Transformer winding vibration signal determination method and system, storage medium and equipment

Abstract

The embodiment of the invention discloses a transformer winding vibration signal determination method, and the method comprises the steps: collecting the vibration data of a transformer, and extracting a box vibration signal corresponding to a to-be-detected transformer from the vibration data; parameters in the time-varying filtering empirical mode decomposition algorithm are optimized, and an optimal parameter combination is obtained; processing the transformer box vibration signal based on a time-varying filtering empirical mode decomposition algorithm of an optimal parameter combination to obtain a plurality of intrinsic mode functions; determining a pseudo observation signal according to the plurality of intrinsic mode functions; and determining a winding vibration signal based on the transformer box body vibration signal and the pseudo observation signal. According to the method, the box body vibration signals are decomposed and integrated, so that the winding vibration signals are accurately extracted from the box body vibration signals of the transformer, and powerful support is provided for monitoring and maintenance of the transformer.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing, and particularly to a method, a system, a storage medium, and a device for determining a vibration signal of a transformer winding. Background Art

[0002] As a core device in the power system, a transformer undertakes the important tasks of electric energy transmission and distribution. With the continuous expansion of the power transmission scale, the transmission of ultra-high voltage and large-capacity electric energy has become a new development trend, which puts forward higher requirements for the reliability and stability of the transformer. Under the harsh working environment, the transformer is prone to failure, resulting in the shutdown of the power system and huge economic losses.

[0003] Traditional transformer fault diagnosis methods, such as short-circuit impedance method, frequency response method, etc., require power-off detection of the transformer, which is cumbersome to operate and lacks timeliness, and it is difficult to meet the requirements of modern power systems for rapid fault diagnosis. The transformer condition assessment technology based on vibration signals has received extensive attention. The change of the internal mechanical structure state of the transformer winding will cause changes in its vibration characteristics. Therefore, by collecting the vibration signals on the surface of the transformer tank and effectively analyzing them, the state of the winding can be evaluated, so as to realize the early warning and diagnosis of transformer faults.

[0004] The vibration signals of the transformer tank usually contain the coupling of the core and winding vibration signals. It is difficult to directly analyze this signal to accurately reflect the state of the winding. Therefore, how to effectively separate the winding vibration signals from the tank vibration signals has become a key problem in the transformer condition assessment technology based on vibration signals. Summary of the Invention

[0005] Based on this, in view of the above problems, it is necessary to propose a method for determining the vibration signal of a transformer winding.

[0006] A method for determining the vibration signal of a transformer winding, the method includes the following steps:

[0007] Collect the vibration data of the transformer, and extract the tank vibration signal corresponding to the transformer to be measured from the vibration data;

[0008] Optimize the parameters in the time-varying filtered empirical mode decomposition algorithm to obtain the optimal parameter combination;

[0009] Process the transformer tank vibration signal based on the time-varying filtered empirical mode decomposition algorithm with the optimal parameter combination to obtain a plurality of intrinsic mode functions;

[0010] Determine the pseudo-observation signal according to the plurality of intrinsic mode functions;

[0011] Determine the winding vibration signal based on the transformer tank vibration signal and the pseudo-observation signal.

[0012] In the above solution, optimizing the parameters in the time-varying filtering empirical mode decomposition algorithm to obtain the optimal parameter combination specifically includes:

[0013] The time-varying filtering empirical mode decomposition algorithm includes a bandwidth threshold ξ and a B-spline order n;

[0014] Iterate the bandwidth threshold ξ and the B-spline order n to obtain the optimal parameter combination.

[0015] In the above solution, iterating the bandwidth threshold ξ and the B-spline order n to obtain the optimal parameter combination specifically includes:

[0016] Initialize the bandwidth threshold ξ and the B-spline order n;

[0017] Randomly generate a number of bandwidth thresholds ξ and B-spline orders n to determine the initial position and population direction of the population;

[0018] Determine a number of parameter combinations of this population according to a number of bandwidth thresholds ξ and B-spline orders n;

[0019] Determine the sample entropy value of the corresponding intrinsic mode function according to the parameter combination;

[0020] Update the position and direction of this population according to the sample entropy value;

[0021] When the maximum number of iterations is reached, obtain the optimal parameter combination of this population.

[0022] In the above solution, determining the sample entropy value of the corresponding intrinsic mode function according to the parameter combination specifically includes:

[0023] Obtain the time series X corresponding to the parameter combination;

[0024] Decompose the time series X into an m-dimensional sequence and determine the maximum difference of the elements at the corresponding positions in the time series X:

[0025]

[0026] In the formula, X is the time series, and i and j are serial numbers;

[0027] Set the similarity tolerance r,

[0028] Determine the number of similar elements where the distance between the elements X(i) and X(j) at the corresponding positions in the time series X is less than the similarity tolerance r;

[0029] Determine the self-similar probability of the element X(i) at the abscissa position in the time series X based on the number of similar elements;

[0030] Obtain the average value of the self-similar probability to obtain the sample entropy value of the sequence;

[0031]

[0032] where B m (r) is the self-similar probability, m is the time series dimension, and N is the time series length.

[0033] In the above solution, after obtaining the average value of the self-similar probability to obtain the sample entropy value of the sequence, it further includes:

[0034] Obtain the sample entropy value of the sequence corresponding to decomposing the time series X into an m + 1-dimensional sequence:

[0035]

[0036] where B m (r) is the self-similar probability, and m is the time series dimension;

[0037] Update the position and direction of the population according to the sample entropy value;

[0038] When the maximum update times are reached, obtain the optimal parameter combination of the bandwidth threshold ξ and the B-spline order n.

[0039] In the above solution, the determining the pseudo-observation signal according to the several intrinsic mode functions specifically includes:

[0040] Select the intrinsic mode function with the highest correlation coefficient with the transformer box vibration signal from the several intrinsic mode functions as the pseudo-observation signal.

[0041] In the above solution, the determining the winding vibration signal based on the transformer box vibration signal and the pseudo-observation signal specifically includes:

[0042] Construct a source signal matrix with the transformer box vibration signal x(t) and the observed signal x’(t);

[0043] Perform normalization preprocessing on the source signal matrix;

[0044] Estimate the separation matrix W i until the difference between W i (k) and W i (k + 1) meets the accuracy requirement:

[0045] W i (k + 1) = W i (k)E{Sg'[W i (k) T S]}-E{Sg'[W i (k)T S]}

[0046] Wherein, W i is the iteration matrix of the separation matrix, S is the source signal matrix, k is the number of iterations, i is the signal source sequence, and g(x) is a non-linear function;

[0047] Obtain the separated signal

[0048]

[0049] Wherein, W is the separation matrix, and X i is the observed signal of the i-th signal source;

[0050] According to the separated signal Determine the winding vibration signal.

[0051] The present application also proposes a transformer winding vibration signal separation system, which includes: a vibration data acquisition unit, a decomposition algorithm optimization unit, a signal processing unit, and a winding vibration signal determination unit;

[0052] The vibration data acquisition unit is used to collect the vibration data of the transformer and extract the box body vibration signal corresponding to the transformer to be measured from the vibration data;

[0053] The decomposition algorithm optimization unit is used to optimize the parameters in the time-varying filtering empirical mode decomposition algorithm to obtain an optimal parameter combination;

[0054] The signal processing unit is used to process the transformer box body vibration signal based on the time-varying filtering empirical mode decomposition algorithm with the optimal parameter combination to obtain a plurality of intrinsic mode functions; determine the pseudo-observed signal according to the plurality of intrinsic mode functions;

[0055] The winding vibration signal determination unit is used to determine the winding vibration signal based on the transformer box body vibration signal and the pseudo-observed signal.

[0056] The present application also proposes a readable storage medium storing a computer program, and when the computer program is executed by a processor, the processor performs the following steps:

[0057] Collect the vibration data of the transformer and extract the box body vibration signal corresponding to the transformer to be measured from the vibration data;

[0058] Optimize the parameters in the time-varying filtering empirical mode decomposition algorithm to obtain an optimal parameter combination;

[0059] Process the transformer box body vibration signal based on the time-varying filtering empirical mode decomposition algorithm with the optimal parameter combination to obtain a plurality of intrinsic mode functions;

[0060] Determine a pseudo-observation signal according to the plurality of intrinsic mode functions;

[0061] Determine a winding vibration signal based on the transformer tank vibration signal and the pseudo-observation signal.

[0062] This application also proposes a computer device, including a memory and a processor, where the memory stores a computer program, and the computer program is executed by the processor to perform the following steps:

[0063] Collect vibration data of the transformer, and extract the corresponding tank vibration signal of the transformer to be measured from the vibration data;

[0064] Optimize the parameters in the time-varying filtered empirical mode decomposition algorithm to obtain an optimal parameter combination;

[0065] Process the transformer tank vibration signal based on the time-varying filtered empirical mode decomposition algorithm with the optimal parameter combination to obtain a plurality of intrinsic mode functions;

[0066] Determine a pseudo-observation signal according to the plurality of intrinsic mode functions;

[0067] Determine a winding vibration signal based on the transformer tank vibration signal and the pseudo-observation signal.

[0068] Adopting the embodiment of the present invention has the following beneficial effects: Collect vibration data of the transformer, and extract the corresponding tank vibration signal of the transformer to be measured from the vibration data; Optimize the parameters in the time-varying filtered empirical mode decomposition algorithm to obtain an optimal parameter combination; Process the transformer tank vibration signal based on the time-varying filtered empirical mode decomposition algorithm with the optimal parameter combination to obtain a plurality of intrinsic mode functions; Determine a pseudo-observation signal according to the plurality of intrinsic mode functions; Determine a winding vibration signal based on the transformer tank vibration signal and the pseudo-observation signal. By decomposing and integrating the tank vibration signal, the present invention realizes accurately extracting the winding vibration signal from the tank vibration signal of the transformer, provides strong support for the monitoring and maintenance of the transformer, and is beneficial to improving the analysis accuracy of the winding state. Description of the Drawings

[0069] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings.

[0070] Among them:

[0071] Figure 1 It is a schematic flow diagram of a method for determining the vibration signal of a transformer winding in an embodiment. Specific implementation manners

[0072] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are only a part rather than all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts belong to the scope of protection of the present invention.

[0073] In the following description, numerous specific details are given to provide a more thorough understanding of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, some well-known technical features are not described to avoid confusion with the present invention. It should be understood that the present invention can be implemented in different forms and should not be construed as limited to the embodiments presented here. On the contrary, providing these embodiments will make the disclosure thorough and complete and will fully convey the scope of the present invention to those skilled in the art.

[0074] The purpose of the terms used herein is only to describe specific embodiments and is not a limitation of the present invention. When used herein, the singular forms "a", "an" and "the" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms "comprising" and / or "including", when used in this specification, determine the presence of the described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups. When used herein, the term "and / or" includes any and all combinations of the related listed items.

[0075] To solve the problem that the traditional vibration signal of a transformer is mainly a coupled signal of the core and winding vibrations and cannot accurately reflect the winding state, the present invention proposes a method for determining the vibration signal of a transformer winding. This method first uses the genetic algorithm for iterative optimization to obtain the optimal parameter combination of the time-varying filtering empirical mode decomposition algorithm (TVFEMD), then brings the vibration signal of the transformer box into the optimized decomposition algorithm to decompose and obtain the intrinsic mode functions, selects the intrinsic mode function with the highest correlation coefficient with the original signal as the effective component, and finally takes the effective component and the original signal together as the observation signal and substitutes it into the decomposition algorithm to decompose and obtain the winding vibration signal.

[0076] To thoroughly understand the present invention, detailed structures will be presented in the following description to illustrate the technical solutions proposed by the present invention; the optional embodiments of the present invention are described in detail below. However, in addition to these detailed descriptions, the present invention may also have other implementation manners.

[0077] As Figure 1 shown, in one embodiment, a method for determining the vibration signal of a transformer winding is provided. The method for determining the vibration signal of the transformer winding includes steps S101 to S105, which are described in detail as follows:

[0078] S101. Collect the vibration data of the transformer and extract the corresponding box vibration signal of the transformer to be measured from the vibration data;

[0079] The purpose of this step is to obtain the overall vibration situation of the transformer, especially for the box part of the transformer to be measured. The vibration data collected by sensors and other devices can reflect the vibration characteristics of the transformer during operation. Extracting the box vibration signal is for subsequent analysis preparation because the vibration of the box is often related to the winding vibration inside the transformer.

[0080] S102. Optimize the parameters in the time-varying filtering empirical mode decomposition algorithm to obtain the optimal parameter combination;

[0081] Time-varying filtering empirical mode decomposition (TVF-EMD) is a signal processing method used to decompose complex non-linear and non-stationary signals into several intrinsic mode functions (IMFs). Optimizing the algorithm parameters is to make the decomposition process more accurate and efficient, so as to obtain intrinsic mode functions that can better reflect the characteristics of the original signal. Obtaining the optimal parameter combination is the key to ensuring the accuracy of subsequent analysis.

[0082] S103. Process the box vibration signal of the transformer based on the time-varying filtering empirical mode decomposition algorithm with the optimal parameter combination to obtain several intrinsic mode functions;

[0083] Using the optimized VF-EMD algorithm to decompose the box vibration signal can obtain a series of intrinsic mode functions. These IMFs represent the characteristics of the signal at different frequencies and scales, which helps the subsequent analysis and extraction of the winding vibration signal. It can adaptively adjust parameters such as the cut-off frequency of the filter according to the frequency change and local characteristics of the signal, and can track the time-varying characteristics of the signal, avoiding mode mixing caused by signal characteristic changes.

[0084] In the aspect of transformer fault diagnosis, mode mixing will interfere with the extraction of fault characteristics. TVFEMD overcomes the problem of mode mixing, making the fault characteristics more clearly reflected in the decomposed IMFs.

[0085] In some embodiments, the bandwidth threshold ξ and the B-spline order n are iterated to obtain an optimal parameter combination, which specifically includes:

[0086] Initialize the bandwidth threshold ξ and the B-spline order n;

[0087] Randomly generate a number of bandwidth thresholds ξ and B-spline orders n to determine the initial position and population direction of the population;

[0088] Determine a number of parameter combinations of the population according to a number of bandwidth thresholds ξ and B-spline orders n;

[0089] Determine the sample entropy value of the corresponding intrinsic mode function according to the parameter combination;

[0090] Update the position and direction of the population according to the sample entropy value;

[0091] When the maximum number of iterations is reached, the optimal parameter combination of the population is obtained.

[0092] Specifically, the sample entropy can measure the complexity and self-similarity of the time series, does not depend on the length of the data during calculation, and has better statistical stability and adaptability. Therefore, the sample entropy is selected as the fitness function.

[0093] In some embodiments, the parameters in the time-varying filtering empirical mode decomposition algorithm are optimized to obtain an optimal parameter combination, which specifically includes:

[0094] The time-varying filtering empirical mode decomposition algorithm includes a bandwidth threshold ξ and a B-spline order n;

[0095] Iterate the bandwidth threshold ξ and the B-spline order n to obtain an optimal parameter combination.

[0096] Preferably, a grid is set within the value range of the bandwidth threshold ξ and the B-spline order n, all grid points are traversed, the decomposition effect under each parameter combination is calculated, and the one with the best effect is selected as the optimal parameter combination. The time-varying filtering empirical mode decomposition algorithm based on the optimal parameter combination is used to process the vibration signal of the transformer box body to obtain a number of intrinsic mode functions.

[0097] Furthermore, the above decomposition effect is evaluated according to the stationarity and integrity of the intrinsic mode function.

[0098] In some embodiments, determining the sample entropy value of the corresponding intrinsic mode function according to the parameter combination specifically includes:

[0099] Obtain the time series X corresponding to the parameter combination;

[0100] Decompose the time series X into an m-dimensional series and determine the maximum difference of the elements at the corresponding positions in the time series X:

[0101]

[0102] Wherein, X is a time series, and i and j are sequence numbers;

[0103] Set a similarity tolerance r.

[0104] Determine the number of similar elements in the time series X where the distance between the elements X(i) and X(j) at the corresponding positions is less than the similarity tolerance r.

[0105] Determine the self-similar probability of the element X(i) at the abscissa position in the time series X based on the number of similar elements.

[0106] Obtain the average value of the self-similar probabilities to obtain the sample entropy value of the sequence.

[0107]

[0108] Wherein, B m (r) is the self-similar probability, m is the dimension of the time series, and N is the length of the time series.

[0109] It can be seen that by calculating the sample entropy values under different parameter combinations, the influence of the parameter combinations on the decomposition effect can be evaluated, and the optimal parameter combination that makes the decomposition effect the best can be found, and the complexity and randomness of the time series can be analyzed: the sample entropy value can be used to analyze the complexity and randomness of the time series, which helps to understand the nature of the time series and provide information for subsequent analysis and applications. By optimizing the parameter combination, the performance of the time-varying filtering empirical mode decomposition algorithm can be improved, thereby improving the accuracy of signal processing.

[0110] In some embodiments, after obtaining the average value of the self-similar probabilities to obtain the sample entropy value of the sequence, it further includes:

[0111] Obtain the sample entropy value of the sequence corresponding to decomposing the time series X into an m + 1-dimensional sequence:

[0112]

[0113] Wherein, B m (r) is the self-similar probability, and m is the dimension of the time series;

[0114] Update the position and direction of the population according to the sample entropy value.

[0115] When the maximum number of updates is reached, obtain the optimal parameter combination of the bandwidth threshold ξ and the B-spline order n.

[0116] Specifically, the original vibration signal of the transformer box collected is substituted into the TVFEMD algorithm with the optimal parameter combination to decompose the intrinsic mode functions, calculate the correlation coefficients between each order of IMF and the original signal, and select the IMF with the largest correlation coefficient as the effective component.

[0117] S104. Determine the pseudo-observation signal according to several intrinsic mode functions;

[0118] The purpose of this step is to screen out the signal components related to winding vibration from the decomposed intrinsic mode functions. The pseudo-observation signal is constructed based on these relevant IMFs and attempts to simulate or reflect the real situation of winding vibration. This step is the key to connecting the box vibration signal and the winding vibration signal.

[0119] In some embodiments, determining the pseudo-observation signal according to several intrinsic mode functions specifically includes:

[0120] Select the intrinsic mode function with the highest correlation coefficient with the transformer box vibration signal from several intrinsic mode functions as the pseudo-observation signal.

[0121] S105. Determine the winding vibration signal based on the transformer box vibration signal and the pseudo-observation signal.

[0122] By comparing and analyzing the box vibration signal and the pseudo-observation signal, the winding vibration signal can be determined. This step may involve signal reconstruction, filtering or other signal processing techniques to separate a purer winding vibration signal from the complex box vibration signal. The finally obtained winding vibration signal can be used to evaluate the health status of the transformer, monitor winding deformation or other potential problems.

[0123] In some embodiments, determining the winding vibration signal based on the transformer box vibration signal and the pseudo-observation signal specifically includes:

[0124] Construct a source signal matrix from the transformer box vibration signal x(t) and the observed signal x'(t);

[0125] Perform normalization preprocessing on the source signal matrix;

[0126] Estimate the separation matrix W i until the difference between W i (k) and W i (k + 1) meets the accuracy requirement:

[0127] W i (k + 1) = W i (k)E{Sg'[W i (k) T S]}-E{Sg'[W i (k)T S]}

[0128] Where, W i is the iteration matrix of the separation matrix, S is the source signal matrix, k is the number of iterations, i is the signal source sequence, and g(x) is a non-linear function;

[0129] Obtain the separated signal

[0130]

[0131] Where, W is the separation matrix, and X i is the observed signal of the i-th signal source;

[0132] According to the separated signal Determine the winding vibration signal.

[0133] It can be seen that this solution can effectively separate the winding vibration signal from the box vibration signal, thereby improving the extraction accuracy of the winding vibration signal. Through signal separation, the interference of noise and other signals can be reduced, making the winding vibration signal clearer. The accurately extracted winding vibration signal can provide a more reliable data basis for winding state evaluation and fault diagnosis, thereby improving the accuracy of diagnosis. It can effectively separate different components in the mixed signal and has a good theoretical basis.

[0134] Preferably, the source signal matrix S(t) is preprocessed by normalization and needs to satisfy:

[0135] E(SS T ) = I.

[0136] Preferably, the separation matrix W i is initially estimated as W i (0) and needs to satisfy:

[0137] ||W i (0)|| = 1.

[0138] Preferably, after further estimating W i , W i (k + 1) is normalized.

[0139]

[0140] Among them, the non-linear function g(x) is usually: g(x) = x 3 ;

[0141] Furthermore, if W i (k) and W iThe difference of (k + 1) does not meet the accuracy requirement, so repeat the iteration step. Otherwise, stop the iteration. At this time, W i (k + 1) is the final W.

[0142] At this time, the separated signal is obtained which includes the core vibration signal and the winding vibration signal. Among them, according to the characteristics of the transformer vibration signal, the frequency of the core vibration signal is mostly between 300 Hz and 500 Hz, the main frequency of the winding vibration signal is 100 Hz, and there are a small number of 100 Hz harmonics. According to the above characteristics, the discrimination of the winding vibration signal can be realized.

[0143] In summary, this solution collects the vibration data of the transformer and extracts the box body vibration signal of the transformer to be measured from it, ensuring that the data source for subsequent analysis is specific to the transformer, thereby improving the pertinence and accuracy of the analysis; optimizing the parameters of the time-varying filtering empirical mode decomposition algorithm to obtain the optimal parameter combination, which helps to improve the processing effect of the algorithm on the vibration signal and makes the intrinsic mode functions obtained by decomposition more accurately reflect the characteristics of the signal; using the time-varying filtering empirical mode decomposition algorithm with the optimal parameter combination to process the box body vibration signal can obtain several intrinsic mode functions, which represent different frequency components of the signal and help to deeply understand the internal structure of the vibration signal, providing a basis for signal separation. The pseudo-observation signal is used for subsequent signal reconstruction and separation processes. Based on the box body vibration signal and the pseudo-observation signal, the winding vibration signal is successfully determined. This step realizes the extraction of the target signal from the complex mixed signal and provides direct data support for the state evaluation and fault diagnosis of the winding.

[0144] Through the above steps, the winding vibration signal can be more accurately identified and extracted, thereby improving the accuracy of the state evaluation and fault diagnosis of the transformer winding. Precise analysis of the winding vibration signal helps to detect potential faults in a timely manner, provides reliable data support for the maintenance and operation of the transformer, reduces the unexpected downtime and maintenance costs, and improves the overall system operation efficiency and reliability.

[0145] This application also proposes a transformer winding vibration signal separation system, which includes: a vibration data acquisition unit, a decomposition algorithm optimization unit, a signal processing unit, and a winding vibration signal determination unit;

[0146] The vibration data acquisition unit is used to collect the vibration data of the transformer and extract the box body vibration signal corresponding to the transformer to be measured from the vibration data;

[0147] The decomposition algorithm optimization unit is used to optimize the parameters in the time-varying filtering empirical mode decomposition algorithm to obtain the optimal parameter combination;

[0148] A signal processing unit, configured to process the vibration signal of the transformer box based on the time-varying filtering empirical mode decomposition algorithm with the optimal parameter combination to obtain a plurality of intrinsic mode functions; and determine a pseudo-observation signal according to the plurality of intrinsic mode functions.

[0149] A winding vibration signal determination unit, configured to determine the winding vibration signal based on the transformer box vibration signal and the pseudo-observation signal.

[0150] The present application also provides a readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the following steps:

[0151] Collect the vibration data of the transformer, and extract the corresponding box vibration signal of the transformer to be measured from the vibration data;

[0152] Optimize the parameters in the time-varying filtering empirical mode decomposition algorithm to obtain the optimal parameter combination;

[0153] Process the transformer box vibration signal based on the time-varying filtering empirical mode decomposition algorithm with the optimal parameter combination to obtain a plurality of intrinsic mode functions;

[0154] Determine a pseudo-observation signal according to the plurality of intrinsic mode functions;

[0155] Determine the winding vibration signal based on the transformer box vibration signal and the pseudo-observation signal.

[0156] The present application also provides a computer device, including a memory and a processor, where the memory stores a computer program, and the computer program is executed by the processor as follows:

[0157] Collect the vibration data of the transformer, and extract the corresponding box vibration signal of the transformer to be measured from the vibration data;

[0158] Optimize the parameters in the time-varying filtering empirical mode decomposition algorithm to obtain the optimal parameter combination;

[0159] Process the transformer box vibration signal based on the time-varying filtering empirical mode decomposition algorithm with the optimal parameter combination to obtain a plurality of intrinsic mode functions;

[0160] Determine a pseudo-observation signal according to the plurality of intrinsic mode functions;

[0161] Determine the winding vibration signal based on the transformer box vibration signal and the pseudo-observation signal.

[0162] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. 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 methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. 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. 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 (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0163] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, 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, it should be considered as the scope described in this specification.

[0164] The above-described embodiments merely represent several implementation manners of the present application. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. The above-disclosed is only the preferred embodiment of the present invention, and of course, it cannot be used to limit the scope of rights of the present invention. Therefore, equivalent changes made according to the claims of the present invention still fall within the scope covered by the present invention.

Claims

1. A method for determining the vibration signal of a transformer winding, characterized in that, The method includes: Collecting vibration data of the transformer and extracting the corresponding box vibration signal of the transformer to be measured from the vibration data; Optimizing the parameters in the time-varying filtering empirical mode decomposition algorithm to obtain the optimal parameter combination; Processing the transformer box vibration signal based on the time-varying filtering empirical mode decomposition algorithm with the optimal parameter combination to obtain a number of intrinsic mode functions; Determining a pseudo-observation signal according to the number of intrinsic mode functions; Determining the winding vibration signal based on the transformer box vibration signal and the pseudo-observation signal.

2. The method for determining the vibration signal of the transformer winding according to claim 1, wherein The optimizing the parameters in the time-varying filtering empirical mode decomposition algorithm to obtain the optimal parameter combination specifically includes: The time-varying filtering empirical mode decomposition algorithm includes a bandwidth threshold ξ and a B-spline order n; Iterating the bandwidth threshold ξ and the B-spline order n to obtain the optimal parameter combination.

3. The method for determining the vibration signal of the transformer winding according to claim 1, wherein The iterating the bandwidth threshold ξ and the B-spline order n to obtain the optimal parameter combination specifically includes: Initializing the bandwidth threshold ξ and the B-spline order n; Randomly generating a number of bandwidth thresholds ξ and B-spline orders n to determine the initial position and population direction of the population; Determining a number of parameter combinations of the population according to the number of bandwidth thresholds ξ and B-spline orders n; Determining the sample entropy value of the corresponding intrinsic mode function according to the parameter combination; Updating the position and direction of the population according to the sample entropy value; When the maximum number of iterations is reached, the optimal parameter combination of the population is obtained.

4. The method for determining the vibration signal of a transformer winding according to claim 3, wherein The determining the sample entropy value of the corresponding intrinsic mode function according to the parameter combination specifically includes: Obtaining the time series X corresponding to the parameter combination; Decomposing the time series X into an m-dimensional series and determining the maximum difference of the elements at the corresponding positions in the time series X: where X is the time series, and i and j are serial numbers; Setting the similarity tolerance r; Determining the number of similar elements where the distance between the elements X(i) and X(j) at the corresponding positions in the time series X is less than the similarity tolerance r; Determining the self-similar probability of the element X(i) at the abscissa position in the time series X based on the number of similar elements; Obtaining the average value of the self-similar probability to obtain the sample entropy value of the sequence; where B m (r) is the self-similar probability, m is the dimension of the time series, and N is the length of the time series.

5. The method for determining the vibration signal of a transformer winding according to claim 4, wherein After the obtaining the average value of the self-similar probability to obtain the sample entropy value of the sequence, it further includes: Obtaining the sample entropy value of the sequence corresponding to decomposing the time series X into an m + 1-dimensional series: where Bm(r) is the self-similar probability and m is the time series dimension; Updating the position and direction of the population according to the sample entropy value; When the maximum number of updates is reached, the optimal parameter combination of the bandwidth threshold ξ and the B-spline order n is obtained.

6. The method for determining the vibration signal of the transformer winding according to claim 1, wherein The determining the pseudo-observation signal according to the number of intrinsic mode functions specifically includes: Selecting the intrinsic mode function with the highest correlation coefficient with the transformer box vibration signal from the number of intrinsic mode functions as the pseudo-observation signal.

7. The method for determining the vibration signal of the transformer winding according to claim 1, characterized in that, The determining the winding vibration signal based on the transformer box vibration signal and the pseudo-observation signal specifically includes: Constructing a source signal matrix from the transformer box vibration signal x(t) and the observed signal x’(t); Performing normalization preprocessing on the source signal matrix; Estimate the separation matrix W according to the following formula i until the difference between W i (k) and W i (k + 1) meets the accuracy requirement: W i (k + 1) = W i (k) ∈ {Sg'[W i (k) T S]} - E{Sg'[W i (k) T S]} Where, W i is the iteration matrix of the separation matrix, S is the source signal matrix, k is the number of iterations, i is the signal source sequence, and g(x) is a non-linear function; Obtain separated signal where \(W\) is the separation matrix and \(X\) i is the observed signal of the \(i\)-th signal source; Based on the separation signal Determine the winding vibration signal.

8. A transformer winding vibration signal separation system, characterized in that The system includes: a vibration data acquisition unit, a decomposition algorithm optimization unit, a signal processing unit, and a winding vibration signal determination unit; The vibration data acquisition unit is configured to acquire vibration data of a transformer and extract a corresponding cabinet vibration signal of the transformer to be measured from the vibration data; The decomposition algorithm optimization unit is configured to optimize parameters in a time-varying filtering empirical mode decomposition algorithm to obtain an optimal parameter combination; The signal processing unit is configured to process the transformer cabinet vibration signal based on the time-varying filtering empirical mode decomposition algorithm with the optimal parameter combination to obtain a plurality of intrinsic mode functions; and determine a pseudo-observation signal according to the plurality of intrinsic mode functions; The winding vibration signal determination unit is configured to determine a winding vibration signal based on the transformer cabinet vibration signal and the pseudo-observation signal.

9. A readable storage medium stores a computer program, which when executed by a processor causes the processor to execute the steps of the method according to any one of claims 1 to 7.

10. A computer device includes a memory and a processor, the memory stores a computer program, which when executed by the processor causes the processor to execute the steps of the method according to any one of claims 1 to 7.

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