CN106840637A - Based on the GIS mechanical oscillation signal Time-Frequency Analysis Methods for improving HHT algorithms - Google Patents
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Abstract
The invention discloses a kind of based on the GIS mechanical oscillation signal Time-Frequency Analysis Methods for improving HHT algorithms, GIS vibration signals under the measurement normal operational shock signals of GIS and failure, the characteristic quantity of vibration signal is extracted using overall set empirical mode decomposition, then the signal that will be obtained by EEMD pretreatments is carried out Hilbert transform pairs GIS mechanical oscillation signals and carries out time frequency analysis.Time frequency analysis are carried out to GIS mechanical oscillation signals using the method for carrying out after the pretreatment of EEMD algorithms HT conversion again can effectively process GIS vibration signals, so as to set up GIS mechanical fault diagnosis databases, to realize that live live detection GIS mechanical breakdowns provide theoretical foundation.
Description
Technical field
The present invention relates to a kind of based on the GIS mechanical oscillation signal Time-Frequency Analysis Methods for improving HHT algorithms.
Background technology
Cubicle Gas-Insulated Switchgear (Gas Insulated Switchgear, GIS) can produce various machineries
Defect, such as loosened screw, circuit breaker operation mechanism failure, transformer vibration etc..Therefore diagnosed by monitoring mechanical breakdown
GIS inside early defect can improve the operation stability of GIS.The vibration signal that GIS internal faults are produced is transmitted by medium
To GIS barrel body, for operating GIS device, sensor often is placed in drum surface, receives the vibration signal for passing over,
So as to detect GIS whether operation exception, that is, break down.Due to vibration signal detection method strong antijamming capability, and and power network
In widely used ultra-high-frequency detection method can form complementation, it is convenient and reliable for low frequency signal in detecting.
But, the Study on Fault for GIS still focuses mostly in shelf depreciation direction at this stage, and the vibration signal of collection is also more
It is frequency electromagnetic wave signal higher, it is less to there is the relatively low mechanical fault signals research of wider, frequency.Due to GIS
The complexity of structure, the vibration characteristics to live operating GIS is less studied, although having document in the prior art in operation
The vibration signal of GIS surveyed, but data acquisition is difficult and data volume is limited, fails to analyze the specific failures of GIS
Problem.Or repeated detection treatment is carried out to normal and abnormal HGIS vibration signals, and draw correspondence vibration amplitude from statistics angle
The related figure on frequency to number of times, but do not indicate failure problems.
The method of conventional treatment vibration signal time-frequency is HHT algorithms, i.e., to the letter by being obtained after the pretreatment of EMD algorithms
HT treatment, but empirical mode decomposition number are carried out as a method for the analysis time frequency signal of maturation, however it remains many problems,
Wherein than it is more serious be exactly to be also easy to produce chaff component and modal overlap phenomenon, be in particular in：
1) disparate yardstick is contained in a single IMF；
2) during same scale appears in different IMF.
The content of the invention
The present invention is in order to solve the above problems, it is proposed that a kind of based on the GIS mechanical oscillation signal time-frequencies for improving HHT algorithms
Analysis method, the present invention is measured to the vibration signal on GIS device surface, and constructs loosened screw and based on winding deformation
Transformer vibrates two kinds of common GIS mechanical breakdowns, and (normal signal, loosened screw, transformer shake three kinds of operating modes of repeated detection
Swing) under GIS vibration signals, improved HHT methods are introduced to process GIS mechanical oscillation signals, three kinds of vibration signals are carried out
Time frequency analysis, so that this kind can be applied to vibration letter deeper into ground more extensively for the processing method of nonlinear and non local boundary value problem
Number process field.
First, to avoid ambiguity, it is as follows that unification carries out explanation of nouns：
GIS：Gas Insulated Switchgear, Cubicle Gas-Insulated Switchgear；
HT：Hilbert Transformation, Hilbert transform；
IMF：Intrinsic Mode Function, intrinsic mode function/intrinsic mode function；
EEMD：Ensemble Empirical Mode Decomposition, overall set empirical mode decomposition；
EMD：Empirical Mode Decomposition, empirical mode decomposition.
To achieve these goals, the present invention is adopted the following technical scheme that：
A kind of GIS mechanical oscillation signal Time-Frequency Analysis Methods based on improvement HHT algorithms, measure the normal operational shocks of GIS
GIS vibration signals under signal and failure, the characteristic quantity of vibration signal is extracted using overall set empirical mode decomposition, then will be passed through
The signal that EEMD pretreatments are obtained carries out Hilbert transform pairs GIS mechanical oscillation signals carries out time frequency analysis.
Abnormal vibrations source, including spiral shell are set at GIS breaker operation mechanisms and disconnecting switch connecting screw and transformer
Silk loosens and the transformer vibration based on winding deformation.
Detect that normal, loosened screw and transformer vibrate shaking in the case of three kinds on the downside of GIS peep-holes using sensor
Dynamic signal.
Primary signal is sieved into intrinsic mode function, a white Gaussian noise letter is added to signal in screening process
Number, the time domain distribution of residual components provides consistent reference configuration after being decomposed for each EMD.
Using the detailed process of overall set empirical mode decomposition method, specifically include：
(1) an overall signal is obtained by being superimposed one group of white Gaussian noise signal to primary signal；
(2) EMD decomposition is carried out to primary signal, obtains each rank IMF components；
(3) different white noises, repeat step (1) and step (2) are added to primary signal；
(4) white Gaussian noise frequency spectrum zero-mean principle is utilized, white Gaussian noise is eliminated as time domain distribution reference structure band
The influence for coming, primary signal is decomposed；
(5) will decompose each IMF for obtaining carries out Hilbert transform, and side is determined according to the hilbert spectrum for obtaining
Border is composed, to characterize the accumulation amplitude distribution of each Frequency point.
In the step (2), the detailed process that EMD is decomposed includes：
(2-1) obtains the average value of its coenvelope and lower envelope according to the maximal point and minimal point of primary signal function；
(2-2) asks for the difference of primary signal function and the average value asked for；
The difference is considered as new primary signal function by (2-3), and repeat step (2-2)-(2-3) is iterated, Zhi Daoji
The difference of calculation meets IMF conditions；
Difference when (2-4) will meet IMF conditions is considered as a new IMF, asks for primary signal function and the new IMF
Difference be new primary signal function, repeat step (2-1)-(2-4), each rank IMF being calculated successively after decomposing and surplus
Remaining component.
In the step (3), added white Gaussian noise number of times is obeyed：Primary signal be added with the IMF of each rank after between
Error amount be equal to white Gaussian noise amplitude and the square root of overall number between difference.
In the step (4), primary signal is resolved into the sum of each rank IMF and residual components.
In the step (5), Hilbert is done by the IMF that EEMD is obtained to each and is converted, obtain analytic signal, according to
The phase of analytic signal, calculates instantaneous frequency.
In the step (5), Hilbert transform is carried out to each IMF, the structure after conversion is designated as Hilbert spectrums,
The definition of marginal spectrum is the integration composed for Hilbert.
Compared with prior art, beneficial effects of the present invention are：
(1) time-domain analysis of the present invention based on average empirical mode decomposition (EEMD), obtains intrinsic mode functions group, can obtain
Vibration signal each composition amplitude and phase condition, it is to avoid the appearance of chaff component and modal overlap phenomenon so that result
It is more accurate；
(2) present invention can obtain the analysis of three kinds of operating mode vibration signal amplitudes and frequency based on improved HHT analyses, obtain
The characteristic criterion of vibration signal under to different faults；
(3) when being carried out to GIS mechanical oscillation signals using the method for carrying out HT conversion again after the pretreatment of EEMD algorithms
Frequency analysis can effectively process GIS vibration signals, powered to realize scene so as to set up GIS mechanical fault diagnosis databases
Detection GIS mechanical breakdowns provide theoretical foundation.
Brief description of the drawings
The Figure of description for constituting the part of the application is used for providing further understanding of the present application, and the application's shows
Meaning property embodiment and its illustrated for explaining the application, does not constitute the improper restriction to the application.
The vibration signal time-domain diagram that Fig. 1 (a), (b), (c) are gathered when being respectively oscillograph under three kinds of operating modes；
Fig. 2 (a), (b), (c) are respectively EEMD decomposition and obtain three kinds of vibration signal time-domain diagrams of operating mode；
Fig. 3 (a), (b), (c) are respectively three kinds of HHT marginal spectrums of the vibration signal of operating mode；
Fig. 4 is schematic flow sheet of the invention.
Specific embodiment：
The invention will be further described with embodiment below in conjunction with the accompanying drawings.
It is noted that described further below is all exemplary, it is intended to provide further instruction to the application.Unless another
Indicate, all technologies and scientific terminology that the present invention is used have logical with the application person of an ordinary skill in the technical field
The identical meanings for understanding.
It should be noted that term used herein above is merely to describe specific embodiment, and be not intended to restricted root
According to the illustrative embodiments of the application.As used herein, unless the context clearly indicates otherwise, otherwise singulative
Be also intended to include plural form, additionally, it should be understood that, when in this manual use term "comprising" and/or " bag
Include " when, it indicates existing characteristics, step, operation, device, component and/or combinations thereof.
As background technology is introduced, prior art the Study on Fault at this stage for GIS still focuses mostly on and is put in locally
Electric direction, the vibration signal of collection is also generally frequency electromagnetic wave signal higher, the machinery relatively low to there is wider, frequency
Fault-signal studies less problem, proposes a kind of based on the GIS mechanical oscillation signal Time-Frequency Analysis Methods for improving HHT algorithms.
In a kind of typical implementation method of the application, as shown in figure 4, vibration signal of the present invention to GIS device surface
Measure, and construct loosened screw and the two kinds of common GIS mechanical breakdowns of vibration of the transformer based on winding deformation, repeatedly inspection
The GIS vibration signals surveyed under three kinds of operating modes (normal signal, loosened screw, transformer vibration), introduce improved HHT methods to locate
Three kinds of vibration signals are carried out time frequency analysis by reason GIS mechanical oscillation signals, so that place of this kind for nonlinear and non local boundary value problem
Reason method can be applied to vibration signal processing field deeper into ground more extensively.
Empirical mode decomposition method is by complicated signal decomposition into some intrinsic mode functions (Intrinsic Mode
Function, IMF) so that being applicable in practice by the instantaneous frequency that HT (Hilbert transform) is obtained.IMF palpuses
Meet following two conditions, i.e.,：1. in whole data segment, the number of extreme point and zero crossing should equal or at most differ 1；②
The average of the lower envelope line that the coenvelope line formed in any point, Local modulus maxima and local minizing point are formed is zero,
I.e. signal is on time shaft Local Symmetric.Specifically processing method is：
Maximal point and minimal point first according to primary signal function s (t) obtain its coenvelope v1(t) and lower envelope v2(t)
Average value
Then the difference h of s (t) and m is obtained, i.e.,
S (t)-m=h (2)
Regard h as new s (t) again to repeat to operate above, be iterated, until when h meets IMF conditions, being now denoted as
c1=h (3)
By c1Regard a new IMF as, make
s(t)-c1=r (4)
Regard r as new s (t), repeat above procedure, c is obtained successively2, c3, c4..., until r (t) is in monotonic trend substantially
Or | r (t) | very littles when can be considered measurement error by stop.Now,
Original signal n IMF has been resolved into now：c1, c2, c3, c4..., cn, and a residual components r.
Empirical mode decomposition is still present many problems, wherein than it is more serious be exactly to be also easy to produce chaff component and mode is mixed
It is folded.Therefore, on the basis of EMD algorithms, it is proposed that EEMD algorithms.
For EMD institutes produced problem, find in addition to first order component, identical band logical to be all presented per the power spectrum of rank IMF
Characteristic, and the average frequency of preceding single order IMF is approximately 2 times of rear single order；Simultaneously it has also been found that signal occurs that frequency alias shows
As.It is total so as to propose based on above-mentioned analysis, it is proposed that add white noise to supplement some yardsticks for lacking in decomposed signal
The average empirical mode decomposition thinking of body.
Therefore, EEMD main algorithms structure is essentially identical with EMD algorithms：Primary signal is sieved into intrinsic mode function,
Due to occurring in that modal overlap phenomenon in screening process, therefore a white Gaussian noise signal is added to signal in screening process
W (t), the time domain distribution of residual components provides consistent reference configuration after being decomposed for each EMD.
1.3 population mean empirical mode decomposition specific steps
1) overall signal is obtained by being superimposed one group of white Gaussian noise signal w (t) to primary signal x (t)：
X (t)=x (t)+w (t) (6)
2) EMD decomposition is carried out to X (t), obtains each rank IMF components：
3) different white noise w are added to primary signali(t), repeat step 1) and 2).
4) white Gaussian noise frequency spectrum zero-mean principle is utilized, white Gaussian noise is eliminated and is brought as time domain distribution reference structure
Influence, now IMF components cnT () is represented by
White Gaussian noise number of times added by EEMD obeys formula (10)
In formula：ε is the amplitude of white Gaussian noise；N is overall number；εnAfter representing that primary signal is added with the IMF of each rank
Between error.In order to ensure the rapidity of Harmonic Detecting Algorithm, the general ε that chooses is 0.01, N=200.
5) therefore, last primary signal x (t) can be analyzed to
Hilbert is converted
Hilbert is to each by the IMF that EEMD is obtained to convert,
xi(t)=ci(t) (12)
Obtain analytic signal,
Z (t)=xi(t)+iyi(t)=a (t) eiθ(t) (14)
A (t) in formula --- instantaneous amplitude,θ (t) --- phase,
Instantaneous frequency is calculated as follows
HT is to each IMF to obtain
Discrepance is now have ignored, above formula is referred to as Hilbert spectrums, is denoted as
Further define marginal spectrum
Marginal spectrum can from the statistical significance characterize the accumulation amplitude distribution of each Frequency point of whole group data.
Present invention experiment is using a whole set of single-phase branch mailbox GIS devices of 110kV of certain switchgear plant.In GIS breaker operation mechanisms
With setting abnormal vibrations source at disconnecting switch connecting screw and transformer, including loosened screw and the transformer based on winding deformation
Vibration.Failure 1 is loosened screw, and failure 2 is transformer winding deformation.Using external piezoelectric acceleration transducer in GIS
Three class vibration signals of peep-hole downside detection (normal, loosened screw, transformer vibration).
Fig. 1 (a), (b), (c) be respectively oscillograph GIS is normal, failure 1, failure 2 when the vibration signal time domain that gathers
Figure, from the point of view of time-domain diagram, the vibration signal amplitude that loosened screw and transformer are vibrated under two kinds of operating modes is all higher than normal vibration letter
Number, and under loosened screw failure, vibration signal amplitude amplification is obvious.Intuitively see, the vibration signal under transformer vibration is close
Collection degree highest, thus it is speculated that there is frequency-doubled signal to be superimposed.
Three groups of vibration signals by after simple denoising, decomposed by above-mentioned EEMD obtain three kinds of operating modes (Fig. 2 (a),
2 (b), 2 (c)) under GIS vibration signals intrinsic mode functions group, each figure be followed successively by from top to bottom each intrinsic mode functions (imf) and
Residual components (res.).Intrinsic modal components accordingly contain the composition of different frequency sections from high to low, and with primary signal
Change and change.
By the way that the yellow spectrum of Hilbert and marginal spectrum can be obtained after Hilbert transform.Can be more intuitive by marginal spectrum
Find out change of the amplitude with frequency distribution.Compared with HHT marginal spectrums (Fig. 3 a) of the vibration signal under normal operation, (the figure of failure 1
3b) and under failure 2 (Fig. 3 c) can very it can be clearly seen that fault signature, loosened screw in the HHT marginal spectrums of GIS vibration signals
There is oscillatory occurences in failure, and amplitude is apparently higher than amplitude during normal operation, transformer oscillator signal at fundamental frequency 100Hz
Energy focus primarily upon 140~160Hz and 440~460Hz scopes, fundamental frequency 100Hz lower far above normal operation is left for its amplitude
Right amplitude.Comprehensive Hilbert Huang analysis of spectrum, it can be deduced that two kinds of characteristic criterions of failure.
The HHT analyticals treatment GIS device vibration signal of application enhancements of the present invention.Using HHT methods, in experiment
Interior, by setting two kinds of common mechanical breakdowns (i.e. loosened screw and transformer vibrate) to GIS device, with the method pair
Vibration signal is analyzed research under three kinds of operating modes of GIS device (normal, failure 1, failure 2).Draw to draw a conclusion：
(1) time-domain analysis based on average empirical mode decomposition (EEMD), obtains intrinsic mode functions group, can be vibrated
Each composition amplitude and phase condition of signal, it is to avoid the appearance of chaff component and modal overlap phenomenon so that result is more accurate
Really.
(2) analyzed based on improved HHT, can obtain the analysis of three kinds of operating mode vibration signal amplitudes and frequency, obtain difference
The characteristic criterion of vibration signal under failure.
(3) spectrum distribution of normal vibration signal is near 100Hz, and frequency band is narrower；Loosened screw fault vibration signal
Frequency spectrum is equally distributed near 100Hz, and frequency band is wider, and amplitude is apparently higher than normal vibration signal；Transformer oscillation fault shakes
The spectrum distribution of dynamic signal is in 140~160Hz and 440~460Hz scopes.
Described above, it is have to carry out time frequency analysis to the mechanical oscillation signal of GIS device based on the method for improving HHT algorithms
Effect.By simulating different types of mechanical breakdown, GIS mechanical fault diagnosis databases can be finally set up.
The preferred embodiment of the application is the foregoing is only, the application is not limited to, for the skill of this area
For art personnel, the application can have various modifications and variations.It is all within spirit herein and principle, made any repair
Change, equivalent, improvement etc., should be included within the protection domain of the application.
Although above-mentioned be described with reference to accompanying drawing to specific embodiment of the invention, not to present invention protection model
The limitation enclosed, one of ordinary skill in the art should be understood that on the basis of technical scheme those skilled in the art are not
Need the various modifications made by paying creative work or deformation still within protection scope of the present invention.
Claims (10)
1. a kind of based on the GIS mechanical oscillation signal Time-Frequency Analysis Methods for improving HHT algorithms, it is characterized in that：Measurement GIS is normally transported
GIS vibration signals under row vibration signal and failure, the characteristic quantity of vibration signal is extracted using overall set empirical mode decomposition, then
The signal that will be obtained by EEMD pretreatments carries out Hilbert transform pairs GIS mechanical oscillation signals carries out time frequency analysis.
2. as claimed in claim 1 a kind of based on the GIS mechanical oscillation signal Time-Frequency Analysis Methods for improving HHT algorithms, it is special
Levying is：Abnormal vibrations source, including screw worked itself loose are set at GIS breaker operation mechanisms and disconnecting switch connecting screw and transformer
Transformer vibration dynamic and based on winding deformation.
3. as claimed in claim 1 or 2 a kind of based on the GIS mechanical oscillation signal Time-Frequency Analysis Methods for improving HHT algorithms, its
It is characterized in：Detect that normal, loosened screw and transformer vibrate the vibration in the case of three kinds on the downside of GIS peep-holes using sensor
Signal.
4. as claimed in claim 1 or 2 a kind of based on the GIS mechanical oscillation signal Time-Frequency Analysis Methods for improving HHT algorithms, its
It is characterized in：Primary signal is sieved into intrinsic mode function, a white Gaussian noise signal is added to signal in screening process,
The time domain distribution of residual components provides consistent reference configuration after being decomposed for each EMD.
5. as claimed in claim 1 or 2 a kind of based on the GIS mechanical oscillation signal Time-Frequency Analysis Methods for improving HHT algorithms, its
It is characterized in：Using the detailed process of overall set empirical mode decomposition method, specifically include：
(1) an overall signal is obtained by being superimposed one group of white Gaussian noise signal to primary signal；
(2) EMD decomposition is carried out to primary signal, obtains each rank IMF components；
(3) different white noises, repeat step (1) and step (2) are added to primary signal；
(4) white Gaussian noise frequency spectrum zero-mean principle is utilized, eliminates what white Gaussian noise brought as time domain distribution reference structure
Influence, primary signal is decomposed；
(5) will decompose each IMF for obtaining carries out Hilbert transform, and limit is determined according to the hilbert spectrum for obtaining
Spectrum, to characterize the accumulation amplitude distribution of each Frequency point.
6. as claimed in claim 5 a kind of based on the GIS mechanical oscillation signal Time-Frequency Analysis Methods for improving HHT algorithms, it is special
Levying is：In the step (2), the detailed process that EMD is decomposed includes：
(2-1) obtains the average value of its coenvelope and lower envelope according to the maximal point and minimal point of primary signal function；
(2-2) asks for the difference of primary signal function and the average value asked for；
The difference is considered as new primary signal function by (2-3), and repeat step (2-2)-(2-3) is iterated, until what is calculated
Difference meets IMF conditions；
Difference when (2-4) will meet IMF conditions is considered as a new IMF, asks for the difference of primary signal function and the new IMF
It is new primary signal function to be worth, repeat step (2-1)-(2-4), each rank IMF and residue point being calculated successively after decomposing
Amount.
7. as claimed in claim 5 a kind of based on the GIS mechanical oscillation signal Time-Frequency Analysis Methods for improving HHT algorithms, it is special
Levying is：In the step (3), added white Gaussian noise number of times is obeyed：Primary signal be added with the IMF of each rank after between mistake
Difference is equal to the difference between the amplitude of white Gaussian noise and the square root of overall number.
8. as claimed in claim 5 a kind of based on the GIS mechanical oscillation signal Time-Frequency Analysis Methods for improving HHT algorithms, it is special
Levying is：In the step (4), primary signal is resolved into the sum of each rank IMF and residual components.
9. as claimed in claim 5 a kind of based on the GIS mechanical oscillation signal Time-Frequency Analysis Methods for improving HHT algorithms, it is special
Levying is：In the step (5), Hilbert is done by the IMF that EEMD is obtained to each and is converted, analytic signal is obtained, according to parsing
The phase of signal, calculates instantaneous frequency.
10. as claimed in claim 5 a kind of based on the GIS mechanical oscillation signal Time-Frequency Analysis Methods for improving HHT algorithms, it is special
Levying is：In the step (5), Hilbert transform is carried out to each IMF, the structure after conversion is designated as Hilbert spectrums, limit
The definition of spectrum is the integration composed for Hilbert.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6408696B1 (en) * | 1999-12-06 | 2002-06-25 | Ai Signal Research, Inc. | Coherent phase line enhancer spectral analysis technique |
| CN105973621A (en) * | 2016-05-02 | 2016-09-28 | 国家电网公司 | Abnormal vibration analysis-based GIS (gas insulated switchgear) mechanical fault diagnosis method and system |
-
2017
- 2017-03-24 CN CN201710183342.5A patent/CN106840637B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6408696B1 (en) * | 1999-12-06 | 2002-06-25 | Ai Signal Research, Inc. | Coherent phase line enhancer spectral analysis technique |
| CN105973621A (en) * | 2016-05-02 | 2016-09-28 | 国家电网公司 | Abnormal vibration analysis-based GIS (gas insulated switchgear) mechanical fault diagnosis method and system |
Non-Patent Citations (3)
| Title |
|---|
| 孙庆生等: "基于EMD算法的GIS机械振动信号分析", 《石化电气》 * |
| 徐天乐等: "基于振动信号HHT方法的GIS设备故障诊断", 《中国电力》 * |
| 洪祥等: "基于EEMD的有载分接开关触头松动故障诊断", 《华电技术》 * |
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