Method, device, storage medium and program product for determining residual magnetism of transformer

Abstract

The present invention discloses a method, device, storage medium and program product for determining residual magnetism of a transformer. Wherein, the method includes: collecting three-phase current signals of the transformer; determining a target identification phase among a plurality of phases corresponding to the transformer based on the three-phase current signals, wherein the similarity between the residual magnetism of the target identification phase and the residual magnetism of the transformer is greater than a similarity threshold; obtaining a target current signal of the target identification phase; performing variational mode decomposition on the target current signal to obtain an approximate entropy sum of the transformer, wherein the approximate entropy sum is used to characterize the stability degree of the target current signal; analyzing the approximate entropy sum to obtain the target residual magnetism of the transformer. The present invention solves the technical problem of being unable to accurately determine the residual magnetism of the transformer.

Description

Technical Field

[0001] The present invention relates to the field of transformers, and in particular, to a method, device, storage medium, and program product for determining residual magnetism of a transformer. Background Art

[0002] Currently, when the main transformer in a substation is put into operation, affected by the residual magnetic field, a large inrush current will be formed at the moment of closing, which is extremely likely to cause safety hazards such as the failure of the main transformer to close, and has an important impact on the stable operation of the power grid. Among them, residual magnetism is an important factor causing the inrush current of the transformer.

[0003] In the related art, it is difficult to directly detect the residual magnetism of the transformer, and there is a technical problem that the residual magnetism of the transformer cannot be accurately determined.

[0004] In view of the above problems, no effective solution has been proposed yet. Summary of the Invention

[0005] Embodiments of the present invention provide a method, device, storage medium, and program product for determining residual magnetism of a transformer, so as to at least solve the technical problem that the residual magnetism of the transformer cannot be accurately determined.

[0006] According to an aspect of an embodiment of the present invention, a method for determining residual magnetism of a transformer is provided, and the method may include: collecting three-phase current signals of the transformer; based on the three-phase current signals, determining a target identification phase among a plurality of phases corresponding to the transformer, where the similarity between the residual magnetism of the target identification phase and the residual magnetism of the transformer is greater than a similarity threshold; obtaining a target current signal of the target identification phase; performing variational mode decomposition on the target current signal to obtain an approximate entropy sum of the transformer, where the approximate entropy sum is used to characterize the stability degree of the target current signal; analyzing the approximate entropy sum to obtain the target residual magnetism of the transformer.

[0007] Optionally, performing variational mode decomposition on the target current signal to obtain an approximate entropy sum of the transformer includes: determining modal components of the target current signal; using the modal components to construct a constraint condition, where the constraint condition is used to constrain the fluctuation degree of the modal components; performing variational mode decomposition on the target current signal through the constraint condition to obtain a plurality of current component signals corresponding to a plurality of intrinsic mode functions of the target current signal; decomposing the plurality of current component signals to obtain a plurality of approximate entropy values of the plurality of current component signals; and determining the sum of the plurality of approximate entropy values as the approximate entropy sum.

[0008] Optionally, analyzing the approximate entropy sum to obtain the target residual magnetism of the transformer includes: obtaining a correlation relationship corresponding to the transformer, where the correlation relationship is used to characterize the relationship between the approximate entropy sum of the transformer and the residual magnetism; and using the correlation relationship and the approximate entropy sum to determine the target residual magnetism corresponding to the approximate entropy sum.

[0009] Optionally, decompose multiple current component signals to obtain multiple approximate entropy values of the multiple current component signals, including: converting the current component signals into multi-dimensional vectors; determining the distances between different vectors in the multi-dimensional vectors; and determining the approximate entropy values based on the distances.

[0010] Optionally, based on the three-phase current signals, determine the target identification phase among multiple phases corresponding to the transformer, including: respectively determining the current peaks of multiple phases in the three-phase current signals to obtain multiple current peaks; determining the target peak among the multiple current peaks, where the target peak is greater than the current peaks other than the target peak among the multiple current peaks; and determining the phase corresponding to the target peak as the target identification phase.

[0011] Optionally, based on the three-phase current signals, determine the target identification phase among multiple phases corresponding to the transformer, including: obtaining a first magnetic flux curve and a second magnetic flux curve of the transformer, where the first magnetic flux curve is used to characterize the relationship between the magnetic flux of the transformer and the current of the transformer, and the second magnetic flux curve is used to characterize the relationship between the magnetic flux of the transformer and the acquisition time of the magnetic flux; performing curve fitting on the first magnetic flux curve and the second magnetic flux curve to obtain a fitted curve; determining the equivalent bias magnetic angles of multiple phases through the fitted curve to obtain multiple equivalent bias magnetic angles; and determining the target identification phase based on the multiple equivalent bias magnetic angles.

[0012] Optionally, analyze the sum of approximate entropies to obtain the target residual magnetism of the transformer, including: predicting the sum of approximate entropies using a prediction model to obtain the target residual magnetism, where the prediction model is constructed through a software tool for power system modeling and simulation.

[0013] Optionally, the method may further include: constructing current signal sample data; determining the correlation relationship between the historical residual magnetism and the historical approximate entropy values in the current sample data; and invoking a software tool for power system modeling and simulation to construct a prediction model according to the correlation relationship.

[0014] Optionally, the method may further include: determining the initial residual magnetism of the target identification phase; comparing the initial residual magnetism with the target residual magnetism to obtain a comparison result, where the comparison result is used to characterize the accuracy of the target residual magnetism.

[0015] Optionally, determining the initial residual magnetism of the target identification phase includes: obtaining the current curve of the transformer; obtaining the magnetic induction intensities of multiple phases in the transformer through the current curve; determining the multiple equivalent bias magnetic quantities of multiple phases based on the multiple magnetic induction intensities; and converting the multiple equivalent bias magnetic quantities into the initial residual magnetism.

[0016] According to another aspect of the embodiments of the present invention, there is also provided a residual magnetism determination device for a transformer, which may include: an acquisition unit for acquiring three-phase current signals of the transformer; a determination unit for determining a target identification phase among a plurality of phases corresponding to the transformer based on the three-phase current signals, wherein the similarity between the residual magnetism of the target identification phase and the residual magnetism of the transformer is greater than a similarity threshold; an acquisition unit for acquiring a target current signal of the target identification phase; a decomposition unit for performing variational mode decomposition on the target current signal to obtain an approximate entropy sum of the transformer, where the approximate entropy sum is used to characterize the stability degree of the target current signal; and an analysis unit for analyzing the approximate entropy sum to obtain the target residual magnetism of the transformer.

[0017] According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, which includes a stored program. When the program runs, it controls the device where the computer-readable storage medium is located to execute the method for determining the residual magnetism of the transformer according to the embodiments of the present invention.

[0018] According to another aspect of the embodiments of the present invention, there is also provided a processor for running a program. When the program is run by the processor, it executes the method for determining the residual magnetism of the transformer according to the embodiments of the present invention.

[0019] According to another aspect of the embodiments of the present invention, there is also provided a program product, which includes computer instructions. When the computer instructions are executed by a processor, they implement the method for determining the residual magnetism of the transformer according to the embodiments of the present invention.

[0020] In the embodiments of the present invention, three-phase current signals of the transformer are acquired; a target identification phase among a plurality of phases corresponding to the transformer is determined based on the three-phase current signals, wherein the similarity between the residual magnetism of the target identification phase and the residual magnetism of the transformer is greater than a similarity threshold; a target current signal of the target identification phase is acquired; variational mode decomposition is performed on the target current signal to obtain an approximate entropy sum of the transformer, where the approximate entropy sum is used to characterize the stability degree of the target current signal; and the approximate entropy sum is analyzed to obtain the target residual magnetism of the transformer. That is to say, in the embodiments of the present invention, by using the three-phase current signals, the target identification phase among a plurality of phases of the transformer is determined, and through processing the target current signal of the target identification phase, the target residual magnetism of the transformer can be accurately determined, thereby solving the technical problem of being unable to accurately determine the residual magnetism of the transformer and achieving the technical effect of being able to accurately determine the residual magnetism of the transformer. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0022] Figure 1 It is a flowchart of a method for determining the residual magnetism of a transformer according to an embodiment of the present invention;

[0023] Figure 2 It is a flowchart of a method for detecting the residual magnetism of a transformer based on waveform characteristics according to an embodiment of the present invention;

[0024] Figure 3 It is a flowchart of a method for signal acquisition and preprocessing according to an embodiment of the present invention;

[0025] Figure 4 It is a flowchart of a method for modal decomposition of current signals according to an embodiment of the present invention;

[0026] Figure 5 It is a schematic diagram of an IMF set according to an embodiment of the present invention;

[0027] Figure 6 It is a flowchart of a method for approximate entropy calculation according to an embodiment of the present invention;

[0028] Figure 7 It is a schematic diagram of an approximate entropy-saturation magnetization curve according to an embodiment of the present invention;

[0029] Figure 8 It is a flowchart of a training algorithm design according to an embodiment of the present invention;

[0030] Figure 9 It is a schematic diagram of a current waveform diagram output under a rated voltage according to an embodiment of the present invention;

[0031] Figure 10 It is a schematic diagram of a current waveform at a rated voltage and a closing angle of 0 degrees according to an embodiment of the present invention;

[0032] Figure 11 It is a schematic diagram of a device for determining the residual magnetism of a transformer according to an embodiment of the present invention. Detailed implementation manners

[0033] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0034] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0035] Embodiment 1

[0036] According to an embodiment of the present invention, an embodiment of a method for determining the residual magnetism of a transformer is provided. The steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0037] In this embodiment, a method for determining the residual magnetism of a transformer is proposed. The method uses three-phase current signals to determine the target identification phase among multiple phases of the transformer, and processes the target current signal of the target identification phase to accurately determine the target residual magnetism of the transformer, thereby solving the technical problem of being unable to accurately determine the residual magnetism of the transformer and achieving the technical effect of being able to accurately determine the residual magnetism of the transformer.

[0038] Figure 1 is a flowchart of a method for determining the residual magnetism of a transformer according to an embodiment of the present invention. As Figure 1 shown, the method may include the following steps:

[0039] Step S102, collect the three-phase current signals of the transformer.

[0040] In the technical solution provided in step S102 of the present invention, the three-phase current signals of the transformer can be collected. Among them, the transformer can be a transformer in a substation. The three-phase current signals can include current signals of multiple items in the transformer, and can be the current in the closing state, the closing current signal (which can be simply referred to as the closing current). For example, the three-phase current signals can include the current signals of the A phase, B phase and C phase of the transformer.

[0041] Step S104, based on the three-phase current signals, determine the target identification phase among the multiple phases corresponding to the transformer, where the similarity between the residual magnetism of the target identification phase and the residual magnetism of the transformer is greater than the similarity threshold.

[0042] In the technical solution provided in step S104 of the present invention above, the three-phase current signals are judged to determine the target identification phase among the multiple phases corresponding to the transformer. Among them, the similarity between the residual magnetism of the target identification phase and the residual magnetism of the transformer is greater than the similarity threshold, and the target identification phase can be the best identification phase among the multiple phases. The similarity threshold can be a preset value, which can be obtained through experience, experiments, etc. It should be noted that this is only an example here, and no specific limitation is imposed on the size of the similarity threshold.

[0043] Optionally, the target identification phase can be one of the A phase, B phase, and C phase, or multiple phases among the A phase, B phase, and C phase, and can be the best identification phase in the transformer. Since the similarity between the residual magnetism of the target identification phase and the residual magnetism of the transformer is greater than the similarity threshold, therefore, by judging the residual magnetism of the target identification phase, the purpose of accurately judging the target residual magnetism of the transformer can be achieved.

[0044] In this embodiment, this embodiment collects and judges the three-phase current signals. By analyzing the three-phase current signals, the distribution of the residual magnetism in the transformer is determined to clarify whether precise prediction of the residual magnetism in the three-phase region is required; at the same time, in terms of precise residual magnetism prediction, the best identification phase among the multiple phases of the transformer can be determined by analyzing the current signals. It is only necessary to analyze the current waveform characteristics of the best identification phase to achieve accurate evaluation of the residual magnetism value, simplifying the complexity of processing the three-phase current signals in the power grid, thereby achieving the purpose of improving the data processing efficiency.

[0045] Optionally, this embodiment can collect the three-phase current signals, determine the current peaks in the three-phase current signals, and based on the current peaks, realize the phase selection determination for precise residual magnetism prediction and the rapid evaluation of the residual magnetism magnitude; use the waveforms of the three-phase current signals to evaluate the uniformity of the residual magnetism distribution to determine the need for separate production prediction for the three phases.

[0046] Step S106, obtain the target current signal of the target identification phase.

[0047] In the technical solution provided in step S106 of the present invention above, the target current signal of the target identification phase can be extracted from the three-phase current signals, and the target current signal is further analyzed. Among them, the target current signal can include the currents corresponding to the target identification phase at multiple moments.

[0048] Step S108, perform variational mode decomposition on the target current signal to obtain the approximate entropy sum of the transformer, where the approximate entropy sum is used to characterize the stability degree of the target current signal.

[0049] In the technical solution provided in step S108 of the present invention, after the target current signal is extracted, the target current signal can be subjected to variational mode decomposition to obtain the approximate entropy sum of the transformer. Among them, the approximate entropy sum can be used to characterize the stability degree of the target current signal. Variational mode decomposition (abbreviated as VMD) can be a signal processing method, which can be used to decompose a complex target current signal into multiple intrinsic mode functions (abbreviated as IMF). The intrinsic mode function can be a basic mode function.

[0050] Optionally, in this embodiment, by using variational mode decomposition, the difference between the signal and each IMF is minimized, and constraint conditions are used to extract different frequency components in the signal to obtain an IMF set. By determining the entropy of multiple IMFs in the IMF set, the approximate entropy sum is further obtained.

[0051] Optionally, the above approximate entropy sum can be the sum of multiple entropies estimated by different mathematical and statistical methods. Commonly used mathematical and statistical methods can include the K-nearest neighbor algorithm, wavelet analysis, etc. The calculation of the approximate entropy sum can simplify the entropy calculation process to a certain extent. In this embodiment, the approximate entropy sum of the transformer can be obtained by performing variational mode decomposition on the target current signal.

[0052] Step S110, analyze the approximate entropy sum to obtain the target residual magnetism of the transformer.

[0053] In the technical solution provided in step S110 of the present invention, the approximate entropy sum can be analyzed to obtain the target residual magnetism of the transformer. Among them, the target residual magnetism can be the magnetic flux remaining in the iron core when the magnetic field in the transformer changes continuously, and can be the residual magnetism identification result.

[0054] Optionally, the residual magnetism is an important parameter for the normal operation of the transformer, which will affect the performance and stability of the transformer. When the residual magnetism exists, it will cause losses, heat and noise during the operation of the transformer. Therefore, reasonable control and management are required in the design and operation.

[0055] Optionally, when the main transformer in the substation is put into operation, affected by the residual magnetic field, a large inrush current will be formed at the moment of closing, which is extremely likely to cause safety hazards such as the failure of the main transformer to close, and has an important impact on the stable operation of the power grid. Among them, the residual magnetism is an important factor causing the inrush current of the transformer. However, due to the difficulty of directly detecting the residual magnetism of the transformer, there are problems such as cumbersome calculation and large calculation errors in the theory of residual magnetism, which greatly restricts the optimization of the residual magnetism detection technology and the precise control of the residual magnetism detection. To solve the above problems, in this embodiment, the target current signal of the target identification phase is extracted from the three-phase current signals, the variational mode decomposition is performed on the target current signal to obtain the approximate entropy sum, and the relationship between the approximate entropy sum and the residual magnetism is used to predict the residual magnetism to obtain the target residual magnetism of the transformer, thus solving the technical problem of being unable to accurately determine the residual magnetism of the transformer and achieving the technical effect of being able to accurately determine the residual magnetism of the transformer.

[0056] Through the above steps S102 and S110 of the present invention, the three-phase current signals of the transformer are collected; based on the three-phase current signals, the target identification phase among the multiple phases corresponding to the transformer is determined, wherein the similarity between the residual magnetism of the target identification phase and the residual magnetism of the transformer is greater than the similarity threshold; the target current signal of the target identification phase is obtained; the variational mode decomposition is performed on the target current signal to obtain the approximate entropy sum of the transformer, wherein the approximate entropy sum is used to characterize the stability degree of the target current signal; the approximate entropy sum is analyzed to obtain the target residual magnetism of the transformer. That is to say, in the embodiment of the present invention, the three-phase current signals are used to determine the target identification phase among the multiple phases of the transformer, and by processing the target current signal of the target identification phase, the target residual magnetism of the transformer is accurately determined, thus solving the technical problem of being unable to accurately determine the residual magnetism of the transformer and achieving the technical effect of being able to accurately determine the residual magnetism of the transformer.

[0057] The above method of this embodiment will be further introduced below.

[0058] As an optional implementation manner, step S108, performing variational mode decomposition on the target current signal to obtain the approximate entropy sum of the transformer, includes: determining the modal components of the target current signal; using the modal components to construct a constraint condition, wherein the constraint condition is used to constrain the fluctuation degree of the modal components; through the constraint condition, performing variational mode decomposition on the target current signal to obtain multiple current component signals corresponding to multiple intrinsic mode functions of the target current signal; decomposing the multiple current component signals to obtain multiple approximate entropy values of the multiple current component signals; and determining the sum of the multiple approximate entropy values as the approximate entropy sum.

[0059] In this embodiment, the modal components of the target current signal can be determined; using the modal components, constraint conditions can be constructed; through the constraint conditions, variational mode decomposition can be performed on the target current signal to obtain multiple current component signals corresponding to multiple intrinsic mode functions of the target current signal; the multiple current component signals can be decomposed to obtain multiple approximate entropy values of the multiple current component signals; the sum of the multiple approximate entropy values is determined as the approximate entropy sum. Among them, the modal components can be obtained by decomposing the target current signal according to the number of modal components.

[0060] Optionally, modal decomposition is performed on the target current signal of the optimal identification phase in the three-phase current signal to obtain the mode for calculating the approximate entropy value.

[0061] In this embodiment, when the optimal identification phase (i.e., the target identification phase) is one of the three phases of the transformer, the target current signal of the target identification phase among the three phases can be obtained; when all three phases are the optimal identification phases, the three-phase current signal can be obtained.

[0062] Optionally, a predetermined number of modal components K can be set in advance. For example, K can be 8. It should be noted that this is only an example here, and no specific limitation is imposed on the magnitude of the number of modal components. Based on the number of modal components, processing is performed on the number of modal components to obtain the modal components (u k ) and the central frequencies (ω k ) of the target current signal. Using the modal components (u k ) and the central frequencies (ω k ), the following constraint conditions can be constructed:

[0063]

[0064] Among them, f can be used to represent the original signal to be decomposed, {u k} can be used to represent all the modal components obtained by decomposition, {ω k} can be used to represent the central frequencies corresponding to each mode, δ(t) can be used to represent the Dirac distribution function, can be used to represent the derivative with respect to time t.

[0065] Optionally, after constructing the constraint conditions, the Lagrange multiplier operator (λ(t)) and the quadratic penalty factor (α) can be introduced, and the constraint conditions are adjusted using the Lagrange multiplier operator and the quadratic penalty factor to transform the constrained variational problem into an unconstrained variational problem:

[0066]

[0067] Optionally, in combination with the alternating direction multiplier algorithm and the Fourier isometric transform, u k , ω can be alternately updatedk , λ, can optimize the obtained modal components and center frequencies through the following formula:

[0068]

[0069]

[0070]

[0071] Among them, and can be used to represent the Fourier transforms of u(t), f(t), and λ(t) respectively, and are ω k and τ can be used to represent the time constant.

[0072] Optionally, judge the updated modal components to determine whether the updated modal components meet the convergence condition. Among them, the convergence condition can be:

[0073]

[0074] Among them, e can be used to represent the given accuracy.

[0075] In this embodiment, if the convergence condition is met, the set of intrinsic mode functions IMF set can be output. Among them, the IMF set can be a current signal containing each intrinsic mode function after the decomposition of the original current signal (i.e., the target current signal), that is, multiple current component signals corresponding to multiple intrinsic mode functions of the target current signal can be obtained. Among them, the current component signals can be represented by IMF 1, IMF 2, IMF 3, IMF 4, IMF 5, IMF 6, IMF 7, IMF 8. It should be noted that this is only an example here, and the representation form of the current component signals is not specifically limited.

[0076] As an optional implementation manner, analyze the approximate entropy sum to obtain the target residual magnetism of the transformer, including: obtaining the correlation relationship corresponding to the transformer, where the correlation relationship is used to characterize the relationship between the approximate entropy sum of the transformer and the residual magnetism; using the correlation relationship and the approximate entropy sum to determine the target residual magnetism corresponding to the approximate entropy sum.

[0077] In this embodiment, the correlation between the approximate entropy sum and the residual magnetism can be pre-established. After determining the approximate entropy sum, the above-mentioned correlation can be obtained, and the target residual magnetism can be determined by using the correlation and the approximate entropy sum. Among them, the correlation can be a linear relationship, an exponential relationship, a non-linear relationship, etc., and can be an identification rule. It should be noted that only examples are given here, and the type of the correlation is not specifically limited.

[0078] For example, the residual magnetism corresponding to different types of transformers at different closing angles and different approximate entropies can be determined in advance to determine the correlation associated with different types of transformers. After determining the approximate entropy sum corresponding to the transformer, the corresponding correlation can be retrieved, and based on the correlation, the target residual magnetism of the transformer can be determined.

[0079] For another example, by setting the no-load closing model of the transformer, three-phase current signals under different residual magnetisms, closing angles and other disturbances are obtained; taking the three-phase current signals as training sample data, combined with the empirical fitting equation, a more accurate mathematical relationship for characterizing the closing magnetic characteristics (i.e., the correlation) is obtained; the approximate entropy sum is obtained through modal decomposition, and the approximate entropy sum is used as an effective determination for residual magnetism prediction; in the residual magnetism identification, the closing angle input is selected to simplify the reverse solution process, so as to realize the rapid and accurate prediction of the residual magnetism value.

[0080] As an optional implementation manner, decomposing a plurality of current component signals to obtain a plurality of approximate entropy values of the plurality of current component signals includes: converting the current component signals into multi-dimensional vectors; determining the distances between different vectors in the multi-dimensional vectors; and determining the approximate entropy values based on the distances.

[0081] In this embodiment, after obtaining the current component signals, the current component signals can be processed to determine the approximate entropy value corresponding to the current component signals. The approximate entropy value corresponding to the current component signals can be determined through the following steps: a plurality of current component signals can be converted into a plurality of multi-dimensional vectors; the distances between different vectors in the multi-dimensional vectors are determined; and the approximate entropy values are determined based on the distances.

[0082] Optionally, after obtaining a plurality of current component signals, the approximate entropy values of each IMF component can be calculated respectively, and the approximate entropy values of the plurality of IMF components are added together to obtain the approximate entropy sum corresponding to the target current signal. Among them, the approximate entropy value can be used to describe the complexity of the time series. The greater the complexity of the sequence, the greater the corresponding approximate entropy value. The quantization result of the approximate entropy value for non-stationary and non-linear sequences is stable.

[0083] Optionally, the approximate entropy values of the intrinsic mode functions (IMFs) obtained by modal decomposition are solved and summed to obtain the observable quantity (i.e., the approximate entropy sum) for residual magnetism prediction.

[0084] Optionally, the approximate entropy value corresponding to the current component signal can be obtained through the following steps: The current signals of each intrinsic mode function (i.e., the current component signal) can be converted into multi-dimensional vectors. For example, the current component signal can be set as a time series X of length N = [x 1 , x 2 ,... x N . The elements of the time series X can be arranged in order to form a vector with m dimensions, that is:

[0085] X i = [x(i), x(i + 1),..., x(i + m - 1)]

[0086] where i = 1, 2,..., N - m + 1

[0087] Optionally, define the distance (d[X i , X j ) in the time series as the distance between the vector X i and the vector X j . Then:

[0088] d[X i , X j = max|x(i + k) - x(j + k)|, k ∈ (0, m - 1)

[0089] In this embodiment, determine the distances between multiple vectors in the multi-dimensional vector, determine the number of d[X i , X j ≤ r, and denote this number as B i , where r can be the similarity tolerance. Calculate the ratio of B i to the total number of vectors N - m + 1, that is:

[0090]

[0091] Furthermore, take the logarithm operation on , and then find its average value for all i, denoted as B m (r):

[0092]

[0093] Let m = m + 1, and repeat the above steps to obtain B m+1 (r)

[0094]

[0095] Optionally, the approximate entropy value (ApEn(m, r, N)) of this time series can be obtained through the following formula:

[0096] ApEn(m,r,N) = B m (r) - B m+1 (r)

[0097] Optionally, the parameter m can be 2 or 3; the magnitude of r can depend on the actual application scenario and can be r = 0.2 * std, where std represents the standard deviation of the original time series. It should be noted that the magnitudes of the above data are only for illustrative purposes and are not specifically limited here.

[0098] Optionally, through the above method, the approximate entropy value corresponding to each current component signal in the IMF set can be determined, and the sum of the approximate entropy values of multiple current component signals can be determined as the approximate entropy sum of the transformer.

[0099] As an alternative implementation, in step S104, based on the three-phase current signals, determining the target identification phase among the multiple phases corresponding to the transformer includes: in the three-phase current signals, respectively determining the current peaks of multiple phases to obtain multiple current peaks; determining the target peak among the multiple current peaks, where the target peak is greater than the current peaks other than the target peak among the multiple current peaks; and determining the phase corresponding to the target peak as the target identification phase.

[0100] In this embodiment, the above target peak can be the maximum value among the current peaks.

[0101] Optionally, the process of determining the target identification phase among the multiple phases corresponding to the transformer based on the three-phase current signals can be obtained through signal processing or through parameter processing. The method of obtaining the target identification phase based on parameter processing is further described below.

[0102] In this embodiment, at any closing angle (for example, no-load closing angle), the current signals of different phases in the transformer are collected to obtain three-phase current signals (I A 、I B 、I C ), and parameter processing can be performed on the three-phase current signals I A 、I B 、I C .

[0103] Optionally, if parameter processing is performed on the three-phase current signals, the current peaks corresponding to each phase (I Apeak 、I Bpeak 、I Cpeak ) can be obtained respectively. By comparing the current peaks of the three phases, the phase with the largest current peak among the three phases can be determined as the best identification phase. Further, the equivalent bias magnetic coefficient of the best identification phase can be determined as cos(θ + θ i + A), where θ is the closing angle, θi The phase difference of the optimal identification phase is φ, and A is the equivalent residual magnetism coefficient. By converting the equivalent residual magnetism coefficient, the initial residual magnetism of the transformer can be obtained. The initial residual magnetism can be the predicted residual magnetism and can be used to measure the accuracy of the target residual magnetism.

[0104] As an optional implementation manner, in step S104, based on the three-phase current signals, determining the target identification phase among multiple phases corresponding to the transformer includes: obtaining a first magnetic flux curve and a second magnetic flux curve of the transformer, where the first magnetic flux curve is used to characterize the relationship between the magnetic flux of the transformer and the current of the transformer, and the second magnetic flux curve is used to characterize the relationship between the magnetic flux of the transformer and the acquisition time of the magnetic flux of the transformer; performing curve fitting on the first magnetic flux curve and the second magnetic flux curve to obtain a fitting curve; determining the equivalent bias magnetic angles of multiple phases through the fitting curve to obtain multiple equivalent bias magnetic angles; and determining the target identification phase based on the multiple equivalent bias magnetic angles.

[0105] In this embodiment, the above-mentioned first magnetic flux curve can be a magnetic flux-current curve (Φ(i) curve) and can be used to characterize the relationship between the magnetic flux of the transformer and the current of the transformer. The second magnetic flux curve can be a magnetic flux-time curve (Φ(t) curve) and can be used to characterize the relationship between the magnetic flux of the transformer and time.

[0106] In this embodiment, if waveform processing is performed on the three-phase current signals, the magnetic flux curve of the transformer can be obtained. Among them, the magnetic flux curve can include a first magnetic flux curve (Φ(i) curve) and a second magnetic flux curve (Φ(t) curve). Curve fitting can be performed on multiple magnetic flux curves according to the fitting equation to obtain a fitting curve.

[0107] Optionally, the fitting equation can be Φ = Φ m cos(ωt + θ) + Φ m cos(θ + α)·e -τt , where τ can be used to characterize the attenuation factor of the bias magnetic factor, Φ m cos(ωt + θ) can be used to characterize the periodic component, Φ m cos(θ + α)·e -τt can be used to characterize the attenuation component, Φ m can be used to characterize the peak value of the magnetic flux density, θ can be used to characterize the original phase angle of the waveform, and α can be used to characterize the equivalent bias magnetic angle.

[0108] Optionally, through the fitting curve, the equivalent bias magnetic angles α A 、α B 、α CThe uniformity of the residual magnetism can be determined by using the equivalent bias magnetic angle of each phase. That is, the errors of the equivalent bias magnetic angles of each phase can be calculated respectively: ɑ A / ɑ max 、ɑ b / ɑ max 、ɑ c / ɑ max If the errors of the equivalent bias magnetic angles of the three phases are all ≤ 30%, only one phase needs to be identified. That is, the phase with the largest peak value of the identification current can be determined as the target identification phase, and only the initial residual magnetism of the target identification phase can be determined. If the condition that the errors of the equivalent bias magnetic angles of the three phases are all ≤ 30% is not satisfied, all three phases can be the target identification phases, and the three-phase currents can be identified to obtain the initial residual magnetism corresponding to each phase.

[0109] As an optional implementation manner, in step S110, analyzing the approximate entropy value to obtain the target residual magnetism of the transformer includes: using a prediction model to predict the approximate entropy sum to obtain the target residual magnetism, where the prediction model is constructed by using a software tool for power system modeling and simulation.

[0110] In this embodiment, the prediction model can be used to analyze the approximate entropy sum to obtain the target residual magnetism. The prediction model can be constructed by using a software tool for power system modeling and simulation (Power Systems Computer Aided Design, abbreviated as PSCAD), and can be a no-load closing model of the transformer.

[0111] Optionally, relevant parameters can be set during the process of constructing the prediction model by using the software tool for power system modeling and simulation. For example, parameters such as the transformer capacity (C N ), rated voltage (U N ), voltage ratio, and reactance (Z) can be set according to the actual situation, and the types and methods of the set parameters are not specifically limited here.

[0112] As an optional implementation manner, the method may further include: constructing current signal sample data; determining the correlation between the historical residual magnetism and the historical approximate entropy value in the current sample data; and calling the software tool for power system modeling and simulation to construct a prediction model according to the correlation.

[0113] In this embodiment, the current signal sample data can be obtained. The current signal sample data can be the current signal data of the transformer at historical moments, can be the closing current sample data, and can include the current signal sample data obtained in advance under different residual magnetism conditions at different closing angles.

[0114] For example, disturbances with closing angles θ ranging from 0 to 120° and residual magnetism Φ B under disturbances ranging from 0 to 0.8 pu can be obtained respectively. According to the obtained current signal sample data and the relationship between the current and magnetic flux during the transformer closing process, the residual magnetism Φ of each sample can be obtained B ,

[0115] Φ = Φ m cos(ωt + θ) + Φ m cos(θ + Φ B )·e -τt

[0116] Through the fitting equation of the current and magnetic flux during the transformer closing process, new current signal sample data Φ(θ, Φ B ) can be obtained. By performing variational mode decomposition on the new current signal sample data, an IMF set with the same number as the current sample data can be obtained. Through the above calculation method, the approximate entropy of each IMF is calculated and the approximate entropy values of each group of IMFs are summed to obtain the approximate entropy sum.

[0117] Based on the approximate entropy sum and the current signal sample data, a grid under disturbance factors is established, which can be composed of the closing angle and the residual magnetism. Further, a cloud diagram of the approximate entropy sum SumApEn represented by the closing angle θ and the residual magnetism Φ B can be drawn. By inputting the closing angle θ' of a specific working condition, the relationship between the residual magnetism (Φ B ) and the approximate entropy sum (SumApEn) at a specific closing angle can be obtained according to the drawn cloud diagram to obtain the correlation relationship. Using the correlation relationship, the residual magnetism can be predicted according to the approximate entropy sum obtained under this working condition.

[0118] As an optional implementation manner, the method may further include: determining the initial residual magnetism of the target identification phase; comparing the initial residual magnetism with the target residual magnetism to obtain a comparison result, where the comparison result is used to characterize the accuracy of the target residual magnetism.

[0119] In this embodiment, the initial residual magnetism of the target identification phase can be determined. The initial residual magnetism can be compared with the target residual magnetism to obtain a comparison result. If the difference between the initial residual magnetism and the target residual magnetism is too large, it can be determined that the comparison result is used to characterize a low accuracy of the target residual magnetism, and the three-phase current signal can be re-obtained to accurately obtain the target residual magnetism of the transformer. If the difference between the initial residual magnetism and the target residual magnetism is small, it can be determined that the comparison result is used to characterize a high accuracy of the target residual magnetism, and the target residual magnetism result of the transformer can be determined to be correct.

[0120] As an alternative implementation, determining the initial residual magnetism of the target identification phase includes: obtaining the current curve of the transformer; obtaining the magnetic induction intensities of multiple phases in the transformer through the current curve; determining the equivalent bias magnetic quantities of multiple phases based on the multiple magnetic induction intensities; and converting the multiple equivalent bias magnetic quantities into the initial residual magnetism.

[0121] In this embodiment, the above-mentioned current curve (B(i) curve) can be used to characterize the relationship between the current and the magnetic induction intensity in the transformer and can be the magnetic induction intensity curve.

[0122] Optionally, the initial residual magnetism can be determined through the following steps: obtaining the magnetic induction intensity curve (B(i) curve) of the transformer, and calculating the residual magnetism estimation result corresponding to the optimal identification phase through the following formula:

[0123] B m [cos(θ + θ i + A) - cos(θ + θ i )]

[0124] where B m can be used to characterize the standard reference magnetic flux density, that is, the magnetic flux density of the input voltage.

[0125] Optionally, if the magnitudes of the current peaks of the three phases are not compared, the initial residual magnetism of the transformer can be directly calculated. The magnetic induction intensity curve (B(i) curve) of the transformer can be obtained. The three-phase magnetic induction intensities (B Apeak , B Bpeak , B Cpeak ) are obtained through the B(i) curve. Based on the three-phase magnetic induction intensities, the equivalent bias magnetic quantity can be obtained as: |B peak | - B m , where B peak can be used to characterize the magnetic flux density corresponding to the current signal I peak . The residual magnetism estimation result is output through the formula ∑ i＝1,2,3 ±(|B peak | - B m ) / 3, where the phase with the largest equivalent bias magnetic quantity takes "+", and the other two phases take "-". The residual magnetism estimation result (that is, the initial residual magnetism) can be output through the above method.

[0126] In the embodiment of the present invention, by using the three-phase current signals to determine the target identification phase among multiple phases of the transformer and processing the target current signal of the target identification phase, the target residual magnetism of the transformer can be accurately determined, thereby solving the technical problem of being unable to accurately determine the residual magnetism of the transformer and achieving the technical effect of accurately determining the residual magnetism of the transformer.

[0127] Embodiment 2

[0128] The technical solutions of the embodiments of the present invention will be illustrated below in conjunction with preferred embodiments.

[0129] At present, when the main transformer in a substation is put into operation, affected by the residual magnetic field, a large inrush current will be formed at the moment of closing, which is extremely likely to cause safety hazards such as the failure of the main transformer to close, and has an important impact on the stable operation of the power grid. Among them, the residual magnetism is an important factor causing the inrush current of the transformer. However, due to the difficulty of directly detecting the residual magnetism of the transformer, there are problems such as cumbersome calculation and large calculation errors in theory for the residual magnetism, which greatly restricts the optimization of the residual magnetism detection technology and the precise control of the residual magnetism detection.

[0130] In the related art, for the detection of the residual magnetism of the transformer, affected by factors such as the closing angle, there are some parameters that cannot be directly measured, or a large amount of data needs to be measured, and it is difficult to face complex working conditions. Therefore, there is a technical problem that the residual magnetism of the transformer core cannot be accurately determined.

[0131] To solve the above problems, the embodiments of the present application propose a fast prediction method applicable to the residual magnetism content of the transformer. This method can include a fast residual magnetism evaluation strategy based on the peak value of the closing current and an accurate residual magnetism prediction based on modal decomposition to obtain approximate entropy. This method realizes the fast evaluation of the residual magnetism content according to the peak value characteristics in the transformer closing current signal; at the same time, using the approximate entropy value in the closing current signal, a mathematical relationship between the approximate entropy value and the residual magnetism is established, so as to realize the accurate prediction of the residual magnetism content of the transformer. A criterion for whether to perform a separate three-phase residual magnetism content evaluation is proposed for the problem of uneven residual magnetism distribution in large power transformers, so as to realize the application of intelligent degaussing and phase selection closing in the engineering application scenario, thereby achieving the technical effect of accurately predicting the residual magnetism of the transformer and solving the technical problem of low accuracy in predicting the residual magnetism of the transformer.

[0132] Optionally, this embodiment uses the current waveform characteristics during the no-load closing of the transformer, and through revealing the internal relationship among the residual magnetism - closing angle - closing current, proposes a fast residual magnetism evaluation strategy based on the current peak value and an accurate residual magnetism prediction method based on current waveform decomposition.

[0133] Optionally, this embodiment proposes a method for fast residual magnetism evaluation, accurate residual magnetism prediction, and setting of the criterion for the uniformity of residual magnetism distribution for the problem of difficult residual magnetism prediction of transformers in engineering, so as to achieve the purpose of better and fast and accurate evaluation of the residual magnetism of large transformers in on-site applications.

[0134] Optionally, in terms of the training model design, by setting the no-load closing model of the transformer, three-phase current signals under different disturbances such as residual magnetism and closing angle are obtained; the three-phase current signals and data are used as training sample data, combined with the empirical fitting equation, to obtain a more accurate mathematical relationship for characterizing the closing magnetic characteristics; the approximate entropy value is obtained through modal decomposition, and the approximate entropy value is selected as an effective determination for residual magnetism prediction; in the residual magnetism identification, the closing angle input is selected to simplify the reverse solution process, so as to realize the fast and accurate prediction of the residual magnetism value.

[0135] Optionally, this embodiment collects three-phase current signals, roughly estimates the residual magnetism magnitude based on the peak values of the three-phase current signals, and conducts a quick evaluation of the precise residual magnetism calculation for phase selection; by analyzing the three-phase current signals, the distribution of the residual magnetism is determined to clarify whether precise prediction of the residual magnetism in the three-phase region is required; at the same time, in terms of precise residual magnetism prediction, the best identification phase can be output by analyzing the current peak value. In practical applications, accurate evaluation of the residual magnetism value can be achieved only by analyzing the waveform characteristics of one-phase current, which simplifies the complexity of processing three-phase current signals in the power grid.

[0136] Optionally, this embodiment uses the sample data of the closing angle, residual magnetism, and approximate entropy sum to draw a cloud map of the approximate entropy sum represented by the closing angle and residual magnetism. According to a specific working condition (for example, determining the closing angle), the obtained approximate entropy sum can be used for residual magnetism prediction.

[0137] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments; the magnitudes of the numbers in this embodiment are only for illustrative purposes and are not specifically limited here. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention, and are not specifically limited here.

[0138] Figure 2 is a flowchart of a method for detecting residual magnetism of a transformer based on waveform characteristics according to an embodiment of the present invention. As Figure 2 shown, the method may include the following steps:

[0139] Step S201, collect three-phase current signals and preprocess the three-phase current signals.

[0140] As an optional embodiment, three-phase current signals can be collected and the collected three-phase current signals can be preprocessed.

[0141] In this embodiment, three-phase current signals can be collected, the current peaks in the three-phase current signals can be determined, and phase selection determination for accurate prediction of residual magnetism and rapid evaluation of the magnitude of residual magnetism can be achieved based on the current peaks; the waveform of the three-phase current signals can be used to evaluate the uniformity of the residual magnetism distribution to determine whether there is a need for three-phase separate production prediction.

[0142] Figure 3 is a flowchart of a signal acquisition and preprocessing method according to an embodiment of the present invention, as Figure 3 shown, the method may include the following steps:

[0143] Step S301, collect three-phase current signals.

[0144] In this embodiment, at any closing angle (for example, no-load closing angle), the current signals of different phases in the transformer are collected to obtain three-phase current signals (I A 、I B 、I C ), and for the three-phase current signals I A 、I B 、I C , parameter processing or signal processing can be performed. Among them, signal processing can also be called waveform processing.

[0145] Optionally, if parameter processing is performed on the three-phase current signals, step S302 can be executed, and if waveform processing is performed on the three-phase current signals, step S308 can be executed.

[0146] Step S302, obtain the current peak.

[0147] In this embodiment, if parameter processing is performed on the three-phase current signals, the current peak corresponding to each phase can be obtained respectively (I Apeak 、I Bpeak 、I Cpeak ).

[0148] Step S303, determine whether to compare the current peaks of the three phases.

[0149] In this embodiment, it is determined whether to compare the current peaks of the three phases. If so, step S304 is implemented; if not, step S306 is implemented.

[0150] Step S304, determine the best identification phase among the three phases.

[0151] In this embodiment, the three-phase current peaks are obtained. If the magnitudes of the three-phase current peaks are compared, the phase with the largest current peak among the three phases is determined as the best identification phase. Further, the equivalent bias magnetic coefficient of the best identification phase can be determined as cos(θ + θ i+A), where θ is the closing angle, and θ i is the phase difference of the optimal identification phase, and A is the equivalent residual magnetism coefficient.

[0152] Step S305, determine the residual magnetism of the optimal identification phase.

[0153] In this embodiment, the equivalent bias magnetic quantity is obtained, and the estimated result of the residual magnetism corresponding to the optimal identification phase is calculated through the following formula:

[0154] B m [cos(θ + θ i + A) - cos(θ + θ i )]

[0155] where B m can be used to represent the standard reference magnetic density, that is, the magnetic density of the input voltage.

[0156] Step S306, obtain the magnetic induction intensity curve of the transformer.

[0157] In this embodiment, if the magnitudes of the current peaks of the three phases are not compared, the residual magnetism of the transformer can be directly calculated.

[0158] Optionally, obtain the magnetic induction intensity curve (B(i) curve) of the transformer.

[0159] Step S307, based on the magnetic induction intensity curve, determine the estimated result of the residual magnetism of the transformer.

[0160] In this embodiment, the three-phase magnetic induction intensities (B Apeak , B Bpeak , B Cpeak ) are obtained through the B(i) curve, and the equivalent bias magnetic quantity is obtained as: |B peak | - B m , where B peak can be used to represent the magnetic density corresponding to the current signal I peak . The estimated result of the residual magnetism is output through the formula ∑ i＝1,2,3 ±(|B peak | - B m ) / 3, where the phase with the largest equivalent bias magnetic quantity takes "+", and the other two phases take "-".

[0161] Optionally, the estimated result of the residual magnetism can be output in the above manner.

[0162] Step S308, obtain multiple flux curves of the transformer.

[0163] In this embodiment, if waveform processing is performed on the three-phase current signals, the magnetic flux curve of the transformer can be obtained. Among them, the magnetic flux curve can include a magnetic flux-current curve (Φ(i) curve) and a magnetic flux-time curve (Φ(t) curve).

[0164] Step S309: Perform curve fitting on multiple magnetic flux curves.

[0165] In this embodiment, multiple magnetic flux curves can be curve-fitted according to the fitting equation to obtain a fitted curve.

[0166] Optionally, the fitting equation can be Φ = Φ m cos(ωt + θ) + Φ m cos(θ + ɑ)·e -τt . Where τ can be used to characterize the attenuation factor of the bias magnetic factor, Φ m cos(ωt + θ) can be used to characterize the periodic component, Φ m cos(θ + ɑ)·e -τt can be used to characterize the attenuation component, Φ m can be used to characterize the peak value of the magnetic flux density, θ can be used to characterize the original phase angle of the waveform, and ɑ can be used to characterize the equivalent bias magnetic angle.

[0167] Step S310: Obtain the equivalent bias magnetic angle of each phase through the fitted curve.

[0168] In this embodiment, the equivalent bias magnetic angles ɑ A 、ɑ B 、ɑ C of the above three phases, namely phase A, phase B, and phase C, can be obtained through the fitted curve.

[0169] Step S311: Determine the uniformity of the residual magnetism.

[0170] In this embodiment, the uniformity of the residual magnetism can be determined by using the equivalent bias magnetic angle of each phase.

[0171] Optionally, calculate the errors of the equivalent bias magnetic angles of each phase respectively: ɑ A / ɑ max 、ɑ b / ɑ max 、ɑ c / ɑ max . If the errors of the equivalent bias magnetic angles of the three phases are all ≤ 30%, only one phase needs to be identified, that is, the residual magnetism of only the phase with the largest current peak can be identified. If the condition that the errors of the equivalent bias magnetic angles of the three phases are all ≤ 30% is not satisfied, the three-phase currents need to be identified to obtain the residual magnetism corresponding to each phase.

[0172] Step S202: Perform modal decomposition on the three-phase current signals.

[0173] As an alternative embodiment, the three-phase current signal is preprocessed to output an identification requirement (i.e., the target current signal). The target current signal with the best identification phase in the three-phase current signal can be subjected to modal decomposition to obtain the modes for calculating the approximate entropy value.

[0174] Figure 4 It is a flowchart of a method for modal decomposition of a current signal according to an embodiment of the present invention, as Figure 4 shown, the method may include the following steps:

[0175] Step S401, obtain the target current signal with the best identification phase.

[0176] In this embodiment, when the best identification phase is one of the three phases, the target current signal with the best identification phase among the three phases can be obtained; when all three phases are the best identification phases, the three-phase current signal can be used.

[0177] Optionally, the number of modal components K can be set in advance. For example, K can be 8. It should be noted that this is only an example here, and no specific limitation is imposed on the magnitude of the number of modal components.

[0178] Step S402, construct the constraint conditions.

[0179] In this embodiment, the modal component (u k ) and the center frequency (ω k ) can be obtained, and using the modal component u k and the center frequency ω k , the constraint conditions can be constructed as:

[0180]

[0181] wherein, f can be used to represent the original signal to be decomposed, {u k} can be used to represent all the modal components obtained by decomposition, {ω k} can be used to represent the center frequencies corresponding to each mode, δ(t) can be used to represent the Dirac distribution function, can be used to represent the derivative with respect to time t.

[0182] Step S403, introduce the Lagrange multiplier operator and the quadratic penalty factor.

[0183] In this embodiment, the Lagrange multiplier operator λ(t) and the quadratic penalty factor α can be introduced, and the constraint conditions are adjusted using the Lagrange multiplier operator and the quadratic penalty factor to transform the constrained variational problem into an unconstrained variational problem:

[0184]

[0185] Step S404, update the modal components and the central frequency.

[0186] In this embodiment, the alternating direction multiplier algorithm and the Fourier equidistant transform can be combined to alternately update u k , ω k , and the Lagrange multiplier (λ). The optimized modal components and central frequency can be obtained through the following formula:

[0187]

[0188]

[0189]

[0190] Wherein, and can be used to represent the Fourier transforms of u(t), f(t), and λ(t) respectively. and are the ω k and after n iterations. τ can be used to represent the time constant.

[0191] Step S405, judge the updated modal components.

[0192] In this embodiment, it can be judged whether the updated modal components meet the convergence condition.

[0193] Optionally, the convergence condition can be:

[0194]

[0195] Wherein, e can be used to represent a given precision.

[0196] In this embodiment, if the convergence condition is met, the set of intrinsic mode functions can be output. Among them, the IMF set can be the current signals of the intrinsic mode functions after the decomposition of the original current signal.

[0197] Figure 5 is a schematic diagram of an IMF set according to an embodiment of the present invention. As Figure 5 shown, the original signal can be the input current signal. By performing the above decomposition on the current signal, the IMF set can be obtained. Among them, the IMF set can include IMF 1, IMF 2, IMF 3, IMF 4, IMF 5, IMF 6, IMF 7, IMF 8, and can include the current signals of the intrinsic mode functions.

[0198] Step S203, determine the approximate entropy sum corresponding to the three-phase current signal.

[0199] As an alternative embodiment, the approximate entropy values of the decomposed IMF components can be calculated, and the approximate entropies of multiple IMF components are added to obtain the approximate entropy sum corresponding to the three-phase current signal. Among them, the approximate entropy value can be used to describe the complexity of the time series. The greater the complexity of the series, the greater the corresponding approximate entropy value. The quantization result of the approximate entropy value is stable for non-stationary and non-linear series.

[0200] Optionally, solve and sum the approximate entropy of the intrinsic mode function (IMF) obtained by modal decomposition to obtain the observable for residual magnetism prediction.

[0201] Figure 6 is a flowchart of an approximate entropy calculation according to an embodiment of the present invention, as Figure 6 shown, the method may include the following steps:

[0202] Step S601, convert the current signal of each intrinsic mode function into a multi-dimensional vector.

[0203] In this embodiment, assume that the current signal of each intrinsic mode function is a time series X = [x 1 , x 2 ,..x. N with a length of N. The elements of the time series X can be arranged in order to form a vector with m dimensions, that is:

[0204] X i = [x(i), x(i + 1),..., x(i + m - 1)]

[0205] where i = 1, 2,..., N - m + 1

[0206] Step S602, determine the distance between multiple vectors in the multi-dimensional vector.

[0207] In this embodiment, define the distance (d[X i , X j ) as the distance between vector X i and X j , then:

[0208] d[X i , X j = max|x(i + k) - x(j + k)|, k ∈ (0, m - 1)

[0209] Step S603, based on the distance between multiple vectors, determine the approximate entropy value of the current signal of each intrinsic mode function.

[0210] In this embodiment, when determining the multi-dimensional vector, the distances between multiple vectors are determined, and the number of d[X i ,X j ≤r is denoted as B i , where r can be the similarity tolerance. Calculate the ratio of B i to the total number of vectors N - m + 1, that is:

[0211]

[0212] Further, take the logarithm operation on , and then find its average value for all i, denoted as B m (r):

[0213]

[0214] Let m = m + 1, and repeat the above steps, then B m+1 (r)

[0215]

[0216] Optionally, the approximate entropy value (ApEn(m, r, N)) of the time series can be obtained through the following formula:

[0217] ApEn(m, r, N) = B m (r) - B m+1 (r)

[0218] Optionally, the parameter m can be 2 or 3; the value of r can depend on the actual application scenario, and can be r = 0.2*std, where std represents the standard deviation of the original time series. It should be noted that the magnitudes of the above data are only for illustrative purposes and are not specifically limited here.

[0219] Optionally, when determining the approximate entropy corresponding to the current signal of each intrinsic mode function in the IMF set, the sum of the approximate entropies of multiple IMFs is determined as the approximate entropy sum of the transformer.

[0220] Step S204, construct the identification rule.

[0221] In this embodiment, the training algorithm draws a cloud map of the approximate entropy sum represented by the closing angle and the residual magnetism, and obtains the corresponding residual magnetism prediction criterion.

[0222] Figure 7 is a schematic diagram of an approximate entropy sum - residual magnetism curve according to an embodiment of the present invention. As Figure 7 shown, in the scenario of closing angle of 0 degrees under the rated voltage, there are differences in the approximate entropy sum - residual magnetism curves corresponding to different phases.

[0223] In this embodiment, since there is a certain relationship between the approximate entropy sum and the residual magnetism and closing angle, in order to predict the residual magnetism based on the magnitude of the approximate entropy sum and the closing angle, the algorithm needs to be trained to obtain a large amount of data to obtain the relationship among the three. Thus, when the closing angle and the approximate entropy sum are determined, the correlation relationship among the three can be used to accurately estimate the residual magnetism.

[0224] Figure 8 It is a flowchart of the design of a training algorithm according to an embodiment of the present invention. As Figure 8 shown, the method may include the following steps:

[0225] Step S801, build a simulation model.

[0226] In this embodiment, the no-load closing model of the transformer can be built using power system computer-aided design, and relevant parameters can be set. For example, the transformer capacity (C N ), rated voltage (U N ), voltage ratio, reactance (Z), and other parameters. The parameters can be set according to the actual situation, and the types and methods of the set parameters are not specifically limited here.

[0227] Step S802, obtain current signal sample data.

[0228] In this embodiment, the above current signal sample data can be closing current sample data. Figure 9 It is a schematic diagram of the current waveform diagram output under the rated voltage according to an embodiment of the present invention. As Figure 9 shown, when the closing angle is 0 degrees, the current signal sample data under different residual magnetism conditions can be obtained in advance.

[0229] For example, the closing current sample data under the disturbances of the closing angle θ of 0 - 120° and the residual magnetism Φ B of 0 - 0.8 pu can be obtained respectively.

[0230] Optionally, according to the obtained current signal sample data and the relationship between the current and magnetic flux during the transformer closing process, the residual magnetism Φ B of each sample can be obtained,

[0231] Φ = Φ m cos(ωt + θ) + Φ m cos(θ + Φ B )·e -τt

[0232] Step S803, determine the approximate entropy sum corresponding to the current signal sample data.

[0233] In this embodiment, new current signal sample data Φ(θ, Φ B ) can be obtained through the fitting equation of current and magnetic flux during the transformer closing process. The new current signal sample data is subjected to variational mode decomposition to obtain an IMF set with the same number as the current sample data. Through the above calculation method, the approximate entropy of each IMF is calculated and the approximate entropy values of each group of IMFs are summed to obtain the sum of approximate entropy.

[0234] Step S804: Determine the relationship between the sum of approximate entropy and the residual magnetism at a specific closing angle.

[0235] In this embodiment, based on the sum of approximate entropy and the current signal sample data, a grid under disturbance factors is established, and this grid can be composed of the closing angle and the residual magnetism. Further, a cloud diagram of the sum of approximate entropy SumApEn represented by the closing angle θ and the residual magnetism Φ B can be drawn. Based on the cloud diagram, the relationship curve between the residual magnetism and the sum of approximate entropy can be obtained.

[0236] Figure 9 is a schematic diagram of a fitting curve graph of the residual magnetism and the sum of approximate entropy at a rated voltage and a closing angle of 0 degrees according to an embodiment of the present invention. As Figure 9 shown, by inputting the closing angle θ' of a specific working condition, the relationship between the residual magnetism (Φ B ) and the sum of approximate entropy (SumApEn) at a specific closing angle can be obtained according to the drawn cloud diagram. The residual magnetism can be predicted based on the sum of approximate entropy obtained under this working condition.

[0237] Step S205: Estimate the residual magnetism based on the obtained sum of approximate entropy.

[0238] In this embodiment, the fitting curve of the residual magnetism and the sum of approximate entropy under a specific working condition can be obtained through the cloud diagram, and the residual magnetism can be predicted based on the obtained sum of approximate entropy.

[0239] For example, assuming that the working condition is a rated voltage and a closing angle of 0 degrees, the prediction process of the residual magnetism may include:

[0240] After closing in this working condition, the three-phase current signals of A, B, and C are collected from 0 to 0.2 s, and the signals as shown in Figure 10 can be obtained. Figure 10 is a schematic diagram of the current waveform at a rated voltage and a closing angle of 0 degrees according to an embodiment of the present invention. As Figure 10 shown, the relationship between the current (i) and time (t) of the transformer under different residual magnetisms is obtained, where the unit of the current can be kiloampere (kA) and the unit of time can be second (s).

[0241] Optionally, by comparing the magnitudes of the three-phase current peaks, it can be found that the current peak of phase A is the largest. Therefore, it is determined that the optimal identification phase is phase A. Perform variational mode decomposition on the target current signal of phase A. The VMD algorithm is mainly affected by the predetermined number of modal components K. For the predetermined number of modal components K, if the value of K is too large, it will lead to over-decomposition and may generate meaningless modes; if the value of K is too small, it will lead to under-decomposition and important modal components in the original signal cannot be decomposed. Therefore, for the above reasons, K = 8 can be set to perform variational mode decomposition on the signal, and the current component signal as shown in Figure 5 is obtained. Further, an IMF set is obtained, and the approximate entropy sum of the obtained IMF set can be calculated. For the cloud diagram of the approximate entropy sum represented by the closing angle and the residual magnetism, when θ = 0° is input, the relationship between the residual magnetism and the approximate entropy sum under this working condition can be obtained, that is, the curve relationship as shown in Figure 7 can be obtained. And a fitting curve is constructed as shown in Figure 9 . If the approximate entropy sum of each intrinsic mode function of the phase A current is x = 0.36206195 at this time, the relationship between the approximate entropy sum of phase A and the residual magnetism is y = 2.7348x - 0.5245 in this case, and y = 0.4656 is obtained. And the actual residual magnetism at this time is 0.44 pu, which is within the allowable error range.

[0242] In this embodiment, the three-phase current signals are collected. By obtaining the closing current peak, the optimal identification phase for rapid residual magnetism testing and accurate residual magnetism prediction can be selected; through the analysis of the current waveform curve, the determination of the residual magnetism uniformity in the three-phase region of the transformer can be realized; in terms of accurate residual magnetism prediction, the residual magnetism can be evaluated through the current waveform characteristics of one phase, which simplifies the complexity of processing the three-phase current signals in the power grid.

[0243] Optionally, the training algorithm of this embodiment obtains the sample data of the closing angle, the residual magnetism, and the approximate entropy sum through the algorithm, draws the cloud diagram of the approximate entropy sum represented by the closing angle and the residual magnetism, and the residual magnetism can be predicted according to the obtained approximate entropy sum under specific working conditions (such as determining the closing angle).

[0244] In the embodiment of the present invention, by using the three-phase current signals, the target identification phase among multiple phases of the transformer is determined, and by processing the target current signal of the target identification phase, the target residual magnetism of the transformer can be accurately determined, thereby solving the technical problem of being unable to accurately determine the residual magnetism of the transformer and achieving the technical effect of being able to accurately determine the residual magnetism of the transformer.

[0245] Embodiment 3

[0246] According to the embodiment of the present invention, a device for determining the residual magnetism of a transformer is further provided. It should be noted that the device for determining the residual magnetism of the transformer in this embodiment can be used to execute the method for determining the residual magnetism of the transformer in Embodiment 1 of the present invention.

[0247] Figure 11 It is a schematic diagram of a residual magnetism determination device for a transformer according to an embodiment of the present invention. As Figure 11 shown, the residual magnetism determination device 110 of the transformer may include: a collection unit 1101, a determination unit 1102, a decomposition unit 1103, and an analysis unit 1104.

[0248] The collection unit 1101 is configured to collect three-phase current signals of the transformer.

[0249] The determination unit 1102 is configured to determine a target identification phase among multiple phases corresponding to the transformer based on the three-phase current signals, wherein the similarity between the residual magnetism of the target identification phase and the residual magnetism of the transformer is greater than a similarity threshold; an acquisition unit is configured to acquire a target current signal of the target identification phase.

[0250] The decomposition unit 1103 is configured to perform variational mode decomposition on the target current signal to obtain an approximate entropy sum of the transformer, where the approximate entropy sum is used to characterize the stability degree of the target current signal.

[0251] The analysis unit 1104 is configured to analyze the approximate entropy sum to obtain the target residual magnetism of the transformer.

[0252] For the residual magnetism determination device of the transformer in this embodiment, the three-phase current signals of the transformer are collected through the collection unit; the target identification phase among multiple phases corresponding to the transformer is determined based on the three-phase current signals through the determination unit, wherein the similarity between the residual magnetism of the target identification phase and the residual magnetism of the transformer is greater than a similarity threshold; the target current signal of the target identification phase is acquired through the acquisition unit; the target current signal is subjected to variational mode decomposition through the decomposition unit to obtain the approximate entropy sum of the transformer, where the approximate entropy sum is used to characterize the stability degree of the target current signal; the approximate entropy sum is analyzed through the analysis unit to obtain the target residual magnetism of the transformer, thereby solving the technical problem of being unable to accurately determine the residual magnetism of the transformer and achieving the technical effect of being able to timely warn of risks during operation.

[0253] Embodiment 4

[0254] According to an embodiment of the present invention, there is also provided a computer-readable storage medium, which includes a stored program, wherein the program executes the method for determining the residual magnetism of the transformer in Embodiment 1.

[0255] Optionally, in this embodiment, the above computer-readable storage medium may be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the mobile terminals in a mobile terminal group.

[0256] In this embodiment, three-phase current signals are used to determine a target identification phase among multiple phases of a transformer. By processing the target current signal of the target identification phase, the target residual magnetism of the transformer can be accurately determined, thereby solving the technical problem of being unable to accurately determine the residual magnetism of the transformer and achieving the technical effect of being able to accurately determine the residual magnetism of the transformer.

[0257] Embodiment 5

[0258] According to an embodiment of the present invention, a processor is further provided. The processor is used to run a program. When the program is run by the processor, the method for determining the residual magnetism of the transformer in Embodiment 1 is executed.

[0259] Optionally, in this embodiment, the above computer terminal may be at least one network device among multiple network devices of a computer network.

[0260] Adopting the embodiment of the present invention, three-phase current signals are used to determine a target identification phase among multiple phases of a transformer. By processing the target current signal of the target identification phase, the target residual magnetism of the transformer can be accurately determined, thereby solving the technical problem of being unable to accurately determine the residual magnetism of the transformer and achieving the technical effect of being able to accurately determine the residual magnetism of the transformer.

[0261] Embodiment 6

[0262] According to an embodiment of the present invention, a computer program product is further provided. The computer program product includes computer instructions. When the computer instructions are executed by a processor, the method for determining the residual magnetism of the transformer in Embodiment 1 is implemented.

[0263] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0264] In the above embodiments of the present invention, the descriptions of the respective embodiments have their own focuses. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0265] In several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of units can be a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of units or modules can be in an electrical or other form.

[0266] The unit described as a separating component may or may not be physically separated. The component displayed as a unit may or may not be a physical unit, that is, it may be located in one place or may be distributed over multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0267] In addition, each functional unit in various embodiments of the present invention may be integrated in a processing unit, may exist separately physically for each unit, or two or more units may be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0268] If the integrated unit 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 such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or 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 for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks or optical discs and other various media that can store program codes.

[0269] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for determining residual magnetism of a transformer, characterized in that: include: Collect three-phase current signals of transformer; Based on the three-phase current signal, determining a target identification phase among the multiple phases corresponding to the transformer, wherein a similarity between the residual magnetism of the target identification phase and the residual magnetism of the transformer is greater than a similarity threshold; Acquire a target current signal of the target identification phase; Performing variational mode decomposition on the target current signal to obtain an approximate entropy sum of the transformer, wherein the approximate entropy sum is used to characterize the stability of the target current signal; Analyze the approximate entropy sum to obtain the target residual magnetism of the transformer; Wherein, performing variational modal decomposition on the target current signal to obtain the approximate entropy sum of the transformer includes: determining a plurality of current component signals based on the target current signal; converting the current component signals into multidimensional vectors; determining the distance between different vectors in the multidimensional vectors; determining an approximate entropy value based on the distance; and determining the sum of the plurality of approximate entropy values ​​as the approximate entropy sum; The step of analyzing the approximate entropy sum to obtain the target residual magnetism of the transformer includes: Obtain an association relationship corresponding to the transformer, wherein the association relationship is used to characterize a relationship between an approximate entropy sum and a residual magnetism of the transformer; and determine the target residual magnetism corresponding to the approximate entropy sum by using the association relationship and the approximate entropy sum.

2. The method according to claim 1, characterized in that: Based on the target current signal, determining the plurality of current component signals comprises: determining a modal component of the target current signal; Using the modal components, constructing constraint conditions, wherein the constraint conditions are used to constrain the degree of fluctuation of the modal components; The target current signal is subjected to variational mode decomposition according to the constraint conditions to obtain a plurality of current component signals corresponding to a plurality of intrinsic mode functions of the target current signal.

3. The method according to claim 1, characterized in that Determining the target identification phase among the multiple phases corresponding to the transformer based on the three-phase current signal includes: In the three-phase current signal, current peak values ​​of the multiple phases are determined respectively to obtain multiple current peak values; determining a target peak value among the plurality of current peak values, wherein the target peak value is greater than the current peak values ​​among the plurality of current peak values ​​except the target peak value; The phase corresponding to the target peak is determined as the target identification phase.

4. The method according to claim 1, characterized in that: Determining the target identification phase among the multiple phases corresponding to the transformer based on the three-phase current signal includes: Acquire a first magnetic flux curve and a second magnetic flux curve of the transformer, wherein the first magnetic flux curve is used to characterize the relationship between the magnetic flux of the transformer and the current of the transformer, and the second magnetic flux curve is used to characterize the relationship between the magnetic flux of the transformer and the acquisition time of the magnetic flux of the transformer; Performing curve fitting on the first magnetic flux curve and the second magnetic flux curve to obtain a fitting curve; Determine the equivalent magnetic bias angles of the multiple phases through the fitting curve to obtain multiple equivalent magnetic bias angles; The target recognition phase is determined based on the multiple equivalent magnetic bias angles.

5. The method according to claim 1, characterized in that Analyzing the approximate entropy sum to obtain the target residual magnetism of the transformer includes: The approximate entropy and are predicted using a prediction model to obtain the target residual magnetism, wherein the prediction model is constructed using a software tool for power system modeling and simulation.

6. The method according to claim 5, characterized in that The method further comprises: Construct current signal sample data; Determine the correlation between the historical remanence and the historical approximate entropy value in the current signal sample data; The power system modeling and simulation software tools are called to construct the prediction model according to the association relationship.

7. The method according to any one of claims 1 to 6, characterized in that The method further comprises: Determining the initial residual magnetism of the target identification phase; The initial residual magnetism and the target residual magnetism are compared to obtain a comparison result, wherein the comparison result is used to characterize the accuracy of the target residual magnetism.

8. The method according to claim 7, characterized in that Determining the initial residual magnetism of the target identification phase includes: Obtaining a current curve of the transformer; Obtaining multiple magnetic induction intensities of multiple phases in the transformer through the current curve; Based on the multiple magnetic induction intensities, determining multiple equivalent magnetic bias quantities of the multiple phases; The multiple equivalent bias magnetization amounts are converted into the initial residual magnetization.

9. A device for determining residual magnetism of a transformer, characterized in that: include: A collection unit, used for collecting three-phase current signals of the transformer; a determination unit, configured to determine, based on the three-phase current signal, a target identification phase among a plurality of phases corresponding to the transformer, wherein a similarity between the residual magnetism of the target identification phase and the residual magnetism of the transformer is greater than a similarity threshold; An acquisition unit, used for acquiring a target current signal of the target identification phase; A decomposition unit, used for performing variational mode decomposition on the target current signal to obtain an approximate entropy sum of the transformer, wherein the approximate entropy sum is used to characterize the stability of the target current signal; An analysis unit, configured to analyze the approximate entropy sum to obtain a target residual magnetism of the transformer; The decomposition unit is used to perform variational mode decomposition on the target current signal to obtain the approximate entropy sum of the transformer through the following steps: determining multiple current component signals based on the target current signal; converting the current component signals into multidimensional vectors; determining the distance between different vectors in the multidimensional vector; determining an approximate entropy value based on the distance; and determining the sum of multiple approximate entropy values ​​as the approximate entropy sum; The analysis unit is used to analyze the approximate entropy sum through the following steps to obtain the target residual magnetism of the transformer: obtaining the association relationship corresponding to the transformer, wherein the association relationship is used to characterize the relationship between the approximate entropy sum and the residual magnetism of the transformer; and determining the target residual magnetism corresponding to the approximate entropy sum using the association relationship and the approximate entropy sum.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 8.

11. A computer program product, characterized in that The method comprises computer instructions, which implement the method according to any one of claims 1 to 8 when executed by a processor.

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