CA2918679C - Pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage gis - Google Patents
Pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage gis Download PDFInfo
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- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
- G01R31/1254—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of gas-insulated power appliances or vacuum gaps
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Abstract
A pattern recognition method for a partial discharge of a three-phase in one enclosure type ultrahigh voltage GIS. The method comprises the steps as follows: detecting a partial discharge of a three-phase in one enclosure type GIS using an ultrahigh frequency, and sampling a partial discharge signal using a UHF sensor; conducting noise elimination processing on the collected partial discharge signal using an improved wavelet threshold filtering method, so as to obtain a real partial discharge signal; extracting feature parameters of the sampled signal through an algorithm based on a phase analysis pattern; conducting dimension reduction processing on a feature space composed of the feature parameters using an improved kernel principal component analysis method, so as to obtain a feature parameter matrix subjected to the dimension reduction; and conducting pattern recognition on an insulation defect type of the GIS using a K-nearest neighbour classification algorithm based on a cluster idea. The pattern recognition method for a partial discharge of a three-phase in one enclosure type ultrahigh voltage GIS can overcome the defects of less functions, a small scope of application and poor accuracy in the prior art, thereby realizing the advantages of more functions, a wide scope of application and good accuracy.
Description
Pattern Recognition Method for Partial Discharge of Three-phase Cylinder Type Ultrahigh Voltage GIS
Technical field The invention involves the technical field of high voltage discharge identification, and specifically involves a pattern recognition method for partial discharge of a three-phase cylinder type ultrahigh voltage GIS.
Background technology Gas Insulated Switchgear (hereafter referred to as GIS) is one of the important equipments in the ultra-high voltage grid. After optimization design, breakers, current transformers, voltage transformers, lightening arresters, disconnecting switches, earthing switches, buses, cable terminals, casings of inlet and outlet line and other parts are respectively installed in corresponding sealed compartments, and finally assembled in an integral casing taking SF6 as insulating medium.
Main defects influencing the performance of insulating medium in GIS include serious installation error, poor contact between conductors, high voltage conductor protrusions, fixed particles, insulator defect, steam, etc..
The development of GIS tends to be three-phase cylinder type, compounding and intelligence. Due to the realization of miniaturization, the general assembly can be completed in the factory. After passing the test, it is transported to the site in the form of interval.
Therefore, it can reduce the installation period, and the reliability is improved.
In terms of internal structure, electric field distribution and other aspects, three-phase cylinder type GIS is obviously different from coaxial type GIS. The existing technology research mainly focuses on coaxial type GIS, but there is less research on the detection pattern recognition of partial discharge of three-phase cylinder type ultrahigh voltage GIS.
Itt the process of realization of the invention, the inventor found that the existing technology at least had fewer functions, small scope of application, poor accuracy and other defects.
Content of the Invention The purpose of the invention is to propose a pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS according to the above problems, so as to realize the advantages of many functions, wide scope of application and good accuracy.
In order to achieve the above purpose, the invention uses the technical solution of a pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS, including the following steps:
Step 1: The ultrahigh frequency is used to detect the partial discharge of three-phase cylinder type GIS, and UHF sensor is used to sample the partial discharge signal;
Step 2: The improved wavelet threshold filtering method is used for denoising processing of the partial discharge signal collected, and the real partial discharge signal is obtained;
Step 3: The characteristic parameters of sampling signal are extracted through the mode algorithm based on the phase analysis;
Step 4: The improved kernel principal component analysis method is used for dimension reduction processing of characteristic space consisting of characteristic parameters, and the characteristic parameter matrix after dimension reduction is obtained; and Step 5: K nearest neighbor algorithm based on the cluster idea is used for pattern recognition of insulation defect type of GIS.
Further, in Step 2, the improved wavelet threshold filtering method is used for denoising processing of the partial discharge signal collected. The calculation method of self-adaptive threshold is used, which is as follows:
Technical field The invention involves the technical field of high voltage discharge identification, and specifically involves a pattern recognition method for partial discharge of a three-phase cylinder type ultrahigh voltage GIS.
Background technology Gas Insulated Switchgear (hereafter referred to as GIS) is one of the important equipments in the ultra-high voltage grid. After optimization design, breakers, current transformers, voltage transformers, lightening arresters, disconnecting switches, earthing switches, buses, cable terminals, casings of inlet and outlet line and other parts are respectively installed in corresponding sealed compartments, and finally assembled in an integral casing taking SF6 as insulating medium.
Main defects influencing the performance of insulating medium in GIS include serious installation error, poor contact between conductors, high voltage conductor protrusions, fixed particles, insulator defect, steam, etc..
The development of GIS tends to be three-phase cylinder type, compounding and intelligence. Due to the realization of miniaturization, the general assembly can be completed in the factory. After passing the test, it is transported to the site in the form of interval.
Therefore, it can reduce the installation period, and the reliability is improved.
In terms of internal structure, electric field distribution and other aspects, three-phase cylinder type GIS is obviously different from coaxial type GIS. The existing technology research mainly focuses on coaxial type GIS, but there is less research on the detection pattern recognition of partial discharge of three-phase cylinder type ultrahigh voltage GIS.
Itt the process of realization of the invention, the inventor found that the existing technology at least had fewer functions, small scope of application, poor accuracy and other defects.
Content of the Invention The purpose of the invention is to propose a pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS according to the above problems, so as to realize the advantages of many functions, wide scope of application and good accuracy.
In order to achieve the above purpose, the invention uses the technical solution of a pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS, including the following steps:
Step 1: The ultrahigh frequency is used to detect the partial discharge of three-phase cylinder type GIS, and UHF sensor is used to sample the partial discharge signal;
Step 2: The improved wavelet threshold filtering method is used for denoising processing of the partial discharge signal collected, and the real partial discharge signal is obtained;
Step 3: The characteristic parameters of sampling signal are extracted through the mode algorithm based on the phase analysis;
Step 4: The improved kernel principal component analysis method is used for dimension reduction processing of characteristic space consisting of characteristic parameters, and the characteristic parameter matrix after dimension reduction is obtained; and Step 5: K nearest neighbor algorithm based on the cluster idea is used for pattern recognition of insulation defect type of GIS.
Further, in Step 2, the improved wavelet threshold filtering method is used for denoising processing of the partial discharge signal collected. The calculation method of self-adaptive threshold is used, which is as follows:
2 Media*Jai 1V2IncV.) \ =
exp 0.6745 \. );
=
N.
In which, j is the scale. is the number of wavelet coefficients on the scale.
"fan(ICA is the median of all the wavelet coefficients on the scale. a' is called the signal-to-noise ratio factor and is the signal-to-noise ratio of signal in the threshold calculation. A is called scale factor and is the estimated error caused when the maximum T
value of wavelet coefficients on the scale corrects the different sampling sequence length.
is the calculated threshold value.
Further, the characteristic parameters in Step 3 include degree of skewness (Sk), degree of steepness (Ku), the number of partial peak points (Pe), cross correlation coefficient (Cc) and discharge factor (Q).
Further, the degree of skewness (Sk) is as follows.
Sk E (x, mu) = p I cr3 In which, w is the number of phase window in the half cycle; Xi is the phase position of the i phase window;
w PYiIYj E ptco, = EP; (01-11)2 In which, =)) is the vertical coordinate of spectrum, representing apparent discharge magnitude (q) or number of discharges (n); parameter represents the central position of partial discharge map collected, cr represents the steepness of symmetry axis at the center of the map. L1X is a parameter related to even distribution of partial discharge map, and g9i is the phase position corresponding to a point in the map;
The degree of skewness (Sk) reflects the skewness of spectrum shape relative to normal distribution. Sk=0 indicates that the spectrum shape is symmetrical; Sk >0 indicates that the
exp 0.6745 \. );
=
N.
In which, j is the scale. is the number of wavelet coefficients on the scale.
"fan(ICA is the median of all the wavelet coefficients on the scale. a' is called the signal-to-noise ratio factor and is the signal-to-noise ratio of signal in the threshold calculation. A is called scale factor and is the estimated error caused when the maximum T
value of wavelet coefficients on the scale corrects the different sampling sequence length.
is the calculated threshold value.
Further, the characteristic parameters in Step 3 include degree of skewness (Sk), degree of steepness (Ku), the number of partial peak points (Pe), cross correlation coefficient (Cc) and discharge factor (Q).
Further, the degree of skewness (Sk) is as follows.
Sk E (x, mu) = p I cr3 In which, w is the number of phase window in the half cycle; Xi is the phase position of the i phase window;
w PYiIYj E ptco, = EP; (01-11)2 In which, =)) is the vertical coordinate of spectrum, representing apparent discharge magnitude (q) or number of discharges (n); parameter represents the central position of partial discharge map collected, cr represents the steepness of symmetry axis at the center of the map. L1X is a parameter related to even distribution of partial discharge map, and g9i is the phase position corresponding to a point in the map;
The degree of skewness (Sk) reflects the skewness of spectrum shape relative to normal distribution. Sk=0 indicates that the spectrum shape is symmetrical; Sk >0 indicates that the
3 spectrum biases to the left relative to normal distribution; Sk<0 indicates that the spectrum biases to the right relative to normal distribution.
Further, the degree of steepness (Ku) is as follows.
Ku = - p idx u4 - 3 In which, w is the number of phase windows in half cycle; Xi is the phase position of the =th phase window;
if _______ = E = pi(p, 0-= Epi(9i i=1 In which, Yi is the vertical coordinate of spectrum, representing apparent discharge magnitude (q) or number of discharges (n); parameter represents the central position of partial discharge map collected, 0" represents the steepness of symmetry axis in the center of the map, AX is a parameter related to even distribution of partial discharge map, and CI is the phase position corresponding to a point in the map;
The degree of steepness (Ku) is used to describe the protruding degree of distribution of a certain shape relative to the normal distribution. The degree of steepness of normal distribution is 0. If Ku > 0, it indicates that the contour of the spectrum is sharper and steeper than that of the normal distribution; if Ku<0, it indicates that the contour of the spectrum is flatter than that of the normal distribution.
Further, the number of partial peak points (Pe) is used to describe the number of partial peak points on the contour of the spectrum; whether there is partial peak at contour point needs to be determined with the following difference equation.
(Y1¨ Yi¨i) >0, (Yi4-1¨ Y1) <0;
The more the phase windows isometrically divided from the phase axis are, the larger the number of partial peak points is.
Further, the degree of steepness (Ku) is as follows.
Ku = - p idx u4 - 3 In which, w is the number of phase windows in half cycle; Xi is the phase position of the =th phase window;
if _______ = E = pi(p, 0-= Epi(9i i=1 In which, Yi is the vertical coordinate of spectrum, representing apparent discharge magnitude (q) or number of discharges (n); parameter represents the central position of partial discharge map collected, 0" represents the steepness of symmetry axis in the center of the map, AX is a parameter related to even distribution of partial discharge map, and CI is the phase position corresponding to a point in the map;
The degree of steepness (Ku) is used to describe the protruding degree of distribution of a certain shape relative to the normal distribution. The degree of steepness of normal distribution is 0. If Ku > 0, it indicates that the contour of the spectrum is sharper and steeper than that of the normal distribution; if Ku<0, it indicates that the contour of the spectrum is flatter than that of the normal distribution.
Further, the number of partial peak points (Pe) is used to describe the number of partial peak points on the contour of the spectrum; whether there is partial peak at contour point needs to be determined with the following difference equation.
(Y1¨ Yi¨i) >0, (Yi4-1¨ Y1) <0;
The more the phase windows isometrically divided from the phase axis are, the larger the number of partial peak points is.
4 Further, the cross correlation coefficient (Cc) is as follows.
E 7,741 ¨ Eq-,¶E iw i=1 Cc.
w IV/ W \ 2 Al (41 Eql rw [E(q;:-.)2 \ 1=1 _L " i.1 1 q q In which, is the discharge quantity in the phase window i. The superscripts "+" and "¨" respectively corresponds to the positive and negative semiaxis of the spectrum; c reflects the correlation betweem the discharge strength and phase distribution in the positive and negative half cycle. If the cross correlation coefficient (Cc) is close to I, it indicates that the contours of "61 spectrum of the positive and negative half cycle are quite similar; if Cc is close to 0, the contour difference of 9 ¨ ("spectrum is great.
Further, the discharge factor (Q) is as follows:
n, qi E 11 1.+ q = 1 Q
n n = =
'7 In which, ni+ and are discharge repetition rate in the phase window i. The superscripts "+" and "¨" respectively correspond to the positive and negative half cycle of the q spectrum.
Further, the improved kernel principal component analysis method in Step 4 is the improved kernel principal component analysis method. The kernel function sampled is as follows.
2' k(X. X )= X. > exp ______ 2 a ;
In which, in (a E R,b N,t7 > 0), parameter a, b and are selected according to the value of elements in the characteristic matrix. The parameter (3 is used to control the range of action in the radial direction of the kernel function; Xi and Xl represent different sample x.
vectors, < ) represents the vector product of sample vectors, R represents the set of real numbers of the value range of vectors, N represents the set of integers, and kxj) represents a new kernel function obtained by combining the advantages of polynomial kernel function and gaussian kernel function.
Further, K nearest neighbor algorithm in Step 5 includes:
Step I : In the training set, first all the partial discharge data is preprocessed and mapped into a spatial vector;
Step 2: From the first category, the similarity of each two data among all the signal data in the category is calculated, the minimum threshold is set, and according to the statistics the clusters with close similarity are obtained;
Step 3: All the signal data in each cluster is merged. And then its central vector is calculated; in addition, the number of clusters/categories is calculated. This value represents the contributing coefficient of the cluster to the category;
Step 4: The new text is preprocessed, and its vector space is obtained;
Step 5: The distance between the spatial vector of new text and the central vector of each cluster generated in Step 3 is calculated. These distances are multiplied by the contributing coefficient of corresponding clusters. The calculated results of clusters in the same category are added. After comparison, the biggest category obtained is the category of partial discharge with typical defect to be classified.
The pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS of the embodiments of the invention has the following steps. The ultrahigh frequency is used to detect the partial discharge of three-phase cylinder type GIS, and UHF sensor is used to sample the partial discharge signal; the improved wavelet threshold filtering method is used for denoising processing of the partial discharge signal collected, and the real partial discharge signal is obtained; the characteristic parameters of sampling signal are extracted through the mode algorithm based on the phase analysis; the improved kernel principal component analysis method is used for dimension reduction processing of characteristic space consisting of characteristic parameters, and the characteristic parameter matrix after dimension reduction is obtained; and K nearest neighbor algorithm based on the cluster idea is used for pattern recognition of insulation defect type of GIS.
Therefore, existing technical defects can be overcome, and the accuracy of the detection pattern recognition of partial discharge of three-phase cylinder type ultrahigh voltage GIS is improved; the defect of less functions, small scope of application and poor accuracy in the existing technology can be overcome, and the advantages of many functions, wide scope of application and good accuracy can be realized.
Other features and advantages of the invention will be introduced in the subsequent description, and part of them are obvious from the description or known through implementation of the invention.
Next, through drawings and embodiments, the technical solution of the invention is future introduced in details.
BRIEF DESCRIPON OF THE DRAWINGS
The drawings are used for further understanding of the invention, constitute a part of the description, and are combined with embodiments of the invention to explain the invention without restricting the scope of the inventionthe invention. In the drawings:
Figure I is the structure diagram of partial discharge test apparatus of GIS
in the pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS in the invention;
Figure 2 is flow diagram of the pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS in the invention. In Figure 2, N and K are both natural numbers;
Figure 3 is the schematic diagram of the relationship between statistical distribution, Sk and Ku in the pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS in the invention. (a) is positive deflection, (b) is no deflection, and (c) is negative deflection. Sk is the deflection of spectrum shape relative to the normal distribution;
(d) is positive deflection, (e) is no deflection, and (f) is negative deflection. The degree of steepness (Ku) is used to describe the protruding degree of distribution of a certain shape relative to the normal distribution;
Figure 4 is the schematic diagram of effect of kernel function in the pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS in the invention. (a) is a polynomial function, (b) is a gaussian kernel function , and (c) is a new kernel function;
Figure 5 is the waveform comparison chart before filtering (a) and after filtering (b) in the pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS in the invention. In Figure 5, the angle of horizontal axis is 0-360 degrees. The vertical axis is the signal amplitude (Q).
Combined with the drawings, the reference numbers in embodiments of the invention are as follows.
1-water resistance; 2-HT testing transformer; 3-transformer; 4-HV bushing; 5-disk insulator.
DETAILED DESCRIPTION
Below is the description of preferred embodiments of the invention combined with the drawings. It should be understood that the preferred embodiments described here are only used to describe and explain the invention, but not to limit the invention.
For the defects in the existing technology, according to the embodiments of the invention, as shown in Figure I-Figure 5, a pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS is provided.
As shown in Figure 1, the partial discharge test apparatus of GIS used in the pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS in the invention includes disk insulators 5, an HV bushing 4 installed on the disk insulators 5, and a water resistance I, an HT testing transformer 2 and a transformer 3 connected to the HV
bushing 4 in turn, and a PDSG connected to the disk insulator 5, and the common end of the water resistance 1 and the FTV bushing 4 is earthed after passing through a voltage divider consisting of capacitors.
The partial discharge test apparatus of GIS mainly includes a transformer 3, a voltage divider consisting of capacitors, an oscilloscope, a three-phase cylinder type GIS, sensors and a PDSG; by setting high voltage conductor metal protrusions, free metal particles, fixed metals on the surface of insulator, air gaps of insulator and other defects respectively in GIS, the corresponding partial discharge signal is detected and the mode is recognized.
The pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS in this embodiment includes the following steps:
1) Ultrahigh frequency (UHF) is used to detect the partial discharge of three-phase cylinder type GIS, and UHF sensors are used to sample the partial discharge (PD) signal;
In Step 1), the ultrahigh frequency (UHF) method is used to detect the partial discharge of three-phase cylinder type GIS. When UHF method is used to detect the partial discharge of GIS, different failure types can be recognized according to the spectral characteristic of signals measured and the position of discharge on the power voltage waveform.
In Step 1), the real three-phase cylinder type GIS is used to measure the partial discharge map under the typical defect conditions. Among them, the three-phase cylinder type GIS shall be provided by a professional high voltage switchgear enterprise. The three-phase conductor is energized as follows. Two phases are earthed, and the other one phase is connected to the high voltage. In the model there is one HV bushing. According to the test results of coaxial type GIS test model, the contrastive typical insulation defects are set. In the cavity of the three-phase cylinder type GIS, there are free metal particles. On the surface of the insulator, there are fixed metal particles. On the insulator, there is air gap defect.
The physical model of partial discharge is set in the GIS stimulated device for the measurement of partial discharge.
2) The improved wavelet threshold filtering method is used for denoising processing of the partial discharge signal collected, and the real partial discharge signal is obtained;
In Step 2), in the improved wavelet threshold filtering method, from the calculation method of Median (Cj, k), it can be seen that the selection of threshold is closely related to the length of analyzed signal. In actual application, we cannot ensure that the proportion and position of the number of points sampled in the entire section of the effective signal in the entire sampling sequence is unchanged. Because the length of signal determines the number of wavelet coefficients on each scale after the wavelet changes (1\1j), it affects the value of Median ( g, 10, so as to affect the threshold. This influence will lead to the following results. The same useful signal sequence (u) is contained in a sampling sequence (s). If the length of s is different (i.e. the width of the time observation window is different), the results after the wavelet threshold filtering will be quite different. For s of different lengths containing the same UHF PD signal, the results of soft threshold filtering algorithm are applied. The sampling frequency of the original signal is 20GHz. The long sampling sequence with wide time window has 50,000 points. The short sampling sequence with narrow time window has 16,000 points. The two sequences both completely contain the useful UHF PD
signal. Obviously, after soft threshold filtering algorithm is applied, UHF PD
signal, especially the part of shock attenuation that will end, has obvious difference. The correlation coefficient of the two is 0.6677.
The primary cause of these consequences is that the calculation formula of threshold completely ignores the amplitude of effective signal and signal-to-noise ratio. Therefore, after several analysises, the embodiment considers the amplitude of effective signal and signal-to-noise ratio, and proposes a new self-adaptive threshold calculation method, which greatly reduces the level of sensitivity of wavelet threshold filtering results to the sampling points. The comparison of figures before and after filtering is as shown in Figure 5. In Figure
E 7,741 ¨ Eq-,¶E iw i=1 Cc.
w IV/ W \ 2 Al (41 Eql rw [E(q;:-.)2 \ 1=1 _L " i.1 1 q q In which, is the discharge quantity in the phase window i. The superscripts "+" and "¨" respectively corresponds to the positive and negative semiaxis of the spectrum; c reflects the correlation betweem the discharge strength and phase distribution in the positive and negative half cycle. If the cross correlation coefficient (Cc) is close to I, it indicates that the contours of "61 spectrum of the positive and negative half cycle are quite similar; if Cc is close to 0, the contour difference of 9 ¨ ("spectrum is great.
Further, the discharge factor (Q) is as follows:
n, qi E 11 1.+ q = 1 Q
n n = =
'7 In which, ni+ and are discharge repetition rate in the phase window i. The superscripts "+" and "¨" respectively correspond to the positive and negative half cycle of the q spectrum.
Further, the improved kernel principal component analysis method in Step 4 is the improved kernel principal component analysis method. The kernel function sampled is as follows.
2' k(X. X )= X. > exp ______ 2 a ;
In which, in (a E R,b N,t7 > 0), parameter a, b and are selected according to the value of elements in the characteristic matrix. The parameter (3 is used to control the range of action in the radial direction of the kernel function; Xi and Xl represent different sample x.
vectors, < ) represents the vector product of sample vectors, R represents the set of real numbers of the value range of vectors, N represents the set of integers, and kxj) represents a new kernel function obtained by combining the advantages of polynomial kernel function and gaussian kernel function.
Further, K nearest neighbor algorithm in Step 5 includes:
Step I : In the training set, first all the partial discharge data is preprocessed and mapped into a spatial vector;
Step 2: From the first category, the similarity of each two data among all the signal data in the category is calculated, the minimum threshold is set, and according to the statistics the clusters with close similarity are obtained;
Step 3: All the signal data in each cluster is merged. And then its central vector is calculated; in addition, the number of clusters/categories is calculated. This value represents the contributing coefficient of the cluster to the category;
Step 4: The new text is preprocessed, and its vector space is obtained;
Step 5: The distance between the spatial vector of new text and the central vector of each cluster generated in Step 3 is calculated. These distances are multiplied by the contributing coefficient of corresponding clusters. The calculated results of clusters in the same category are added. After comparison, the biggest category obtained is the category of partial discharge with typical defect to be classified.
The pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS of the embodiments of the invention has the following steps. The ultrahigh frequency is used to detect the partial discharge of three-phase cylinder type GIS, and UHF sensor is used to sample the partial discharge signal; the improved wavelet threshold filtering method is used for denoising processing of the partial discharge signal collected, and the real partial discharge signal is obtained; the characteristic parameters of sampling signal are extracted through the mode algorithm based on the phase analysis; the improved kernel principal component analysis method is used for dimension reduction processing of characteristic space consisting of characteristic parameters, and the characteristic parameter matrix after dimension reduction is obtained; and K nearest neighbor algorithm based on the cluster idea is used for pattern recognition of insulation defect type of GIS.
Therefore, existing technical defects can be overcome, and the accuracy of the detection pattern recognition of partial discharge of three-phase cylinder type ultrahigh voltage GIS is improved; the defect of less functions, small scope of application and poor accuracy in the existing technology can be overcome, and the advantages of many functions, wide scope of application and good accuracy can be realized.
Other features and advantages of the invention will be introduced in the subsequent description, and part of them are obvious from the description or known through implementation of the invention.
Next, through drawings and embodiments, the technical solution of the invention is future introduced in details.
BRIEF DESCRIPON OF THE DRAWINGS
The drawings are used for further understanding of the invention, constitute a part of the description, and are combined with embodiments of the invention to explain the invention without restricting the scope of the inventionthe invention. In the drawings:
Figure I is the structure diagram of partial discharge test apparatus of GIS
in the pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS in the invention;
Figure 2 is flow diagram of the pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS in the invention. In Figure 2, N and K are both natural numbers;
Figure 3 is the schematic diagram of the relationship between statistical distribution, Sk and Ku in the pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS in the invention. (a) is positive deflection, (b) is no deflection, and (c) is negative deflection. Sk is the deflection of spectrum shape relative to the normal distribution;
(d) is positive deflection, (e) is no deflection, and (f) is negative deflection. The degree of steepness (Ku) is used to describe the protruding degree of distribution of a certain shape relative to the normal distribution;
Figure 4 is the schematic diagram of effect of kernel function in the pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS in the invention. (a) is a polynomial function, (b) is a gaussian kernel function , and (c) is a new kernel function;
Figure 5 is the waveform comparison chart before filtering (a) and after filtering (b) in the pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS in the invention. In Figure 5, the angle of horizontal axis is 0-360 degrees. The vertical axis is the signal amplitude (Q).
Combined with the drawings, the reference numbers in embodiments of the invention are as follows.
1-water resistance; 2-HT testing transformer; 3-transformer; 4-HV bushing; 5-disk insulator.
DETAILED DESCRIPTION
Below is the description of preferred embodiments of the invention combined with the drawings. It should be understood that the preferred embodiments described here are only used to describe and explain the invention, but not to limit the invention.
For the defects in the existing technology, according to the embodiments of the invention, as shown in Figure I-Figure 5, a pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS is provided.
As shown in Figure 1, the partial discharge test apparatus of GIS used in the pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS in the invention includes disk insulators 5, an HV bushing 4 installed on the disk insulators 5, and a water resistance I, an HT testing transformer 2 and a transformer 3 connected to the HV
bushing 4 in turn, and a PDSG connected to the disk insulator 5, and the common end of the water resistance 1 and the FTV bushing 4 is earthed after passing through a voltage divider consisting of capacitors.
The partial discharge test apparatus of GIS mainly includes a transformer 3, a voltage divider consisting of capacitors, an oscilloscope, a three-phase cylinder type GIS, sensors and a PDSG; by setting high voltage conductor metal protrusions, free metal particles, fixed metals on the surface of insulator, air gaps of insulator and other defects respectively in GIS, the corresponding partial discharge signal is detected and the mode is recognized.
The pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS in this embodiment includes the following steps:
1) Ultrahigh frequency (UHF) is used to detect the partial discharge of three-phase cylinder type GIS, and UHF sensors are used to sample the partial discharge (PD) signal;
In Step 1), the ultrahigh frequency (UHF) method is used to detect the partial discharge of three-phase cylinder type GIS. When UHF method is used to detect the partial discharge of GIS, different failure types can be recognized according to the spectral characteristic of signals measured and the position of discharge on the power voltage waveform.
In Step 1), the real three-phase cylinder type GIS is used to measure the partial discharge map under the typical defect conditions. Among them, the three-phase cylinder type GIS shall be provided by a professional high voltage switchgear enterprise. The three-phase conductor is energized as follows. Two phases are earthed, and the other one phase is connected to the high voltage. In the model there is one HV bushing. According to the test results of coaxial type GIS test model, the contrastive typical insulation defects are set. In the cavity of the three-phase cylinder type GIS, there are free metal particles. On the surface of the insulator, there are fixed metal particles. On the insulator, there is air gap defect.
The physical model of partial discharge is set in the GIS stimulated device for the measurement of partial discharge.
2) The improved wavelet threshold filtering method is used for denoising processing of the partial discharge signal collected, and the real partial discharge signal is obtained;
In Step 2), in the improved wavelet threshold filtering method, from the calculation method of Median (Cj, k), it can be seen that the selection of threshold is closely related to the length of analyzed signal. In actual application, we cannot ensure that the proportion and position of the number of points sampled in the entire section of the effective signal in the entire sampling sequence is unchanged. Because the length of signal determines the number of wavelet coefficients on each scale after the wavelet changes (1\1j), it affects the value of Median ( g, 10, so as to affect the threshold. This influence will lead to the following results. The same useful signal sequence (u) is contained in a sampling sequence (s). If the length of s is different (i.e. the width of the time observation window is different), the results after the wavelet threshold filtering will be quite different. For s of different lengths containing the same UHF PD signal, the results of soft threshold filtering algorithm are applied. The sampling frequency of the original signal is 20GHz. The long sampling sequence with wide time window has 50,000 points. The short sampling sequence with narrow time window has 16,000 points. The two sequences both completely contain the useful UHF PD
signal. Obviously, after soft threshold filtering algorithm is applied, UHF PD
signal, especially the part of shock attenuation that will end, has obvious difference. The correlation coefficient of the two is 0.6677.
The primary cause of these consequences is that the calculation formula of threshold completely ignores the amplitude of effective signal and signal-to-noise ratio. Therefore, after several analysises, the embodiment considers the amplitude of effective signal and signal-to-noise ratio, and proposes a new self-adaptive threshold calculation method, which greatly reduces the level of sensitivity of wavelet threshold filtering results to the sampling points. The comparison of figures before and after filtering is as shown in Figure 5. In Figure
5, (a) is the graph before filtering, and (b) is the graph after filtering.
Arledia4Cbk 10 in ______ (N ( \
=
a exp 0.6745 \. i;
In which, j is the scale. Ali is the number of wavelet coefficients on the scale.
tt1edia4C =
is the median of all the wavelet coefficients on the scale, a called the signal to noise factor is the signal-to-noise ratio of signal in the threshold calculation. A called scale factor is the estimated error caused when the maximum value of wavelet coefficients on the scale corrects the different sampling sequence length.
-/ is the calculated threshold value.
The effectiveness of the wavelet denoising method mainly depends on a wavelet primary function, a wavelet decomposition scale, a threshold function, threshold selection and other aspects. In the embodiment, a large number of simulation experiments, laboratory simulation and field measured data are used to analyze and verify the effectiveness of the method used.
The results show that compared with the denoising method with other threshold rules, the wavelet denoising method obviously improves the denoising ability in the partial discharge signal processing, and has advantages such as small distortion of signal waveform after the processing, more accurate extraction and few influencing factors.
3) Characteristic parameters of sampling signal are extracted with the phase analysis mode algorithm. Preferably, the parameters include degree of skewness (Sk), degree of steepness (Ku), the number of partial peak points (Pe), cross correlation coefficient (Cc) and discharge factor (Q).
In Step 3), for the signal after sampling, characteristic parameters are extracted with phase resolved partial discharge (PRPD) mode. Among them, the definition of degree of skewness (Sk) is as follows:
Sk = 143 = piAx (73 =
Jr..]
In which, w is the number of phase window in the half cycle; xi is the phase position of the VI' phase window;
11.
w H1 =ylYi Al =I p ico, = (9i JO2 1=1 V i=1 In which, Yi is the vertical coordinate of spectrum, representing apparent discharge magnitude (q) or number of discharge (n); parameter '41 represents the central position of partial discharge map collected, .7 represents the steepness of symmetry axis in the center of the map, 11X is a parameter related to even distribution of partial discharge map, and is the phase position corresponding to a point in the map;
The degree of skewness (Sk) reflects the skewness of spectrum shape relative to normal distribution. Sk=0 indicates that the spectrum shape is symmetrical; Sk > 0 indicates that the spectrum biases to the left relative to normal distribution; Sk<0 indicates that the spectrum biases to the right relative to normal distribution.
Definition of the phase window: Construction method of ci"qqlspace curved surface:
The power frequency phase is divided into 256 sections according to 0-360 degrees. The discharge pulse amplitude (q) is divided into 128 sections by maximum amplitude, so that "(I plane is divided into 128x256 sections; the number of discharge in each section on tt) -q plane is counted, and '1)'*(1-11 space curved face is obtained.
The degree of steepness (Ku) is defined as below.
Ku = E(x, -1.1)4 p,Ax 1 a4 ¨ 3 In which, the definition of each variable is the same as that of variable in the degree of skewness. The degree of steepness (Ku) is used to describe the protruding degree of distribution of a certain shape relative to the normal distribution. The degree of steepness of normal distribution is 0. If Ku > 0, it indicates that the contour of the spectrum is sharper and steeper than that of the normal distribution; if Ku<0, it indicates that the contour of the spectrum is flatter than that of the normal distribution.
The number of partial peak points (Pe) is used to describe the number of partial peak points on the contour of the spectrum; whether there is partial peak at contour point needs to be determined with the following formula:
d dYi.1 <0 Y i--, ., 0 do , and The above formula is converted into the following difference equation.
Yi - Yi-i Yi-1-1 - Y=
i q)/ - Cpi-i >0, Pi+1 - q)i <0;
The difference equation can be simplified as below.
_ v ( .7 v i ) >0, ( Yi i = -=
Y' ) <0;
In the actual calculation, the number of partial peak points is closely related to the number of phase windows of the spectrum. Generally, the more the phase windows isometrically divided from the phase axis are, the larger the number of partial peak points is.
The cross correlation coefficient (Cc) is defined as below:
w (ii' w \
Eteqi- - EqTE q7 1W
CC
i..1 k,e--1 itt i I
= _ Eqi) ify E(0 _1( zq- iff, In which, q: ' is the discharge quantity in the phase window i. The superscripts "+" and "¨" respectively correspond to the positive and negative semiaxis of the spectrum; Cc reflects the correlation between the discharge strength and phase distribution in the positive and negative half cycle. If the cross correlation coefficient (Cc) is close to 1, it indicates that the contour of 9-(1. spectrum of the positive and negative half cycle is quite similar; if Cc is aVe close to 0, the contour difference of spectrum is great.
The discharge factor (Q) is as follows:
n n q Q= , =
ri n =
n n In which, and l are discharge repetition rate in the phase window i. The superscripts "+" and "¨" respectively corresponds to the positive and negative half cycle of the (I) ¨ qspectrum. The discharge quantity factor (Q) reflects the difference of average discharge quantity within the positive and negative half cycle of the ¨ '1 spectrum.
According to the above formula of statistical operator, through analysis spectrum and spectrum the statistical operator is calculated. The characteristic parameters degree of skewness (SK), degree of steepness (Ku), the number of partial peak points (Pe), discharge factor (Q) and cross correlation coefficient (Cc) are extracted for the pattern recognition.
4) The improved kernel principal component analysis method is used for dimension reduction processing of characteristic space consisting of characteristic parameters, and the characteristic parameter matrix after dimension reduction is obtained;
In Step 4), we do not know which characteristic parameters can construct the simplest characteristic space of UHF PD signal in advance, namely the non-redundant full rank characteristic parameters matrix, the constructed characteristic space has large number of dimensions, and there may be redundant number of dimensions, which is bad for the operation and recognition results, so the dimension reduction processing of characteristic space is required.
In the application of KPCA, the selection of nonlinear transformation (i.e.
kernel function) is very important. The commonly used kernel functions include polynomial kernel function, Gaussian kernel function and Sigmoid kernel function as shown below:
k(X= Xj )= ("< Xi, X = > a)b ( k(x,,xj)= exp XAI2 a"
;
k(Xi , X .)= tanh(< xõ x > +a) (a e R) In which, < Xi is the sample vector, which is the vector product of Xiand Xi.
11x, is Euclidean norm of the two. The polynomial kernel function is the bth power of , .
distance ("o(<,%> +a) , and the monotonic increasing function of < x1 X >
After conversion, if (<xõ..r., >+a)>1, the original distance <x oxl >will be magnified; on the contrary, if (<xoxt >+a)<1, the original distance <i> will be reduced. It can be seen that the effect of polynomial kernel function is reduction of small distance and further increase of large distance. Gaussian kernel function, also known as radial basis kernel function, is usually defined as the index monotonic decline function of two vector Euclidean distances. It is a kind of scalar function with radial symmetry. Among them, 0 is called the width parameter used to control the radial range of action of the function, namely width of gaussian pulse. But usually the range of action of Gaussian kernel function is small. The effect is just opposite to that of polynomial kernel function, namely increasing small distance and decreasing big distance.
Actually the effect of kernel function shall be further increase of the original distance, or reduction of the distance between similar samples and increase of the distance between different samples. Therefore, this embodiment combines the advantages and disadvantages of polynomial kernel function and gaussian kernel function, and proposes a new kernel function, whose effect is as shown in Figure 4.
In Step 4), the kernel function used by the improved kernel principal component analysis method is as follows:
2i - X -J
k X = X X > +4' exp 20'2 In which, in (a E R,b > 0), parameter a, b and 0 are selected according to the value of elements in the characteristic matrix. The parameter 0 is used to control the range of action in the radial direction of the kernel function: Xi and Xi represent different sample xi xi vectors, ( represents the vector product of sample vectors, R represents the set of kkxj) real numbers of the value range of vectors, N represents the set of integers, and represents the new kernel function obtained by combining the advantages of polynomial kernel function and gaussian kernel function. Here, a=5 and b=1, so that the distance after conversion changes proportionally with the original distance. The parameter 0 is used to control the range of action in the radial direction of the kernel function.
The distance between two vectors in the original characteristic matrix is generally not more than 7, so 0 =7. It can be seen from Figure 4 that the new kernel function proposed in the invention increases the original small distance and appropriately reduces the big distance.
5) K nearest neighbor classification method based on the cluster idea is used for pattern recognition of insulation defect type of GIS:
In Step 5), the main idea of K nearest neighbor classification method is as follows. The test documents are given. The system searches for K nearest neighbors in the classified training set. According to the category distribution of these neighbors, the categories of the test documents are obtained. The similarity of these neighbors with test documents can be weighed, so as to obtain the good classification effect. The cluster refers to the set of a category of documents with similar properties. The invention considers partial discharge signal data subset in the training set with the maximum distance between texts in the same category as one cluster. Therefore, the algorithm of K nearest neighbor classification method can be described as follows:
Step 1: In the training set, first of all the partial discharge data is preprocessed and mapped into a spatial vector;
Step 2: From the first category, the similarity of each two data among all the signal data in the category is calculated, the minimum threshold is set, and according to the statistics the clusters with close similarity are obtained;
Step 3: All the signal data in each cluster is merged. And then its central vector is calculated; in addition, the number of clusters/categories is calculated. This value represents the contributing coefficient of the cluster to the category, recorded as C;
Step 4: The new text is preprocessed, and its vector space is obtained;
Step 5: The distance between the spatial vector of new text with each cluster of central vector generated in Step 3 is calculated. These distances are multiplied by the contributing coefficient of corresponding cluster. The calculated results of clusters in the same category are added. After comparison, the biggest category obtained is the category of partial discharge with typical defect to be classified.
The basis for this algorithm is to find which texts in the same category belong to the same cluster. Below the idea of generated cluster algorithm of finding clusters of the same category is given. Assume the category:
c=fdl, d2, ....... dm) Step 1: The threshold of similarity (a) is set;
Step 2: First a cluster is created, recorded as TO. The number of documents contained in the cluster is recorded with Ki. The total number of clusters created is obtained. The processed document i=2 is initialized;
Step 3: Start from di;
Step 4: The similarity with the first text in Tn is calculated, and s is obtained;
Step 5: If S > a, and in Tn there is text not compared with this text, then continue the similarity calculation, and update s; if where is no text not compared, then the data is added to Cluster Tn; if S< a and there are other clusters not compared, then n++ and return to Step 4;
if there is no cluster not compared, then new cluster is created, and recorded as T++total; the document shall be in Cluster T++total;
Step 6: If .'rn, then i++; return to Step 3; Otherwise, end.
In order to overcome the defect of high erroneous judgment of the nearest neighbor method, the nearest neighbor is expanded to K nearest neighbor. K nearest neighbor method does not select the nearest neighbor for classification, but select K
representative points nearest to the text to be classified. And then according the category information of the K
representative points, the category of text to be classified is determined.
For the characteristic parameter matrix after dimensionality reduction, half of the samples are used for training of K nearest neighbor classifier. The other half is used to test the performance of the classifier. For the characteristic parameter matrix after dimensionality reduction with KPCA, RST and CCMDR algorithm, K nearest neighbor classifier is used to recognize GIS insulation defect type. In this embodiment, under C language software environment, program documents are prepared, and design, training and classification recognition test of the classifier are realized. Because the output of the classifier designed in the embodiment does not have scattered distribution centered on a point like BP neural network, but corresponds to 4 GIS defect types, and the output value only includes 4 results [1, 2, 3, 4], so the pattern recognition result is only expressed as the accuracy rate of recognition, as shown in Table 1.
Table 1: The accuracy rate of K nearest neighbor algorithm pattern recognition Defect type The accuracy rate of K nearest neighbor method High voltage conductor metal protrusions 92%
Free metal particles 91.5%
Fixed metals on the surface of insulator 88%
Air gap defect of insulator 90%
In conclusion, the pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS of the embodiments of the invention has the following steps. The ultrahigh frequency is used to detect the partial discharge of three-phase in one enclosure GIS, and UHF sensor is used to sample the partial discharge (PD) signal; the improved wavelet threshold filtering method is used for denoising processing of the partial discharge signal collected, and the real partial discharge signal is obtained;
the characteristic parameters of sampling signal are extracted through the mode algorithm based on the phase analysis, including degree of skewness (Sk), degree of steepness (Ku), the number of partial peak points (Pe), cross correlation coefficient (Cc) and discharge factor (Q);
the improved kernel principal component analysis method is used for dimension reduction processing of characteristic space consisting of characteristic parameters, and the characteristic parameter matrix after dimension reduction is obtained; and K nearest neighbor algorithm based on the cluster idea is used for pattern recognition of insulation defect type of GIS.
The pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS at least can have the following beneficial effects. Existing technical defects can be overcome, and the accuracy of the detection pattern recognition of partial discharge of three-phase cylinder type ultrahigh voltage GIS is improved.
Finally it should be noted that above is only the preferred embodiments of the invention, and not used to restrict the invention. Although the invention is described in details with the reference of the above embodiments, those skilled in this field can still revise the technical solution recorded in the above embodiments, or equally replace part of the technical features.
Any revision, equal replacement and improvement within the spirit and principles of the invention shall be within the scope of protection of the invention.
Arledia4Cbk 10 in ______ (N ( \
=
a exp 0.6745 \. i;
In which, j is the scale. Ali is the number of wavelet coefficients on the scale.
tt1edia4C =
is the median of all the wavelet coefficients on the scale, a called the signal to noise factor is the signal-to-noise ratio of signal in the threshold calculation. A called scale factor is the estimated error caused when the maximum value of wavelet coefficients on the scale corrects the different sampling sequence length.
-/ is the calculated threshold value.
The effectiveness of the wavelet denoising method mainly depends on a wavelet primary function, a wavelet decomposition scale, a threshold function, threshold selection and other aspects. In the embodiment, a large number of simulation experiments, laboratory simulation and field measured data are used to analyze and verify the effectiveness of the method used.
The results show that compared with the denoising method with other threshold rules, the wavelet denoising method obviously improves the denoising ability in the partial discharge signal processing, and has advantages such as small distortion of signal waveform after the processing, more accurate extraction and few influencing factors.
3) Characteristic parameters of sampling signal are extracted with the phase analysis mode algorithm. Preferably, the parameters include degree of skewness (Sk), degree of steepness (Ku), the number of partial peak points (Pe), cross correlation coefficient (Cc) and discharge factor (Q).
In Step 3), for the signal after sampling, characteristic parameters are extracted with phase resolved partial discharge (PRPD) mode. Among them, the definition of degree of skewness (Sk) is as follows:
Sk = 143 = piAx (73 =
Jr..]
In which, w is the number of phase window in the half cycle; xi is the phase position of the VI' phase window;
11.
w H1 =ylYi Al =I p ico, = (9i JO2 1=1 V i=1 In which, Yi is the vertical coordinate of spectrum, representing apparent discharge magnitude (q) or number of discharge (n); parameter '41 represents the central position of partial discharge map collected, .7 represents the steepness of symmetry axis in the center of the map, 11X is a parameter related to even distribution of partial discharge map, and is the phase position corresponding to a point in the map;
The degree of skewness (Sk) reflects the skewness of spectrum shape relative to normal distribution. Sk=0 indicates that the spectrum shape is symmetrical; Sk > 0 indicates that the spectrum biases to the left relative to normal distribution; Sk<0 indicates that the spectrum biases to the right relative to normal distribution.
Definition of the phase window: Construction method of ci"qqlspace curved surface:
The power frequency phase is divided into 256 sections according to 0-360 degrees. The discharge pulse amplitude (q) is divided into 128 sections by maximum amplitude, so that "(I plane is divided into 128x256 sections; the number of discharge in each section on tt) -q plane is counted, and '1)'*(1-11 space curved face is obtained.
The degree of steepness (Ku) is defined as below.
Ku = E(x, -1.1)4 p,Ax 1 a4 ¨ 3 In which, the definition of each variable is the same as that of variable in the degree of skewness. The degree of steepness (Ku) is used to describe the protruding degree of distribution of a certain shape relative to the normal distribution. The degree of steepness of normal distribution is 0. If Ku > 0, it indicates that the contour of the spectrum is sharper and steeper than that of the normal distribution; if Ku<0, it indicates that the contour of the spectrum is flatter than that of the normal distribution.
The number of partial peak points (Pe) is used to describe the number of partial peak points on the contour of the spectrum; whether there is partial peak at contour point needs to be determined with the following formula:
d dYi.1 <0 Y i--, ., 0 do , and The above formula is converted into the following difference equation.
Yi - Yi-i Yi-1-1 - Y=
i q)/ - Cpi-i >0, Pi+1 - q)i <0;
The difference equation can be simplified as below.
_ v ( .7 v i ) >0, ( Yi i = -=
Y' ) <0;
In the actual calculation, the number of partial peak points is closely related to the number of phase windows of the spectrum. Generally, the more the phase windows isometrically divided from the phase axis are, the larger the number of partial peak points is.
The cross correlation coefficient (Cc) is defined as below:
w (ii' w \
Eteqi- - EqTE q7 1W
CC
i..1 k,e--1 itt i I
= _ Eqi) ify E(0 _1( zq- iff, In which, q: ' is the discharge quantity in the phase window i. The superscripts "+" and "¨" respectively correspond to the positive and negative semiaxis of the spectrum; Cc reflects the correlation between the discharge strength and phase distribution in the positive and negative half cycle. If the cross correlation coefficient (Cc) is close to 1, it indicates that the contour of 9-(1. spectrum of the positive and negative half cycle is quite similar; if Cc is aVe close to 0, the contour difference of spectrum is great.
The discharge factor (Q) is as follows:
n n q Q= , =
ri n =
n n In which, and l are discharge repetition rate in the phase window i. The superscripts "+" and "¨" respectively corresponds to the positive and negative half cycle of the (I) ¨ qspectrum. The discharge quantity factor (Q) reflects the difference of average discharge quantity within the positive and negative half cycle of the ¨ '1 spectrum.
According to the above formula of statistical operator, through analysis spectrum and spectrum the statistical operator is calculated. The characteristic parameters degree of skewness (SK), degree of steepness (Ku), the number of partial peak points (Pe), discharge factor (Q) and cross correlation coefficient (Cc) are extracted for the pattern recognition.
4) The improved kernel principal component analysis method is used for dimension reduction processing of characteristic space consisting of characteristic parameters, and the characteristic parameter matrix after dimension reduction is obtained;
In Step 4), we do not know which characteristic parameters can construct the simplest characteristic space of UHF PD signal in advance, namely the non-redundant full rank characteristic parameters matrix, the constructed characteristic space has large number of dimensions, and there may be redundant number of dimensions, which is bad for the operation and recognition results, so the dimension reduction processing of characteristic space is required.
In the application of KPCA, the selection of nonlinear transformation (i.e.
kernel function) is very important. The commonly used kernel functions include polynomial kernel function, Gaussian kernel function and Sigmoid kernel function as shown below:
k(X= Xj )= ("< Xi, X = > a)b ( k(x,,xj)= exp XAI2 a"
;
k(Xi , X .)= tanh(< xõ x > +a) (a e R) In which, < Xi is the sample vector, which is the vector product of Xiand Xi.
11x, is Euclidean norm of the two. The polynomial kernel function is the bth power of , .
distance ("o(<,%> +a) , and the monotonic increasing function of < x1 X >
After conversion, if (<xõ..r., >+a)>1, the original distance <x oxl >will be magnified; on the contrary, if (<xoxt >+a)<1, the original distance <i> will be reduced. It can be seen that the effect of polynomial kernel function is reduction of small distance and further increase of large distance. Gaussian kernel function, also known as radial basis kernel function, is usually defined as the index monotonic decline function of two vector Euclidean distances. It is a kind of scalar function with radial symmetry. Among them, 0 is called the width parameter used to control the radial range of action of the function, namely width of gaussian pulse. But usually the range of action of Gaussian kernel function is small. The effect is just opposite to that of polynomial kernel function, namely increasing small distance and decreasing big distance.
Actually the effect of kernel function shall be further increase of the original distance, or reduction of the distance between similar samples and increase of the distance between different samples. Therefore, this embodiment combines the advantages and disadvantages of polynomial kernel function and gaussian kernel function, and proposes a new kernel function, whose effect is as shown in Figure 4.
In Step 4), the kernel function used by the improved kernel principal component analysis method is as follows:
2i - X -J
k X = X X > +4' exp 20'2 In which, in (a E R,b > 0), parameter a, b and 0 are selected according to the value of elements in the characteristic matrix. The parameter 0 is used to control the range of action in the radial direction of the kernel function: Xi and Xi represent different sample xi xi vectors, ( represents the vector product of sample vectors, R represents the set of kkxj) real numbers of the value range of vectors, N represents the set of integers, and represents the new kernel function obtained by combining the advantages of polynomial kernel function and gaussian kernel function. Here, a=5 and b=1, so that the distance after conversion changes proportionally with the original distance. The parameter 0 is used to control the range of action in the radial direction of the kernel function.
The distance between two vectors in the original characteristic matrix is generally not more than 7, so 0 =7. It can be seen from Figure 4 that the new kernel function proposed in the invention increases the original small distance and appropriately reduces the big distance.
5) K nearest neighbor classification method based on the cluster idea is used for pattern recognition of insulation defect type of GIS:
In Step 5), the main idea of K nearest neighbor classification method is as follows. The test documents are given. The system searches for K nearest neighbors in the classified training set. According to the category distribution of these neighbors, the categories of the test documents are obtained. The similarity of these neighbors with test documents can be weighed, so as to obtain the good classification effect. The cluster refers to the set of a category of documents with similar properties. The invention considers partial discharge signal data subset in the training set with the maximum distance between texts in the same category as one cluster. Therefore, the algorithm of K nearest neighbor classification method can be described as follows:
Step 1: In the training set, first of all the partial discharge data is preprocessed and mapped into a spatial vector;
Step 2: From the first category, the similarity of each two data among all the signal data in the category is calculated, the minimum threshold is set, and according to the statistics the clusters with close similarity are obtained;
Step 3: All the signal data in each cluster is merged. And then its central vector is calculated; in addition, the number of clusters/categories is calculated. This value represents the contributing coefficient of the cluster to the category, recorded as C;
Step 4: The new text is preprocessed, and its vector space is obtained;
Step 5: The distance between the spatial vector of new text with each cluster of central vector generated in Step 3 is calculated. These distances are multiplied by the contributing coefficient of corresponding cluster. The calculated results of clusters in the same category are added. After comparison, the biggest category obtained is the category of partial discharge with typical defect to be classified.
The basis for this algorithm is to find which texts in the same category belong to the same cluster. Below the idea of generated cluster algorithm of finding clusters of the same category is given. Assume the category:
c=fdl, d2, ....... dm) Step 1: The threshold of similarity (a) is set;
Step 2: First a cluster is created, recorded as TO. The number of documents contained in the cluster is recorded with Ki. The total number of clusters created is obtained. The processed document i=2 is initialized;
Step 3: Start from di;
Step 4: The similarity with the first text in Tn is calculated, and s is obtained;
Step 5: If S > a, and in Tn there is text not compared with this text, then continue the similarity calculation, and update s; if where is no text not compared, then the data is added to Cluster Tn; if S< a and there are other clusters not compared, then n++ and return to Step 4;
if there is no cluster not compared, then new cluster is created, and recorded as T++total; the document shall be in Cluster T++total;
Step 6: If .'rn, then i++; return to Step 3; Otherwise, end.
In order to overcome the defect of high erroneous judgment of the nearest neighbor method, the nearest neighbor is expanded to K nearest neighbor. K nearest neighbor method does not select the nearest neighbor for classification, but select K
representative points nearest to the text to be classified. And then according the category information of the K
representative points, the category of text to be classified is determined.
For the characteristic parameter matrix after dimensionality reduction, half of the samples are used for training of K nearest neighbor classifier. The other half is used to test the performance of the classifier. For the characteristic parameter matrix after dimensionality reduction with KPCA, RST and CCMDR algorithm, K nearest neighbor classifier is used to recognize GIS insulation defect type. In this embodiment, under C language software environment, program documents are prepared, and design, training and classification recognition test of the classifier are realized. Because the output of the classifier designed in the embodiment does not have scattered distribution centered on a point like BP neural network, but corresponds to 4 GIS defect types, and the output value only includes 4 results [1, 2, 3, 4], so the pattern recognition result is only expressed as the accuracy rate of recognition, as shown in Table 1.
Table 1: The accuracy rate of K nearest neighbor algorithm pattern recognition Defect type The accuracy rate of K nearest neighbor method High voltage conductor metal protrusions 92%
Free metal particles 91.5%
Fixed metals on the surface of insulator 88%
Air gap defect of insulator 90%
In conclusion, the pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS of the embodiments of the invention has the following steps. The ultrahigh frequency is used to detect the partial discharge of three-phase in one enclosure GIS, and UHF sensor is used to sample the partial discharge (PD) signal; the improved wavelet threshold filtering method is used for denoising processing of the partial discharge signal collected, and the real partial discharge signal is obtained;
the characteristic parameters of sampling signal are extracted through the mode algorithm based on the phase analysis, including degree of skewness (Sk), degree of steepness (Ku), the number of partial peak points (Pe), cross correlation coefficient (Cc) and discharge factor (Q);
the improved kernel principal component analysis method is used for dimension reduction processing of characteristic space consisting of characteristic parameters, and the characteristic parameter matrix after dimension reduction is obtained; and K nearest neighbor algorithm based on the cluster idea is used for pattern recognition of insulation defect type of GIS.
The pattern recognition method for partial discharge of three-phase cylinder type ultrahigh voltage GIS at least can have the following beneficial effects. Existing technical defects can be overcome, and the accuracy of the detection pattern recognition of partial discharge of three-phase cylinder type ultrahigh voltage GIS is improved.
Finally it should be noted that above is only the preferred embodiments of the invention, and not used to restrict the invention. Although the invention is described in details with the reference of the above embodiments, those skilled in this field can still revise the technical solution recorded in the above embodiments, or equally replace part of the technical features.
Any revision, equal replacement and improvement within the spirit and principles of the invention shall be within the scope of protection of the invention.
Claims (9)
1. An insulation defect type determining method for partial discharge of three-phase cylinder type ultrahigh voltage Gas Insulated Switchgear (GIS), which is characterized by including the following steps:
using ultrahigh frequency to detect the partial discharge of three-phase in one enclosure GIS, and using ultrahigh frequency sensor to sample a partial discharge signal;
denoising processing of the partial discharge signal collected by using an improved wavelet threshold filtering method, and obtaining a real partial discharge signal;
extracting a characteristic parameters of sampling signal through a mode algorithm based on phase analysis;
using an improved kernel principal component analysis method for dimension reduction processing of characteristic space consisting of characteristic parameters, and obtaining the characteristic parameter matrix after dimension reduction; and identifying insulation defect type of GIS by using K nearest neighbor algorithm based on a cluster mode;
wherein K nearest neighbor algorithm includes:
Step 1: in a training set, first of all partial discharge data is preprocessed and mapped into a spatial vector;
Step 2: from a first category, the similarity of each two data among all signal data in the first category is calculated, a minimum threshold is set, and according to the statistics clusters with close similarity are obtained;
Step 3: all signal data in each cluster is merged, then its central vector is calculated; in addition, the number of clusters/categories is calculated and this value represents a contributing coefficient of the cluster to the category;
Step 4: a new text is preprocessed, and its vector space is obtained;
Step 5: a distance between a spatial vector of new text and central vector of each cluster generated in Step 3 is calculated, these distances are multiplied by the contributing coefficient of corresponding cluster, calculated results of clusters in the same category are added, and after comparison, the biggest category obtained is a category of partial discharge with typical defect to be classified.
using ultrahigh frequency to detect the partial discharge of three-phase in one enclosure GIS, and using ultrahigh frequency sensor to sample a partial discharge signal;
denoising processing of the partial discharge signal collected by using an improved wavelet threshold filtering method, and obtaining a real partial discharge signal;
extracting a characteristic parameters of sampling signal through a mode algorithm based on phase analysis;
using an improved kernel principal component analysis method for dimension reduction processing of characteristic space consisting of characteristic parameters, and obtaining the characteristic parameter matrix after dimension reduction; and identifying insulation defect type of GIS by using K nearest neighbor algorithm based on a cluster mode;
wherein K nearest neighbor algorithm includes:
Step 1: in a training set, first of all partial discharge data is preprocessed and mapped into a spatial vector;
Step 2: from a first category, the similarity of each two data among all signal data in the first category is calculated, a minimum threshold is set, and according to the statistics clusters with close similarity are obtained;
Step 3: all signal data in each cluster is merged, then its central vector is calculated; in addition, the number of clusters/categories is calculated and this value represents a contributing coefficient of the cluster to the category;
Step 4: a new text is preprocessed, and its vector space is obtained;
Step 5: a distance between a spatial vector of new text and central vector of each cluster generated in Step 3 is calculated, these distances are multiplied by the contributing coefficient of corresponding cluster, calculated results of clusters in the same category are added, and after comparison, the biggest category obtained is a category of partial discharge with typical defect to be classified.
2. The method according to claim 1, wherein the improved wavelet threshold filtering method for denoising processing of the partial discharge signal collected uses a calculation method of self-adaptive threshold, which is as follows, wherein, j is scale, N j is number of wavelet coefficients on the scale, Median(¦C j,k.¦) is median of all the wavelet coefficients on the scale, .alpha. called a signal to noise factor, is signal-to-noise ratio of signal in a threshold calculation, .beta.j called scale factor, is an estimated error caused when the maximum value of wavelet coefficients on the scale corrects a different sampling sequence length, and T j is a calculated threshold value.
3. The method according to claim 1 or 2, wherein extracted parameter characteristics include degree of skewness (Sk), degree of steepness (Ku), number of partial peak points (Pe), cross correlation coefficient (Cc) and discharge factor (Q).
4. The method according to claim 3, wherein the degree of skewness (Sk) is as follows:
wherein, W is number of phase window in a half cycle; x i is a phase position of the i th phase window;
wherein, .gamma. i is vertical coordinate of spectrum, representing apparent discharge magnitude (q) or number of discharge (n); parameter µ represents a central position of partial discharge map collected, .sigma. represents steepness of symmetry axis in the center of the map, .DELTA.x is a parameter related to even distribution of partial discharge map, and .PHI. i is the phase position corresponding to a point in the map;
the degree of skewness (Sk) reflects the skewness of spectrum shape relative to normal distribution, Sk = 0 indicates that the spectrum shape is symmetrical;
Sk > 0 indicates that a spectrum biases to the left relative to normal distribution;
Sk < 0 indicates that a spectrum biases to the right relative to normal distribution.
wherein, W is number of phase window in a half cycle; x i is a phase position of the i th phase window;
wherein, .gamma. i is vertical coordinate of spectrum, representing apparent discharge magnitude (q) or number of discharge (n); parameter µ represents a central position of partial discharge map collected, .sigma. represents steepness of symmetry axis in the center of the map, .DELTA.x is a parameter related to even distribution of partial discharge map, and .PHI. i is the phase position corresponding to a point in the map;
the degree of skewness (Sk) reflects the skewness of spectrum shape relative to normal distribution, Sk = 0 indicates that the spectrum shape is symmetrical;
Sk > 0 indicates that a spectrum biases to the left relative to normal distribution;
Sk < 0 indicates that a spectrum biases to the right relative to normal distribution.
5. The method according to claim 3, wherein the degree of steepness (Ku) is as follows:
wherein, W is number of phase window in a half cycle; x, iis a phase position of the i th phase window;
wherein, Y i is vertical coordinate of spectrum, representing apparent discharge magnitude (q) or number of discharge (n); parameter µ represents a central position of partial discharge map collected, a represents steepness of symmetry axis in the center of the map, .DELTA.x is a parameter related to even distribution of partial discharge map, and .PHI. i is the phase position corresponding to a point in the map;
the degree of steepness (Ku) is used to describe a protruding degree of distribution of a certain shape relative to the normal distribution, the degree of steepness (Ku) of normal distribution is 0; if Ku > 0, it indicates that a contour of the spectrum is sharper and steeper than that of the normal distribution; if Ku < 0, it indicates that the contour of the spectrum is flatter than that of the normal distribution.
wherein, W is number of phase window in a half cycle; x, iis a phase position of the i th phase window;
wherein, Y i is vertical coordinate of spectrum, representing apparent discharge magnitude (q) or number of discharge (n); parameter µ represents a central position of partial discharge map collected, a represents steepness of symmetry axis in the center of the map, .DELTA.x is a parameter related to even distribution of partial discharge map, and .PHI. i is the phase position corresponding to a point in the map;
the degree of steepness (Ku) is used to describe a protruding degree of distribution of a certain shape relative to the normal distribution, the degree of steepness (Ku) of normal distribution is 0; if Ku > 0, it indicates that a contour of the spectrum is sharper and steeper than that of the normal distribution; if Ku < 0, it indicates that the contour of the spectrum is flatter than that of the normal distribution.
6. The method according to claim 3, wherein the number of partial peak points (Pe) is used to describe the number of partial peak points on a contour of the spectrum;
whether there is partial peak at contour point (.PHI. i, y i) needs to be decided with the following difference equation:
(y i - y i-l) > 0, (y i+l y i) < 0;
the more the phase windows isometrically divided from the phase axis are, the larger the number of partial peak points is.
whether there is partial peak at contour point (.PHI. i, y i) needs to be decided with the following difference equation:
(y i - y i-l) > 0, (y i+l y i) < 0;
the more the phase windows isometrically divided from the phase axis are, the larger the number of partial peak points is.
7. The method according to claim 3, wherein the cross correlation coefficient (Ce) is as follows:
wherein, qi+, qi is discharge quantity in a phase window i, the superscripts "+"
and "_" respectively correspond to the positive and negative semiaxis of the spectrum; c reflects correlation between discharge strength and phase distribution in the positive and negative half cycle, if the cross correlation coefficient (Ce) is close to 1, it indicates that the contour of .PHI..q, spectrum of the positive and negative half cycle is quite similar; if Ce is close to 0, the contour difference of .PHI. - g ave spectrum is great.
wherein, qi+, qi is discharge quantity in a phase window i, the superscripts "+"
and "_" respectively correspond to the positive and negative semiaxis of the spectrum; c reflects correlation between discharge strength and phase distribution in the positive and negative half cycle, if the cross correlation coefficient (Ce) is close to 1, it indicates that the contour of .PHI..q, spectrum of the positive and negative half cycle is quite similar; if Ce is close to 0, the contour difference of .PHI. - g ave spectrum is great.
8. The method according to claim 3, wherein the discharge factor (Q) is as follows:
wherein, n i+ and n i are discharge repetition rate in a phase window 1, the superscripts "+"
and "_" respectively correspond to the positive and negative half cycle of .PHI. - q spectrum.
wherein, n i+ and n i are discharge repetition rate in a phase window 1, the superscripts "+"
and "_" respectively correspond to the positive and negative half cycle of .PHI. - q spectrum.
9. The method according to claim 1 or 2, wherein the improved kernel principal component analysis method is an improved kernel principal component analysis and a kernel function sampled is as follows:
wherein, in (.alpha. .epsilon. R, b .epsilon. N, .sigma. > 0), parameter a, b and a are selected according to the value of elements in the characteristic matrix, the parameter a is used to control the range of action in the radial direction of the kernel function; x i and x i represent different sample vectors, (x i, x j) represents vector product of sample vectors, R represents the set of real numbers of the value range of vectors, N represents the set of integers, and k(x i, x j) represents a new kernel function obtained by combining advantages of polynomial kernel function and gaussian kernel function.
wherein, in (.alpha. .epsilon. R, b .epsilon. N, .sigma. > 0), parameter a, b and a are selected according to the value of elements in the characteristic matrix, the parameter a is used to control the range of action in the radial direction of the kernel function; x i and x i represent different sample vectors, (x i, x j) represents vector product of sample vectors, R represents the set of real numbers of the value range of vectors, N represents the set of integers, and k(x i, x j) represents a new kernel function obtained by combining advantages of polynomial kernel function and gaussian kernel function.
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