CN116008735A - Partial discharge signal extraction method and system based on density - Google Patents
Partial discharge signal extraction method and system based on density Download PDFInfo
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Abstract
The invention discloses a density-based partial discharge signal extraction method, which comprises the following steps: (1) acquiring a first original partial discharge signal sequence; (2) Extracting partial discharge pulse based on a pulse edge searching method of a second-order envelope curve to obtain a second-order envelope curve sequence and a pulse index matrix; (3) Comparing the second-order envelope maxima V in the second-order envelope sequence max And set threshold V th If V max ≥V th Removing the partial discharge signal as an interference signal to obtain a second original partial discharge signal; (4) Extracting equivalent bandwidth F of a pulse index matrix of a second original partial discharge signal as a frequency domain characteristic quantity, and extracting first-order envelope information as a time domain characteristic quantity; removing the interference signal for the second time based on the frequency domain characteristic quantity and the time domain characteristic quantity to obtain a third initial partial discharge signal; (5) Aiming at the third initial partial discharge signal, a clustering center is searched by a density-based pulse clustering method, and outliers deviating from the clustering center are taken as interference signals to be removed.
Description
Technical Field
The present invention relates to a signal extraction method and system, and in particular, to a partial discharge signal extraction method and system.
Background
In the manufacturing and running of the power equipment, the generated insulation defects can cause insulation local field intensity concentration, thereby causing insulation local breakdown and triggering local discharge; therefore, in order to ensure safe operation of the power equipment, partial discharge signal (Partial Discharge, PD) detection, which is a key detection technique reflecting the insulation condition of the high-voltage power equipment, is required for the power equipment.
The partial discharge type can be judged according to the detected partial discharge signals, the damage degree and the damage mechanism of the partial discharge of different defect types to the insulation are different, and the identification of the partial discharge defect type has important theoretical significance and practical value for the diagnosis and evaluation of the insulation state of the power equipment.
In the field actual monitoring process, various noises always exist in the detection field aiming at the partial discharge signals, and if the original signals are directly collected for diagnosis, erroneous judgment can be caused; therefore, in order to accurately identify the type of partial discharge defect, it is necessary to first perform separation of the partial discharge signal and the interference signal with respect to the collected original signal.
Based on the above, the invention is expected to obtain a density-based partial discharge signal extraction method and system, which can quickly and accurately separate effective partial discharge signals based on detected original signals.
Disclosure of Invention
The invention aims to provide a density-based partial discharge signal extraction method, which can firstly adopt a threshold strategy to remove part of interference signals in partial discharge signals, then extract pulse frequency domain characteristic quantity and time domain characteristic quantity to further separate part of interference signals which are easy to remove, finally remove the interference signals for the third time based on density-based self-adaptive pulse clustering to realize effective separation of the partial discharge signals and the interference signals, and can be effectively applied to the electric power field and has good popularization and application prospects.
Based on the above object, the present invention provides a density-based partial discharge signal extraction method, which includes the steps of:
(1) The acquisition device acquires a first original partial discharge signal sequence of the power equipment, wherein the first original partial discharge signal comprises an effective partial discharge signal and an interference signal;
(2) Partial discharge pulse extraction is carried out by adopting a pulse edge searching method based on a second-order envelope curve so as to obtain a second-order envelope curve sequence and a pulse index matrix;
(3) Maximum value V of second-order envelope curve in second-order envelope curve sequence max And a set threshold V th Comparing to remove the interference signal for the first time to obtain a second original partial discharge signal: if V max ≥V th Then it is rejected as an interference signal;
(4) Extracting equivalent bandwidth F of a pulse index matrix of the second original partial discharge signal as a frequency domain feature quantity, and extracting first-order envelope information of the pulse index matrix of the second original partial discharge signal as a time domain feature quantity; removing the interference signal for the second time based on the frequency domain characteristic quantity and the time domain characteristic quantity to obtain a third initial partial discharge signal;
(5) Aiming at a third initial partial discharge signal, a density-based pulse clustering method is adopted to find a clustering center, outliers deviating from the clustering center are used as interference signals, and the interference signals are removed for the third time, so that an effective partial discharge signal is obtained.
In order to improve the accuracy of partial discharge fault diagnosis, the partial discharge signal and the interference signal in the detected original signal are required to be separated, so that the invention designs a partial discharge signal extraction method which can separate the partial discharge signal and the interference signal.
In the technical scheme, the invention provides a density-based partial discharge signal extraction method, which can firstly adopt a threshold strategy to remove part of interference signals in partial discharge signals, then extract pulse frequency domain characteristic quantity and time domain characteristic quantity to further separate part of interference signals which are easy to remove, finally remove the interference signals for the third time based on density-based self-adaptive pulse clustering, so as to realize effective separation of the partial discharge signals and the interference signals.
Further, in the partial discharge signal extraction method of the present invention, in step (1), a fourth-order hilbert fractal antenna is used to collect a partial discharge UHF (ultra high frequency) signal within a period of time, so as to serve as a first original partial discharge signal sequence.
Further, in the partial discharge signal extraction method according to the present invention, between the step (1) and the step (2), further includes: and normalizing the first original partial discharge signal.
In the above technical solution of the present invention, the normalization processing may be further performed on the first original partial discharge signal obtained in the step (1), and the technical means of the normalization processing adopted belong to the prior art known to those skilled in the art, and are not described in detail herein.
Further, in the partial discharge signal extraction method according to the present invention, step (2) includes:
(2a) For the first original partial discharge signal sequence x= [ x ] 1 ,x 2 ,…,x N ]Processing according to the following formula to obtain a processed sequence
Wherein x is i For the first original partial discharge signal amplitude, N is the length of the first original partial discharge signal sequence.
(2b) Pair sequence
Searching the maximum value point to obtain a sequence V peak Then to sequence V peak Searching maximum value points to obtain sequences
Pair sequence
Performing linear interpolation and setting the value smaller than the preset value a to zero to obtain a second-order envelope line sequence with the length of N +.>
(2c) Searching for a maximum sequence of white noise levels having a second order envelope sequence amplitude higher than the first original partial discharge signal sequence
And recording its index V in the first original partial discharge signal sequence loc To
As starting point, searching the second-order envelope zero point leftwards and rightwards respectively, and recording the matrix P with the index of the single pulse starting point and the ending point Index ＝[P s1 ,P e1 ；P s2 ,P e2 ；...；P sm ,P em ]The method comprises the steps of carrying out a first treatment on the surface of the Wherein m represents the maximum number of second-order envelope sequences above the white noise level; p (P) s1 To P sm For single pulse starting point, P e1 To P em Is a single pulse termination point;
(2d) For single pulse starting point and ending point matrix P Index The same pulse judgment is carried out to obtain a pulse index matrix
Wherein n represents the number of maximum pulse points after the same pulse judgment; p (P) s1 To P sn For single pulse starting point, P e1 To P en Is a single pulse termination point; p (P) max1 To P maxn Representing the extreme point of each single pulse.
In the above technical solution of the present invention, the step (2) of the partial discharge signal extraction method of the present invention may further include steps (2 a) - (2 d), which may perform partial discharge pulse extraction according to a pulse edge search method of a second-order envelope based on the collected first original partial discharge signal sequence, so as to obtain a second-order envelope sequence and a pulse index matrix.
Further, in the partial discharge signal extraction method of the present invention, the preset value a is 3 to 5 times of the vertical precision of the acquisition device.
Further, in the partial discharge signal extraction method according to the present invention, in step (4), the first-order envelope information includes: first-order envelope sequence, pulse envelope wavefront 1-norm A s Pulse envelope tail 1-norm a e The method comprises the steps of carrying out a first treatment on the surface of the The step of extracting first-order envelope information of the pulse index matrix of the second original partial discharge signal as the time domain feature quantity comprises the following steps:
(4a) For the second original partial discharge signal sequence x= [ X ] 1 ,X 2 ,…,X M ]Processing according to the following formula to obtain a processed sequence
Wherein X is i M is the length of the second original partial discharge signal sequence;
(4b) Pair sequence
Searching maximum points to obtain a maximum point sequence, and performing linear interpolation on the maximum point sequence to obtain a first-order envelope sequence V= [ V ] 1 ,V 2 ,...,V p ,...,V N1 ]Wherein N1 represents the number of pulse signal sequences, V p Is the maximum value of the first-order envelope curve;
(4c) Obtaining the pulse envelope wavefront 1-norm A based on the following formula s Pulse envelope tail 1-norm a e ：
Wherein V is i Representing elements in the first order envelope sequence V.
In the above-mentioned technical solution of the present invention, in the step (4), in addition to the first order including line information heard in the steps (4 a) - (4 c), an equivalent bandwidth F of the pulse index matrix of the second original partial discharge signal needs to be extracted as the frequency domain feature quantity.
In the invention, the pulse index matrix of the second original partial discharge signal is subjected to frequency domain analysis, and the equivalent bandwidth F is selected as the frequency domain characteristic quantity, wherein the equivalent bandwidth F can be obtained by the following formula:
in the above formula, S (f) is fourier transform of the time domain signal, and f is the second original partial discharge signal that needs to be subjected to frequency domain processing.
Further, in the partial discharge signal extraction method of the present invention, in step (5), a density peak value fast search and clustering algorithm is adopted to find a clustering center.
Further, in the partial discharge signal extraction method according to the present invention, step (5) includes:
(5a) Calculating the local density and distance of each sample point in the sequence of the third initial partial discharge signal, and carrying out normalization processing;
(5b) Calculating a clustering index based on the local density and the distance;
(5c) Constructing N clustering index subsets, calculating the standard deviation of each clustering index subset, and rounding downwards to obtain a standard deviation sequence;
(5d) Searching the label difference non-zero value to obtain a clustering center.
Accordingly, another object of the present invention is to provide a density-based partial discharge signal extraction system, which can be used to implement the above-mentioned partial discharge signal extraction method of the present invention, and can quickly and accurately separate effective partial discharge signals based on detected original signals, so that the system has a very good application prospect, and can be effectively applied to the electric field.
Based on the above object, the present invention also proposes a density-based partial discharge signal extraction system, comprising:
an acquisition device for executing the step (1) of the partial discharge signal extraction method of the present invention;
an extraction module that performs steps (2) - (5) of the partial discharge signal extraction method of the present invention described above.
Further, in the partial discharge signal extraction system of the present invention, the acquisition device includes a fourth-order hilbert fractal antenna, and the fourth-order hilbert fractal antenna is used to acquire a partial discharge UHF signal within a period of time, so as to serve as a first original partial discharge signal sequence.
Compared with the prior art, the density-based partial discharge signal extraction method and system have the following advantages:
the density-based partial discharge signal extraction method and system can effectively solve the problem of noise existing in the original signals detected and collected in the prior art, and can quickly and accurately separate out effective partial discharge signals based on the detected original signals; based on the separated effective partial discharge signals, the type of the partial discharge defect can be accurately identified, and the insulation state of the power equipment can be diagnosed.
According to the density-based partial discharge signal extraction method, a threshold strategy can be adopted to remove part of interference signals in partial discharge signals, then pulse frequency domain characteristic quantity and time domain characteristic quantity are extracted to further separate part of interference signals which are easy to remove, finally the density-based self-adaptive pulse clustering is adopted to remove the interference signals for the third time, so that effective separation of the partial discharge signals and the interference signals is achieved, and the method has good popularization and application prospects and can be effectively applied to the electric power field.
Accordingly, the density-based partial discharge signal extraction system of the present invention can be used to implement the partial discharge signal extraction method of the present invention, which also has the advantages and benefits described above.
Drawings
Fig. 1 schematically shows a flow chart of a density-based partial discharge signal extraction method according to the present invention in one embodiment.
Detailed Description
The density-based partial discharge signal extraction method and system according to the present invention will be further explained and illustrated with reference to the drawings and specific embodiments, but the explanation and illustration do not unduly limit the technical solution of the present invention.
Fig. 1 schematically shows a flow chart of a density-based partial discharge signal extraction method according to the present invention in one embodiment.
As shown in fig. 1, in the present embodiment, the density-based partial discharge signal extraction method according to the present invention may include the following steps (1) - (5):
(1) The acquisition device acquires a first original partial discharge signal sequence of the power equipment, wherein the first original partial discharge signal comprises an effective partial discharge signal and an interference signal.
It should be noted that, in this embodiment, the acquisition device further includes a fourth-order hilbert fractal antenna, and the partial discharge UHF (ultra high frequency) signal in a period of time can be acquired by using the fourth-order hilbert fractal antenna to be used as the first original partial discharge signal sequence.
It should be noted that in this embodiment, a four-order hilbert fractal antenna may be used to collect several groups, for example, 400 groups of partial discharge UHF signals, as the first original partial discharge signal sequence.
Correspondingly, in this embodiment, normalization processing is further required for the first original partial discharge signal sequence acquired; of course, in some other embodiments, normalization may not be performed. The technical means of normalization processing adopted belongs to the prior art known to the person skilled in the art, and is not repeated here.
(2) And carrying out partial discharge pulse extraction based on a pulse edge searching method of the second-order envelope curve to obtain a second-order envelope curve sequence and a pulse index matrix.
In this embodiment, based on the normalized first original partial discharge signal sequence, partial discharge pulse extraction may be performed according to a pulse edge search method of the second-order envelope, so as to obtain the second-order envelope sequence and the pulse index matrix.
In the above step (2) of the present invention, the specific process of obtaining the second-order envelope sequence and the pulse index matrix based on the first original partial discharge signal sequence may include the following steps (2 a) - (2 d):
(2a) For the first original partial discharge signal sequence x= [ x ] 1 ,x 2 ,…,x N ]Processing according to the following formula (1) to obtain a processed sequence
In the above formula (1), x i For the first original partial discharge signal amplitude, N is the length of the first original partial discharge signal sequence.
(2b) Pair sequence
Searching the maximum value point to obtain a sequence V peak Then to sequence V peak Searching maximum value points to obtain sequences
For sequences->
Performing linear interpolation and setting the value smaller than the preset value a to zero to obtain a second-order envelope line sequence with the length of N +.>
Wherein k represents the sequence V peak Maximum number of (2).
In this embodiment, in the step (2 b), the preset value a may be preferably set to 3 to 5 times the vertical accuracy of the acquisition device, and may be set to 1.5mV.
Accordingly, the second order obtained in step (2 b) is based on the above-mentioned line sequence
The pulse edge search method may be further adopted to extract partial discharge pulses, and the specific operations thereof are as shown in the following steps (2 c) and (2 d). />
(2c) Searching for a maximum sequence of white noise levels having a second order envelope sequence amplitude higher than the first original partial discharge signal sequence
And recording its index V in the first original partial discharge signal sequence loc To
As starting point, searching the second-order envelope zero point leftwards and rightwards respectively, and recording the matrix P with the index of the single pulse starting point and the ending point Index ＝[P s1 ,P e1 ；P s2 ,P e2 ；...；P sm ,P em ]The method comprises the steps of carrying out a first treatment on the surface of the Wherein m represents the maximum number of second-order envelope sequences above the white noise level; p (P) s1 To P sm For single pulse starting point, P e1 To P em Is a single pulse termination point.
(2d) For single pulse starting point and ending point matrix P Index The same pulse judgment is carried out to obtain a pulse index matrix
Wherein n represents the number of maximum pulse points after the same pulse judgment; p (P) s1 To P sn For single pulse starting point, P e1 To P en Is a single pulse termination point; p (P) max1 To P maxn Representing the extreme point of each single pulse.
In the step (2 d), since there may be a case where the same pulse has multiple second-order peak points, which results in a case where the edge search in the step (2 c) has repetition of the single-pulse start point and the single-pulse end point, it is necessary to delete the single-pulse start point and end point matrix P Index Is a repeated row of (c).
During the experiment, the inventors found that the existence of a discontinuous second-order envelope in the single pulse resulted in a single pulse start and end point matrix P Index Is embodied in two rows and P en And P sn+1 The phase difference is small.
For this purpose, in the present invention, the matrix P of the starting point and the ending point of the single pulse is further increased Index And judging the distance dplurse between the two points of the second column of the nth row and the first column of the (n+1) th row so as to carry out the same pulse judgment. For example, in the present embodiment, the threshold value of the distance dplurse may be controlled to be 0.125 μs, if the distance dplurse is>0.125 mus (the time required to form 10 data points), then two pulses are determined, otherwise the n-th row and n+1-th row of a single pulse are considered to be combined into one row.
(3) Maximum value V of second-order envelope curve in second-order envelope curve sequence max And a set threshold V th Comparing to remove the interference signal for the first time to obtain a second original partial discharge signal: if V max ≥V th Then it is rejected as an interference signal.
In the step (3), a threshold strategy may be adopted to reject a part of the interference signals in the partial discharge signals. The operator can preset the threshold value V th And the pulse shape above this threshold is considered to be an interfering signal. The present invention thus provides a second-order envelope maximum value V of the second-order envelope sequence obtained in the above step (2 b) max And a set threshold V th Comparing if V max ≥V th And eliminating the partial discharge signal as an interference signal to obtain a second original partial discharge signal.
(4) Extracting equivalent bandwidth F of a pulse index matrix of the second original partial discharge signal as a frequency domain feature quantity, and extracting first-order envelope information of the pulse index matrix of the second original partial discharge signal as a time domain feature quantity; and removing the interference signal for the second time based on the frequency domain characteristic quantity and the time domain characteristic quantity to obtain a third initial partial discharge signal.
In the invention, the pulse index matrix of the second original partial discharge signal can be subjected to frequency domain analysis, the equivalent bandwidth F is selected as the frequency domain characteristic quantity, and the equivalent bandwidth F can be obtained by the following formula (2):
in the above formula (2), S (f) is fourier transform of the time domain signal, and f is the second original partial discharge signal that needs to be subjected to frequency domain processing.
In the present invention, extracting first-order envelope information of the pulse index matrix of the second original partial discharge signal as the time-domain feature quantity may further include the following steps (4 a) - (4 c):
(4a) For the second original partial discharge signal sequence x= [ X ] 1 ,X 2 ,…,X M ]Processing according to the following formula to obtain a processed sequence
Wherein X is i And M is the length of the second original partial discharge signal sequence.
(4b) Pair sequence
Searching maximum points to obtain a maximum point sequence, and performing linear interpolation on the maximum point sequence to obtain a first-order envelope sequence V= [ V ] 1 ,V 2 ,...,V p ,...,V N1 ]Wherein N1 represents the number of pulse signal sequences, V p Is the first order envelope maximum.
(4c) Obtaining the pulse envelope wavefront 1-norm A based on the following formula s Pulse envelope tail 1-norm a e ：
Wherein V is i Representing elements in the first order envelope sequence V.
Because different types of pulses have different ranges of characteristic values, the interference signals can be removed for the second time based on the frequency domain characteristic values and the time domain characteristic values by a method for analyzing the characteristic values so as to obtain a third initial partial discharge signal.
Therefore, in the step (4) of the present invention, after the frequency domain feature quantity and the time domain feature quantity are obtained, the features of the partial discharge signal and the pulse interference signal can be further analyzed, and through the test, the features of the pulse interference signal are as follows: the amplitude of the signal is larger, the oscillation starts to decay after oscillation increases by one cycle, and the frequency spectrum is shown to have a double-frequency peak value and larger standard deviation; the pulse amplitude of the partial discharge signal is small, the partial discharge signal has exponential decay trend, a single frequency peak point is formed on the frequency spectrum, and the fluctuation amplitude is small, so that the single partial discharge waveform is more stable than the pulse interference signal generated by the variable frequency power supply.
The comparison shows that the time-frequency domain difference between the interference signal and the partial discharge signal is obvious, so that after the frequency domain characteristic quantity and the time domain characteristic quantity are obtained, a pulse time domain signal image and a frequency spectrum image can be established, the rough distinction of the signal types is carried out based on the pulse time domain signal image and the frequency spectrum image, and the interference signal is removed for the second time, so that a third original partial discharge signal is obtained.
Therefore, in the partial discharge signal extraction method disclosed by the invention, in the step (3) and the step (4), a threshold strategy is adopted to remove part of the interference signals, and then the pulse frequency domain characteristic quantity and the time domain characteristic quantity are extracted to further separate part of the interference signals which are easy to remove.
However, in actual engineering, the third original partial discharge signal obtained after the processing in the step (3) and the step (4) still contains discrete noise partially overlapped in the partial discharge signal, so that the following step (5) is further needed to be adopted to reject the interference signal for the third time.
(5) Aiming at a third initial partial discharge signal, a density-based pulse clustering method is adopted to find a clustering center, outliers deviating from the clustering center are used as interference signals, and the interference signals are removed for the third time, so that an effective partial discharge signal is obtained.
In this embodiment, a density peak fast search and clustering algorithm (Clustering by Fast Search and Find ofDensity Peaks, CFSFDP) may be preferably used to find the cluster center.
In the invention, the CFSFDP algorithm is a density-based clustering algorithm, and the clustering center and the number of types can be determined by calculating the local density and the distance of the sample points by adopting the algorithm, so that better separation of partial discharge pulse signals and noise signals is realized.
In the clustering, the sequence of the third initial partial discharge signals is expressed as
d ij ＝dist(x i ,x j ) Sample point x in the sequence representing the third initial partial discharge signal i And x j Distance between them.
Accordingly, in the present invention, for any sample point x in the sequence D of the third original partial discharge signal i Defining the local density ρ i As shown in the following equation (6), the distance delta is defined i 7 as shown in the following formula (7):
wherein I is s Number indicating sequence value, I s ＝{1,2,……,N}；d c Is the cut-off distance, and d c >0, which is generally selected as d ij The distance values at 1-2% of the positions are arranged in ascending order, with delta for the point of greatest local density i ＝max(d ij )。
Therefore, in the invention, the local density and distance of all sample points can be calculated, the sample points are projected to a two-dimensional plane formed by the local density and the distance, and the local density and distance value of the sample cluster density center is large, so that the sample cluster center and the number of types can be determined through a local density-distance value graph. Then, attribute tags (class 1, class2, …, class n) are further assigned to the cluster centers, other sample points are assigned to clusters closest thereto and having high-density value sample points, and noise points are removed by an average local density index.
It should be noted that, the CFSFDP algorithm is implemented by manually determining the number of sample clusters, but in practical application, the number of sample clusters is unknown and is affected by subjective factors, and for the same decision diagram, different users can obtain different clustering results. For this reason, in this embodiment, the present invention preferably employs an adaptive clustering center decision algorithm in step (5), and the specific steps thereof further include the following steps (5 a) - (5 d):
(5a) And calculating the local density and the distance of each sample point in the sequence of the third initial partial discharge signal, and carrying out normalization processing.
In the step (5 a), the local density and distance of each sample point in the sequence of the third initial partial discharge signal according to the present invention may be calculated by the above formula (6) and formula (7); accordingly, the normalization processing performed in the step (5 a) belongs to a technical means known in the prior art, and is not described in detail herein.
(5b) And calculating a clustering index based on the local density and the distance.
In the above step (5 b), in the present embodiment, the clustering index γ can be further calculated based on the local density and distance of the sample points, as shown in the following formula (8):
wherein alpha is a cluster index normalization coefficient, and e is a natural constant; experiments prove that in the embodiment, when alpha=100 is controlled, a more stable clustering result can be obtained.
After obtaining the clustering index corresponding to each sample point, the clustering index gamma is arranged in ascending order to obtain gamma= [ gamma ] 1 ,γ 2 ,γ 3 ,…γ N ]The method comprises the steps of carrying out a first treatment on the surface of the At the same time, the index loc= [ loc ] in the original sample point sequence can also be obtained 1 ,loc 2 ,loc 3 ,…loc N ]The method comprises the steps of carrying out a first treatment on the surface of the Where N is the total number of sample points.
(5c) And constructing N clustering index subsets, calculating the standard deviation of each clustering index subset, and rounding downwards to obtain a standard deviation sequence.
In the step (5 c), N subsets of cluster indicators need to be constructed, namely: γγ1= [ γ1], γγ2= [ γ1, γ2], γγ3= [ γ1, γ2, γ3], …, γn= [ γ1, γ2, …, γk, …, γn ]; where N is the total number of sample points.
In the invention, after N cluster index subsets are constructed and obtained, the standard deviation of each cluster index subset needs to be further calculated and rounded down to obtain a standard deviation sequence sigma= [ sigma ] 1 ,σ 2 ,...,σ k ,...,σ N ]The method comprises the steps of carrying out a first treatment on the surface of the Where N is the total number of sample points. k is E [1,2, …, N]。
(5d) Searching the label difference non-zero value to obtain a clustering center.
In the above step (5 d), the marked difference non-zero value σ in the standard deviation sequence σ can be found k From this, the cluster center [ sigma ] can be obtained k-1 ,σ k ,...,σ N ]Simultaneous clustering center [ sigma ] k-1 ,σ k ,...,σ N ]The total number of (2) is the number of cluster centers, which corresponds to [ loc ] k-1 ,loc k ,...,loc N ]Is the index position of the cluster center in the original sample point sequence.
In the present embodiment, the larger the value of the clustering index γ determined by the above formula (8), the more likely it is to be a clustering center. In the embodiment, the invention can automatically acquire the number of sample clusters and the center sample point by adopting the self-adaptive clustering center decision algorithm.
In this way, in the step (5) of the partial discharge signal extraction method according to the present invention, the partial discharge signal cluster can be further determined by performing the CFSFDP density-based adaptive clustering on the third original partial discharge signal, the cluster center can be found by using the density-based adaptive pulse clustering method, and the outlier deviating from the cluster center can be used as the interference signal, and the interference signal can be removed for the third time, thereby obtaining the effective partial discharge signal.
It should be noted that, in this embodiment, in order to implement the above-mentioned partial discharge extraction method according to the present invention, the implementation of the present invention employs a partial discharge signal extraction system, which may include: the device comprises a collecting device and an extracting module. Wherein the acquisition means in the system may be used to perform the operation of step (1) above of the partial discharge extraction method of the invention; the extraction module in the system may be used to perform the operations of steps (2) -step (5) described above of the partial discharge extraction method of the present invention.
In summary, the density-based partial discharge signal extraction method and system can effectively solve the problem of noise existing in the original signals detected and collected in the prior art, and can quickly and accurately separate effective partial discharge signals based on the original signals obtained by detection; based on the separated effective partial discharge signals, the type of the partial discharge defect can be accurately identified, and the insulation state of the power equipment can be diagnosed.
It should be noted that the prior art part in the protection scope of the present invention is not limited to the embodiments set forth in the present application, and all prior art that does not contradict the scheme of the present invention, including but not limited to the prior patent document, the prior publication, the prior disclosure, the use, etc., can be included in the protection scope of the present invention.
In addition, the combination of the features described in the present application is not limited to the combination described in the claims or the combination described in the embodiments, and all the features described in the present application may be freely combined or combined in any manner unless contradiction occurs between them.
It should also be noted that the above-recited embodiments are merely specific examples of the present invention. It is apparent that the present invention is not limited to the above embodiments, and similar changes or modifications will be apparent to those skilled in the art from the present disclosure, and it is intended to be within the scope of the present invention.
Claims (10)
1. The partial discharge signal extraction method based on the density is characterized by comprising the following steps:
(1) The acquisition device acquires a first original partial discharge signal sequence of the power equipment, wherein the first original partial discharge signal comprises an effective partial discharge signal and an interference signal;
(2) Partial discharge pulse extraction is carried out by adopting a pulse edge searching method based on a second-order envelope curve so as to obtain a second-order envelope curve sequence and a pulse index matrix;
(3) Maximum value V of second-order envelope curve in second-order envelope curve sequence max And a set threshold V th Comparing to remove the interference signal for the first time to obtain a second original partial discharge signal: if V max ≥V th Then it is rejected as an interference signal;
(4) Extracting equivalent bandwidth F of a pulse index matrix of the second original partial discharge signal as a frequency domain feature quantity, and extracting first-order envelope information of the pulse index matrix of the second original partial discharge signal as a time domain feature quantity; removing the interference signal for the second time based on the frequency domain characteristic quantity and the time domain characteristic quantity to obtain a third initial partial discharge signal;
(5) Aiming at a third initial partial discharge signal, a density-based pulse clustering method is adopted to find a clustering center, outliers deviating from the clustering center are used as interference signals, and the interference signals are removed for the third time, so that an effective partial discharge signal is obtained.
2. The partial discharge signal extraction method as defined in claim 1, wherein in step (1), a fourth-order hilbert fractal antenna is used to collect partial discharge UHF signals over a period of time as the first original partial discharge signal sequence.
3. The partial discharge signal extraction method as claimed in claim 1, further comprising, between the step (1) and the step (2): and normalizing the first original partial discharge signal.
4. The partial discharge signal extraction method as claimed in claim 1, wherein the step (2) includes:
(2a) For the first original partial discharge signal sequence x= [ x ] 1 ,x 2 ,…,x N ]Processing according to the following formula to obtain a processed sequence
Wherein x is i N is the length of the first original partial discharge signal sequence;
(2b) Pair sequence
Searching the maximum value point to obtain a sequence V peak Then to sequence V peak Searching maximum value points to obtain sequences
Pair sequence
Performing linear interpolation and setting the value smaller than the preset value a to zero to obtain a second-order envelope line sequence with the length of N +.>
(2c) Searching for a maximum sequence of white noise levels having a second order envelope sequence amplitude higher than the first original partial discharge signal sequence
And record it in the first original partIndex V in discharge signal sequence loc To
As starting point, searching the second-order envelope zero point leftwards and rightwards respectively, and recording the matrix P with the index of the single pulse starting point and the ending point Index ＝[P s1 ,P e1 ；P s2 ,P e2 ；...；P sm ,P em ]The method comprises the steps of carrying out a first treatment on the surface of the Wherein m represents the maximum number of second-order envelope sequences above the white noise level; p (P) s1 To P sm For single pulse starting point, P e1 To P em Is a single pulse termination point;
(2d) For single pulse starting point and ending point matrix P Index The same pulse judgment is carried out to obtain a pulse index matrix
Wherein n represents the number of maximum pulse points after the same pulse judgment; p (P) s1 To P sn For single pulse starting point, P e1 To P en Is a single pulse termination point; p (P) max1 To P maxn Representing the extreme point of each single pulse.
5. The partial discharge signal extraction method of claim 4, wherein the preset value a is 3-5 times the vertical accuracy of the acquisition device.
6. The partial discharge signal extraction method as claimed in claim 4, wherein in step (4), the first-order envelope information includes: first-order envelope sequence, pulse envelope wavefront 1-norm A s Pulse envelope tail 1-norm a e The method comprises the steps of carrying out a first treatment on the surface of the The step of extracting first-order envelope information of the pulse index matrix of the second original partial discharge signal as the time domain feature quantity comprises the following steps:
(4a) For the second original partial discharge signal sequence x= [ X ] 1 ,X 2 ,…,X M ]Processing according to the following formula to obtain a processed sequence
Wherein X is i M is the length of the second original partial discharge signal sequence;
(4b) Pair sequence
Searching maximum points to obtain a maximum point sequence, and performing linear interpolation on the maximum point sequence to obtain a first-order envelope sequence V= [ V ] 1 ,V 2 ,...,V p ,...,V N1 ]Wherein N1 represents the number of pulse signal sequences, V p Is the maximum value of the first-order envelope curve;
(4c) Obtaining the pulse envelope wavefront 1-norm A based on the following formula s Pulse envelope tail 1-norm a e ：
Wherein V is i Representing elements in the first order envelope sequence V.
7. The partial discharge signal extraction method as claimed in claim 1, wherein in step (5), a density peak fast search and clustering algorithm is used to find a cluster center.
8. The partial discharge signal extraction method as claimed in claim 7, wherein the step (5) includes:
(5a) Calculating the local density and distance of each sample point in the sequence of the third initial partial discharge signal, and carrying out normalization processing;
(5b) Calculating a clustering index based on the local density and the distance;
(5c) Constructing N clustering index subsets, calculating the standard deviation of each clustering index subset, and rounding downwards to obtain a standard deviation sequence;
(5d) Searching the label difference non-zero value to obtain a clustering center.
9. A density-based partial discharge signal extraction system, comprising:
acquisition means performing step (1) of the partial discharge signal extraction method according to any of claims 1, 3-8;
an extraction module performing steps (2) - (5) of the partial discharge signal extraction method as claimed in any of claims 1, 3-8.
10. The partial discharge signal extraction system of claim 9, wherein the acquisition means comprises a fourth order hilbert fractal antenna, and wherein the fourth order hilbert fractal antenna is used to acquire partial discharge UHF signals over a period of time as the first original partial discharge signal sequence.
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