High-frequency transformer loss model correction method and system based on nanocrystalline magnetic core

Abstract

The invention provides a high-frequency transformer loss model correction method and system based on a nanocrystalline magnetic core, and belongs to the technical field of magnetic material loss modeling and power electronic converter magnetic assembly design. Extracting waveform characteristic parameters such as a rising edge constant and a duty ratio; on the basis of a Bertotti loss separation model, introducing the waveform characteristics as correction factors into eddy current loss term and residual loss term coefficients, and establishing a magnetic core loss correction model suitable for trapezoidal waves and rectangular waves containing zero voltage intervals; deriving a loss conversion coefficient between sine excitation and non-sine excitation through the model; and performing multivariate nonlinear fitting calibration on a model coefficient based on high-frequency magnetic characteristic experimental data, and verifying prediction precision by using independent data. According to the method, the reliability of loss prediction under non-sine excitation is improved.

Description

Technical Field

[0001] This invention belongs to the field of magnetic material loss modeling and power electronic converter magnetic component design technology, and in particular relates to a method and system for correcting high-frequency transformer loss models based on nanocrystalline magnetic cores. Background Technology

[0002] Nanocrystalline magnetic cores are widely used in high-frequency transformers and high-frequency power conversion equipment due to their high saturation flux density, high permeability, and low loss. In practical applications, high-frequency transformers are often driven by switching circuits such as PWM, and their winding terminal voltages typically exhibit non-sinusoidal waveforms such as trapezoidal waves or rectangular waves containing zero-voltage intervals. Because the rate of change of magnetic flux density with time varies piecewise under non-sinusoidal excitation, eddy current losses and residual losses are highly sensitive to waveform shape, making the classical core loss model based on the sinusoidal wave assumption prone to significant prediction errors under these operating conditions.

[0003] While existing technologies include loss calculation methods such as the Bertotti loss separation model, their parameters are typically calibrated based on sinusoidal excitation conditions. When directly applied to non-sinusoidal excitation, these methods struggle to accurately reflect the loss differences caused by waveform shape, thus impacting loss assessment, temperature rise design, and thermal management margin configuration for high-frequency transformers. Summary of the Invention

[0004] To achieve the above objectives, the technical solution of the present invention is implemented as follows: Firstly, this solution discloses a method for correcting the loss model of a high-frequency transformer based on a nanocrystalline magnetic core, including: Obtain the non-sinusoidal excitation waveform of the winding terminal voltage of the target high-frequency transformer under the target operating condition, and identify the excitation waveform as a trapezoidal wave or a rectangular wave containing a zero voltage range; Waveform characteristic parameters are extracted from the excitation waveform, wherein the rising edge constant is used to characterize the proportion of the voltage rising edge time to the falling edge time in one cycle, and the duty cycle is used to characterize the proportion of the non-zero voltage duration in one cycle. Based on the Bertotti loss separation model, the rising edge constant or duty cycle is introduced as a correction factor into the eddy current loss term coefficient and the residual loss term coefficient to construct a core loss correction model corresponding to the excitation waveform type. The input of the core loss correction model includes at least the excitation frequency, the peak magnetic flux density, and the rising edge constant or duty cycle, and the output is the total core loss per unit volume. Based on the core loss correction model, the loss conversion coefficient between sinusoidal excitation and non-sinusoidal excitation under the same peak magnetic flux density condition is derived. Experimental data on the loss of nanocrystalline magnetic cores under different excitation frequencies, different peak magnetic flux densities, and different rise time constants or duty cycles were collected, and the undetermined coefficients of the core loss correction model were determined by multivariate nonlinear fitting to complete the model calibration. The target operating condition parameters are input into the calibrated core loss correction model to obtain the predicted loss, and then compared with the corresponding measured loss to output the model error.

[0005] Furthermore, the rising edge constant is the ratio of the sum of the voltage rising edge time and the falling edge time to one cycle; the duty cycle is the ratio of the non-zero voltage duration to one cycle.

[0006] Furthermore, the identification of the trapezoidal wave includes: detecting the rising edge segment, plateau segment, and falling edge segment of the voltage within one cycle, and calculating the rising edge time and falling edge time respectively to obtain the rising edge constant; the identification of the rectangular wave includes: detecting the non-zero voltage duration interval within one cycle and calculating the non-zero voltage duration to obtain the duty cycle.

[0007] Furthermore, the core loss correction model includes a hysteresis loss term, an eddy current loss term, and a residual loss term, wherein the coefficient of the hysteresis loss term is a material-related constant, and the coefficients of the eddy current loss term and the residual loss term are respectively expressed as functions of the rising edge constant or the duty cycle.

[0008] Furthermore, when the excitation waveform is a trapezoidal wave, the eddy current loss term coefficient and the residual loss term coefficient are parameterized using a polynomial or exponential form with respect to the rising edge constant, respectively; when the excitation waveform is a rectangular wave, the eddy current loss term coefficient and the residual loss term coefficient are parameterized using a polynomial or exponential form with respect to the duty cycle, respectively.

[0009] Furthermore, the loss conversion coefficient includes an eddy current loss conversion coefficient and a residual loss conversion coefficient. The eddy current loss conversion coefficient is used to characterize the ratio of the eddy current loss component under non-sinusoidal excitation to the eddy current loss component under sinusoidal excitation under the same peak magnetic flux density condition. The residual loss conversion coefficient is used to characterize the ratio of the residual loss component under non-sinusoidal excitation to the residual loss component under sinusoidal excitation under the same peak magnetic flux density condition.

[0010] Furthermore, when deriving the loss conversion coefficient, the excitation voltage waveform is expressed in segments according to the period, and a correlation between the excitation voltage waveform and the rate of change of magnetic flux density is established based on Faraday's law of electromagnetic induction. Then, the correlation is substituted into the eddy current loss term and the residual loss term of the loss separation model to obtain the eddy current loss conversion coefficient and the residual loss conversion coefficient.

[0011] Furthermore, the experimental data were obtained through high-frequency magnetic characteristic testing, and the measurement conditions covered different rise time constants, different duty cycles, different excitation frequencies, and different peak magnetic flux densities.

[0012] Furthermore, the multivariate nonlinear fitting includes: constructing a nonlinear least squares objective function with the total core loss per unit volume of the experimental data as the target value and the output of the core loss correction model as the fitting value, and iteratively solving the undetermined coefficients to obtain the calibrated coefficient set.

[0013] Furthermore, the model error includes any one of the mean relative error, maximum relative error, or root mean square error, and accuracy verification is performed by separating the calibration dataset from the independent validation dataset.

[0014] Furthermore, it also includes: establishing a three-dimensional finite element model of the target high-frequency transformer, calculating the magnetic flux density distribution and core loss distribution based on the non-sinusoidal excitation waveform, and comparing the simulation results with the output of the core loss correction model or experimental data for model verification or fitting constraints.

[0015] Secondly, this solution discloses a high-frequency transformer loss model correction system based on nanocrystalline magnetic cores, including: The parameter input module is used to receive or extract the excitation waveform and output the rising edge constant and duty cycle. The core calculation module is connected to the parameter input module and internally stores the core loss correction model. It is used to calculate the total core loss per unit volume based on the input excitation frequency, peak magnetic flux density, and rise time constant or duty cycle. The coefficient management module, connected to the core computing module, is used to store, update, and retrieve model coefficients determined by multivariate nonlinear fitting. The system also includes a coefficient fitting module, which is used to perform the multivariate nonlinear fitting based on experimental data and write the fitted model coefficients into the coefficient management module; The system also includes a conversion coefficient generation module, which is used to output the loss conversion coefficient based on the core loss correction model, and to perform loss component conversion between sinusoidal excitation and non-sinusoidal excitation.

[0016] Thirdly, this solution discloses an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described above, or to implement the system function described above.

[0017] Fourthly, this solution discloses a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described above or the system functions described above. Compared with existing technologies, the high-frequency transformer loss model correction method and system based on nanocrystalline magnetic cores described in this invention have the following advantages: (1) This invention extracts waveform characteristic parameters such as rising edge constant γ and duty cycle D from non-sinusoidal excitation waveforms and introduces them as correction factors into the eddy current loss term and residual loss term of the loss separation model, so that the model can be adaptively corrected as the waveform shape changes, thereby improving the accuracy of loss prediction under non-sinusoidal excitation. (2) Based on the modified model, the present invention derives the loss conversion coefficient between sinusoidal and non-sinusoidal excitation, so that the eddy current loss component and the residual loss component under different waveforms can be quantitatively converted, which is beneficial to uniformly evaluate the impact of different driving strategies on core loss in the design stage. (3) This invention uses high-frequency magnetic property experimental data to perform multivariate nonlinear fitting calibration of material correlation coefficients, so that the model can be applied to specific nanocrystalline materials and the accuracy can be verified using independent datasets, which facilitates material selection, structure and thermal design optimization in engineering applications. Attached Figure Description

[0018] 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 undue limitation of the invention. In the drawings: Figure 1 This is a schematic diagram of a three-dimensional model of the nanocrystalline high-frequency transformer described in an embodiment of the present invention; Figure 2 This is a schematic diagram of the relationship between trapezoidal wave voltage and magnetic induction intensity according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the relationship between the voltage and magnetic induction intensity of a rectangular wave with zero voltage as described in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the variation of the trapezoidal wave conversion coefficient with the rising edge coefficient according to an embodiment of the present invention; Figure 5 This is a schematic diagram illustrating the variation of the rectangular wave conversion coefficient with duty cycle according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the method described in an embodiment of the present invention. Detailed Implementation

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0020] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0021] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0022] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] A method and system for correcting the loss model of a high-frequency transformer based on a nanocrystalline magnetic core are disclosed. In practical applications, this method achieves accurate loss prediction through a series of sequentially executed steps. The method involves acquiring the non-sinusoidal excitation voltage waveform of the high-frequency transformer during actual operation and extracting key parameters characterizing the waveform, including the rise time constant. Duty cycle Secondly, based on the classic Bertotti loss separation model, the rising edge constant is... Duty cycle As a correction factor, a core loss calculation model applicable to trapezoidal and rectangular wave excitation was constructed. To verify the model's correctness and applicability, waveform conversion coefficients were further derived from the model. These coefficients quantitatively describe the proportional relationship between eddy current loss and residual loss components under different excitation waveforms. Loss data of specific nanocrystalline core samples under different excitation conditions were obtained through experimental measurements. Based on this data, the material correlation coefficients in the loss calculation model were fitted and calibrated to adapt the model to specific materials. The calibrated model was used for loss prediction under actual working conditions, and the accuracy of the model was finally verified by comparing the predicted values ​​with the measured values.

[0024] Example 1: Establishment of the Correction Model and Analysis of Waveform Conversion Coefficients Establish a three-dimensional model of a nanocrystalline high-frequency transformer, such as Figure 1 As shown.

[0025] like Figure 2 The trapezoidal wave shown has a constant rising edge. Defined as the sum of the proportions of the time it takes for the voltage to rise from zero to its peak value (or fall from its peak value to zero) within half a cycle. For example, Figure 3 The rectangular wave shown has a duty cycle of Defined as the ratio of the high-level duration to the entire cycle.

[0026] In one specific implementation, to obtain waveform characteristic parameters for model correction, the voltage at the winding terminals of the high-frequency transformer can be synchronously sampled. During sampling, an isolated voltage probe or a differential probe can be used, ensuring the sampling bandwidth covers the main frequency band of the voltage rising edge. The sampled sequence can be de-biased and low-pass filtered to reduce the impact of switching spikes, ringing, and measurement noise on edge recognition. Period segmentation can be achieved by detecting consecutive rising edge trigger points or detecting repetitive voltage characteristic points. When jitter exists, a hysteresis threshold plus a minimum duration method can be used to filter short-pulse false triggers, ensuring that the start and end points of each cycle are stable and consistent.

[0027] For trapezoidal waves, the rise time constant can be obtained by extracting the time of the voltage rise and fall segments: first, determine the voltage peak value, then use a certain proportion of the peak value as a threshold, such as a lower threshold and a higher threshold, and calculate the duration of the voltage rising from the lower threshold to the higher threshold and the duration of the voltage falling from the higher threshold to the lower threshold, respectively. Normalize both within half a period or the entire period to obtain the rise time constant. For rectangular waves containing zero-voltage intervals, the duty cycle can be obtained by the ratio of the high-level duration to the period, where the high-level duration can be determined by threshold comparison. When the waveform exhibits slight ringing, the high level can be considered a valid interval continuously above the threshold to avoid the high-level interval being split due to ringing.

[0028] Furthermore, to ensure that the excitation frequency, peak flux density, and waveform parameters are traceable within the same data link, the excitation current can be acquired simultaneously with voltage sampling. Based on known structural parameters such as the number of turns in the transformer winding and the effective cross-sectional area of ​​the magnetic core, the voltage waveform is integrated to obtain the change in flux or flux linkage over time, which is then converted to obtain the change in flux density over time, and the peak flux density is determined accordingly. For drift caused by the integration process, periodic average correction or endpoint constraint correction can be used to ensure that the flux density waveform closes within one cycle, thereby improving the stability of the peak flux density extraction.

[0029] According to Faraday's law of electromagnetic induction Excitation voltage waveform and magnetic induction intensity This is an integral relationship. By integrating the voltage expressions for trapezoidal and rectangular waves piecewise, we can obtain the value within half a cycle. The piecewise function expressions are shown in formulas (1) and (2) in the instruction manual, respectively.

[0030] (1) (2) These two Substituting the expression into the integral form of the classic Bertotti loss separation model, and through mathematical derivation, the eddy current losses under trapezoidal wave and rectangular wave excitation can be obtained respectively. and residual loss The expressions are shown in formulas (3) and (4).

[0031] (3) (4) Dividing the eddy currents and residual losses under non-sinusoidal excitation in equations (3) and (4) by the corresponding losses under sinusoidal excitation, respectively, yields the loss conversion coefficients of trapezoidal waves relative to sinusoidal waves with the same peak flux density. : (5) Similarly, in the rectangular wave duty cycle Under actual operating conditions of ≥0.5, the loss conversion factor of the rectangular wave to the sine wave can be obtained. : (6) Figure 4 and Figure 5 This demonstrates the trend of the conversion factor as a function of waveform parameters. Analysis shows that when the waveform approaches a triangular wave (…),… When the waveform approaches a square wave, the conversion factor is at its maximum, meaning that eddy currents and residual losses are at their highest; when the waveform approaches a square wave ( or When the conversion factors are constant (approximately 0.913 and 0.810), the loss is lower than that of a sine wave. This explains the phenomenon observed in the experiment: triangular wave loss > sine wave loss > square wave loss, theoretically verifying the correctness of the modified model.

[0032] By combining equations (5) and (6) to correct the core loss using waveform coefficients, a fitting formula for the loss of high-frequency nanocrystalline magnetic cores excited by trapezoidal and rectangular waves is finally obtained. In a specific implementation, experimental data can be organized according to three dimensions: excitation frequency, peak magnetic flux density, and waveform parameters, covering multiple representative working conditions to ensure that the fitting results have an engineering applicability. The measured loss can be obtained by the magnetic characteristic experimental system: a trapezoidal wave or a rectangular wave with a zero voltage range is generated by the waveform generator and power drive unit to excite the sample core; the instantaneous power is obtained by voltage and current sampling, and the total loss is obtained by averaging within the steady-state period. To facilitate comparability between different samples and cores of different sizes, the total loss can be divided by the core volume to obtain the unit volume loss as the fitting target value; the core volume can be obtained by geometric dimension measurement or material data sheet.

[0033] During fitting calibration, multivariate nonlinear regression can be used to solve for the undetermined coefficients of the model. To improve the stability of the fit, initial values ​​of the coefficients and convergence criteria can be set, and necessary physical constraints can be imposed on the fitting process, such as limiting the loss component to non-negativity and limiting the model's trend of variation with frequency and magnetic flux density peak values ​​within typical operating conditions to avoid abnormal fluctuations. If there are a few outliers, such as abnormal loss values ​​caused by insufficient stability of the measurement transient or sampling saturation, they can be removed based on the residual threshold or a robust fitting strategy can be adopted to reduce the impact of outliers and obtain a more reliable set of coefficients. (7) (8) Example 2: Model-Aided Analysis and Verification Based on Finite Element Simulation In one specific implementation, to avoid the fitting results only conforming to the calibration data and becoming inaccurate for new operating conditions, the measured data can be divided into a calibration dataset and an independent validation dataset. The calibration dataset is used for coefficient solving, and the validation dataset is used for accuracy evaluation. The accuracy evaluation can output at least one error index, such as mean relative error, maximum relative error, or root mean square error, and can be statistically analyzed in different frequency ranges, different magnetic flux density peak ranges, and different waveform parameter ranges to illustrate the error level of the model in each typical operating region. When the input operating condition exceeds the coverage of the calibration data, the system can output an applicable range prompt, or use the coefficients of the nearest boundary operating condition for extrapolation calculation, and indicate in the output results that the result belongs to the extrapolation estimate, so as to reduce the risk of misuse.

[0034] To ensure the loss correction model is applicable to nanocrystalline magnetic cores, the material correlation coefficients need to be determined. In practice, measured loss data of the magnetic core under various operating conditions need to be obtained, covering different rise time constants. Duty cycle Excitation frequency and peak magnetic flux density .

[0035] Furthermore, by correlating the measured data with the model expression and fitting it using a multivariate nonlinear regression algorithm, a set of optimal coefficient values ​​can be obtained, thus completing the model calibration. The purpose of this fitting process is to establish a precise quantitative relationship between waveform parameters and losses.

[0036] Furthermore, the optimal coefficients obtained from the fitting are substituted into the loss model to obtain a calibrated loss predictor specific to the material. In practical applications, the waveform parameters under the target operating condition are input ( or ),frequency and magnetic flux density The model can directly calculate the predicted loss value. To verify the model's accuracy, the predicted value can be compared with another set of independent measured loss data. In one specific implementation, a three-dimensional finite element model of a nanocrystalline high-frequency transformer can be established. The model includes the magnetic core, windings, and necessary structural components, and is set with the same geometric dimensions and material properties as the prototype. The excitation conditions can be a non-sinusoidal voltage waveform or an equivalent excitation current waveform consistent with the actual measurements; the distribution of magnetic flux density at different locations on the magnetic core within one or more steady-state cycles is calculated. Subsequently, the modified loss model is used to calculate the unit volume loss density at each location under the corresponding local magnetic flux density peak and equivalent frequency conditions, and the total loss is obtained by integrating the loss density over the magnetic core volume. By comparing the total loss obtained from finite element integration with the experimentally measured total loss and the predicted loss of the modified model under the overall equivalent magnetic flux density peak condition, the applicability of the model in the presence of spatially non-uniform magnetic flux density distribution can be verified, and loss hotspot regions can be identified, providing a basis for structural optimization and heat dissipation design. The results show that the model corrected by this method has significantly better prediction accuracy than the classic Bertotti model without waveform parameter correction.

[0037] Example 3: Software Implementation of Loss Model Correction System In one specific implementation, the loss model correction system can run on a host computer, embedded controller, or the computing unit of a testing instrument. The system includes at least a waveform acquisition and analysis module, a parameter extraction module, a model selection and calculation module, a coefficient management module, and a result output module. The waveform acquisition and analysis module receives the winding terminal voltage sampling sequence and performs period segmentation; the parameter extraction module calculates the rise time constant and duty cycle and outputs the corresponding waveform type; the model selection and calculation module calls the corresponding loss correction model according to the waveform type, inputs the excitation frequency, peak flux density, and waveform parameters, and outputs the total loss per unit volume and the loss of each component; the coefficient management module stores the calibration coefficients of different materials or different batches of magnetic cores and supports coefficient updates and version records; the result output module outputs the predicted loss, conversion coefficients, and error indicators, and can provide the calculation results to the transformer design, thermal design, or test report generation process in the form of a file or interface.

[0038] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for correcting a high-frequency transformer loss model based on a nanocrystalline magnetic core, characterized in that, The method comprises: obtaining a winding end voltage non-sinusoidal excitation waveform of a target high-frequency transformer under a target operating condition, and identifying the excitation waveform as a trapezoidal waveform or a rectangular waveform with a zero voltage interval; extracting waveform feature parameters from the excitation waveform, wherein a rising edge constant is used to represent the proportion of voltage rising edge time and falling edge time in a period, and a duty cycle is used to represent the proportion of non-zero voltage duration in a period; based on a Bertotti loss separation model, introducing the rising edge constant or the duty cycle as a correction factor into a coefficient of an eddy current loss term and a coefficient of a residual loss term to construct a magnetic core loss correction model corresponding to the type of the excitation waveform, wherein the input of the magnetic core loss correction model at least includes an excitation frequency, a magnetic flux density peak value and the rising edge constant or the duty cycle, and the output is a total loss per unit volume of the magnetic core; deriving a loss conversion coefficient of sinusoidal excitation and the non-sinusoidal excitation under the condition of the same magnetic flux density peak value based on the magnetic core loss correction model; collecting loss experimental data of nanocrystalline magnetic cores under different excitation frequencies, different magnetic flux density peak values and different rising edge constants or duty cycles, and determining undetermined coefficients of the magnetic core loss correction model by using multivariate nonlinear fitting to complete model calibration; inputting target operating condition parameters into the calibrated magnetic core loss correction model to obtain predicted loss, and comparing the predicted loss with the corresponding measured loss to output model error.

2. The method of claim 1, wherein, The rising edge constant is the ratio of the sum of voltage rising edge time and falling edge time to a period; and the duty cycle is the ratio of non-zero voltage duration to a period.

3. The method of claim 1, wherein, The identification of the trapezoidal waveform comprises detecting a rising edge section, a platform section and a falling edge section of the voltage in a period, and calculating the rising edge time and the falling edge time to obtain the rising edge constant; and the identification of the rectangular waveform comprises detecting a non-zero voltage duration interval in a period and calculating the non-zero voltage duration to obtain the duty cycle.

4. The method of claim 1, wherein, The magnetic core loss correction model comprises a hysteresis loss term, an eddy current loss term and a residual loss term, wherein the coefficient of the hysteresis loss term is a material-related constant, and the coefficients of the eddy current loss term and the residual loss term are represented as functions of the rising edge constant or the duty cycle.

5. The method of claim 4, wherein, When the excitation waveform is a trapezoidal waveform, the coefficients of the eddy current loss term and the residual loss term are parameterized in a polynomial form or an exponential form with respect to the rising edge constant; and when the excitation waveform is a rectangular waveform, the coefficients of the eddy current loss term and the residual loss term are parameterized in a polynomial form or an exponential form with respect to the duty cycle.

6. The method of claim 1, wherein, The loss conversion coefficient comprises an eddy current loss conversion coefficient and a residual loss conversion coefficient, and the eddy current loss conversion coefficient is used to represent the ratio relationship of the eddy current loss component of the non-sinusoidal excitation to the eddy current loss component of the sinusoidal excitation under the condition of the same magnetic flux density peak value; and the residual loss conversion coefficient is used to represent the ratio relationship of the residual loss component of the non-sinusoidal excitation to the residual loss component of the sinusoidal excitation under the condition of the same magnetic flux density peak value.

7. The method of claim 1, wherein, In deducing the loss conversion coefficients, the excitation voltage waveform is expressed in segments in a period, and a correlation between the excitation voltage waveform and the rate of change of magnetic flux density is established based on Faraday's law of electromagnetic induction, and then the correlation is substituted into the eddy current loss term and the residual loss term of the loss separation model to obtain the eddy current loss conversion coefficient and the residual loss conversion coefficient.

8. The method of claim 1, wherein, The experimental data are obtained through high-frequency magnetic property tests, and the measurement conditions cover different rise time constants, different duty cycles, different excitation frequencies, and different peak values of magnetic flux density.

9. The method of claim 1, wherein, The multi-element nonlinear fitting includes: taking the total loss per unit volume of the magnetic core of the experimental data as a target value, taking the output of the magnetic core loss correction model as a fitting value, constructing a nonlinear least squares objective function, and iteratively solving the undetermined coefficients to obtain the calibrated coefficient array.

10. A nanocrystalline core-based high-frequency transformer loss model correction system, characterized by, Comprise: A parameter input module for receiving or extracting an excitation waveform and outputting a rise time constant and a duty cycle; A core calculation module connected to the parameter input module, internally storing the magnetic core loss correction model of any one of claims 1 to 9, for calculating the total loss per unit volume of the magnetic core according to the input excitation frequency, peak value of magnetic flux density, and rise time constant or duty cycle; A coefficient management module connected to the core calculation module, for storing, updating and calling the model coefficients determined by the multi-element nonlinear fitting; The system further comprises a coefficient fitting module for performing the multi-element nonlinear fitting based on the experimental data and writing the fitted model coefficients into the coefficient management module; The system further comprises a conversion coefficient generation module for outputting the loss conversion coefficients based on the magnetic core loss correction model, and for converting the loss components between sinusoidal excitation and non-sinusoidal excitation.

Images