A high-efficiency and high-precision compensation system and method for intelligent mutual inductor

Abstract

The present invention discloses an efficient and high-precision compensation system and method for an intelligent mutual inductor, and specifically relates to the technical field of mutual inductors. The system generates a hysteresis loop diagram by collecting magnetic flux density and magnetic field strength data of the mutual inductor under different current and voltage change rates, extracts hysteresis loop width, residual magnetism and coercive force characteristics, and establishes a hysteresis effect error model. The system identifies harmonic distortion by combining spectrum analysis, predicts the degree of harmonic distortion in a high-frequency environment, comprehensively analyzes the hysteresis effect lag and harmonic distortion, and evaluates the accuracy of the compensation algorithm in processing nonlinear errors. The system finally divides the compensation signal into two categories, high accuracy and low accuracy, directly outputs the high-accuracy signal, and performs error correction on the low-accuracy signal before outputting it, thereby effectively solving the problem that conventional compensation algorithms cannot correct nonlinear errors, and improving the measurement accuracy of the mutual inductor in extreme environments and the operational stability of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of data visualization, and in particular to a high-efficiency and high-precision compensation system and method for an intelligent mutual inductor. Background Art

[0002] Efficient, high-precision compensation for intelligent transformers uses intelligent algorithms and digital signal processing to dynamically adjust and compensate for transformer errors under varying operating conditions, thereby improving measurement accuracy. Since transformers are affected by a variety of factors during operation, such as the environment, load, and temperature, which can cause deviations between the output signal and the actual signal, the compensation function of intelligent transformers effectively corrects these errors, ensuring data accuracy and reliability.

[0003] Furthermore, efficient and high-precision compensation not only improves the measurement accuracy of instrument transformers but also enhances their operating efficiency. Through real-time monitoring and adjustment, intelligent instrument transformers can reduce energy loss and resource waste without increasing hardware complexity, thereby achieving more energy-efficient operation. This compensation technology is widely used in power systems to help ensure stable transmission and distribution of electrical energy.

[0004] The existing technology has the following shortcomings:

[0005] In extreme electromagnetic environments or high-frequency, high-voltage operating scenarios, instrument transformers may produce severe nonlinear errors that cannot be effectively corrected using conventional compensation algorithms. For example, the transformer's core may saturate under extreme conditions, exacerbating the nonlinear response of the induced signal. Intelligent algorithms are typically based on linear models or assume that transformer errors are predictable, but nonlinear errors are often difficult to accurately model. If the system fails to account for these nonlinear characteristics, the compensation algorithm may make incorrect adjustments, causing the transformer output signal to become even more distorted. Furthermore, long-term error accumulation can even cause equipment failure or unstable power system operation, especially in environments with high precision requirements, such as power dispatch centers or automation systems for protection equipment. Summary of the Invention

[0006] The object of the present invention is to provide a high-efficiency and high-precision compensation system and method for an intelligent mutual inductor, so as to solve the deficiencies in the background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a high-efficiency and high-precision compensation method for an intelligent mutual inductor, comprising the following steps:

[0008] S1: Under different current and voltage change rates, collect the magnetic flux density and magnetic field strength data of the transformer and generate a hysteresis loop diagram;

[0009] S2: Based on the hysteresis loop diagram, the hysteresis loop width characteristics, remanence and coercive force characteristics in the hysteresis loop are extracted respectively, and the hysteresis effect error model of the mutual inductor is established. The hysteresis of the hysteresis effect is determined based on the output results of the model;

[0010] S3: Use digital signal processing to perform spectrum analysis on the output signal of the transformer, identify the harmonic distortion of different frequency components in the signal, and predict the degree of harmonic distortion of the output signal of the transformer in a high-frequency environment;

[0011] S4: Comprehensively analyze the hysteresis effect and the degree of harmonic distortion of the transformer output signal in a high-frequency environment to evaluate the signal accuracy of the compensation algorithm when dealing with the transformer nonlinear error;

[0012] S5: Divide the signal accuracy of the compensation algorithm when dealing with the nonlinear error of the transformer into different levels, namely, high-accuracy signals and low-accuracy signals. The high-accuracy signal is directly used as the final output signal of the transformer, and the low-accuracy signal is output after error correction.

[0013] Preferably, in S2, according to the hysteresis loop diagram, the hysteresis loop width characteristics and the remanence and coercive force characteristics in the hysteresis loop are respectively extracted, a hysteresis effect error model of the mutual inductor is established, and the hysteresis of the hysteresis effect is determined according to the model output result, specifically:

[0014] The hysteresis loop width indicates the degree of hysteresis between the magnetic field intensity H and the magnetic flux density B during the magnetization and demagnetization process of the transformer. It is calculated by measuring the difference in magnetic field intensity when the magnetic flux density is zero.

[0015] In the hysteresis loop diagram, find the point where the magnetic flux density B = 0. At B = 0, record the positive and negative magnetic field strengths, denoted as Hpositive and Hnegative, respectively. Calculate the expression: Wh = Hpositive - Hnegative. Where: Wh is the width of the hysteresis loop, reflecting the hysteresis during magnetization and demagnetization. Hpositive is the positive magnetic field strength when the magnetic flux density is zero, and Hnegative is the negative magnetic field strength when the magnetic flux density is zero.

[0016] Remanence is the value of magnetic flux density B when magnetic field strength H=0, indicating the magnetic flux retained in the transformer core after demagnetization. In the hysteresis loop diagram, find the point where magnetic field strength H=0 and record the magnetic flux density value at this time, recorded as Br. The calculation expression is: Br=B(H=0); where Br is remanence, indicating the magnetic flux density in the magnetization loop when the magnetic field strength is zero;

[0017] Coercive force is the reverse magnetic field strength required to make the magnetic flux density B = 0, which indicates the magnetic field strength required by the transformer core to eliminate residual magnetism. In the hysteresis loop diagram, find the point where the magnetic flux density B = 0 and record the corresponding reverse magnetic field strength, recorded as Hc. The calculation expression is: Hc = |H(B = 0)|; where Hc is the coercive force, which indicates the reverse magnetic field strength required to return the magnetic flux density to zero;

[0018] The hysteresis effect error model reflects the nonlinear error caused by the hysteresis effect in the operation of the transformer. The hysteresis effect error model is established by the hysteresis loop width Wh, remanence Br, and coercive force Hc. The hysteresis effect error is described as the hysteresis relationship between the input magnetic field intensity and the actual output magnetic flux density. Under a given magnetic field intensity H, the error ε caused by the hysteresis effect is mag (H) expression is expressed as: ε mag (H) = α·Wh + β·Br + γ·Hc; where α, β, and γ are unknown coefficients.

[0019] Preferably, in S3, digital signal processing is used to perform spectrum analysis on the output signal of the mutual inductor, identify the harmonic distortion of different frequency components in the signal, and predict the degree of harmonic distortion of the output signal of the mutual inductor in a high-frequency environment, specifically:

[0020] The output signal of the transformer is collected in real time. Before spectrum analysis, the DC component of the collected transformer output signal is eliminated by removing the mean value of the signal. The expression is: Where x(t) is the original signal and N is the number of signal samples. Applying a window function to the signal enhances the resolution of the spectrum within a limited sampling length. The expression is: windowed =x(t)·w(t); where w(t) is the window function; the preprocessed time domain signal x(t) is subjected to a fast Fourier transform operation to obtain a frequency domain signal X(f), which is expressed as: X(k) is the kth discrete frequency component in the frequency domain, N is the total number of sampling points, k represents different frequency components, x(n) is the nth sampling point in the time domain signal, is the kernel function of Fourier transform. After obtaining frequency domain data through fast Fourier transform operation, a spectrum diagram is drawn with the horizontal axis as frequency and the vertical axis as signal amplitude. In the spectrum diagram, the harmonic signal will appear as a peak at an integer multiple of the fundamental frequency f0.

[0021] Through spectrum analysis, the harmonic distortion in the signal is identified and quantified. It is expressed as the ratio of the sum of the squares of all harmonic components in the signal to the square of the fundamental component. The expression is: Where THD is the total harmonic distortion, V1 is the amplitude of the fundamental frequency, and V8, V9, ... are the amplitudes of each harmonic.

[0022] Preferably, the harmonic distortion degree of the output signal of the mutual inductor in a high-frequency environment is predicted to generate a harmonic distortion anomaly index, and the method for obtaining the harmonic distortion anomaly index is:

[0023] Select the wavelet basis function and perform multi-scale wavelet decomposition on the harmonic distortion data to obtain detail coefficients and approximate coefficients at different scales. Use the detail coefficients and approximate coefficients to reconstruct the signal and obtain the reconstructed signal at different scales. The specific calculation expression is: Among them, AX j％,- and DX j,- are the reconstructed approximate coefficient and detail coefficient respectively, and BX(t) is the reconstructed signal. The harmonic distortion anomaly index is calculated based on the reconstructed signal. By calculating the energy change of the detail coefficient at different scales, the variation of the output signal of the mutual inductor is obtained. The energy of the detail coefficient at each scale is calculated. The specific calculation expression is: E j =∑ - AD j,- A 8 Among them, E j is the energy of the jth layer. The harmonic distortion anomaly index is calculated based on the energy changes at different scales. The specific calculation expression is: Where QV is the harmonic distortion anomaly index.

[0024] Preferably, in S4, the hysteresis of the hysteresis effect and the degree of harmonic distortion of the output signal of the transformer in a high-frequency environment are comprehensively analyzed, specifically:

[0025] The hysteresis effect and the harmonic distortion anomaly index are converted into the first eigenvector, and the first eigenvector is used as the input of the machine learning model. The machine learning model uses each group of first eigenvectors to predict the signal accuracy value label of the compensation algorithm when responding to the nonlinear error of the mutual inductor as the prediction target, and takes minimizing the sum of the prediction errors of the signal accuracy value labels of all compensation algorithms when responding to the nonlinear error of the mutual inductor as the training target. The machine learning model is trained until the sum of the prediction errors reaches convergence, and the model training is stopped. The signal accuracy value of the compensation algorithm when responding to the nonlinear error of the mutual inductor is determined according to the model output result, wherein the machine learning model is a polynomial regression model.

[0026] Preferably, in S5, the signal accuracy of the compensation algorithm in dealing with the nonlinear error of the mutual inductor is divided into different levels, namely, high-accuracy signals and low-accuracy signals, specifically:

[0027] The obtained signal accuracy value of the compensation algorithm in response to the nonlinear error of the mutual inductor is compared with a preset reference threshold value of the signal accuracy value. If the signal accuracy value of the compensation algorithm in response to the nonlinear error of the mutual inductor is greater than or equal to the reference threshold value of the signal accuracy value, it means that the signal accuracy of the compensation algorithm in response to the nonlinear error of the mutual inductor is high. In this case, no early warning signal is generated, and the signal of the compensation algorithm in response to the nonlinear error of the mutual inductor is classified as a high-accuracy signal, which is directly used as the final output signal of the mutual inductor;

[0028] If the signal accuracy value when the compensation algorithm responds to the nonlinear error of the transformer is less than the reference threshold value of the signal accuracy value, it means that the signal accuracy when the compensation algorithm responds to the nonlinear error of the transformer is low. At this time, a warning signal is generated, and the signal when the compensation algorithm responds to the nonlinear error of the transformer is divided into a low-accuracy signal, and the error is corrected before output.

[0029] The present invention also provides an efficient and high-precision compensation system for an intelligent mutual inductor, comprising a data acquisition module, a hysteresis effect modeling module, a spectrum analysis and harmonic detection module, a comprehensive error analysis and evaluation module, and a signal classification and output module;

[0030] Data acquisition module: collects the magnetic flux density and magnetic field strength data of the transformer under different current and voltage change rates and generates a hysteresis loop diagram;

[0031] Hysteresis effect modeling module: Based on the hysteresis loop diagram, the hysteresis loop width characteristics, remanence and coercive force characteristics in the hysteresis loop are extracted respectively, and the hysteresis effect error model of the mutual inductor is established. The hysteresis of the hysteresis effect is determined based on the model output results;

[0032] Spectrum analysis and harmonic detection module: Uses digital signal processing to perform spectrum analysis on the output signal of the transformer, identifies the harmonic distortion of different frequency components in the signal, and predicts the degree of harmonic distortion of the output signal of the transformer in a high-frequency environment;

[0033] Comprehensive error analysis and evaluation module: This module comprehensively analyzes the hysteresis effect and the degree of harmonic distortion of the transformer's output signal in a high-frequency environment, and evaluates the signal accuracy of the compensation algorithm when dealing with transformer nonlinear errors.

[0034] Signal classification and output module: The signal accuracy when the compensation algorithm responds to the nonlinear error of the transformer is divided into different levels, namely high-accuracy signals and low-accuracy signals. The high-accuracy signal is directly used as the final output signal of the transformer, and the low-accuracy signal is output after error correction.

[0035] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0036] 1. The present invention collects the magnetic flux density and magnetic field strength data of the transformer under different current and voltage change rates, extracts the hysteresis effect characteristics in combination with the hysteresis loop diagram, establishes an error model, and evaluates the hysteresis of the transformer; and uses digital signal processing technology to perform spectrum analysis on the transformer output signal to identify and predict harmonic distortion. Through a comprehensive analysis of the hysteresis of the hysteresis effect and the harmonic distortion anomaly index, a machine learning model is used to evaluate the signal accuracy of the compensation algorithm, thereby achieving effective correction of high-frequency nonlinear errors. According to the accuracy of the compensation algorithm, the signal is divided into a high-accuracy signal and a low-accuracy signal. The high-accuracy signal is directly output, and the low-accuracy signal is output after error correction.

[0037] 2. This invention effectively addresses the inability of conventional compensation algorithms in existing technologies to address transformer nonlinearity errors. This significantly improves the accuracy and stability of transformer output signals, particularly in high-frequency and extreme electromagnetic environments, and reduces the risk of system instability caused by error accumulation. Through adaptive optimization using a machine learning model, the adaptability of the compensation algorithm is further enhanced, ensuring long-term reliable operation of the system under complex operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0039] Figure 1 Flow chart of the method of the present invention.

[0040] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0041] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0042] Example 1, please refer to Figure 1 and Figure 2 As shown, the efficient and high-precision compensation method for an intelligent mutual inductor described in this embodiment includes the following steps:

[0043] S1: Under different current and voltage change rates, collect the magnetic flux density and magnetic field strength data of the transformer and generate a hysteresis loop diagram;

[0044] S2: Based on the hysteresis loop diagram, the hysteresis loop width characteristics, remanence and coercive force characteristics in the hysteresis loop are extracted respectively, and the hysteresis effect error model of the mutual inductor is established. The hysteresis of the hysteresis effect is determined based on the output results of the model;

[0045] S3: Use digital signal processing to perform spectrum analysis on the output signal of the transformer, identify the harmonic distortion of different frequency components in the signal, and predict the degree of harmonic distortion of the output signal of the transformer in a high-frequency environment;

[0046] S4: Comprehensively analyze the hysteresis effect and the degree of harmonic distortion of the transformer output signal in a high-frequency environment to evaluate the signal accuracy of the compensation algorithm when dealing with the transformer nonlinear error;

[0047] S5: Divide the signal accuracy of the compensation algorithm when dealing with the nonlinear error of the transformer into different levels, namely, high-accuracy signals and low-accuracy signals. The high-accuracy signal is directly used as the final output signal of the transformer, and the low-accuracy signal is output after error correction.

[0048] Among them, in S1, under different current and voltage change rates, the magnetic flux density and magnetic field strength data of the mutual inductor are collected to generate a hysteresis loop diagram, specifically:

[0049] The selected transformer to be tested should have operating conditions that cover different current and voltage change rates to ensure that the experimental results have wide applicability. A high-precision power supply is used to provide variable voltage and current. The power supply should be able to operate at different frequencies and voltages and support voltage and current changes at different rates. Sensors include: Current sensor: used to monitor the input current changes of the transformer in real time. Magnetic flux density sensor (Hall sensor): placed on or around the core of the transformer to measure magnetic flux density (B). The Hall sensor can sense changes in the transformer's magnetic field and thus obtain magnetic flux information. Magnetic field strength sensor (current clamp, BH curve tester): used to measure the transformer's magnetic field strength (H). The current clamp is usually connected to the excitation power supply to monitor the magnetic field strength in the transformer's core. Collect sensor data in real time and transmit the data to a computer for analysis. The data acquisition system should support a high sampling rate to ensure accurate capture of magnetic field characteristics under rapidly changing voltage and current conditions.

[0050] Set the operating current and voltage ranges of the transformer based on the actual application scenario. Common voltage change rates can range from 0.1V / s to several hundred V / s, and current change rates can range from low frequencies (e.g., 50Hz) to high frequencies (e.g., several thousand Hz). The current and voltage change rates should cover conditions from normal operating conditions to extreme operating conditions to fully evaluate the hysteresis effect of the transformer. Before beginning the experiment, calibrate all sensors to ensure the accuracy of equipment such as Hall effect sensors and current clamps, eliminate system noise interference, and ensure measurement accuracy.

[0051] Gradually apply current and voltage of varying amplitudes, controlling the rate of change of voltage and current. For example, start with a low voltage and increase it at a set rate, gradually applying it to a high voltage, and record the corresponding changes in magnetic field strength and magnetic flux density. Start with a lower voltage and current rate of change (such as low frequency and low voltage), apply a smaller magnetic field strength, and record the relationship between magnetic field strength and magnetic flux density. During this period, the transformer core generally does not experience significant saturation, and the measured data is relatively linear. Gradually increase the voltage and current amplitude and rate of change until the transformer core gradually approaches or enters magnetic saturation. At this point, the relationship between magnetic field strength and magnetic flux density becomes nonlinear, and hysteresis effects become more pronounced. To generate a complete hysteresis loop, apply voltage and current in a cycle of positive and negative cycles. For example, apply a positive voltage and gradually decrease it, then apply a negative voltage, and repeat this process, collecting data to generate a closed hysteresis loop. At each current and voltage rate of change, collect magnetic field strength (H) and magnetic flux density (B) data and record them at a high sampling rate. When the voltage and current are switched between positive and negative, accurate recording of each sampling point is ensured to form a complete magnetization loop.

[0052] The magnetic field strength and flux density data collected during the experiment may contain noise and require preprocessing. Common preprocessing steps include: Filtering: Using digital filtering techniques (such as low-pass filtering or Kalman filtering) to remove high-frequency noise and ensure smoother data. Correcting for zero drift or offset in the data to ensure that the starting point of the measurement is consistent with the reference value. Because the collected data points may be sparse at certain voltage change rates, interpolation algorithms (such as linear interpolation and spline interpolation) can be used to supplement intermediate points to generate a smoother curve.

[0053] The processed magnetic field strength (H) and magnetic flux density (B) data are visualized to generate a hysteresis loop. This loop should be a closed, circular curve, representing the relationship between magnetic field strength and magnetic flux density during the transformer's magnetization and demagnetization processes. During the magnetization process, as the current or voltage gradually increases, the magnetic flux density increases with the magnetic field strength, forming the positive half of the hysteresis loop. As the current or voltage gradually decreases, the relationship between magnetic flux density and magnetic field strength lags, forming the negative half of the hysteresis loop.

[0054] The characteristic parameters of the hysteresis effect of the transformer are extracted from the generated hysteresis loop diagram, including: Hysteresis loop width: measures the width of the hysteresis loop, reflecting the energy loss in the iron core. The wider the width, the more severe the hysteresis effect. Remanence (Br): When the magnetic field strength (H) is reduced to zero, the remaining value of the magnetic flux density in the loop represents the hysteresis characteristics of the hysteresis effect. Coercive force (Hc): The reverse magnetic field strength required to return the magnetic flux density to zero, reflecting the closure of the hysteresis loop. The magnetic saturation of the transformer core is evaluated by observing the change in the shape of the loop. When the core is saturated, the top of the loop will tend to be flat, indicating that the magnetic flux density no longer increases linearly with the magnetic field strength. This saturation phenomenon will affect the accuracy of the transformer.

[0055] Repeat the above steps to generate multiple sets of hysteresis loops under different frequency (e.g., 50 Hz, 1 kHz) and load (e.g., light load, heavy load) conditions. The hysteresis effect may exhibit different characteristics under different frequency and load conditions, and the shape and width of the loop will vary. Compare the loop plots under different conditions to analyze the differences in the hysteresis effect of the transformer under various operating conditions. The hysteresis effect is generally more pronounced at higher frequencies and heavier loads.

[0056] The collected raw data and processed hysteresis loops are saved for subsequent compensation algorithm input and system optimization. Based on the experimental results, an analysis report is generated, detailing the hysteresis characteristics of the transformer at different current and voltage change rates, as well as the magnetic saturation conditions. This data can be used in subsequent compensation algorithm design and nonlinear error correction.

[0057] In this application, the magnetic flux density and magnetic field strength data of the transformer are collected at different current and voltage change rates. This allows the generation of accurate hysteresis loops and the extraction of key parameters. This data not only helps evaluate the hysteresis effect and nonlinear characteristics of the transformer, but also provides basic data for the subsequent compensation algorithm design, ensuring that the system can effectively cope with complex operating conditions.

[0058] S2: According to the hysteresis loop diagram, the hysteresis loop width characteristics and the remanence and coercive force characteristics in the hysteresis loop are extracted respectively, and the hysteresis effect error model of the mutual inductor is established. The hysteresis of the hysteresis effect is determined according to the output results of the model.

[0059] The hysteresis loop width indicates the lag between the magnetic field intensity H and the magnetic flux density B during the magnetization and demagnetization processes of the transformer and is an important characteristic of the hysteresis effect. The hysteresis loop width can be calculated by measuring the difference in magnetic field intensity when the magnetic flux density is zero.

[0060] In the hysteresis loop diagram, find the point where the magnetic flux density B = 0. When B = 0, record the positive and negative magnetic field strengths, respectively recorded as Hpositive and Hnegative, and calculate the expression: Wh = Hpositive - Hnegative; where: Wh is the width of the hysteresis loop, reflecting the hysteresis in the magnetization and demagnetization process, Hpositive is the positive magnetic field strength when the magnetic flux density is zero, and Hnegative is the negative magnetic field strength when the magnetic flux density is zero.

[0061] Remanence is the value of magnetic flux density B when magnetic field strength H = 0. It represents the magnetic flux retained in the transformer core after demagnetization. In the hysteresis loop diagram, find the point where magnetic field strength H = 0 and record the magnetic flux density value at this time, recorded as Br. The calculation expression is: Br = B(H = 0); where Br is remanence, which represents the magnetic flux density in the magnetization loop when the magnetic field strength is zero.

[0062] Coercive force is the reverse magnetic field strength required to reduce magnetic flux density B to 0. It represents the magnetic field strength required to eliminate residual magnetism in the transformer core and is an important characteristic of the hysteresis effect. In the hysteresis loop, find the point where magnetic flux density B = 0 and record the corresponding reverse magnetic field strength, denoted as Hc. The calculation expression is: Hc = |H(B = 0)|; where Hc is the coercive force, which represents the reverse magnetic field strength required to reduce magnetic flux density to zero.

[0063] The hysteresis effect error model reflects the nonlinear error caused by the hysteresis effect in the operation of the transformer. The hysteresis effect error model is established by the hysteresis loop width Wh, remanence Br, and coercive force Hc. The hysteresis effect error is described as the hysteresis relationship between the input magnetic field intensity and the actual output magnetic flux density. Under a given magnetic field intensity H, the error ε caused by the hysteresis effect is mag (H) expression is expressed as: ε mag (H) = α·Wh + β·Br + γ·Hc; where α, β, and γ are unknown coefficients determined by fitting experimental data and reflect the contribution of different parameters to the hysteresis error.

[0064] Through multiple experimental data collection, Wh, Br, and Hc are calculated for different currents and voltage rates of change. Combined with the actual measured transformer output signal errors, data fitting methods (such as the least squares method) are used to determine the coefficients α, β, and γ. The fitting steps are as follows: Experimental Data Collection: Wh, Br, and Hc are collected under multiple operating conditions along with the actual measured transformer output errors. Multiple regression or least squares methods are used to fit the error formula and solve for α, β, and γ. The fitting process is repeatedly adjusted to ensure the model accurately predicts errors under various conditions.

[0065] The error model can be used to determine the hysteresis effect of the transformer, that is, the influence of the hysteresis effect on the output signal of the transformer under different working conditions.

[0066] Hysteresis effect τ mag By estimating the maximum error of the error model, the effect of hysteresis on the output under different current and voltage changes is calculated. The expression is: mag =maxLε mag (H)M; compare the calculated hysteresis effect with the preset hysteresis reference threshold of the hysteresis effect. If the hysteresis of the hysteresis effect is greater than or equal to the hysteresis reference threshold, it means that the hysteresis of the hysteresis effect is serious, and a hysteresis effect hysteresis signal is generated at this time; if the hysteresis of the hysteresis effect is less than the hysteresis reference threshold, it means that the hysteresis of the hysteresis effect is not serious, and no hysteresis effect hysteresis signal is generated at this time.

[0067] S3: Use digital signal processing to perform spectrum analysis on the output signal of the transformer, identify the harmonic distortion of different frequency components in the signal, and predict the degree of harmonic distortion of the output signal of the transformer in a high-frequency environment.

[0068] The output signal of the transformer is collected in real time. In order to accurately capture the harmonic components of the signal, the sampling frequency fs must satisfy the Nyquist sampling theorem, that is, the sampling frequency is at least twice the highest frequency component of the signal. Generally, the selected sampling frequency should be greater than 10 times the fundamental frequency. For example, if the fundamental frequency of the power system is 50Hz, the sampling frequency should be at least 500Hz, or even higher. In order to obtain a high-resolution spectrum, it is important to collect sufficiently long signal samples. The signal length T is related to the spectrum resolution Δf, and Therefore, the longer the signal acquisition time T, the higher the spectral resolution. When acquiring signals, pay attention to dealing with environmental noise and interfering signals. You can use analog or digital filters (such as low-pass filters) to pre-process the signal to reduce high-frequency noise and interference in the sampled signal.

[0069] Before performing spectrum analysis on the collected transformer output signal, the DC offset must be removed because the signal may contain a DC component to ensure that the spectrum analysis is focused on the AC part. The DC component can be eliminated by removing the mean value of the signal. The expression is: Where x(t) is the original signal and N is the number of signal samples. To reduce spectral leakage effects, a window function can be applied to the signal. Common window functions include Hanning window, Hamming window, and Heyman window. The choice of window function helps to enhance the resolution of the spectrum within a limited sampling length. The expression is: windowed=x(t)·w(t); where w(t) is the window function. The preprocessed time domain signal x(t) is subjected to a fast Fourier transform to obtain the frequency domain signal X(f), which is expressed as: X(k) is the kth discrete frequency component in the frequency domain, N is the total number of sampling points, k represents different frequency components, x(n) is the nth sampling point in the time domain signal, is the kernel function of the Fourier transform. The fast Fourier transform (FFT) is used to obtain the amplitude and phase information of a signal at different frequencies. Harmonic components typically appear at integer multiples of the fundamental frequency f0, such as 2f0 (second harmonic) and 3f0 (third harmonic).

[0070] After obtaining frequency domain data through a Fast Fourier Transform (FFT) operation, a spectrum is plotted with frequency (Hz) on the horizontal axis and signal amplitude (or power density) on the vertical axis. In the spectrum, harmonic signals appear as peaks at integer multiples of the fundamental frequency f0. Typically, the fundamental frequency of a power system is 50Hz or 60Hz, so second, third, and fifth harmonics appear at positions such as 100Hz, 150Hz, and 250Hz.

[0071] Through spectrum analysis, we can identify and quantify the harmonic distortion in the signal. Total harmonic distortion is an important indicator to measure harmonic distortion. It represents the ratio of the sum of the squares of all harmonic components in the signal to the square of the fundamental component. The expression is: Where THD is total harmonic distortion, V1 is the amplitude of the fundamental frequency (50Hz or 60Hz), and V8, V9, ... are the amplitudes of the respective harmonics (such as the second harmonic, the third harmonic, etc.).

[0072] The higher the THD, the more harmonic components in the signal and the more severe the distortion. To identify the frequency components and amplitudes of each specific harmonic, we usually focus on the amplitudes of low-order harmonics (such as the second and third harmonics). These harmonics often have a significant impact on the measurement accuracy of the transformer. The amplitude of each harmonic V Q It can be obtained through spectrum analysis.

[0073] The harmonic distortion degree of the output signal of the transformer in a high-frequency environment is predicted to generate a harmonic distortion anomaly index. The method for obtaining the harmonic distortion anomaly index is as follows:

[0074] Select wavelet basis functions, such as Daubechies (db), Symlets (sym), Coiflets (coif), etc., and perform multi-scale wavelet decomposition on the harmonic distortion data to obtain detail coefficients and approximate coefficients at different scales. The specific calculation expression is: Where, X(t) is the transformer output signal, φ j％,- (t) and ψ j,-(t) are scaling function and wavelet function respectively, A j％,- is the approximation coefficient, D j,- is the detail coefficient;

[0075] The signal is reconstructed using detail coefficients and approximation coefficients to obtain reconstructed signals at different scales. The specific calculation expression is: Among them, AX j％,- and DX j,- are the reconstructed approximate coefficient and detail coefficient respectively, and BX(t) is the reconstructed signal. The harmonic distortion anomaly index is calculated based on the reconstructed signal. By calculating the energy change of the detail coefficient at different scales, the variation of the output signal of the mutual inductor is obtained. The energy of the detail coefficient at each scale is calculated. The specific calculation expression is: E j =∑ - AD j,- A 8 Among them, E j is the energy of the jth layer. The harmonic distortion anomaly index is calculated based on the energy changes at different scales. The specific calculation expression is: Where QV is the harmonic distortion anomaly index.

[0076] A higher HTD index indicates a higher proportion of high-frequency harmonics in the transformer's output signal compared to the total signal energy. This indicates a higher degree of harmonic distortion, particularly at high frequencies, where harmonic distortion can significantly increase, affecting signal quality and measurement accuracy. A high HTD index often indicates the presence of large nonlinear loads or unstable electrical equipment in the system, potentially leading to equipment malfunction, measurement errors, and even power system instability.

[0077] Conversely, a smaller harmonic distortion index indicates a lower level of high-frequency harmonics in the transformer's output signal, resulting in a lower degree of harmonic distortion. In this case, the transformer's output signal is closer to the ideal fundamental signal, resulting in higher measurement accuracy and more stable system operation. This generally indicates that the impact of high-frequency harmonics is negligible, signal quality is good, and the power system is operating normally and stably.

[0078] S4: Comprehensively analyze the hysteresis effect and the degree of harmonic distortion of the transformer's output signal in a high-frequency environment to evaluate the signal accuracy of the compensation algorithm when dealing with the transformer's nonlinear error.

[0079] The hysteresis effect and the harmonic distortion anomaly index are converted into the first eigenvector, and the first eigenvector is used as the input of the machine learning model. The machine learning model uses each group of first eigenvectors to predict the signal accuracy value label of the compensation algorithm when responding to the nonlinear error of the mutual inductor as the prediction target, and takes minimizing the sum of the prediction errors of the signal accuracy value labels of all compensation algorithms when responding to the nonlinear error of the mutual inductor as the training target. The machine learning model is trained until the sum of the prediction errors reaches convergence, and the model training is stopped. The signal accuracy value of the compensation algorithm when responding to the nonlinear error of the mutual inductor is determined according to the model output result, wherein the machine learning model is a polynomial regression model.

[0080] The method for obtaining the signal accuracy value when the compensation algorithm deals with the nonlinear error of the mutual inductor is as follows: from the first eigenvector training data of the trained machine learning model, the corresponding function expression is obtained: CR = FUτ mag , QVV; where F is the output function of the model, τ mag is the hysteresis of the hysteresis effect, QV is the harmonic distortion anomaly index, and CR is the signal accuracy value when the compensation algorithm deals with the nonlinear error of the transformer.

[0081] S5: Divide the signal accuracy of the compensation algorithm when dealing with the nonlinear error of the transformer into different levels, namely, high-accuracy signals and low-accuracy signals. The high-accuracy signal is directly used as the final output signal of the transformer, and the low-accuracy signal is output after error correction.

[0082] The obtained signal accuracy value of the compensation algorithm when responding to the nonlinear error of the transformer is compared with the pre-set reference threshold of the signal accuracy value. If the signal accuracy value of the compensation algorithm when responding to the nonlinear error of the transformer is greater than or equal to the reference threshold of the signal accuracy value, it means that the signal accuracy of the compensation algorithm when responding to the nonlinear error of the transformer is high. At this time, no early warning signal is generated, and the signal of the compensation algorithm when responding to the nonlinear error of the transformer is classified as a high-accuracy signal, which is directly used as the final output signal of the transformer; if the signal accuracy value of the compensation algorithm when responding to the nonlinear error of the transformer is less than the reference threshold of the signal accuracy value, it means that the signal accuracy of the compensation algorithm when responding to the nonlinear error of the transformer is low. At this time, a early warning signal is generated, and the signal of the compensation algorithm when responding to the nonlinear error of the transformer is classified as a low-accuracy signal, which is output after error correction.

[0083] When the transformer's output signal is evaluated as highly accurate, its error is within the allowable range and meets the predetermined accuracy standard. In this case, no further processing or correction is required for the signal; it can be directly transmitted as the transformer's final output signal to the power system's control center or monitoring equipment. This processing method ensures rapid signal transmission, reduces unnecessary delays, and maintains efficient system operation and accurate measurement, especially in scenarios requiring real-time response, such as power dispatching or automated control systems.

[0084] When the output signal of the transformer is evaluated as low accuracy, the signal error exceeds the set tolerance range and must be corrected before output to prevent the erroneous signal from affecting the safety and stability of the power system. The error correction process includes the following steps:

[0085] Signal Analysis: First, perform a detailed analysis of the low-accuracy signal to identify the source of the error. Common error types include: Harmonic distortion: The presence of high-order harmonic components causes severe signal distortion.

[0086] Hysteresis effect: The transformer core saturates under high load or high voltage conditions, resulting in increased nonlinear errors.

[0087] Noise interference: Environmental noise or electromagnetic interference introduces high-frequency noise components.

[0088] Spectrum analysis and model detection: Use tools such as fast Fourier transform (FFT) or wavelet transform to perform spectrum analysis on the signal, determine the frequency characteristics of harmonics or noise interference, and use the hysteresis effect model to make error judgments.

[0089] If a signal exhibits significant harmonic distortion, digital filters (such as band-stop filters and low-pass filters) can be applied to suppress harmonics in specific frequency bands. Digital filters can effectively remove high-frequency noise and higher-order harmonics, correcting the distorted portion of the signal. Adaptive filtering: For situations where harmonic frequencies are not fixed, adaptive filters can be used to dynamically adjust the filter's frequency response, correcting the signal's high-frequency noise and distortion components in real time.

[0090] For nonlinear errors (such as nonlinear responses caused by hysteresis), nonlinear compensation algorithms (such as fuzzy logic control and neural networks) can be used to dynamically compensate for signal output by analyzing the error model. Neural networks can automatically adjust compensation parameters based on historical training data, while fuzzy logic can handle fuzzy error characteristics.

[0091] If the signal contains random noise or multiple interferences, the Kalman filter can correct the signal by making an optimal estimate of the noise. It is suitable for continuously correcting low-precision signals in dynamic systems, making the output signal smoother and more accurate.

[0092] The corrected signal is monitored in real time via a feedback loop to ensure that the error-corrected signal meets requirements. If the error correction fails to achieve the desired effect, the feedback system triggers readjustment until the signal accuracy meets the required standards. Comparison with a high-precision reference signal or model verifies that the corrected signal is within the predetermined error range, ensuring that the compensated signal can be safely output.

[0093] After the signal is error-corrected, the error is reduced to an acceptable range. The corrected signal is then used as the final output of the transformer and transmitted to the control system or monitoring platform to ensure safe operation and stable control of the system.

[0094] In this application, through this two-layer processing mechanism, high-accuracy signals can be directly used as the final output of the transformer, ensuring efficient signal transmission and rapid system response. For low-accuracy signals, after using multiple error correction methods such as harmonic filtering, nonlinear compensation, and adaptive correction, the accuracy of the signal before output is improved. Ultimately, this solution can significantly improve the measurement accuracy and reliability of the transformer under complex operating conditions, ensuring the stable operation of the power system.

[0095] In this embodiment, magnetic flux density and magnetic field strength data are collected at different current and voltage change rates to generate a hysteresis loop diagram. The hysteresis loop width, remanence, and coercive force characteristics are extracted from the hysteresis loop to establish a hysteresis effect error model and determine the hysteresis. Digital signal processing is used to perform spectral analysis on the output signal to identify and predict the degree of harmonic distortion. A comprehensive analysis of the hysteresis effect and the degree of harmonic distortion is performed to evaluate the signal accuracy of the compensation algorithm. Signal accuracy is then graded, with high-accuracy signals directly used as the final output, while low-accuracy signals are output after error correction. This method ensures high-precision measurement and signal output of the transformer under complex operating conditions.

[0096] Embodiment 2, an efficient and high-precision compensation system for an intelligent mutual inductor described in this embodiment, includes a data acquisition module, a hysteresis effect modeling module, a spectrum analysis and harmonic detection module, a comprehensive error analysis and evaluation module, and a signal classification and output module;

[0097] Data acquisition module: collects the magnetic flux density and magnetic field strength data of the transformer under different current and voltage change rates and generates a hysteresis loop diagram;

[0098] Hysteresis effect modeling module: Based on the hysteresis loop diagram, the hysteresis loop width characteristics, remanence and coercive force characteristics in the hysteresis loop are extracted respectively, and the hysteresis effect error model of the mutual inductor is established. The hysteresis of the hysteresis effect is determined based on the model output results;

[0099] Spectrum analysis and harmonic detection module: Uses digital signal processing to perform spectrum analysis on the output signal of the transformer, identifies the harmonic distortion of different frequency components in the signal, and predicts the degree of harmonic distortion of the output signal of the transformer in a high-frequency environment;

[0100] Comprehensive error analysis and evaluation module: This module comprehensively analyzes the hysteresis effect and the degree of harmonic distortion of the transformer's output signal in a high-frequency environment, and evaluates the signal accuracy of the compensation algorithm when dealing with transformer nonlinear errors.

[0101] Signal classification and output module: The signal accuracy when the compensation algorithm responds to the nonlinear error of the transformer is divided into different levels, namely high-accuracy signals and low-accuracy signals. The high-accuracy signal is directly used as the final output signal of the transformer, and the low-accuracy signal is output after error correction.

[0102] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0103] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0104] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0105] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. An efficient and high-precision compensation method for an intelligent mutual inductor, characterized by: The following steps are included: S1: Under different current and voltage change rates, collect the magnetic flux density and magnetic field strength data of the transformer and generate a hysteresis loop diagram; S2: Based on the hysteresis loop diagram, the hysteresis loop width characteristics, remanence and coercive force characteristics in the hysteresis loop are extracted respectively, and the hysteresis effect error model of the mutual inductor is established. The hysteresis of the hysteresis effect is determined based on the output results of the model; S3: Use digital signal processing to perform spectrum analysis on the output signal of the transformer, identify the harmonic distortion of different frequency components in the signal, and predict the degree of harmonic distortion of the output signal of the transformer in a high-frequency environment; S4: Comprehensively analyze the hysteresis of the hysteresis effect and the degree of harmonic distortion of the output signal of the transformer in a high-frequency environment, and evaluate the signal accuracy of the compensation algorithm in responding to the nonlinear error of the transformer. Specifically, convert the hysteresis of the hysteresis effect and the harmonic distortion anomaly index into a first eigenvector, and use the first eigenvector as the input of the machine learning model. The machine learning model uses each group of first eigenvectors to predict the signal accuracy value label of the compensation algorithm in responding to the nonlinear error of the transformer as the prediction target, and uses minimizing the sum of the prediction errors of the signal accuracy value labels of all compensation algorithms in responding to the nonlinear error of the transformer as the training target. The machine learning model is trained until the sum of the prediction errors reaches convergence, and the model training is stopped. The signal accuracy value of the compensation algorithm in responding to the nonlinear error of the transformer is determined according to the model output result, wherein the machine learning model is a polynomial regression model; S5: Divide the signal accuracy of the compensation algorithm when dealing with the nonlinear error of the transformer into different levels, namely, high-accuracy signals and low-accuracy signals. The high-accuracy signal is directly used as the final output signal of the transformer, and the low-accuracy signal is output after error correction.

2. The high-efficiency and high-precision compensation method for an intelligent mutual inductor according to claim 1, characterized in that: In S2, according to the hysteresis loop diagram, the hysteresis loop width characteristics and the remanence and coercive force characteristics in the hysteresis loop are extracted respectively, and the hysteresis effect error model of the mutual inductor is established. The hysteresis of the hysteresis effect is determined according to the output results of the model, specifically: The hysteresis loop width indicates the degree of hysteresis between the magnetic field intensity H and the magnetic flux density B during the magnetization and demagnetization process of the transformer. It is calculated by measuring the difference in magnetic field intensity when the magnetic flux density is zero. In the hysteresis loop diagram, find the point where the magnetic flux density B = 0. At B = 0, record the positive and negative magnetic field strengths, denoted as Hpositive and Hnegative, respectively. Calculate the expression: Wh = Hpositive - Hnegative. Where: Wh is the width of the hysteresis loop, reflecting the hysteresis during magnetization and demagnetization. Hpositive is the positive magnetic field strength when the magnetic flux density is zero, and Hnegative is the negative magnetic field strength when the magnetic flux density is zero. Remanence is the value of magnetic flux density B when magnetic field strength H=0, indicating the magnetic flux retained in the transformer core after demagnetization. In the hysteresis loop diagram, find the point where magnetic field strength H=0 and record the magnetic flux density value at this time, recorded as Br. The calculation expression is: Br=B(H=0); where Br is remanence, indicating the magnetic flux density in the magnetization loop when the magnetic field strength is zero; Coercive force is the reverse magnetic field strength required to make the magnetic flux density B = 0, which indicates the magnetic field strength required by the transformer core to eliminate residual magnetism. In the hysteresis loop diagram, find the point where the magnetic flux density B = 0 and record the corresponding reverse magnetic field strength, recorded as Hc. The calculation expression is: Hc = |H(B = 0)|; where Hc is the coercive force, which indicates the reverse magnetic field strength required to return the magnetic flux density to zero; The hysteresis effect error model reflects the nonlinear error caused by the hysteresis effect in the operation of the transformer. The hysteresis effect error model is established by the hysteresis loop width Wh, remanence Br, and coercive force Hc. The hysteresis effect error is described as the hysteresis relationship between the input magnetic field intensity and the actual output magnetic flux density. Under a given magnetic field intensity H, the error ε caused by the hysteresis effect is mag (H) expression is expressed as: ε mag (H) = α·Wh + β·Br + γ·Hc; where α, β, and γ are unknown coefficients.

3. The high-efficiency and high-precision compensation method for an intelligent mutual inductor according to claim 2, characterized in that: In S3, digital signal processing is used to perform spectrum analysis on the output signal of the transformer, identify the harmonic distortion of different frequency components in the signal, and predict the degree of harmonic distortion of the output signal of the transformer in a high-frequency environment. Specifically: The output signal of the transformer is collected in real time. Before spectrum analysis, the DC component of the collected transformer output signal is eliminated by removing the mean value of the signal. The expression is: Where x(t) is the original signal and N is the number of signal samples. Applying a window function to the signal enhances the resolution of the spectrum within a limited sampling length. The expression is: windowed =x(t)·w(t); where w(t) is the window function; the preprocessed time domain signal x(t) is subjected to a fast Fourier transform operation to obtain a frequency domain signal X(f), which is expressed as: X(k) is the kth discrete frequency component in the frequency domain, N is the total number of sampling points, k represents different frequency components, x(n) is the nth sampling point in the time domain signal, is the kernel function of Fourier transform. After obtaining frequency domain data through fast Fourier transform operation, a spectrum diagram is drawn with the horizontal axis as frequency and the vertical axis as signal amplitude. In the spectrum diagram, the harmonic signal will appear as a peak at an integer multiple of the fundamental frequency f0. Through spectrum analysis, the harmonic distortion in the signal is identified and quantified. It is expressed as the ratio of the sum of the squares of all harmonic components in the signal to the square of the fundamental component. The expression is: Where THD is the total harmonic distortion, V1 is the amplitude of the fundamental frequency, and V2, V3, ... are the amplitudes of each harmonic.

4. The high-efficiency and high-precision compensation method for an intelligent mutual inductor according to claim 3, characterized in that: The harmonic distortion degree of the output signal of the transformer in a high-frequency environment is predicted to generate a harmonic distortion anomaly index. The method for obtaining the harmonic distortion anomaly index is as follows: Select the wavelet basis function and perform multi-scale wavelet decomposition on the harmonic distortion data to obtain detail coefficients and approximate coefficients at different scales. Use the detail coefficients and approximate coefficients to reconstruct the signal and obtain the reconstructed signal at different scales. The specific calculation expression is: Among them, AX j0,k and DX j,k are the reconstructed approximate coefficient and detail coefficient respectively, and BX(t) is the reconstructed signal. The harmonic distortion anomaly index is calculated based on the reconstructed signal. By calculating the energy change of the detail coefficient at different scales, the variation of the output signal of the mutual inductor is obtained. The energy of the detail coefficient at each scale is calculated. The specific calculation expression is: E j =∑k|D j,k | 2 Among them, E j is the energy of the jth layer. The harmonic distortion anomaly index is calculated based on the energy changes at different scales. The specific calculation expression is: Where QV is the harmonic distortion anomaly index.

5. The high-efficiency and high-precision compensation method for an intelligent mutual inductor according to claim 1, characterized in that: In S5, the signal accuracy of the compensation algorithm in dealing with the nonlinear error of the mutual inductor is divided into different levels, namely high-accuracy signals and low-accuracy signals, specifically: The obtained signal accuracy value of the compensation algorithm in response to the nonlinear error of the mutual inductor is compared with a preset reference threshold value of the signal accuracy value. If the signal accuracy value of the compensation algorithm in response to the nonlinear error of the mutual inductor is greater than or equal to the reference threshold value of the signal accuracy value, it means that the signal accuracy of the compensation algorithm in response to the nonlinear error of the mutual inductor is high. In this case, no early warning signal is generated, and the signal of the compensation algorithm in response to the nonlinear error of the mutual inductor is classified as a high-accuracy signal, which is directly used as the final output signal of the mutual inductor; If the signal accuracy value when the compensation algorithm responds to the nonlinear error of the transformer is less than the reference threshold value of the signal accuracy value, it means that the signal accuracy when the compensation algorithm responds to the nonlinear error of the transformer is low. At this time, a warning signal is generated, and the signal when the compensation algorithm responds to the nonlinear error of the transformer is divided into a low-accuracy signal, and the error is corrected before output.

6. A high-efficiency and high-precision compensation system for an intelligent mutual inductor, used to implement the high-efficiency and high-precision compensation method for an intelligent mutual inductor according to any one of claims 1 to 5, characterized in that: It includes data acquisition module, hysteresis effect modeling module, spectrum analysis and harmonic detection module, comprehensive error analysis and evaluation module, and signal classification and output module; Data acquisition module: collects the magnetic flux density and magnetic field strength data of the transformer under different current and voltage change rates and generates a hysteresis loop diagram; Hysteresis effect modeling module: Based on the hysteresis loop diagram, the hysteresis loop width characteristics, remanence and coercive force characteristics in the hysteresis loop are extracted respectively, and the hysteresis effect error model of the mutual inductor is established. The hysteresis of the hysteresis effect is determined based on the model output results; Spectrum analysis and harmonic detection module: Uses digital signal processing to perform spectrum analysis on the output signal of the transformer, identifies the harmonic distortion of different frequency components in the signal, and predicts the degree of harmonic distortion of the output signal of the transformer in a high-frequency environment; Comprehensive error analysis and evaluation module: This module comprehensively analyzes the hysteresis effect and the degree of harmonic distortion of the transformer's output signal in a high-frequency environment, and evaluates the signal accuracy of the compensation algorithm when dealing with transformer nonlinear errors. Signal classification and output module: The signal accuracy when the compensation algorithm responds to the nonlinear error of the transformer is divided into different levels, namely high-accuracy signals and low-accuracy signals. The high-accuracy signal is directly used as the final output signal of the transformer, and the low-accuracy signal is output after error correction.

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