A Method for Suppressing Measurement Interference of Current Transformers under High-Pressure Electromagnetic Interference

Abstract

This application relates to the technical field of current transformer measurement, and specifically relates to a method for suppressing measurement interference of a current transformer under high-voltage strong electromagnetic interference. The method includes: collecting relevant signals for analyzing the interference impact on the current transformer measurement; for the high-frequency interference generated by pulse noise and instantaneous surges, analyzing the composite degree of the high-frequency interference caused by the two factors, and analyzing its coupling characteristics to quantify the measurement interference intensity characteristics of the high-frequency noise received by the current transformer at historical moments; using the measurement interference intensity characteristics to improve the step size parameter in the LMS adaptive filtering algorithm to obtain the measured current signal after interference suppression. The purpose of this application is to accurately quantify the spatio-temporal correlation characteristics of pulse-surges, so that the improved LMS algorithm can establish a dynamic mapping relationship of interference intensity and reduce the risk of measurement interference of the current transformer.

Description

Technical Field

[0001] The present application relates to the technical field of current transformer measurement, and particularly relates to a method for suppressing interference in current transformer measurement under high-voltage strong electromagnetic interference. Background Art

[0002] In an environment of high-voltage strong electromagnetic interference, the LMS adaptive filtering technology is usually used to suppress interference in current transformer measurement. Its core is to dynamically adjust the filter weights through the gradient descent algorithm to minimize the output error. However, the unique characteristics of high-frequency interferences such as impulse noise and instantaneous surges severely restrict the effectiveness of traditional algorithms. Impulse noise is manifested as high-amplitude spikes at the microsecond level, and its suddenness causes the gradient estimation to deviate from the true error surface, leading to disorder in the weight update direction, resulting in signal overshoot or under-compensation; instantaneous surges have broadband spectral characteristics and time-varying energy distributions.

[0003] Most importantly, the spatio-temporal coupling effect of the two types of interferences forms a composite interference pattern, and its non-stationary characteristics make the fixed step-size parameter face the contradiction between the convergence speed and the steady-state accuracy. A small step size is difficult to track the rapid rising edge of the surge interference, while a large step size can improve the transient response speed but will cause weight oscillations caused by impulse noise.

[0004] Since the prior art fails to establish a dynamic mapping relationship of the interference intensity and cannot accurately quantify the spatio-temporal correlation characteristics of impulse-surge, it is easy to cause the measurement of the current transformer to be interfered. Summary of the Invention

[0005] In order to solve the above technical problems, the present application provides a method for suppressing interference in current transformer measurement under high-voltage strong electromagnetic interference to solve the existing problems.

[0006] A method for suppressing interference in current transformer measurement under high-voltage strong electromagnetic interference of the present application adopts the following technical solutions:

[0007] An embodiment of the present application provides a method for suppressing interference in current transformer measurement under high-voltage strong electromagnetic interference, and the method includes the following steps:

[0008] Collect the current signal of the current transformer, as well as the voltage signal of the circuit where it is located, the magnetic field signal around it in the working state, and the ground potential signal;

[0009] Perform high-frequency reconstruction on various signals respectively to extract high-frequency interference signals; detect different abnormal signals in the high-frequency interference signals;

[0010] Accumulate the signal amplitude values within the corresponding time domain range between different abnormal signals adjacent in time domain, and perform positive fusion with the proportion of the number of signal pairs composed of different abnormal signals adjacent in time domain, and use the fusion result as the composite interference feature of various signals;

[0011] Calculate the covariance between the calculated current and voltage signals and the magnetic field and ground potential signals respectively; divide all types of signals into two categories, and perform regression analysis with one category as the independent variable and the other category as the dependent variable respectively to obtain two regression coefficient vectors; positively fuse the mean value of the composite interference characteristics of all types of signals, the sum value of all calculated covariances, and the differences between the two regression coefficient vectors, and use the fusion result as the measured interference intensity characteristic.

[0012] According to the measured interference intensity characteristic calculated in real time, improve the step size parameter in the LMS adaptive filtering algorithm for the current signal to obtain the current data measured by the current transformer after interference suppression.

[0013] Preferably, the detection method for different types of abnormal signals in the high-frequency interference signal is determined as:

[0014] Use a preset time-domain threshold to detect pulse noise signals in the high-frequency interference signal;

[0015] Use a preset envelope threshold to detect instantaneous surge signals in the analytic signal of the high-frequency interference signal.

[0016] Preferably, the use of the preset time-domain threshold to detect pulse noise signals in the high-frequency interference signal includes:

[0017] Calculate the amplitude mean value μ and standard deviation σ of the high-frequency interference signal;

[0018] Set the time-domain threshold to μ + kσ, where k is a constant greater than zero;

[0019] Detect the signal points in the high-frequency interference signal that exceed the time-domain threshold;

[0020] Mark the detected signal points as pulse noise signals.

[0021] Preferably, the use of the preset envelope threshold to detect instantaneous surge signals in the analytic signal of the high-frequency interference signal includes:

[0022] Perform Hilbert transform on the high-frequency interference signal to obtain the analytic signal and calculate its envelope;

[0023] Set the envelope threshold to the P% value of the envelope, where P is a positive number less than 100;

[0024] Detect the signals in the envelope that exceed the envelope threshold;

[0025] Mark the detected signals as instantaneous surge signals.

[0026] Preferably, the method for obtaining different types of abnormal signals adjacent in time domain is determined as:

[0027] When the time length between the corresponding moments of different types of abnormal signals is less than a preset time window, these two different types of abnormal signals are regarded as different types of abnormal signals that are adjacent in the time domain.

[0028] Preferably, the composite interference feature of the current signal is denoted as A, , where a is the number of signal pairs in the high-frequency interference signal of the current signal, N is the number of signal points of the collected current signal, is the total number of combinations of these signal points taken two at a time, represents the signal within the corresponding time domain range between the two abnormal signals in signal pair i, represents the cumulative value of the signal amplitudes within the corresponding time domain range between the two abnormal signals in signal pair i.

[0029] Preferably, the measurement interference intensity feature is further determined by the product result of the mean value of the composite interference features of all types of signals, the calculated sum of all covariance values, and the difference between the two regression coefficient vectors.

[0030] Preferably, the improved formula for the step size parameter is: , where, is the improved step size, is the initial step size, is the currently calculated measurement interference intensity feature, is the maximum value of the historically calculated measurement interference intensity features.

[0031] Preferably, the method for dividing all types of signals into two categories is: dividing them according to internal influence signals and external influence signals, classifying current and voltage signals as internal influence signals, and classifying magnetic field and ground potential signals as external influence signals.

[0032] Preferably, the independent variable or the dependent variable during regression analysis is a matrix constructed from all types of signals in the corresponding category.

[0033] This application has at least the following beneficial effects:

[0034] The present application proposes a method for suppressing measurement interference of current transformers under high-voltage electromagnetic interference. Aiming at the problem of high-frequency interference distortion caused by pulse noise and instantaneous surges, various data are reconstructed for high-frequency components, and the spatio-temporal coupling intensity of high-frequency interference signals is analyzed to solve the measurement deviation problem caused by insufficient quantification of transient composite interference by traditional methods. Aiming at the asymmetric interference caused by the coupling of internal and external electromagnetic interferences, the non-linear correlation characteristics between internal circuit parameters and the external electromagnetic environment are analyzed through covariance and regression analysis, excluding the limitations of single interference source analysis. Based on the measurement interference intensity characteristics calculated in real time, the step size parameter of the LMS adaptive filtering algorithm is dynamically adjusted to realize a weight update mechanism with adaptive interference intensity, so that the improved LMS algorithm establishes a dynamic mapping relationship of interference intensity, accurately quantifies the spatio-temporal correlation characteristics of pulses and surges, and reduces the risk of measurement of current transformers being interfered. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0036] Figure 1 It is a flowchart of a method for suppressing measurement interference of a current transformer under high-voltage electromagnetic interference provided by the present application;

[0037] Figure 2 It is a flowchart of the construction process of the measurement interference intensity characteristics of high-frequency noise suffered by the current transformer provided by the present application;

[0038] Figure 3 It is a schematic diagram of the signal convergence process of the traditional LMS and the improved LMS with step size provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] In order to further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following, in conjunction with the drawings and preferred embodiments, details the specific implementation manners, structures, features and effects of a method for suppressing measurement interference of a current transformer under high-voltage electromagnetic interference proposed according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0041] The following specifically describes the specific solution of a method for suppressing measurement interference of a current transformer under high - intensity electromagnetic interference in conjunction with the accompanying drawings.

[0042] A method for suppressing measurement interference of a current transformer under high - intensity electromagnetic interference provided by an embodiment of the present application.

[0043] Specifically, a method for suppressing measurement interference of a current transformer under high - intensity electromagnetic interference is provided as follows. Please refer to Figure 1 , and the method includes the following steps:

[0044] Step 1: Collect relevant signals for analyzing the interference effect on the measurement of the current transformer.

[0045] To achieve the accuracy of current transformer measurement under high - intensity electromagnetic interference, the present application collects the current signal of the current transformer, as well as the voltage signal of its circuit, the magnetic field signal around it during its working state, and the ground potential signal. In this embodiment, a data collector is used to collect the current measured by the current transformer, and a voltage transformer with a data collector is installed on the circuit where the current transformer is located to obtain voltage data; a three - axis magnetic field sensor is installed around the current transformer to collect the magnetic field intensity signal; a ground potential sensor is installed near the grounding point to collect the ground potential fluctuation signal.

[0046] In this embodiment, the acquisition frequencies of all sensors are uniformly set to 100 kHz, and time - stamp alignment is achieved through a multi - channel synchronous signal acquisition system to ensure the strict synchronous acquisition of current, voltage, magnetic field, and ground potential signals. And the collected signals are subjected to overall normalization processing to provide support for high - resolution signals for quantifying the non - stationary characteristics of high - intensity electromagnetic interference.

[0047] The normalized signals of each type are sorted in ascending order of time to obtain the current signal, voltage signal, magnetic field signal, and ground potential signal. In this embodiment, the current transformer is monitored for a long time to determine its anti - interference state. Taking the power - on of the circuit as the initial acquisition moment of the signal, the current, voltage, magnetic field, and ground potential signals within 1 hour are collected in this embodiment. Suppose there are N elements in each type of signal collected.

[0048] Step 2: For the high - frequency interference generated by pulse noise and instantaneous surges, analyze the composite degree of the high - frequency interference caused by the two factors, and analyze its coupling characteristics to quantify the measurement interference intensity characteristics of the high - frequency noise suffered by the current transformer at historical moments.

[0049] In the present application, the flow chart of the construction process of the measurement interference intensity characteristics of the high - frequency noise suffered by the current transformer is as shown in the appendix Figure 2 as follows:

[0050] S1. Reconstruct the high-frequency components of various signals respectively to extract high-frequency interference signals.

[0051] In a high-voltage and strong electromagnetic interference environment, high-frequency noises in the current signals measured by current transformers, such as pulse noises and instantaneous surges, are the main sources of interference that cause measurement errors and misoperations of protection devices. The high-frequency noises will be superimposed on the current signals, resulting in the measured values deviating from the true values, and will also mask the low-frequency components of useful signals, leading to signal distortion.

[0052] In order to extract the high-frequency interference components in relevant signals, reconstruct the high-frequency components of various signals respectively to extract high-frequency interference signals.

[0053] In this embodiment, four signals, namely current signals, voltage signals, magnetic field signals, and ground potential signals, are used as inputs respectively. Here, taking the current signal as an example, select the Coiflet wavelet basis function suitable for extracting high-frequency components, set the decomposition level to 4, and use the discrete wavelet transform (DWT) to perform multi-scale decomposition on the current signal respectively. Output 4 high-frequency detail coefficients and 1 low-frequency approximation coefficient of the current signal. The detail coefficients capture the high-frequency components of the signal at different scales, including high-frequency interferences such as pulse noises and instantaneous surges. By reconstructing the detail coefficients, the high-frequency components in the signal can be restored. Taking the 4 high-frequency detail coefficients decomposed by DWT as inputs, use the inverse discrete wavelet transform (IDWT) to reconstruct the input detail coefficients to obtain the high-frequency interference signal of the current signal.

[0054] Among them, the methods suitable for extracting high-frequency components also include discrete Fourier transform, etc., which are specifically set by the implementer. In addition, the discrete wavelet transform of signals and the inverse discrete wavelet transform for reconstructing the decomposed signals are both well-known technologies and will not be elaborated here. The setting of the decomposition level can be set by the implementer himself.

[0055] S2. Detect different abnormal signals in the high-frequency interference signals.

[0056] As a preferred implementation method, the detection method for different abnormal signals in the high-frequency interference signals is determined as follows: Use a preset time-domain threshold to detect pulse noise signals in the high-frequency interference signals; use a preset envelope threshold to detect instantaneous surge signals in the analytic signals of the high-frequency interference signals.

[0057] In other implementation methods, the detection of different abnormal signals in the high-frequency interference signals can be carried out through frequency-domain analysis, the 3σ principle, etc. Or directly identify different abnormal signals manually.

[0058] In high-frequency interference signals, impulse noise usually appears as high-amplitude spikes that suddenly occur in the signal and have an extremely short duration. Therefore, in this embodiment, a preset time-domain threshold is used to detect impulse noise signals in high-frequency interference signals, including: calculating the amplitude mean of the high-frequency interference signal and the standard deviation , setting the time-domain threshold to , where k is a constant greater than zero; detecting signal points in the high-frequency interference signal that exceed the time-domain threshold, marking each signal point that exceeds the time-domain threshold, and respectively recording all the marked points as impulse noise signals. At the same time, record the occurrence times of all impulse noise signals.

[0059] Instantaneous surges usually appear as short-term high-energy fluctuations in the signal, which have a longer duration than impulse noise. Accordingly, in this embodiment, a preset envelope threshold is used to detect instantaneous surge signals in the analytic signal of the high-frequency interference signal, including: performing a Hilbert transform on the high-frequency interference signal, outputting the analytic signal, and calculating the envelope of the analytic signal. Set the envelope threshold. In this embodiment, the envelope threshold is set to the P% of the envelope, where P is a positive number less than 100, and the value in this embodiment is 95 to more flexibly control the sensitivity of instantaneous surge detection. Detect signals in the envelope that exceed the envelope threshold, mark them as instantaneous surge signals, and record the occurrence times of all instantaneous surge signals. Among them, the Hilbert transform is a well-known technology and will not be elaborated here.

[0060] S3, positively fuse the sum of the signal amplitude values within the corresponding time-domain range between different types of abnormal signals that are temporally adjacent, and the proportion of the number of signal pairs composed of different types of abnormal signals that are temporally adjacent, and use the fusion result as the composite interference feature of various signals.

[0061] As a preferred implementation manner, the method for obtaining different types of abnormal signals that are temporally adjacent is determined as: when the time length between the corresponding times of different types of abnormal signals is less than a preset time window, then these two different types of abnormal signals are used as different types of abnormal signals that are temporally adjacent.

[0062] Specifically, in this embodiment, the time window is used to determine whether the impulse noise signal and the instantaneous surge signal occur in the same time period, that is, whether they are adjacent in the neighborhood. The size of the time window in this embodiment is 10 ms. For each element in the impulse noise signal, calculate the time interval between its occurrence time and the occurrence times of all elements in the instantaneous surge signal. If the time interval is less than the size of the time window, it is considered that the two are temporally related, that is, the two types of abnormal signals are temporally adjacent, and then these two signals are recorded as a signal pair. Traverse all the occurrence times of impulse noise to obtain signal pairs that meet the above-mentioned temporally adjacent conditions. Suppose a total of a signal pairs are obtained.

[0063] For each signal pair, taking signal pair i as an example, the signals within the corresponding time domain range between the two abnormal signals in signal pair i are . For example, the occurrence time of the impulse noise in signal pair i is , and the occurrence time of the instantaneous surge is . If , then is the high-frequency interference signal of the current signal between , and vice versa.

[0064] Based on the above analysis, in this application, the sum of the signal amplitude values within the corresponding time domain range between different types of abnormal signals adjacent in time domain is positively fused with the proportion of the number of signal pairs composed of different types of abnormal signals adjacent in time domain, and the fusion result is used as the composite interference feature of various signals.

[0065] It can be understood that positive fusion is a fusion method such as addition and multiplication between data. The specific positive fusion method is determined by the implementer according to the actual situation to select a suitable fusion method, and this application does not make special restrictions.

[0066] Specifically, in this embodiment, the composite interference feature of the current signal is calculated , and the specific calculation formula is: , where a is the number of signal pairs in the high-frequency interference signal of the current signal, N is the number of signal points of the collected current signal, is the total number of pairwise combinations of these signal points, represents the signal within the corresponding time domain range between the two abnormal signals in signal pair i, represents the sum of the signal amplitude values within the corresponding time domain range between the two abnormal signals in signal pair i.

[0067] It should be understood that represents the proportion of the number of signal pairs in all possible combinations, reflecting the correlation intensity of impulse noise and instantaneous surge signals in time. When it is larger, it means that the proportion of signal pairs is higher. The larger the value, the stronger the composite effect of interference; is used to reflect the interference intensity of a single pulse-surge coupling event. is the secondary summation of the sum of the amplitude values of all a signal pairs, representing the total amplitude intensity of the composite interference. The larger its value, the higher the intensity of the composite interference. The larger the value.

[0068] It is used to quantify the combined effects of pulse noise and instantaneous surge signals in a high-voltage and strong electromagnetic interference environment, describe the intensity of the simultaneous occurrence of pulse noise and instantaneous surge signals in a high-voltage and strong electromagnetic interference environment and their impact on the system. When the value of A is larger, it indicates that the combined interference effect is stronger, which may lead to an increase in measurement error or an increased risk of misoperation of the protection device.

[0069] Subsequently, using the same method, calculate the combined interference characteristics of voltage, magnetic field, and ground potential signals, and calculate the mean value of the combined interference characteristics of the four types of data. .

[0070] S4. Calculate the covariance between current and voltage signals and magnetic field and ground potential signals respectively; divide all types of signals into two categories, and use one category as the independent variable and the other category as the dependent variable for regression analysis to obtain two regression coefficient vectors; perform positive fusion on the differences between the mean value of the combined interference characteristics of all types of signals, the calculated sum value of all covariances, and the two regression coefficient vectors, and use the fusion result as the measurement interference intensity characteristic.

[0071] During the measurement process of the current transformer, the sources of its high-frequency noise are mainly divided into two types: external electromagnetic interference and internal circuit interference. The types of external electromagnetic interference include but are not limited to high-voltage equipment operation, lightning surges, interference from electromagnetic components, and poor grounding; the types of internal circuit interference include but are not limited to parasitic parameters and power supply fluctuations.

[0072] Therefore, the four types of data collected are divided into two categories. Among them, current and voltage signals are classified as internal influence signals, and ground potential and magnetic field signals are classified as external influence signals; current and voltage are direct electrical quantities in the power system, reflecting the electrical state of the system and mainly affected by internal circuit interference, while ground potential and magnetic field are indirect physical quantities in the power system, reflecting the influence of the external environment on the system and mainly affected by external electromagnetic interference.

[0073] At different moments, the interference generated by external electromagnetic interference and internal circuit interference on the current transformer is different, that is, external electromagnetic interference and internal circuit interference are not constant. In order to analyze their respective effects on the current transformer, in this application, the covariance between current and voltage signals and magnetic field and ground potential signals is calculated respectively.

[0074] During the calculation process of the covariance, there are 4 variables: current, voltage, magnetic field, and ground potential. Among them, the covariance calculated for the four groups of current and magnetic field, current and ground potential, voltage and magnetic field, and voltage and ground potential are all the covariance between internal and external interferences, reflecting the coupling relationship between internal and external interferences. Thus, the coupling strength between internal and external interferences is obtained, and its value is the sum value of all calculated covariances.

[0075] Furthermore, all types of signals in this application are divided into two categories. One category is used as the independent variable and the other as the dependent variable for regression analysis to obtain two regression coefficient vectors. Among them, both the independent variable and the dependent variable in the regression analysis are matrices constructed from all types of signals in the corresponding category.

[0076] In this embodiment, let the current signal be I, the voltage signal be V, the magnetic field signal be G, and the ground potential signal be M. The four signals of current, voltage, ground potential, and magnetic field are respectively represented to form matrices X and Y, where , . Subsequently, using the partial least squares regression algorithm, with matrix X as the independent variable and matrix Y as the dependent variable, the regression coefficient vector is output. Similarly, with matrix X as the dependent variable and matrix Y as the independent variable, the partial least squares regression algorithm is used to output the regression coefficient vector . Among them, the partial least squares regression algorithm is a well-known technology and will not be elaborated here. The regression analysis method can also adopt multiple linear regression, principal component regression, etc.

[0077] Based on the above analysis, this application positively fuses the composite interference characteristic mean of all types of signals, the calculated sum value of all covariances, and the difference between the two regression coefficient vectors, and uses the fusion result as the measurement interference intensity characteristic.

[0078] As a preferred implementation manner, the measurement interference intensity characteristic is further determined by the product result of the composite interference characteristic mean of all types of signals, the calculated sum value of all covariances, and the difference between the two regression coefficient vectors.

[0079] In this embodiment, the specific calculation formula for the measurement interference intensity characteristic B is: , where is the composite interference characteristic mean of all types of signals, which reflects the composite interference intensity of impulse noise and instantaneous surge signals in the system. The larger its value, the stronger the composite interference in the system, the greater the risk of measurement error increase or misoperation of the protection device, and B increases accordingly; is the calculated sum value of all covariances, which describes the interaction intensity between internal interference and external interference. The larger its value, the stronger the coupling effect between internal and external interferences, the higher the complexity of the interference, and the increase of the B value; is the difference between the two regression coefficient vectors, which is the Euclidean distance between the two regression coefficient vectors in this embodiment. It describes the difference in the influence of internal interference and external interference on the measurement result of the current transformer. The larger its value, the greater the difference in the influence of internal and external interferences on the measurement result, the stronger the asymmetry of the interference, the more complex the influence on the measurement of the current transformer, and the B value increases accordingly.

[0080] B reflects the overall intensity of high-frequency interference in the system, the coupling effect of internal and external interference, and the asymmetry of interference. Specifically, the larger the value of B, the stronger the composite interference suffered by the system, the more significant the interference coupling effect, and the higher the asymmetry of interference, which will lead to an increase in the measurement error of the current transformer and an increase in the risk of misoperation of the protection device.

[0081] Step 3: Improve the step size parameter in the LMS adaptive filtering algorithm using the measured interference intensity characteristics to obtain the measured current signal after interference suppression.

[0082] The traditional method uses the LMS adaptive filtering algorithm to suppress the measurement interference of the current transformer. By dynamically adjusting the filter weights in real time, it can accurately track and cancel the non-stationary electromagnetic noise signal, effectively separate the fundamental wave and high-frequency noise, increase the signal-to-noise ratio by more than 20 dB, significantly improve the measurement accuracy, and has environmental self-adaptability compared with traditional fixed-parameter filtering. The convergence speed can still be controlled within 5 ms under sudden interference.

[0083] However, the weight update mechanism of LMS based on gradient descent has a lagging response to non-stationary and high-dynamic-range transient interference. Its fixed step size parameter is difficult to balance the convergence speed and stability. A too small step size results in a tracking delay for impulse noise and cannot cancel high-amplitude spikes in time; a too large step size causes weight oscillation or even divergence due to the sparsity and strong amplitude characteristics of impulse noise, and it is difficult to meet the high-precision measurement requirements.

[0084] From the above analysis, it can be seen that the fixed step size parameter of the traditional LMS algorithm When facing non-stationary high-frequency interference, it is easy to lead to a contradiction between the convergence speed and the steady-state error: a too large step size will cause oscillation, and a too small step size cannot quickly track the change of interference. Therefore, in this application, according to the measured interference intensity characteristics calculated in real time, the step size parameter in the LMS adaptive filtering algorithm for the current signal is improved to obtain the current data measured by the current transformer after interference suppression.

[0085] In this embodiment, the measured interference intensity characteristic B calculated in real time is used to dynamically adjust the step size to make the step size match the current interference intensity and asymmetry. The specific improvement formula is: , where is the improved step size, is the initial step size, which takes a value of 0.01 in this embodiment, is the currently calculated measured interference intensity characteristic, is the maximum value of the measured interference intensity characteristic calculated historically.

[0086] In this embodiment, the schematic diagrams of the signal convergence processes of the traditional LMS and the LMS after step size improvement are asFigure 3 As shown, Figure 3 The first figure shows the traditional LMS with a fixed step size. The signal amplitude filtering effect of the current signal measured by the current transformer is shown in the figure below. The second figure shows the LMS in the adaptive step size after the step size is improved. The signal amplitude filtering effect of the current signal measured by the current transformer is shown below.

[0087] The step size parameters in LMS are dynamically adjusted by the step size adaptive mechanism based on the measured interference intensity characteristics, which can accurately perceive the non-stationary characteristics of interference. When impulse noise or transient surge occurs, the B value increases in real time to trigger the step size. Proportional expansion significantly improves the algorithm's tracking speed for sudden high-frequency interference, making the weight update rate match the dynamic characteristics of the interference; The calibrated nonlinear constraint mechanism enables the algorithm to maintain weight stability in the strong interference coupling stage (such as internal and external interference resonance caused by switching operation), avoiding the divergence risk caused by the traditional fixed step size due to parameter solidification. The advanced LMS algorithm can establish a dynamic mapping relationship of interference intensity, accurately quantify the temporal and spatial correlation characteristics of pulse-surge, and reduce the risk of interference in the measurement of current transformers.

[0088] The above technical features constitute the best implementation example of the present application, which has strong adaptability and optimal implementation effect. Non-essential technical features can be added or reduced according to actual needs to meet the needs of different situations.

Claims

1. A method for suppressing current transformer measurement interference under high-pressure electromagnetic interference, characterized in that The method includes the following steps: Collect the current signal of the current transformer, as well as the voltage signal of the circuit where it is located, the magnetic field signal around it under its working state, and the ground potential signal; Perform high-frequency reconstruction on various signals respectively to extract high-frequency interference signals; detect different abnormal signals in the high-frequency interference signals; Perform positive fusion on the cumulative value of the signal amplitudes within the corresponding time domain range between different abnormal signals adjacent in time domain and the proportion of the number of signal pairs composed of different abnormal signals adjacent in time domain, and use the fusion result as the composite interference feature of various signals; Calculate the covariance between the current and voltage signals and the magnetic field and ground potential signals respectively; divide all types of signals into two categories, and perform regression analysis with one category as the independent variable and the other category as the dependent variable to obtain two regression coefficient vectors; take the product result of the mean value of the composite interference features of all types of signals, the sum value of all calculated covariances, and the difference between the two regression coefficient vectors as the measurement interference intensity feature; According to the measured interference intensity feature calculated in real time, improve the step size parameter in the LMS adaptive filtering algorithm for the current signal to obtain the current data measured by the current transformer after interference suppression; Among them, the coincidence interference feature of the current signal obtained by the forward fusion is denoted as A, , where a is the number of signal pairs in the high-frequency interference signal of the current signal, and N is the number of signal points of the collected current signal, is the total number of pairwise combinations of these signal points, represents the signal within the corresponding time domain range between the two abnormal signals in signal pair i, represents the cumulative value of the signal amplitudes within the corresponding time domain range between the two abnormal signals in signal pair i.

2. The current transformer measurement interference suppression method under high-pressure electromagnetic interference according to claim 1, characterized in that The method for detecting different abnormal signals in the high-frequency interference signals is determined as: Use a preset time domain threshold to detect pulse noise signals in the high-frequency interference signals; Use a preset envelope threshold to detect instantaneous surge signals in the analytic signal of the high-frequency interference signals.

3. A method for suppressing current transformer measurement interference under high-pressure electromagnetic interference as described in claim 2, characterized in that The step of using a preset time domain threshold to detect pulse noise signals in the high-frequency interference signals includes: Calculate the amplitude mean value μ and standard deviation σ of the high-frequency interference signal; Set the time domain threshold to μ + kσ, where k is a constant greater than zero; Detect the signal points in the high-frequency interference signal that exceed the time domain threshold; Mark the detected signal points as pulse noise signals.

4. A method for suppressing current transformer measurement interference under high-pressure electromagnetic interference according to claim 2, characterized in that, The step of using a preset envelope threshold to detect instantaneous surge signals in the analytic signal of the high-frequency interference signals includes: Perform Hilbert transform on the high-frequency interference signal to obtain the analytic signal and calculate its envelope; Set the envelope threshold to the P% value of the envelope, where P is a positive number less than 100; Detect the signals in the envelope that exceed the envelope threshold; Mark the detected signals as instantaneous surge signals.

5. A method for suppressing measurement interference of a current transformer under high-pressure electromagnetic interference, characterized in that, The method for obtaining different abnormal signals adjacent in time domain is determined as: When the time length between the corresponding moments of different abnormal signals is less than a preset time window, these two different abnormal signals are regarded as different abnormal signals adjacent in time domain.

6. A method for suppressing current transformer measurement interference under high-pressure electromagnetic interference, characterized in that The improved formula for the step size parameter is as follows: , where is the improved step size, is the initial step size, is the currently calculated measurement interference intensity feature, is the maximum value of the historically calculated measurement interference intensity feature.

7. A method for suppressing measurement interference of a current transformer under high-pressure electromagnetic interference, characterized in that, The method for dividing all types of signals into two categories is: divide them according to internal influence signals and external influence signals, divide the current and voltage signals into internal influence signals, and divide the magnetic field and ground potential signals into external influence signals.

8. A method for suppressing current transformer measurement interference under high-pressure electromagnetic interference, characterized in that Both the independent variable and the dependent variable in the regression analysis are matrices constructed from all types of signals in the corresponding category.

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