A dynamic calibration method for reference error of a powered magnetic core sensor

Abstract

The present application relates to the technical field of magnetic core sensor, and discloses a kind of dynamic calibration method of reference error of energy-taking type magnetic core sensor, comprising: by collecting primary side and secondary side current and energy-taking loop parameter, construct anti-phase noise proportion module and calculate signal energy reference value.Separate out virtual reactive power projection quantity, combine secondary side parameter quantization magnetic domain asymmetry factor, deduce dynamic equivalent impedance and equivalent voltage trace value.Further analyze dynamic excitation current and magnetic permeability degradation index, calibrate magnetic core saturation ratio.Finally, the reference error compensation amplitude and reference phase shift are calculated, the dynamic calibration measurement output value is output, and the calibration validity is judged based on the measurement integrity evaluation factor.The present application can effectively suppress the nonlinear disturbance caused by energy-taking high-frequency switch to the magnetic core, solve the phase extraction singularity problem, and execute blocking when the magnetic core is deeply saturated, improve the dynamic measurement precision and system defense capability.

Description

Technical Field

[0001] This invention relates to the field of magnetic core sensor technology, specifically to a dynamic calibration method for the reference error of an energy-harvesting magnetic core sensor. Background Technology

[0002] Energy-harvesting magnetic core sensors are widely used in smart grids and high-voltage power transmission and distribution monitoring systems. Their core advantage lies in their ability to directly draw power from the primary bus to power the monitoring and communication modules on the secondary side. However, in actual operation, the operation of the high-frequency energy conversion switch in the energy harvesting circuit can cause unidirectional pulse impacts on the sensor core. This transient impact easily induces non-uniform magnetic domain reversal within the core, leading to a nonlinear drift in the dynamic equivalent impedance on the secondary side. This drift not only invalidates traditional Ohm's law and classical impedance models but also generates a specific virtual reactive power projection in the secondary circuit. This virtual reactive power projection does not generate actual work but severely interferes with the calculation of the energy factor, ultimately causing dynamic errors in the sensor's measurement reference. Existing measurement reference calibration techniques mostly rely on static compensation under steady-state conditions. Such static compensation methods struggle to track the nonlinear magnetic domain disturbances caused by transient energy harvesting impacts and cannot adapt to complex and changing dynamic conditions. Some improved solutions attempt to dynamically correct by extracting asymmetric energy components. However, in the process of separating the virtual reactive power projection, a phase separation method based on zero-crossing detection is typically used. This method is prone to singularity calculation abrupt changes near the zero-crossing point when there are harmonics or strong phase noise interference in the power grid, causing the separation algorithm to fail. In addition, existing compensation strategies often lack the ability to determine the fuse protection when the magnetic core enters the deep physical saturation region under extremely harsh operating conditions, which poses a risk of blind compensation.

[0003] In summary, there is an urgent need for a measurement method that is highly resistant to phase noise, can accurately quantify magnetic domain distortion, and has dynamic self-calibration and overcompensation prevention capabilities. Summary of the Invention

[0004] This invention provides a dynamic calibration method for the reference error of an energy-harvesting magnetic core sensor, which helps to solve the problems mentioned in the background art.

[0005] This invention provides the following technical solution: a dynamic calibration method for the reference error of an energy-harvesting magnetic core sensor, comprising: The phase noise immunity proportional modulus is calculated using the current measurements on the primary and secondary sides and the turns ratio coefficient of the secondary winding, and the signal energy reference value is calculated by combining the voltage and current measurements of the energy harvesting circuit. The virtual reactive power projection is calculated based on the signal energy reference value, and the reactive power fluctuation coefficient is calculated in combination with the voltage measurement value of the energy harvesting circuit. The domain reversal asymmetry factor is calculated using the reactive power fluctuation coefficient and the secondary winding internal resistance. The dynamic equivalent impedance is calculated based on the magnetic domain reversal asymmetry factor, and the equivalent voltage trace value is calculated in combination with the internal resistance of the secondary winding. The dynamic excitation current is calculated based on the dynamic equivalent impedance and the equivalent voltage trace value, and the permeability degradation index is calculated in combination with the phase noise proportional modulus. The core saturation ratio is calculated using the dynamic excitation current and the permeability degradation index. The reference error compensation amplitude is calculated based on the core saturation ratio and the dynamic equivalent impedance, and the reference phase offset is calculated in combination with the virtual reactive power projection. The dynamic calibration measurement output value is calculated using the reference error compensation amplitude and the reference phase offset. The measurement integrity evaluation factor is calculated by combining the magnetic core saturation ratio. Based on the comparison result between the measurement integrity evaluation factor and the preset measurement reliability safety threshold, the dynamic calibration measurement output value is selected to be output or blocked.

[0006] Optionally, the step of calculating the phase noise immunity proportional modulus using the current measurements on the primary and secondary sides and the turns ratio coefficient of the secondary winding, and calculating the signal energy reference value in conjunction with the voltage and current measurements of the energy harvesting circuit, includes: A current acquisition unit is configured on the primary side of the energy-harvesting magnetic core sensor, and synchronous current acquisition units and voltage acquisition units are configured in the measurement winding circuit and energy harvesting circuit on the secondary side, respectively. Obtain the turns ratio of the secondary winding, the measured value of the primary current, and the measured value of the secondary current. The measured value of the secondary current is multiplied by the turns ratio coefficient of the secondary winding to obtain the converted current value of the secondary side. The primary side current measurement value and the secondary side equivalent current value are squared and summed, and the square root operation is performed on the summation result to extract the anti-phase noise proportional modulus. Obtain the measured values ​​of the voltage and current in the energy harvesting circuit; The apparent power of the energy harvesting circuit is obtained by multiplying the measured voltage value of the energy harvesting circuit with the measured current value of the energy harvesting circuit. The signal energy reference value is obtained by calculating the ratio of the apparent power of the energy harvesting circuit to the phase noise immunity proportional modulus.

[0007] Optionally, the step of calculating the virtual reactive power projection based on the signal energy reference value and calculating the reactive power fluctuation coefficient in conjunction with the voltage measurement value of the energy harvesting circuit includes: The difference between the measured value of the primary current and the converted value of the secondary current is calculated to obtain the ampere-turn imbalance between the primary and secondary sides. The virtual reactive power projection is obtained by multiplying the ampere-turn imbalance with the signal energy reference value. The intrinsic apparent power on the secondary side is obtained by multiplying the measured voltage value of the energy harvesting circuit with the measured current value on the secondary side. The total basic reactive power fluctuation is obtained by summing the virtual reactive power projection and the secondary side inherent apparent power. The reactive power fluctuation coefficient is obtained by calculating the ratio between the virtual reactive power projection and the basic reactive power fluctuation.

[0008] Optionally, the calculation of the domain reversal asymmetry factor using the reactive power fluctuation coefficient and the secondary winding internal resistance includes: Obtain the internal resistance of the secondary winding; The measured current value of the energy harvesting circuit is squared, and the result is multiplied with the internal resistance of the secondary winding to obtain the heat loss power of the energy harvesting circuit. The power distortion factor is obtained by calculating the ratio of the heat loss power of the energy harvesting circuit to the inherent apparent power of the secondary side. The power distortion factor and the reactive power fluctuation coefficient are multiplied to obtain the domain reversal asymmetry factor.

[0009] Optionally, the step of calculating the dynamic equivalent impedance based on the domain reversal asymmetry factor and calculating the equivalent voltage trace value in conjunction with the secondary winding internal resistance includes: The base impedance value is obtained by calculating the ratio of the measured voltage value of the energy harvesting circuit to the measured current value of the energy harvesting circuit. The nonlinear adjustment weight is obtained by adding the magnetic domain reversal asymmetry factor to a preset unit constant. The dynamic equivalent impedance is obtained by multiplying the base impedance value with the nonlinear adjustment weight. The first voltage drop component is obtained by multiplying the dynamic equivalent impedance with the measured secondary current value. The second voltage drop component is obtained by multiplying the internal resistance of the secondary winding with the measured current of the energy extraction circuit. The equivalent voltage trace value is obtained by summing the first voltage drop component and the second voltage drop component.

[0010] Optionally, the step of calculating the dynamic excitation current based on the dynamic equivalent impedance and the equivalent voltage trace value, and calculating the permeability degradation index in conjunction with the anti-phase noise proportional modulus, includes: The equivalent excitation leakage current is obtained by calculating the ratio of the equivalent voltage trace value to the dynamic equivalent impedance. The dynamic excitation current is extracted by subtracting the equivalent magnetizing leakage current from the ampere-turn imbalance. The ratio of the dynamic excitation current to the anti-phase noise proportional modulus is calculated to obtain the basic excitation degradation rate; Subtracting the magnetic domain reversal asymmetry factor from the preset unit constant yields the asymmetric decay weight; The permeability degradation index is obtained by multiplying the basic excitation degradation rate with the asymmetric attenuation weight.

[0011] Optionally, the step of calculating the core saturation ratio using the dynamic excitation current and the permeability degradation index includes: Perform a square operation on the dynamic excitation current to obtain the dynamic excitation square component; The primary side current measurement value is squared to obtain the square component of the primary side current; The basic square component is obtained by summing the dynamic excitation square component and the primary current square component. The ratio of the dynamic excitation square component to the basic square total is calculated to obtain the basic saturation mapping coefficient; The core saturation ratio is obtained by multiplying the basic saturation mapping coefficient with the permeability degradation index.

[0012] Optionally, the step of calculating the reference error compensation amplitude based on the core saturation ratio and the dynamic equivalent impedance, and calculating the reference phase offset in conjunction with the virtual reactive power projection, includes: The basic amplitude deviation is obtained by multiplying the measured value of the secondary current with the core saturation ratio. The total series impedance is obtained by summing the dynamic equivalent impedance with the internal resistance of the secondary winding. The ratio of the dynamic equivalent impedance to the total series impedance is calculated to obtain the impedance voltage division ratio. The reference error compensation amplitude is obtained by multiplying the basic amplitude deviation by the impedance voltage division ratio. The virtual reactive power projection is multiplied by the core saturation ratio to obtain the phase shift numerator. The phase shift denominator term is obtained by multiplying the measured voltage value of the energy harvesting circuit with the anti-phase noise proportional modulus. The reference phase offset is obtained by calculating the ratio between the numerator and denominator of the phase offset.

[0013] Optionally, the step of calculating the dynamic calibration measurement output value using the reference error compensation amplitude and the reference phase offset, calculating the measurement integrity evaluation factor in conjunction with the core saturation ratio, and selecting to output or block the dynamic calibration measurement output value based on the comparison result of the measurement integrity evaluation factor and a preset measurement reliability safety threshold includes: The dynamic excitation current is multiplied by the reference phase offset to obtain the phase equivalent deduction. The secondary current measurement value is summed with the reference error compensation amplitude, and the phase equivalent deduction is subtracted from it to obtain the dynamic calibration measurement output value; The primary side current measurement value is multiplied by the secondary side winding turns ratio coefficient to obtain the ideal converted value of the primary side. The ratio of the dynamic calibration measurement output value to the primary side ideal conversion value is calculated to obtain the macroscopic transfer accuracy index. Subtracting the core saturation ratio from the preset unit constant yields the microscopic physical health index. The measurement integrity assessment factor is obtained by multiplying the macroscopic transmission accuracy index with the microscopic physical health index. Configure a measurement reliability security threshold, and compare the measurement integrity evaluation factor with the measurement reliability security threshold in terms of numerical values; When the measurement integrity assessment factor is greater than or equal to the measurement reliability safety threshold, the dynamic calibration measurement output value is selected for acceptance and output. When the measurement integrity assessment factor is less than the measurement reliability safety threshold, a blocking operation is performed to intercept the output of the dynamic calibration measurement output value.

[0014] The present invention has the following beneficial effects: 1. By utilizing the current measurements on the primary and secondary sides of the energy-harvesting core sensor and the parameters of the energy harvesting circuit, this scheme calculates the phase noise reduction proportional modulus and the signal energy reference value. It then separates the virtual reactive power projection, quantifies the domain reversal asymmetry factor, and derives the dynamic equivalent impedance and core saturation ratio. Based on this, it calculates the reference error compensation amplitude and reference phase offset. Finally, it combines the measurement integrity evaluation factor to determine whether to output or block the dynamic calibration measurement output value. In specific operating environments containing both the energy harvesting circuit and the secondary side measurement winding circuit, the sensor induces complex energy distribution changes during energy harvesting, leading to dynamic deviations in the measurement reference. This scheme quantifies the energy harvesting... The magnetic domain reversal asymmetry and permeability degradation index caused by the action transform the attenuation of the underlying magnetic properties into a solvable dynamic circuit parameter. The beneficial effects of this scheme are that it can adapt to the impact of the energy harvesting circuit operation on the magnetic core, use the anti-phase noise proportional modulus to suppress the computational interference caused by the environment, and realize dynamic error compensation calculation for the current saturation characteristics. Furthermore, the calibration status is determined based on the comparison result between the measurement integrity assessment factor and the preset measurement reliability safety threshold. When the integrity assessment factor is less than the safety threshold, the interception and blocking operation is performed, which reduces the probability of outputting distorted data under extreme and harsh conditions and ensures the reliability and robustness of the measurement compensation mechanism in specific system environments.

[0015] 2. By configuring synchronous current and voltage acquisition units on the primary and secondary sides of the energy-harvesting magnetic core sensor, and performing summation and square root operations based on the acquired measured values ​​and turns ratio coefficient, a geometric modulus unaffected by severe phase fluctuations, namely an anti-phase noise proportional modulus, is constructed. This design effectively avoids the division-by-zero singularity problem that is easily generated by traditional zero-crossing detection in strong electromagnetic environments or in the presence of harmonic interference, and improves the numerical stability of modulus extraction under complex electrical conditions. Then, the total apparent power of the energy harvesting circuit is normalized using this modulus to obtain a signal energy reference value unaffected by high-frequency switching noise, providing a stable and reliable energy reference basis for subsequent separation of microscopic interference components.

[0016] 3. The ampere-turn imbalance is obtained by calculating the difference between the currents converted from the primary and secondary sides. This imbalance is then projected onto a pre-established signal energy reference value to extract a specific virtual reactive power projection. This process algebraically separates the illusory power consumption that does not generate actual work but causes energy measurement deviations, thus eliminating its interference with the real energy factor. Subsequently, the reactive power fluctuation coefficient is established by calculating the ratio of the inherent apparent power on the secondary side to the total reactive power fluctuation. This coefficient can quantitatively reflect the characteristics of violent reactive power fluctuations caused by energy extraction, providing an intermediate correlation index for subsequent quantification of the nonlinear changes in the microstate of the magnetic medium, and realizing the numerical capture of the transient impact characteristics of energy extraction.

[0017] 4. By obtaining the internal resistance of the secondary winding and combining it with the current measurement value of the energy harvesting circuit, the heat loss power of the energy harvesting circuit, which represents the internal heating and loss state, is calculated. Then, the power distortion factor is obtained by using the ratio of this loss power to the inherent apparent power of the secondary side. This factor is then combined with the reactive power fluctuation coefficient obtained earlier to calculate the magnetic domain reversal asymmetry factor, which represents the degree of uneven domain flipping caused by the unidirectional impact of high-frequency switching in the magnetic core. This step directly maps the macroscopic current, voltage and heat loss parameters into quantitative factors that reflect the microscopic magnetic characteristics. This transforms the magnetic domain nonlinear disturbance, which is difficult to measure directly, into a circuit algebraic variable that can be accurately represented in the control algorithm, laying the physical parameter foundation for correcting nonlinear impedance.

[0018] 5. By calculating the basic impedance value using the voltage and current measurements of the energy harvesting circuit, and multiplying it by the nonlinear adjustment weight composed of the magnetic domain reversal asymmetry factor, the dynamic equivalent impedance that truly reflects the current physical disturbance state is calculated. This operation overcomes the limitation of the traditional classical impedance model failing under transient power impact due to the assumption of constant impedance, allowing the impedance parameter to be adaptively adjusted according to the decay of magnetic characteristics. Based on this dynamic equivalent impedance and the winding internal resistance, the voltage drop components of each part in the secondary circuit are calculated respectively, and the equivalent voltage trace value is obtained by combining them. This truly restores the voltage drop law inside the system under nonlinear conditions, ensuring the physical rationality of the subsequent excitation state separation derivation.

[0019] 6. By using the ratio of the equivalent voltage trace value to the dynamic equivalent impedance as the equivalent excitation leakage current and subtracting it from the system's ampere-turn imbalance, the dynamic excitation current component truly used to establish the magnetic field is extracted. This process is based on the objective Ampere's circuital law, eliminating the masking effect of loss current on excitation analysis. Furthermore, the basic excitation degradation rate is calculated using the dynamic excitation current and the anti-phase noise proportional modulus, and combined with the attenuation weight derived from the domain reversal asymmetry factor, the permeability degradation index, which characterizes the overall magnetic permeability degradation of the magnetic core, is calculated. Thus, without intruding into the internal physical structure of the sensor, online quantitative evaluation of the dynamic magnetic permeability characteristics of the magnetic core is achieved through algebraic reconstruction of external port parameters.

[0020] 7. By performing squaring operations on the primary current and the separated dynamic excitation current respectively, a basic total square is constructed. The basic saturation mapping coefficient is calculated by the proportion of the dynamic excitation square component in the basic total. This operation transforms the energy-magnetic field conversion relationship into a proportional structure that reflects the approach to saturation. Subsequently, this mapping coefficient is combined with the permeability degradation index to calibrate the core saturation ratio that comprehensively reflects the nonlinear characteristics. This ratio unifies the permeability decay at the micro level with the excitation requirements of the macro circuit, quantitatively giving the degree to which the current core medium approaches the physical saturation limit, and becoming the core mapping index connecting the decay of the underlying physical state and the compensation of the upper measurement reference.

[0021] 8. By multiplying the secondary current measurement, the core saturation ratio, and the impedance voltage division ratio established based on the dynamic equivalent impedance, the reference error compensation amplitude reflecting the amplitude attenuation is calculated. This process accurately allocates the nonlinear disturbance to the amplitude correction stage, avoiding the shortcomings of single static coefficient compensation. At the same time, the phase offset numerator is constructed using the virtual reactive power projection and the core saturation ratio, and the reference phase offset is calculated by using the product term including the anti-phase noise proportional modulus as the denominator. The asymmetric energy component that causes the phase error is converted into a dimensionless radian offset factor, realizing the synchronous and independent quantification extraction of the nonlinear deviation of the amplitude and phase dimensions in the measurement reference under complex dynamic electromagnetic environment.

[0022] 9. By using the obtained phase offset to map the phase angle deviation to an equivalent current deduction on the orthogonal axis, and combining it with the reference error compensation amplitude to correct the secondary current, a dynamic calibration measurement output value is obtained. This completes the pure algebraic dimension error compensation that conforms to physical laws. At the same time, a measurement integrity evaluation factor that correlates macroscopic accuracy and microscopic health is constructed. This evaluation factor is compared with a preset measurement reliability safety threshold. When the magnetic core condition does not exceed the physical limit, a reliable calibration result is output. When the evaluation factor falls below the threshold due to deep saturation caused by impact, a fuse blocking mechanism is executed. This gives the measurement system the self-diagnosis and protection capability against blind overcompensation, improving the safety of the measurement output under extreme and harsh conditions. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the basic process of the present invention.

[0024] Figure 2 This is a schematic diagram of the parameter acquisition for the energy-harvesting magnetic core sensor of the present invention.

[0025] Figure 3 This is a schematic diagram showing the relationship between the virtual reactive power projection, reactive power fluctuation coefficient, and dynamic equivalent impedance of the present invention.

[0026] Figure 4This is a schematic diagram illustrating the dynamic excitation current, core saturation ratio, and calibration effectiveness determination of the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Example 1, refer to Figure 1 A dynamic calibration method for the reference error of an energy-harvesting magnetic core sensor, comprising: The phase noise immunity proportional modulus is calculated using the current measurements on the primary and secondary sides and the turns ratio coefficient of the secondary winding, and the signal energy reference value is calculated by combining the voltage and current measurements of the energy harvesting circuit. The virtual reactive power projection is calculated based on the signal energy reference value, and the reactive power fluctuation coefficient is calculated in combination with the voltage measurement value of the energy harvesting circuit. The domain reversal asymmetry factor is calculated using the reactive power fluctuation coefficient and the secondary winding internal resistance. The dynamic equivalent impedance is calculated based on the magnetic domain reversal asymmetry factor, and the equivalent voltage trace value is calculated in combination with the internal resistance of the secondary winding. The dynamic excitation current is calculated based on the dynamic equivalent impedance and the equivalent voltage trace value, and the permeability degradation index is calculated in combination with the phase noise proportional modulus. The core saturation ratio is calculated using the dynamic excitation current and the permeability degradation index. The reference error compensation amplitude is calculated based on the core saturation ratio and the dynamic equivalent impedance, and the reference phase offset is calculated in combination with the virtual reactive power projection. The dynamic calibration measurement output value is calculated using the reference error compensation amplitude and the reference phase offset. The measurement integrity evaluation factor is calculated by combining the magnetic core saturation ratio. Based on the comparison result between the measurement integrity evaluation factor and the preset measurement reliability safety threshold, the dynamic calibration measurement output value is selected to be output or blocked.

[0029] The calculation of the phase noise immunity proportional modulus using the current measurements from the primary and secondary sides and the turns ratio coefficient of the secondary winding, and the calculation of the signal energy reference value in conjunction with the voltage and current measurements from the energy harvesting circuit, includes: A current acquisition unit is configured on the primary side of the energy-harvesting magnetic core sensor, and synchronous current acquisition units and voltage acquisition units are configured in the measurement winding circuit and energy harvesting circuit on the secondary side, respectively. Obtain the turns ratio of the secondary winding, the measured value of the primary current, and the measured value of the secondary current. The measured value of the secondary current is multiplied by the turns ratio coefficient of the secondary winding to obtain the converted current value of the secondary side. The primary side current measurement value and the secondary side equivalent current value are squared and summed, and the square root operation is performed on the summation result to extract the anti-phase noise proportional modulus. Obtain the measured values ​​of the voltage and current in the energy harvesting circuit; The apparent power of the energy harvesting circuit is obtained by multiplying the measured voltage value of the energy harvesting circuit with the measured current value of the energy harvesting circuit. The signal energy reference value is obtained by calculating the ratio of the apparent power of the energy harvesting circuit to the phase noise immunity proportional modulus.

[0030] The calculation of the virtual reactive power projection based on the signal energy reference value, and the calculation of the reactive power fluctuation coefficient in conjunction with the voltage measurement value of the energy harvesting circuit, includes: The difference between the measured value of the primary current and the converted value of the secondary current is calculated to obtain the ampere-turn imbalance between the primary and secondary sides. The virtual reactive power projection is obtained by multiplying the ampere-turn imbalance with the signal energy reference value. The intrinsic apparent power on the secondary side is obtained by multiplying the measured voltage value of the energy harvesting circuit with the measured current value on the secondary side. The total basic reactive power fluctuation is obtained by summing the virtual reactive power projection and the secondary side inherent apparent power. The reactive power fluctuation coefficient is obtained by calculating the ratio between the virtual reactive power projection and the basic reactive power fluctuation.

[0031] The calculation of the domain reversal asymmetry factor using the reactive power fluctuation coefficient and the secondary winding internal resistance includes: Obtain the internal resistance of the secondary winding; The measured current value of the energy harvesting circuit is squared, and the result is multiplied with the internal resistance of the secondary winding to obtain the heat loss power of the energy harvesting circuit. The power distortion factor is obtained by calculating the ratio of the heat loss power of the energy harvesting circuit to the inherent apparent power of the secondary side. The power distortion factor and the reactive power fluctuation coefficient are multiplied to obtain the domain reversal asymmetry factor.

[0032] The step of calculating the dynamic equivalent impedance based on the domain reversal asymmetry factor and calculating the equivalent voltage trace value in combination with the secondary winding internal resistance includes: The base impedance value is obtained by calculating the ratio of the measured voltage value of the energy harvesting circuit to the measured current value of the energy harvesting circuit. The nonlinear adjustment weight is obtained by adding the magnetic domain reversal asymmetry factor to a preset unit constant. The dynamic equivalent impedance is obtained by multiplying the base impedance value with the nonlinear adjustment weight. The first voltage drop component is obtained by multiplying the dynamic equivalent impedance with the measured secondary current value. The second voltage drop component is obtained by multiplying the internal resistance of the secondary winding with the measured current of the energy extraction circuit. The equivalent voltage trace value is obtained by summing the first voltage drop component and the second voltage drop component.

[0033] The calculation of the dynamic excitation current based on the dynamic equivalent impedance and the equivalent voltage trace value, and the calculation of the permeability degradation index in conjunction with the phase noise reduction proportional modulus, includes: The equivalent excitation leakage current is obtained by calculating the ratio of the equivalent voltage trace value to the dynamic equivalent impedance. The dynamic excitation current is extracted by subtracting the equivalent magnetizing leakage current from the ampere-turn imbalance. The ratio of the dynamic excitation current to the anti-phase noise proportional modulus is calculated to obtain the basic excitation degradation rate; Subtracting the magnetic domain reversal asymmetry factor from the preset unit constant yields the asymmetric decay weight; The permeability degradation index is obtained by multiplying the basic excitation degradation rate with the asymmetric attenuation weight.

[0034] The calculation of the core saturation ratio using the dynamic excitation current and the permeability degradation index includes: Perform a square operation on the dynamic excitation current to obtain the dynamic excitation square component; The primary side current measurement value is squared to obtain the square component of the primary side current; The basic square component is obtained by summing the dynamic excitation square component and the primary current square component. The ratio of the dynamic excitation square component to the basic square total is calculated to obtain the basic saturation mapping coefficient; The core saturation ratio is obtained by multiplying the basic saturation mapping coefficient with the permeability degradation index.

[0035] The step of calculating the reference error compensation amplitude based on the core saturation ratio and the dynamic equivalent impedance, and calculating the reference phase offset in conjunction with the virtual reactive power projection, includes: The basic amplitude deviation is obtained by multiplying the measured value of the secondary current with the core saturation ratio. The total series impedance is obtained by summing the dynamic equivalent impedance with the internal resistance of the secondary winding. The ratio of the dynamic equivalent impedance to the total series impedance is calculated to obtain the impedance voltage division ratio. The reference error compensation amplitude is obtained by multiplying the basic amplitude deviation by the impedance voltage division ratio. The virtual reactive power projection is multiplied by the core saturation ratio to obtain the phase shift numerator. The phase shift denominator term is obtained by multiplying the measured voltage value of the energy harvesting circuit with the anti-phase noise proportional modulus. The reference phase offset is obtained by calculating the ratio between the numerator and denominator of the phase offset.

[0036] The process of calculating the dynamic calibration measurement output value using the reference error compensation amplitude and the reference phase offset, calculating the measurement integrity evaluation factor using the core saturation ratio, and selecting to output or block the dynamic calibration measurement output value based on the comparison result of the measurement integrity evaluation factor and the preset measurement reliability safety threshold includes: The dynamic excitation current is multiplied by the reference phase offset to obtain the phase equivalent deduction. The secondary current measurement value is summed with the reference error compensation amplitude, and the phase equivalent deduction is subtracted from it to obtain the dynamic calibration measurement output value; The primary side current measurement value is multiplied by the secondary side winding turns ratio coefficient to obtain the ideal converted value of the primary side. The ratio of the dynamic calibration measurement output value to the primary side ideal conversion value is calculated to obtain the macroscopic transfer accuracy index. Subtracting the core saturation ratio from the preset unit constant yields the microscopic physical health index. The measurement integrity assessment factor is obtained by multiplying the macroscopic transmission accuracy index with the microscopic physical health index. Configure a measurement reliability security threshold, and compare the measurement integrity evaluation factor with the measurement reliability security threshold in terms of numerical values; When the measurement integrity assessment factor is greater than or equal to the measurement reliability safety threshold, the dynamic calibration measurement output value is selected for acceptance and output. When the measurement integrity assessment factor is less than the measurement reliability safety threshold, a blocking operation is performed to intercept the output of the dynamic calibration measurement output value.

[0037] Example 2: A dynamic calibration method for the reference error of an energy-harvesting magnetic core sensor, comprising: Reference Figure 2 , Figure 2 The primary side, secondary side, measurement winding circuit, energy harvesting circuit, and synchronous acquisition relationship of the energy harvesting type magnetic core sensor are shown, which are used to explain the basis for obtaining the primary side current measurement value, secondary side current measurement value, energy harvesting circuit voltage measurement value, and energy harvesting circuit current measurement value; The calculation of the phase noise immunity proportional modulus using the current measurements from the primary and secondary sides and the turns ratio coefficient of the secondary winding, and the calculation of the signal energy reference value in conjunction with the voltage and current measurements from the energy harvesting circuit, includes: The purpose of this step is to bypass traditional zero-crossing detection by constructing a geometric modulus that is not affected by drastic phase fluctuations to avoid mathematical singularities and to calculate the basic signal energy benchmark. A high-frequency current sampling probe is arranged on the primary side of the energy-harvesting magnetic core sensor, and synchronous current and voltage sensors are arranged in the secondary side measurement winding circuit and energy harvesting circuit, respectively. Before performing the calculation, first set the turns ratio coefficient of the secondary winding. The value is determined based on the physical winding ratio of the sensor at the factory. A larger value indicates a higher current reduction ratio, but an excessively large value may result in a very small secondary induced current, thus increasing the impact on the signal-to-noise ratio; an excessively small value will negate the isolation function of the current transformer. The secondary winding turns ratio coefficient is a pre-calibrated same-direction conversion coefficient, used to map the primary side current measurement value and the secondary side current measurement value to the same calculation basis. Obtain primary side current measurement value and secondary current measurement values ; First, calculate the phase noise immunity modulus under the current state, using the following formula:

[0038] In the formula, The phase noise reduction ratio; This is the measured value of the primary side current; This is the measured value of the secondary side current; This refers to the turns ratio coefficient of the secondary winding; Obtain the voltage measurement value of the energy harvesting circuit. and the measured value of the energy harvesting circuit current ; The total apparent power of the energy harvesting circuit is normalized using the phase noise immunity modulus, and the signal energy reference value is calculated using the following formula:

[0039] In the formula, This serves as a reference value for signal energy. This is the measured value of the energy harvesting circuit voltage; This is the measured value of the energy harvesting circuit current; This is the phase noise reduction proportional modulus.

[0040] By configuring synchronous current and voltage acquisition units on the primary and secondary sides of the energy-harvesting magnetic core sensor, and performing summation and square root operations based on the acquired measured values ​​and turns ratio coefficient, a geometric modulus, namely an anti-phase noise proportional modulus, that is unaffected by severe phase fluctuations is constructed. This design effectively avoids the division-by-zero singularity problem that is easily generated by traditional zero-crossing detection in strong electromagnetic environments or in the presence of harmonic interference, and improves the numerical stability of modulus extraction under complex electrical conditions. Then, the total apparent power of the energy harvesting circuit is normalized using this modulus to obtain a signal energy reference value that is unaffected by high-frequency switching noise, providing a stable and reliable energy reference basis for subsequent separation of microscopic interference components.

[0041] Reference Figure 3 , Figure 3 The correlation between the signal energy reference value, virtual reactive power projection, reactive power fluctuation coefficient, magnetic domain inversion asymmetry factor, and dynamic equivalent impedance is shown to illustrate the transmission process of energy harvesting disturbance to nonlinear impedance reconstruction. Figure 3 The working conditions 1 through 5 are respectively steady-state working condition, minor impact, moderate impact, severe impact, and extremely harsh working condition; The calculation of the virtual reactive power projection based on the signal energy reference value, and the calculation of the reactive power fluctuation coefficient in conjunction with the voltage measurement value of the energy harvesting circuit, includes: The purpose of this step is to use the stable energy benchmark determined in step one, combined with the ampere-turn imbalance on the primary and secondary sides, to extract the virtual reactive power projection that does not actually do work. First, calculate the virtual reactive power projection using the following formula:

[0042] In the formula, This is the virtual reactive power projection quantity; This serves as a reference value for signal energy. This is the measured value of the primary side current; This is the measured value of the secondary side current; This refers to the turns ratio coefficient of the secondary winding; Subsequently, the virtual reactive power projection is fused with the secondary side's inherent apparent power to calculate the reactive power fluctuation coefficient, which serves as a weighting factor for subsequent quantization of magnetic domain distortion. The formula is as follows:

[0043] In the formula, This refers to the reactive power fluctuation coefficient. This is the virtual reactive power projection quantity; This is the measured value of the energy harvesting circuit voltage; This is the measured value of the secondary side current.

[0044] The ampere-turn imbalance is obtained by calculating the difference between the currents converted from the primary and secondary sides. This imbalance is then projected onto a pre-established signal energy reference value to extract a specific virtual reactive power projection. This process algebraically separates the illusory power consumption that does not generate actual work but causes energy measurement deviations, thus eliminating its interference with the real energy factor. Subsequently, by calculating the ratio of the inherent apparent power on the secondary side to the total reactive power fluctuation, a reactive power fluctuation coefficient is established. This coefficient can quantitatively reflect the characteristics of violent reactive power fluctuations caused by energy extraction, providing an intermediate correlation index for subsequent quantification of the nonlinear changes in the microstate of the magnetic medium, and realizing the numerical capture of the transient impact characteristics of energy extraction.

[0045] The calculation of the domain reversal asymmetry factor using the reactive power fluctuation coefficient and the secondary winding internal resistance includes: The purpose of this step is to use the reactive power fluctuation characteristics extracted in the previous step, combined with the internal heat loss power of the energy harvesting circuit, to quantify the nonlinear magnetic domain disturbance of the magnetic core caused by high-frequency switching. Before performing this step of the calculation, first obtain the inherent parameters of the equipment, such as the internal resistance of the secondary winding. ; The formula for calculating the domain inversion asymmetry factor is as follows:

[0046] In the formula, It is the domain reversal asymmetry factor; This refers to the reactive power fluctuation coefficient. This is the measured value of the energy harvesting circuit current; This refers to the internal resistance of the secondary winding. This is the measured value of the energy harvesting circuit voltage; This is the measured value of the secondary side current.

[0047] By obtaining the internal resistance of the secondary winding and combining it with the current measurement value of the energy harvesting circuit, the heat loss power of the energy harvesting circuit, which represents the internal heating and loss state, is calculated. Then, the power distortion factor is obtained by using the ratio of this loss power to the inherent apparent power of the secondary side. This factor is then combined with the reactive power fluctuation coefficient obtained earlier to calculate the magnetic domain reversal asymmetry factor, which represents the degree of uneven domain flipping caused by the unidirectional impact of high-frequency switching in the magnetic core. This step directly maps the macroscopic current, voltage and heat loss parameters into quantitative factors that reflect the microscopic magnetic characteristics. This transforms the magnetic domain nonlinear disturbance, which is difficult to measure directly, into a circuit algebraic variable that can be accurately represented in the control algorithm, laying the physical parameter foundation for correcting nonlinear impedance.

[0048] The step of calculating the dynamic equivalent impedance based on the domain reversal asymmetry factor and calculating the equivalent voltage trace value in combination with the secondary winding internal resistance includes: The purpose of this step is to map the captured microscopic magnetic domain reversal state onto the macroscopic circuit impedance characteristics and calculate the dynamic equivalent impedance including nonlinear distortion effects. First, calculate the dynamic equivalent impedance using the following formula:

[0049] In the formula, For dynamic equivalent impedance; This is the measured value of the energy harvesting circuit voltage; This is the measured value of the energy harvesting circuit current; It is the domain reversal asymmetry factor; Then, the equivalent voltage trace value of the secondary system is calculated to prepare for the subsequent derivation of the excitation state. The formula is as follows:

[0050] In the formula, This represents the equivalent voltage trace value; For dynamic equivalent impedance; This is the measured value of the secondary side current; This refers to the internal resistance of the secondary winding. This is the measured value of the current in the energy harvesting circuit.

[0051] By calculating the basic impedance value using the voltage and current measurements of the energy harvesting circuit and multiplying it by a nonlinear adjustment weight composed of a magnetic domain reversal asymmetry factor, the dynamic equivalent impedance that truly reflects the current physical disturbance state is calculated. This operation overcomes the limitation of the traditional classical impedance model failing under transient power impacts due to the assumption of constant impedance, allowing the impedance parameter to adaptively adjust with the decay of magnetic properties. Based on this dynamic equivalent impedance and the winding internal resistance, the voltage drop components of each part in the secondary circuit are calculated and combined to obtain the equivalent voltage trace value, which truly restores the voltage drop law inside the system under nonlinear conditions and ensures the physical rationality of the subsequent excitation state separation derivation.

[0052] Reference Figure 4 , Figure 4 The progressive relationship between dynamic excitation current, permeability degradation index, core saturation ratio, reference error compensation amplitude, dynamic calibration measurement output value, and measurement integrity evaluation factor is shown to illustrate the calibration output and interruption determination process. Figure 4 The normalized index values ​​are used to represent the relative trends of dynamic excitation current, permeability degradation index, and core saturation ratio at different sampling stages, and are not used to limit specific numerical ranges. The calculation of the dynamic excitation current based on the dynamic equivalent impedance and the equivalent voltage trace value, and the calculation of the permeability degradation index in conjunction with the phase noise reduction proportional modulus, includes: The principle of this step is based on Ampere's circuital law and dynamic impedance, which separates the current component that is actually consumed in establishing the magnetic field, and at the same time evaluates the degree of attenuation of the magnetic core's permeability due to energy extraction. First, calculate the dynamic excitation current using the following formula:

[0053] In the formula, It is the dynamic excitation current; This is the measured value of the primary side current; This is the measured value of the secondary side current; This refers to the turns ratio coefficient of the secondary winding; This represents the equivalent voltage trace value; For dynamic equivalent impedance; Next, combining the magnetic domain asymmetry factor, the permeability degradation index, which characterizes the decline in magnetic properties, is calculated, as shown in the following formula:

[0054] In the formula, The permeability degradation index; It is the dynamic excitation current; The phase noise reduction ratio; It is the domain reversal asymmetry factor.

[0055] By using the ratio of the equivalent voltage trace value to the dynamic equivalent impedance as the equivalent magnetizing leakage current and subtracting it from the system's ampere-turn imbalance, the dynamic excitation current component truly used to establish the magnetic field is extracted. This process, based on the objective Ampere's circuital law, eliminates the masking effect of loss current on the excitation analysis. Furthermore, the basic excitation degradation rate is calculated using the dynamic excitation current and the anti-phase noise proportional modulus, and combined with the attenuation weight derived from the domain reversal asymmetry factor, the permeability degradation index, which characterizes the overall magnetic permeability degradation of the magnetic core, is calculated. Thus, without intruding into the internal physical structure of the sensor, online quantitative evaluation of the dynamic magnetic permeability characteristics of the magnetic core is achieved through algebraic reconstruction of external port parameters.

[0056] The calculation of the core saturation ratio using the dynamic excitation current and the permeability degradation index includes: The purpose of this step is to perform energy state mapping on the degraded permeability and excitation current, and to calculate the nonlinear ratio of the current magnetic core approaching the saturation state. The formula for calculating the core saturation ratio is as follows:

[0057] In the formula, This refers to the core saturation ratio. It is the dynamic excitation current; This is the measured value of the primary side current; is the permeability degradation index.

[0058] By performing squaring operations on the primary current and the separated dynamic excitation current respectively, a basic total square is constructed. The basic saturation mapping coefficient is then calculated by the proportion of the dynamic excitation square component in the basic total. This operation transforms the energy-magnetic field conversion relationship into a proportional structure that reflects the approaching saturation trend. Subsequently, this mapping coefficient is combined with the permeability degradation index to calibrate the core saturation ratio that comprehensively reflects the nonlinear characteristics. This ratio unifies the permeability decay at the micro level with the excitation requirements of the macro circuit, quantitatively giving the degree to which the current core medium approaches the physical saturation limit, and becoming the core mapping index connecting the decay of the underlying physical state and the compensation of the upper-level measurement reference.

[0059] The step of calculating the reference error compensation amplitude based on the core saturation ratio and the dynamic equivalent impedance, and calculating the reference phase offset in conjunction with the virtual reactive power projection, includes: The purpose of this step is to determine the specific amplitude compensation difference and phase offset angle for correcting the sensor measurement reference based on the obtained saturation characteristics and various dynamic parameters. First, calculate the reference error compensation amplitude using the following formula:

[0060] In the formula, The reference error compensation amplitude; This is the measured value of the secondary side current; This refers to the core saturation ratio. For dynamic equivalent impedance; This refers to the internal resistance of the secondary winding. Next, the reference phase offset is calculated using the following formula:

[0061] In the formula, This is the reference phase offset. This is the virtual reactive power projection quantity; This refers to the core saturation ratio. This is the measured value of the energy harvesting circuit voltage; This is the phase noise reduction proportional modulus.

[0062] By multiplying the secondary current measurement, the core saturation ratio, and the impedance voltage division ratio established based on the dynamic equivalent impedance, the reference error compensation amplitude reflecting the amplitude attenuation is calculated. This process accurately allocates the nonlinear disturbance to the amplitude correction stage, avoiding the shortcomings of single static coefficient compensation. At the same time, the phase offset numerator is constructed using the virtual reactive power projection and the core saturation ratio, and the reference phase offset is calculated by using the product term including the anti-phase noise proportional modulus as the denominator. The asymmetric energy component that causes the phase error is converted into a dimensionless radian offset factor, realizing the synchronous and independent quantification extraction of the nonlinear deviation of the amplitude and phase dimensions in the measurement reference under complex dynamic electromagnetic environment.

[0063] The process of calculating the dynamic calibration measurement output value using the reference error compensation amplitude and the reference phase offset, calculating the measurement integrity evaluation factor using the core saturation ratio, and selecting to output or block the dynamic calibration measurement output value based on the comparison result of the measurement integrity evaluation factor and the preset measurement reliability safety threshold includes: The purpose of this step is to use the obtained error compensation amount to correct the original measurement value, calculate the reliability index, and decide whether to accept the calibration result based on it. First, calculate the dynamic calibration measurement output value using the following formula:

[0064] In the formula, For dynamic calibration of measurement output values; This is the measured value of the secondary side current; The reference error compensation amplitude; It is the dynamic excitation current; This is the reference phase offset. Next, the measurement integrity assessment factor is calculated based on the output data, using the following formula:

[0065] In the formula, For measuring integrity assessment factors; For dynamic calibration of measurement output values; This is the measured value of the primary side current; This refers to the turns ratio coefficient of the secondary winding; This refers to the core saturation ratio. Set a measurement reliability safety threshold ; The measurement reliability and security threshold These are pre-calibrated values, representing the minimum integrity level at which dynamic calibration measurement outputs are allowed to be accepted; Finally, based on the comparison between the measurement integrity assessment factor and the measurement reliability safety threshold, a calibration validity determination is performed: when When the calibration is deemed valid, the dynamic calibration measurement output value is accepted and output. when When the calibration circuit is activated, the output of the dynamic calibration measurement value is blocked.

[0066] By using the obtained phase offset to map the phase angle deviation to an equivalent current deduction on the orthogonal axis, and combining the reference error compensation amplitude to correct the secondary current, a dynamic calibration measurement output value is obtained. This completes the pure algebraic dimension error compensation that conforms to physical laws. At the same time, a measurement integrity evaluation factor that correlates macroscopic accuracy and microscopic health is constructed. This evaluation factor is compared with a preset measurement reliability safety threshold. When the magnetic core condition does not exceed the physical limit, a reliable calibration result is output. However, when the evaluation factor falls below the threshold due to deep saturation caused by an impact, a fuse blocking mechanism is executed. This gives the measurement system the self-diagnosis and protection capability against blind overcompensation, improving the safety of the measurement output under extreme and harsh conditions.

[0067] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0068] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A dynamic calibration method for the reference error of an energy-harvesting magnetic core sensor, characterized in that, include: The phase noise immunity proportional modulus is calculated using the current measurements on the primary and secondary sides and the turns ratio coefficient of the secondary winding, and the signal energy reference value is calculated by combining the voltage and current measurements of the energy harvesting circuit. The virtual reactive power projection is calculated based on the signal energy reference value, and the reactive power fluctuation coefficient is calculated in combination with the voltage measurement value of the energy harvesting circuit. The domain reversal asymmetry factor is calculated using the reactive power fluctuation coefficient and the secondary winding internal resistance. The dynamic equivalent impedance is calculated based on the magnetic domain reversal asymmetry factor, and the equivalent voltage trace value is calculated in combination with the internal resistance of the secondary winding. The dynamic excitation current is calculated based on the dynamic equivalent impedance and the equivalent voltage trace value, and the permeability degradation index is calculated in combination with the phase noise proportional modulus. The core saturation ratio is calculated using the dynamic excitation current and the permeability degradation index. The reference error compensation amplitude is calculated based on the core saturation ratio and the dynamic equivalent impedance, and the reference phase offset is calculated in combination with the virtual reactive power projection. The dynamic calibration measurement output value is calculated using the reference error compensation amplitude and the reference phase offset. The measurement integrity evaluation factor is calculated by combining the magnetic core saturation ratio. Based on the comparison result between the measurement integrity evaluation factor and the preset measurement reliability safety threshold, the dynamic calibration measurement output value is selected to be output or blocked.

2. The dynamic calibration method for the reference error of an energy-harvesting magnetic core sensor according to claim 1, characterized in that, The calculation of the phase noise immunity proportional modulus using the current measurements from the primary and secondary sides and the turns ratio coefficient of the secondary winding, and the calculation of the signal energy reference value in conjunction with the voltage and current measurements from the energy harvesting circuit, includes: A current acquisition unit is configured on the primary side of the energy-harvesting magnetic core sensor, and synchronous current acquisition units and voltage acquisition units are configured in the measurement winding circuit and energy harvesting circuit on the secondary side, respectively. Obtain the turns ratio of the secondary winding, the measured value of the primary current, and the measured value of the secondary current. The measured value of the secondary current is multiplied by the turns ratio coefficient of the secondary winding to obtain the converted current value of the secondary side. The primary side current measurement value and the secondary side equivalent current value are squared and summed, and the square root operation is performed on the summation result to extract the anti-phase noise proportional modulus. Obtain the measured values ​​of the voltage and current in the energy harvesting circuit; The apparent power of the energy harvesting circuit is obtained by multiplying the measured voltage value of the energy harvesting circuit with the measured current value of the energy harvesting circuit. The signal energy reference value is obtained by calculating the ratio of the apparent power of the energy harvesting circuit to the phase noise immunity proportional modulus.

3. The dynamic calibration method for the reference error of an energy-harvesting magnetic core sensor according to claim 2, characterized in that, The calculation of the virtual reactive power projection based on the signal energy reference value, and the calculation of the reactive power fluctuation coefficient in conjunction with the voltage measurement value of the energy harvesting circuit, includes: The difference between the measured value of the primary current and the converted value of the secondary current is calculated to obtain the ampere-turn imbalance between the primary and secondary sides. The virtual reactive power projection is obtained by multiplying the ampere-turn imbalance with the signal energy reference value. The intrinsic apparent power on the secondary side is obtained by multiplying the measured voltage value of the energy harvesting circuit with the measured current value on the secondary side. The total basic reactive power fluctuation is obtained by summing the virtual reactive power projection and the secondary side inherent apparent power. The reactive power fluctuation coefficient is obtained by calculating the ratio between the virtual reactive power projection and the basic reactive power fluctuation.

4. The dynamic calibration method for the reference error of an energy-harvesting magnetic core sensor according to claim 3, characterized in that, The calculation of the domain reversal asymmetry factor using the reactive power fluctuation coefficient and the secondary winding internal resistance includes: Obtain the internal resistance of the secondary winding; The measured current value of the energy harvesting circuit is squared, and the result is multiplied with the internal resistance of the secondary winding to obtain the heat loss power of the energy harvesting circuit. The power distortion factor is obtained by calculating the ratio of the heat loss power of the energy harvesting circuit to the inherent apparent power of the secondary side. The power distortion factor and the reactive power fluctuation coefficient are multiplied to obtain the domain reversal asymmetry factor.

5. The dynamic calibration method for the reference error of an energy-harvesting magnetic core sensor according to claim 4, characterized in that, The step of calculating the dynamic equivalent impedance based on the domain reversal asymmetry factor and calculating the equivalent voltage trace value in combination with the secondary winding internal resistance includes: The base impedance value is obtained by calculating the ratio of the measured voltage value of the energy harvesting circuit to the measured current value of the energy harvesting circuit. The nonlinear adjustment weight is obtained by adding the magnetic domain reversal asymmetry factor to a preset unit constant. The dynamic equivalent impedance is obtained by multiplying the base impedance value with the nonlinear adjustment weight. The first voltage drop component is obtained by multiplying the dynamic equivalent impedance with the measured secondary current value. The second voltage drop component is obtained by multiplying the internal resistance of the secondary winding with the measured current of the energy extraction circuit. The equivalent voltage trace value is obtained by summing the first voltage drop component and the second voltage drop component.

6. The dynamic calibration method for the reference error of an energy-harvesting magnetic core sensor according to claim 5, characterized in that, The calculation of the dynamic excitation current based on the dynamic equivalent impedance and the equivalent voltage trace value, and the calculation of the permeability degradation index in conjunction with the phase noise reduction proportional modulus, includes: The equivalent excitation leakage current is obtained by calculating the ratio of the equivalent voltage trace value to the dynamic equivalent impedance. The dynamic excitation current is extracted by subtracting the equivalent magnetizing leakage current from the ampere-turn imbalance. The ratio of the dynamic excitation current to the anti-phase noise proportional modulus is calculated to obtain the basic excitation degradation rate; Subtracting the magnetic domain reversal asymmetry factor from the preset unit constant yields the asymmetric decay weight; The permeability degradation index is obtained by multiplying the basic excitation degradation rate with the asymmetric attenuation weight.

7. The dynamic calibration method for the reference error of an energy-harvesting magnetic core sensor according to claim 6, characterized in that, The calculation of the core saturation ratio using the dynamic excitation current and the permeability degradation index includes: Perform a square operation on the dynamic excitation current to obtain the dynamic excitation square component; The primary side current measurement value is squared to obtain the square component of the primary side current; The basic square component is obtained by summing the dynamic excitation square component and the primary current square component. The ratio of the dynamic excitation square component to the basic square total is calculated to obtain the basic saturation mapping coefficient; The core saturation ratio is obtained by multiplying the basic saturation mapping coefficient with the permeability degradation index.

8. The dynamic calibration method for the reference error of an energy-harvesting magnetic core sensor according to claim 7, characterized in that, The step of calculating the reference error compensation amplitude based on the core saturation ratio and the dynamic equivalent impedance, and calculating the reference phase offset in conjunction with the virtual reactive power projection, includes: The basic amplitude deviation is obtained by multiplying the measured value of the secondary current with the core saturation ratio. The total series impedance is obtained by summing the dynamic equivalent impedance with the internal resistance of the secondary winding. The ratio of the dynamic equivalent impedance to the total series impedance is calculated to obtain the impedance voltage division ratio. The reference error compensation amplitude is obtained by multiplying the basic amplitude deviation by the impedance voltage division ratio. The virtual reactive power projection is multiplied by the core saturation ratio to obtain the phase shift numerator. The phase shift denominator term is obtained by multiplying the measured voltage value of the energy harvesting circuit with the anti-phase noise proportional modulus. The reference phase offset is obtained by calculating the ratio between the numerator and denominator of the phase offset.

9. The dynamic calibration method for the reference error of an energy-harvesting magnetic core sensor according to claim 8, characterized in that, The process of calculating the dynamic calibration measurement output value using the reference error compensation amplitude and the reference phase offset, calculating the measurement integrity evaluation factor using the core saturation ratio, and selecting to output or block the dynamic calibration measurement output value based on the comparison result of the measurement integrity evaluation factor and the preset measurement reliability safety threshold includes: The dynamic excitation current is multiplied by the reference phase offset to obtain the phase equivalent deduction. The secondary current measurement value is summed with the reference error compensation amplitude, and the phase equivalent deduction is subtracted from it to obtain the dynamic calibration measurement output value; The primary side current measurement value is multiplied by the secondary side winding turns ratio coefficient to obtain the ideal converted value of the primary side. The ratio of the dynamic calibration measurement output value to the primary side ideal conversion value is calculated to obtain the macroscopic transfer accuracy index. Subtracting the core saturation ratio from the preset unit constant yields the microscopic physical health index. The measurement integrity assessment factor is obtained by multiplying the macroscopic transmission accuracy index with the microscopic physical health index. Configure a measurement reliability security threshold, and compare the measurement integrity evaluation factor with the measurement reliability security threshold in terms of numerical values; When the measurement integrity assessment factor is greater than or equal to the measurement reliability safety threshold, the dynamic calibration measurement output value is selected for acceptance and output. When the measurement integrity assessment factor is less than the measurement reliability safety threshold, a blocking operation is performed to intercept the output of the dynamic calibration measurement output value.

Images