CN115267446A - Power equipment partial discharge detection method based on multi-signal classification positioning algorithm - Google Patents

Power equipment partial discharge detection method based on multi-signal classification positioning algorithm Download PDF

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CN115267446A
CN115267446A CN202210841311.5A CN202210841311A CN115267446A CN 115267446 A CN115267446 A CN 115267446A CN 202210841311 A CN202210841311 A CN 202210841311A CN 115267446 A CN115267446 A CN 115267446A
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王昭雷
孟荣
王永红
段志勇
梁雪峰
贺梦天
苑旭楠
张贤
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Super High Voltage Branch Of State Grid Hebei Electric Power Co ltd
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Abstract

The invention discloses a power equipment partial discharge detection method based on a multi-signal classification positioning algorithm, which comprises the following steps of: s1, receiving a partial discharge signal sent by power equipment through an eight-element circular microphone array sensor; s2, carrying out Fourier transform on the received time domain digital signal with the sound source information, and converting the time domain digital signal into a frequency domain; s3, extracting characteristic frequency domain of the frequency domain discharge signal obtained in the step S2, and finding out a frequency value which can represent the received discharge signal most; s4, analyzing and positioning the received discharge signal by utilizing a multi-signal classification sound source positioning algorithm suitable for the conditions of small signal-to-noise ratio and small snapshot number and combining the signal characteristic frequency value obtained in the step S3; the detection method provided by the invention adopts multi-parameter joint estimation, is beneficial to more accurately positioning the partial discharge position of the power equipment, and achieves the purpose of effectively detecting the partial discharge.

Description

Power equipment partial discharge detection method based on multi-signal classification positioning algorithm
Technical Field
The invention relates to the field of partial discharge detection of power equipment, in particular to a partial discharge detection method of the power equipment based on a multi-signal classification positioning algorithm.
Background
The partial abnormal discharge is an important factor causing a reduction in the operating life of the electric power equipment, and is also the most common abnormal operating state of various electric power equipment. The positioning detection of partial discharge is a key engineering technology and is an important basis for judging whether the running states of a plurality of electric power equipment are normal or not. The method has important significance for preventing serious faults of the power equipment by timely finding abnormal discharge and accurately positioning the abnormal discharge.
The current methods for detecting partial discharge mainly include: electrical, optical, opto-acoustic, and ultrahigh frequency methods. The ultrasonic wave has the characteristics of high frequency and short wavelength, and has strong directional sense on signal transmission, so the detection process is relatively simple. Meanwhile, the ultrasonic measurement method is widely researched due to the characteristics of easiness in realizing online detection, convenience in spatial positioning, small electric interference and the like.
The invention provides a power equipment partial discharge detection method based on a multi-signal classification positioning algorithm.
Disclosure of Invention
The invention aims to provide a power equipment partial discharge detection method based on a multi-signal classification positioning algorithm, so as to obtain a better detection effect in the process of detecting the partial discharge positioning of the power equipment.
In order to achieve the purpose, the invention provides the following technical scheme:
a partial discharge detection method for power equipment based on a multi-signal classification positioning algorithm comprises the following steps:
s1, receiving a partial discharge signal sent by power equipment through an eight-element circular microphone array sensor;
s2, carrying out Fourier transform on the received time domain digital signal with the sound source information, and converting the time domain digital signal into a frequency domain;
s3, extracting characteristic frequency of the frequency domain discharge signal obtained in the step S2, and finding a frequency value which can represent the received discharge signal most;
s4, analyzing and positioning the received discharge signal by utilizing a multi-signal classification sound source positioning algorithm suitable for the conditions of small signal-to-noise ratio and small snapshot number and combining the signal characteristic frequency value obtained in the step S3;
as a further scheme of the invention: in step S1, the eight-element circular array employed is a planar microphone sensor array.
As a further scheme of the invention: the eight-element circular array in the step S1 can receive two position parameters of a pitch angle and an azimuth angle of a signal, and the two position parameters can be jointly estimated, so that space three-dimensional effective positioning can be realized.
As a further scheme of the invention: in step S2, the object for which the fourier transform is directed is the time domain state of the received discharge digital signal, wherein the amount of signal data received by a single microphone sensor is associated with the number of snapshots and is a signal length parameter in the fourier transform.
As a further scheme of the invention: in step S2, the signal strength after conversion into the frequency domain is represented by the size of the ordinate, and the frequency corresponding to the portion having a large signal strength should be considered more in step S3.
As a further scheme of the invention: in step S4, the application condition of the sound source localization algorithm suitable for multi-signal classification in the case of small signal-to-noise ratio and small snapshot number is that when the signal can be considered as a narrowband signal, it can be expressed as:
Sk(t-t1)≈Sk(t)
where t1 is the time required for delay between the array microphone units.
As a further scheme of the invention: in step S4, the received discharge signal is first processed by obtaining a covariance matrix, and then is subjected to subsequent signal classification.
Figure BDA0003750485850000021
Wherein X (i) is the ith signal data received, XH(i) The Hermite matrix of X (i), N being the total number of data received.
As a further scheme of the invention: due to the planar array, in step S4, the time delay tau involved in the algorithmm,kThe method comprises the following steps:
Figure BDA0003750485850000031
wherein, taum,kIs the relative delay, theta, of the signal source k and the array element m relative to the center of the arraykAnd
Figure BDA0003750485850000032
the pitch angle and azimuth angle of a signal source k, r is the radius of the circular array, and M is the number of array elements.
As a further scheme of the invention: since the array is a planar array, in step S4, the direction matrix a involved in the algorithm should be:
Figure BDA0003750485850000033
wherein,
Figure BDA0003750485850000034
a direction vector generated for the array to receive the ith signal source information.
As a further scheme of the invention: in step S4, the spectral function for determining the final search result of the multi-signal classification algorithm is:
Figure BDA0003750485850000035
wherein,
Figure BDA0003750485850000036
for the conventional spectral function P vs. azimuth angle
Figure BDA0003750485850000037
The result of the second-order partial derivative is solved,
Figure BDA0003750485850000038
for the conventional spectral function P to the pitch angle thetakAnd solving the result of the second-order partial derivative.
Compared with the prior art, the invention has the beneficial effects that:
1. the eight-element cross microphone array is adopted to receive and receive the radio signals, the plane array structure of the eight-element cross microphone array enables the estimation of the sound source position to be expanded into two parameters of a pitch angle and an azimuth angle, and the multi-parameter joint estimation method is beneficial to more accurately determining the position information of the discharge sound source and effectively finishing the spatial three-dimensional positioning of partial discharge.
2. The frequency spectrum information of the discharge signal is obtained through Fourier transform of the received signal, and the main characteristic frequency is extracted from the frequency spectrum information and used as the numerical value of the frequency parameter in the positioning algorithm, so that the value of the frequency parameter in the multi-signal classification algorithm is refined, and the positioning reliability of the signal is enhanced.
3. In the step S4, a new spatial spectrum estimation function is used as a basis for determining a peak after global search, and compared with a conventional spatial spectrum function, the method has a better positioning detection effect on partial discharge of the power equipment under the conditions of a small signal-to-noise ratio and a small snapshot count of a signal.
Drawings
FIG. 1 is a flow chart of the algorithm of the present invention.
Fig. 2 is a simulation diagram of an eight-element circular sensor array used in the present invention receiving far-field discharge signals.
Detailed Description
The technical scheme of the patent is further explained in detail by combining the attached drawings.
Referring to fig. 1, a partial discharge detection method for power equipment based on a multi-signal classification positioning algorithm includes the following steps:
s1, receiving a partial discharge signal sent by power equipment through an eight-element circular microphone array sensor;
when the partial discharge of the power equipment is detected, the approximate direction of the eight-element circular microphone array is firstly directed to the power equipment to be detected for a period of time, and the discharge signal of the power equipment is received.
S2, carrying out Fourier transform on the received time domain digital signal with the sound source information, and converting the time domain digital signal into a frequency domain;
and carrying out Fourier transform on the signals received by each microphone, and converting the signals from a time domain to a frequency domain for analysis. Wherein the object for which the fourier transform is directed is the time domain state of the received discharge digital signal, the amount of signal data received by a single microphone sensor is associated with the number of snapshots and is a length parameter of the signal in the fourier transform.
S3, extracting characteristic frequency of the frequency domain discharge signal obtained in the step S2, and finding a frequency value which can represent the received discharge signal most;
due to the fact that the discharge signal has certain fluctuation and interference of surrounding noise, even after filtering processing is carried out, obtained frequency domain information is not a specific frequency, but superposition of different intensities of a plurality of frequency signals is achieved. The discharge signal part with the higher signal intensity is selected as the main frequency of the received discharge signal in the area with the higher signal amplitude, and the value is brought into the signal frequency parameter related to the algorithm in the algorithm of the next step S4.
S4, analyzing and positioning the received discharge signal by using a multi-signal classification sound source positioning algorithm suitable for the conditions of small signal-to-noise ratio and small snapshot number and combining the signal characteristic frequency value obtained in the step S3
The specific improved multi-signal classification algorithm principle in step S4 of the present invention is as follows:
the multi-signal classification algorithm has the characteristics of high resolution, high precision and high stability, and is suitable for abnormal discharge positioning scenes in power routing inspection.
In this scenario, when it is used to determine the incoming wave direction of the signal, there are the following advantages:
1. a plurality of sound sources can be positioned;
2. the detection effect has high precision;
3. the antenna beam signal has a high resolution;
4. it is applicable to short data situations.
To facilitate theoretical analysis, we make the following provisions:
1. each test signal source has the same but uncorrelated polarization.
2. The signal source is narrow band with each source having the same center frequency ω 0.
3. Assuming that the number of the test signal sources is D;
the acquisition array is a circular array consisting of M (M > D) array elements; in the present invention, the value of M is specifically 8, that is, the present invention adopts an eight-element circular array to receive the discharge signal, but in the description of the principle, the number of array elements in the array is assumed to be M.
Each element has the same characteristics and is isotropic in every direction.
4. The microphone element spacing is d, and the array element spacing is not more than half of the wavelength of the highest frequency signal.
5. The microphone array belongs to a far-field scene, namely, a received sound source signal can be simulated into a plane wave;
6. the array elements and the test signals are uncorrelated;
variance σ2Is zero mean Gaussian noise nm(t)；
7. The characteristics of the received signal branches are the same.
Assuming that the number of signal sources propagating to the microphone array is k (k =1,2, \8230;, D), the wavefront signal is Sk(t), we assume that it is a narrowband signal, S due to the simplified condition of the narrowband signalk(t) can be expressed as:
Sk(t)＝sk(t)exp[jωk(t)]
wherein s isk(t) is Sk(t) complex envelope.
ωk(t) is Sk(t) angular frequency.
Center frequency ω of all signals0The same is true.
Thus, the device
Figure BDA0003750485850000061
Wherein c is the signal wave velocity.
λ is the wavelength.
At the same time, the user can select the desired position,
λ＝c/f
and substituting the frequency value obtained in the step S3 in the invention, and taking 340m/S as c to obtain lambda.
Let the time required for delay between the array microphone units be t1。
Under the condition that the signal is narrow-band, the following signals are provided:
Sk(t-t1)≈Sk(t)
thus, the delayed wavefront signal is
Figure BDA0003750485850000062
The center of the circular array in the invention is used as a reference point of the microphone sensor.
At time t, the sensing signal of array element M (M =1, 2.. Multidot.m) to the kth signal source is
Figure BDA0003750485850000063
Wherein, akIs the influence coefficient of the array element m on the signal source k.
Since each array element has no directivity, let ak＝1；
τm,kIs the relative delay of the signal source k and the array element m relative to the center of the array, and can be expressed as
Figure BDA0003750485850000064
Wherein r is the array radius.
Figure BDA0003750485850000065
And thetakRespectively the azimuth angle and the pitch angle of the signal source k.
Figure BDA0003750485850000066
The negative angle between the signal source and the positive x-axis in fig. 2 ranges from 0 deg. to 360 deg..
θkIs the angle between the signal source and the positive direction of the array center normal in figure 2, and ranges from 0 deg. to 90 deg..
Considering the upper noise and the incoming waves of all signal sources, the output signal of the m-th array element is:
Figure BDA0003750485850000071
wherein n ism(t)To measure noise.
Writing the above formula into vector form, and let akIf l, then there are
X(t)＝α(θk)Sk(t)+N(t)
Wherein,
X(t)＝[x1(t)，x2(t)，…xM(t)]T
Figure BDA0003750485850000072
N(t)＝[n1(t)，n2(t)，…，nM(t)]T
then the user can use the device to make a visual display,
X(t)＝AS(t)+N(t)
wherein
S(t)＝[S1(t)，S2(t)，…，SD(t)]T
Figure BDA0003750485850000073
So far, the problem becomes xm(t) sampling, then from { x }m(i) I =1, 2.. M } the incoming wave direction of the signal source k is estimated.
For the array output x (t), its covariance matrix R is
R＝E[X(t)XH(t)]
The signal and noise are uncorrelated and the noise is zero average white noise.
The following results can thus be obtained:
R＝E[(AS+N)(AS+N)H]
＝AE[SSH]AH+E[NNH]
＝APAH+RN
where P is the correlation matrix of the spatial signal, RNThe correlation matrix for noise can be expressed as follows
P＝E[S(t)SH(t)]
RN＝σ2I
Wherein σ2Is the noise power, and I is an M identity matrix.
When theta is measuredi≠θj，
Figure BDA0003750485850000081
When i ≠ j, the matrix a is a van der mond matrix defined by the above equation.
Since A is a Van der Mond matrix, each column is independent of the other.
Thus, if P is a non-singular array, there are:
rank(APAH)＝D
since P is positive, the matrix APAHThe eigenvalues of (a) are positive, i.e., there are a total of D positive eigenvalues. Thus, R is a full rank matrix with M eigenvalues.
We sort the eigenvalues in descending order, having
λ1≥λ2≥…≥λM＞0
Of these M eigenvalues, the larger D eigenvalues correspond to signals, while the remaining M-D eigenvalues, which are smaller, correspond to noise.
Therefore, the eigenvalue (eigenvector) of R can be decomposed into a signal eigenvalue (eigenvector) and a noise eigenvalue (eigenvector).
Let λ i be the ith eigenvalue of matrix R, vi be the eigenvector corresponding to λ i, then:
Rvi＝λivi
let λi＝σ2Is the minimum eigenvalue of R, then
Rvi＝σ2vi，i＝D+1，D+2，…，M
σ2vi＝(APAH+σ2I)vi，i＝D+1，D+2，…，M
Finishing to obtain:
APAHvz＝0
because A isHA is D × D dimension full rank matrix, so (A)HA)-1Presence of, P-1Are also present. From above, the two sides of the upper formula are simultaneously multiplied by P-1(AHA)-1AHThe following can be obtained:
P-1(AHA)-1AHAP Hvi＝0
therefore, the temperature of the molten metal is controlled,
AHvi＝0，i＝D+1，D+2，…，M
i.e. the noise feature vector is perpendicular to the column vector of the signal matrix.
That is, we find a vector that is orthogonal (or closest to orthogonal) to the noise subspace, whose direction represents the direction of arrival.
Using the noise eigenvalues as each column, a noise matrix is constructed:
En＝[VD+1，VD+2，…，VM]
further defining a two-dimensional spatial spectrum:
Figure BDA0003750485850000091
where the denominator is the inner product of the signal matrix and the noise matrix.
Ideally, the denominator is zero.
However, since the denominator is only the minimum value due to the presence of noise in the environment when detecting the power equipment, the denominator is only the minimum value
Figure BDA0003750485850000092
There is one peak.
Searching the two-dimensional space to sum
Figure BDA0003750485850000093
And (4) changing, searching a peak value of the two-dimensional space spectrum, and further determining the position of the sound source.
However, when the signal-to-noise ratio of the received signal is low and the number of signal data is small, the spatial spectrum estimation sound source position cannot achieve a good effect.
According to the invention, second-order derivatives are respectively obtained for the pitch angle and the azimuth angle at the spectral peak, a new spatial spectrum function is constructed, and the resolution of the algorithm is further improved.
Assuming the range of azimuth angles is angle
Figure BDA0003750485850000094
In the range of
Figure BDA0003750485850000095
The search interval is
Figure BDA0003750485850000096
The range of pitch angle theta is R theta, and the search interval is Delta theta.
Then order
Figure BDA0003750485850000097
Figure BDA0003750485850000101
The spatial spectrum function can be expressed as:
Figure BDA0003750485850000102
the partial derivative of the variable is calculated by a binary discrete function in
Figure BDA0003750485850000103
From discrete function P to argument
Figure BDA0003750485850000104
The first partial derivative of (a) is:
Figure BDA0003750485850000105
note book
Figure BDA0003750485850000106
Is P'φk。
In that
Figure BDA0003750485850000107
Is aligned with
Figure BDA0003750485850000108
The second derivative of (d) is:
Figure BDA0003750485850000109
note the book
Figure BDA00037504858500001010
Is P ″)φk。
The same can be said
Figure BDA00037504858500001011
The first and second derivatives of the discrete function P with respect to the argument θ are:
Figure BDA00037504858500001012
Figure BDA00037504858500001013
note the book
Figure BDA00037504858500001014
And
Figure BDA00037504858500001015
are respectively as
Figure BDA00037504858500001016
According to analysis, the second derivative of the maximum point of the original spectrum function forms a sharp negative spectrum peak at the arrival angles of the azimuth angle and the pitch angle.
The new spectral functions can thus be collated: i.e. values with partial derivatives greater than 0 are reduced to 0, a new spectral function is obtained as:
Figure BDA0003750485850000111
Figure BDA0003750485850000112
due to P pairs
Figure BDA0003750485850000113
And θ, the second order partial derivatives are independent of each other, so the second derivative P "of the original spectral function can be expressed as:
Figure BDA0003750485850000114
a new spatial spectrum function can be formed by the formula;
in step S4, after traversing the spatial angle through the algorithm, finding a spectrum peak of a new spatial spectrum function, where the corresponding azimuth angle and pitch angle are the estimated spatial position of the power equipment discharge sound source.
The beneficial effects of the invention are:
1. the eight-element cross microphone array is adopted to receive signals, the plane array structure of the eight-element cross microphone array enables the estimation of the sound source position to be expanded into two parameters of a pitch angle and an azimuth angle, and the multi-parameter joint estimation method is beneficial to more accurately determining the position information of the discharge sound source and effectively finishing the spatial three-dimensional positioning of partial discharge.
2. The frequency spectrum information of the discharge signal is obtained through Fourier transform of the received signal, and the main characteristic frequency is extracted from the frequency spectrum information and is used as the numerical value of the frequency parameter in the positioning algorithm, so that the value of the frequency parameter in the multi-signal classification algorithm is refined, and the positioning reliability of the signal is enhanced.
3. In the step S4, a new spatial spectrum estimation function is used as a basis for determining a peak after global search, and compared with a conventional spatial spectrum function, the method has a better positioning detection effect on partial discharge of the power equipment under the conditions of a small signal-to-noise ratio and a small snapshot count of a signal.
The above description is only an embodiment of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modifications made by using the design concept should fall within the scope of infringing the present invention.

Claims (10)

1. A power equipment partial discharge detection method based on a multi-signal classification positioning algorithm is characterized by comprising the following steps: s1, receiving a partial discharge signal sent by power equipment through an eight-element circular microphone array sensor; s2, carrying out Fourier transformation on the received time domain digital signal with the sound source information, and converting the time domain digital signal into a frequency domain; s3, extracting characteristic frequency of the frequency domain discharge signal obtained in the step S2, and finding out a frequency value which can represent the received discharge signal most; and S4, analyzing and positioning the received discharge signal by using a multi-signal classification sound source positioning algorithm suitable for the conditions of small signal-to-noise ratio and small snapshot number and combining the signal characteristic frequency value obtained in the step S3. Wherein the more detailed operation of S4 is as follows: and (4) extracting the signal characteristic frequency value obtained in the step (S3), taking the frequency value as an integral frequency value representing the received discharge signal, bringing the frequency value into a signal frequency parameter designed by a multi-signal classification sound source positioning algorithm suitable for the conditions of small signal-to-noise ratio and small snapshot number, and further analyzing and positioning the received discharge signal.
2. The partial discharge detection method for the power equipment based on the multi-signal classification positioning algorithm according to claim 1, characterized in that: in step S1, the eight-element circular array employed is a planar microphone sensor array.
3. The partial discharge detection method for the power equipment based on the multi-signal classification positioning algorithm as claimed in claim 1, wherein: the eight-element circular array in the step S1 can receive two position parameters of a pitch angle and an azimuth angle of a signal, and the two position parameters can be jointly estimated, so that space three-dimensional effective positioning can be realized.
4. The partial discharge detection method for the power equipment based on the multi-signal classification positioning algorithm according to claim 1, characterized in that: in step S2, the object for which the fourier transform is directed is the time domain state of the received discharge digital signal, wherein the amount of signal data received by a single microphone sensor is associated with the number of snapshots and is a signal length parameter in the fourier transform.
5. The partial discharge detection method for the power equipment based on the multi-signal classification positioning algorithm as claimed in claim 1, wherein: in step S2, the signal strength after conversion into the frequency domain is represented by the size of the ordinate, and the frequency corresponding to the portion having a large signal strength should be considered more in step S3.
6. The partial discharge detection method for the power equipment based on the multi-signal classification positioning algorithm according to claim 1, characterized in that: in step S4, the sound source localization algorithm applicable to multi-signal classification in the case of small signal-to-noise ratio and small snapshot count is applied on the condition that when the signal can be regarded as a narrowband signal, for the narrowband signal Sk(t) has:
Sk(t-t1)≈Sk(t)
wherein, t1The time required for the delay between the array microphone units.
7. The partial discharge detection method for the power equipment based on the multi-signal classification positioning algorithm according to claim 1, characterized in that: in step S4, the received discharge signal is first processed by obtaining a covariance matrix, and then is subjected to subsequent signal classification.
Figure FDA0003750485840000021
Wherein X (i) is the ith received signal data, XH(i) The Hermite matrix of X (i), N being the total number of data received.
8. The partial discharge detection method for the power equipment based on the multi-signal classification positioning algorithm according to claim 1, characterized in that: due to the planar array, in step S4, the time delay tau involved in the algorithmm,kThe method comprises the following steps:
Figure FDA0003750485840000022
wherein, taum,kIs the relative delay, theta, of the signal source k and the array element m relative to the center of the arraykAnd
Figure FDA0003750485840000023
the pitch angle and azimuth angle of a signal source k, r is the radius of the circular array, and M is the number of array elements.
9. The partial discharge detection method for the power equipment based on the multi-signal classification positioning algorithm according to claim 1, characterized in that: since the array is a planar array, in step S4, the direction matrix a involved in the algorithm should be:
Figure FDA0003750485840000024
wherein,
Figure FDA0003750485840000025
a direction vector generated for the array to receive the D-th signal source information.
10. The partial discharge detection method for the power equipment based on the multi-signal classification positioning algorithm as claimed in claim 1, wherein: the spatial spectrum function of the improved multi-signal classification algorithm in step S4 is
Figure FDA0003750485840000026
Wherein,
Figure FDA0003750485840000027
and is disclosed in
Figure FDA0003750485840000028
Respectively, is a conventional spatial spectrum function P
Figure FDA00037504858400000210
Is aligned with
Figure FDA0003750485840000029
And thetanThe second derivative of (a).
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Citations (2)

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