A transformer short-circuit resistance analysis method and system based on data collection

Abstract

The present application relates to the technical field of data processing, in particular to a transformer short-circuit resistance analysis method and system based on data collection, comprising: analyzing the distance between the core energy content distance of the transformer normal operation state representation and the preset threshold value, calculating the compression force loss rate of the transformer in the axial direction; combined with the analysis of the abnormal high-frequency vibration state generated by the transformer when vibrating and the distance between the energy contained in the abnormal high-frequency vibration state and the core energy content, the radial stiffness loss rate of the transformer in the radial direction is calculated; integrate the compression force loss rate and the radial stiffness loss rate, calculate the short-circuit resistance strength of the transformer; according to the short-circuit resistance strength, the short-circuit resistance of the transformer is analyzed. The present application improves the response efficiency of the transformer short-circuit resistance analysis result.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and specifically to a method and system for analyzing the short-circuit withstand capability of transformers based on data acquisition. Background Technology

[0002] Power transformers are core hub equipment in power systems, and their operational reliability directly affects the safety and stability of the entire power system. Among all possible faults, short-circuit faults pose the most severe challenge to transformers due to their high probability of occurrence and enormous impact. The massive current generated by a short circuit (typically tens of times the rated current) induces powerful electrodynamic forces and a rapid temperature rise in the transformer windings, posing serious threats to its mechanical structure (dynamic stability) and insulation materials (thermal stability), respectively. If the transformer's actual short-circuit withstand capability is insufficient to withstand this impact, it may lead to winding deformation, insulation damage, or even catastrophic accidents such as explosions and fires. Therefore, accurately analyzing and effectively monitoring the short-circuit withstand capability of transformers is a crucial link in ensuring circuit safety and preventing major accidents.

[0003] There are two main methods for testing the short-circuit withstand capability of transformers: direct testing and indirect testing (impedance measurement). Direct testing involves applying a short-circuit power supply capable of generating rated current to one winding of the transformer (usually the low-voltage side), while the other winding (usually the high-voltage side) is artificially short-circuited. The transformer's short-circuit withstand capability is analyzed by measuring and recording the electrodynamic and thermal effects during the test and its state afterward. Indirect testing primarily determines the transformer's state based on the change in the transformer winding's short-circuit impedance under a certain current. In practical scenarios, these existing testing methods all require offline testing, making real-time analysis of the transformer's state impossible, resulting in a significant delay in the analysis of the transformer's short-circuit withstand capability. Summary of the Invention

[0004] This invention provides a method and system for analyzing the short-circuit withstand capability of transformers based on data acquisition, in order to solve the existing problems: existing detection methods all need to be carried out offline in actual scenarios, and cannot analyze the transformer status in real time, resulting in a significant delay in the analysis of the transformer's short-circuit withstand capability.

[0005] The present invention provides a method and system for analyzing the short-circuit withstand capability of transformers based on data acquisition, which adopts the following technical solution:

[0006] This invention proposes a method for analyzing the short-circuit withstand capability of transformers based on data acquisition. The method includes the following steps:

[0007] Obtain load current data sequences, axial vibration spectrum data sequences, and radial vibration spectrum data sequences; the load current data sequences, axial vibration spectrum data sequences, and radial vibration spectrum data sequences contain the same number of data points;

[0008] Based on the load current data sequence and the axial vibration spectrum data sequence, the distance between the core energy content representing the normal operating state of the transformer and the preset threshold is analyzed in the axial direction of the transformer in the overall time sequence, and the clamping force loss rate of the transformer in the axial direction is calculated.

[0009] Based on the radial vibration spectrum data sequence, the radial stiffness loss rate of the transformer in the radial direction within the overall time sequence is calculated by combining the analysis of the abnormal high-frequency vibration state generated by the transformer during vibration and the distance between the energy contained in the abnormal high-frequency vibration state and the core energy content.

[0010] By integrating the axial clamping force loss rate and the radial stiffness loss rate of the transformer, the overall short-circuit resistance capacity of the transformer is comprehensively analyzed, and the short-circuit resistance strength of the transformer is calculated. Based on the short-circuit resistance strength, the short-circuit resistance capacity of the transformer is analyzed.

[0011] Preferably, the method for obtaining the clamping force loss rate is as follows:

[0012] The degree of core vibration energy in the axial vibration spectrum data sequence of the transformer is analyzed, and the core energy proportion of the transformer in the axial direction is calculated. The intensity of disturbance to the energy expression of the core vibration energy in the axial vibration spectrum data sequence in terms of time is analyzed, and the ideal current expression intensity of the core energy of the transformer in the axial direction is calculated. The overall difference between the ideal current expression intensity and the load current data sequence is taken as the vibration deviation degree of the transformer in the axial direction. The clamping force loss rate of the transformer in the axial direction is calculated by combining the core energy proportion degree and the difference between the vibration deviation degree and the predetermined threshold.

[0013] Preferably, the method for obtaining the core energy percentage is as follows:

[0014] The fundamental frequency band and effective frequency band are obtained from the axial vibration spectrum data sequence; the energy proportion of the fundamental frequency band in the effective frequency band is calculated as the core energy proportion of the transformer in the axial direction.

[0015] Preferably, the method for obtaining the intensity of the ideal current expression is as follows:

[0016] Obtain the number of imaginary points in the fundamental frequency band of the axial vibration spectrum data sequence; calculate the total power of the axial vibration spectrum data sequence in the fundamental frequency band; combine the total power and the number of imaginary points to calculate the ideal current expression intensity of the core energy of the transformer in the axial direction.

[0017] Preferably, the method for obtaining the degree of vibration deviation is as follows:

[0018] The average load current of the load current data sequence is obtained, and the difference between the ideal current expression intensity and the average load current quality is calculated as the degree of vibration deviation of the transformer in the axial direction.

[0019] Preferably, the method for obtaining the radial stiffness loss rate is as follows:

[0020] The radial vibration spectrum data sequence is divided into several sub-spectrum data segments. The high-frequency anomaly distribution of energy in different sub-spectrum data segments is analyzed, and the frequency concentration in the radial direction of each sub-spectrum data segment is calculated. Based on the frequency concentration, several radial core frequency data segments are selected and decomposed from the sub-spectrum data segments. The energy content between the radial core frequency data segments and the fundamental frequency band is compared to calculate the abnormally high energy level of the transformer in the radial direction. Combining the frequency concentration and the abnormally high energy level, the radial stiffness loss rate of the transformer in the radial direction is calculated.

[0021] Preferably, the method for obtaining the frequency concentration is as follows:

[0022] Based on the spectral energy contained in each sub-spectral data segment, calculate the probability density of each sub-spectral data segment; based on the probability density, analyze the effective information content contained in each sub-spectral data segment, and calculate the frequency concentration of each sub-spectral data segment in the radial direction.

[0023] Preferably, the method for obtaining the abnormally high energy level is as follows:

[0024] By combining the spectral energy contained in all radial core frequency data segments, the high-frequency spectral energy value of the transformer in the radial direction is calculated; based on the high-frequency spectral energy value, the proportion of energy contained between the high-frequency spectral energy value and the fundamental frequency band is compared to calculate the abnormally high energy level of the transformer in the radial direction.

[0025] Preferably, the method for obtaining the short-circuit withstand capability is as follows:

[0026] The sum of the axial clamping force loss rate and the radial stiffness loss rate of the transformer is used as the short-circuit withstand capability of the transformer.

[0027] The present invention also proposes a transformer short-circuit withstand capability analysis system based on data acquisition, including a memory and a processor. The processor executes a computer program stored in the memory to implement the steps of the above-mentioned transformer short-circuit withstand capability analysis method based on data acquisition.

[0028] The beneficial effects of the technical solution of this invention are as follows: This invention analyzes the distance between the core energy content representing the normal operating state of the transformer and a preset threshold in the axial direction within the overall time sequence of the transformer, and calculates the clamping force loss rate in the axial direction of the transformer; this makes the distribution of the overall clamping force loss in the axial direction of the transformer clearer; then, in the radial direction within the overall time sequence of the transformer, it combines the analysis of the abnormal high-frequency vibration state generated by the transformer during vibration and the distance between the energy contained in the abnormal high-frequency vibration state and the core energy content, and calculates the radial stiffness loss rate in the radial direction of the transformer; this makes the stiffness loss in the radial direction of the transformer more concrete; then, it integrates the clamping force loss rate and the radial stiffness loss rate to calculate the transformer's short-circuit withstand capability; by fusing the abnormal vibration performance of the transformer in the axial and radial directions, it quantifies the transformer's ability to continue resisting line short circuits, making the abnormal performance of the transformer in the radial and axial directions easier to distinguish. This invention calculates the short-circuit withstand capability by fusing the analysis of the abnormal vibration performance of the transformer in the axial and radial directions during short circuits, realizing real-time analysis of the transformer's state and improving the response efficiency of the transformer's short-circuit withstand capability analysis results. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a flowchart illustrating the steps of a transformer short-circuit withstand capability analysis method based on data acquisition according to the present invention.

[0031] Figure 2 This is a schematic diagram of the axial and radial directions of the transformer of the present invention. Detailed Implementation

[0032] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a transformer short-circuit withstand capability analysis method and system based on data acquisition proposed in accordance with the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0034] The following description, in conjunction with the accompanying drawings, details the specific scheme of the transformer short-circuit withstand capability analysis method and system based on data acquisition provided by this invention.

[0035] Please see Figure 1 The diagram illustrates a flowchart of a method for analyzing the short-circuit withstand capability of a transformer based on data acquisition, according to an embodiment of the present invention. The method includes the following steps:

[0036] Step S001: Obtain the load current data sequence, axial vibration spectrum data sequence, and radial vibration spectrum data sequence; the load current data sequence, axial vibration spectrum data sequence, and radial vibration spectrum data sequence contain the same number of data.

[0037] It should be noted that there are two main methods for testing the short-circuit withstand capability of transformers: direct testing and indirect testing (impedance measurement). Direct testing involves applying a short-circuit power supply capable of generating rated current to one winding of the transformer (usually the low-voltage side), while the other winding (usually the high-voltage side) is artificially short-circuited. The transformer's short-circuit withstand capability is analyzed by measuring and recording the electrodynamic and thermal effects during the test, as well as its state afterward. Indirect testing, on the other hand, primarily determines the transformer's state based on the change in the transformer winding's short-circuit impedance under a certain current. In practical scenarios, these existing testing methods all require offline testing, making real-time analysis of the transformer's state impossible, resulting in a significant delay in the analysis of the transformer's short-circuit withstand capability.

[0038] In one specific implementation of this invention, the method for obtaining the load current data sequence, the axial vibration spectrum data sequence, and the radial vibration spectrum data sequence is as follows:

[0039] After installing a three-dimensional vibration accelerometer on the top of the transformer, the vibration data recorded in the data monitoring platform is used as axial vibration data; after installing a three-dimensional vibration accelerometer on the sidewall of the transformer, the vibration data recorded in the data monitoring platform is used as radial vibration data; simultaneously, the data monitoring platform records the transformer's current data in real time. Taking the moment of the latest data acquisition as an example, the moment of the latest data acquisition is taken as the target moment; the sequence of all axial vibration data recorded before the target moment is taken as the axial vibration data sequence; the sequence of all radial vibration data recorded before the target moment is taken as the radial vibration data sequence; and the sequence of all current data recorded before the target moment is taken as the load current data sequence.

[0040] It should be noted that a three-dimensional vibration acceleration sensor is installed on the top of the transformer to measure the axial vibration of the transformer; a three-dimensional vibration acceleration sensor is installed on the side wall of the transformer to measure the radial vibration of the transformer.

[0041] It should be noted that in this embodiment, the sampling frequency of the three-dimensional vibration acceleration sensor is described as 10kHz, the sampling duration is 1s, and the sampling interval is 3min. Specific values ​​can be adjusted according to the implementation situation. Please refer to... Figure 2 It shows a schematic diagram of the transformer's axial and radial directions.

[0042] Furthermore, the sequence of axial spectrum data obtained by performing a Fourier transform on the axial vibration data sequence is used as the axial vibration spectrum data sequence; the sequence of radial spectrum data obtained by performing a Fourier transform on the radial vibration data sequence is used as the radial vibration spectrum data sequence. The process of obtaining spectrum data through Fourier transform is a well-known technique and will not be described in detail in this embodiment.

[0043] It should be noted that each spectral data in the axial vibration spectrum data sequence and the radial vibration spectrum data sequence corresponds to a spectral frequency and an energy value.

[0044] Thus, the load current data sequence, axial vibration spectrum data sequence, and radial vibration spectrum data sequence were obtained through the above method.

[0045] Step S002: Based on the load current data sequence and the axial vibration spectrum data sequence, analyze the distance of the core energy content representing the normal operating state of the transformer from the preset threshold in the axial direction within the overall time sequence, and calculate the clamping force loss rate of the transformer in the axial direction.

[0046] It should be noted that transformers exhibit characteristic vibrations during normal operation due to core magnetostriction and winding load current. While a transformer may not immediately fail after a short-circuit impact, it will experience winding deformation to some extent, leading to reduced mechanical stability and a continuous decline in its short-circuit withstand capability. Once this deformation accumulates to a certain degree, a serious fault will occur when subjected to a larger short-circuit current. The winding deformation that occurs in a transformer can be mainly divided into two types: axial loosening and radial deformation. The abnormal behavior of the transformer differs in these two cases, therefore, specific analysis is required for each situation.

[0047] It should be further explained that, under normal circumstances, the silicon steel sheets used in the transformer core undergo minute, periodic deformations in response to changes in the magnetic field strength under the influence of an alternating magnetic field. This physical phenomenon is called "magnetostriction." Although this deformation is extremely small, it is sufficient to cause vibration in the core, and this deformation is a normal vibration of the transformer in the axial direction, where the vibration energy constitutes the majority of the energy. If the transformer's short-circuit withstand capability in the axial direction is poor, then the degree to which the vibration energy constitutes the majority of the energy will be disrupted. Therefore, based on the load current data sequence and the axial vibration spectrum data sequence, the distance between the core energy content representing the normal operating state of the transformer and a preset threshold can be analyzed in the axial direction over the entire time series, and the clamping force loss rate of the transformer in the axial direction can be calculated.

[0048] Preferably, in some implementations of the present invention, the method for obtaining the clamping force loss rate is as follows: Analyzing the extent of core vibration energy in the axial vibration spectrum data sequence of the transformer, and calculating the proportion of core energy in the axial direction of the transformer; analyzing the intensity of temporal disturbance to the energy expression of the core vibration energy contained in the axial vibration spectrum data sequence, and calculating the ideal current expression intensity of the core energy in the axial direction of the transformer; taking the overall difference between the ideal current expression intensity and the load current data sequence as the degree of vibration deviation of the transformer in the axial direction; and comprehensively considering the differences between the core energy proportion and the vibration deviation degree from predetermined thresholds, calculating the clamping force loss rate of the transformer in the axial direction. The specific process is as follows:

[0049] Preferably, in some implementations of the present invention, the method for obtaining the core energy proportion is as follows: obtaining the fundamental frequency band and the effective frequency band from the axial vibration spectrum data sequence; calculating the energy proportion of the fundamental frequency band in the effective frequency band as the core energy proportion of the transformer in the axial direction. The specific process is as follows:

[0050] Preset a baseband range and an effective frequency band range The spectral frequencies in the axial vibration spectrum data sequence belong to the fundamental frequency band. The data segment is used as the axial fundamental frequency data segment; the spectral frequencies in the axial vibration spectrum data sequence that belong to the effective frequency band range are considered. The data segment is used as the axial effective frequency waiting data segment. In this embodiment, it is... This example is used for illustration; no specific limitations are set in this embodiment. It depends on the specific implementation situation.

[0051] Furthermore, as an example, the core energy percentage can be calculated using the following formula:

[0052]

[0053] In the formula, This indicates the proportion of core energy in the transformer along the axial direction; This represents the value obtained after performing a definite integral operation on the axial fundamental frequency data segment; This represents the value obtained by performing a definite integral operation on the effective frequency data segment along the axis. This represents the preset hyperparameters. In this embodiment, we use... For example, this is used to prevent the denominator from being 0.

[0054] It should be noted that the greater the proportion of core energy, the more normal energy is contained in the vibration energy generated by the transformer in the axial direction, and the more core it occupies in the overall vibration energy composition.

[0055] It should be further explained that performing definite integral operations on data segments composed of spectral data, such as the axial fundamental frequency data segment and the axial effective frequency data segment, can characterize the spectral energy contained in the corresponding data segment; and the process of performing definite integral operations on spectral data to represent spectral energy is well known, and will not be described in detail in this embodiment.

[0056] It should be noted that each spectral data point corresponds to a complex number, and each complex number contains a real part and an imaginary part. The real part corresponds to the amplitude, directly reflecting the magnitude of the spectral energy; the imaginary part corresponds to the phase, directly reflecting the time of energy generation. The number of imaginary parts directly interferes with the accuracy of frequency resolution in the spectral data. When abnormal axial vibration of the transformer occurs, the vibration frequency usually decreases as the amplitude increases, reducing the number of imaginary parts and increasing the interference with frequency resolution accuracy. Consequently, more of the true current data is hidden, resulting in a larger current data under normal ideal conditions. Therefore, it is necessary to analyze the intensity of energy interference in the time aspect of the core vibration energy contained in the axial vibration spectral data sequence and calculate the ideal current expression intensity of the core energy of the transformer in the axial direction.

[0057] Preferably, in some implementations of the present invention, the method for obtaining the ideal current expression intensity is as follows: obtaining the number of imaginary points contained in the axial vibration spectrum data sequence in the fundamental frequency band; calculating the total power of the axial vibration spectrum data sequence in the fundamental frequency band; and combining the total power and the number of imaginary points to calculate the ideal current expression intensity of the transformer's core energy in the axial direction. The specific process is as follows:

[0058] In the axial fundamental frequency data segment, the number of imaginary parts of all complex numbers contained therein is taken as the imaginary part point value of the axial fundamental frequency data segment; the total power in the axial fundamental frequency data segment is obtained. The process of obtaining the total power in the spectrum data segment is a well-known technique and will not be described in detail in this embodiment.

[0059] It should be noted that each spectral data point in the axial vibration spectral data sequence corresponds to a complex number, and each complex number contains a real part and an imaginary part.

[0060] Furthermore, as an example, the intensity of an ideal current expression can be calculated using the following formula:

[0061]

[0062] In the formula, The ideal current expression intensity representing the core energy of a transformer in the axial direction; This represents the total power in the axial fundamental frequency data segment; This represents the imaginary part value of the axial fundamental frequency data segment; This represents the mean of the imaginary part coefficients of all complex numbers in the axial fundamental frequency data segment; Indicates taking the absolute value; This represents the preset denominator hyperparameter, which is used in this embodiment. For example, this is used to prevent the denominator from being 0.

[0063] It should be noted that the greater the intensity of the ideal current, the less energy the transformer wastes in other aspects when vibrating in the axial direction, and the greater the value of the ideal current corresponding to the amplitude generated by the transformer when vibrating in the axial direction.

[0064] It should be noted that, according to the existing Lorentz force model, there is a relationship between the magnitude of the current and the movement of the charge perpendicular to the current direction under the force. In real-world scenarios, the transformer current moves along the winding coils surrounding the cylindrical body. Therefore, the Lorentz force will alter the direction of charge movement perpendicular to the current (i.e., the axial direction), revealing a difference between the magnitude of the current and the axial vibration in the transformer. Thus, the overall difference between the ideal current intensity and the load current data sequence can be used as the degree of deviation of the transformer's vibration in the axial direction.

[0065] Preferably, in some implementations of the present invention, the method for obtaining the degree of vibration deviation is as follows: obtaining the average load current of the load current data sequence, calculating the difference between the ideal current expression intensity and the average load current quality, and using this difference as the degree of vibration deviation of the transformer in the axial direction. The specific process is as follows:

[0066] As an example, the degree of vibration deviation can be calculated using the following formula:

[0067]

[0068] In the formula, This indicates the degree of vibration deviation of the transformer in the axial direction; The ideal current expression intensity representing the core energy of a transformer in the axial direction; The calibration coefficients representing the vibration amplitude and current; This represents the mean of all current data in the load current data sequence. This represents an exponential function with the natural constant as its base. The example uses... The model is used to represent the inverse proportional relationship and for normalization processing. As input to the model, implementers can choose between an inverse proportional function and a normalization function based on the actual situation.

[0069] It should be noted that the greater the degree of vibration deviation, the more the transformer's vibration amplitude in the axial direction deviates from the normal vibration amplitude.

[0070] It should be noted that the calibration coefficients of vibration amplitude and current are obtained through no-load testing. The process of obtaining these coefficients is well-known in no-load testing techniques and will not be described in detail in this embodiment.

[0071] Furthermore, as an example, the clamping force loss rate can be calculated using the following formula:

[0072]

[0073] In the formula, This indicates the rate of clamping force loss of the transformer in the axial direction; This indicates the degree of vibration deviation of the transformer in the axial direction; This indicates the proportion of core energy in the transformer along the axial direction; This indicates the maximum permissible value for the preset axial vibration deviation. In this embodiment, the preset value is... The following is an example; the specific values ​​can be adjusted according to the actual scenario. This represents a normal value indicating the preset core energy percentage; in this embodiment, it is preset. The following is an example; the specific values ​​can be adjusted according to the actual scenario. The weighting of the vibration deviation is preset in this embodiment. The following is an example; the specific values ​​can be adjusted according to the actual scenario. The allocation weights representing the proportion of core energy are preset in this embodiment. The following is an example; the specific values ​​can be adjusted according to the actual scenario. Indicates taking the absolute value;

[0074] It should be noted that, This indicates the degree of attenuation of the overall clamping force of the transformer. The larger the value, the greater the overall loss of the overall clamping force of the transformer. This indicates the fundamental frequency vibration deviation. The larger the value, the more uneven the distribution of the clamping force. If the clamping force loss rate is larger, it indicates that the overall clamping force loss of the transformer is greater and the distribution is more uneven, reflecting that the transformer's ability to resist short circuits in the axial direction is worse.

[0075] Thus, the axial clamping force loss rate of the transformer is obtained using the above method.

[0076] Step S003: Based on the radial vibration spectrum data sequence, in the radial direction of the transformer within the overall time sequence, combined with the analysis of the abnormal high-frequency vibration state generated by the transformer during vibration and the distance between the energy contained in the abnormal high-frequency vibration state and the core energy content, calculate the radial stiffness loss rate of the transformer in the radial direction.

[0077] It should be noted that under normal circumstances, when the load current flows through the transformer windings, a strong electromagnetic force is generated between the high-voltage and low-voltage windings and inside the winding turns. These forces cause periodic deformation and vibration of the windings. If the transformer has poor short-circuit withstand capability in the radial direction, it indicates a loss of radial stiffness in the winding coils, leading to changes in structural damping characteristics and inducing high-frequency resonance frequency shift. Stiffness attenuation causes the material to enter the plastic deformation stage, exciting vibration harmonics. Therefore, based on the radial vibration spectrum data sequence, the radial stiffness loss rate of the transformer can be calculated in the radial direction within the overall time sequence of the transformer, combined with the analysis of the abnormal high-frequency vibration state generated by the transformer during vibration and the distance between the energy contained in the abnormal high-frequency vibration state and the core energy content.

[0078] Preferably, in some implementations of the present invention, the method for obtaining the radial stiffness loss rate is as follows: The radial vibration spectrum data sequence is divided into several sub-spectrum data segments; the high-frequency abnormal distribution of energy in different sub-spectrum data segments is analyzed, and the frequency concentration in the radial direction of each sub-spectrum data segment is calculated; based on the frequency concentration, several radial core frequency data segments are selected and decomposed from the sub-spectrum data segments; the energy content between the radial core frequency data segments and the fundamental frequency band is compared to calculate the abnormally high energy level of the transformer in the radial direction; and the radial stiffness loss rate of the transformer in the radial direction is calculated by combining the frequency concentration and the abnormally high energy level. The specific process is as follows:

[0079] The radial vibration spectrum data sequence is divided into equal parts according to the maximum frequency range involved. Each sub-spectrum data segment. In this embodiment, the sub-spectrum data segment is used. This example is used for illustration; no specific limitations are set in this embodiment. It depends on the specific implementation situation.

[0080] Preferably, in some implementations of the present invention, the method for obtaining frequency concentration is as follows: Based on the spectral energy contained in each sub-spectrum data segment, the probability density of each sub-spectrum data segment is calculated; based on the probability density, the effective information content contained in each sub-spectrum data segment is analyzed, and the frequency concentration of each sub-spectrum data segment in the radial direction is calculated. The specific process is as follows:

[0081] The value obtained by performing a definite integral operation on each sub-spectrum data segment is used as the sub-spectrum energy of each sub-spectrum data segment. Based on the sub-spectrum energy, the probability density of each sub-spectrum data segment is calculated. Based on the probability density, the inversely proportional normalized value of the information entropy of each sub-spectrum data segment is calculated, which is used as the frequency concentration of each sub-spectrum data segment in the radial direction. The calculation processes for probability density and information entropy are well-known techniques and will not be described in detail in this embodiment. Furthermore, this embodiment employs... The model is used to represent the inverse proportional relationship and normalization process. Implementers can choose the inverse proportional function and the normalization function according to the actual situation.

[0082] It should be noted that the greater the frequency concentration, the more concentrated the radial vibration spectrum energy, reflecting a more severe loss of transformer stiffness in the radial direction.

[0083] Furthermore, a frequency concentration threshold is preset. The frequency concentration is greater than The sequence of sub-spectrum data segments is used as the main spectrum data sequence; VDM decomposition is performed on the main spectrum data sequence to obtain several intrinsic mode functions (IMFs); and each IMF is used as the radial core frequency data segment. In this embodiment, [the following is an example of a specific implementation / implementation]... This example is used for illustration; no specific limitations are set in this embodiment. It depends on the specific implementation situation.

[0084] It should be noted that the process of obtaining the intrinsic mode functions (IMFs) is a well-known part of the VDM (Variational Mode Decomposition) algorithm. In this embodiment, the termination condition for obtaining the number of IMFs is that the number of IMFs obtained is consistent with the number of sub-spectral data segments contained in the main spectrum data sequence.

[0085] Preferably, in some implementations of the present invention, the method for obtaining the abnormally high energy level is as follows: The high-frequency spectral energy value of the transformer in the radial direction is calculated by comprehensively considering the spectral energy contained in all radial core frequency data segments; based on the high-frequency spectral energy value, the proportion of energy contained between the high-frequency spectral energy value and the fundamental frequency band is compared to calculate the abnormally high energy level of the transformer in the radial direction. The specific process is as follows:

[0086] The value obtained by definite integration of each radial core frequency data segment is taken as the core energy value of each radial core frequency data segment; the average of the core energy values ​​of all radial core frequency data segments is taken as the high-frequency spectral energy value of the transformer in the radial direction; and the spectral frequencies in the radial vibration spectrum data sequence that belong to the fundamental frequency band are considered as the core energy value. The data segment is used as the radial fundamental frequency data segment; the value obtained by performing a definite integral operation on the radial fundamental frequency data segment is used as the radial fundamental frequency energy value of the radial fundamental frequency data segment; the ratio of the high-frequency spectrum energy value to the radial fundamental frequency energy value is used as the abnormally high energy level of the transformer in the radial direction.

[0087] It should be noted that the greater the abnormally high energy level, the less dispersed the energy distribution of the transformer in the radial direction across different frequency bands, reflecting a more severe loss of stiffness in the transformer in the radial direction.

[0088] Furthermore, the product of frequency concentration and abnormally high energy level is taken as the radial stiffness loss rate of the transformer in the radial direction.

[0089] It should be noted that the greater the radial stiffness loss rate, the more severe the loss of stiffness in the radial direction of the transformer.

[0090] Thus, the radial stiffness loss rate of the transformer in the radial direction is obtained through the above method.

[0091] Step S004: Integrate the transformer's axial clamping force loss rate and radial stiffness loss rate in the radial direction, comprehensively analyze the transformer's overall ability to resist short circuits, and calculate the transformer's short circuit withstand capability; based on the short circuit withstand capability, conduct a short circuit withstand capability analysis of the transformer.

[0092] Preferably, in some specific implementations of the embodiments of the present invention, the method for obtaining the short-circuit withstand capability is as follows: the cumulative mapping result of the transformer's clamping force loss rate in the axial direction and the transformer's radial stiffness loss rate in the radial direction is used as the transformer's short-circuit withstand capability. The specific process is as follows:

[0093] The inversely proportional normalized value of the product between the transformer's axial clamping force loss rate and its radial stiffness loss rate is taken as the transformer's short-circuit withstand capability.

[0094] It should be noted that the greater the short-circuit withstand capability, the less obvious the abnormal situation of the transformer in the radial and axial directions, the less obvious the abnormal vibration and deformation state of the transformer, and the stronger the transformer's ability to continue to resist line short circuits.

[0095] In a specific implementation of this invention, the specific process of analyzing the transformer's short-circuit withstand capability based on the short-circuit withstand capability strength is as follows: A short-circuit withstand capability strength threshold range is preset. If the short-circuit withstand capability is less than It was determined that the transformer's ability to withstand short-circuit current was insufficient, necessitating shutdown and reinforcement; if the short-circuit withstand capability was within... In the process, if the transformer's ability to withstand short-circuit current decreases, an early warning is issued; if the short-circuit withstand capability is greater than... It is assumed that the transformer's ability to withstand short-circuit current is sufficient, and no additional operation is required. This embodiment uses... This example is used for illustration; no specific limitations are set in this embodiment. It depends on the specific implementation situation.

[0096] The above steps complete the method for analyzing the short-circuit withstand capability of transformers based on data acquisition.

[0097] Another embodiment of the present invention provides a transformer short-circuit withstand capability analysis system based on data acquisition. The system includes a memory and a processor. When the processor executes the computer program stored in the memory, it performs the above-described method steps S001 to S004.

[0098] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for analyzing short circuit resistance of a transformer based on data acquisition, characterized in that, The method comprises the following steps: Obtaining a load current data sequence, an axial vibration frequency spectrum data sequence, and a radial vibration frequency spectrum data sequence; the load current data sequence, the axial vibration frequency spectrum data sequence, and the radial vibration frequency spectrum data sequence have consistent data quantity; According to the load current data sequence and the axial vibration frequency spectrum data sequence, analyzing the distance between the core energy content distance preset threshold value of the transformer in the axial direction within the overall time sequence, and calculating the compression force loss rate of the transformer in the axial direction; According to the radial vibration frequency spectrum data sequence, in the radial direction of the transformer within the overall time sequence, combining the analysis of the abnormal high-frequency vibration state generated by the transformer during vibration and the distance between the energy contained in the abnormal high-frequency vibration state and the core energy content, the radial stiffness loss rate of the transformer in the radial direction is calculated; The compression force loss rate of the transformer in the axial direction and the radial stiffness loss rate of the transformer in the radial direction are integrated to comprehensively analyze the ability range of the transformer resisting short circuit, and the anti-short circuit capacity strength of the transformer is calculated; according to the anti-short circuit capacity strength, the anti-short circuit capacity of the transformer is analyzed.

2. The method for analyzing the short circuit resistance of a transformer based on data collection according to claim 1, characterized in that, The method for obtaining the compression force loss rate is: The core vibration energy in the axial vibration frequency spectrum data sequence is analyzed, the core energy proportion in the axial direction of the transformer is calculated; the intensity of the energy expression of the core energy in the axial direction of the transformer is calculated by analyzing the time aspect of the core vibration energy contained in the axial vibration frequency spectrum data sequence; the overall difference between the ideal current expression intensity and the load current data sequence is taken as the vibration deviation degree of the transformer in the axial direction; the difference between the core energy proportion and the vibration deviation degree is calculated, and the compression force loss rate of the transformer in the axial direction is calculated.

3. The method for analyzing the short circuit resistance of a transformer based on data collection according to claim 2, characterized in that, The method for obtaining the core energy proportion is: The fundamental frequency band and the effective frequency band are obtained from the axial vibration frequency spectrum data sequence; the energy proportion of the fundamental frequency band in the effective frequency band is calculated as the core energy proportion in the axial direction of the transformer.

4. The method for analyzing the short circuit resistance of a transformer based on data collection according to claim 3, characterized in that, The method for obtaining the ideal current expression intensity is: The number of imaginary points contained in the fundamental frequency band in the axial vibration frequency spectrum data sequence is obtained; the total power of the axial vibration frequency spectrum data sequence in the fundamental frequency band is calculated; the ideal current expression intensity of the core energy in the axial direction of the transformer is calculated by integrating the total power and the number of imaginary points.

5. The method for analyzing the short circuit resistance of a transformer based on data collection according to claim 2, characterized in that, The method for obtaining the vibration deviation degree is: The average load current of the load current data sequence is obtained, and the difference between the ideal current expression intensity and the average load current is calculated as the vibration deviation degree of the transformer in the axial direction.

6. The method for analyzing the short circuit resistance of a transformer based on data collection according to claim 3, characterized in that, The method for obtaining the radial stiffness loss rate is: The radial vibration frequency spectrum data sequence is divided into several sub-frequency spectrum data segments; the high-frequency abnormal distribution of the energy occupied by different sub-frequency spectrum data segments is analyzed, and the frequency concentration of each sub-frequency spectrum data segment in the radial direction is calculated; according to the frequency concentration, several radial core frequency data segments are screened and decomposed from the sub-frequency spectrum data segments; the energy content occupied between the radial core frequency data segments and the fundamental frequency band is compared, and the abnormal high-energy degree of the transformer in the radial direction is calculated; The radial stiffness loss rate of the transformer in the radial direction is calculated by comprehensively considering the frequency concentration and the abnormal high-energy degree.

7. The method for analyzing the short circuit resistance of a transformer based on data collection according to claim 6, characterized in that, The frequency concentration is obtained by: According to the frequency spectrum energy contained in each sub-frequency spectrum data segment, the probability density of each sub-frequency spectrum data segment is calculated; According to the probability density, the effective information amount contained in each sub-frequency spectrum data segment is analyzed, and the frequency concentration of each sub-frequency spectrum data segment in the radial direction is calculated.

8. The method for analyzing the short circuit resistance of a transformer based on data collection according to claim 6, characterized in that, The abnormal high-energy degree is obtained by: The high-frequency spectrum energy value of the transformer in the radial direction is calculated by comprehensively considering the frequency spectrum energy contained in all radial core frequency data segments; according to the high-frequency spectrum energy value, the proportion of the energy value contained between the high-frequency spectrum energy value and the fundamental frequency band is compared, and the abnormal high-energy degree of the transformer in the radial direction is calculated.

9. The method for analyzing the short circuit resistance of a transformer based on data collection according to claim 1, characterized in that, The anti-short circuit capacity strength is obtained by: The anti-short circuit capacity strength of the transformer is obtained by mapping the cumulative results of the axial compression force loss rate of the transformer in the axial direction and the radial stiffness loss rate of the transformer in the radial direction.

10. A data acquisition based transformer short circuit withstand capability analysis system comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The computer program is executed by the processor to realize the steps of the transformer anti-short circuit capacity analysis method based on data acquisition according to any one of claims 1-9.

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