CN104698248B - All-fiber current transformator denoising method and device based on FPGA - Google Patents
All-fiber current transformator denoising method and device based on FPGA Download PDFInfo
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- CN104698248B CN104698248B CN201410839645.4A CN201410839645A CN104698248B CN 104698248 B CN104698248 B CN 104698248B CN 201410839645 A CN201410839645 A CN 201410839645A CN 104698248 B CN104698248 B CN 104698248B
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Abstract
The present invention relates to technical field technical field of integrated circuits, and in particular to a kind of all-fiber current transformator denoising method and device based on FPGA.The all-fiber current transformator denoising method based on FPGA of the present invention improves precision by the way of Floating-point Computation, avoids the occurrence of the occupied situation of ample resources with reference to FPGA timing management, while solve the problems, such as complex matrix computing.
Description
Technical field
The present invention relates to technical field technical field of integrated circuits, and in particular to a kind of full optical-fiber current based on FPGA is mutual
Sensor denoising method and device.
Background technology
All-fiber current transformator high voltage, high current measurement in terms of have compared with traditional transformer it is obvious excellent
Gesture, but as traditional transformer, the measurement error that grass comes are can not avoid the problem of.
Mainly have currently for the method for reducing noise：
For source noise, if the wavelength change as caused by temperature, then can with direct measurement temperature tuning wavelength, it is no
Wavelength must be then measured to be corrected.If the influence as caused by return light, optoisolator, signal attenuator or choosing can be used
With the low-coherence light source of superluminescent diode (SLD) etc, to reduce the interference effect of reflected light and flashlight, suppress the Rayleigh back of the body
To shot noise.
For detecting electrical signal noise, amplifier noise can be electronically reduced, and shot noise can only pass through choosing
Light source power as big as possible and low-loss optical fibre channel are selected to strengthen optical signal, improves signal to noise ratio.
For sensing loop noise, one kind is to use short-coherence light source, second, phase-modulation is carried out in one end of fiber optic coils,
Suitable modulating frequency is selected, the Polarization Modulation phase of two beam Rayleigh scattering lights of left rotation and right rotation is just differed 180, can eliminate
Return to the additional amplitude zoop of the optical signal of light source.In addition, ambient noise, thermal noise, generation-recombination noise, circuit
Noise, dark current noise, electronic noise, ambient noise (such as temperature change), acoustic agitation, mechanical oscillation and any other big rule
Mould disturbance etc. (such as magnetic field of the earth changes) is also all the noise source for influenceing performance.It can be seen that common denoising method is simply to light path
Various pieces noise suppressed, and lack control to total system noise.
A kind of in consideration of it, the defects of overcoming the above in the prior art, there is provided new all-fiber current transformator denoising method
As this area technical problem urgently to be resolved hurrily.
The content of the invention
It is an object of the invention to the drawbacks described above for prior art, the purpose of the present invention can be arranged by following technology
Apply to realize：
A kind of all-fiber current transformator denoising method based on FPGA, compared with prior art, its difference is,
Comprise the following steps：
Step S1：Gather the status signal of current transformer and the status signal is pre-processed to obtain state variable
Data xi；
Step S2：To gained status variable data xiIt is weighted to obtain weighted data collection { χi}；
Step S3：To gained weighted data collection { χiCarry out nonlinear transportation acquisition data set { yi}；
Step S4：According to data set { yi, data set { yiMean value computation covariance matrix Pyy, according to state variable number
According to xi, status variable data xiAverage, data set { yiAnd data set { yiMean value computation covariance matrix Pxy；
Step S5：According to covariance matrix PxyWith covariance matrix PyyCarry out posterior value calculating and export the posterior value.
Preferably, also include after the step S1, before step S2 to status variable data xiThe step initialized
Suddenly：Wherein, x1、x2、x3Ac current signal is represented respectively
Amplitude, frequency and phase.
Preferably, the step S2 is specifically included：
Step S21：Calculate status variable data xiAverage xi；
Step S22：Calculate status variable data xiWith its average xiCovariance matrix Pxx；
Step S23：It is shown according to the following formula to carry out resampling to obtain weighted data collection { χi,
χi=xi, i=4；
Wherein, n=7, λ=α2N-n, 0.0001≤α≤1, weights are
Preferably, nonlinear transportation is in the step S3
Preferably, in the step S5：xi(k)=xi(k)+Kk[uin(k)-y (k)], wherein, uinTo be pre-
Processing gained status variable data xi；By gained xi(k) substitute intoObtain uout。
Present invention also offers a kind of all-fiber current transformator denoising device based on FPGA, including：
Pretreatment module, for gathering the status signal of current transformer and being pre-processed to the status signal with acquisition
Status variable data xi；
Weighted calculation module, for gained status variable data xiIt is weighted to obtain weighted data collection { χi}；
Nonlinear processing module, for gained weighted data collection { χiCarry out nonlinear transportation acquisition data set
{yi}；
Covariance computing module, for according to data set { yi, data set { yiMean value computation covariance matrix Pyy, root
According to status variable data xi, status variable data xiAverage, data set { yiAnd data set { yiMean value computation covariance square
Battle array Pxy；
Posterior value computing module, for according to covariance matrix PxyWith covariance matrix PyyProgress posterior value calculating simultaneously will
The posterior value exports.
Preferably, the weighted calculation module includes：First mean module, the first covariance module and resampling module,
First covariance module is connected with pretreatment module, the first mean module and resampling module respectively, resampling module and institute
State Nonlinear processing module connection.
Preferably, the covariance computing module includes：Second mean module, the second covariance module and the 3rd covariance
Module, the second mean module, the second covariance module and the 3rd covariance module are connected with Nonlinear processing module, and second is equal
Value module is connected with the second covariance module and the 3rd covariance module respectively, the second covariance module and the 3rd covariance module
It is connected with posterior value computing module.
The all-fiber current transformator denoising method based on FPGA of the present invention improves precision by the way of Floating-point Computation,
The occupied situation of ample resources is avoided the occurrence of with reference to FPGA timing management, while solves the problems, such as complex matrix computing.
Brief description of the drawings
Fig. 1 is the schematic diagram of whole FPGA signal-data processings；
Fig. 2 is the flow chart of the denoising method of the present invention；
Fig. 3 is the schematic diagram of the denoising method of the embodiment of the present invention 1；
Fig. 4 is the structured flowchart of the denoising device of embodiment 2.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, below in conjunction with the accompanying drawings and specific implementation
Example is described in further detail to the present invention.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention,
It is not intended to limit the present invention.
It is whole FPGA principles of signal processing figure as shown in Figure 1, wherein, demodulation filtering feedback serves in the signal processing
The effect formed a connecting link, the value after removal random noise is fed back to and enters horizontal phasing control in DAC, in order to remove total system
Noise and not only simply the noises of the various pieces of light path is suppressed, the present invention to demodulated in Fig. 1 filtering feedback it
Improved at place, there is provided a kind of all-fiber current transformator denoising method based on FPGA, compared with prior art, it is not
It is with part, as shown in Fig. 2 this method comprises the following steps：
Step S1：Gather the status signal of current transformer and the status signal is pre-processed to obtain state variable
Data xi；
Step S2：To gained status variable data xiIt is weighted to obtain weighted data collection { χi}；
Step S3：To gained weighted data collection { χiCarry out nonlinear transportation acquisition data set { yi}；
Step S4：According to data set { yi, data set { yiMean value computation covariance matrix Pyy, according to state variable number
According to xi, status variable data xiAverage, data set { yiAnd data set { yiMean value computation covariance matrix Pxy；
Step S5：According to covariance matrix PxyWith covariance matrix PyyCarry out posterior value calculating and export the posterior value.
Specifically, the above-mentioned all-fiber current transformator denoising method based on FPGA is improved by the way of Floating-point Computation
Precision, the occupied situation of ample resources is avoided the occurrence of with reference to FPGA timing management, while solve complex matrix computing
Problem.
Correspondingly, present invention also offers a kind of all-fiber current transformator denoising device based on FPGA, including：
Pretreatment module, for gathering the status signal of current transformer and being pre-processed to the status signal with acquisition
Status variable data xi；
Weighted calculation module, for gained status variable data xiIt is weighted to obtain weighted data collection { χi}；
Nonlinear processing module, for gained weighted data collection { χiCarry out nonlinear transportation acquisition data set
{yi}；
Covariance computing module, for according to data set { yi, data set { yiMean value computation covariance matrix Pyy, root
According to status variable data xi, status variable data xiAverage, data set { yiAnd data set { yiMean value computation covariance square
Battle array Pxy；
Posterior value computing module, for according to covariance matrix PxyWith covariance matrix PyyProgress posterior value calculating simultaneously will
The posterior value exports.
Embodiment 1：
A kind of all-fiber current transformator denoising method based on FPGA is present embodiments provided, the present embodiment uses
XILINX spartan6 Series FPGAs, algorithm is completed using wherein abundant DSP, RAM resource and Floating-point Computation IP kernel
In involved complicated matrix operation.As shown in figure 3, system should initialize the status variable data in 1 first when working
xi, wherein,Here x1、x2、x3Alternating current is represented respectively
Amplitude, frequency and the phase of signal.The average value x of each state variable is obtained by mean module 2i, association is calculated by covariance module 3
Variance matrix PxxAfterwards again through resampling module 4 according to obtained by equation below：
χi=xi, i=4；
Wherein, n=7, λ=α2N-n, 0.0001≤α≤1, weights are
Utilized again by priori value computing module 6{ the y of priori point set 7 of calculationi}.Done again by ADC
Relate to the u demodulatedin, the average x of status variable datai, { the y of priori point set 7iAnd tetra- groups of data of corresponding average y again by association
Variance module 9,10 is calculated derive from covariance matrix P respectivelyyyWith cross covariance battle array Pxy.The increasing most asked afterwards through 11 posterior value computing modules
BenefitAfterwards just it is estimated that new status variable data xi(k)=xi(k)+Kk[uin(k)-y (k)], again by xi
(k) back substitution entersIn can with arrive output quantity uout.It is parallel that whole process takes full advantage of FPGA
The characteristics of property.
Embodiment 2：
A kind of all-fiber current transformator denoising device based on FPGA is present embodiments provided, realizes going for embodiment 1
Method for de-noising, as shown in figure 4, the denoising device includes：Pretreatment module 101, weighted calculation module 102, Nonlinear processing module
103rd, covariance computing module 104 and posterior value computing module 105, wherein,
Pretreatment module 101, for gather the status signal of current transformer and the status signal is pre-processed with
Obtain status variable data xi；
Weighted calculation module 102, for gained status variable data xiIt is weighted to obtain weighted data collection
{χi}；
Nonlinear processing module 103, for gained weighted data collection { χiCarry out nonlinear transportation acquisition data
Collect { yi}；
Covariance computing module 104, for according to data set { yi, data set { yiMean value computation covariance matrix
Pyy, according to status variable data xi, status variable data xiAverage, data set { yiAnd data set { yiMean value computation association
Variance matrix Pxy；
Posterior value computing module 105, for according to covariance matrix PxyWith covariance matrix PyyPosterior value is carried out to calculate simultaneously
The posterior value is exported.
Wherein, weighted calculation module 102 further comprises：First mean module 1021, the and of the first covariance module 1022
Resampling module 1023, the first covariance module 1022 is respectively with pretreatment module 101, the first mean module 1021 and adopting again
Egf block 1023 connects.
Wherein, covariance computing module 104 further comprises：Second mean module 1041, the second covariance module 1042
With the 3rd covariance module 1043, Nonlinear processing module 103 is all connected with above three respectively, the second covariance module 1042
It is connected with the 3rd covariance module 1043 with posterior value computing module 105, the second covariance module 1042 is according to data set
{yi, data set { yiMean value computation covariance matrix Pyy, what the 3rd covariance module 1043 went out according to ADC interferometric demodulations
uin, status variable data xiAverage, data set { yiAnd data set { yiMean value computation covariance matrix Pxy。
The gain that posteriority module 105 is askedAfterwards just it is estimated that new status variable data xi(k)=xi
(k)+Kk[uin(k)-y (k)], again by xi(k) back substitution entersIn can with arrive output quantity uout。
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
All any modification, equivalent and improvement made within refreshing and principle etc., should be included in the scope of the protection.
Claims (8)
1. a kind of all-fiber current transformator denoising method based on FPGA, it is characterised in that comprise the following steps：
Step S1：Gather the status signal of current transformer and the status signal is pre-processed to obtain status variable data
xi；
Step S2：To gained status variable data xiIt is weighted to obtain weighted data collection { χi}；
Step S3：To gained weighted data collection { χiCarry out nonlinear transportation acquisition data set { yi}；
Step S4：According to data set { yi, data set { yiMean value computation covariance matrix Pyy, according to status variable data xi、
Status variable data xiAverage, data set { yiAnd data set { yiMean value computation covariance matrix Pxy；
Step S5：According to covariance matrix PxyWith covariance matrix PyyCarry out posterior value calculating and export the posterior value.
2. the all-fiber current transformator denoising method according to claim 1 based on FPGA, it is characterised in that the step
Also include after rapid S1, before step S2 to status variable data xiThe step of being initialized：
Wherein, x1、x2、x3Ac current signal is represented respectively
Amplitude, frequency and phase.
3. the all-fiber current transformator denoising method according to claim 2 based on FPGA, it is characterised in that the step
Rapid S2 is specifically included：
Step S21：Calculate status variable data xiAverage xi；
Step S22：Calculate status variable data xiWith its average xiCovariance matrix Pxx；
Step S23：It is shown according to the following formula to carry out resampling to obtain weighted data collection { χi,
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Wherein, n=7, λ=α2N-n, 0.0001≤α≤1, weights are
4. the all-fiber current transformator denoising method according to claim 3 based on FPGA, it is characterised in that the step
Nonlinear transportation is in rapid S3
5. the all-fiber current transformator denoising method according to claim 4 based on FPGA, it is characterised in that the step
In rapid S5：xi(k)=xi(k)+Kk[uin(k)-y (k)], wherein, uinFor pretreatment gained status variable data
xi；By gained xi(k) substitute intoObtain uout。
6. a kind of all-fiber current transformator denoising device based on FPGA, it is characterised in that the device includes：
Pretreatment module, for gathering the status signal of current transformer and being pre-processed the status signal to obtain state
Variable data xi；
Weighted calculation module, for gained status variable data xiIt is weighted to obtain weighted data collection { χi}；
Nonlinear processing module, for gained weighted data collection { χiCarry out nonlinear transportation acquisition data set { yi}；
Covariance computing module, for according to data set { yi, data set { yiMean value computation covariance matrix Pyy, according to shape
State variable data xi, status variable data xiAverage, data set { yiAnd data set { yiMean value computation covariance matrix
Pxy；
Posterior value computing module, for according to covariance matrix PxyWith covariance matrix PyyCarry out posterior value calculating and by the posteriority
Value output.
7. the all-fiber current transformator denoising device according to claim 6 based on FPGA, it is characterised in that described to add
Power computing module includes：First mean module, the first covariance module and resampling module, the first covariance module respectively with advance
Processing module, the first mean module and the connection of resampling module, resampling module are connected with the Nonlinear processing module.
8. the all-fiber current transformator denoising device according to claim 6 based on FPGA, it is characterised in that the association
Variance computing module includes：Second mean module, the second covariance module and the 3rd covariance module, the second mean module,
Two covariance modules and the 3rd covariance module are connected with Nonlinear processing module, the second mean module respectively with the second association side
Difference module and the connection of the 3rd covariance module, the second covariance module and the 3rd covariance module connect with posterior value computing module
Connect.
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Citations (5)
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|---|---|---|---|---|
| US6549687B1 (en) * | 2001-10-26 | 2003-04-15 | Lake Shore Cryotronics, Inc. | System and method for measuring physical, chemical and biological stimuli using vertical cavity surface emitting lasers with integrated tuner |
| CN101299147A (en) * | 2008-04-25 | 2008-11-05 | 北京航空航天大学 | Closed-loop control method and device of optical fiber current transformer system noise suppression |
| CN102082606A (en) * | 2011-01-10 | 2011-06-01 | 深圳市方隅光电科技有限公司 | Optical fiber current transformer phase difference detecting and processing device |
| CN102854360A (en) * | 2012-08-14 | 2013-01-02 | 北京航空航天大学 | Stability control device for transmission spectrums of optical fiber current transducer |
| CN203894324U (en) * | 2014-03-17 | 2014-10-22 | 淮安信息职业技术学院 | Phase difference detection device of fiber current transformer |
-
2014
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6549687B1 (en) * | 2001-10-26 | 2003-04-15 | Lake Shore Cryotronics, Inc. | System and method for measuring physical, chemical and biological stimuli using vertical cavity surface emitting lasers with integrated tuner |
| CN101299147A (en) * | 2008-04-25 | 2008-11-05 | 北京航空航天大学 | Closed-loop control method and device of optical fiber current transformer system noise suppression |
| CN102082606A (en) * | 2011-01-10 | 2011-06-01 | 深圳市方隅光电科技有限公司 | Optical fiber current transformer phase difference detecting and processing device |
| CN102854360A (en) * | 2012-08-14 | 2013-01-02 | 北京航空航天大学 | Stability control device for transmission spectrums of optical fiber current transducer |
| CN203894324U (en) * | 2014-03-17 | 2014-10-22 | 淮安信息职业技术学院 | Phase difference detection device of fiber current transformer |
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