CN103091612A - Separation and recognition algorithm for transformer oiled paper insulation multiple partial discharging source signals - Google Patents

Abstract

The invention discloses a separation and recognition algorithm for transformer oiled paper insulation multiple partial discharging source signals. According to the separation and recognition algorithm, time-frequency analysis and close-neighbor similar transmission clusters are adopted to conduct separation and recognition of the multiple partial discharging source signals. The separation and recognition algorithm comprises the steps of firstly, using S transformation to conduct the time-frequency analysis for partial discharging pulses, obtaining an S transformation amplitude (STA) matrix, calculating similarity of the pulses, secondly, using the similarity matrix to conduct the close-neighbor transmission cluster, achieving automatic separation of the discharging pulses, finally, extracting fingerprint feature recognition discharging source modes of a pulse phase partial distribution (PRPD) pattern map of subclass pulses, and using the multiple discharging source signals which are collected through tests and combined by manual work for verification of effectiveness of the separation and recognition algorithm. According to the separation and recognition algorithm for the transformer oiled paper insulation multiple partial discharging source signals, the number of the clusters and corresponding pulse groups can be provided according to the similarity among the pulses and free of influence of the pulse width. When certain pulse width is used for extracting a single pulse wave form for collected PD original data, separation of the multiple discharging sources can be well achieved.

Description

A kind of separation and recognizer of many shelf depreciations of transformer oil paper insulation source signal

Technical field

The present invention relates to insulation of electrical installation on-line monitoring and fault diagnosis field, particularly a kind of separation and recognizer of many Partial Discharge Sources of transformer oil paper insulation pulse signal.

Background technology

Power transformer is the nucleus equipment in electrical network, thereby inevitably can produce the shelf depreciation (PD) that insulation defect causes in device fabrication, transportation and During Process of Long-term Operation, has had a strong impact on the operational reliability of transformer.Therefore, the analysis and diagnosis of shelf depreciation source information is the important content in Transformer State Assessment, provides reference frame for formulating rational maintenance and Strategies of Maintenance.

Classic method often adopts impulse phase distribution pattern (PRPD) the figure spectral shape of discharge pulse signal and identifies the discharge source type according to the characteristic parameter of its extraction.But in actual moving process, because transformer oil paper insulation mostly is liquid-solid two-phase compound inslation, its inner structure is complicated, often in meeting many places initiation shelf depreciation; Simultaneously, abominable running environment also might cause sleeve surface to discharge.Therefore, the transformer insulated local discharge signal that field monitoring the obtains stack of many places discharge source signal often, the PRPD collection of illustrative plates that causes gained is the overlay information of a plurality of discharge source.Therefore, adopt PRPD figure spectrum signature or the parameter of traditional single discharge source directly to identify and to diagnose many shelf depreciations source signal, need to carry out pulse separation to the many discharge source superposed signal that receives before discharge source type identification and diagnosis.In theory, the signal from same Partial Discharge Sources that sensor receives has similar characteristic, having certain representative feature by extraction makes the pulse with similar characteristics can be gathered into a class, after realizing the automatic separation of many discharge source pulse, in conjunction with the discharge fingerprint of single defective, can diagnose many discharge source.

Summary of the invention

In view of this, technical matters to be solved by this invention is to provide a kind of separation and recognizer of many Partial Discharge Sources of transformer oil paper insulation pulse signal.

The object of the present invention is achieved like this:

Separation and the recognizer of a kind of many shelf depreciations of transformer oil paper insulation source signal provided by the invention comprise the following steps:

S1: gather many shelf depreciations source signal and single shelf depreciation source signal;

S2: many shelf depreciations source signal is analyzed obtained single PD pulse train and phase-amplitude matrix data;

S3: calculate single PD pulse train and carry out similarity and generate similarity matrix;

S4: the phase-amplitude matrix data is carried out cluster in conjunction with similarity matrix and generates subclass pulse PRPD spectrum data by neighbour's propagation clustering;

S5: identify subclass pulse PRPD spectrum data and separate many shelf depreciations source signal to form different discharge source types according to the PRPD collection of illustrative plates fingerprint characteristic of single shelf depreciation source signal.

Further, the generation of described similarity matrix specifically comprises the following steps:

S31: single PD pulse train is carried out the S conversion and generates STA amplitude matrix;

S32: adopt following formula to calculate the similarity of STA amplitude matrix:

$S(i,j)=\frac{\underset{k=1}{\overset{m}{\mathrm{\Σ}}}\underset{l=1}{\overset{n}{\mathrm{\Σ}}}(A_i(k,l)-\stackrel{-}{A_i})(A_j(k,l)-\stackrel{-}{A_j})}{\sqrt{\left(\underset{k=1}{\overset{m}{\mathrm{\Σ}}}\underset{l=1}{\overset{n}{\mathrm{\Σ}}}{(A_i(k,l)-\stackrel{-}{A_i})}^2\right)}\left(\underset{k=1}{\overset{m}{\mathrm{\Σ}}}\underset{l=1}{\overset{n}{\mathrm{\Σ}}}{(A_j(k,l)-\stackrel{-}{A_j})}^2\right)}---\left(1\right)$

In formula, S (i, j) characterizes i PD pulse A in STA amplitude matrix _iWith j pulse A _jSimilarity, m and n are respectively line number and the columns of STA amplitude matrix,

With

Be A in STA amplitude matrix _iAnd A _jAverage.

Further, described neighbour's propagation clustering specifically comprises the following steps:

S41: initialization r (i, k) and a (i, k),

Wherein, r (i, k) is Attraction Degree, characterizes sample A _kBe suitable as sample A _iThe degree of cluster centre; A (i, k) is degree of membership, characterizes sample A _iSelect sample A _kAppropriateness as its cluster centre;

S42: upgrade Attraction Degree r (i, k) according to formula (2)～(5) iteration:

r ^old(i,k)=r(i,k) (2)

$r(i,k)=S(i,k)-\underset{j:j\≠k}{\max }[a(i,j)+S(i,j)]---\left(3\right)$

r ^new(i,k)=(1-λ)r(i,k)+λr ^old(i,k) (4)

r(i,k)=r ^new(i,k) (5)

S43: upgrade degree of membership a (i, k) according to formula (6)～(10) iteration:

a ^old(i,k)=a(i,k) (6)

$a(k,k)=\underset{j:j\≠k}{\mathrm{\Σ}}\max \{0,r(j,k)\}---\left(7\right)$

a ^new(i,k)=(1-λ)a(i,k)+λa ^old(i,k) (9)

a(i,k)=a ^new(i,k) (10)

In formula (2)～(10), r ^old(i, k) and a ^old(i, k) characterizes the value of last iteration, r ^new(i, k) and a ^new(i, k) is the value of current iteration, and r (i, k) and a (i, k) are the intermediate variable in iterative process;

S44: iterate operating procedure S42 and S43 are until export one group of stable class center and cluster result thereof;

S45: output cluster result:

$c_i^{*}=\underset{k}{\arg \max }[r(i,k)+a(i,k)]$

Wherein,

The cluster result of expression output, i, k represents sample number;

Further, the PRPD collection of illustrative plates fingerprint characteristic of described single shelf depreciation source signal is realized by following steps:

S51: single shelf depreciation source signal is carried out analyzing and processing form the phase-amplitude matrix data;

S52: the PRPD spectrum data is processed and generated to the phase-amplitude matrix data;

S53: fingerprint characteristic is processed and generated to the PRPD spectrum data.

The invention has the advantages that: the present invention adopts the similar propagation clustering of time frequency analysis and neighbour carry out separating of many Partial Discharge Sources pulse signal and identify, employing S transfer pair partial discharge pulse carries out time frequency analysis, obtain ST amplitude (STA) matrix, and based on the at first similarity of STA matrix computations pulse; Then, adopt similarity matrix to carry out neighbour's propagation clustering, realize the automatic separation of discharge pulse.At last, extract the fingerprint characteristic identification discharge source type of impulse phase distribution pattern (PRPD) collection of illustrative plates of subclass pulse, and adopt artificial combination and test the many discharge source signal that gathers the validity of method has been carried out verification, remarkable result is as follows:

(1) adopt the APC algorithm that many discharge source signal is separated and be based on the ST similarity matrix, can automatically provide clusters number and corresponding pulse train according to the similarity between pulse.

(2) the method is not subjected to the impact of pulsewidth, extracts when the PD raw data that gathers adopts certain pulsewidth the separation that the single pulse waveform can be realized many discharge source preferably.

(3) extract the single pulse waveform when the PD raw data that gathers adopts certain pulsewidth, can realize preferably the separation of many discharge source.

Other advantage of the present invention, target and feature will be set forth to a certain extent in the following description, and to a certain extent, based on being apparent to those skilled in the art to investigating hereinafter, perhaps can be instructed from the practice of the present invention.The objectives and other advantages of the present invention can realize and obtain by specifically noted structure in following instructions and accompanying drawing.

Description of drawings

In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention is described in further detail below in conjunction with accompanying drawing, wherein:

Fig. 1 is many Partial Discharge Sources identification process figure;

Fig. 2 is along face and corona commingle discharging PRPD collection of illustrative plates;

Fig. 3 is along face and corona commingle discharging signal separating resulting;

Fig. 4 is corona and interlayer commingle discharging PRPD collection of illustrative plates;

Fig. 5 is interlayer and corona commingle discharging signal separating resulting.

Embodiment

Below with reference to accompanying drawing, the preferred embodiments of the present invention are described in detail; Should be appreciated that preferred embodiment only for the present invention is described, rather than in order to limit protection scope of the present invention.

Embodiment 1

Fig. 1 is many Partial Discharge Sources identification process figure, and as shown in the figure: separation and the recognizer of a kind of many shelf depreciations of transformer oil paper insulation source signal provided by the invention comprise the following steps:

S1: gather many shelf depreciations source signal and single shelf depreciation source signal;

S2: many shelf depreciations source signal is analyzed obtained single PD pulse train and phase-amplitude matrix data;

S3: calculate single PD pulse train and carry out similarity and generate similarity matrix;

S4: the phase-amplitude matrix data is carried out cluster in conjunction with similarity matrix and generates subclass pulse PRPD spectrum data by neighbour's propagation clustering;

S5: identify subclass pulse PRPD spectrum data and separate many shelf depreciations source signal to form different discharge source types according to the PRPD collection of illustrative plates fingerprint characteristic of single shelf depreciation source signal.

The generation of described similarity matrix specifically comprises the following steps:

S31: single PD pulse train is carried out the S conversion and generates STA amplitude matrix;

S32: adopt following formula to calculate the similarity of STA amplitude matrix:

$S(i,j)=\frac{\underset{k=1}{\overset{m}{\mathrm{\Σ}}}\underset{l=1}{\overset{n}{\mathrm{\Σ}}}(A_i(k,l)-\stackrel{-}{A_i})(A_j(k,l)-\stackrel{-}{A_j})}{\sqrt{\left(\underset{k=1}{\overset{m}{\mathrm{\Σ}}}\underset{l=1}{\overset{n}{\mathrm{\Σ}}}{(A_i(k,l)-\stackrel{-}{A_i})}^2\right)}\left(\underset{k=1}{\overset{m}{\mathrm{\Σ}}}\underset{l=1}{\overset{n}{\mathrm{\Σ}}}{(A_j(k,l)-\stackrel{-}{A_j})}^2\right)}---\left(1\right)$

In formula, S (i, j) characterizes i PD pulse A in STA amplitude matrix _iWith j pulse A _jSimilarity, m and n are respectively line number and the columns of STA amplitude matrix,

With Be A in STA amplitude matrix _iAnd A _jAverage.

Described neighbour's propagation clustering specifically comprises the following steps:

S41: initialization r (i, k) and a (i, k),

Wherein, r (i, k) is Attraction Degree, characterizes sample A _kBe suitable as sample A _iThe degree of cluster centre; A (i, k) is degree of membership, characterizes sample A _iSelect sample A _kAppropriateness as its cluster centre;

S42: according to formula (2) ^～(5) iteration is upgraded Attraction Degree r (i, k):

r ^old(i,k)=r(i,k) (2)

$r(i,k)=S(i,k)-\underset{j:j\≠k}{\max }\left[(a(i,j)+S(i,j)\right]---\left(3\right)$

r ^new(i,k)=(1-λ)r(i,k)+λr ^old(i,k) (4)

r(i,k)=r ^new(i,k) (5)

S43: upgrade degree of membership a (i, k) according to formula (6)～(10) iteration:

a ^old(i,k)=a(i,k) (6)

$a(k,k)=\underset{j:j\≠k}{\mathrm{\Σ}}\max \left\{0,r(j,k)\right\}---\left(7\right)$

$a(i,k)=\min \{0,\left(r(k,k)+\underset{j:j\∉\{i,k\}}{\mathrm{\Σ}}\max \{0,r(j,k)\}\right)\}---\left(8\right)$

a ^new(i,k)=(1-λ)a(i,k)+λa ^old(i,k) (9)

a(i,k)=a ^new(i,k) (10)

In formula (2)～(10), r ^old(i, k) and a ^old(i, k) characterizes the value of last iteration, r ^new(i, k) and a ^new(i, k) is the value of current iteration, and r (i, k) and a (i, k) are the intermediate variable in iterative process;

S44: iterate operating procedure S42 and S43 are until export one group of stable class center and cluster result thereof;

S45: output cluster result:

$c_i^{*}=\underset{k}{\arg \max }\left[r(i,k+a(i,k)\right]$

Wherein, The cluster result of expression output, i, k represents sample number;

The PRPD collection of illustrative plates fingerprint characteristic of described single shelf depreciation source signal is realized by following steps:

S51: single shelf depreciation source signal is carried out analyzing and processing form the phase-amplitude matrix data;

S52: the PRPD spectrum data is processed and generated to the phase-amplitude matrix data;

S53: fingerprint characteristic is processed and generated to the PRPD spectrum data.

Separating and recognizer of many shelf depreciations of transformer oil paper insulation source signal based on pulse time frequency analysis and neighbour's propagation clustering provided by the invention, solved many Partial Discharge Sources PRPD collection of illustrative plates exist intersect, the overlapping and problem that can't identify, for Transformer State Assessment with formulate and reasonably safeguard with Strategies of Maintenance reference frame is provided.

Embodiment 2

The difference of the present embodiment and embodiment 1 only is:

The present embodiment analogue transformer paper oil insulation faultiness design three-type-person work model: creeping discharge model, corona discharge model and interlayer discharging model.

Fig. 2 is the PRPD collection of illustrative plates after the signal with creeping discharge and corona discharge manually synthesizes, and sampled voltage is 22kV, and sampling rate is 100MS/s, comprises 567 pulses of creeping discharge and 235 pulses of corona discharge.

The first step of algorithm is to calculate similarity matrix, and the dimension of S conversion amplitude matrix (STA) is 100 * 200.Choose damping factor λ=0.5, prevent the vibration in neighbour's propagation clustering algorithm (APC).Simultaneously, arrange reference vector p be p (1)=p (2)=...=p (i)=...=p (802)=-20.Through interative computation, the APC algorithm provides separating resulting automatically: the group number be 2 with the dither matrix that belongs to each group.Along the separating resulting of face and corona commingle discharging as shown in Figure 3.Can find out corresponding to group 1 obviously different with the STA matrix of the pulse of group 2 from figure (e) and figure (f).

Group 1 is respectively 568 and 234 with the number of pulses of organizing in 2.By paired observation, the pulse of group in 1 be mainly from creeping discharge, and organize pulse in 2 mainly from corona discharge.Be 99.63% by the separation accuracy that calculates the PD signal.

Embodiment 3

The difference of the present embodiment and embodiment 1 only is:

Fig. 4 has provided corona and the interlayer discharge signal PRPD collection of illustrative plates after manually synthetic, the sampled voltage of corona discharge is 19kV, the sampled voltage of interlayer discharge is 10kV, and sampling rate is 100MS/s, and the signal that collects comprises 416 corona pulses and 854 interlayer discharge pulses.

Choose equally damping factor λ=0.5, reference vector p be set to p (1)=p (2)=...=p (i)=...=p (802)=-20.Through interative computation, the separating resulting that the APC algorithm obtains as shown in Figure 5.Group 1 is respectively 411 and 859 with the number of pulses of organizing in 2.By observing, the pulse in group 1 is mainly from corona discharge, and the pulse of organizing in 2 is mainly discharged from interlayer.The separation accuracy that calculates the PD signal is 98.35%.

The above is only the preferred embodiments of the present invention, is not limited to the present invention, and obviously, those skilled in the art can carry out various changes and modification and not break away from the spirit and scope of the present invention the present invention.Like this, if within of the present invention these are revised and modification belongs to the scope of claim of the present invention and equivalent technologies thereof, the present invention also is intended to comprise these changes and modification interior.

Claims (4)

1. separation and the recognizer of many shelf depreciations of transformer oil paper insulation source signal is characterized in that: comprise the following steps:

S1: gather many shelf depreciations source signal and single shelf depreciation source signal;

S2: many shelf depreciations source signal is analyzed obtained single PD pulse train and phase-amplitude matrix data;

S3: calculate single PD pulse train and carry out similarity and generate similarity matrix;

S4: the phase-amplitude matrix data is carried out cluster in conjunction with similarity matrix and generates subclass pulse PRPD spectrum data by neighbour's propagation clustering;

S5: identify subclass pulse PRPD spectrum data and separate many shelf depreciations source signal to form different discharge source types according to the PRPD collection of illustrative plates fingerprint characteristic of single shelf depreciation source signal.

2. separation and the recognizer of many shelf depreciations of transformer oil paper insulation source signal according to claim 1, it is characterized in that: the generation of described similarity matrix specifically comprises the following steps:

S31: single PD pulse train is carried out the S conversion and generates STA amplitude matrix;

S32: adopt following formula to calculate the similarity of STA amplitude matrix:

In formula, S (i, j) characterizes i PD pulse A in STA amplitude matrix _iWith j pulse A _jSimilarity, m and n are respectively line number and the columns of STA amplitude matrix,

With

Be A in STA amplitude matrix _iAnd A _jAverage, A _iA in (k, l) expression STA amplitude matrix _iThe capable l column element of k.

3. separation and the recognizer of many shelf depreciations of transformer oil paper insulation source signal according to claim 1, it is characterized in that: described neighbour's propagation clustering specifically comprises the following steps:

S41: initialization r (i, k) and a (i, k),

Wherein, r (i, k) is Attraction Degree, characterizes sample A _kBe suitable as sample A _iThe degree of cluster centre; A (i, k) is degree of membership, characterizes sample A _iSelect sample A _kAppropriateness as its cluster centre;

S42: upgrade Attraction Degree r (i, k) according to formula (2)～(5) iteration:

r ^old(i,k)=r(i,k) (2)

r ^new(i,k)=(1-λ)r(i,k)+λr ^old(i,k) (4)

r(i,k)=r ^new(i,k) (5)

S43: upgrade degree of membership a (i, k) according to formula (6)～(10) iteration:

a ^old(i,k)=a(i,k) (6)

a ^new(i,k)=(1-λ)a(i,k)+λa ^old(i,k) (9)

a(i,k)=a ^new(i,k) (10)

In formula (2)～(10), r ^old(i, k) and a ^old(i, k) characterizes the value of last iteration, r ^new(i, k) and a ^new(i, k) is the value of current iteration, and r (i, k) and a (i, k) they are the intermediate variable in iterative process, and λ represents damping factor, i, and j, k represents sample number, i.e. i, j, k sample;

S44: iterate operating procedure S42 and S43 are until export one group of stable class center and cluster result thereof;

S45: output cluster result:

Wherein,

The cluster result of expression output, i, k represents sample number.

4. separation and the recognizer of many shelf depreciations of transformer oil paper insulation source signal according to claim 1, it is characterized in that: the PRPD collection of illustrative plates fingerprint characteristic of described single shelf depreciation source signal is realized by following steps:

S51: single shelf depreciation source signal is carried out analyzing and processing form the phase-amplitude matrix data;

S52: the PRPD spectrum data is processed and generated to the phase-amplitude matrix data;

S53: fingerprint characteristic is processed and generated to the PRPD spectrum data.

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