CN109541455A - A kind of OLTC impact characteristics extracting method based on S-transformation time-frequency spectrum SVD noise reduction - Google Patents
A kind of OLTC impact characteristics extracting method based on S-transformation time-frequency spectrum SVD noise reduction Download PDFInfo
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- 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
- G01R31/327—Testing of circuit interrupters, switches or circuit-breakers
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- 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
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Abstract
The invention discloses a kind of OLTC impact characteristics extracting methods based on S-transformation time-frequency spectrum SVD noise reduction, comprising the following steps: (1) is acquired by acceleration transducer to the vibration signal under load ratio bridging switch OLTC normal condition；(2) S-transformation is carried out to collected vibration signal, then carries out discretization and obtains S-transformation time-frequency spectrum；(3) spectral coefficient matrix A is obtained based on time-frequency spectrum, singular value decomposition is carried out to matrix A, obtains each singular value of matrix A；(4) a threshold value σ is setth, σ will be less than or equal in unusual value sequence ΣthSingular value be set to zero, then rebuild S-transformation time-frequency spectrum coefficient matrix B；(5) clock synchronization spectral coefficient matrix B carries out S inverse transformation, obtains the time domain impulse feature of vibration signal.The present invention achieves preferable effect to the shock characteristic of tap switch vibration signal.
Description
Technical field
The present invention relates to load ratio bridging switch fault diagnosis technology fields, and in particular to one kind is based on S-transformation time-frequency spectrum SVD
The OLTC vibration signal processing method of noise reduction.
Background technique
Core component one of of the on-load tap changers of transformers OLTC as transformer plays stablize in the power system
Load center voltage adjusts reactive power flow, increases the important function such as dispatching of power netwoks flexibility.In load ratio bridging switch operating process
In, collision or friction between mechanism components can generate vibration signal, these vibration signals include equipment state abundant
Information.But collection in worksite to vibration signal often contain abnormal data and various noises, will affect the knot of analysis of vibration signal
Fruit.So selecting suitable method to propose the shock response of OLTC switching at that moment before to analysis of vibration signal
It takes, facilitates the characteristic quantity for finding failure when the fault diagnosis to OLTC.
In traditional impact spy into extraction process method, for the processing of shock characteristic signal, existing method is main
It is that there is the spy of high susceptibility to impact ingredient using signal high-order statistic, the methods of kurtosis, degree of skewness or kurtosis
Point realizes the detection of shock characteristic.High-order statistic method constructs blind deconvolution filter using optimization object function, detects signal
In weak impact ingredient, but filter length is difficult to determine in the method, limits its application.Kurtosis method uses kurtosis index
The weighted kurtosis index combined with cross-correlation coefficient be optimization aim, using random resonance detection method extract signal impact at
Point, there is certain feasibility, however for the signal of low signal-to-noise ratio, weighted kurtosis index has some limitations, and joins
Number optimization is more difficult, influences testing result.The present invention proposes that a kind of OLTC impact based on S-transformation time-frequency spectrum SVD noise reduction is special
Property extracting method, using directly noise reduction is carried out to the shock characteristic signal of noise mixing, to extract impact ingredient, this side
Formula simple, intuitive, it is with strong points.
Summary of the invention
To solve deficiency in the prior art, the present invention provides a kind of OLTC impact based on S-transformation time-frequency spectrum SVD noise reduction
Feature extraction method, excellent noise reduction effect, algorithm is simple, strong operability.
In order to achieve the above objectives, the present invention adopts the following technical scheme: it is a kind of based on S-transformation time-frequency spectrum SVD noise reduction
OLTC impact characteristics extracting method, comprising the following steps:
Step 1: the vibration signal under acquisition load ratio bridging switch OLTC normal condition；
Step 2: S-transformation being carried out to collected vibration signal, discretization is then carried out, obtains S-transformation time-frequency spectrum；
Step 3: spectral coefficient matrix A being obtained based on time-frequency spectrum, singular value decomposition is carried out to matrix A, obtains each of matrix A
A singular value；
Step 4: according to each singular value in matrix A, removing noise therein, rebuild S-transformation time-frequency spectral coefficient
Matrix B；
Step 5: clock synchronization spectral coefficient matrix B carries out S inverse transformation, obtains the time domain impulse feature of vibration signal.
The present invention further comprises following preferred embodiment:
In step 1, the top cover that vibrating sensor will be accelerated to be placed in OLTC collects the vibration letter of OLTC switching moment
Number.
In step 2, that the signal by acceleration vibrating sensor acquisition is noise mixed signal x (t)=s (t)+n
(t), s (t) is contact impact vibration signal, and n (t) is noise.S-transformation is carried out to signal is collected according to formula (1),
In formula, S (τ, f) indicates the continuous S-transformation of x (t), and t indicates the time, and f indicates frequency, and parameter τ indicates analysis window ω
The position on t axis, analysis window ω are defined as Gaussian window in time domain, it may be assumed that
σ=1/ in formula | f |, discretization then, which is carried out, according to formula (3) obtains S-transformation time-frequency spectrum.
In formula: T be x (t) sampling interval, N be x (t) number of samples, X [] be Fourier transformation, j, n, m=0,1 ...
N-1。
In step 3, unusual decomposition (SVD) is carried out to S-transformation time-frequency spectrum to handle, obtain its preceding 1000 points of singular value
Spectrum, the specific steps are as follows:
The time-frequency spectrum of the S-transformation obtained in the previous step can regard the matrix A of order r≤n of a N × N as, and N is
The sampling length of continuous time signal x (t).Unusual decomposition is carried out to A,
A=UDVT (4)
U in formula, V can be solved must existing symmetrical matrix.
In formula: σ1, σ2…σNFor the singular value of A, then these singular values are arranged according to the sequence successively decreased, i.e. σ1≥σ2
≥…≥σN, and enabling non-zero singular value sequence is Σ=(σ1, σ2…σr).To obtain singular value spectrum.
In step 4, Σ=(σ is composed by the singular value of signal1, σ2…σr) find out the singular value Difference Spectrum P=(ρ of signal1,
ρ2…ρr-1) as shown in figure 5, wherein element
ρi=σi-σi+1, i=1,2 ... r-1 (6)
After obtaining singular value Difference Spectrum, one group of peak point that Difference Spectrum foremost part is more concentrated, and its amplitude is aobvious
The point for the amplitude for being greater than subsequent peak point is write as threshold value σthThe position coordinates at place will be less than or equal in unusual value sequence Σ
σthSingular value be set to zero, then formula (7) rebuild S-transformation time-frequency spectrum coefficient matrix B.
B=UD*VT (7)
D in formula*It is that singular value in D is less than threshold value σthDiagonal matrix, U, V are the above-mentioned matrixes acquired.
In steps of 5, after the time-frequency spectrum coefficient matrix B after the SVD noise reduction that gets application, as shown in fig. 7, formula
(8) inverse operation for carrying out S-transformation, obtains the time domain impulse feature of vibration signal.
In formula: N is the number of samples of x (t), and T is the sampling interval of x (t), j, n ,=0,1 ... N-1.
Advantageous effects of the invention:
(1) present invention has multi-resolution characteristics, for the high frequency in signal compared to conventional impact feature extracting method
Impacting ingredient has compared with hypersensitivity, meets linear superposition theorem, there is no the interference of cross term, is suitable for processing and analysis is non-
Stationary signal, especially as OLTC shock characteristic signal；
(2) the OLTC shock characteristic extracting method based on S-transformation time-frequency spectrum SVD noise reduction is a kind of new method, simple straight
It sees, is with strong points, being easily achieved.As the shock characteristic frequency of occurrences of most important information, can completely effectively extract.
(3) through analysis of experimental data it is found that the present invention can low frequency part to signal and high frequency section can obtain compared with
Noise reduction effect well.
Detailed description of the invention
Fig. 1 is flow chart of the invention.
Fig. 2 is to collect vibration signal original graph.
Fig. 3 is to obtain time-frequency spectrum through S-transformation.
Fig. 4 is singular value spectrogram.
Fig. 5 is singular value difference spectrogram.
Fig. 6 is threshold value σthThe position coordinates figure at place.
Fig. 7 is to rebuild S-transformation time-frequency spectral coefficient map.
Fig. 8 is the shock characteristic figure that S inverse transformation obtains.
Specific embodiment
Below with reference to attached drawing, technical solution of the present invention is described in detail.
The present invention provides a kind of OLTC impact characteristics extracting method based on S-transformation time-frequency spectrum SVD noise reduction, detailed processes
See Fig. 1, the described method comprises the following steps:
Step 1: the vibration signal under acquisition load ratio bridging switch OLTC normal condition；
Step 2: S-transformation being carried out to collected vibration signal, discretization is then carried out, obtains S-transformation time-frequency spectrum；
Step 3: spectral coefficient matrix A being obtained based on time-frequency spectrum, singular value decomposition is carried out to matrix A, obtains each of matrix A
A singular value；
Step 4: according to each singular value in matrix A, removing noise therein, rebuild S-transformation time-frequency spectral coefficient
Matrix B；
Step 5: clock synchronization spectral coefficient matrix B carries out S inverse transformation, obtains the time domain impulse feature of vibration signal.
There is load to decompose switch as research object using M type below, uses the vibration acceleration sensor of model JF2020
The vibration signal in OLTC handoff procedure is acquired with Nicolet data collecting instrument, it is right in conjunction with flow chart 1 of the invention
Extracting method of the invention is described in further detail.
In step 1, when OLTC is acted switching in a flash, vibration signal is there are two types of route of transmission: contact → connection
Bar → OLTC top cover or contact → OLTC oil → transformer oil → oil tank of transformer side wall.The rigidity on top is conducive to vibrate well
The transmission of signal, therefore acceleration vibrating sensor is placed in the top cover of OLTC, collected vibration signal is as shown in Figure 2.
In step 2, that the signal by acceleration vibrating sensor acquisition is noise mixed signal x (t)=s (t)+n
(t), s (t) is contact impact vibration signal, and n (t) is noise.S-transformation is carried out to signal is collected according to formula (1),
In formula, S (τ, f) indicates the continuous S-transformation of x (t), and t indicates the time, and f indicates frequency, and parameter τ indicates analysis window ω
The position on t axis, analysis window ω are defined as Gaussian window in time domain, it may be assumed that
σ=1/ in formula | f |, discretization then is carried out according to formula (3) and obtains S-transformation time-frequency spectrum, as shown in Figure 3.
In formula: T be x (t) sampling interval, N be x (t) number of samples, X [] be Fourier transformation, j, n, m=0,1 ...
N-1。
In step 3, SVD processing is carried out to S-transformation time-frequency spectrum, obtains its preceding 1000 points of singular value spectrum specific steps such as
Under:
The time-frequency spectrum of the S-transformation obtained in the previous step can regard the matrix A of order r≤n of a N × N as, and N is
The sampling length of continuous time signal x (t).Unusual decomposition is carried out to A,
A=UDVT (4)
U in formula, V can be solved must existing symmetrical matrix.
In formula: σ1, σ2…σNFor the singular value of A, then these singular values are arranged according to the sequence successively decreased, i.e. σ1≥σ2
≥…≥σN, and enabling non-zero singular value sequence is Σ=(σ1, σ2…σr).To obtain singular value spectrum, as shown in figure 4, if A is
The matrix collectively constituted by signal and noise, then the singular value σ of matrix A1, σ2…σNIt can reflect signal and noise energy collection
In situation.If by σ1, σ2…σNIt lines up according to the sequence successively decreased, i.e. σ1≥σ2≥…≥σi≥…≥σr>=0, that
, preceding i biggish singular values will mainly reflect signal, lesser singular value σi+1..., σrThen mainly reflect noise, this portion
Divide the singular value zero setting of reflection noise, so that it may remove the noise in signal.
In step 4, Σ=(σ is composed by the singular value of signal1, σ2…σr) find out the singular value Difference Spectrum P=(ρ of signal1,
ρ2…ρr-1) as shown in figure 5, wherein element
ρi=σi-σi+1, i=1,2 ... r-1 (6)
After obtaining singular value Difference Spectrum, one group of peak point that Difference Spectrum foremost part is more concentrated, and its amplitude is aobvious
The point for the amplitude for being greater than subsequent peak point is write as threshold value σthThe position coordinates at place, as shown in Figure 6.I.e. by σ3650As threshold
Value will be less than or equal to σ in unusual value sequence Σ3650Singular value be set to zero, then formula (7) rebuild S-transformation time-frequency
Spectral coefficient matrix B.
B=UD*VT (7)
D in formula*It is that singular value in D is less than or equal to threshold value σ3650Diagonal matrix after all setting 0, U, V are above-mentioned acquired
Matrix.
In steps of 5, after the time-frequency spectrum coefficient matrix B after the SVD noise reduction that gets application, as shown in fig. 7, formula
(8) inverse operation for carrying out S-transformation, obtains the time domain impulse feature of vibration signal, as shown in Figure 8.
In formula: N is the number of samples of x (t), and T is the sampling interval of x (t), j, n ,=0,1 ... N-1.
Finally signal-to-noise ratio is calculated using the criterion formula (9) of signal-to-noise ratio is judged
In formula: x (i) is original signal, and z (i) is the signal after noise reduction, brings the equal of original signal and the signal after noise reduction into
Square error RMSE is smaller, then for the signal after noise reduction just closer to original signal, noise reduction effect is better.Preceding step is acquired
To initial data and the noise reduction that acquires after signal bring formula (9) into and obtain RMSE=0.6841.In general, 0≤RMSE
≤ 1, excellent noise reduction effect；1 < RMSE≤10, noise reduction effect are general；10 < RMSE, substantially without noise reduction effect.According to what is be calculated
RMSE=0.6841 illustrates excellent noise reduction effect, and the shock characteristic dose-effect fruit extracted using the present invention is fine.
The foregoing is merely a kind of case study on implementation of the invention, it is noted that for the ordinary skill people of the art
For member, under the premise of not departing from inventive technique principle, several improvements and modifications, these improvements and modifications can also be made
It should be regarded as protection scope of the present invention.
Claims (11)
1. a kind of OLTC impact characteristics extracting method based on S-transformation time-frequency spectrum SVD noise reduction, it is characterised in that: including following step
It is rapid:
Step 1: the vibration signal under acquisition load ratio bridging switch OLTC normal condition；
Step 2: S-transformation being carried out to collected vibration signal, discretization is then carried out, obtains S-transformation time-frequency spectrum；
Step 3: spectral coefficient matrix A being obtained based on time-frequency spectrum, singular value decomposition is carried out to matrix A, obtains each surprise of matrix A
Different value；
Step 4: according to each singular value in matrix A, removing noise therein, rebuild S-transformation time-frequency spectrum coefficient matrix
B；
Step 5: clock synchronization spectral coefficient matrix B carries out S inverse transformation, obtains the time domain impulse feature of vibration signal.
2. a kind of OLTC impact characteristics extracting method based on S-transformation time-frequency spectrum SVD noise reduction according to claim 1,
Be characterized in: wherein in step 1, by acceleration transducer to the vibration signal under load ratio bridging switch OLTC normal condition into
Row acquisition.
3. a kind of OLTC impact characteristics extracting method based on S-transformation time-frequency spectrum SVD noise reduction according to claim 2,
It is characterized in: wherein in step 1, acceleration sensor is placed in the top cover of load ratio bridging switch OLTC.
4. a kind of OLTC impact characteristics extracting method based on S-transformation time-frequency spectrum SVD noise reduction according to claim 1,
Be characterized in: wherein in step 2, the signal acquired by acceleration vibrating sensor is noise mixed signal x (t)=s (t)+n
(t), s (t) is contact impact vibration signal, and n (t) is noise, carries out S-transformation to signal is collected according to formula (1),
In formula, S (τ, f) indicates the continuous S-transformation of x (t), and t indicates the time, and f indicates frequency, and parameter τ indicates analysis window ω in t axis
Upper position, analysis window ω are defined as Gaussian window in time domain, it may be assumed that
σ=1/ in formula | f |.
5. a kind of OLTC impact characteristics extracting method based on S-transformation time-frequency spectrum SVD noise reduction according to claim 4,
It is characterized in: wherein in step 2, after carrying out S-transformation to collected signal, carries out discretization according to formula (3) and obtain S-transformation
Time-frequency spectrum:
In formula: T is the sampling interval of x (t), and N is the number of samples of x (t), and X [] is Fourier transformation, j, n, m=0,1 ... N-1.
6. a kind of OLTC impact characteristics extracting method based on S-transformation time-frequency spectrum SVD noise reduction according to claim 1,
It is characterized in: wherein in step 3, SVD processing is carried out to S-transformation time-frequency spectrum, obtains its preceding 1000 points of singular value spectrum, specific steps
It is as follows:
The time-frequency spectrum of the S-transformation obtained in the previous step can regard the matrix A of order r≤n of a N × N as, and N is continuous
The sampling length of time signal x (t) carries out unusual decomposition to A,
A=UDVT (4)
U in formula, V can be solved must existing symmetrical matrix；
In formula: σ1, σ2…σNFor the singular value of A, then these singular values are arranged according to the sequence successively decreased, i.e. σ1≥σ2≥…≥
σN, and enabling non-zero singular value sequence is Σ=(σ1, σ2…σr), to obtain singular value spectrum.
7. a kind of OLTC impact characteristics extracting method based on S-transformation time-frequency spectrum SVD noise reduction according to claim 1,
Be characterized in: wherein step 4 specifically includes: one threshold value σ of settingth, σ will be less than or equal in unusual value sequence ΣthSingular value
It is set to zero, then rebuilds S-transformation time-frequency spectrum coefficient matrix B.
8. a kind of OLTC impact characteristics extracting method based on S-transformation time-frequency spectrum SVD noise reduction according to claim 7,
It is characterized in: Σ=(σ is composed by the singular value of signal1, σ2…σr) find out the singular value Difference Spectrum P=(ρ of signal1,ρ2…ρr-1),
Middle element
ρi=σi-σi+1, i=1,2 ... r-1 (6)
After obtaining singular value Difference Spectrum, one group of peak point that Difference Spectrum foremost part is more concentrated, and its amplitude is significantly big
In subsequent peak point amplitude point as threshold value σthThe position coordinates at place will be less than or equal to σ in unusual value sequence Σth
Singular value be set to zero, then formula (7) rebuild S-transformation time-frequency spectrum coefficient matrix B:
B=UD*VT (7)
D in formula*It is that singular value in D is less than threshold value σthDiagonal matrix, U, V are the above-mentioned matrixes acquired.
9. a kind of OLTC impact characteristics extracting method based on S-transformation time-frequency spectrum SVD noise reduction according to claim 1,
Be characterized in: wherein in step 5, after the time-frequency spectrum coefficient matrix B after the SVD noise reduction that gets application, formula (8) carries out S change
The inverse operation changed obtains the time domain impulse feature of vibration signal:
In formula: N is the number of samples of x (t), T is the sampling interval of x (t), j, n ,=0,1 ... N-1.
10. a kind of OLTC impact characteristics extracting method based on S-transformation time-frequency spectrum SVD noise reduction according to claim 1,
Be characterized in: wherein load ratio bridging switch OLTC includes but is not limited to M type load ratio bridging switch.
11. a kind of OLTC impact characteristics extracting method based on S-transformation time-frequency spectrum SVD noise reduction according to claim 1,
Be characterized in: wherein acceleration transducer includes but is not limited to vibration acceleration sensor.
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| CN110826017A (en) * | 2019-09-25 | 2020-02-21 | 中国地质大学（武汉） | Signal denoising method based on parameter optimization Hankel matrix and singular value decomposition |
| CN111308285A (en) * | 2020-03-03 | 2020-06-19 | 西南交通大学 | Narrow-band interference noise reduction method |
| CN111308285B (en) * | 2020-03-03 | 2021-04-13 | 西南交通大学 | Narrow-band interference noise reduction method |
| CN112464811A (en) * | 2020-11-26 | 2021-03-09 | 淮阴工学院 | Method for accurately filtering high-frequency random noise in pumped storage unit runout signal |
| CN113640660A (en) * | 2021-08-05 | 2021-11-12 | 国网江苏省电力有限公司电力科学研究院 | Method and device for reducing noise of vibration signal of on-load tap-changer |
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