Current direction intelligent identification method and system based on current transformer

Abstract

The invention provides a current direction intelligent identification method and system based on a current transformer, and relates to the technical field of current direction identification, and the method comprises the steps: obtaining a secondary side output signal related to a to-be-detected current through the current transformer; performing time-frequency decomposition on the secondary side output signal by using an S transformation algorithm based on a Morlet wavelet kernel, and extracting a secondary side output effective signal; carrying out phase correction on an effective signal output by the secondary side in combination with a Preisach hysteresis model; constructing a current direction sensitivity factor based on the time-frequency distribution of the secondary side output signal obtained by time-frequency decomposition; mapping the time-frequency distribution to a sparse domain, and extracting a current direction sensitive component of the current to be detected; determining a direction judgment threshold for judging the current direction by combining the current direction sensitive component and the current direction sensitivity factor; and judging the current direction of the current to be detected according to the direction judgment threshold. According to the invention, the current direction identification accuracy and the identification application range are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of current direction identification, and in particular to a current direction intelligent identification method and system based on a current transformer. Background Art

[0002] A current transformer (CT) is an electrical device that converts current into a smaller proportional current or voltage signal. It is commonly used to measure and monitor current in industrial field control equipment. Its primary function is to sense the current in the main circuit and generate a proportional small current or voltage signal for use by measuring instruments or protection devices, enabling 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 systems. Intelligent current direction identification can accurately identify the current flow direction and promptly detect 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, the existing current direction identification process does not take current signal noise into consideration. Especially when directly identifying weak signals, signal noise can easily affect the phase of the weak signal, which is closely related to the current direction, resulting in the inability to identify the accurate phase, and then leading to inaccurate phase difference calculation and a sharp increase in the current direction misjudgment rate. Summary of the Invention

[0005] In order to solve the technical problem in the prior art that current signal noise is not taken into account during current direction identification, especially 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, and further leading to inaccurate phase difference calculation and a sharp increase in the current direction misjudgment rate, the present invention provides a current direction intelligent identification method and system based on current transformer.

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

[0007] First aspect

[0008] An embodiment of the present invention provides a method for intelligently identifying current direction based on a current transformer, comprising:

[0009] S1: Obtain the secondary side 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 secondary side output effective signal;

[0011] S3: Combined with the Preisach hysteresis model, the phase of the effective signal output on the secondary side is corrected;

[0012] 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 valid signal and whether the secondary-side output valid signal can represent the current direction. If so, proceed to step S5; otherwise, return to step S1;

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

[0014] S6: Determine a direction discrimination threshold for judging the current direction by combining the current direction sensitive component and the current direction sensitivity factor;

[0015] S7: Determine the current direction of the current to be measured according to the direction determination threshold.

[0016] Second aspect

[0017] An embodiment of the present invention provides a current direction intelligent identification system based on a current transformer, comprising:

[0018] processor;

[0019] The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the current direction intelligent identification method based on the current transformer as in the first aspect is implemented.

[0020] The third aspect

[0021] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for intelligently identifying current direction based on a current transformer according to the first aspect is implemented.

[0022] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0023] In an embodiment of the present invention, the secondary side output signal is decomposed in time and frequency by using an S-transform algorithm based on the Morlet wavelet kernel, which effectively separates the noise and effective components in the signal and improves the recognition ability of weak signals. The phase is corrected in combination with the Preisach hysteresis model, which can effectively eliminate the phase error caused by noise or electromagnetic interference, thereby avoiding the common phase misalignment problem in a small current environment. In addition, a current direction sensitivity factor is constructed, and the validity of the signal is judged in combination with the time-frequency distribution, ensuring that the obtained signal can accurately reflect the current direction. The direction discrimination threshold for judging the current direction is determined by combining the current direction sensitive component and the current direction sensitivity factor, and the relationship between the current direction and the signal characteristics is quantified, further improving the accuracy of the current direction judgment, especially in low-load or complex industrial field control equipment, avoiding misjudgment caused by signal interference or calculation delay, and improving the accuracy of current direction recognition and the scope of application of the recognition method. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 A schematic diagram of a flow chart of a method for intelligently identifying current direction based on a current transformer provided by an embodiment of the present invention;

[0026] Figure 2 A schematic structural diagram of a current direction intelligent identification system based on a current transformer provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0028] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0029] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0030] Reference Manual Figure 1, shows a flow chart of a method for intelligently identifying current direction based on a current transformer provided by an embodiment of the present invention.

[0031] An embodiment of the present invention provides a method for intelligently identifying current direction based on a current transformer. This method can be implemented by a device for intelligently identifying current direction based on a current transformer, which can be a terminal or a server. The process flow of the method for intelligently identifying current direction based on a current transformer may include the following steps:

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

[0033] The "measured current" refers to the target current that the current transformer needs to measure and analyze, typically a current signal from a device or circuit within an industrial field control system. The measured current can be high and is typically converted by a current transformer into a smaller, easily measurable signal for subsequent current direction identification and monitoring. The "secondary output signal" refers to the signal generated by the secondary (i.e., output) terminal of the current transformer when measuring the measured current. Current transformers are used to convert high currents into a smaller, easily measurable current signal for current monitoring and control.

[0034] The secondary output signal of the current being measured is obtained through a current transformer. This signal is the result of the current transformer converting the high current in the main circuit into a low current or voltage signal. Current transformers provide isolation and convert high current into a measurable small signal, effectively protecting the measurement equipment. Furthermore, accurate current acquisition provides reliable data support for subsequent current direction identification, ensuring signal accuracy and security.

[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 secondary side output effective signal.

[0036] Among them, the S-transform algorithm of the Morlet wavelet kernel is a time-frequency analysis method used to decompose the signal into time-frequency components of different frequencies. The Morlet wavelet is a complex wavelet based on the Gaussian waveform. It has good frequency resolution in the frequency domain and good localization characteristics in the time domain. The S-transform algorithm applies the Morlet wavelet to the signal and decomposes the signal in time and frequency. It can obtain the time information and frequency information of the signal at the same time, which helps to identify the changing patterns and characteristics of the signal. The effective signal output by the secondary side refers to the effective part of the signal generated by the current transformer after S-transform processing when measuring the current to be measured. Through time-frequency decomposition, the effective components in the current signal can be extracted, and the noise and irrelevant frequency components can be removed, providing an accurate signal basis for the judgment of the current direction.

[0037] It should be noted that the Morlet wavelet kernel-based S-transform algorithm is used to perform time-frequency decomposition on the secondary output signal, extracting the frequency and time characteristics of the effective signal. This process effectively separates useful information from noise in the signal, preventing noise from interfering with current direction identification, improving the recognition accuracy of weak signals, and ensuring the effectiveness and accuracy of the signal in subsequent processing steps.

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

[0039] S201: Establishing a hybrid model that describes the dynamic characteristics of the secondary-side output signal, including fundamental wave, harmonics, noise, and residual magnetic effect.

[0040] The hybrid model is specifically:

[0041]

[0042] Where, s(t) represents the secondary side output signal at time t, A0 represents the initial strength of the residual magnetism of the current transformer core, e represents the natural constant, β represents the residual magnetism attenuation coefficient of the current transformer core, ω represents the fundamental frequency, and cos represents the cosine function. Indicates the fundamental phase shift, L k (t) represents the time-varying harmonic coefficient describing the time-varying amplitude of the k-th order harmonic at time t, k = 2, 3, …, N, 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 term describes the core remanence decay characteristics.

[0044] It is important to note that the established hybrid model takes into account the fundamental wave, harmonics, noise, and residual magnetism to comprehensively describe the dynamic characteristics of the current transformer secondary output signal. By combining the fundamental wave, harmonics, residual magnetism attenuation, and noise factors, this model provides a more realistic signal representation, accurately capturing the signal changes under different current transformer operating conditions. The hybrid model effectively separates and identifies different frequency components, especially in low current or noise conditions, enhancing signal accuracy and reliability.

[0045] S202: Performing time-frequency decomposition on the secondary-side output signal using an S-transform algorithm to obtain a time-frequency distribution of the secondary-side output signal.

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

[0047]

[0048] Among them, S(τ,f) represents the time-frequency distribution of s(t) at the time scale τ and frequency scale f, π represents pi, represents the imaginary unit, and d represents the differential sign.

[0049] The time scale represents the time position when performing time-frequency analysis, that is, the moment of analyzing the signal. Typically, the S transform is used to analyze the characteristics of the signal within a small time window, and the time scale τ is the central moment of this window.

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

[0051] S203: Extracting a secondary-side output valid signal from the secondary-side output signal according to the time-frequency distribution.

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

[0053] It is important to note that a hybrid model is established by considering the influence of the fundamental wave, harmonics, noise, and residual magnetic effect, accurately describing the dynamic characteristics of the secondary output signal of the current transformer. First, the hybrid model can effectively separate and identify different frequency components, especially in the presence of low current and noise interference. Next, the S-transform algorithm is used to perform time-frequency decomposition on the signal and extract the signal's time-frequency distribution. This can simultaneously capture the signal's time and frequency characteristics and reveal 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. It can accurately identify current signals in complex environments, filter out noise, and enhance the accuracy of current direction identification.

[0054] S3: Combined with the Preisach hysteresis model, the phase of the secondary side output effective signal is corrected.

[0055] The Preisach hysteresis model is a mathematical model that describes the hysteresis effect of magnetic materials, particularly useful for simulating their nonlinear and history-dependent behavior. In current transformers, the nonlinear permeability of the core causes hysteresis when the current changes. This means that when the current signal changes, the core's magnetic response is not instantaneous but delayed. The Preisach model simulates this hysteresis effect through discretized state variables, effectively capturing and correcting it and reducing the phase shift caused by hysteresis.

[0056] It is understandable that in a current transformer, the nonlinear magnetic permeability of the core will cause the phase shift of the signal, especially in low current scenarios. This means that when the CT core responds to current changes, it does not simply change linearly, but there is a hysteresis effect, which is more obvious under low current conditions. This hysteresis effect can lead to misjudgment of the current direction because the phase of the signal will be offset, affecting the detection of the current direction. Combined with the Preisach hysteresis model, the phase of the effective signal output on the secondary side is corrected, especially to compensate for the hysteresis effect in the current transformer core. This process can eliminate the phase offset caused by the nonlinear characteristics of the core, especially in low current environments. By accurately correcting the phase, the accuracy of the signal is improved, the accurate judgment of the current direction is ensured, and the misjudgment of the current direction caused by the hysteresis effect is avoided.

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

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

[0059] The Preisach hysteresis model is specifically:

[0060]

[0061] Where B(t) represents the magnetic flux density at time t, H(t) represents the magnetic field intensity at time t, η1 and η2 represent state variables describing the open and closed 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 intensity obtained by offline calibration of the BH curve with respect to η1 and η2. It represents the hysteresis operator describing the hysteresis effect between the core magnetic flux density and the core magnetic field intensity with respect to η1 and η2.

[0062] The state variable η1 = 1 indicates that the circuit is open, increasing the magnetic flux density; η1 = -1 indicates that the circuit is closed, decreasing the magnetic flux density; and the same applies to η2. μ(η1, η2) determines the effect of magnetic field strength on magnetic flux density based on these states.

[0063] The BH curve describes the relationship between magnetic flux density and magnetic field strength. The response function determines the rate at which a material's magnetic flux density changes when the magnetic field changes. This function, like the magnetic permeability of an iron core, describes the material's ability to respond to changes in the magnetic field.

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

[0065] It is important to note that the Preisach hysteresis model is established to simulate the nonlinear relationship between the magnetic flux density and magnetic field strength of the core in a current transformer. This model accounts for the variations in the core material's magnetic permeability and its nonlinear response to changes in magnetic field strength by describing the hysteresis effect. By introducing state variables to simulate the delay effect of changes in magnetic field strength, it is possible to more accurately simulate and compensate for hysteresis in the current transformer, precisely describing and correcting the phase shift caused by hysteresis. This ensures accurate current direction identification and signal reliability, especially under low current conditions.

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

[0067] The calculation method of 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 intensity 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 the magnetic flux density and magnetic field strength in real time, dynamic compensation can be performed for the phase offset in the effective secondary output signal. The goal of compensation is to restore the correct phase of the effective secondary output signal, thereby eliminating the error caused by the core hysteresis effect and avoiding the misjudgment of the current direction caused by this error.

[0071] Among them, the phase lag angle refers to the phase difference between the magnetic flux density and the magnetic field strength. In the current transformer, the changes in the magnetic flux density and the magnetic field strength are usually not synchronized, and the magnetic field changes will lag behind the changes in the magnetic flux density. The phase lag angle indicates the degree of this hysteresis effect and is usually used to describe the response delay of nonlinear magnetic materials (such as iron cores) to the magnetic field. By calculating the phase lag angle between the magnetic flux density and the magnetic field strength in real time, the phase offset in the signal can be dynamically compensated. The goal of this compensation is to eliminate the error caused by the hysteresis effect of the iron core, ensure the phase of the signal is accurate, and avoid the error affecting the judgment of the current direction. The advantage is that this method can accurately correct the phase distortion caused by the hysteresis effect in low current environments, and improve the accuracy and reliability of current direction identification, especially in the case of large noise or interference.

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

[0073] Specifically, the Preisach hysteresis model is established to accurately simulate the relationship between the nonlinear magnetic flux density and magnetic field strength of the core of a current transformer. This model accounts for 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 lag angle, the signal phase is adjusted in real time to eliminate errors caused by the hysteresis effect. Ultimately, phase correction improves signal accuracy and avoids error-induced misjudgment of current direction, especially in the presence of high noise or interference, ensuring high-precision and reliable current direction identification.

[0074] 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 valid signal and whether the secondary side output valid signal can represent the current direction. If so, proceed to step S5; otherwise, return to step S1.

[0075] The time-frequency distribution refers to a two-dimensional representation that combines the signal's frequency components with its time information. This distribution reveals the frequency variations of a signal over different time periods, helping to identify the signal's time and frequency domain characteristics. The current direction sensitivity factor quantifies the relationship between the signal and current direction by analyzing the characteristics of the current signal in the time-frequency distribution. This factor reflects the validity of the signal and determines whether it accurately represents the direction of the current. Under different current variation patterns, the sensitivity factor can help assess the signal's reliability 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 effectively represents the current direction. Time-frequency distribution analysis ensures that both the frequency and time domain characteristics of the signal are fully considered, filtering out invalid signals and noise. This improves signal validity 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 a possible implementation manner, the current direction sensitivity factor is specifically a ratio of the target fundamental frequency signal energy in the secondary-side output effective signal to the total frequency domain signal energy.

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

[0079] The formula form of the current direction sensitivity factor is:

[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 Indicates the maximum frequency of the effective signal output on the secondary side, and || indicates the absolute value.

[0082] It's important to note that calculating the current direction sensitivity factor quantifies the energy contribution of the target fundamental frequency signal to the effective secondary-side output signal. This factor reflects the relationship between current direction and the fundamental frequency signal, accurately assessing whether the signal represents the current direction. By focusing on the target fundamental frequency (such as 50Hz or 60Hz) and considering the energy contribution 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, and avoiding misjudgments.

[0083] In a possible implementation manner, determining in S4 whether the valid signal outputted by the secondary side can represent the current direction is specifically as follows:

[0084] It is determined whether the current direction sensitivity factor is greater than or equal to the preset current direction sensitivity factor. If so, it is determined that the secondary side output valid signal can represent the current direction; otherwise, it is determined that the secondary side output valid signal cannot represent the current direction.

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

[0086] Specifically, the preset current direction sensitivity factor is a threshold set in actual applications based on the characteristics of the current signal. It is used to measure whether the signal can represent the current direction. This factor is derived through simulation experiments and statistical analysis of actual current signals. The purpose is to ensure sufficient reliability in determining the current direction and avoid misjudgments. Specifically: First, the system is simulated by injecting current signals and noise samples of 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 calculated for different preset current direction sensitivity factors. The detection probability refers to the probability that the system accurately determines the current direction as positive or negative. The false alarm rate refers to the probability that the system incorrectly determines the current direction. Then, through curve fitting, the current direction sensitivity factor that maximizes the ratio of the detection probability to the 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 a sparse domain and extract the current direction sensitive component of the current to be measured.

[0088] Among them, the sparse domain refers to mapping the signal to a space with sparse representation during the signal processing process, usually through transformation techniques (such as Fourier transform, wavelet transform, etc.) to represent the signal as a sparse set of coefficients. In the sparse domain, only a few coefficients have large values, while most coefficients are zero or close to zero, which can effectively highlight the main features of the signal and compress unnecessary information. The current direction sensitive component refers to the signal part extracted in the sparse domain that is closely related to the current direction. By mapping to the sparse domain, these components related to the change in current direction can be accurately extracted, thereby identifying the key characteristics of the current direction in the complex current waveform.

[0089] It is important to note that the time-frequency distribution is mapped to a sparse domain, from which sensitive components closely related to current direction are extracted. This approach highlights the key features of the signal and reduces the impact of redundant information and noise. Sparse representation effectively captures the important components of current direction, reduces computational complexity, and improves the accuracy and efficiency of current direction identification in low signal-to-noise ratio and complex environments.

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

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

[0092] S502: According to 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 of the mapping process is:

[0094]

[0095] Where D represents an overcomplete dictionary, α represents the target sparse representation coefficient representing the representation of the time-frequency distribution signal S in the overcomplete dictionary D, represents α at the minimum function value, represents the square of the Frobenius norm, ||α|| 2,1 represents the group sparse norm of the L2 norm of each row in the time-frequency distribution signal S, TV(α) represents the total variation regularization term with respect to parameter α for reducing the noise of the time-frequency distribution signal, λ1 represents the first regularization parameter that controls the degree of sparsity influence, and λ2 represents the second regularization parameter that controls the degree of influence of the total variation regularization term.

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

[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 to ensure that the sparse representation can accurately capture the key features of the signal. The use of an overcomplete dictionary, combined with the Frobenius norm, L2 norm, and total variation regularization, can effectively reduce signal noise and maintain 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 in the case of large noise interference.

[0098] S503: Reconstruct the time-frequency distribution signal according to the mapping result, ie, the target sparse representation coefficient, to obtain the current direction sensitive component.

[0099] In practical applications, an overcomplete dictionary is constructed, combined with power frequency fundamental and harmonic templates, to accurately capture sensitive components related to current direction. First, a dictionary is constructed based on the fundamental and harmonic characteristics of the target signal. Then, a dictionary transform is used to map the time-frequency distribution to a sparse domain, resulting in sparse representation coefficients. In this process, the Frobenius norm, L2 norm, and total variation regularization are combined to effectively reduce signal noise and ensure that the key features of the signal are preserved. Finally, the reconstructed sparse representation coefficients are used to recover the sensitive components of current direction. Sparse representation can reduce redundant information, improve signal processing efficiency and accuracy, and enhance the reliability of current direction identification, especially in noisy or interfering environments.

[0100] S6: Determine a direction discrimination threshold for judging the current direction by combining the current direction sensitive component and the current direction sensitivity factor.

[0101] Among them, the direction discrimination threshold is a key criterion for judging whether the current is in the forward or reverse direction. This threshold is calculated by combining the current direction sensitive component and the current direction sensitivity factor, and is intended to quantify the direction 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 direction of current flow. Combining the phase gradient and the current direction sensitivity factor, the direction discrimination threshold obtained provides a standard for judging the forward and reverse direction of the current. When the direction confidence exceeds the threshold, the system judges that the current is in the forward direction. If it is lower than the threshold, it is judged to be in the reverse direction, which can ensure the accuracy of current direction identification, especially in complex environments.

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

[0103] S601: Perform synchronous compression 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′). represents the Dirac function of the frequency scale f″ selected on the time scale τ to coincide with the instantaneous frequency arg[S(τ,f′)], T s (τ, f″) represents the current direction sensitive component obtained by redistribution with respect to τ and f′.

[0106] Among them, the Dirac function only selects the part that is consistent with the instantaneous frequency of the signal for operation.

[0107] It is important to note that the current direction-sensitive components are processed using a synchronously compressed wavelet transform, redistributing them according to signal energy. This process utilizes the Dirac function to select the portion that aligns with the instantaneous frequency, accurately extracting key signal information. This approach enhances the signal's features related to current direction and reduces the effects of redundant information and noise. This effectively improves signal resolution and accuracy, particularly in complex current waveforms and with significant noise interference, enhancing the precision and robustness of current direction identification.

[0108] S602: Calculate the phase gradient of the redistributed current direction sensitive component according to the redistribution result.

[0109] The phase gradient calculation formula is as follows:

[0110]

[0111] in, Represents 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 redistributed current direction-sensitive component. This process involves differentiating the redistributed signal phase to capture the speed of current direction changes. By calculating the phase gradient, rapidly changing portions of the current signal can be accurately identified, further enhancing the current direction information in the signal. This improves the accuracy and reliability of current direction identification, especially in low signal-to-noise ratio or dynamically changing current environments.

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

[0114] The calculation method of 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 is equal to the total duration of the time scale τ.

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

[0118] It should be noted that the phase gradient It reflects the rate of change of the current direction. If the change of the current signal is in the positive direction, then its phase gradient will be positive, indicating that the current is flowing in the positive direction. Conversely, if the phase gradient is negative, it means that the current is flowing in the reverse direction. When the calculated direction discrimination threshold result is positive, it indicates that the current direction is consistent with the positive direction and is judged to be the positive direction. If the result is negative, it indicates that the current direction is consistent with the reverse direction and is judged to be the negative direction. More specifically, in the discrimination of the current direction, the judgment of the 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 the 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 reverse 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 method accurately extracts directional information from current signals through synchronous compression wavelet transform, phase gradient calculation, and setting of direction discrimination thresholds. By redistributing signal energy, features related to current direction are enhanced and noise interference is reduced. Phase gradient calculation captures the instantaneous speed of current change, further enhancing signal accuracy. Combining the phase gradient with the current direction sensitivity factor, the direction discrimination threshold is calculated, enabling precise 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 current direction of the current to be measured according to the direction determination threshold.

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

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

[0123] The phase gradient reflects the rate of change of the current signal over time. When the direction of current flow changes, the phase of the signal also changes accordingly. By calculating the phase gradient, the speed of the current direction change 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 sets a threshold to determine whether the current is forward or reverse based on the combined influence of the phase gradient and the sensitivity factor. 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 measured current 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 current signal's phase gradient and sensitivity factor.

[0125] In actual applications, the entire current direction identification process starts with obtaining the secondary side output signal of the current to be measured from the current transformer through precise signal acquisition and processing. After time-frequency decomposition, phase correction, sensitivity factor construction, sparse domain mapping and other steps, the forward or reverse direction of the current is finally determined. The S transform algorithm of the Morlet wavelet kernel effectively extracts the frequency and time characteristics of the signal. The Preisach hysteresis model eliminates the influence of the hysteresis effect. The sensitivity factor and sparse representation enhance the effectiveness of the signal. Through the direction discrimination threshold judgment, the system can reliably distinguish the current direction. This method has high precision, high robustness and strong adaptability. It can ensure the accuracy and real-time performance of current direction judgment in complex environments, especially under low current or noise interference conditions, and improve the stability and safety of industrial field control equipment.

[0126] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0127] In an embodiment of the present invention, the secondary side output signal is decomposed in time and frequency by using an S-transform algorithm based on the Morlet wavelet kernel, which effectively separates the noise and effective components in the signal and improves the recognition ability of weak signals. The phase is corrected in combination with the Preisach hysteresis model, which can effectively eliminate the phase error caused by noise or electromagnetic interference, thereby avoiding the common phase misalignment problem in a small current environment. In addition, a current direction sensitivity factor is constructed, and the validity of the signal is judged in combination with the time-frequency distribution, ensuring that the obtained signal can accurately reflect the current direction. The direction discrimination threshold for judging the current direction is determined by combining the current direction sensitive component and the current direction sensitivity factor, and the relationship between the current direction and the signal characteristics is quantified, further improving the accuracy of the current direction judgment, especially in low-load or complex industrial field control equipment, avoiding misjudgment caused by signal interference or calculation delay, and improving the accuracy of current direction recognition and the scope of application of the recognition method.

[0128] Reference Manual Figure 2 , shows a structural schematic diagram of a current direction intelligent identification system based on current transformer provided by the present invention.

[0129] The present invention further provides a current direction intelligent identification system 20 based on a current transformer, which is 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 method for intelligently identifying the current direction based on the current transformer in the method embodiment is implemented.

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

[0133] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0134] In an embodiment of the present invention, the secondary side output signal is decomposed in time and frequency by using an S-transform algorithm based on the Morlet wavelet kernel, which effectively separates the noise and effective components in the signal and improves the recognition ability of weak signals. The phase is corrected in combination with the Preisach hysteresis model, which can effectively eliminate the phase error caused by noise or electromagnetic interference, thereby avoiding the common phase misalignment problem in a small current environment. In addition, a current direction sensitivity factor is constructed, and the validity of the signal is judged in combination with the time-frequency distribution, ensuring that the obtained signal can accurately reflect the current direction. The direction discrimination threshold for judging the current direction is determined by combining the current direction sensitive component and the current direction sensitivity factor, and the relationship between the current direction and the signal characteristics is quantified, further improving the accuracy of the current direction judgment, especially in low-load or complex industrial field control equipment, avoiding misjudgment caused by signal interference or calculation delay, and improving the accuracy of current direction recognition and the scope of application of the recognition method.

[0135] It should be understood that the processor in the embodiments of the present invention may be a central processing unit (CPU), but may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

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

[0137] The above embodiments can be implemented in whole or in part through software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function according to the embodiments of the present invention is generated in whole or in part. 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 a wired method (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available media can be magnetic media (such as floppy disks, hard disks, tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0138] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

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

[0140] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean 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 appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0142] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

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

[0144] Units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

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

[0146] If the function is implemented in the form of 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 the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and other media that can store program codes.

[0147] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for intelligently identifying the direction of current based on a current transformer as described in the method embodiment is implemented.

[0148] The computer-readable storage medium provided by the present invention can implement the steps and effects of the current direction intelligent identification method based on the current transformer of the above method embodiment. To avoid repetition, the present invention will not elaborate on them.

[0149] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0150] In an embodiment of the present invention, the secondary side output signal is decomposed in time and frequency by using an S-transform algorithm based on the Morlet wavelet kernel, which effectively separates the noise and effective components in the signal and improves the recognition ability of weak signals. The phase is corrected in combination with the Preisach hysteresis model, which can effectively eliminate the phase error caused by noise or electromagnetic interference, thereby avoiding the common phase misalignment problem in a small current environment. In addition, a current direction sensitivity factor is constructed, and the validity of the signal is judged in combination with the time-frequency distribution, ensuring that the obtained signal can accurately reflect the current direction. The direction discrimination threshold for judging the current direction is determined by combining the current direction sensitive component and the current direction sensitivity factor, and the relationship between the current direction and the signal characteristics is quantified, further improving the accuracy of the current direction judgment, especially in low-load or complex industrial field control equipment, avoiding misjudgment caused by signal interference or calculation delay, and improving the accuracy of current direction recognition and the scope of application 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 modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

[0152] There are a few points to note:

[0153] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention. Other structures may refer to conventional designs.

[0154] (2) For the sake of clarity, the thickness of layers or regions in the drawings used to describe the embodiments of the present invention are exaggerated or reduced, that is, these drawings are not drawn to scale. It is understood that when an element such as a layer, film, region, or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or intervening elements may be present.

[0155] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to form new embodiments.

[0156] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for intelligently identifying current direction based on current transformer, characterized in that: include: S1: Obtaining a secondary side output signal of the current to be measured through the current transformer; S2: Using the S-transform algorithm based on the Morlet wavelet kernel to perform time-frequency decomposition on the secondary side output signal to extract the secondary side output effective signal; S3: performing phase correction on the secondary side output effective signal in combination with the Preisach hysteresis model; 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 valid signal and determine whether the secondary-side output valid signal can represent the current direction. If so, proceed to step S5; otherwise, return to step S1; S5: Mapping the time-frequency distribution to a sparse domain, and extracting a current direction sensitive component of the current to be measured; S6: Determine a direction discrimination threshold for judging the current direction by combining the current direction sensitive component and the current direction sensitivity factor; S7: Determine the current direction of the current to be measured according to the direction determination threshold.

2. The method for intelligently identifying current direction based on current transformer according to claim 1, characterized in that: The S2 specifically includes: S201: establishing a hybrid model that describes the dynamic characteristics of the secondary side output signal, including fundamental wave, harmonics, noise, and residual magnetic effect; S202: performing time-frequency decomposition on the secondary-side output signal using the S-transform algorithm to obtain a time-frequency distribution of the secondary-side output signal; S203: Extracting the secondary-side output valid signal from the secondary-side output signal according to the time-frequency distribution.

3. The method for intelligently identifying current direction based on current transformer according to claim 1, characterized in that: The S3 specifically includes: S301: Establishing a Preisach hysteresis model that simulates the nonlinear relationship between the core magnetic flux density and the core magnetic field strength in the current transformer; S302: Calculating a phase lag angle between the magnetic flux density and the magnetic field strength; S303: Performing phase correction on the secondary-side output effective signal according to the phase lag angle.

4. The method for intelligently identifying current direction based on current transformer according to claim 1, characterized in that: The current direction sensitivity factor is specifically a ratio of the target fundamental frequency signal energy in the secondary-side output effective signal to the total frequency domain signal energy.

5. The method for intelligently identifying current direction based on current transformer according to claim 4, characterized in that: The step of determining whether the valid signal outputted by the secondary side can represent the current direction in S4 is specifically as follows: Determine whether the current direction sensitivity factor is greater than or equal to a preset current direction sensitivity factor; if so, determine that the secondary side output valid signal can represent the current direction; otherwise, determine that the secondary side output valid signal cannot represent the current direction.

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

7. The method for intelligently identifying current direction based on current transformer according to claim 6, characterized in that: The S6 specifically includes: S601: performing synchronous compression wavelet transform on the current direction sensitive component to redistribute the current direction sensitive component according to signal energy; S602: Calculating the phase gradient of the redistributed current direction sensitive component according to the redistribution result; S603: Determine the direction discrimination threshold by combining the phase gradient and the current direction sensitivity factor.

8. The method for intelligently identifying current direction based on current transformer according to claim 1, characterized in that: The S7 specifically includes: When the direction determination threshold is greater than 0, it is determined that the current to be measured is in a positive direction; otherwise, it is determined that the current to be measured is in a negative direction.

9. A current direction intelligent identification system based on current transformer, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method for intelligently identifying the current direction based on a current transformer according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for intelligently identifying the direction of current based on a current transformer as claimed in any one of claims 1 to 8 is implemented.

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