Dynamic magnetostriction modeling and parameter identification method and system for oriented silicon steel

Abstract

The invention belongs to the technical field of parameter identification, and provides an oriented silicon steel dynamic magnetostriction modeling and parameter identification method and system in order to solve the defects of existing magnetostriction modeling in the aspects of precision, universality and the like, and the method comprises the steps that hysteresis characteristics and magnetostriction characteristics of oriented silicon steel in the rolling direction are obtained; based on a Jiles-Atherton model, taking the magnetic induction intensity as a main and independent variable to establish a dynamic Jiles-Atherton model; determining a dynamic magnetostriction model of the oriented silicon along the rolling direction by introducing even polynomial combination of irreversible magnetization intensity; an external pressure stress coupling item is introduced into an effective magnetic field expression of the Jiles-Atherton model, and a force-magnetic coupling model is constructed; and performing global identification on parameters in the model by adopting a dynamic inertial particle swarm optimization algorithm to finally obtain an optimal parameter combination. According to the scheme, the adaptability, precision and engineering applicability of the model are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of parameter identification technology, and in particular relates to a method and system for dynamic magnetostriction modeling and parameter identification of oriented silicon steel. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Magnetostriction is the dimensional change in ferromagnetic materials caused by the rearrangement of magnetic domains under the influence of a magnetic field. It is a major source of electromagnetic vibration and noise in equipment such as power transformers and motors. With the development of power systems towards ultra-high voltage and large capacity, the impact of magnetostrictive behavior on equipment performance and noise control is becoming increasingly significant.

[0004] Traditional modeling methods, such as magnetic domain theory and thermodynamic models, can reflect the magnetostrictive properties, but they generally suffer from insufficient model accuracy and universality, difficulty in describing the dynamic magnetostrictive characteristics of grain-oriented silicon steel, and often neglect the influence of applied stress. The Jiles-Atherton model has a good physical foundation, but it still has shortcomings in expressing strain hysteresis, handling stress coupling, and parameter adaptability.

[0005] Existing technologies include measuring the hysteresis loop and magnetostriction loop of silicon steel sheets under different stresses; establishing a magnetostrictive constitutive model by combining microscopic magnetic domain theory with macroscopic thermodynamic relationships; extracting magnetostriction values ​​from magnetic property curves to determine key magnetization characteristics in electrical-oriented silicon steel; and improving the magnetostrictive constitutive model by introducing a sixth-order magnetization term and simplifying the expressions of key parameters in previous constitutive models. However, these technologies primarily model static or quasi-static magnetostrictive behavior, do not consider dynamic loss effects, and lack sufficient response capability to high-frequency excitations. Furthermore, the stress coupling mechanism, based on macroscopic thermodynamic relationships, limits its adaptability under complex stress conditions.

[0006] Therefore, there is an urgent need for a magnetostrictive modeling method to describe the dynamic magnetostrictive properties of grain-oriented silicon steel, so as to meet the engineering requirements for accurate modeling and prediction of magnetostriction under complex stress conditions. Summary of the Invention

[0007] To overcome the shortcomings of the prior art, this invention provides a method and system for dynamic magnetostriction modeling and parameter identification of oriented silicon steel, which significantly improves the adaptability, accuracy and engineering applicability of the model.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for dynamic magnetostriction modeling and parameter identification of grain-oriented silicon steel, comprising: To obtain the hysteresis and magnetostrictive properties of grain-oriented silicon steel along the rolling direction; Based on the Jiles-Atherton model, a dynamic Jiles-Atherton model is established with magnetic flux density as the main independent variable; By introducing an even-order polynomial combination of irreversible magnetization, a dynamic magnetostriction model for oriented silicon along the rolling direction is determined. An external pressure stress coupling term is introduced into the effective magnetic field expression of the Jiles-Atherton model to construct a force-magnetic coupling model. The parameters in the force-magnetic coupling model, the dynamic Jiles-Atherton model, and the dynamic magnetostrictive model are globally identified using a dynamic inertial particle swarm optimization algorithm, and the optimal parameter combination is finally obtained.

[0009] Secondly, the present invention provides a dynamic magnetostriction modeling and parameter identification system for grain-oriented silicon steel, comprising: The acquisition module is configured to acquire the hysteresis and magnetostrictive properties of grain-oriented silicon steel along the rolling direction. The Jiles-Atherton model building module is configured to: establish a dynamic Jiles-Atherton model based on the Jiles-Atherton model, with magnetic induction intensity as the main independent variable; The dynamic magnetostriction model construction module is configured to: determine the dynamic magnetostriction model of oriented silicon along the rolling direction by introducing an even-order polynomial combination of irreversible magnetization. The force-magnetic coupling model building module is configured to: introduce an external compressive stress coupling term into the effective magnetic field expression of the Jiles-Atherton model to build a force-magnetic coupling model; The parameter identification module is configured to use a dynamic inertial particle swarm optimization algorithm to globally identify the parameters in the force-magnetic coupling model, the dynamic Jiles-Atherton model, and the dynamic magnetostrictive model, and finally obtain the optimal parameter combination.

[0010] Thirdly, the present invention provides an electronic device including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0011] Fourthly, the present invention provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in the first aspect.

[0012] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect.

[0013] The above one or more technical solutions have the following beneficial effects: In this invention, a dynamic Jiles-Atherton model is established using magnetic flux density as the main independent variable. By introducing an even-order polynomial combination of irreversible magnetization, a dynamic magnetostrictive model for grain-oriented silicon along the rolling direction is determined. This overcomes the shortcomings of existing models that mistakenly treat magnetostriction as a single-valued function and cannot describe the hysteresis characteristics of butterfly curves. The technical solution of this invention can accurately predict the magnetostrictive behavior of grain-oriented silicon steel under excitation at different frequencies and magnetic flux densities. Experiments show that its hysteresis fitting accuracy error is less than ±5%, significantly improving the dynamic modeling capability compared to traditional models.

[0014] In this invention, a compressive stress correction term is introduced during the force-magnetic coupling modeling process to modify the effective magnetic field expression, enabling the force-magnetic coupling model to dynamically reflect the changes in the magnetostrictive response of silicon steel material under external clamping or prestressing conditions.

[0015] In this invention, a dynamic particle swarm optimization algorithm and the Newton-Raphson method are used to solve the model parameters. The results show that the method still maintains high prediction accuracy in the range of 1.5MPa–2.0MPa compressive stress, breaking through the limitation of existing models that cannot describe stress-magnetic coupling behavior. It can be widely used in material property modeling tasks under complex force field conditions such as transformer noise control and electromagnetic-structural coupling simulation.

[0016] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0017] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0018] Figure 1 This is a schematic diagram of the dynamic magnetostriction modeling and parameter identification method for oriented silicon steel in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the single-chip measurement method in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the laser Doppler vibration meter in Embodiment 1 of the present invention; Figure 4 This is a flowchart of the dynamic inertial particle swarm algorithm in Embodiment 1 of the present invention. Detailed Implementation

[0019] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0020] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0021] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0022] Example 1 This embodiment discloses a method for dynamic magnetostriction modeling and parameter identification of grain-oriented silicon steel, including: To obtain the hysteresis and magnetostrictive properties of grain-oriented silicon steel along the rolling direction; Based on the Jiles-Atherton model, a dynamic Jiles-Atherton model is established with magnetic flux density as the main independent variable; By introducing an even-order polynomial combination of irreversible magnetization, a dynamic magnetostriction model for oriented silicon along the rolling direction is determined. An external pressure stress coupling term is introduced into the effective magnetic field expression of the Jiles-Atherton model to construct a force-magnetic coupling model. The dynamic inertial particle swarm optimization algorithm is used to globally identify the parameters in the force-magnetic coupling model, the dynamic Jiles-Atherton model, and the dynamic magnetostrictive model, and finally obtain the optimal parameter combination.

[0023] This embodiment is based on the improved Jiles-Atherton model. By introducing a dynamic loss term and a magnetostrictive model with magnetic induction intensity as the independent variable, and by introducing an even-order polynomial of irreversible magnetization intensity, the dynamic hysteresis behavior under excitation of different frequencies and waveforms is effectively characterized. A stress coupling term is explicitly added to the effective magnetic field to realize the dynamic response to applied stress. The dynamic inertial particle swarm optimization algorithm is used to globally optimize and identify 11 parameters, which significantly improves the accuracy and adaptability of the model.

[0024] The following is combined Figure 1 This embodiment provides a detailed description of a method for dynamic magnetostriction modeling and parameter identification of grain-oriented silicon steel. Step 1: Test the hysteresis and magnetostriction properties of the sample along the rolling direction using experimental equipment.

[0025] Step 1 involves testing the hysteresis and magnetostrictive properties of the sample along the rolling direction using experimental equipment. The specific process includes: S11: The magnetic properties under compressive stress and third harmonic stress are measured using sinusoidal excitation at frequencies of 50Hz and 60Hz.

[0026] The experimental data systematically revealed the evolution of magnetostrictive behavior of grain-oriented silicon steel under different service conditions, providing important experimental basis and data support for the subsequent establishment of a magnetostrictive model. In this embodiment, B27R090, B18R060, and 30QGD silicon steels were used as objects, and the magnetic induction intensity B and magnetic field intensity H were obtained by single-piece measurement method, such as... Figure 2 As shown.

[0027] S12: Measure the magnetostrictive strain of materials under different frequencies (e.g., 50Hz, 60Hz), different maximum magnetic flux densities, and applied compressive stresses (e.g., 1.5MPa, 2MPa) using a laser Doppler vibrometer. Figure 3 This is a schematic diagram of a laser Doppler vibration meter.

[0028] The magnetostrictive strain of materials under different frequencies, maximum magnetic flux densities, and applied compressive stress conditions was measured using a laser Doppler vibrometer. ; According to Ampere's circuital law, the current flowing through the primary winding... The magnetic field strength generated afterwards for:

[0029] In the formula, The number of turns of the primary coil. L represents the magnitude of the current passing through the primary coil, and L is the length of the magnetic circuit, i.e., the effective length of the sample.

[0030] According to Faraday's law of electromagnetic induction, the induced voltage on the secondary winding... for:

[0031] In the formula, This represents the cross-sectional area of ​​the sample. This represents the number of turns in the secondary coil. From this, the magnetic flux density can be obtained. :

[0032] The magnetostrictive strain of materials under different frequencies (e.g., 50Hz, 60Hz), different maximum magnetic flux densities, and applied compressive stresses (e.g., 1.5MPa, 2MPa) was measured using a laser Doppler vibrometer. :

[0033] In the formula, c is the speed of light; f is the frequency of light; The wavelength of light; This refers to the Doppler frequency shift that occurs after a laser beam is reflected from the surface of a moving object. It is the speed of an object's motion.

[0034] Therefore, it can be seen that by measuring the Doppler frequency shift of the coherent laser light wave reflected from the surface of the object... This allows us to obtain the object's velocity V. Integrating over time yields the corresponding displacement, which is the magnetostrictive strain of the grain-oriented silicon steel sample. The schematic diagram of laser Doppler vibration measurement is shown below. Figure 2 As shown, data is recorded via a host computer to form a high-precision dataset required for modeling.

[0035] Step 2: Based on the Jiles-Atherton model, establish a dynamic Jiles-Atherton model with magnetic induction intensity as the main independent variable.

[0036] This example constructs a dynamic magnetostriction modeling framework based on magnetic domain theory and the Jiles-Atherton model. Using magnetic flux density B as the main independent variable, this example builds a dynamic magnetostriction model based on magnetic flux density B, building upon the traditional Jiles-Atherton model.

[0037] According to the law of conservation of energy, the static Jiles-Atherton model is:

[0038] in, The magnetization intensity, The non-hysteresis magnetization intensity Where is the irreversible magnetization, and k is the pinning constant. B e For effective magnetic induction intensity, B e The expression is:

[0039] in, The permeability of free space, is the average magnetic field coefficient of interdomain coupling.

[0040] Considering the eddy current loss and abnormal loss of ferromagnetic materials, the loss P d for:

[0041] In the formula, It is eddy current loss. It is abnormal loss.

[0042] In the dynamic Jiles-Atherton model, k e This is the eddy current loss coefficient. k ex Let be the abnormal loss coefficient. , At this point, the dynamic Jiles-Atherton model is:

[0043] Where e is the thickness of the steel sheet, ρ Let S be the conductor conductivity, S be the cross-sectional area of ​​the steel sheet, and G be a dimensionless parameter. V 0 represents the statistical distribution of the local coercive magnetic field.

[0044] In the magnetization process of ferromagnetic materials, in addition to irreversible magnetization, there is also reversible magnetization. When the applied magnetic field strength is low, the magnetization process is mainly reversible magnetization, including reversible domain translation, reversible domain wall bending, and reversible domain rotation.

[0045] The magnetization M can be divided into reversible components. and irreversible components Two parts:

[0046] and The value of is not constantly changing; the reversible magnetization component occurs after the reversible magnetization in the lower phase of the magnetic field ends. It will generally stop changing, and the final magnetization intensity will... With irreversible magnetization End. Therefore, using the differential form is more advantageous for distinguishing the contributions of reversible and irreversible magnetic susceptibility to the differential magnetic susceptibility. Reversible magnetization. It can also be used with hysteresis-free magnetization. With irreversible magnetization The difference represents:

[0047]

[0048] In the formula, c is the reversible magnetization coefficient; It is the direction parameter, H e This indicates the strength of the effective magnetic field.

[0049] Combining the above three equations, a dynamic hysteresis model based on magnetic induction intensity B is constructed.

[0050] Find the equation for both sides of the above dynamic Jiles-Atherton model with respect to... The differential, and Substituting into the dynamic Jiles-Atherton model, we get:

[0051] Will Represented by the magnetic induction intensity B, and obtained through differentiation:

[0052] The dynamic Jiles-Atherton model can be obtained by organizing the data:

[0053] in, k For coefficients, , n The average pinning density of the magnetic material. To determine the energy required to overcome the pinning energy during magnetization, consider a pinning point located on the domain wall of a magnetic domain. m Let be the magnetic moment per unit volume of the left magnetic domain of the domain wall.

[0054] In the formula, dynamic loss P d It can be represented as:

[0055] The causes of pinning are diverse, ranging from structural defects at the lattice scale to external non-uniform stress. However, when quantifying the pinning effect, the Jiles-Atherton theory employs an "averaging" approach to measure the overall hindering effect. This means it doesn't concern itself with the specific reasons for the obstruction of magnetic domain movement, but only with understanding the overall energy change of the magnetic material itself. Therefore, when calculating the total magnetization, another portion of the magnetization should be subtracted.

[0056] Step 3: By introducing an even-order polynomial combination of irreversible magnetization, a dynamic magnetostriction model for oriented silicon along the rolling direction is determined.

[0057] In addition, the model also introduces additional A term is added to simulate hysteresis characteristics. To ensure the symmetry of the magnetostriction curve, [the following is missing from the original text]. The terms are still added in the form of even-power terms. (like ） Finally, it was verified through calculation. Form than and The combination of these is better.

[0058] And for Item, in essence, can be related to The terms are combined, therefore, relying solely on this term to increase the hysteresis of the material is not sufficient; for For this project, due to the large number of parameters introduced, it is difficult to determine reasonable initial values, and the subsequent parameter solving process is quite challenging, leading to convergence difficulties. Ultimately, a dynamic magnetostrictive model along the rolling direction was determined:

[0059] In the formula, , , The hysteresis coefficient is... is the magnetostriction coefficient along the rolling direction.

[0060] This embodiment introduces an even-order polynomial for irreversible magnetization. By combining various methods, a dynamic magnetostrictive model along the rolling direction is determined, and a magnetostrictive model with magnetic induction intensity B as the independent variable is constructed. This overcomes the shortcomings of existing models that mistakenly treat magnetostriction as a single-valued function and cannot describe the hysteresis characteristics of the butterfly curve. This technical solution can accurately predict the magnetostrictive behavior of grain-oriented silicon steel under excitation at different frequencies and magnetic flux densities. Experiments show that its hysteresis fitting accuracy error is less than ±5%, significantly improving the dynamic modeling capability compared to traditional models. This innovation requires a deep understanding of the irreversible physical mechanism of the magnetization process and the construction of a specific mathematical form based on the magnetization characteristics that can guarantee the symmetry of the curve, which is not easily obtained through conventional trial and error.

[0061] Step 4: Introduce an external pressure stress coupling term into the effective magnetic field expression of the Jiles-Atherton model to construct a force-magnetic coupling model.

[0062] To simulate the mechanical clamping effect in actual engineering conditions, the effective magnetic field of the Jiles-Atherton model was used. H e Introducing stress coupling terms into the expression Establish a force-magnetic coupling modeling mechanism:

[0063]

[0064] in, For external compressive stress, ρ represents the vacuum permeability. This coupled expression can dynamically reflect the amplification or contraction effect of magnetostrictive behavior under different stress conditions.

[0065] This embodiment introduces a compressive stress correction term during the modeling process. The expression for the effective magnetic field was modified so that the model could dynamically reflect the changes in the magnetostrictive response of silicon steel under external clamping or prestressing conditions.

[0066] Step 5: Use the dynamic inertial particle swarm optimization algorithm to globally identify the parameters in the force-magnetic coupling model, the dynamic Jiles-Atherton model, and the dynamic magnetostrictive model, and finally obtain the optimal parameter combination.

[0067] Eleven parameters, including hysteresis model parameters, were globally identified in the model using the Dynamic Inertial Particle Swarm Optimization (DIPSO) algorithm. Dynamic loss coefficient and magnetostriction-related parameters .

[0068] By dynamically adjusting the inertia weights, the algorithm is ensured to have global search capabilities in the early stages and local convergence capabilities in the later stages, thereby improving modeling accuracy.

[0069] like Figure 4 As shown, the specific process of globally identifying the 11 parameters in the model using the Dynamic Inertial Particle Swarm Optimization (DIPSO) algorithm includes: S41: Parameters and the position of each particle Initialize the particle swarm, and set the particle swarm size, learning factors c1 and c2, and inertia coefficient w.

[0070] S42: Calculate the fitness value for each particle; The fitness function is:

[0071] In the formula, N To determine the number of data collection points for the magnetostrictive hysteresis loop in the experiment, H max , H min These are the maximum and minimum values ​​of the measured magnetic field strength. H model The magnetic field strength calculated by the model. H real This represents the actual measured magnetic field strength. λ max , λ min The maximum and minimum values ​​of the measured magnetostriction are given. λ model Magnetostriction calculated using the magnetostriction model. λ real This refers to the actual measured magnetostriction.

[0072] S43: Compare the fitness value of each particle with the best position in the previous iterations of this cycle, and select the better parameters as the current best position pbest; S44: Compare the fitness value of each particle with the best position in all iterations globally. If it is better, reset the population best position gbest. S45: If the expected calculation accuracy is achieved, i.e. the error meets the requirements, the algorithm exits; otherwise, the particle's velocity and position are updated, and the algorithm returns to S42 to continue the update iteration until the optimal parameter combination is obtained.

[0073] This embodiment uses the Newton-Raphson method to solve for the implicitly expressed hysteresis-free magnetization. Approximation through multiple iterations The numerical solution is obtained, and the result of the previous calculation is used as the initial value in the solution process. The process is as follows: S51: Setting the Equation The initial value is 0, the error is 10⁻⁶, and the maximum number of iterations is 500. M an The magnetization is hysteresis-free. M s It is the saturation magnetization; S52: Adopted Absolute error of iterative calculation The process has ended. It is the solution to the implicit function equation.

[0074] The proposed model in this embodiment was used for parameter identification and calculation. The magnetic properties of B27R090 and B18R060 silicon steels under conditions containing a 10% third harmonic component were analyzed. The optimal parameter results obtained after model identification are shown in Tables 1 and 2. For 30SQGD oriented silicon steel sheets under conditions of a maximum magnetic flux density of 1.8T and stresses of 2MPa and 1.5MPa, the fitting parameter results obtained by using the dynamic inertial particle swarm optimization algorithm based on the proposed force-magnetic coupling model are shown in Table 3.

[0075] Table 1 B27R090 in B m Fitting parameters for 1.8T and 1.3T excitations containing 10% third harmonic.

[0076] Table 2 B18R060 in B m Fitting parameters for 1.8T and 1.5T excitations containing 10% third harmonic.

[0077] Table 3 30SQGD in B m Fitting parameters for stresses of 2 MPa and 1.5 MPa at a stress of 1.8T.

[0078] This embodiment utilizes a dynamic particle swarm optimization algorithm and the Newton-Raphson method to solve for model parameters. Results show that this method maintains high prediction accuracy within the compressive stress range of 1.5MPa–2.0MPa, overcoming the limitation of existing models in describing stress-magnetic coupling behavior. It can be widely applied to material property modeling tasks under complex force field conditions, such as transformer noise control and electromagnetic-structural coupling simulation. Coupled stress into the basic model in a specific differential form requires cross-disciplinary knowledge integration. The systematic use of intelligent algorithms to solve multi-parameter optimization problems constitutes a complete non-obvious technical solution, significantly enhancing the model's engineering practical value.

[0079] Example 2 The purpose of this embodiment is to provide a dynamic magnetostriction modeling and parameter identification system for grain-oriented silicon steel, including: The acquisition module is configured to acquire the hysteresis and magnetostrictive properties of grain-oriented silicon steel along the rolling direction. The Jiles-Atherton model building module is configured to: establish a dynamic Jiles-Atherton model based on the Jiles-Atherton model, with magnetic induction intensity as the main independent variable; The dynamic magnetostriction model construction module is configured to: determine the dynamic magnetostriction model of oriented silicon along the rolling direction by introducing an even-order polynomial combination of irreversible magnetization. The force-magnetic coupling model building module is configured to: introduce an external compressive stress coupling term into the effective magnetic field expression of the Jiles-Atherton model to build a force-magnetic coupling model; The parameter identification module is configured to use a dynamic inertial particle swarm optimization algorithm to globally identify the parameters in the force-magnetic coupling model, the dynamic Jiles-Atherton model, and the dynamic magnetostrictive model, and finally obtain the optimal parameter combination.

[0080] In further embodiments, the following is also provided: An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor. When executed by the processor, the computer instructions perform the method described in Embodiment 1. For brevity, further details are omitted here.

[0081] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0082] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0083] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in Embodiment 1.

[0084] The method in Embodiment 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0085] A computer program product includes a computer program that, when executed by a processor, implements the method described in Embodiment 1.

[0086] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which execute in a device on a target real or virtual processor to perform the processes / methods described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided among program modules as needed. The machine-executable instructions for the program modules can execute within a local or distributed device. In a distributed device, the program modules can reside in both local and remote storage media.

[0087] The computer program code used to implement the methods of the present invention may be written in one or more programming languages. This computer program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the computer or other programmable data processing device, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0088] In the context of this invention, computer program code or related data may be carried by any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals may include electrical, optical, radio, sound, or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0089] Those skilled in the art will recognize that the units and algorithm steps described in conjunction with the embodiments herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0090] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for dynamic magnetostriction modeling and parameter identification of grain-oriented silicon steel, characterized in that, include: To obtain the hysteresis and magnetostrictive properties of grain-oriented silicon steel along the rolling direction; Based on the Jiles-Atherton model, a dynamic Jiles-Atherton model is established with magnetic flux density as the main independent variable; By introducing an even-order polynomial combination of irreversible magnetization, a dynamic magnetostriction model for oriented silicon along the rolling direction is determined. An external pressure stress coupling term is introduced into the effective magnetic field expression of the Jiles-Atherton model to construct a force-magnetic coupling model. The parameters in the force-magnetic coupling model, the dynamic Jiles-Atherton model, and the dynamic magnetostrictive model are globally identified using a dynamic inertial particle swarm optimization algorithm, and the optimal parameter combination is finally obtained.

2. The method for dynamic magnetostriction modeling and parameter identification of grain-oriented silicon steel as described in claim 1, characterized in that, The hysteresis and magnetostrictive properties of grain-oriented silicon steel along the rolling direction are obtained as follows: The magnetic induction intensity and magnetic field intensity of grain-oriented silicon steel were obtained using a single-piece measurement method. A vibration meter was used to measure the magnetostrictive strain of grain-oriented silicon steel under different frequencies, different maximum magnetic flux densities, and external compressive stress conditions.

3. The method for dynamic magnetostriction modeling and parameter identification of grain-oriented silicon steel as described in claim 1, characterized in that, Based on the Jiles-Atherton model, a dynamic Jiles-Atherton model is established with magnetic flux density as the main independent variable, specifically: Based on the law of conservation of energy, a static Jiles-Atherton model is constructed; Considering the eddy current loss and abnormal loss of ferromagnetic materials, and dividing the magnetization intensity into reversible magnetization intensity and irreversible magnetization intensity, the final dynamic Jiles-Atherton model is obtained through differential operation; where reversible magnetization intensity is expressed as the difference between hysteresis-free magnetization intensity and irreversible magnetization intensity.

4. The method for dynamic magnetostriction modeling and parameter identification of grain-oriented silicon steel as described in claim 1, characterized in that, The dynamic magnetostriction model is as follows: ； in, , , The hysteresis coefficient is... is the magnetostriction coefficient along the rolling direction; M is the magnetization intensity; It represents the irreversible magnetization intensity.

5. The method for dynamic magnetostriction modeling and parameter identification of grain-oriented silicon steel as described in claim 1, characterized in that, The force-magnetic coupling model is specifically as follows: ； ； Where H is the magnetic field strength. For external compressive stress, The permeability of free space, λ represents the external compressive stress coupling term; λ represents the dynamic magnetostrictive stress. is the average magnetic field coefficient of interdomain coupling.

6. The method for dynamic magnetostriction modeling and parameter identification of grain-oriented silicon steel as described in claim 1, characterized in that, The parameters in the force-magnetic coupling model, the dynamic Jiles-Atherton model, and the dynamic magnetostrictive model are globally identified using a dynamic inertial particle swarm optimization algorithm, ultimately obtaining the optimal parameter combination, specifically: S41: Initialize the parameters and the position of each particle; S42: Calculate the fitness value for each particle; S43: Compare the fitness value of each particle with the best position in the previous iterations and select the better parameters as the current best position; S44: Compare the fitness value of each particle with the best position in all iterations globally. If it is better, reset the population's best position. S45: If the expected calculation accuracy is achieved, the process ends; otherwise, continue to update the particle's velocity and position, and return to S42 to continue the update iteration until the optimal parameter combination is obtained.

7. The method for dynamic magnetostriction modeling and parameter identification of grain-oriented silicon steel as described in claim 3, characterized in that, The implicitly expressed hysteresis-free magnetization is solved using the Newton-Raphson method, and the numerical solution of hysteresis-free magnetization is approximated through multiple iterations.

8. A system for dynamic magnetostriction modeling and parameter identification of grain-oriented silicon steel, characterized in that, include: The acquisition module is configured to acquire the hysteresis and magnetostrictive properties of grain-oriented silicon steel along the rolling direction. The Jiles-Atherton model building module is configured to: establish a dynamic Jiles-Atherton model based on the Jiles-Atherton model, with magnetic induction intensity as the main independent variable; The dynamic magnetostriction model construction module is configured to: determine the dynamic magnetostriction model of oriented silicon along the rolling direction by introducing an even-order polynomial combination of irreversible magnetization. The force-magnetic coupling model building module is configured to: introduce an external compressive stress coupling term into the effective magnetic field expression of the Jiles-Atherton model to build a force-magnetic coupling model; The parameter identification module is configured to use a dynamic inertial particle swarm optimization algorithm to globally identify the parameters in the force-magnetic coupling model, the dynamic Jiles-Atherton model, and the dynamic magnetostrictive model, and finally obtain the optimal parameter combination.

9. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, perform the method described in any one of claims 1-7.

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