CN103048593B - A kind of recognition methods of gas-insulated switchgear insulation defect kind - Google Patents
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- 239000000126 substance Substances 0.000 description 3
- 239000000725 suspension Substances 0.000 description 3
- 230000015556 catabolic process Effects 0.000 description 2
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Abstract
The invention discloses a kind of recognition methods of gas-insulated switchgear insulation defect kind, it comprises step: the local discharge superhigh frequency signal 1) gathering gas-insulated switchgear; 2) to shelf depreciation ultra-high frequency signal filtering noise reduction; 3) envelope of local discharge superhigh frequency signal is obtained; 4) from envelope, peak value of pulse V is extracted
top, behind peak, begin to show trough V
v1, behind peak, begin to show crest V
p1, time existing trough V behind peak
v2with behind peak existing crest V
p2five eigenwerts; 5) form construction feature duration set by above-mentioned five eigenwerts, three characteristic quantities getting discrimination the highest in characteristic quantity set build a three-dimensional feature space; 6) sample data of the local discharge signal of corresponding typical shelf depreciation type is adopted to train sample space training classifier; 7) by trained sample space training classifier to the shelf depreciation classification of type be mapped in three-dimensional feature space and identification.
Description
Technical field
The present invention relates to a kind of signal detecting method, be specifically related to a kind of GIS partial discharge signal detecting method.
Background technology
Gas-insulated switchgear (Gas Insulated Switchgear, GIS) equipment is little with its floor area, and high reliability, is widely used in electric system.Gas insulated combined electrical equipment can produce shelf depreciation before insulation defect generation insulation breakdown.Shelf depreciation is sign and the form of expression of GIS insulation defect.The phenomenon of GIS partial discharge is detected, more early can find the insulation defect of its inside, to take appropriate measures, thus prevent from it from further developing causing the accident.
Shelf depreciation in GIS device is caused by a series of current impulse with the extremely short rise time, and can along with multiple physical chemical phenomenons such as sound, light, chemical products when it occurs.Therefore, shelf depreciation in GIS device can just be monitored by detecting these physical chemical phenomenons.
Carrying out monitoring and identifying to shelf depreciation (Partial discharge, PD, hereinafter referred to as " office the puts ") phenomenon of GIS, is find premature insulator fault, trouble-saving important means.In all multi-methods that monitoring GIS office puts, ultrahigh frequency (Ultra-High Frequency, UHF) method has that highly sensitive, antijamming capability is strong, identifiable design fault type and can playing a game puts the row accurately advantage such as location into, is the focus studied both at home and abroad over nearly 20 years.
Insulation defect in GIS has polytype, as stained in suspension electrode, free metal particle, insulator surface etc., the discharge characteristic that different defects produces is different, also different to the extent of injury of GIS, in order to assess the state of insulation of GIS exactly, the type of correct identification electric discharge is most important.
Be usually used in identification office at present and put the method for type mainly based on phase resolved plot, this method does not have office to put pulse shape information, also needs in addition to provide operating frequency phase synchronizing information.
Summary of the invention
The object of this invention is to provide a kind of recognition methods of gas-insulated switchgear insulation defect kind, when not needing operating frequency phase synchronizing information, this recognition methods puts the waveform of pulse based on office, the UHF signal (frequency range is 300MHz-3000MHz) shelf depreciation put demodulates low frequency signal to obtain the envelope of local discharge signal as the low frequency pulse signal modulated by high-frequency signal, thus corresponding envelope characteristic is extracted from local discharge superhigh frequency envelope, and the identification of high-frequency local discharging type is realized by recognition mode, it can judge the type of GIS partial discharge rapidly and accurately, effectively improve efficiency and the accuracy of diagnosis GIS insulation defect, for assessment GIS state of insulation and to formulate rational maintenance policy most important, avoid the generation of the security incident caused due to GIS insulation fault.
In order to realize foregoing invention object, the invention provides a kind of recognition methods of gas-insulated switchgear insulation defect kind, comprising the following steps:
(1) the local discharge superhigh frequency signal of gas-insulated switchgear is gathered;
(2) filtering noise reduction is carried out to obtain the local discharge superhigh frequency signal of high s/n ratio to the local discharge superhigh frequency signal collected;
(3) envelope of local discharge superhigh frequency signal is obtained;
Because the local discharge superhigh frequency signal collected shows as high-frequency oscillation signal, in order to local discharge superhigh frequency signal general trend in the time domain can be obtained, then need the envelope obtaining local discharge superhigh frequency signal, the envelope obtaining local discharge superhigh frequency signal can adopt Hilbert transform method, detection-filter method, high pass absolute value demodulation method and SPL method etc.;
(4) from envelope, peak value of pulse V is extracted
top, behind peak, begin to show trough V
v1, behind peak, begin to show crest V
p1, time existing trough V behind peak
v2with behind peak existing crest V
p2five eigenwerts;
The envelope of dissimilar local discharge superhigh frequency signal in shape homogeneous phase is similar to the double-exponential function of oscillatory extinction, but oscillation frequency, damping time constant often also exist obvious difference outward, now then need the data by extracting several key point to identify the type of shelf depreciation, the technical program extracts peak value of pulse V
top, behind peak, begin to show trough V
v1, behind peak, begin to show crest V
p1, time existing trough V behind peak
v2with behind peak existing crest V
p2these five eigenwerts;
(5) with above-mentioned five eigenwert construction feature duration sets
in characteristic quantity set
in get three the highest characteristic quantities of discrimination and build three-dimensional feature space { x, y, a z};
In order to obtain three characteristic quantities distinguishing best results, need the discrimination D calculating above-mentioned four characteristic quantities
ab:
In formula, D
abfor the discrimination of stochastic variable a, b on same characteristic quantity,
represent the sample mean of the stochastic variable of a certain same characteristic features amount of any two kinds of different electric discharge types respectively, σ
a, σ
brepresent the unbiased sample standard deviation of the stochastic variable of a certain same characteristic features amount of any two kinds of different electric discharge types respectively, (such as, characteristic quantity be calculated
to the discrimination of creeping discharge and these two kinds of discharge signals of floating potential, then creeping discharge signal
with floating potential discharge signal
a respectively in corresponding formula, b) is wherein { a by the sample of n a collected
1, a
2, a
3, a
4a
n, the sample of m b is { b
1, b
2, b
3, b
4b
m, can be in the hope of:
The sample mean of a is:
The sample mean of b is:
The unbiased sample standard deviation of a is:
The unbiased sample standard deviation of b is:
By what try to achieve
σ
aand σ
bsubstitution formula (1) is to obtain discrimination D
abvalue.
Discrimination D
abvalue larger, then illustrate that two kinds of different electric discharge types on same characteristic quantity occur that the possibility obscured is less.
That is, in the technical program, respectively different office is put the same characteristic features amount combination of two of type, and ask for the discrimination D of two kinds of shelf depreciation types that this identical characteristic quantity is chosen for this
ab.In order to construct the three-dimensional feature space distinguishing different electric discharge type, need the principle following " the most bad do not enter " when selected characteristic amount, namely avoid adopting as far as possible and put the poorest characteristic quantity of type performance to distinguishing some or several office, based on this principle by the step that the poorest characteristic quantity is rejected be: different electric discharge type in pairs first, then carry out discrimination D with regard to a certain same characteristic features amount of each centering
abcalculate, characteristic quantity is four, is
and
then from the discrimination of four characteristic quantities, choose arbitrarily three carry out suing for peace to form different summation combinations, then the discrimination of difference summation combination is corresponded to corresponding office and put type to formation list, finally type centering difference summation combination is put for same office to carry out contrasting the summation " distinguishing the most bad " each office to be put type centering and combine and rejected, retain other high three characteristic quantities of summation combined value to form the parameter { x in three-dimensional feature space, y, z}.Due to discrimination D
absummation numerical value more can distinguish different electric discharge type more greatly, and therefore, the numerical value putting a certain summation combination of type centering when a certain office more hour, illustrates during this summation is combined that the characteristic quantity of rejecting can distinguish different electric discharge types, otherwise also as the same.
Discrimination concept and calculating are known by those skilled in the art, therefore do not carry out introduction detailed further at this herein.
(6) sample data of the local discharge signal of corresponding typical shelf depreciation type is adopted to train sample space training classifier;
(7) { the shelf depreciation type in x, y, z} carries out classifying and identifying to being mapped to three-dimensional feature space to adopt trained sample space training classifier.
Further, in above-mentioned steps (2), employing passband is that the bandpass filter of 300MHz ~ 850MHz carries out filtering noise reduction.In filtering, adopt passband to be that the reason of the bandpass filter of 300MHz ~ 850MHz is: 1) dissimilar Partial discharge signal is similar in the frequency content of below 300MHz, do not possess and distinguish the operability that type is put in different office; 2) noise band is essentially at amplitude maximum place (about about 870MHz) in the signal spectrum of some shelf depreciation; 3) frequency component greatly about more than 900MHz is little on the impact of time domain plethysmographic signal envelope, can not provide effective numerical basis for the envelope obtaining local discharge superhigh frequency signal.
Further, in step (2), 12 rank Butterworth bandpass filter are adopted to carry out filtering noise reduction.
Further, in step (3), Hilbert transform method is adopted to obtain the envelope of local discharge superhigh frequency signal.
Certainly, detection-filter method also can be adopted to obtain envelope, but adopting what obtain in this way is the envelope of the positive half cycle center line of signal, its envelope accuracy not adopting Hilbert transform method to obtain is high.
In addition, high pass absolute-value scheme also can be adopted to obtain envelope, but adopt the envelope that the envelope obtained in this way is also signal center line, the envelope accuracy that its accuracy does not adopt Hilbert transform method to obtain yet is high.
In addition, SPL method can also be adopted to obtain envelope, but the interpolation point selection principle of SPL method compares and is difficult to determine, and this algorithm is not strong for the adaptability of unlike signal.
Hilbert transform method is also known by those skilled in that art, therefore only does simple introduction at this herein:
For continued time domain signal x (t), its with
convolution be
The analytic signal of continued time domain signal x (t) is
Wherein, j represents imaginary unit;
So the mould of this analytic signal is
This mould is exactly the envelope of continued time domain signal x (t).
Be that relation between discrete signal sequence x (n) of m and FFT (Fast Fourier Transform (FFT)) sequence X (k) thereof can be expressed as length:
Wherein, the FFT sequence that the discrete analytic signal a (n) that A (k) is x (n) is corresponding, substitutes into formula by the A (k) of gained in formula after IFFT (inverse fast fourier transform) process
in ask modulus value to obtain envelope E (n):
E(n)＝|a(n)|＝|IFFT[A(k)]|。
Further, in step (6), sample space training classifier adopts neural network or support vector machine.
Further, in step (6), typical shelf depreciation type comprises: creeping discharge signal, floating potential signal and metal particle signal at least one of them.
The recognition methods of gas-insulated switchgear insulation defect kind of the present invention, put the waveform of pulse based on office and do not need operating frequency phase synchronizing information, it is as follows that this recognition methods has advantage:
(1) the different electric discharge types of gas-insulated switchgear can be classified and distinguish exactly;
(2) efficiency judging gas-insulated switchgear insulation defect is greatly improved;
(3) for gas-insulated switchgear breakdown maintenance strategy provides important evidence;
(4) diagnostic result of gas-insulated switchgear insulation defect is obtained in time to avoid the generation of serious accident.
Accompanying drawing explanation
Fig. 1 shows the local discharge superhigh frequency signal Spectrum Analysis of creeping discharge insulation fault.
Fig. 2 shows the local discharge superhigh frequency signal Spectrum Analysis of floating potential insulation fault.
Fig. 3 shows the local discharge superhigh frequency signal Spectrum Analysis of metal particle insulation fault.
Fig. 4 shows the local discharge superhigh frequency signal through pretreated creeping discharge.
Fig. 5 shows the local discharge superhigh frequency signal through pretreated floating potential.
Fig. 6 shows the local discharge superhigh frequency signal through pretreated metal particle.
Fig. 7 shows peak value of pulse V in the envelope of local discharge superhigh frequency signal
top, behind peak, begin to show trough V
v1, behind peak, begin to show crest V
p1, time existing trough V behind peak
v2with behind peak existing crest V
p2deng five eigenwerts.
Fig. 8 shows the characteristic quantity of three kinds of local discharge superhigh frequency signals
value.
Fig. 9 shows the characteristic quantity of three kinds of local discharge superhigh frequency signals
value.
Figure 10 shows the characteristic quantity of three kinds of local discharge superhigh frequency signals
value.
Figure 11 shows the characteristic quantity of three kinds of local discharge superhigh frequency signals
value.
Figure 12 shows the distribution in the sample data three-dimensional feature space of the local discharge signal of three kinds of shelf depreciation types.
Embodiment
Below in conjunction with specific embodiment and Figure of description, the recognition methods to gas-insulated switchgear insulation defect kind of the present invention is further explained explanation, but this explanation illustrates the improper restriction do not formed technical scheme involved in the present invention.
Relate to the insulation fault of three kinds of typical gas-insulated switchgears in the present embodiment, be respectively creeping discharge, floating potential and metal particle, the spectrum analysis of the local discharge signal of these three kinds of Exemplary insulative faults as shown in Figure 1 to Figure 3.Can be found out by Fig. 1 to Fig. 3, three kinds of typical shelf depreciation type signals are similar in the frequency content of below 300MHz, do not possess the operability distinguishing different shelf depreciation type; In the signal spectrum of the shelf depreciation caused by metal particle and creeping discharge, amplitude maximum (about about 870MHz) is essentially noise band; Type signal is put in three kinds of typical offices to be affected little about the frequency component of more than 900MHz greatly on time domain plethysmographic signal envelope, can not provide effective numerical basis for the envelope obtaining local discharge superhigh frequency signal.
The step adopting technical solutions according to the invention to be used for identifying above-mentioned three kinds of insulation defect kinds of gas-insulated switchgear is as follows:
(1) the local discharge superhigh frequency signal of gas-insulated switchgear is gathered by uhf electromagnetic wave signal transducer.
(2) in order to obtain the local discharge superhigh frequency signal of high s/n ratio, filtering noise reduction process is carried out to the 12 rank Butterworth bandpass filter that the local discharge superhigh frequency signal employing passband collected is 300MHz ~ 850MHz; Creeping discharge after filtering after noise reduction, the local discharge superhigh frequency signal of floating potential and metal particle as shown in Figs. 4-6.
(3) in order to local discharge superhigh frequency signal general trend in the time domain can be obtained, Hilbert transform method is adopted to obtain the envelope of local discharge superhigh frequency signal.
(4) respectively from creeping discharge, in the envelope of the local discharge superhigh frequency signal of floating potential and metal particle, peak value of pulse V is extracted
top, behind peak, begin to show trough V
v1, behind peak, begin to show crest V
p1, time existing trough V behind peak
v2with behind peak existing crest V
p2five eigenwerts, as shown in Figure 7.
(5) with five eigenwert construction feature duration sets in step (4)
fig. 8 to Figure 11 shows the distribution plan of four characteristic quantities in set, and 1,2 and 3 in Fig. 8 to Figure 11 represents creeping discharge respectively, the local discharge signal of floating potential and metal particle three kinds of defects.
(6) in characteristic quantity set
in get three the highest characteristic quantities of discrimination and build three-dimensional feature space { parameter of x, y, z}, discriminations
wherein,
represent the sample mean of the stochastic variable of a certain same characteristic features amount of any two kinds of different electric discharge types respectively, σ
a, σ
brepresent the unbiased sample standard deviation of the stochastic variable of a certain same characteristic features amount of any two kinds of different electric discharge types respectively, characteristic quantity set
in the sample mean of stochastic variable of each characteristic quantity and unbiased sample standard deviation as shown in table 1.Be { a by the sample of n a collected
1, a
2, a
3, a
4a
n, the sample of m b is { b
1, b
2, b
3, b
4b
m, can be in the hope of the sample mean of a
the sample mean of b is
and the unbiased sample standard deviation of a is
the unbiased sample standard deviation of b is
by three kinds of shelf depreciation types in pairs, namely along face-suspension, suspension-metal and metal-along face, its corresponding each characteristic quantity is calculated corresponding discrimination, obtains table 2.Then from the discrimination D of four characteristic quantities
abchoose arbitrarily three in value to carry out suing for peace to form different summation combinations, then by the discrimination D of difference summation combination
abvalue correspond to corresponding each play a game and put type, obtain table 3.The data that bold Italic data representation discrimination in table 3 is the most bad.Therefore, in the present embodiment, the characteristic quantity can distinguishing these three kinds of shelf depreciation types is
and
these three characteristic quantities are adopted to form three-dimensional feature space { x, y, z}.
(7) adopt creeping discharge, the sample data of the local discharge signal of floating potential and metal particle three kinds of shelf depreciation types is to one or three layers of BP (Back Propagation) neural network, and namely error-duration model neural network is trained.
The training process of neural network is known by those skilled in that art, therefore does not repeat them here concrete training process herein.
(8) adopt trained three layers of BP neural network to being mapped to three-dimensional feature space { x, y, three kinds of shelf depreciation types in z}, i.e. creeping discharge, the shelf depreciation type of floating potential and metal particle is classified, and as shown in figure 12, finding that the random data of characteristic quantity gathers is significantly three regions, can identify for above-mentioned three kinds of shelf depreciation types, recognition result is as shown in table 4.
The sample mean of the stochastic variable of each characteristic quantity and unbiased sample standard deviation in the characteristic quantity set of table 1 three kinds of shelf depreciations
The discrimination D of each characteristic quantity during table 2 three kinds of shelf depreciations are paired
abnumerical value
| Characteristic quantity | Along face-suspension | Suspension-metal | Metal-along face |
| V v1/V top | 1.2285 | 0.6528 | 1.6987 |
| V p1/V top | 0.1716 | 1.0149 | 1.4725 |
| V v2/V top | 1.0082 | 0.8089 | 1.6389 |
| V p2/V top | 1.4932 | 0.7310 | 0.0697 |
The numerical value of type centering different discrimination summation combination is put in each office of table 3
The discrimination of table 4 three kinds of shelf depreciation types in the confusion matrix of three layers of BP neural network
As shown in Table 4, adopt the recognition methods effect of gas-insulated switchgear insulation defect kind of the present invention better, different electric discharge types can be identified rapidly and accurately.
That enumerates it should be noted that above is only specific embodiments of the invention, obviously the invention is not restricted to above embodiment, has many similar changes thereupon.If all distortion that those skilled in the art directly derives from content disclosed by the invention or associates, protection scope of the present invention all should be belonged to.
Claims (6)
1. a recognition methods for gas-insulated switchgear insulation defect kind, the method comprising the steps of:
(1) the local discharge superhigh frequency signal of gas-insulated switchgear is gathered;
(2) filtering noise reduction is carried out to the local discharge superhigh frequency signal collected;
It is characterized in that, the method also comprises step:
(3) envelope of local discharge superhigh frequency signal is obtained;
(4) from envelope, peak value of pulse V is extracted
top, behind peak, begin to show trough V
v1, behind peak, begin to show crest V
p1, time existing trough V behind peak
v2with behind peak existing crest V
p2five eigenwerts;
(5) with above-mentioned five eigenwert construction feature duration sets
in characteristic quantity set
in get three the highest characteristic quantities of discrimination and build three-dimensional feature space { x, y, a z};
(6) sample data of the local discharge signal of corresponding typical shelf depreciation type is adopted to train sample space training classifier;
(7) { the shelf depreciation type in x, y, z} carries out classifying and identifying to being mapped to three-dimensional feature space to adopt trained sample space training classifier.
2. the recognition methods of gas-insulated switchgear insulation defect kind as claimed in claim 1, is characterized in that: in step (2), and employing passband is that the bandpass filter of 300MHz ~ 850MHz carries out filtering noise reduction.
3. the recognition methods of gas-insulated switchgear insulation defect kind as claimed in claim 2, is characterized in that: in step (2), adopts 12 rank Butterworth bandpass filter to carry out filtering noise reduction.
4. the recognition methods of gas-insulated switchgear insulation defect kind as claimed in claim 1, is characterized in that, in step (3), adopts Hilbert transform method to obtain the envelope of local discharge superhigh frequency signal.
5. the recognition methods of gas-insulated switchgear insulation defect kind as claimed in claim 1, is characterized in that, in step (6), sample space training classifier adopts neural network or support vector machine.
6. the recognition methods of gas-insulated switchgear insulation defect kind as claimed in claim 1, it is characterized in that, in step (6), typical shelf depreciation type comprises: creeping discharge signal, floating potential signal and metal particle signal at least one of them.
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