A current direction intelligent identification method and system based on a current transformer

Abstract

The application provides a current direction intelligent recognition method and system based on a current transformer, and relates to the technical field of current direction recognition.The method comprises the following steps: obtaining a secondary side output signal about a to-be-tested current through a current transformer; performing time-frequency decomposition on the secondary side output signal by using an S transform algorithm based on a Morlet wave kernel, and extracting a secondary side output effective signal; performing phase correction on the secondary side output effective signal in combination with a Preisach magnetic hysteresis model; constructing a current direction sensitivity factor based on the time-frequency distribution of the secondary side output signal obtained through time-frequency decomposition; mapping the time-frequency distribution to a sparse domain, and extracting a current direction sensitive component of the to-be-tested current; determining a direction discrimination threshold for judging the current direction in combination with the current direction sensitive component and the current direction sensitivity factor; and judging the current direction of the to-be-tested current according to the direction discrimination threshold.The application improves the accuracy and the applicable range of current direction recognition.

Description

Technical Field

[0001] This invention relates to the field of current direction recognition technology, and in particular to a method and system for intelligent current direction recognition based on a current transformer. Background Technology

[0002] A current transformer (CT) is an electrical device that converts current into a smaller proportional current or voltage signal. It is commonly used in industrial field control equipment to measure and monitor current. Its main function is to generate a small, proportional current or voltage signal by sensing the current in the main circuit, which is then used by measuring instruments or protection devices to achieve accurate current monitoring and analysis.

[0003] Intelligent current direction identification is crucial for the operation and protection of industrial field control equipment. Traditional current measurement may not be able to directly determine the direction of current flow, especially in complex multi-power supply situations. By intelligently identifying the current direction, the direction of current flow can be accurately determined, allowing for the timely detection of potential reverse current, short circuits, or abnormal power flow in the system, thereby improving the safety and reliability of industrial field control equipment.

[0004] However, existing current direction identification processes do not consider current signal noise. In particular, when weak signals are directly identified, signal noise can easily affect the phase of the weak signal that is closely related to the current direction, resulting in the inability to identify the accurate phase. This leads to inaccurate phase difference calculation and a sharp increase in the current direction misjudgment rate. Summary of the Invention

[0005] To address the technical problem that existing technologies fail to consider current signal noise during current direction identification, especially when directly identifying weak signals, signal noise can easily affect the phase of the weak signal that is closely related to the current direction, leading to inaccurate phase identification, inaccurate phase difference calculation, and a sharp increase in the current direction misjudgment rate, this invention provides a current direction intelligent identification method and system based on a current transformer.

[0006] The technical solutions provided by the embodiments of the present invention are as follows:

[0007] First aspect

[0008] This invention provides a method for intelligent current direction identification based on a current transformer, comprising:

[0009] S1: Obtain the secondary output signal of the current to be measured through the current transformer;

[0010] S2: Use the S-transform algorithm based on the Morlet wavelet kernel to perform time-frequency decomposition on the secondary side output signal and extract the effective signal of the secondary side output;

[0011] S3: Phase correction of the effective output signal on the secondary side is performed by combining the Preisach hysteresis model;

[0012] S4: Based on the time-frequency distribution of the secondary side output signal obtained by time-frequency decomposition, construct a current direction sensitivity factor to determine the validity of the secondary side output signal. Determine whether the secondary side output signal can represent the current direction. If yes, proceed to step S5; otherwise, return to step S1.

[0013] S5: Map the time-frequency distribution to the sparse domain and extract the current direction sensitive component of the current to be measured;

[0014] S6: Combine the current direction sensitive component and the current direction sensitivity factor to determine the direction discrimination threshold for judging the current direction;

[0015] S7: Determine the direction of the current to be measured based on the direction discrimination threshold.

[0016] Second aspect

[0017] This invention provides an intelligent current direction identification system based on a current transformer, comprising:

[0018] processor;

[0019] The memory stores computer-readable instructions, which, when executed by a processor, implement the intelligent current direction identification method based on a current transformer as described in the first aspect.

[0020] Third aspect

[0021] The present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the intelligent current direction identification method based on a current transformer as described in the first aspect.

[0022] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0023] In this embodiment of the invention, the secondary output signal is decomposed in time and frequency using an S-transform algorithm based on the Morlet wavelet kernel, effectively separating noise and effective components from the signal and improving the recognition capability of weak signals. Phase correction using the Preisach hysteresis model effectively eliminates phase errors caused by noise or electromagnetic interference, thus avoiding the phase misalignment problem commonly encountered in low-current environments. Furthermore, a current direction sensitivity factor is constructed, and the validity of the signal is determined by combining the time-frequency distribution, ensuring that the obtained signal accurately reflects the current direction. The current direction sensitivity component and the current direction sensitivity factor are combined to determine the direction discrimination threshold for judging the current direction, quantifying the relationship between the current direction and signal characteristics, further improving the accuracy of current direction judgment. Especially in low-load or complex industrial field control equipment, this avoids misjudgments caused by signal interference or computational delays, improving the accuracy of current direction recognition and the applicability of the recognition method. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart illustrating an intelligent current direction identification method based on a current transformer, provided in an embodiment of the present invention;

[0026] Figure 2 This is a schematic diagram of a current direction intelligent identification system based on a current transformer, provided as an embodiment of the present invention. Detailed Implementation

[0027] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0028] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0029] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0030] Reference manual attached Figure 1The diagram shows a flowchart of an intelligent current direction identification method based on a current transformer provided by an embodiment of the present invention.

[0031] This invention provides a method for intelligent current direction identification based on a current transformer. This method can be implemented by a device for intelligent current direction identification based on a current transformer, which can be a terminal or a server. The processing flow of the method for intelligent current direction identification based on a current transformer may include the following steps:

[0032] S1: Obtain the secondary output signal of the current to be measured through the current transformer.

[0033] The current under test (DUT) refers to the target current that the current transformer needs to measure and analyze. It is typically a current signal from a device or circuit in an industrial field control system. The DUT may be a high current, and it is usually converted into a smaller, easily measurable signal by the current transformer for subsequent current direction identification and monitoring. The secondary output signal is the signal generated at the secondary (output) terminal of the current transformer when measuring the DUT. The current transformer is used to convert high current into a smaller, easily measurable current signal for current monitoring and control.

[0034] The secondary output signal of the current to be measured is obtained through a current transformer. This signal is the output result of the current transformer converting the high current in the main circuit into a low current or voltage signal. The current transformer can provide isolation and convert high current into a measurable small signal, effectively protecting the measuring equipment. At the same time, through accurate current acquisition, it provides reliable data support for subsequent current direction identification, ensuring the accuracy and safety of the signal.

[0035] S2: Use the S-transform algorithm based on the Morlet wavelet kernel to perform time-frequency decomposition on the secondary side output signal and extract the effective signal of the secondary side output.

[0036] The S-transform algorithm using the Morlet wavelet kernel is a time-frequency analysis method used to decompose a signal into time-frequency components of different frequencies. The Morlet wavelet, a complex wavelet based on a Gaussian waveform, possesses good frequency resolution in the frequency domain and good localization characteristics in the time domain. By applying the Morlet wavelet to the signal and performing time-frequency decomposition, the S-transform algorithm can simultaneously obtain both time and frequency information, aiding in the identification of signal variation patterns and characteristics. The effective output signal on the secondary side refers to the effective portion of the signal generated by the current transformer when measuring the current under test, after S-transform processing. Through time-frequency decomposition, the effective components of the current signal can be extracted, and noise and irrelevant frequency components can be removed, providing an accurate signal basis for determining the current direction.

[0037] It should be noted that the S-transform algorithm using the Morlet wavelet kernel is used to perform time-frequency decomposition on the secondary output signal to extract the frequency and time features from the effective signal. This process effectively separates useful information from noise in the signal, avoids noise interference with current direction identification, improves the identification accuracy of weak signals, and ensures the effectiveness and accuracy of the signal in subsequent processing steps.

[0038] In one possible implementation, S2 specifically includes:

[0039] S201: Establish a hybrid model that includes the fundamental frequency, harmonics, noise, and remanent magnetization effect to describe the dynamic characteristics of the secondary output signal.

[0040] The hybrid model is specifically as follows:

[0041]

[0042] Where s(t) represents the secondary output signal at time t, A0 represents the initial strength of the remanent magnetization of the current transformer core, e represents the natural constant, β represents the remanent magnetization attenuation coefficient of the current transformer core, ω represents the fundamental frequency, and cos represents the cosine function. L represents the fundamental phase shift. k (t) represents the time-varying harmonic coefficient describing the time-varying amplitude of the k-th harmonic at time t, k = 2, 3, ..., N, where N represents the highest order of the harmonic, and ∈(t) represents the colored noise of the secondary side output signal at time t.

[0043] Specifically, A0e -βt This item describes the characteristics of remanent magnetization decay in the iron core.

[0044] It should be noted that the established hybrid model considers the fundamental frequency, harmonics, noise, and remanence effects to comprehensively describe the dynamic characteristics of the secondary output signal of the current transformer. By combining the fundamental frequency, harmonics, remanence decay, and noise factors, this model provides a more realistic signal representation, enabling accurate capture of signal changes in the current transformer under different operating conditions. The hybrid model effectively separates and identifies different frequency components, especially under low current or noise interference conditions, enhancing signal accuracy and reliability.

[0045] S202: The time-frequency distribution of the secondary side output signal is obtained by performing time-frequency decomposition on the secondary side output signal using the S-transform algorithm.

[0046] The time-frequency decomposition formula is as follows:

[0047]

[0048] Where S(τ,f) represents the time-frequency distribution of s(t) on the time scale τ and the frequency scale f, and π represents pi. d represents the imaginary unit, and d represents the differential symbol.

[0049] Here, the time scale represents the time position during time-frequency analysis, i.e., the moment when the signal is being analyzed. Typically, the S-transform is used to analyze the characteristics of a signal within a small time window, and this time scale τ is the center time of this window.

[0050] It should be noted that the S-transform algorithm decomposes the secondary output signal into time and frequency components to obtain the signal's time-frequency distribution on both the time and frequency scales. This method can simultaneously provide both time and frequency information of the signal, making it particularly suitable for analyzing non-stationary signals. The S-transform can accurately extract the frequency characteristics of a signal within a small time window, revealing the instantaneous frequency changes. The advantage of this method is its ability to efficiently identify the frequency components of a signal across different time periods, helping to extract useful information and filter out noise, thus improving the accuracy and reliability of the signal analysis.

[0051] S203: Extract the effective secondary output signal from the secondary output signal based on the time-frequency distribution.

[0052] Specifically, a frequency range and time window can be set to filter effective signals based on the analysis target and signal characteristics. For example, the fundamental frequency range and harmonic frequency range of the current to be measured can be set, that is, the components of the signal in these frequency ranges can be selected from the time-frequency distribution based on a threshold through time-frequency analysis.

[0053] It should be noted that a hybrid model was established by considering the effects of the fundamental frequency, harmonics, noise, and remanent magnetization, accurately describing the dynamic characteristics of the secondary output signal of the current transformer. First, the hybrid model effectively separates and identifies different frequency components, especially under low current and noise interference conditions. Next, the S-transform algorithm is used to perform time-frequency decomposition of the signal, extracting its time-frequency distribution, which simultaneously captures the signal's time and frequency characteristics, revealing instantaneous frequency changes. Finally, by selecting a specific frequency range and time window, the effective signal is extracted from the time-frequency distribution, thereby improving the signal's accuracy and reliability. This model can accurately identify current signals in complex environments, filter out noise, and enhance the accuracy of current direction identification.

[0054] S3: Phase correction of the effective output signal on the secondary side is performed by combining the Preisach hysteresis model.

[0055] The Preisach hysteresis model is a mathematical model describing the hysteresis effect of magnetic materials, particularly used to simulate the nonlinear and history-dependent behavior of magnetic materials. In current transformers, the nonlinear permeability of the core leads to hysteresis when the current changes; that is, when the current signal changes, the magnetic response of the core is not instantaneous but delayed. The Preisach model simulates this hysteresis effect through discretized state variables, effectively capturing and correcting this hysteresis and reducing phase shift problems caused by hysteresis.

[0056] Understandably, in current transformers, the nonlinear permeability of the core causes phase shifts in the signal, especially in low-current scenarios. This means that the CT core does not simply change linearly in response to current variations, but exhibits a hysteresis effect, which is more pronounced under low-current conditions. This hysteresis effect can lead to misjudgments of the current direction because the signal phase shifts, affecting current direction detection. By combining the Preisach hysteresis model, phase correction is performed on the effective output signal of the secondary side, specifically compensating for the hysteresis effect in the current transformer core. This process eliminates the phase shift caused by the nonlinear characteristics of the core, particularly in low-current environments. By accurately correcting the phase, signal accuracy is improved, ensuring accurate current direction determination and avoiding misjudgments of the current direction caused by the hysteresis effect.

[0057] In one possible implementation, S3 specifically includes:

[0058] S301: Establish a Preisach hysteresis model to simulate the nonlinear relationship between the core flux density and the core magnetic field strength in a current transformer.

[0059] The Preisach hysteresis model is specifically as follows:

[0060]

[0061] Where B(t) represents the magnetic flux density at time t, H(t) represents the magnetic field strength at time t, η1 and η2 are both state variables describing the opening and closing states of the binary hysteresis loop, and μ(η1,η2) represents the response function of the core magnetic flux density to the core magnetic field strength obtained from the offline calibrated BH curve with respect to η1 and η2. The hysteresis operator describes the hysteresis effect between the magnetic flux density and magnetic field strength of the iron core with respect to η1 and η2.

[0062] In this context, the state variable η1 = 1 indicates that the loop is open and the magnetic flux density increases, while η1 = -1 indicates that the loop is closed and the magnetic flux density decreases, and the same applies to η2. μ(η1,η2) determines the magnitude of the influence of the magnetic field strength on the magnetic flux density based on these states.

[0063] The BH curve refers to the relationship between magnetic flux density and magnetic field strength. The value of the response function determines the rate of change of the material's magnetic flux density when the magnetic field changes. It can be understood as the permeability of the iron core, describing the material's ability to respond to changes in the magnetic field.

[0064] The hysteresis effect refers to the fact that when the magnetic field strength changes, the change in magnetic flux density does not immediately follow, but rather there is a certain delay. This delay leads to a nonlinear response in the magnetic flux density.

[0065] It should be noted that the Preisach hysteresis model is used to simulate the relationship between the nonlinear magnetic flux density and magnetic field strength of the core in a current transformer. This model, by describing the hysteresis effect, considers the change in the permeability of the core material and its nonlinear response to changes in magnetic field strength. By introducing state variables to simulate the delay effect when the magnetic field strength changes, the hysteresis phenomenon in the current transformer can be simulated and compensated more accurately, precisely describing and correcting the phase shift caused by the hysteresis effect, especially under low current conditions, ensuring the accuracy of current direction identification and the reliability of the signal.

[0066] S302: Calculate the phase lag angle between magnetic flux density and magnetic field strength.

[0067] The specific method for calculating the phase lag angle is as follows:

[0068]

[0069] Where Δθ represents the phase lag angle between the magnetic flux density B(t) at time t and the magnetic field strength H(t) at time t, and arg represents the calculated phase angle.

[0070] It should be noted that by calculating the phase lag angle between magnetic flux density and magnetic field strength in real time, the phase shift in the effective output signal of the secondary side can be dynamically compensated. The goal of the compensation is to restore the correct phase of the effective output signal of the secondary side, thereby eliminating the error caused by the core hysteresis effect and avoiding misjudgment of the current direction caused by the error.

[0071] The phase lag angle refers to the phase difference between magnetic flux density and magnetic field strength. In current transformers, changes in magnetic flux density and magnetic field strength are usually not synchronous; the change in magnetic field lags behind the change in magnetic flux density. The phase lag angle indicates the degree of this lag effect and is commonly used to describe the response delay of nonlinear magnetic materials (such as iron cores) to magnetic fields. By calculating the phase lag angle between magnetic flux density and magnetic field strength in real time, phase shifts in the signal can be dynamically compensated. The goal of this compensation is to eliminate errors caused by the hysteresis effect of the iron core, ensuring accurate signal phase and preventing errors from affecting the determination of current direction. The advantage is that this method can accurately correct phase distortion caused by hysteresis under low-current conditions, improving the accuracy and reliability of current direction identification, especially under conditions of high noise or interference.

[0072] S303: Perform phase correction on the effective signal output from the secondary side based on the phase lag angle.

[0073] Specifically, a Preisach hysteresis model is established to accurately simulate the relationship between the nonlinear magnetic flux density and magnetic field strength of the core in a current transformer. This model considers the hysteresis effect, describing the situation where changes in the magnetic field lag behind changes in the magnetic flux density, helping to compensate for phase shifts in low-current environments. By calculating the phase hysteresis angle, the signal phase is adjusted in real time, thereby eliminating errors caused by the hysteresis effect. Ultimately, phase correction improves signal accuracy, avoiding misjudgments of current direction caused by errors, especially under conditions of high noise or interference, ensuring high accuracy and reliability in current direction identification.

[0074] S4: Based on the time-frequency distribution of the secondary side output signal obtained by time-frequency decomposition, construct a current direction sensitivity factor to determine the validity of the secondary side output signal. Determine whether the secondary side output signal can represent the current direction. If yes, proceed to step S5; otherwise, return to step S1.

[0075] Time-frequency distribution refers to a two-dimensional representation that combines the frequency components of a signal with time information. It reveals the frequency variations of a signal over different time periods, helping to identify the signal's time-domain and frequency-domain characteristics. The current direction sensitivity factor is a parameter that quantifies the relationship between a signal and its direction by analyzing the characteristics of the current signal in the time-frequency distribution. This factor reflects the signal's validity and determines whether it accurately represents the current direction. Under different current variation patterns, the sensitivity factor can help assess the reliability of the signal in identifying the current direction.

[0076] It should be noted that by constructing a current direction sensitivity factor, it is possible to accurately determine whether the secondary-side output signal can effectively represent the current direction. Time-frequency distribution analysis ensures that the frequency and time domain characteristics of the signal are fully considered, eliminating invalid signals and noise effects, and improving the signal effectiveness in complex industrial field control equipment, especially under low current or interference conditions, thereby enhancing the accuracy and robustness of current direction identification.

[0077] In one possible implementation, the current direction sensitivity factor is specifically the percentage of the target fundamental frequency signal energy in the effective signal output from the secondary side to the total frequency domain signal energy.

[0078] The target base frequency (e.g., power frequency, 50Hz or 60Hz) specifically corresponds to the main frequency of the current to be detected.

[0079] The formula for the current direction sensitivity factor is as follows:

[0080]

[0081] Where Γ represents the current direction sensitivity factor, f0 represents the target fundamental frequency, Δf represents the target fundamental frequency tolerance, and f max This represents the maximum frequency of the effective output signal on the secondary side, and || represents taking the absolute value.

[0082] It should be noted that calculating the current direction sensitivity factor quantifies the energy proportion of the target fundamental frequency signal in the effective output signal on the secondary side. This factor reflects the relationship between the current direction and the fundamental frequency signal, and can accurately assess whether the signal represents the current direction. By focusing on the target fundamental frequency (such as 50Hz or 60Hz) and considering the energy proportion within the frequency range, noise and other irrelevant components can be effectively filtered out, improving the accuracy and reliability of current direction determination, especially under low current conditions, thus avoiding misjudgments.

[0083] In one possible implementation, determining whether the valid output signal from the secondary side can represent the current direction in step S4 specifically involves:

[0084] Determine whether the current direction sensitivity factor is greater than or equal to the preset current direction sensitivity factor. If so, determine that the effective signal output by the secondary side can represent the current direction; otherwise, determine that the effective signal output by the secondary side cannot represent the current direction.

[0085] It should be noted that those skilled in the art can set the value of the preset current direction sensitivity factor according to actual needs, and this invention does not limit it.

[0086] Specifically, the preset current direction sensitivity factor is a threshold set based on the characteristics of the current signal in practical applications to measure whether the signal can represent the current direction. This factor is derived through simulation experiments and statistical analysis of actual current signals, aiming to ensure sufficient reliability in current direction judgment and avoid misjudgment. Specifically: First, the system is simulated by injecting current signals and noise samples from different directions. These signals and noise samples include current signal conditions under various real-world operating environments, such as normal current and reverse current. Then, the detection probability and false alarm rate are statistically analyzed under different preset current direction sensitivity factors. The detection probability refers to the probability that the system accurately judges the current direction as positive or negative. The false alarm rate refers to the probability that the system incorrectly judges the current direction. Afterwards, through curve fitting, the current direction sensitivity factor that maximizes the ratio of detection probability to false alarm rate is found. This factor is used as the preset current direction sensitivity factor and serves as a reference standard in actual signal analysis.

[0087] S5: Map the time-frequency distribution to the sparse domain and extract the current direction sensitive component of the current to be measured.

[0088] In signal processing, the sparse domain refers to mapping a signal to a space with a sparse representation. This is typically achieved through transform techniques (such as Fourier transform and wavelet transform) to represent the signal as a sparse set of coefficients. In the sparse domain, only a few coefficients have large values, while most are zero or close to zero, effectively highlighting the main features of the signal and compressing unnecessary information. Current direction-sensitive components are the signal parts extracted from the sparse domain that are closely related to the current direction. By mapping to the sparse domain, these components related to changes in current direction can be accurately extracted, thus identifying key features of the current direction in complex current waveforms.

[0089] It should be noted that mapping the time-frequency distribution to a sparse domain allows for the extraction of sensitive components closely related to the current direction. This method highlights the main features of the signal, reducing the impact of redundant information and noise. Sparse representation can effectively capture important components of the current direction, reduce computational complexity, and improve the accuracy and efficiency of current direction identification in low signal-to-noise ratio and complex environments.

[0090] In one possible implementation, S5 specifically includes:

[0091] S501: Construct an overcomplete dictionary for capturing current direction-sensitive components based on the power frequency fundamental wave template and harmonic wave template of the current to be measured.

[0092] S502: Based on the overcomplete dictionary, the time-frequency distribution is mapped to the sparse domain through dictionary transformation to obtain the target sparse representation coefficients.

[0093] The specific formula for the mapping process is:

[0094]

[0095] Where D represents an overcomplete dictionary, and α represents the target sparse representation coefficients of the time-frequency distributed signal S in the overcomplete dictionary D. This represents taking the minimum function value of α. Let ||α|| denote the square of the Frobenius norm. 2,1 denoted as the group sparse norm for calculating the L2 norm of each row in the time-frequency distributed signal S, TV(α) represents the total variation regularization term with respect to parameter α used to reduce noise in the time-frequency distributed signal, λ1 represents the first regularization parameter controlling the degree of influence of sparsity, and λ2 represents the second regularization parameter controlling the degree of influence of the total variation regularization term.

[0096] Specifically, the square of the Frobenius norm The difference between S and the signal reconstructed using dictionary D and coefficients α is measured. The goal is to minimize this error, making the reconstructed signal as close as possible to the original signal. Total Variation Regularization (TV) is a regularization method commonly used in signal and image processing, primarily for reducing noise in signals while preserving edge features or variations. It smooths the signal by limiting the total amount of variation, reducing unwanted noise while preserving important features as much as possible.

[0097] It should be noted that the time-frequency distribution is mapped to the sparse domain through dictionary transformation to obtain the target sparse representation coefficients. This process aims to minimize the signal reconstruction error through optimization formulas, ensuring that the sparse representation can accurately capture the key features of the signal. Using an overcomplete dictionary, combined with Frobenius norm, L2 norm, and total variation regularization, signal noise can be effectively reduced while preserving the sparsity and important components of the signal. Sparse representation can reduce redundant information and noise, accurately extract sensitive components related to the current direction, and improve the efficiency and accuracy of signal processing, especially under conditions of high noise interference.

[0098] S503: Reconstruct the time-frequency distribution signal based on the mapping result, i.e., the target sparse representation coefficients, to obtain the current direction sensitive component.

[0099] In practical applications, a comprehensive dictionary is constructed, combined with power frequency fundamental and harmonic templates, to accurately capture sensitive components related to current direction. First, a dictionary is built based on the fundamental and harmonic features of the target signal. Then, dictionary transformation is used to map the time-frequency distribution to the sparse domain, obtaining sparse representation coefficients. During this process, Frobenius norm, L2 norm, and total variation regularization are combined to effectively reduce signal noise and ensure that key signal features are preserved. Finally, the current direction sensitive component is recovered using the reconstructed sparse representation coefficients. Sparse representation reduces redundant information, improves signal processing efficiency and accuracy, and enhances the reliability of current direction identification, especially in environments with high noise or interference.

[0100] S6: Combine the current direction sensitive component and the current direction sensitivity factor to determine the direction discrimination threshold for judging the current direction.

[0101] The direction discrimination threshold is a key criterion used to determine whether the current is in the positive or negative direction. This threshold is calculated by combining the current direction-sensitive component and the current direction sensitivity factor, aiming to quantify the directional information of the current signal. Specifically, by calculating the phase gradient of the current direction-sensitive component, the instantaneous frequency change information of the current signal can be obtained, which is closely related to the current flow direction. Combining the phase gradient and the current direction sensitivity factor, the resulting direction discrimination threshold provides a standard for determining the positive or negative direction of the current. When the direction confidence exceeds the threshold, the system determines the current to be in the positive direction. If it is below the threshold, it is determined to be in the negative direction. This ensures the accuracy of current direction identification, especially in complex environments.

[0102] In one possible implementation, S6 specifically includes:

[0103] S601: Perform synchronous compressed wavelet transform on the current direction sensitive component to redistribute the current direction sensitive component according to signal energy.

[0104]

[0105] Where δ() represents the Dirac function, S(τ,f′) represents the current direction-sensitive component with respect to the time scale τ and the frequency scale f′, and arg[S(τ,f′)] represents the phase representing the instantaneous frequency of S(τ,f′). T represents the Dirac function that chooses a frequency scale f″ that is consistent with the instantaneous frequency arg[S(τ,f′)] on the time scale τ. s (τ,f″) represents the current direction sensitive components with respect to τ and f′ obtained by redistribution.

[0106] The Dirac function only operates on the portion of the signal that is the same as its instantaneous frequency.

[0107] It should be noted that the current direction-sensitive components are processed using synchronous compressed wavelet transform, and then redistributed according to signal energy. This process utilizes the Dirac function to select the portion consistent with the instantaneous frequency for operation, accurately extracting key information from the signal. In this way, the current direction-related features in the signal are enhanced, while reducing the influence of redundant information and noise. This effectively improves the signal resolution and accuracy, especially in cases of complex current waveforms and significant noise interference, enhancing the accuracy and robustness of current direction identification.

[0108] S602: Calculate the phase gradient of the current direction sensitive component after redistribution based on the redistribution results.

[0109] The specific formula for calculating the phase gradient is as follows:

[0110]

[0111] in, This indicates the phase of the current direction-sensitive component after redistribution.

[0112] It should be noted that the instantaneous rate of change of the signal is extracted by calculating the phase gradient of the current direction-sensitive component after redistribution. This process involves differentiating the phase of the redistributed signal to capture the speed of change in the current direction. By calculating the phase gradient, the rapidly changing components in the current signal can be accurately identified, further enhancing the current direction information in the signal. This is particularly beneficial in low signal-to-noise ratio or dynamically changing current environments, improving the accuracy and reliability of current direction identification.

[0113] S603: Determine the direction discrimination threshold by combining the phase gradient and the current direction sensitivity factor.

[0114] The specific calculation method for the direction discrimination threshold is as follows:

[0115]

[0116] Where C represents the direction discrimination threshold, sgn represents the sign function representing the function value, t0 represents the starting point of integration, Δt represents the duration of integration, and t0+Δt equals the total duration of the time scale τ.

[0117] In this context, the opposite direction of electron flow is the positive direction, and the actual direction of electron flow represents the negative direction of the current.

[0118] It should be noted that the phase gradient This reflects the rate of change in current direction. If the current signal changes in the positive direction, its phase gradient will be positive, indicating that the current is flowing in the positive direction. Conversely, if the phase gradient is negative, it indicates that the current is flowing in the opposite direction. When the calculated direction discrimination threshold is positive, it indicates that the current direction is consistent with the positive direction and is determined to be positive. If the result is negative, it indicates that the current direction is consistent with the opposite direction and is determined to be negative. More specifically, in the determination of current direction, the determination of positive and negative directions is related to the sign of the phase gradient. By calculating the integral of the phase gradient, we can obtain a measure of the change in current direction. If this measure is positive, it means that the current is flowing in the positive direction. If it is negative, it means that the current is flowing in the opposite direction. Therefore, a direction discrimination threshold greater than 0 indicates that the current is in the positive direction, and less than 0 indicates that the current is in the negative direction.

[0119] In practical applications, the direction information in the current signal is accurately extracted through synchronous compressed wavelet transform, phase gradient calculation, and setting a direction discrimination threshold. By redistributing signal energy, the characteristics related to the current direction are enhanced, and noise interference is reduced. The instantaneous velocity of current change is captured by calculating the phase gradient, further improving signal accuracy. Combining the phase gradient and the current direction sensitivity factor, a direction discrimination threshold is calculated to achieve accurate current direction determination. This improves the accuracy, robustness, and reliability of current direction identification in noisy or dynamically changing current environments.

[0120] S7: Determine the direction of the current to be measured based on the direction discrimination threshold.

[0121] In one possible implementation, S7 specifically includes:

[0122] If the direction discrimination threshold is greater than 0, the current to be measured is determined to be in the positive direction; otherwise, the current to be measured is determined to be in the negative direction.

[0123] The phase gradient reflects the rate of change of the current signal over time. When the current flow direction changes, the phase of the signal also changes accordingly. By calculating the phase gradient, the rate of change of the current direction can be captured. The current direction sensitivity factor quantifies the relationship between the signal and the current direction, helping to determine whether the signal reliably represents the direction of the current. The direction discrimination threshold, based on the combined effects of the phase gradient and the sensitivity factor, sets a threshold to determine whether the current is positive or negative. When the direction discrimination threshold is greater than 0, it indicates that the current direction is consistent with the positive direction; otherwise, it is negative.

[0124] It should be noted that the current direction is determined by setting a direction discrimination threshold. Specifically, when the direction discrimination threshold is greater than 0, the current to be measured is determined to be in the positive direction. Conversely, when the threshold is less than or equal to 0, the current is determined to be in the negative direction. This determination is based on the weighted result of the phase gradient of the current signal and the sensitivity factor.

[0125] In practical applications, the entire current direction identification process involves precise signal acquisition and processing. Starting with the secondary output signal of the current to be measured from the current transformer, it goes through steps such as time-frequency decomposition, phase correction, sensitivity factor construction, and sparse domain mapping to ultimately determine the forward or reverse direction of the current. The Morlet wavelet kernel S-transform algorithm effectively extracts the frequency and time features of the signal, the Preisach hysteresis model eliminates the influence of hysteresis, and the sensitivity factor and sparse representation enhance the effectiveness of the signal. By using a direction discrimination threshold, the system can reliably distinguish the current direction. This method features high accuracy, high robustness, and strong adaptability, ensuring the accuracy and real-time performance of current direction determination in complex environments, especially under low current or noise interference conditions, thereby improving the stability and safety of industrial field control equipment.

[0126] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0127] In this embodiment of the invention, the secondary output signal is decomposed in time and frequency using an S-transform algorithm based on the Morlet wavelet kernel, effectively separating noise and effective components from the signal and improving the recognition capability of weak signals. Phase correction using the Preisach hysteresis model effectively eliminates phase errors caused by noise or electromagnetic interference, thus avoiding the phase misalignment problem commonly encountered in low-current environments. Furthermore, a current direction sensitivity factor is constructed, and the validity of the signal is determined by combining the time-frequency distribution, ensuring that the obtained signal accurately reflects the current direction. The current direction sensitivity component and the current direction sensitivity factor are combined to determine the direction discrimination threshold for judging the current direction, quantifying the relationship between the current direction and signal characteristics, further improving the accuracy of current direction judgment. Especially in low-load or complex industrial field control equipment, this avoids misjudgments caused by signal interference or computational delays, improving the accuracy of current direction recognition and the applicability of the recognition method.

[0128] Reference manual attached Figure 2 The diagram shows a schematic of the structure of an intelligent current direction identification system based on a current transformer provided by the present invention.

[0129] The present invention also provides a current direction intelligent identification system 20 based on a current transformer, applied to the above-mentioned current direction intelligent identification method based on a current transformer, comprising:

[0130] Processor 201.

[0131] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201, the intelligent current direction recognition method based on the current transformer, as described in the method embodiment, is implemented.

[0132] The intelligent current direction recognition system 20 based on current transformer provided by the present invention can execute the above-mentioned intelligent current direction recognition method based on current transformer and achieve the same or similar technical effects. To avoid repetition, the present invention will not elaborate further.

[0133] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0134] In this embodiment of the invention, the secondary output signal is decomposed in time and frequency using an S-transform algorithm based on the Morlet wavelet kernel, effectively separating noise and effective components from the signal and improving the recognition capability of weak signals. Phase correction using the Preisach hysteresis model effectively eliminates phase errors caused by noise or electromagnetic interference, thus avoiding the phase misalignment problem commonly encountered in low-current environments. Furthermore, a current direction sensitivity factor is constructed, and the validity of the signal is determined by combining the time-frequency distribution, ensuring that the obtained signal accurately reflects the current direction. The current direction sensitivity component and the current direction sensitivity factor are combined to determine the direction discrimination threshold for judging the current direction, quantifying the relationship between the current direction and signal characteristics, further improving the accuracy of current direction judgment. Especially in low-load or complex industrial field control equipment, this avoids misjudgments caused by signal interference or computational delays, improving the accuracy of current direction recognition and the applicability of the recognition method.

[0135] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0136] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0137] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0138] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0139] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0140] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0141] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0142] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0143] In the embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0144] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0145] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0146] If the functionality is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0147] This invention provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the intelligent current direction identification method based on a current transformer as described in the method embodiment.

[0148] The present invention provides a computer-readable storage medium that can implement the steps and effects of the intelligent current direction identification method based on current transformers in the above-described method embodiments. To avoid repetition, the present invention will not elaborate further.

[0149] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0150] In this embodiment of the invention, the secondary output signal is decomposed in time and frequency using an S-transform algorithm based on the Morlet wavelet kernel, effectively separating noise and effective components from the signal and improving the recognition capability of weak signals. Phase correction using the Preisach hysteresis model effectively eliminates phase errors caused by noise or electromagnetic interference, thus avoiding the phase misalignment problem commonly encountered in low-current environments. Furthermore, a current direction sensitivity factor is constructed, and the validity of the signal is determined by combining the time-frequency distribution, ensuring that the obtained signal accurately reflects the current direction. The current direction sensitivity component and the current direction sensitivity factor are combined to determine the direction discrimination threshold for judging the current direction, quantifying the relationship between the current direction and signal characteristics, further improving the accuracy of current direction judgment. Especially in low-load or complex industrial field control equipment, this avoids misjudgments caused by signal interference or computational delays, improving the accuracy of current direction recognition and the applicability of the recognition method.

[0151] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0152] The following points need to be explained:

[0153] (1) The accompanying drawings of the embodiments of the present invention only involve the structures involved in the embodiments of the present invention. Other structures can refer to the general design.

[0154] (2) For clarity, the thickness of layers or regions is enlarged or reduced in the drawings used to describe embodiments of the present invention; that is, these drawings are not drawn to actual scale. It is understood that when an element such as a layer, film, region, or substrate is referred to as being “above” or “below” another element, the element may be “directly” located “above” or “below” the other element, or there may be intermediate elements.

[0155] (3) Where there is no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0156] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for intelligent identification of current direction based on a current transformer, characterized in that, include: S1: Obtain the secondary output signal of the current to be measured through the current transformer; S2: The secondary side output signal is decomposed into time and frequency using the S-transform algorithm based on the Morlet wavelet kernel to extract the effective signal of the secondary side output; S3: Phase correction is performed on the effective output signal of the secondary side using the Preisach hysteresis model; S4: Based on the time-frequency distribution of the secondary side output signal obtained by time-frequency decomposition, a current direction sensitivity factor is constructed to determine the validity of the secondary side output signal. If the secondary side output signal can represent the current direction, proceed to step S5; otherwise, return to step S1. S5: Map the time-frequency distribution to a sparse domain and extract the current direction sensitive component of the current to be measured; S6: Combine the current direction sensitive component and the current direction sensitivity factor to determine the direction discrimination threshold for judging the current direction; S7: Determine the current direction of the current to be measured based on the direction discrimination threshold.

2. The intelligent current direction identification method based on a current transformer according to claim 1, characterized in that, S2 specifically includes: S201: Establish a hybrid model that includes fundamental frequency, harmonics, noise, and remanent magnetization effects to describe the dynamic characteristics of the secondary output signal; S202: The time-frequency distribution of the secondary side output signal is obtained by performing time-frequency decomposition on the secondary side output signal using the S-transform algorithm. S203: Extract the effective secondary output signal from the secondary output signal according to the time-frequency distribution.

3. The intelligent current direction identification method based on a current transformer according to claim 1, characterized in that, S3 specifically includes: S301: Establish a Preisach hysteresis model to simulate the nonlinear relationship between the core magnetic flux density and the core magnetic field strength in the current transformer. S302: Calculate the phase hysteresis angle between the magnetic flux density and the magnetic field strength; S303: Based on the phase lag angle, perform phase correction on the effective signal output from the secondary side.

4. The intelligent current direction identification method based on a current transformer according to claim 1, characterized in that, The current direction sensitivity factor is specifically the percentage of the target fundamental frequency signal energy in the effective output signal of the secondary side to the total frequency domain signal energy.

5. The intelligent current direction identification method based on a current transformer according to claim 4, characterized in that, The determination in S4 of whether the valid signal output from the secondary side can represent the current direction specifically involves: Determine whether the current direction sensitivity factor is greater than or equal to the preset current direction sensitivity factor. If so, determine that the effective signal output by the secondary side can represent the current direction; otherwise, determine that the effective signal output by the secondary side cannot represent the current direction.

6. The intelligent current direction identification method based on a current transformer according to claim 1, characterized in that, S5 specifically includes: S501: Construct an overcomplete dictionary for capturing the direction-sensitive component of the current based on the power frequency fundamental wave template and harmonic wave template of the current to be measured. S502: Based on the overcomplete dictionary, the time-frequency distribution is mapped to the sparse domain through dictionary transformation to obtain the target sparse representation coefficients; S503: Reconstruct the time-frequency distribution signal based on the mapping result, i.e., the target sparse representation coefficients, to obtain the current direction sensitive component.

7. The intelligent current direction identification method based on a current transformer according to claim 6, characterized in that, S6 specifically includes: S601: Perform synchronous compressed wavelet transform on the current direction sensitive component to redistribute the current direction sensitive component according to signal energy; S602: Calculate the phase gradient of the current direction sensitive component after redistribution based on the redistribution results; S603: Determine the direction discrimination threshold by combining the phase gradient and the current direction sensitivity factor.

8. The intelligent current direction identification method based on a current transformer according to claim 1, characterized in that, Specifically, S7 includes: If the direction discrimination threshold is greater than 0, the current to be measured is determined to be in the positive direction; otherwise, the current to be measured is determined to be in the negative direction.

9. A current direction intelligent identification system based on a current transformer, characterized in that, include: processor; A memory storing computer-readable instructions, which, when executed by the processor, implement the intelligent current direction identification method based on a current transformer as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the intelligent current direction identification method based on current transformer as described in any one of claims 1 to 8.

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