Method, system, device and medium for real-time acquisition of transformer winding frequency response curve

Abstract

The present invention provides a method, system, device, and medium for acquiring a transformer winding frequency response curve in real time. The method comprises: installing a capacitive coupling sensor on the exterior of a transformer bushing and determining a time domain signal acquisition length; acquiring a sequence of signals to be converted during the transformer's operation in real time via the capacitive coupling sensor according to a preset acquisition frequency and time domain signal acquisition length; the sequence of signals to be converted comprising an excitation signal sequence to be converted and a response signal sequence to be converted; performing time-frequency conversion on the sequence of signals to be converted using an SDFT algorithm to obtain a corresponding time series spectrum; the time series spectrum comprising an excitation time series spectrum and a response time series spectrum; and obtaining a transformer winding frequency response curve based on the time series spectrum. The present invention utilizes an SDFT algorithm combined with correlation coefficient processing to process time-frequency data, which not only enables efficient real-time monitoring of the winding frequency response curve, but also reduces interference in drawing the frequency response curve, thereby improving the accuracy of the winding frequency response curve.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer winding state monitoring, and in particular to a method, system, computer equipment and storage medium for real-time acquisition of transformer winding frequency response curve based on sliding discrete Fourier transform. Background Art

[0002] Large transformers are critical hub equipment in power systems, connecting grids of varying voltage levels. Failures can lead to major power outages, including voltage fluctuations, equipment damage, and widespread power outages. These can cause severe economic losses and social impacts. The operating status of transformers is directly related to the reliability and stability of the power grid, making real-time monitoring of transformer failures extremely important. While transformer failures vary, statistics on transformer failures in power grids show that winding failures are the most common mechanical defects in transformers and pose the greatest threat to transformer operation. Therefore, effectively assessing the status of transformer windings is crucial.

[0003] Commonly used methods for detecting mechanical faults in transformer windings are mainly divided into offline frequency response analysis (FRA) and online frequency response analysis (FRA). Both methods obtain the frequency response characteristics of the winding's high-frequency equivalent distributed parameter network and infer changes in the winding state from changes in the frequency response. However, in the current pulse frequency response method, the excitation and response signals are measured in the time domain. The frequency response curve is mostly obtained using the fast Fourier transform (FFT) algorithm. This algorithm requires N sampling results to calculate a single result, rather than calculating the instantaneous spectrum of each sample value. This results in significant latency and cannot truly achieve online, real-time monitoring of transformer winding deformation. Furthermore, it does not consider interference factors during the time-to-frequency conversion process, resulting in inaccurate winding frequency response curves, which directly reduces the application value of transformer fault monitoring and analysis. Summary of the Invention

[0004] The present invention aims to provide a real-time acquisition method for transformer winding frequency response curves. By processing time-frequency data using a sliding discrete Fourier transform (SDFT) combined with correlation coefficient processing during online detection of transformer winding status using a pulse frequency response method, the present invention addresses the application defects of existing transformer winding frequency response curve acquisition methods in terms of real-time performance and accuracy. The present invention can continuously refresh spectrum data along with signal acquisition, achieve efficient real-time monitoring of the winding frequency response curve, effectively select reliable signal values, reduce interference in frequency response curve drawing, improve the accuracy of the winding frequency response curve, and thus enhance the application value of transformer fault monitoring and analysis.

[0005] In order to achieve the above objectives, it is necessary to provide a method and system for real-time acquisition of transformer winding frequency response curve in response to the above technical problems.

[0006] In a first aspect, an embodiment of the present invention provides a method for real-time acquisition of a transformer winding frequency response curve, the method comprising the following steps:

[0007] Install the capacitive coupling sensor outside the transformer bushing and determine the time domain signal acquisition length;

[0008] According to the preset acquisition frequency and the time domain signal acquisition length, the capacitive coupling sensor is used to collect the signal sequence to be converted during the operation of the transformer in real time; the signal sequence to be converted includes an excitation signal sequence to be converted and a response signal sequence to be converted;

[0009] Using the SDFT algorithm to perform time-frequency conversion on the signal sequence to be converted to obtain a corresponding time series spectrum; the time series spectrum includes an excitation time series spectrum and a response time series spectrum;

[0010] A transformer winding frequency response curve is obtained according to the time series spectrum.

[0011] Furthermore, the step of determining the time domain signal acquisition length includes:

[0012] Obtain signal regions that meet periodicity conditions, and perform spectrum analysis on each signal region to obtain the corresponding signal length;

[0013] An average value of each signal length is obtained, and the average value is used as the time domain signal acquisition length.

[0014] Furthermore, the step of collecting the signal sequence to be converted during the transformer operation process in real time includes:

[0015] Real-time collection of original signal sequences during transformer operation; the original signal sequences include original excitation signal sequences and original response signal sequences;

[0016] The original signal sequence is subjected to low-pass filtering to obtain the signal sequence to be converted.

[0017] Furthermore, the step of performing time-frequency conversion on the signal sequence to be converted by using the SDFT algorithm to obtain a corresponding time series spectrum includes:

[0018] Obtain the previous DFT conversion result corresponding to the previous signal sequence to be converted, and obtain a time series spectrum corresponding to the signal sequence to be converted based on the previous DFT conversion result, the signal sequence to be converted, and the signal sequence to be converted before the time domain signal acquisition length; the time series spectrum is expressed as:

[0019]

[0020] Where M represents the acquisition length of the time domain signal; X n+1 (k) and X n (k) represents the spectrum value of the kth frequency point of the time series spectrum corresponding to the signal sequence to be converted at the n+1th moment and the nth moment respectively; x(n+1) and x(n-M+1) represent the signal sequence to be converted and the signal sequence to be converted before the time domain signal acquisition length, respectively.

[0021] Furthermore, the step of obtaining a time series spectrum corresponding to the signal sequence to be converted based on the previous DFT conversion result, the signal sequence to be converted, and the signal sequence to be converted before the time domain signal acquisition length includes:

[0022] The signal sequence to be converted and the previous signal sequence to be converted are grouped according to a preset number, and correlation analysis is performed on each corresponding group of the signal sequence to be converted and the previous signal sequence to be converted to obtain corresponding correlation coefficients;

[0023] Perform signal screening on the signal sequence to be converted according to each correlation coefficient to obtain a corresponding available signal sequence;

[0024] A time series spectrum corresponding to the signal sequence to be converted is obtained according to the previous DFT conversion result, the available signal sequence, and the signal sequence to be converted before the time domain signal acquisition length.

[0025] Furthermore, the step of grouping the signal sequence to be converted and the previous signal sequence to be converted according to a preset number includes:

[0026] Filtering the signal sequence to be converted and the previous signal sequence to be converted according to a preset frequency band range to obtain a corresponding frequency band signal sequence;

[0027] The signal sequences of each frequency band are grouped equally according to a preset number.

[0028] Furthermore, the step of performing signal screening on the signal sequence to be converted according to each correlation coefficient to obtain a corresponding available signal sequence includes:

[0029] If the correlation coefficient is not greater than a preset threshold, the corresponding group data of the signal sequence to be converted is determined as the data of the available signal sequence; otherwise, the corresponding group data of the signal sequence to be converted is not selected as the data of the available signal sequence.

[0030] In a second aspect, an embodiment of the present invention provides a system for real-time acquisition of a transformer winding frequency response curve, the system comprising:

[0031] A pre-processing module is used to install the capacitive coupling sensor on the outside of the transformer bushing and determine the time domain signal acquisition length;

[0032] A real-time acquisition module, configured to acquire, in real time, a sequence of signals to be converted during the operation of the transformer through the capacitive coupling sensor according to a preset acquisition frequency and the time domain signal acquisition length; the sequence of signals to be converted includes a sequence of excitation signals to be converted and a sequence of response signals to be converted;

[0033] A time-frequency conversion module is used to perform time-frequency conversion on the signal sequence to be converted using the SDFT algorithm to obtain a corresponding time series spectrum; the time series spectrum includes an excitation time series spectrum and a response time series spectrum;

[0034] The curve acquisition module is used to obtain the transformer winding frequency response curve according to the time series spectrum.

[0035] In a third aspect, an embodiment of the present invention further provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0036] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0037] The above-mentioned application provides a method and system for real-time acquisition of the frequency response curve of a transformer winding. Through the method, a capacitive coupling sensor is installed on the outside of the transformer bushing, and after determining the time domain signal acquisition length, the capacitive coupling sensor is used to collect the signal sequence to be converted, including the excitation signal sequence to be converted and the response signal sequence to be converted, in real time during the operation process of the transformer according to the preset acquisition frequency and time domain signal acquisition length. The signal sequence to be converted is subjected to time-frequency conversion using the SDFT algorithm to obtain a corresponding time series spectrum including the excitation time series spectrum and the response time series spectrum, as well as a technical solution for obtaining the frequency response curve of the transformer winding based on the time series spectrum. Compared with the existing technology, the real-time acquisition method of the transformer winding frequency response curve uses the sliding discrete Fourier transform (SDFT) combined with correlation coefficient processing to process the time-frequency data during the online detection of the transformer winding status using the pulse frequency response method. It can not only continuously refresh the spectrum data along with the signal acquisition to achieve efficient real-time monitoring of the winding frequency response curve, but also effectively select reliable signal values, reduce the interference of the frequency response curve drawing, and improve the accuracy of the winding frequency response curve, thereby enhancing the application value of transformer fault monitoring and analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Schematic diagram of a high-frequency equivalent model of a transformer for transformer winding deformation detection according to an embodiment of the present invention;

[0039] Figure 2 1 is a flow chart of a method for real-time acquisition of a transformer winding frequency response curve according to an embodiment of the present invention;

[0040] Figure 3 2 is a schematic structural diagram of a system for obtaining a transformer winding frequency response curve in real time according to an embodiment of the present invention;

[0041] Figure 4 1 is a diagram showing the internal structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and beneficial effects of this application more clear, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. Obviously, the embodiments described below are part of the embodiments of the present invention and are only used to illustrate the present invention, but are not used to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0043] The principle of the present invention to detect transformer winding deformation based on FRA (Frequency Response Analysis) is that at high frequencies (>1kHz), due to the skin effect, the iron core of the transformer can be almost ignored, and the transformer can be equivalent to Figure 1 The two-port network shown in the figure consists of a resistor, an inductor, and a capacitor; where C hg 、C lg Indicates the capacitance between the high voltage winding and the low voltage winding to the ground (transformer housing), L h and L l Represents the equivalent inductance of the high-voltage winding and the low-voltage winding, C hl Represents the equivalent capacitance between the high-voltage winding and the low-voltage winding, C sh and C sl The FRA curve represents the capacitance between the high-voltage and low-voltage windings. When a winding deforms or experiences a short-circuit fault, this corresponds to a change in the resistive, inductive, or capacitive parameters of the two-port network. This is reflected in the frequency spectrum as a shift in the FRA curve. This shift in the FRA curve can be used to determine whether the winding's health has changed. The following examples will detail the method for acquiring a transformer winding frequency response curve in real time.

[0044] In one embodiment, Figure 2 As shown, a method for real-time acquisition of transformer winding frequency response curve is provided, comprising the following steps:

[0045] S11. Install the capacitive coupling sensor on the outside of the transformer bushing and determine the time domain signal acquisition length; wherein, the determination of the time domain signal acquisition length can be understood as the selection of the signal length in the time domain. Considering that the selection of the time domain signal length will affect the correctness of the DFT (Discrete Fourier Transform) operation, and the signals in actual applications are often random and have no definite period, this embodiment preferably obtains a reasonable time domain signal length by estimating several typical and periodic signal regions for spectrum analysis and then taking the average value thereof; specifically, the step of determining the time domain signal acquisition length includes:

[0046] Obtain signal regions that meet periodicity conditions, and perform spectrum analysis on each signal region to obtain the corresponding signal length;

[0047] Obtaining an average value of each signal length, and using the average value as the time domain signal acquisition length;

[0048] S12. According to a preset acquisition frequency and the time domain signal acquisition length, the capacitive coupling sensor is used to collect, in real time, a sequence of signals to be converted during the operation of the transformer; the sequence of signals to be converted includes a sequence of excitation signals to be converted and a sequence of response signals to be converted, which can be understood as being obtained by coupling the signal into the winding through the capacitive coupling sensor installed at the bushing, injecting an excitation signal with a relatively high voltage amplitude and rich in high-frequency signal components at one end of the power transformer, and detecting a response signal causing oscillation of the two-port network at the other end;

[0049] According to the sampling theorem, frequency domain aliasing can only be avoided when the sampling frequency is greater than twice the highest frequency of the signal. However, the duration of the actual signal is limited, and the theoretical spectrum width is infinite. The sampling frequency in practical applications does not meet the sampling theorem very well. Considering that the high-frequency components exceeding a certain range have a weaker effect on the signal, this embodiment preferably performs low-pass filtering on the original signal sequence obtained in real time during the transformer operation process before using the SDFT algorithm for signal processing. Specifically, the steps of real-time acquisition of the signal sequence to be converted during the transformer operation process include:

[0050] Real-time collection of original signal sequences during transformer operation; the original signal sequences include original excitation signal sequences and original response signal sequences;

[0051] Performing low-pass filtering on the original signal sequence to obtain the signal sequence to be converted;

[0052] S13. Use the SDFT algorithm to perform time-frequency conversion on the signal sequence to be converted to obtain a corresponding time series spectrum; the time series spectrum includes an excitation time series spectrum and a response time series spectrum; wherein the SDFT algorithm uses a sliding window of fixed length that slides over time to select samples and data acquisition, and calculates an M-point discrete Fourier transform DFT (Discrete Fourier Transform) algorithm within a sliding window. The SDFT algorithm has the advantage of low computational complexity and can use the spectrum calculation result calculated at the previous moment to iteratively obtain the spectrum at the next moment. The calculation speed is extremely fast and the requirement for memory space is greatly reduced. In addition, the algorithm has implicit periodicity and the sampled data can form a periodic sequence, which is convenient for the analysis and study of the frequency response curve. Specifically, the step of using the SDFT algorithm to perform time-frequency conversion on the signal sequence to be converted to obtain the corresponding time series spectrum includes:

[0053] Obtain the previous DFT conversion result corresponding to the previous signal sequence to be converted, and obtain a time series spectrum corresponding to the signal sequence to be converted based on the previous DFT conversion result, the signal sequence to be converted, and the signal sequence to be converted before the time domain signal acquisition length; the time series spectrum is expressed as:

[0054]

[0055] Where M represents the acquisition length of the time domain signal; X n+1 (k) and X n (k) represents the spectrum value of the kth frequency point of the time series spectrum corresponding to the signal sequence to be converted at the n+1th moment and the nth moment respectively; x(n+1) and x(n-M+1) represent the signal sequence to be converted and the signal sequence to be converted before the time domain signal acquisition length, respectively.

[0056] Specifically, the process of the SDFT algorithm processing the signal sequence to be converted can be understood as:

[0057] For a discrete time sequence x(n) (the previous signal sequence to be converted), its M-point DFT transform (discrete Fourier transform) is:

[0058]

[0059] Where k is the spectrum sequence index; q = n-M+1 (n is the time series index), which indicates the position of the first sample in the sequence undergoing DFT change in the entire large sequence; X n (k) represents the value of the kth frequency point of the DFT transformation at the nth moment; M represents the time domain signal acquisition length, that is, the sequence length of each frame for DFT transformation.

[0060] At time n+1 x(n+1) (previous signal sequence to be converted), the spectrum value of the kth frequency point of the M-point DFT in the sliding window is:

[0061]

[0062] The corresponding time series spectrum is:

[0063]

[0064] From formula (3), we can know the DFT calculation result (time series spectrum) at time n+1. We only need to convert the previous DFT calculation result X corresponding to the previous signal sequence to be converted into n (k) Add the current sampling signal value (the signal sequence to be converted x(n+1)), subtract the sampling signal value x(n-M+1) before M moments, and then perform phase shift.

[0065] In this embodiment, the existing FFT algorithm must be performed after the signal is completely collected, which has a shortcoming of low real-time performance. A method of using SDFT to process the excitation signal and the response signal is provided to achieve real-time monitoring of the transformer winding frequency response curve.

[0066] In addition, considering that in the process of actually applying formula (3) to update the time series spectrum in real time, there will be a large number of data updates with minimal overall impact, which will increase the amount of calculation and increase the interference of the algorithm calculation, thereby reducing the accuracy of the winding frequency response curve acquisition, this embodiment preferably sets a rule to judge the correlation coefficient of the signal value added by each SDFT to select the truly effective sequence data for update and use, so as to reduce the impact of subtle interference in the algorithm calculation on the accuracy of the winding frequency response curve. Specifically, the step of obtaining the time series spectrum corresponding to the signal sequence to be converted based on the previous DFT conversion result, the signal sequence to be converted, and the signal sequence to be converted before the time domain signal acquisition length includes:

[0067] The signal sequence to be converted and the previous signal sequence to be converted are grouped according to a preset number, and correlation analysis is performed on each corresponding group of the signal sequence to be converted and the previous signal sequence to be converted in turn to obtain a corresponding correlation coefficient; wherein, the preset number can be selected according to actual application requirements in principle. In this embodiment, considering that the actual drawing of the winding frequency response curve requires the use of 0-1MHz time domain point data, preferably, the preset number N is set to 10 ^5 , that is, taking each N points as a group and calculating the correlation coefficient separately, it needs to be divided into 10 ^6 / N groups, a total of 10 groups of correlation coefficient calculations are required;

[0068] The step of grouping the signal sequence to be converted and the previous signal sequence to be converted according to a preset number includes:

[0069] The signal sequence to be converted and the previous signal sequence to be converted are respectively filtered according to a preset frequency band range to obtain a corresponding frequency band signal sequence; wherein the frequency band signal sequence can be understood as time domain range data obtained by filtering according to a frequency of 0 to 1 MHz; it should be noted that the range of the frequency band signal sequence filtering here can also be adjusted according to actual analysis requirements;

[0070] Grouping the signal sequences of each frequency band into equal groups according to a preset number;

[0071] After obtaining the corresponding groups of the current signal sequence to be converted and the previous signal sequence to be converted in each time domain range by the above method, the correlation coefficient of each group can be obtained according to the following method:

[0072] Assume that the signal sequence to be converted and the signal sequence corresponding to a certain group in the previous signal sequence to be converted are X(i) and Y(i), respectively, i = 0, 1, ..., N-1, then the standard deviations of X(i) and Y(i) are shown in equations (4) and (5) respectively:

[0073]

[0074]

[0075] The covariance of sequences X(i) and Y(i) is defined as shown in formula (6):

[0076]

[0077] The corresponding normalized covariance coefficient is shown in formula (7):

[0078]

[0079] The obtained correlation coefficient is shown in formula (8):

[0080]

[0081] After obtaining the correlation coefficients of each group through equations (4)-(8), the following method can be used to analyze the magnitude of the changes in various data and determine whether the corresponding group signal data needs to be updated based on the magnitude of the changes;

[0082] According to each correlation coefficient, the signal sequence to be converted is subjected to signal screening to obtain a corresponding usable signal sequence; wherein the usable signal sequence can be understood as a signal sequence obtained by retaining grouped data with correlation coefficients less than a preset threshold in the signal sequence to be converted and replacing grouped data with correlation coefficients greater than the preset threshold with 0; specifically, the step of performing signal screening on the signal sequence to be converted according to each correlation coefficient to obtain a corresponding usable signal sequence includes:

[0083] If the correlation coefficient is not greater than a preset threshold, the corresponding group data of the signal sequence to be converted is determined as the data of the available signal sequence; otherwise, the corresponding group data of the signal sequence to be converted is not selected as the data of the available signal sequence;

[0084] After the correlation coefficient of the signal value added by SDFT is determined by the above steps to obtain the available signal sequence, the following steps can be used to replace the signal sequence to be converted x(n+1) in the SDFT algorithm in formula (3) with the available signal sequence, so that only the data with large changes are updated, and the data with minimal overall impact is not updated, thereby reducing the overall data update amount, effectively reducing the algorithm calculation amount, and further reducing the impact of subtle interference in the algorithm on data processing accuracy;

[0085] According to the previous DFT conversion result, the available signal sequence, and the signal sequence to be converted before the time domain signal acquisition length, the time series spectrum corresponding to the signal sequence to be converted is obtained. It should be noted that the formula for obtaining the time series spectrum using the available signal sequence can be found in formula (3), which will not be repeated here;

[0086] S14. Obtain a transformer winding frequency response curve according to the time series spectrum. The calculation formula of the transformer winding frequency response curve is shown in formula (9):

[0087]

[0088] Among them, U i (n) and U o (n) represents the excitation time spectrum and response time spectrum respectively, and H(f) represents the transformer winding frequency response curve.

[0089] The embodiments of the present application address the application defects of existing methods for obtaining transformer winding frequency response curves in terms of real-time and accuracy by using a sliding discrete Fourier transform (SDFT) combined with correlation coefficient processing to perform time-frequency conversion processing on the collected excitation signal and response signal. This method not only continuously refreshes the spectrum data as the signal is collected, thereby achieving efficient real-time monitoring of the winding frequency response curve, but also effectively selects reliable signal values, reduces interference in drawing the frequency response curve, improves the accuracy of the winding frequency response curve, and thus enhances the application value of transformer fault monitoring and analysis.

[0090] In one embodiment, Figure 3 As shown, a system for real-time acquisition of transformer winding frequency response curve is provided, the system comprising:

[0091] Preprocessing module 1, used to install the capacitive coupling sensor outside the transformer bushing and determine the time domain signal acquisition length;

[0092] A real-time acquisition module 2 is configured to acquire, in real time, a sequence of signals to be converted during the operation of the transformer through the capacitive coupling sensor according to a preset acquisition frequency and the time domain signal acquisition length; the sequence of signals to be converted includes a sequence of excitation signals to be converted and a sequence of response signals to be converted;

[0093] The time-frequency conversion module 3 is used to perform time-frequency conversion on the signal sequence to be converted using the SDFT algorithm to obtain a corresponding time series spectrum; the time series spectrum includes an excitation time series spectrum and a response time series spectrum;

[0094] The curve acquisition module 4 is used to obtain the transformer winding frequency response curve according to the time series spectrum.

[0095] The specific limitations of a system for acquiring a transformer winding frequency response curve in real time can be found in the limitations of a method for acquiring a transformer winding frequency response curve in real time above and will not be further elaborated here. Each module in the aforementioned system for acquiring a transformer winding frequency response curve in real time can be implemented in whole or in part through software, hardware, or a combination thereof. Each of these modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0096] Figure 4 FIG. 1 shows an internal structure diagram of a computer device in one embodiment, which may be a terminal or a server. Figure 4 As shown, the computer device includes a processor, memory, network interface, display, and input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for obtaining a transformer winding frequency response curve in real time is implemented. The display screen of the computer device can be a liquid crystal display or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a key, trackball, or touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse.

[0097] It can be understood by those skilled in the art that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computing device may include more or fewer components than shown in the figure, or combine certain components, or have the same component arrangement.

[0098] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0099] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0100] In summary, embodiments of the present invention provide a method and system for real-time acquisition of a transformer winding frequency response curve. The method implements a method for real-time acquisition of a transformer winding frequency response curve by installing a capacitive coupling sensor on the outside of a transformer bushing. After determining a time domain signal acquisition length, the capacitive coupling sensor collects a signal sequence to be converted, including an excitation signal sequence to be converted and a response signal sequence to be converted, in real time during the transformer's operation process, according to a preset acquisition frequency and time domain signal acquisition length. The signal sequence to be converted is then time-to-frequency converted using an SDFT algorithm to obtain a corresponding time series spectrum including an excitation time series spectrum and a response time series spectrum. Furthermore, a technical solution for obtaining a transformer winding frequency response curve based on the time series spectrum is provided. This method processes time-frequency data using a sliding discrete Fourier transform (SDFT) combined with correlation coefficient processing during online detection of the transformer winding state using a pulse frequency response method. This method not only continuously updates spectrum data as signal acquisition progresses, achieving efficient real-time monitoring of the winding frequency response curve, but also effectively selects reliable signal values, reduces interference in drawing the frequency response curve, and improves the accuracy of the winding frequency response curve, thereby enhancing the application value of transformer fault monitoring and analysis.

[0101] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the various technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0102] The above-described embodiments merely represent several preferred implementations of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art could make several improvements and substitutions without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be based on the scope of protection of the claims.

Claims

1. A method for real-time acquisition of transformer winding frequency response curve, characterized in that: The method comprises the following steps: Install the capacitive coupling sensor outside the transformer bushing and determine the time domain signal acquisition length; According to the preset acquisition frequency and the time domain signal acquisition length, the capacitive coupling sensor is used to collect the signal sequence to be converted during the operation of the transformer in real time; the signal sequence to be converted includes an excitation signal sequence to be converted and a response signal sequence to be converted; Using the SDFT algorithm to perform time-frequency conversion on the signal sequence to be converted to obtain a corresponding time series spectrum; the time series spectrum includes an excitation time series spectrum and a response time series spectrum; Obtaining a transformer winding frequency response curve according to the time series spectrum; The step of performing time-frequency conversion on the signal sequence to be converted by using the SDFT algorithm to obtain the corresponding time series spectrum includes: Obtaining a previous DFT conversion result corresponding to a previous signal sequence to be converted, and obtaining a time series spectrum corresponding to the signal sequence to be converted based on the previous DFT conversion result, the signal sequence to be converted, and the signal sequence to be converted before the time domain signal acquisition length, including: The signal sequence to be converted and the previous signal sequence to be converted are grouped according to a preset number, and correlation analysis is performed on each corresponding group of the signal sequence to be converted and the previous signal sequence to be converted to obtain corresponding correlation coefficients; Perform signal screening on the signal sequence to be converted according to each correlation coefficient to obtain a corresponding available signal sequence; A time series spectrum corresponding to the signal sequence to be converted is obtained according to the previous DFT conversion result, the available signal sequence, and the signal sequence to be converted before the time domain signal acquisition length.

2. The method for real-time acquisition of transformer winding frequency response curve according to claim 1, characterized in that: The step of determining the time domain signal acquisition length comprises: Obtain signal regions that meet periodicity conditions, and perform spectrum analysis on each signal region to obtain the corresponding signal length; An average value of each signal length is obtained, and the average value is used as the time domain signal acquisition length.

3. The method for real-time acquisition of transformer winding frequency response curve according to claim 1, characterized in that: The step of real-time acquisition of a signal sequence to be converted during the transformer operation process comprises: Real-time collection of original signal sequences during transformer operation; the original signal sequences include original excitation signal sequences and original response signal sequences; The original signal sequence is subjected to low-pass filtering to obtain the signal sequence to be converted.

4. The method for real-time acquisition of transformer winding frequency response curve according to claim 1, characterized in that: The time series spectrum is expressed as: Where M represents the acquisition length of the time domain signal; X n+1 (k) and X n (k) represents the spectrum value of the kth frequency point of the time series spectrum corresponding to the signal sequence to be converted at the n+1th moment and the nth moment respectively; x(n+1) and x(n-M+1) represent the signal sequence to be converted and the signal sequence to be converted before the time domain signal acquisition length, respectively.

5. The method for real-time acquisition of transformer winding frequency response curve according to claim 1, characterized in that: The step of grouping the signal sequence to be converted and the previous signal sequence to be converted according to a preset number comprises: Filtering the signal sequence to be converted and the previous signal sequence to be converted according to a preset frequency band range to obtain a corresponding frequency band signal sequence; The signal sequences of each frequency band are grouped equally according to a preset number.

6. The method for real-time acquisition of transformer winding frequency response curve according to claim 1, characterized in that: The step of performing signal screening on the signal sequence to be converted according to each correlation coefficient to obtain a corresponding available signal sequence includes: If the correlation coefficient is not greater than a preset threshold, the corresponding group data of the signal sequence to be converted is determined as the data of the available signal sequence; otherwise, the corresponding group data of the signal sequence to be converted is not selected as the data of the available signal sequence.

7. A real-time acquisition system for transformer winding frequency response curve, characterized in that: The system comprises: A pre-processing module is used to install the capacitive coupling sensor on the outside of the transformer bushing and determine the time domain signal acquisition length; A real-time acquisition module, configured to acquire, in real time, a sequence of signals to be converted during the operation of the transformer through the capacitive coupling sensor according to a preset acquisition frequency and the time domain signal acquisition length; the sequence of signals to be converted includes a sequence of excitation signals to be converted and a sequence of response signals to be converted; A time-frequency conversion module is used to perform time-frequency conversion on the signal sequence to be converted using the SDFT algorithm to obtain a corresponding time series spectrum; the time series spectrum includes an excitation time series spectrum and a response time series spectrum; A curve acquisition module, configured to obtain a transformer winding frequency response curve based on the time series spectrum; The step of performing time-frequency conversion on the signal sequence to be converted by using the SDFT algorithm to obtain the corresponding time series spectrum includes: Obtaining a previous DFT conversion result corresponding to a previous signal sequence to be converted, and obtaining a time series spectrum corresponding to the signal sequence to be converted based on the previous DFT conversion result, the signal sequence to be converted, and the signal sequence to be converted before the time domain signal acquisition length, including: The signal sequence to be converted and the previous signal sequence to be converted are grouped according to a preset number, and correlation analysis is performed on each corresponding group of the signal sequence to be converted and the previous signal sequence to be converted to obtain corresponding correlation coefficients; Perform signal screening on the signal sequence to be converted according to each correlation coefficient to obtain a corresponding available signal sequence; A time series spectrum corresponding to the signal sequence to be converted is obtained according to the previous DFT conversion result, the available signal sequence, and the signal sequence to be converted before the time domain signal acquisition length.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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