CN106596899A - Evaluation method of random error between oil-immersed transformer on-line monitoring data and on-line detection data - Google Patents
Evaluation method of random error between oil-immersed transformer on-line monitoring data and on-line detection data Download PDFInfo
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Abstract
The invention discloses an evaluation method of a random error between oil-immersed transformer on-line monitoring data and on-line detection data. The method comprises 1, respectively acquiring on-line monitoring data and on-live detection data, and pre-processing the data to obtain two time series, 2, calculating the difference of the two time series to obtain an error series, carrying out decomposition to obtain multiple intrinsic mode functions, and building a random error series from the intrinsic mode functions meeting noise characteristics, 3, decomposing the on-line monitoring time series into multiple intrinsic mode functions, removing the intrinsic mode functions meeting noise characteristics so that a real signal series, constructing an error reference series, calculating xt and xt' according to formulas of xt=St+Nt and xt'=St+Nt', and calculating corresponding signal-noise ratios dt and dt', and 4, calculating an evaluation coefficient rho of the random error according to a formula of rho=na/n.
Description
Technical field
The present invention relates to signal analysis field, and in particular to a kind of oil-filled transformer online monitoring data and live detection
The evaluation methodology of random error between data.
Background technology
The monitoring means of oil-filled transformer are divided into two kinds, and one kind is used in line monitoring, i.e., remote using infrared survey spectrometer
Journey monitors equipment on-line；One kind is live detection, i.e., professional is sampled detection to transformer oil to scene.Online data
Short, the advantage of low cost is spaced with monitoring, its data noise and random error problem are often Utilities Electric Co.'s focus of attention.
However, still falling within still unsolved technical barrier with regard to transformer online monitoring data noise and error estimation at present, research is sent out
Exhibition has important value for transformer online monitoring device random error evaluation methodology.
The online monitoring data of transformator belongs to non-linear, non-stationary time serieses, analysis and the process of such data
Sufficiently complex, traditional method is difficult to be suitable for.Science of Economics often rejects low in nonstationary time series using the method for HP filtering
The long-term trend of frequency, and then (Hodrick R J, Prescott E are measured in the random short-term fluctuation to high frequency
C.Postwar U.S.Business Cycles:AnEmpirical Investigation[J].Journal of Money
Credit&Banking,1997,29(1):1-16.)；Signal analysis field then more uses power spectral density (i.e. PSD methods)
(John L, Hadley J.Correlated errors in geodetic are analyzed to the noise power under different frequency
time series:Implications for time-dependent deformation[M]//Journal of
Geophysical Research:Solid Earth(1978–2012).1997:591–603.)；These methods are to online data
The tolerance of random fluctuation has certain effect, but its theoretical basis is all from seasonal effect in time series analysis of spectrum, belongs to frequency-domain analysiss side
Method, it is impossible to which the temporal signatures of transformator online data random fluctuation are accurately measured.
In the realistic case, the fluctuating margin of our more concern online datas, the amplitude of random fluctuation is excessive, often
Affect judgement of the related personnel to Transformer Operation Status, therefore the temporal signatures (local of transformator online data random fluctuation
Availability) it is our primary metric target.In signal analysis field, Fourier analyses are undoubtedly important point of signal characteristic
Analysis method, but its makes it exist compared with overall situation in terms of the temporal signatures of non-stationary signal and description signal are processed the characteristics of of overall importance
Limit.Therefore, on the basis of Fourier analyses, related scholar proposes the analysis method for non-stationary signal, including in short-term
Fourier conversion, Wigner-Ville distribution, wavelet transformation etc..The ultimate principle of Instant Fourier Transform is to signal sequence
Row add window function, and non-stationary signal is changed into multiple stationary signals shorter at interval, then carry out Fourier to signal in window
Conversion, so as to obtain the time varying spectrum of primary signal, reaches what the high-frequency signal to different periods (random noise) was analyzed
Purpose (Jurado F, Saenz J R.Comparison between discrete STFT and wavelets for the
analysis of power quality events[J].Electric Power Systems Research,2002,62
(3):183-190.)；Wigner-Ville distribution is then that the covariance function to primary signal carries out Fourier conversion, one
Determine to solve the limitation that window function brings Instant Fourier Transform in degree.Meanwhile, Wigner-Ville distribution has many
Useful characteristic, including time shift, symmetry etc., time varying characteristic (Martin W, the Flandrin of signal can be described preferably
P.Wigner-Ville spectral analysis of nonstationary processes[J].IEEE
Transactions on Acoustics Speech&Signal Processing,1985,33(6):1461-1470.)；It is little
Wave conversion is proposed by Donoho, is a kind of multiple dimensioned signal decomposition method, and the process of its Noise Estimation may be summarized to be：(1)
Wavelet decomposition is carried out to primary signal by arranging wavelet basis function.(2) with the intermediate value absolute deviation of ground floor wavelet decomposition come
Estimate noise (Johnstone I M, Silverman B W.Wavelet threshold estimators for data
with correlated noise[J].Journal of the royal statistical society:series B
(statistical methodology),1997,59(2):319-351.)(Donoho D L.De-noising by soft-
thresholding[J].IEEE Transactions on Information Theory,1995,41(3):613-627.)。
Compare compared with preceding method, wavelet transformation has more preferable time-frequency characteristic and a noise measurement index, Donoho propose based on small echo
The noise estimation method of conversion is most commonly used non-linear, non-stationary signal the noise estimation method of current application.
Said method is improved traditional Fourier analyses, and for non-linear, non-stationary signal noise section is provided
, system method of estimation, however, these methods still suffer from many under the scene that transformator online data random error is estimated asking
Topic.One is noise extraction aspect, and said method cannot realize the extracted in self-adaptive of random noise, and the effect of estimating of its noise depends on
In the selection of design parameter, for example, the selection of wavelet basis function affects huge to its noise reduction capability, but small echo in actual applications
The determination of basic function is but insoluble problem.Two is Noise Estimation aspect, and existing method itself has certain time variation,
But noise rating index is then more started with from statistical property, such as noise variance, intermediate value variation, Allan variances, TheoH side
Difference etc. (Allan variances and TheoH variances are a kind of methods of estimation to specific noise, such as white Gaussian noise, flicker noise, with
(Allan D W, Barnes J A.A modified " Allan variance " the with increased such as machine migration noise
oscillator characterization ability[C]//Thirty Fifth Annual Frequency Control
Symposium.1981.IEEE,1981:470-475.)(McGee J A,Howe D A.TheoH and Allan
deviation as power-law noise estimators[J].ieee transactions on ultrasonics,
ferroelectrics,and frequency control,2007,54(2):448-452.)).These evaluation indexes have complete
Office's property, it is impossible to reflect the characteristics of local of online data is available.Meanwhile, these methods are mainly used in the random error of precision instrument
Measurement, it is very sensitive to " rough error (significant errors) ", and the situation of transformer online monitoring device random error is complex, needs
Have more the evaluation index of universality.
In sum, existing method acts on extremely limited in the case of the estimation of transformator online data random error.
The content of the invention
The invention provides random error is commented between a kind of oil-filled transformer online monitoring data and live detection data
The temporal signatures of transformer online monitoring data random fluctuation can be measured accurately by valency method, be calculated
The evaluation coefficient ρ of random error can be with the certainty of measurement of accurate description on-Line Monitor Device.
The evaluation methodology of random error, its feature between a kind of oil-filled transformer online monitoring data and live detection data
It is to comprise the steps：
(1) content of characteristic gas in oil-filled transformer oil, is obtained by remote online monitoring equipment, online prison is designated as
Survey data；The content of characteristic gas in oil-filled transformer oil is obtained by manual sampling, is designated as in live detection data；Respectively
Pretreatment is carried out to online monitoring data and live detection data, obtains that time interval is identical and time point is mutually corresponding online
Monitoring feature gas content-time serieses and live detection characteristic gas content-time serieses；
(2), will on-line monitoring characteristic gas content-time serieses and live detection characteristic gas content-time serieses phase
Subtract, obtain error sequence, recycle set ensemble empirical mode decomposition method that decomposition is carried out to error sequence and obtain multiple eigen mode letters
Number, meets the intrinsic mode functions composition random error series of noise characteristic, is designated as Nt；
(3) carry out decomposing to monitoring characteristic gas content-time serieses on-line using set ensemble empirical mode decomposition method
To multiple intrinsic mode functions, to reject and obtain authentic signal sequence S after the intrinsic mode functions for meeting noise characteristict, according to true letter
Number sequence StAnd the required precision instrument error reference sequences N of on-Line Monitor Devicet′；Again according to formula xt=St+NtWith xt'=
St+Nt' respectively obtain sequence xtAnd xt', it is computed respectively obtaining sequence xtAnd xt' corresponding signal to noise ratio d and d '；
(4) sequence x is consideredtTemporal signatures, using rectangular window function respectively to xtWith xtNoise on ' each time point
Than being estimated, d is obtainedtWith dt', the evaluation coefficient ρ of random error is obtained further according to lower formula I, with this to on-Line Monitor Device
Certainty of measurement evaluated；
ρ=na/n (Ⅰ)；
In formula, n is time point sum in on-line monitoring characteristic gas content-time serieses, naIt is that random error is less than in fact
The number of the time point of border requirements.
More practical online data random error evaluation of programme is proposed in the present invention.For noise extracts problem, I
Have selected empirical mode decomposition and its improved method and replace the existing wavelet transformation, reason to be：(1) empirical mode decomposition category
In adaptive noise extracting method, parameter setting need not be carried out before signal processing, be particularly suitable for non-stationary when
Between sequence carry out feature extraction (Wu Z, Huang N E.Ensemble empirical mode decomposition:a
noise-assisted data analysis method[J].Advances in adaptive data analysis,
2009,1(01):1-41.)(Wu Z,Huang N E,Long S R,et al.On the trend,detrending,and
variability of nonlinear and nonstationary time series[J].Proceedings of the
National Academy of Sciences,2007,104(38):14889-14894.).(2) in a large number empirical researchs show,
Effect of the ensemble empirical mode decomposition method in terms of signal decomposition is better than wavelet transformation (Peng Z K, Peter W T, Chu F L.A
comparison study of improved Hilbert–Huang transform and wavelet transform:
application to fault diagnosis for rolling bearing[J].Mechanical systems and
signal processing,2005,19(5):974-988.)(Labate D,La Foresta F,Occhiuto G,et
al.Empirical mode decomposition vs.wavelet decomposition for the extraction
of respiratory signal from single-channel ECG:A comparison[J].IEEE Sensors
Journal,2013,13(7):2666-2674.)(Dai W J,Ding X L,Zhu J J,et al.EMD filter
method and its application in GPS multipath[J].Acta Geodaetica et
Cartographica Sinica,2006.).Therefore, compare than existing methods, empirical mode decomposition can help us more
Accurately extract the random error of transformator online data；For Noise Estimation problem, we select signal to noise ratio as noise
Metric.On the one hand, signal to noise ratio has higher universality, it is adaptable to any type of noise measurement.On the other hand, with
Statistical indicator is compared, and signal to noise ratio has real physical significance, it is possible to increase interpretation of scheme ability.Meanwhile, in order that index
With temporal signatures, we add time slip-window to the random error series for extracting, and are compared to the noise of signal in window
For the evaluation index of the moment random noise, embody to greatest extent online data local it is available the characteristics of.
In step (1), described characteristic gas include hydrogen, total hydrocarbon, carbon monoxide or carbon dioxide, and described total hydrocarbon can
Being the Hydrocarbon such as methane, ethane, ethylene, acetylene；
The content of the characteristic gas is characterized the mass concentration or volumetric concentration of gas.
The certainty of measurement for being mainly used in description on-Line Monitor Device of the random error of transformer online monitoring device.At random
The fluctuation of error is stronger, and the certainty of measurement of device is lower, and stability is poorer.National grid has to the stability of on-Line Monitor Device
And clearly require, be i.e. the fluctuation of on-Line Monitor Device is not to be exceeded the 10% of measured value, the monitoring dress poor to certainty of measurement
Put to give and return factory's correction process.
During random error horizontal estimated, the live detection data of oil-filled transformer have important reference price
Value, therefore, we, as reference sequences, and are carried out to online monitoring data equally using live detection data with live detection data
Data prediction so as to become that time interval is identical, time point mutually corresponding time serieses.
In practical operation, the pretreatment made is that, with one day as time interval, there is taking the mean for multiple data in one day,
Replaced with linear interpolation without data.
After carrying out complete data prediction, we are poor with live detection data by online monitoring data, obtain online
The error sequence of Monitoring Data, and enter row set empirical mode decomposition to error sequence.Set ensemble empirical mode decomposition method is one
Signal processing method is planted, its Main Function is that original signal sequence is decomposed into into multiple intrinsic mode functions, each eigen mode
Formula function has the characteristic of stationary time series.Research shows, through the intrinsic mode function tool of the noise of empirical mode decomposition
There is certain statistical property.We according to pertinent literature method, the eigen mode that each is decomposited from online monitoring data
Formula function is calculated, using first decomposite, average significantly for 0 intrinsic mode function as noise sequence, so as to obtain
The random error series N of online monitoring datat。
The concrete grammar decomposed to error sequence using set ensemble empirical mode decomposition method is as follows：
(a) error identifying sequence NtAll maximum points and all minimum points in (being designated as x (t) herein), and by institute
There is maximum point to be coupled together with a curve and obtain coenvelope line emax(t), then obtain lower envelope line by all minimum points
emin(t)；
B () calculates coenvelope line emax(t) and lower envelope line eminMeansigma methodss m (t) of (t), and signal calculated NtWith m (t)
Difference d (t)；
C whether () inspection d (t) be intrinsic mode functions, and touchstone is extreme point and zero crossing in whole time range
The average of number at most difference one and upper and lower envelope is zero：If d (t) is an intrinsic mode functions, note imf (t)=d
T () is the intrinsic mode functions output of this process, make residual error r (t)=x (t)-d (t), original series x (t)=r (t)；If d
T () is not intrinsic mode functions, then make x (t)=d (t)；Repetition above step till it can not decomposite intrinsic mode functions again；
So as to primary signal is expressed as into intrinsic mode functions and residual error item sum：
Wherein imf (t) represents intrinsic mode functions item, and i is the item number of intrinsic mode functions, and N is the total item of intrinsic mode functions,
R (t) represents residual error item.
Meanwhile, in order to preferably be analyzed, we utilize same method, to the on-line monitoring through pretreatment
Data enter row set empirical mode decomposition, and the intrinsic mode function for wherein meeting noise statisticses is rejected, and are supervised online
Survey the authentic signal sequence S of devicet。
Survey whether device meets the requirements to understand on-line monitoring, we have also been constructed an error reference sequences Nt', will be
The precision of line monitoring device is designated as ε, instrument error reference sequences Nt', Nt'=St×mt, wherein, mtIt is amplitude between-ε to ε
The white noise sequence being randomly assigned.Therefore, we only need to contrast NtWith Nt' fluctuation situation, you can understand on-line monitoring dress
Put and whether meet the requirements.
During relative analyses, we are using signal to noise ratio to NtWith Nt' fluctuating level quantified.If known letter
Number sequence x (t) be made up of noise sequence n (t) and signal sequence s (t), then in x (t) signal and noise ratio (signal to noise ratio, singly
Position：DB), it is defined as follows：
In formula, PsFor signal power, PnFor noise power, its computing formula is respectively：
In formula, t represents seasonal effect in time series time point, the length of T representation signal sequences.
We are by xt=St+NtWith xt'=St+Nt' above formula is substituted into, signal to noise ratio d and d ' are obtained, if d>D ' explanation on-line monitorings
The fluctuation of device random error is less, it is possible to use.
In addition, the local availability in order to investigate on-Line Monitor Device, we introduce the concept of rectangular window function, to time domain
In the range of signal to noise ratio on each time point estimated.After adding window function, we can obtain error sequence in time domain scale
The distribution situation of the signal to noise ratio of row.If the signal to noise ratio of online monitoring data random error series is dt, artificial reference noise sequence
Signal to noise ratio be dt', d in time domain scalet>dt' time point number be na, then substitute into and be obtained in formula I random error
Evaluation coefficient ρ.Wherein dtWith dt' computational methods it is as follows：
In formula, Pst, Pnt, Pnt' calculate by rectangular window function gained, the length of rectangular window function is designated as into w, then Pst, Pnt,
Pnt' computing formula be respectively：
In formula, t represents seasonal effect in time series time point, the length of T representation signal sequences.
The span of evaluation coefficient ρ is [0,1], and its value is bigger, illustrates the availability of on-line equipment in time domain scale
It is higher；When evaluation coefficient ρ=1, illustrate that fluctuation of the on-Line Monitor Device in the range of whole evaluation time meets reality
Demand, availability is very high.When result is 0, fluctuation of the on-Line Monitor Device in the range of whole evaluation time is illustrated not
Meet actual demand, should give correction.
Compared with prior art, the present invention has advantages below：
(1) in terms of noise decomposition, the present invention has adaptivity, automatically can contain on-line monitoring characteristic gas
Amount-Time Series are noise sequence and signal sequence.
(2) present invention considers the temporal signatures of error sequence, with dynamic characteristic, can be to monitoring characteristic gas on-line
Content-seasonal effect in time series local availability is estimated.
Description of the drawings
Give in Fig. 1 the characteristic gas content of pretreated certain oil-filled transformer infrared spectrograph on-line monitoring-
Characteristic gas content-the time graph of time graph and live detection；
Fig. 2 is to decompose the random error for obtaining on the basis of two stack features gas contents-time serieses in embodiment
Sequence Nt；
Fig. 3 is that monitoring on-line in embodiment decompose the online prison for obtaining on the basis of characteristic gas content-time serieses
Survey data authentic signal sequence St；
Fig. 4 is the random error series (left figure) of the online monitoring data decomposited using 4 kinds of different wavelet basis functions
With authentic signal sequence (right figure)；
Fig. 5 is using random error series (upper left) and the calculated time domain scale of man made noise's reference sequences (upper right)
Interior signal to noise ratio comparison diagram.
Specific embodiment
With reference to specific embodiment, the present invention is described further, but the invention is not restricted to following embodiments.
Embodiment：
The certainty of measurement for being mainly used in description on-Line Monitor Device of the random error of transformer online monitoring device.At random
The fluctuation of error is stronger, and the certainty of measurement of device is lower, and stability is poorer.National grid has to the stability of on-Line Monitor Device
And clearly require, be i.e. the fluctuation of on-Line Monitor Device is not to be exceeded the 10% of measured value, the monitoring dress poor to certainty of measurement
Put to give and return factory's correction process.
During random error horizontal estimated, the live detection data of oil-filled transformer have important reference price
Value, therefore, we, as reference sequences, and are carried out to online monitoring data equally using live detection data with live detection data
Data prediction so as to become that time interval is identical, time point mutually corresponding time serieses.In practical operation, between the time
Every generally taking one day, for the problem that online data on each time point or charged data are lacked, we then take linear interpolation
Data are filled up by method, make the time span of the two identical, and time point is corresponded, pretreated on-line monitoring
Characteristic gas content-the time graph of characteristic gas content-time graph and live detection is as shown in Figure 1.
After carrying out complete data prediction, we are poor with live detection data by online monitoring data, obtain online
The error sequence of Monitoring Data, and enter row set empirical mode decomposition to error sequence.Set ensemble empirical mode decomposition method is one
Signal processing method is planted, its Main Function is that original signal sequence is decomposed into into multiple intrinsic mode functions, each eigen mode
Formula function has the characteristic of stationary time series.Research shows, through the intrinsic mode function tool of the noise of empirical mode decomposition
There is certain statistical property.We are calculated the intrinsic mode function that each is decomposited from online monitoring data, will
First decomposite, average significantly for 0 intrinsic mode function as noise sequence, so as to obtain the random of online monitoring data
Error sequence Nt, as shown in Figure 2.
Meanwhile, in order to preferably be analyzed, we utilize same method, to the on-line monitoring through pretreatment
Data enter row set empirical mode decomposition, and the intrinsic mode function for wherein meeting noise statisticses is rejected, and are supervised online
Survey the authentic signal sequence S of devicet, corresponding time domain scattergram is as shown in Figure 3.
Because national grid is distinctly claimed to the stability of on-Line Monitor Device, i.e. the fluctuation of on-Line Monitor Device should not surpass
The 10% of measured value is crossed, in the present embodiment so that required precision is for 10% as an example, according to the actual signal of online monitoring data, is utilized
Amplitude white noise sequence m between -0.1 to 0.1tConstruct artificial noise reference sequence Nt', and to actual with chance error
Difference sequence NtWith man made noise reference sequences Nt' carried out relative analyses, and using rectangular window function calculate its signal to noise ratio when
Distribution situation in the range of domain, rectangular window function length of window is set to one month, as a result as shown in Figure 5.The portion of red circle is drawn in Fig. 5
Divide the fluctuation situation for representing online data random error more than noise reference sequence, these situations have in signal to noise ratio comparison diagram
Preferably reflection.
It is the advantage for illustrating set experience decomposition method in terms of noise extraction in the present embodiment, we are according to same
Step, using the noise and actual signal of wavelet transformation and 4 kinds of different wavelet basis functions to the transformer online monitoring data
Separation and Extraction is carried out, the random error series (left figure) for respectively obtaining four groups of online monitoring datas are (right with authentic signal sequence
Figure), as shown in Figure 4, it is found that different wavelet basis functions is extracted to noise has material impact, small echo with signal decomposition
The problem that basic function is difficult to determine is that the method is dfficult to apply to actual key.
Claims (9)
1. between a kind of oil-filled transformer online monitoring data and live detection data random error evaluation methodology, its feature exists
In comprising the steps：
(1) content of characteristic gas in oil-filled transformer oil, is obtained by remote online monitoring equipment, is designated as monitoring number on-line
According to；The content of characteristic gas in oil-filled transformer oil is obtained by manual sampling, is designated as in live detection data；Respectively to
Line Monitoring Data and live detection data carry out pretreatment, obtain that time interval is identical and time point mutually corresponding on-line monitoring
Characteristic gas content-time serieses and live detection characteristic gas content-time serieses；
(2), on-line monitoring characteristic gas content-time serieses and live detection characteristic gas content-time serieses are subtracted each other, is obtained
To error sequence, recycle set ensemble empirical mode decomposition method that decomposition is carried out to error sequence and obtain multiple intrinsic mode functions, accord with
The intrinsic mode functions composition random error series of noise characteristic are closed, N is designated ast；
(3) decomposition is carried out to on-line monitoring characteristic gas content-time serieses using set ensemble empirical mode decomposition method and obtains many
Individual intrinsic mode functions, to reject and obtain authentic signal sequence S after the intrinsic mode functions for meeting noise characteristict, according to actual signal sequence
Row StAnd the required precision instrument error reference sequences N of on-Line Monitor Devicet′；Again according to formula xt=St+NtWith xt'=St+Nt′
Respectively obtain sequence xtAnd xt', it is computed respectively obtaining sequence xtAnd xt' corresponding signal to noise ratio d and d '；
(4) sequence x is consideredtTemporal signatures, using rectangular window function respectively to xtWith xtSignal to noise ratio on ' each time point is entered
Row estimation, obtains dtWith dt', the evaluation coefficient ρ of random error is obtained further according to lower formula I, with this survey to on-Line Monitor Device
Accuracy of measurement is evaluated；
ρ=na/n (Ⅰ)；
In formula, n is time point sum in on-line monitoring characteristic gas content-time serieses, naActual demand is less than for random error
The number of the time point of value.
2. random error is commented between oil-filled transformer online monitoring data according to claim 1 and live detection data
Valency method, it is characterised in that in step (1), described characteristic gas include hydrogen, methane, ethane, ethylene, acetylene, an oxidation
Carbon or carbon dioxide；
The content of the characteristic gas is characterized the mass concentration or volumetric concentration of gas.
3. random error is commented between oil-filled transformer online monitoring data according to claim 1 and live detection data
Valency method, it is characterised in that in step (1), described pretreatment is：
With one day as time interval, there is taking the mean for multiple data in one day, being replaced with linear interpolation without data.
4. random error is commented between oil-filled transformer online monitoring data according to claim 1 and live detection data
Valency method, it is characterised in that in step (2), using set ensemble empirical mode decomposition method error sequence is decomposed it is concrete
Method is as follows：
(a) error identifying sequence NtAll maximum points and all minimum points in (being designated as x (t) herein), and will be all very big
Value o'clock is coupled together with a curve and obtains coenvelope line emax(t), then obtain lower envelope line e by all minimum pointsmin(t)；
B () calculates coenvelope line emax(t) and lower envelope line eminMeansigma methodss m (t) of (t), and signal calculated NtWith the difference of m (t)
d(t)；
C whether () inspection d (t) be intrinsic mode functions, and touchstone is the number of extreme point and zero crossing in whole time range
The average of at most difference one and upper and lower envelope is zero：If d (t) is an intrinsic mode functions, note imf (t)=d (t) is
The intrinsic mode functions output of this process, makes residual error r (t)=x (t)-d (t), original series x (t)=r (t)；If d (t) is not
It is intrinsic mode functions, then makes x (t)=d (t)；Repetition above step till it can not decomposite intrinsic mode functions again；So as to incite somebody to action
Primary signal is expressed as intrinsic mode functions and residual error item sum：
Wherein imf (t) represents intrinsic mode functions item, and i is the item number of intrinsic mode functions, N for intrinsic mode functions total item, r (t)
Represent residual error item.
5. random error is commented between oil-filled transformer online monitoring data according to claim 1 and live detection data
Valency method, it is characterised in that in step (2), it is significantly 0 that the specific features for meeting noise are the averages of intrinsic mode functions.
6. random error is commented between oil-filled transformer online monitoring data according to claim 1 and live detection data
Valency method, it is characterised in that in step (3), error reference sequences Nt' building method it is as follows：
The precision of on-Line Monitor Device is designated as into ε, instrument error reference sequences Nt', Nt'=St×mt, wherein, mtIt is amplitude in-ε
To the white noise sequence being randomly assigned between ε.
7. random error is commented between oil-filled transformer online monitoring data according to claim 1 and live detection data
Valency method, it is characterised in that in step (3), the computational methods such as following formula (II -1) of signal to noise ratio d：
In formula, PsFor signal power, PnFor noise power, its computing formula is respectively：
In formula, t represents seasonal effect in time series time point, the length of T representation signal sequences；
The computational methods of signal to noise ratio d ' such as following formula (II -2)：
In formula, Pn' also it is noise power, its computing formula is：
8. random error is commented between oil-filled transformer online monitoring data according to claim 1 and live detection data
Valency method, it is characterised in that in step (4), dtWith dt' computational methods it is as follows：
In formula, Pst, Pnt, Pnt' calculate by rectangular window function gained, the length of rectangular window function is designated as into w, then Pst, Pnt, Pnt′
Computing formula be respectively：
In formula, t represents seasonal effect in time series time point, the length of T representation signal sequences.
9. random error is commented between oil-filled transformer online monitoring data according to claim 8 and live detection data
Valency method, it is characterised in that meet dt>dt' time point think random error less than actual demand value time point, number
It is designated as na。
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