CN108318761A - Wind power generating set power quality detection method based on compressed sensing - Google Patents
Wind power generating set power quality detection method based on compressed sensing Download PDFInfo
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Abstract
The invention discloses the wind power generating set power quality detection methods based on compressed sensing, are related to intelligent grid field.Voltage signal under 1, acquisition wind power generating set working condition that the method comprising the steps of；The voltage signal is obtained sparse signal by step 2 by rarefaction representation；The sparse signal is carried out compression sampling by step 3 using sparse random measurement matrix, obtains the signal after compression sampling；Signal after the compression sampling is reconstructed step 4 using OMP algorithms；Step 5, the Short Term Flicker severity for calculating reconstruction signal, and detect reconstruction signal flicker envelope.Method through the invention is by compressive sensing theory, it is applied to wind power generating set power quality sample detecting field, breach traditional nyquist sampling theorem, sampling and compression are carried out at the same time, sampled data output can be greatly reduced, shorten the sampling time, reduces the requirement to wind power generating set electric energy quality signal sample detecting hardware system, stored and transmitted convenient for data.
Description
Technical field
The present invention relates to intelligent grid field, the wind power generating set power quality based on compressed sensing is more particularly related to
Detection method.
Background technology
Traditional power quality data acquisition analysis system is realized based on nyquist sampling theorem, which includes
Signal acquisition-compression-storage/transmission-signal reconstruction-analyzing processing, to undistorted recovery original signal, sampling frequency
Rate must be twice of original signal highest frequency or more than.
Power quality sampling processing system based on nyquist sampling theorem is primarily present two aspect disadvantages, is on the one hand
Meet Nyquist sampling thheorems, it is desirable that sample devices must keep higher sample rate, and then increase hardware device at
This；On the other hand, in order to store and calculate collected a large amount of electric energy quality signal data, sizable memory space is needed
And computing capability, the pressure for increasing data storage and calculating.
Therefore, there are data collection capacity is big and computation complexity for the existing method to wind power generating set Power Quality Detection
Height leads to the problem high to requirements for hardware.
Invention content
The embodiment of the present invention provides the wind power generating set power quality detection method based on compressed sensing, existing to solve
There is skill data during operation collection capacity big and computation complexity height, leads to the problem high to requirements for hardware.
The embodiment of the present invention provides the wind power generating set power quality detection method based on compressed sensing, including：Step
1, the voltage flicker signal under wind power generating set working condition is acquired；
The voltage flicker signal is obtained sparse signal by step 2 by rarefaction representation；
The sparse signal is carried out compression sampling by step 3 using sparse random measurement matrix, after obtaining compression sampling
Signal；
Signal after the compression sampling is reconstructed step 4 using OMP algorithms；
Step 5, the Short Term Flicker severity for calculating reconstruction signal, and detect reconstruction signal flicker envelope.
Preferably, the voltage flicker signal under the wind power generating set working condition is：
Wherein, in formula (1)：N is the sum of flickering signal, and A is the amplitude of voltage, miIt is the tune of i-th flicker envelope
System, fiIt is the frequency of i-th flicker envelope,It is the phase angle of i-th flicker envelope.
The voltage flicker signal is obtained into sparse signal by rarefaction representation include preferably, described：Select Fourier
The voltage flicker signal is carried out rarefaction representation by transformation as sparse basis array.
Preferably, the sparse signal is carried out compression sampling using sparse random measurement matrix, after obtaining compression sampling
Signal include：
By it is described use sparse random measurement Matrix Multiplication to obtain with the sparse signal compression sampling after signal.
Preferably, the Short Term Flicker severity for calculating reconstruction signal, including：Weight is calculated using following formula (2)
The Short Term Flicker severity of structure signal：
Wherein, in formula (2), P0.1、P1、P3、P10、P50Five parameters indicate instantaneous flicker visual sense within ten minutes respectively
Degree S (t) perceives unit value more than 0.1%, 1%, 3%, 10%, 50% time.
Preferably, the detection reconstruction signal flicker envelope includes：
The instantaneous envelope and instantaneous frequency of reconstruction signal are extracted using Teager energy operators；
Wherein, the extraction of the instantaneous envelope of reconstruction signal uses following formula for (3)；The instantaneous frequency of reconstruction signal carries
It takes and uses following formula for (4) and (5)；
Wherein, in formula (3), a (n) is instantaneous envelope, and y (n) is reconstruction signal, y1 (n)=y (n)-y (n-1), ψd[y
(n)] energy operator of reconstruction signal y (n) is indicated；
Instantaneous frequency calculation formula is：
Wherein, in formula (4), ω (n) is instantaneous angular frequency；
Wherein, in formula (5), f (n) is instantaneous frequency.
By by compressive sensing theory, being applied to wind power generating set power quality sample detecting neck in the embodiment of the present invention
Domain breaches traditional nyquist sampling theorem, and sampling and compression are carried out at the same time, sampled data output can be greatly reduced, and contracts
The short sampling time reduces the requirement to wind power generating set electric energy quality signal sample detecting hardware system, is stored convenient for data
And transmission；And the sparse random measurement matrix used adopts wind power generating set electric energy quality signal observation compression processing effect ratio
The computation complexity for making great efforts matrix with gaussian random matrix and shellfish is low, and is easily achieved, and can also reduce to wind power generating set electricity
The requirement of energy quality signal sample detection hardware system.
Description of the drawings
Fig. 1 is the stream of the wind power generating set power quality detection method provided in an embodiment of the present invention based on compressed sensing
Journey schematic diagram；
Fig. 2 is that classical signal provided in an embodiment of the present invention obtains and processing procedure and the signal based on compressive sensing theory
It obtains and processing procedure comparison diagram；
Fig. 3 is voltage flicker signal waveforms provided in an embodiment of the present invention；
Fig. 4 is voltage flicker signal spectrum figure provided in an embodiment of the present invention；
Fig. 5 is voltage flicker signal provided in an embodiment of the present invention and reconstruction signal comparison diagram；
Fig. 6 is that voltage flicker signal transient flickering provided in an embodiment of the present invention regards sensitivity map；
Fig. 7 is the instantaneous vermicularizing alloy figure of reconstruction signal provided in an embodiment of the present invention；
Fig. 8 is the waveform and detection error result figure that flicker envelope provided in an embodiment of the present invention changes over time.
Specific implementation mode
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation describes, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
The flow of wind power generating set power quality detection method provided in an embodiment of the present invention based on compressed sensing is shown
It is intended to, as shown in Figure 1, this method includes：
Voltage flicker signal under step 1, acquisition wind power generating set working condition.
Wherein, the voltage flicker signal under the wind power generating set working condition is：
In formula：N is the sum of flickering signal, and A is the amplitude of voltage, miIt is the modulation degree of i-th flicker envelope, fiIt is i-th
The frequency of item flicker envelope,It is the phase angle of i-th flicker envelope.
The voltage flicker signal is obtained sparse signal by step 2 by rarefaction representation.
The premise of applied compression perception theory be signal itself with sparsity or in some transform domain with sparse
Property, that is, assume signal f ∈ RN×1Length be N, signal itself or contain only K nonzero value person after certain transformation it be exhausted
Remaining numerical value, and K are far longer than to value<<N, then one can consider that signal f is sparse signal, its degree of rarefication is K.It is natural
In boundary, most of signals itself are not proper sparse signals, but are sparse signal, wind-power electricity generation in certain domains
Unit electric energy quality signal is also such.In order to meet the precondition, wind power generating set electric energy quality signal need to be carried out
Rarefaction representation, the sparse basis array ψ ∈ R used hereinN×NIt is Fourier transformation, sparse vector is x ∈ RN×1, i.e.,：
F=ψ x, (2)
The sparse signal is carried out compression sampling by step 3 using sparse random measurement matrix, obtains the letter after compression sampling
Number.
Wherein, sparse random measurement matrix φ realizes that letter meets RIP criterion, and realization sparse signal is from higher-dimension to low-dimensional
Compression is realized in projection, and ensure that original signal information important in reduction process is not lost.Signal is projected to one and
The incoherent calculation matrix φ ∈ R of sparse transformation base ψ heightM×NOn, obtain the observation signal u that length is M.The process mathematical table
It is up to formula：
In formulaReferred to as perceive matrix or sensing matrix.
Calculation matrix used herein is sparse random measurement matrix, and more other calculation matrix are compared, in practical applications,
Sparse random measurement matrix is easy to implement and preserves, by simulation results show, for wind power generating set electric energy quality signal,
Its quality reconstruction is got well than widely used gaussian random calculation matrix and shellfish make great efforts matrix reconstruction effect.
Signal after the compression sampling is reconstructed step 4 using OMP algorithms.
Wherein, the process of reconstruct is considered as decompression process, i.e., is decompressed from the signal data of low-dimensional with restructing algorithm
Contract the high dimensional signal of script.The restructing algorithm applied at present mainly has two major classes：Greedy algorithm, convex optimized algorithm.It is greedy
Algorithm includes mainly：The various improvement based on match tracing such as matching pursuit algorithm (MP), orthogonal matching pursuit algorithm (OMP) are calculated
Method.Convex optimized algorithm includes：Base tracks (BP), interior point method, GRADIENT PROJECTION METHODS (GPSR) etc..OMP algorithms are used to realize herein
The reconstruct of signal, in contrast calculation amount is smaller for the algorithm, and speed is fast, and reconstruction signal is efficient.
Step 5, the Short Term Flicker severity for calculating reconstruction signal, and detect reconstruction signal flicker envelope.
Wherein, the Short Term Flicker severity of the calculating reconstruction signal, including：Reconstruct letter is calculated using following formula (2)
Number Short Term Flicker severity：
Wherein, in formula (2), P0.1、P1、P3、P10、P50Five parameters indicate instantaneous flicker visual sense within ten minutes respectively
Degree S (t) perceives unit value more than 0.1%, 1%, 3%, 10%, 50% time.
In addition, the P being calculatedstThe P being calculated with original signalstAnd PstTheoretical value is compared, and error is examined to be
It is no to be less than 5%, then coincidence measurement standard in the range of standard sequential, illustrate compressive sensing theory being applied to wind-force hair
Motor group power quality fields of measurement is feasible.
Furthermore the detection reconstruction signal flicker envelope includes：The wink of reconstruction signal is extracted using Teager energy operators
When envelope and instantaneous frequency；
Wherein, the extraction of the instantaneous envelope of reconstruction signal uses following formula for (5)；The instantaneous frequency of reconstruction signal carries
It takes and uses following formula for (6) and (7)；
Wherein, in formula (5), a (n) is instantaneous envelope, and y (n) is reconstruction signal, y1 (n)=y (n)-y (n-1), ψd[y
(n)] energy operator of reconstruction signal y (n) is indicated.
Instantaneous frequency calculation formula is：
Wherein, in formula (6), ω (n) is instantaneous angular frequency.
Wherein, in formula (7), f (n) is instantaneous frequency.
In the embodiment of the present invention, by classical signal obtain and processing procedure and signal acquisition based on compressive sensing theory and
Shown in processing procedure comparison diagram 2, data sampling and two processes of compression are combined into one by method of the invention from Fig. 2, and reduction is adopted
Sample data volume shortens the sampling time, reduces the requirement to wind power generating set electric energy quality signal sample detecting hardware system, just
It is stored and transmitted in data, while the efficiency of signal processing can be improved, reduce the cost of signal processing.Effectively compensate for traditional electricity
Deficiency in energy quality signal acquisition processing system.
In the embodiment of the present invention, by taking single-frequency voltage flicker signal as an example, applied compression perception theory carries out compression and adopts
Sample and reconstruct, and calculate Short Term Flicker severity：
The expression formula of single-frequency voltage flicker signal is：
In above formula, work frequency carrier amplitude is takenω0=2 π f0, take work frequency carrier frequency f0=50Hz takes just
Phase angleTake the modulation degree m of flicker envelopei=0.125, take the frequency f of flicker envelopei=8.8Hz, takes flicker envelope
Phase angleIt obtains being illustrated in fig. 3 shown below waveform.
According to the definition of sparse signal and oscillogram it is found that normal voltage signal is not sparse signal in time domain,
In order to meet the precondition of applied compression perception theory, it is necessary to by transform domain so that signal meets sparsity, voltage be dodged
Varying signal carries out Fourier transformation, and the frequency-domain waveform of voltage flicker signal is as shown in Figure 4.
Be apparent from from Fig. 4, voltage flicker signal is sparse signal in frequency domain, select as a result, Fourier transformation as
Wind power generating set electric energy quality signal is carried out rarefaction representation by sparse basis array.
In the embodiment of the present invention, voltage flicker signal behavior work frequency carrier amplitude isWork frequency carrier frequency is
50Hz, the sine wave that initial phase angle is 0 are used as carrier signal, and the modulation degree of flicker envelope is 0.125, modulating frequency 8.8Hz, just
Phase angle is 0, sample frequency 800Hz, sampling time 10s, it is known that actual samples points are 8000.This emulates selection
Observation number is 64, selects Fourier transformation as sparse basis array, by making great efforts square to gaussian random calculation matrix, shellfish
Battle array and the research of sparse random measurement matrix, analysis are dilute by simulation results show using three kinds of different calculation matrix observation signals
It dredges random measurement matrix and is directed to wind power generating set electric energy quality signal, quality reconstruction is measured than widely used gaussian random
Matrix and shellfish, which make great efforts matrix reconstruction effect, to get well.
Finally the sparse random measurement matrix of application realizes projection of the sparse signal from higher-dimension to low-dimensional as calculation matrix, most
The reconstruct of normal voltage signal is realized by OMP algorithms afterwards.Quality reconstruction is as shown in Figure 5.
Wherein, in order to avoid the randomness of experimental result and contingency, to different calculation matrix emulation experiment 20 times, meter
The average value of the root-mean-square error of reconstruction signal is calculated, result of calculation is as shown in table 1, it is seen then that sparse random measurement matrix effect is most
It is good afterwards.Can research directly be further analyzed to the signal of reconstruct, interference will not be brought.
The reconstructed error of the different calculation matrix of the application of table 1
Primary voltage flickering signal and the sparse random matrix of application are calculated as measurement square using the method that IEC standard is recommended
The Short Term Flicker severity of battle array and the voltage flicker signal of reconstruct, verifies whether to comply with standard regulation.
It is 0.125 by flicker envelope modulation degree is calculated, modulating frequency 8.8Hz, the voltage flicker that initial phase angle is 0
The Short Term Flicker severity P of signal and the voltage flicker signal of applied compression perception theory sample reconstructionstIt is 0.7241, reason
It is 0.714 by value, error 1.4201% is less than 5% specified in standard, meets the requirements.
If being sampled using nyquist sampling theorem, because the highest frequency of original signal is 50Hz, sampling
Rate is at least 100Hz, then the required sampling number of nyquist sampling theorem is 1000, and obtained herein by observation
Signal reconstruction can be realized in 64 points.
By comparison it is found that applied compression perception theory may be implemented in sampling number less than nyquist sampling points
In the case of accurate reconstruction signal, select Fourier transformation as sparse basis array, signal realized with sparse random measurement matrix
Higher-dimension and by OMP algorithm reconstruction signals, works well to the projection of low-dimensional, by verification, meets IEC standard.
Fig. 6 is that voltage flicker signal transient flickering provided in an embodiment of the present invention regards sensitivity map；Fig. 7 is the embodiment of the present invention
The instantaneous vermicularizing alloy figure of the reconstruction signal of offer；Voltage flicker signal and reconstruction signal instantaneous vermicularizing alloy data are protected
It deposits, then calls the statistics program finished in advance according to formula (4), it is 0.125 that flicker envelope modulation degree, which is calculated, modulation frequency
Rate is 8.8Hz, the voltage flicker signal of voltage flicker signal and applied compression perception theory sample reconstruction that initial phase angle is 0 it is short
When flickering severity PstIt is 0.7241, theoretical value 0.714, error 1.4201% is less than specified in standard
5%, it meets the requirements.
If being sampled using nyquist sampling theorem, because the highest frequency of original signal is 50Hz, sampling
Rate is at least 100Hz, then the required sampling number of nyquist sampling theorem is 1000, and obtained herein by observation
Signal reconstruction can be realized in 64 points.
By comparison it is found that applied compression perception theory may be implemented in sampling number less than nyquist sampling points
In the case of accurate reconstruction signal, select Fourier transformation as sparse basis array, signal realized with sparse random measurement matrix
Higher-dimension and by OMP algorithm reconstruction signals, works well to the projection of low-dimensional, by verification, meets IEC standard.
Application Teager energy operators of the embodiment of the present invention detect reconstruction signal flicker envelope
According to IEC61000-4-15 standard setting flickering voltage parameters, single-frequency All Time voltage flicker letter is added
Number expression formula is：
Flickering signal is added in normal voltage All Time section, the modulation degree of flickering voltage envelope is 0.125, flicker envelope
Frequency be 8.8Hz, the initial phase angle of flicker envelope is 0.Flickering signal waveforms are as shown in Figure 3：
Reconstruction signal is detected using Teager energy operators, can obtain oscillogram that flicker envelope changes over time and
Detection error is as a result, as shown in Figure 8：
It can obviously obtain from the graph, flicker envelope detection error is minimum, works well, and changes with time and presets
Value is consistent.Teager energy operators can accurately realize the detection of reconstruction signal.
In conclusion compressive sensing theory is suitable for wind power generating set electric energy quality signal detection field.
By by compressive sensing theory, being applied to wind power generating set power quality sample detecting neck in the embodiment of the present invention
Domain breaches traditional nyquist sampling theorem, and sampling and compression are carried out at the same time, sampled data output can be greatly reduced, and contracts
The short sampling time reduces the requirement to wind power generating set electric energy quality signal sample detecting hardware system, is stored convenient for data
And transmission；And the sparse random measurement matrix used adopts wind power generating set electric energy quality signal observation compression processing effect ratio
The computation complexity for making great efforts matrix with gaussian random matrix and shellfish is low, and is easily achieved, and can also reduce to wind power generating set electricity
The requirement of energy quality signal sample detection hardware system.
Disclosed above is only several specific embodiments of the present invention, and those skilled in the art can carry out the present invention
Various modification and variations without departing from the spirit and scope of the present invention, if these modifications and changes of the present invention belong to the present invention
Within the scope of claim and its equivalent technologies, then the present invention is also intended to include these modifications and variations.
Claims (6)
1. the wind power generating set power quality detection method based on compressed sensing, which is characterized in that including：
Voltage flicker signal under step 1, acquisition wind power generating set working condition；
The voltage flicker signal is obtained sparse signal by step 2 by rarefaction representation；
The sparse signal is carried out compression sampling by step 3 using sparse random measurement matrix, obtains the letter after compression sampling
Number；
Signal after the compression sampling is reconstructed step 4 using OMP algorithms；
Step 5, the Short Term Flicker severity for calculating reconstruction signal, and detect reconstruction signal flicker envelope.
2. the wind power generating set power quality detection method according to claim 1 based on compressed sensing, feature exist
In the voltage flicker signal under the wind power generating set working condition is：
Wherein, in formula (1)：N is the sum of flickering signal, and A is the amplitude of voltage, miIt is the modulation degree of i-th flicker envelope,
fiIt is the frequency of i-th flicker envelope,It is the phase angle of i-th flicker envelope.
3. the wind power generating set power quality detection method according to claim 1 based on compressed sensing, feature exist
In described the voltage flicker signal is obtained sparse signal by rarefaction representation to include：Select Fourier transformation as sparse
The voltage flicker signal is carried out rarefaction representation by basic matrix.
4. the wind power generating set power quality detection method according to claim 1 based on compressed sensing, feature exist
In by the sparse signal using sparse random measurement matrix progress compression sampling, obtaining the signal after compression sampling includes：
By it is described use sparse random measurement Matrix Multiplication to obtain with the sparse signal compression sampling after signal.
5. the wind power generating set power quality detection method according to claim 1 based on compressed sensing, feature exist
In, the Short Term Flicker severity for calculating reconstruction signal, including：Reconstruction signal is calculated in short-term using following formula (2)
Flickering severity：
Wherein, in formula (2), P0.1、P1、P3、P10、P50Five parameters indicate instantaneous vermicularizing alloy S within ten minutes respectively
(t) perceive unit value more than 0.1%, 1%, 3%, 10%, 50% time.
6. the wind power generating set power quality detection method according to claim 1 based on compressed sensing, feature exist
In the detection reconstruction signal flicker envelope includes：
The instantaneous envelope and instantaneous frequency of reconstruction signal are extracted using Teager energy operators；
Wherein, the extraction of the instantaneous envelope of reconstruction signal uses following formula for (3)；The extraction of the instantaneous frequency of reconstruction signal is adopted
It is (4) and (5) with following formula；
Wherein, in formula (3), a (n) is instantaneous envelope, and y (n) is reconstruction signal, y1 (n)=y (n)-y (n-1), ψd[y (n)] table
Show the energy operator of reconstruction signal y (n)；
Instantaneous frequency calculation formula is：
Wherein, in formula (4), ω (n) is instantaneous angular frequency；
Wherein, in formula (5), f (n) is instantaneous frequency.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111582137A (en) * | 2020-04-30 | 2020-08-25 | 燕山大学 | Rolling bearing signal reconstruction method and system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102955068A (en) * | 2012-09-28 | 2013-03-06 | 江苏大学 | Harmonic detection method based on compressive sampling orthogonal matching pursuit |
| 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 |
| CN107561416A (en) * | 2017-07-03 | 2018-01-09 | 国家电网公司 | A kind of local discharge signal acquisition system and method based on compressed sensing |
-
2018
- 2018-02-06 CN CN201810116685.4A patent/CN108318761A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102955068A (en) * | 2012-09-28 | 2013-03-06 | 江苏大学 | Harmonic detection method based on compressive sampling orthogonal matching pursuit |
| 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 |
| CN107561416A (en) * | 2017-07-03 | 2018-01-09 | 国家电网公司 | A kind of local discharge signal acquisition system and method based on compressed sensing |
Non-Patent Citations (5)
| Title |
|---|
| 安海清: "电压波动与闪变的检测及分析", 《中国优秀硕士学位论文全文数据库工程科技II辑》 * |
| 梁社潮: "基于压缩传感理论的电能质量数据压缩的研究", 《中国优秀硕士学位论文全文数据库工程科技II辑》 * |
| 沈跃: "基于压缩感知理论的电力系统数据检测与压缩方法研究", 《中国博士学位论文全文数据库工程科技II辑》 * |
| 王大为: "数据压缩方法研究及其在电力系统中的应用", 《中国优秀硕士学位论文全文数据库工程科技II辑》 * |
| 王旭 等: "基于FFT和HHT的Kaiser窗校正的风力发电机组电压闪变测量", 《电子测量与仪器学报》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111582137A (en) * | 2020-04-30 | 2020-08-25 | 燕山大学 | Rolling bearing signal reconstruction method and system |
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Application publication date: 20180724 |