CN110245321A - Duration power quality disturbances method and device based on match tracing sparse decomposition - Google Patents
Duration power quality disturbances method and device based on match tracing sparse decomposition Download PDFInfo
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Abstract
The present invention relates to the duration power quality disturbances method and device based on match tracing sparse decomposition, the duration power quality disturbances method therein based on match tracing sparse decomposition the following steps are included: one, establish redundant dictionary；Two, match tracing decomposes: three, carrying out discretization to atomic parameter；Four, based on the electrical energy power quality disturbance parameter detecting of MP algorithm, the residual error of signal is done inner product operation with all atoms in discrete Gabor dictionary in a manner of traversing, finds out the matched atoms with the maximum absolute value of residual error inner product by each iteration.Present invention combination inner product recursive calculation, realize the rapid computations of MP, and devise synthesis dictionary, finally realize the extraction of electrical energy power quality disturbance matching waveform and the quick detection of parameter, calculating speed is greatly improved, disturbance waveform is extracted from white noise using less iteration, shows good noiseproof feature.
Description
Technical field
The present invention relates to the methods for being detected and being monitored to electrical energy power quality disturbance, and in particular to be that one kind is based on
The duration power quality disturbances method and device of match tracing sparse decomposition.
Background technique
Electrical equipment in use can to the work quality of other electrical equipments of external spatial emission electromagnetic wave influence,
As the usage amount of power equipment gradually increases, electrical energy power quality disturbance is detected and is monitored is extremely important.
In recent decades, the researcher of domestic and international related fields has delivered largely about harmonic wave and m-Acetyl chlorophosphonazo source, danger
The document of evil, detection and inhibition etc..However, being still had at present to the accurate measurement of harmonic wave and m-Acetyl chlorophosphonazo parameter many tired
Difficulty, therefore, for a long time, the detection of harmonic wave and m-Acetyl chlorophosphonazo is always an important research direction in electric energy quality monitoring.It is humorous
Wave and m-Acetyl chlorophosphonazo detection along with power system development overall process, the frequency domain theory that has been born and when domain theory, form a variety of
Harmonic wave, inter-harmonic wave measuring method, in terms of Measurement of Harmonics in Power System, including Fourier's analysis method, Prony algorithm Martin Hilb
The detection such as special Huang, instantaneous reactive power theory method, Hilbert-Huang converter technique, Power estimation method, wavelet analysis method
Method.
But there was only basic theories in the prior art, the detection method that different detection quotient uses in reality is different,
There is also biggish difference for the precision of detection, therefore, are badly in need of complete set and the duration power quality disturbances scheme that optimizes.
Summary of the invention
It is this object of the present invention is to provide a kind of duration power quality disturbances method based on match tracing sparse decomposition
Duration power quality disturbances method based on match tracing sparse decomposition is used to improve the extraction of electrical energy power quality disturbance matching waveform
With the detection speed of parameter.It is a further object to provide the electrical energy power quality disturbance inspections based on match tracing sparse decomposition
Survey device.
The technical solution adopted by the present invention to solve the technical problems is: this electric energy based on match tracing sparse decomposition
Quality disturbance detection method, includes the following steps:
One, redundant dictionary is established, using one group of function waveform with extensive time-frequency local characteristics as in sparse decomposition
Redundant dictionary, each waveform is as the atom a in redundant dictionaryγ(t), the method for redundant dictionary is established are as follows:
A1, the set of coordinates for establishing first waveform: amplitude direction is stirred as Y direction, with wave travel direction using waveform
For X-direction, the A column in Excel table sequentially input first waveform along the corresponding X axis coordinate value in the direction of propagation,
B column in Excel table sequentially input first waveform along the corresponding Y axis coordinate value in amplitude direction, constitute first waveform
Set of coordinates, the difference of two neighboring X axis coordinate value indicate the differential step size value of first waveform；
A2, the set of coordinates for establishing second waveform: the C column in Excel table sequentially input second waveform along propagation
The corresponding X axis coordinate value in direction, the D column in Excel table sequentially input second waveform and sit along the corresponding Y-axis in amplitude direction
Scale value constitutes the set of coordinates of second waveform, the differential step size value phase of the differential step size value of second waveform and first waveform
Deng；
A3, the set of coordinates for establishing third waveform: the E column in Excel table sequentially input third waveform along propagation
The corresponding X axis coordinate value in direction, the F column in Excel table sequentially input third waveform and sit along the corresponding Y-axis in amplitude direction
Scale value constitutes the set of coordinates of second waveform, the differential step size value phase of the differential step size value of third waveform and first waveform
Deng；
A4, the set of coordinates for establishing waveform to be matched: the G column in Excel table sequentially input waveform to be matched along propagation
The corresponding X axis coordinate value in direction, the H column in Excel table sequentially input waveform to be matched and sit along the corresponding Y-axis in amplitude direction
Scale value constitutes the set of coordinates of waveform to be matched, and the difference of two neighboring X axis coordinate value indicates the differential step size value of waveform to be matched,
The differential step size value of waveform to be matched is equal with the differential step size value of first waveform；
Two, match tracing decomposes, and defines redundant dictionary D={ aγ}γ∈Г, Г is the set a of parameter group γγ, aγIt is redundancy
Atom in dictionary D, | | aγ| |=1, decomposition method are as follows:
In B1, Dictionary of Computing with the atom a of the maximum absolute value of signal f inner productγo, meet | < f, aγo>|=sup |<f, aγ
>|(γ∈Г)；
B2, signal f is decomposed into f=< f, aγo>aγo+R1, < f, aγo>aγoFor in most matched atoms aγoOn vertical throwing
Shadow component, R1For in aγoOn approach the residue signal after f；
B3, the decomposable process that A1 and A2 is repeated to residue signal, obtain new remnants, after n times iteration, signal f=
∑<Rm, aγm>aγm+Rn, the sparse bayesian learning expression formula of m=0 → (n-1), signal f are f '=∑ < Rm, aγm>aγm, m=0 → (n-
1), the residual error R of each iterationnCalculating formula is Rn-1-<Rn-1, aγn-1>aγn-a, as the specified the number of iterations n of satisfaction or reach precision
Stop iteration after it is required that；
Three, discretization is carried out to atomic parameter, using Gabor dictionary, discretization method be γ=(s, u, v, φ)=
(2j, p2jΔ u, q2- jΔ v, i Δ φ), N is signal length, 0 < j≤log2N, Δ φ=π/6, Δ u=1/2, Δ v=π, 0≤
P < N ﹒ 2-j+1, 0≤q < 2j+1,0≤i≤12；
Four, based on the electrical energy power quality disturbance parameter detecting of MP algorithm, each iteration is by the residual error of signal in a manner of traversing
Inner product operation is done with all atoms in discrete Gabor dictionary, finds out the matched atoms with the maximum absolute value of residual error inner product.
A kind of duration power quality disturbances device based on match tracing sparse decomposition include: electric power signal acquisition module,
Memory module, computing module and display module, memory module are electrically connected with electric power signal acquisition module and computing module respectively,
Computing module is electrically connected with display module, and electric power signal acquisition module is for acquiring Power Disturbance Wave data and by Power Disturbance
Wave data sends memory module to, and computing module reads data from memory module and carries out operation, and computing module is by operation
Result data sends display module to, and the operation method of computing module is the electric energy matter based on match tracing sparse decomposition
Measure disturbance detecting method.
The invention has the following advantages:
1, the present invention combines inner product recursive calculation, realizes the rapid computations of MP, and devise synthesis dictionary, final to realize
The extraction of electrical energy power quality disturbance matching waveform and the quick detection of parameter.Under MATLAB platform, to containing single and compound
The decomposition experiment of the electric energy quality signal of disturbance shows as N=1024, and the inner product of each iteration of MP algorithm calculates the time and is about
65s, and using the inner product calculating time of this method is about 0.022s, calculating speed is greatly improved, and is changed using less
In generation, extracts disturbance waveform from white noise, shows good noiseproof feature.It is disturbed in combination with the power quality in the present invention
Motion detection device realizes complete set and specific duration power quality disturbances scheme.
2, the present invention establishes first waveform, the second waveform, third wave using Excel table auxiliary when establishing redundant dictionary
The set of coordinates of shape and waveform to be matched is convenient for manual matching operation, data visualization and more intuitive.
Detailed description of the invention
Fig. 1 is the flow diagram of the duration power quality disturbances method based on match tracing sparse decomposition；
Fig. 2 is the method flow schematic diagram for establishing redundant dictionary；
Fig. 3 is the flow diagram for embodying match tracing decomposition method.
Specific embodiment
The present invention is further illustrated below:
With reference to Fig. 1, Fig. 2 and Fig. 3, this duration power quality disturbances method based on match tracing sparse decomposition, including
Following steps:
S1: establishing redundant dictionary, using one group of function waveform with extensive time-frequency local characteristics as in sparse decomposition
Redundant dictionary, each waveform is as the atom a in redundant dictionaryγ(t), the method for redundant dictionary is established are as follows:
A1: amplitude direction is stirred as Y direction, using wave travel direction as X-direction, in Excel table using waveform
A column sequentially input first waveform along the corresponding X axis coordinate value in the direction of propagation, the B column in Excel table sequentially input the
One waveform constitutes the set of coordinates of first waveform along the corresponding Y axis coordinate value in amplitude direction, two neighboring X axis coordinate value it
Difference indicates the differential step size value of first waveform；
A2: the C column in Excel table sequentially input second waveform along the corresponding X axis coordinate value in the direction of propagation,
D column in Excel table sequentially input second waveform along the corresponding Y axis coordinate value in amplitude direction, constitute second waveform
Set of coordinates, the differential step size value of second waveform are equal with the differential step size value of first waveform；
A3: the E column in Excel table sequentially input third waveform along the corresponding X axis coordinate value in the direction of propagation,
F column in Excel table sequentially input third waveform along the corresponding Y axis coordinate value in amplitude direction, constitute second waveform
Set of coordinates, the differential step size value of third waveform are equal with the differential step size value of first waveform；
A4: the G column in Excel table sequentially input waveform to be matched along the corresponding X axis coordinate value in the direction of propagation,
H column in Excel table sequentially input waveform to be matched along the corresponding Y axis coordinate value in amplitude direction, constitute waveform to be matched
Set of coordinates, the difference of two neighboring X axis coordinate value indicate the differential step size value of waveform to be matched, the differential step size value of waveform to be matched
It is equal with the differential step size value of first waveform；
S2: match tracing decomposes, and defines redundant dictionary D={ aγ}γ∈Г, Г is the set a of parameter group γγ, aγIt is redundancy
Atom in dictionary D, | | aγ| |=1, decomposition method is
In B1, Dictionary of Computing with the atom a of the maximum absolute value of signal f inner productγo, meet | < f, aγo>|=sup |<f, aγ
>|(γ∈Г)；
B2, signal f is decomposed into f=< f, aγo>aγo+R1, < f, aγo>aγoFor in most matched atoms aγoOn vertical throwing
Shadow component, R1For in aγoOn approach the residue signal after f；
B3, the decomposable process that A1 and A2 is repeated to residue signal, obtain new remnants, after n times iteration, signal f=
∑<Rm, aγm>aγm+Rn, the sparse bayesian learning expression formula of m=0 → (n-1), signal f are f '=∑ < Rm, aγm>aγm, m=0 → (n-
1), the residual error R of each iterationnCalculating formula is Rn-1-<Rn-1, aγn-1>aγn-a, as the specified the number of iterations n of satisfaction or reach precision
Stop iteration after it is required that；
S3: carrying out discretization to atomic parameter, using Gabor dictionary, discretization method be γ=(s, u, v, φ)=
(2j, p2jΔ u, q2- jΔ v, i Δ φ), N is signal length, 0 < j≤log2N, Δ φ=π/6, Δ u=1/2, Δ v=π, 0≤
P < N ﹒ 2-j+1, 0≤q < 2j+1,0≤i≤12；
S4: the electrical energy power quality disturbance parameter detecting based on MP algorithm, each iteration is by the residual error of signal in a manner of traversing
Inner product operation is done with all atoms in discrete Gabor dictionary, finds out the matched atoms with the maximum absolute value of residual error inner product.
This method combination inner product recursive calculation, realizes the rapid computations of MP, and devise synthesis dictionary, finally realizes
Electrical energy power quality disturbance matches the extraction of waveform and the quick detection of parameter.Under MATLAB platform, to containing single and compound disturb
The decomposition experiment of dynamic electric energy quality signal shows as N=1024, and the inner product of each iteration of MP algorithm calculates the time and is about
65s, and using the inner product calculating time of this method is about 0.022s, calculating speed is greatly improved, and is changed using less
In generation, extracts disturbance waveform from white noise, shows good noiseproof feature.
When establishing redundant dictionary using Excel table auxiliary establish first waveform, the second waveform, third waveform and to
The set of coordinates of waveform is matched, manual matching operation, data visualization and more intuitive are convenient for, matching operation method is, will be to be matched
The coordinate value of every a line of waveform is carried out with the coordinate value in the corresponding row of first waveform, the second waveform and third waveform respectively
Ask poor, since the coordinate value of X-axis is identical, so only need to retain the difference of the coordinate value of Y-axis, the difference of the coordinate value of Y-axis according to
In cell in cell in cell in the secondary I example for being stored in Excel table, in J and in K, to I, J, K
In all numerical value calculated the variance for obtaining the data in I, J, K respectively, to judge the fluctuation journey of the data in I, J, K
Size is spent, variance is bigger, and degree of fluctuation is bigger, and matching degree is lower.
A kind of duration power quality disturbances device based on match tracing sparse decomposition include: electric power signal acquisition module,
Memory module, computing module and display module, memory module are electrically connected with electric power signal acquisition module and computing module respectively,
Computing module is electrically connected with display module, and electric power signal acquisition module is for acquiring Power Disturbance Wave data and by Power Disturbance
Wave data sends memory module to, and computing module reads data from memory module and carries out operation, and computing module is by operation
Result data sends display module to, and the operation method of computing module is the electric energy matter based on match tracing sparse decomposition
Measure disturbance detecting method.
Claims (2)
1. a kind of duration power quality disturbances method based on match tracing sparse decomposition, it is characterised in that include the following steps:
One, redundant dictionary is established, using one group of function waveform with extensive time-frequency local characteristics as the redundancy in sparse decomposition
Dictionary, each waveform is as the atom a in redundant dictionaryγ(t), the method for redundant dictionary is established are as follows:
A1, the set of coordinates for establishing first waveform: amplitude direction is stirred as Y direction, using wave travel direction as X-axis using waveform
Direction, the A column in Excel table sequentially input first waveform along the corresponding X axis coordinate value in the direction of propagation, in Excel table
B column in lattice sequentially input first waveform along the corresponding Y axis coordinate value in amplitude direction, constitute the set of coordinates of first waveform,
The difference of two neighboring X axis coordinate value indicates the differential step size value of first waveform；
A2, the set of coordinates for establishing second waveform: the C column in Excel table sequentially input second waveform along the direction of propagation
Corresponding X axis coordinate value, the D column in Excel table sequentially input second waveform along the corresponding Y axis coordinate in amplitude direction
Value, constitutes the set of coordinates of second waveform, the differential step size value of second waveform is equal with the differential step size value of first waveform；
A3, the set of coordinates for establishing third waveform: the E column in Excel table sequentially input third waveform along the direction of propagation
Corresponding X axis coordinate value, the F column in Excel table sequentially input third waveform along the corresponding Y axis coordinate in amplitude direction
Value, constitutes the set of coordinates of second waveform, the differential step size value of third waveform is equal with the differential step size value of first waveform；
A4, the set of coordinates for establishing waveform to be matched: the G column in Excel table sequentially input waveform to be matched along the direction of propagation
Corresponding X axis coordinate value, the H column in Excel table sequentially input waveform to be matched along the corresponding Y axis coordinate in amplitude direction
Value, constitutes the set of coordinates of waveform to be matched, and the difference of two neighboring X axis coordinate value indicates the differential step size value of waveform to be matched, to
The differential step size value for matching waveform is equal with the differential step size value of first waveform；
Two, match tracing decomposes, and defines redundant dictionary D={ aγ}γ∈Г, Г is the set a of parameter group γγ, aγIt is redundant dictionary D
In atom, | | aγ| |=1, decomposition method are as follows:
In B1, Dictionary of Computing with the atom a of the maximum absolute value of signal f inner productγo, meet | < f, aγo>|=sup |<f, aγ>|
(γ∈Г)；
B2, signal f is decomposed into f=< f, aγo>aγo+R1, < f, aγo>aγoFor in most matched atoms aγoOn upright projection point
Amount, R1For in aγoOn approach the residue signal after f；
B3, the decomposable process that A1 and A2 is repeated to residue signal, obtain new remnants, after n times iteration, signal f=∑ < Rm,
aγm>aγm+Rn, the sparse bayesian learning expression formula of m=0 → (n-1), signal f are f '=∑ < Rm, aγm>aγm, m=0 → (n-1), often
The residual error R of secondary iterationnCalculating formula is Rn-1-<Rn-1, aγn-1>aγn-a, as the specified the number of iterations n of satisfaction or reach required precision
After stop iteration；
Three, discretization is carried out to atomic parameter, using Gabor dictionary, discretization method is γ=(s, u, v, φ)=(2j, p2j
Δ u, q2- jΔ v, i Δ φ), N is signal length, 0 < j≤log2N, Δ φ=π/6, Δ u=1/2, Δ v=π, 0≤p < N ﹒ 2-j+1, 0≤q < 2j+1,0≤i≤12；
Four, based on the electrical energy power quality disturbance parameter detecting of MP algorithm, each iteration by the residual error of signal in a manner of traversing with from
All atoms dissipated in Gabor dictionary do inner product operation, find out the matched atoms with the maximum absolute value of residual error inner product.
2. a kind of duration power quality disturbances device based on match tracing sparse decomposition, which is characterized in that this based on matching
The duration power quality disturbances device of tracking sparse decomposition includes electric power signal acquisition module, memory module, computing module and shows
Show that module, memory module are electrically connected with electric power signal acquisition module and computing module respectively, computing module and display module electricity
Connection, electric power signal acquisition module is for acquiring Power Disturbance Wave data and sending Power Disturbance Wave data to storage mould
Block, computing module read data from memory module and carry out operation, and computing module gives operation result data transmission to display mould
Block, the operation method of computing module are the duration power quality disturbances method based on match tracing sparse decomposition.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112075932A (en) * | 2020-10-15 | 2020-12-15 | 中国医学科学院生物医学工程研究所 | High-resolution time-frequency analysis method for evoked potential signals |
| CN112597816A (en) * | 2020-12-07 | 2021-04-02 | 合肥工业大学 | Electric energy quality signal feature extraction method |
| CN114675079A (en) * | 2022-04-11 | 2022-06-28 | 东南大学溧阳研究院 | Method and system for extracting high signal-to-noise ratio voltage sag disturbance signal for reconstructing steady state waveform |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030108101A1 (en) * | 2001-11-30 | 2003-06-12 | International Business Machines Corporation | System and method for encoding three-dimensional signals using a matching pursuit algorithm |
| CN103995973A (en) * | 2014-05-27 | 2014-08-20 | 哈尔滨工业大学 | Signal sparse decomposition method based on set partitioning of over-complete dictionary |
| CN109213036A (en) * | 2018-08-28 | 2019-01-15 | 深圳智芯数据服务有限公司 | A kind of method of data collection system and the acquisition of wearable device data, modeling |
-
2019
- 2019-05-09 CN CN201910385901.XA patent/CN110245321A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030108101A1 (en) * | 2001-11-30 | 2003-06-12 | International Business Machines Corporation | System and method for encoding three-dimensional signals using a matching pursuit algorithm |
| CN103995973A (en) * | 2014-05-27 | 2014-08-20 | 哈尔滨工业大学 | Signal sparse decomposition method based on set partitioning of over-complete dictionary |
| CN109213036A (en) * | 2018-08-28 | 2019-01-15 | 深圳智芯数据服务有限公司 | A kind of method of data collection system and the acquisition of wearable device data, modeling |
Non-Patent Citations (1)
| Title |
|---|
| 陈雷，郑德忠，赵兴涛，廖文喆，李占友: "基于匹配追踪稀疏分解的电能质量扰动检测", 《仪器仪表学报》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112075932A (en) * | 2020-10-15 | 2020-12-15 | 中国医学科学院生物医学工程研究所 | High-resolution time-frequency analysis method for evoked potential signals |
| CN112075932B (en) * | 2020-10-15 | 2023-12-05 | 中国医学科学院生物医学工程研究所 | High-resolution time-frequency analysis method for evoked potential signals |
| CN112597816A (en) * | 2020-12-07 | 2021-04-02 | 合肥工业大学 | Electric energy quality signal feature extraction method |
| CN112597816B (en) * | 2020-12-07 | 2022-09-13 | 合肥工业大学 | Electric energy quality signal feature extraction method |
| CN114675079A (en) * | 2022-04-11 | 2022-06-28 | 东南大学溧阳研究院 | Method and system for extracting high signal-to-noise ratio voltage sag disturbance signal for reconstructing steady state waveform |
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