Method, device, medium and equipment for constructing simulation monitoring model of transformer

Abstract

The application discloses a kind of construction method, device, medium and equipment of simulation monitoring model of transformer, first acquisition transformer under specific frequency band signal measured frequency response curve;Then construct simulation monitoring model, and randomly initialize each module quantity in model;Next, carry out simulation calculation simulation frequency response curve;Through calculating amplitude correlation degree, the consistency between measured and simulation data is measured;If amplitude correlation degree and current iteration number are not in accordance with preset condition, then through multi-objective optimization algorithm iteration adjustment model parameter, until amplitude correlation degree and / or current iteration number meet preset condition.This application updates simulation monitoring model in real time, and the dynamic comparison with measured data improves the accuracy and real-time performance of transformer monitoring.Compared with traditional offline fault diagnosis model, it effectively reduces the difference between simulation result and actual data, and realizes more accurate online monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power equipment modeling, and in particular to a transformer simulation monitoring model construction method, device, medium and equipment. BACKGROUND

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

[0003] Therefore, it is necessary to provide a transformer simulation monitoring model construction method, device, medium and equipment to solve the problem that most existing transformer frequency response simulation models are mainly used for offline fault diagnosis, and there is a large difference between the simulation results and the actual measured transformer frequency response.

[0004] A transformer simulation monitoring model construction method, the method comprises:

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

[0006] Construct 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 represent the body structure of the transformer winding, and the cross correlation module is used to represent the correlation between the transformer windings, and between the windings and the iron box and the core;

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

[0008] Calculate the amplitude correlation degree according to the measured frequency response curve and the simulation 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 simulation frequency response curve;

[0009] If the amplitude correlation degree does not satisfy the preset correlation degree condition and the current iteration number is less than the preset iteration number 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 executed until the amplitude correlation degree satisfies the preset correlation degree condition and / or the current iteration number is equal to the preset iteration number threshold.

[0010] In one of the embodiments, the amplitude correlation degree is calculated according to the measured frequency response curve and the simulation frequency response curve, including:

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

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

[0013]

[0014] In the above formula, m di is the amplitude corresponding to the i-th frequency point in the simulation amplitude-frequency response curve; is the average amplitude corresponding to all frequency points in the simulation amplitude-frequency response curve; m i is the amplitude corresponding to the i-th frequency point in the measured amplitude-frequency response curve; m is the average amplitude 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 simulation 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 of the embodiments, the measured frequency response curve includes a measured amplitude-frequency response curve and a measured phase-frequency response curve, the simulation frequency response curve includes a simulation amplitude-frequency response curve and a simulation phase-frequency response curve, and the calculation formula of the amplitude-phase interaction correlation degree is:

[0016]

[0017] In the above formula, m di is the amplitude value corresponding to the i-th frequency point in the simulated amplitude-frequency response curve; is the average amplitude value corresponding to all frequency points in the simulated amplitude-frequency response curve; m i is the amplitude value corresponding to the i-th frequency point in the measured amplitude-frequency response curve; is the average 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 average phase value 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 average phase value 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 coexistence correlation degree is greater than or equal to a preset first correlation degree threshold, and the amplitude-phase interaction correlation degree is greater than or equal to a preset second correlation degree threshold, it is determined that the amplitude correlation degree satisfies a preset correlation degree condition.

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

[0021] A measurement module for obtaining a measured frequency response curve of the transformer after injecting a preset frequency band signal through a test system;

[0022] A model construction and initialization module for 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 represent the body structure of the transformer winding, and the cross correlation modules are used to represent the correlation relationship between the transformer windings, and between the windings and the iron box and the iron core;

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

[0024] A correlation degree calculation module for calculating an 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 features of the amplitude and phase in the measured frequency response curve and the features of the amplitude and phase in the simulated frequency response curve;

[0025] An iterative optimization module is configured to, if the amplitude correlation degree does not satisfy the preset correlation degree condition and the current iteration number is less than the preset iteration number threshold, optimize the number of basic subunit modules and cross correlation modules in the simulation monitoring model by using a multi-objective optimization algorithm, and return to call the simulation module until the amplitude correlation degree satisfies the preset correlation degree condition and / or the current iteration number is equal to the preset iteration number threshold.

[0026] In one of the embodiments, the correlation degree calculation module is specifically configured to:

[0027] The amplitude-phase coexistence correlation degree and the amplitude-phase interaction correlation degree are calculated according to the measured frequency response curve and the simulation frequency response curve, wherein the amplitude-phase coexistence correlation degree is used to measure the correlation degree between the common change degree of the amplitude and the phase in the measured frequency response curve and the common change degree of the amplitude and the phase in the simulation frequency response curve, and the amplitude-phase interaction correlation degree is used to measure the correlation degree between the interaction degree of the amplitude and the phase in the measured frequency response curve and the interaction degree of the amplitude and the phase in the simulation frequency response curve.

[0028] In one of the embodiments, the measured frequency response curve includes a measured amplitude-frequency response curve and a measured phase-frequency response curve, the simulation frequency response curve includes a simulation amplitude-frequency response curve and a simulation phase-frequency response curve, and the calculation formula of the amplitude-phase coexistence correlation degree is as follows:

[0029]

[0030] In the above formula, m di is the amplitude corresponding to the i-th frequency point in the simulation amplitude-frequency response curve; is the average amplitude of all frequency points in the simulation 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 average amplitude of all frequency points in the measured amplitude-frequency response curve; p di is the phase corresponding to the i-th frequency point in the simulation 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, and the computer program is executed by a processor to make the processor execute the steps of the method for constructing the simulation monitoring model of the transformer.

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

[0033] The application provides a transformer simulation monitoring model construction method, device, medium and equipment. First, the measured frequency response curve of the transformer under a specific frequency band signal is acquired. Then, a simulation monitoring model is constructed, and the number of each module in the model is randomly initialized. Next, simulation calculation of the simulation frequency response curve is performed. The consistency between the measured data and the simulation data is measured through calculation of the amplitude correlation degree. If the amplitude correlation degree and the current iteration number do not meet the preset condition, the model parameters are iteratively adjusted through a multi-objective optimization algorithm until the amplitude correlation degree and / or the current iteration number meet the preset condition. The simulation monitoring model is updated in real time, and the dynamic comparison with the measured data is performed, so that the accuracy and real-time performance of the transformer monitoring are improved. Compared with the traditional offline fault diagnosis model, the application effectively reduces the difference between the simulation result and the actual data, and realizes more accurate online monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0035] Among them:

[0036] Figure 1 The flowchart of the transformer simulation monitoring model construction method is shown in the figure.

[0037] Figure 2 The structure diagram of the transformer simulation monitoring model construction device is shown in the figure.

[0038] Figure 3 The structure block diagram of the terminal device is shown in the figure. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0040] The terms "first", "second", and the like in the description and in the claims of the present application and above-described drawings are used to distinguish between similar objects, not to describe a particular sequential order. Moreover, the terms "include", and "have", and any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, product, or device that comprises a list of steps or units is not limited to the listed steps or units, but can optionally further include additional steps or units not listed, or can optionally further include other steps or units inherent to such processes, methods, products, or devices.

[0041] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. It is expressly understood that the embodiments described herein are merely examples from among a great variety of embodiments that can be made.

[0042] As Figure 1 shown, Figure 1 is a flowchart of a method for constructing a simulation monitoring model of a transformer, the method for constructing a simulation monitoring model of a transformer in the embodiment provides the following steps:

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

[0044] The test system is a combination of hardware and software for detecting and recording 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 actual frequency response data of the transformer under the action of the excitation signal of the test system.

[0045] Specifically, the test system injects a signal of a specific frequency band (for example, 1 kHz-1 MHz) into the transformer and monitors the measured response data of the transformer, and then draws a measured frequency response curve based on the measured data. The curve generally takes frequency as the horizontal axis and the amplitude difference or phase difference of the input and output 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-association modules in the simulation monitoring model.

[0047] The simulation monitoring model is used to simulate the frequency response characteristics of the transformer in different working states in real time, thereby assisting in state monitoring and fault diagnosis. The basic subunit module is used to represent the body 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, and the number of corresponding modules is represented as N H , N M , N L , and of course, other structure basic subunit modules can also be included.

[0048] The cross correlation module is used to represent the correlation between the transformer windings and between the windings and the iron tank and the iron core, and the number of corresponding modules is represented as N C . The electromagnetic field of the transformer winding will affect the iron tank and the iron core, and vice versa, the physical properties of the iron tank and the iron core will affect the electrical performance of the winding. The cross correlation module simulates the influence 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 by using inductors, resistors, and capacitors, and the cross correlation module represents the relationship between the winding and the iron core and the iron tank by defining the coupling between different modules (such as magnetic coupling and electrical coupling). And 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 sine wave signals of different frequencies into the current simulation monitoring model, and then draw a simulation frequency response curve by simulating current, voltage, and other output parameters.

[0052] S104, calculating an amplitude correlation degree according to the measured frequency response curve and the simulation frequency response curve.

[0053] The amplitude correlation degree is used to measure the correlation between the amplitude and phase characteristics of the measured frequency response curve and the amplitude and phase characteristics of the simulation frequency response curve. That is, it compares the differences in amplitude and phase between the actual measurement data and the simulation results to evaluate their similarity or consistency. The Pearson correlation coefficient formula can be used to calculate the amplitude correlation degree.

[0054] Optionally, the calculation of the amplitude correlation degree according to the measured frequency response curve and the simulation frequency response curve in S104 includes calculating an amplitude-phase coexistence correlation degree and an amplitude-phase interaction correlation degree according to the measured frequency response curve and the simulation frequency response curve.

[0055] The amplitude-phase coexistence correlation degree is used to measure the correlation degree between the mutual interaction degree of the amplitude and the phase in the measured frequency response curve and the mutual interaction degree of the amplitude and the phase in the simulation frequency response curve. It investigates whether the amplitude and the phase have a mutual influence relationship, and especially whether the amplitude and the phase will produce nonlinearity or interaction due to certain factors at certain frequencies. The interaction between the amplitude and the phase is quantified by a cross-correlation coefficient or a nonlinear modeling.

[0056] The amplitude-phase coexistence correlation degree is used to measure the correlation degree between the mutual interaction degree of the amplitude and the phase in the measured frequency response curve and the mutual interaction degree of the amplitude and the phase in the simulation frequency response curve. It investigates whether the amplitude and the phase have a mutual influence relationship, and especially whether the amplitude and the phase will produce nonlinearity or interaction due to certain factors at certain frequencies. The interaction between the amplitude and the phase is quantified by a cross-correlation coefficient or a 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 simulation frequency response curve includes a simulation amplitude-frequency response curve and a simulation phase-frequency response curve. The calculation formula of the amplitude-phase coexistence correlation degree is:

[0058]

[0059] In the above formula, m di is the amplitude corresponding to the i-th frequency point in the simulation amplitude-frequency response curve; is the mean value of the amplitudes corresponding to all frequency points in the simulation 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 value of the amplitudes 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 simulation 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 in the above formula calculates the linear correlation between the simulation and the measured amplitude-frequency response curves. The numerator calculates the covariance of the simulation and the measured amplitudes, and measures their common variation degree. The denominator corresponds to the standard deviation of the simulation and the measured amplitudes, and respectively measures the fluctuation degree of the simulation and the measured amplitudes. The second part then calculates the difference between the measured and the simulation phase-frequency response curves in a weighted manner. The greater the value of the amplitude-phase coexistence correlation degree, the more similar the simulation and the measured frequency response curves in the coexistence relationship of the amplitude and the phase, which can help to analyze the matching degree between the transformer simulation model and the measured data.

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

[0062]

[0063] In the above formula, m di is the amplitude value corresponding to the i-th frequency point in the simulated amplitude-frequency response curve; is the average amplitude value corresponding to all frequency points in the simulated amplitude-frequency response curve; m i is the amplitude value corresponding to the i-th frequency point in the measured amplitude-frequency response curve; is the average amplitude value corresponding to all frequency points in the measured amplitude-frequency response curve; p di is the phase value corresponding to the i-th frequency point in the simulated phase-frequency response curve; is the average phase value corresponding to all frequency points in the simulated phase-frequency response curve; p d is the phase value corresponding to the i-th frequency point in the measured phase-frequency response curve; is the average phase value 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 differences between simulated and measured frequency response curves, and provide a more powerful tool for model optimization and fault detection.

[0065] S105, determine whether the amplitude correlation degree does not satisfy 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 satisfy the preset correlation degree condition and the current iteration number is less than the preset iteration number threshold, S106 is executed and the subsequent steps are returned to execute S103. If the amplitude correlation degree satisfies the preset correlation degree condition and / or the current iteration number is equal to the preset iteration number threshold, the process ends.

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

[0067] Optionally, to determine whether the preset correlation degree condition is satisfied, the method for constructing the simulation monitoring model of the transformer further executes the following steps: when the amplitude-phase coexistence correlation degree is greater than or equal to a preset first correlation degree threshold (for example, set to 0.95) and the amplitude-phase interaction correlation degree is greater than or equal to a preset second correlation degree threshold (for example, set to 0.95), it is determined that the amplitude correlation degree satisfies the preset correlation degree condition.

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

[0069] S106, the number of basic subunit modules and cross correlation modules in the simulation monitoring model is optimized by a multi-objective optimization algorithm.

[0070] Among them, the multi-objective optimization algorithm is an algorithm for optimizing multiple conflicting objective functions. Unlike single-objective optimization, which only considers one optimization target, multi-objective optimization considers the optimal solution of multiple targets 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 module number:

[0072] A, initialize the population: randomly generate a set of candidate solutions, each solution 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 value of two objective functions, including objective function 1: amplitude correlation (i.e. calculate the amplitude-phase coexistence and amplitude-phase interaction correlation by 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, forming several Pareto fronts. The rule of Pareto dominance is: a solution A is better than a solution B, if A is superior to B in a certain target, and not worse than B in other targets.

[0075] D, selection: within each Pareto front, sort according to the crowding distance. Crowding distance is used to ensure the uniform distribution of solutions, avoiding all solutions concentrated in a certain part of the Pareto frontier.

[0076] E, crossover and mutation: use crossover operation to generate new candidate solutions. For example, combine the module numbers of two parent solutions to form a child solution: parent 1: [8, 6], parent 2: [10, 5], then the child solution may be [8, 5] or [10, 6]. Use mutation operation to randomly adjust the module number, for example, [8, 5] is mutated to [9, 5].

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

[0078] The method for constructing the simulation monitoring model of the transformer comprises the following steps: firstly, obtaining a 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 each module in the model; then, performing simulation calculation on a simulation frequency response curve; calculating an amplitude correlation degree to measure the consistency between the measured data and the simulation data; if the amplitude correlation degree and the current iteration number do not meet the preset conditions, adjusting the model parameters through a multi-objective optimization algorithm until the amplitude correlation degree and / or the current iteration number meet the preset conditions. The method can improve the accuracy and real-time performance of the transformer monitoring by updating the simulation monitoring model in real time and dynamically comparing the simulation monitoring model with the measured data. Compared with the traditional offline fault diagnosis model, the method can effectively reduce the difference between the simulation result and the actual data and realize more accurate online monitoring.

[0079] In one embodiment, as shown in Figure 2 a device for constructing a simulation monitoring model of a transformer is proposed, which comprises:

[0080] a measured module 201 configured to obtain a measured frequency response curve of the transformer after injecting a preset frequency band signal through a test system;

[0081] a model construction and initialization module 202 configured to construct 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 represent the body structure of the transformer winding, and the cross correlation modules are used to represent the correlation between the transformer windings and between the windings and the iron box and the iron core;

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

[0083] a correlation degree calculation module 204 configured to calculate an amplitude correlation degree according to the measured frequency response curve and the simulation frequency response curve; wherein the amplitude correlation degree is used to measure the correlation degree between the amplitude and phase features in the measured frequency response curve and the amplitude and phase features in the simulation frequency response curve;

[0084] an iteration optimization module 205 configured to, 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, optimize the number of the basic subunit modules and the cross correlation modules in the simulation monitoring model through a multi-objective optimization algorithm, return to call the simulation module 203, until the amplitude correlation degree meets the preset correlation degree condition and / or the current iteration number is equal to the preset iteration number threshold.

[0085] Figure 3 An internal structure diagram of a terminal device in one embodiment is shown. As Figure 3As shown, the terminal device includes a processor, a memory and a network interface connected through 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 can also store a computer program, which, when executed by the processor, can enable the processor to implement the construction method of the simulation monitoring model of the transformer. The computer program can also be stored in the internal memory, which, when executed by the processor, can enable the processor to execute the construction method of the simulation monitoring model of the transformer. Those skilled in the art can understand that Figure 3 The structure shown in the figure is only a block diagram of part of the 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 can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0086] A computer readable storage medium stores a computer program, which, when executed by a processor, implements the following steps: obtaining a measured frequency response curve of the transformer after injecting a preset frequency band signal 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 module is used to represent the body structure of the transformer winding, and the cross correlation module is used to represent 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 an amplitude correlation degree according to the measured frequency response curve and the simulation 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 simulation frequency response curve; if the amplitude correlation degree does not satisfy a preset correlation degree condition, and the current iteration number is less than a preset iteration number threshold, then the number of basic subunit modules and cross correlation modules in the simulation monitoring model is optimized through a multi-objective optimization algorithm, and the step of simulating the current simulation monitoring model to obtain a simulation frequency response curve and the subsequent steps are returned to be executed until the amplitude correlation degree satisfies the preset correlation degree condition, and / or the current iteration number is equal to the preset iteration number threshold.

[0087] A terminal device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the following steps when executing the computer program: obtaining a measured frequency response curve of a transformer after injecting a preset frequency band signal 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 module is used to represent the body structure of the transformer winding, and the cross correlation module is used to represent 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 an amplitude correlation degree according to the measured frequency response curve and the simulation 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 simulation frequency response curve; if the amplitude correlation degree does not satisfy a preset correlation degree condition, and the current iteration number is less than a preset iteration number threshold, then the number of basic subunit modules and cross correlation modules in the simulation monitoring model is optimized through 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 executed until the amplitude correlation degree satisfies the preset correlation degree condition, and / or the current iteration number is equal to the preset iteration number threshold.

[0088] It should be noted that the above method, device, equipment and computer readable storage medium for constructing a simulation monitoring model of a transformer belong to one general inventive concept, and the contents in the simulation monitoring model of the transformer construction method, device, equipment and computer readable storage medium embodiments can be mutually applicable.

[0089] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant 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 above-mentioned embodiment methods. Any reference to memory, storage, database or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synch link) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0090] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0091] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A method of constructing a simulation monitoring model of a transformer, characterized by, The method comprises: obtaining a measured frequency response curve of the transformer after injecting a preset frequency band signal 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 module is used to represent the body structure of the transformer winding, and the cross correlation module is used to represent 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 an amplitude correlation degree according to the measured frequency response curve and the simulation 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 simulation frequency response curve; if the amplitude correlation degree does not satisfy a preset correlation degree condition, and the current iteration number is less than a preset iteration number threshold, optimizing the number of basic subunit modules and cross correlation modules in the simulation monitoring model through a multi-objective optimization algorithm, returning to execute the step of simulating the current simulation monitoring model to obtain a simulation frequency response curve and subsequent steps until the amplitude correlation degree satisfies the preset correlation degree condition, and / or the current iteration number is equal to the preset iteration number threshold; the calculation of the amplitude correlation degree according to the measured frequency response curve and the simulation frequency response curve comprises: calculating an amplitude-phase coexistence correlation degree and an amplitude-phase interaction correlation degree according to the measured frequency response curve and the simulation frequency response curve; wherein the amplitude-phase coexistence correlation degree is used to measure the correlation degree between the common change degree of the amplitude and phase in the measured frequency response curve and the common change degree of the amplitude and phase in the simulation frequency response curve, and the amplitude-phase interaction correlation degree is used to measure the correlation degree between the interaction degree of the amplitude and phase in the measured frequency response curve and the interaction degree of the amplitude and phase in the simulation frequency response curve.

2. The method of claim 1, wherein, The measured frequency response curve comprises a measured amplitude-frequency response curve and a measured phase-frequency response curve, the simulation frequency response curve comprises a simulation amplitude-frequency response curve and a simulation phase-frequency response curve, and the calculation formula of the amplitude-phase coexistence correlation degree is: In the above formula, is the amplitude value corresponding to the i-th frequency point in the simulated amplitude-frequency response curve; is the average amplitude value corresponding to all frequency points in the simulated amplitude-frequency response curve; is the amplitude value corresponding to the i-th frequency point in the measured amplitude-frequency response curve; is the average amplitude value corresponding to all frequency points in the measured amplitude-frequency response curve; is the phase corresponding to the i-th frequency point in the simulated phase-frequency response curve; is the phase corresponding to the i-th frequency point in the measured phase-frequency response curve, I is a preset coefficient, and n is the total number of frequency points.

3. The method of claim 1, wherein, The measured frequency response curve comprises a measured amplitude-frequency response curve and a measured phase-frequency response curve, the simulation frequency response curve comprises a simulation amplitude-frequency response curve and a simulation phase-frequency response curve, and the calculation formula of the amplitude-phase interaction correlation degree is: In the above formula, is the amplitude value corresponding to the i-th frequency point in the simulated amplitude-frequency response curve; is the average amplitude value corresponding to all frequency points in the simulated amplitude-frequency response curve; is the amplitude value corresponding to the i-th frequency point in the measured amplitude-frequency response curve; is the average amplitude value corresponding to all frequency points in the measured amplitude-frequency response curve; is the phase corresponding to the i-th frequency point in the simulated phase-frequency response curve; is the average phase value corresponding to all frequency points in the simulated phase-frequency response curve; is the phase corresponding to the i-th frequency point in the measured phase-frequency response curve; is the average phase value corresponding to all frequency points in the measured phase-frequency response curve.

4. The method of claim 1, wherein, The method further comprises: when the calculated amplitude-phase coexistence correlation degree is greater than or equal to a preset first correlation degree threshold, and the amplitude-phase interaction correlation degree is greater than or equal to a preset second correlation degree threshold, it is determined that the amplitude correlation degree satisfies the preset correlation degree condition.

5. An apparatus for constructing a simulation monitoring model of a transformer, characterized by The construction device of the simulation monitoring model of the transformer comprises: a measured module for obtaining a measured frequency response curve of the transformer after injecting a preset frequency band signal through a test system; The model construction and initialization module is configured to construct 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; the basic subunit modules are configured to represent the body structure of the transformer winding, and the cross correlation modules are configured to represent the correlation between the transformer windings and between the windings and the iron box and the iron core; The simulation module is configured to simulate the current simulation monitoring model to obtain a simulation frequency response curve; The correlation degree calculation module is configured to calculate an amplitude correlation degree according to the measured frequency response curve and the simulation frequency response curve; the amplitude correlation degree is configured to measure the correlation degree between the characteristics of the amplitude and the phase in the measured frequency response curve and the characteristics of the amplitude and the phase in the simulation frequency response curve; The iterative optimization module is configured to, if the amplitude correlation degree does not satisfy a preset correlation degree condition and a current iteration number is less than a preset iteration number threshold, optimize the number of the basic subunit modules and the cross correlation modules in the simulation monitoring model by using a multi-objective optimization algorithm, return to call the simulation module, until the amplitude correlation degree satisfies the preset correlation degree condition and / or the current iteration number is equal to the preset iteration number threshold. The correlation degree calculation module is specifically configured to: calculate an amplitude-phase coexistence correlation degree and an amplitude-phase interaction correlation degree according to the measured frequency response curve and the simulation frequency response curve; the amplitude-phase coexistence correlation degree is configured to measure the correlation degree between the common variation degree of the amplitude and the phase in the measured frequency response curve and the common variation degree of the amplitude and the phase in the simulation frequency response curve, and the amplitude-phase interaction correlation degree is configured to measure the correlation degree between the interaction degree of the amplitude and the phase in the measured frequency response curve and the interaction degree of the amplitude and the phase in the simulation frequency response curve.

6. The apparatus for constructing an emulation monitoring model according to claim 5, wherein The measured frequency response curve includes a measured amplitude-frequency response curve and a measured phase-frequency response curve, the simulation frequency response curve includes a simulation amplitude-frequency response curve and a simulation phase-frequency response curve, and the calculation formula of the amplitude-phase coexistence correlation degree is: In the above formula, is the amplitude value corresponding to the i-th frequency point in the simulated amplitude-frequency response curve; is the average amplitude value corresponding to all frequency points in the simulated amplitude-frequency response curve; is the amplitude value corresponding to the i-th frequency point in the measured amplitude-frequency response curve; is the average amplitude value corresponding to all frequency points in the measured amplitude-frequency response curve; is the phase corresponding to the i-th frequency point in the simulated phase-frequency response curve; is the phase corresponding to the i-th frequency point in the measured phase-frequency response curve, I is a preset coefficient, and n is the total number of frequency points.

7. A computer-readable storage medium, characterized in that, The computer program is stored in the memory and is executed by the processor to enable the processor to perform the steps of the method according to any one of claims 1 to 4.

8. A terminal device, comprising: The computer program is stored in the memory and is executed by the processor to enable the processor to perform the steps of the method according to any one of claims 1 to 4.

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