An analysis method for the transient process characteristics of a converter transformer

Abstract

The present invention relates to the technical field of power systems, and discloses an analysis method for transient process characteristics of a converter transformer, including calculating the calculated values of individual following selection parameters and carrying out different types of predation behaviors; calculating the position information of the individual in this iteration according to the predation behavior of the individual; updating the position information of the individual and calculating the fitness value of the individual; calculating the position information of the individual in this iteration at this time according to the death flip process; updating the position information of the individual again and calculating the fitness value of the individual, and defining the individual with the largest fitness value as the atom most matching the signal to be measured. Optimizing the atom decomposition algorithm based on the behavioral characteristics of the predation competition behavior can simplify the analysis process and reduce the computational complexity. In addition, compared with the existing signal decomposition methods, the atom decomposition algorithm provided by the present invention has stronger adaptability and lower complexity.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and particularly to an analysis method for transient process characteristics of a converter transformer. Background Art

[0002] According to operation experience and research, the transient process may cause insulation breakdown problems in the converter transformer, thereby affecting the normal operation of the converter transformer and ultimately resulting in huge economic losses. Therefore, quickly and accurately identifying the transient process characteristics of the converter transformer helps engineering technicians promptly identify the cause of the accident and can also provide a basis for proposing transient process suppression methods and improving insulation coordination. Therefore, the decomposition process of transient process signals is a key step in feature recognition. Atomic decomposition is an analysis method for non-stationary signals. When the atomic decomposition algorithm is applied to the decomposition of converter transformer transient process signals, it has stronger time-frequency analysis and anti-noise capabilities. However, the atomic decomposition with exhaustive iteration has the problems of a large amount of calculation and low calculation efficiency. Summary of the Invention

[0003] Based on this, in view of the problems of large calculation amount and low calculation efficiency existing in the atomic decomposition with exhaustive iteration, it is necessary to provide an analysis method for transient process characteristics of a converter transformer.

[0004] An analysis method for transient process characteristics of a converter transformer includes calculating values of selection parameters followed by individuals to carry out different types of predation behaviors; calculating the position information of the individuals in this iteration according to the predation behaviors of the individuals; updating the position information of the individuals and calculating the fitness values of the individuals; calculating the position information of the individuals in this iteration according to the death flip process; updating the position information of the individuals again and calculating the fitness values of the individuals.

[0005] In the above analysis method for transient process characteristics of a converter transformer, the atoms in the decaying sine wave atom library are regarded as individuals performing predation behaviors, and a set of optimal linear combinations are searched in the atom library according to the characteristics of individuals during predation in nature to approximate the signal to be measured. Optimizing the atomic decomposition algorithm based on the behavioral characteristics of predation competition behaviors can simplify the analysis process and reduce its computational complexity. The analysis method for transient process characteristics of a converter transformer provided by the present invention solves problems by simulating the principle of the predation behaviors of organisms in nature. Compared with existing signal decomposition methods, it has stronger adaptability and lower complexity.

[0006] In one embodiment, the predation behavior includes the cruising method and the lurking method. The individual follows the calculated value of the selection parameter and conducts different types of predation behaviors, including calculating the selection parameter of the individual according to the selection function. When the selection parameter of the individual is less than or equal to the preset value, it is determined that the individual conducts the predation behavior of the cruising method. When the selection parameter of the individual is greater than the preset value, it is determined that the individual conducts the predation behavior of the lurking method.

[0007] In one embodiment, the calculation formula of the selection function includes:

[0008] E i =(w1T + w2H i ) / L

[0009] Where E i is the selection parameter of the individual, w1 and w2 are weight coefficients, T is the environmental temperature, H i is the hunger level of the individual, and L is the depth of the river water.

[0010] In one embodiment, the cruising method includes:

[0011] The individual floats on the water surface and searches for food over a large range during cruising. This process is expressed by the equation:

[0012]

[0013] Where x i K is the position of individual i at iteration step K, x i K+1 is the position of individual i at iteration step K + 1, Step is the iteration step size, and θ1 and θ2 are the angles of head rotation when the individual turns its head to look left and right, respectively;

[0014] After the individual discovers a suitable prey object, it dives and slowly approaches underwater. When the attack range is satisfied, it rushes out from underwater and launches a fierce attack. This process is expressed by the equation:

[0015]

[0016] Where x best is the position of the best individual in the population, x worst is the position of the worst individual in the population, x i K is the position of individual i at iteration step K, x i K+1 is the position of individual i at iteration step K + 1, and x i is the initial position of individual i.

[0017] In one embodiment, the lurking method includes:

[0018] Individuals search for gentle tidal flats and lurk in the waters near the shore. When animals come to drink water, they launch a surprise attack. This process can be expressed by the following equation:

[0019]

[0020] In the formula, x best is the position of the best individual in the population, x worst is the position of the worst individual in the population, x i K is the position of individual i at the Kth iteration step, x i K+1 is the position of individual i at the (K + 1)th iteration step.

[0021] In one of the embodiments, the death flip process includes:

[0022] When an individual in the group successfully bites the prey, other individuals will swim towards the prey, bite the prey, and flip their bodies in circles multiple times. With the cooperation of the group, the prey is decomposed. This process can be expressed by the following equation:

[0023]

[0024] In the formula, x best is the position of the best individual in the population, x i K is the position of individual i at the Kth iteration step, x i K+1 is the position of individual i at the (K + 1)th iteration step, and Step is the iteration step size.

[0025] A method for analyzing the transient process characteristics of a converter transformer includes: when the voltage amplitude of an electronic device is greater than a preset value, recording the electrical signal of the electronic device and defining the electrical signal of the electronic device as a signal to be measured; performing atomic decomposition on the signal to be measured by using the method for analyzing the transient process characteristics of a converter transformer according to any one of the above embodiments to obtain effective atomic parameters; and analyzing the transient characteristics of the electronic device according to the effective atomic parameters.

[0026] In one embodiment, the analysis method for the transient process characteristics of a commutation transformer as described in any of the above embodiments is used to perform atomic decomposition on the signal to be measured, obtaining effective atomic parameters including a design iteration formula, and constructing a damped sinusoidal atom library based on the signal to be measured; regarding the atoms in the damped sinusoidal atom library as individuals performing predation behavior; initializing the basic parameters of the predation behavior competition optimization algorithm; searching for the atom in the damped sinusoidal atom library that best matches the signal to be measured through the predation behavior competition optimization algorithm; the individual with the largest fitness value is the atom that best matches the signal to be measured; updating the residual signal as the new signal to be measured; repeating the steps of searching for the atom that best matches the signal to be measured and updating the residual signal as the new signal to be measured until the number of iterations reaches a preset iteration termination number; defining the optimal atom obtained in the last iteration process as the best matching atom, and obtaining the effective atomic parameters of the best matching atom.

[0027] In one embodiment, the expression of the damped sinusoidal atom library includes:

[0028]

[0029] where g(t) is a Gaussian window function; define Let γ be the index of g γ (t), where s is the scale parameter, τ is the displacement factor, ξ is the frequency factor, is the phase factor, Z γ is the conversion coefficient, e is the base of the power function, ω is the frequency, φ is the phase angle, ρ is the damping coefficient, t s is the start time, t e is the end time, and u(t) is the unit step.

[0030] In one embodiment, the basic parameters include the population size, the iteration termination number, and the iteration step size. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in this specification. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0032] Figure 1 is a schematic flowchart of the analysis method for the transient process characteristics of a commutation transformer according to one embodiment of the present invention;

[0033] Figure 2Schematic flowchart of a method for selecting a suitable foraging method according to the selection parameters of an individual in one embodiment of the present invention;

[0034] Figure 3 Schematic flowchart of a method for analyzing the transient process characteristics of a converter transformer in another embodiment of the present invention;

[0035] Figure 4 Schematic flowchart of a method for finding the atom that best matches a signal to be measured based on a predation behavior competition optimization algorithm in one embodiment of the present invention;

[0036] Figure 5 Schematic diagram of an overvoltage waveform in one embodiment of the present invention;

[0037] Figure 6 Statistical chart of the cumulative energy distribution of overvoltage in one embodiment of the present invention. Detailed implementation manners

[0038] For ease of understanding the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Preferred embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the disclosure of the present invention more thorough and comprehensive.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used in the description of the present invention in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0040] The decomposition of transient process signals is a key step in fault feature recognition. Existing signal decomposition methods mainly include wavelet transform, Prony algorithm, empirical mode decomposition (EMD) combined with Hilbert-Huang transform (HHT), S transform, etc. When using wavelet transform to decompose transient process signals and calculating energy features in the high-frequency band for recognition, both the selection of wavelet basis and the signal decomposition scale need to be set artificially, resulting in a relatively complex algorithm. When using the Prony algorithm to analyze transient features, there are certain errors in the recognition of non-stationary signals, so some information may be missed. When using EMD-Hilbert transform to calculate features such as amplitude spectrum features, marginal spectrum features, and time-frequency spectrum features of transient process signals, the phenomenon of mode mixing is likely to occur. When using S transform to perform spectral analysis on transient information and extract features under the time-frequency matrix, when the number of signal sampling points is too large, the calculation rate of S transform is too low, and its parameter settings lack unified regulations and are susceptible to the types of research objects.

[0041] Atom decomposition is an analysis method for non-stationary signals. Atom decomposition has advantages such as stronger time-frequency analysis and anti-noise capabilities when analyzing transient features. However, due to the large computational amount of exhaustive iterative atom decomposition, the computational efficiency is low. Therefore, the present invention will simplify it based on the predation behavior competition optimization method to reduce the computational complexity of the atom decomposition algorithm.

[0042] Figure 1 It is a schematic flowchart of the method for analyzing the transient process characteristics of a converter transformer according to an embodiment of the present invention. In one embodiment, the method for analyzing the transient process characteristics of a converter transformer includes the following steps S10 to S60.

[0043] Step S10: Individuals follow the calculated value of the selection parameter and carry out different types of predation behaviors.

[0044] The atoms in the decaying sinusoidal atom library are regarded as individuals performing predation behaviors. In the predation behavior competition optimization algorithm, there are at least two predation behaviors for individuals, and the predation behavior of an individual depends on whether the river is in the flood season or the dry season. In this embodiment, it is possible to judge whether the river is in the flood season or the dry season according to the calculated value of the selection parameter.

[0045] Step S20: Calculate the position information of the individual in this iteration according to the predation behavior of the individual.

[0046] In this embodiment, the decaying sinusoidal atom library is discretized and placed in the solution space. According to the selection parameter calculated in step S10, judge which foraging behavior to choose, and calculate the position of the individual in the solution space in this iteration according to the foraging behavior.

[0047] Step S30: Update the position information of the individuals and calculate the fitness values of the individuals.

[0048] Based on the positions of the individuals in the solution space calculated in Step S20, update the positions in the solution space. Each position has multiple parameter information, including frequency f, damping coefficient ρ, phase angle φ, start time t s , end time t e and other parameters. After the position information in the solution space is updated, recalculate the fitness values G of each individual. The fitness value G is the absolute value of <R n f,g γn >. < > is the inner product operator, that is, when calculating the fitness value G of an individual, it is necessary to take the absolute value after performing the inner product operation on the signal R n f after the nth iteration and the optimal atom g γn obtained in the nth iteration.

[0049] Step S40: Calculate the position information of the individuals in this iteration according to the death flip process.

[0050] In the predation behavior in nature, when an individual in the group successfully bites the prey, other individuals will swim towards the prey, bite the prey, and turn around in circles many times, and decompose the prey under the cooperation of the group. Simulate this process to recalculate the position information of the individuals in the solution space at this time in this iteration.

[0051] In one embodiment, the death flip process can be expressed by the following equation:

[0052]

[0053] In the formula, x best is the position of the best individual in the population (Gmax), x i K is the position of individual i at the Kth iteration step, x i K+1 is the position of individual i at the (K + 1)th iteration step, and Step is the iteration step size.

[0054] Step S50: Update the position information of the individuals again and calculate the fitness values of the individuals.

[0055] Based on the positions of the individuals in the solution space calculated in Step S40, update the positions in the solution space again. Similarly, each position has multiple parameter information, including frequency f, damping coefficient ρ, phase angle φ, start time t s , end time t eParameters such as etc. After the position information in the solution space is updated again, the fitness value G of each individual is recalculated once again. Since in this embodiment, the approximation of the atom to the signal to be measured is described by the fitness value G, among the atoms in the atom library, the atom with the largest fitness value G is the atom that best matches the signal to be measured.

[0056] The above analysis method for the transient process characteristics of the converter transformer regards the atoms in the decaying sine wave atom library as individuals performing predation behavior, searches for an optimal linear combination in the atom library according to the characteristics of individuals during predation in nature to approximate the signal to be measured, and regards the atom closest to the signal to be measured as the best matching atom. Optimizing the atom decomposition algorithm based on the behavioral characteristics of predation competition behavior can simplify the analysis process and reduce its computational complexity. The analysis method for the transient process characteristics of the converter transformer provided in this embodiment solves problems by simulating the principle of the predation behavior of organisms in nature. Compared with the existing signal decomposition methods, it has stronger adaptability and lower complexity.

[0057] Figure 2 It is a schematic flowchart of the method for selecting a suitable foraging method according to the selection parameters of an individual in one embodiment of the present invention. In one embodiment, the individual follows the calculated value of the selection parameter and conducts different types of predation behaviors, including the following steps S31 to S35.

[0058] Step S31: Calculate the selection parameter of the individual according to the selection function.

[0059] In this embodiment, the predation behavior includes the cruising method and the lurking method, and it can be judged which predation behavior the individual chooses according to the selection parameter of the individual's predation behavior. Specifically, the selection parameter of the individual's predation behavior can be calculated by the following selection function, and the calculation formula of the selection function includes:

[0060] E i =(w1T + w2H i ) / L

[0061] In the formula, E i is the selection parameter of the individual, w1 and w2 are weight coefficients, T is the environmental temperature, and H i is the hunger level of the individual, and L is the depth of the river water.

[0062] Step S33: When the selection parameter of the individual is less than or equal to the preset value, it is determined that the individual conducts the predation behavior of the cruising method.

[0063] In this embodiment, the preset value is 0.3. That is, when the selection parameter E i ≤0.3 of the individual, it is determined that the river is in the flood period, and the individual adopts the cruising foraging method.

[0064] In one embodiment, when an individual uses the cruising foraging method, it includes two processes. One is the process of searching for food, and the other is the process of attacking food. When searching for food, the individual floats on the water surface and searches for food over a large range during the cruising process. This process can be expressed by the following equation:

[0065]

[0066] In the formula, x i K is the position of individual i in the solution space at the K-th iteration step, x i K+1 is the position of individual i in the solution space at the (K + 1)-th iteration step, Step is the iteration step size, and θ1 and θ2 are the angles of head rotation when the individual turns its head to look left and right respectively. In this embodiment, both θ1 and θ2 are random numbers between 0° and 60°.

[0067] When attacking food, after the individual discovers a suitable prey, it starts to dive and slowly approaches the prey underwater. After meeting the attack range, it rushes out from underwater and launches a fierce attack. This process can be expressed by the following equation:

[0068]

[0069] In the formula, x best is the position of the best individual in the population (Gmax), x worst is the position of the worst individual in the population (Gmin), x i K is the position of individual i in the solution space at the K-th iteration step, x i K+1 is the position of individual i in the solution space at the (K + 1)-th iteration step, and x i is the initial position of individual i. Among them, the best individual position Gmax refers to the position of the individual with the largest fitness value G in the solution space, and the worst individual position Gmin refers to the position of the individual with the smallest fitness value G in the solution space.

[0070] Step S35: When the selection parameter of an individual is greater than the preset value, it is determined that the individual conducts the foraging behavior of the lurking method.

[0071] In this embodiment, the preset value is 0.3. That is, when the selection parameter E i > 0.3, it is determined that the river is in the dry season, and the individual adopts the lurking foraging method.

[0072] In one embodiment, when an individual uses the latent foraging method, the individual needs to first find a gentle tidal flat and lurk in the waters near the shore. When an animal comes to drink water, a surprise attack is launched. This process can be expressed by the following equation:

[0073]

[0074] In the formula, x best is the position of the best individual in the population (Gmax), x worst is the position of the worst individual in the population (Gmin), x i K is the position of individual i in the solution space at the K-th iteration step, and x i K+1 is the position of individual i in the solution space at the (K + 1)-th iteration step. Among them, the best individual position Gmax refers to the position of the individual with the largest fitness value G in the solution space, and the worst individual position Gmin refers to the position of the individual with the smallest fitness value G in the solution space.

[0075] Figure 3 FIG. is a schematic flow chart of a method for analyzing the transient process characteristics of a converter transformer according to another embodiment of the present invention. In one embodiment, the method for analyzing the transient process characteristics of a converter transformer includes the following steps S100 to S300.

[0076] Step S100: When the voltage amplitude of the electronic device is greater than a preset value, record the electrical signal of the electronic device and define the electrical signal of the electronic device as a signal to be measured.

[0077] In this embodiment, the electronic device is a converter transformer. When the converter transformer is in a transient process, its voltage amplitude will increase. Therefore, the electrical signal of the converter transformer is monitored in real time. When the voltage amplitude at a certain moment is greater than the preset value, record the electrical signal of the converter transformer. The electrical signal obtained by recording is the transient process signal that needs to be analyzed, and this transient process signal is defined as the signal to be measured.

[0078] Step S200: Use the method for analyzing the transient process characteristics of a converter transformer described in any of the above embodiments to perform atomic decomposition on the signal to be measured to obtain effective atomic parameters.

[0079] The atomic decomposition algorithm approximates the original signal by using a sparse data representation, and uses an over-complete redundant function library of an atomic library to replace the orthogonal function basis to decompose the signal. Since the over-complete function does not need to meet any orthogonality requirements, the elements in the atomic library are not called function bases, but atoms. There are no restrictions on the composition of atoms, so they can be optimized to better approximate the signal to be measured.

[0080] In this embodiment, the problem is solved by simulating the principle of the predation behavior of organisms in nature. Based on the behavioral characteristics of predation competition behavior, the atomic decomposition algorithm is optimized to simplify the analysis process and reduce its computational complexity. A set of optimal linear combinations is searched in the atomic library to approximate the signal to be measured, and the atom closest to the signal to be measured is regarded as the best matching atom, thereby obtaining the effective atom parameters.

[0081] Step S300: Analyze the transient characteristics of the electronic device according to the effective atom parameters.

[0082] Based on the effective atom parameters obtained by decomposition, the composition components of the signal to be measured can be accurately deduced and have a good time positioning function, realizing the decomposition of the transient process signal. After decomposing the signal to be measured, different overvoltage types can be distinguished, which is used for analyzing the power quality and locating faults.

[0083] The analysis method for the transient process characteristics of the converter transformer provided in this embodiment uses the atomic decomposition algorithm to decompose the transient process of the converter transformer. Applying the analysis method for the transient process characteristics of the converter transformer to the decomposition of the transient process signal of the converter transformer, the analysis process has stronger time-frequency analysis ability and anti-noise ability. In addition, compared with the existing methods for decomposing transient process signals, the analysis method for the transient process characteristics of the converter transformer provided in this embodiment also has stronger adaptability and lower complexity.

[0084] Figure 4 This is a schematic flowchart of the method for finding the atom most matching the signal to be measured based on the predation behavior competition optimization algorithm in one embodiment of the present invention. In one embodiment, the analysis method for the transient process characteristics of the converter transformer is used to perform atomic decomposition on the signal to be measured, and the steps for obtaining the effective atom parameters include the following steps S210 to S260.

[0085] Step S210: Design an iterative formula and construct a damped sinusoidal atom library according to the signal to be measured; regard the atoms in the damped sinusoidal atom library as individuals performing predation behavior.

[0086] The atoms in the atom library can be constructed according to the characteristics of the signal to be measured to improve the signal decomposition effect and reduce the calculation amount. In each iterative calculation, the atom library is scanned according to the indexing method to obtain the atom most relevant to the analysis signal in this iteration, and then the best atom component is extracted from the signal to be measured to form a new residual signal. The iterative relationship is:

[0087] R m+1 f=R m f-<R m f,g γm >g γm

[0088] Wherein, R m+1 f is the residual after m-step iteration, R m f is the signal after m-step iteration, g γn is the optimal atom obtained in the m-th step of iteration.

[0089] After n iterations, the current residual value is R m+1 f, then the original signal f t can be expressed as:

[0090]

[0091] Wherein, g γn is the optimal atom obtained in the n-th step of iteration, R n f is the signal after n-step iteration, <> is the inner product operator, <R n f, g γn > the absolute value of is the fitness value G, R m+1 f is the residual after n-step iteration, f t is the signal to be measured.

[0092] In addition, <R n f, g γn > g γn is the projection of the original signal on the atom, and the residual signal is the new signal to be decomposed. In each step of iteration, a new atom and its residual signal can be obtained until the number of iterations reaches the preset iteration termination number m.

[0093] According to the characteristics of the signal to be measured, a decaying sinusoidal atom library is constructed. The overvoltage signal is composed of high-frequency sinusoidal oscillations and low-frequency sinusoidal harmonics, and can be decomposed and expressed by a group of sinusoidal waveforms with decay characteristics (damping characteristics) or pure sinusoidal waveforms without decay. Based on this characteristic, the atom library adopted in this embodiment is a group of parameterized damped sinusoidal atoms, and the sinusoidal function atom library is used as the atom library model. In this embodiment, based on the atom library, a damped sinusoidal atom library suitable for the decomposition application of hybrid overvoltage and its parameter range are constructed according to the parameter characteristic range of the overvoltage. When indexing the decaying sinusoidal atom library subsequently, the atoms in the decaying sinusoidal atom library are regarded as individuals performing predation behavior, and an optimal solution is found according to the characteristics of individuals performing predation in nature.

[0094] In one embodiment, the expression of the decaying sinusoidal atom library includes:

[0095]

[0096] Wherein, g(t) is the Gaussian window function; it is defined as Let γ be g γThe index of (t), where s is the scale parameter, τ is the displacement factor, ξ is the frequency factor, is the phase factor, Z γ is the conversion coefficient, e is the base of the power function, ω is the frequency, φ is the phase angle, ρ is the damping coefficient, t s is the start time, t e is the end time, and u(t) is the unit step.

[0097] Step S220: Initialize the basic parameters of the predation behavior competition optimization algorithm.

[0098] Initialize the basic parameters of the predation behavior competition optimization algorithm. In this embodiment, the population size N, the number of iterations m and other related parameters are set. Construct an optimization object library, and use the frequency ω, the phase angle φ, the damping coefficient ρ, the start time t s , and the end time t e as the optimization objects. That is, use the parameter information of the best matching atom as the optimization object.

[0099] Step S230: Use the predation behavior competition optimization algorithm to find the atom in the decaying sinusoidal atom library that best matches the signal to be measured; the individual with the largest fitness value is the atom that best matches the signal to be measured.

[0100] In actual calculation, the approximation of the atom to the signal to be measured is described by the fitness value G. Therefore, the atom with the largest fitness value G in the atom library is the atom that best matches the signal to be measured.

[0101] Step S240: Update the residual signal as the new signal to be measured.

[0102] Calculate the residual signal after this iteration according to the iterative relationship, and use the residual signal as the new signal to be measured.

[0103] R m+1 f = R m f - <R m f, g γm > g γm

[0104] In the formula, R m+1 f is the residual after m steps of iteration, R m f is the signal after m steps of iteration, and g γn is the optimal atom obtained in the mth step of iteration.

[0105] Step S250: Repeat the steps of finding the atom that best matches the signal to be measured and updating the residual signal as the new signal to be measured until the number of iterations reaches the preset number of iteration termination.

[0106] Repeat steps S230 and S240. In each iteration, an optimal atom that best matches the signal to be measured and its residual signal will be found. Use the residual signal in this iteration as the signal to be measured in the next iteration, and search for the atom that best matches the new signal to be measured in the decaying sinusoidal atom library. Repeat steps S230 and S240. When the number of iterations reaches the preset iteration termination number m, the iteration terminates.

[0107] In one embodiment, it is also possible to set to stop the iteration when the energy of the residual signal is less than a certain threshold.

[0108] Step S260: Define the optimal atom obtained in the last iteration as the best matching atom, and obtain the effective atom parameters of the best matching atom.

[0109] Take the final optimal atom obtained in the last iteration as the best matching atom. Since the optimal atom obtained in each iteration is the best matching atom, the residual signal in the last iteration is omitted, and the best matching atom information is substituted into the original signal f t in the expression, and the signal can be decomposed into The best matching atom information refers to the parameter information of the position of the best individual (Gmax) in the last iteration process, including frequency f, damping coefficient ρ, phase angle φ, start time t s , end time t e and other parameters.

[0110] In one embodiment, the basic parameters include the population number N, the iteration termination number m, and the iteration step size Step. In this embodiment, the numerical values of the parameters are set according to the algorithm parameter setting table shown in Table 1. The algorithm parameters shown in Table 1 are used to Figure 5 decompose the transient overvoltage signal of the converter transformer shown. Figure 5 This is a schematic diagram of the overvoltage waveform of one embodiment of the present invention.

[0111] Table 1 Algorithm Parameter Setting Table

[0112]

[0113] In this embodiment, substitute the above parameter values into the analysis method of the transient process characteristics of the converter transformer, and set the weight coefficient w1 to 0.6, the weight coefficient w2 to 1.1, the river depth L to 3.7, the ambient temperature T to 29, the iteration step size Step to 0.03, the population number N to 150, and the iteration termination number m to 300.

[0114] For Figure 5After decomposing the transient overvoltage signal of the converter transformer shown in Figure 6 The energy distribution diagram is shown. Figure 6 This is a statistical diagram of the cumulative distribution of overvoltage energy in one embodiment of the present invention. Figure 6 It is known that Figure 5 After decomposing the complex signal shown, the various frequency components mixed in the signal can be distinguished. It can be seen that the analysis method of the transient process characteristics of the converter transformer provided by the present invention can accurately decompose the various components of the signal. After decomposing the transient overvoltage of the converter transformer, the transient process characteristics of the converter transformer can be quickly and accurately identified, which helps engineering and technical personnel to find out the cause of the accident in time, and at the same time provides a basis for proposing a transient process suppression method and improving insulation coordination.

[0115] It should be understood that although Figures 1-4 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figures 1-4 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0116] In the description of this specification, the description with reference to the terms "some embodiments", "other embodiments", "ideal embodiments", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example.

[0117] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0118] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for analyzing the transient process characteristics of a converter transformer, characterized in that, Including: When the voltage amplitude of the electronic device is greater than a preset value, recording the electrical signal of the electronic device and defining the electrical signal of the electronic device as a signal to be measured; Performing atomic decomposition on the signal to be measured to obtain effective atomic parameters; Analyzing the transient characteristics of the electronic device according to the effective atomic parameters; the effective atomic parameters are used to determine the composition components of the signal to be measured; Wherein, the performing atomic decomposition on the signal to be measured to obtain effective atomic parameters includes: Regarding the atoms in the decaying sinusoidal atom library as individuals performing predation behaviors; the decaying sinusoidal atom library is constructed according to the signal to be measured; The individuals follow the calculated value of the selection parameter to carry out different types of predation behaviors; the predation behaviors include the cruising method and the lurking method; the individuals following the calculated value of the selection parameter to carry out different types of predation behaviors includes: Calculating the selection parameter of the individual according to the selection function; the calculation formula of the selection function includes: E i = (w1T + w2H i ) / L, where E i is the selection parameter of the individual, w1 and w2 are weight coefficients, T is the environmental temperature, and H i is the hunger level of the individual, and L is the river depth; When the selection parameter of the individual is less than or equal to the preset value, it is determined that the individual carries out the predation behavior of the cruising method; the cruising method includes: The individual floats on the water surface and searches for food in a large range during the cruising process, and this process is expressed by an equation as: where x i K is the position of individual i at the K - th iteration step, and x i K+1 is the position of individual i at the (K + 1)-th iteration step. Step is the iteration step size, and θ1 and θ2 are the angles of head rotation when the individual turns its head to look left and right respectively; After the individual discovers a suitable prey, it dives and slowly approaches underwater. When the attack range is satisfied, it rushes out from underwater and launches a fierce attack, and this process is expressed by an equation as: where x best is the position of the best individual in the population, x worst is the position of the worst individual in the population, x i K is the position of individual i at iteration step K, x i K+1 is the position of individual i at iteration step K + 1, x i is the initial position of individual i; When the selection parameter of the individual is greater than the preset value, it is determined that the individual carries out the predation behavior of the lurking method; the lurking method includes: The individual searches for a gentle shoal and lurks in the waters near the shore, and launches a sudden attack when an animal comes to drink water, and this process is expressed by an equation as: where x best is the position of the best individual in the population, x worst is the position of the worst individual in the population, x i K is the position of individual i at iteration step K, x i K+1 is the position of individual i at iteration step K+1; Calculating the position information of the individual in this iteration according to the predation behavior of the individual; Updating the position information of the individual and calculating the fitness value of the individual; Calculating the position information of the individual in this iteration according to the death flipping process; the death flipping process includes: When an individual in the group successfully bites the prey, other individuals will swim towards the prey, bite the prey, and flip their bodies in circles multiple times, and decompose the prey under the cooperative effect of the group, and this process is expressed by an equation as: where x best is the position of the best individual in the population, x i K is the position of individual i at iteration step K, x i K+1 is the position of individual i at iteration step K + 1, and Step is the iteration step size; Updating the position information of the individual again and calculating the fitness value of the individual; Regarding the atom with the largest fitness value in the decaying sinusoidal atom library as the best matching atom of the signal to be measured, and obtaining effective atomic parameters based on the best matching atom.

2. The analysis method for the transient process characteristics of a commutation transformer according to claim 1, characterized in that The performing atomic decomposition on the signal to be measured to obtain effective atomic parameters includes: Designing an iterative formula and constructing a decaying sinusoidal atom library according to the signal to be measured; regarding the atoms in the decaying sinusoidal atom library as individuals performing predation behaviors; Initializing the basic parameters of the predation behavior competition optimization algorithm; Searching for the atom most matching the signal to be measured in the decaying sinusoidal atom library through the predation behavior competition optimization algorithm; the individual with the largest fitness value is the atom most matching the signal to be measured; Updating the residual signal as a new signal to be measured; Repeat the steps of finding the atom that best matches the signal to be measured and updating the residual signal as the new signal to be measured until the number of iterations reaches the preset iteration termination number; Define the optimal atom obtained in the last iteration as the best matching atom, and obtain the effective atom parameters of the best matching atom.

3. The analysis method for the transient process characteristics of a commutation transformer according to claim 2, characterized in that The expression of the damped sinusoidal atom library includes: where \(g(t)\) is the Gaussian window function; it is defined that let \(\gamma\) be the index of \(g\) γ (t), where \(s\) is the scale parameter, \(\tau\) is the displacement factor, \(\xi\) is the frequency factor, is the phase factor, \(Z\) γ is the conversion coefficient, \(e\) is the base of the exponential function, \(\omega\) is the frequency, \(\varphi\) is the phase angle, \(\rho\) is the damping coefficient, \(t\) s is the start time, \(t\) e is the end time, and \(u(t)\) is the unit step.

4. The analysis method for the transient process characteristics of a commutation transformer according to claim 2, characterized in that The basic parameters include the population size, the iteration termination number, and the iteration step size.

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