Methods, systems, equipment, and media for predicting high-frequency losses in nanocrystalline ribbon cores and magnetic powder cores.

Abstract

This application belongs to the technical field of high-frequency iron core loss analysis of soft magnetic materials, and discloses a method, system, equipment, and medium for predicting high-frequency losses of nanocrystalline ribbon iron cores and magnetic powder cores. The method includes: determining the iron loss coefficient of low-frequency measurement data; determining the fitting relationship between the iron loss coefficient and frequency; predicting the high-frequency iron loss coefficient based on the fitting relationship between the iron loss coefficient and frequency; and calculating the high-frequency iron core loss based on the predicted high-frequency iron loss coefficient. This method achieves accurate prediction and calculation of high-frequency iron loss through low-frequency measurement data, solving the practical problem that the capacity limitation of existing high-frequency power supplies makes it difficult to directly measure iron loss.

Description

Technical Field

[0001] This application relates to the field of high-frequency core loss analysis technology for soft magnetic materials, and in particular to a method, system, equipment and medium for predicting high-frequency losses of nanocrystalline ribbon cores and magnetic powder cores. Background Technology

[0002] Driven by the development of high-speed switching semiconductors, the operating frequency of electrical equipment is constantly increasing, reaching up to 10 MHz. As a key component in electrical equipment, the accurate calculation of the high-frequency magnetic properties of soft magnetic materials is of great significance for evaluating the overall performance of electromagnetic components such as transformers and inductors.

[0003] Currently, research on predicting iron core losses is extensive, with main methods including the Steinmetz equation, loss statistics theory, and hysteresis models. These methods have made progress in applicability and computational accuracy through continuous improvement. Among them, the Steinmetz equation is widely used in engineering practice due to its simplicity and ease of calculation. Loss statistics theory, starting from physical mechanisms, decomposes total iron loss into three parts: static hysteresis loss, classical eddy current loss, and abnormal loss. Based on this theory, Barbisio proposed that under the same peak magnetic flux density, the difference between the total loss and the corresponding classical eddy current loss at two frequencies varies linearly with the square root of the frequency. Static hysteresis loss and excessive loss coefficients can be separated through linear fitting, but this method has a significantly larger error at higher excitation frequencies. In addition, hysteresis models can accurately describe the magnetization behavior mechanism of materials and are used for iron loss calculation. However, the above methods and models have problems such as heavy reliance on measured data at the frequency to be predicted, large computational load, and low prediction accuracy. Under high-frequency conditions, the iron core loss measurement system has high power supply capacity requirements, making it difficult to accurately measure iron loss under high-frequency conditions. This makes it difficult to reliably predict iron loss at high frequencies using existing methods. Summary of the Invention

[0004] To address the aforementioned issues, this application provides a method, system, equipment, and medium for predicting high-frequency losses in nanocrystalline ribbon cores and magnetic powder cores. Addressing the practical challenge of directly measuring iron losses due to limitations in high-frequency power supply capacity, this application achieves accurate prediction and calculation of high-frequency iron losses through low-frequency measurement data.

[0005] According to the first aspect of this application, a method for predicting high-frequency losses of nanocrystalline ribbon cores and magnetic powder cores is provided, the method comprising:

[0006] Determine the iron loss coefficient of low-frequency measurement data;

[0007] Determine the fitting relationship between the iron loss coefficient and the frequency;

[0008] The high-frequency iron loss coefficient is predicted based on the fitting relationship between the iron loss coefficient and the frequency, and the high-frequency iron core loss is calculated based on the predicted high-frequency iron loss coefficient.

[0009] Further, determining the iron loss coefficient of the low-frequency measurement data includes:

[0010] Calculate the iron loss coefficient using the following Fourier decomposition formula:

[0011] ;

[0012] Where H(t) is the magnetic field strength, and ω is the excitation angular frequency; H mn (n=1, 3, 5…) represents the amplitude of the nth harmonic component obtained after Fourier decomposition of the magnetic field strength; φ n (n=1, 3, 5…) represents the phase value of the nth harmonic component of the magnetic field strength after Fourier decomposition; H m1 Defined as the first iron loss coefficient, and cosφ1 as the second iron loss coefficient.

[0013] Furthermore, determining the fitting relationship between the iron loss coefficient and the frequency includes:

[0014] The first iron loss coefficient H is determined according to the following formula. m1 Fitting formula:

[0015]

[0016] Where f is the excitation frequency; a1, a2, and a3 are the first undetermined parameters, which are obtained by fitting the first iron loss coefficient after Fourier decomposition of the actual measurement data with the excitation frequency.

[0017] Furthermore, determining the fitting relationship between the iron loss coefficient and the frequency also includes determining the fitting formula for the second iron loss coefficient cosφ1 according to the following formula:

[0018]

[0019] Among them, b1, b2, and b3 are the second undetermined parameters, which are obtained by fitting the second iron loss coefficient cosφ1 after Fourier decomposition of the actual measurement data with the excitation frequency.

[0020] Furthermore, the calculation of high-frequency core loss based on the predicted high-frequency iron loss coefficient includes calculating the core loss according to the following formula:

[0021]

[0022] Among them, P iron For core loss, B mThe amplitude of the excitation magnetic flux density; ρ is the density of the soft magnetic material; H m1 cosφ1 is the first and second iron loss coefficients obtained at the corresponding high frequency using the determined fitting formula.

[0023] Furthermore, the calculation of high-frequency core loss based on the predicted high-frequency iron loss coefficient also includes:

[0024] The relative absolute change of iron loss δ is introduced to measure the change in the predicted value obtained by the number of consecutive sampling frequency points. By comparing the predicted values ​​obtained after fitting two sets of consecutive sampling points, it is determined whether the predicted undetermined coefficients have reached stability. When the relative absolute change of iron loss δ is less than the set threshold after two consecutive increases in sampling points, it is considered that the predicted undetermined coefficients have reached stability, and the predicted iron core loss value corresponding to the current sampling frequency point is taken as the high-frequency iron core loss.

[0025] Furthermore, the formula for calculating the relative absolute change δ of iron loss is as follows:

[0026]

[0027] Where N is the total number of loss sampling points, and i is the index of the sampling frequency point; P M (i) and P M+1 (i) represents the calculated iron loss value at the i-th sampling frequency point after obtaining the expression for the iron loss coefficient using the iron loss measurement values ​​at M and (M+1) sampling frequency points as fitting data.

[0028] According to the second technical solution of this application, a system for predicting high-frequency losses of nanocrystalline ribbon cores and magnetic powder cores is provided, the system comprising:

[0029] The first determining module is configured to determine the iron loss coefficient of low-frequency measurement data;

[0030] The second determining module is configured to determine the fitting relationship between the iron loss coefficient and the frequency;

[0031] The high-frequency iron core loss prediction module is configured to predict the high-frequency iron loss coefficient based on the fitting relationship between the iron loss coefficient and the frequency, and to calculate the high-frequency iron core loss based on the predicted high-frequency iron loss coefficient.

[0032] According to the third technical solution of this application, an electronic device is provided, the electronic device comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the method described above.

[0033] According to the fourth technical solution of this application, a non-transitory computer-readable storage medium storing instructions is provided, which, when executed by a processor, performs the method described above.

[0034] The methods, systems, equipment, and media for predicting high-frequency losses of nanocrystalline ribbon cores and magnetic powder cores according to the various schemes of this application have at least the following technical effects:

[0035] This application accurately extracts the first and second iron loss coefficients by performing Fourier decomposition on low-frequency measurement data, and establishes fitting formulas between the two and the frequency. This allows for the prediction of high-frequency iron loss coefficients using readily available low-frequency iron loss data. Combined with the iron loss calculation formula, high-frequency iron core loss is accurately calculated, effectively solving the problem of direct measurement of iron loss due to the limitation of high-frequency power supply capacity. At the same time, by introducing the relative absolute change of iron loss to measure the change amplitude of the predicted value obtained by the number of consecutive sampling points, and setting a condition that the fitting coefficient tends to stabilize when the change is less than a preset threshold after two consecutive increases in sampling points, the reliability and accuracy of the prediction results are ensured, and random errors are avoided. Thus, high-precision prediction of high-frequency iron core loss is achieved while reducing the measurement difficulty.

[0036] Additional aspects and advantages of this application 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 this application. Attached Figure Description

[0037] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0038] Figure 1 A flowchart illustrating a method for predicting high-frequency losses of nanocrystalline ribbon cores and magnetic powder cores provided in this application embodiment;

[0039] Figure 2 This is a schematic diagram of the structure of the nanocrystalline magnetic ring provided in the embodiments of this application;

[0040] Figure 3 This is a schematic diagram of the structure of the magnetic powder core magnetic ring provided in the embodiments of this application;

[0041] Figure 4 The first iron loss coefficient H of the nanocrystalline magnetic ring provided in the embodiments of this application m1 A comparison image of the fitting results and the first iron loss coefficient of the actual measured data;

[0042] Figure 5 A comparison image of the fitting result of the second iron loss coefficient cosφ1 of the nanocrystalline magnetic ring provided in the embodiments of this application and the second iron loss coefficient of the actual measured data;

[0043] Figure 6 The first iron loss coefficient H of the magnetic powder core magnetic ring provided in the embodiments of this application m1A comparison image of the fitting results and the first iron loss coefficient of the actual measured data;

[0044] Figure 7 A comparison image of the fitting result of the second iron loss coefficient cosφ1 of the magnetic powder core magnetic ring provided in the embodiments of this application and the second iron loss coefficient of the actual measured data;

[0045] Figure 8 A comparison image of the predicted and measured core loss results of the nanocrystalline magnetic ring obtained according to the method of this embodiment, provided for the purposes of this application.

[0046] Figure 9 A comparison image of the predicted and measured core loss results of the magnetic powder core ring obtained according to the method of this embodiment, provided for the embodiments of this application.

[0047] Figure 10 This is a schematic diagram of the structure of a system for predicting high-frequency losses of nanocrystalline ribbon cores and magnetic powder cores, provided in an embodiment of this application. Detailed Implementation

[0048] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0049] This application provides a method for predicting high-frequency losses in nanocrystalline ribbon cores and magnetic powder cores based on temperature measurement data and deep learning. Please refer to [link to relevant documentation]. Figure 1 This is a flowchart illustrating a method for predicting high-frequency losses in nanocrystalline ribbon cores and magnetic powder cores, as provided in an embodiment of this application. The method includes the following steps S101-S103.

[0050] S101: Determine the iron loss coefficient of low-frequency measurement data.

[0051] In this step, for example, the following is measured experimentally: Figure 2 The nanocrystalline magnetic ring shown is Figure 3 The magnetic properties of the powder core magnetic ring at low frequencies are shown. The outer ring diameter of the nanocrystalline magnetic ring is 70 mm, the inner ring diameter is 50 mm, the height is 10 mm, and the width of the single-layer strip is 0.025 mm; the outer ring diameter of the powder core is 43 mm, the inner ring diameter is 31 mm, and the height is 6 mm. The iron loss coefficient under low-frequency excitation is calculated. The iron loss coefficient under low-frequency excitation is calculated according to the following Fourier decomposition formula:

[0052] ;

[0053] Where H(t) is the magnetic field strength, and ω is the excitation angular frequency; H mn (n=1, 3, 5…) represents the amplitude of the nth harmonic component obtained after Fourier decomposition of the magnetic field strength; φ n (n=1, 3, 5…) represents the phase value of the nth harmonic component of the magnetic field strength after Fourier decomposition. H m1 cosφ1 is defined as the first iron loss coefficient and the second iron loss coefficient.

[0054] S102: Determine the fitting relationship between the iron loss coefficient and the frequency.

[0055] When both B and H are complex numbers, the formula for calculating magnetic permeability is as follows:

[0056] ;

[0057] Among them, B m To excite the magnetic flux density amplitude, H m1 Given the magnitude of the first component of the Fourier decomposition of the magnetic field strength, and combining it with the definition of complex permeability, the expression for the complex permeability φ1 can be obtained as follows:

[0058] ;

[0059] Where μ' is the real part of the complex permeability and μ'' is the imaginary part of the complex permeability, the fitting relationship between the second iron loss coefficient cosφ1 and the frequency is obtained by combining the Cole-Cole model as follows:

[0060] ;

[0061] Where f is the excitation frequency; b1, b2, and b3 are the second undetermined parameters, which need to be obtained by fitting the second iron loss coefficient cosφ1 after Fourier decomposition of the actual measurement data with the excitation frequency.

[0062] Similarly, a first iron loss coefficient H was proposed. m1 The fitting relationship with frequency is as follows:

[0063] ;

[0064] Among them, a1, a2, and a3 are the first undetermined parameters, which need to be determined based on the first iron loss coefficient H obtained by Fourier decomposition of the actual measurement data. m1 It is obtained by fitting with the excitation frequency.

[0065] like Figure 4 , Figure 5 As shown, the first iron loss coefficient H of the nanocrystalline magnetic ring obtained according to step S102 is given when the excitation magnetic flux density amplitude is 0.2 T. m1By comparing the fitting result of the second iron loss coefficient cosφ1 with the iron loss coefficient of the actual measured data, and obtaining the undetermined parameters based on known measurement data within 2.5 kHz, the first iron loss coefficient H at 15 kHz can be accurately predicted. m1 The results, along with the second iron loss coefficient cosφ1, verified the accuracy of the fitted formula.

[0066] like Figure 6 , Figure 7 As shown, the first iron loss coefficient H of the magnetic powder core ring obtained according to step S102 is given when the excitation magnetic flux density amplitude is 0.2 T. m1 By comparing the fitting result of the second iron loss coefficient cosφ1 with the iron loss coefficient of the actual measured data, and obtaining the undetermined parameters based on known measurement data within 4.5 kHz, the first iron loss coefficient H at 15 kHz can be accurately predicted. m1 The results, along with the second iron loss coefficient cosφ1, verified the accuracy of the fitted formula.

[0067] S103: Predict the high-frequency iron loss coefficient based on the fitting relationship between the iron loss coefficient and the frequency, and calculate the high-frequency iron core loss based on the predicted high-frequency iron loss coefficient.

[0068] In this step, for example, based on the principle of trigonometric orthogonality, under sinusoidal excitation, the following formula for calculating core loss is obtained:

[0069] ;

[0070] Among them, P iron For core loss, B m The amplitude of the excitation magnetic flux density; ρ is the density of the soft magnetic material; H m1 cosφ1 is the first and second iron loss coefficients obtained at the corresponding high frequency using the determined fitting formula.

[0071] By comparing the predicted values ​​obtained after fitting two sets of continuous sampling points, it is determined whether the undetermined coefficients of the prediction have reached stability. The relative absolute change in iron loss, δ, is introduced to measure the magnitude of change in the predicted values ​​obtained from the number of continuous sampling points.

[0072] ;

[0073] Where N is the total number of loss sampling points. P M (i) and P M+1(i) represents the calculated iron loss value at the i-th sampling frequency point after using the iron loss measurements from M and (M+1) sampling frequency points as fitting data to obtain the expression for the iron loss coefficient. M is at least 4. As the number of fitting points increases, the absolute change decreases, and the undetermined fitting coefficients gradually approach the true value. To avoid randomness, when the value of δ is less than 3% after two consecutive increases in sampling points, the expression for the iron loss coefficient is considered to have stabilized. At this time, P... M+1 (i) is the predicted core loss value at the i-th sampling frequency point.

[0074] like Figure 8 , Figure 9 As shown, a comparison is presented between the predicted and measured core loss results of nanocrystalline magnetic rings and powder core magnetic rings obtained according to the method of this embodiment when the excitation magnetic flux density amplitude is 0.2 T. This verifies the accuracy of the high-frequency loss prediction of nanocrystalline strip cores and powder cores based on low-frequency measurement data in this embodiment. Based on measurement data within 2.5 kHz, the iron loss of nanocrystalline magnetic rings at 15 kHz can be predicted; based on measurement data within 4 kHz, the iron loss of powder core magnetic rings at 15 kHz can be predicted.

[0075] In summary, the method for predicting high-frequency losses of nanocrystalline ribbon cores and magnetic powder cores provided in this embodiment addresses the practical problem that the iron loss is difficult to measure directly due to the limitation of high-frequency power supply capacity. The method proposes to achieve accurate prediction and calculation of high-frequency iron loss through low-frequency measurement data.

[0076] Another aspect of this application provides a system for predicting high-frequency losses in nanocrystalline ribbon cores and magnetic powder cores, such as... Figure 10 The diagram shown is a structural diagram of a system for predicting high-frequency losses of nanocrystalline ribbon cores and magnetic powder cores according to an embodiment of this application. The system for predicting high-frequency losses of nanocrystalline ribbon cores and magnetic powder cores includes:

[0077] The first determining module 10 is configured to determine the iron loss coefficient of low-frequency measurement data;

[0078] The second determining module 20 is configured to determine the fitting relationship between the iron loss coefficient and the frequency;

[0079] The high-frequency iron core loss prediction module 30 is used to construct a prediction model based on the calculation method and the fitting relationship, and to calculate and output the corresponding high-frequency iron core loss prediction value by inputting low-frequency iron loss measurement data.

[0080] For example, the first determining module 10 includes:

[0081] The first calculation unit is used to calculate the iron loss coefficient according to the following Fourier decomposition formula:

[0082] ;

[0083] Where H(t) is the magnetic field strength, and ω is the excitation angular frequency; H mn (n=1, 3, 5…) represents the amplitude of the nth harmonic component obtained after Fourier decomposition of the magnetic field intensity waveform; φ n (n=1, 3, 5…) represents the phase value of the nth harmonic component of the magnetic field strength after Fourier decomposition. H m1 cosφ1 is defined as the first iron loss coefficient and the second iron loss coefficient.

[0084] For example, the second determining module 20 includes:

[0085] The second calculation unit is used to determine the iron loss coefficient H according to the following formula. m1 Fitting formula:

[0086] ;

[0087] Where f is the excitation frequency; a1, a2, and a3 are parameters to be determined, which need to be calculated based on the iron loss coefficient H obtained from the Fourier decomposition of the actual measurement data. m1 It is obtained by fitting with the excitation frequency.

[0088] For example, the second determining module includes:

[0089] The third calculation unit is used to determine the fitting formula for the iron loss coefficient cosφ1 based on the following formula:

[0090] ;

[0091] Among them, b1, b2, and b3 are the second undetermined parameters, which need to be obtained by fitting the second iron loss coefficient cosφ1 after Fourier decomposition of the actual measurement data with the excitation frequency.

[0092] For example, the high-frequency core loss prediction module 30 includes:

[0093] The fourth calculation unit is used to calculate the core loss according to the following formula:

[0094] ;

[0095] Among them, B m The amplitude of the excitation magnetic flux density; ρ is the density of the soft magnetic material; H m1 cosφ1 is the first and second iron loss coefficients obtained at the corresponding high frequency using the determined fitting formula.

[0096] The fifth calculation unit compares the predicted values ​​obtained after fitting two sets of continuous sampling points to determine whether the predicted undetermined coefficients have reached stability. It introduces the relative absolute change in iron loss, δ, to measure the magnitude of change in the predicted values ​​obtained from the number of continuous sampling points.

[0097] ;

[0098] Where N is the total number of loss sampling points. P M (i) and P M+1 (i) represents the calculated iron loss value at the i-th sampling frequency point after using the iron loss measurements from M and (M+1) sampling frequency points as fitting data to obtain the expression for the iron loss coefficient. M is at least 4. As the number of fitting points increases, the absolute change decreases, and the undetermined fitting coefficients gradually approach the true value. To avoid randomness, when the value of δ is less than 3% after two consecutive increases in sampling points, the expression for the iron loss coefficient is considered to have stabilized. At this time, P... M+1 (i) is the predicted core loss value at the i-th sampling frequency point.

[0099] It should be noted that the device for predicting high-frequency losses of nanocrystalline ribbon cores and magnetic powder cores provided in the above embodiments and the method for predicting high-frequency losses of nanocrystalline ribbon cores and magnetic powder cores provided in the aforementioned embodiments belong to the same concept. The specific ways in which each module and unit performs its operation have been described in detail in the method embodiments, and will not be repeated here.

[0100] Another aspect of this application provides an electronic device, including: a controller; and a memory for storing one or more programs, which, when executed by the controller, perform the methods described in the various embodiments above.

[0101] Another aspect of this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method as described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0102] Another aspect of this application provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various embodiments described above.

[0103] According to one aspect of the embodiments of this application, a computer system is also provided, including a central processing unit (CPU), which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) or a program loaded from storage into random access memory (RAM), such as performing the methods described above. Various programs and data required for system operation are also stored in the RAM. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0104] For example, a computer system includes a Central Processing Unit (CPU), which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) or loaded from storage into random access memory (RAM), such as executing the methods described in the above embodiments. The RAM also stores various programs and data required for system operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0105] The following components are connected to the I / O interface: input sections including keyboards, mice, etc.; output sections including cathode ray tubes (CRTs), liquid crystal displays (LCDs), and speakers; storage sections including hard drives; and communication sections including network interface cards such as LAN (Local Area Network) cards and modems. The communication sections perform communication processing via networks such as the Internet. Drives are also connected to the I / O interface as needed. Removable media, such as disks, optical discs, magneto-optical discs, semiconductor memories, etc., are installed on the drive as needed so that computer programs read from them can be installed into the storage section as required.

[0106] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs various functions defined in the system of this application.

[0107] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0108] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0109] The module units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0110] The above embodiments are only used to illustrate this application and are not intended to limit this application. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of this application. Therefore, all equivalent technical solutions also fall within the scope of this application, and the patent protection scope of this application should be defined by the claims.

Claims

1. A method for predicting high-frequency losses in nanocrystalline ribbon cores and magnetic powder cores, characterized in that, The method includes: Determine the iron loss coefficient of low-frequency measurement data; Determine the fitting relationship between the iron loss coefficient and the frequency; The high-frequency iron loss coefficient is predicted based on the fitting relationship between the iron loss coefficient and the frequency, and the high-frequency iron core loss is calculated based on the predicted high-frequency iron loss coefficient. The determination of the iron loss coefficient of the low-frequency measurement data includes: Calculate the iron loss coefficient using the following Fourier decomposition formula: ； Where H(t) is the magnetic field strength, and ω is the excitation angular frequency; H mn (n=1, 3, 5…) represents the amplitude of the nth harmonic component obtained after Fourier decomposition of the magnetic field strength; φ n (n=1, 3, 5…) represents the phase value of the nth harmonic component of the magnetic field strength after Fourier decomposition; H m1 Defined as the first iron loss coefficient, and cosφ1 as the second iron loss coefficient; The determination of the fitting relationship between the iron loss coefficient and the frequency includes: The first iron loss coefficient H is determined according to the following formula. m1 Fitting formula: Where f is the excitation frequency; a1, a2, and a3 are the first undetermined parameters, which are obtained by fitting the first iron loss coefficient after Fourier decomposition of the actual measurement data with the excitation frequency; The determination of the fitting relationship between the iron loss coefficient and the frequency also includes determining the fitting formula for the second iron loss coefficient cosφ1 according to the following formula: Among them, b1, b2, and b3 are the second undetermined parameters, which are obtained by fitting the second iron loss coefficient cosφ1 after Fourier decomposition of actual measurement data with the excitation frequency; The calculation of high-frequency core loss based on the predicted high-frequency iron loss coefficient includes calculating the core loss according to the following formula: Among them, P iron For core loss, B m The amplitude of the excitation magnetic flux density; ρ is the density of the soft magnetic material; H m1 cosφ1 is the first and second iron loss coefficients obtained at the corresponding high frequency using the determined fitting formula.

2. The method according to claim 1, characterized in that, The calculation of high-frequency core loss based on the predicted high-frequency iron loss coefficient also includes: The relative absolute change of iron loss δ is introduced to measure the change in the predicted value obtained by the number of consecutive sampling frequency points. By comparing the predicted values ​​obtained after fitting two sets of consecutive sampling points, it is determined whether the predicted undetermined coefficients have reached stability. When the relative absolute change of iron loss δ is less than the set threshold after two consecutive increases in sampling points, it is considered that the predicted undetermined coefficients have reached stability. The predicted value of iron core loss corresponding to the current sampling frequency point is used as the high-frequency iron core loss.

3. The method according to claim 1, characterized in that, The formula for calculating the relative absolute change δ of iron loss is: Where N is the total number of loss sampling points, and i is the index of the sampling frequency point; P M (i) and P M+1 (i) represents the calculated iron loss value at the i-th sampling frequency point after obtaining the expression for the iron loss coefficient using the iron loss measurement values ​​at M and (M+1) sampling frequency points as fitting data.

4. A system for predicting high-frequency losses in nanocrystalline ribbon cores and magnetic powder cores, characterized in that, The system includes: The first determining module is configured to determine the iron loss coefficient of low-frequency measurement data, including: Calculate the iron loss coefficient using the following Fourier decomposition formula: ； Where H(t) is the magnetic field strength, and ω is the excitation angular frequency; H mn (n=1, 3, 5…) represents the amplitude of the nth harmonic component obtained after Fourier decomposition of the magnetic field strength; φ n (n=1, 3, 5…) represents the phase value of the nth harmonic component of the magnetic field strength after Fourier decomposition; H m1 Defined as the first iron loss coefficient, and cosφ1 as the second iron loss coefficient; The second determining module is configured to determine the fitting relationship between the iron loss coefficient and the frequency; The high-frequency iron core loss prediction module is configured to predict the high-frequency iron loss coefficient based on the fitting relationship between the iron loss coefficient and the frequency, and to calculate the high-frequency iron core loss based on the predicted high-frequency iron loss coefficient. The determination of the fitting relationship between the iron loss coefficient and the frequency includes: The first iron loss coefficient H is determined according to the following formula. m1 Fitting formula: Where f is the excitation frequency; a1, a2, and a3 are the first undetermined parameters, which are obtained by fitting the first iron loss coefficient after Fourier decomposition of the actual measurement data with the excitation frequency; The determination of the fitting relationship between the iron loss coefficient and the frequency also includes determining the fitting formula for the second iron loss coefficient cosφ1 according to the following formula: Among them, b1, b2, and b3 are the second undetermined parameters, which are obtained by fitting the second iron loss coefficient cosφ1 after Fourier decomposition of actual measurement data with the excitation frequency; The calculation of high-frequency core loss based on the predicted high-frequency iron loss coefficient includes calculating the core loss according to the following formula: Among them, P iron For core loss, B m The amplitude of the excitation magnetic flux density; ρ is the density of the soft magnetic material; H m1 cosφ1 is the first and second iron loss coefficients obtained at the corresponding high frequency using the determined fitting formula.

5. An electronic device, characterized in that, The electronic device includes: Memory, used to store computer programs; A processor for executing the computer program to implement the method as described in any one of claims 1 to 3.

6. A non-transitory computer-readable storage medium storing instructions, characterized in that, When the instructions are executed by the processor, the method according to any one of claims 1 to 3 is performed.

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