Parameter optimization method for phase-shifting transformer with double-core structure

Abstract

The invention discloses a parameter optimization method for a phase-shifting transformer with a double-core structure, which comprises the following steps of: firstly, determining performance parameters of the phase-shifting transformer with the double-core structure according to requirements of a user on a high-integration-level phase-shifting transformer and related standards, setting constraint conditions and optimizing a value range of parameter variables; secondly, constructing a multi-objective optimization function considering the high integration level of the phase-shifting transformer; further, solving an optimization problem by adopting an improved multi-target bionic grasshopper optimization algorithm; finally, the optimization result is corrected and verified, and a design result is output. According to the method, the multi-objective optimization function containing the high-integration index is constructed, and the improved multi-objective bionic grasshopper optimization algorithm is adopted, so that the high-integration rapid design of the phase-shifting transformer can be realized.

Description

Technical Field

[0001] The invention relates to a parameter optimization method for a dual-core structure phase-shifting transformer, and belongs to the field of transformers. Background Art

[0002] The phase-shifting transformer superimposes a suitable voltage phase on the line input voltage to change the voltage phase difference on both sides of the line, thereby achieving the purpose of regulating the line flow. There are two types of controllable phase-shifting transformers: single-core structure and double-core structure. The single-core structure is simple, but the capacity is small, and the voltage-regulating winding and the on-load switch are directly connected to the line end, and the ability to withstand overvoltage and short-circuit current is weak. The advantage of the double-core structure of the phase-shifting transformer is that its voltage-regulating winding is not directly connected to the system line, so the voltage and current of the voltage-regulating winding can be flexibly designed according to the rated performance parameters of the tap switch, and optimized in combination with it.

[0003] The dual-core symmetrical phase-shifting transformer can be used as a low-cost, high-reliability power flow controller in the renewable energy power system to cope with the uncertainty of renewable energy power generation. However, the dual-core phase-shifting transformer has a dual body and is designed for special applications. The design process cannot be carried out completely according to the traditional transformer design method. How to highly integrate the series transformer and the parallel transformer to improve the mobility of the transformer and use modern intelligent algorithms to achieve rapid optimization design is an urgent problem to be solved. Summary of the invention

[0004] In order to solve the problem of integrated optimization design of dual-core structure phase-shifting transformer in the prior art, the present invention provides a low-cost and high-integration optimization method of a phase-shifting transformer.

[0005] In order to achieve the above object, the technical solution proposed in the present invention is: a parameter optimization method of a dual-core structure phase-shifting transformer, wherein the dual-core structure phase-shifting transformer includes a series transformer and a parallel transformer, and includes the following steps:

[0006] Step S1: Determine the performance parameters of the dual-core phase-shifting transformer, set constraints and optimize the value range of parameter variables;

[0007] Step S2: constructing a multi-objective optimization function considering the high integration of the phase-shifting transformer;

[0008] The objectives of the multi-objective optimization function include the material cost of the double-core phase-shifting transformer, the size of the double-core phase-shifting transformer body, and the difference between the series transformer body and the parallel transformer body;

[0009] Step S3: using an improved multi-objective bionic grasshopper optimization algorithm to solve a multi-objective optimization function;

[0010] Step S4: Output the optimization result.

[0011] The further design of the above technical solution is as follows: in the step S1, according to the voltage level U of the double-core phase-shifting transformer N , flow capacity I and maximum phase shift angle α max Determine the following parameters:

[0012] The phase-shifting winding capacity of the transformer is

[0013] The structural capacity is

[0014] Series transformer high voltage coil current: I S1 =I L1 =I;

[0015] Series transformer high voltage coil voltage: ΔV1 = 2U N sin(0.5α max );

[0016] Series transformer low voltage coil voltage: V2 = 2K -1 U N sin(0.5α max );

[0017] Series transformer low voltage coil current: I2 = KIcos (0.5α max );

[0018] Parallel transformer high voltage coil voltage: V3 = U N cos(0.5α max );

[0019] The current of the high voltage coil of the parallel transformer: I3 = 2Isin (0.5α max );

[0020] Parallel transformer low voltage coil voltage:

[0021] Current of low voltage coil of parallel transformer:

[0022] Among them, K is the transformation ratio between the high-voltage and low-voltage coils of the series transformer, I S1 and I L1 They represent the amplitude of the current at the input and output ends of the high-voltage coil of the series transformer respectively.

[0023] In step S1, the constraints include transformation ratio deviation constraint, impedance voltage design value constraint, load loss constraint, no-load current constraint, no-load loss constraint, core magnetic flux density limit constraint and winding conductor current density constraint.

[0024] The optimization parameter variables include the diameter D1 of the series transformer core, the core magnetic flux density B m1 , high voltage winding current density J 11 , low voltage winding current density J 21 , the diameter of the parallel transformer core D2, the core flux density B m2 , high voltage winding current density J 12 , low voltage winding current density J 22 And the transformation ratio K of the series transformer.

[0025] The multi-objective optimization function in step S2 is:

[0026] minf(x)=min[f1(x)+f2(x)+f3(x)]

[0027] Among them, the optimization target f1(x) represents the core material cost, and the specific expression is as follows:

[0028]

[0029] Including the price c1 and quality w1(x) of the series transformer core, the price c2 and quality w2(x) of the low-voltage winding, the price c3 and quality w3(x) of the high-voltage winding, the price c4 and quality w4(x) of the parallel transformer core, the price c5 and quality w5(x) of the low-voltage winding, and the price c6 and quality w6(x) of the high-voltage winding;

[0030] The optimization target f2(x) represents the body size of the dual-core phase-shifting transformer. The specific expression is as follows:

[0031]

[0032] Including the volume v1(x) of the series transformer body and the volume v2(x) of the parallel transformer body;

[0033] The optimization target f3(x) represents the difference between the series transformer body and the parallel transformer body. The specific expression is as follows:

[0034] f3(x)=(v1(x)-v2(x)) 2 .

[0035] In step S3, the improved multi-objective bionic grasshopper optimization algorithm specifically includes the following steps:

[0036] S31, initializing the parameters of the algorithm and initializing the initial position of each grasshopper;

[0037] S32, calculate the fitness f(x) of each grasshopper and select the optimal solution T d ;

[0038] S33, updating the adaptive coefficient c;

[0039] S34, updating the position of each grasshopper;

[0040] S35, calculating the fitness of each grasshopper after updating its position, and selecting the optimal solution;

[0041] S36, determine whether the maximum number of iterations has been reached, if not, return to step S33, if yes, proceed to step S37;

[0042] S37. Output the current optimal solution and the best fitness.

[0043] The grasshopper position initialization in the multi-objective bionic grasshopper optimization algorithm is performed using the following formula:

[0044]

[0045] Among them, y i,lb Represents the lower bound of the variable y, y i,ub Represents the upper bound of the variable y.

[0046] In the improved multi-objective bionic grasshopper optimization algorithm, the grasshopper group has N grasshoppers, and the position of the i-th grasshopper is expressed as:

[0047]

[0048] in, f and l are the attraction strength and attraction length between grasshoppers, respectively. When the value is negative, it means there is repulsion between grasshoppers, and when it is positive, it means there is attraction between grasshoppers. d ij =|y j -y i | represents the distance between the two grasshoppers, g represents the acceleration due to gravity, represents the unit vector pointing to the center of the earth, u represents the wind speed, A unit vector representing the wind direction.

[0049] The selection function of the adaptive coefficient is:

[0050]

[0051] Among them, c max and c min Respectively represent the maximum and minimum values ​​of the adaptive coefficient, t max represents the maximum number of iterations, and t represents the current number of iterations.

[0052] The position update method of the grasshopper is:

[0053]

[0054] Where c is the adaptive coefficient, Td is the optimal position of the current iteration, y i,ub and i,lb They represent the upper and lower bounds of the search space respectively.

[0055] The beneficial effects of the present invention are:

[0056] The parameter optimization method of the present invention selects parameters such as the core diameter of the series transformer and the parallel transformer, the core flux density, the high and low voltage winding current density, and the transformation ratio of the series transformer as optimization variables, takes the lowest body material cost, the smallest body volume, and the smallest body volume difference of the series transformer and the parallel transformer of the phase-shifting transformer as optimization goals, and adopts an improved multi-objective bionic grasshopper algorithm to quickly solve the optimization problem. A low-cost, high-integration optimization design solution for the phase-shifting transformer can be quickly obtained while meeting the standard requirements of user needs, thereby improving the mobility of the transformer. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A parameter optimization method flow for highly integrated phase-shifting transformers.

[0058] Figure 2 This is the wiring diagram of the double-core phase-shifting transformer winding.

[0059] Figure 3 General process for general transformer parameter optimization.

[0060] Figure 4 Flowchart of the optimization algorithm.

[0061] Figure 5 This is the relationship diagram between the fitness and the number of iterations of the optimization algorithm.

[0062] Figure 6 This is the structural design diagram of the phase-shifting transformer parameter optimization system. DETAILED DESCRIPTION

[0063] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0064] Embodiment 1

[0065] The parameter optimization method of the highly integrated dual-core phase-shifting transformer of this embodiment adopts an intelligent optimization algorithm to quickly solve the optimization problem, thereby achieving multi-objective comprehensive optimization of the dual-core phase-shifting transformer body material cost and integration level.

[0066] like Figure 1 As shown, the method specifically includes:

[0067] Step S1, determining the performance parameters of the dual-core phase-shifting transformer according to the user's requirements for the highly integrated phase-shifting transformer and relevant standards, setting the constraint conditions and the value range of the optimized parameter variables;

[0068] This embodiment implements a design parameter optimization method for user requirements with a voltage level of 110 kV, a current capacity of 520 A, and a maximum phase shift angle of ±8°.

[0069] The phase-shifting transformer model constructed in this embodiment is as follows: Figure 2 The figure shows the winding connection diagram of a double-core phase-shifting transformer, including a series transformer and a parallel transformer.

[0070] according to Figure 3 The general process of conventional transformer parameter optimization is shown to determine the technical parameters of the transformer of this embodiment, namely: the phase-shifting winding capacity and structural capacity of the double-core phase-shifting transformer. Among them, the phase-shifting winding capacity is:

[0071]

[0072] Here S N Take an approximate value of 100MVA.

[0073] The structure capacity is:

[0074]

[0075] Here S J Take the approximate value of 13.9MVA.

[0076] Substitute S N =100MVA, U N =110kV, the voltage and current levels of each winding on the series and parallel sides of the phase-shifting transformer can be calculated to determine the reference diameter and insulation voltage level of the winding:

[0077] Current of high voltage coil of series transformer:

[0078] Series transformer high voltage coil voltage: ΔV1 = 2U N sin(0.5α max )=15346.42V

[0079] Series transformer low voltage coil voltage: V2 = 2K -1 U N sin(0.5α max )=15346.42K -1 V

[0080] Series transformer low voltage coil current: I2 = KIcos (0.5α max )=524.58KA

[0081] Parallel transformer high voltage coil voltage: V3 = U N cos(0.5α max )=109732.05V

[0082] The current of the high voltage coil of the parallel transformer: I3 = 2Isin (0.5α max )=73.22A

[0083] Parallel transformer low voltage coil voltage:

[0084] Current of low voltage coil of parallel transformer:

[0085] According to the relevant standards and specifications of transformers, the following variables are selected as optimization variables: the diameter of the series transformer core D1, the core magnetic flux density B m1 , high voltage winding current density J 11 , low voltage winding current density J 21 , the diameter of the parallel transformer core D2, the core flux density B m2 , high voltage winding current density J 12 , low voltage winding current density J 22 , the transformation ratio K of the series transformer.

[0086] According to the relevant standards and specifications of transformers, set J optimization parameter variables x = [x1, x2, ..., x N ] T Inequality optimization constraints:

[0087] g j (x)≤0,j=1,…,J

[0088] Including transformation ratio deviation constraint, impedance voltage design value constraint, load loss constraint, no-load current constraint, no-load loss constraint, core magnetic flux density limit constraint, winding conductor current density constraint, etc.; the details are as follows:

[0089] 1) Transformation ratio deviation constraint:

[0090] The voltage transformation ratio deviation is less than half of the value specified in the standard:

[0091] |V%|≤0.25%

[0092] 2) Impedance voltage design value constraints:

[0093] The impedance voltage design value needs to meet the deviation limit of the reference value:

[0094]

[0095] Among them, UK % is the calculated impedance voltage value; U KS % is the reference impedance voltage; The impedance voltage value is the deviation limit from the required reference value, usually 2.5%.

[0096] 3) Load loss constraints:

[0097] Load loss is less than the load loss limit:

[0098]

[0099] Among them, P KS is the standard value of load loss, i.e. 50% of rated power; is the constraint coefficient for load loss limitation, usually taken as 1.1.

[0100] 4) No-load current constraint:

[0101] No-load current is less than no-load current limit:

[0102]

[0103] Among them, I OS % is the standard value of no-load current, i.e. 30% of the rated current; is the constraint coefficient for no-load current limitation, usually taken as 1.2.

[0104] 5) No-load loss constraints:

[0105] No-load loss is less than no-load loss limit:

[0106]

[0107] Among them, P OS is the standard value of no-load loss, here it is taken as 34.9kW; It is the constraint coefficient of no-load loss limit, usually taken as 1.2.

[0108] 6) Core magnetic flux density limit constraints:

[0109] The phase-shifting transformer involved in the embodiment has a capacity between 10MVA and 1000MVA, which is a large transformer with a maximum magnetic density of B m The value is generally 1.7~1.75T.

[0110] 7) Current density constraints of winding wires:

[0111] The current density of the winding conductor ranges from 2.7 to 3.3 (A / mm 2 ).

[0112] According to the relevant standards and specifications of the transformer, the value ranges of N optimization parameter variables are set:

[0113] x i,min ≤x i ≤x i,max ,i=1,…,N

[0114] where x i,min and x i,max Represent the optimization parameter variables x i The minimum and maximum values ​​of are shown in Table 1:

[0115] Table 1 Optimization parameter value range

[0116]

[0117] In the table, the value of subscript i is 1, 2; when i is 1, it represents a series transformer; when i is 2, it represents a parallel transformer.

[0118] Step S2: construct a multi-objective optimization function considering the high integration of the phase-shifting transformer; set the specific expression of the multi-objective optimization function considering the high integration of the dual-core structure phase-shifting transformer to:

[0119] minf(x)=min[f1(x)+f2(x)+f3(x)]

[0120] f1(x) represents the material cost, which is represented by the price of the core and winding copper wire of the excitation transformer and the series transformer, and the unit is 10,000 yuan; f2(x) represents the size of the phase shifter, which is represented by the sum of the volumes of the core with windings of the excitation transformer and the series transformer, and the unit is m 3 f3(x) represents the volume difference of the two phase shifter bodies, which is represented by the square of the volume difference between the excitation transformer and the series transformer with winding core, and the unit is m 6 .

[0121] According to the survey, the price of the selected iron core is c1=c4=12300 yuan / ton, the price of copper is c2=c5=c3=c6=83400 yuan / ton; the density of silicon steel sheet γ is set to 7.65kg / dm 3 , copper density ρ Cu , take the fixed value of 8.9kg / dm 3 Based on the optimization parameter variables, combined with the relevant transformer parameter calculation formula and the empirical coefficient value, the calculation expression of the optimization target f1(x) can be obtained as follows:

[0122]

[0123] The calculation expression of the optimization target f2(x) is as follows:

[0124]

[0125] The calculation expression of the optimization target f3(x) is as follows:

[0126]

[0127] Step 3: Use the improved multi-objective bionic grasshopper optimization algorithm to solve the multi-objective optimization function.

[0128] Combined with Figure 4 The optimization algorithm flow shown is used to complete the phase shifter parameter optimization work.

[0129] S31, initializing the parameters of the algorithm and initializing the initial position of each grasshopper;

[0130] The grasshopper position initialization of the multi-objective bionic grasshopper optimization algorithm is carried out using the following formula:

[0131]

[0132] Among them, y i,lb Represents the lower bound of the variable y, y i,ub Represents the upper bound of the variable y;

[0133] Assume that the grasshopper group has N grasshoppers, and the position of the i-th grasshopper is expressed as:

[0134]

[0135] in, f and l are the attraction strength and attraction length between grasshoppers, respectively. When the value is negative, it means there is repulsion between grasshoppers, and when it is positive, it means there is attraction between grasshoppers. d ij =|y j -y i | represents the distance between the two grasshoppers, g represents the acceleration due to gravity, represents the unit vector pointing to the center of the earth, u represents the wind speed, A unit vector representing wind direction;

[0136] However, when the grasshopper swarm reaches the comfort zone (i.e., the attraction and repulsion between grasshoppers are equal), the nymph swarm still does not converge. In order to adapt to the solution of the optimization problem and coordinate the global and local optimization processes, parameters are introduced and the position update method is changed to distinguish the optimization at different stages.

[0137] At this time, the location update method is:

[0138]

[0139] Where c is the adaptive coefficient, T d is the optimal position of the current iteration, yi,ub and i,lb Respectively represent the upper and lower bounds of the search space; the adaptive coefficient selection function is

[0140]

[0141] where c max and c min Respectively represent the maximum and minimum values ​​of the adaptive coefficient, t max represents the maximum number of iterations, and t represents the current number of iterations.

[0142] S32, after completing the initialization of the grasshopper position, calculate the fitness of each grasshopper, that is, calculate the position Y of each grasshopper i The size of the objective function f(x). Select the optimal solution T d , that is, after comparison, the minimum value of the objective function f(x) is obtained and the corresponding grasshopper position is recorded.

[0143] S33, updating the adaptive coefficient c;

[0144] S34, updating the position of each grasshopper;

[0145] S35, according to the relationship between grasshoppers, update the position of grasshoppers, calculate the fitness of each grasshopper after the updated position, and select the optimal solution T again d .

[0146] S36, determine whether the maximum number of iterations has been reached, if not, return to step S33, if yes, proceed to step S37;

[0147] At this point, complete Figure 4 The step of "determining whether the maximum number of iterations has been reached" is to compare the current number of iterations with the maximum number of iterations. If the maximum number of iterations has not been reached, the adaptive coefficient is updated and the grasshopper position is adjusted. If the maximum number of iterations has been reached, the step of S37 is continued.

[0148] S37, output the current optimal solution T d And the best fitness f(x) min .

[0149] The grasshopper position initialization of the multi-objective bionic grasshopper optimization algorithm is carried out using the following formula:

[0150]

[0151] Among them, y i,lb Represents the lower bound of the variable, y i,ub Indicates the upper bound of a variable.

[0152] Set the optimization program related parameters as follows:

[0153] Table 2 Optimization program related parameter setting values

[0154]

[0155] Running the optimization program, we can get Figure 5 The relationship between the fitness and the number of iterations of the optimization algorithm shown in the figure obtains the optimized values ​​of the following parameters:

[0156] Table 3 Optimized parameter values

[0157]

[0158]

[0159] At this point, the optimized objective function values ​​are as follows:

[0160] Table 4. Values ​​of each objective function after optimization

[0161]

[0162] Step 4: Combined with the transformer design engineering practice, the optimization results are corrected and verified, and the design results are output. According to the user requirements of voltage level of 110kV, current capacity of 520A, and maximum phase shift angle of ±8°, the data in Table 5 are appropriately corrected, and the final design results are obtained as follows:

[0163] The diameter D1 of the series transformer core is 720mm, and the diameter D2 of the parallel transformer core is 760mm;

[0164] Series transformer magnetic flux density B m1 is 1.7T, the magnetic flux density of the parallel transformer is B m2 1.75T;

[0165] Current density of high voltage winding of series transformer J 11 2.306A / mm 2 , current density of low voltage winding of series transformer J 21 2.443A / mm 2 ;

[0166] Current density J of high voltage winding of parallel transformer 12 2.306A / mm 2 , current density of low voltage winding of parallel transformer J 22 2.443A / mm 2 ;

[0167] The transformation ratio K of the series transformer is 1.24, and the number of turns of the high-voltage and low-voltage windings are set to 272 / 220 respectively.

[0168] Embodiment 2

[0169] This embodiment implements the parameter optimization method of the high-integration phase-shifting transformer described in the first embodiment to realize modular setting and optimization output through computer programming, including a phase-shifting transformer parameter setting module, an improved multi-objective bionic grasshopper optimization algorithm parameter setting module, an improved multi-objective bionic grasshopper optimization algorithm engine module, an optimization result verification module and a design result output module. The relationship between the modules is as follows: Figure 6 As shown in the figure, after the user inputs the parameters in the phase-shifting transformer parameter setting module, the program will enter the improved multi-objective bionic grasshopper optimization algorithm parameter setting module and call the improved multi-objective bionic grasshopper optimization algorithm engine module for optimization. After the improved multi-objective bionic grasshopper optimization algorithm engine module is optimized, the optimization results are output to the improved multi-objective bionic grasshopper optimization algorithm parameter setting module, and finally the optimization result verification module and the design result output module are entered to output the design results.

[0170] The program of the present invention may be implemented in hardware, firmware, or as software or computer code that may be stored in a recording medium (such as a CDROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded over a network and will be stored in a local recording medium, so that the method described herein may be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that a computer, processor, microprocessor controller, or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, processor, or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the method shown herein.

[0171] The technical solutions of the present invention are not limited to the above-mentioned embodiments, and any technical solutions obtained by equivalent replacement methods fall within the scope of protection required by the present invention.

Claims

1. A parameter optimization method for a dual-core phase-shifting transformer, wherein the dual-core phase-shifting transformer comprises a series transformer and a parallel transformer, characterized in that: The following steps are involved: Step S1: Determine the performance parameters of the dual-core phase-shifting transformer, set constraints and optimize the value range of parameter variables; Step S2: constructing a multi-objective optimization function considering the high integration of the phase-shifting transformer; The objectives of the multi-objective optimization function include the material cost of the double-core phase-shifting transformer, the size of the double-core phase-shifting transformer body, and the difference between the series transformer body and the parallel transformer body; Step S3: using an improved multi-objective bionic grasshopper optimization algorithm to solve a multi-objective optimization function; Step S4: Output the design optimization results.

2. The parameter optimization method of the dual-core phase-shifting transformer according to claim 1 is characterized in that: In step S1, according to the voltage level U of the double-core phase-shifting transformer N , flow capacity I and maximum phase shift angle α max Determine the following parameters: The phase-shifting winding capacity of the transformer is The structural capacity is Series transformer high voltage coil current: I S1 =I L1 =I; Series transformer high voltage coil voltage: ΔV1 = 2U N sin(0.5α max ); Series transformer low voltage coil voltage: V2 = 2K -1 U N sin(0.5α max ); Series transformer low voltage coil current: I2 = KIcos (0.5α max ); Parallel transformer high voltage coil voltage: V3 = U N cos(0.5α max ); The current of the high voltage coil of the parallel transformer: I3 = 2Isin (0.5α max ); Parallel transformer low voltage coil voltage: Current of low voltage coil of parallel transformer: Where K is the transformation ratio between the high-voltage and low-voltage coils of the series transformer; I S1 and I L1 They represent the amplitude of the current at the input and output ends of the high-voltage coil of the series transformer respectively.

3. The parameter optimization method of the dual-core phase-shifting transformer according to claim 2 is characterized in that: In step S1, the constraints include transformation ratio deviation constraint, impedance voltage design value constraint, load loss constraint, no-load current constraint, no-load loss constraint, core magnetic flux density limit constraint and winding conductor current density constraint.

4. The parameter optimization method of the dual-core phase-shifting transformer according to claim 3 is characterized in that: The optimization parameter variables include the diameter D1 of the series transformer core, the core magnetic flux density B m1 , high voltage winding current density J 11 , low voltage winding current density J 21 , the diameter of the parallel transformer core D2, the core flux density B m2 , high voltage winding current density J 12 , low voltage winding current density J 22 And the transformation ratio K of the series transformer.

5. The parameter optimization method of the dual-core phase-shifting transformer according to claim 3 is characterized in that: The multi-objective optimization function in step S2 is: minf(x)=min[f1(x)+f2(x)+f3(x)] Among them, the optimization target f1(x) represents the core material cost, and the specific expression is as follows: Including the price c1 and quality w1(x) of the series transformer core, the price c2 and quality w2(x) of the low-voltage winding, the price c3 and quality w3(x) of the high-voltage winding, the price c4 and quality w4(x) of the parallel transformer core, the price c5 and quality w5(x) of the low-voltage winding, and the price c6 and quality w6(x) of the high-voltage winding; The optimization target f2(x) represents the body size of the dual-core phase-shifting transformer. The specific expression is as follows: Including the volume v1(x) of the series transformer body and the volume v2(x) of the parallel transformer body; The optimization target f3(x) represents the difference between the series transformer body and the parallel transformer body. The specific expression is as follows: f3(x)=(v1(x)-v2(x)) 2 。 6. The parameter optimization method of the dual-core phase-shifting transformer according to claim 5 is characterized in that: In step S3, the improved multi-objective bionic grasshopper optimization algorithm specifically includes the following steps: S31, initializing the parameters of the algorithm and initializing the initial position of each grasshopper; S32, calculate the fitness f(x) of each grasshopper and select the optimal solution T d ; S33, updating the adaptive coefficient c; S34, updating the position of each grasshopper; S35, calculating the fitness of each grasshopper after updating its position, and selecting the optimal solution; S36, determine whether the maximum number of iterations has been reached, if not, return to step S33, if yes, proceed to step S37; S37. Output the current optimal solution and the best fitness.

7. The parameter optimization method of the dual-core phase-shifting transformer according to claim 6 is characterized in that: The grasshopper position initialization in the multi-objective bionic grasshopper optimization algorithm is performed using the following formula: Among them, y i,lb Represents the lower bound of the variable y, y i,ub Represents the upper bound of the variable y.

8. The parameter optimization method of the dual-core phase-shifting transformer according to claim 7 is characterized in that: In the improved multi-objective bionic grasshopper optimization algorithm, the grasshopper group has N grasshoppers, and the position of the i-th grasshopper is expressed as: in, f and l are the attraction strength and attraction length between grasshoppers, respectively. When the value is negative, it means there is repulsion between grasshoppers, and when it is positive, it means there is attraction between grasshoppers. d ij =|y j -y i | represents the distance between the two grasshoppers, g represents the acceleration due to gravity, represents the unit vector pointing to the center of the earth, u represents the wind speed, A unit vector representing the wind direction.

9. The parameter optimization method of the dual-core phase-shifting transformer according to claim 8 is characterized in that: The selection function of the adaptive coefficient is: Among them, c max and c min Respectively represent the maximum and minimum values ​​of the adaptive coefficient, t max represents the maximum number of iterations, and t represents the current number of iterations.

10. The parameter optimization method of the dual-core phase-shifting transformer according to claim 9, characterized in that: The position update method of the grasshopper is: Where c is the adaptive coefficient, T d is the optimal position of the current iteration, y i,ub and i,lb They represent the upper and lower bounds of the search space respectively.

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