CN110380415A - A kind of mains by harmonics electric current dynamic compensation method based on wavelet transformation - Google Patents
A kind of mains by harmonics electric current dynamic compensation method based on wavelet transformation Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/01—Arrangements for reducing harmonics or ripples
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/40—Arrangements for reducing harmonics
Abstract
The present invention provides a kind of mains by harmonics electric current dynamic compensation method based on wavelet transformation, this method carries out wavelet transformation analysis to the power network current of sampling first, power network current data are decomposed using Matllat algorithm, isolate the wavelet coefficient of fundamental current in power network current；Then three wavelet coefficients that 120 ° of difference is chosen from the wavelet coefficient of fundamental current carry out operation to these three wavelet coefficients using sine wave phase feature, obtain fundamental current penalty coefficient；The wavelet coefficient of fundamental current is individually reconstructed, the fundamental current in power network current is calculated, fundamental current is compensated using fundamental current penalty coefficient, obtains high-precision real-time fundamental current；High-precision harmonic current in power grid is obtained from fundamental current is subtracted in power network current.The present invention is solved using dynamic property poor disadvantage when wavelet transformation progress harmonic current real-time detection, can be effectively improved the precision of wavelet transformation mains by harmonics real-time current detection, be improved its dynamic response performance.
Description
Technical field
The invention belongs to technical field of power systems more particularly to a kind of mains by harmonics electric current dynamics based on wavelet transformation
Compensation method.
Technical background
With society, expanding economy, more and more power electronic equipments are applied in electric system, these are non-thread
Property load extensive application serious harmonic pollution is caused to power grid so that the harmonic current in power grid increases significantly, greatly
Reduce power quality.Harmonic current in power grid brings great harm to the safety of electric system and economical operation.One side
Face, harmonic current in electric system is endangered to the production of electric energy and transmission belt, while also influencing the normal fortune of electronic equipment
Row；On the other hand, harmonic current also results in the significant wastage of electric energy, brings environmental pollution.Harmonic wave in electric system is controlled
Reason and power quality analysis, need the harmonic current in real-time detection power grid.
A kind of time-frequency combination analysis means of the wavelet transformation as variable resolution, in Harmonious Waves in Power Systems current detecting
Successful application is arrived.When being applied to real-time harmonic detection, need to carry out border extension to data, due to the power network current period
The characteristics of property, data are extended through the method frequently with period expansion.If power network current be it is stable, by power network current
Data, which carry out period expansion, can obtain preferable effect, but when power network current changes, by power network current data into
Row period expansion, which will lead to Harmonic currents detection, can generate error, and dynamic detection precision reduces, and harmonic detecting has a cycle
Lag.Therefore, the disadvantage poor for wavelet transformation harmonic detecting dynamic property, need to detect wavelet transformation real-time harmonic into
Row improves, to improve its real-time detection accuracy.
Summary of the invention
The present invention proposes a kind of mains by harmonics electric current dynamic compensation method based on wavelet transformation, and this method mainly solves to adopt
When detecting mains by harmonics electric current with wavelet transformation, when power network current changes, Harmonic currents detection precision is improved, is protected simultaneously
When card power network current does not change, detection accuracy is constant, guarantees that wavelet transformation Harmonic currents detection obtains optimum detection precision.
In order to achieve the above objectives, the technical scheme adopted by the invention is as follows: propose it is a kind of using power grid fundamental current phase believe
The method compensated to wavelet transformation Harmonic currents detection is ceased, this method differs 120 ° of feature to base using sine wave phase
Wave electric current compensates, and improves Harmonic currents detection precision.
Technical solution specific steps proposed by the present invention include:
Step 1: data sampling being carried out to power network current and obtains power network current discrete signal；
Step 2: wavelet transformation decomposition is carried out to power network current discrete signal using Mallat algorithm；
Step 3: repeating step 2 and carry out the decomposition of small echo residual coefficient, until only including fundamental current in small echo residual coefficient
Ingredient；
Step 4: from the wavelet coefficient c of fundamental currentN, kIt is middle to choose 120 ° of phase phase difference of three wavelet coefficients, and utilize
Sine wave phase feature carries out operation to these three wavelet coefficients and obtains fundamental current penalty coefficient；
Step 5: the fundamental current wavelet coefficient isolated individually being reconstructed, time domain fundamental current signal is obtained；
Step 6: dynamic in real time being carried out to time domain fundamental current signal using fundamental current penalty coefficient and is compensated, is obtained high-precision
Spend fundamental current signal；
Step 7: subtracting high-precision fundamental current signal from former power network current signal and obtain harmonic current signal；
Preferably, carrying out data sampling to power network current described in step 1 specifically:
Power network current sample frequency is fsIt is sampled, obtains power network current discrete signal xN, discrete signal xNIn include
Highest frequency is fH=fs/2；
Preferably, carrying out wavelet transformation decomposition to power network current discrete signal using Mallat algorithm described in step 2
Specifically:
By power network current discrete signal xNAs highest resolution SPACE V0In general picture c0, k, i.e. c0, k=xN, according to small echo
The Mallat algorithm of transformation is decomposed, decomposition formula are as follows:
Wherein, n is maximum decomposition level number, and k is discrete data length；J is current decomposition layer, the scale of corresponding wavelet transformation
Space；cJ-1, kFor the residual coefficient of j-1 scale space, dJ-1, kFor the wavelet coefficient of j-1 scale space, cJ, kFor j scale space
Residual coefficient, dJ, kFor the wavelet coefficient of j scale space；H (m) is the wavelet decomposition filter coefficient with low-pass nature, g
It (m) is the wavelet decomposition filter coefficient with high-pass nature.
Discrete signal cJ-1, mAfter h (m), output low frequency general picture cJ, k, after g (m), export high frequency detail dJ, k, point
Low frequency general picture c after solutionJ, kWith high frequency detail dJ, kBandwidth only has former discrete signal cJ-1, mHalf, therefore, power network current is discrete
Signal xNAfter being decomposed by above-mentioned formula, obtain comprising 0~f of low frequencyHThe residual coefficient c of/2 data1, kWith include high frequency fH/ 2~
fHThe wavelet coefficient d of data1, k；
Preferably, repeating step 2 described in step 3 carries out residual coefficient decomposition, until only wrapping in small echo residual coefficient
Ingredient containing fundamental current, specifically:
To the data c comprising power grid fundamental frequencyJ, kThe decomposition of Matllat algorithmic formula is continued through, until small echo residue system
Number cN, kIn only include fundamental current ingredient；
To power network current discrete signal xNFundamental current signal and harmonic current Signal separator can be gone out by n-layer decomposition
Come, Decomposition order n and sampling signal frequency fsBetween relationship are as follows:
Wherein, f0For fundamental frequency；
Preferably, from the wavelet coefficient c of fundamental current described in step 4N, fIt is middle to choose 120 ° of phase phase difference three small
Wave system number are as follows:
From wavelet coefficient cN, kMiddle three wavelet coefficient c for choosing 120 ° of phase phase differenceN, k1、cN, k2And cN, k3；
Operation is carried out described in step 4 and using sine wave phase feature to these three wavelet coefficients, obtains fundamental current
Penalty coefficient are as follows:
Calculating fundamental current penalty coefficient by these three data is c, and operational formula is as follows:
C=a (cN, k1+cN, k2+cN, k3)
Wherein, wherein a is adjustment factor；
cN, k1、cN, k2And cN, k3The wavelet coefficient of 120 ° of positions of sine wave phase difference is respectively represented, if their corresponding time domains
Signal is respectively x1=A1sin(ωt-120°)、x2=A2sinωt、x3=A3Sin (ω t+120 °), defined by wavelet transformation and
Property can obtain:
C=a (cN, k1+cN, k2+cN, k3)=a (< x1, φ>+<x2, φ>+<x3, φ>)
=a (< x1+x2+x3, φ >)
Wherein φ is wavelet basis, x1+x2+x3It can indicate are as follows:
x1+x2+x3=A1sin(ωt-120°)+A2sinωt+A3sin(ωt+120°)
According to the property of SIN function, by formula x1+x2+x3It is available, when power network current does not change, x1+x2+
x3=0；When power network current increases, x1+x2+x3Amplitude be greater than 0；When power network current reduces, x1+x2+x3Amplitude be less than
0；By formula c=a (< x1+x2+x3, φ >) it is found that fundamental current penalty coefficient c by x1+x2+x3It is obtained by wavelet transformation,
With x1+x2+x3Size it is corresponding, therefore c reflects x1+x2+x3The size and Orientation of variation；When power network current does not change
When, c=0；When power network current changes, c ≠ 0, symbol and size reflect the direction and size that power network current changes, because
This c contains the characteristic information of fundamental current variation, can be compensated with the fundamental current that c analyzes harmonic conversion；
Preferably, the fundamental current wavelet coefficient isolated individually is reconstructed described in step 5 specifically:
To the wavelet coefficient c for only including fundamental current signalN, kCarry out n-layer reconstruct, reconstruct number of plies n and the wavelet decomposition number of plies
It is identical, it is determined by step 3.
The signal reconstruction algorithm of time domain fundamental current described in step 5 are as follows:
To the wavelet coefficient c for only including fundamental current signalN, kN-layer reconstruct, available space are carried out by above-mentioned formula
V0Middle signal general picture c0, k, due to cN, kFor the wavelet coefficient of fundamental current signal, therefore V0General picture c in space0, kIt only include base
Wave current information, therefore time domain fundamental current signalAre as follows:
Preferably, being moved in real time using fundamental current penalty coefficient to time domain fundamental current signal described in step 6
State compensation specifically:
The time domain fundamental current signal that step 5 is calculated in the fundamental current penalty coefficient c being calculated using step 4It carries out dynamic in real time to compensate, obtains fundamental current signal x0NReal-time dynamic compensation formula are as follows:
Preferably, harmonic current signal described in step 7 are as follows:
xh=xN-x0N
Technical effect of the invention are as follows:
Above-mentioned wavelet transformation harmonic current real-time detection dynamic compensation method proposed by the present invention, by choosing phase mutual deviation
120 ° of fundamental wave wavelet coefficient obtains the feature of fundamental wave variation according to the phase feature of sine wave, and fundamental current benefit is calculated
Coefficient c is repaid, wavelet transformation real-time harmonic is detected and carries out dynamic compensation.If power network current does not change, fundamental current
Penalty coefficient c is zero, does not influence detection accuracy.When power network current changes, due to period expansion, it will lead to and detect
Harmonic current precision reduces, and at this moment fundamental current penalty coefficient c is not equal to zero, and c contains the characteristic information of fundamental current variation,
C is used to compensate Harmonic currents detection at this time, detection accuracy can be improved, obtain good detection effect.
Detailed description of the invention
Fig. 1: the method for the present invention flow chart；
Fig. 2: power network current；
Fig. 3: power network current and fundamental current；
Fig. 4: fundamental current compensates waveform；
Fig. 5: power network current and compensated fundamental current；
Fig. 6: harmonic current.
Specific embodiment
Technical solution of the present invention is clearly and completely described below in conjunction with attached drawing, it is clear that described implementation
Example is a part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, ordinary skill
Personnel's every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
As shown in Figure 1, be flow chart of the method for the present invention, specifically includes the following steps:
Step 1: data sampling being carried out to power network current and obtains power network current discrete signal, as shown in Figure 2；
Data sampling is carried out to power network current described in step 1 specifically:
Power network current sample frequency is fs=9.6kHz is sampled, and power network current discrete signal x is obtainedN, discrete signal xN
In include highest frequency be
Step 2: wavelet transformation decomposition being carried out to power network current discrete signal using Mallat algorithm, wavelet basis uses
Daubechies small echo db10；
Wavelet transformation decomposition is carried out to power network current discrete signal using Mallat algorithm described in step 2 specifically:
By power network current discrete signal xNAs highest resolution SPACE V0In general picture c0, k, i.e. c0, k=xN, according to small echo
The Mallat algorithm of transformation is decomposed, decomposition formula are as follows:
Wherein, n is maximum decomposition level number, and k is discrete data length；J is current decomposition layer, the scale of corresponding wavelet transformation
Space；cJ-1, kFor the residual coefficient of j-1 scale space, dJ-1, kFor the wavelet coefficient of j-1 scale space, cJ, kFor j scale space
Residual coefficient, dJ, kFor the wavelet coefficient of j scale space；H (m) is the wavelet decomposition filter coefficient with low-pass nature, g
It (m) is the wavelet decomposition filter coefficient with high-pass nature.
Discrete signal cJ-1, mAfter h (m), output low frequency general picture cJ, k, after g (m), export high frequency detail dJ, k, point
Low frequency general picture c after solutionJ, kWith high frequency detail dJ, kBandwidth only has former discrete signal cJ-1, mHalf, therefore, power network current is discrete
Signal xNAfter decomposing by above-mentioned formula, the residual coefficient c comprising 0~4.8kHz of low frequency data is obtained1, kWith include high frequency
The wavelet coefficient d of 4.8kHz~9.6kHz data1, k；
Step 3: repeating step 2 and carry out the decomposition of small echo residual coefficient, until only including fundamental current in small echo residual coefficient
Ingredient；
Step 2 described in step 3 carries out the decomposition of small echo residual coefficient, until only including fundamental current in small echo residual coefficient
Ingredient specifically:
To the data c comprising power grid fundamental frequencyJ, kThe decomposition of Matllat algorithmic formula is continued through, until small echo residue system
Number cN, kIn only include fundamental current ingredient；
To power network current discrete signal xNFundamental current signal and harmonic current Signal separator can be gone out by n-layer decomposition
Come, Decomposition order n are as follows:
Step 4: from the wavelet coefficient c of fundamental current6, kIt is middle to choose 120 ° of phase phase difference of three wavelet coefficients, and utilize
Sine wave phase feature carries out operation to these three wavelet coefficients and obtains fundamental current penalty coefficient；
From the wavelet coefficient c of fundamental current described in step 46, kMiddle three wavelet coefficients for choosing 120 ° of phase phase difference are as follows:
From wavelet coefficient c6, kMiddle three wavelet coefficient c for choosing 120 ° of phase phase difference6, k1、cE, k2And c6, k3；
Operation is carried out described in step 4 and using sine wave phase feature to these three wavelet coefficients, obtains fundamental current
Penalty coefficient are as follows:
Calculating fundamental current penalty coefficient by these three data is c, and operational formula is as follows:
C=a (c6, k1+c6, k2+c6, k3)
Wherein, wherein a=0.002 is adjustment factor；
c6, k1、c6, k2And c6, k3The wavelet coefficient of 120 ° of positions of sine wave phase difference is respectively represented, if their corresponding time domains
Signal is respectively x1=A1sin(ωt-120°)、x2=A2sin ωt、x3=A3Sin (ω t+120 °), is defined by wavelet transformation
It can be obtained with property:
C=a (c6, k1+c6, k2+c6, k3)=a (< x1, φ>+<x2, φ>+<x3, φ >)
=a (< x1+x2+x3, φ >)
Wherein φ is wavelet basis, x1+x2+x3It can indicate are as follows:
x1+x2+x3=A1sin(ωt-120°)+A2sinωt+A3sin(ωt+120°)
According to the property of SIN function, by formula x1+x2+x3It is available, when power network current does not change, x1+x2+
x3=0；When power network current increases, x1+x2+x3Amplitude be greater than 0；When power network current reduces, x1+x2+x3Amplitude be less than
0；By formula c=a (< x1+x2+x3, φ >) it is found that fundamental current penalty coefficient c by x1+x2+x3It is obtained by wavelet transformation,
With x1+x2+x3Size it is corresponding, therefore c reflects x1+x2+x3The size and Orientation of variation；When power network current does not change
When, c=0；When power network current changes, c ≠ 0, symbol and size reflect the direction and size that power network current changes, because
This c contains the characteristic information of fundamental current variation, can be compensated with the fundamental current that c analyzes harmonic conversion；
Step 5: to the fundamental current wavelet coefficient c isolated6, kIt is individually reconstructed, obtains time domain fundamental current signal；
The fundamental current wavelet coefficient isolated individually is reconstructed described in step 5 specifically:
To the wavelet coefficient c for only including fundamental current signal6, kCarry out n-layer reconstruct, reconstruct number of plies n=6 and wavelet decomposition layer
Number is identical, is determined by step 3.
The signal reconstruction algorithm of time domain fundamental current described in step 5 are as follows:
To the wavelet coefficient c for only including fundamental current signal6, k6 layers of reconstruct, available space are carried out by above-mentioned formula
V0Middle signal general picture c0, k, due to c6, kFor the wavelet coefficient of fundamental current signal, therefore V0General picture c in space0, kIt only include base
Wave current information, therefore time domain fundamental current signalAre as follows:
Power network current and fundamental current signalWaveform is as shown in Figure 3；
Step 6: dynamic in real time being carried out to time domain fundamental current signal using fundamental current penalty coefficient and is compensated, is obtained high-precision
Spend fundamental current signal；
It is specific to time domain fundamental current signal progress dynamic compensation in real time using fundamental current penalty coefficient described in step 6
Are as follows:
The time domain fundamental current signal that step 5 is calculated in the fundamental current penalty coefficient c being calculated using step 4It carries out dynamic in real time to compensate, obtains high-precision fundamental current signal x0N, real-time dynamic compensation formula are as follows:
Compensation current waveform is as shown in figure 4, the fundamental current obtained after compensation is as shown in Figure 5.Comparison diagram 3 and Fig. 5, can be with
See after being compensated using fundamental current penalty coefficient to fundamental current, a cycle after power network current changes,
The detection accuracy of fundamental current is greatly improved, and dynamic property is greatly improved.
Step 7: subtracting high-precision fundamental current signal from former power network current signal and obtain harmonic current signal xh, harmonic wave
Current waveform is as shown in fig. 6, Harmonics Calculation formula are as follows:
xh=xN-x0N
The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations.Although referring to aforementioned each implementation
Invention is explained in detail for example, those skilled in the art still can according to previous embodiment or attached drawing into
Other various forms of modifications of row or variation.Here can not all embodiments or technical solution be carried out with exhaustion, all hairs
Modification, replacement in bright principle etc., should be included in the present invention claims protection scope in.
Claims (8)
1. a kind of mains by harmonics electric current dynamic compensation method based on wavelet transformation, which comprises the steps of:
Step 1: data sampling being carried out to power network current and obtains power network current discrete signal；
Step 2: wavelet transformation decomposition is carried out to power network current discrete signal using Mallat algorithm；
Step 3: repeating step 2 and carry out the decomposition of small echo residual coefficient, until only including fundamental current ingredient in small echo residual coefficient；
Step 4: from the wavelet coefficient c of fundamental currentN, kMiddle three wavelet coefficients for choosing 120 ° of phase phase difference, and utilize sine
Wave phase feature carries out operation to these three wavelet coefficients and obtains fundamental current penalty coefficient；
Step 5: the fundamental current wavelet coefficient isolated individually being reconstructed, time domain fundamental current signal is obtained；
Step 6: dynamic in real time being carried out to time domain fundamental current signal using fundamental current penalty coefficient and is compensated, high-precision base is obtained
Signal wave current；
Step 7: subtracting high-precision fundamental current signal from former power network current signal and obtain harmonic current signal.
2. the mains by harmonics electric current dynamic compensation method according to claim 1 based on wavelet transformation, it is characterised in that: step
Data sampling is carried out to power network current described in rapid 1 specifically:
Power network current sample frequency is fsIt is sampled, obtains power network current discrete signal xN, discrete signal xNIn include highest
Frequency is fH=fs/2。
3. the mains by harmonics electric current dynamic compensation method according to claim 1 based on wavelet transformation, which is characterized in that step
Wavelet transformation decomposition is carried out to power network current discrete signal using Mallat algorithm described in rapid 2 specifically:
By power network current discrete signal xNAs highest resolution SPACE V0In general picture c0, k, i.e. c0, k=xN, according to wavelet transformation
Mallat algorithm decomposed, decomposition formula are as follows:
Wherein, n is maximum decomposition level number, and k is discrete data length；J is current decomposition layer, and the scale of corresponding wavelet transformation is empty
Between；cJ-1, kFor the residual coefficient of j-1 scale space, dJ-1, kFor the wavelet coefficient of j-1 scale space, cJ, kFor j scale space
Residual coefficient, dJ, kFor the wavelet coefficient of j scale space；H (m) is the wavelet decomposition filter coefficient with low-pass nature, g (m)
For the wavelet decomposition filter coefficient with high-pass nature；
Discrete signal cJ-1, mAfter h (m), output low frequency general picture cJ, k, after g (m), export high frequency detail dJ, k, after decomposition
Low frequency general picture cJ, kWith high frequency detail dJ, kBandwidth only has former discrete signal cJ-1, mHalf, therefore, power network current discrete signal
xNAfter being decomposed by above-mentioned formula, obtain comprising 0~f of low frequencyHThe residual coefficient c of/2 data1, kWith include high frequency fH/ 2~fHNumber
According to wavelet coefficient d1, k。
4. the mains by harmonics electric current dynamic compensation method according to claim 1 based on wavelet transformation, it is characterised in that: step
Step 2 is repeated described in rapid 3 and carries out residual coefficient decomposition, until only including fundamental current ingredient in small echo residual coefficient, specifically
Are as follows:
To the data c comprising power grid fundamental frequencyJ, kThe decomposition of Matllat algorithmic formula is continued through, until small echo residual coefficient
cN, kIn only include fundamental current ingredient；
To power network current discrete signal xNFundamental current signal and harmonic current Signal separator can be come out by n-layer decomposition, point
Solve number of plies n and sampling signal frequency fsBetween relationship are as follows:
Wherein, f0For fundamental frequency.
5. the mains by harmonics electric current dynamic compensation method according to claim 1 based on wavelet transformation, it is characterised in that: step
From the wavelet coefficient c of fundamental current described in rapid 4N, kMiddle three wavelet coefficients for choosing 120 ° of phase phase difference are as follows:
From wavelet coefficient cN, kMiddle three wavelet coefficient c for choosing 120 ° of phase phase differenceN, k1、cN, k2And cN, k3；
Operation is carried out described in step 4 and using sine wave phase feature to these three wavelet coefficients, obtains fundamental current compensation
Coefficient are as follows:
Calculating fundamental current penalty coefficient by these three data is c, and operational formula is as follows:
C=a (cN, k1+cN, k2+cN, k3)
Wherein, wherein a is adjustment factor；
cN, k1、cN, k2And cN, k3The wavelet coefficient of 120 ° of positions of sine wave phase difference is respectively represented, if their corresponding time-domain signals
Respectively x1=A1sin(ωt-120°)、x2=A2sinωt、x3=A3Sin (ω t+120 °), is defined and property by wavelet transformation
It can obtain:
C=a (cN, k1+cN, k2+cN, k3)=a (< x1, φ>+<x2, φ>+<x3, φ >)
=a (< x1+x2+x3, φ >)
Wherein φ is wavelet basis, x1+x2+x3It can indicate are as follows:
x1+x2+x3=A1sin(ωt-120°)+A2sinωt+A3sin(ωt+120°)
According to the property of SIN function, by formula x1+x2+x3It is available, when power network current does not change, x1+x2+x3=
0；When power network current increases, x1+x2+x3Amplitude be greater than 0；When power network current reduces, x1+x2+x3Amplitude less than 0；By
Formula c=a (< x1+x2+x3, φ >) it is found that fundamental current penalty coefficient c by x1+x2+x3It is obtained by wavelet transformation, with x1+
x2+x3Size it is corresponding, therefore c reflects x1+x2+x3The size and Orientation of variation；When power network current does not change, c
=0；When power network current changes, c ≠ 0, symbol and size reflect the direction and size that power network current changes, therefore c
Characteristic information containing fundamental current variation, can be compensated with the fundamental current that c analyzes harmonic conversion.
6. the mains by harmonics electric current dynamic compensation method according to claim 1 based on wavelet transformation, it is characterised in that: step
The fundamental current wavelet coefficient isolated individually is reconstructed described in rapid 5 specifically:
To the wavelet coefficient c for only including fundamental current signalN, kN-layer reconstruct is carried out, reconstruct number of plies n is identical as the wavelet decomposition number of plies,
It is determined by step 3；
The signal reconstruction algorithm of time domain fundamental current described in step 5 are as follows:
To the wavelet coefficient c for only including fundamental current signalN, kN-layer reconstruct, available SPACE V are carried out by above-mentioned formula0In
Signal general picture c0, k, due to cN, kFor the wavelet coefficient of fundamental current signal, therefore V0General picture C in space0, kOnly comprising fundamental wave electricity
Stream information, therefore time domain fundamental current signalAre as follows:
7. the mains by harmonics electric current dynamic compensation method according to claim 1 based on wavelet transformation, it is characterised in that: step
Dynamic in real time is carried out to time domain fundamental current signal using fundamental current penalty coefficient described in rapid 6 to compensate specifically:
The time domain fundamental current signal that step 5 is calculated in the fundamental current penalty coefficient c being calculated using step 4
It carries out dynamic in real time to compensate, obtains fundamental current signal x0NReal-time dynamic compensation formula are as follows:
8. the mains by harmonics electric current dynamic compensation method according to claim 1 based on wavelet transformation, it is characterised in that: step
Harmonic current signal described in rapid 7 are as follows:
xh=xN-x0N。
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| CN104880592A (en) * | 2015-06-24 | 2015-09-02 | 国网浙江省电力公司电力科学研究院 | Electric energy calculating method and system under harmonic wave condition |
| CN109212310A (en) * | 2017-06-30 | 2019-01-15 | 南京理工大学 | A kind of micro-capacitance sensor harmonic detecting method |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112116827A (en) * | 2020-09-18 | 2020-12-22 | 广州言几方科技有限公司 | Intelligent display screen for parking space information of large parking garage |
| CN112116827B (en) * | 2020-09-18 | 2021-07-30 | 广州言几方科技有限公司 | Intelligent display screen for parking space information of large parking garage |
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