CN111740632B - quasi-Z-source inverter discrete time average model prediction control device and method - Google Patents

quasi-Z-source inverter discrete time average model prediction control device and method Download PDF

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CN111740632B
CN111740632B CN202010603056.1A CN202010603056A CN111740632B CN 111740632 B CN111740632 B CN 111740632B CN 202010603056 A CN202010603056 A CN 202010603056A CN 111740632 B CN111740632 B CN 111740632B
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current
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moment
time
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CN111740632A (en
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张潇桐
马大中
蔡智阳
孙峰
孙秋野
李胜辉
王盼峰
朱钰
孙家正
付尧
程科
赵清松
黄雨佳
白雪
李林娟
王志伟
刘丽月
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Electric Power Research Institute of State Grid Liaoning Electric Power Co Ltd
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Electric Power Research Institute of State Grid Liaoning Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
    • H02M7/00—Conversion of ac power input into dc power output; Conversion of dc power input into ac power output
    • H02M7/42—Conversion of dc power input into ac power output without possibility of reversal
    • H02M7/44—Conversion of dc power input into ac power output without possibility of reversal by static converters
    • H02M7/48—Conversion of dc power input into ac power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode
    • H02M7/53—Conversion of dc power input into ac power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal
    • H02M7/537—Conversion of dc power input into ac power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only, e.g. single switched pulse inverters
    • H02M7/5387—Conversion of dc power input into ac power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only, e.g. single switched pulse inverters in a bridge configuration
    • H02M7/53871—Conversion of dc power input into ac power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only, e.g. single switched pulse inverters in a bridge configuration with automatic control of output voltage or current
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
    • H02M1/00—Details of apparatus for conversion
    • H02M1/0003—Details of control, feedback or regulation circuits

Abstract

The invention provides a device and a method for controlling a quasi-Z-source inverter discrete time average model in a prediction mode, and relates to the technical field of Z/quasi-Z-source inverter control. The device comprises a main power circuit, a control circuit and a detection circuit; the self-optimizing control is realized based on the controlled quantity discrete time average model, and the computational complexity of prediction calculation is greatly simplified; the fixed switching frequency operation of the inverter is realized, and the design difficulty of a filter circuit is reduced while the heating of a switching tube is reduced; the decoupling control of the direct current side and the alternating current side is realized, and the dynamic stability of the system is enhanced through the separation control of the direct duty ratio and the modulation coefficient.

Description

quasi-Z-source inverter discrete time average model prediction control device and method
Technical Field
The invention relates to the technical field of Z/quasi Z source inverter control, in particular to a quasi Z source inverter discrete time average model prediction control device and method.
Background
The quasi-Z source inverter can simultaneously realize the step-up/step-down conversion of input direct current on a single-stage conversion structure due to the special impedance source network characteristic, so that the quasi-Z source inverter is more and more widely applied to a distributed power generation grid-connected system. However, the quasi-Z source network capacitor voltage and the inductor current are obviously affected by load fluctuation, and the system stability is seriously damaged. Therefore, the development of research aiming at the quasi-Z source inverter control method has important and profound significance for the popularization of the quasi-Z source inverter control method in the application of the quasi-Z source inverter control method in renewable distributed power grid connection.
The current control methods for the quasi-Z source inverter include: PI control, fuzzy control, neural network control, SMC control and model prediction control, wherein the traditional PI control is easy to realize, but the compromise between the dynamic response speed and the stability of the system is required when the parameters of a controller are designed; although the dynamic response of the quasi-Z source inverter can be greatly improved by fuzzy control and neural network control, the structure of the controller is more complex, the requirement on the computing capacity of a digital processor is high, and the multivariable control is difficult to realize; SMC control is easy to implement by a digital processor and is not sensitive to changes in system parameters, but causes changes in switching frequency and thus increases switching losses.
Model Predictive Control (MPC) is an ideal quasi-Z-source inverter control method, can realize faster dynamic response and better steady-state performance of a system, and effectively reduces switching loss. The model prediction control is realized by constructing a state equation of system variables, discretizing the state equation based on a forward Euler equation to obtain a functional relation between a value of a control variable at the next sampling moment and a current state variable value, constructing a cost function containing a plurality of control variables of the system, iteratively optimizing and finding out an optimal voltage vector based on all possible output voltage vectors at the next moment of the inverter, and applying a corresponding switch sequence to a switch tube, so that the optimal control of the capacitance voltage, the inductance current and the output current of the quasi Z-source inverter is realized.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a device and a method for predicting and controlling a quasi-Z-source inverter discrete time average model, and aims to remove the constraint of cost functions in the traditional MPC, reduce the complexity of calculation and realize constant switching frequency.
The technical scheme adopted by the invention is as follows:
on one hand, the invention provides a quasi-Z source inverter discrete time average model prediction control device, which comprises a main power circuit, a control circuit and a detection circuit;
the main power circuit comprises an input voltage source Vinquasi-Z-source three-phase two-level inverter, L-type low-pass filter and three-phase load RLThe quasi Z source three-phase two-level inverter comprises a quasi Z source network and an inverter bridge, and the input voltage source VinThe output end of the quasi Z-source three-phase two-level inverter is connected with the input end of the L-type low-pass filter, and the output end of the L-type low-pass filter is connected with a three-phase load RLConnection in which the quasi-Z source network includes an inductance L1Inductor L2Capacitor C1Capacitor C2And a diode D, an inductor L1And a diode D and an inductor L2Series connection, capacitor C2Connecting the diode input terminal to the inductor L2Output terminal, capacitor C1Connecting the output of the diode to an input voltage source VinThe negative electrode of (1);
the control circuit comprises a first input port, a second input port and a third input port; the detection circuit comprises a direct current side quasi Z source network capacitor voltage detection circuit, a direct current side quasi Z source network inductive current detection circuit and an alternating current side output current detection circuit, wherein the input end of the direct current side quasi Z source network inductive current detection circuit and the inductance L of the quasi Z source network1The output end of the quasi-Z source network inductive current detection circuit is connected with a first input port of the control circuit, and the input end of the quasi-Z source network capacitor voltage detection circuit is connected with a capacitor C of the quasi-Z source network1The output end of the quasi Z source network capacitor voltage detection circuit is connected with a second input port of the control circuit, the input end of the alternating current side output current detection circuit is connected with the output end of the quasi Z source three-phase two-level inverter, and the output end of the alternating current side output current detection circuit is connected with a third input port of the control circuit;
on the other hand, the quasi-Z source inverter discrete time average model prediction control method is realized by the quasi-Z source inverter discrete time average model prediction control device: the method comprises the following steps:
step 1: constructing an output current prediction outer ring, and generating an inductive current reference at the moment k +1 based on a discrete time average prediction model of the output current of the quasi-Z source three-phase two-level inverter at the current moment k
Figure GDA0003147376580000021
Step 1.1: generating output current I at the moment k +1 based on a discrete time average prediction model of output current of a quasi-Z source three-phase two-level inverter at the current moment ko(k +1), the specific formula is as follows:
Figure GDA0003147376580000022
where M (k) is the modulation factor at time k, VPN(k) DC link voltage at time k, TSFor the sampling period, L is the filter inductance, Io(k) Output current at time k, RLIs a three-phase load;
step 1.2: inductive current reference at the moment of generating k +1 based on active power transmission balance
Figure GDA0003147376580000023
The specific formula is as follows:
Figure GDA0003147376580000024
wherein v isin(k) A direct current input voltage at time k;
step 2: constructing an inductive current prediction inner loop, and combining a discrete time average prediction model of the inductive current of the quasi-Z source network based on the current time k with the inductive current reference obtained in the step 1
Figure GDA0003147376580000025
Generating a through duty ratio D (k +1) at the moment k + 1;
step 2.1: inductive current i at k +2 moment is predicted based on discrete time average prediction model of inductive currentL1(k +2), the specific formula is as follows:
Figure GDA0003147376580000031
wherein iL1quasi-Z source network inductance L with (k +1) as k +1 moment1Current, iPN(k) For the current flowing into the inverter bridge at the current moment k, R and R are equivalent series resistance of the quasi-Z source network inductor and the capacitor respectively, and vC1(k) quasi-Z source network capacitor C1Voltage of L1A quasi-Z source network inductor;
step 2.2: let iL1(k+2)＝IL *Parallel knotCombining the discrete average prediction model of the inductance at the k +1 moment to generate a direct duty ratio D (k +1) at the k +1 moment, wherein the specific formula is as follows:
Figure GDA0003147376580000032
wherein D (k) is the through duty cycle of the current time k, iL1(k) The current of the quasi-Z source network inductor L1 at the current moment k;
and step 3: generating modulation signal V at the moment k +1 based on discrete time average prediction model of output current of quasi-Z source three-phase two-level inverter at the current moment km(k+1)；
Step 3.1: generating output current I at the moment k +2 based on a discrete time average prediction model of the output current of the quasi-Z source inverter at the current moment ko(k +2), the specific formula is as follows:
Figure GDA0003147376580000033
wherein, M (k +1) and M (k) are modulation coefficients at k +1 and k time respectively;
step 3.2: let Io(k+2)＝Io *(k) Substituting step 3.1 to calculate the modulation coefficient M (k +1) at the time of k +1, the specific formula is as follows:
Figure GDA0003147376580000034
wherein Io *(k) A given reference for the output current at time k;
step 3.3: generating PWM modulation signal V of quasi-Z source inverter based on modulation coefficient M (k +1) obtained in step 3.2mThe concrete formula is as follows:
Figure GDA0003147376580000041
vma、vmb、vmcthe three-phase modulation signals respectively correspond to A, B and C, w represents angular frequency, and t represents time;
and 4, step 4: modulating signal V based on k +1 moment obtained in step 3m(k +1) synthetic spatial reference vector
Figure GDA0003147376580000042
Judging the sector N where the sector N is located;
order to
Figure GDA0003147376580000043
Wherein u isαAnd uβAre respectively as
Figure GDA0003147376580000044
Component on the alpha and beta axes, if Uref1>0, then a equals 1, otherwise a equals 0; if U isref2>0, then B equals 1, otherwise B equals 0; if U isref3>0, then C is 1, otherwise C is 0; wherein U isref1For transition reference vector 1, Uref2For transition reference vector 2, Uref3The vector is a transition reference vector 3, A is a transition parameter 1, B is a transition parameter 2, C is a transition parameter 3, and W is a space vector sector judgment parameter;
let W be 4 × C +2 × B + a, the relationship between W and sector N can be obtained as shown in the following table:
W 3 1 5 4 6 2
sector N 1 2 3 4 5 6
And 5: and determining the conduction time of the switching tube based on the direct-through duty ratio D (k +1) at the moment of k +1 obtained in the step 2 and the sector N where the reference vector obtained in the step 4 is located.
When N is equal to 1, calculating the conduction time of the switching tube by combining the through duty ratio D (k +1) at the moment k +1 as follows:
Figure GDA0003147376580000045
when N is 2, the on-time of the switching tube is calculated by combining the through duty ratio D (k +1) at the time k +1 as follows:
Figure GDA0003147376580000046
when N is 3, the on-time of the switching tube is calculated by combining the through duty ratio D (k +1) at the time k +1 as follows:
Figure GDA0003147376580000051
when N is 4, calculating the conduction time of the switching tube by combining the through duty ratio D (k +1) at the moment k +1 as follows:
Figure GDA0003147376580000052
when N is 5, calculating the conduction time of the switching tube by combining the through duty ratio D (k +1) at the moment k +1 as follows:
Figure GDA0003147376580000053
when N is 6, the on-time of the switching tube is calculated by combining the through duty ratio D (k +1) at the time k +1 as follows:
Figure GDA0003147376580000054
wherein T ismin-，Tmin+，Tmid-，Tmid+，Tmax-，Tmax+The time T of the conduction of the switching tube of the three-phase bridge arm from left to right and from top to bottom respectivelymin、Tmid、TmaxThe time T is the sequential conduction time of the three-phase switching tubes A, B and C from left to right in the space vector modulation of the traditional voltage source invertershIs the time of the direct zero vector contribution.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in:
according to the quasi-Z source inverter prediction control device and method based on the discrete time average model, cost function constraint in the traditional model prediction control is removed, meanwhile, the calculation complexity is reduced, and constant switching frequency is achieved. The constraint of a cost function and the adjustment of a corresponding weight factor are avoided, the self-optimizing control is realized on the basis of the controlled discrete time average model, and the calculation complexity of the prediction calculation is greatly simplified; the fixed switching frequency operation of the inverter is realized, and the design difficulty of a filter circuit is reduced while the heating of a switching tube is reduced; the decoupling control of the direct current side and the alternating current side is realized, and the dynamic stability of the system is enhanced through the separation control of the direct duty ratio and the modulation coefficient.
Drawings
FIG. 1 is a schematic structural diagram of a quasi-Z-source inverter discrete time average model predictive control device according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a main power circuit of a quasi-Z-source three-phase two-level inverter according to an embodiment of the invention;
FIG. 3 is a schematic circuit diagram of a quasi-Z-source three-phase two-level inverter according to an embodiment of the present invention operating in a non-shoot-through mode;
FIG. 4 is a schematic circuit diagram of a quasi-Z-source three-phase two-level inverter according to an embodiment of the present invention operating in a pass-through mode;
FIG. 5 is a graph of quasi-Z source network capacitance voltage in an embodiment of the present invention;
FIG. 6 is a graph of quasi-Z source network inductor current in an embodiment in which the invention is employed;
FIG. 7 is a DC side link voltage diagram in an embodiment of the present invention;
fig. 8 is a diagram of the output current of the ac side of the quasi-Z source three-phase two-level inverter in the embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings.
In one aspect, the present invention provides a quasi-Z source inverter discrete time average model prediction control apparatus, as shown in fig. 1, including a main power circuit 1, a control circuit 5 and a detection circuit;
the main power circuit 1 comprises an input voltage source Vinquasi-Z-source three-phase two-level inverter, L-type low-pass filter and three-phase load RLThe quasi Z source three-phase two-level inverter comprises a quasi Z source network and an inverter bridge, and the input voltage source VinThe output end of the quasi Z-source three-phase two-level inverter is connected with the input end of the L-type low-pass filter, and the output end of the L-type low-pass filter is connected with a three-phase load RLConnection in which the quasi-Z source network includes an inductance L1Inductor L2Capacitor C1Capacitor C2And a diode D, an inductor L1And a diode D and an inductor L2Series connection, capacitor C2Connecting the diode input terminal to the inductor L2Output terminal, capacitor C1Connecting the output and input of the diodeVoltage source VinThe negative electrode of (1);
the control circuit 5 comprises a first input port, a second input port and a third input port; the detection circuit comprises a direct current side quasi Z source network capacitor voltage detection circuit 3, a direct current side quasi Z source network inductive current detection circuit 2 and an alternating current side output current detection circuit 4, wherein the input end of the direct current side quasi Z source network inductive current detection circuit is connected with an inductor L1 of a quasi Z source network, the output end of the quasi Z source network inductive current detection circuit is connected with a first input port of a control circuit, the input end of the quasi Z source network capacitor voltage detection circuit is connected with a capacitor C1 of the quasi Z source network, the output end of the quasi Z source network capacitor voltage detection circuit is connected with a second input port of the control circuit, the input end of the alternating current side output current detection circuit is connected with the output end of a quasi Z source three-phase two-level inverter, and the output end of the alternating current side output current detection circuit is connected with a third input port of the control circuit;
the control circuit 5 adopts a dual-core control structure of TMS320F28335 DSP + XC6SLX9 FPGA.
Fig. 2 is a main power circuit of a quasi-Z source three-phase two-level inverter, and hereinafter, the subscript (k) of a variable band indicates the value of the variable at the moment k, and the subscript (k +1) indicates the value of the variable at the moment k +1, the invention sets: DC side quasi-Z source network capacitor C1＝C2Inductance L1＝L2Inductance value L of three-phase filter inductora＝Lb＝LcL, resistance R of three-phase resistive loada＝Rb＝RcR. According to kirchhoff's voltage and current law, the state equations of the inductor current and the output current of the quasi-Z source inverter working in the non-through mode as shown in fig. 3 are as follows:
Figure GDA0003147376580000071
the state equations of the inductor current and the output current of the quasi-Z source inverter operating in the through mode as shown in fig. 4 are as follows:
Figure GDA0003147376580000072
further, it can be seen from equations (1) and (2) that one sampling period T is reachedSInternal quasi-Z source network inductor L1Voltage v acrossL1Is represented as follows:
Figure GDA0003147376580000073
voltage v at two ends of filter inductor at alternating current side of quasi Z-source inverterLIs represented as follows:
Figure GDA0003147376580000074
using the first order forward Euler equation (5):
Figure GDA0003147376580000075
a discrete time average prediction model of the quasi-Z source network inductive current at the moment k +1 can be obtained:
Figure GDA0003147376580000076
in the same way, a discrete time average prediction model of the output current of the quasi-Z source inverter at the moment k +1 can be obtained:
Figure GDA0003147376580000081
in the above formula iL1(k) For flowing through the inductance L1Current of vC1(k) Is a capacitor C1Voltage across, vin(k) For DC input voltage, R and R are respectively inductance L1And a capacitor C1Equivalent series resistance of iPN(k) For the current flowing in the inverter bridge, VPN(k) For DC link voltage, D (k) is the through duty cycle, M (k) is the modulation factor, L andRLrespectively a filter inductor and a load.
On the other hand, the quasi-Z source inverter discrete time average model prediction control method is realized through the quasi-Z source inverter prediction control system based on the discrete time average model, the traditional quasi-Z source inverter model prediction control method is based on a cost function to perform traversal optimization on all possible output voltage vectors of an inverter at the next moment so as to determine an optimal switching sequence, and the problems of large calculation amount, complex adjustment of weight factors in the cost function, variable switching frequency and the like exist. The method is improved aiming at the traditional prediction control method, the direct-through duty ratio and the modulation coefficient at the next moment are predicted based on the discrete time average prediction model of the controlled variable, and then the driving signal of the switch is generated through the corresponding modulation method, the traversal optimization process of the cost function in the traditional model prediction is eliminated, the calculation complexity of the prediction control is greatly simplified while the fixed switching frequency is realized, and the control design of the direct current side and the alternating current side is decoupled. The method comprises the following steps:
step 1: constructing an output current prediction outer ring, and generating an inductive current reference at the moment k +1 based on a discrete time average prediction model of the output current of the quasi-Z source three-phase two-level inverter at the current moment k
Figure GDA0003147376580000082
Step 1.1: generating output current I at the moment k +1 based on a discrete time average prediction model of output current of a quasi-Z source three-phase two-level inverter at the current moment ko(k +1), the specific formula is as follows:
Figure GDA0003147376580000083
where M (k) is the modulation factor at time k, VPN(k) DC link voltage at time k, TSFor the sampling period, L is the filter inductance, Io(k) Output current at time k, RLIs a three-phase load;
in this example, take TS＝25us，L＝15mH，RL10 Ω, then there are:
Io(k+1)＝8.3×10-4VPN(k)M(k)+0.983Io(k)；
step 1.2: inductive current reference at the moment of generating k +1 based on active power transmission balance
Figure GDA0003147376580000084
The specific formula is as follows:
Figure GDA0003147376580000085
wherein v isin(k) A direct current input voltage at time k; in this example, R is takenL10 Ω then
Figure GDA0003147376580000091
Step 2: constructing an inductive current prediction inner loop, and combining a discrete time average prediction model of the inductive current of the quasi-Z source network based on the current time k with the inductive current reference obtained in the step 1
Figure GDA0003147376580000092
Generating a through duty ratio D (k +1) at the moment k + 1;
step 2.1: inductive current i at k +2 moment is predicted based on discrete time average prediction model of inductive currentL1(k +2), the specific formula is as follows:
Figure GDA0003147376580000093
wherein iL1The current L1 of the quasi-Z source network inductor at the moment that (k +1) is k +1PN(k) For the current flowing into the inverter bridge at the current moment k, R and R are equivalent series resistance of the quasi-Z source network inductor and the capacitor respectively, and vC1(k) Voltage, L, of quasi-Z source network capacitor C11A quasi-Z source network inductor;
in this example, take TS＝25us，L1When 0.5mH, 0.35 Ω, 0.19 Ω, there are:
iL1(k+2)＝0.05[(2D(k+1)-1)vC1(k)+(1-D(k+1))vin(k)+0.35(1-D(k+1))iPN(K)]+0.973iL1(k+1)
step 2.2: let iL1(k+2)＝IL *And generating a direct-through duty ratio D (k +1) at the moment k +1 by combining a discrete average prediction model of the inductance at the moment k +1, wherein the specific formula is as follows:
Figure GDA0003147376580000094
wherein D (k) is the through duty cycle of the current time k, iL1(k) The current of the quasi-Z source network inductor L1 at the current moment k;
in this example, take TS＝25us，L1When 0.5mH, 0.35 Ω, 0.19 Ω, there are:
Figure GDA0003147376580000101
and step 3: generating modulation signal V at the moment k +1 based on discrete time average prediction model of output current of quasi-Z source three-phase two-level inverter at the current moment km(k+1)；
Step 3.1: generating output current I at the moment k +2 based on a discrete time average prediction model of the output current of the quasi-Z source inverter at the current moment ko(k +2), the specific formula is as follows:
Figure GDA0003147376580000102
wherein, M (k +1) and M (k) are modulation coefficients at k +1 and k time respectively; in this example, take TS＝25us，L＝15mH，RL10 Ω, then there are:
Io(k+2)＝4.17×10-4VPN(k)M(k+1)+4.099×10-4VPN(k)M(k)+0.9663Io(k)
step 3.2: let Io(k+2)＝Io *(k) Substituting step 3.1 to calculate the modulation coefficient M (k +1) at the time of k +1, the specific formula is as follows:
Figure GDA0003147376580000103
wherein Io *(k) A given reference for the output current at time k;
in this example, take TS＝25us，L＝15mH，RL10 Ω, then there are:
Figure GDA0003147376580000104
step 3.3: generating PWM modulation signal V of quasi-Z source inverter based on modulation coefficient M (k +1) obtained in step 3.2mThe concrete formula is as follows:
Figure GDA0003147376580000105
vma、vmb、vmcthe three-phase modulation signals respectively correspond to A, B and C, w represents angular frequency, and t represents time;
in this embodiment, the following are provided:
Figure GDA0003147376580000111
and 4, step 4: modulating signal V based on k +1 moment obtained in step 3m(k +1) synthetic spatial reference vector
Figure GDA0003147376580000112
Judging the sector N where the sector N is located;
order to
Figure GDA0003147376580000113
Wherein u isαAnd uβAre respectively as
Figure GDA0003147376580000114
Component on the alpha and beta axes, if Uref1>0, then a equals 1, otherwise a equals 0; if U isref2>0, then B equals 1, otherwise B equals 0; if U isref3>0, then C is 1, otherwise C is 0;
let W be 4 × C +2 × B + a, the relationship between W and sector N can be obtained as shown in the following table:
W 3 1 5 4 6 2
sector N 1 2 3 4 5 6
In this embodiment, let
Figure GDA0003147376580000115
Further order:
Figure GDA0003147376580000116
if U isref1>0, then a equals 1, otherwise a equals 0;
if U isref2>0, then B equals 1, otherwise B equals 0;
if U isref3>0, then C is 1, otherwise C is 0;
when W is 4 × C +2 × B + a, the following table further confirms that
Figure GDA0003147376580000117
The located sector N;
W 3 1 5 4 6 2
sector N 1 2 3 4 5 6
And 5: and determining the conduction time of the switching tube based on the direct-through duty ratio D (k +1) at the moment of k +1 obtained in the step 2 and the sector N where the reference vector obtained in the step 4 is located.
When N is equal to 1, calculating the conduction time of the switching tube by combining the through duty ratio D (k +1) at the moment k +1 as follows:
Figure GDA0003147376580000121
when N is 2, the on-time of the switching tube is calculated by combining the through duty ratio D (k +1) at the time k +1 as follows:
Figure GDA0003147376580000122
when N is 3, the on-time of the switching tube is calculated by combining the through duty ratio D (k +1) at the time k +1 as follows:
Figure GDA0003147376580000123
when N is 4, calculating the conduction time of the switching tube by combining the through duty ratio D (k +1) at the moment k +1 as follows:
Figure GDA0003147376580000124
when N is 5, calculating the conduction time of the switching tube by combining the through duty ratio D (k +1) at the moment k +1 as follows:
Figure GDA0003147376580000125
when N is 6, the on-time of the switching tube is calculated by combining the through duty ratio D (k +1) at the time k +1 as follows:
Figure GDA0003147376580000126
wherein T ismin-，Tmin+，Tmid-，Tmid+，Tmax-，Tmax+The time T of the conduction of the switching tube of the three-phase bridge arm from left to right and from top to bottom respectivelymin、Tmid、TmaxThe time T is the sequential conduction time of the three-phase switching tubes A, B and C from left to right in the space vector modulation of the traditional voltage source invertershIs the time of the direct zero vector contribution.
Simulation results of the examples are shown in the figure: fig. 5 shows the quasi-Z source network capacitor voltage, fig. 6 shows the quasi-Z source network inductor current, fig. 7 shows the dc side link voltage, and fig. 8 shows the ac side output current of the quasi-Z source inverter, and the simulation parameters are shown in table 1.
TABLE 1 simulation parameters
Figure GDA0003147376580000131
As can be seen from the simulation results of embodiment 1, the quasi-Z source inverter discrete time average model prediction control device and method provided by the invention can quickly track the change of the reference value of the output current of the inverter when the reference value changes, and the quasi-Z source network capacitor voltage on the direct current side and the direct current bus link voltage basically have no fluctuation, so that higher stability is maintained; the inductive current responds to the power requirement of the load side in a very short time, and the dynamic response speed is high; therefore, synchronous optimal control of the capacitor voltage, the inductive current and the output current of the quasi-Z source inverter is realized.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions and scope of the present invention as defined in the appended claims.

Claims (1)

1. A quasi-Z source inverter discrete time average model prediction control device is characterized in that: the device comprises a main power circuit, a control circuit and a detection circuit;
the main power circuit comprises an input voltage source Vinquasi-Z-source three-phase two-level inverter, L-type low-pass filter and three-phase load RLThe quasi Z source three-phase two-level inverter comprises a quasi Z source network and an inverter bridge, and the input voltage source VinThe output end of the quasi Z-source three-phase two-level inverter is connected with the input end of the L-type low-pass filter, and the output end of the L-type low-pass filter is connected with a three-phase load RLConnection in which the quasi-Z source network includes an inductance L1Inductor L2Capacitor C1Capacitor C2And a diode D, an inductor L1And a diode D and an inductor L2Series connection, capacitor C2Connecting the diode input terminal to the inductor L2Output terminal, capacitor C1Connecting the output of the diode to an input voltage source VinThe negative electrode of (1);
the control circuit comprises a first input port, a second input port and a third input port; the detection circuit comprises a direct current side quasi Z source network capacitor voltage detection circuit, a direct current side quasi Z source network inductive current detection circuit and an alternating current side output current detection circuit, wherein the input end of the direct current side quasi Z source network inductive current detection circuit and the inductance L of the quasi Z source network1The output end of the quasi-Z source network inductive current detection circuit is connected with a first input port of the control circuit, and the input end of the quasi-Z source network capacitor voltage detection circuit is connected with a capacitor C of the quasi-Z source network1The output end of the quasi Z source network capacitor voltage detection circuit is connected with a second input port of the control circuit, the input end of the alternating current side output current detection circuit is connected with the output end of the quasi Z source three-phase two-level inverter, and the output end of the alternating current side output current detection circuit is connected with a third output port of the control circuitThe input port is connected;
the quasi-Z source inverter discrete time average model prediction control device is used for realizing the following quasi-Z source inverter discrete time average model prediction control method, and specifically comprises the following steps:
step 1: constructing an output current prediction outer ring, and generating an inductive current reference at the moment k +1 based on a discrete time average prediction model of the output current of the quasi-Z source three-phase two-level inverter at the current moment k
Figure FDA0003147376570000011
The step 1 specifically comprises the following steps:
step 1.1: generating output current I at the moment k +1 based on a discrete time average prediction model of output current of a quasi-Z source three-phase two-level inverter at the current moment ko(k +1), the specific formula is as follows:
Figure FDA0003147376570000012
where M (k) is the modulation factor at time k, VPN(k) DC link voltage at time k, TSFor the sampling period, L is the filter inductance, Io(k) Output current at time k, RLIs a three-phase load;
step 1.2: inductive current reference at the moment of generating k +1 based on active power transmission balance
Figure FDA0003147376570000013
The specific formula is as follows:
Figure FDA0003147376570000014
wherein v isin(k) A direct current input voltage at time k;
step 2: constructing an inductive current prediction inner ring, and combining steps based on a discrete time average prediction model of the inductive current of the quasi-Z source network at the current moment kReference to the inductor current obtained in step 1
Figure FDA0003147376570000021
Generating a through duty ratio D (k +1) at the moment k + 1;
the step 2 specifically comprises:
step 2.1: inductive current i at k +2 moment is predicted based on discrete time average prediction model of inductive currentL1(k +2), the specific formula is as follows:
Figure FDA0003147376570000022
wherein iL1quasi-Z source network inductance L with (k +1) as k +1 moment1Current, iPN(k) For the current flowing into the inverter bridge at the current moment k, R and R are equivalent series resistance of the quasi-Z source network inductor and the capacitor respectively, and vC1(k) quasi-Z source network capacitor C1Voltage of L1A quasi-Z source network inductor;
step 2.2: order to
Figure FDA0003147376570000023
And generating a direct-through duty ratio D (k +1) at the moment k +1 by combining a discrete average prediction model of the inductance at the moment k +1, wherein the specific formula is as follows:
Figure FDA0003147376570000024
wherein D (k) is the through duty cycle of the current time k, iL1(k) The current of the quasi-Z source network inductor L1 at the current moment k;
and step 3: generating modulation signal V at the moment k +1 based on discrete time average prediction model of output current of quasi-Z source three-phase two-level inverter at the current moment km(k+1)；
The step 3 specifically includes:
step 3.1: discrete time average prediction of quasi-Z source inverter output current based on current time kOutput current I at the moment of model generation k +2o(k +2), the specific formula is as follows:
Figure FDA0003147376570000025
wherein, M (k +1) and M (k) are modulation coefficients at k +1 and k time respectively;
step 3.2: order to
Figure FDA0003147376570000031
Substituting step 3.1 to calculate the modulation coefficient M (k +1) at the time of k +1, wherein the specific formula is as follows:
Figure FDA0003147376570000032
wherein Io *(k) A given reference for the output current at time k;
step 3.3: generating PWM modulation signal V of quasi-Z source inverter based on modulation coefficient M (k +1) obtained in step 3.2mThe concrete formula is as follows:
Figure FDA0003147376570000033
vma、vmb、vmcthe three-phase modulation signals respectively correspond to A, B and C, w represents angular frequency, and t represents time;
and 4, step 4: modulating signal V based on k +1 moment obtained in step 3m(k +1) synthetic spatial reference vector
Figure FDA0003147376570000034
Judging the sector N where the sector N is located;
order to
Figure FDA0003147376570000035
Wherein u isαAnd uβAre respectively as
Figure FDA0003147376570000036
Component on the alpha and beta axes, if Uref1>0, then a equals 1, otherwise a equals 0; if U isref2>0, then B equals 1, otherwise B equals 0; if U isref3>0, then C is 1, otherwise C is 0; wherein U isref1For transition reference vector 1, Uref2For transition reference vector 2, Uref3The vector is a transition reference vector 3, A is a transition parameter 1, B is a transition parameter 2, C is a transition parameter 3, and W is a space vector sector judgment parameter;
let W be 4 × C +2 × B + a, the relationship between W and sector N can be obtained as shown in the following table:
W 3 1 5 4 6 2 sector N 1 2 3 4 5 6
And 5: determining the conduction time of the switching tube based on the direct duty ratio D (k +1) at the moment of k +1 obtained in the step 2 and the sector N where the reference vector obtained in the step 4 is located;
when N is equal to 1, calculating the conduction time of the switching tube by combining the through duty ratio D (k +1) at the moment k +1 as follows:
Figure FDA0003147376570000041
when N is 2, the on-time of the switching tube is calculated by combining the through duty ratio D (k +1) at the time k +1 as follows:
Figure FDA0003147376570000042
when N is 3, the on-time of the switching tube is calculated by combining the through duty ratio D (k +1) at the time k +1 as follows:
Figure FDA0003147376570000043
when N is 4, calculating the conduction time of the switching tube by combining the through duty ratio D (k +1) at the moment k +1 as follows:
Figure FDA0003147376570000044
when N is 5, calculating the conduction time of the switching tube by combining the through duty ratio D (k +1) at the moment k +1 as follows:
Figure FDA0003147376570000045
when N is 6, the on-time of the switching tube is calculated by combining the through duty ratio D (k +1) at the time k +1 as follows:
Figure FDA0003147376570000046
wherein T ismin-，Tmin+，Tmid-，Tmid+，Tmax-，Tmax+The time T of the conduction of the switching tube of the three-phase bridge arm from left to right and from top to bottom respectivelymin、Tmid、TmaxThe time T is the sequential conduction time of the three-phase switching tubes A, B and C from left to right in the space vector modulation of the traditional voltage source invertershIs the time of the direct zero vector contribution.
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CN112688587B (en) * 2020-12-28 2022-02-15 珠海创芯科技有限公司 Robust prediction control method of impedance source inverter
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105897099A (en) * 2016-05-27 2016-08-24 同济大学 Control method for bidirectional quasi-Z-source inversion type motor driving system
CN107681915A (en) * 2017-10-17 2018-02-09 南京理工大学 Based on the multi-electrical level inverter combination method and device for determining frequency finite aggregate model prediction
CN107769259A (en) * 2017-12-01 2018-03-06 北京航空航天大学 A kind of inverter current forecast Control Algorithm based on discrete averaging model
CN107959431A (en) * 2017-12-01 2018-04-24 北京航空航天大学 Quasi- Z-source inverter direct current bus voltage control method is predicted based on straight-through duty cycle
CN108153150A (en) * 2017-11-21 2018-06-12 中国矿业大学 Dual-level matrix frequency converter Model Predictive Control strategy based on space vector modulation
CN109639153A (en) * 2018-12-26 2019-04-16 北京航空航天大学 A kind of model predictive control method of the source Quasi-Z indirect matrix converter
CN110190766A (en) * 2019-05-13 2019-08-30 浙江工业大学 A kind of quasi- Z-source inverter reduces the model predictive control method of switching frequency

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10581336B2 (en) * 2017-12-23 2020-03-03 Enphase Energy, Inc. Three-phase AC/AC converter with quasi-sine wave HF series resonant link

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105897099A (en) * 2016-05-27 2016-08-24 同济大学 Control method for bidirectional quasi-Z-source inversion type motor driving system
CN107681915A (en) * 2017-10-17 2018-02-09 南京理工大学 Based on the multi-electrical level inverter combination method and device for determining frequency finite aggregate model prediction
CN108153150A (en) * 2017-11-21 2018-06-12 中国矿业大学 Dual-level matrix frequency converter Model Predictive Control strategy based on space vector modulation
CN107769259A (en) * 2017-12-01 2018-03-06 北京航空航天大学 A kind of inverter current forecast Control Algorithm based on discrete averaging model
CN107959431A (en) * 2017-12-01 2018-04-24 北京航空航天大学 Quasi- Z-source inverter direct current bus voltage control method is predicted based on straight-through duty cycle
CN109639153A (en) * 2018-12-26 2019-04-16 北京航空航天大学 A kind of model predictive control method of the source Quasi-Z indirect matrix converter
CN110190766A (en) * 2019-05-13 2019-08-30 浙江工业大学 A kind of quasi- Z-source inverter reduces the model predictive control method of switching frequency

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
"A Discrete-Time Average Model-Based Predictive Control for a Quasi-Z-Source Inverter";Yushan Liu et al;《IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS》;20180831;第65卷(第8期);第6044-6054页 *

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