CN103048593B - A kind of recognition methods of gas-insulated switchgear insulation defect kind - Google Patents
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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 invention relates to a signal detection method, in particular to a GIS partial discharge signal detection method.
Background
Gas Insulated Switchgear (GIS) devices have been widely used in power systems due to their advantages of small footprint, high reliability, and the like. The gas insulated switchgear generates partial discharge before insulation breakdown occurs in an insulation defect. Partial discharges are a sign and manifestation of GIS insulation defects. The GIS partial discharge phenomenon is detected, and the insulation defect in the GIS partial discharge phenomenon can be found early so as to take proper measures and prevent the GIS partial discharge phenomenon from further developing to cause accidents.
Partial discharges in GIS devices are caused by a series of current pulses with extremely short rise times and occur with a variety of physicochemical phenomena, acoustic, optical, chemical products, etc. Therefore, by detecting these physicochemical phenomena, partial discharges in GIS devices can be monitored.
Monitoring and identifying Partial Discharge (PD) phenomenon of GIS is an important means for finding early insulation fault and preventing accidents. Among the methods for monitoring the GIS partial discharge, the Ultra-High Frequency (UHF) method has the advantages of High sensitivity, strong anti-interference capability, capability of identifying fault types, capability of accurately positioning the partial discharge and the like, and is a hot spot of domestic and foreign research for nearly 20 years.
Insulation defects in the GIS are of various types, such as floating electrodes, free metal particles, insulator surface fouling and the like, the discharge characteristics generated by different defects are different, the damage degree of the GIS is different, and the correct identification of the discharge type is important for accurately evaluating the insulation state of the GIS.
At present, a method commonly used for identifying the partial discharge type is mainly based on a phase distribution map, and the method has no partial discharge pulse waveform information and needs to provide power frequency phase synchronization information.
Disclosure of Invention
The invention aims to provide a method for identifying the type of insulation defect of gas insulated switchgear, which can be used for identifying the type of the insulation defect of the gas insulated switchgear, the identification method is based on the waveform of the partial discharge pulse, a UHF signal (the frequency range is 300MHz-3000MHz) of partial discharge is taken as a low-frequency pulse signal modulated by a high-frequency signal to demodulate a low-frequency signal so as to obtain an envelope curve of the partial discharge signal, thereby extracting corresponding envelope characteristics from the partial discharge ultrahigh frequency envelope curve, realizing the identification of the ultrahigh frequency partial discharge type through an identification mode, it can quickly and accurately judge the type of GIS partial discharge, effectively improves the efficiency and accuracy of diagnosing GIS insulation defects, the method is of great importance for evaluating the insulation state of the GIS and formulating a reasonable maintenance strategy, and safety accidents caused by GIS insulation faults are avoided.
In order to achieve the above object, the present invention provides a method for identifying the type of insulation defect of a gas insulated switchgear, comprising the steps of:
(1) collecting a local discharge ultrahigh frequency signal of the gas insulated switchgear;
(2) filtering and denoising the collected partial discharge ultrahigh frequency signal to obtain a partial discharge ultrahigh frequency signal with a high signal-to-noise ratio;
(3) obtaining an envelope line of a partial discharge ultrahigh frequency signal;
the acquired local discharge ultrahigh frequency signal is represented as a high-frequency oscillation signal, and in order to obtain the general trend of the local discharge ultrahigh frequency signal in the time domain, an envelope curve of the local discharge ultrahigh frequency signal needs to be obtained, and the envelope curve of the local discharge ultrahigh frequency signal can be obtained by a Hilbert transform method, a detection-filtering method, a high-pass absolute value demodulation method, a spline curve method and the like;
(4) extracting the pulse peak value V from the envelope curvetopInitial trough V after peakv1First appearance wave peak V after peakp1Second occurrence of trough V after peakv2Secondary peak V after peak summationp2Five eigenvalues;
the envelopes of the different types of partial discharge ultrahigh-frequency signals are similar to a double-exponential function of oscillation attenuation in appearance, but the oscillation frequency and the attenuation time constant are often obviously different, at the moment, the type of partial discharge needs to be identified by extracting data of a plurality of key points, and the technical scheme extracts a pulse peak value VtopInitial trough V after peakv1First appearance wave peak V after peakp1Second occurrence of trough V after peakv2Secondary peak V after peak summationp2These five eigenvalues;
(5) constructing a feature quantity set by using the five feature valuesIn feature quantity setThree characteristic quantities with highest distinguishing degrees are taken to construct a three-dimensional characteristic space { x, y, z };
in order to obtain the distinguishing effectPreferably, the three characteristic quantities need to be calculated to obtain the discrimination D of the four characteristic quantitiesab：
In the formula, DabThe discrimination of the random variables a and b on the same characteristic quantity,sample mean, σ, of random variables of a same characteristic quantity, each representing any two different discharge typesa、σbUnbiased sample standard deviation of random variables of a same characteristic quantity respectively representing any two different discharge types (for example, to calculate the characteristic quantity)Discriminating between creeping discharge and floating potential discharge signalsAnd discharge signals of floating potentialRespectively corresponding to a and b) in the formula, wherein the samples of n a collected by the collection are { a1,a2,a3,a4…anSample of m b is { b }1,b2,b3,b4…bmGet out:
the sample average of a is:
the sample average of b is:
the standard deviation of unbiased samples of a is:
the standard deviation of unbiased samples of b is:
will find outσaAnd sigmabSubstitution of formula (1) to obtain the discrimination DabThe value is obtained.
Degree of distinction DabThe larger the value of (a) is, the smaller the possibility of confusion between two different discharge types on the same characteristic quantity is.
That is to say, in the present technical solution, two feature quantities of the same type are combined in pairs, and the degree of distinction D between the two selected partial discharge types and the feature quantity of the same type is obtainedab. In order to construct a three-dimensional feature space capable of distinguishing different discharge types, the principle of 'worst not entering' needs to be followed when selecting feature quantities, namely, the feature quantities which are worst for distinguishing one or more partial discharge types are avoided as much as possible, and the step of rejecting the worst feature quantities based on the principle is as follows: firstly, pairwise pairing different discharge types, and then performing discrimination D on a certain same characteristic quantity in each pairabCalculating to obtain four characteristic quantities, namelyAndand then, randomly selecting three feature quantities from the discrimination degrees of the four feature quantities to sum to form different summation combinations, forming a list by corresponding the discrimination degrees of the different summation combinations to corresponding partial discharge type pairs, finally, comparing the different summation combinations in the same partial discharge type pair to eliminate the summation combination with the worst discrimination in each partial discharge type pair, and reserving the other three feature quantities with high summation combination values to form parameters { x, y, z } of the three-dimensional feature space. Due to the discrimination DabThe larger the summation value is, the more different discharge types can be distinguished, so that when the value of a summation combination in a certain partial discharge type pair is smaller, the more the characteristic quantity removed in the summation combination can be used for distinguishing different discharge types, and vice versa.
The discrimination concepts and calculations are well known to those skilled in the art and are not described in further detail herein.
(6) Training a sample space training classifier by adopting sample data of a partial discharge signal corresponding to a typical partial discharge type;
(7) and (3) adopting a trained sample space to train a classifier to classify and identify the partial discharge type mapped into the three-dimensional characteristic space { x, y, z }.
Furthermore, in the step (2), a band-pass filter with a passband of 300MHz to 850MHz is used for filtering and denoising. The reason why the band-pass filter with the passband of 300 MHz-850 MHz is adopted in the filtering process is as follows: 1) the frequency components of different types of partial discharge signals below 300MHz are similar, and the operability for distinguishing different partial discharge types is not provided; 2) some partial discharges have a signal spectrum that is essentially a noise band where the amplitude is largest (around 870 MHz); 3) the frequency components above 900MHz have little effect on the envelope of the time domain waveform of the signal, and cannot provide an effective numerical basis for obtaining the envelope of the partial discharge ultrahigh frequency signal.
Further, in step (2), a 12-step Butterworth band-pass filter is used for filtering and denoising.
Further, in the step (3), an envelope of the partial discharge ultrahigh frequency signal is obtained by using a hilbert transform method.
Of course, the envelope can be obtained by a detection-filtering method, but the envelope obtained by the method is the envelope of the positive semi-cycle central line of the signal, and the envelope obtained by the Hilbert transform method is not accurate.
The envelope may be obtained by a high-pass absolute value method, but the envelope obtained by this method is also an envelope of a signal line, and the accuracy is not high as that obtained by the hilbert transform method.
In addition, a spline curve method can be adopted to obtain the envelope line, but the interpolation point selection principle of the spline curve method is difficult to determine, and the algorithm has poor adaptability to different signals.
The hubert transform is also well known to those skilled in the art, and is therefore described here only briefly:
for a continuous time domain signal x (t), it is compared withIs convoluted into
The analytic signal of the continuous time domain signal x (t) is
Wherein j represents an imaginary unit;
then the modulus of the analytic signal is
This mode is the envelope of the continuous time domain signal x (t).
The relationship between the discrete signal sequence x (n) of length m and its FFT (fast fourier transform) sequence x (k) can be expressed as:
wherein A (k) is the FFT sequence corresponding to the discrete analytic signal a (n) of x (n), and A (k) obtained in the formula is processed by IFFT (inverse fast Fourier transform) to obtain the formulaThe envelope e (n) can be obtained by the modulus:
E(n)＝|a(n)|＝|IFFT[A(k)]|。
further, in step (6), the sample space training classifier employs a neural network or a support vector machine.
Further, in step (6), typical partial discharge types include: at least one of a creeping discharge signal, a floating potential signal, and a metal particle signal.
The method for identifying the type of the insulation defect of the gas insulated switchgear is based on the waveform of partial discharge pulse and does not need power frequency phase synchronization information, and has the advantages that:
(1) different discharge types of the gas insulated switchgear can be accurately classified and distinguished;
(2) the efficiency of judging the insulation defect of the gas insulated switchgear is greatly improved;
(3) providing an important basis for a fault maintenance strategy of the gas insulated switchgear;
(4) the diagnosis result of the insulation defect of the gas insulated switchgear is obtained in time to avoid the occurrence of major safety accidents.
Drawings
Fig. 1 shows a spectral analysis of a partial discharge uhf signal for a creeping discharge insulation fault.
Fig. 2 shows a spectral analysis of a partial discharge uhf signal of a floating potential insulation fault.
Fig. 3 shows a spectral analysis of a partial discharge uhf signal for a metal particle insulation fault.
FIG. 4 shows the partial discharge UHF signal of the creeping discharge after pretreatment.
FIG. 5 shows the partial discharge UHF signal of the floating potential after pretreatment.
FIG. 6 shows the partial discharge UHF signal of the pretreated metal particles.
FIG. 7 shows the mid-pulse of the envelope of the partial discharge UHF signalPeak impact value VtopInitial trough V after peakv1First appearance wave peak V after peakp1Second occurrence of trough V after peakv2Secondary peak V after peak summationp2And five eigenvalues are equal.
FIG. 8 shows the characteristic quantities of three partial discharge UHF signalsThe value of (a).
FIG. 9 shows characteristic quantities of three partial discharge UHF signalsThe value of (a).
FIG. 10 shows the characteristic quantities of three partial discharge UHF signalsThe value of (a).
FIG. 11 shows the characteristic quantities of three partial discharge UHF signalsThe value of (a).
Fig. 12 shows the distribution within the three-dimensional feature space of sample data of the partial discharge signals of the three partial discharge types.
Detailed Description
The method for identifying the type of insulation defect of a gas insulated switchgear according to the present invention will be further explained with reference to the following embodiments and the drawings, but the explanation is not intended to unduly limit the technical solutions according to the present invention.
In the present embodiment, three typical insulation faults of the gas insulated switchgear, i.e., creeping discharge, floating potential, and metal particles, are involved, and the frequency spectrum analysis of the partial discharge signals of the three typical insulation faults is shown in fig. 1 to 3. As can be seen from fig. 1 to 3, the frequency components of the three typical partial discharge type signals below 300MHz are similar, and do not have operability for distinguishing different partial discharge types; the signal spectrum of the partial discharge caused by the metal particles and the creeping discharge has a noise band at the maximum amplitude (about 870 MHz); frequency components of three typical partial discharge type signals above about 900MHz have little influence on the envelope of the time domain waveform of the signals, and effective numerical basis cannot be provided for obtaining the envelope of the partial discharge ultrahigh frequency signals.
The steps for identifying the three insulation defect types of the gas insulated switchgear by adopting the technical scheme of the invention are as follows:
(1) and collecting the local discharge ultrahigh frequency signal of the gas insulated switchgear through an ultrahigh frequency electromagnetic wave signal sensor.
(2) In order to obtain a partial discharge ultrahigh frequency signal with a high signal-to-noise ratio, a 12-order Butterworth band-pass filter with a passband of 300 MHz-850 MHz is adopted for filtering and denoising the collected partial discharge ultrahigh frequency signal; the surface discharge, the suspension potential and the local discharge ultrahigh frequency signal of the metal particles after filtering and noise reduction are shown in fig. 4 to 6.
(3) In order to obtain the general trend of the partial discharge ultrahigh frequency signal in the time domain, a Hilbert transform method is adopted to obtain an envelope curve of the partial discharge ultrahigh frequency signal.
(4) Respectively extracting pulse peak value V from envelope lines of surface discharge, suspension potential and partial discharge ultrahigh frequency signals of metal particlestopInitial trough V after peakv1First appearance wave peak V after peakp1Second occurrence of trough V after peakv2Secondary peak V after peak summationp2Five eigenvalues, as shown in fig. 7.
(5) Constructing a feature quantity set by using the five feature values in the step (4)Fig. 8 to 11 show distribution diagrams of four feature quantities in the set, and 1, 2 and 3 in fig. 8 to 11 represent partial discharge signals of three defects of creeping discharge, floating potential and metal particles, respectively.
(6) In feature quantity setThree characteristic quantities with highest discrimination are obtained to construct parameters of a three-dimensional characteristic space { x, y, z }, and the discrimination isWherein,sample mean, σ, of random variables of a same characteristic quantity, each representing any two different discharge typesa、σbUnbiased sample standard deviation of random variables of a same characteristic quantity respectively representing any two different discharge types, and characteristic quantity setThe sample mean and unbiased sample standard deviation of the random variables of each feature quantity in (1) are shown in table. The sample passing through the collected n a is { a1,a2,a3,a4…anSample of m b is { b }1,b2,b3,b4…bmThe average of the samples of a can be found to beb sample average ofAnd a has an unbiased sample standard deviation ofb unbiased sample standard deviation ofThe three partial discharge types, namely along-plane-suspension, suspension-metal and metal-along-plane, are paired in pairs, and corresponding discrimination degrees are calculated according to the corresponding characteristic quantities to obtain table 2. Then from the four feature quantities of the degree of differentiation DabSelecting three values arbitrarily to sum to form different summation combinations, and then dividing the difference D of the different summation combinationsabThe values correspond to each respective pair of partial discharge types, resulting in table 3. The bold italic data in table 3 represents the data with the worst discriminative power. Therefore, in the present embodiment, the feature quantity capable of distinguishing the three types of partial discharge is Andand (5) forming a three-dimensional feature space { x, y, z } by using the three feature quantities.
(7) Sample data of partial discharge signals of three types of partial discharge of creeping discharge, suspension potential and metal particles are adopted to train a three-layer BP (Back propagation) neural network, namely an error back propagation neural network.
The training process of the neural network is well known to those skilled in the art, and thus, a detailed description thereof is omitted here.
(8) Three partial discharge types, namely creeping discharge, suspension potential and the partial discharge type of metal particles, mapped into a three-dimensional feature space { x, y, z } are classified by adopting a trained three-layer BP neural network, as shown in FIG. 12, it is found that random data of feature quantities are obviously gathered into three areas, namely, the three partial discharge types can be identified, and the identification result is shown in Table 4.
TABLE 1 sample mean value and standard deviation of unbiased sample of random variables of each feature quantity in feature quantity set of three partial discharges
TABLE 2 discrimination D of each feature in three partial discharge pairingsabNumerical value
| Characteristic amount | Surface-suspension | Suspension-metal | Metal edge surface |
| Vv1/Vtop | 1.2285 | 0.6528 | 1.6987 |
| Vp1/Vtop | 0.1716 | 1.0149 | 1.4725 |
| Vv2/Vtop | 1.0082 | 0.8089 | 1.6389 |
| Vp2/Vtop | 1.4932 | 0.7310 | 0.0697 |
TABLE 3 numerical values of different discrimination sum combinations in each partial discharge type pair
TABLE 4 recognition rate of three partial discharge types in the confusion matrix of three-layer BP neural network
As can be seen from table 4, the method for identifying the type of the insulation defect of the gas insulated switchgear according to the present invention has a good effect, and can quickly and accurately identify different discharge types.
It is to be noted that the above lists only specific embodiments of the present invention, and it is obvious that the present invention is not limited to the above embodiments, and many similar variations follow. All modifications which would occur to one skilled in the art and which are, therefore, directly derived or suggested from the disclosure herein are deemed to be within the scope of the present invention.
Claims (6)
1. A method for identifying the type of insulation defect of a gas insulated switchgear, the method comprising the steps of:
(1) collecting a local discharge ultrahigh frequency signal of the gas insulated switchgear;
(2) filtering and denoising the collected partial discharge ultrahigh frequency signal;
characterized in that the method further comprises the steps of:
(3) obtaining an envelope line of a partial discharge ultrahigh frequency signal;
(4) extracting the pulse peak value V from the envelope curvetopAfter the peakInitial trough Vv1First appearance wave peak V after peakp1Second occurrence of trough V after peakv2Secondary peak V after peak summationp2Five eigenvalues;
(5) constructing a feature quantity set by using the five feature valuesIn feature quantity setThree characteristic quantities with highest distinguishing degrees are taken to construct a three-dimensional characteristic space { x, y, z };
(6) training a sample space training classifier by adopting sample data of a partial discharge signal corresponding to a typical partial discharge type;
(7) and (3) adopting a trained sample space to train a classifier to classify and identify the partial discharge type mapped into the three-dimensional characteristic space { x, y, z }.
2. The method for identifying the kind of insulation defect of the gas insulated switchgear according to claim 1, wherein: in the step (2), a band-pass filter with a passband of 300 MHz-850 MHz is adopted for filtering and noise reduction.
3. The method for identifying the kind of insulation defect of the gas insulated switchgear according to claim 2, wherein: in the step (2), a 12-order Butterworth band-pass filter is adopted for filtering and denoising.
4. The method for identifying the type of the insulation defect of the gas insulated switchgear as claimed in claim 1, wherein in the step (3), the envelope of the partial discharge UHF signal is obtained by using a Hilbert transform method.
5. The method for identifying the type of the insulation defect of the gas insulated switchgear according to claim 1, wherein in the step (6), the sample space training classifier uses a neural network or a support vector machine.
6. The method for identifying the kind of insulation defect of gas insulated switchgear according to claim 1, wherein in the step (6), the typical partial discharge type includes: at least one of a creeping discharge signal, a floating potential signal, and a metal particle signal.
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