CN105740578A - Maximum entropy-spectrum method applied to disconnecting switch vibration monitoring - Google Patents
Maximum entropy-spectrum method applied to disconnecting switch vibration monitoring Download PDFInfo
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Abstract
The invention discloses a maximum entropy-spectrum method applied to disconnecting switch vibration monitoring.Measured data except for limited data is not subjected to any assumption of definiteness, solving is carried out through an extrapolation method according to data except for known limited data self-correlation sequences on the premise of the maximum information entropy, and the power spectral density of signals to be detected is estimated; consistent data extrapolation is carried out on observed data through an AR prediction model, a modern spectrum estimation method based on maximum entropy-spectrum estimation is adopted for carrying out frequency spectrum estimation on non-stationary signals of a disconnecting switch, and the analysis precision and accuracy of the method are further improved.By means of maximum entropy-spectrum estimation analysis on the vibration signals of the actual disconnecting switch, small sample data is adopted under the condition of not changing the sampling frequency of the signals, consistent data extrapolation is carried out on observed data through the AR prediction model, and the frequency resolution rate higher than classic spectrum estimation and spectrum estimation accuracy are obtained and adopted as a theoretical basis for adjusting the disconnected and connected state of the disconnecting switch.
Description
Technical field
The present invention relates to the non-stationary signal spectrum estimation technique field of isolation switch, be specifically related to a kind of Maximum Entropy Spectrum Method being applied to isolation switch vibration monitoring.
Background technology
High voltage isolator is the power transmission and transforming equipment disconnecting or connecting high-tension line in no-load situation, and the electrical equipments such as the high voltage bus overhauled, chopper and charged high-tension line carry out the equipment of electrical isolation.All the time, high voltage isolator is all make one of consumption is maximum, range of application is the widest high-voltage electrical equipment in power system.Due to reasons such as device fabrication, environmental pollution, foundation deformation, longtime running, overload operation, contact oxidation, electric arc impacts, there is the problems such as not in place, the contact slap of Guan Bi, during operation, easily there is heating potential safety hazard.If found not in time, it is easy to cause equipment breakdown, causing substantial amounts of property loss, this phenomenon seems especially prevalent in load growth area faster.
Owing to the economy of China is in a varied orbit with rapid changepl. never-ending changes and improvements, the setting up and develop also at the step immediately following the epoch of electrical network, the scope universal along with electrical network expands gradually, it is desirable to use more equipment, brings very big working strength and pressure just to detection staff.First have to the monitoring service data according to electric power be analyzed, it is possible to the practical situation of equipment is had a clear and definite conclusion, namely the whether normal of equipment is done a judgement.Then, comprehensive, with multi-angle control system is analyzed, for instance manufacture produce material, element quality, modular construction, current capacity, the problem such as potential safety hazard.Being can be seen that by the present situation, the maintenance of high voltage isolator and supervision seem simple, but practical operation is extremely difficult.
Remotely after the operation of isolation switch deciliter, even if auxiliary contact correctly return, also accurately whether can not close a floodgate and put in place by detection and isolation switch, must have field personnel confirm errorless after just can carry out subsequent operation, increase workload and maintenance difficulties, not easily find the problem hidden.And the vibration monitoring isolating switch judges deciliter state by monitoring the change of the natural frequency of isolation switch, criterion is clear, and being that the one to existing criterion is active and effective supplements, and can give warning in advance, and decreases workload and potential safety hazard.
Based on the classical spectrum estimation of FFT technique owing to being the Asymptotic upbiased estimation to signal real power spectrum, but not consistent Estimation.When analyzing sample length and increasing, the variance of spectrum estimation, deviation and resolution can not improve simultaneously；Further, since real-time engineering detection signal is non-stationary, will necessarily there is non-synchronous sampling errors, windowing truncation frequently can lead to spectrum energy leakage and spectrum estimation deviation, finally affects the accuracy of electromechanical equipment fault diagnosis.Adopt the modern spectral estimation method based on Maximum Entropy Spectral Estimation (MESE) that the non-stationary signal of isolation switch is carried out spectrum estimation herein, to improving its analysis precision and accuracy further.
Summary of the invention
For problem above, the invention provides a kind of Maximum Entropy Spectrum Method being applied to isolation switch vibration monitoring, adopt the modern spectral estimation method based on Maximum Entropy Spectral Estimation that the non-stationary signal of isolation switch is carried out spectrum estimation, further increase its analysis precision and accuracy.By the Maximum Entropy Spectral Estimation analysis to actual isolation switch vibration signal, when not changing the sample frequency of signal, adopt less sample data, conforming Data Extrapolation is carried out by the AR forecast model data to observing, obtain the accuracy of the frequency resolution higher than classical spectrum estimate and Power estimation, and switch the theoretical foundation of deciliter state as detection and isolation.
To achieve these goals, the technical solution used in the present invention is as follows: a kind of Maximum Entropy Spectrum Method being applied to isolation switch vibration monitoring, data beyond measured finite data are not made any discriminating hypotheses, try to achieve according to the data extrapolation beyond known finite data autocorrelation sequence under the premise that comentropy is maximum, and estimate the power spectral density of signal to be detected；And the data that AR forecast model is to observing carry out conforming Data Extrapolation.
The expression formula of the power spectral density of Maximum Entropy Spectral Estimation is:
In formula, a (k), k=1 ..., p is the coefficient of p rank linear prediction filter；σ2Forecast error power for predictive filter；
The power spectral density of Maximum Entropy Spectral Estimation and autoregression (AR) model has identical form, and therefore Burg maximum entropy power and AR power spectrum equivalence, directly solve exponent number p and coefficient a (k) with AR model；
According to Burg algorithm, obtain the recurrence formula of AR model parameter a (k) and the recurrence formula of prediction mean square error Pm first with Levinson-Durbin recursive algorithm:
I=1 ..., m-1 (1-2)
am(m)=Km(1-3)
Pm=(1-| Km|2)Pm-1(1-4)
In formula, Km is called reflex system；
Then the mean power minimum principle according to priori prediction errors and posteriori prediction errors, obtains the recurrence formula of reflection coefficient Km:
Wherein,
Respectively m rank priori prediction errors and posteriori prediction errors.fm(n) and gmN the recurrence formula of () is
fm(n)=fm-1(n)+Kmgm-1(n-1)(1-8)
The step of Maximum Entropy Spectral Estimation Burg algorithm is as follows:
(1) initial value of forecast error power is calculatedInitial value f with forward and backward forecast error0(n)=g0N ()=x (n) also makes m=1；
(2) negate according to formula (1-5) and penetrate COEFFICIENT K m；
(3) forward prediction wave filter system a is calculated according to formula (1-2) and formula (1-3)m(i), i=1 ..., m；
(4) forecast error power P m is calculated according to formula (1-4)；
(5) predictive filter output f is calculated according to formula (1-8) and formula (1-9)m(n) and gm(n)；
(6) make m ← m+1, repeat step 2～5, until forecast error power PmNo longer significantly reduce；
(7) finally by the coefficient a of predictionm(i), i=1 ..., m obtains power spectral density for people's formula (1-1), and Maximum Entropy Spectral Estimation Burg algorithm utilizes limited data, application linear forecasting technology and adaptive principle by iterating, and obtains the system number making model tend towards stability.
The system of selection of described AR forecast model exponent number: first transmitting the Function Criterion criterion as model order using Final prediction error criterion, information criterion, autoregression, according to the p value that above three criterion calculates, empirical equation is:
Beneficial effects of the present invention:
The present invention adopts the modern spectral estimation method based on Maximum Entropy Spectral Estimation that the non-stationary signal of isolation switch is carried out spectrum estimation, further increases its analysis precision and accuracy.By the Maximum Entropy Spectral Estimation analysis to actual isolation switch vibration signal, when not changing the sample frequency of signal, adopt less sample data, conforming Data Extrapolation is carried out by the AR forecast model data to observing, obtain the accuracy of the frequency resolution higher than classical spectrum estimate and Power estimation, and switch the theoretical foundation of deciliter state as detection and isolation.
Accompanying drawing explanation
Fig. 1 is the oscillogram of maximum entropy spectrum exponent number p=5 of the present invention.
Fig. 2 is the oscillogram of maximum entropy spectrum exponent number p=20 of the present invention.
Fig. 3 is the oscillogram of maximum entropy spectrum exponent number p=40 of the present invention.
Fig. 4 is that the present invention isolates switch and is in non-"on" position figure.
Fig. 5 is that the present invention isolates switch and is in combined floodgate intermediateness figure.
Fig. 6 be the present invention isolate switch be in conjunction tight state diagram.
In accompanying drawing,
The fundamental frequency of Fig. 4 is 2.675, and second-order frequency is 5.513
The fundamental frequency of Fig. 5 is 3.3hz, and second-order frequency is 5.675hz
The fundamental frequency of Fig. 6 is 3.313hz, and second-order frequency is 5.813hz
Detailed description of the invention
In order to make the purpose of the present invention, technical scheme and advantage clearly understand, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein is only in order to explain the present invention, is not intended to limit the present invention.
Embodiment:
A kind of Maximum Entropy Spectrum Method being applied to isolation switch vibration monitoring, data beyond measured finite data are not made any discriminating hypotheses, try to achieve according to the data extrapolation beyond known finite data autocorrelation sequence under the premise that comentropy is maximum, and estimate the power spectral density of signal to be detected；And the data that AR forecast model is to observing carry out conforming Data Extrapolation.
The expression formula of the power spectral density of Maximum Entropy Spectral Estimation is:
In formula, a (k), k=1 ..., p is the coefficient of p rank linear prediction filter；σ2Forecast error power for predictive filter；
The power spectral density of Maximum Entropy Spectral Estimation and autoregression (AR) model has identical form, and therefore Burg maximum entropy power and AR power spectrum equivalence, directly solve exponent number p and coefficient a (k) with AR model；
According to Burg algorithm, obtain the recurrence formula of AR model parameter a (k) and the recurrence formula of prediction mean square error Pm first with Levinson-Durbin recursive algorithm:
I=1 ..., m-1 (1-2)
am(m)=Km(1-3)
Pm=(1-| Km|2)Pm-1(1-4)
In formula, Km is called reflex system；
Then the mean power minimum principle according to priori prediction errors and posteriori prediction errors, obtains the recurrence formula of reflection coefficient Km:
Wherein,
Respectively m rank priori prediction errors and posteriori prediction errors.fm(n) and gmN the recurrence formula of () is
fm(n)=fm-1(n)+Kmgm-1(n-1)(1-8)
The step of Maximum Entropy Spectral Estimation Burg algorithm is as follows:
(1) initial value of forecast error power is calculatedInitial value f with forward and backward forecast error0(n)=g0N ()=x (n) also makes m=1；
(2) negate according to formula (1-5) and penetrate COEFFICIENT K m；
(3) forward prediction wave filter system a is calculated according to formula (1-2) and formula (1-3)m(i), i=1 ..., m；
(4) forecast error power P m is calculated according to formula (1-4)；
(5) predictive filter output f is calculated according to formula (1-8) and formula (1-9)m(n) and gm(n)；
(6) make m ← m+1, repeat step 2～5, until forecast error power PmNo longer significantly reduce；
(7) finally by the coefficient a of predictionm(i), i=1 ..., m obtains power spectral density for people's formula (1-1), and Maximum Entropy Spectral Estimation Burg algorithm utilizes limited data, application linear forecasting technology and adaptive principle by iterating, and obtains the system number making model tend towards stability.
The system of selection of described AR forecast model exponent number: first transmitting the Function Criterion criterion as model order using Final prediction error criterion, information criterion, autoregression, according to the p value that above three criterion calculates, empirical equation is:
Maximum Entropy Spectrum Method is introduced: entropy was the measuring of quantity of information size in theory of information originally, in stochastic process, it may also be used for weighing the power of the randomness of a process, its spectrum entropy of sequence that randomness is the strongest is also maximum. and the basic thought of maximum entropy spectrum analysis is that the data beyond measured finite data are not made any deterministic hypothesis, but under the premise that comentropy is maximum, by unknown correlation function by alternative manner recursion out.
For stationary random signal, its power spectral density is the Fourier transformation of auto-correlation function, and owing to stationary random sequence is endless, its auto-correlation function is also unlimited, and the data gathered in practice are always limited, estimate the power spectrum of signal only by the finite data gathered.In traditional power Spectral Estimation, often assume that the data beyond the finite data gathered are zero, be equivalent to data windowing, by the data estimation auto-correlation function gathered, power spectrum is obtained again through FFT, so will necessarily producing error, and it is low to there is resolution, secondary lobe occurs and the problems such as spectral line leakage.The basic thought of Maximum Entropy Spectral Estimation is that the data beyond measured finite data are not made any discriminating hypotheses, but try to achieve according to the data extrapolation beyond known finite data autocorrelation sequence under the premise that comentropy is maximum, and estimate the power spectral density of signal to be detected.
The selection of the maximum entropy spectrum exponent number P of isolation switch vibration monitoring:
Under isolation switch operating condition, use environmental excitation and the method adopting high-sensitivity vibration sensor, gather vibration signal.The vibration signal of scissor moving contact and fixed contact of isolating switch contact position taken from by this test data, due to environmental excitation noise containing typical non-stationary signal of the signal packet that collects, therefore adopt low frequency anti-aliasing filtering number to adopt card and gather this signal, and the analysis that is for data processing, obtain isolation switch deciliter state characteristic information, implement the status monitoring to isolation switch.
Such as Fig. 1 to Fig. 3 it can be seen that based on the maximum entropy spectra method engineering non-stationary signal Power estimation it is crucial that the feature of time series signal of the Engineering Signal obtained according to actual monitoring determines recursive algorithm, and choose suitable order.These data sample data are the vibration acceleration signal gathered when scissor moving contact and fixed contact of isolating switch separates, and frequency acquisition Fs=51.2Hz/s, the time of collection is 20s.When adopting AR, the sample that fetches data is 1024, relation between the model order upper bound and sample length N can be determined by (0.01～0.02) N, determine that lower limit is Pr=10, test finds: the spectrum estimation result of order p=18～23 is similar, for reducing amount of calculation, taking its order is p=20.The spectrogram that existing spy takes two ultimate value p=5 and p=40 and p=20 contrasts, and sees shown in Fig. 1, Fig. 2 and Fig. 3 respectively.In FIG, as p=5, owing to model order is relatively low, spectral line is excessively smooth, and the spectral peak of the 2.7Hz in spectrogram cannot characterize, and the spectral peak of 5.5Hz there occurs distortion simultaneously, and whole spectrogram all there occurs change, substantially can not relatively accurately characterize Spectral structure.And as p=40, spectrogram is substantially close with p=20, but near 2.7Hz, occur in that two obvious pseudo-peaks, calculate the time length compared with p=20 simultaneously.Test also finds, as p=60, in spectrogram, the pseudo-peak energy near 2.7Hz strengthens, occur in that other pseudo-peak simultaneously, making spectrogram thicken, some locally even there will be the phenomenon of sharply change and vibration, to a certain extent, the Power estimation of gained has more false detail, reduces the accuracy of Power estimation on the contrary.
The maximum entropy spectrum application of isolation switch vibration monitoring:
After determining order, just set about studying keeping apart and close the change of some eigenvalues in making process, see Fig. 4 to Fig. 6；
In the making process of scissor isolation switch, along with the increase of dynamic/static contact contact pressure, fundamental frequency there occurs migration, changes to 3.313hz from 2.675hz, and rate of change is 23.9%.Fundamental frequency is the frequency of vibration that isolation switchs most body, switchs "on" position for detection and isolation.This criterion repeatability is consistent, and reliability is high.Second-order frequency is changed to 5.813 by 5.513, and rate of change is 5.4%, and excursion is inconspicuous, it is impossible to switch the foundation of opening and closing state as detection and isolation.Being drawn by repeatedly reperformance test, when first-harmonic frequency of vibration is lower than 2.8Hz, isolation switch is in gate-dividing state；Think to close a floodgate more than 3.3Hz and put in place；Frequency thinks there is hidden danger between 2.8-3.3Hz.
The present invention is by the Maximum Entropy Spectral Estimation analysis to actual isolation switch vibration signal, when not changing the sample frequency of signal, adopt less sample data, conforming Data Extrapolation is carried out by the AR forecast model data to observing, the accuracy of the frequency resolution higher than classical spectrum estimate and Power estimation can be obtained, it is possible to switch the theoretical foundation of deciliter state as detection and isolation.
Based on above-mentioned, the present invention adopts the modern spectral estimation method based on Maximum Entropy Spectral Estimation that the non-stationary signal of isolation switch is carried out spectrum estimation, further increases its analysis precision and accuracy.By the Maximum Entropy Spectral Estimation analysis to actual isolation switch vibration signal, when not changing the sample frequency of signal, adopt less sample data, conforming Data Extrapolation is carried out by the AR forecast model data to observing, obtain the accuracy of the frequency resolution higher than classical spectrum estimate and Power estimation, and switch the theoretical foundation of deciliter state as detection and isolation.
The foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention, all any amendment, equivalent replacement and improvement etc. made within the spirit and principles in the present invention, should be included within protection scope of the present invention.
Claims (1)
1. the Maximum Entropy Spectrum Method being applied to isolation switch vibration monitoring, it is characterized in that: the data beyond measured finite data are not made any discriminating hypotheses, try to achieve according to the data extrapolation beyond known finite data autocorrelation sequence under the premise that comentropy is maximum, and estimate the power spectral density of signal to be detected；And the data that AR forecast model is to observing carry out conforming Data Extrapolation,
The expression formula of the power spectral density of Maximum Entropy Spectral Estimation is:
In formula, a (k), k=1 ..., p is the coefficient of p rank linear prediction filter；σ2Forecast error power for predictive filter；
The power spectral density of Maximum Entropy Spectral Estimation and autoregression (AR) model has identical form, and therefore Burg maximum entropy power and AR power spectrum equivalence, directly solve exponent number p and coefficient a (k) with AR model；
According to Burg algorithm, obtain the recurrence formula of AR model parameter a (k) and the recurrence formula of prediction mean square error Pm first with Levinson-Durbin recursive algorithm:
I=1 ..., m-1 (1-2)
am(m)=Km(1-3)
Pm=(1-| Km|2)Pm-1(1-4)
In formula, Km is called reflex system；
Then the mean power minimum principle according to priori prediction errors and posteriori prediction errors, obtains the recurrence formula of reflection coefficient Km:
Wherein,
Respectively m rank priori prediction errors and posteriori prediction errors, fm(n) and gmN the recurrence formula of () is
fm(n)=fm-1(n)+Kmgm-1(n-1)(1-8)
The step of Maximum Entropy Spectral Estimation Burg algorithm is as follows:
(1) initial value of forecast error power is calculatedInitial value f with forward and backward forecast error0(n)=g0N ()=x (n) also makes m=1；
(2) negate according to formula (1-5) and penetrate COEFFICIENT K m；
(3) forward prediction wave filter system a is calculated according to formula (1-2) and formula (1-3)m(i), i=1 ..., m；
(4) forecast error power P m is calculated according to formula (1-4)；
(5) predictive filter output f is calculated according to formula (1-8) and formula (1-9)m(n) and gm(n)；
(6) make m ← m+1, repeat step 2～5, until forecast error power PmNo longer significantly reduce；
(7) finally by the coefficient a of predictionm(i), i=1 ..., m obtains power spectral density for people's formula (1-1), and Maximum Entropy Spectral Estimation Burg algorithm utilizes limited data, application linear forecasting technology and adaptive principle by iterating, and obtains the system number making model tend towards stability,
The system of selection of described AR forecast model exponent number: first transmitting the Function Criterion criterion as model order using Final prediction error criterion, information criterion, autoregression, according to the p value that above three criterion calculates, empirical equation is:
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| CN110243562A (en) * | 2019-06-24 | 2019-09-17 | 同济大学 | A kind of online recursion spectral analysis method suitable for engineering structure vibration monitoring signal |
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| CN109327859B (en) * | 2018-11-26 | 2021-06-01 | 中山大学 | Frequency spectrum compression transmission method for railway GSM-R air interface monitoring system |
| CN110243562A (en) * | 2019-06-24 | 2019-09-17 | 同济大学 | A kind of online recursion spectral analysis method suitable for engineering structure vibration monitoring signal |
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Inventor after: Hu Changsheng Inventor after: Zu Jianqiang Inventor after: Zhu Jinkang Inventor after: Dan Deshuai Inventor after: Guo Lei Inventor after: Chen Dapeng Inventor after: Zhang Nan Inventor after: Yang Limin Inventor after: Zhang Wei Inventor after: Bai Ming Inventor after: According to Mamumaimaidi Vaav Inventor after: Zhang Licheng Inventor after: Ding Ge Inventor after: Li Zhen Inventor before: Zhang Nan Inventor before: Dan Deshuai Inventor before: Guo Lei Inventor before: Chen Dapeng Inventor before: Yang Limin Inventor before: Bai Ming Inventor before: According to the Waafu Mohammad. Inventor before: Zhang Licheng Inventor before: Ding Ge Inventor before: Li Zhen Inventor before: Zu Jianqiang Inventor before: Zhu Jinkang |
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| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20160706 |