CN110011359A - A kind of gird-connected inverter parameter identification method under finite aggregate Model Predictive Control - Google Patents
A kind of gird-connected inverter parameter identification method under finite aggregate Model Predictive Control 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
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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
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Abstract
The invention discloses the gird-connected inverter parameter identification methods under a kind of finite aggregate Model Predictive Control, its step includes: under simplified model predictive control method, it constructs twin voltage and predicts error equation, obtain the relationship between filter inductance and its equivalent resistance true value and parametric nominal value, true inductance is recognized with resistance in real time, accurate prediction model is constructed, so as to improve the quality of grid-connected current, improves the robustness of finite aggregate Model Predictive Control.
Description
Technical field
The invention belongs to gird-connected inverter control technology fields, more particularly to a kind of in simplified model prediction
Parameter identification method under control improves under finite aggregate Model Predictive Control for improving grid-connected inverters electric current waveform quality
The parameter robustness of gird-connected inverter.
Background technique
Model Predictive Control (Model Predictive Control, MPC) is generated from later period the 1970s
A kind of computer control algorithm, its concept is intuitive, be easy to model, without accurate model and complex control parameter designing, to gram
The problems such as taking non-linear and uncertain in industrial control process has extraordinary effect, and is easy to increase constraint, dynamic
Respond fast, strong robustness.
In view of its huge advantage, in last decade, one of MPC FCS-MPC converters, motor driven,
The related fieldss such as electric system are widely used and develop, and what can not be avoided is that FCS-MPC faces in practical applications
Numerous challenges, if on-line calculation is big, if calculate time-out, switch motion in application, be not the moment optimized switching movement,
The effect of PREDICTIVE CONTROL is reduced, and when there are modeling errors, so that following the optimized switching letter of traditional FCS-MPC algorithms selection
Array close, optimality has been lost when being imposed in three-phase inverter, cause system controlled volume real response curve with by
There is deviation in the optimal response curve that FCS-MPC algorithm determines, the final control performance for influencing system.However at present about grid-connected
The discrimination method of inverter model PREDICTIVE CONTROL parameter mismatch is all based on traditional model prediction current control, original
It is a large amount of calculate on the basis of be superimposed the calculating of a large amount of identification algorithm, increase the risk for calculating time-out, and it is current mostly
Number parameter identification schemes only recognize inductance, have ignored the resistance mismatch of filter inductance, when resistance wave in a big way
When dynamic, the steady-state error of grid-connected current be will increase, current quality decline.
Summary of the invention
The present invention is provided under a kind of finite aggregate Model Predictive Control to avoid above-mentioned deficiency of the prior art
Gird-connected inverter parameter identification method, to by introducing two predicted voltage error equations, real-time identification inductance and its parasitism
Resistance reduces the calculation amount of PREDICTIVE CONTROL, to improve the parameter of inverter model PREDICTIVE CONTROL while obtaining accurate model
Robustness improves grid-connected inverters electric current waveform quality.
The present invention adopts the following technical scheme that in order to solve the technical problem
The characteristics of gird-connected inverter parameter identification method under a kind of finite aggregate Model Predictive Control of the present invention is the side
Method carries out as follows:
Step 1: when using formula (1) building k moment parameter mismatch, simplified parallel network reverse under finite aggregate Model Predictive Control
The nominal discrete model of device:
In formula (1),It is the optimal output voltage of gird-connected inverter under k moment two-phase α β rest frame, eα,β kFor k
Network voltage under moment two-phase α β rest frame, r ' are nominal resistance, and L ' is nominal inductance；iα,β_ref k+1For the k+1 moment
Reference current under two-phase α β rest frame, iα,β kFor the grid-connected current under k moment two-phase α β rest frame, Ts is sampling
Time interval；
When being run using formula (2) building k moment actual parameter, simplified gird-connected inverter under finite aggregate Model Predictive Control
True discrete model:
In formula (2), Vα,β kIt is the actual output voltage of gird-connected inverter under k moment two-phase α β rest frame, iα,β k+1For
Actual current under k+1 moment two-phase α β rest frame, r is actual resistance, and L is actual inductance；
Obtain the optimal output electricity of gird-connected inverter under k-1 moment two-phase α β rest frame respectively by formula (1) and formula (2)
PressureAnd under k-1 moment actual two-phase α β rest frame gird-connected inverter actual output voltage Vα,β k-1；
Step 2: using formula (3) obtain under k-1 moment two-phase α β rest frame optimal output voltage and virtual voltage it
Poor Δ Vα,β k-1:
In formula (3), iα,β_ref kFor the reference current under k moment two-phase α β rest frame, iα,β k-1For k-1 moment two-phase α
Grid-connected current under β rest frame；
The difference of the optimal voltage evaluated error under k-1 moment and k-2 moment two-phase α β rest frame is obtained using formula (4)
Δδα,β k-1:
In formula (4), iα,β_ref k-1For the reference current under k-1 moment two-phase α β rest frame, iα,β k-2For the k-2 moment
Grid-connected current under two-phase α β rest frame；
The identification expression formula L of actual inductance L under k moment two-phase α β rest frame is constructed using formula (5)α,β_est k:
Step 3: obtaining the estimator R of actual resistance r under k moment two-phase α β rest frame using formula (6)α,β_est k:
Step 4: actual inductance L being estimated using α axle inductance estimator shown in formula (7), obtains the α axis at k moment
Inductance estimated value Lα_est k:
In formula (7), Lα_est k-1For the α axle inductance estimated value at k-1 moment, Δ δα k-1For the α axis at k-1 moment and k-2 moment
The difference of optimal voltage evaluated error, iαref k, iαref k-1Respectively k moment, the α axis reference current at k-1 moment, iα k, iα k-1, iα k-2
Respectively k moment, k-1 moment, the α axis grid-connected current at k-2 moment, Vα k-2, Vα k-1At the respectively k-2 moment, the α axis at k-1 moment is simultaneously
The output voltage of net inverter；
Step 5: actual resistance r being estimated using formula (8), obtains the resistance estimated value R at k momentest k:
In formula (8), θ is the power grid phase angle at k-1 moment, variables A=[1 π/4,3 π/4] ∪ [5 π/4,7 π/4], variable B=
[0,1 π/4] ∪ [3 π/4,5 π/4] ∪ [7 π/4~π]；
Step 6: by the α axle inductance estimated value L at the k momentα_est kAs nominal inductance L ', by the resistance at the k moment
Estimated value Rest kIt as nominal resistance r ' and substitutes into formula (1), obtains the nominal discrete model at k moment；
Step 7: being substituted into formula (1) after k is replaced with k+1, obtain two-staged prediction model；
Step 8: the optimal output voltage of k+1 moment gird-connected inverter is obtained according to the two-staged prediction model；
Step 9: according to the optimal output voltage, utility value function optimization method obtains obtaining the grid-connected of k+1 moment
Inverter switching device pipe action signal Sa k+1,Sb k+1,Sc k+1, and gird-connected inverter switching tube action is exported after a cycle that is delayed
Signal Sa k+1,Sb k+1,Sc k+1, realize to gird-connected inverter the k+1 moment switch motion；
Step 10: at the k+1 moment, after k+1 is assigned to k, return step 1 is executed.
Compared with the prior art, the invention has the advantages that:
1, the present invention is on the basis of simplified finite aggregate Model Predictive Control, by constructing twin voltage error equation,
While picking out inductance, the dead resistance of inductance is picked out, parameter mismatch is completely eliminated, has improved the matter of grid-connected current
Amount；The overall calculation amount of the finite aggregate Model Predictive Control based on parameter identification is reduced, thus reduces in a cycle and counts
Calculate the risk of time-out.
2, parameter identification is carried out on the basis of the finite aggregate Model Predictive Control model that the present invention simplifies in step 1, kept away
Exempt to carry out the huge of totality calculation amount caused by parameter identification on the basis of Classical forecast current control, greatly reduce total
The calculation amount of body alleviates the computation burden of controller；
3, the present invention is distinguished while picking out inductance in step 2 and step 3 by constructing twin voltage error equation
Know the dead resistance for inductance, completely eliminate the mismatch of parameter, improves the robustness of finite aggregate Model Predictive Control.
4, the present invention to the estimation inductance in step 2 and step 3 and estimates resistance expression formula in step 4 and step 5
Singular point problem has carried out detailed analysis, proposes simple and effective solution, avoids the parameter Estimation in previous research
The complexity of formula singular point case study.
Detailed description of the invention
Fig. 1 is grid-connected inverter system structural block diagram in the present invention；
Fig. 2 is the schematic diagram of singular point processing in inductance of the present invention estimation；
Fig. 3 is the schematic diagram of singular point processing in resistance of the present invention estimation；
Fig. 4 is the effect picture that parameter Estimation before and after parameter identification is added in the present invention；
Fig. 5 is the effect picture that grid-connected current before and after parameter Estimation is added in the present invention；
Fig. 6 is the fft analysis figure that grid-connected current before parameter Estimation is added in the present invention；
Fig. 7 is the fft analysis figure that grid-connected current after parameter Estimation is added in the present invention.
Specific embodiment
Referring to Fig. 1, in the present embodiment, a kind of gird-connected inverter parameter under simplified finite aggregate Model Predictive Control is distinguished
Knowledge method is: constructing simplified Model Predictive Control mismatch parameter prediction model and the realistic model based on accurate parameter, root
According to above-mentioned model construction by building twin voltage error equation, true inductance is recognized with resistance in real time, building is quasi-
True prediction model, calculates optimal output voltage, and two-staged prediction obtains optimal inverter finally by cost function optimizing and opens
Close pipe movement.Specifically, it is to carry out as follows:
Step 1: when using formula (1) building k moment parameter mismatch, the base of gird-connected inverter under finite aggregate Model Predictive Control
In the nominal discrete model for calculating optimal voltage:
In formula (1),It is the optimal output voltage of gird-connected inverter under k moment two-phase α β rest frame, eα,β kFor k
Network voltage under moment two-phase α β rest frame, r ' are nominal resistance, and L ' is nominal inductance；iα,β_ref k+1For the k+1 moment
Reference current under two-phase α β rest frame, iα,β kFor the grid-connected current under k moment two-phase α β rest frame, Ts is sampling
Time interval；
When being run using formula (2) building k moment actual parameter, the base of the gird-connected inverter under finite aggregate Model Predictive Control
In the calculating true discrete model of optimal voltage:
In formula (2), Vα,β kIt is the actual output voltage of gird-connected inverter under k moment two-phase α β rest frame, iα,β k+1For
Actual current under k+1 moment two-phase α β rest frame, r is actual resistance, and L is actual inductance；
Obtain the optimal of gird-connected inverter under the two-phase α β rest frame that the k-1 moment calculates respectively by formula (1) and formula (2)
Output voltageAnd under k-1 moment actual two-phase α β rest frame gird-connected inverter actual output voltage
Vα,β k-1, but actual output voltage Vα,β k-1It can be acted by the inverter switching device at k-1 moment and inverter direct-current voltage is direct
It obtains；It is only necessary to the calculating of 1 suboptimum voltage for the prediction model calculated based on optimal voltage, compare Classical forecast current model
8 predicted currents calculating, calculation amount substantially reduces, therefore Model Predictive Control based on the simplification for calculating optimal voltage
Parameter identification can substantially reduce overall calculation amount.
Step 2: the optimal output voltage of the inverter that step 1 is calculatedWith actual output voltage Vα,β k-1It is poor to make:Optimal output voltage and reality under k-1 moment two-phase α β rest frame are obtained using formula (3)
Difference in voltage:
In formula (3), iα,β_ref kFor the reference current under k moment two-phase α β rest frame, iα,β k-1For k-1 moment two-phase α
Grid-connected current under β rest frame；
Further construct the difference of front and back moment optimal voltage evaluated error: Δ δα,β k-1=Δ Vα,β k-1-ΔVα,β k-2, utilize
Formula (4) obtains the difference at k-1 moment and the optimal voltage evaluated error under k-2 moment two-phase α β rest frame:
In formula (4), iα,β_ref k-1For the reference current under k-1 moment two-phase α β rest frame, iα,β k-2For the k-2 moment
Grid-connected current under two-phase α β rest frame；
In formula (4), generally have:
Therefore ignore the resistive term in formula (4), obtain actual inductance L under k moment two-phase α β rest frame shown in formula (6)
Identification expression formula Lα,β_est k:
Step 3: the formula (6) in step 2 being substituted into voltage error expression formula (3), the k-1 moment as shown in formula (7) is obtained
Relational expression J under two-phase α β rest frame about actual resistance r and nominal resistance r 'α,β, as shown in formula (7):
The estimator R of actual resistance r under k moment two-phase α β rest frame is obtained by formula (7)α,β_est k:
Step 4: when the inductance calculating formula (6) that step 2 is calculated calculates, molecule item is if 0, then peak occurs in estimated value
Value, is considered as singular point, to obtain stable estimated value, it is necessary to avoid singular point.Analysis presence is directly carried out to the singular point problem of inductance
The voltage vector relational expression as shown in formula (9) can be obtained according to inverter switching device vector correlation Fig. 2 in difficulty:
In formula (9), Vα,β k-2, Vα,β k-1Inverter under respectively k-2, k-1 moment two-phase α β rest frame exports electricity
Pressure, Vgrid_α,β k-2, Vgrid_α,β k-1Network voltage under respectively k-2, k-1 moment two-phase α β rest frame.
In the case where sample frequency is sufficiently large, the amplitude of two neighboring moment network voltage is approximately equal, and formula (9) can turn
It turns to:
The form of the singular point of step 2 Chinese style (6) can be converted to by formula (10) difference of former and later two switch motions, it is significantly simple
The deterministic process of singular point is changed.By inverter switching device polar plot 2 it is found that the singular point of α axle inductance estimator is merely present in two-phase
Adjacent moment inverter switching device acts in identical situation, and the singular point of β axle inductance estimator is not only deposited in these cases
In inverter switching device movement between 000 and 100, between 001 and 101, between 010 and 110, converted between 111 and 011
When there is also, singular point numbers to be more than α axle inductance estimator, therefore the response speed of α axle inductance estimation is faster than β axle inductance estimator, therefore
Actual inductance is estimated using α axle inductance estimator.
Actual inductance L is estimated using α axle inductance estimator shown in formula (11), the α axle inductance for obtaining the k moment is estimated
Evaluation Lα_est k:
In formula (11), Lα_est k-1For the α axle inductance estimated value at k-1 moment, Δ δα k-1For the α axis at k-1 moment and k-2 moment
The difference of optimal voltage evaluated error, iαref k, iαref k-1Respectively k moment, the α axis reference current at k-1 moment, iα k, iα k-1, iα k-2
Respectively k moment, k-1 moment, the α axis grid-connected current at k-2 moment, Vα k-2, Vα k-1At the respectively k-2 moment, the α axis at k-1 moment is simultaneously
The output voltage of net inverter；
Step 5: there is also singular points for step 3 resistance estimator (8), but singular point form is simple, as the zero passage of two shaft currents
Point, and the current phase of two axis differs 90 ° under two-phase α β rest frame, the singular point phase of two axis resistance estimators also differs
90°.Axis is estimated by switching, it is easy to avoid the estimator singular point of each axis, i.e., in singular point a certain range of α axis estimator,
Estimated with β axis resistance estimator, α axis stops estimation, when reaching in singular point a certain range of β axis, is estimated with the resistance of α axis
Formula is estimated that β axis stops estimation.But the selection of the range of two axis near its singularities will affect the effect of estimation, theoretically get over
Far from singular point, the concussion of estimated value is smaller, and the effect of estimation is better.
In step 3 formula (7), r ' be the k-1 moment prediction model in resistance practical application value, and the estimated value of r be k when
The applicable value at quarter, in the sufficiently high situation of sample frequency, it is believed that moment amplitude is equal before and after electric current, and formula (7) can be approximately
The ohmically voltage at k-1 moment subtracts the ohmically voltage at k moment, obtains (12):
Jα,β≈Vr k-1-Vr k (12)
In formula (14), Vr k-1, Vr kVoltage on the resistance r at respectively k-1, k moment, both sides with divided by sampling period Ts,
Obtain formula (13):
Jα,β/Ts≈(Vr k-1-Vr k)/Ts=- Δ Vr/Ts (13)
In formula (13), Δ VrDifference in voltage for k, on the resistance r at k-1 moment.
Known to, Jα,βLag Vr k-190 °, and voltage and current in phase on resistance r, therefore Jα,βAlso i is laggedα,β k-190 °,
Then Jα/iα k-1It is represented by (14):
Jα/iα k-1=J_αsin(θ-90)/I_α× sin (θ)=J_α/I_αtan(θ-90) (14)
In formula (14), JαFor the α axis component of the voltage difference of adjacent moment on resistance r, iα k-1For the α of k-1 moment grid-connected current
Axis component, J_αFor voltage difference amplitude α axis component, I on adjacent moment resistance_αFor the amplitude of the α axis component of grid-connected current, θ k-
The power grid phase angle at 1 moment.
Similarly, Jβ/iβ k-1It is represented by formula (15):
Jβ/iβ k-1==J_β/I_βtanθ (15)
In formula (15), JβFor the beta -axis component of the voltage difference of adjacent moment on resistance r, iβ k-1For the β of k-1 moment grid-connected current
Axis component, J_βFor voltage difference beta -axis component amplitude, I on adjacent moment resistance_βFor the amplitude of the beta -axis component of grid-connected current.
By formula (14), the graphic diagram 3 of formula (15) is it is found that in pi/2, and near 3 pi/2s, the concussion of α axis resistance estimated value is smaller,
And the concussion of β axis resistance estimated value is smaller near π, 2 π, therefore take in sustained oscillation o'clock as the critical of two axis identification conversion, always
On body, the estimation of inductance is more steady.
Therefore enable formula (14) equal with formula (15), obtain formula (16):
|J_α/I_αTan (θ -90) |=| J_β/I_βtanθ| (16)
Solve the critical angle of two axis resistance identification conversion under two-phase α β rest frame are as follows: the π/4,7 of π/4,5 of π/4,3 π/
4, actual resistance r is estimated using formula (17), obtains the resistance estimated value R at k momentest k:
In formula (17), θ is the power grid phase angle at k-1 moment, variables A=[1 π/4,3 π/4] ∪ [5 π/4,7 π/4], variable B=
[0,1 π/4] ∪ [3 π/4,5 π/4] ∪ [7 π/4~π]；The resistance estimated value totality shock range that such method obtains is minimum, and
In periodical concussion, therefore moving average filter can be used, obtains smooth resistance estimation curve.
Step 6: by the α axle inductance estimated value L at k momentα_est kAs nominal inductance L ', by the resistance estimated value at k moment
Rest kIt as nominal resistance r ' and substitutes into formula (1), obtains the nominal discrete model at k moment；
Step 7: being substituted into formula (1) after k is replaced with k+1, obtain two-staged prediction model；
Step 8: the optimal output voltage of k+1 moment gird-connected inverter is obtained according to two-staged prediction model；
Step 9: according to optimal output voltage, the gird-connected inverter that utility value function optimization method obtains the k+1 moment is opened
Close pipe action signal Sa k+1,Sb k+1,Sc k+1, and gird-connected inverter switching tube action signal S is exported after a cycle that is delayeda k+1,
Sb k+1,Sc k+1, realize to gird-connected inverter the k+1 moment switch motion；
Step 10: at the k+1 moment, after k+1 is assigned to k, return step 1 is executed.
For the validity for verifying parameter identification method proposed by the present invention, specified appearance is built in Matlab/simulink
Measuring is 18kw three-phase grid-connected inverter model, and the nominal value of inductance and resistance is respectively 1mH, 0.5 Ω, and actual inductance and resistance are
5mH, 0.3 Ω.Fig. 4 is the identification effect figure of inductance and resistance before and after parameter identification is added, and Identification of parameter is added in 0.5s,
It is nominal value before middle 0.5s, is the actual inductance value of identification after 0.5s.Figure 4, it is seen that identification inductance and resistance can be with
Rapidly and accurately track the actual value of inductive resistance.Fig. 5 is the grid-connected current waveform being added before and after parameter identification, and ginseng is added in 0.1s
Number identification algorithm, it can be seen that the distortion of 0.1s three-phase grid-connected current is serious, and current waveform is greatly improved after 0.1s.Fig. 6
Before parameter identification is added, the fft analysis result figure of grid-connected current, grid-connected current amplitude deviates considerably from current instruction value 40A, electricity
Stream wave distortion is serious, and THD value is excessively high.Fig. 7 is the fft analysis result figure of grid-connected current after parameter identification is added, grid-connected current
Amplitude deviation be improved significantly, in sine, THD value is greatly reduced current waveform.It is proposed by the invention based on simplification
Model Predictive Control under parameter identification, can improve well the grid-connected current amplitude as caused by parameter mismatch deviate with electricity
Stream distortion, substantially increases the robustness of finite aggregate Model Predictive Control.
Claims (1)
1. the gird-connected inverter parameter identification method under a kind of finite aggregate Model Predictive Control, characterized in that the method be by
What following steps carried out:
Step 1: when using formula (1) building k moment parameter mismatch, simplified gird-connected inverter under finite aggregate Model Predictive Control
Nominal discrete model:
In formula (1), Vα,β k*It is the optimal output voltage of gird-connected inverter under k moment two-phase α β rest frame, eα,β kFor the k moment
Network voltage under two-phase α β rest frame, r ' are nominal resistance, and L ' is nominal inductance；iα,β_ref k+1For k+1 moment two-phase α
Reference current under β rest frame, iα,β kFor the grid-connected current under k moment two-phase α β rest frame, Ts is the sampling time
Interval；
When being run using formula (2) building k moment actual parameter, simplified gird-connected inverter is true under finite aggregate Model Predictive Control
Real discrete model:
In formula (2), Vα,β kIt is the actual output voltage of gird-connected inverter under k moment two-phase α β rest frame, iα,β k+1When for k+1
The actual current under two-phase α β rest frame is carved, r is actual resistance, and L is actual inductance；
Obtain the optimal output voltage of gird-connected inverter under k-1 moment two-phase α β rest frame respectively by formula (1) and formula (2)
Vα,β k-1*And under k-1 moment actual two-phase α β rest frame gird-connected inverter actual output voltage Vα,β k-1；
Step 2: obtaining the difference Δ of optimal output voltage and virtual voltage under k-1 moment two-phase α β rest frame using formula (3)
Vα,β k-1:
In formula (3), iα,β_ref kFor the reference current under k moment two-phase α β rest frame, iα,β k-1It is quiet for k-1 moment two-phase α β
The only grid-connected current under coordinate system；
The difference Δ of the optimal voltage evaluated error under k-1 moment and k-2 moment two-phase α β rest frame is obtained using formula (4)
δα,β k-1:
In formula (4), iα,β_ref k-1For the reference current under k-1 moment two-phase α β rest frame, iα,β k-2For k-2 moment two-phase α β
Grid-connected current under rest frame；
The identification expression formula L of actual inductance L under k moment two-phase α β rest frame is constructed using formula (5)α,β_est k:
Step 3: obtaining the estimator R of actual resistance r under k moment two-phase α β rest frame using formula (6)α,β_est k:
Step 4: actual inductance L being estimated using α axle inductance estimator shown in formula (7), obtains the α axle inductance at k moment
Estimated value Lα_est k:
In formula (7), Lα_est k-1For the α axle inductance estimated value at k-1 moment, Δ δα k-1It is optimal for k-1 moment and the α axis at k-2 moment
The difference of voltage evaluated error, iαref k, iαref k-1Respectively k moment, the α axis reference current at k-1 moment, iα k, iα k-1, iα k-2Respectively
For k moment, k-1 moment, the α axis grid-connected current at k-2 moment, Vα k-2, Vα k-1The α axis at respectively k-2 moment, k-1 moment is grid-connected inverse
Become the output voltage of device；
Step 5: actual resistance r being estimated using formula (8), obtains the resistance estimated value R at k momentest k:
In formula (8), θ is the power grid phase angle at k-1 moment, variables A=[1 π/4,3 π/4] ∪ [5 π/4,7 π/4], variable B=[0,1
π/4] ∪ [π/4 3 π/4,5] ∪ [7 π/4~π]；
Step 6: by the α axle inductance estimated value L at the k momentα_est kAs nominal inductance L ', the resistance at the k moment is estimated
Value Rest kIt as nominal resistance r ' and substitutes into formula (1), obtains the nominal discrete model at k moment；
Step 7: being substituted into formula (1) after k is replaced with k+1, obtain two-staged prediction model；
Step 8: the optimal output voltage of k+1 moment gird-connected inverter is obtained according to the two-staged prediction model；
Step 9: according to the optimal output voltage, utility value function optimization method obtains obtaining the parallel network reverse at k+1 moment
Device switching tube action signal Sa k+1,Sb k+1,Sc k+1, and gird-connected inverter switching tube action signal is exported after a cycle that is delayed
Sa k+1,Sb k+1,Sc k+1, realize to gird-connected inverter the k+1 moment switch motion；
Step 10: at the k+1 moment, after k+1 is assigned to k, return step 1 is executed.
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Cited By (5)
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|---|---|---|---|---|
| CN111030486A (en) * | 2019-12-06 | 2020-04-17 | 合肥工业大学 | Non-parameter finite set model prediction control method of three-level grid-connected inverter |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111030486A (en) * | 2019-12-06 | 2020-04-17 | 合肥工业大学 | Non-parameter finite set model prediction control method of three-level grid-connected inverter |
| CN111740575A (en) * | 2020-07-01 | 2020-10-02 | 电子科技大学 | Inverter model parameter self-adaptive identification method based on steepest descent method |
| CN111740575B (en) * | 2020-07-01 | 2023-06-09 | 电子科技大学 | Inverter model parameter self-adaptive identification method based on steepest descent method |
| CN112018809A (en) * | 2020-08-14 | 2020-12-01 | 长安大学 | Single-phase grid-connected inverter fixed frequency model prediction current control method |
| CN112018809B (en) * | 2020-08-14 | 2022-03-08 | 长安大学 | Single-phase grid-connected inverter fixed frequency model prediction current control method |
| CN113517822A (en) * | 2021-05-10 | 2021-10-19 | 南京工程学院 | Inverter parameter rapid identification method under finite set model predictive control |
| CN113517822B (en) * | 2021-05-10 | 2022-12-06 | 南京工程学院 | Inverter parameter rapid identification method under finite set model predictive control |
| CN113489358A (en) * | 2021-06-29 | 2021-10-08 | 南京工程学院 | Parameter online identification method for inverter |
| CN113489358B (en) * | 2021-06-29 | 2022-07-01 | 南京工程学院 | Parameter online identification method for inverter |
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