thickness of the high-voltage winding is a stacking coefficient; z is Z dyzx Is the axial dimension of the low-voltage winding; w (W) lk The wire width of the low-voltage winding is measured; b (B) dybr The number of the low-voltage wires is the number of the parallel windings; h dyfx Is the radial total size of the low-voltage winding; h dyfx1 Is the radial dimension of the low-voltage winding inside the air passage; h dyfx2 The radial dimension of the low-voltage winding outside the air passage; c (C) dy1 The number of layers of the low-voltage winding on the inner side of the air passage; c (C) dy2 The number of layers of the low-voltage winding at the outer side of the air passage; h is a dyjy The thickness of the interlayer insulation of the low-voltage winding is; x is x dydx Is a low voltage winding stack thickness coefficient.
And calculating short circuit impedance, no-load loss, load loss and winding temperature rise, randomly calculating relevant winding parameters when the parameters are not qualified, and readjusting the diameter of the iron core if the parameters are not qualified.
Generating a population with the size of 10 according to the method, and setting the maximum iteration algebra T as 100 generations;
after the initial population is generated, the fitness function calculation mode is set as follows:
fitness(X i )＝f norm (X i )+v norm (X i )
wherein the middle fitness (X i ) The fitness value is simply called fitness;
the smaller the fitness of the individual, the better the individual is represented.
The main material cost of the dry type transformer is set as a fitness function, namely:
f(X)＝[W Fe ×V Fe +W Low ×V Low +V High ×V High +f am ]
wherein f (X) is the cost of the main material, W Fe Is the weight of the iron core, V Fe Is iron core unit price, W Low For low voltage wire weight, V Low Is the unit price of low-voltage wire, W High For the weight of the high-voltage wire, V High Is the unit price of high-voltage wires, f am For clips and accessoriesThe cost is high;
smaller objective functions represent better designs.
The foregoing embodiments are merely preferred embodiments of the present invention, and should not be construed as limiting the present invention, and the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict. The protection scope of the invention is defined by the technical proposal of the claims, and the equivalent substitution of technical characteristics in the technical proposal of the claims is taken as the protection scope. I.e., equivalent replacement modifications within the scope of this patent are also within the scope of protection of the present invention.
Claims (10)
1. A transformer parameter optimization design method based on an MSPBO algorithm is characterized by comprising the following steps of: the method comprises the following steps:
s1, determining performance parameters according to the requirements of users or standards of the dry type transformer, and setting constraint conditions and optimizing the value range of parameter variables;
s2, generating codes of all the dimension optimization variables and corresponding actual value arrays, then randomly generating the positions of the first generation population, and evaluating the fitness of the population;
s3, dividing the population in the step S2 into four types of individuals, and carrying out update iteration according to an SPBO algorithm;
s4, after updating iteration of four species of individuals in the population, performing fitness evaluation and Metropolis criterion execution in a simulated annealing algorithm, and returning new species of individuals to an initial judgment condition;
s5, stopping the algorithm when the algorithm reaches the set maximum iteration times, and outputting the optimal design scheme in the population; if the output condition is not met, carrying out individual iterative updating again; and sequencing the cost prices of the main materials of the final population from low to high, and outputting the transformer design scheme with the lowest cost of the main materials meeting the performance standard to generate a calculation sheet.
2. The transformer parameter optimization design method based on the MSPBO algorithm as set forth in claim 1, wherein the method is characterized in that: in step S3, the specific implementation method of the SPBO algorithm is as follows:
four types of individuals with the group division correspond to four levels of the SPBO algorithm respectively: the optimal students, good students, common students and randomly fluctuation score students update iteration according to the following corresponding iteration modes when t+1 exams are carried out:
s301, the highest total score of the examination is listed as the optimal student, and the iteration formula is as follows:
if rand()＞rand
else
where a=2a decreases monotonically,taking max generation=100;
j is examination subject, k is 1 or 2 at random; i students are any students except the optimal students; random takes any value in [0,1 ];
s302, the students with excellent performance for each subject are listed as good students, and an iteration formula is as follows:
wherein b=2·a·rand () -a; in the formula, for good students, two numbers r are required to be taken from (0, 1) before an iterative formula is selected 1 And r 2 When r is 1 ＞r 2 If so, taking the formula (4), otherwise, pressingThe iteration is carried out in the formula (5),class average performance for subject j at t exams;
s303, the students with learning performance close to the average class level are listed as ordinary students, and an iterative formula is as follows:
s304, the students with random states of all course subjects to be treated are listed as the students with random fluctuation of the results, and the iterative formula is as follows:
wherein X is min,j And X max,j The lowest score and the highest score of subject j at the t-th examination, respectively.
3. The transformer parameter optimization design method based on the MSPBO algorithm as set forth in claim 1, wherein the method is characterized in that: in step S4, the simulated annealing algorithm is divided into internal and external cycles, the external cycle temperature is continuously reduced, the internal cycle is in different states generated under the continuous disturbance of the external temperature, when the external temperature is higher, the disturbance probability is also higher, when the external cycle temperature is reduced to be stable in area, the disturbance probability is also reduced, and finally the internal cycle also tends to be stable, wherein the internal cycle is executed according to the metapolis criterion: initial state X of the relative position characterization of the given particle old The energy of the state is E old The method comprises the steps of carrying out a first treatment on the surface of the Then, the randomly selected particles are randomly changed in a tiny way through a perturbation device, so that the particles acquire a new state X new The energy value of the new state is E new ；
From state X in system old Change to X new The probability of acceptance of (c) is:
wherein T is the temperature, ifWhen the random number is greater than the interval [0,1], the state X is still accepted new As the current state.
4. The transformer parameter optimization design method based on the MSPBO algorithm as set forth in claim 1, wherein the method is characterized in that: in step S5, the optimization objective of the optimal design scheme includes economic indicators, where the economic indicators include main material cost, core cost, low-voltage winding cost, high-voltage winding cost, and clip cost of the dry-type transformer, and the corresponding objective function expression is:
wherein f (X) is the cost of the main material of the transformer, W Fe Is the weight of the iron core, V Fe Is iron core unit price, W Low For low voltage wire weight, V Low Is the unit price of low-voltage wire, W High For the weight of the high-voltage wire, V High Is the unit price of high-voltage wires, f am Costs for accessories and clips; x is X 1 Is iron core laminated thickness X 2 For the number of layers, X, of low-voltage windings 3 Is the width of the low-voltage wire, X 4 Is the thickness X of the low-voltage wire 5 Is the width of high-voltage wire, X 6 The thickness of the high-voltage wire is equal to that of the high-voltage wire; the smaller the objective function f (X) is, the better the design is.
5. The transformer parameter optimization design method based on the MSPBO algorithm as set forth in claim 1, wherein the method is characterized in that: in step S5, the optimization targets of the optimal design scheme include technical performance indexes and materials and process technologies, and constraint conditions are as follows:
(1) No-load loss：P 0 (X)≤P 0r ；
(2) Load loss: p (P) k (X)≤P kr ；
(3) No-load current: i 0 (X)≤I 0r ；
(4) Impedance voltage:
(5) Low voltage winding temperature rise:
(6) Temperature rise of the high-voltage winding:
(7) Low voltage winding wire current density:
(8) High voltage winding wire current density:
(9) Magnetic flux density:
(10) Efficiency is that: eta (X) is not less than eta r ；
Wherein P is 0 (X)、P 0r The actual value and the rated value of the no-load loss are respectively;
P k (X)、P kr load loss actual and rated values, respectively;
I 0 (X)、I 0r the actual value and the rated value of the no-load current are respectively;
U k (X) is the actual value of the short-circuit impedance,respectively a minimum value and a maximum value of the short-circuit impedance;
T l (X) is the actual temperature rise value of the low-voltage winding,the maximum limit value of the temperature rise of the low-voltage winding is set;
T h (X) is the actual temperature rise value of the high-voltage winding,the maximum limit value of the temperature rise of the high-voltage winding is set;
J 1 (X)、J h (X) is the actual value of the current density of the low and high voltage wires,the current density of the low-voltage wire and the high-voltage wire are respectively the maximum limiting values;
B m (X) is the actual value of the magnetic flux density of the iron core,and->Respectively the minimum value and the maximum value of the magnetic flux density of the iron core;
η(X)、η r the actual efficiency and the nominal efficiency, respectively.
6. A transformer parameter optimization design system based on an MSPBO algorithm is characterized in that: the system comprises a dry-type transformer parameter setting module, an MSPBO algorithm and optimization variable upper and lower limit setting module, an MSPBO algorithm engine module and an optimization result output module.
7. The transformer parameter optimization design system based on the MSPBO algorithm as set forth in claim 6, wherein: the dry-type transformer parameter setting module is used for setting and optimizing the rated capacity, iron core materials, iron core forms and insulation grade parameters of the dry-type transformer, and allowing parameter adjustment to be performed manually to meet actual product requirements.
8. The transformer parameter optimization design system based on the MSPBO algorithm as set forth in claim 6, wherein: the SPBO algorithm and optimization variable upper and lower limit setting module is used for setting population scale and maximum iteration number parameters of the MSPBO algorithm, and setting upper and lower limits of iron core lamination thickness, upper and lower limits of low voltage layer number, upper and lower limits of high voltage winding line width or line thickness and upper and lower limits of low voltage winding line width or line thickness.
9. The transformer parameter optimization design system based on the MSPBO algorithm as set forth in claim 6, wherein: the MSPBO algorithm engine module is used for performing MSPBO algorithm related calculation, including random encoding and generating an initial variable population, calculating an objective function value and an fitness value of population individuals of the generated initial population, updating the population position, finally generating a new child population, and performing iterative calculation until convergence conditions are met, and stopping calculation.
10. The transformer parameter optimization design system based on the MSPBO algorithm as set forth in claim 6, wherein: the optimization result output module is used for outputting a plurality of dry-type transformer parameter optimization schemes meeting the requirements of clients or performance parameter standards, which are acquired by the MSPBO algorithm, and selecting the optimal transformer parameter optimization design scheme according to the low manufacturing cost and the low loss requirement.
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