Construction method and device of simulation monitoring model of transformer, medium and equipment

Abstract

The invention discloses a method and a device for constructing a simulation monitoring model of a transformer, a medium and equipment. The method comprises the following steps: firstly, acquiring an actually measured frequency response curve of the transformer under a specific frequency band signal; then constructing a simulation monitoring model, and randomly initializing the number of modules in the model; then performing simulation calculation to obtain a simulation frequency response curve; measuring the consistency between the actually measured data and the simulation data by calculating the amplitude-phase correlation degree; and if the amplitude-phase correlation degree and the current number of iterations do not meet the preset condition, iteratively adjusting model parameters through a multi-objective optimization algorithm until the amplitude-phase correlation degree and / or the current number of iterations meet the preset condition. By updating the simulation monitoring model in real time and dynamically comparing the simulation monitoring model with actually measured data, the accuracy and the real-time performance of transformer monitoring are improved. Compared with a traditional off-line fault diagnosis model, the method has the advantages that the difference between a simulation result and actual data is effectively reduced, and more accurate on-line monitoring is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment modeling, and in particular to a method, device, medium and equipment for constructing a simulation monitoring model of a transformer. Background Art

[0002] Online frequency response analysis is a widely used technology for online monitoring of transformer windings and cores. The transformer can be regarded as a two-port network consisting of inductance, resistance, capacitance and conductance. When the status of each component of the transformer changes, the parameters of its equivalent circuit will also change, which allows us to infer its status by analyzing the changes in the transformer frequency response. However, the current problem is that most of the existing transformer frequency response simulation models are mainly used for offline fault diagnosis, and there is a large difference between their simulation results and the actual measured transformer frequency response. Summary of the invention

[0003] Based on this, it is necessary to provide a method, device, medium and equipment for constructing a simulation monitoring model of a transformer to solve the problem that most existing transformer frequency response simulation models are mainly used for offline fault diagnosis and their simulation results are significantly different from the actual measured transformer frequency response.

[0004] A method for constructing a simulation monitoring model of a transformer, the method comprising:

[0005] Obtaining the measured frequency response curve of the transformer after injecting a preset frequency band signal through the test system;

[0006] Constructing a simulation monitoring model of a transformer, and randomly initializing the number of basic subunit modules and cross-correlation modules in the simulation monitoring model; wherein the basic subunit modules are used to characterize the main structure of the transformer winding, and the cross-correlation modules are used to characterize the correlation between the transformer windings, and between the windings and the iron box and the iron core;

[0007] Simulating the current simulation monitoring model to obtain a simulation frequency response curve;

[0008] Calculating the amplitude correlation coefficient according to the measured frequency response curve and the simulated frequency response curve; wherein the amplitude correlation coefficient is used to measure the degree of correlation between the characteristics of the amplitude and phase in the measured frequency response curve and the characteristics of the amplitude and phase in the simulated frequency response curve;

[0009] If the amplitude correlation degree does not meet the preset correlation condition and the current number of iterations is less than the preset iteration threshold, the number of basic subunit modules and cross-correlation modules in the simulation monitoring model is optimized by a multi-objective optimization algorithm, and the step of simulating the current simulation monitoring model to obtain a simulation frequency response curve and subsequent steps are returned until the amplitude correlation degree meets the preset correlation condition and / or the current number of iterations is equal to the preset iteration threshold.

[0010] In one embodiment, the calculating the amplitude correlation coefficient according to the measured frequency response curve and the simulated frequency response curve includes:

[0011] The amplitude-phase symbiotic correlation and the amplitude-phase mutual correlation are calculated according to the measured frequency response curve and the simulated frequency response curve; wherein the amplitude-phase symbiotic correlation is used to measure the correlation between the degree of common variation of amplitude and phase in the measured frequency response curve and the degree of common variation of amplitude and phase in the simulated frequency response curve, and the amplitude-phase mutual correlation is used to measure the correlation between the degree of interaction between amplitude and phase in the measured frequency response curve and the degree of interaction between amplitude and phase in the simulated frequency response curve.

[0012] In one embodiment, the measured frequency response curve includes a measured amplitude-frequency response curve and a measured phase-frequency response curve, the simulated frequency response curve includes a simulated amplitude-frequency response curve and a simulated phase-frequency response curve, and the calculation formula of the amplitude-phase symbiotic correlation degree is:

[0013]

[0014] In the above formula, m di is the amplitude corresponding to the i-th frequency point in the simulated amplitude-frequency response curve; is the mean amplitude value corresponding to all frequency points in the simulated amplitude-frequency response curve; m i is the amplitude corresponding to the ith frequency point in the measured amplitude-frequency response curve; m is the mean amplitude corresponding to all frequency points in the measured amplitude-frequency response curve; p di is the phase corresponding to the ith frequency point in the simulated phase-frequency response curve; p i is the phase corresponding to the i-th frequency point in the measured phase-frequency response curve.

[0015] In one embodiment, the measured frequency response curve includes a measured amplitude-frequency response curve and a measured phase-frequency response curve, the simulated frequency response curve includes a simulated amplitude-frequency response curve and a simulated phase-frequency response curve, and the calculation formula of the amplitude-phase cross-correlation is:

[0016]

[0017] In the above formula, m di is the amplitude corresponding to the i-th frequency point in the simulated amplitude-frequency response curve; is the mean amplitude value corresponding to all frequency points in the simulated amplitude-frequency response curve; m i is the amplitude corresponding to the i-th frequency point in the measured amplitude-frequency response curve; is the mean amplitude value corresponding to all frequency points in the measured amplitude-frequency response curve; p di is the phase corresponding to the ith frequency point in the simulated phase-frequency response curve; is the phase mean corresponding to all frequency points in the simulated phase-frequency response curve; p d is the phase corresponding to the i-th frequency point in the measured phase-frequency response curve; is the phase mean corresponding to all frequency points in the measured phase-frequency response curve.

[0018] In one embodiment, the method further comprises:

[0019] When the calculated amplitude-phase symbiotic correlation is greater than or equal to a preset first correlation threshold, and the amplitude-phase interaction correlation is greater than or equal to a preset second correlation threshold, it is determined that the amplitude correlation satisfies a preset correlation condition.

[0020] A device for constructing a simulation monitoring model of a transformer, the device for constructing a simulation monitoring model of a transformer comprising:

[0021] A measurement module, used to obtain a measured frequency response curve of the transformer after a preset frequency band signal is injected through a test system;

[0022] A model building and initialization module is used to build a simulation monitoring model of the transformer and randomly initialize the number of basic subunit modules and cross-correlation modules in the simulation monitoring model; wherein the basic subunit modules are used to characterize the main structure of the transformer winding, and the cross-correlation modules are used to characterize the relationship between the transformer windings, and between the windings and the iron box and the iron core;

[0023] A simulation module, used for simulating the current simulation monitoring model to obtain a simulation frequency response curve;

[0024] A correlation degree calculation module, used to calculate the amplitude correlation degree according to the measured frequency response curve and the simulated frequency response curve; wherein the amplitude correlation degree is used to measure the correlation degree between the characteristics of the amplitude and phase in the measured frequency response curve and the characteristics of the amplitude and phase in the simulated frequency response curve;

[0025] An iterative optimization module is used to optimize the number of basic subunit modules and cross-correlation modules in the simulation monitoring model through a multi-objective optimization algorithm if the amplitude correlation degree does not meet the preset correlation degree condition and the current number of iterations is less than the preset iteration number threshold, and return to calling the simulation module until the amplitude correlation degree meets the preset correlation degree condition and / or the current number of iterations is equal to the preset iteration number threshold.

[0026] In one embodiment, the association degree calculation module is specifically used to:

[0027] The amplitude-phase symbiotic correlation and the amplitude-phase mutual correlation are calculated according to the measured frequency response curve and the simulated frequency response curve; wherein the amplitude-phase symbiotic correlation is used to measure the correlation between the degree of common variation of amplitude and phase in the measured frequency response curve and the degree of common variation of amplitude and phase in the simulated frequency response curve, and the amplitude-phase mutual correlation is used to measure the correlation between the degree of interaction between amplitude and phase in the measured frequency response curve and the degree of interaction between amplitude and phase in the simulated frequency response curve.

[0028] In one embodiment, the measured frequency response curve includes a measured amplitude-frequency response curve and a measured phase-frequency response curve, the simulated frequency response curve includes a simulated amplitude-frequency response curve and a simulated phase-frequency response curve, and the calculation formula of the amplitude-phase symbiotic correlation degree is:

[0029]

[0030] In the above formula, m di is the amplitude corresponding to the i-th frequency point in the simulated amplitude-frequency response curve; is the mean amplitude value corresponding to all frequency points in the simulated amplitude-frequency response curve; m i is the amplitude corresponding to the i-th frequency point in the measured amplitude-frequency response curve; is the mean amplitude value corresponding to all frequency points in the measured amplitude-frequency response curve; p di is the phase corresponding to the ith frequency point in the simulated phase-frequency response curve; p i is the phase corresponding to the i-th frequency point in the measured phase-frequency response curve.

[0031] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the processor executes the steps of the method for constructing a simulation monitoring model of the transformer.

[0032] A terminal device includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for constructing a simulation monitoring model of the transformer.

[0033] The present invention provides a method, device, medium and equipment for constructing a simulation monitoring model of a transformer. First, the measured frequency response curve of the transformer under a specific frequency band signal is obtained; then a simulation monitoring model is constructed, and the number of modules in the model is randomly initialized; then a simulation frequency response curve is simulated and calculated; the consistency between the measured and simulated data is measured by calculating the amplitude correlation coefficient; if the amplitude correlation coefficient and the current number of iterations do not meet the preset conditions, the model parameters are iteratively adjusted through a multi-objective optimization algorithm until the amplitude correlation coefficient and / or the current number of iterations meet the preset conditions. The present invention improves the accuracy and real-time performance of transformer monitoring by dynamically comparing the simulation monitoring model with the measured data in real time. Compared with the traditional offline fault diagnosis model, it effectively reduces the difference between the simulation results and the actual data, and realizes more accurate online monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0035] in:

[0036] Figure 1 A flowchart of a method for constructing a simulation monitoring model for a transformer;

[0037] Figure 2 A schematic diagram of the structure of a device for constructing a simulation monitoring model of a transformer;

[0038] Figure 3 This is a structural block diagram of the terminal equipment. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0041] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0042] like Figure 1 As shown, Figure 1 The figure is a flow chart of a method for constructing a simulation monitoring model of a transformer in one embodiment. The steps provided in the method for constructing a simulation monitoring model of a transformer in this embodiment include:

[0043] S101, obtaining, through a test system, a measured frequency response curve of the transformer after a preset frequency band signal is injected.

[0044] The test system is a combination of hardware and software used to detect and record the frequency response of the transformer. It includes a signal generator, a frequency response analyzer, a sensor, and a data acquisition and processing unit. The measured frequency response curve refers to the curve of the frequency response data actually generated by the transformer under the excitation signal of the test system.

[0045] Specifically, the test system injects a signal of a specific frequency band (e.g., 1kHz-1 MHz) into the transformer and monitors the measured response data of the transformer, and then draws a measured frequency response curve based on these measured data. The curve graph generally uses frequency as the horizontal axis and the amplitude difference or phase difference between the input and output ends as the vertical axis.

[0046] S102, constructing a simulation monitoring model of the transformer, and randomly initializing the number of basic subunit modules and cross-correlation modules in the simulation monitoring model.

[0047] Among them, the simulation monitoring model is used to simulate the frequency response characteristics of the transformer under different working conditions in real time, thereby assisting in state monitoring and fault diagnosis. The basic subunit module is used to characterize the main structure of the transformer winding, and can include a high-voltage winding basic subunit module, a medium-voltage winding basic subunit module, and a low-voltage winding basic subunit module. The corresponding number of modules is represented by N H 、N M 、N L , of course, it can also include basic sub-unit modules of other structures.

[0048] The cross-correlation module is used to characterize the relationship between transformer windings, and between windings and iron box and core. The corresponding number of modules is expressed as N. C The electromagnetic fields of the transformer windings affect the iron box and core, and conversely, the physical properties of the iron box and core affect the electrical performance of the windings. The cross-correlation module simulates the effects of these interactions.

[0049] Specifically, we can use circuit simulation software (such as MATLAB) to build a simulation model of the transformer. The basic subunit module simulates the winding structure of the transformer through components such as inductance, resistance and capacitance, and the cross-correlation module characterizes the relationship between the winding and the iron core and the iron box by defining the coupling between different modules (such as magnetic coupling and electric coupling). The number of basic subunit modules and cross-correlation modules can be randomly generated between 1 and 10.

[0050] S103, simulating the current simulation monitoring model to obtain a simulation frequency response curve.

[0051] Specifically, we use circuit simulation software to inject sinusoidal wave signals of different frequencies into the current simulation monitoring model, and then draw the simulation frequency response curve by simulating output parameters such as current and voltage.

[0052] S104, calculating the amplitude correlation coefficient according to the measured frequency response curve and the simulated frequency response curve.

[0053] Among them, the amplitude correlation is used to measure the degree of correlation between the amplitude and phase characteristics in the measured frequency response curve and the amplitude and phase characteristics in the simulated frequency response curve. That is, it evaluates the similarity or consistency between the actual measured data and the simulation results by comparing their differences in amplitude and phase. The amplitude correlation can be calculated using the Pearson correlation coefficient formula.

[0054] Optionally, calculating the amplitude-phase correlation degree according to the measured frequency response curve and the simulated frequency response curve in S104 includes: calculating the amplitude-phase symbiotic correlation degree and the amplitude-phase mutual correlation degree according to the measured frequency response curve and the simulated frequency response curve.

[0055] Among them, the amplitude-phase symbiotic correlation is used to measure the correlation between the degree of common change of amplitude and phase in the measured frequency response curve and the degree of common change of amplitude and phase in the simulated frequency response curve. It examines whether the two characteristics of amplitude and phase change at the same time and whether their change trends are consistent, or whether the amplitude and phase are affected by the same factor. The Pearson correlation coefficient formula can be used to calculate the amplitude-phase symbiotic correlation.

[0056] The amplitude-phase cross-correlation is used to measure the degree of correlation between the interaction between amplitude and phase in the measured frequency response curve and the interaction between amplitude and phase in the simulated frequency response curve. It examines whether the amplitude and phase have a mutual influence, especially at certain frequencies, whether the amplitude and phase will produce nonlinearity or interaction due to certain factors. The interaction between amplitude and phase is quantified through cross-correlation coefficients or nonlinear modeling.

[0057] Optionally, the measured frequency response curve includes a measured amplitude-frequency response curve and a measured phase-frequency response curve, and the simulated frequency response curve includes a simulated amplitude-frequency response curve and a simulated phase-frequency response curve. The calculation formula of the amplitude-phase symbiotic correlation degree is:

[0058]

[0059] In the above formula, m di is the amplitude corresponding to the i-th frequency point in the simulated amplitude-frequency response curve; is the mean amplitude value corresponding to all frequency points in the simulated amplitude-frequency response curve; m i is the amplitude corresponding to the i-th frequency point in the measured amplitude-frequency response curve; is the mean amplitude value corresponding to all frequency points in the measured amplitude-frequency response curve; p di is the phase corresponding to the ith frequency point in the simulated phase-frequency response curve; p i is the phase corresponding to the i-th frequency point in the measured phase-frequency response curve.

[0060] The first part of the above formula The linear correlation between the simulated and measured amplitude-frequency response curves is calculated. The numerator calculates the covariance of the simulated and measured amplitudes, which measures the degree of their common variation. The denominator corresponds to the standard deviation of the simulated and measured amplitudes, which measures the degree of fluctuation of the simulated and measured amplitudes, respectively. The difference between the measured and simulated phase-frequency response curves is calculated in a weighted manner. The closer the value of the amplitude-phase symbiotic correlation is, the more similar the simulated and measured frequency response curves are in the symbiotic relationship of amplitude and phase, which can help analyze the matching degree between the transformer simulation model and the measured data.

[0061] Optionally, the calculation formula of the amplitude-phase mutual correlation is:

[0062]

[0063] In the above formula, m di is the amplitude corresponding to the i-th frequency point in the simulated amplitude-frequency response curve; is the mean amplitude value corresponding to all frequency points in the simulated amplitude-frequency response curve; m i is the amplitude corresponding to the i-th frequency point in the measured amplitude-frequency response curve; is the mean amplitude value corresponding to all frequency points in the measured amplitude-frequency response curve; p di is the phase corresponding to the i-th frequency point in the simulated phase-frequency response curve; is the phase mean corresponding to all frequency points in the simulated phase-frequency response curve; p d is the phase corresponding to the i-th frequency point in the measured phase-frequency response curve; It is the phase mean corresponding to all frequency points in the measured phase-frequency response curve.

[0064] Since there may be complex interactions between amplitude and phase in the frequency response of devices such as transformers, the design of the above formula emphasizes the nonlinear relationship between amplitude and phase, which can more accurately quantify the interaction difference between simulated and measured frequency response curves, providing a more powerful tool for model optimization and fault detection.

[0065] S105, judging whether the amplitude correlation degree does not meet the preset correlation degree condition, and whether the current iteration number is less than the preset iteration number threshold. If the amplitude correlation degree does not meet the preset correlation degree condition, and the current iteration number is less than the preset iteration number threshold, then executing S106, and returning to execute S103 and subsequent steps. If the amplitude correlation degree meets the preset correlation degree condition, and / or the current iteration number is equal to the preset iteration number threshold, then ending.

[0066] This step is used to determine whether the simulation model optimization has met the stop condition, which involves two key judgments. Among them, whether the amplitude correlation meets the preset correlation condition: that is, check whether the current amplitude correlation is high enough to meet the preset accuracy requirements. Whether the current number of iterations is less than the preset iteration threshold: that is, determine whether the optimization process has reached the set maximum number of iterations (for example, set to 100 times).

[0067] Optionally, in order to determine whether the preset correlation condition is met, the method for constructing the simulation monitoring model of the transformer also performs the following steps: when the calculated amplitude-phase symbiotic correlation is greater than or equal to a preset first correlation threshold (for example, set to 0.95), and the amplitude-phase interaction correlation is greater than or equal to a preset second correlation threshold (for example, set to 0.95), it is determined that the amplitude correlation meets the preset correlation condition.

[0068] If neither the correlation condition nor the iteration number condition is met, the system will execute S105 to continue the optimization process, otherwise the optimization is stopped.

[0069] S106, optimizing the number of basic sub-unit modules and cross-correlation modules in the simulation monitoring model through a multi-objective optimization algorithm.

[0070] Among them, the multi-objective optimization algorithm is an algorithm used to optimize multiple conflicting objective functions. Unlike the single-objective optimization algorithm, which only considers one optimization goal, the multi-objective optimization considers the optimal solution of multiple goals at the same time.

[0071] For example, taking the non-dominated sorting genetic algorithm II (NSGA-II) as an example, we perform the following steps to optimize the number of modules:

[0072] A. Initialize the population: randomly generate a set of candidate solutions, each of which represents a simulation model configuration, such as the number of basic subunit modules and cross-correlation modules.

[0073] B. Objective function calculation: For each candidate solution, calculate the values ​​of two objective functions, including objective function 1: amplitude correlation degree (i.e., calculate amplitude-phase symbiosis and amplitude-phase cross-correlation degree through the above formula). Objective function 2: model complexity (i.e., the total number of basic subunit modules and cross-correlation modules).

[0074] C. Non-dominated sorting: Sort the population according to the Pareto dominance relationship to form several Pareto levels (ParetoFronts). The rule of Pareto dominance is: a solution A is better than solution B if A is better than B in some goal and not worse than B in other goals.

[0075] D. Selection: Within each Pareto level, sort by crowding distance. Crowding distance is used to ensure uniform distribution of solutions and avoid all solutions being concentrated in a certain part of the Pareto front.

[0076] E. Crossover and mutation: Use the crossover operation to generate new candidate solutions. For example, the number of modules of two parents is combined to form a child: Parent 1: [8,6], Parent 2: [10,5], then the child may be [8,5] or [10,6]. Use the mutation operation to randomly adjust the number of modules, for example, [8,5] mutates to [9,5].

[0077] F. Generate a new population: merge the parent and child generations, screen out the next generation population, and repeat steps B, C, D, and E until the termination condition is reached (such as the maximum number of generations or the convergence of the objective function).

[0078] The method for constructing the simulation monitoring model of the above transformer first obtains the measured frequency response curve of the transformer under a specific frequency band signal; then constructs a simulation monitoring model and randomly initializes the number of modules in the model; then performs simulation calculation of the simulation frequency response curve; measures the consistency between the measured and simulated data by calculating the amplitude correlation coefficient; if the amplitude correlation coefficient and the current number of iterations do not meet the preset conditions, iteratively adjusts the model parameters through a multi-objective optimization algorithm until the amplitude correlation coefficient and / or the current number of iterations meet the preset conditions. This method improves the accuracy and real-time performance of transformer monitoring by dynamically comparing the simulation monitoring model with the measured data in real time. Compared with the traditional offline fault diagnosis model, it effectively reduces the difference between the simulation results and the actual data, and realizes more accurate online monitoring.

[0079] In one embodiment, Figure 2 As shown, a device for constructing a simulation monitoring model of a transformer is proposed, and the device includes:

[0080] The actual measurement module 201 is used to obtain the actual measured frequency response curve of the transformer after the preset frequency band signal is injected through the test system;

[0081] The model building and initialization module 202 is used to build a simulation monitoring model of the transformer and randomly initialize the number of basic subunit modules and cross-correlation modules in the simulation monitoring model; wherein the basic subunit module is used to characterize the main structure of the transformer winding, and the cross-correlation module is used to characterize the correlation between the transformer windings, and between the windings and the iron box and the iron core;

[0082] The simulation module 203 is used to simulate the current simulation monitoring model to obtain a simulation frequency response curve;

[0083] A correlation degree calculation module 204 is used to calculate the amplitude correlation degree according to the measured frequency response curve and the simulated frequency response curve; wherein the amplitude correlation degree is used to measure the correlation degree between the characteristics of the amplitude and phase in the measured frequency response curve and the characteristics of the amplitude and phase in the simulated frequency response curve;

[0084] The iterative optimization module 205 is used to optimize the number of basic sub-unit modules and cross-correlation modules in the simulation monitoring model through a multi-objective optimization algorithm if the amplitude correlation degree does not meet the preset correlation degree condition and the current number of iterations is less than the preset number of iterations threshold, and return to call the simulation module 203 until the amplitude correlation degree meets the preset correlation degree condition and / or the current number of iterations is equal to the preset number of iterations threshold.

[0085] Figure 3 FIG. 2 shows an internal structure diagram of a terminal device in an embodiment. Figure 3As shown, the terminal device includes a processor, a memory and a network interface connected via a system bus. Among them, the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the terminal device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor can implement a method for constructing a simulation monitoring model of a transformer. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor can execute a method for constructing a simulation monitoring model of a transformer. Those skilled in the art can understand that Figure 3 The structure shown in the figure is only a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the terminal device to which the scheme of the present application is applied. The specific terminal device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0086] A computer-readable storage medium stores a computer program, which implements the following steps when executed by a processor: obtaining a measured frequency response curve of a transformer after a preset frequency band signal is injected through a test system; constructing a simulation monitoring model of the transformer, and randomly initializing the number of basic subunit modules and cross-correlation modules in the simulation monitoring model; wherein the basic subunit modules are used to characterize the main structure of the transformer winding, and the cross-correlation modules are used to characterize the correlation between the transformer windings, and between the windings and the iron box and the iron core; simulating the current simulation monitoring model to obtain a simulated frequency response curve; and according to the measured frequency response curve and The amplitude correlation coefficient is calculated by simulating the frequency response curve; wherein the amplitude correlation coefficient is used to measure the degree of correlation between the characteristics of the amplitude and phase in the measured frequency response curve and the characteristics of the amplitude and phase in the simulated frequency response curve; if the amplitude correlation coefficient does not meet the preset correlation condition, and the current number of iterations is less than the preset number of iterations threshold, the number of basic subunit modules and cross-correlation modules in the simulation monitoring model is optimized by a multi-objective optimization algorithm, and the step of simulating the current simulation monitoring model to obtain the simulation frequency response curve and subsequent steps are returned to execute, until the amplitude correlation coefficient meets the preset correlation condition, and / or the current number of iterations is equal to the preset number of iterations threshold.

[0087] A terminal device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: obtaining a measured frequency response curve of a transformer after a preset frequency band signal is injected through a test system; constructing a simulation monitoring model of the transformer, and randomly initializing the number of basic subunit modules and cross-correlation modules in the simulation monitoring model; wherein the basic subunit modules are used to characterize the main structure of the transformer winding, and the cross-correlation modules are used to characterize the correlation between the transformer windings, and between the windings and the iron box and the iron core; simulating the current simulation monitoring model to obtain a simulated frequency response curve; and simulating the current simulation monitoring model according to the measured frequency response curve. The amplitude correlation coefficient is calculated based on the frequency response curve and the simulated frequency response curve; wherein the amplitude correlation coefficient is used to measure the degree of correlation between the characteristics of the amplitude and phase in the measured frequency response curve and the characteristics of the amplitude and phase in the simulated frequency response curve; if the amplitude correlation coefficient does not meet the preset correlation condition, and the current number of iterations is less than the preset number of iterations threshold, the number of basic subunit modules and cross-correlation modules in the simulation monitoring model is optimized by a multi-objective optimization algorithm, and the step of simulating the current simulation monitoring model to obtain the simulated frequency response curve and subsequent steps are returned until the amplitude correlation coefficient meets the preset correlation condition, and / or the current number of iterations is equal to the preset number of iterations threshold.

[0088] It should be noted that the above-mentioned method, device, equipment and computer-readable storage medium for constructing a simulation monitoring model of the transformer belong to a general inventive concept, and the contents of the embodiments of the method, device, equipment and computer-readable storage medium for constructing a simulation monitoring model of the transformer are applicable to each other.

[0089] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0090] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above 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.

[0091] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for constructing a simulation monitoring model of a transformer, characterized in that: The method comprises: Obtaining the measured frequency response curve of the transformer after injecting a preset frequency band signal through the test system; Constructing a simulation monitoring model of a transformer, and randomly initializing the number of basic subunit modules and cross-correlation modules in the simulation monitoring model; wherein the basic subunit modules are used to characterize the main structure of the transformer winding, and the cross-correlation modules are used to characterize the correlation between the transformer windings, and between the windings and the iron box and the iron core; Simulating the current simulation monitoring model to obtain a simulation frequency response curve; Calculating the amplitude correlation coefficient according to the measured frequency response curve and the simulated frequency response curve; wherein the amplitude correlation coefficient is used to measure the degree of correlation between the characteristics of the amplitude and phase in the measured frequency response curve and the characteristics of the amplitude and phase in the simulated frequency response curve; If the amplitude correlation degree does not meet the preset correlation condition and the current number of iterations is less than the preset iteration threshold, the number of basic subunit modules and cross-correlation modules in the simulation monitoring model is optimized by a multi-objective optimization algorithm, and the step of simulating the current simulation monitoring model to obtain a simulation frequency response curve and subsequent steps are returned until the amplitude correlation degree meets the preset correlation condition and / or the current number of iterations is equal to the preset iteration threshold.

2. The method according to claim 1, characterized in that The calculating the amplitude correlation coefficient according to the measured frequency response curve and the simulated frequency response curve includes: The amplitude-phase symbiotic correlation and the amplitude-phase mutual correlation are calculated according to the measured frequency response curve and the simulated frequency response curve; wherein the amplitude-phase symbiotic correlation is used to measure the correlation between the degree of common variation of amplitude and phase in the measured frequency response curve and the degree of common variation of amplitude and phase in the simulated frequency response curve, and the amplitude-phase mutual correlation is used to measure the correlation between the degree of interaction between amplitude and phase in the measured frequency response curve and the degree of interaction between amplitude and phase in the simulated frequency response curve.

3. The method according to claim 2, characterized in that The measured frequency response curve includes a measured amplitude-frequency response curve and a measured phase-frequency response curve, the simulated frequency response curve includes a simulated amplitude-frequency response curve and a simulated phase-frequency response curve, and the calculation formula of the amplitude-phase symbiotic correlation degree is: In the above formula, m di is the amplitude corresponding to the i-th frequency point in the simulated amplitude-frequency response curve; is the mean amplitude value corresponding to all frequency points in the simulated amplitude-frequency response curve; m i is the amplitude corresponding to the i-th frequency point in the measured amplitude-frequency response curve; is the mean amplitude value corresponding to all frequency points in the measured amplitude-frequency response curve; p di is the phase corresponding to the ith frequency point in the simulated phase-frequency response curve; p i is the phase corresponding to the i-th frequency point in the measured phase-frequency response curve.

4. The method according to claim 2, characterized in that: The measured frequency response curve includes a measured amplitude-frequency response curve and a measured phase-frequency response curve, the simulated frequency response curve includes a simulated amplitude-frequency response curve and a simulated phase-frequency response curve, and the calculation formula of the amplitude-phase mutual correlation is: In the above formula, m di is the amplitude corresponding to the i-th frequency point in the simulated amplitude-frequency response curve; is the mean amplitude value corresponding to all frequency points in the simulated amplitude-frequency response curve; m i is the amplitude corresponding to the i-th frequency point in the measured amplitude-frequency response curve; is the mean amplitude value corresponding to all frequency points in the measured amplitude-frequency response curve; p di is the phase corresponding to the ith frequency point in the simulated phase-frequency response curve; is the phase mean corresponding to all frequency points in the simulated phase-frequency response curve; p d is the phase corresponding to the i-th frequency point in the measured phase-frequency response curve; is the phase mean corresponding to all frequency points in the measured phase-frequency response curve.

5. The method according to claim 2, characterized in that: The method further comprises: When the calculated amplitude-phase symbiotic correlation is greater than or equal to a preset first correlation threshold, and the amplitude-phase interaction correlation is greater than or equal to a preset second correlation threshold, it is determined that the amplitude correlation satisfies a preset correlation condition.

6. A device for constructing a simulation monitoring model of a transformer, characterized in that: The construction device of the transformer simulation monitoring model includes: A measurement module, used to obtain a measured frequency response curve of the transformer after a preset frequency band signal is injected through a test system; A model building and initialization module is used to build a simulation monitoring model of the transformer and randomly initialize the number of basic subunit modules and cross-correlation modules in the simulation monitoring model; wherein the basic subunit modules are used to characterize the main structure of the transformer winding, and the cross-correlation modules are used to characterize the relationship between the transformer windings, and between the windings and the iron box and the iron core; A simulation module, used for simulating the current simulation monitoring model to obtain a simulation frequency response curve; A correlation degree calculation module, used to calculate the amplitude correlation degree according to the measured frequency response curve and the simulated frequency response curve; wherein the amplitude correlation degree is used to measure the correlation degree between the characteristics of the amplitude and phase in the measured frequency response curve and the characteristics of the amplitude and phase in the simulated frequency response curve; An iterative optimization module is used to optimize the number of basic subunit modules and cross-correlation modules in the simulation monitoring model through a multi-objective optimization algorithm if the amplitude correlation degree does not meet the preset correlation degree condition and the current number of iterations is less than the preset iteration number threshold, and return to call the simulation module until the amplitude correlation degree meets the preset correlation degree condition and / or the current number of iterations is equal to the preset iteration number threshold.

7. The device for constructing a simulation monitoring model according to claim 6, characterized in that: The association degree calculation module is specifically used for: The amplitude-phase symbiotic correlation and the amplitude-phase mutual correlation are calculated according to the measured frequency response curve and the simulated frequency response curve; wherein the amplitude-phase symbiotic correlation is used to measure the correlation between the degree of common variation of amplitude and phase in the measured frequency response curve and the degree of common variation of amplitude and phase in the simulated frequency response curve, and the amplitude-phase mutual correlation is used to measure the correlation between the degree of interaction between amplitude and phase in the measured frequency response curve and the degree of interaction between amplitude and phase in the simulated frequency response curve.

8. The device for constructing a simulation monitoring model according to claim 7, characterized in that: The measured frequency response curve includes a measured amplitude-frequency response curve and a measured phase-frequency response curve, the simulated frequency response curve includes a simulated amplitude-frequency response curve and a simulated phase-frequency response curve, and the calculation formula of the amplitude-phase symbiotic correlation degree is: In the above formula, m di is the amplitude corresponding to the i-th frequency point in the simulated amplitude-frequency response curve; is the mean amplitude value corresponding to all frequency points in the simulated amplitude-frequency response curve; m i is the amplitude corresponding to the i-th frequency point in the measured amplitude-frequency response curve; is the mean amplitude value corresponding to all frequency points in the measured amplitude-frequency response curve; p di is the phase corresponding to the ith frequency point in the simulated phase-frequency response curve; p i is the phase corresponding to the i-th frequency point in the measured phase-frequency response curve.

9. A computer-readable storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 5.

10. A terminal device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 5.

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