A low-noise transformer design method and system considering temperature effects

Abstract

The present invention relates to a low-noise transformer design method and system that considers temperature effects. The method comprises: first, using the basic principles of transformers and the Steinmtz equation for derivation to establish an electromagnetic-temperature-vibration multi-physics field model; then, using the established electromagnetic-temperature-vibration multi-physics field model, with vibration acceleration suppression as the guide and the transformer electromagnetic constraints as the constraints, establishing a transformer optimization design model; finally, using a wolf pack algorithm to solve the established vibration acceleration optimization design model, obtaining transformer optimization design parameters guided by vibration acceleration, reducing the noise level emitted by the designed transformer, and achieving vibration suppression design for the transformer while meeting the basic transformer design parameters. This effectively improves the transformer's quietness performance and contributes to the safe, quiet, and stable operation of the electrical system.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer design, and in particular to a low-noise transformer design method and system considering temperature effects. Background Art

[0002] As a crucial component of electrical systems, the safe and stable operation of transformers directly impacts their reliability. During power system operation, transformers often generate significant vibration noise. Therefore, reducing transformer vibration noise is crucial to achieving quiet power system operation.

[0003] The electromagnetic force generated by alternating current in the transformer core causes vibration, a major source of transformer noise. Past research has focused on process improvements to noise reduction technologies, such as optimizing structural design, employing sound insulation materials, and replacing low-noise fans. However, there has been limited exploration of direct vibration reduction design methods. Furthermore, few studies have systematically considered the impact of temperature effects on transformer vibration noise and how to mitigate this impact through design. Summary of the Invention

[0004] In view of this, it is necessary to provide a low-noise transformer design method that takes temperature effects into consideration to address the above-mentioned defects of the prior art.

[0005] In order to solve the above problems, in a first aspect, an embodiment of the present invention provides a low-noise transformer design method considering temperature effects, comprising:

[0006] S1, determining the parameters to be optimized of the transformer, and establishing an electromagnetic-temperature-vibration multi-physics model based on the electromagnetic relationship of the transformer and magnetostriction under the influence of transformer temperature changes; wherein the electromagnetic-temperature-vibration multi-physics model includes an electromagnetic model of the transformer and a transformer temperature-vibration model;

[0007] S2, constructing an objective function and electromagnetic constraints with the goal of minimizing transformer vibration acceleration based on the electromagnetic-temperature-vibration multi-physics field model, and establishing a transformer optimization design model;

[0008] S3, solving the transformer optimization design model by using a wolf pack algorithm to obtain transformer optimization parameters.

[0009] Preferably, in step S1, the parameters to be optimized of the transformer include:

[0010] Transformer turns N, transformer winding axial height H, transformer magnetic flux density B m And the transformer core diameter D.

[0011] Preferably, in step S1, establishing an electromagnetic-temperature-vibration multi-physics model based on the electromagnetic relationship of the transformer and magnetostriction under the influence of transformer temperature change includes:

[0012] According to the electromagnetic relationship of the transformer, an electromagnetic model of the transformer is established; wherein the electromagnetic model of the transformer includes a transformer mass formula and a transformer electromagnetic constraint formula;

[0013] According to the magnetostriction under the influence of transformer temperature change, the temperature-vibration model of the transformer is derived.

[0014] Preferably, the transformer quality formula is:

[0015] G T =G e +G Fe =C1ND+C2HD 2 +C3D 2 +C4D 3

[0016] Where G T is the transformer mass, G e is the transformer coil mass, G Fe is the transformer core mass, C1 is the transformer coil coefficient, C2 is the transformer core column mass coefficient, C3 is the transformer side column mass coefficient, and C4 is the transformer four corner mass coefficient;

[0017] The transformer electromagnetic constraint formula includes the constraint relationship between the transformer voltage and the transformer structure and the constraint relationship between the transformer short-circuit impedance voltage and the transformer structure, where:

[0018] The constraint relationship between transformer voltage and transformer structure is:

[0019]

[0020] Where, C5 is the voltage coefficient of the transformer structure, U N is the rated voltage of the transformer coil, f is the frequency, K D It is the ratio of the effective cross-sectional area of ​​the core column to the area of ​​the circle determined by the core diameter D;

[0021] The constraint relationship between the transformer short-circuit impedance voltage and the transformer structure is:

[0022]

[0023] Where C6 is the transformer structural impedance voltage coefficient, μ0 is the core magnetic permeability, I is the winding current, k d is the air path coefficient between coils, a p is the transformer coil coefficient, ρ1 is the longitudinal Rockwell coefficient, kq is the additional leakage reactance coefficient, u x is the transformer short-circuit impedance voltage.

[0024] Preferably, the derivation of the temperature-vibration model of the transformer according to magnetostriction under the influence of temperature change of the transformer includes:

[0025] Assume that the total transformer loss under the action of sine wave is P t , the winding loss is P w , the core loss is P c , the following relationship is obtained from the Steinmtz equation:

[0026]

[0027] Where F1 and F2 are AC winding coefficients, N1 and N2 are the turns of the primary winding and the secondary winding of the transformer respectively, ρ2 and ρ3 are the resistivity of the primary winding and the secondary winding of the transformer respectively, MLT1 and MLT2 are the average turn length of the primary winding and the average turn length of the secondary winding of the transformer respectively, and K m , superscript α, superscript β are fitting coefficients, V c is the core volume; I1 and I2 are the transformer primary winding current and transformer secondary winding current respectively;

[0028] The total transformer loss is P t Finally, a finite element simulation model of transformer temperature rise is established and solved to obtain the transformer temperature rise ΔT under the initial parameter conditions;

[0029] Assume that magnetic induction intensity B(t) = B m sinωt, t is time, ω is angular frequency, then:

[0030]

[0031] Where B(t) is the magnetic induction intensity, χ m is the magnetic susceptibility of the medium, M(t) is the time function of the magnetization intensity, and H(t) is the time function of the magnetic field intensity;

[0032] From the above formula we can get:

[0033] Where μ is the magnetic permeability of the material;

[0034] Considering the influence of transformer temperature change, the expression of transformer magnetostriction λ is obtained as follows:

[0035] λ(M)=(γ 11 M 2 +γ 21 M 4 )+ΔT(γ13 M 2 +γ 23 M 4 )

[0036] Where, γ 11 , γ 21 , γ 13 , γ 23 is the magnetostriction coefficient of the material, M is the magnetization intensity;

[0037] Substituting the expression of magnetic induction intensity into the expression of magnetostriction, we get:

[0038]

[0039] make: but:

[0040] λ=Asin 2 ωt+B sin 4 ωt

[0041] in,

[0042] Then we get:

[0043]

[0044] By taking the derivative of magnetostriction λ, we get the expression of transformer vibration acceleration a:

[0045]

[0046] Then the effective value of the transformer vibration acceleration |a| is:

[0047]

[0048] Based on the relationship between magnetostriction λ and transformer temperature change ΔT, and the expression of the effective value of transformer vibration acceleration |a|, the temperature-vibration model of the transformer is obtained.

[0049] Preferably, in step S2, based on the electromagnetic-temperature-vibration multi-physics field model, an objective function and electromagnetic constraints with the goal of minimizing transformer vibration acceleration are constructed to establish a transformer optimization design model, including:

[0050] Taking the minimum effective value of transformer vibration acceleration |a| as the optimization goal, the objective function of the transformer optimization design model is established;

[0051] According to the transformer mass formula, the constraint relationship between transformer voltage and transformer structure, and the constraint relationship between transformer short-circuit impedance voltage and transformer structure, the electromagnetic constraint conditions of the transformer optimization design model are obtained as follows:

[0052]

[0053] Where G Tmax is the maximum mass of the transformer;

[0054] The transformer optimization design model is composed of the objective function and the electromagnetic constraint conditions.

[0055] Preferably, in step S3, solving the transformer optimization design model by a wolf pack algorithm to obtain transformer optimization parameters includes:

[0056] S31, initialize the position X of the artificial wolf in the wolf pack i And the population size N', the maximum number of iterations k max , exploration scale factor α', maximum number of walks T max , distance determination factor ω', step size factor S, update scale factor β'; where the artificial wolf position corresponds to the parameters to be optimized of the transformer;

[0057] S32, at the beginning of each iteration, the wolf with the highest odor concentration in the current population is selected as the leader wolf; the odor concentration corresponds to the objective function of the transformer optimization design model;

[0058] According to the wolf detection ratio factor α', a certain number of scout wolves are randomly selected, and the remaining artificial wolves are used as fierce wolves; the number of scout wolves is S_ num , S_ num Randomly select an integer between [N' / (α'+1), N' / α'];

[0059] The scouting wolf performs wandering behavior, searching for prey in the solution space, and selects the one with the strongest smell and greater than the current location's smell concentration Y i0 Wherein, searching for prey means searching for the optimal solution of the objective function;

[0060]

[0061] Among them, step a d is the walking step length, h is the total number of walking directions, p=1,2,…,h; x id To explore the position of wolf i in the d-dimensional space, is the position of the wolf i in the d-dimensional space after performing a wandering behavior;

[0062] Until the concentration of prey odor detected by the scouting wolf i reaches Y i , greater than the prey odor concentration Y perceived by the leader wolf lead Or reach the maximum number of walks T max , then proceed to step S33;

[0063] S33, the wolf rushes towards the prey. If the concentration of the prey odor sensed by the wolf is Y i >Y lead , then it replaces the alpha wolf and initiates the summoning behavior: the fierce wolves around the alpha wolf approach the alpha wolf's position with a larger running stride length;

[0064]

[0065] Among them, g d k is the position of the kth generation wolf leader in the d-dimensional space, step b d For the running stride length, is the position of wolf i in the k-th generation wolf pack in the d-th dimensional space, is the position of the exploratory wolf i in the k+1 generation wolf pack in the d-dimensional space;

[0066] If Y i <Y lead , the artificial wolf continues to charge, and the process goes to step S34;

[0067] S34, updates the position of the artificial wolf participating in the siege behavior, trying to get closer to the prey;

[0068]

[0069] Among them, G d k is the position of the prey in the d-dimensional space in the k-th generation population, step c d is the attack step length, λ' is a random factor uniformly distributed between [-1,1], and the siege step length step c d The random factor λ' is used to adjust the position update; during the siege, if the odor concentration of artificial wolf i is greater than the odor concentration of the original position, the position is updated; otherwise, the position of the artificial wolf remains unchanged;

[0070] S35, updating the position of the alpha wolf according to the winner-takes-all alpha wolf generation rule, where the alpha wolf position represents the optimal solution of the current objective function;

[0071] The wolf pack is updated according to the survival of the fittest wolf pack update mechanism, and the R artificial wolves with the lowest odor concentration are eliminated, and R artificial wolves are randomly generated at the same time;

[0072] S36, repeat steps S32 to S35 until the maximum number of iterations k is reached max Or the position of the alpha wolf meets the optimization accuracy requirements, the position of the alpha wolf is output and the optimal solution of the objective function is obtained;

[0073] S37, after each iteration, the number of transformer turns N and the current optimized parameter values ​​of the transformer parameters to be optimized are obtained by the wolf pack algorithm, and a transformer temperature rise finite element simulation model is established based on the current optimized parameter values ​​to obtain the actual temperature rise ΔT of the transformer under the current optimized parameter values. (0) , the expression of transformer magnetostriction λ is modified:

[0074] λ(M)=(γ 11 M 2 +γ 21 M 4 )+ΔT (0) (γ 13 M 2 +γ 23 M 4 )

[0075] In the formula, the superscript (0) indicates the 0th iteration;

[0076] According to the corrected transformer magnetostriction, the corrected transformer vibration acceleration effective value |a| is obtained. (0) :

[0077]

[0078] The effective value of transformer vibration acceleration |a| (0) Minimum is the optimization goal, and the revised objective function is established;

[0079] S38, use the wolf pack algorithm to solve the corrected objective function and obtain the corrected optimal magnetic flux density B m (1) , according to the electromagnetic constraints of the transformer optimization design model, the corrected number of turns N is calculated (1) and winding axial height H (1) ;

[0080] S39, repeat steps S36 to S38, and iteratively solve the optimal solution B by continuously iteratively optimizing the objective function. m (k) , until the maximum number of iterations is reached or the optimal solution changes |B m (k+1) -B m (k) | is less than the preset threshold ε, the optimal magnetic flux density B of the output transformer m (k) , the optimal number of turns of the transformer N (k) , the optimal winding axial height H of the transformer (k) , the optimal transformer core diameter D (k) and the minimum effective value of the transformer core acceleration |a| (k).

[0081] In a second aspect, an embodiment of the present invention provides a low-noise transformer design system that considers temperature effects, including:

[0082] A model building module is configured to establish an electromagnetic-temperature-vibration multi-physics model based on the electromagnetic relationship of the transformer and the magnetostriction under the influence of transformer temperature changes; wherein the electromagnetic-temperature-vibration multi-physics model includes the electromagnetic model of the transformer and the transformer temperature-vibration model;

[0083] An optimization design module is used to construct an objective function and electromagnetic constraints with the goal of minimizing transformer vibration acceleration based on the electromagnetic-temperature-vibration multi-physics field model, and establish a transformer optimization design model;

[0084] The optimization parameter solving module is used to solve the transformer optimization design model through the wolf pack algorithm to obtain the transformer optimization parameters.

[0085] In a third aspect, the present invention further provides an electronic device comprising a memory and a processor, wherein:

[0086] The memory is used to store programs;

[0087] The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the low-noise transformer design method considering temperature effects as described in the embodiment of the first aspect of the present invention.

[0088] In a fourth aspect, the present invention also provides a computer-readable storage medium for storing computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the low-noise transformer design method considering temperature effects as described in the embodiment of the first aspect of the present invention.

[0089] The present invention provides a low-noise transformer design method and system that considers temperature effects. First, an electromagnetic-temperature-vibration multi-physics model is established using the basic principles of transformers and the Steinmtz equation. Then, the established electromagnetic-temperature-vibration multi-physics model is used to establish a transformer optimization design model guided by vibration acceleration suppression and subject to the transformer's electromagnetic constraints. Finally, the established vibration acceleration optimization design model is solved using a wolf pack algorithm to obtain transformer optimization design parameters guided by vibration acceleration, thereby reducing the noise level emitted by the designed transformer. This achieves vibration suppression design for the transformer while meeting the basic transformer design parameters. This effectively improves the transformer's quietness performance and contributes to the safe, quiet, and stable operation of the electrical system.

[0090] Compared with the prior art, the present invention has the following beneficial effects:

[0091] 1) The present invention can optimize transformer parameters, reduce transformer vibration noise from the source, and make the transformer run quietly and stably.

[0092] 2) The present invention comprehensively considers the impact of electromagnetic and temperature on transformer noise. Based on comprehensive analysis, the design of the transformer can be optimized more accurately, thereby improving the transformer's adaptability to different environments.

[0093] 3) The present invention can use the wolf pack algorithm to solve the optimization design model to obtain efficient and reliable transformer optimization design parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] Figure 1 A flow chart of the low-noise transformer design method considering temperature effects provided by the present invention;

[0095] Figure 2 Schematic diagram of the wolf pack algorithm provided by the present invention;

[0096] Figure 3 A structural block diagram of a low-noise transformer design system considering temperature effects provided by the present invention;

[0097] Figure 4 This is a structural block diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0098] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0099] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0100] When AC current is applied to a transformer's core, it generates electromagnetic force, which in turn causes vibration. This is one of the main sources of transformer noise. Currently, research on transformer noise reduction technologies focuses on process improvements, such as optimizing structural design, using sound insulation materials, and replacing low-noise fans. However, there is less research on direct vibration reduction design methods. Furthermore, few studies have systematically considered the impact of temperature effects on transformer vibration noise and how to mitigate this impact through design.

[0101] In light of this, the present invention provides a low-noise transformer design method that considers temperature effects. This method comprehensively considers the impact of electromagnetic and temperature on transformer noise. Based on comprehensive analysis, it can more accurately optimize the transformer design, effectively improving the transformer's quietness performance and contributing to the safe, quiet, and stable operation of electrical systems. This method will be described and introduced below through multiple embodiments.

[0102] Figure 1 The flow chart of the low noise transformer design method considering temperature effect provided by the present invention. Figure 1 As shown, the low noise transformer design method considering temperature effect includes steps S1 to S3, wherein:

[0103] Step S1, determining the parameters to be optimized of the transformer, and establishing an electromagnetic-temperature-vibration multi-physics model based on the electromagnetic relationship of the transformer and the magnetostriction under the influence of transformer temperature changes; wherein the electromagnetic-temperature-vibration multi-physics model includes the electromagnetic model of the transformer and the transformer temperature-vibration model.

[0104] In this embodiment, the parameters to be optimized for the transformer include: the number of turns N of the transformer, the axial height H of the transformer winding, the magnetic flux density B of the transformer, m And the transformer core diameter D.

[0105] Step S1 may specifically include step S11 and step S12; wherein,

[0106] S11, establishing an electromagnetic model of the transformer based on the electromagnetic relationship of the transformer; wherein the electromagnetic model of the transformer includes a transformer mass formula and a transformer electromagnetic constraint formula;

[0107] In this embodiment, the electromagnetic model of the transformer is first established. The electromagnetic model of the transformer includes the transformer mass formula and the transformer electromagnetic constraint formula. Specifically, based on the electromagnetic relationship of the transformer, the following relationship can be obtained:

[0108]

[0109] Where, N is the number of series turns per phase of the winding; I is the winding line current; K D It is the ratio of the effective cross-sectional area of ​​the core column to the area of ​​the circle determined by the core column diameter D; A is the line load; B m is the transformer flux density; m is the number of phases; H is the axial height of the winding; f is the frequency; Φ m is the maximum magnetic flux; E is the electromotive force, S N is the total capacity of the transformer.

[0110] The quality of the transformer includes the quality of the transformer coil and the quality of the transformer core. The quality expression of the transformer coil is:

[0111]

[0112] Where G e is the transformer coil mass, ρ1 is the transformer coil material density, k d is the air channel coefficient between coils, J is the current density, and C1 is the transformer coil coefficient.

[0113] The volume of the transformer core includes the volume of the core column, the part between the center distances of the two side columns, and the actual part of the four corners of the iron yoke. Therefore, the expression of the transformer core mass is:

[0114]

[0115] From equations (2) to (8), the transformer quality formula is:

[0116] G T =G e +G Fe =C1ND+C2HD 2 +C3D 2 +C4D 3 (9)

[0118] In the above formula, A is the effective cross-sectional area of ​​the iron yoke; K Fe is the superposition coefficient; h 0为 Distance from coil end to iron yoke; a z The center distance between two adjacent core columns, ρ2 is the density of the transformer core material; G T is the transformer mass, G e is the transformer coil mass, G Fe is the quality of the transformer core, C1 is the transformer coil coefficient, C2 is the transformer core column quality coefficient, C3 is the quality coefficient between the transformer side columns, and C4 is the transformer four corner quality coefficient.

[0119] The transformer electromagnetic constraint formula includes the constraint relationship between the transformer voltage and the transformer structure, and the constraint relationship between the transformer short-circuit impedance voltage and the transformer structure, where:

[0120] From the rated voltage UN of the transformer coil, we can get the constraint relationship between the transformer voltage and the transformer structure:

[0121]

[0122] Where, C5 is the voltage coefficient of the transformer structure, U N is the rated voltage of the transformer coil, f is the frequency, K D It is the ratio of the effective cross-sectional area of ​​the core column to the area of ​​the circle determined by the core diameter D;

[0123] Transformer short-circuit impedance voltage u x It is also the rated parameter of the transformer. The constraint relationship between the transformer short-circuit impedance voltage and the transformer structure is:

[0124]

[0125] Where C6 is the transformer structural impedance voltage coefficient, μ0 is the core magnetic permeability, I is the winding current, k d is the air path coefficient between coils, a p is the transformer coil coefficient, ρ1 is the longitudinal Rockwell coefficient, k q is the additional leakage reactance coefficient, u x is the transformer short-circuit impedance voltage.

[0126] In this embodiment, the transformer mass formula shown in formula (9), the constraint relationship between the transformer short-circuit impedance voltage and the transformer structure in formula (11), and the constraint relationship between the transformer short-circuit impedance voltage and the transformer structure shown in formula (13) constitute the electromagnetic model of the transformer.

[0127] S12, based on the magnetostriction under the influence of transformer temperature change, derive the temperature-vibration model of the transformer.

[0128] Assume that the total transformer loss under the action of sine wave is P t , the winding loss is P w , the core loss is P c , the following relationship is obtained from the Steinmtz equation:

[0129]

[0130] Where F1 and F2 are AC winding coefficients, N1 and N2 are the turns of the primary winding and the secondary winding of the transformer respectively, ρ2 and ρ3 are the resistivity of the primary winding and the secondary winding of the transformer respectively, MLT1 and MLT2 are the average turn length of the primary winding and the average turn length of the secondary winding of the transformer respectively, and K m , superscript α, superscript β are fitting coefficients, V c is the core volume; I1 and I2 are the transformer primary winding current and transformer secondary winding current respectively.

[0131] The Steinmetz equation is used to define the approximate heat energy generated by the hysteresis released per unit area of ​​a magnetic material per cycle. The Steinmetz equation can be used to calculate the magnetic loss per unit volume of ferromagnetic materials (such as transformer cores).

[0132] The total transformer loss is P tFinally, a finite element simulation model of transformer temperature rise is established and solved to obtain the transformer temperature rise ΔT under the initial parameter conditions;

[0133] Assume that magnetic induction intensity B(t) = B m sinωt, t is time, ω is angular frequency, then:

[0134]

[0135] Where B(t) is the magnetic induction intensity, χ m is the magnetic susceptibility of the medium, M(t) is the time function of the magnetization intensity, and H(t) is the time function of the magnetic field intensity;

[0136] From the above formula (15), we can get:

[0137]

[0138] Where μ is the magnetic permeability of the material;

[0139] It is understood that magnetic field, stress, and temperature are the main factors affecting the magnetostriction coefficient of a transformer. As temperature changes, the magnetostriction of the transformer's internal materials changes, which in turn affects its vibration characteristics. In this embodiment, considering the effect of transformer temperature change ΔT on magnetostriction, the expression for the transformer's magnetostriction λ is obtained as:

[0140] λ(M)=(γ 11 M 2 +γ 21 M 4 )+ΔT(γ 13 M 2 +γ 23 M 4 ) (17)

[0141] Where, γ 11 , γ 21 , γ 13 , γ 23 is the material's magnetostriction coefficient, and M is the magnetization intensity. Magnetic field, stress, and temperature are the primary factors influencing the transformer's magnetostriction coefficient. Temperature changes cause changes in the magnetostriction of the transformer's internal materials, which in turn affects its vibration characteristics.

[0142] Substituting the magnetic induction intensity expression (15) into the magnetostriction expression (17), we obtain:

[0143]

[0144] make:

[0145]

[0146] Then, from equations (18) and (19), the expression of magnetostriction λ is simplified to:

[0147] λ=Asin 2 ωt+Bsin 4 ωt (20)

[0149] in,

[0150]

[0151] From formula (20) and formula (21), we can get:

[0152]

[0153] The magnetostrictive acceleration is obtained by differentiating the magnetostrictive acceleration λ. The magnetostrictive acceleration is used as the transformer vibration acceleration. The expression of the transformer vibration acceleration a is:

[0154]

[0155] Then the effective value of the transformer vibration acceleration |a| is:

[0156]

[0157] Based on the relationship between magnetostriction λ and transformer temperature change ΔT, as well as the expression for the effective value of transformer vibration acceleration |a|, the transformer temperature-vibration model is obtained. Specifically, the transformer temperature-vibration model can be constructed using equations (18), (19), and (24).

[0158] Step S2: constructing an objective function and electromagnetic constraints with the goal of minimizing transformer vibration acceleration based on the electromagnetic-temperature-vibration multi-physics field model, and establishing a transformer optimization design model.

[0159] Specifically, based on formula (24), the objective function of the transformer optimization design model is established with the minimum effective value of transformer vibration acceleration |a| as the optimization goal.

[0160] According to the transformer quality formula shown in formula (9), the constraint relationship between the transformer short-circuit impedance voltage and the transformer structure in formula (11), and the constraint relationship between the transformer short-circuit impedance voltage and the transformer structure shown in formula (13), the electromagnetic constraint conditions of the transformer optimization design model are obtained as follows:

[0161]

[0162] Where G Tmax is the maximum mass of the transformer;

[0163] The transformer optimization design model is composed of the above objective function and electromagnetic constraints.

[0164] Step S3: solving the transformer optimization design model by using a wolf pack algorithm to obtain transformer optimization parameters.

[0165] Specifically, after establishing a transformer optimization design model, this embodiment solves the transformer optimization design model using a wolf pack algorithm to obtain optimized transformer parameters. The wolf pack algorithm, based on wolf group intelligence, simulates wolf hunting behavior and prey distribution. It abstracts three intelligent behaviors: roaming, summoning, and siege, as well as the "winner takes all" rule for alpha wolf generation and the "survival of the fittest" wolf pack renewal mechanism, proposing a new swarm intelligence algorithm.

[0166] Figure 2 The wolf pack algorithm principle diagram provided by the present invention is shown in FIG. Figure 2 , step S3 of the present invention may include the following steps S31 to S39:

[0167] S31, initialize the position X of the artificial wolf in the wolf pack i And the population size N', the maximum number of iterations k max , exploration scale factor α', maximum number of walks T max , distance determination factor ω', step size factor S, update scale factor β'; where the artificial wolf position corresponds to the parameters to be optimized of the transformer;

[0168] S32, at the beginning of each iteration, the wolf with the highest odor concentration in the current population is selected as the leader wolf; the odor concentration corresponds to the objective function of the transformer optimization design model;

[0169] According to the wolf detection ratio factor α', a certain number of scout wolves are randomly selected, and the remaining artificial wolves are used as fierce wolves; the number of scout wolves is S_ num , S_ num Randomly select an integer between [N' / (α'+1), N' / α'];

[0170] The scouting wolf performs wandering behavior, searching for prey in the solution space, and selects the one with the strongest smell and greater than the current location's smell concentration Y i0 Wherein, searching for prey means searching for the optimal solution of the objective function;

[0171]

[0172] Among them, step a d is the walking step length, h is the total number of walking directions, p=1,2,…,h; x id To explore the position of wolf i in the d-dimensional space, is the position of the wolf i in the d-dimensional space after performing a wandering behavior;

[0173] Until the concentration of prey odor detected by the scouting wolf i reaches Y i , greater than the prey odor concentration Y perceived by the leader wolf lead Or reach the maximum number of walks T max , then proceed to step S33;

[0174] S33, the wolf rushes towards the prey. If the concentration of the prey odor sensed by the wolf is Y i >Y lead , then it replaces the alpha wolf and initiates the summoning behavior: the fierce wolves around the alpha wolf approach the alpha wolf's position with a larger running stride length;

[0175]

[0176] Among them, g d k is the position of the kth generation wolf leader in the d-dimensional space, step b d For the running stride length, is the position of wolf i in the k-th generation wolf pack in the d-th dimensional space, is the position of the exploratory wolf i in the k+1 generation wolf pack in the d-dimensional space;

[0177] If Y i <Y lead , the artificial wolf continues to charge, and the process goes to step S34;

[0178] S34, updates the position of the artificial wolf participating in the siege behavior, trying to get closer to the prey;

[0179]

[0180] Among them, G d k is the position of the prey in the d-dimensional space in the k-th generation population, step c d is the attack step length, λ' is a random factor uniformly distributed between [-1,1], and the siege step length step c d The random factor λ' is used to adjust the position update; during the siege, if the odor concentration of artificial wolf i is greater than the odor concentration of the original position, the position is updated; otherwise, the position of the artificial wolf remains unchanged;

[0181] S35, updating the position of the alpha wolf according to the winner-takes-all alpha wolf generation rule, where the alpha wolf position represents the optimal solution of the current objective function;

[0182] The wolf pack is updated according to the survival of the fittest wolf pack update mechanism, and the R artificial wolves with the lowest odor concentration are eliminated, and R artificial wolves are randomly generated at the same time;

[0183] S36, repeat steps S32 to S35 until the maximum number of iterations k is reached max Or the position of the alpha wolf meets the optimization accuracy requirements, the position of the alpha wolf is output and the optimal solution of the objective function is obtained;

[0184] S37, after each iteration, the number of transformer turns N and the current optimized parameter values ​​of the transformer parameters to be optimized are obtained by the wolf pack algorithm, and a transformer temperature rise finite element simulation model is established based on the current optimized parameter values ​​to obtain the actual temperature rise ΔT of the transformer under the current optimized parameter values. (0) , the expression of transformer magnetostriction λ is modified:

[0185] λ(M)=(γ 11 M 2 +γ 21 M 4 )+ΔT (0) (γ 13 M 2 +γ 23 M 4 ) (29)

[0186] In the formula, the superscript (0) indicates the 0th iteration;

[0187] Referring to equations (19) to (23), based on the corrected transformer magnetostriction, the corrected transformer vibration acceleration effective value |a| is obtained. (0) :

[0188]

[0189] The effective value of transformer vibration acceleration |a| (0) Minimum is the optimization goal, and the revised objective function is established;

[0190] S38, use the wolf pack algorithm to solve the corrected objective function and obtain the corrected optimal magnetic flux density B m (1) , according to the electromagnetic constraints of the transformer optimization design model, the corrected number of turns N is calculated (1) and winding axial height H (1) ;

[0191] S39, repeat steps S36 to S38, and iteratively solve the optimal solution B by continuously iteratively optimizing the objective function. m (k) , until the maximum number of iterations is reached or the optimal solution changes |B m (k+1)-B m (k) | is less than the preset threshold ε, the optimal magnetic flux density B of the output transformer m (k) , the optimal number of turns of the transformer N (k) , the optimal winding axial height H of the transformer (k) , the optimal transformer core diameter D (k) and the minimum effective value of the transformer core acceleration |a| (k) .

[0192] The present invention provides a low-noise transformer design method that considers temperature effects. First, the electromagnetic-temperature-vibration multi-physics model is established using the basic principles of transformers and the Steinmtz equation for derivation. Then, the established electromagnetic-temperature-vibration multi-physics model is used to establish a transformer optimization design model guided by vibration acceleration suppression and with the transformer electromagnetic constraints as constraints. Finally, the established vibration acceleration optimization design model is solved using a wolf pack algorithm to obtain transformer optimization design parameters guided by vibration acceleration, thereby reducing the noise level emitted by the designed transformer. This method achieves vibration suppression design for the transformer while meeting the basic transformer design parameters. This method effectively improves the transformer's quietness performance and contributes to the safe, quiet, and stable operation of the electrical system.

[0193] Compared with the prior art, the present invention has the following beneficial effects:

[0194] 1) The present invention can optimize transformer parameters, reduce transformer vibration noise from the source, and make the transformer run quietly and stably.

[0195] 2) The present invention comprehensively considers the impact of electromagnetic and temperature on transformer noise. Based on comprehensive analysis, the design of the transformer can be optimized more accurately, thereby improving the transformer's adaptability to different environments.

[0196] 3) The present invention can use the wolf pack algorithm to solve the optimization design model to obtain efficient and reliable transformer optimization design parameters.

[0197] Figure 3 The structural block diagram of the low noise transformer design system considering temperature effect provided by the present invention is shown in FIG. Figure 3 The low noise transformer design system 300 considering temperature effect includes:

[0198] The model building module 301 establishes an electromagnetic-temperature-vibration multi-physics model based on the electromagnetic relationship of the transformer and the magnetostriction under the influence of the transformer temperature change; wherein the electromagnetic-temperature-vibration multi-physics model includes the electromagnetic model of the transformer and the transformer temperature-vibration model;

[0199] An optimization design module 302 is configured to construct an objective function and electromagnetic constraints with the goal of minimizing transformer vibration acceleration based on the electromagnetic-temperature-vibration multi-physics field model, and establish a transformer optimization design model;

[0200] The optimization parameter solving module 303 is used to solve the transformer optimization design model by using a wolf pack algorithm to obtain transformer optimization parameters.

[0201] The low-noise transformer design system considering temperature effects provided by the present invention executes the low-noise transformer design methods considering temperature effects provided by the above-mentioned embodiments through the model building module 301, the optimization design module 302 and the optimization parameter solving module 303. The low-noise transformer design methods considering temperature effects have been described in detail in the above-mentioned embodiments and will not be repeated in this embodiment.

[0202] The low-noise transformer design system, which considers temperature effects, provides a vibration suppression design for the transformer while meeting basic transformer design parameters. This effectively improves the transformer's quietness and contributes to the safe, quiet, and stable operation of electrical systems.

[0203] Figure 4 The structural block diagram of the electronic device provided by the present invention is as follows: Figure 4 As shown, the present invention further provides an electronic device, wherein the electronic device 400 can be a computing device such as a mobile terminal, a desktop computer, a notebook computer, a PDA, or a server. The electronic device 400 includes a processor 401 and a memory 402, wherein the memory 402 stores a low-noise transformer design program 404 that considers temperature effects.

[0204] In some embodiments, the memory 402 may be an internal storage unit of a computer device, such as a hard disk or memory of the computer device. In other embodiments, the memory 402 may also be an external storage device of the computer device, such as a plug-in hard disk equipped on the computer device, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Furthermore, the memory 402 may also include both an internal storage unit of the computer device and an external storage device. The memory 402 is used to store application software and various types of data installed on the computer device, such as program codes installed on the computer device. The memory 402 may also be used to temporarily store data that has been output or is to be output. In one embodiment, when the low-noise transformer design program 404 considering temperature effects is executed by the processor 401, the following steps are implemented:

[0205] S1, determining the parameters to be optimized of the transformer, and establishing an electromagnetic-temperature-vibration multi-physics model based on the electromagnetic relationship of the transformer and magnetostriction under the influence of transformer temperature changes; wherein the electromagnetic-temperature-vibration multi-physics model includes an electromagnetic model of the transformer and a transformer temperature-vibration model;

[0206] S2, constructing an objective function and electromagnetic constraints with the goal of minimizing transformer vibration acceleration based on the electromagnetic-temperature-vibration multi-physics field model, and establishing a transformer optimization design model;

[0207] S3, solving the transformer optimization design model by using a wolf pack algorithm to obtain transformer optimization parameters.

[0208] In some embodiments, the processor 401 may be a central processing unit (CPU), a microprocessor, or other data processing chip, configured to execute program codes or process data stored in the memory 402, such as executing a low-noise transformer design program that considers temperature effects.

[0209] This embodiment further provides a computer-readable storage medium storing a low-noise transformer design program that considers temperature effects. When the low-noise transformer design program that considers temperature effects is executed by a processor, the following steps are implemented:

[0210] S1, determining the parameters to be optimized of the transformer, and establishing an electromagnetic-temperature-vibration multi-physics model based on the electromagnetic relationship of the transformer and magnetostriction under the influence of transformer temperature changes; wherein the electromagnetic-temperature-vibration multi-physics model includes an electromagnetic model of the transformer and a transformer temperature-vibration model;

[0211] S2, constructing an objective function and electromagnetic constraints with the goal of minimizing transformer vibration acceleration based on the electromagnetic-temperature-vibration multi-physics field model, and establishing a transformer optimization design model;

[0212] S3, solving the transformer optimization design model by using a wolf pack algorithm to obtain transformer optimization parameters.

[0213] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0214] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A low-noise transformer design method considering temperature effects, characterized in that: include: S1, determine the parameters to be optimized of the transformer, and establish an electromagnetic-temperature-vibration multi-physics model based on the electromagnetic relationship of the transformer and the magnetostriction under the influence of transformer temperature change; wherein, the electromagnetic-temperature-vibration multi-physics model includes the electromagnetic model of the transformer and the transformer temperature-vibration model; wherein, the parameters to be optimized of the transformer include: the number of transformer turns N, the axial height H of the transformer winding, the magnetic flux density B of the transformer m and the transformer core diameter D; In step S1, the electromagnetic-temperature-vibration multi-physics model is established based on the electromagnetic relationship of the transformer and the magnetostriction under the influence of the transformer temperature change, including: According to the electromagnetic relationship of the transformer, an electromagnetic model of the transformer is established; wherein the electromagnetic model of the transformer includes a transformer mass formula and a transformer electromagnetic constraint formula; Based on the magnetostriction under the influence of transformer temperature change, the temperature-vibration model of the transformer is derived. The derivation process of the temperature-vibration model includes: Assume that the total transformer loss under the action of sine wave is P t , the winding loss is P w , the core loss is P c , the following relationship is obtained from the Steinmtz equation: Where F1 and F2 are AC winding coefficients, N1 and N2 are the turns of the primary winding and the secondary winding of the transformer respectively, ρ2 and ρ3 are the resistivity of the primary winding and the secondary winding of the transformer respectively, MLT1 and MLT2 are the average turn length of the primary winding and the average turn length of the secondary winding of the transformer respectively, and K m , superscript α, superscript β are fitting coefficients, V c is the core volume; I1 and I2 are the transformer primary winding current and transformer secondary winding current respectively; The total transformer loss is P t Finally, a finite element simulation model of transformer temperature rise is established and solved to obtain the transformer temperature rise ΔT under the initial parameter conditions; Assume that magnetic induction intensity B(t) = B m sinωt, t is time, ω is angular frequency, then: Where B(t) is the magnetic induction intensity, χ m is the magnetic susceptibility of the medium, M(t) is the time function of the magnetization intensity, and H(t) is the time function of the magnetic field intensity; From the above formula we can get: Where μ is the magnetic permeability of the material; Considering the influence of transformer temperature change, the expression of transformer magnetostriction λ is obtained as follows: λ(M)=(γ 11 M 2 +g 21 M 4 )+ΔT(γ 13 M 2 +g 23 M 4 ) Where, γ 11 , γ 21 , γ 13 , γ 23 is the magnetostriction coefficient of the material, M is the magnetization intensity; Substituting the expression of magnetic induction intensity into the expression of magnetostriction, we get: make: but: λ=Asin 2 ωt+B sin 4 ωt in, Then we get: By taking the derivative of magnetostriction λ, we get the expression of transformer vibration acceleration a: Then the effective value of the transformer vibration acceleration |a| is: Based on the relationship between magnetostriction λ and transformer temperature change ΔT, and the expression of the effective value of transformer vibration acceleration |a|, the temperature-vibration model of the transformer is obtained; S2, constructing an objective function and electromagnetic constraints with the goal of minimizing transformer vibration acceleration based on the electromagnetic-temperature-vibration multi-physics field model, and establishing a transformer optimization design model; S3, solving the transformer optimization design model by using a wolf pack algorithm to obtain transformer optimization parameters.

2. The method for designing a low-noise transformer considering temperature effects according to claim 1, characterized in that: The transformer quality formula is: G T =G e +G Fe =C1ND+C2HD 2 +C3D 2 +C4D 3 Where G T is the transformer mass, G e is the transformer coil mass, G Fe is the mass of the transformer core, C1 is the mass coefficient of the transformer coil, C2 is the mass coefficient of the transformer core column, C3 is the mass coefficient between the transformer side columns, and C4 is the mass coefficient of the transformer four corners; The transformer electromagnetic constraint formula includes the constraint relationship between the transformer voltage and the transformer structure and the constraint relationship between the transformer short-circuit impedance voltage and the transformer structure, where: The constraint relationship between transformer voltage and transformer structure is: Where, C5 is the voltage coefficient of the transformer structure, U N is the rated voltage of the transformer coil, f is the frequency, K D It is the ratio of the effective cross-sectional area of ​​the core column to the area of ​​the circle determined by the core diameter D; The constraint relationship between the transformer short-circuit impedance voltage and the transformer structure is: Where C6 is the transformer structural impedance voltage coefficient, μ0 is the core magnetic permeability, I is the winding current, k d is the air path coefficient between coils, a p is the transformer coil coefficient, ρ1 is the longitudinal Rockwell coefficient, k q is the additional leakage reactance coefficient, u x is the transformer short-circuit impedance voltage.

3. The method for designing a low-noise transformer considering temperature effects according to claim 1, wherein: In step S2, based on the electromagnetic-temperature-vibration multi-physics field model, an objective function and electromagnetic constraints with the goal of minimizing transformer vibration acceleration are constructed to establish a transformer optimization design model, including: Taking the minimum effective value of transformer vibration acceleration |a| as the optimization goal, the objective function of the transformer optimization design model is established; According to the transformer mass formula, the constraint relationship between transformer voltage and transformer structure, and the constraint relationship between transformer short-circuit impedance voltage and transformer structure, the electromagnetic constraint conditions of the transformer optimization design model are obtained as follows: Where G Tmax is the maximum mass of the transformer; The transformer optimization design model is composed of the objective function and the electromagnetic constraint conditions.

4. The method for designing a low-noise transformer considering temperature effects according to claim 3, characterized in that: In step S3, the transformer optimization design model is solved by the wolf pack algorithm to obtain transformer optimization parameters, including: S31, initialize the position X of the artificial wolf in the wolf pack i And the population size N', the maximum number of iterations k max , wolf exploration ratio factor α', maximum number of wandering times T max , distance determination factor ω', step size factor S, update scale factor β'; where the artificial wolf position corresponds to the parameters to be optimized of the transformer; S32, at the beginning of each iteration, the wolf with the highest odor concentration in the current population is selected as the leader wolf; the odor concentration corresponds to the objective function of the transformer optimization design model; According to the wolf detection ratio factor α', a certain number of scout wolves are randomly selected, and the remaining artificial wolves are used as fierce wolves; the number of scout wolves is S_ num , S_ num Randomly select an integer between [N' / (α'+1), N' / α']; The scouting wolf performs wandering behavior, searching for prey in the solution space, and selects the one with the strongest smell and greater than the current location's smell concentration Y i0 Wherein, searching for prey means searching for the optimal solution of the objective function; Among them, step a d is the walking step length, h is the total number of walking directions, p=1,2,…,h; x id To explore the position of wolf i in the d-dimensional space, is the position of the wolf i in the d-dimensional space after performing a wandering behavior; Until the concentration of prey odor detected by the scouting wolf i reaches Y i , greater than the prey odor concentration Y perceived by the leader wolf lead Or reach the maximum number of walks T max , then proceed to step S33; S33, the wolf rushes towards the prey. If the concentration of the prey odor sensed by the wolf is Y i >Y lead , then it replaces the alpha wolf and initiates the summoning behavior: the fierce wolves around the alpha wolf approach the alpha wolf's position with a larger running stride length; Among them, g d k is the position of the kth generation wolf leader in the d-dimensional space, step b d For the running stride length, is the position of wolf i in the k-th generation wolf pack in the d-th dimensional space, is the position of the exploratory wolf i in the k+1 generation wolf pack in the d-dimensional space; If Y i <Y lead , the artificial wolf continues to charge, and the process goes to step S34; S34, updates the position of the artificial wolf participating in the siege behavior, trying to get closer to the prey; Among them, G d k is the position of the prey in the d-dimensional space in the k-th generation population, step c d is the attack step length, λ' is a random factor uniformly distributed between [-1,1], and the siege step length step c d The random factor λ' is used to adjust the position update; during the siege, if the odor concentration of artificial wolf i is greater than the odor concentration of the original position, the position is updated; otherwise, the position of the artificial wolf remains unchanged; S35, updating the position of the alpha wolf according to the winner-takes-all alpha wolf generation rule, where the alpha wolf position represents the optimal solution of the current objective function; The wolf pack is updated according to the survival of the fittest wolf pack update mechanism, and the R artificial wolves with the lowest odor concentration are eliminated, and R artificial wolves are randomly generated at the same time; S36, repeat steps S32 to S35 until the maximum number of iterations k is reached max Or the position of the alpha wolf meets the optimization accuracy requirements, the position of the alpha wolf is output and the optimal solution of the objective function is obtained; S37, after each iteration, the number of transformer turns N and the current optimized parameter values ​​of the transformer parameters to be optimized are obtained by the wolf pack algorithm, and a transformer temperature rise finite element simulation model is established based on the current optimized parameter values ​​to obtain the actual temperature rise ΔT of the transformer under the current optimized parameter values. (0) , the expression of transformer magnetostriction λ is modified: λ(M)=(γ 11 M 2 +g 21 M 4 )+ΔT (0) (c 13 M 2 +g 23 M 4 ) In the formula, the superscript (0) indicates the 0th iteration; According to the corrected transformer magnetostriction, the corrected transformer vibration acceleration effective value |a| is obtained. (0) : The effective value of transformer vibration acceleration |a| (0) Minimum is the optimization goal, and the revised objective function is established; S38, use the wolf pack algorithm to solve the corrected objective function and obtain the corrected optimal magnetic flux density B m (1) , according to the electromagnetic constraints of the transformer optimization design model, the corrected number of turns N is calculated (1) and winding axial height H (1) ; S39, repeat steps S36 to S38, and iteratively solve the optimal solution B by continuously iteratively optimizing the objective function. m (k) , until the maximum number of iterations is reached or the optimal solution changes |B m (k+1) -B m (k) | is less than the preset threshold ε, the optimal magnetic flux density B of the output transformer m (k) , the optimal number of turns of the transformer N (k) , the optimal winding axial height H of the transformer (k) , the optimal transformer core diameter D (k) and the minimum effective value of the transformer core acceleration |a| (k) .

5. A low-noise transformer design system considering temperature effects, the system being used to execute the low-noise transformer design method considering temperature effects according to any one of claims 1 to 4, characterized in that: include: A model building module is configured to establish an electromagnetic-temperature-vibration multi-physics model based on the electromagnetic relationship of the transformer and the magnetostriction under the influence of transformer temperature changes; wherein the electromagnetic-temperature-vibration multi-physics model includes the electromagnetic model of the transformer and the transformer temperature-vibration model; An optimization design module is used to construct an objective function and electromagnetic constraints with the goal of minimizing transformer vibration acceleration based on the electromagnetic-temperature-vibration multi-physics field model, and establish a transformer optimization design model; The optimization parameter solving module is used to solve the transformer optimization design model through the wolf pack algorithm to obtain the transformer optimization parameters.

6. An electronic device, It is characterized by: comprising a memory and a processor, wherein, The memory is used to store programs; The processor is coupled to the memory and is configured to execute the program stored in the memory to implement the steps of the method for designing a low-noise transformer considering temperature effects as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps of the low-noise transformer design method considering temperature effects as described in any one of claims 1 to 4.

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