CN110426569B - Noise reduction processing method for acoustic signals of transformer - Google Patents
Noise reduction processing method for acoustic signals of transformer Download PDFInfo
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- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H1/00—Measuring characteristics of vibrations in solids by using direct conduction to the detector
- G01H1/12—Measuring characteristics of vibrations in solids by using direct conduction to the detector of longitudinal or not specified vibrations
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- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1209—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing using acoustic measurements
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Abstract
一种变压器声信号降噪处理方法，属监控领域。其以多路测量信号为基础，通过稀疏分量分析算法分离出变压器运行状态声信号；用变分模态分解将信号分解为四层本征模态函数；通过对本征模态函数分量进行滤波处理，得到新的本征模态函数分量；将其重构可得最终去除噪信号。其通过基于势函数的稀疏分量分析算法和变分模态分解对声传感器采集信号进行去噪处理，能够有效分离变电站运行声信号和环境噪声，去噪效果良好。可广泛用于无人值守变电站的运行监控领域。
The invention relates to a noise reduction processing method for transformer acoustic signals, which belongs to the field of monitoring. Based on multi-channel measurement signals, the transformer operating state acoustic signal is separated by sparse component analysis algorithm; the signal is decomposed into four-layer eigenmode functions by variational modal decomposition; the eigenmode function components are filtered by filtering. , to obtain a new eigenmode function component; reconstruct it to obtain the final denoised signal. The sparse component analysis algorithm based on potential function and variational modal decomposition are used to de-noise the signal collected by the acoustic sensor, which can effectively separate the substation operating acoustic signal and the environmental noise, and the de-noising effect is good. It can be widely used in the field of operation monitoring of unattended substations.
Description
Technical Field
The invention belongs to the field of monitoring, and particularly relates to a noise reduction processing method for a transformer acoustic signal.
Background
As one of the most important electrical devices in an electrical power system, a power transformer needs to be ensured in a reliable operation state.
At present, the troubleshooting of the transformer mainly depends on regular polling and visual inspection by special polling personnel.
The inspection mode consumes a large amount of manpower, has strong subjectivity, and the transformer fault often cannot be fed back and solved in real time due to periodic inspection, thereby finally causing huge property loss to the national power system.
When the transformer is in operation, the iron core and the winding generate vibration, and vibration sound signals which are radiated and mixed around are transmitted through the oil and the box body. In addition, when partial discharge occurs in the transformer, an acoustic signal is often emitted. Therefore, the vibration sound signal of the transformer contains abundant information of the running state of the transformer, and can be used as a basis for judging the running state of the transformer.
The traditional transformer operation state detection method comprises vibration signal detection, ultrasonic detection, infrared imaging monitoring and the like. Compared with the traditional method, the sound signal detection method has no direct contact with a power system and is easy to collect, and meanwhile compared with the method of locally mounting a sensor on the transformer to collect signals, the sound signal detection method can collect audio generated by the integral vibration of the transformer, and is generally due to the vibration signal detection method. Therefore, the transformer fault detection by the acoustic signal is feasible and reliable.
However, the acoustic sensor collects the acoustic signal of the operating state of the transformer, and also has corona acoustic signals, environmental noise and the like, so that effective operating state information is submerged in various interferences and is difficult to perform subsequent processing. Therefore, how to effectively extract the acoustic signal of the transformer becomes the key for accurately judging the fault of the transformer in the follow-up process.
Disclosure of Invention
The invention aims to solve the technical problem of providing a transformer acoustic signal noise reduction processing method. The method carries out denoising processing on the signals acquired by the acoustic sensor through a sparse component analysis algorithm and variational modal decomposition based on a potential function. Firstly, separating out the sound signals of the running state of the transformer by a sparse component analysis algorithm, decomposing the signals into four layers of intrinsic mode functions by using variational mode decomposition, filtering the intrinsic mode function components to obtain new intrinsic mode function components, and reconstructing the new intrinsic mode function components to obtain the final noise-removed signals. The method can effectively separate the operation sound signal and the environmental noise of the transformer substation, and has good denoising effect.
The technical scheme of the invention is as follows: the method for noise reduction processing of the acoustic signal of the transformer comprises the field acquisition of the acoustic signal of the running state of the transformer, and is characterized in that:
1) separating out the sound signals of the running state of the transformer by a sparse component analysis algorithm;
2) decomposing the signal into four layers of intrinsic mode functions by using variational mode decomposition;
3) filtering the intrinsic mode function component to obtain a new intrinsic mode function component;
4) reconstructing it to obtain the final de-noised signal.
Specifically, the method for noise reduction processing of the acoustic signal of the transformer is based on a plurality of paths of measurement signals, and the processing process mainly comprises the following steps:
1) after windowing and framing through a Hamming window, carrying out short-time Fourier transform on the sound signal to enable the sound signal to have frequency domain sparse characteristics;
2) obtaining a preliminary de-noising signal through underdetermined blind source separation;
3) further carrying out four-layer eigenmode function decomposition on the signals by a variation mode decomposition method;
4) carrying out 6-layer wavelet decomposition on the high-frequency component and the low-frequency component of the intrinsic mode function, and carrying out filtering processing on the high-frequency component and the low-frequency component of the intrinsic mode function by using a 3 sigma principle;
5) and performing signal reconstruction according to the high-frequency component, the low-frequency component and the middle-frequency component of the new eigenmode function.
Furthermore, the transformer acoustic signal noise reduction processing method is characterized in that after windowing and framing through a Hamming window, short-time Fourier transform is carried out on the acoustic signal to enable the acoustic signal to have frequency domain sparse characteristics; then, obtaining a preliminary de-noising signal through underdetermined blind source separation; then, further carrying out four-layer eigenmode function decomposition on the signals by a variation mode decomposition method; carrying out 6-layer wavelet decomposition on the high-frequency component and the low-frequency component of the intrinsic mode function, and carrying out filtering processing on the high-frequency component and the low-frequency component of the intrinsic mode function by using a 3 sigma principle; and finally, performing signal reconstruction according to the high-frequency component, the low-frequency component and the middle-frequency component of the new eigenmode function.
According to the transformer acoustic signal noise reduction processing method, the noise reduction processing is performed on the acoustic sensor acquisition signals through the sparse component analysis algorithm and the variational modal decomposition based on the potential function, the transformer substation operation acoustic signals and the environmental noise can be effectively separated, and the noise reduction effect is good.
According to the transformer acoustic signal noise reduction processing method, noise reduction processing is carried out on the acquired signals of the acoustic sensor through a sparse component analysis algorithm and variational modal decomposition based on a potential function.
Specifically, the transformer acoustic signal noise reduction processing method includes the steps of firstly separating out an acoustic signal in a transformer running state through a sparse component analysis algorithm, decomposing the signal into four layers of intrinsic mode functions through variational mode decomposition, obtaining new intrinsic mode function components through filtering the intrinsic mode function components, and reconstructing the new intrinsic mode function components to obtain a final noise removal signal.
Furthermore, the transformer acoustic signal noise reduction processing method comprises the steps of firstly obtaining multiple paths of acoustic signals, windowing and framing the acoustic signals through a Hamming window, and then carrying out short-time Fourier transform to enable the acoustic signals to have frequency domain sparse characteristics; then obtaining a multi-channel output signal through an SCA separation unit based on an underdetermined blind source separation strategy: split signal 1, split signal 2, split signal 3, split signal 4; then, performing variation modal decomposition on the separation signal 1 through a VMD decomposition unit to obtain a high-frequency component IMF1, a low-frequency component IMF4, a medium-frequency component IMF2 and an IMF 3; the high-frequency component IMF1 and the low-frequency component IMF4 are processed by a wavelet decomposition unit and a 3 sigma threshold filtering unit respectively, and finally the filtered signals, the intermediate-frequency components IMF2 and IMF3 are subjected to signal reconstruction to reconstruct the transformer acoustic signal through a signal reconstruction unit.
Compared with the prior art, the invention has the advantages that:
and denoising the acquired signals of the acoustic sensor by a sparse component analysis algorithm and variational modal decomposition based on a potential function. Firstly, separating out the sound signals of the running state of the transformer by a sparse component analysis algorithm, decomposing the signals into four layers of intrinsic mode functions by using variational mode decomposition, filtering the intrinsic mode function components to obtain new intrinsic mode function components, and reconstructing the new intrinsic mode function components to obtain the final noise-removed signals. The method can effectively separate the operation sound signal and the environmental noise of the transformer substation, and has good denoising effect.
Drawings
FIG. 1 is a flow chart of a transformer acoustic signal noise reduction processing method of the present invention;
FIG. 2 is a schematic diagram of a monitoring input signal of a multi-path noisy transformer according to the present invention;
FIG. 3 is a schematic diagram of blind source separation based on sparse component analysis algorithm of the present invention;
FIG. 4 is a waveform diagram of an isolated source signal reconstructed after eigenmode function component filtering according to the present invention;
fig. 5 is a waveform diagram of a noise reduction output signal obtained by reconstructing low, medium and high frequency signals according to the present invention.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
The technical scheme of the invention provides a transformer acoustic signal noise reduction processing method, which comprises the steps of carrying out field acquisition on an acoustic signal in a transformer running state and based on obtained multi-channel measurement signals, and is characterized in that:
1) separating out the sound signals of the running state of the transformer by a sparse component analysis algorithm;
2) decomposing the signal into four layers of intrinsic mode functions by using variational mode decomposition;
3) filtering the intrinsic mode function component to obtain a new intrinsic mode function component;
4) reconstructing it to obtain the final noise-removed signal.
Specifically, the method for noise reduction processing of the acoustic signal of the transformer is based on a plurality of paths of measurement signals, and the processing process mainly comprises the following steps:
1) after windowing and framing through a Hamming window, carrying out short-time Fourier transform on the sound signal to enable the sound signal to have frequency domain sparse characteristics;
2) obtaining a preliminary de-noising signal through underdetermined blind source separation;
3) further carrying out four-layer eigenmode function decomposition on the signals by a variation mode decomposition method;
4) carrying out 6-layer wavelet decomposition on the high-frequency component and the low-frequency component of the intrinsic mode function, and carrying out filtering processing on the high-frequency component and the low-frequency component of the intrinsic mode function by using a 3 sigma principle;
5) and performing signal reconstruction according to the high-frequency component, the low-frequency component and the middle-frequency component of the new eigenmode function.
Furthermore, the transformer acoustic signal noise reduction processing method is characterized in that after windowing and framing through a Hamming window, short-time Fourier transform is carried out on the acoustic signal to enable the acoustic signal to have frequency domain sparse characteristics; then, obtaining a preliminary de-noising signal through underdetermined blind source separation; then, further carrying out four-layer eigenmode function decomposition on the signals by a variation mode decomposition method; carrying out 6-layer wavelet decomposition on the high-frequency component and the low-frequency component of the intrinsic mode function, and carrying out filtering processing on the high-frequency component and the low-frequency component of the intrinsic mode function by using a 3 sigma principle; and finally, performing signal reconstruction according to the high-frequency component, the low-frequency component and the middle-frequency component of the new eigenmode function.
According to the transformer acoustic signal noise reduction processing method, the noise reduction processing is performed on the acoustic sensor acquisition signals through the sparse component analysis algorithm and the variational modal decomposition based on the potential function, the transformer substation operation acoustic signals and the environmental noise can be effectively separated, and the noise reduction effect is good.
According to the transformer acoustic signal noise reduction processing method, noise reduction processing is carried out on the acquired signals of the acoustic sensor through a sparse component analysis algorithm and variational modal decomposition based on a potential function.
Specifically, the transformer acoustic signal noise reduction processing method includes the steps of firstly separating out an acoustic signal in a transformer running state through a sparse component analysis algorithm, decomposing the signal into four layers of intrinsic mode functions through variational mode decomposition, obtaining new intrinsic mode function components through filtering the intrinsic mode function components, and reconstructing the new intrinsic mode function components to obtain a final noise removal signal.
Furthermore, the transformer acoustic signal noise reduction processing method comprises the steps of firstly obtaining multiple paths of acoustic signals, windowing and framing the acoustic signals through a Hamming window, and then carrying out short-time Fourier transform to enable the acoustic signals to have frequency domain sparse characteristics; then obtaining a multi-channel output signal through an SCA separation unit based on an underdetermined blind source separation strategy: split signal 1, split signal 2, split signal 3, split signal 4; then, performing variation modal decomposition on the separation signal 1 through a VMD decomposition unit to obtain a high-frequency component IMF1, a low-frequency component IMF4, a medium-frequency component IMF2 and an IMF 3; the high-frequency component IMF1 and the low-frequency component IMF4 are processed by a wavelet decomposition unit and a 3 sigma threshold filtering unit respectively, and finally the filtered signals, the intermediate-frequency components IMF2 and IMF3 are subjected to signal reconstruction to reconstruct the transformer acoustic signal through a signal reconstruction unit.
Specifically, fig. 1 is a flow chart of a transformer acoustic signal noise reduction processing method according to the present invention.
According to the technical scheme, firstly, a plurality of paths of acoustic signals are obtained, and after windowing and framing through a Hamming window, short-time Fourier transform is carried out, so that the acoustic signals have frequency domain sparse characteristics; then obtaining a multi-channel output signal through an SCA separation unit 1 based on an underdetermined blind source separation strategy: split signal 1, split signal 2, split signal 3, split signal 4; the separation signal 1 is then subjected to a variational modal decomposition by the VMD decomposition unit 2 to obtain a high-frequency component IMF1, a low-frequency component IMF4 and medium-frequency components IMF2, IMF 3. The high-frequency component IMF1 and the low-frequency component IMF4 are processed by a wavelet decomposition unit 3 and a 3 sigma threshold filtering unit 4 respectively, and finally the filtered signals and the intermediate-frequency components IMF2 and IMF3 pass through a signal reconstruction unit 5 to reconstruct the transformer acoustic signal.
The waveform of the multi-path transformer monitoring input signal with noise is shown in fig. 2, wherein measurement signals of the transformer noise are respectively obtained from two sensors.
Fig. 3 is a frequency domain scattergram example obtained by performing blind source separation on two paths of observation signals by a sparse component analysis algorithm.
Fig. 4 shows an example of output signals obtained by performing a metamorphic mode decomposition on input signals after blind source separation.
Fig. 5 is a waveform of a noise reduction output signal obtained by filtering an output signal after the variable mode decomposition through a wavelet decomposition unit and a 3 σ threshold filtering unit and finally reconstructing low, medium and high frequency signals.
According to the technical scheme, the signals acquired by the acoustic sensor are denoised through a sparse component analysis algorithm and variational modal decomposition based on a potential function. Firstly, separating out the sound signals of the running state of the transformer by a sparse component analysis algorithm, decomposing the signals into four layers of intrinsic mode functions by using variational mode decomposition, filtering the intrinsic mode function components to obtain new intrinsic mode function components, and reconstructing the new intrinsic mode function components to obtain the final noise-removed signals. The method can effectively separate the operation sound signal and the environmental noise of the transformer substation, and has good denoising effect.
The invention can be widely applied to the field of operation monitoring of the unattended transformer substation.
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| CN110426569A (en) | 2019-11-08 |
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