Method and device for calculating iron loss of transformer under deep saturation in reverse power flow condition

Abstract

This invention relates to a method and apparatus for calculating iron losses in transformers under deep saturation conditions with reverse power flow, comprising: introducing a saturation adaptive adjustment factor λ. sat Calculate the total magnetic susceptibility dM / dH of the J-A model; calculate the hysteresis loss power P. h Introducing the harmonic coefficient K f and temperature coefficient K t ; Calculate the eddy current loss power P e With residual power loss P r ; Calculate the total iron loss power P applicable to reverse power flow conditions. F This invention constructs an adaptive direction coefficient δ adp Adaptive piecewise integral correction of hysteresis loss significantly improves calculation accuracy under deep saturation conditions; dynamic coupling of the J-A model with excitation voltage and excitation current is achieved, eliminating the need for additional complex parameter calibration and enhancing ease of engineering use; harmonic coefficients K are introduced for eddy current loss and residual loss. f With temperature coefficient K t It can accurately reflect the nonlinear enhancement effect of loss under deep saturation, and the calculation results are more in line with the actual operating conditions.

Description

Technical Field

[0001] This invention relates to the field of power equipment loss calculation technology, and in particular to a method and equipment for calculating iron losses of transformers under deep saturation conditions in reverse power flow. Background Technology

[0002] With the large-scale grid connection of new energy sources such as photovoltaics and wind power, the power flow distribution of the power system exhibits significant bidirectional flow characteristics, and transformers frequently operate in reverse power flow mode. In traditional power grids, transformers mostly supply power in one direction with forward power flow, and the iron core operates in the linear magnetization region. Conventional iron loss calculation methods, based on symmetrical hysteresis loops and linear material assumptions, can meet the accuracy requirements of engineering calculations. However, in the scenario of new energy grid connection, reverse power flow will cause residual magnetism shift and magnetic flux waveform distortion in the transformer iron core. The iron core is very likely to enter the deep saturation region, and the hysteresis loop will show obvious asymmetric distortion, making the traditional iron loss calculation model no longer applicable.

[0003] Currently, transformer iron loss calculations generally employ the classical loss separation method, which divides iron loss into three parts: hysteresis loss, eddy current loss, and residual loss, and calculates them independently. However, the classical loss separation method has significant limitations: First, it does not consider the asymmetric characteristics of the hysteresis loop caused by reverse power flow, and the hysteresis loss calculation is based solely on the integral of the symmetric loop, leading to a significant increase in error under deep saturation. Second, eddy current loss and residual loss are calculated using fixed coefficients, failing to couple the nonlinear changes in the core magnetization state and thus failing to reflect the nonlinear enhancement effect of losses under deep saturation. Third, traditional models do not establish a dynamic correlation between circuit parameters and magnetic field parameters, making it difficult to adapt to operating conditions where excitation voltage and excitation current change in real time, resulting in significant deviations between the calculated results and actual losses.

[0004] Furthermore, among existing hysteresis models, although the JA model can accurately describe the domain magnetization process, conventional applications have not optimized for the asymmetric magnetization and deep saturation characteristics caused by reverse power flow, have not combined the direction coefficient to achieve piecewise correction of hysteresis loss, and have not completed the nonlinear adaptation of eddy current loss and residual loss, resulting in the model being unable to be directly used for accurate calculation of deep saturation iron loss of transformers under reverse power flow.

[0005] In summary, existing technologies lack methods that are adapted to new energy grid-connected scenarios and can accurately calculate the deep saturation iron loss of transformers under reverse power flow, making it difficult to meet the engineering requirements for transformer loss assessment, efficiency optimization, and safe operation. There is an urgent need for an iron loss correction calculation method based on advanced hysteresis theory that considers asymmetric magnetization and deep saturation characteristics. Summary of the Invention

[0006] To address the shortcomings of existing transformer iron loss calculation methods in reverse power flow and deep saturation conditions, such as insufficient accuracy and failure to consider asymmetric distortion of the hysteresis loop and nonlinear enhancement of losses, the primary objective of this invention is to provide an iron loss calculation method for transformers in deep saturation under reverse power flow conditions that significantly improves calculation accuracy under deep saturation conditions, accurately reflects the nonlinear enhancement effect of losses under deep saturation, and provides calculation results that are more consistent with actual operating conditions.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a method for calculating iron losses of transformers under deep saturation conditions in reverse power flow, the method comprising the following sequential steps:

[0008] (1) Introduce saturated adaptive adjustment factor λ sat Construct adaptive directional coefficient δ adp The total magnetic susceptibility dM / dH of the JA model is calculated by dynamically correcting the irreversible motion characterization of magnetic domains based on real-time saturation.

[0009] (2) Based on the total magnetic susceptibility dM / dH of the JA model, and considering the asymmetric magnetization of the iron core caused by the reverse power flow, the hysteresis loss power P is calculated by using saturation adaptive variable step size piecewise integration. h ;

[0010] (3) Introduce the harmonic coefficient K f To accommodate magnetic flux distortion caused by harmonics under reverse power flow, a temperature coefficient K is introduced. t The effect of core temperature on the magnetic permeability, electrical conductivity and domain wall motion resistance of silicon steel sheets;

[0011] (4) Use harmonic coefficient K f With temperature coefficient K t Calculate the eddy current loss power P e With residual power loss P r ;

[0012] (5) Based on the hysteresis loss power P h Eddy current loss power P e With residual power loss P r Calculate the total iron loss power P applicable to reverse power flow conditions. F .

[0013] Step (1) specifically refers to the fact that in the JA model, the total magnetization M is divided into reversible magnetization components M0 and M1. rev and irreversible magnetization component M irr :

[0014] (1);

[0015] Among them, the reversible magnetization component M rev satisfy:

[0016] (2);

[0017] In the formula, c is the invertibility coefficient; M an For hysteresis-free magnetization, the Langevin function is used to describe it:

[0018] (3);

[0019] Where coth is the hyperbolic cotangent function, M s H represents the saturation magnetization, where a is a material-related parameter; e The effective magnetic field strength is determined by the interaction between the external magnetic field H and the total magnetization M:

[0020] (4);

[0021] Where α is the interdomain coupling coefficient;

[0022] Combining equations (1) and (2), the irreversible magnetization component M is obtained. irr for:

[0023] (5);

[0024] To adapt to the deep saturated nonlinear characteristics, the directional coefficient δ of the JA model is adaptively improved by introducing a saturation adaptive adjustment factor λ. sat According to the real-time saturation |B| / B s Dynamically adjust the amplitude of the direction coefficient δ to construct an adaptive direction coefficient δ. adp :

[0025] (6);

[0026] (7);

[0027] Where γ is the saturation adjustment coefficient, 0 < γ ≤ 0.3; |B| is the absolute value of the real-time magnetic flux density; B s dH / dt is the saturation magnetic flux density; sign is the sign function; dH / dt is the time rate of change of the magnetic field strength.

[0028] According to the energy conservation equation for magnetization of ferromagnetic materials:

[0029] (8);

[0030] Where k is the pinning coefficient and μ0 is the free permeability;

[0031] Combining equations (1) to (8), the total magnetic susceptibility dM / dH of the JA model is obtained as follows:

[0032] (9).

[0033] Step (2) specifically refers to: using the adaptive direction coefficient δ adp By combining adaptive variable-step-size piecewise integration of saturation to characterize the asymmetric magnetization characteristics under deep saturation, the hysteresis loss power P is thus determined. h The integral form is:

[0034] (10);

[0035] Where f is the frequency; H is the external magnetic field; M is the magnetization; and μ0 is the free permeability.

[0036] The total magnetic susceptibility dM / dH of the JA model is approximated by local linearization. The approximated total magnetic susceptibility dM / dH is:

[0037] (11);

[0038] In the formula, c is the reversibility coefficient; α is the interdomain coupling coefficient;

[0039] Combining Ohm's law for magnetic circuits and the law of electromagnetic induction, and in conjunction with equation (11), the hysteresis loss power P of the coupled circuit parameters is calculated by performing piecewise integration on equation (10). h :

[0040] ;

[0041] Among them, U e I e These represent the excitation voltage and excitation current, respectively; ϕ is the excitation voltage U. e With excitation current I e The phase difference; N and A are the number of turns and cross-sectional area of ​​the winding on the excitation side, respectively; k is the pinning factor; λ sat This is a saturated adaptive adjustment factor.

[0042] Step (3) specifically refers to: introducing the harmonic coefficient K f With temperature coefficient K t for:

[0043] ;

[0044] Where ξ is the harmonic influence coefficient, ζ is the temperature sensitivity coefficient, and both ξ and ζ are dimensionless coefficients; T is the current core temperature; T0 is the reference room temperature; and THD is the total harmonic distortion rate of the excitation voltage.

[0045] Step (4) specifically refers to: since eddy current loss is proportional to the square of the rate of change of magnetic flux density, combined with Faraday's law of electromagnetic induction and the introduced harmonic coefficient K f With temperature coefficient K t Calculate the eddy current loss power P e :

[0046] ;

[0047] Among them, K e =σd s 2 V / 12 is the eddy current loss coefficient, d s U is the thickness of a single silicon steel sheet, σ is the conductivity, V is the core volume; N and A are the number of turns and cross-sectional area of ​​the winding on the excitation side, respectively; U e This is the excitation voltage;

[0048] Based on Bertotti's loss separation theory, combined with the harmonic coefficient K f With temperature coefficient K t Calculate the remaining power loss P r :

[0049] ;

[0050] Among them, K r =(σGV0) 0.5 is the residual loss coefficient; G is a dimensionless constant, taken as 0.1356; V0 is the local effective electric field corresponding to unit domain wall motion.

[0051] Step (5) specifically refers to: the total iron loss power P F for:

[0052] .

[0053] Another object of the present invention is to provide an electronic device comprising:

[0054] Processor; and

[0055] The memory stores computer program instructions that, when executed by the processor, cause the processor to perform the iron loss calculation method described above, applicable to deep saturation of transformers under reverse power flow conditions.

[0056] The present invention also provides a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, cause the processor to perform the iron loss calculation method described above for transformer deep saturation under reverse power flow conditions.

[0057] As can be seen from the above technical solution, the beneficial effects of the present invention are as follows: First, in response to the asymmetric distortion of the hysteresis loop caused by reverse power flow, the present invention constructs an adaptive directional coefficient δ adp Adaptive piecewise integral correction of hysteresis loss significantly improves computational accuracy under deep saturation conditions; simultaneously, based on the JA hysteresis model and starting from the magnetic domain mechanism, it distinguishes the reversible magnetization component M. rev and irreversible magnetization component M irr First, this invention clarifies the physical meaning of loss correction and makes the theory rigorous. Second, it achieves dynamic coupling between the JA model and excitation voltage and current, eliminating the need for additional complex parameter calibration and enhancing its ease of use in engineering. Furthermore, it introduces a harmonic coefficient K to address eddy current loss and residual loss. f With temperature coefficient K t First, it can accurately reflect the nonlinear enhancement effect of loss under deep saturation, and the calculation results are more in line with the actual operating conditions. Second, the present invention is adapted to the reverse power flow operation scenario of grid-connected transformers of new energy sources such as photovoltaics, and can provide high-precision support for transformer loss assessment, efficiency optimization, condition monitoring and safe operation, and has significant engineering application value. Attached Figure Description

[0058] Figure 1 This is a flowchart of the method of the present invention;

[0059] Figure 2 This is a comparison chart of the experimental results of the present invention, traditional calculation methods, and small-scale models.

[0060] Figure 3 This is a comparison chart showing the error analysis of experimental measured data by the present invention and traditional calculation methods. Detailed Implementation

[0061] like Figure 1 As shown, a method for calculating iron losses in transformers with deep saturation under reverse power flow conditions is proposed. This method includes the following steps in sequence:

[0062] (1) Introduce saturated adaptive adjustment factor λ sat Construct adaptive directional coefficient δ adp The total magnetic susceptibility dM / dH of the JA model is calculated by dynamically correcting the irreversible motion characterization of magnetic domains based on real-time saturation.

[0063] (2) Based on the total magnetic susceptibility dM / dH of the JA model, and considering the asymmetric magnetization of the iron core caused by the reverse power flow, the hysteresis loss power P is calculated by using saturation adaptive variable step size piecewise integration. h ;

[0064] (3) Introduce the harmonic coefficient K f To accommodate magnetic flux distortion caused by harmonics under reverse power flow, a temperature coefficient K is introduced. tThe effect of core temperature on the magnetic permeability, electrical conductivity and domain wall motion resistance of silicon steel sheets;

[0065] (4) Use harmonic coefficient K f With temperature coefficient K t Calculate the eddy current loss power P e With residual power loss P r ;

[0066] (5) Based on the hysteresis loss power P h Eddy current loss power P e With residual power loss P r Calculate the total iron loss power P applicable to reverse power flow conditions. F .

[0067] Step (1) specifically refers to the fact that in the JA model, the total magnetization M is divided into reversible magnetization components M0 and M1. rev and irreversible magnetization component M irr :

[0068] (1);

[0069] Among them, the reversible magnetization component M rev satisfy:

[0070] (2);

[0071] In the formula, c is the invertibility coefficient, 0 ≤ c ≤ 1; M an For hysteresis-free magnetization, the Langevin function is used to describe it:

[0072] (3);

[0073] Where coth is the hyperbolic cotangent function, M s H represents the saturation magnetization, where a is a material-related parameter; e The effective magnetic field strength is determined by the interaction between the external magnetic field H and the total magnetization M:

[0074] (4);

[0075] Where α is the interdomain coupling coefficient;

[0076] Combining equations (1) and (2), the irreversible magnetization component M is obtained. irr for:

[0077] (5);

[0078] To adapt to the deep saturated nonlinear characteristics, the directional coefficient δ of the JA model is adaptively improved by introducing a saturation adaptive adjustment factor λ.sat According to the real-time saturation |B| / B s Dynamically adjust the amplitude of the direction coefficient δ to construct an adaptive direction coefficient δ. adp :

[0079] (6);

[0080] (7);

[0081] Where γ is the saturation adjustment coefficient, determined by fitting the magnetization curve of the iron-core silicon steel sheet material, and is a dimensionless empirical coefficient, 0 < γ ≤ 0.3; |B| is the absolute value of the real-time magnetic flux density; B s dH / dt is the saturation magnetic flux density; sign is the sign function; dH / dt is the time rate of change of the magnetic field strength.

[0082] According to the energy conservation equation for magnetization of ferromagnetic materials:

[0083] (8);

[0084] Where k is the pinning coefficient and μ0 is the free permeability;

[0085] Combining equations (1) to (8), the total magnetic susceptibility dM / dH of the JA model is obtained as follows:

[0086] (9).

[0087] Using the acquired excitation current as the initial input, the real-time magnetization intensity and the reversible magnetization component M are calculated through numerical iteration. rev and irreversible magnetization component M irr Complete the basic parameter solution of the JA model.

[0088] Step (2) specifically refers to: using the adaptive direction coefficient δ adp By combining adaptive variable-step-size piecewise integration of saturation to characterize the asymmetric magnetization characteristics under deep saturation, the hysteresis loss power P is thus determined. h The integral form is:

[0089] (10);

[0090] Where f is the frequency; H is the external magnetic field; M is the magnetization; and μ0 is the free permeability.

[0091] The integration interval is one power frequency cycle, through δ adp The adaptive switching enables segmented integration of the hysteresis loop, accurately matching the asymmetric magnetization characteristics under reverse power flow.

[0092] The total magnetic susceptibility dM / dH of the JA model is approximated by local linearization. The approximated total magnetic susceptibility dM / dH is:

[0093] (11);

[0094] In the formula, c is the reversibility coefficient; α is the interdomain coupling coefficient;

[0095] Combining Ohm's law for magnetic circuits and the law of electromagnetic induction, and in conjunction with equation (11), the hysteresis loss power P of the coupled circuit parameters is calculated by performing piecewise integration on equation (10). h :

[0096] ;

[0097] Among them, U e I e These represent the excitation voltage and excitation current, respectively; ϕ is the excitation voltage U. e With excitation current I e The phase difference; N and A are the number of turns and cross-sectional area of ​​the winding on the excitation side, respectively; k is the pinning factor; λ sat This is a saturated adaptive adjustment factor.

[0098] Step (3) specifically refers to: introducing the harmonic coefficient K f With temperature coefficient K t for:

[0099] ;

[0100] Where ξ is the harmonic influence coefficient, ζ is the temperature sensitivity coefficient, and both ξ and ζ are dimensionless coefficients; T is the current core temperature; T0 is the reference room temperature, which is taken as 25℃; THD is the total harmonic distortion rate of the excitation voltage.

[0101] Step (4) specifically refers to: since eddy current loss is proportional to the square of the rate of change of magnetic flux density, combined with Faraday's law of electromagnetic induction and the introduced harmonic coefficient K f With temperature coefficient K t Calculate the eddy current loss power P e It accurately reflects the nonlinear enhancement effect of eddy current loss under deep saturation:

[0102] ;

[0103] Among them, K e =σd s 2 V / 12 is the eddy current loss coefficient, d s U is the thickness of a single silicon steel sheet, σ is the conductivity, V is the core volume; N and A are the number of turns and cross-sectional area of ​​the winding on the excitation side, respectively; U eThis is the excitation voltage;

[0104] Based on Bertotti's loss separation theory, combined with the harmonic coefficient K f With temperature coefficient K t Calculate the remaining power loss P r This addresses the error problem of fixed residual loss in traditional models:

[0105] ;

[0106] Among them, K r =(σGV0) 0.5 is the residual loss coefficient; G is a dimensionless constant, taken as 0.1356; V0 is the local effective electric field corresponding to unit domain wall motion.

[0107] Step (5) specifically refers to: the total iron loss power P F for:

[0108] .

[0109] The transformer excitation voltage U is collected in real time. e Excitation current I e As input, steps (1) to (5) are executed cyclically to update the magnetization component, magnetic field strength, losses of each part and total iron loss power in real time, so as to realize the dynamic and accurate calculation of iron loss under different reverse current strengths and different saturation depths.

[0110] like Figure 2 As shown, iron loss was tested under operating conditions where the low-voltage side voltage was 1 to 1.2 times the rated value, with the secondary side rated voltage as the reference and in 2% rated voltage increments. Figure 2 The study compared the measured losses, the losses calculated by traditional methods, and the losses calculated by the present invention. The results showed that as the voltage increased and the iron core entered deep saturation, the traditional method significantly underestimated the losses, while the calculation results of the present invention were in high agreement with the measured losses, effectively improving the accuracy of iron loss calculation under deep saturation conditions of reverse power flow.

[0111] like Figure 3 As shown, the relative errors of the traditional calculation method and the present invention were calculated based on the measured loss. Figure 3 It is evident that when the low-voltage side voltage is low and the reverse power is small, the calculation errors of both methods are small. As the voltage increases and the core enters deep saturation, the error of the traditional method increases sharply, exceeding 30% at its highest, and cannot accurately reflect the actual iron loss. However, the error of the present invention remains at a low level, with a maximum relative error of less than 5%, which significantly improves the calculation accuracy of iron loss under deep saturation conditions of reverse power flow.

[0112] In summary, to address the asymmetric distortion of the hysteresis loop caused by reverse power flow, this invention constructs an adaptive direction coefficient δ adp Adaptive piecewise integral correction of hysteresis loss significantly improves computational accuracy under deep saturation conditions; simultaneously, based on the JA hysteresis model and starting from the magnetic domain mechanism, it distinguishes the reversible magnetization component M. rev and irreversible magnetization component M irr This makes the physical meaning of loss correction clear and the theory rigorous; the invention realizes the dynamic coupling of the JA model with excitation voltage and excitation current, without the need for additional complex parameter calibration, and is highly easy to use in engineering; and a harmonic coefficient K is introduced for eddy current loss and residual loss. f With temperature coefficient K t It can accurately reflect the nonlinear enhancement effect of loss under deep saturation, and the calculation results are more in line with the actual operating conditions. This invention is suitable for the reverse power flow operation scenario of grid-connected transformers of new energy sources such as photovoltaics, and can provide high-precision support for transformer loss assessment, efficiency optimization, condition monitoring and safe operation, and has significant engineering application value.

[0113] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for calculating iron losses in transformers under deep saturation conditions applicable to reverse power flow, characterized in that: The method includes the following steps in sequence: (1) Introduce saturated adaptive adjustment factor λ sat Construct adaptive directional coefficient δ adp The total magnetic susceptibility dM / dH of the JA model is calculated by dynamically correcting the irreversible motion characterization of magnetic domains based on real-time saturation. (2) Based on the total magnetic susceptibility dM / dH of the JA model, and considering the asymmetric magnetization of the iron core caused by the reverse power flow, the hysteresis loss power P is calculated by using saturation adaptive variable step size piecewise integration. h ; (3) Introduce the harmonic coefficient K f To accommodate magnetic flux distortion caused by harmonics under reverse power flow, a temperature coefficient K is introduced. t The effect of core temperature on the magnetic permeability, electrical conductivity and domain wall motion resistance of silicon steel sheets; (4) Use harmonic coefficient K f With temperature coefficient K t Calculate the eddy current loss power P e With residual power loss P r ; (5) Based on the hysteresis loss power P h Eddy current loss power P e With residual power loss P r Calculate the total iron loss power P applicable to reverse power flow conditions. F .

2. The method for calculating iron loss of transformers under deep saturation conditions according to claim 1, characterized in that: Step (1) specifically refers to the fact that in the JA model, the total magnetization M is divided into reversible magnetization components M0 and M1. rev and irreversible magnetization component M irr : （1）； Among them, the reversible magnetization component M rev satisfy: （2）； In the formula, c is the invertibility coefficient; M an For hysteresis-free magnetization, the Langevin function is used to describe it: （3）； Where coth is the hyperbolic cotangent function, M s H represents the saturation magnetization, where a is a material-related parameter; e The effective magnetic field strength is determined by the interaction between the external magnetic field H and the total magnetization M: （4）； Where α is the interdomain coupling coefficient; Combining equations (1) and (2), the irreversible magnetization component M is obtained. irr for: （5）； To adapt to the deep saturation nonlinearity, the directional coefficient δ of the JA model is adaptively improved by introducing a saturation adaptive adjustment factor λ. sat According to the real-time saturation |B| / B s Dynamically adjust the magnitude of the direction coefficient δ to construct an adaptive direction coefficient δ. adp : （6）； （7）； Where γ is the saturation adjustment coefficient, 0 < γ ≤ 0.3; |B| is the absolute value of the real-time magnetic flux density; B s dH / dt is the saturation magnetic flux density; sign is the sign function; dH / dt is the time rate of change of the magnetic field strength. According to the energy conservation equation for magnetization of ferromagnetic materials: （8）； Where k is the pinning coefficient and μ0 is the free permeability; Combining equations (1) to (8), the total magnetic susceptibility dM / dH of the JA model is obtained as follows: （9）。 3. The method for calculating iron losses of transformers under deep saturation conditions according to claim 1, characterized in that: Step (2) specifically refers to: using the adaptive direction coefficient δ adp By combining adaptive variable-step-size piecewise integration of saturation to characterize the asymmetric magnetization characteristics under deep saturation, the hysteresis loss power P is thus determined. h The integral form is: （10）； Where f is the frequency; H is the external magnetic field; M is the magnetization; and μ0 is the free permeability. The total magnetic susceptibility dM / dH of the JA model is approximated by local linearization. The approximated total magnetic susceptibility dM / dH is: （11）； In the formula, c is the reversibility coefficient; α is the interdomain coupling coefficient; Combining Ohm's law for magnetic circuits and the law of electromagnetic induction, and in conjunction with equation (11), the hysteresis loss power P of the coupled circuit parameters is calculated by performing piecewise integration on equation (10). h : ； Among them, U e I e These represent the excitation voltage and excitation current, respectively; ϕ is the excitation voltage U. e With excitation current I e The phase difference; N and A are the number of turns and cross-sectional area of ​​the winding on the excitation side, respectively; k is the pinning factor; λ sat This is a saturated adaptive adjustment factor.

4. The method for calculating iron loss of transformers under deep saturation conditions according to claim 1, characterized in that: Step (3) specifically refers to: introducing the harmonic coefficient K f With temperature coefficient K t for: ； Where ξ is the harmonic influence coefficient, ζ is the temperature sensitivity coefficient, and both ξ and ζ are dimensionless coefficients; T is the current core temperature; T0 is the reference room temperature; and THD is the total harmonic distortion rate of the excitation voltage.

5. The method for calculating iron losses of transformers under deep saturation conditions according to claim 1, characterized in that: Step (4) specifically refers to: since eddy current loss is proportional to the square of the rate of change of magnetic flux density, combined with Faraday's law of electromagnetic induction and the introduced harmonic coefficient K f With temperature coefficient K t Calculate the eddy current loss power P e : ； Among them, K e =σd s 2 V / 12 is the eddy current loss coefficient, d s U is the thickness of a single silicon steel sheet, σ is the conductivity, V is the core volume; N and A are the number of turns and cross-sectional area of ​​the winding on the excitation side, respectively; U e This is the excitation voltage; Based on Bertotti's loss separation theory, combined with the harmonic coefficient K f With temperature coefficient K t Calculate the remaining power loss P r : ； Among them, K r =(σGV0) 0.5 is the residual loss coefficient; G is a dimensionless constant, taken as 0.1356; V0 is the local effective electric field corresponding to unit domain wall motion.

6. The method for calculating iron losses of transformers under deep saturation conditions according to claim 1, characterized in that: Step (5) specifically refers to: the total iron loss power P F for: 。 7. An electronic device, comprising: processor; as well as A memory storing computer program instructions that, when executed by the processor, cause the processor to perform the iron loss calculation method for transformer deep saturation under reverse power flow conditions as described in any one of claims 1-6.

8. A computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, cause the processor to perform the iron loss calculation method for transformer deep saturation under reverse power flow conditions as described in any one of claims 1-6.

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