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

Abstract

The present invention provides a method and system for electromagnetic transient simulation of a magnetic valve-type current transformer, relating to the technical field of power system simulation. The specific steps include: collecting electromagnetic, magnetic field, control, and environmental data; extracting magnetic flux peak value, magnetic flux harmonic distortion rate, and current harmonic distortion rate; and constructing monitoring feature data; constructing a data prediction model to dynamically predict electromagnetic parameters at the next moment, obtaining electromagnetic data, magnetic flux peak value, magnetic flux harmonic distortion rate, and current harmonic distortion rate correction values; performing electromagnetic transient simulation using a finite element-circuit coupling model, calculating amplitude deviation rate and phase deviation rate, and generating a comprehensive operating state coefficient to determine the operating state of the current transformer. Through multi-dimensional data acquisition, time-series dynamic modeling, and coupled simulation, the present invention addresses the problems of poor fixed parameter adaptability, insufficient capture of time-series features, and low simulation accuracy, significantly improving the accuracy of saturation characteristic simulation, transient prediction accuracy, and secondary-side current simulation.

Description

Technical Field

[0001] The present invention relates to the technical field of power system simulation, and in particular to a method and system for simulating electromagnetic transients of a magnetic valve type current transformer. Background Art

[0002] Magnetic valve current transformers (MTs) are widely used in power system protection and renewable energy grid integration due to their controllable core saturation characteristics and highly accurate transient response. Their core function is to precisely transmit high currents from the primary side to low currents on the secondary side through electromagnetic coupling. Accurately simulating saturation characteristics during electromagnetic transients is key to evaluating their reliability.

[0003] Publication No. CN117910239A provides a method, system, device, and medium for simulating electromagnetic transients of a current transformer. The method includes the following steps: simulating the hysteresis characteristics of the current transformer using a nonlinear element, simulating the excitation current using an injected current source, and constructing an electromagnetic transient model of the current transformer; and performing electromagnetic transient simulation of the current transformer based on the electromagnetic transient model. The electromagnetic transient model of the current transformer developed in this method easily extracts magnetization and hysteresis parameters from measured data, which are 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. As can be seen from the above statements, the existing method only constructs a model by simulating hysteresis characteristics through nonlinear elements and simulating excitation current through injected current sources, and does not fully consider the coupling influence of multiple factors and dynamic timing characteristics in the electromagnetic transient process of the magnetic valve current transformer: on the one hand, the electromagnetic transient process of the magnetic valve current transformer is affected by the coupling of multi-dimensional variables such as load conditions, frequency range, environmental parameters, and control signals, but the existing technology does not mention the collection, analysis and parameter correction mechanism of the above dynamic variables, and the model is difficult to adapt to complex working conditions; on the other hand, the transient response of the magnetic valve current transformer has a 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 dynamically model historical data. It is unable to 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 this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

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

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for simulating electromagnetic transients of a magnetic valve type current transformer, comprising the following steps:

[0009] S1. Obtain the initial saturation magnetic flux density and initial remanence coefficient of the core material in a standard environment and without external electromagnetic excitation. Collect electromagnetic, magnetic field, control, and environmental data of the current transformer at multiple consecutive test moments. Based on the collected electromagnetic and magnetic field data, extract the peak magnetic flux density, magnetic flux harmonic distortion rate, and current harmonic distortion rate to construct monitoring feature data.

[0010] S2. Build a data prediction model based on LSTM, using monitoring feature data from multiple test moments as input and electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate from the next test moment as label outputs to train the data prediction model.

[0011] 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 density peak, magnetic density harmonic distortion rate and current harmonic distortion rate at time t+1;

[0012] 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 the respective preset threshold value, and obtain the corrected value of the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at time t+1 based on the comparison result;

[0013] S5. 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, 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 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;

[0014] S6. Construct a finite element-circuit coupling model, input the corrected initial saturation magnetic flux density and initial remanence coefficient from the simulation characteristic data as parameters, perform electromagnetic transient simulation using the harmonic component currents as circuit excitation conditions, obtain the simulated value of the secondary side current waveform, and calculate the amplitude deviation rate and phase deviation rate of the secondary side current simulation value and the reference value;

[0015] S7. Process the amplitude deviation rate and the phase deviation rate to generate a comprehensive operating status coefficient, compare the comprehensive operating status coefficient with a preset threshold, and determine the operating status of the magnetic valve type current transformer based on the comparison result.

[0016] Furthermore, the electromagnetic data includes the secondary side output current and voltage; the magnetic field data is the magnetic flux density data at the winding point, and the control data includes the waveform, amplitude, frequency, and phase of the primary side excitation current; the environmental data includes the ambient temperature and ambient 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.

[0017] Furthermore, the standard deviation of each parameter is compared with the respective preset thresholds. Based on the comparison results, 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 correction value at time t+1 are obtained. The specific process is as follows:

[0018] when When The parameters are stable at time t+1 and the previous n-1 moments, and the mean value is used as the correction value;

[0019] ;

[0020] in, is the corrected secondary side output current, is the corrected secondary side output voltage, is the corrected magnetic density peak value, is the corrected magnetic density harmonic distortion rate, is the corrected current harmonic distortion rate;

[0021] Where, is the average value of the secondary current at time t+1 and the previous n-1 moments, is the average value of the secondary side voltage at time t+1 and the previous n-1 moments, is the average of the peak magnetic density at time t+1 and the previous n-1 moments, is the average value of the magnetic field harmonic distortion rate at time t+1 and the previous n-1 moments, is the average value of the current harmonic distortion rate at time t+1 and the previous n-1 moments;

[0022] Where, For the The standard deviation of the parameters, For the The threshold corresponding to the standard deviation of the parameters, , They 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 respectively;

[0023] when When The parameters are unstable at time t+1 and the previous n-1 times. After removing the outliers, the median is selected as the correction value. The specific steps are as follows:

[0024] Sort all parameters from small to large to form an ordered sequence corresponding to each parameter;

[0025] For the ordered sequence corresponding to the same parameter, calculate the 25th percentile in the ordered sequence and the 75th percentile ;

[0026] Calculate the interquartile range :

[0027] ;

[0028] Outlier range:

[0029] ;

[0030] According to the outlier range, all data points falling within the outlier range are removed from the ordered sequence to obtain a valid data subset;

[0031] For the valid data subset, calculate the median as the correction value:

[0032] When the number of valid data is odd, the median is the value in the middle of the subset;

[0033] When the number of valid data is even, the median is the average of the two middle values ​​of the subset.

[0034] Furthermore, according to the initial saturation magnetic flux density and the corrected magnetic flux harmonic distortion rate, the corrected initial saturation magnetic flux density is obtained according to the following formula:

[0035] ;

[0036] in, is the corrected initial saturation flux density, is the initial saturation magnetic flux density, is the corrected magnetic density harmonic distortion rate;

[0037] Based on the initial remanence coefficient, the corrected peak magnetic flux density and the initial saturation magnetic flux density, the corrected initial remanence coefficient is obtained according to the following formula:

[0038] ;

[0039] in, is the corrected initial remanence coefficient, is the initial remanence coefficient, is the proportionality coefficient, .

[0040] Furthermore, the amplitude deviation rate between the secondary side current simulation value and the reference value is calculated. The specific process is as follows:

[0041] The simulated value of the secondary current is extracted from the finite element-circuit coupling model. Based on the current transformer ratio and the actual value of the primary current, the reference value of the secondary current is calculated according to the following formula:

[0042] ;

[0043] in, is the reference value of the secondary side current, is the actual value of the primary side current, is the current transformer ratio;

[0044] ;

[0045] in, is the amplitude deviation rate between the secondary side current simulation value and the reference value, is the secondary side current simulation value;

[0046] Extract the secondary side current simulation waveform from the finite element-circuit coupling model, and obtain the phase angle of the secondary side current simulation value based on the secondary side current simulation waveform;

[0047] ;

[0048] in, 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.

[0049] Furthermore, the amplitude deviation rate and phase deviation rate are processed to generate the comprehensive coefficient of the operating status, according to the following formula:

[0050] ;

[0051] in, is the comprehensive coefficient of operating status, 、 The amplitude deviation rate and phase deviation rate of the secondary side current simulation value and the reference value;

[0052] Where, is the weight coefficient of the amplitude deviation rate, is the weight coefficient of the phase deviation rate, and and The specific value of is determined by the hierarchical analysis method.

[0053] Furthermore, based on the comparison results, the operating state of the magnetic valve type current transformer is determined. The specific process is as follows:

[0054] when When , the magnetic valve type current transformer is in normal operating state;

[0055] when When , the magnetic valve current transformer is in the warning state;

[0056] when When , the magnetic valve type current transformer is in fault state.

[0057] in, To divide the critical values ​​of normal operation and warning state, It is the critical value for dividing the warning and fault states.

[0058] To achieve the above object, the present invention further provides the following technical solutions:

[0059] A magnetic valve type current transformer electromagnetic transient simulation system, the system being used to execute any of the above-mentioned magnetic valve type current transformer electromagnetic transient simulation methods, comprising:

[0060] The data acquisition module is used to obtain the initial saturation magnetic flux density and initial remanence coefficient of the core material in a standard environment and without external electromagnetic excitation. It also collects electromagnetic, magnetic field, control, and environmental data of the current transformer at multiple consecutive test moments. Based on the collected electromagnetic and magnetic field data, it extracts the peak magnetic flux density, magnetic flux harmonic distortion rate, and current harmonic distortion rate to construct monitoring feature data.

[0061] The data prediction model construction module is used to build a data prediction model based on LSTM. It uses the monitoring feature data of multiple test moments as input and the electromagnetic data, magnetic density peak value, magnetic density harmonic distortion rate, and current harmonic distortion rate of the next test moment as label output to train the data prediction model.

[0062] The simulation module is used to input the monitoring characteristic data of the current time t and the previous 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;

[0063] The predicted value correction module is 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 the respective preset threshold value, and obtain the correction value of the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at time t+1 based on the comparison result;

[0064] A simulation data correction module is used to obtain a corrected initial saturation magnetic flux density based on the initial saturation magnetic flux density and the corrected magnetic flux harmonic distortion rate, obtain a corrected initial remanence coefficient based on the initial remanence coefficient, the corrected magnetic flux peak value and the initial saturation magnetic flux density, extract each harmonic component 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 each harmonic component;

[0065] A 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, perform electromagnetic transient simulation using each harmonic component current as a circuit excitation condition, obtain the simulated value of the simulated waveform of the secondary side current, and calculate the amplitude deviation rate and phase deviation rate of the simulated value of the secondary side current and the reference value;

[0066] The judgment module is used to process the amplitude deviation rate and the phase deviation rate to generate an operating state comprehensive coefficient, compare the operating state comprehensive coefficient with a preset threshold, and judge the operating state of the magnetic valve type current transformer based on the comparison result.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] The present invention collects the initial saturation magnetic flux density and initial remanence coefficient of the core material under standard conditions, and combines them with real-time monitored data such as magnetic flux peak value and harmonic distortion rate to dynamically correct the initial parameters, extract characteristic quantities such as magnetic flux peak value, magnetic flux harmonic distortion rate, and current harmonic distortion rate, and construct a monitoring system that includes steady-state accuracy (amplitude) and dynamic process (harmonics);

[0069] Input current and historical monitoring characteristic data into the prediction model to predict the electromagnetic parameters, magnetic flux density peak value, and harmonic distortion rate at time t+1, and calculate the correction value. Use the corrected initial saturation magnetic flux density and initial remanence coefficient as parameter inputs, and each harmonic component current as the circuit excitation condition to perform electromagnetic transient simulation, obtain the simulation value of the secondary side current simulation waveform, and use this to calculate the amplitude deviation rate and phase deviation rate of the secondary side current simulation value and the reference value;

[0070] The amplitude deviation rate and the phase deviation rate are used to generate a comprehensive operating status coefficient, which is then compared with a preset threshold value. The operating status of the magnetic valve type current transformer is determined based on the comparison result.

[0071] In summary, through multi-dimensional data acquisition, timing dynamic modeling and coupled simulation, the problems of poor adaptability of fixed parameters, insufficient capture of timing characteristics and low simulation accuracy have been solved, and the saturation characteristic simulation accuracy, transient prediction accuracy and secondary side current simulation accuracy have been significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 Schematic diagram of the overall method flow of the present invention;

[0073] Figure 2 It is a block diagram of the module composition of the present invention;

[0074] Figure 3 Schematic diagram of the fitting of the corrected magnetic flux density harmonic distortion rate and the corrected initial saturation magnetic flux density according to the present invention;

[0075] Figure 4 Schematic diagram of the fitting of the corrected peak magnetic flux density and the corrected initial remanence coefficient of the present invention;

[0076] Figure 5 Schematic diagram of the fitting of the corrected initial saturation magnetic flux density and the corrected initial remanence coefficient of the present invention;

[0077] Figure 6 Schematic diagram of the fitting of the amplitude deviation rate and the comprehensive coefficient of the operating state of the present invention;

[0078] Figure 7 Schematic diagram of the fitting of the phase deviation rate and the comprehensive coefficient of the operating state of the present invention. DETAILED DESCRIPTION

[0079] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0080] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0081] Example 1:

[0082] See also Figure 1 , the present invention provides a technical solution:

[0083] A method for simulating electromagnetic transients of a magnetic valve type current transformer, comprising the following steps:

[0084] S1. Obtain the initial saturation magnetic flux density and initial remanence coefficient of the core material in a standard environment and without external electromagnetic excitation. Collect electromagnetic, magnetic field, control, and environmental data of the current transformer at multiple consecutive test moments. Based on the collected electromagnetic and magnetic field data, extract the peak magnetic flux density, magnetic flux harmonic distortion rate, and current harmonic distortion rate to construct monitoring feature data.

[0085] Based on 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 point, and the control data includes the waveform, amplitude, frequency, and phase of the primary side excitation current; the environmental data includes the ambient temperature and ambient 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.

[0086] The collection method 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, ambient temperature and humidity is as follows:

[0087] A high-precision current transformer is directly connected in series with the primary side circuit. Based on the principle of electromagnetic induction, the primary current change is sensed by a coil wound on a non-ferromagnetic material frame. The output voltage is proportional to the current differential. The current waveform is restored by the integration circuit. According to Ampere's loop law, the magnetic field generated by the primary current induces an electromotive force in the coil. The electromotive force is converted into a voltage signal through a sampling resistor or an integration circuit. The waveform and amplitude data are obtained after digitization by an analog-to-digital converter.

[0088] Perform fast Fourier transform on the collected current waveform to extract the fundamental frequency component, or calculate the time interval between adjacent cycles through the zero-crossing detection circuit to calculate the cycle, and then calculate the frequency according to the formula , , For the period; for the frequency signal, period , the phase change rate per unit time is Therefore, the time difference between the zero crossing point of the measurement signal and the zero crossing point of the reference is converted into an absolute phase.

[0089] By connecting a shunt resistor (non-inductive resistor) in series with the secondary circuit, the current signal is converted into a voltage signal (for example, a 1A / 5A current is converted into a 1V / 5V voltage through a 1Ω resistor), and then filtered and amplified by a differential amplifier and sent to the ADC (analog-to-digital converter) to obtain the secondary side output current.

[0090] A voltage transformer is connected in parallel at both ends of the secondary winding. The principle of electromagnetic induction is used to reduce the voltage according to the transformation ratio. Two resistors are connected in series to form a voltage divider circuit. This circuit is connected in parallel at both ends of the secondary winding and 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 free of interference. Similar to current acquisition, the voltage signal is converted into a digital value to obtain the secondary side output voltage data.

[0091] When using a Hall effect magnetometer probe, the probe needs to be close to the surface of the iron core where the winding is wound. The mechanical translation stage drives the probe to move along the axial direction of the winding, collects the magnetic density distribution point by point, constructs a two-dimensional magnetic density cloud map, and obtains the magnetic density data at the winding.

[0092] Use an integrated digital temperature sensor or thermocouple to measure the ambient temperature through the digital input channel of the DAQ or a dedicated temperature module.

[0093] Using a capacitive humidity sensor, the output voltage changes linearly with humidity, connected to the analog input channel to measure the ambient temperature.

[0094] Based on the above embodiment, the initial saturation magnetic flux density and initial remanence coefficient of the core material are obtained in a standard environment and without external electromagnetic excitation. The specific acquisition method is as follows:

[0095] The initial saturation magnetic flux density refers to the maximum value of the magnetic flux density when the core material approaches saturation as the magnetic field strength increases during the magnetization process. Using the DC magnetization curve method, the primary winding is connected to a DC power supply, and a standard resistor is connected in series to monitor the excitation current; the secondary winding is open-circuited, and a Tesla meter probe is connected to measure the surface magnetic flux density of the core. The DC current is slowly increased from zero. After each stabilization, the excitation current and the corresponding surface magnetic flux density of the core are recorded, the magnetic field strength is calculated, and a curve of the surface magnetic flux density of the core - magnetic field strength is drawn. When the surface magnetic flux density of the core tends to be flat as the magnetic field strength increases (the slope of the curve approaches zero), the surface magnetic flux density of the core is the initial saturation magnetic flux density.

[0096] 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. The DC method is used to excite the iron core to the saturation magnetic flux density, and the DC power supply is quickly disconnected. At the same time, the residual magnetic flux density of the iron core is measured with a Tesla meter to calculate the remanence coefficient.

[0097] On the basis of the above embodiment, the magnetic flux density peak value, magnetic flux density harmonic distortion rate and current harmonic distortion rate are extracted based on the collected electromagnetic data and magnetic field data. The specific method is as follows:

[0098] The magnetic field data (magnetic density data at the winding location) is filtered and denoised to eliminate high-frequency interference and baseline drift. The time series magnetic density data is framed, and the time window of each frame of data is determined according to the test frequency. An extreme value search is performed on each frame of magnetic density data to obtain the maximum magnetic density within the time period, i.e., the magnetic density peak value.

[0099] The magnetic density data is subjected to fast Fourier transform to convert the time domain signal into a frequency domain spectrum to obtain the amplitude and phase information of each harmonic. The fundamental frequency (such as the power frequency 50Hz) is defined as f0, and the amplitudes of the second and above harmonics (2f0, 3f0...) are extracted. The calculation formula for the magnetic density harmonic distortion rate is: ,in, is the magnetic field harmonic distortion rate, is the fundamental magnetic flux density amplitude, For the Subharmonic magnetic flux amplitude, is the index of the harmonic, , is the harmonic order.

[0100] Perform fast Fourier transform on the secondary output current in the electromagnetic data to obtain the fundamental current amplitude Hedi Subharmonic current amplitude , the calculation formula of current harmonic distortion rate is ,in, is the current harmonic distortion rate.

[0101] Based on the above embodiments, the collected or extracted electromagnetic data, magnetic field data, control data, environmental data, magnetic density peak value, magnetic density harmonic distortion rate and current harmonic distortion rate are normalized, and the data subsequently analyzed and processed are all the normalized electromagnetic data, magnetic field data, control data, environmental data, magnetic density peak value, magnetic density harmonic distortion rate and current harmonic distortion rate.

[0102] S2. Build a data prediction model based on LSTM, using monitoring feature data from multiple test moments as input and electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate from the next test moment as label outputs to train the data prediction model.

[0103] Based on the above embodiment, the training process of the data prediction model is as follows:

[0104] Input data: Characteristic data of the previous test moments (such as electromagnetic data, magnetic field data, control data, environmental data, magnetic density peak value, magnetic density harmonic distortion rate and current harmonic distortion rate of the previous 10 test moments) to form a "time series segment". For example, if the prediction The electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate and current harmonic distortion rate at the time of test are input as follows: Monitoring characteristic data at all times;

[0105] Output labels: electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate and current harmonic distortion rate at the next test moment;

[0106] The electromagnetic data, magnetic field data, control data, environmental data, magnetic density peak value, magnetic density harmonic distortion rate and current harmonic distortion rate are processed in windows, and the continuous data is cut into multiple "input-output pairs" (such as → , → );

[0107] Input layer: used to receive time series segments;

[0108] LSTM (Long Short-Term Memory) layers: 1-3 layers, each containing several "memory cells" to capture long-term dependencies;

[0109] Output layer: used to set the number of neurons according to the prediction target;

[0110] Time step: the number of test moments entered;

[0111] Training process:

[0112] Batch training: Update parameters with a small batch of data each time to avoid insufficient memory;

[0113] Loss function: measures the prediction error and uses mean square error;

[0114] Optimizer: Adopt Adam (Adaptive Moment Estimation) optimizer;

[0115] Termination condition: reaching the preset training rounds.

[0116] 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 density peak, magnetic density harmonic distortion rate and current harmonic distortion rate at time t+1;

[0117] 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 n-1 moments before it, compare the standard deviation of each parameter with the respective preset thresholds, and obtain the corrected 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 time t+1 based on the comparison results;

[0118] Among them, when n is 6, that is, the average 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 and the previous 5 times is calculated, and the average 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 a total of n times, and the time interval between the first n-1 times is calculated.

[0119] Based on the above embodiment, the standard deviation of each parameter is compared with the respective preset thresholds. According to the comparison results, 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 are obtained. The specific process is as follows:

[0120] when When The parameters at time t+1 and the previous n-1 moments are stably used as the mean value of the correction value;

[0121] ;

[0122] in, is the corrected secondary side output current, is the corrected secondary side output voltage, is the corrected magnetic density peak value, is the corrected magnetic density harmonic distortion rate, is the corrected current harmonic distortion rate;

[0123] Where, is the average value of the secondary current at time t+1 and the previous n-1 moments, is the average value of the secondary side voltage at time t+1 and the previous n-1 moments, is the average of the peak magnetic density at time t+1 and the previous n-1 moments, is the average value of the magnetic field harmonic distortion rate at time t+1 and the previous n-1 moments, is the average value of the current harmonic distortion rate at time t+1 and the previous n-1 moments;

[0124] Where, For the The standard deviation of the parameters, For the The threshold corresponding to the standard deviation of the parameters, , They 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 respectively;

[0125] when When When the parameters are unstable at time t+1 and the previous n-1 moments, the median is selected as the correction value after removing the outliers. The specific steps are as follows:

[0126] Sort all parameters from small to large to form an ordered sequence corresponding to each parameter;

[0127] For the ordered sequence corresponding to the same parameter, calculate the 25th percentile in the ordered sequence and the 75th percentile ;

[0128] Calculate the interquartile range :

[0129] ;

[0130] Outlier range:

[0131] ;

[0132] According to the outlier range, all data points falling within the outlier range are removed from the ordered sequence to obtain a valid data subset;

[0133] For the valid data subset, calculate the median as the correction value:

[0134] When the number of valid data is odd, the median is the value in the middle of the subset;

[0135] When the number of valid data is even, the median is the average of the two middle values ​​of the subset.

[0136] S5. Based on the initial saturation magnetic flux density and the corrected magnetic flux density harmonic distortion rate, obtain a corrected value of the initial saturation magnetic flux density, obtain a 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;

[0137] Table 1. Changes in the corrected initial saturation flux density with the predicted value of the magnetic flux harmonic distortion rate

[0138]

[0139] Table 1 shows that the corrected initial saturation flux density and the harmonic distortion rate of the magnetic flux density are significantly negatively correlated. Specifically, as the corrected harmonic distortion rate increases from 1% to 25%, the corrected initial saturation flux density decreases continuously from 1.784 to 1.432, and the overall downward trend exhibits nonlinear characteristics. While the decrease in some adjacent data points fluctuates (such as between numbers 4 and 5, and between numbers 7 and 8), the overall negative correlation trend is clear. This indicates that an increase in the corrected harmonic distortion rate leads to a decrease in the corrected initial saturation flux density.

[0140] according to Figure 3 It can be seen that as the corrected magnetic flux 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.

[0141] On the basis of the above embodiment, the corrected initial saturation magnetic flux density is obtained according to the initial saturation magnetic flux density and the corrected magnetic flux harmonic distortion rate, according to the following formula:

[0142] ;

[0143] in, is the corrected initial saturation flux density, is the initial saturation magnetic flux density, is the corrected magnetic density harmonic distortion rate;

[0144] On this basis, it should be noted that:

[0145] When the corrected magnetic field harmonic distortion rate When it increases, it indicates that the high-order harmonic content in the magnetic flux waveform increases, and the nonlinear magnetization effect of the core intensifies, including early saturation and increased hysteresis loss. The high-order harmonics will cause the core to enter the saturation state at a lower magnetic flux density, so the actual initial saturation magnetic flux density decreases;

[0146] When the corrected magnetic field harmonic distortion rate When it decreases, it indicates that the magnetic flux waveform is closer to a sine wave, the core operates in the linear magnetization region, and the saturation degree decreases. At this time, the core can withstand a higher magnetic flux density before entering the saturation state. Therefore, the actual initial saturation magnetic flux density approaches the initial value.

[0147] Therefore, the corrected initial saturation magnetic flux density and the corrected magnetic flux harmonic distortion rate are negatively correlated. Therefore, the above-mentioned 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 harmonic distortion rate.

[0148] By using the saturation flux density correction value, the model can more accurately simulate the saturation characteristics of the core in a harmonic environment, avoiding the underestimation of the saturation starting point and depth by traditional fixed-parameter models. In scenarios with high harmonic content, such as fault current (such as short circuit) or inrush current (such as no-load closing), the corrected saturation flux density makes the simulation results closer to the actual waveform distortion and phase shift.

[0149] Table 2. Changes of the corrected initial remanence coefficient with the initial remanence coefficient, corrected magnetic flux peak, corrected initial saturation flux density, and proportional coefficient

[0150]

[0151] According to Table 2, when the initial remanence coefficient (fixed at 0.9) and the proportional coefficient (fixed at 0.2) remain unchanged, as the corrected magnetic flux peak value increases from 1.2 to 1.68 and the corrected initial saturation magnetic flux density decreases from 1.68 to 1.44, the corrected initial remanence coefficient gradually increases from 0.81 to 0.9253. Therefore, the corrected initial remanence coefficient is positively correlated with the magnetic flux peak value and negatively correlated with the initial saturation magnetic flux density.

[0152] according to Figure 4 、 5 It can be seen that the corrected initial remanence coefficient increases positively with the increase of the corrected magnetic flux peak value, and the fitting line is a straight line, reflecting an approximately linear positive correlation law; the corrected initial remanence coefficient decreases negatively with the increase of the corrected initial saturation magnetic flux density, and the fitting line is a straight line, reflecting an approximately linear negative correlation law.

[0153] On the basis of the above embodiment, based on the initial remanence coefficient, the corrected magnetic flux density peak value and the initial saturation magnetic flux density, the corrected initial remanence coefficient is obtained according to the following formula:

[0154] ;

[0155] in, is the corrected initial remanence coefficient, is the initial remanence coefficient;

[0156] Where, is the proportionality coefficient, The essence of is to quantify the sensitivity of physical relationships. In current transformers, the remanence coefficient is limited by the material properties and the nonlinearity of the magnetization curve, and its changes are usually gradual rather than drastic fluctuations. Therefore, A smaller value is required. The lower limit of 0.1 ensures that the formula has sufficient response to the change of time parameters to avoid correction failure; the upper limit of 0.3 prevents the correction value from exceeding the maximum fluctuation range possible in physics, ensuring the rationality of the model.

[0157] On this basis, it should be noted that:

[0158] Corrected magnetic flux density peak Reflects the maximum magnetic induction intensity reached by the magnetic material during the magnetization process. When it increases, it means that the magnetic field strength applied to the material during the magnetization process is greater, and the magnetic domains inside the material will turn more fully to 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, the higher the degree of orderly 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 corrected initial remanence coefficient will increase accordingly.

[0159] Corrected initial saturation flux density It 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 flux density An increase in the remanence coefficient means that the material requires a higher magnetic field strength to reach saturation, meaning that the material's magnetic domains are "more difficult to fully magnetize." The remanence coefficient is inversely proportional to the difficulty of magnetizing the material to saturation. If the material is more difficult to saturate, then under the same magnetizing conditions, the magnetic domains will be less ordered, and the residual magnetic induction intensity after the external magnetic field is removed will also be smaller. For example, if a material has a higher saturation flux density, it is equivalent to having a larger "magnetic domain activity space," making it more difficult to "fill" it during magnetization, so the corrected initial remanence coefficient will also be reduced accordingly.

[0160] Therefore, the corrected initial remanence coefficient and the corrected magnetic flux density peak value are positively correlated, and the corrected initial remanence coefficient and the corrected initial saturation magnetic flux density are negatively correlated.

[0161] Therefore, the above-mentioned function form is used to express the functional relationship between the corrected initial remanence coefficient and the corrected magnetic flux peak value and the initial saturation magnetic flux density.

[0162] On the basis of the above embodiment, based on the corrected electromagnetic data and current harmonic distortion rate, each harmonic component is extracted by 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:

[0163] Extract the time-domain simulation waveform, sampling frequency, and number of sampling points of the secondary current from the finite element-circuit coupling model, ensuring that the sampling time is an integer multiple of the fundamental wave period;

[0164] The preprocessed time domain signal is converted into frequency domain representation to obtain a series of complex numbers, each of which corresponds 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;

[0165] For each complex frequency component, calculate its amplitude and phase;

[0166] Perform frequency domain conversion. According to the known fundamental frequency, find the closest frequency point in the frequency domain data. The complex number corresponding to this point is the fundamental component.

[0167] The original amplitude of the fundamental component is corrected to obtain the actual fundamental amplitude, and the phase angle of the fundamental is extracted as the reference for all harmonic phases. The amplitude correction formula is:

[0168] ;

[0169] in, is the corrected fundamental amplitude, is the original amplitude calculated by fast Fourier transform, is the number of sampling points;

[0170] Traverse each harmonic, starting from the second harmonic, and calculate the position of each harmonic in the frequency domain;

[0171] If the harmonic frequency exceeds half the sampling frequency, the harmonic is ignored;

[0172] Calculate the harmonic parameters and repeat the amplitude correction and phase extraction steps of the fundamental wave for each effective harmonic to obtain the actual amplitude of each harmonic and the phase angle relative to the fundamental wave;

[0173] The extracted fundamental wave and each harmonic component are superimposed according to their respective amplitudes and phases to generate a new time domain waveform, which should be close to the original simulation waveform but only contains the selected harmonic components;

[0174] 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 the harmonic component in the total signal;

[0175] Compare the calculated harmonic distortion rate with the predicted value of current harmonic distortion rate obtained through statistical correction to ensure that the error between the two is within the allowable range of the project.

[0176] S6. 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, perform electromagnetic transient simulation using the harmonic component currents as circuit excitation conditions, obtain the simulated value of the secondary side current waveform, and calculate the amplitude deviation rate and phase deviation of the secondary side current simulation value from the reference value;

[0177] Based on the above embodiment, a finite element-circuit coupling model is constructed. The corrected initial saturation magnetic flux density and initial remanence coefficient in the simulation characteristic data are input as parameters. The current of each harmonic component is used as the circuit excitation condition to perform electromagnetic transient simulation. The specific process is as follows:

[0178] Finite element analysis: Based on electromagnetic field theory, a three-dimensional or two-dimensional finite element model of the current transformer core and winding is established, defining the nonlinear magnetic properties of the core material (such as magnetization curve and residual magnetic properties), winding structure parameters (number of turns, conductor cross-sectional area), and boundary conditions (such as magnetic field strength and eddy current effects).

[0179] Circuitry: Construct the primary-side excitation circuit and the secondary-side load circuit, taking into account power supply characteristics (e.g., sinusoidal wave, non-sinusoidal wave with harmonics), load impedance (resistive, inductive, or nonlinear load), and control components (e.g., switches, lightning arresters).

[0180] Coupling mechanism: Through the magnetic field-circuit interface, the magnetic field distribution (such as magnetic flux density) calculated by finite element analysis interacts with the current and voltage parameters calculated by circuit analysis in real time, achieving collaborative simulation of electromagnetic transient processes.

[0181] The simulation characteristic data generated by S5, including the corrected initial saturation flux density, the corrected initial remanence coefficient, and each harmonic component current, are embedded into the model:

[0182] Saturation magnetic flux density: correct the core saturation characteristics and adjust the inflection point of the magnetization curve;

[0183] Initial remanence coefficient: modifies the hysteresis loop and affects the remanence distribution;

[0184] Each harmonic component current is used as an excitation source and loaded into the primary winding to simulate the actual current waveform containing harmonics;

[0185] The finite element method combined with the finite difference time domain method is used to discretize the simulation time domain into Maxwell's equations and circuit equations are solved step by step with a time step of

[0186] In view of the core saturation and hysteresis effect, a piecewise linearization method is used to update the magnetic permeability and remanence coefficient in real time;

[0187] Coupled solution of magnetic field distribution and circuit response:

[0188] The finite element module calculates the winding induced electromotive force and feeds it back to the circuit module as a voltage source;

[0189] The circuit module calculates the winding current, which serves as the excitation source for the finite element module to update the magnetic field distribution.

[0190] Based on the above embodiment, the amplitude deviation rate between the secondary side current simulation value and the reference value is calculated. The specific process is as follows:

[0191] The simulated value of the secondary current is extracted from the finite element-circuit coupling model. Based on the current transformer ratio and the actual value of the primary current, the reference value of the secondary current is calculated according to the following formula:

[0192] ;

[0193] in, is the reference value of the secondary side current, is the actual value of the primary side current, is the current transformer ratio;

[0194] ;

[0195] in, is the amplitude deviation rate between the secondary side current simulation value and the reference value, is the secondary side current simulation value;

[0196] Extract the secondary side current simulation waveform from the finite element-circuit coupling model, and obtain the phase angle of the secondary side current simulation value based on the secondary side current simulation waveform;

[0197] ;

[0198] in, 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.

[0199] S7. Process the amplitude deviation rate and the phase deviation rate to generate a comprehensive operating status coefficient, compare the comprehensive operating status coefficient with a preset threshold, and determine the operating status of the magnetic valve type current transformer based on the comparison result.

[0200] Table 3. Variation of comprehensive coefficient of operating status with amplitude deviation rate and phase deviation rate

[0201]

[0202] According to Table 3, the overall operating status comprehensive coefficient shows a positive correlation with the amplitude deviation rate and phase deviation rate. However, due to weight coefficients or data fluctuations, some data (such as serial numbers 8, 13, and 16) make the comprehensive coefficient and the change of a single variable not completely linear, reflecting that the actual relationship is affected by multiple factors and has local nonlinear characteristics. However, the dominant law of positive correlation is clear.

[0203] 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.

[0204] Based on the above embodiment, the amplitude deviation rate and the phase deviation rate are processed to generate the comprehensive coefficient of the operating state, according to the following formula:

[0205] ;

[0206] in, The comprehensive coefficient of the operating state is used to combine 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 greater the degree to which the current transformer deviates from the ideal operating state.

[0207] On this basis, it should be noted that:

[0208] In the amplitude deviation rate When it 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 current transformer core material has nonlinear characteristics. When the primary current increases to a certain extent, the core enters the saturation zone, the magnetic permeability decreases, and the excitation current surges, resulting in the secondary current amplitude cannot be amplified proportionally, resulting in amplitude attenuation; when the amplitude deviation rate increases, the internal leakage flux of the transformer increases, and the winding resistance loss (copper loss) and the core eddy current loss (iron loss) also increase accordingly. These losses will further affect the magnetization efficiency of the core, forming a vicious cycle of "saturation-loss-saturation aggravation", which significantly increases the degree to which the transformer deviates from the ideal state.

[0209] Phase deviation rate When the voltage increases, there is a hysteresis effect in the magnetization curve of the core material. When the primary current changes, the change of the core magnetic flux lags behind the excitation 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 produces phase delays for currents of different frequencies. Especially when high-order harmonics exist, the phase deviation is more significant. When the core is saturated, the harmonic components in the excitation current increase, the nonlinear phase shift intensifies, and the phase difference deviates from the ideal value in the linear region.

[0210] Therefore, the comprehensive coefficient of the operating state is positively correlated with the amplitude deviation rate and the phase deviation rate.

[0211] The amplitude deviation rate is the degree to which the output signal of a transformer deviates from the ideal transmission ratio during steady-state operation. It essentially reflects steady-state characteristics such as the core material's magnetic permeability stability and winding losses. For example, core saturation can cause a decrease in magnetic permeability, causing the output amplitude to fall below the theoretical value, resulting in amplitude deviation.

[0212] The magnetization curve of the core material has a hysteresis loop. When the primary current changes, the change in magnetic flux density lags behind the magnetic field strength, causing the secondary current phase to lag behind the primary current, forming a phase deviation. The worse the core material (such as large hysteresis loss of silicon steel sheet), the higher the phase deviation rate.

[0213] 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, so the degree to which the current transformer deviates from the ideal operating state can be regarded as the superposition of two independent components.

[0214] In summary, the above function form is used to express the functional relationship between the error comprehensive coefficient, proportional deviation rate and time difference.

[0215] Where, is the weight coefficient of the amplitude deviation rate, is the weight coefficient of the phase deviation rate, and and The specific value of is determined by the hierarchical analysis method, and the specific logic is as follows:

[0216] The two indicators of amplitude deviation rate and phase deviation rate are marked, and the relative importance between them is determined by the nine-scale method to construct a judgment matrix, in which the index of amplitude deviation rate is marked as 1 and the index of phase deviation rate is marked as 2. The constructed judgment matrix for:

[0217] ;

[0218] in, 、 are all indices of indicators, and , , Indicates that the index is The importance of the amplitude deviation rate relative to the phase deviation rate indexed by v to the comprehensive coefficient of the operating state, The specific value is determined by relevant experts using a 1-9 scoring method. Indicates that the index is The amplitude deviation rate is more important to the comprehensive coefficient of the operating state than the phase deviation rate with index v. Indicates that the index is The amplitude deviation rate is extremely unimportant to the comprehensive coefficient of the operating state compared to the phase deviation rate indexed as v;

[0219] Each element value in the judgment matrix is ​​divided by the sum of its columns to obtain a normalized judgment matrix. The mean of the element values ​​in each row of the normalized judgment matrix is ​​calculated, and the mean of the element values ​​in the first row is used as the proportional coefficient of the amplitude deviation rate, and the mean of the element values ​​in the second row is used as the proportional coefficient of the phase deviation rate. With the constraint that the sum of the scaled values ​​is equal to 1, the amplitude deviation rate and the phase deviation rate are scaled in equal proportion, and the scaled values ​​are used as the corresponding weights.

[0220] On the basis of the above embodiment, the operating state of the magnetic valve type current transformer is judged according to the comparison result. The specific process is as follows:

[0221] when When , the magnetic valve type current transformer is in normal operating state;

[0222] when When , the magnetic valve current transformer is in the warning state;

[0223] when When , the magnetic valve type current transformer is in fault state.

[0224] in, To divide the critical values ​​of normal operation and warning state, Critical values ​​for classifying warning and fault states;

[0225] According to the accuracy level of the current transformer (such as 0.1 level, 0.5 level, etc.), its rated error is usually clearly limited (for example, the rated current error of a 0.5 level transformer does not exceed ±0.5%). It can be set to the upper limit 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 failure, and record The mutation point or gradual growth trend is determined by combining the fault initiation characteristics. .

[0226] See also Figure 2 , the present invention also provides a technical solution:

[0227] A magnetic valve type current transformer electromagnetic transient simulation system, the system being used to execute any of the above-mentioned magnetic valve type current transformer electromagnetic transient simulation methods, comprising:

[0228] The data acquisition module is used to obtain the initial saturation magnetic flux density and initial remanence coefficient of the core material in a standard environment and without external electromagnetic excitation. It also collects electromagnetic, magnetic field, control, and environmental data of the current transformer at multiple consecutive test moments. Based on the collected electromagnetic and magnetic field data, it extracts the peak magnetic flux density, magnetic flux harmonic distortion rate, and current harmonic distortion rate to construct monitoring feature data.

[0229] The data prediction model construction module is used to build a data prediction model based on LSTM. It uses the monitoring feature data of multiple test moments as input and the electromagnetic data, magnetic density peak value, magnetic density harmonic distortion rate, and current harmonic distortion rate of the next test moment as label output to train the data prediction model.

[0230] The simulation module is used to input the monitoring characteristic data of the current time t and the previous 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;

[0231] The predicted value correction module is 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 the respective preset threshold value, and obtain the correction value of the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at time t+1 based on the comparison result;

[0232] A simulation data correction module is used to obtain a corrected initial saturation magnetic flux density based on the initial saturation magnetic flux density and the corrected magnetic flux harmonic distortion rate, obtain a corrected initial remanence coefficient based on the initial remanence coefficient, the corrected magnetic flux peak value and the initial saturation magnetic flux density, extract each harmonic component 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 each harmonic component;

[0233] A 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, perform electromagnetic transient simulation using each harmonic component current as a circuit excitation condition, obtain the simulated value of the simulated waveform of the secondary side current, and calculate the amplitude deviation rate and phase deviation rate of the simulated value of the secondary side current and the reference value;

[0234] The judgment module is used to process the amplitude deviation rate and the phase deviation rate to generate an operating state comprehensive coefficient, compare the operating state comprehensive coefficient with a preset threshold, and judge the operating state of the magnetic valve type current transformer based on the comparison result.

[0235] 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.

[0236] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other 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 appreciate that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by computer software, electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0237] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0238] 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. A method for simulating electromagnetic transients 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 core material in a standard environment and without external electromagnetic excitation. Collect electromagnetic, magnetic field, control, and environmental data of the current transformer at multiple consecutive test moments. Based on the collected electromagnetic and magnetic field data, extract the peak magnetic flux density, magnetic flux harmonic distortion rate, and current harmonic distortion rate to construct monitoring feature data. S2. Build a data prediction model based on LSTM, using monitoring feature data from multiple test moments as input and electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate from the next test moment 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 density peak, magnetic 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 the respective preset threshold value, and obtain the corrected value of the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at time t+1 based on the comparison result; S5. 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, 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 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; S6. Construct a finite element-circuit coupling model, input the corrected initial saturation magnetic flux density and initial remanence coefficient from the simulation characteristic data as parameters, perform electromagnetic transient simulation using the harmonic component currents as circuit excitation conditions, obtain the simulated value of the secondary side current waveform, and calculate the amplitude deviation rate and phase deviation rate of the secondary side current simulation value and the reference value; S7. Process the amplitude deviation rate and the phase deviation rate to generate a comprehensive operating status coefficient, compare the comprehensive operating status coefficient with a preset threshold, and determine the operating status of the magnetic valve type current transformer based on the comparison result.

2. The electromagnetic transient simulation method of a magnetic valve type current transformer according to claim 1, characterized in that: Electromagnetic data includes secondary side output current and voltage; magnetic field data is the magnetic flux density data at the winding winding point, and control data includes the waveform, amplitude, frequency, and phase of the primary side excitation current; environmental data includes ambient temperature and ambient humidity; 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 a magnetic valve type current transformer according to claim 2, characterized in that: Compare the standard deviation of each parameter with the respective preset thresholds. Based on the comparison results, obtain 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 correction value at time t+1. The specific process is as follows: when When The parameters are stable at time t+1 and the previous n-1 moments, and the mean value is used as the correction value; ； in, is the corrected secondary side output current, is the corrected secondary side output voltage, is the corrected magnetic density peak value, is the corrected magnetic density harmonic distortion rate, is the corrected current harmonic distortion rate; Where, is the average value of the secondary current at time t+1 and the previous n-1 moments, is the average value of the secondary side voltage at time t+1 and the previous n-1 moments, is the average of the peak magnetic density at time t+1 and the previous n-1 moments, is the average value of the magnetic field harmonic distortion rate at time t+1 and the previous n-1 moments, is the average value of the current harmonic distortion rate at time t+1 and the previous n-1 moments; Where, For the The standard deviation of the parameters, For the The threshold corresponding to the standard deviation of the parameters, , They 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 respectively; when When The parameters are unstable at time t+1 and the previous n-1 times. 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; For the 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, all data points falling within the outlier range are removed from the ordered sequence to obtain a valid data subset; For the valid data subset, calculate the median as the correction value: When the number of valid data is odd, the median is the value in the middle of the subset; When the number of valid data is even, the median is the average of the two middle values ​​of the subset.

4. The electromagnetic transient simulation method of a magnetic valve type current transformer according to claim 3, characterized in that: According to the initial saturation magnetic flux density and the corrected magnetic flux harmonic distortion rate, the corrected initial saturation magnetic flux density is obtained according to the following formula: ； in, is the corrected initial saturation 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 peak magnetic flux density and the initial saturation magnetic flux density, the corrected initial remanence coefficient is obtained according to the following formula: ； in, is the corrected initial remanence coefficient, is the initial remanence coefficient, is the proportionality coefficient, .

5. The electromagnetic transient simulation method of a magnetic valve type current transformer according to claim 4, characterized in that: Calculate the amplitude deviation rate between the secondary side current simulation value and the reference value. The specific process is as follows: The simulated value of the secondary current is extracted from the finite element-circuit coupling model. Based on the current transformer ratio and the actual value of the primary current, the reference value of the secondary current is calculated according to the following formula: ； in, is the reference value of the secondary side current, is the actual value of the primary side current, is the current transformer ratio; ； in, 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 obtain the phase angle of the secondary side current simulation value based on the secondary side current simulation waveform; ； in, 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 a magnetic valve type current transformer according to claim 1, characterized in that: The amplitude deviation rate and phase deviation rate are processed to generate the comprehensive coefficient of the operating status. The formula is as follows: ； in, is the comprehensive coefficient of operating status, 、 The amplitude deviation rate and phase deviation rate of the secondary side current simulation value and the reference value; Where, is the weight coefficient of the amplitude deviation rate, is the weight coefficient of the phase deviation rate, and and The specific value of is determined by the hierarchical analysis method.

7. The electromagnetic transient simulation method of a magnetic valve type current transformer according to claim 6, characterized in that: According to the comparison results, the operating status of the magnetic valve current transformer is judged. The specific process is as follows: when When , the magnetic valve type current transformer is in normal operating state; when When , the magnetic valve current transformer is in the warning state; when When , the magnetic valve current transformer is in fault state; in, To divide the critical values ​​of normal operation and warning state, It is the critical value for dividing the warning and fault states.

8. A magnetic valve type current transformer electromagnetic transient simulation system, the system being used to execute the magnetic valve type current transformer electromagnetic transient simulation method according to any one of claims 1 to 7, characterized in that: include: The data acquisition module is used to obtain the initial saturation magnetic flux density and initial remanence coefficient of the core material in a standard environment and without external electromagnetic excitation. It also collects electromagnetic, magnetic field, control, and environmental data of the current transformer at multiple consecutive test moments. Based on the collected electromagnetic and magnetic field data, it extracts the peak magnetic flux density, magnetic flux harmonic distortion rate, and current harmonic distortion rate to construct monitoring feature data. The data prediction model construction module is used to build a data prediction model based on LSTM. It uses the monitoring feature data of multiple test moments as input and the electromagnetic data, magnetic density peak value, magnetic density harmonic distortion rate, and 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 time t and the previous 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; The predicted value correction module is 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 the respective preset threshold value, and obtain the correction value of the electromagnetic data, magnetic flux density peak value, magnetic flux density harmonic distortion rate, and current harmonic distortion rate at time t+1 based on the comparison result; A simulation data correction module is used to obtain a corrected initial saturation magnetic flux density based on the initial saturation magnetic flux density and the corrected magnetic flux harmonic distortion rate, obtain a corrected initial remanence coefficient based on the initial remanence coefficient, the corrected magnetic flux peak value and the initial saturation magnetic flux density, extract each harmonic component 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 each harmonic component; A 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, perform electromagnetic transient simulation using each harmonic component current as a circuit excitation condition, obtain the simulated value of the simulated waveform of the secondary side current, and calculate the amplitude deviation rate and phase deviation rate of the simulated value of the secondary side current and the reference value; The judgment module is used to process the amplitude deviation rate and the phase deviation rate to generate an operating state comprehensive coefficient, compare the operating state comprehensive coefficient with a preset threshold, and judge the operating state of the magnetic valve type current transformer based on the comparison result.

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