CN116699337A - Urban underground cable partial discharge positioning method based on time delay estimation - Google Patents
Urban underground cable partial discharge positioning method based on time delay estimation Download PDFInfo
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- 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
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Abstract
The invention relates to a local discharge positioning method of an urban underground cable based on time delay estimation, which comprises the following steps: s1, acquiring an ultrasonic signal when partial discharge occurs; s2, performing VMD decomposition on the ultrasonic signals, and dividing IMF components into three cases: a pure signal component, a noisy component, and a noise component; s3, extracting detail signals from the soft threshold of the dual-tree complex wavelet for the noise-containing component, and reconstructing the detail signals and the pure signal component to obtain denoising signals respectively corresponding to the two sensors; s4, calculating a cross correlation function and an autocorrelation function for the two denoising signals, and solving the moment of the peak point as an inter-signal time delay estimation; s5, determining models of received signals corresponding to the two sensors respectively, and obtaining the position of the target signal source. Compared with the prior art, the invention has the advantages of high positioning precision and the like.
Description
Technical Field
The invention relates to the technical field of cable partial discharge positioning, in particular to a method for positioning urban underground cable partial discharge based on time delay estimation.
Background
When partial discharge occurs, other phenomena such as current, temperature, sound and the like often occur, and different measurement modes are provided for different phenomena. Detection methods can be broadly classified into two types, namely, an electrical detection method and a non-electrical detection method, depending on whether the detection is electrically related or not.
The non-electrical detection method can be classified into a photodetection method, a decomposed component detection method, an ultrasonic method, and the like. The photodetection method is to detect the position of partial discharge point according to the difference of light wavelength in discharge; the decomposition component detection method is to judge the partial discharge state according to the concentration of the chemical gas; the ultrasonic method is to detect an ultrasonic signal inside by using an ultrasonic sensor.
The electrical detection method can be further classified into a capacitive coupling method, an electromagnetic coupling method, an ultrahigh frequency method, and the like. The detection principle of the electromagnetic coupling method is that a cable passes through the rogowski coil and the grounding wire of the cable shielding layer, local pulse current flowing through the cable shielding layer is collected, and then the local pulse current is transmitted to a subsequent processing system for analysis and processing. The ultrahigh frequency method can detect ultrahigh frequency electromagnetic pulse signals up to GHZ generated when partial discharge occurs through an ultrahigh frequency antenna sensor, and then detect the partial discharge signals.
At present, the research of the photodetection method is still in a starting stage, and the partial discharge positioning of large-volume electrical equipment is difficult; the ultrasonic method is easy to attenuate in the transmission process, has low sensitivity and insufficient accuracy; the capacitive coupling method needs to be detected under the condition of damaging the insulating layer, so that electrical equipment is damaged, and the risk coefficient is high; the ultra-high frequency sensor needs to meet the requirements of ultra-high frequency and broadband to design a detection antenna, and the manufacturing cost is high.
Disclosure of Invention
The invention aims to provide a local discharge positioning method for an urban underground cable based on time delay estimation, which aims to improve positioning accuracy and ensure safety.
The aim of the invention can be achieved by the following technical scheme:
a local discharge positioning method of urban underground cable based on time delay estimation comprises the following steps:
s1, acquiring ultrasonic signals when partial discharge occurs based on two sensors at two ends of a cable;
s2, performing VMD decomposition on the ultrasonic signals to obtain k IMF components, calculating classification parameter values corresponding to each component, and dividing the IMF components into three cases: a pure signal component, a noisy component, and a noise component;
s3, extracting detail signals from the soft threshold of the dual-tree complex wavelet for the noise-containing component, and reconstructing the detail signals and the pure signal component to obtain denoising signals respectively corresponding to the two sensors;
s4, calculating a cross-correlation function and an autocorrelation function for the two denoising signals, calculating a weighted cross-power spectrum function, carrying out inverse Fourier transform on the weighted cross-power spectrum function, obtaining extremum for the function on a time domain, and obtaining the moment of a peak point as an inter-signal time delay estimation, wherein the cross-correlation function comprises a third-time related second cross-correlation function;
s5, determining models of the received signals corresponding to the two sensors respectively according to the inter-signal time delay estimation and the background noise signal to obtain the position of a target signal source, wherein the position of the target signal source is a partial discharge position.
Further, the classification parameter value is determined based on the approximate entropy of the IMF component and the cross-correlation coefficient of the IMF component and the original ultrasonic signal, and the classification parameter value is specifically a ratio of the approximate entropy and the cross-correlation coefficient.
Further, for the ith IMF component, if its classification parameter value is smallest among all IMF components, it is classified into a pure signal component, if its classification parameter value satisfies F i -F min And < lambda, is divided into noise-containing components, if the classification parameter value satisfies F i -F min Not less than lambda, is divided into noise components, wherein F i For the classification parameter value of the ith IMF component, F min For the smallest classification parameter value of all IMF components, λ is the threshold value.
Further, let two denoising signals be x 1(n) and x2 (n) the cross-correlation function comprises a first cross-correlation function between the two de-noised signals and a second cross-correlation function between the first cross-correlation function and an autocorrelation function of one of the de-noised signals, the autocorrelation function being an autocorrelation function of the two de-noised signals respectively.
Further, the first cross-correlation function is
R 12 (τ)＝E[x 1 (n)x 2 (n-τ)]
Where E is a mathematical expectation, expressed as:
wherein: t is the probability density, f (T) is the probability density function, and in general, the probability density function of two signals in the effective area defaults to 1, and τ is the time delay.
Further, the autocorrelation function is:
R 11 (τ)＝E[x 1 (n)x 1 (n-τ)]
R 22 (τ)＝E[x 2 (n)x 2 (n-τ)]
wherein E is mathematical expectation, τ is time delay, R 11 (τ) is the autocorrelation function of one of the denoised signals, R 22 (τ) is an autocorrelation function of another denoised signal.
Further, the second cross-correlation function is:
wherein ,R12 (τ) is a first cross-correlation function, R 11 E is a mathematical expectation and τ is a time delay for the autocorrelation function of one of the denoised signals.
Further, the weighted cross-power spectral function is:
wherein F is Fourier transform, F * For taking conjugate after Fourier transform, R 22 An autocorrelation function for another denoising signal;
the weighting function is:
wherein ,r represents 12 R 11 Power spectral density,/, of (2)>R represents 22 F represents frequency.
Further, the model of the received signals corresponding to the two sensors respectively is:
x 1 (t)＝s(t)+n 1 (t)
x 2 (t)＝gs(t-D)+n 2 (t)
wherein s (t) represents a target signal source; d represents an inter-signal delay estimate; 0 < g.ltoreq.1 is the attenuation coefficient, x 1 (t) is the received signal corresponding to one of the sensors, x 2 (t) is the received signal corresponding to another sensor, n 1 (t)、n 2 And (t) is the background noise signal received by array elements A and B, respectively.
Further, the attenuation coefficient is 1.
Compared with the prior art, the invention has the following beneficial effects:
(1) The method comprises the steps of firstly dividing the acquired signal into a pure signal component, a noise component and a noise component by using a VMD algorithm, and then carrying out secondary noise reduction on the noise component by using double-tree complex wavelet transformation, thereby improving the noise removal effect; in the aspect of time delay estimation, a generalized three-time correlation time delay estimation algorithm of a weighting function is provided, a cross correlation function and an autocorrelation function are calculated, the threshold value of errors occurring under low signal to noise ratio is reduced by utilizing multiple correlations, the anti-noise performance is improved, the precision of time delay estimation is improved, and then the precision of partial discharge positioning is improved.
(2) The invention has the advantages of no need of damaging an insulating layer, high safety, capability of meeting the requirements by adopting a common sensor, good noise reduction effect, no need of an ultrahigh frequency sensor and cost reduction.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of generalized third-order correlation delay estimation of the weighting function of the present invention;
fig. 3 is a block diagram of the sensor and discharge location of the present invention.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples. The present embodiment is implemented on the premise of the technical scheme of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection scope of the present invention is not limited to the following examples.
Noun interpretation:
SCOT: smooth coherent transformations.
VMD: and decomposing the variation mode.
The invention provides a local discharge positioning method of an urban underground cable based on time delay estimation. A flow chart of the method is shown in fig. 1. The method comprises the steps of firstly classifying acquired signals by using a VMD algorithm, then carrying out secondary noise reduction by using double-tree complex wavelet transformation, improving the signal-to-noise ratio and preparing for subsequent more accurate positioning; the invention provides a generalized three-time related time delay estimation algorithm based on the SCOT weighting function, which improves the precision of time delay estimation and further improves the precision of partial discharge positioning.
The method of the invention comprises the following steps:
s1, acquiring ultrasonic signals when partial discharge occurs based on two sensors at two ends of a cable;
s2, performing VMD decomposition on the ultrasonic signals to obtain k IMF components, calculating classification parameter values corresponding to each component, and dividing the IMF components into three cases: a pure signal component, a noisy component, and a noise component;
s3, extracting detail signals from the soft threshold of the dual-tree complex wavelet for the noise-containing component, and reconstructing the detail signals and the pure signal component to obtain denoising signals respectively corresponding to the two sensors;
s4, calculating a cross-correlation function and an autocorrelation function for the two denoising signals, calculating a weighted cross-power spectrum function, carrying out inverse Fourier transform on the weighted cross-power spectrum function, and obtaining an extremum value of the function on a time domain to obtain the moment of a peak point as an inter-signal time delay estimation;
s5, determining models of the received signals corresponding to the two sensors respectively according to the inter-signal delay estimation and the background noise signal to obtain the position of a target signal source, wherein the position of the target signal source is a partial discharge position.
In S1, the invention uses two sensors to realize the acquisition of ultrasonic signals generated when partial discharge occurs. The sensors are arranged at two ends of the cable and are used for collecting ultrasonic signals when partial discharge occurs.
S2 and S3 are dual-tree complex wavelet denoising processes adopting VMD, and Variational Modal Decomposition (VMD) can effectively process nonlinear signals and overcome the defects of modal aliasing, end-point effect and the like, but noise still exists in the actually processed signals. The dual-tree complex wavelet transformation is derived from offline wavelet transformation and complex wavelet transformation, and compared with classical wavelet transformation, the dual-tree complex wavelet transformation has higher operation efficiency, translational stability and reconstruction completeness, avoids frequency aliasing in the signal processing process, and can better retain detail characteristics in signals. The method comprises the steps of firstly adopting a VMD algorithm to reduce noise of signals, and then utilizing double-tree complex wavelets to reduce noise of preprocessed signals for the second time to obtain noise-reduced signals.
The specific process of S2 and S3 is as follows: firstly, carrying out VMD decomposition on the acquired signals to obtain k IMF components, and assuming that the value of a single component classification parameter is F and the approximate entropy of each IMF component is A AF The cross-correlation coefficient of the IMF component and the original signal is V CC Let f=a AE /V CC . If the IMF component contains more target signal features, the smaller the approximate entropy is, the larger the cross-correlation coefficient between the IMF component and the original signal is, and the smaller the classification parameter value is. Assuming that the threshold is λ, the IMF components obtained by decomposition can be classified into the following three categories:
(1) pure signal component: i.e. the classification parameter value is minimum F min Components of (2);
(2) noise-containing componentThe amount is as follows: satisfy F i -F min The component less than lambda still contains obvious noise, and secondary noise reduction is needed;
(3) noise component: i.e. with noise dominant component, with F i -F min And lambda is not less than. And secondly, performing double-tree complex wavelet transform (DT-CWT) on the noise-containing components obtained after classification, wherein the effective signals are generally contained in the low-frequency part of the wavelet coefficients, and the noise-containing signals are mostly contained in the high-frequency part, so that the effective signals can be extracted by setting a threshold value. The selection method of the threshold value mainly comprises a hard threshold value method and a soft threshold value method, and the soft threshold value method is generally superior to the hard threshold value method in continuity and denoising effect, so that a soft threshold value method is adopted to extract a detail signal, and then the obtained IMF component and a pure signal component are reconstructed to obtain a final denoising signal.
The process of extracting detail signals by using a soft threshold value of double tree complex wavelet (DT-CWT) is as follows:
firstly, a proper threshold value is set, wavelet coefficients lower than the threshold value are set to be zero, wavelet coefficients higher than the threshold value are reserved, and then wavelet inverse transformation is carried out to obtain a reconstructed detail signal. The soft threshold expression is:
wherein gamma is a global threshold and the expression is
f (x) is a wavelet coefficient after thresholding, x is a wavelet coefficient, N is a signal length, and σ is a noise standard deviation.
In S4, a three-time correlation time delay estimation improvement algorithm based on SCOT function weighting is provided, and the noise immunity and the time delay estimation precision of the three-time correlation are further improved.
The specific steps of S4 are as follows: let x be 1(n) and x2 (n) signals received by the two sensors respectively; first, find x 1(n) and x2 Cross-correlation function of (n): r is R 12 (τ)＝E[x 1 (n)x 2 (n-τ)]The method comprises the steps of carrying out a first treatment on the surface of the Then, respectively find x 1(n) and x2 Autocorrelation function of (n): r is R 11 (τ)＝E[x 1 (n)x 1 (n-τ)]；R 22 (τ)＝E[x 2 (n)x 2 (n-τ)]；R 11 (τ) and R 12 The cross-correlation function of (τ) is:then, the third-order correlation function is calculated as: />The weighting function is:
the cross-power spectral function of the three-time correlation algorithm is:and carrying out inverse Fourier transform on the weighted cross power spectrum function, and solving an extremum for the function in the time domain, wherein the moment of the peak point is the estimated value of the time delay between signals, and the improved three-time related time delay estimation algorithm schematic diagram is shown in figure 2.
In fig. 3, A, B are two sensors that receive signals, and S is a target to be measured. In S5, the model of the A, B received signal may be expressed as: x is x 1 (t)＝s(t)+n 1 (t)；x 2 (t)＝gs(t-D)+n 2 (t); wherein s (t) represents a target signal source; d represents an estimated time delay; 0 < g.ltoreq.1 is the attenuation coefficient, usually 1; n is n 1 (t)、n 2 (t) is the background noise signal expression received by array elements A and B, respectively. In this model, the distance between array elements is usually fixed, and the properties such as the speed of signal propagation are also known, so that the position of the anchor point can be obtained only by requiring the estimated value of the delay.
The invention utilizes VMD algorithm to divide the collected signals into three types of pure signal component, noise component and noise component; and then, the noise component is subjected to secondary noise reduction by utilizing the double-tree complex wavelet transformation, and finally, the pure signal component and the signal component subjected to secondary noise reduction are reconstructed, so that the noise removal effect of the signal is improved. Meanwhile, the influence of two channels on the signal power spectrum is considered by the SCOT weighting function, and theoretically, the performance of the SCOT algorithm is superior to that of the traditional method. And compared with the secondary correlation, the tertiary correlation can improve the noise immunity of the signal and has higher time delay precision. The invention combines the two to further improve the precision of time delay estimation, thereby improving the positioning precision.
The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention by one of ordinary skill in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (10)
1. The urban underground cable partial discharge positioning method based on time delay estimation is characterized by comprising the following steps of:
s1, acquiring ultrasonic signals when partial discharge occurs based on two sensors at two ends of a cable;
s2, performing VMD decomposition on the ultrasonic signals to obtain k IMF components, calculating classification parameter values corresponding to each component, and dividing the IMF components into three cases: a pure signal component, a noisy component, and a noise component;
s3, extracting detail signals from the soft threshold of the dual-tree complex wavelet for the noise-containing component, and reconstructing the detail signals and the pure signal component to obtain denoising signals respectively corresponding to the two sensors;
s4, calculating a cross-correlation function and an autocorrelation function for the two denoising signals, calculating a weighted cross-power spectrum function, carrying out inverse Fourier transform on the weighted cross-power spectrum function, obtaining extremum for the function on a time domain, and obtaining the moment of a peak point as an inter-signal time delay estimation, wherein the cross-correlation function comprises a third-time related second cross-correlation function;
s5, determining models of the received signals corresponding to the two sensors respectively according to the inter-signal time delay estimation and the background noise signal to obtain the position of a target signal source, wherein the position of the target signal source is a partial discharge position.
2. The method for positioning partial discharge of an urban underground cable based on time delay estimation according to claim 1, wherein the classification parameter value is determined based on the approximate entropy of the IMF component and the cross-correlation coefficient of the IMF component and the original ultrasonic signal, and the classification parameter value is specifically the ratio of the approximate entropy and the cross-correlation coefficient.
3. The method of claim 2, wherein for the ith IMF component, if the classification parameter value is smallest among all IMF components, the IMF component is classified as a pure signal component, and if the classification parameter value satisfies F i -F min And < lambda, is divided into noise-containing components, if the classification parameter value satisfies F i -F min Not less than lambda, is divided into noise components, wherein F i For the classification parameter value of the ith IMF component, F min For the smallest classification parameter value of all IMF components, λ is the threshold value.
4. The method for positioning partial discharge of urban underground cable based on time delay estimation according to claim 1, wherein two denoising signals are set as x 1(n) and x2 (n) the cross-correlation function comprises a first cross-correlation function between the two de-noised signals and a second cross-correlation function between the first cross-correlation function and an autocorrelation function of one of the de-noised signals, the autocorrelation function being an autocorrelation function of the two de-noised signals respectively.
5. The method for positioning partial discharge of an urban underground cable based on time delay estimation according to claim 4, wherein the first cross correlation function is
R 12 (τ)＝E[x 1 (n)x 2 (n-τ)]
Where E is a mathematical expectation, expressed as:
wherein: t is the probability density, f (T) is the probability density function, and in general, the probability density function of two signals in the effective area defaults to 1, and τ is the time delay.
6. The method for positioning partial discharge of an urban underground cable based on time delay estimation according to claim 5, wherein the autocorrelation function is:
R 11 (τ)＝E[x 1 (n)x 1 (n-τ)]
R 22 (τ)＝E[x 2 (n)x 2 (n-τ)]
wherein E is mathematical expectation, τ is time delay, R 11 (τ) is the autocorrelation function of one of the denoised signals, R 22 (τ) is an autocorrelation function of another denoised signal.
7. The method for positioning partial discharge of an underground cable in a city based on time delay estimation of claim 6, wherein the second cross correlation function is:
wherein ,R12 (τ) is a first cross-correlation function, R 11 E is a mathematical expectation and τ is a time delay for the autocorrelation function of one of the denoised signals.
8. The method for positioning partial discharge of an underground cable in a city based on time delay estimation according to claim 7, wherein the weighted cross power spectrum function is:
wherein F is Fourier transform, F * For taking conjugate after Fourier transform, R 22 An autocorrelation function for another denoising signal;
the weighting function is:
wherein ,r represents 12 R 11 Power spectral density,/, of (2)>R represents 22 F represents frequency.
9. The method for positioning partial discharge of an urban underground cable based on time delay estimation according to claim 1, wherein the model of the received signals respectively corresponding to the two sensors is as follows:
x 1 (t)＝s(t)+n 1 (t)
x 2 (t)＝gs(t-D)+n 2 (t)
wherein s (t) represents a target signal source; d represents an inter-signal delay estimate; 0 < g.ltoreq.1 is the attenuation coefficient, x 1 (t) is the received signal corresponding to one of the sensors, x 2 (t) is the received signal corresponding to another sensor, n 1 (t)、n 2 And (t) is the background noise signal received by array elements A and B, respectively.
10. The method for positioning partial discharge of an urban underground cable based on time delay estimation according to claim 9, wherein the attenuation coefficient is 1.
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