A Noise Processing Method for Ultrasonic Detection of Transformer Hot Spot Temperature

Abstract

The present invention relates to the technical field of transformer acoustic wave detection, and specifically to a noise processing method for ultrasonic detection of hot spot temperature in transformers, which includes multiplying a pseudo-random sequence coding signal by an electrical signal at the transmitting end of an ultrasonic sensor to generate a modulation signal with a pseudo-random binary coding sequence; performing time-domain windowing processing on the collected ultrasonic signals to obtain a spectrogram; performing dynamic frequency-domain segmentation based on the spectrogram; performing clustering analysis on the low-frequency subbands to distinguish steady-state vibration noise from transient interference, and establishing a corresponding noise feature library; performing wavelet threshold denoising on each windowed signal to retain high-frequency ultrasonic signals; performing cross-correlation operation on the reconstructed and spliced complete signal and the pseudo-random sequence coding signal to extract the peak value of the cross-correlation function. By combining the pseudo-random sequence coding signal with the ultrasonic signal, effective coding of the ultrasonic signal is achieved, the anti-noise performance is enhanced, and the problem of noise interference in the process of ultrasonic detection of transformers is effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer acoustic wave detection, and particularly to a noise processing method for ultrasonic detection of hot spot temperature of transformers. Background Art

[0002] During the operation of power transformers, hot spot temperature monitoring is crucial for equipment safety. As a method for indirectly measuring the hot spot temperature of transformers by utilizing the functional relationship between the medium temperature and the acoustic wave propagation characteristics, the ultrasonic detection technology has the advantages of high precision, real-time continuous measurement, and convenient maintenance. As a non-invasive detection technology, it has the characteristics of being free from electromagnetic interference and high safety, and has been gradually applied in the field of transformer internal detection.

[0003] However, during the normal operation of oil-immersed power transformers, there are phenomena such as magnetostriction of the iron core, axial and radial vibrations of the windings, and vibrations of the cooling system. These vibrations will ultimately be transmitted to the surface of the transformer shell through the transformer oil and rigid connection parts, which will superimpose noise interference on the ultrasonic signals received by the ultrasonic sensors, resulting in a low signal-to-noise ratio of the received signals and a large calculation error of the propagation time. Existing methods mostly adopt fixed threshold filtering or single-domain processing, and it is difficult to effectively separate noise from useful signals. Summary of the Invention

[0004] To solve the above problems, the present invention provides a noise processing method for ultrasonic detection of hot spot temperature of transformers, including:

[0005] Generating a pseudo-random sequence coding signal, multiplying the pseudo-random sequence coding signal by the electrical signal at the transmitting end of the ultrasonic sensor to generate a modulated signal with a pseudo-random binary coding sequence, and loading the modulated signal to transmit the modulated ultrasonic signal;

[0006] Performing time-domain windowing processing on the ultrasonic signals collected by the receiving end of the ultrasonic sensor, performing Fourier transform on the windowed signal to obtain a spectrogram; based on the spectrogram, performing dynamic frequency-domain segmentation, dividing the spectrum into multiple sub-bands, and the sub-bands include high-frequency sub-bands and low-frequency sub-bands;

[0007] Performing clustering analysis on the low-frequency sub-bands to distinguish steady-state vibration noise from transient interference, and establishing a corresponding noise feature library;

[0008] Performing wavelet threshold denoising on each windowed signal based on the noise feature library to retain high-frequency ultrasonic signals; inverse-transforming the denoised signals into time-domain signals, and splicing the time-domain signals after transformation of each windowed signal into a complete signal;

[0009] Perform cross-correlation operation on the reconstructed and spliced complete signal and the pseudo-random sequence coded signal, and extract the peak value of the cross-correlation function; calculate the propagation duration of the ultrasonic signal according to the peak position, and inversely determine the hot spot temperature based on the sound speed-temperature model.

[0010] The time-domain windowing process for the ultrasonic signal collected by the receiving end of the ultrasonic sensor is specifically as follows:

[0011] Estimate the time window length according to the propagation path of the ultrasonic wave. The time window is:

[0012] ,

[0013] where, t represents the time window; L is the ultrasonic wave propagation path length; v 0 represents the sound speed; represents the sound speed offset caused by temperature change;

[0014] Perform windowing segmentation on the collected ultrasonic signal according to the time window length, and intercept the windowed signal through the window function.

[0015] In the specific implementation manner, in order to effectively reduce spectral leakage, the window function selects the Blackman window function, which can improve the accuracy of signal processing.

[0016] In order to effectively separate the ultrasonic signal from the vibration noise, and thus adopt different processing strategies for different components, the high-frequency sub-band is the main component of the ultrasonic signal; the low-frequency sub-band is the main component of the vibration noise.

[0017] The noise feature library includes a steady-state noise feature library and a transient noise feature library, which can comprehensively cover different types of noise features, making noise recognition and classification more accurate.

[0018] Among them, the steady-state noise feature library is the eigenvector corresponding to the steady-state cluster center, including energy distribution and spectral shape. By analyzing the energy distribution and spectral shape, the steady-state vibration noise can be more accurately separated from the signal.

[0019] The transient noise feature library is the eigenvector corresponding to the transient cluster center, including time-domain features, which are helpful for capturing the suddenness and irregularity of transient noise.

[0020] The wavelet threshold denoising of each windowed signal based on the noise feature library is specifically operated as follows:

[0021] Select a wavelet basis that matches the characteristics of the ultrasonic signal, and determine the decomposition level according to the signal sampling frequency and the ultrasonic main frequency range;

[0022] Adaptive threshold setting is performed based on the noise feature library. For the detail coefficients of each layer of the wavelet decomposition layer, its dominant component is judged according to the noise feature library. The steady-state noise feature library is matched for the low-frequency layer, and the transient noise feature library is matched for the high-frequency layer;

[0023] Discrete wavelet transform is performed on each windowed signal to obtain the detail coefficients of each layer; denoising is performed according to the detail thresholds of each layer of the set wavelet decomposition layer.

[0024] The symbol width of the pseudo-random sequence coding signal is determined by the ultrasonic frequency, and the formula for the symbol width is:

[0025] ,

[0026] In the formula, Tc represents the symbol width; f 0 is the ultrasonic frequency; k is a positive integer value.

[0027] Beneficial effects: The present invention is a noise processing method for ultrasonic detection of the hot spot temperature of a transformer. By combining the pseudo-random sequence coding signal with the ultrasonic signal, effective coding of the ultrasonic signal is achieved, and the anti-noise performance is enhanced; the sub-bands are dynamically divided according to the spectral characteristics of the vibration noise, and the non-stationary noise is suppressed by combining time-domain windowing. The vibration noise feature library is established through cluster analysis to realize the dynamic matching of the noise components, providing a basis for subsequent denoising; by performing wavelet threshold denoising on each windowed signal, the high-frequency ultrasonic signal is effectively retained and the noise interference is removed; finally, the propagation duration of the acoustic signal can be accurately calculated by performing cross-correlation operation on the received and processed signal and the pseudo-random sequence coding signal, significantly improving the signal-to-noise ratio, thereby effectively solving the problem of noise interference existing in the ultrasonic detection process of oil-immersed power transformers and improving the accuracy of hot spot temperature detection. Brief Description of the Drawings

[0028] By reading the detailed description of the preferred embodiments below, the solutions and advantages of the present application will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention.

[0029] Figure 1 It is a flowchart of the noise processing method for ultrasonic detection of the hot spot temperature of a transformer. Detailed Embodiments

[0030] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the drawings.

[0031] This embodiment provides a noise processing method for ultrasonic detection of the hot spot temperature of a transformer, which is applied to a transformer ultrasonic detection system. The transformer ultrasonic detection system includes a signal generating end, an ultrasonic signal transmitting end, an ultrasonic signal receiving end, and a processing terminal. By arranging the transformer ultrasonic detection system outside the power transformer box body, the acoustic signal propagation in the box body is collected through the ultrasonic signal transmitting end and the ultrasonic signal receiving end, and the hot spot temperature of the transformer is detected according to the changes in the propagation speed, propagation path, and propagation time of the acoustic signal.

[0032] To process the influence of vibration noise superposition on the propagation of ultrasonic signals in the power transformer box body, see Figure 1 , the specific implementation steps of the method are as follows:

[0033] Step 1: Generate a pseudo-random sequence coding signal, multiply the pseudo-random sequence coding signal by the electrical signal of the ultrasonic sensor transmitting end to generate a modulation signal with a pseudo-random binary coding sequence, and load the modulation signal to transmit the modulated ultrasonic signal;

[0034] The signal generating end generates a periodic pseudo-random sequence coding signal, and multiplies the pseudo-random sequence coding signal by the electrical signal of the ultrasonic sensor transmitting end to generate a modulation signal with a pseudo-random binary coding.

[0035] Among them, the symbol width of the pseudo-random sequence coding signal is determined according to the ultrasonic frequency of the ultrasonic sensor transmitting end. The formula for the symbol width is:

[0036] ,

[0037] In the formula, Tc represents the symbol width; f 0 is the ultrasonic frequency; k is a positive integer value.

[0038] The generated modulation signal is transmitted to the ultrasonic sensor transmitting end to drive the ultrasonic sensor at the transmitting end to emit ultrasonic signals into the power transformer box body.

[0039] Step 2: Perform time-domain windowing processing on the ultrasonic signals collected by the ultrasonic sensor receiving end, perform Fourier transform on the windowed signals to obtain a spectrogram; perform dynamic frequency-domain segmentation based on the spectrogram, and divide the spectrum into multiple sub-bands, including high-frequency sub-bands and low-frequency sub-bands;

[0040] When the ultrasonic sensor receiving end collects ultrasonic signals, the ultrasonic sensor receiving end will transmit the ultrasonic signals to the processing terminal. The processing terminal performs time-domain windowing processing on the ultrasonic signals collected by the ultrasonic sensor receiving end. The specific operation is as follows:

[0041] Estimate the time window length according to the propagation path of the ultrasonic wave. The time window is as follows:

[0042] ,

[0043] where t represents the time window; L is the length of the ultrasonic wave propagation path; v 0 represents the speed of sound; represents the offset of the speed of sound caused by temperature change;

[0044] Perform windowing segmentation on the collected ultrasonic signals according to the time window length; select the Blackman window function as the window function to intercept the windowed signal.

[0045] Perform a fast Fourier transform (FFT) on the windowed signal, take the amplitude energy of the transform coefficients, transform the frequency to the Mel scale, take the logarithm, and then obtain the spectrogram of the ultrasonic signal through a discrete cosine transform.

[0046] Perform dynamic frequency domain segmentation on the spectrogram according to the vibration noise energy distribution, divide the spectrum into multiple sub-bands. Among them, the high-frequency sub-band is the main component of the ultrasonic signal; the low-frequency sub-band is the main component of the vibration noise.

[0047] Step 3: Perform clustering analysis on the low-frequency sub-band to distinguish steady-state vibration noise from transient interference, and establish a corresponding noise feature library;

[0048] Take the low-frequency sub-band as the object of noise analysis, and extract features for each frequency point of the low-frequency sub-band, including energy value, time-domain statistic, and frequency-domain statistic; the energy value is calculated as the square of the amplitude of each frequency point, representing the energy distribution; the time-domain statistic is the mean, variance, and peak factor of the signal within the window; the frequency-domain statistic is the bandwidth and flatness of the sub-band spectrum.

[0049] Use the K-means clustering algorithm to perform clustering analysis on the low-frequency sub-band, and divide the low-frequency sub-band data into two categories, including steady-state vibration noise and transient interference. In this embodiment, randomly select 2 initial cluster centers (corresponding to the two types of noise); calculate the Euclidean distance from each data point of each low-frequency sub-band to the cluster center, and assign it to the nearest cluster; recalculate the cluster center; recalculate the Euclidean distance from each data point to the cluster center for reallocation until the change in the cluster center is less than the threshold or the maximum number of iterations is reached; perform two-category cluster label marking based on the cluster, and mark each data point as belonging to steady-state vibration noise or transient interference noise. It should be noted that the K-means clustering algorithm belongs to the prior art, and the specific steps will not be elaborated here.

[0050] Based on the distinguished steady-state vibration noise and transient interference, a corresponding noise feature library is established, including a steady-state noise feature library and a transient noise feature library; the steady-state noise feature library is established by extracting the feature vectors corresponding to the steady-state cluster centers, including energy distribution and spectral shape; the transient noise feature library is constructed by extracting the feature vectors corresponding to the transient cluster centers, including time-domain features.

[0051] Step 4: Perform wavelet threshold denoising on each windowed signal based on the noise feature library, and retain the high-frequency ultrasonic signals; inverse-transform the denoised signals into time-domain signals, and splice the time-domain signals after transformation of each windowed signal into a complete signal;

[0052] The specific operation of performing wavelet threshold denoising on each windowed signal based on the noise feature library is as follows:

[0053] Select a wavelet basis that matches the characteristics of the ultrasonic signal, and determine the decomposition level according to the signal sampling frequency and the ultrasonic main frequency range; set the wavelet decomposition level to 5 layers, and ensure that the high-frequency ultrasonic components are located in the lower decomposition layers;

[0054] Set an adaptive threshold based on the noise feature library. For the detail coefficients of each layer of the wavelet decomposition, judge its dominant component according to the noise feature library, match the steady-state noise feature library for the low-frequency layer, and match the transient noise feature library for the high-frequency layer;

[0055] Perform discrete wavelet transform on each windowed signal to obtain the detail coefficients of each layer; perform denoising according to the detail thresholds of each layer of the set wavelet decomposition layer.

[0056] Step 5: Perform cross-correlation operation on the reconstructed and spliced complete signal and the pseudo-random sequence coded signal, and extract the peak value of the cross-correlation function; calculate the propagation duration of the ultrasonic signal according to the peak position, and inversely determine the hot spot temperature based on the sound speed-temperature model;

[0057] When the transmitting end of the ultrasonic sensor emits an ultrasonic signal, it will transmit the pseudo-random sequence coded signal to the processing terminal for storage; when the processing terminal obtains the reconstructed and spliced complete signal through Step 2 - Step 4, it calls the pseudo-random sequence coded signal for cross-correlation operation.

[0058] The formula for performing cross-correlation operation on the reconstructed and spliced complete signal and the pseudo-random sequence coded signal is:

[0059] ,

[0060] In the formula, r(t) represents the spliced complete signal; s(t) represents the pseudo-random sequence coded signal; t represents the time window; R rs (τ)is the cross-correlation function value, representing the degree of similarity between two signals at different time offsets;

[0061] Determine the peak position of the cross-correlation function according to the cross-correlation function value τ peak , and determine the propagation duration of the ultrasonic signal according to the peak position t d = τ peak + t .

[0062] Determine the propagated sound wave according to the propagation duration and the propagation path, and inversely deduce the hot spot temperature based on the sound speed-temperature model.

Claims

1. A noise processing method for ultrasonic detection of the hot spot temperature of a transformer, characterized in that, Including: Generating a pseudo-random sequence encoded signal, multiplying the pseudo-random sequence encoded signal with the electrical signal at the transmitting end of the ultrasonic sensor to generate a modulated signal with a pseudo-random binary coded sequence, and loading the modulated signal to transmit the modulated ultrasonic signal; Performing time-domain windowing processing on the ultrasonic signal collected by the receiving end of the ultrasonic sensor, performing Fourier transform on the windowed signal to obtain a spectrogram; performing dynamic frequency-domain segmentation based on the spectrogram, dividing the spectrum into multiple sub-bands, where the sub-bands include a high-frequency sub-band and a low-frequency sub-band; Performing clustering analysis on the low-frequency sub-band to distinguish steady-state vibration noise and transient interference, and establishing a corresponding noise feature library; Performing wavelet threshold denoising on each windowed signal based on the noise feature library to retain the high-frequency ultrasonic signal; inverse-transforming the denoised signal into a time-domain signal, and splicing the time-domain signals after transformation of each windowed signal into a complete signal; Performing cross-correlation operation on the reconstructed and spliced complete signal and the pseudo-random sequence encoded signal, and extracting the peak value of the cross-correlation function; calculating the propagation duration of the ultrasonic signal according to the peak position, and inversely determining the hot spot temperature based on the sound speed-temperature model.

2. The noise processing method for ultrasonic detection of the hot spot temperature of a transformer according to claim 1, wherein, The performing time-domain windowing processing on the ultrasonic signal collected by the receiving end of the ultrasonic sensor specifically is: Estimating the time window length according to the propagation path of the ultrasonic wave, and the time window is: ， Among them, t represents the time window; L is the ultrasonic propagation path length; v 0 represents the sound speed; represents the sound speed offset caused by temperature change; Performing windowing segmentation on the collected ultrasonic signal according to the time window length, and intercepting the windowed signal through a window function.

3. The noise processing method for ultrasonic detection of the hot spot temperature of a transformer according to claim 2, characterized in that The window function selects a Blackman window function.

4. The noise processing method for ultrasonic detection of the hot spot temperature of a transformer according to claim 1, wherein The high-frequency sub-band is the main component of the ultrasonic signal; the low-frequency sub-band is the main component of the vibration noise.

5. The noise processing method for ultrasonic detection of the hot spot temperature of a transformer according to claim 1, characterized in that, The noise feature library includes a steady-state noise feature library and a transient noise feature library.

6. The noise processing method for ultrasonic detection of the hot spot temperature of a transformer according to claim 5, characterized in that, The steady-state noise feature library is the eigenvector corresponding to the steady-state cluster center, including energy distribution and spectral shape.

7. The noise processing method for ultrasonic detection of the hot spot temperature of a transformer according to claim 5, wherein The transient noise feature library is the eigenvector corresponding to the transient cluster center, including time-domain features.

8. The noise processing method for ultrasonic detection of the hot spot temperature of a transformer according to claim 5, characterized in that, The performing wavelet threshold denoising on each windowed signal based on the noise feature library, its specific operation is: Selecting a wavelet basis matching the characteristics of the ultrasonic signal, and determining the decomposition level according to the signal sampling frequency and the ultrasonic main frequency range; Performing adaptive threshold setting based on the noise feature library, for each layer of detail coefficients in the wavelet decomposition layer, judging its dominant component according to the noise feature library, matching the steady-state noise feature library for the low-frequency layer, and matching the transient noise feature library for the high-frequency layer; Performing discrete wavelet transform on each windowed signal to obtain each layer of detail coefficients; performing denoising according to the set detail thresholds of each layer in the wavelet decomposition layer.

9. The noise processing method for ultrasonic detection of the hot spot temperature of a transformer according to claim 1, wherein, The code element width of the pseudo-random sequence encoded signal is determined by the ultrasonic frequency, and the formula for the code element width is: ， In the formula, Tc represents the symbol width; f 0 is the ultrasonic frequency; k is a positive integer value.

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