CN103034757B - Wind energy turbine set time-frequency domain modeling method based on empirical mode decomposition - Google Patents
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Abstract
A kind of wind energy turbine set time-frequency domain modeling method based on empirical mode decomposition, described method is：The wind power measured data obtaining is normalized after Filtering Processing, carry out EMD and be processed as some intrinsic mode function IMF, and carry out Hilbert transform respectively and generate its corresponding time-frequency spectrum, respectively these time-frequency spectrum are fitted again, and by fitting result respectively by anti-Hilbert transform, obtain time-domain expression, these time-domain expressions are summed up, that is, obtain the wind energy turbine set analytical model comprising various fluctuation characteristics.By time-frequency domain modeling means, the analytical model set up out includes the typical wave characteristic information of wind energy turbine set to modeling method of the present invention, and this model is used for simulating the impact analysis to line voltage, frequency for the wind energy turbine set fluctuation with non-stationary randomness.
Description
Technical field
The present invention relates to wind farm grid-connected impact analysis technical field is and in particular to comprise the wind energy turbine set reliable model of fluctuation information
Set up.
Background technology
Empirical mode decomposition (Empirical Mode Decomposition, i.e. EMD) is HHT (Hilbert-Huang
Transform ingredient), is " the The being delivered in 1998 by Norden E.Huang and Steven R.Long et al.
Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary
A kind of method for processing non-stationary signal proposing in Time Series Analysis ".The effect of EMD is to believe non-stationary
Number it is decomposed into some intrinsic mode function IMF (intrinsic Mode Function) meeting Hilbert transform, to these IMF
Carry out Hilbert transform, the distribution situation of signal time-frequency spectrum can be obtained.EMD has complete adaptivity, can be according to tool
Body signal carrys out self adaptation and produces " base ", and, it is not necessary to default basic function, this is also exactly for the IMF being produced by " screening " process
The advantage of EMD is located.
Wind farm grid-connected impact analysis is that following wind energy turbine set realizes the grid-connected necessary work of extensive Thief zone.Given this grid-connected impact
Analysis mainly relies on simulation study, therefore setting up reliable farm model is prerequisite, and this requires farm model must be able to
Enough comprise the power swing characteristic that wind energy turbine set carries non-stationary randomness.And wind energy turbine set modeling approach is mainly based upon equivalent wind at present
The wind energy turbine set equivalent model of speed, its basic ideas is Lai an equivalent wind energy turbine set with separate unit or several Fans, and blower fan is defeated
Enter wind speed then to be substituted with equivalent wind speed.Comprise two key issues among these, one is the computational methods of equivalent wind speed, another
It is the correction of fan parameter.Due to the complexity of wind energy turbine set self structure, the exact physical model comprising structural information is difficult to
Set up, this Equivalent Modeling thinking provides a kind of shortcut.However, due to the presence of the internal inertia of wind energy turbine set and smoothing effect,
It is difficult to find that accurate corresponding relation between input wind speed and output, lead to the equivalent model can not reproducing output
Wave characteristic.This is extremely disadvantageous for wind farm grid-connected impact analysis.
Content of the invention
The purpose of the present invention be set up comprise output-power fluctuation characteristic information farm model, for wind farm grid-connected lower to electricity
The impact analysis of net.
The present invention adopts Hilbert transform method, to having interval intrinsic of different frequency after empirical mode decomposition (EMD)
Mode function IMF signal carries out Time-frequency Spectrum Analysis, simulates the less smooth curve of residual error, and obtains via anti-Hilbert transform
To time-domain expression, i.e. wind energy turbine set analytical model.
The wind energy turbine set time-frequency domain modeling method Ji Yu empirical mode decomposition (EMD) for the present invention comprises the following steps that：
1. gather output data p of every unit in wind energy turbine setwind_i(t), by the wind energy turbine set collecting every unit defeated
Go out power pwind_iT () sums up, try to achieve wind energy turbine set gross output pwindf(t), wind energy turbine set gross output pwindfT () is wind-powered electricity generation
Every unit blower fan output p inwind_i(t) sum, that is,Wherein t is the time.
To wind energy turbine set gross output pwindfT () is normalized, choose wind energy turbine set total rated output power peT () is as base
Value.
Data after normalization is carried out with Fourier transformation, generates frequency spectrum, observe spectral characteristic, estimate the data after normalization
Frequency distribution, and be filtered by lowpass digital filter, filter off the larger noise spot of error, the data obtaining is designated as p (t),
P (t) is carried out following process as Power Output for Wind Power Field data.
2. pair Power Output for Wind Power Field p (t) carries out empirical mode decomposition (EMD) process, and p (t) is decomposed into n intrinsic mode
Function c1(t),c2(t),...,cn(t) and residual components rn(t), that is,：
C in formula1(t),c2(t),...,cnT fluctuation that () may be considered the filtered data of Power Output for Wind Power Field normalization is special
Levy function, rnT () may be considered the trend function of the filtered data of Power Output for Wind Power Field normalization.
3. respectively to intrinsic mode function c1(t),c2(t),...,cnT () does Hilbert transform, generate time-frequency spectrum, concretely comprise the following steps：
C in formulanT () is cnThe Hilloert function of (t)
Its contravariant is changed to：
Obtain analytic signal：
Z (t)=cn(t)+iCn(t)=a (t) eiθ(t)(4)
In formula, a (t) is instantaneous amplitude,
θ (t) is phase place,
Instantaneous frequency f (t) is calculated as follows：
Process intrinsic mode function c by above-mentioned steps successively1(t),c2(t),...,cn(t), you can respectively obtain described intrinsic mode
Time-frequency spectrum f of function1(t),f2(t),...,fn(t).
4. respectively to f1(t),f2(t),...,fnT () is fitted, that is, obtain their fitting function.For residual components rnT () then
Directly it is fitted in the time domain.
5. respectively to step 4) in these time-frequency domain matched curves of obtaining carry out anti-Hilbert transform, you can obtain corresponding
Analytical expression in time domain.Concrete grammar is：
θ can be obtained according to formula (8)nT the expression formula of () is as follows：
θn(t)=2 π ∫ fn(t)dt (8)
By formula (8) and formula (2), formula (6) simultaneous, you can to solve expression formula cn(t).Time domain according to said method can be solved successively
In all analytical expressions.
6. by c1(t),c2(t),...,cn(t) and rnT () time-domain expression sums up, and carry out adding with the expression formula of residual components
With obtain the analytical expression of farm model.
In above-mentioned formula and expression formula, t express time.
The wind energy turbine set interpretive model set up out by above-mentioned steps can access in power system simulation model directly as power source, leads to
Cross collection and analysis system voltage, frequency change, the impact to line voltage, frequency for the wind energy turbine set fluctuation can be analyzed.
Present invention advantage compared with prior art is：
(1) the modeling foothold of the present invention is measured data, by the method for Digital Signal Processing it is established that can be used in counting
The farm model of calculation machine emulation.Compared to existing equivalent modeling method, the modeling method of the present invention is not subject to complexity in wind energy turbine set
Structure and the interference of various random factors, can be good at the various typical wave features of reproducing Power Output for Wind Power Field, are used for
The wind farm grid-connected lower impact analysis to line voltage, frequency, has higher reliability.
(2) also the spectrum analyses to Power Output for Wind Power Field improve the present invention.To be built upon existing spectrum analyses right more
Primary signal is directly processed, and the spectral frequencies distribution obtaining is wide in range and complicated.The present invention passes through EMD method, will include multiple
The primary signal of miscellaneous frequency content is decomposed into some signals with separate frequency bands, can more clearly from observe and analyze wind energy turbine set
Fluctuation characteristic.
Brief description
Fig. 1 is the overview flow chart of modeling method of the present invention；
Fig. 2 is empirical mode decomposition (EMD) flow chart；
Fig. 3 is the matching flow chart based on Hilbert transform frequency spectrum；
Fig. 4 is Power Output for Wind Power Field (active) time domain beamformer；
Fig. 5 is by the time domain beamformer carrying out after 9 empirical mode decompositions of Power Output for Wind Power Field P (t)；
Fig. 6 is that component c1-c9 each in Fig. 5 is carried out the time-frequency domain oscillogram after Hilbert transform；
Fig. 7 is senior middle school's low-frequency range each several part oscillogram；
Fig. 8 is system frequency variation diagram after original wind power accesses；
Fig. 9 is system frequency variation diagram after the access of empirical mode decomposition HFS；
Figure 10 is system frequency variation diagram after the access of empirical mode decomposition intermediate-frequency section；
Figure 11 decomposes system frequency variation diagram after low frequency part accesses for EMD.
Specific embodiment
Below in conjunction with the drawings and specific embodiments, the invention will be further described.
The comprising the following steps that of modeling method of the present invention：
1. the collection of active power of wind power field data
Gather the output P of the every unit of wind energy turbine set in certain wind fieldwind_i(t), and sum up, acquired results are this wind energy turbine set
Gross output Pwind_f(t), that is,：
2. normalized
Choosing total rated output power Pe (t) of this wind energy turbine set is base value, to the P obtaining in step 1wind_fT () is normalized,
Obtain data
3. data filtering is processed
The discrete serieses P that step 2 is obtainedorT () generates power spectrum by numerical method, observe Por(t) frequency distribution situation,
Choose rational lowpass digital filter to PorT () is filtered, filter off the larger noise spot of error, and the data obtaining is designated as
P (t), using P (t) as subsequent treatment Power Output for Wind Power Field, as shown in Figure 4.
4. carry out empirical mode decomposition (EMD)
As shown in Fig. 2 empirical mode decomposition is carried out to P, Power Output for Wind Power Field P (t) is decomposed into n intrinsic mode function
c1(t),c2(t)...cn(t) and residual components rn(t).Comprise the following steps that：
According to the definition of intrinsic mode function, use mkRepresent and signal P (t) is asked k time be all worth to mP (t), and signal P (t) and
The difference of mP (t) is defined as intrinsic mode function component c1, that is,：
c1=P (t)-mP (t)
Obtain c1Afterwards, with P (t)-c1Decomposed again as pending signal, be can get the 2nd intrinsic mode function component c2,
The rest may be inferred to n-th, obtains n-th intrinsic mode function component cn, the decomposition expression formula of its intrinsic mode function of each time is：
Can stop decomposing after decomposition result meets prespecified condition.Finally can get result：
The comprising the following steps that of empirical mode decomposition (EMD) method：
(1) observe the signal waveform of Power Output for Wind Power Field P (t), estimate frequency distribution, from continuously alternate local maximum
Point and minimum point, fit to coenvelope and lower envelope respectively with 3 battens.Coenvelope is averaged with lower envelope, obtains
Envelope and average m of lower envelope1.Define component h1For signal P (t) and average m1Difference, that is,：
P(t)-m1=h1
(2) due in 3 spline-fits, some problems occur：Produce new extreme point, translation and amplify existing numerical point
And the larger swing of two ends Near The Extreme Point producing ratio etc., need to repeat to be processed according to step described in (1).The 2nd
In secondary process, component h1As pending data, then：
h1-m11=h11
If result is unsatisfied with, can continue with, until kth time is processed, hkBecome an intrinsic mode function component,
I.e.：
h1(k-1)-m1k=h1k
By h1kIt is defined as:
c1=h1k
c1It is exactly required intrinsic mode function component.
Must determine a criterion stopping processing to repeating to process, this criterion may be selected to be following standard deviation eSD, that is,：
eSDRepresentative value may be set between 0.2～0.3.
(3) isolate intrinsic mode function component c from original signal P (t)1Afterwards, have：
P(t)-c1=r1
The r obtaining1It is that original power data P (t) weeds out fluctuation c1Data afterwards.
By r1Carry out repeating to process by method described in step (2) as new data, obtain：
r1-c2=r2
In the same manner, r2It is r1Weed out fluctuation c2Data afterwards, it is also contemplated that r2It is that original power data P (t) weeds out fluctuation c1And c2
Data afterwards.
So continue with down, have：
Result above is combined, that is, obtains：
Then, just original signal P (t) is resolved into n intrinsic mode function component ck(k=1,2 ... n), and 1 residue
Component rn.Wherein c1(t),c2(t)...cnT () may be considered the fluctuation characteristic function of Power Output for Wind Power Field P (t), rnIt is considered that
It is the trend function of Power Output for Wind Power Field P (t).Empirical mode decomposition only need to know extreme point it is not necessary to know average or
Person zero reference value, because obtaining in processing procedure to zero reference value of each intrinsic mode function component.
The condition finally stopping has following 2 kinds：1. intrinsic mode function component cnOr residual components rnBecome the predetermined value than regulation
Hour；②rnBecome monotonic function, therefrom or else can process and draw intrinsic mode function component.For the function containing trend term
P (t), rnIt is exactly the trend term in P (t).In embodiment, P (t) is carried out nine empirical mode decompositions, each IMF after process
Component and rnAs shown in Figure 5.
5. generate time-frequency spectrum
Respectively to intrinsic mode function c1(t),c2(t)...cn(t) and rnT () is done Hilbert transform and is generated time-frequency spectrum, concretely comprise the following steps：
Its contravariant is changed to：
Obtain analytic signal：
Z (t)=cn(t)+iCn(t)=a (t) eiθ(t)(4)
In formula, a (t) is instantaneous amplitude,
θ (t) is phase place,
Instantaneous frequency is calculated as follows：
In formula, CnT () is cnThe Hilloert function of (t).
Process c by above-mentioned steps successively1(t),c2(t)...cn(t), you can respectively obtain their time-frequency spectrum f1(t),f2(t)...fn(t).Figure
6 is the time-frequency spectrum carrying out generating after Hilbert transform by nine intrinsic mode functions (IMF) in step 5.According to each
Levy mode function (IMF) component energy proportion, in conjunction with time-frequency spectrum, as shown in figure 3, can be divided into high, medium and low
3 frequency ranges.f1Between 1Hz-2Hz, referred to as high band；f2～f7Between 0.02-1Hz, referred to as Mid Frequency；
f8,f9Less than 0.02Hz, referred to as low-frequency range.The waveform of each several part and energy proportion are as shown in Figure 7.
6. time-frequency spectrum matching
Respectively to f1(t),f2(t)...fnT () is fitted, that is, obtain their fitting function.
7. return time domain
Respectively to step 5) these time-frequency domain matched curves of obtaining carry out anti-Hilbert transform, you can obtain corresponding time domain
Expression formula.Concrete grammar is：
θ can be obtained according to formula (7)nT the expression formula of () is as follows：
θn(t)=2 π ∫ fn(t)dt (8)
By formula (8) and formula (2), formula (6) simultaneous, you can to solve expression formula cn(t).According to said method can solve all successively
Analytical expression in time domain.By c1(t),c2(t)...cn(t) and rnT () sums up, acquired results are the parsing mould of wind energy turbine set
Type.
In above-mentioned formula and expression formula, t express time.
The farm model set up out by above-mentioned steps is accessed in power system simulation model as power source, obtains system frequency
Change is as shown in Figure 8.Fig. 9～Figure 11 is respectively the change that high frequency, intermediate frequency and low frequency component access system frequency when in power system
Change situation.By situation shown in figure can clearly differentiate each frequency component to the difference of effect on power system it was demonstrated that this
Bright practicality.
Claims (1)
1. a kind of wind energy turbine set time-frequency domain modeling method based on empirical mode decomposition is it is characterised in that described modeling method has
Body step is as follows：
1) by the output p of every unit in the wind energy turbine set collectingwind_iT () sums up, obtain wind energy turbine set and always export
Power pwindfT (), chooses wind energy turbine set total rated output power pe(t) as normalized base value, to wind energy turbine set gross output
pwindfT () is normalized, the data obtaining is carried out with Fourier transformation and generates frequency spectrum, estimate normalization after normalized
The frequency distribution of data afterwards, and the data after normalization is carried out with low-pass filtering, filter off the unreasonable number causing because of measurement error
Strong point, in above-mentioned expression formula, t is the time；
2) represent that filtered data is carried out to normalization through normalization filtered wind energy turbine set gross output data with p (t)
Empirical mode decomposition, generates multiple intrinsic mode function components and residual components：
Wherein cjT () represents j-th intrinsic mode function component IMF of signalj, rnT () represents that signal is residual after n time is decomposed
Cofunction component；J-th intrinsic mode function component IMFjBe distributed in different frequency range and by from high to low order row, that is,
f1(t) ＞ f2(t) ＞ ... ＞ fnT (), that is, the time-frequency domain achieving signal separates, and in above-mentioned expression formula, t is the time；
3) respectively to step 2) the intrinsic mode function c that obtains1(t),c2(t),...,cnT () carries out Hilbert transform, obtain its phase
Time-frequency spectrum f answered1(t),f2(t),...,fnT (), concretely comprises the following steps：
C in formulanT () is cnThe Hilloert function of (t)
Its contravariant is changed to：
Obtain analytic signal：
Z (t)=cn(t)+iCn(t)=a (t) eiθ(t)(4)
In formula, a (t) is instantaneous amplitude,
θ (t) is phase place,
Time-frequency spectrum f (t) is calculated as follows：
Process intrinsic mode function c by above-mentioned steps successively1(t),c2(t),...,cn(t), you can respectively obtain described intrinsic mode function
Time-frequency spectrum f1(t),f2(t),...,fnT (), in above-mentioned formula and expression formula, t is the time；
4) respectively to step 3) time-frequency spectrum f of intrinsic mode function that obtains1(t),f2(t),...,fnT () is fitted, obtain intrinsic
The fitting function of mode function, for residual components rnT () then directly is fitted obtaining fitting function r in the time domainn'(t)；Respectively
Anti- Hilbert transform is carried out to the time-frequency domain fitting function of intrinsic mode function：
θ is obtained according to formula (7)nT the expression formula of () is as follows：
θn(t)=2 π ∫ fn(t)dt (8)
By formula (8) and formula (2), formula (6) simultaneous, you can solve expression formula cn(t)；According to said method solve all time domains successively
In analytical expression；
The time-domain expression of intrinsic modal components fitting result and residual components fitting result is added and obtains farm model
Analytical expression：X (t)=c1(t)+c2(t)+...+cn(t)+rn(t)；In above-mentioned expression formula, t is the time.
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| CN103178538B (en) * | 2013-04-11 | 2014-12-03 | 华北电力大学 | Wind power fluctuation suppression method of hybrid energy storage wind power generation system |
| CN105181334A (en) * | 2015-09-21 | 2015-12-23 | 燕山大学 | Rolling bearing fault detection method based on cascade multistable stochastic resonance and empirical mode decomposition (EMD) |
| CN107607065A (en) * | 2017-09-22 | 2018-01-19 | 河海大学 | A kind of impact echo signal analysis method based on variation mode decomposition |
| CN107729288B (en) * | 2017-09-30 | 2020-11-06 | 中国人民解放军战略支援部队航天工程大学 | Polynomial phase signal time-frequency transformation method based on particle swarm optimization |
| CN108169540A (en) * | 2017-12-13 | 2018-06-15 | 内蒙古工业大学 | A kind of measuring method of wind power generating set voltage flicker |
| CN108508268A (en) * | 2018-04-02 | 2018-09-07 | 山西大学 | A kind of novel digital type flickermeter decomposed based on time-frequency domain |
| CN112103939B (en) * | 2019-06-18 | 2024-01-26 | 上海电机学院 | Power system stability analysis method and device |
| CN111274867B (en) * | 2019-12-30 | 2023-03-31 | 西北大学 | Method for simulating field gust |
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| CN111476402A (en) * | 2020-03-16 | 2020-07-31 | 云南电网有限责任公司 | Wind power generation capacity prediction method coupling meteorological information and EMD technology |
| CN112200069B (en) * | 2020-09-30 | 2022-11-04 | 山东大学 | Tunnel filtering method and system combining time-frequency domain spectral subtraction and empirical mode decomposition |
| CN112182975A (en) * | 2020-10-10 | 2021-01-05 | 国网江苏省电力有限公司扬州供电分公司 | Power frequency domain decomposition optimization method based on empirical mode decomposition |
| CN115496627A (en) * | 2022-11-22 | 2022-12-20 | 国网天津市电力公司电力科学研究院 | Method and system for evaluating response potential of adjustable resource |
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Inventor after: Tang Xisheng Inventor after: Hu Xiao Inventor after: Miao Fufeng Inventor after: He Huimin Inventor after: Wu Tao Inventor after: Li Shanying Inventor before: Tang Xisheng Inventor before: Hu Xiao Inventor before: Miao Fufeng |
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