A transformer cost optimization design system and method based on GWO algorithm

Abstract

The present invention belongs to the field of transformer optimization design and specifically discloses a transformer cost optimization design system and method based on the GWO algorithm. The method comprises the following steps: 1) establishing a loss model and a cost model for the transformer electrical portion based on the transformer's loss parameters, and setting initial values ​​for the design parameters to be optimized in the loss model and the cost model; 2) setting the transformer's optimization objective function and constraints based on the loss model and the cost model; 3) improving the population initialization, convergence factor, and position update weights of the traditional GWO algorithm to obtain an improved GWO algorithm; and 4) optimizing the transformer design using the improved GWO algorithm in step 3 through an operational optimization system to determine the optimal design parameters for multiple groups of transformers. The improved GWO algorithm of the present invention achieves significantly better results than the traditional GWO algorithm, significantly reducing transformer losses and operating costs, thus offering significant practical value.

Description

Technical Field

[0001] The present invention belongs to the field of transformer optimization design, and in particular relates to a transformer cost optimization design system and method based on a GWO algorithm. Background Art

[0002] Oil-immersed transformers, as a common 1000-kV power transformer, are often accompanied by inevitable operating losses during operation, resulting in corresponding operating costs. Transformer losses mainly include core loss, winding loss, and additional loss. Core loss (iron loss) is a significant factor in the transformer's total loss and dominates no-load loss, while winding loss (copper loss) dominates load loss.

[0003] The mathematical models and algorithms used for optimizing the design of oil-immersed transformers vary depending on the loss component. Commonly used algorithms in this field include particle swarm optimization, the gray wolf optimization algorithm (GWO algorithm), the honey bee algorithm, the ant algorithm, and genetic algorithms. However, these traditional algorithms often suffer from numerous drawbacks for transformer design: These include excessive computational complexity and low solution efficiency; an inability to ensure global optimization; limited versatility and program portability; and an inability to address complex mixed discrete variable optimization problems. Therefore, in actual transformer design, algorithms often require targeted improvements tailored to design requirements before implementation. Summary of the Invention

[0004] In view of the problems and shortcomings in the prior art, the purpose of the present invention is to reduce the transformer loss as much as possible, thereby reducing the operating cost of the transformer, and to design a transformer cost optimization design system and method based on the GWO algorithm.

[0005] To achieve the purpose of the invention, the technical solution adopted by the present invention is as follows:

[0006] A first aspect of the present invention provides a transformer cost optimization design method based on the GWO algorithm, comprising the following steps:

[0007] Step 1: Based on the transformer loss parameters, establish the loss model and cost model of the transformer electrical part, and set initial values ​​for the design parameters to be optimized in the loss model and cost model;

[0008] Step 2: Based on the loss model and cost model, set the optimization objective function and constraints of the transformer;

[0009] Step 3: Based on the traditional GWO algorithm, the population initialization, convergence factor and position update weight are improved to obtain the improved GWO algorithm;

[0010] Step 4: Using the improved GWO algorithm in step 3, the transformer is optimized through an operational optimization system to solve the transformer design parameters.

[0011] According to the transformer cost optimization design method based on the GWO algorithm, further, a mutation operation is introduced in step 3 to improve the distribution properties of the wolf pack during the initialization of the GWO algorithm. The individuals in the mutation operation are generated by formula (1):

[0012]

[0013] In formula (1), D ij (t+1) represents the distance between individuals i and j in the t+1th generation population, t is the current iteration number, X (X∈[0,2]) is the scaling factor, r1, r2 and r3 are random integers not equal to i, and satisfy r1, r2, r3∈[1,N].

[0014] According to the transformer cost optimization design method based on the GWO algorithm, further, in the improved GWO algorithm, D=|C·X(t)-X(t)| and X(t+1)=X P The calculation formulas for the C and A coefficients in (t)-A·D are as shown in equations (2) and (3):

[0015] A=2.8a·r1-a (2)

[0016] C=2.8r2 (3)

[0017] In formula (2) and formula (3), r1 and r2 are random numbers between [0, 1]; a is the nonlinear convergence factor of the improved GWO algorithm, and its update formula is shown in formula (4):

[0018] a(t)=2-(log(1+μtan(t / T) 3 )) p (4)

[0019] In formula (4), t is the current iteration number, T is the maximum iteration number of the population, and the adjustment coefficients μ∈[1.2, 4.1] and p∈[1, 10].

[0020] According to the transformer cost optimization design method based on the GWO algorithm, further, in step S3, the improved GWO algorithm adopts an adaptive position update strategy, redistributes weights according to the fitness values ​​of α wolf, β wolf and δ wolf, and then solves the hunting position; the weight nonlinear update strategy is shown in formulas (5), (6) and (7):

[0021]

[0022]

[0023]

[0024] In formula (5), formula (6) and formula (7), f α 、f β 、f δ Represent the fitness of α wolf, β wolf and δ wolf respectively, and the weights w1, w2 and w3 correspond to the learning rates of α wolf, β wolf and δ wolf respectively;

[0025] The adaptive position update formula is shown in formula (8):

[0026]

[0027] In formula (8), X1, X2, and X3 represent the direction and step length that the individual ω wolf will follow along with α wolf, β wolf, and δ wolf, respectively, and X(t+1) represents the final position of ω wolf in the iteration cycle.

[0028] According to the transformer cost optimization design method based on the GWO algorithm, further, the optimization objective function in step S2 requires that the sum of the no-load loss, load loss and additional loss of the transformer is minimized, and the additional loss must meet f2(x)

[0029] Where L represents the additional loss, δ Indicates the actual leakage inductance of the transformer, L const Indicates a given standard value of leakage inductance.

[0030] According to the transformer cost optimization design method based on the GWO algorithm, further, the constraint condition in step S2 is:

[0031] (a) The distance between different windings of the transformer, between different layers of the same winding, and between the winding and the core is greater than the insulation distance;

[0032] (b) The magnetic flux density amplitude of the transformer core should satisfy Bm<0.6T;

[0033] (c) The porosity of transformer windings should be greater than 0.7.

[0034] According to the transformer cost optimization design method based on the GWO algorithm, further, after solving the problem using the design method, a non-dominated sorting is introduced for solutions that do not meet the constraints, and a penalty operation is added before the sorting.

[0035] According to the transformer cost optimization design method based on the GWO algorithm, further, the specific operation of the non-dominated sorting is: first determine whether each solution meets the requirements of the constraint conditions, and separately list all solutions that do not meet the requirements, and add a penalty value to the optimization objective function of these solutions; then perform non-dominated sorting, sorting the solutions that do not meet the requirements after the solutions that meet the requirements, and the individual sorting distances satisfy a uniform distribution.

[0036] A second aspect of the present invention provides a transformer cost optimization system based on the GWO algorithm. The optimization system includes a memory and a processor. The memory is used to store executable instructions that can be run on the processor. The processor is used to execute the improved GWO algorithm when running the executable instructions to implement the transformer cost optimization design method based on the GWO algorithm described in the first aspect.

[0037] A third aspect of the present invention provides a storage medium storing program instructions executable by a processor, wherein the program instructions are used to execute the transformer cost optimization design method based on the GWO algorithm as described in the first aspect.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] (1) This invention uses an improved GWO algorithm to optimize the total transformer losses and operating costs, maximizing the availability of multiple feasible optimization solutions for designers to choose from. The improved GWO algorithm yields significantly improved results compared to the traditional GWO algorithm, significantly reducing transformer losses and operating costs, demonstrating its practical value.

[0040] (2) The present invention adjusts the search process by improving the convergence factor and improves the position update equation based on the improvement of the weight formula, which effectively solves the problems of the traditional gray wolf algorithm, which has strong development ability but weak exploration ability, and strong global search ability but slightly poor accuracy.

[0041] (3) The present invention also introduces a mutation operation when the GWO algorithm generates initial values ​​to maintain the diversity of the initial population, improve the optimization range, and avoid the lack of sufficient randomness in the traditional GWO algorithm when generating initial values. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Flowchart of the transformer cost optimization design method based on the improved GWO algorithm of the present invention;

[0043] Figure 2 It is the input interface of the optimization system in Example 1 of the present invention;

[0044] Figure 3 This is the optimization result given by the optimization system in Example 2 of the present invention. DETAILED DESCRIPTION

[0045] In order to enable those skilled in the art to more clearly understand the technical solution of the present invention, the technical solution of the present invention will be described in detail below with reference to specific embodiments.

[0046] Example 1

[0047] A parameter optimization design method for oil-immersed transformer based on improved GWO algorithm. The overall method flow is as follows: Figure 1 As shown, the following steps are included:

[0048] Step 1: Based on the transformer loss parameters, establish a mathematical model for the transformer electrical part loss and cost, and set initial values ​​for the design parameters to be optimized in the loss model and cost model;

[0049] 1. Establish the total loss model of the electrical part of the transformer:

[0050] When sinusoidal excitation is applied to the transformer, the relationship between the transformer's no-load loss and other parameters can be expressed as follows:

[0051]

[0052] In formula (1), the three coefficients K, α, and β are obtained from the no-load loss data under sinusoidal excitation provided by the manufacturer; f is the sinusoidal excitation frequency, B m is the magnetic flux density amplitude of the core.

[0053] When non-sinusoidal excitation is applied to the transformer, the calculation formula for the transformer no-load loss is as follows:

[0054]

[0055]

[0056] In formula (2), T is the period of the non-sinusoidal excitation signal, k i is the parameter calculated by the second formula in (2) from the above K, α and the angle parameter θ in the non-sinusoidal excitation signal function.

[0057] The calculation of transformer load losses is based on a one-dimensional electromagnetic field model that simulates the magnetic field distribution in several foil conductors. This model superimposes several foil conductors to form an infinitely long solenoid. This solenoid is treated as a transformer winding, and the total loss parameters and leakage magnetic field in the transformer winding can be derived.

[0058] Therefore, the total loss of the electrical part of the transformer is calculated as follows:

[0059]

[0060] In formula (3), F r,n is the AC resistance coefficient at the nth harmonic, R dc is the DC resistance of the winding conductor, I f is the current flowing through the busbar per unit width.

[0061] Leakage inductance is often calculated based on leakage flux energy, and its calculation formula is as follows:

[0062]

[0063] In formula (4), W leakage is the leaked magnetic field energy, I1 is the peak value of the primary winding excitation current, H is the magnetic field density, L δ is the total leakage inductance, including the converted secondary winding leakage inductance, all converted to the primary winding.

[0064] 2. Establish the total cost model of the electrical part of the transformer:

[0065] Because the operating cost of a transformer is mainly determined by losses, which mainly include core losses, winding losses and additional losses, among which additional losses are mainly determined by leakage magnetic energy, the present invention uses leakage magnetic energy to approximately replace additional losses. The operating cost of a transformer is expressed by the following formula:

[0066] C h =C L,h +C N,h +C A,h (5)

[0067] In formula (5), C h is the total operating cost of the transformer, C L,h is the load loss cost, C N,h is the no-load loss cost, C A,h is the total additional loss cost.

[0068] 3. Set initial values ​​for the design parameters to be optimized in the transformer's total loss model and total cost model:

[0069] The initializer program sets the parameters as needed and assigns each parameter in the total loss model and total cost model a minimum design limit as its initial value. The algorithm then optimizes based on this minimum limit, gradually moving the parameter values ​​from the minimum limit toward the optimal value. These initial values ​​cannot all be 0 or the same value, and they cannot be very large or very small.

[0070] Step 2: Based on the above total loss model and total cost model, set the transformer optimization objective function and constraints

[0071] 1. Set the objective function of transformer optimization design as follows:

[0072] (a) Since the cost of the transformer of the present invention mainly depends on the total loss of the transformer, the total loss of the transformer is always set to the minimum value, that is, the sum of the no-load loss and the load loss is minimized:

[0073] f1(x)=min{P core +P windings} (6)

[0074] (b) Since the total loss is the sum of no-load loss, load loss, and additional loss, and the additional loss is directly related to the leakage inductance, to ensure the minimum additional loss, we have:

[0075]

[0076] In formula (7), L δ is the actual leakage inductance of the transformer, L const is the given standard value of leakage inductance.

[0077] 2. Set the constraints for transformer optimization design as follows:

[0078] (a) Ensure that the distance between different windings of the transformer, between different layers of the same winding, and between the winding and the core is greater than the insulation distance;

[0079] (b) The magnetic flux density amplitude of the transformer core should satisfy Bm < 0.6T to avoid core saturation;

[0080] (c) The porosity of transformer windings should be greater than 0.7.

[0081] Step 3: Initialize the population based on the gray wolf optimization algorithm, and design an improved GWO algorithm based on the mathematical model established in step 1;

[0082] Assume that the number of gray wolf individuals in the population is m, then the gray wolf individuals in the population can be expressed as X = {X i , i=1,2,...,m}, the position of the i-th gray wolf in the search space can be expressed as X i ={X i1 , X i2 ,...,X in}, then:

[0083] ① Initialize the gray wolf algorithm parameters and the gray wolf individual positions, population size, number of iterations, and weights of each cost function. Determine the search upper and lower bounds based on the transformer loss mathematical model established in step 1, randomly initialize the gray wolf individual positions, and perform iterative search.

[0084] ② Set a search boundary for all gray wolf individual locations and adjust gray wolf individuals that exceed the boundary;

[0085] ③ Calculate the fitness value of the current gray wolf individual and obtain the top three individuals with the best fitness in the current population: α wolf, β wolf, and δ wolf;

[0086] ④ The remaining individual ω wolves update their own positions according to the positions of the first three levels of gray wolf individuals. The distance and position between the gray wolf individuals and the prey are updated as shown in formulas (8) and (9):

[0087] D=|C·X(t)-X(t)| (8)

[0088] X(t+1)=X P (t)-A·D (9)

[0089] In formula (8) and formula (9), D is the distance between the gray wolf and the prey, t is the current iteration number, C and A are coefficient vectors, X P is the position vector of the prey, X(t) is the position vector of a single gray wolf, and X(t+1) is the position vector of the gray wolf at time t+1. Since the search space of the traditional gray wolf algorithm is large, the population and the number of iterations are relatively small, in order to avoid the algorithm falling into the local optimum, the calculation formulas of the C and A coefficients in the original algorithm are improved as shown in formulas (10) and (11):

[0090] A=2.8a·r1-a (10)

[0091] C=2.8r2 (11)

[0092] In formula (10) and formula (11), r1 and r2 are random numbers between [0, 1] to improve the search range of the algorithm and avoid falling into the local optimum; a is the convergence factor of the improved gray wolf algorithm, which adopts a nonlinear convergence method. Its update formula is shown in formula (12):

[0093] a(t)=2-(log(1+μtan(t / T) 3 )) p (12)

[0094] In formula (12), t is the current iteration number, T is the maximum iteration number of the population, and the adjustment coefficients μ∈[1.2, 4.1] and p∈[1, 10]. The present invention found that when μ=1.3 and p=6, the solution is relatively optimal, and the solution accuracy is at a relatively stable level. The present invention uses this update method to find the global optimal solution, by reducing the decay rate of a in the early stage, avoiding falling into the local optimal solution in the later stage.

[0095] According to the distance between the individual ω wolf and the prey and the position update formula, the distance between the current ω wolf and the top three wolves and the direction of movement towards the prey are obtained. The expression of the process of each gray wolf tracking the prey is:

[0096]

[0097] In formula (13), D α , D β , D δ Respectively represent the distances between α wolf, β wolf and δ wolf and other ω wolf individuals, X α , X β , X δ They represent the current positions of wolf α, wolf β and wolf δ respectively, X(t) represents the current position information of wolf ω, and C1, C2 and C3 are coefficients.

[0098] During the hunting process, the remaining ω wolf individuals will search according to the steps and directions of α wolf, β wolf and δ wolf according to formula (14):

[0099]

[0100] Where X1, X2, and X3 represent the direction and step size that the ω wolf will follow in the direction of α wolf, β wolf, and δ wolf, respectively; A1, A2, and A3 represent the corresponding coefficients when generating α wolf, β wolf, and δ wolf, respectively.

[0101] Because the traditional GWO has problems such as strong development capability but weak exploration capability, and strong global search capability but slightly poor accuracy, in order to solve the problems of weak search capability and insufficient accuracy of the algorithm and prevent the algorithm from converging prematurely or falling into local optimality, the present invention uses an adaptive position update strategy to redistribute weights according to the fitness values ​​of α wolf, β wolf, and δ wolf, and then solves the hunting position. The weight nonlinear update strategy is shown in Equations (15), (16), and (17):

[0102]

[0103]

[0104]

[0105] The position update formula is shown in formula (18):

[0106]

[0107] In formula (15), formula (16) and formula (17), f α 、f β 、f δ They represent the fitness of gray wolves α, β and δ respectively, and the weights w1, w2 and w3 correspond to the learning rates of gray wolves α, β and δ respectively; in formula (18), X(t+1) represents the final position of wolf ω in this iteration cycle.

[0108] In addition to improving the convergence factor and position update method in the traditional GWO algorithm, the present invention also includes improvements such as introducing mutation operations and non-dominated sorting operations to generate initial values ​​in the improved algorithm:

[0109] Since the traditional GWO algorithm lacks sufficient randomness when generating initial values, it is not conducive to finding the optimal solution. Therefore, the mutation operation is introduced when the GWO algorithm generates initial values ​​to maintain the diversity of the initial population and improve the optimization range. At this time, the individual is generated by formula (19):

[0110]

[0111] In formula (19), D ij (t+1) represents the distance between individuals i and j in the t+1th generation population, t is the current iteration number, X (X∈[0,2]) is the scaling factor, r1, r2 and r3 are random integers not equal to i, and satisfy r1, r2, r3∈[1,N].

[0112] Because the traditional GWO algorithm cannot filter out individuals that don't meet the constraints, to avoid errors and allow them to be re-entered into the algorithm's optimization process, this invention introduces a non-dominated sorting method for individuals that don't meet the constraints, and adds a penalty operation before sorting. The implementation steps are as follows: First, determine whether each solution meets the constraint function requirements, list all solutions that don't meet the requirements, and add a penalty value to the objective function of these solutions; then perform a non-dominated sorting, sorting the solutions that don't meet the requirements after the solutions that meet the requirements, and ensuring that the individual sorting distances are evenly distributed.

[0113] Step 4: Using the improved GWO algorithm in step 3, the transformer is optimized through an operational optimization system;

[0114] First, the improved GWO algorithm is written in a computer language as a program segment that can be recognized and called by the current setup environment. This program segment can be directly called as a function. Next, an optimization system is built. The optimization system consists of a software program. The input and output parameters are stored in the software's data storage. The software program is primarily responsible for executing the executable program code of the algorithm and model components. Specifically, the model code is executed while calling the improved GWO algorithm program segment. When the system is fed with the basic design parameters and constraint parameters of a specified transformer, the optimization system calls the improved GWO algorithm program to optimize the transformer and output several sets of optimized transformer design parameters for design reference.

[0115] The optimization system is run and used in the Creo Paramatric TOOLKIT environment. Its input loop variables include main parameters such as no-load loss, load loss, leakage inductance, and other parameters such as core diameter, low-voltage wire gauge, low-voltage winding turns, and high-voltage wire gauge. The input interface of the optimization system is as follows: Figure 2 shown.

[0116] The memory includes a computer-readable storage medium, and the storage medium stores a program file capable of implementing all of the above methods. The program file can be stored in the storage medium in the form of a software product, including several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc., which can store program code, or a terminal device such as a computer, server, mobile phone, or tablet.

[0117] Example 2

[0118] Based on the optimization system built in Example 1, an example analysis is performed. Figure 2 The input interface shown is used to input the basic parameters of the 10KV oil-immersed distribution transformer shown in Table 1 and the transformer constraint parameters shown in Table 2 into the optimization system.

[0119] Table 1 Basic parameters of 10KV oil-immersed distribution transformer

[0120]

[0121] Table 2 Transformer constraint parameters input by the platform

[0122]

[0123] In order to ensure that the actual engineering manufacturing has more sufficient reference significance, the optimization system will generate as many feasible optimization data results as possible, such as Figure 3 As shown, at this time, the system result display page has generated a total of 241 groups of results.

[0124] Depend on Figure 3 It can be seen that the generated feasible solutions include multiple parameters such as voltage, loss, current and temperature rise. Therefore, the appropriate data set can be selected according to the actual engineering needs and applied in actual engineering manufacturing after precision processing.

[0125] In order to further verify the performance of the improved Grey Wolf Algorithm, the optimization system is equipped with the traditional Grey Wolf Algorithm as a reference group, and the same transformer loss and cost upper limit are set. The same set of data shown in Table 1 and Table 2 is substituted into the solution. For ease of expression, all the parameter results are summarized as loss and cost data as shown in Table 3:

[0126] Table 3 Parameter results obtained by different algorithms

[0127]

[0128] As shown in Table 3, the improved GWO algorithm significantly improves the solution results compared to the traditional GWO algorithm. The improved GWO algorithm significantly reduces transformer losses and the operating costs of the transformer. This proves that the optimization design method provided by the present invention can solve the optimization design problem of transformer loss parameters and that the proposed improved GWO algorithm has good practical application value.

[0129] The above embodiments are specific implementation methods of the present invention, but the implementation methods of the present invention are not limited to the above embodiments. Any other combination, change, modification, substitution, and simplification that does not exceed the design concept of the present invention shall fall within the scope of protection of the present invention.

Claims

1. A transformer cost optimization design method based on GWO algorithm, characterized in that: The following steps are involved: Step 1: Based on the transformer loss parameters, establish the loss model and cost model of the transformer electrical part, and set initial values ​​for the design parameters to be optimized in the loss model and cost model; Step 2: Based on the loss model and cost model, set the optimization objective function and constraints of the transformer; Step 3: Based on the traditional GWO algorithm, improve the population initialization, convergence factor and position update weight to obtain the improved GWO algorithm; Step 4: Using the improved GWO algorithm in step 3, the transformer is optimized through an operational optimization system to solve the optimal design parameters of multiple sets of transformers; The specific method for improving the population initialization in step 3 is to improve the distribution properties of the wolf pack during population initialization of the GWO algorithm by introducing a mutation operation. The individuals in the mutation operation are generated by formula (1): In formula (1), D ij (t+1) represents the distance between individuals i and j in the t+1 generation population, t is the current iteration number, X is the scaling factor, X∈[0,2], r1, r2 and r3 are random integers not equal to i, and satisfy r1∈[1,N], r2∈[1,N], r3∈[1,N]; In the improved GWO algorithm, D = |C·X(t)-X(t)| and X(t+1) = X P (t)-A·D, A=2.8a·r1-a (2) C＝2.8r2 (3) In formula (2) and formula (3), r1 and r2 are random numbers between [0,1]; a is the nonlinear convergence factor of the improved GWO algorithm, and its update formula is: a(t)=2-(log(1+μtan(t / T) 3 )) p (4) In formula (4), t is the current iteration number, T is the maximum iteration number of the population, and the adjustment coefficients μ∈[1.2, 4.1] and p∈[1, 10]. In step S3, the improved GWO algorithm adopts an adaptive position update strategy, redistributes weights according to the fitness values ​​of α wolf, β wolf, and δ wolf, and then solves the hunting position. The weight nonlinear update formulas are shown in formulas (5), (6), and (7): In formula (5), formula (6) and formula (7), f α 、f β 、f δ Represent the fitness of α wolf, β wolf and δ wolf respectively, and the weights w1, w2 and w3 correspond to the learning rates of α wolf, β wolf and δ wolf respectively; The formula for the adaptive position update is: In formula (8), X1, X2, and X3 represent the direction and step length that the individual ω wolf will follow along with α wolf, β wolf, and δ wolf, respectively, and X(t+1) represents the final position of ω wolf in the iteration cycle.

2. The transformer cost optimization design method based on the GWO algorithm according to claim 1 is characterized in that: The optimization objective function in step S2 requires that the sum of the transformer's no-load loss, load loss, and additional loss be minimized, and the additional loss must satisfy Where f2(x) represents the additional loss, L δ Indicates the actual leakage inductance of the transformer, L const Indicates a given standard value of leakage inductance.

3. The transformer cost optimization design method based on the GWO algorithm according to claim 2 is characterized in that: The constraints in step S2 are: (a) The distance between different windings of the transformer, between different layers of the same winding, and between the winding and the core is greater than the insulation distance; (b) The magnetic flux density amplitude of the transformer core should satisfy Bm<0.6T; (c) The porosity of transformer windings should be greater than 0.

7.

4. The transformer cost optimization design method based on the GWO algorithm according to claim 3 is characterized in that: When solving the design parameters of the transformer in step 4, non-dominated sorting is introduced for solutions that do not meet the constraints, and a penalty operation is added before sorting.

5. The transformer cost optimization design method based on the GWO algorithm according to claim 4 is characterized in that: The specific operation of the non-dominated sorting is as follows: first determine whether each solution meets the requirements of the constraints, and list all solutions that do not meet the requirements separately, and add penalty values ​​to the optimization objective function of these solutions; then perform non-dominated sorting, sorting the solutions that do not meet the requirements after the solutions that meet the requirements, and the individual sorting distances meet the uniform distribution.

6. A transformer cost optimization system based on GWO algorithm, characterized in that: The optimization system includes a memory and a processor, wherein the memory is used to store executable instructions that can be run on the processor, and the processor is used to execute the improved GWO algorithm when running the executable instructions, thereby implementing the transformer cost optimization design method based on the GWO algorithm as described in any one of claims 1 to 5.

7. A storage medium, characterized in that: Program instructions executable by a processor are stored, and the program instructions are used to execute the transformer cost optimization design method based on the GWO algorithm according to any one of claims 1 to 5.

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