Electromagnetic transient simulation method and system for magnetic valve type current transformer

Abstract

The invention provides an electromagnetic transient simulation method and system for a magnetic valve type current transformer, and relates to the technical field of power system simulation, and the method specifically comprises the steps: collecting electromagnetic, magnetic field, control and environment data, extracting a flux density peak value, a flux density harmonic distortion rate and a current harmonic distortion rate, and constructing monitoring feature data; constructing a data prediction model, dynamically predicting electromagnetic parameters at the next moment, obtaining electromagnetic data, a flux density peak value, a flux density harmonic distortion rate and a current harmonic distortion rate correction value, performing electromagnetic transient simulation through a finite element-circuit coupling model, calculating an amplitude deviation rate and a phase deviation rate, and performing current harmonic distortion rate correction. And generating a running state comprehensive coefficient to judge the running state of the current transformer. Through multi-dimensional data acquisition, time sequence dynamic modeling and coupling simulation, the problems of poor adaptability of fixed parameters, insufficient time sequence characteristic capture and low simulation precision are solved, and the saturation characteristic simulation precision, the transient prediction accuracy and the secondary side current simulation precision are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system simulation, and specifically provides a magnet valve type current transformer electromagnetic transient simulation method and system. Background Technique

[0002] Magnet valve type current transformers are widely used in power system protection, new energy grid connection and other scenarios due to their advantages of controllable iron core saturation characteristics and high transient response accuracy. Its core function is to accurately transfer large current on the primary side to small current on the secondary side through electromagnetic coupling, and the accurate simulation of the saturation characteristics during the electromagnetic transient process is the key to evaluating its reliability.

[0003] In the prior art, a current transformer electromagnetic transient simulation method, system, device and medium disclosed in CN117910239A includes the following steps: simulating the hysteresis characteristics of the current transformer based on a nonlinear element, simulating the exciting current based on an injected current source, and constructing an electromagnetic transient model of the current transformer; based on the electromagnetic transient model, performing electromagnetic transient simulation of the current transformer. The electromagnetic transient model of this method can easily obtain magnetization parameters and hysteresis parameters from measured data, which is consistent with the actual characteristics of the current transformer, thereby improving the simulation accuracy and numerical stability of the electromagnetic transient model.

[0004] However, there are still the following deficiencies. From the above statements, it can be seen that the existing method only constructs a model by simulating the hysteresis characteristics with a nonlinear element and simulating the exciting current with an injected current source, without fully considering the coupling effects of multiple factors and the dynamic timing characteristics during the electromagnetic transient process of the magnet valve type current transformer: on the one hand, the electromagnetic transient process of the magnet valve type current transformer is affected by the coupling action of multi-dimensional variables such as load conditions, frequency ranges, environmental parameters, and control signals, but the existing technology does not mention the acquisition, analysis, and parameter correction mechanisms for the above dynamic variables, and the model is difficult to adapt to complex working conditions; on the other hand, the transient response of the magnet valve type current transformer has strong timing correlation. However, the existing technology constructs an electromagnetic transient model based on static model parameters and does not introduce a timing prediction model to perform dynamic modeling on historical data, and cannot capture the evolution law of "historical state - current state - future state", resulting in the model being unable to accurately track the dynamic characteristics of the transient process.

[0005] The above information disclosed in the background art section is only used to strengthen the understanding of the background of the present disclosure, and therefore it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide a magnet valve type current transformer electromagnetic transient simulation method and system to solve the problems raised in the above background technique.

[0007] To achieve the above object, the present invention provides the following technical solutions: An electromagnetic transient simulation method for a magnetic valve type current transformer, the specific steps include: S1. Obtain the initial saturation magnetic flux density and initial remanence coefficient of the iron core material in a standard environment and without external electromagnetic excitation, collect the electromagnetic, magnetic field, control and environmental data of the current transformer at multiple consecutive test times, and based on the collected electromagnetic data and magnetic field data, extract the magnetic flux density peak value, magnetic flux density harmonic distortion rate and current harmonic distortion rate, and construct monitoring characteristic data; S2. Build a data prediction model based on LSTM, use the monitoring characteristic data of the previous multiple test times as input, and use the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate and current harmonic distortion rate of the next test time as label output, and train the data prediction model; S3. Input the monitoring characteristic data of the current time t and the previous multiple test times into the data prediction model, and predict the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate and current harmonic distortion rate at time t + 1; S4. Calculate the standard deviation of the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at time t + 1 and the previous n - 1 times, compare the standard deviation of each parameter with its respective preset threshold, and according to the comparison result, obtain the correction values of the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at time t + 1; S5. According to the initial saturation magnetic flux density and the corrected magnetic flux density harmonic distortion rate, obtain the corrected initial saturation magnetic flux density. Based on the initial remanence coefficient, the corrected magnetic flux density peak value and the initial saturation magnetic flux density, obtain the corrected initial remanence coefficient. Based on the corrected electromagnetic data and current harmonic distortion rate, extract each harmonic component through Fourier transform, superimpose the extracted harmonic components, reconstruct the waveform and extract the current of each harmonic component; S6. Build a finite element - circuit coupling model, input the corrected initial saturation magnetic flux density and initial remanence coefficient in the simulation characteristic data as parameters, use the current of each harmonic component as the circuit excitation condition for electromagnetic transient simulation, obtain the simulation value of the secondary side current simulation waveform, and calculate the amplitude deviation rate and phase deviation rate between the secondary side current simulation value and the reference value; S7. Process the amplitude deviation rate and phase deviation rate to generate an operation state comprehensive coefficient, compare the operation state comprehensive coefficient with a preset threshold, and according to the comparison result, judge the operation state of the magnetic valve type current transformer.

[0008] Further, the electromagnetic data includes the secondary side output current and voltage; the magnetic field data is the magnetic flux density data at the winding winding location, the control data includes the waveform, amplitude, frequency, and phase of the primary side excitation current; the environmental data includes the environmental temperature and humidity; the monitoring characteristic data includes electromagnetic data, magnetic field data, control data, environmental data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate.

[0009] Further, compare the standard deviation of each parameter with its preset threshold, and according to the comparison result, obtain the correction values of the secondary side output current, secondary side output voltage, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at time t+1. The specific process is as follows: When it indicates that the th parameter is stable at time t+1 and the previous n-1 moments, and the mean value is used as the correction value; ; Among them, is the corrected secondary side output current, is the corrected secondary side output voltage, is the corrected magnetic flux density peak value, is the corrected magnetic flux density harmonic distortion rate, is the corrected current harmonic distortion rate; In the formula, is the mean value of the secondary side current at time t+1 and the previous n-1 moments, is the mean value of the secondary side voltage at time t+1 and the previous n-1 moments, is the mean value of the magnetic flux density peak value at time t+1 and the previous n-1 moments, is the mean value of the magnetic flux density harmonic distortion rate at time t+1 and the previous n-1 moments, is the mean value of the current harmonic distortion rate at time t+1 and the previous n-1 moments; In the formula, is the standard deviation of the th parameter, is the threshold corresponding to the standard deviation of the th parameter, , respectively correspond to the secondary side output current, secondary side output voltage, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate; When it indicates that the th parameter is unstable at time t+1 and the previous n-1 moments. After removing the outliers, the median is selected as the correction value. The specific steps are as follows: Sort all parameters from small to large to form an ordered sequence corresponding to each parameter one by one; For an ordered sequence corresponding to the same parameter, calculate the 25th percentile in the ordered sequence and the 75th percentile ; Calculate the interquartile range : ; Outlier range: ; According to the outlier range, remove all data points falling within the outlier range from the ordered sequence to obtain a valid data subset; For the valid data subset, calculate its median as the correction value: When the number of valid data is odd, the median is the value at the middle position in the subset; When the number of valid data is even, the median is the average of the two middle values in the subset.

[0010] Furthermore, based on the initial saturation magnetic flux density and the corrected magnetic density harmonic distortion rate, obtain the corrected initial saturation magnetic flux density, according to the following formula: ; where, is the corrected initial saturation magnetic flux density, is the initial saturation magnetic flux density, is the corrected magnetic density harmonic distortion rate; Based on the initial remanence coefficient, the corrected magnetic density peak value, and the initial saturation magnetic flux density, obtain the corrected initial remanence coefficient, according to the following formula: ; where, is the corrected initial remanence coefficient, is the initial remanence coefficient, is the proportionality coefficient, .

[0011] Furthermore, calculate the amplitude deviation rate between the secondary side current simulation value and the reference value, and the specific process is as follows: Extract the simulation value of the secondary side current from the finite element - circuit coupling model, and calculate the reference value of the secondary side current based on the current transformer turns ratio and the actual value of the primary side current, according to the following formula: ; where, is the reference value of the secondary side current, is the actual value of the primary side current, is the current transformer turns ratio; ; Among them, is the amplitude deviation rate between the secondary-side current simulation value and the reference value, is the secondary-side current simulation value; Extract the secondary-side current simulation waveform from the finite element-circuit coupling model, and based on the secondary-side current simulation waveform, obtain the phase angle of the secondary-side current simulation value; ; Among them, is the phase deviation rate between the secondary-side current simulation value and the reference value, is the phase angle of the secondary-side current simulation value, is the phase angle of the secondary-side current reference value.

[0012] Furthermore, perform data processing on the amplitude deviation rate and the phase deviation rate to generate a comprehensive operation state coefficient. The formula is as follows: ; Among them, is the comprehensive operation state coefficient, , are the amplitude deviation rate and the phase deviation rate between the secondary-side current simulation value and the reference value; In the formula, is the weight coefficient of the amplitude deviation rate, is the weight coefficient of the phase deviation rate, and and The specific values of are determined by the analytic hierarchy process.

[0013] Furthermore, according to the comparison result, judge the operation state of the magnet valve type current transformer. The specific process is as follows: When , the magnet valve type current transformer is in the normal operation state; When , the magnet valve type current transformer is in the warning state; When , the magnet valve type current transformer is in the fault state.

[0014] Among them, is the critical value for dividing the normal operation and the warning state, is the critical value for dividing the warning and the fault state.

[0015] To achieve the above object, the present invention also provides the following technical solution: A magnet valve type current transformer electromagnetic transient simulation system, the system is used to execute any one of the above-mentioned magnet valve type current transformer electromagnetic transient simulation methods, including: The data acquisition module is used to obtain the initial saturation magnetic flux density and the initial remanence coefficient of the iron core material under the standard environment and without external electromagnetic excitation, collect the electromagnetic, magnetic field, control and environmental data of the current transformer at multiple consecutive test moments, extract the magnetic flux density peak value, the magnetic flux density harmonic distortion rate and the current harmonic distortion rate based on the collected electromagnetic data and magnetic field data, and construct the monitoring characteristic data; The data prediction model construction module is used to construct a data prediction model based on LSTM, use the monitoring characteristic data of the previous multiple test moments as input, and use the electromagnetic data, the magnetic flux density peak value, the magnetic flux density harmonic distortion rate and the current harmonic distortion rate of the next test moment as label output to train the data prediction model; The simulation module is used to input the monitoring characteristic data of the current moment t and the previous multiple test moments into the data prediction model to predict the electromagnetic data, the magnetic flux density peak value, the magnetic flux density harmonic distortion rate and the current harmonic distortion rate at the moment t + 1; The predicted value correction module is used to calculate the standard deviation of the electromagnetic data, the magnetic flux density peak value, the magnetic flux density harmonic distortion rate, and the current harmonic distortion rate at the moment t + 1 and the previous n - 1 moments, compare the standard deviation of each parameter with its preset threshold, and obtain the correction values of the electromagnetic data, the magnetic flux density peak value, the magnetic flux density harmonic distortion rate, and the current harmonic distortion rate at the moment t + 1 according to the comparison result; The simulation data correction module is used to obtain the corrected initial saturation magnetic flux density according to the initial saturation magnetic flux density and the corrected magnetic flux density harmonic distortion rate, obtain the corrected initial remanence coefficient based on the initial remanence coefficient, the corrected magnetic flux density peak value and the initial saturation magnetic flux density, extract the harmonic components of each order through Fourier transform based on the corrected electromagnetic data and the current harmonic distortion rate, superimpose the extracted harmonic components, reconstruct the waveform and extract the current of the harmonic components of each order; The simulation module is used to construct a finite element - circuit coupling model, input the corrected initial saturation magnetic flux density and the corrected initial remanence coefficient in the simulation characteristic data as parameters, use the current of the harmonic components of each order as the circuit excitation condition to perform electromagnetic transient simulation, obtain the simulation value of the secondary side current waveform, and calculate the amplitude deviation rate and the phase deviation rate between the secondary side current simulation value and the reference value; The judgment module is used to process the amplitude deviation rate and the phase deviation rate to generate a comprehensive operation state coefficient, compare the comprehensive operation state coefficient with the preset threshold, and judge the operation state of the magnetic valve type current transformer according to the comparison result.

[0016] Compared with the prior art, the beneficial effects of the present invention are: The present invention collects the initial saturation magnetic flux density and the initial remanence coefficient of the iron core material under standard conditions, combines data such as the peak magnetic density and harmonic distortion rate monitored in real time, dynamically corrects the initial parameters, extracts characteristic quantities such as the peak magnetic density, magnetic density harmonic distortion rate, and current harmonic distortion rate, and constructs a monitoring system including steady-state accuracy (amplitude) and dynamic process (harmonics); Input the current and historical monitoring characteristic data into the prediction model to predict the electromagnetic parameters, peak magnetic density, and harmonic distortion rate at time t + 1, calculate the correction value, use the corrected initial saturation magnetic flux density and initial remanence coefficient as parameter inputs, and use the current of each harmonic component as the circuit excitation condition for electromagnetic transient simulation to obtain the simulation value of the secondary side current waveform, and calculate the amplitude deviation rate and phase deviation rate between the simulation value of the secondary side current and the reference value based on this; Use the amplitude deviation rate and phase deviation rate to generate a comprehensive operation state coefficient, compare the comprehensive operation state coefficient with a preset threshold, and judge the operation state of the magnetic valve type current transformer according to the comparison result.

[0017] In summary, through multi-dimensional data acquisition, time-series dynamic modeling, and coupled simulation, the problems of poor adaptability of fixed parameters, insufficient capture of time-series characteristics, and low simulation accuracy are solved, and the simulation accuracy of saturation characteristics, the accuracy of transient prediction, and the simulation accuracy of the secondary side current are significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the overall method flow of the present invention; Figure 2 It is a block diagram of the module composition of the present invention; Figure 3 It is a fitting schematic diagram of the corrected magnetic density harmonic distortion rate and the corrected initial saturation magnetic flux density of the present invention; Figure 4 It is a fitting schematic diagram of the corrected peak magnetic density and the corrected initial remanence coefficient of the present invention; Figure 5 It is a fitting schematic diagram of the corrected initial saturation magnetic flux density and the corrected initial remanence coefficient of the present invention; Figure 6 It is a fitting schematic diagram of the amplitude deviation rate and the comprehensive operation state coefficient of the present invention; Figure 7 It is a fitting schematic diagram of the phase deviation rate and the comprehensive operation state coefficient of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0019] In order to make the purpose, technical solution, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to specific embodiments.

[0020] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar terms used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Words such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. Words such as "connected" or "linked" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right", etc. are only used to represent relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0021] Embodiment 1: Please refer to Figure 1 , the present invention provides a technical solution: An electromagnetic transient simulation method for a magnetic valve type current transformer, the specific steps include: S1. Obtain the initial saturation magnetic flux density and initial remanence coefficient of the iron core material in a standard environment and under the state of not being externally electromagnetically excited, collect the electromagnetic, magnetic field, control and environmental data of the current transformer at a continuous plurality of test times, and based on the collected electromagnetic data and magnetic field data, extract the magnetic flux density peak value, magnetic flux density harmonic distortion rate and current harmonic distortion rate, and construct monitoring characteristic data; On the basis of the above embodiment, the electromagnetic data includes the secondary side output current and voltage; the magnetic field data is the magnetic flux density data at the winding winding place, the control data includes the waveform, amplitude, frequency, and phase of the primary side excitation current; the environmental data includes the environmental temperature and environmental humidity; the monitoring characteristic data includes electromagnetic data, magnetic field data, control data, environmental data, magnetic flux density peak value, magnetic flux density harmonic distortion rate and current harmonic distortion rate.

[0022] The acquisition methods of the primary side excitation current waveform, current amplitude, frequency, phase, secondary side output current and voltage data, magnetic flux density data at the winding winding place, environmental temperature and environmental humidity are as follows: A high-precision current transformer is directly connected in series to the primary side circuit. Based on the principle of electromagnetic induction, the change of the primary current is induced by the coil wound on the non-ferromagnetic material skeleton. The output voltage is proportional to the differential of the current. The current waveform is restored through an integration circuit. According to Ampere's circuital law, the magnetic field generated by the primary current induces an electromotive force in the coil, and the electromotive force is converted into a voltage signal through a sampling resistor or an integration circuit, and the waveform and amplitude data are obtained after being digitized by an analog-to-digital converter.

[0023] Perform a fast Fourier transform on the collected current waveform to extract the fundamental frequency component, or calculate the period by calculating the time interval between adjacent periods through a zero-crossing detection circuit. According to the formula frequency , , where is the period; for a signal with a frequency of , the period . Therefore, measure the time difference between the zero-crossing of the measured signal and the reference zero-crossing and convert it to the absolute phase.

[0024] Connect a shunt resistor (non-inductive resistor) in series in the secondary circuit to convert the current signal into a voltage signal (e.g., a 1A / 5A current is converted into a 1V / 5V voltage through a 1Ω resistor), and then filter and amplify it through a differential amplifier and send it to the ADC (analog-to-digital converter) to obtain the secondary-side output current.

[0025] A voltage transformer is connected in parallel across the secondary winding. Using the principle of electromagnetic induction, the voltage is reduced according to the turns ratio. A voltage-dividing circuit is formed by two resistors connected in series and connected in parallel across the secondary winding. The voltage is reduced according to the resistance ratio. The divided voltage may need to be amplified (or filtered to ensure that the signal sent to the ADC is stable and interference-free. Similar to the current acquisition, the voltage signal is converted into a digital value to obtain the secondary-side output voltage data.

[0026] Use a Hall effect magnetometer probe. The probe needs to be close to the surface of the iron core around which the winding is wound. Drive the probe to move axially along the winding through a mechanical displacement stage, collect the magnetic flux density distribution point by point, construct a two-dimensional magnetic flux density cloud map, and obtain the magnetic flux density data at the winding winding.

[0027] Adopt an integrated digital temperature sensor or thermocouple, and connect it through the digital input channel of the DAQ or a dedicated temperature module to measure the ambient temperature.

[0028] Use a capacitive humidity sensor. The output voltage changes linearly with humidity and is connected to the analog input channel to measure the ambient temperature.

[0029] On the basis of the above embodiments, obtain the initial saturation magnetic flux density and initial remanence coefficient of the iron core material in a standard environment and under the state of not being externally electromagnetically excited. The specific acquisition method is as follows: The initial saturation magnetic flux density refers to the maximum value when the magnetic flux density tends to saturate as the magnetic field strength increases during the magnetization process of the iron core material. Using the DC magnetization curve method, the primary side winding is connected to a DC power supply, and a standard resistor is connected in series to monitor the excitation current; the secondary side winding is open-circuited, and a Tesla meter probe is connected to measure the magnetic flux density on the surface of the iron core. Slowly increase the DC current from zero, record the excitation current and the corresponding magnetic flux density on the surface of the iron core after each stabilization, calculate the magnetic field strength, and plot the curve of magnetic flux density on the surface of the iron core - magnetic field strength. When the magnetic flux density on the surface of the iron core tends to flatten as the magnetic field strength increases (the slope of the curve approaches zero), the magnetic flux density on the surface of the iron core at this time is the initial saturation magnetic flux density; The initial remanence coefficient refers to the ratio of the residual magnetic flux density to the saturation magnetic flux density when the magnetic field strength is removed after the iron core material is magnetized to saturation. Using the DC method, the iron core is excited to the saturation magnetic flux density, the DC power supply is quickly disconnected, and at the same time, the residual magnetic flux density of the iron core is measured with a Tesla meter to calculate the remanence coefficient.

[0030] Based on the above embodiments, based on the collected electromagnetic data and magnetic field data, the magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate are extracted. The specific method is as follows: Filter and denoise the magnetic field data (magnetic flux density data at the winding winding location) to eliminate high-frequency interference and baseline drift. Perform frame processing on the time-series magnetic flux density data, determine the time window of each frame of data according to the test frequency, and perform extreme value search on each frame of magnetic flux density data to obtain the maximum magnetic flux density within this time period, that is, the magnetic flux density peak value.

[0031] Perform a fast Fourier transform on the magnetic flux density data to convert the time-domain signal into a frequency-domain spectrum, obtain the amplitude and phase information of each harmonic, define the fundamental frequency (such as the power frequency of 50 Hz) as f0, extract the amplitudes of the harmonics of the 2nd and above (2f0, 3f0...), and the calculation formula for the magnetic flux density harmonic distortion rate is: where, is the magnetic flux density harmonic distortion rate, is the fundamental magnetic flux density amplitude, is the th harmonic magnetic flux density amplitude, is the index of the harmonic, , is the harmonic order.

[0032] Perform a fast Fourier transform on the secondary side output current in the electromagnetic data to obtain the fundamental current amplitude and the th harmonic current amplitude , and the calculation formula for the current harmonic distortion rate is where, is the current harmonic distortion rate.

[0033] Based on the above embodiments, the collected or extracted electromagnetic data, magnetic field data, control data, environmental data, peak magnetic density, magnetic density harmonic distortion rate, and current harmonic distortion rate are normalized, and the subsequent data for analysis and processing are the normalized electromagnetic data, magnetic field data, control data, environmental data, peak magnetic density, magnetic density harmonic distortion rate, and current harmonic distortion rate.

[0034] S2. Construct a data prediction model based on LSTM. Use the monitoring feature data at previous multiple test times as input, and use the electromagnetic data, peak magnetic density, magnetic density harmonic distortion rate, and current harmonic distortion rate at the next test time as the label output to train the data prediction model. Based on the above embodiments, the training process of the data prediction model is as follows: Input data: The feature data at previous multiple test times (such as the electromagnetic data, magnetic field data, control data, environmental data, peak magnetic density, magnetic density harmonic distortion rate, and current harmonic distortion rate at the previous 10 test times) form a "time series segment". For example, if predicting the electromagnetic data, peak magnetic density, magnetic density harmonic distortion rate, and current harmonic distortion rate at the test time, the input is the monitoring feature data at the time. Output label: The electromagnetic data, peak magnetic density, magnetic density harmonic distortion rate, and current harmonic distortion rate at the next test time; Window the electromagnetic data, magnetic field data, control data, environmental data, peak magnetic density, magnetic density harmonic distortion rate, and current harmonic distortion rate, and cut the continuous data into multiple "input-output pairs" (such as → , → ); Input layer: Used to receive the time series segment; LSTM (Long Short-Term Memory Network) layer: 1-3 layers, each layer contains several "memory units" for capturing long-term dependencies; Output layer: Used to set the number of neurons according to the prediction target; Time step: The number of input test times; Training process: Batch training: Update the parameters with a small batch of data each time to avoid out-of-memory; Loss function: Measure the prediction error, using the mean squared error; Optimizer: Use the Adam (Adaptive Moment Estimation) optimizer; Termination condition: Reach the preset number of training epochs.

[0035] S3. Input the monitoring feature data at the current moment \(t\) and the previous multiple test moments into the data prediction model to predict the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at the moment \(t + 1\). S4. Calculate the standard deviations of the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at the moment \(t + 1\) and the previous \(n - 1\) moments. Compare the standard deviations of each parameter with their respective preset thresholds, and obtain the correction values of the predicted values of the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at the moment \(t + 1\) according to the comparison results. Among them, when \(n = 6\), that is, calculate the mean values of the secondary side output current, secondary side output voltage, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at the moment \(t + 1\) and the previous 5 moments. The mean values of the secondary side output current, secondary side output voltage, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate for a total of \(n\) moments are equally spaced between the previous \(n - 1\) moments.

[0036] Based on the above embodiments, compare the standard deviations of each parameter with their respective preset thresholds, and obtain the correction values of the secondary side output current, secondary side output voltage, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at the moment \(t + 1\) according to the comparison results. The specific process is as follows: When it indicates that the th parameter is stable at the moment \(t + 1\) and the previous \(n - 1\) moments, and the mean value is used as the correction value. ; Among them, is the corrected secondary side output current, is the corrected secondary side output voltage, is the corrected magnetic flux density peak value, is the corrected magnetic flux density harmonic distortion rate, is the corrected current harmonic distortion rate; In the formula, is the mean value of the secondary side current at the moment \(t + 1\) and the previous \(n - 1\) moments, is the mean value of the secondary side voltage at the moment \(t + 1\) and the previous \(n - 1\) moments, is the mean value of the magnetic flux density peak value at the moment \(t + 1\) and the previous \(n - 1\) moments, is the mean value of the magnetic flux density harmonic distortion rate at the moment \(t + 1\) and the previous \(n - 1\) moments, is the mean value of the current harmonic distortion rate at the moment \(t + 1\) and the previous \(n - 1\) moments; In the formula, is the standard deviation of the th parameter, is the threshold corresponding to the standard deviation of the th parameter, , correspond to the secondary side output current, secondary side output voltage, peak magnetic flux density, magnetic flux density harmonic distortion rate, and current harmonic distortion rate respectively; When , it indicates that for the th parameter, after removing the outliers that are unstable at time t + 1 and the previous n - 1 moments, the median is selected as the correction value. The specific steps are as follows: Sort all parameters from smallest to largest to form an ordered sequence corresponding to each parameter one by one; For the ordered sequence corresponding to the same parameter, calculate the 25th percentile and the 75th percentile ; Calculate the interquartile range : ; Outlier range: ; According to the outlier range, remove all data points that fall within the outlier range from the ordered sequence to obtain an effective data subset; For the effective data subset, calculate its median as the correction value: When the number of effective data is odd, the median is the value at the middle position in the subset; When the number of effective data is even, the median is the average of the two middle values in the subset.

[0037] S5. According to the initial saturation magnetic flux density and the corrected magnetic flux density harmonic distortion rate, obtain the correction value of the initial saturation magnetic flux density. Based on the initial remanence coefficient, the corrected peak magnetic flux density, and the initial saturation magnetic flux density, obtain the corrected initial remanence coefficient. Based on the corrected electromagnetic data and the current harmonic distortion rate, extract each harmonic component through Fourier transform, superimpose the extracted harmonic components, reconstruct the waveform, and extract the current of each harmonic component; Table 1. Variation of the corrected initial saturation magnetic flux density with the predicted value of the magnetic flux density harmonic distortion rate

[0038] As can be seen from Table 1, the corrected initial saturation magnetic flux density is significantly negatively correlated with the magnetic flux density harmonic distortion rate. Specifically, when the corrected magnetic flux density harmonic distortion rate increases from 1% to 25%, the corrected initial saturation magnetic flux density continuously decreases from 1.784 to 1.432, and the overall decreasing trend shows a non - linear characteristic. The decrease amplitude of some adjacent data points fluctuates (such as from serial number 4 to 5, serial number 7 to 8), but the overall negative correlation trend is clear. This indicates that the increase in the corrected magnetic flux density harmonic distortion rate will lead to a decrease in the corrected initial saturation magnetic flux density.

[0039] According to Figure 3 it can be seen that as the corrected magnetic flux density harmonic distortion rate increases, the corrected initial saturation magnetic flux density gradually decreases, and the fitting line is a straight line, reflecting an approximately linear negative correlation between the two.

[0040] Based on the above embodiments, according to the initial saturation magnetic flux density and the corrected magnetic flux density harmonic distortion rate, the corrected initial saturation magnetic flux density is obtained, and the formula is as follows: ; wherein, is the corrected initial saturation magnetic flux density, is the initial saturation magnetic flux density, is the corrected magnetic flux density harmonic distortion rate; On this basis, it should be noted that: When the corrected magnetic flux density harmonic distortion rate increases, it indicates that the content of high-order harmonics in the magnetic flux density waveform increases, and the non-linear magnetization effect of the iron core intensifies, including earlier saturation and increased hysteresis loss. High-order harmonics will cause the iron core to enter the saturation state at a lower magnetic flux density, so the actual initial saturation magnetic flux density decreases; When the corrected magnetic flux density harmonic distortion rate decreases, it indicates that the magnetic flux density waveform is closer to a sine wave, the iron core operates in the linear magnetization region, and the saturation degree weakens. At this time, the iron core can withstand a higher magnetic flux density before entering the saturation state, so the actual initial saturation magnetic flux density approaches the initial value.

[0041] Therefore, the corrected initial saturation magnetic flux density and the corrected magnetic flux density harmonic distortion rate are negatively correlated. Therefore, the above inverse proportional function is used to express the functional relationship between the corrected initial saturation magnetic flux density, the initial saturation magnetic flux density, and the corrected magnetic flux density harmonic distortion rate.

[0042] Through the saturation magnetic flux density correction value, the model can more accurately simulate the saturation characteristics of the iron core in a harmonic environment, avoid underestimation of the saturation starting point and depth by the traditional fixed-parameter model, and in scenarios with high harmonic content such as fault current (e.g., short circuit) or inrush current (e.g., no-load closing), the corrected saturation magnetic flux density makes the simulation results closer to the actual waveform distortion and phase shift.

[0043] Table 2. Variation of the corrected initial remanence coefficient with the initial remanence coefficient, the corrected magnetic flux density peak value, the corrected initial saturation magnetic flux density, and the proportionality coefficient

[0044] According to Table 2, when the initial remanence coefficient (fixed at 0.9) and the proportionality coefficient (fixed at 0.2) remain unchanged, as the peak magnetic flux density after correction increases from 1.2 to 1.68 and the initial saturation magnetic flux density after correction decreases from 1.68 to 1.44, the initial remanence coefficient after correction gradually increases from 0.81 to 0.9253. Therefore, the initial remanence coefficient after correction is positively correlated with the peak magnetic flux density and negatively correlated with the initial saturation magnetic flux density.

[0045] According to Figure 4 , 5 it can be seen that the initial remanence coefficient after correction increases positively with the increase of the peak magnetic flux density after correction, and the fitting line is a straight line, reflecting an approximately linear positive correlation law; the initial remanence coefficient after correction decreases negatively with the increase of the initial saturation magnetic flux density after correction, and the fitting line is a straight line, reflecting an approximately linear negative correlation law.

[0046] Based on the above embodiments, based on the initial remanence coefficient, the peak magnetic flux density after correction, and the initial saturation magnetic flux density, the initial remanence coefficient after correction is obtained, and the formula is as follows: ; where is the initial remanence coefficient after correction, is the initial remanence coefficient; In the formula, is the proportionality coefficient, The essence of is to quantify the sensitivity of the physical relationship. In a current transformer, the remanence coefficient is restricted by material properties and the nonlinearity of the magnetization curve, and its change is usually gradual rather than drastic fluctuations. Therefore, needs to take a smaller value, , the lower limit of 0.1 ensures that the formula has sufficient response to changes in time parameters and avoids correction failure; the upper limit of 0.3 prevents the corrected value from exceeding the maximum possible physical fluctuation range and ensures the rationality of the model.

[0047] On this basis, it should be noted that: The peak magnetic flux density after correction reflects the maximum magnetic induction intensity reached by the magnetic material during the magnetization process. When the peak magnetic flux density after correction increases, it means that a greater magnetic field intensity is applied to the material during the magnetization process, and the magnetic domains inside the material will turn more fully towards the direction of the external magnetic field, resulting in a deeper magnetization depth; remanence refers to the magnetic induction intensity retained by the material after the external magnetic field is removed. The deeper the magnetization degree, the higher the degree of ordered arrangement of the magnetic domains, and the more consistent the direction of the "retained" magnetic domains after the external magnetic field is removed. Therefore, the initial remanence coefficient after correction will increase accordingly.

[0048] The initial saturation magnetic flux density after correction is the upper limit of the magnetic induction intensity when the material is magnetized to the saturation state, reflecting the maximum ability of the material's magnetic domains to be arranged in an orderly manner. When the corrected initial saturation magnetic flux density increases, it means that the material requires a higher magnetic field strength to reach saturation, that is, the magnetic domains of the material are "more difficult to be fully magnetized"; the remanence coefficient is inversely proportional to the "difficulty" of magnetizing the material to saturation. If it is more difficult for the material to reach saturation, then under the same magnetization conditions, the degree of orderly arrangement of the magnetic domains is lower, and the residual magnetic induction intensity after the external magnetic field is removed is also smaller. For example, if a material has a higher saturation magnetic flux density, it is equivalent to having a larger "magnetic domain activity space" and being more difficult to be "filled up" during magnetization. Therefore, the corrected initial remanence coefficient will also decrease accordingly.

[0049] Therefore, the corrected initial remanence coefficient and the corrected peak magnetic density are positively correlated, and the corrected initial remanence coefficient and the corrected initial saturation magnetic flux density are negatively correlated.

[0050] Therefore, the above functional form is used to express the functional relationship between the corrected initial remanence coefficient, the corrected peak magnetic density, and the initial saturation magnetic flux density.

[0051] Based on the above embodiments, based on the corrected electromagnetic data and the current harmonic distortion rate, each harmonic component is extracted through Fourier transform, the extracted harmonic components are superimposed, the waveform is reconstructed, and the current of each harmonic component is extracted. The specific steps are as follows: Extract the time-domain simulation waveform, sampling frequency, and number of sampling points of the secondary-side current from the finite element-circuit coupling model, ensuring that the sampling duration is an integer multiple of the fundamental wave period; Convert the preprocessed time-domain signal into a frequency-domain representation to obtain a series of complex numbers, each complex number corresponding to a component of a specific frequency. The frequency resolution depends on the number of sampling points and the sampling frequency, that is, the interval between adjacent frequency points; For each complex number of the frequency component, calculate its amplitude and phase; Perform a frequency-domain conversion. According to the known fundamental wave frequency, find the closest frequency point in the frequency-domain data. The complex number corresponding to this point is the fundamental wave component; Correct the original amplitude of the fundamental wave component to obtain the actual fundamental wave amplitude, and extract the phase angle of the fundamental wave as the reference benchmark for the phases of all harmonics. The amplitude correction formula: ; Among them, is the corrected fundamental wave amplitude, is the original amplitude calculated by the fast Fourier transform, is the number of sampling points; Traverse each harmonic. Starting from the second harmonic, calculate the position of each harmonic in the frequency domain in turn; If the harmonic frequency exceeds half of the sampling frequency, the harmonic is ignored; Calculate the harmonic parameters. For each valid harmonic, repeat the steps of fundamental amplitude correction and phase extraction to obtain the actual amplitude of each harmonic and the phase angle relative to the fundamental wave; Superimpose the extracted fundamental wave and harmonic components according to their respective amplitudes and phases to generate a new time-domain waveform. This waveform should be close to the original simulation waveform but only contain the selected harmonic components; Calculate the harmonic distortion rate of the reconstructed waveform: that is, the ratio of the square root of the sum of the squares of the amplitudes of each harmonic to the amplitude of the fundamental wave, which reflects the proportion of harmonic components in the total signal; Compare the calculated harmonic distortion rate with the predicted value of the current harmonic distortion rate obtained by statistical correction before to ensure that the error between the two is within the engineering allowable range.

[0052] S6. Construct a finite element-circuit coupling model. Input the corrected initial saturation flux density and corrected initial remanence coefficient in the simulation characteristic data as parameters, and use the harmonic component currents of each order as the circuit excitation conditions to conduct electromagnetic transient simulation to obtain the simulation value of the secondary-side current waveform, and calculate the amplitude deviation rate and phase deviation of the secondary-side current simulation value and the reference value; On the basis of the above embodiments, construct a finite element-circuit coupling model. Input the corrected initial saturation flux density and initial remanence coefficient in the simulation characteristic data as parameters, and use the harmonic component currents of each order as the circuit excitation conditions to conduct electromagnetic transient simulation. The specific process is as follows: Finite element part: Based on the electromagnetic field theory, establish a three-dimensional or two-dimensional finite element model of the current transformer core and winding, and define the nonlinear magnetic characteristics of the core material (such as magnetization curve, remanence characteristics), winding structure parameters (number of turns, wire cross-sectional area), and boundary conditions (such as magnetic field strength, eddy current effect); Circuit part: Construct the primary-side excitation circuit and secondary-side load circuit, considering power supply characteristics (such as sine wave, non-sine wave containing harmonics), load impedance (resistive, inductive or nonlinear load), and control components (such as switches, lightning arresters); Coupling mechanism: Through the magnetic field-circuit interface, interact the magnetic field distribution (such as magnetic flux density) calculated by the finite element and the current and voltage parameters calculated by the circuit in real time to realize the co-simulation of the electromagnetic transient process; Embed the simulation characteristic data generated in S5, including the corrected initial saturation flux density, corrected initial remanence coefficient, and harmonic component currents of each order, into the model: Saturation flux density: Correct the core saturation characteristics and adjust the inflection point of the magnetization curve; Initial remanence coefficient: Correct the hysteresis loop and affect the remanence distribution; The current of each harmonic component is used as an excitation source and loaded onto the primary winding to simulate the actual current waveform containing harmonics; Using the finite element method combined with the finite difference time domain method, the simulation time domain is discretized into time step lengths, and the Maxwell's equations and circuit equations are gradually solved; For the core saturation and hysteresis effects, a piecewise linearization method is used to update the permeability and remanence coefficient in real time; Coupled solution of the magnetic field distribution and circuit response: The finite element module calculates the induced electromotive force of the winding and feeds it back to the circuit module as a voltage source; The circuit module calculates the winding current, which is used as the excitation source of the finite element module to update the magnetic field distribution.

[0053] Based on the above embodiments, calculate the amplitude deviation rate between the simulated value and the reference value of the secondary current. The specific process is as follows: Extract the simulated value of the secondary current from the finite element-circuit coupling model. Based on the current transformer ratio and the actual value of the primary current, calculate the reference value of the secondary current. The formula is as follows: ; where, is the reference value of the secondary current, is the actual value of the primary current, is the current transformer ratio; ; where, is the amplitude deviation rate between the simulated value and the reference value of the secondary current, is the simulated value of the secondary current; Extract the simulated waveform of the secondary current from the finite element-circuit coupling model. Based on the simulated waveform of the secondary current, obtain the phase angle of the simulated value of the secondary current; ; where, is the phase deviation rate between the simulated value and the reference value of the secondary current, is the phase angle of the simulated value of the secondary current, is the phase angle of the reference value of the secondary current.

[0054] S7. Process the amplitude deviation rate and phase deviation rate to generate a comprehensive operation state coefficient. Compare the comprehensive operation state coefficient with a preset threshold. According to the comparison result, judge the operation state of the magnet valve type current transformer.

[0055] Table 3. Variation of the comprehensive operation state coefficient with the amplitude deviation rate and phase deviation rate

[0056] According to Table 3, the comprehensive coefficient of the operating state is generally positively correlated with the amplitude deviation rate and the phase deviation rate. However, for some data (such as item numbers 8, 13, and 16), due to the weight coefficient or data fluctuation, the comprehensive coefficient and the single variable do not change completely linearly, reflecting that the actual relationship is affected by multiple factors and has local non-linear characteristics. However, the dominant law of positive correlation is clear.

[0057] According to Figure 6 、 7 it can be seen that the comprehensive coefficient of the operating state increases positively with the increase of the amplitude deviation rate, and the fitting line is a straight line, reflecting an approximately linear positive correlation; the comprehensive coefficient of the operating state increases positively with the increase of the phase deviation rate, and the fitting line is a straight line, reflecting an approximately linear positive correlation.

[0058] On the basis of the above embodiments, the amplitude deviation rate and the phase deviation rate are processed to generate the comprehensive coefficient of the operating state. The formula is as follows: ; Among them, is the comprehensive coefficient of the operating state. The comprehensive coefficient of the operating state is used to combine two indicators, namely the amplitude deviation rate and the phase deviation rate, to comprehensively reflect the quantitative index of the operating state of the current transformer. The larger the comprehensive coefficient of the operating state, the higher the degree of deviation of the current transformer from the ideal operating state; On this basis, it should be noted that: When the amplitude deviation rate increases, it means that the deviation between the secondary current amplitude and the ideal value increases. The core reason is that the magnetization curve of the magnetic core material of the current transformer has non-linear characteristics. When the primary current increases to a certain extent, the magnetic core enters the saturation region, the magnetic permeability decreases, and the exciting current surges, resulting in the inability of the secondary current amplitude to be amplified proportionally and the occurrence of amplitude attenuation; when the amplitude deviation rate rises, the internal leakage flux of the transformer increases, and the winding resistance loss (copper loss) and the magnetic core eddy current loss (iron loss) also increase accordingly. These losses will further affect the magnetization efficiency of the magnetic core, forming a vicious cycle of "saturation - loss - increased saturation", significantly increasing the degree of deviation of the transformer from the ideal state.

[0059] When the phase deviation rate increases, there is a hysteresis effect in the magnetization curve of the magnetic core material. When the primary current changes, the magnetic core magnetic flux changes lagging behind the exciting current, resulting in a phase shift of the secondary current; the leakage inductance and distributed capacitance of the transformer winding form an LC network, which causes phase delay for currents of different frequencies, especially when high-order harmonics exist, the phase deviation is more significant; when the magnetic core is saturated, the harmonic components in the exciting current increase, and the non-linear phase shift intensifies, causing the phase difference to deviate from the ideal value in the linear region.

[0060] Therefore, the comprehensive coefficient of the operating state is positively correlated with both the amplitude deviation rate and the phase deviation rate.

[0061] The amplitude deviation rate is the degree of deviation of the output signal from the ideal transmission ratio when the mutual inductor is in steady-state operation. Its essence reflects the steady-state characteristics such as the magnetic permeability stability of the core material and winding losses. For example, core saturation will cause a decrease in magnetic permeability, resulting in an output amplitude lower than the theoretical value, forming an amplitude deviation.

[0062] There is a hysteresis loop in the magnetization curve of the core material. When the primary current changes, the change in magnetic flux density lags behind the magnetic field strength, resulting in the phase of the secondary current lagging behind the primary current, forming a phase deviation. The worse the core material (such as large hysteresis loss of silicon steel sheets), the higher the phase deviation rate.

[0063] The physical mechanisms of amplitude deviation and phase deviation are independent of each other. Amplitude deviation is mainly determined by "energy attenuation" factors such as magnetic circuit loss and winding impedance; phase deviation is dominated by "time offset" factors such as time delay and hysteresis effect in electromagnetic induction; the two do not directly affect each other. Therefore, the degree of deviation of the current transformer from the ideal operating state can be regarded as the superposition of two independent components.

[0064] In summary, the above functional form is used to express the functional relationship between the error comprehensive coefficient, the proportional deviation rate, and the time difference.

[0065] In the formula, is the weight coefficient of the amplitude deviation rate, is the weight coefficient of the phase deviation rate, and and The specific values of are determined by the analytic hierarchy process. The specific logic is as follows: Mark the two indicators of the amplitude deviation rate and the phase deviation rate, determine the numerical values of the relative importance between each pair through the nine-scale method, and construct a judgment matrix. Among them, mark the index of the amplitude deviation rate as 1 and the index of the phase deviation rate as 2. The constructed judgment matrix is: ; Among them, , are all the indices of the indicators, and , , represents that the amplitude deviation rate with index is the importance of the operating state comprehensive coefficient relative to the phase deviation rate with index v, The specific value of is determined by relevant experts using the 1-9 scoring method, represents that the amplitude deviation rate with index is extremely important for the operating state comprehensive coefficient compared to the phase deviation rate with index v. Indicates that the amplitude deviation rate with index is extremely unimportant to the comprehensive operation state coefficient compared to the phase deviation rate with index v; Divide each element value in the judgment matrix by the sum of its column to obtain a normalized judgment matrix. Calculate the mean value of each row element value in the normalized judgment matrix, and use the mean value of the first row element value as the proportionality coefficient of the amplitude deviation rate, and use the mean value of the second row element value as the proportionality coefficient of the phase deviation rate. Under the constraint that the sum of the scaled values is equal to 1, perform equal-proportion scaling on the amplitude deviation rate and the phase deviation rate, and use the scaled values as the corresponding weights.

[0066] Based on the above embodiments, according to the comparison results, judge the operation state of the magnetic valve type current transformer. The specific process is as follows: When , the magnetic valve type current transformer is in a normal operation state; When , the magnetic valve type current transformer is in a warning state; When , the magnetic valve type current transformer is in a fault state.

[0067] Wherein, is the critical value for dividing the normal operation and the warning state, is the critical value for dividing the warning and the fault state; According to the accuracy level of the current transformer (such as 0.1 level, 0.5 level, etc.), its rated error usually has clear limitations (for example, the rated current error of a 0.5-level transformer does not exceed ±0.5%), can be set as the upper limit value of the rated error (such as 0.5%), indicating the maximum allowable error during normal operation; artificially set abnormal working conditions such as magnetic valve saturation and winding faults, record mutation points or progressive growth trends, and determine in combination with the initial characteristics of the fault.

[0068] Please refer to Figure 2 , the present invention also provides a technical solution: A magnetic valve type current transformer electromagnetic transient simulation system, the system is used to execute any one of the above-mentioned magnetic valve type current transformer electromagnetic transient simulation methods, including: A data acquisition module, which is used to obtain the initial saturation magnetic flux density and the initial remanence coefficient of the iron core material in a standard environment and under the state of not being externally electromagnetically excited, collect the electromagnetic, magnetic field, control and environmental data of the current transformer at multiple consecutive test times, extract the magnetic density peak value, the magnetic density harmonic distortion rate and the current harmonic distortion rate based on the collected electromagnetic data and magnetic field data, and construct monitoring characteristic data; A data prediction model construction module, which is used to construct a data prediction model based on LSTM, take the monitoring feature data at multiple previous test moments as input, and take the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at the next test moment as label outputs to train the data prediction model; A simulation module, which is used to input the monitoring feature data at the current moment t and multiple previous test moments into the data prediction model to predict the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at the t+1 moment; A predicted value correction module, which is used to calculate the standard deviations of the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at the t+1 moment and the previous n-1 moments, compare the standard deviations of each parameter with their respective preset thresholds, and obtain the corrected values of the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at the t+1 moment according to the comparison results; A simulation data correction module, which is used to obtain the corrected initial saturation magnetic flux density according to the initial saturation magnetic flux density and the corrected magnetic flux density harmonic distortion rate, obtain the corrected initial residual magnetism coefficient based on the initial residual magnetism coefficient, the corrected magnetic flux density peak value, and the initial saturation magnetic flux density, extract the harmonic components of each order through Fourier transform based on the corrected electromagnetic data and current harmonic distortion rate, superimpose the extracted harmonic components, reconstruct the waveform and extract the current of the harmonic components of each order; A simulation module, which is used to construct a finite element-circuit coupling model, input the corrected initial saturation magnetic flux density and the corrected initial residual magnetism coefficient in the simulation feature data as parameters, take the current of the harmonic components of each order as the circuit excitation condition to perform electromagnetic transient simulation, obtain the simulation value of the secondary side current simulation waveform, and calculate the amplitude deviation rate and phase deviation rate between the secondary side current simulation value and the reference value; A judgment module, which is used to process the amplitude deviation rate and phase deviation rate to generate a comprehensive operation state coefficient, compare the comprehensive operation state coefficient with a preset threshold, and judge the operation state of the magnetic valve type current transformer according to the comparison results.

[0069] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0070] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will realize that the units and algorithm steps of each example described in connection with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0071] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units. They may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0072] As described above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application.

Claims

1. A method for electromagnetic transient simulation of a magnetic valve type current transformer, characterized in that, The specific steps include: S1. Obtain the initial saturation magnetic flux density and initial remanence coefficient of the iron core material in a standard environment and under the state of no external electromagnetic excitation. Collect the electromagnetic, magnetic field, control, and environmental data of the current transformer at multiple consecutive test times. Based on the collected electromagnetic data and magnetic field data, extract the magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate, and construct monitoring characteristic data; S2. Build a data prediction model based on LSTM. Use the monitoring characteristic data of the previous multiple test times as input, and use the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate of the next test time as label outputs to train the data prediction model; S3. Input the monitoring characteristic data of the current time t and the previous multiple test times into the data prediction model to predict the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at time t + 1; S4. Calculate the standard deviations of the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at time t + 1 and the previous n - 1 times. Compare the standard deviations of each parameter with their respective preset thresholds. According to the comparison results, obtain the correction values of the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at time t + 1; S5. According to the initial saturation magnetic flux density and the corrected magnetic flux density harmonic distortion rate, obtain the corrected initial saturation magnetic flux density. Based on the initial remanence coefficient, the corrected magnetic flux density peak value, and the initial saturation magnetic flux density, obtain the corrected initial remanence coefficient. Based on the corrected electromagnetic data and current harmonic distortion rate, extract each harmonic component through Fourier transform, superimpose the extracted harmonic components, reconstruct the waveform, and extract the current of each harmonic component; S6. Build a finite element - circuit coupling model. Input the corrected initial saturation magnetic flux density and initial remanence coefficient in the simulation characteristic data as parameters, and use the current of each harmonic component as the circuit excitation condition for electromagnetic transient simulation to obtain the simulation value of the secondary side current simulation waveform, and calculate the amplitude deviation rate and phase deviation rate between the secondary side current simulation value and the reference value; S7. Process the amplitude deviation rate and phase deviation rate to generate a comprehensive operation state coefficient. Compare the comprehensive operation state coefficient with the preset threshold. According to the comparison results, judge the operation state of the magneto - valve current transformer.

2. The electromagnetic transient simulation method of the magnetic valve type current transformer according to claim 1, wherein, The electromagnetic data includes the secondary side output current and voltage; the magnetic field data is the magnetic flux density data at the winding winding place. The control data includes the waveform, amplitude, frequency, and phase of the primary side excitation current; the environmental data includes the environmental temperature and environmental humidity; the monitoring characteristic data includes electromagnetic data, magnetic field data, control data, environmental data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate.

3. The electromagnetic transient simulation method of the magnetic valve type current transformer according to claim 2, characterized in that Compare the standard deviations of each parameter with their respective preset thresholds. According to the comparison results, obtain the correction values of the secondary side output current, secondary side output voltage, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at time t + 1. The specific process is as follows: When it indicates that the th parameter is stable at the (t + 1)-th moment and the previous n - 1 moments, and the mean value is used as the correction value; ； Among them, is the corrected secondary side output current, is the corrected secondary side output voltage, is the corrected peak magnetic flux density, is the corrected magnetic flux density harmonic distortion rate, is the corrected current harmonic distortion rate; Wherein, is the average value of the secondary-side current at the moment t + 1 and the previous n - 1 moments, is the average value of the secondary-side voltage at the moment t + 1 and the previous n - 1 moments, is the average value of the peak magnetic flux density at the moment t + 1 and the previous n - 1 moments, is the average value of the magnetic flux density harmonic distortion rate at the moment t + 1 and the previous n - 1 moments, is the average value of the current harmonic distortion rate at the moment t + 1 and the previous n - 1 moments; In the formula, is the standard deviation of the th parameter, is the threshold corresponding to the standard deviation of the th parameter, , correspond to the secondary side output current, secondary side output voltage, peak magnetic flux density, magnetic flux density harmonic distortion rate, and current harmonic distortion rate respectively; When , it indicates that the th parameter is unstable at the (t + 1)-th moment and the previous n - 1 moments. After removing the outliers, the median is selected as the correction value. The specific steps are as follows: Sort all kinds of parameters from small to large to form an ordered sequence corresponding to each kind of parameter one by one; For an ordered sequence corresponding to the same parameter, calculate the 25th percentile in the ordered sequence and the 75th percentile ; Calculate the interquartile range : ； Outlier range: ； Remove all data points within the outlier range from the ordered sequence to obtain a valid data subset; For the valid data subset, calculate its median as the correction value: When the number of valid data is odd, the median is the value at the middle position of the subset; When the number of valid data is even, the median is the average of the two middle values in the subset.

4. The electromagnetic transient simulation method of the magnetic valve type current transformer according to claim 3, wherein Based on the initial saturation magnetic flux density and the corrected magnetic flux density harmonic distortion rate, obtain the corrected initial saturation magnetic flux density, according to the following formula: ； Among them, is the corrected initial saturation magnetic flux density, is the initial saturation magnetic flux density, is the harmonic distortion rate of magnetic flux density after correction; Based on the initial remanence coefficient, the corrected magnetic flux density peak value, and the initial saturation magnetic flux density, obtain the corrected initial remanence coefficient, according to the following formula: ； Among them, is the corrected initial remanence coefficient, is the initial remanence coefficient, is the proportionality coefficient, .

5. The electromagnetic transient simulation method of the magnetic valve type current transformer according to claim 4, wherein, Calculate the amplitude deviation rate between the simulated value and the reference value of the secondary-side current. The specific process is as follows: Extract the simulated value of the secondary-side current from the finite element-circuit coupling model. Based on the current transformer turns ratio and the actual value of the primary-side current, calculate the reference value of the secondary-side current, according to the following formula: ； wherein, is the reference value of the secondary side current, is the actual value of the primary side current, is the turns ratio of the current transformer; ； Among them, is the amplitude deviation rate between the secondary side current simulation value and the reference value, is the secondary side current simulation value; Extract the simulated waveform of the secondary-side current from the finite element-circuit coupling model. Based on the simulated waveform of the secondary-side current, obtain the phase angle of the simulated value of the secondary-side current; ； Among them, is the phase deviation rate between the secondary side current simulation value and the reference value, is the phase angle of the secondary side current simulation value, is the phase angle of the secondary side current reference value.

6. The electromagnetic transient simulation method of the magnetic valve type current transformer according to claim 1, characterized in that Perform data processing on the amplitude deviation rate and the phase deviation rate to generate a comprehensive operation state coefficient, according to the following formula: ； Among them, is the comprehensive coefficient of the operating state, , are the amplitude deviation rate and phase deviation rate of the secondary side current simulation value and the reference value; wherein, is the weight coefficient of the amplitude deviation rate, is the weight coefficient of the phase deviation rate, and and The specific values of are determined by the analytic hierarchy process.

7. The electromagnetic transient simulation method of the magnetic valve type current transformer according to claim 6, characterized in that, Based on the comparison result, judge the operation state of the magnetic valve type current transformer. The specific process is as follows: When the magnetic valve type current transformer is in the normal operation state; When the magnetic valve type current transformer is in a warning state; When the magnet valve type current transformer is in a fault state; Among them, is the critical value for dividing the normal operation and warning states, is the critical value for dividing the warning and failure states.

8. An electromagnetic transient simulation system for a magnetic valve type current transformer, the system being used to execute an electromagnetic transient simulation method for a magnetic valve type current transformer according to any one of claims 1-7, characterized in that, Including: A data acquisition module, used to obtain the initial saturation magnetic flux density and the initial remanence coefficient of the iron core material in a standard environment and without external electromagnetic excitation, collect the electromagnetic, magnetic field, control, and environmental data of the current transformer at multiple consecutive test times, extract the magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate based on the collected electromagnetic data and magnetic field data, and construct monitoring characteristic data; A data prediction model construction module, used to construct a data prediction model based on LSTM, use the monitoring characteristic data of the previous multiple test times as input, and output the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate of the next test time as labels, and train the data prediction model; A simulation module, used to input the monitoring characteristic data of the current time t and the previous multiple test times into the data prediction model to predict the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at time t + 1; A predicted value correction module, used to calculate the standard deviation of the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at time t + 1 and the previous n - 1 times, compare the standard deviation of each parameter with its respective preset threshold, and based on the comparison result, obtain the corrected values of the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at time t + 1; A simulated data correction module, used to obtain the corrected initial saturation magnetic flux density based on the initial saturation magnetic flux density and the corrected magnetic flux density harmonic distortion rate, obtain the corrected initial remanence coefficient based on the initial remanence coefficient, the corrected magnetic flux density peak value, and the initial saturation magnetic flux density, extract each harmonic component through Fourier transform based on the corrected electromagnetic data and current harmonic distortion rate, superimpose the extracted harmonic components, reconstruct the waveform and extract the current of each harmonic component; A simulation module, which is used to construct a finite element-circuit coupling model, input the corrected initial saturation magnetic flux density and the corrected initial remanence coefficient in the simulation characteristic data as parameters, take the harmonic component currents as circuit excitation conditions to perform electromagnetic transient simulations, obtain the simulation values of the secondary side current waveforms, and calculate the amplitude deviation rate and phase deviation rate between the secondary side current simulation values and the reference values; A judgment module, which is used to process the amplitude deviation rate and phase deviation rate to generate a comprehensive operation state coefficient, compare the comprehensive operation state coefficient with a preset threshold, and judge the operation state of the magneto-valve current transformer according to the comparison result.

Images