CN112560331B - Energy-saving and material-saving optimization design system and method for amorphous alloy dry type transformer - Google Patents
Energy-saving and material-saving optimization design system and method for amorphous alloy dry type transformer Download PDFInfo
- Publication number
- CN112560331B CN112560331B CN202011373438.6A CN202011373438A CN112560331B CN 112560331 B CN112560331 B CN 112560331B CN 202011373438 A CN202011373438 A CN 202011373438A CN 112560331 B CN112560331 B CN 112560331B
- Authority
- CN
- China
- Prior art keywords
- amorphous alloy
- voltage
- optimization
- population
- type transformer
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01F—MAGNETS; INDUCTANCES; TRANSFORMERS; SELECTION OF MATERIALS FOR THEIR MAGNETIC PROPERTIES
- H01F27/00—Details of transformers or inductances, in general
- H01F27/24—Magnetic cores
- H01F27/245—Magnetic cores made from sheets, e.g. grain-oriented
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
Abstract
The invention discloses an energy-saving and material-saving optimization design system and method for an amorphous alloy dry-type transformer, wherein the system comprises an amorphous alloy dry-type transformer optimization design system main interface parameter setting module, an amorphous alloy dry-type transformer optimization design system product operation parameter input module, an amorphous alloy dry-type transformer optimization design system improved NSGA-II optimization algorithm parameter and optimization variable parameter upper limit and lower limit setting module, an amorphous alloy dry-type transformer optimization design system improved NSGA-II algorithm optimization processing module and an amorphous alloy dry-type transformer optimization design system output optimization result module; the method comprises the following steps: acquiring an initial design scheme of the amorphous alloy dry-type transformer through manual design; compared with the traditional transformer design method, the optimization system is simple to operate, high in automation degree and short in design period, can effectively reduce the manufacturing cost of the amorphous alloy dry-type transformer, remarkably improves the loss performance of the amorphous alloy dry-type transformer, and has important significance for popularization of the amorphous alloy dry-type transformer.
Description
Technical Field
The invention relates to the technical field of amorphous alloy transformer optimization design, in particular to an energy-saving and material-saving optimization design system and method for an amorphous alloy dry type transformer.
Background
The demand of new capital construction on electric power is continuously increased, and in the process of electric power production, a large amount of pollutants such as greenhouse gas and dust are often accompanied, so that the environment is greatly polluted. In the electric energy loss of China, the electric energy loss in a power distribution network approximately accounts for about 70% of the whole network loss. In power distribution networks, there are long-term lightly loaded or unloaded distribution transformers whose no-load losses account for a large portion of the losses in the distribution network. Therefore, the reduction of the no-load loss of the transformer can bring huge energy-saving and environmental-protection benefits.
With the popularization of energy conservation and emission reduction and green environmental protection concepts, amorphous alloy iron core transformers with low no-load loss are paid more and more attention and are applied. Compared with the traditional iron core transformer made of silicon steel sheets, the amorphous alloy iron core transformer has great advantages in energy conservation. Compared with a silicon steel sheet iron core transformer, the no-load loss of the amorphous alloy iron core transformer is reduced by 70-80%, and the no-load current is reduced by about 80%, so that the amorphous alloy iron core transformer is a distribution transformer with ideal energy-saving effect at present. However, compared with the conventional silicon steel sheet iron core, the amorphous alloy iron core is more difficult to manufacture and uses more materials, so that the price of the amorphous alloy iron core transformer is higher than that of the conventional silicon steel sheet iron core transformer. Therefore, the amorphous alloy transformer needs to be optimally designed.
At present, due to the lack and shortage of related amorphous alloy transformer optimization design tool software, a large amount of waste of raw materials of products and insufficient quality stability are caused. In addition, the traditional transformer design method has low automation degree, large design workload and long design period, and an optimal design scheme is difficult to obtain. Therefore, in the optimization problem of multi-objective, multi-variable, multi-constraint condition, discreteness and non-linearity in transformer optimization design, how to reduce the manufacturing cost of the amorphous alloy transformer, improve the loss performance of the transformer and accelerate the design cycle of the transformer becomes a urgent task in the amorphous alloy transformer design industry.
Disclosure of Invention
The invention aims to provide an energy-saving and material-saving optimization design method and system for an amorphous alloy dry type transformer; the optimized design method disclosed by the invention can effectively reduce the manufacturing cost of the amorphous alloy transformer and improve the loss performance of the transformer; in addition, the optimal design system disclosed by the invention can solve the problems of low automation degree, large design workload and long design period of the traditional transformer design method.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
the energy-saving and material-saving optimization design system for the amorphous alloy dry type transformer comprises a main interface parameter setting module of the amorphous alloy dry type transformer optimization design system, an amorphous alloy dry type transformer optimization design system product operation parameter input module, an NSGA-II optimization algorithm parameter and optimization variable parameter upper limit and lower limit setting module improved by the amorphous alloy dry type transformer optimization design system, an NSGA-II algorithm optimization processing module improved by the amorphous alloy dry type transformer optimization design system and an amorphous alloy dry type transformer optimization design system output optimization result module;
wherein;
the amorphous alloy dry-type transformer optimization design system main interface parameter setting module is used for determining rated capacity, iron core form, iron core material, coupling group and insulation grade operation parameters of an optimized object;
the amorphous alloy dry-type transformer optimization design system product operation parameter input module is used for inputting transformer optimization calculation operation parameters, and the transformer optimization calculation operation parameters comprise rated parameters, structural parameters, insulation distances, determination coefficients, line mode margins, performance tolerances, air channel types and material unit price parameters;
the optimized design system of the amorphous alloy dry-type transformer is characterized in that an improved NSGA-II optimized algorithm parameter and optimized variable parameter upper bound and lower bound setting module is used for setting the coding length, the population size, the genetic algebra, the selection probability, the cross probability and the variation probability of an optimized algorithm, and setting the value upper bound and lower bound of an iron core stack thickness, the number of low-voltage layers, the high-voltage line width, the high-voltage line thickness, the low-voltage line width and the low-voltage line thickness optimized variable;
the optimized design system of the amorphous alloy dry-type transformer is characterized in that an improved NSGA-II algorithm optimized processing module is used for establishing a transformer optimized model, and the economic index and the loss index of the amorphous alloy dry-type transformer are optimized and solved by adopting an NSGA-II algorithm under the condition that the constraint conditions of electrical performance, material performance and material specification are met;
the output optimization result module of the amorphous alloy dry type transformer optimization design system is used for outputting a plurality of optimization schemes which are obtained by an improved NSGA-II algorithm and meet performance requirements, and selecting an optimal high-efficiency energy-saving low-cost optimization design scheme according to different requirements of customers.
The rated parameters in the operation parameter input module of the amorphous alloy dry-type transformer optimization design system are specifically used for setting parameters of the product model specification, the product rated capacity, the high-voltage rated voltage, the low-voltage rated voltage, the working frequency, the load loss, the no-load loss, the impedance voltage, the no-load current and the temperature rise limit value of the high-low voltage winding.
The structural parameters in the operation parameter input module of the amorphous alloy dry-type transformer optimization design system are specifically used for setting the material and shape of high-voltage and low-voltage windings, the winding and parallel winding form of high-voltage wires, the number of iron core stacks and parallel, the number of high-voltage winding sections, the thickness of an upper clamping piece, the thickness of a lower clamping piece and the thickness of an end clamping piece.
The insulation distance in the operation parameter input module of the amorphous alloy dry-type transformer optimization design system is specifically used for setting parameters of iron core gaps, small window gaps, thicknesses of epoxy cylinders or paperboards, thicknesses of reinforcing plates in the middle of the epoxy cylinders, air flue thicknesses, insulation thicknesses of high-voltage wires, low-voltage outer layer thicknesses, main air flue thicknesses, high-voltage inner layer thicknesses, high-voltage outer layer thicknesses, end insulation, insulation thicknesses between high-voltage wire layers and insulation thicknesses between low-voltage wire layers.
The determined coefficients in the operation parameter input module of the amorphous alloy dry type transformer optimization design system are specifically used for setting parameters of an iron core lamination coefficient, an iron core loss coefficient, an iron core lap joint coefficient, a low-voltage winding overlapping thickness coefficient, a high-voltage winding overlapping thickness coefficient, a load loss coefficient, a high-voltage winding temperature rise coefficient, a low-voltage winding temperature rise coefficient and a copper foil coefficient;
the performance tolerance in the operation parameter input module of the amorphous alloy dry-type transformer optimization design system is used for setting the allowable upper limit and lower limit of impedance voltage, and the no-load loss tolerance value and the load loss tolerance value;
the line mode margin in the operation parameter input module is used for setting a line mode width margin and a line mode length margin;
the air channel type in the operation parameter input module is used for setting the high-pressure side air channel type and the low-pressure side air channel type.
Further optimizing, wherein the material unit price in the operation parameter input module of the amorphous alloy dry-type transformer optimization design system is specifically used for setting amorphous alloy unit price, high-voltage aluminum flat wire unit price, high-voltage copper round wire unit price, low-voltage copper foil unit price, low-voltage aluminum foil unit price, clamping piece unit price and accessory unit price parameters;
based on the setting of the upper and lower bounds of the optimization algorithm parameters and the optimization variable parameters, an improved NSGA-II algorithm optimization processing module in the amorphous alloy dry type transformer optimization design system is used for carrying out program internal optimization calculation on the established transformer optimization model;
the output optimization result module of the amorphous alloy dry-type transformer optimization design system is specifically used for displaying various feasible optimization results and outputting main transformer parameter values in an optimization scheme by selecting one optimization result;
the obtained optimization scheme can be output and stored in 3 forms, namely, in the form of a Txt text; secondly, outputting and storing in the form of an Excel calculation sheet; and thirdly, outputting and storing in the form of an Access database.
The energy-saving and material-saving optimization design method for the amorphous alloy dry type transformer comprises the following steps:
step 1:
determining optimization variables and constraint conditions in an improved NSGA-II optimization model according to an optimization target of the amorphous alloy dry-type transformer; the optimization variables comprise iron core stack thickness, low-voltage layer number, a high-voltage wire gauge and a low-voltage wire gauge; the constraint conditions comprise electrical property constraint, material property constraint and process constraint;
step 2:
acquiring an initial design scheme of the amorphous alloy dry-type transformer by manual design, and determining the upper and lower value limits of the optimized variables;
and step 3:
determining the number and the length of genes contained in a single individual of a population in the NSGA-II algorithm according to the number of optimized variables, coding the genes of the population individual by setting the size of the population and the upper and lower value limits of each gene in a real number coding and integer coding mode, and generating an initial population by a chaotic mapping mode so as to enhance the diversity of the population; wherein the initial population comprises an internal populationPWith external populationsP e The internal population participates in genetic evolution, is continuously initialized and updated in a chaotic mapping mode and participates in the selection operation of the population; the number of genes contained in the population individual is determined by the optimization variable;
and 4, step 4:
calculating an objective function value of each individual in the current-generation population by the optimized objective function, and performing rapid non-dominated sorting operation on the objective function values by comparing the objective function values of each individual to obtain sorted population individuals;
and 5:
performing virtual fitness calculation, namely congestion distance calculation on the sorted population individuals of each domination level, and obtaining offspring population after selecting, crossing and mutating the population individualsQ；
Step 6:
combining the parent population and the offspring population into a new populationRFirstly, carrying out variation operation on individuals with dense repetition in the combined population and calculating the objective function value of the individuals, then carrying out rapid non-dominant sorting operation on the combined population, and before selection, selecting the combined populationNTaking excellent individuals as a new parent population;
and 7:
performing initialization updating operation on the external population by adopting a chaotic mapping mode, and setting the non-dominated sequencing sequence number in the new parent population as 1 (F 1 ) Storing the individuals into the updated external population, and if the population genetic algebra does not reach the maximum iteration number, returning the new parent population and the updated external population to execute the steps 3 to 6 to perform the next genetic operation;
and 8:
and if the population genetic algebra reaches the maximum iteration times, selecting an optimal design scheme with high efficiency, energy conservation and low manufacturing cost according to different requirements of customers according to a plurality of groups of feasible amorphous alloy dry type transformer optimal design schemes output in the population.
Further optimizing, the optimization target comprises an economic index (main material cost) and a loss index (total loss), wherein the economic index is the main material cost, and the loss index is the total loss:
wherein, the first and the second end of the pipe are connected with each other,APC(X) Cost of the main material;NLL(X) No load loss;LL(X) Is a load loss;X=[X 1 , X 2 , ... , X n ]to optimize variable parameters;
wherein the cost of the main materialf 1 (X) And total lossf 2 (X) Respectively as follows:
wherein the content of the first and second substances,
C core 、C low 、C high the unit price of the amorphous alloy iron core, the low-voltage winding and the high-voltage winding is respectively;
W core 、W low 、W high the weights of the amorphous alloy iron core, the low-voltage winding and the high-voltage winding are respectively set;
K、α、βis the core material coefficient;
V core is the volume of the iron core;
ρ core is the amorphous alloy density;
fthe working frequency of the transformer;
B m is the core flux density;
k 1 、k 2 the loss coefficients of the high-voltage winding and the low-voltage winding are respectively;
I high 、I low high and low voltage winding phase currents, respectively;
R high 、R low respectively high and low voltage winding resistances.
Further optimizing, wherein the electrical performance constraint, the material performance constraint and the material specification constraint are as follows:
1) The electrical performance constraints are as follows:
a. no-load loss:P 0 < k P0 ×P 0r ；
b. load loss:P k < k Pk ×P kr ；
c. no-load current:I 0 < I 0r ；
d. short-circuit impedance:U k min < U k < U k max ；
e. efficiency:η > η r ；
2) The material property constraints are as follows:
a. core magnetic flux density:B m min < B m < B m max ；
b. current density of low-voltage wire:J l < J l max ；
c. current density of the high-voltage wire:J h < J h max ；
d. temperature rise of the low-voltage winding:T l < T l max ；
e. temperature rise of the high-voltage winding:T h < T h max ；
3) The material specification constraints are as follows:
a. iron core lamination thickness:D min < D t < D max ；
b. the number of layers of the low-voltage winding is as follows:N c min < N c < N c max ；
c. high-voltage wire width:W h min < W h < W h max ；
d. and (3) thickness of the high-voltage wire:D h min < D h < D h max ；
e. low voltage conductor width:W l min < W l < W l max ；
f, thickness of the low-voltage wire:D l min < D l < D l max 。
wherein the content of the first and second substances,
P 0 、P 0r respectively representing the actual value and the rated value of no-load loss;
k P0 is the no-load loss coefficient;
P k 、P kr respectively a load loss actual value and a rated value;
k Pk is the load loss factor;
I 0 、I 0r respectively an actual value and a rated value of the no-load current;
U k for the actual value of the short-circuit impedance,U k max 、U k min the short circuit impedance values are respectively an upper bound and a lower bound;
η、η r actual efficiency and rated efficiency respectively;
B m max 、B m min an upper limit and a lower limit of the magnetic flux density of the iron core are respectively set;
J l 、J h are the actual current density values of the high-voltage wire and the low-voltage wire respectively,J l max 、J h max the maximum limiting values of the current density of the high-voltage wire and the low-voltage wire are respectively set;
T l 、T h are respectively the actual temperature rise values of the high-voltage winding and the low-voltage winding,T l max 、T h max respectively setting the maximum limit values of the temperature rise of the high-voltage winding and the low-voltage winding;
D max 、D min respectively taking an upper bound and a lower bound for the iron core lamination thickness;
N c max 、N c min respectively taking an upper bound and a lower bound for the layer number of the low-voltage winding;
W h max 、W h min the width of the high-voltage wire is respectively provided with an upper boundary and a lower boundary;
D h min 、D h max respectively taking a lower bound and an upper bound for the thickness of the high-voltage lead;
W l max 、W l min the width of the low-voltage wire is respectively provided with an upper boundary and a lower boundary;
D l min 、D l max the thickness of the low-voltage wire is respectively taken as a lower bound and an upper bound.
The mode of generating the initial population by the chaotic mapping mode comprises the following steps:
wherein, the first and the second end of the pipe are connected with each other,
L i,j is as followsiThe first of an individualjA dimension chaotic variable;
γis a chaotic factor and is used for generating a chaotic signal,γ∈(0, 4)；
when the temperature is higher than the set temperatureγWhen = 4, the chaotic variable is in a complete chaotic state;
X i,j is a firstiThe first of an individualjA dimension variable parameter;X j min , X j max respectively the lower bound and the upper bound of the variable parameter of the j dimension of the population individuals;
wherein, the first and the second end of the pipe are connected with each other,
the calculation method of the virtual fitness (crowding distance) of the population individual is as follows:
wherein, the first and the second end of the pipe are connected with each other,
Corwd_dist i 1 (X) Is a firstiFirst objective function of individualf 1 (X) The degree of crowding;
Corwd_dist i 2 (X) Is as followsiIndividual second objective functionf 2 (X) The degree of crowding;
Corwd_dist i (X) Is as followsiIndividual population virtual fitness (crowding distance);
pre_ f 1 (X)、next_ f 1 (X) Are respectively the firsti1 individual to the firsti+A first objective function value for 1 individual;f 1 max 、f 1 min respectively the maximum value and the minimum value of the first objective function;
pre_ f 2 (X)、next_ f 2 (X) Are respectively the firsti-1 individual toi+A second objective function value for 1 individual;
f 2 max 、f 2 min respectively the maximum value and the minimum value of the second objective function;
in each domination level population individual, sorting according to the sizes of the first objective function value and the second objective function value, and taking the crowdedness of the first individual and the last individual as infinity;
the selection, crossing and variation of population individuals generate offspring populations, and the genetic operations of the offspring populations respectively adopt championship selection operation, simulated binary crossing operation and polynomial variation operation.
Compared with the prior art, the invention has the following beneficial effects:
the method adopts the improved NSGA-II multi-objective optimization algorithm under the constraint condition to optimize the cost and the total loss of the main materials of the amorphous alloy transformer, and can obtain a plurality of feasible optimization schemes to the maximum extent for designers to select. Compared with manual design, the obtained optimization scheme can effectively reduce the manufacturing cost of the amorphous alloy transformer and improve the loss performance of the transformer. In addition, based on an improved NSGA-II optimization algorithm, the invention also provides an energy-saving and material-saving optimization design system for the amorphous alloy dry-type transformer, the system has reasonable design flow, accurate optimization calculation result and high calculation speed, and the problems of low automation degree, large design workload and long design period of the traditional transformer design method are effectively solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a flow chart of the energy-saving and material-saving optimization design system of the dry type transformer based on the improved NSGA-II amorphous alloy.
FIG. 2 is a structural design drawing of an energy-saving and material-saving optimization design system of the amorphous alloy dry type transformer.
FIG. 3 is an operation flow chart of the energy-saving and material-saving optimization design system of the amorphous alloy dry type transformer.
FIG. 4 is a main interface of the energy-saving and material-saving optimized design system of the amorphous alloy dry type transformer.
FIG. 5 is a product parameter setting interface of the energy-saving and material-saving optimization design system of the amorphous alloy dry type transformer.
Fig. 6 is an interface of the improved NSGA-II algorithm operation parameter and the optimized variable parameter set by the energy-saving and material-saving optimized design system for the amorphous alloy dry-type transformer of the present invention.
FIG. 7 is an interface of the optimized result output by the optimized energy-saving material-saving design system of the amorphous alloy dry-type transformer of the present invention.
FIG. 8 is an optimization result distribution curve of the energy-saving and material-saving optimization design system of the amorphous alloy dry type transformer based on the NSGA-II algorithm.
FIG. 9 is a distribution curve of the energy-saving and material-saving optimization design system of the amorphous alloy dry type transformer based on the improved NSGA-II algorithm.
Detailed Description
The present invention will be further described with reference to the following examples, which are intended to illustrate only some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, other embodiments used by those skilled in the art without any creative effort belong to the protection scope of the present invention.
Example one
The invention discloses an energy-saving and material-saving optimization design system for an amorphous alloy dry-type transformer, which comprises an amorphous alloy dry-type transformer optimization design system main interface parameter setting module, an amorphous alloy dry-type transformer optimization design system product operation parameter input module, an amorphous alloy dry-type transformer optimization design system improved NSGA-II optimization algorithm parameter and optimization variable parameter upper limit and lower limit setting module, an amorphous alloy dry-type transformer optimization design system improved NSGA-II algorithm optimization processing module and an amorphous alloy dry-type transformer optimization design system output optimization result module;
wherein;
the amorphous alloy dry-type transformer optimization design system main interface parameter setting module is used for determining rated capacity, iron core form, iron core material, coupling group and insulation grade operation parameters of an optimized object;
the amorphous alloy dry-type transformer optimization design system product operation parameter input module is used for inputting transformer optimization calculation operation parameters, and the transformer optimization calculation operation parameters comprise rated parameters, structural parameters, insulation distances, determination coefficients, line mode margins, performance tolerances, air channel types and material unit price parameters;
the optimized design system of the amorphous alloy dry-type transformer is characterized in that an improved NSGA-II optimized algorithm parameter and optimized variable parameter upper bound and lower bound setting module is used for setting the coding length, the population size, the genetic algebra, the selection probability, the cross probability and the variation probability of an optimized algorithm, and setting the value upper bound and lower bound of an iron core stack thickness, the number of low-voltage layers, the high-voltage line width, the high-voltage line thickness, the low-voltage line width and the low-voltage line thickness optimized variable;
the optimized design system of the amorphous alloy dry type transformer is characterized in that an improved NSGA-II algorithm optimization processing module is used for establishing a transformer optimization model, and under the condition that constraint conditions of electrical performance, material performance and material specification are met, an NSGA-II algorithm is adopted to carry out optimization solution on economic indexes and loss indexes of the amorphous alloy dry type transformer;
the output optimization result module of the amorphous alloy dry type transformer optimization design system is used for outputting a plurality of optimization schemes which are obtained by an improved NSGA-II algorithm and meet performance requirements, and selecting an optimal high-efficiency energy-saving low-cost optimization design scheme according to different requirements of customers.
The rated parameters in the operation parameter input module of the amorphous alloy dry-type transformer optimization design system are specifically used for setting parameters of the product model specification, the product rated capacity, the high-voltage rated voltage, the low-voltage rated voltage, the working frequency, the load loss, the no-load loss, the impedance voltage, the no-load current and the temperature rise limit value of the high-low voltage winding.
The structural parameters in the operation parameter input module of the amorphous alloy dry-type transformer optimization design system are specifically used for setting the material and shape of high-voltage and low-voltage windings, the winding and parallel winding form of high-voltage wires, the number of iron core stacks and parallel, the number of high-voltage winding sections, the thickness of an upper clamping piece, the thickness of a lower clamping piece and the thickness of an end clamping piece.
The insulation distance in the operation parameter input module of the amorphous alloy dry-type transformer optimization design system is specifically used for setting parameters of iron core gaps, small window gaps, thicknesses of epoxy cylinders or paperboards, thicknesses of reinforcing plates in the middle of the epoxy cylinders, air flue thicknesses, high-voltage lead insulation thicknesses, low-voltage outer layer thicknesses, main air flue thicknesses, high-voltage inner layer thicknesses, high-voltage outer layer thicknesses, end insulation, high-voltage lead interlayer insulation thicknesses and low-voltage lead interlayer insulation thicknesses.
The determined coefficients in the operation parameter input module of the amorphous alloy dry type transformer optimization design system are specifically used for setting parameters of an iron core lamination coefficient, an iron core loss coefficient, an iron core lap joint coefficient, a low-voltage winding overlapping thickness coefficient, a high-voltage winding overlapping thickness coefficient, a load loss coefficient, a high-voltage winding temperature rise coefficient, a low-voltage winding temperature rise coefficient and a copper foil coefficient;
the performance tolerance in the operation parameter input module of the amorphous alloy dry-type transformer optimization design system is used for setting an allowable upper limit and a lower limit of impedance voltage, a no-load loss tolerance value and a load loss tolerance value;
the linear mode margin in the operation parameter input module is used for setting a linear mode width margin and a linear mode length margin;
the air channel type in the operation parameter input module is used for setting the high-pressure side air channel type and the low-pressure side air channel type.
Further optimizing, wherein the material unit price in the operation parameter input module of the amorphous alloy dry-type transformer optimization design system is specifically used for setting amorphous alloy unit price, high-voltage aluminum flat wire unit price, high-voltage copper round wire unit price, low-voltage copper foil unit price, low-voltage aluminum foil unit price, clamping piece unit price and accessory unit price parameters;
based on the setting of the upper and lower bounds of the optimization algorithm parameters and the optimization variable parameters, an improved NSGA-II algorithm optimization processing module in the amorphous alloy dry type transformer optimization design system is used for carrying out program internal optimization calculation on the established transformer optimization model;
the output optimization result module of the amorphous alloy dry-type transformer optimization design system is specifically used for displaying various feasible optimization results and outputting main transformer parameter values in an optimization scheme by selecting one of the optimization results;
the obtained optimization scheme can be output and stored in 3 forms, namely, the optimization scheme is output and stored in a Txt text form; secondly, outputting and storing in the form of an Excel calculation sheet; and thirdly, outputting and storing in the form of an Access database.
The energy-saving and material-saving optimization design method for the amorphous alloy dry type transformer comprises the following steps:
step 1:
determining optimization variables and constraint conditions in an improved NSGA-II optimization model according to an optimization target of the amorphous alloy dry-type transformer; the optimization variables comprise iron core stack thickness, low-voltage layer number, a high-voltage wire gauge and a low-voltage wire gauge; the constraint conditions comprise electrical property constraint, material property constraint and process constraint;
step 2:
acquiring an initial design scheme of the amorphous alloy dry-type transformer by manual design, and determining the upper and lower value limits of the optimized variables;
and 3, step 3:
determining the number and the length of genes contained in a single individual of a population in the NSGA-II algorithm according to the number of optimized variables, encoding the genes of the population individuals by setting the size of the population and the upper and lower value boundaries of each gene in a real number encoding and integer encoding mode, and generating an initial population by a chaotic mapping mode so as to enhance the diversity of the population; wherein the initial population comprises an internal populationPWith external populationsP e The internal population participates in genetic evolution, is continuously initialized and updated in a chaotic mapping mode and participates in the selection operation of the population; the number of genes contained in the population individual is determined by the optimization variable;
and 4, step 4:
calculating an objective function value of each individual in the current-generation population by the optimized objective function, and performing rapid non-dominated sorting operation on each individual by comparing the objective function values of the individuals to obtain sorted population individuals;
and 5:
performing virtual fitness calculation, namely congestion distance calculation on the sorted population individuals of each domination level, and obtaining offspring population after selecting, crossing and mutating the population individualsQ；
And 6:
combining the parent population and the offspring population into a new populationRFirstly, carrying out variation operation on individuals with dense repetition in the combined population and calculating the objective function value of the individuals, then carrying out rapid non-dominant sorting operation on the combined population, and before selection, selecting the combined populationNTaking excellent individuals as a new parent population;
and 7:
performing initialization updating operation on an external population by adopting a chaotic mapping mode, and setting the non-dominated sequencing sequence number in the new parent population to be 1 (F 1 ) Storing the individuals into the updated external population, and if the population genetic algebra does not reach the maximum iteration number, returning the new parent population and the updated external population to execute the steps 3 to 6 to perform the next genetic operation;
and 8:
and if the population genetic algebra reaches the maximum iteration times, selecting an optimal design scheme with high efficiency, energy conservation and low manufacturing cost according to different requirements of customers according to a plurality of groups of feasible amorphous alloy dry-type transformers output in the population.
Further optimizing, the optimization target comprises an economic index (main material cost) and a loss index (total loss), wherein the economic index is the main material cost, and the loss index is the total loss:
wherein the content of the first and second substances,APC(X) Cost of the main material;NLL(X) No load loss;LL(X) Is the load loss;X=[X 1 , X 2 , ... , X n ]to optimize variable parameters;
wherein the cost of the main materialf 1 (X) And total lossf 2 (X) Respectively as follows:
wherein, the first and the second end of the pipe are connected with each other,
C core 、C low 、C high the unit price of the amorphous alloy iron core, the unit price of the low-voltage winding and the unit price of the high-voltage winding are respectively;
W core 、W low 、W high the weights of the amorphous alloy iron core, the low-voltage winding and the high-voltage winding are respectively set;
K、α、βis the core material coefficient;
V core is the volume of the iron core;
ρ core is the amorphous alloy density;
fthe working frequency of the transformer;
B m is the core flux density;
k 1 、k 2 the loss coefficients of the high-voltage winding and the low-voltage winding are respectively;
I high 、I low respectively high-voltage winding phase current and low-voltage winding phase current;
R high 、R low respectively high and low voltage winding resistances.
Further optimizing, wherein the electrical performance constraint, the material performance constraint and the material specification constraint are as follows:
1) The electrical performance constraints are as follows:
a. no-load loss:P 0 < k P0 ×P 0r ；
b. load loss:P k < k Pk ×P kr ；
c. no-load current:I 0 < I 0r ；
d. short-circuit impedance:U k min < U k < U k max ；
e. efficiency:η > η r ；
2) The material property constraints are as follows:
a. core magnetic flux density:B m min < B m < B m max ；
b. low-voltage wire current density:J l < J l max ；
c. current density of the high-voltage wire:J h < J h max ；
d. temperature rise of the low-voltage winding:T l < T l max ；
e. temperature rise of the high-voltage winding:T h < T h max ；
3) The material specification constraints are as follows:
a, iron core lamination thickness:D min < D t < D max ；
b, the number of layers of the low-voltage winding is as follows:N c min < N c < N c max ；
c, high voltage wire width:W h min < W h < W h max ；
d, high-voltage wire thickness:D h min < D h < D h max ；
e, low voltage conductor width:W l min < W l < W l max ；
f, thickness of the low-voltage wire:D l min < D l < D l max 。
wherein the content of the first and second substances,
P 0 、P 0r respectively an actual value and a rated value of no-load loss;
k P0 is the no-load loss coefficient;
P k 、P kr respectively representing the actual value and the rated value of the load loss;
k Pk is the load loss factor;
I 0 、I 0r respectively an actual value and a rated value of the no-load current;
U k for the actual value of the short-circuit impedance,U k max 、U k min the upper and lower bounds are taken for the short circuit impedance respectively;
η、η r actual efficiency and rated efficiency respectively;
B m max 、B m min an upper limit and a lower limit of the magnetic flux density of the iron core are respectively set;
J l 、J h are the actual current density values of the high-voltage wire and the low-voltage wire respectively,J l max 、J h max the maximum limiting values of the current density of the high-voltage wire and the low-voltage wire are respectively;
T l 、T h are respectively the actual temperature rise values of the high-voltage winding and the low-voltage winding,T l max 、T h max the maximum limit values of the temperature rise of the high-voltage winding and the low-voltage winding are respectively set;
D max 、D min respectively taking an upper bound and a lower bound for the iron core overlapping thickness;
N c max 、N c min respectively taking an upper bound and a lower bound for the layer number of the low-voltage winding;
W h max 、W h min the width of the high-voltage wire is respectively taken as an upper bound and a lower bound;
D h min 、D h max respectively taking a lower bound and an upper bound for the thickness of the high-voltage wire;
W l max 、W l min the width of the low-voltage wire is respectively taken as an upper bound and a lower bound;
D l min 、D l max the thickness of the low-voltage wire is respectively taken as a lower bound and an upper bound.
The mode of generating the initial population by the chaotic mapping mode comprises the following steps:
wherein the content of the first and second substances,
L i,j is a firstiThe first of an individualjA dimension chaotic variable;
γis a chaotic factor and is used for generating a chaotic signal,γ∈(0, 4)；
when the temperature is higher than the set temperatureγWhen = 4, the chaotic variable is in a complete chaotic state;
X i,j is a firstiThe first of an individualjA dimension variable parameter;X j min , X j max respectively the lower bound and the upper bound of the variable parameter of the j dimension of the population individual;
wherein, the first and the second end of the pipe are connected with each other,
the calculation method of the virtual fitness (crowding distance) of the population individuals is as follows:
wherein the content of the first and second substances,
Corwd_dist i 1 (X) Is as followsiFirst objective function of individualf 1 (X) The degree of crowding;
Corwd_dist i 2 (X) Is as followsiIndividual second objective functionf 2 (X) The degree of congestion;
Corwd_dist i (X) Is as followsiIndividual population virtual fitness (crowding distance);
pre_ f 1 (X)、next_ f 1 (X) Are respectively the firsti-1 individual toi+A first objective function value for 1 individual;f 1 max 、f 1 min respectively the maximum value and the minimum value of the first objective function;
pre_ f 2 (X)、next_ f 2 (X) Are respectively the firsti-1 individual toi+A second objective function value for 1 individual;
f 2 max 、f 2 min respectively the maximum value and the minimum value of the second objective function;
in each domination level population individual, sorting according to the sizes of the first objective function value and the second objective function value respectively, and taking the crowdedness of the first individual and the last individual as infinity;
the selection, crossing and variation of population individuals generate offspring populations, and the genetic operations of the offspring populations respectively adopt a championship selection operation, a simulated binary crossing operation and a polynomial variation operation.
To further facilitate a more intuitive understanding of the present invention to those skilled in the art, the present invention is further described in the following detailed description.
An energy-saving and material-saving optimization design method for an amorphous alloy dry type transformer (see figure 1) adopts an improved NSGA-II optimization algorithm and carries out optimization design on amorphous alloy through an operable optimization system.
The specific operation process is as follows:
the first step is as follows: setting the main interface parameters (see fig. 4): and determining the rated capacity, the iron core form, the iron core material, the connection group, the insulation grade and other operation parameters of the optimized object, and selecting the optimized design.
The embodiment of the invention takes an amorphous alloy dry type transformer of SCLBH15-315/10 as an example, the rated capacity is 315kVA, the iron core is in a three-phase five-column type, the iron core is made of amorphous alloy, the coupling group is Dyn11, and the insulation grade is F grade.
The second step: set product run parameters (see fig. 5): and determining rated parameters, structural parameters, insulation distance, determination coefficient, line mode margin, performance tolerance, air channel type and unit price of materials of the product.
The third step: setting improved NSGA-II algorithm operation parameters and optimization variable parameters (see FIG. 6): setting the coding length, the population size, the genetic algebra, the selection probability, the cross probability and the mutation probability of an optimization algorithm; setting the upper and lower value limits of optimized variables such as iron core stack thickness, low-voltage layer number, high-voltage line width, high-voltage line thickness, low-voltage line width, low-voltage line thickness and the like;
the improved NSGA-II algorithm of the embodiment of the invention adopts a real number coding and integer coding mode to code the optimized variable, and adopts a chaos mapping mode to generate an initial population. The optimized variable parameter coding length is set to be 6, and the population sizes of the internal population and the external population set in the initial population are set to be 6N=100, the population generation number is 200, the selection probability is 0.8 for the crossover probability, and the mutation probability is 1/6.
Setting the value interval of optimized variableX j min , X j max ]Wherein the value interval of the lamination thickness of the iron core is set as [50, 100 ]]The value interval of the low-voltage layer number is set to be [15, 25 ]]The high-voltage line width value interval is set to be [2, 16 ]]The value interval of the high-voltage line thickness is set to be [0.8, 5.6 ]]The low-voltage line width value interval is set to [20, 1400 ]]The value interval of the low-voltage line thickness is set to be [0.2, 2.5 ]]。
And fourthly, calling an improved NSGA-II algorithm to carry out optimization calculation on the SCLBH15-315/10 amorphous alloy dry-type transformer.
Firstly, generating an initial population in a set optimization variable value interval by adopting a chaotic mapping mode, wherein the initial population comprises an internal populationPWith external populationsP e 。
Calculating an objective function for each individual in the contemporary populationf 1 (X) Andf 2 (X) The value of (4) is obtained by comparing the objective function value of each individual to perform rapid non-dominated sorting operation on the individual to obtain the sorted population individuals;
performing virtual fitness (crowding distance,Corwd_ dist i (X) ) is calculated. Obtaining offspring population by selecting, crossing and mutating population individualsQ；
Secondly, the parent population isPAnd progeny populationQAre combined into a new populationRFirstly, carrying out variation operation on individuals with dense repetition in the combined population and calculating the objective function value of the individuals, then carrying out rapid non-dominant sorting operation on the combined population, and before selection, selecting the combined populationNThe excellent individuals serve as a new parent population;
initializing and updating the external population by chaotic mapping, and setting the non-dominated sequencing sequence number in the new parent population to 1 (1: (F 1 ) The individuals are stored in the updated external population, if the population genetic algebra does not reach the maximum genetic algebra of 200, the new parent population and the updated external population are returned to execute the steps (3) to (6) to carry out the next genetic operation;
finally, if the population genetic algebra reaches the maximum genetic algebra of 200, selecting an optimal design scheme with high efficiency, energy saving and low manufacturing cost according to different requirements of customers according to a plurality of groups of feasible amorphous alloy dry-type transformers output from the population;
and fourthly, outputting a plurality of optimization schemes which are acquired by the improved NSGA-II algorithm and meet the performance requirements, wherein the optimization schemes comprise the following tables (in the tables, the unit is mm):
and selecting an optimal efficient energy-saving low-manufacturing-cost design scheme from the table according to different requirements of customers, wherein specific parameters of the optimal scheme are displayed on an output product optimization result interface (see fig. 7).
In the embodiment, an amorphous alloy dry-type transformer of SCLBH15-315/10 is taken as an example, and the optimization design is carried out by respectively adopting an NSGA-II algorithm and an improved NSGA-II algorithm. As can be seen from fig. 8 and 9, the improved NSGA-II algorithm enhances the diversity of the population, effectively improves the phenomenon that denser individuals repeatedly appear in the population, makes the obtained Pareto solutions more uniformly distributed, and provides more feasible design solutions for designers. In this embodiment, the initially designed optimized variable parameter values and objective function values of the amorphous alloy dry-type transformer of SCLBH15-315/10 are as follows: iron core stack thickness (X 1 ) 83mm, number of low-pressure layers (X 2 ) 20 layers, high voltage linewidth: (X 3 ) 4.75mm, high voltage line thickness: (X 4 ) 1.9mm, low voltage line width: (X 5 ) 600mm, low-voltage line thickness: (X 6 ) 0.7mm, the main material costf 1 (X) 29590 yuan, total lossf 2 (X) Is 3745W. From the above table, on the premise of meeting the performance requirement of the amorphous alloy dry-type transformer, compared with the original design, the 2 nd to 8 th optimization schemes obtained by adopting the improved NSGA-II algorithm can effectively reduce the manufacturing cost of the transformer and improve the loss of the transformer. In addition, the optimization design system for the amorphous alloy dry-type transformer is simple to operate, the optimization calculation time of the embodiment is within 1 minute each time, the design period is greatly shortened, and the design process is accelerated.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the invention. The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, it should be noted that any modifications, equivalents and improvements made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A be used for energy-conserving material-saving optimal design system of metallic glass dry-type transformer, its characterized in that:
the optimized design system comprises a main interface parameter setting module of an amorphous alloy dry type transformer optimized design system, an amorphous alloy dry type transformer optimized design system product operation parameter input module, an improved NSGA-II optimized algorithm parameter and optimized variable parameter upper limit and lower limit setting module of the amorphous alloy dry type transformer optimized design system, an improved NSGA-II algorithm optimized processing module of the amorphous alloy dry type transformer optimized design system and an amorphous alloy dry type transformer optimized design system output optimized result module;
wherein the content of the first and second substances,
the amorphous alloy dry-type transformer optimization design system main interface parameter setting module is used for determining rated capacity, iron core form, iron core material, coupling group and insulation grade operation parameters of an optimized object;
the amorphous alloy dry-type transformer optimization design system product operation parameter input module is used for inputting transformer optimization calculation operation parameters, and the transformer optimization calculation operation parameters comprise rated parameters, structural parameters, insulation distances, determination coefficients, line mode margins, performance tolerances, air channel types and material unit price parameters;
the optimized design system of the amorphous alloy dry type transformer is characterized in that an improved NSGA-II optimized algorithm parameter and optimized variable parameter upper bound and lower bound setting module is used for setting the coding length, the population size, the genetic algebra, the selection probability, the cross probability and the variation probability of an optimized algorithm, and setting the value upper bound and lower bound of the iron core stack thickness, the low voltage layer number, the high voltage line width, the high voltage line thickness, the low voltage line width and the low voltage line optimized variable;
the optimized design system of the amorphous alloy dry-type transformer is characterized in that an improved NSGA-II algorithm optimized processing module is used for establishing a transformer optimized model, and the economic index and the loss index of the amorphous alloy dry-type transformer are optimized and solved by adopting an NSGA-II algorithm under the condition that the constraint conditions of electrical performance, material performance and material specification are met;
the output optimization result module of the amorphous alloy dry type transformer optimization design system is used for outputting a plurality of optimization schemes which are obtained by an improved NSGA-II algorithm and meet performance requirements, and selecting an optimal high-efficiency energy-saving low-cost optimization design scheme according to different requirements of customers.
2. The energy-saving and material-saving optimization design system for the amorphous alloy dry type transformer according to claim 1, characterized in that: the rated parameters in the operation parameter input module of the amorphous alloy dry type transformer optimization design system are specifically used for setting parameters of the product model specification, the product rated capacity, the high-voltage rated voltage, the low-voltage rated voltage, the working frequency, the load loss, the no-load loss, the impedance voltage, the no-load current and the temperature rise limit value of the high-low voltage winding.
3. The energy-saving and material-saving optimization design system for the amorphous alloy dry type transformer according to claim 1, characterized in that: the structural parameters in the operation parameter input module of the amorphous alloy dry type transformer optimization design system are specifically used for setting the material and the shape of a high-voltage winding and a low-voltage winding, the winding and parallel form of a high-voltage lead, the number of the iron core, the number of the sections of the high-voltage winding, the thickness of an upper clamping piece, the thickness of a lower clamping piece and the thickness of an end clamping piece.
4. The energy-saving and material-saving optimization design system for the amorphous alloy dry type transformer according to claim 1, is characterized in that: the insulation distance in the operation parameter input module of the amorphous alloy dry type transformer optimization design system is specifically used for setting iron core gaps, small window gaps, the thickness of an epoxy cylinder or a paper plate, the thickness of an epoxy cylinder middle reinforcing plate, the thickness of an air channel, the insulation thickness of a high-voltage lead, the thickness of a low-voltage outer layer, the thickness of a main air channel, the thickness of a high-voltage inner layer, the thickness of a high-voltage outer layer, end insulation, the insulation thickness between high-voltage lead layers and the insulation thickness between low-voltage lead layers.
5. The energy-saving and material-saving optimization design system for the amorphous alloy dry type transformer according to claim 1, characterized in that: the determined coefficients in the operation parameter input module of the amorphous alloy dry type transformer optimization design system are specifically used for setting parameters of an iron core lamination coefficient, an iron core loss coefficient, an iron core lap joint coefficient, a low-voltage winding overlapping thickness coefficient, a high-voltage winding overlapping thickness coefficient, a load loss coefficient, a high-voltage winding temperature rise coefficient, a low-voltage winding temperature rise coefficient and a copper foil coefficient;
the performance tolerance in the operation parameter input module of the amorphous alloy dry-type transformer optimization design system is used for setting an allowable upper limit and a lower limit of impedance voltage, a no-load loss tolerance value and a load loss tolerance value;
the line mode margin in the operation parameter input module is used for setting a line mode width margin and a line mode length margin;
the air channel type in the operation parameter input module is used for setting the high-pressure side air channel type and the low-pressure side air channel type.
6. The energy-saving and material-saving optimization design system for the amorphous alloy dry type transformer according to claim 1, characterized in that: the material unit price in the operation parameter input module of the amorphous alloy dry-type transformer optimization design system is specifically used for setting parameters of amorphous alloy unit price, high-voltage flat aluminum wire unit price, high-voltage flat copper wire unit price, high-voltage round copper wire unit price, low-voltage copper foil unit price, low-voltage aluminum foil unit price, clamping piece unit price and accessory unit price;
based on the setting of the upper and lower bounds of the optimization algorithm parameters and the optimization variable parameters, an improved NSGA-II algorithm optimization processing module in the amorphous alloy dry type transformer optimization design system is used for carrying out program internal optimization calculation on the established transformer optimization model;
the output optimization result module of the amorphous alloy dry-type transformer optimization design system is specifically used for displaying various feasible optimization results and outputting main transformer parameter values in an optimization scheme by selecting one of the optimization results;
the obtained optimization scheme can be output and stored in 3 forms, namely, in the form of a Txt text; secondly, outputting and storing in the form of an Excel calculation sheet; and thirdly, outputting and storing in the form of an Access database.
7. The energy-saving and material-saving optimization design method for the amorphous alloy dry type transformer is characterized by comprising the following steps of:
step 1:
determining optimization variables and constraint conditions in an improved NSGA-II optimization model according to an optimization target of the amorphous alloy dry-type transformer; the optimization variables comprise iron core stack thickness, low-voltage layer number, a high-voltage wire gauge and a low-voltage wire gauge; the constraint conditions comprise electrical property constraint, material property constraint and process constraint;
step 2:
acquiring an initial design scheme of the amorphous alloy dry-type transformer by manual design, and determining the upper and lower value limits of the optimized variables;
and 3, step 3:
determining the number and the length of genes contained in a single individual of a population in the NSGA-II algorithm according to the number of optimized variables, coding the genes of the population individual by setting the size of the population and the upper and lower value limits of each gene in a real number coding and integer coding mode, and generating an initial population by a chaotic mapping mode so as to enhance the diversity of the population; wherein the initial population comprises an internal populationPWith external populationsP e The internal population participates in genetic evolution, is continuously initialized and updated in a chaotic mapping mode and participates in the selection operation of the population; the number of genes contained in the population individual is determined by the optimization variable;
and 4, step 4:
calculating an objective function value of each individual in the current-generation population by the optimized objective function, and performing rapid non-dominated sorting operation on the objective function values by comparing the objective function values of each individual to obtain sorted population individuals;
and 5:
performing virtual fitness calculation, namely congestion distance calculation on the sorted population individuals of each domination level, and obtaining offspring population after selecting, crossing and mutating the population individualsQ；
Step 6:
combining the parent population and the offspring population into a new populationRFirstly, carrying out variation operation on individuals with dense repetition in the combined population and calculating the objective function value of the individuals, then carrying out rapid non-dominant sorting operation on the combined population, and before selection, selecting the combined populationNThe excellent individuals serve as a new parent population;
and 7:
performing initialization updating operation on an external population by adopting a chaotic mapping mode, and setting the non-dominated sequencing sequence number in the new parent population to be 1 (F 1 ) Storing the individuals into the updated external population, and if the population genetic algebra does not reach the maximum iteration number, returning the new parent population and the updated external population to execute the steps 3 to 6 to perform the next genetic operation;
and 8:
and if the population genetic algebra reaches the maximum iteration times, selecting an optimal design scheme with high efficiency, energy conservation and low manufacturing cost according to different requirements of customers according to a plurality of groups of feasible amorphous alloy dry-type transformers output in the population.
8. The energy-saving and material-saving optimization design method for the amorphous alloy dry type transformer according to claim 7, characterized in that:
the optimization target comprises an economic index and a loss index, wherein the economic index is the main material cost, and the loss index is the total loss:
wherein, the first and the second end of the pipe are connected with each other,APC(X) Cost of the main material;NLL(X) No load loss;LL(X) Is the load loss;X=[X 1 , X 2 , ... , X n ]to optimize variable parameters;
wherein the cost of the main materialf 1 (X) And total lossf 2 (X) Respectively as follows:
wherein the content of the first and second substances,
C core 、C low 、C high the unit price of the amorphous alloy iron core, the low-voltage winding and the high-voltage winding is respectively;
W core 、W low 、W high the weights of the amorphous alloy iron core, the low-voltage winding and the high-voltage winding are respectively set;
K、α、βis the core material coefficient;
V core is the volume of the iron core;
ρ core is the amorphous alloy density;
fthe working frequency of the transformer;
B m is the core flux density;
k 1 、k 2 the loss coefficients of the high-voltage winding and the low-voltage winding are respectively;
I high 、I low high and low voltage winding phase currents, respectively;
R high 、R low respectively high and low voltage winding resistances.
9. The energy-saving and material-saving optimization design method for the amorphous alloy dry type transformer according to claim 7 or 8, wherein the method comprises the following steps: the electrical property constraint, the material property constraint and the material specification constraint are as follows:
the electrical performance constraints are as follows:
no-load loss:P 0 < k P0 ×P 0r ；
load loss:P k < k Pk ×P kr ；
no-load current:I 0 < I 0r ；
short-circuit impedance:U k min < U k < U k max ；
efficiency:η > η r ；
the material property constraints are as follows:
core magnetic flux density:B m min < B m < B m max ；
current density of low-voltage wire:J l < J l max ；
current density of the high-voltage wire:J h < J h max ；
temperature rise of the low-voltage winding:T l < T l max ；
temperature rise of the high-voltage winding:T h < T h max ；
the material specification constraints are as follows:
core stack thickness:D min < D t < D max ；
number of layers of low-voltage winding:N c min < N c < N c max ；
high voltage wire width:W h min < W h < W h max ；
the high-voltage wire is thick:D h min < D h < D h max ；
low voltage conductive line width:W l min < W l < W l max ；
low voltage wire thickness:D l min < D l < D l max ；
wherein the content of the first and second substances,
P 0 、P 0r respectively representing the actual value and the rated value of no-load loss;
k P0 is the no-load loss coefficient;
P k 、P kr respectively a load loss actual value and a rated value;
k Pk is the load loss factor;
I 0 、I 0r respectively an actual value and a rated value of the no-load current;
U k for the actual value of the short-circuit impedance,U k max 、U k min the short circuit impedance values are respectively an upper bound and a lower bound;
η、η r actual efficiency and rated efficiency respectively;
B m max 、B m min an upper limit and a lower limit of the magnetic flux density of the iron core are respectively set;
J l 、J h are the actual current density values of the high-voltage wire and the low-voltage wire respectively,J l max 、J h max the maximum limiting values of the current density of the high-voltage wire and the low-voltage wire are respectively set;
T l 、T h are respectively the actual temperature rise values of the high-voltage winding and the low-voltage winding,T l max 、T h max respectively setting the maximum limit values of the temperature rise of the high-voltage winding and the low-voltage winding;
D max 、D min respectively taking an upper bound and a lower bound for the iron core lamination thickness;
N c max 、N c min respectively taking an upper bound and a lower bound for the layer number of the low-voltage winding;
W h max 、W h min the width of the high-voltage wire is respectively provided with an upper boundary and a lower boundary;
D h min 、D h max respectively taking a lower bound and an upper bound for the thickness of the high-voltage wire;
W l max 、W l min the width of the low-voltage wire is respectively taken as an upper bound and a lower bound;
D l min 、D l max the thickness of the low-voltage wire is respectively taken as a lower bound and an upper bound.
10. The energy-saving and material-saving optimization design method for the amorphous alloy dry type transformer according to claim 9, characterized in that: the mode of generating the initial population by the chaotic mapping mode comprises the following steps:
wherein the content of the first and second substances,
L i,j is as followsiThe first of an individualjA dimension chaotic variable;
γis a chaotic factor and is used as a chaotic factor,γ∈(0, 4)；
when in useγ When = 4, the chaotic variable is in a complete chaotic state;
X i,j is as followsiThe first of an individualjA dimension variable parameter;X j min , X j max respectively the lower bound and the upper bound of the variable parameter of the j dimension of the population individual;
wherein the content of the first and second substances,
the calculation method of the crowd distance of the population individual virtual fitness is as follows:
wherein, the first and the second end of the pipe are connected with each other,
Corwd_dist i 1 (X) Is as followsiFirst objective function of individualf 1 (X) The degree of crowding;
Corwd_dist i 2 (X) Is as followsiIndividual second objective functionf 2 (X) The degree of crowding;
Corwd_dist i (X) Is as followsiIndividual population virtual fitness;
pre_ f 1 (X)、next_ f 1 (X) Are respectively the firsti-1 individual toi+A first objective function value for 1 individual;f 1 max 、f 1 min respectively the maximum value and the minimum value of the first objective function;
pre_ f 2 (X)、next_ f 2 (X) Are respectively the firsti1 individual to the firsti+A second objective function value for 1 individual;
f 2 max 、f 2 min respectively the maximum value and the minimum value of the second objective function;
in each domination level population individual, sorting according to the sizes of the first objective function value and the second objective function value respectively, and taking the crowdedness of the first individual and the last individual as infinity;
the selection, crossing and variation of population individuals generate offspring populations, and the genetic operations of the offspring populations respectively adopt a championship selection operation, a simulated binary crossing operation and a polynomial variation operation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011373438.6A CN112560331B (en) | 2020-11-30 | 2020-11-30 | Energy-saving and material-saving optimization design system and method for amorphous alloy dry type transformer |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011373438.6A CN112560331B (en) | 2020-11-30 | 2020-11-30 | Energy-saving and material-saving optimization design system and method for amorphous alloy dry type transformer |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112560331A CN112560331A (en) | 2021-03-26 |
| CN112560331B true CN112560331B (en) | 2022-11-22 |
Family
ID=75045424
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011373438.6A Active CN112560331B (en) | 2020-11-30 | 2020-11-30 | Energy-saving and material-saving optimization design system and method for amorphous alloy dry type transformer |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112560331B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115270607B (en) * | 2022-07-06 | 2023-04-28 | 明珠电气股份有限公司 | Dry-type transformer optimization design method for nuclear power station by integrating expert experience |
| CN115310353B (en) * | 2022-07-26 | 2024-02-20 | 明珠电气股份有限公司 | Power transformer design method based on rapid multi-objective optimization |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104317979A (en) * | 2014-08-20 | 2015-01-28 | 江苏科技大学 | High-frequency high-voltage transformer design optimization method based on genetic algorithm |
| CN106257477A (en) * | 2016-07-29 | 2016-12-28 | 南京工程学院 | A kind of intermediate frequency amorphous alloy transformer optimization method based on multi-objective genetic algorithm |
| CN106528996A (en) * | 2016-11-04 | 2017-03-22 | 江西理工大学 | Transformer optimal design method based on adaptive teaching-learning-based optimization |
| CN108053250A (en) * | 2017-12-15 | 2018-05-18 | 北京理工大学 | A kind of transformer Price optimization method and device based on genetic algorithm |
| CN109583072A (en) * | 2018-11-23 | 2019-04-05 | 华中科技大学 | A kind of genetic algorithm optimization method and system of insulating core transformer compensating parameter |
| CN110517874A (en) * | 2019-08-05 | 2019-11-29 | 三峡大学 | A kind of high-power intermediate-frequency power transformer design method |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202258680U (en) * | 2011-08-02 | 2012-05-30 | 广东海鸿变压器有限公司 | Oil-immersed type three-dimensional rolled iron-core amorphous-alloy transformer |
| WO2015175923A1 (en) * | 2014-05-16 | 2015-11-19 | HST Solar Farms, Inc. | Systems & methods for solar photovoltaic array engineering |
| US20160285265A1 (en) * | 2015-03-25 | 2016-09-29 | Eleon Energy, Inc. | Methods and systems for power restoration planning employing simulation and a frequency analysis test |
-
2020
- 2020-11-30 CN CN202011373438.6A patent/CN112560331B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104317979A (en) * | 2014-08-20 | 2015-01-28 | 江苏科技大学 | High-frequency high-voltage transformer design optimization method based on genetic algorithm |
| CN106257477A (en) * | 2016-07-29 | 2016-12-28 | 南京工程学院 | A kind of intermediate frequency amorphous alloy transformer optimization method based on multi-objective genetic algorithm |
| CN106528996A (en) * | 2016-11-04 | 2017-03-22 | 江西理工大学 | Transformer optimal design method based on adaptive teaching-learning-based optimization |
| CN108053250A (en) * | 2017-12-15 | 2018-05-18 | 北京理工大学 | A kind of transformer Price optimization method and device based on genetic algorithm |
| CN109583072A (en) * | 2018-11-23 | 2019-04-05 | 华中科技大学 | A kind of genetic algorithm optimization method and system of insulating core transformer compensating parameter |
| CN110517874A (en) * | 2019-08-05 | 2019-11-29 | 三峡大学 | A kind of high-power intermediate-frequency power transformer design method |
Non-Patent Citations (6)
| Title |
|---|
| NSGA-II-Based Codesign Optimization for Power Conversion and Controller Stages of Interleaved Boost Converters in Electric Vehicle Drivetrains;Dai-Duong Tran等;《Energies》;20201004;第13卷(第19期);5167:1-31 * |
| Optimization Design of Amorphous Metal Distribution Transformer Based on Improved Fast and Elitist Multi-objective Genetic Algorithm;Daosheng Liu等;《2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE)》;20201215;1-4 * |
| 基于IPSO算法的非晶合金变压器优化设计;梁礼明等;《科技通报》;20181130;第34卷(第11期);120-124 * |
| 基于二代非支配排序遗传算法的电子变压器多目标优化;杨慧娜等;《华北电力大学学报(自然科学版)》;20130531;第40卷(第05期);C042-131 * |
| 基于改进型NSGA-Ⅱ算法的深海高频变压器优化研究;罗柏文等;《中国海洋平台》;20160531;第31卷(第05期);51-56 * |
| 大功率电子变压器优化设计;王刚;《中国优秀博硕士学位论文全文数据库(硕士)工程科技Ⅱ辑》;20140115(第(2014)01期);C042-131 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112560331A (en) | 2021-03-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112560331B (en) | Energy-saving and material-saving optimization design system and method for amorphous alloy dry type transformer | |
| CN107591799B (en) | Power distribution network short-term planning method based on maximum power supply capacity | |
| CN110517874B (en) | Design method of high-power medium-frequency power transformer | |
| CN106096106B (en) | High-frequency high-voltage transformer for electrostatic dust collection optimum design method | |
| CN104484517B (en) | A kind of MMC bridge arms reactor parameter optimization method | |
| WO2024037576A1 (en) | Method and terminal for calculating carbon emissions of transformer substation | |
| CN105118647B (en) | The determination method of Large Copacity high frequency transformer optimum working frequency | |
| Arjona et al. | Hybrid optimum design of a distribution transformer based on 2-D FE and a manufacturer design methodology | |
| Liu et al. | Optimization design of amorphous metal distribution transformer based on improved fast and elitist multi-objective genetic algorithm | |
| CN108053250B (en) | Genetic algorithm-based transformer price optimization method and device | |
| CN110137985A (en) | A kind of phase-change switch control method and relevant apparatus | |
| CN115085396A (en) | Multi-parameter optimization method of three-coil coupling mechanism based on inductive decoupling | |
| CN111697607A (en) | Multi-terminal flexible direct-current transmission receiving-end power grid access method and system | |
| CN110807538A (en) | Power distribution network planning method considering permeability of electric vehicles in residential area | |
| CN107992703A (en) | A kind of UI/UU air-gap-free inductance intelligent design systems and method based on bar shaped magnetic core | |
| CN109840614B (en) | Transformer optimal equipment utilization rate control method based on life cycle cost | |
| CN2773870Y (en) | Hopper-sensing insulated transformer with big power UPS and double iron core | |
| CN115203857A (en) | Amorphous alloy transformer parameter optimization design method based on particle swarm optimization | |
| CN105743099A (en) | Optimization method of ultra-high voltage power grid series compensation degree | |
| CN207039464U (en) | A kind of electric power electric transformer based on cascade connection multi-level | |
| CN110266024A (en) | A kind of method for limiting short circuit current of power grid based on current distribution entropy | |
| Wang et al. | Multiobjective optimization design of high frequency transformer based on NSGA-II algorithm | |
| CN117408206B (en) | Electroacoustic transducer broadband impedance matching design method based on pareto optimization | |
| CN116992736A (en) | Multi-objective optimization design method for low-frequency transformer | |
| Gao et al. | Optimum design of permanent magnet synchronous motor based on gene handling genetic algorithms |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |