CN110045184A - A kind of sub-harmonic wave measurement method based on compressed sensing MACSMP - Google Patents
A kind of sub-harmonic wave measurement method based on compressed sensing MACSMP Download PDFInfo
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- CN110045184A CN110045184A CN201910261684.3A CN201910261684A CN110045184A CN 110045184 A CN110045184 A CN 110045184A CN 201910261684 A CN201910261684 A CN 201910261684A CN 110045184 A CN110045184 A CN 110045184A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
- G01R23/16—Spectrum analysis; Fourier analysis
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- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
- H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
- H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
Abstract
The present invention relates to a kind of sub-harmonic wave measurement methods based on compressed sensing MACSMP, comprising the following steps: 1) carries out sliding-model control, sub-harmonic wave discrete sampling sequence x (n) to system sub-harmonic wave signal；2) discrete Fourier transform is carried out to obtaining the signal X (k) after system discretization, the signal X (k) after obtaining system discretization；3) by introducing spectral resolution of the interpolation factor to improve signal X (k)；4) the model formula after raising spectral resolution is equivalent to compressed sensing model；5) compressed sensing model is solved using MACSMP algorithm, finally obtains measurement vector.Compared with prior art, the present invention has a case that operational efficiency is high, it is unknown to be adapted to signal degree of rarefication.
Description
Technical field
The present invention relates to the detection fields of the sub-harmonic wave in power quality, are based on compressed sensing more particularly, to one kind
The sub-harmonic wave measurement method of MACSMP.
Background technique
With the continuous development of power electronic technique, switching frequency reaches thousands of full-control type power electronics to several hundred kHz
Device is more and more, such as photovoltaic DC-to-AC converter, electric automobile charging pile, energy saver equipment.The operation of these equipment can generate 2
The sub-harmonic wave of~150kHz frequency range.This rahmonic largely introduces power distribution network, has caused many power quality new problems, has been
The harmonic pollution problems for administering power electronic equipment and other sub-harmonic wave sources, are just particularly important the detection of harmonic wave.
In recent years, small with more widely having at present about the detection method of harmonic wave in Electric Power Harmonic Analysis field
Wave analysis method and fourier transform method.However, Fourier transformation and wavelet analysis method have some disadvantages, document " power train
System harmonic detecting and denoising method research " in mention Fourier transformation and cannot characterize the certain local information of disturbing signal, can not
Disturbing signal amplitude, frequency and the phase for tracking variation, impact analysis；" backtracking Adaptive matching tracks electric energy to document
Quality signal reconstructing method " in mention wavelet analysis there are computationally intensive, algorithm complexity is high, and real-time is poor, small echo letter
The defects of number is not unique, in addition, wavelet transformation is there is also the defect of " edge effect " so that need on boundary to data into
Row processing, causes certain processing error.And based on Nyquist sampling thheorem, i.e., these harmonic detecting methods are all
fs≥2fmax, at least need the sample frequency of 300K or more for the measurement of sub-harmonic wave (2K-150K), however such sampling
Sharply increasing for data volume is directly resulted in, causes huge pressure to transmit and store to subsequent data.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind to be based on compressed sensing
The sub-harmonic wave measurement method of MACSMP.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of sub-harmonic wave measurement method based on compressed sensing MACSMP, comprising the following steps:
1) sliding-model control, sub-harmonic wave discrete sampling sequence x (n) are carried out to system sub-harmonic wave signal；
2) discrete Fourier transform is carried out to obtaining the signal X (k) after system discretization, the letter after obtaining system discretization
Number X (k)；
3) by introducing spectral resolution of the interpolation factor to improve signal X (k)；
4) the model formula after raising spectral resolution is equivalent to compressed sensing model；
5) compressed sensing model is solved using MACSMP algorithm, finally obtains measurement vector.
In the step 1), the expression formula of sub-harmonic wave discrete sampling sequence x (n) are as follows:
Wherein, Ash、θshFor the amplitude and phase of ultraharmonics, fshFor the frequency of ultraharmonics, TsFor the sampling period, n is continuous
The sampled value serial number of signal, and n=0,1 ..., N-1, N are signal sequence length, and sh is the number of sub-harmonic wave.
In the step 2), the expression formula of the signal X (k) after system discretization are as follows:
Wherein, N is measured signal length, and k is the sampled value serial number of discrete signal, and k ∈ [0, N-1], AN() is Di
Sharp Cray kernel function.
In the step 3), so that frequency resolution Δ f is increased to original P times by introducing interpolation factor P, then has:
N '=NP
Wherein, r=rshIndicate fshR when frequency resolution is Δ ' fshSpectral line, N ' are after introducing interpolation factor
Score number of lines.
The introducing interpolation factor P is positive integer, and value is no more than 10.
In the step 4), the expression of compressed sensing model are as follows:
y≈Aα
y≈Aα≈Φx≈ΦΨα
Wherein, y is observation vector, speciallyΦ(k,r)For N × N ' calculation matrix, Ψ(k,r)For the dilute of N × N
Matrix is dredged, α is the measurement vector of N ' × 1, and A is N × N ' sensing matrix, A(k,r)For (k, r) a member in sensing matrix A
Element.
The step 5) specifically includes the following steps:
51) initial sparse degree K is set0=1, supported collection
52) basisAtom is corresponded to calculate in residual error r and calculation matrix Φ
Related coefficient u, and by K0In the corresponding index deposit supported collection F of a maximum value；
If 53)Then K0=K0+ 1, it goes to step 52), wherein F0Most to be matched in Φ with residual error
K0The corresponding indexed set of a atom,Indicate manipulative indexing collection F in Φ0Atom set, δkFor the equidistant property of constraint of Φ
(RIP) parameter；
54) initial residual error is calculatedWhereinForGeneralized inverse matrix；
55) it initializes: stage stage=1, the number of iterations n=1, supported collection size size=K0, indexed set
56) byRelated coefficient u is calculated, and selects 2*size maximum value pair
It carries out Regularization, and then manipulative indexing is stored in S；
57) S is merged into supported collection F, according to formulaRebuild measurement vectorAnd retain
Vector is measured with reconstruction in supported collection FMost matched size element, other elements zero setting, and update AF；
58) measurement vector is rebuild againSimultaneously according to formulaObtain new residual error rnew；
If 59) meet and stop iterated conditionalThen stop iteration, otherwise carries out step 510)；
If 510) meet and expand supported collection length condition | | rnew||2≥||r||2, then step 511) is carried out；Otherwise r=
rnew, the number of iterations n=n+1, and return step 56)；
If 511) meet variable step conditionThen step-length step=[step/2], size=size+step,
Stage=stage+1, return step 56), otherwise step-length is constant, size=size+step, stage=stage+1, returns to step
It is rapid 56).
Compared with prior art, the invention has the following advantages that
One, the present invention uses compressive sensing theory, to be far below the sample frequency based on nyquist sampling theorem to letter
Number Sampling Compression and reconstruct are carried out, reduces calculation amount, operational efficiency is high.
Two, MACSMP algorithm is by being applied to the signal reconstruction process of compressed sensing by the present invention, signal degree of rarefication not
The accurate reconstruct that compressed sub-harmonic wave signal is realized in the case where knowing, realizes the precise measurement of sub-harmonic wave.
Detailed description of the invention
Fig. 1 is MACSMP algorithm iteration flow chart.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.
Embodiment:
The present invention provides the sub-harmonic wave Measurement Algorithm of compressed sensing MACSMP a kind of, comprising the following steps:
System sub-harmonic wave signal is subjected to sliding-model control first；
By the signal of discretization utilizes deformed discrete Fourier transform (Discrete Fourier in step 1
Transform, DFT) it is handled；
By being introduced into interpolation factor to improve the spectral resolution through treated in step 2 discrete signal；
Through step 3, treated that formula is equivalent at compressed sensing model；
Compressed sensing models coupling MACSMP algorithm is sought into measurement vector.
Further, in the step (1), the sliding-model control process of system sub-harmonic wave signal is as follows:
If system voltage, electric current discrete signal are as follows:
In formula: fhIndicate harmonic wave, and fh=hf1, f1Indicate fundamental frequency.H indicates overtone order, AhAnd θhIs characterized respectively
The amplitude and phase of h subharmonic ingredient.
2~150kHz frequency range, the 32 even time interval data window over harmonic wave measurement recommended for standard IEC 61000-4-30
Method equally spacedly extracts the time window that 32 groups of length are 0.5ms, has obtained corresponding over harmonic wave component discrete sampling sequence:
DFT transform is carried out to x (n), may be expressed as:
Gained X (k) is the signal after system discretization.
Further, in the step (2), by the signal of discretization utilizes the change of deformed discrete fourier in step 1
It changes (Discrete Fourier Transform, DFT) to be handled, process is as follows:
During carrying out the discrete Fourier transform of discrete signal, in order to keep the coefficient of DFT transform of x (n) not
Become, to formula (3) equal sign both sides with multiplied byThen it obtains:
According to Geometric Sequence sum formula in formula, can obtain:
Similarly, have:
Then the final expression formula of the DFT about x (n) is obtained:
In formula: 0≤k < N；AN() is Dirichlet kernel.
Ignore negative frequency pointInfluence, then the N point DFT transform of x (n) can finally indicate
Are as follows:
In formula: fshFor the frequency of certain ultraharmonics, spectral resolution Δ f=fs/N。
Further, in the step (3), by being introduced into interpolation factor P to improve through treated in step 2 discrete signal
Spectral resolution, process is as follows:
In the spectrogram after by DFT, in order to improve spectral resolution, by introducing interpolation factor P (positive integer)
So that frequency resolution is increased to original P times, i.e. Δ ' f=Δ f/P, score number of lines becomes N '=NP by N, to obtain:
In formula (10): rshIndicate fshR when frequency resolution is Δ ' fshSpectral line；δshIndicate fshIn frequency point
Non-integer spectral line when resolution is Δ ' f, and | δsh|≤0.5.When round numbers spectral line,And r is replaced with rsh, in
It is that formula (9) can approximate representation are as follows:
In formula, r ∈ [0, N ' -1], k ∈ [0, N-1].
Selection about interpolation factor P: being generally basede on actual needs and theory analysis, and combines calculation amount and calculate essence
Degree, chooses interpolation factor P moderately, and specifically, the value of interpolation factor is often no more than 10, and based on preliminary survey as a result, should make
The integral multiple of frequency resolution after the close enough raising of frequency of tested over harmonic wave component.
Further, in the step (4), through step 3, treated that formula can be equivalent at compressed sensing model, process
It is as follows:
After introducing interpolation, formula (11) is convertible into compressed sensing model:
y≈Aα (12)
y≈Aα≈Φx≈ΦΨα (16)
In formula: y is observation vector, is representedX (k) is the coefficient of the DFT transform of measured signal；N is tested letter
Number length；Φ is N × N ' calculation matrix；Ψ is the sparse matrix of N × N；α is the measurement vector of N ' × 1, and degree of rarefication is not
Know；A is N × N ' sensing matrix, A(k,r)For (k, r) a element in formula (15).
Further, in the step (5), compressed sensing models coupling MACSMP algorithm is sought into measurement vector α, process is such as
Under:
The iterative process of MACSMP algorithm:
1) MACSMP algorithm inputs parameter: observation vector y, sensing matrix A, initial stage step-length step ≠ 0.
2) MACSMP algorithm output parameter: the estimated value of measurement vector α
3) MACSMP algorithm iteration flow chart is as shown in Figure 1.
Step 1: initial sparse degree K0=1, supported collection
Step 2: byRelated coefficient u is calculated, and by K0A maximum value is corresponding
In index deposit supported collection F；
Step 3: ifThen K0=K0+ 1, go to step 2；
Step 4: initial residual error
Step 5: initialization: stage stage=1, the number of iterations n=1, supported collection size size=K0, indexed set
Step 6: byRelated coefficient u is calculated, and selects 2size maximum value
Regularization is carried out to it, then manipulative indexing is stored in S；
Step 7: S being merged into supported collection F, formula is utilizedRebuild measurement vectorAnd it protects
Stay in F withMost matched size element, other elements zero setting, and update AF；
Step 8: rebuilding measurement vector againSimultaneously according to formulaObtain new residual error rnew；
Step 9: stopping iterated conditional if meetingThen stop iteration, otherwise goes to step 10；
Step 10: expanding supported collection length condition if meeting | | rnew||2≥||r||2, then 11 are gone to step；Otherwise r=rnew,
The number of iterations n=n+1, and go to step 6；
Step 11: if meeting variable step conditionThen step-length step=[step/2], size=size+
Step, stage=stage+1 go to step 6；Otherwise step-length is constant, size=size+step, stage=stage+1, turns step
Rapid 6；
Wherein, step 1 to step 3 complete degree of rarefication pre-estimation function, step 6 to step 8 mainly realize to atom into
Row regularization screening and backtracking thought processing, and step 9 is then by two threshold epsilons to step 111、ε2To control respectively repeatedly
In generation, stops whether halving with iteration step length.In MACSMP algorithm, the operand of approximation signal degree of rarefication part is relatively small, mainly
Calculation amount concentrates on estimating signal this part using least square method, but due to being obtained using the method for degree of rarefication pre-estimation
As soon as degree of rarefication coarse value also reduces indirectly in this way to the number of signal estimation in algorithm iteration early period, so can be effective
Reduce the operand of algorithm.
Claims (7)
1. a kind of sub-harmonic wave measurement method based on compressed sensing MACSMP, which comprises the following steps:
1) sliding-model control, sub-harmonic wave discrete sampling sequence x (n) are carried out to system sub-harmonic wave signal；
2) discrete Fourier transform is carried out to obtaining the signal X (k) after system discretization, the signal X after obtaining system discretization
(k)；
3) by introducing spectral resolution of the interpolation factor to improve signal X (k)；
4) the model formula after raising spectral resolution is equivalent to compressed sensing model；
5) compressed sensing model is solved using MACSMP algorithm, finally obtains measurement vector.
2. a kind of sub-harmonic wave measurement method based on compressed sensing MACSMP according to claim 1, which is characterized in that
In the step 1), the expression formula of sub-harmonic wave discrete sampling sequence x (n) are as follows:
Wherein, Ash、θshFor the amplitude and phase of ultraharmonics, fshFor the frequency of ultraharmonics, TsFor the sampling period, n is continuous signal
Sampled value serial number, and n=0,1 ..., N-1, N is signal sequence length, and sh is the number of sub-harmonic wave.
3. a kind of sub-harmonic wave measurement method based on compressed sensing MACSMP according to claim 2, which is characterized in that
In the step 2), the expression formula of the signal X (k) after system discretization are as follows:
Wherein, N is measured signal length, and k is the sampled value serial number of discrete signal, and k ∈ [0, N-1], AN() is Di Li Cray
Kernel function.
4. a kind of sub-harmonic wave measurement method based on compressed sensing MACSMP according to claim 3, which is characterized in that
In the step 3), so that frequency resolution Δ f is increased to original P times by introducing interpolation factor P, then has:
N '=NP
Wherein, r=rshIndicate fshR when frequency resolution is Δ ' fshSpectral line, N ' are total after introducing interpolation factor
Compose number of lines.
5. a kind of sub-harmonic wave measurement method based on compressed sensing MACSMP according to claim 4, which is characterized in that
The introducing interpolation factor P is positive integer, and value is no more than 10.
6. a kind of sub-harmonic wave measurement method based on compressed sensing MACSMP according to claim 4, which is characterized in that
In the step 4), the expression of compressed sensing model are as follows:
y≈Aα
y≈Aα≈Φx≈ΦΨα
Wherein, y is observation vector, speciallyΦ(k,r)For N × N ' calculation matrix, Ψ(k,r)For the sparse square of N × N
Battle array, α are the measurement vector of N ' × 1, and A is N × N ' sensing matrix, A(k,r)For (k, r) a element in sensing matrix A.
7. a kind of sub-harmonic wave measurement method based on compressed sensing MACSMP according to claim 6, which is characterized in that
The step 5) specifically includes the following steps:
51) initial sparse degree K is set0=1, supported collection
52) basisAtom is corresponded to calculate in residual error r and calculation matrix ΦPhase
Relationship number u, and by K0In the corresponding index deposit supported collection F of a maximum value；
If 53)Then K0=K0+ 1, it goes to step 52), wherein F0For in Φ with the most matched K of residual error0
The corresponding indexed set of a atom,Indicate manipulative indexing collection F in Φ0Atom set, δkFor the equidistant property of constraint of Φ
(RIP) parameter；
54) initial residual error is calculatedWhereinForGeneralized inverse matrix；
55) it initializes: stage stage=1, the number of iterations n=1, supported collection size size=K0, indexed set
56) byCalculate related coefficient u, and select 2*size maximum value to its into
Then manipulative indexing is stored in S by row Regularization；
57) S is merged into supported collection F, according to formulaRebuild measurement vectorAnd retain support
Collect in F and measures vector with reconstructionMost matched size element, other elements zero setting, and update AF；
58) measurement vector is rebuild againSimultaneously according to formulaObtain new residual error rnew；
If 59) meet and stop iterated conditionalThen stop iteration, otherwise carries out step 510)；
If 510) meet and expand supported collection length condition | | rnew||2≥||r||2, then step 511) is carried out；Otherwise r=rnew, repeatedly
Generation number n=n+1, and return step 56)；
If 511) meet variable step conditionThen step-length step=[step/2], size=size+step,
Stage=stage+1, return step 56), otherwise step-length is constant, size=size+step, stage=stage+1, returns to step
It is rapid 56).
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110716088A (en) * | 2019-10-30 | 2020-01-21 | 国网上海市电力公司 | Ultrahigh harmonic measurement method based on compressive sensing MACSMP |
| CN111351984A (en) * | 2020-03-02 | 2020-06-30 | 上海电力大学 | CS-RBAPVS-based super-harmonic measurement method |
| CN115902397A (en) * | 2023-01-06 | 2023-04-04 | 四川大学 | Rapid super-harmonic estimation method based on simplified compressed sensing model |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103384836A (en) * | 2010-12-22 | 2013-11-06 | 皇家飞利浦电子股份有限公司 | Rapid parallel reconstruction for arbitrary k-space trajectories containing GRAPPA operator |
| CN103780521A (en) * | 2014-02-28 | 2014-05-07 | 重庆邮电大学 | Sparsity self-adaptive OFDM system channel estimation method |
| CN103983850A (en) * | 2014-05-13 | 2014-08-13 | 天津大学 | Power system harmonious wave compressed signal reconstruction and detection method based on compressed sensing |
| CN107192878A (en) * | 2017-04-07 | 2017-09-22 | 中国农业大学 | A kind of trend of harmonic detection method of power and device based on compressed sensing |
| CN107527371A (en) * | 2017-09-07 | 2017-12-29 | 中国科学院光电技术研究所 | One kind approaches smooth L in compressed sensing0The design constructing method of the image reconstruction algorithm of norm |
| US20180115064A1 (en) * | 2016-10-21 | 2018-04-26 | C-Com Satellite Systems Inc. | Method and apparatus for phased antenna array calibration |
| CN108881076A (en) * | 2018-06-30 | 2018-11-23 | 宜春学院 | A kind of compressed sensing based MIMO-FBMC/OQAM system channel estimation method |
| CN108918964A (en) * | 2018-07-11 | 2018-11-30 | 国网江苏省电力有限公司宿迁供电分公司 | A kind of harmonic analysis method of sparsity enhancing |
| CN109164298A (en) * | 2018-07-25 | 2019-01-08 | 陕西科技大学 | A kind of compressed sensing based ultra harmonics detection device and detection method |
-
2019
- 2019-04-02 CN CN201910261684.3A patent/CN110045184A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103384836A (en) * | 2010-12-22 | 2013-11-06 | 皇家飞利浦电子股份有限公司 | Rapid parallel reconstruction for arbitrary k-space trajectories containing GRAPPA operator |
| CN103780521A (en) * | 2014-02-28 | 2014-05-07 | 重庆邮电大学 | Sparsity self-adaptive OFDM system channel estimation method |
| CN103983850A (en) * | 2014-05-13 | 2014-08-13 | 天津大学 | Power system harmonious wave compressed signal reconstruction and detection method based on compressed sensing |
| US20180115064A1 (en) * | 2016-10-21 | 2018-04-26 | C-Com Satellite Systems Inc. | Method and apparatus for phased antenna array calibration |
| CN107192878A (en) * | 2017-04-07 | 2017-09-22 | 中国农业大学 | A kind of trend of harmonic detection method of power and device based on compressed sensing |
| CN107527371A (en) * | 2017-09-07 | 2017-12-29 | 中国科学院光电技术研究所 | One kind approaches smooth L in compressed sensing0The design constructing method of the image reconstruction algorithm of norm |
| CN108881076A (en) * | 2018-06-30 | 2018-11-23 | 宜春学院 | A kind of compressed sensing based MIMO-FBMC/OQAM system channel estimation method |
| CN108918964A (en) * | 2018-07-11 | 2018-11-30 | 国网江苏省电力有限公司宿迁供电分公司 | A kind of harmonic analysis method of sparsity enhancing |
| CN109164298A (en) * | 2018-07-25 | 2019-01-08 | 陕西科技大学 | A kind of compressed sensing based ultra harmonics detection device and detection method |
Non-Patent Citations (3)
| Title |
|---|
| ANDREW WABNITZ等: "Upscaling Ambisonic sound scenes using compressed sensing techniques", 《 2011 IEEE WORKSHOP ON APPLICATIONS OF SIGNAL PROCESSING TO AUDIO AND ACOUSTICS (WASPAA)》 * |
| 倪加明等: "一种改进的稀疏度自适应压缩采样匹配追踪算法", 《通信技术》 * |
| 庄双勇等: "基于压缩感知OMP的超谐波测量新算法", 《仪器仪表学报》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110716088A (en) * | 2019-10-30 | 2020-01-21 | 国网上海市电力公司 | Ultrahigh harmonic measurement method based on compressive sensing MACSMP |
| CN111351984A (en) * | 2020-03-02 | 2020-06-30 | 上海电力大学 | CS-RBAPVS-based super-harmonic measurement method |
| CN115902397A (en) * | 2023-01-06 | 2023-04-04 | 四川大学 | Rapid super-harmonic estimation method based on simplified compressed sensing model |
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Application publication date: 20190723 |