CN111221253A - Robust model prediction control method suitable for three-phase grid-connected inverter - Google Patents

Robust model prediction control method suitable for three-phase grid-connected inverter Download PDF

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CN111221253A
CN111221253A CN202010164286.2A CN202010164286A CN111221253A CN 111221253 A CN111221253 A CN 111221253A CN 202010164286 A CN202010164286 A CN 202010164286A CN 111221253 A CN111221253 A CN 111221253A
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disturbance
connected inverter
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state
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CN111221253B (en
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桂永光
张浩浩
吴江
高爱杰
杨宗翰
钱德周
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State Grid Jiangsu Electric Power Co ltd Suqian Power Supply Branch
State Grid Corp of China SGCC
State Grid Jiangsu Electric Power Co Ltd
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State Grid Jiangsu Electric Power Co Ltd Suqian Power Supply Branch
Sihong Power Supply Branch Company State Grid Jiangsu Electric Power Co
State Grid Corp of China SGCC
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Abstract

The invention discloses a robust model prediction control method suitable for a three-phase grid-connected inverter, which observes concentrated disturbance caused by mismatching of grid-connected reactance parameters by constructing a sliding mode disturbance observer and compensates a disturbance observation value to a system prediction model. On the basis, active current and reactive current output control of the three-phase grid-connected inverter are superposed into a value function of model prediction control, values of the value function in all effective switching states are sequentially obtained, the effective switching state with the minimum value function is made to be the optimal switching state, and the optimal switching state is selected to trigger the grid-connected inverter. The robust model prediction control method disclosed by the invention has the advantages that: the tracking error of model predictive control can be reduced when the nominal parameter of the grid-connected reactor is not matched with the actual parameter of the grid-connected reactor, the disturbance resistance of the model predictive control is improved, and the robustness of the system is enhanced.

Description

Robust model prediction control method suitable for three-phase grid-connected inverter
Technical Field
The invention belongs to the technical field of electric power, and particularly relates to a robust model prediction control method suitable for a three-phase grid-connected inverter.
Background
Under the influence of the problems of energy shortage, environmental pollution and the like, the proportion of renewable energy sources such as wind energy, solar energy and the like is increased year by year in recent years. As an interface for accessing renewable energy into a power grid, the performance of a grid-connected inverter directly influences the safe and stable operation of the whole new energy power generation system. The performance of the grid-connected inverter is directly related to the control method adopted by the grid-connected inverter, so that the research on the output current control of the grid-connected inverter is of great significance.
The traditional linear controller has limited dynamic performance and complicated parameter design. Hysteresis control is a nonlinear controller, and although dynamic performance is good, a high sampling frequency is required. In addition, the algorithm of the methods such as fuzzy control and the like applied to the grid-connected inverter is particularly complex, and the practical difficulty of the methods applied to the engineering is higher.
The model prediction control has the advantages of good dynamic performance, simple algorithm, capability of simultaneously processing a plurality of control targets and the like, and is applied to the output control of the grid-connected inverter at present. However, model predictive control is highly dependent on a system model, and when an actual system model and an established model have errors or are not matched, a tracking error is generated, and the system stability is seriously influenced.
In actual engineering, due to the influence of factors such as grid-connected reactor magnetic saturation and manufacturing tolerance, the established system model is difficult to match with an actual model. Therefore, a robust model prediction control method which is suitable for a three-phase grid-connected inverter and can reduce tracking errors caused by parameter mismatching is urgently needed at present.
Disclosure of Invention
The invention aims to solve the technical problem that aiming at the defects in the prior art, the robust model prediction control method suitable for the three-phase grid-connected inverter is provided, the tracking error of the model prediction control can be reduced when the nominal parameter of the grid-connected reactor is not matched with the actual parameter of the grid-connected reactor, the disturbance resistance of the model prediction control is improved, and the robustness of the system is enhanced.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a robust model prediction control method suitable for a three-phase grid-connected inverter comprises the following steps:
(1) establishing a system discrete prediction model based on nominal parameters of a grid-connected reactor, wherein state variablesx=[i d i q]TIncluding active currenti dAnd reactive currenti qAnd the current prediction error caused by the mismatching of the nominal parameter and the actual parameter of the grid-connected reactor is defined as a centralized disturbance variableN=[N d N q]T；N dAs active disturbance variable sumN qIs a reactive disturbance variable.
(2) Assuming that disturbance variables in a single control period are kept unchanged, expanding the active disturbance variables Nd and the reactive disturbance variables Nq into system state variables, and constructing a sliding mode disturbance observer based on the prediction model established in the step (1); sliding mode disturbance observer based on construction and last control period expansion state variable estimation value
Figure 892465DEST_PATH_IMAGE001
Calculating the current control period expansion state variable estimated value
Figure 198776DEST_PATH_IMAGE002
Wherein the extended state variable estimate value
Figure 243741DEST_PATH_IMAGE003
Including an active current estimate
Figure 230551DEST_PATH_IMAGE004
Estimate of reactive current
Figure 910317DEST_PATH_IMAGE005
Estimate of active disturbance
Figure 957171DEST_PATH_IMAGE006
And reactive disturbance estimate
Figure 778845DEST_PATH_IMAGE007
；kIs a variable that increases with the operation of the grid-connected inverter, and in practical engineering, the algorithm is iteratively implemented by a Digital Signal Processor (DSP),krefers to the current control period;
(3) defining a cost function including active current control and reactive current controlJ；
(4) Using the active disturbance estimated value in the prediction model established in the step (1)
Figure 93455DEST_PATH_IMAGE008
Replacing active disturbance variablesN dUsing estimated value of reactive disturbance
Figure 935909DEST_PATH_IMAGE009
Substitution of reactive disturbance variablesN qSequentially calculating the switch state asS 1ToS 7Comparing the values of the lower time cost function to obtain the cost functionJMinimum optimal switch stateS iopmAnd then selecting the optimal switching state to trigger the grid-connected inverter.
The established system discrete prediction model is as follows:
Figure 612526DEST_PATH_IMAGE010
；
wherein the content of the first and second substances,
Figure 904712DEST_PATH_IMAGE012
，
Figure 233325DEST_PATH_IMAGE014
，
Figure 887683DEST_PATH_IMAGE015
，
Figure DEST_PATH_IMAGE001
，
Figure 505894DEST_PATH_IMAGE017
，
Figure DEST_PATH_IMAGE002
。
L onrepresents the nominal inductance parameter of the grid-connected reactor,R ona nominal resistance parameter of the grid-connected reactor is represented,V dcrepresenting the dc bus voltage of the three-phase grid-connected inverter,T srepresents a control period;
Figure 557784DEST_PATH_IMAGE019
is a constant, which refers to the angular frequency of the power frequency grid.
Figure DEST_PATH_IMAGE003
，
Figure DEST_PATH_IMAGE004
，
Figure DEST_PATH_IMAGE005
，
Figure 130628DEST_PATH_IMAGE023
。i a(k)，i b(k) Andi c(k) A sampled value representing the three-phase output current of the present control cycle,v a(k)，v b(k) Andv c(k) And the sampling value of the three-phase output current in the current control period is represented.M=[M a M b M c]TThe output level vector of the three-phase grid-connected inverter is represented by the calculation formula
Figure DEST_PATH_IMAGE006
WhereinS a，S bAndS cthe switching states of the three phases a, b and c are defined as follows:
Figure 991320DEST_PATH_IMAGE025
，
Figure 178150DEST_PATH_IMAGE026
，
Figure 438630DEST_PATH_IMAGE027
。
the sliding mode disturbance observer is as follows:
Figure DEST_PATH_IMAGE007
Figure DEST_PATH_IMAGE008
Figure DEST_PATH_IMAGE009
wherein the content of the first and second substances,
Figure 76683DEST_PATH_IMAGE031
，
Figure 371921DEST_PATH_IMAGE032
，
Figure DEST_PATH_IMAGE010
，
Figure 100991DEST_PATH_IMAGE034
，
Figure DEST_PATH_IMAGE011
a matrix of the observer gains is represented,sat(x) The saturation function is expressed to reduce buffeting of the sliding mode observer, and the calculation formula is
Figure DEST_PATH_IMAGE012
。
The cost functionJComprises the following steps:
Figure 82221DEST_PATH_IMAGE037
the derived cost functionJMinimum optimal switch stateS iopmThe process comprises the following steps:
(1) initializing, defining minimum value of cost functionJ minNumbering the switch states as positive infinityiIs 1;
(2) on-line consulting the following table to find out and switch stateS i Corresponding control vectorM d(k) AndM q(k) (ii) a Md and Mq denote output level vectorsM=[MaMbMc]T park transformed vector, i.e. control vector.
TABLE 2SAndM dqin relation to (2)
Figure DEST_PATH_IMAGE013
The following needs to be addedM d(k)，M q(k) Corresponding value
Namely:S 1=[0 0 0]when the temperature of the water is higher than the set temperature,M dq(k)=[0 0]T；
S 2=[0 0 1]when the temperature of the water is higher than the set temperature,M dq(k)=
Figure 54049DEST_PATH_IMAGE039
；
S 3=[0 1 0]when the temperature of the water is higher than the set temperature,M dq(k)=
Figure 691090DEST_PATH_IMAGE040
；
S 4=[0 1 1]when the temperature of the water is higher than the set temperature,M dq(k)=
Figure 866119DEST_PATH_IMAGE041
；
S 5=[1 0 0]when the temperature of the water is higher than the set temperature,M dq(k)=
Figure 645810DEST_PATH_IMAGE042
；
S 6=[1 0 1]when the temperature of the water is higher than the set temperature,M dq(k)=
Figure DEST_PATH_IMAGE014
；
S 7=[1 1 0]when the temperature of the water is higher than the set temperature,M dq(k)=
Figure DEST_PATH_IMAGE015
。
(3) evaluating the state of the switchS i Lower cost functionJ；
(4) Obtained by judging calculationJWhether or not less thanJ minIf it isThen optimal switch state numberingi opmNumber equal to current switch statei，J minIs equal toJThen entering (5); if not, directly entering into step (5);
(5) judging switch state numberiIf the value is increased to 7, if yes, the state of the selection switch isS iopm And driving the trigger IGBT; if not, increasing the switch state numberiAnd (2) returning to continue to calculate the cost function in other effective switch states.
Compared with the prior art, the invention has the beneficial effects that: the method can reduce the tracking error of model predictive control when the nominal parameter of the grid-connected reactor is not matched with the actual parameter of the grid-connected reactor, improve the disturbance resistance of the model predictive control and enhance the robustness of the system.
Drawings
Fig. 1 is a circuit topology of a three-phase grid-connected inverter.
Detailed Description
The circuit topology of the three-phase grid-connected inverter is shown in fig. 1. Three-phase three-bridge arm two-level inverterL a，L b，L cWhen three filter inductors are connected in parallel to the public power grid, the actual value of the filter inductor can be expressed asL oAnd its equivalent resistance value can be expressed asR o。v a，v b，v cRespectively, represent the three-phase grid voltage,u a，u b，u crespectively represent the output voltages of the three-phase grid-connected inverter,i a，i b，i ceach represents an output current of the three-phase grid-connected inverter, and a predetermined positive direction thereof is as shown in the figure. The DC side input power supply voltage is availableV dcAnd (4) showing.
By usingS xy Represents a drive signal of an Insulated Gate Bipolar Transistor (IGBT), whereinxE.g. { a, b, c } represents the bridge arm number,xe {1, 2} represents an IGBT number. When the bridge arm works normally, the switching signals of the upper IGBT and the lower IGBT in the same bridge arm are complementary and can be outputTwo levels +1 and-1. Can useS=[S a S b S c]TIndicating the switching state of a three-phase grid-connected inverter, whereinS a、S b、S cThe definitions of (A) and (B) are respectively shown in formulas (1) to (3).
Figure 558698DEST_PATH_IMAGE045
(1)
Figure 734288DEST_PATH_IMAGE046
(2)
Figure 541669DEST_PATH_IMAGE047
(3)
According to kirchhoff voltage and current laws, the three-phase grid-connected inverter meets a differential equation shown in a formula (4).
Figure 275838DEST_PATH_IMAGE048
(4)
Output voltage of grid-connected inverteru a，u b，u cAnd the on-off stateSCan be expressed by equation (5).
Figure DEST_PATH_IMAGE016
(5)
According to equation (5), an output level vector of the grid-connected inverter can be definedM=[M a M b M c]TAs shown in equation (6).
Figure DEST_PATH_IMAGE017
(6)
Thus, it is possible to provideMAndSthere is a one-to-one correspondence as shown in table 1.
In Table 1MAndSrelation simulation parameter of
Figure DEST_PATH_IMAGE018
Further, bringing formulae (1) and (6) into formula (4) can give:
Figure DEST_PATH_IMAGE019
(7)
the equation (7) is subjected to constant amplitude park transformation to obtain
Figure DEST_PATH_IMAGE020
(8)
Wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE021
，
Figure DEST_PATH_IMAGE022
，
Figure DEST_PATH_IMAGE023
，
Figure 953641DEST_PATH_IMAGE057
，
Figure 69326DEST_PATH_IMAGE058
。
order tox=[i d i q]TRepresenting the state variables of the system, the continuous mathematical model of the three-phase grid-connected inverter system can be represented by equation (9),
Figure 56346DEST_PATH_IMAGE059
(9)
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE024
，
Figure DEST_PATH_IMAGE025
，
Figure DEST_PATH_IMAGE026
，
Figure DEST_PATH_IMAGE027
，
Figure DEST_PATH_IMAGE028
。
for the convenience of digital control, equation (9) is according to the sampling periodT sDiscretizing to obtain a discrete mathematical model (10) of the system,
Figure 757655DEST_PATH_IMAGE065
(10)
wherein the content of the first and second substances,
Figure 704890DEST_PATH_IMAGE066
，
Figure 400922DEST_PATH_IMAGE067
，
Figure 406101DEST_PATH_IMAGE068
，kandk+1 respectively representskT sAnd (a)k+1)T sThe time of day.
The sliding mode observer adopts a high-gain switching function of which the gain sign depends on the error between the state variable estimated value and the actual value, and forces the state variable estimated value to converge to the actual value as soon as possible. Compared with the traditional linear LongBege observer, the sliding-mode observer increases the disturbance resistance of the system. The method adopts a sliding-mode observer to observe the concentrated error caused by the error of the grid-connected reactance parameter in the three-phase grid-connected inverter system.
For the sake of distinction, nominal parameters of the grid-connected reactance (including inductance and resistance) are used herein respectivelyL onAndR onrepresenting, and the parameter matrix derived therefrom being usedA dn、B 1dn、B 2dnEtc., as shown in formulas (11) to (13).
Figure 820374DEST_PATH_IMAGE069
(11)
Figure 160578DEST_PATH_IMAGE070
(12)
Figure 239346DEST_PATH_IMAGE071
(13)
In the formulae (11) to (13),
Figure DEST_PATH_IMAGE029
，
Figure DEST_PATH_IMAGE030
，
Figure DEST_PATH_IMAGE031
。
but by the actual parameters of the grid-connected reactanceL oAndR othe derived parameter matrix is still usedA d、B 1d、B 2dEtc. It should be noted that the grid-connected reactance actual parameter is in the actual systemL oAndR odue to manufacturing tolerances, operating conditions, etc., are difficult to measure accurately, so only nominal parameters are present in the controllerL onAndR onmay be used. Assuming a real system parameter matrixA d、B 1d、B 2dAnd a nominal parameter matrixA dn、B 1dn、B 2dnCan be expressed by the expressions (14) to (16),
Figure 143234DEST_PATH_IMAGE075
(14)
Figure 122174DEST_PATH_IMAGE076
(15)
Figure 387595DEST_PATH_IMAGE077
(16)
wherein, DeltaA d，ΔB 1d，ΔB 2dRepresenting model errors due to grid-tied reactance parameter uncertainty. Bringing (14) - (16) into (10) can convert the system discrete mathematical model into
Figure 939185DEST_PATH_IMAGE079
Figure 370567DEST_PATH_IMAGE080
(17)
According to equation (17), the lumped disturbances caused by the uncertainty of the grid-tied reactance parameters can be used as shown in equation (18)N(k)=[N d(k)N q(k)]TAnd (4) showing.
Figure 814490DEST_PATH_IMAGE081
(18)
In order to realize accurate model prediction control of a system when uncertainty of parameters of grid-connected reactance exists, a sliding mode state observer is designed to observe concentrated disturbance caused by the uncertainty of the parameters and compensate the concentrated disturbance into a prediction model of the system.
Due to the small control period of the system, the disturbance amountN d(k) AndN q(k) Can be assumed to be constant within a single control cycle and can be extended to the state variables of the system, the discrete model of the system is transformed into equation (19).
Figure DEST_PATH_IMAGE032
Figure DEST_PATH_IMAGE033
Figure DEST_PATH_IMAGE034
Figure DEST_PATH_IMAGE035
(19)
Wherein
Figure DEST_PATH_IMAGE036
，
Figure DEST_PATH_IMAGE037
，
Figure DEST_PATH_IMAGE038
。
The output equation of the system can be expressed by equation (20),
Figure DEST_PATH_IMAGE039
(20)
wherein the content of the first and second substances,
Figure 525748DEST_PATH_IMAGE090
。
therefore, according to the equations (19) and (20), a sliding-mode observer as shown in equation (21) can be constructed,
Figure DEST_PATH_IMAGE040
Figure 2751DEST_PATH_IMAGE092
Figure 773694DEST_PATH_IMAGE093
(21)
wherein the content of the first and second substances,
Figure 719791DEST_PATH_IMAGE094
and
Figure 263168DEST_PATH_IMAGE095
representing state variablesXIs detected by the measured values of (a) and (b),
Figure 967510DEST_PATH_IMAGE096
representing the gain matrix of the observer by a saturation functionsat(x) Instead of a sign function to reduce the buffeting of the sliding mode observer. The value of the saturation function can be obtained by equation (22).
Figure 988380DEST_PATH_IMAGE097
(22)
In addition to this, the present invention is,
Figure 454397DEST_PATH_IMAGE098
and
Figure 260327DEST_PATH_IMAGE099
representing concentrated perturbationsNCan be obtained from equation (23).
Figure 991004DEST_PATH_IMAGE100
(23)
The control targets of the three-phase grid-connected inverter system include two: first, make the active currenti dTrack its instruction valuesi dref(ii) a Second, make the reactive current orderi qTrack its instruction valuesi qref. The defined cost function is thus shown in equation (24).
Figure 725653DEST_PATH_IMAGE101
(24)
According to the formula (14),i dref(k+1) andi qref(k+1) can be predicted from equation (25) and equation (26), respectively,
Figure 380763DEST_PATH_IMAGE102
Figure 713086DEST_PATH_IMAGE103
(25)
Figure 333823DEST_PATH_IMAGE104
Figure 520790DEST_PATH_IMAGE105
(26)
at the same time, the amount of disturbanceN d(k) AndN q(k) Can be estimated respectively by the same
Figure 614734DEST_PATH_IMAGE106
And
Figure 300675DEST_PATH_IMAGE107
instead. In addition, park transformation can be performed under three-phase symmetrical power grid conditionsv qIf =0, the formula (24) can be expressed as the formula (27).
Figure 604090DEST_PATH_IMAGE108
Figure 583861DEST_PATH_IMAGE109
Figure 307313DEST_PATH_IMAGE110
(27)
In the formula (27), in order to quickly obtainM d(k) AndM q(k) The corresponding values of the effective switch states are shown in the table 2, and the algorithm can be worked out by looking up the table on line during operationM d(k) AndM q(k). As can be seen from Table 2, the 8 valid switch states only yield 7 different control vectorsM dqTherefore onlyS 1ToS 7The cost function for the 7 switch states needs to be calculated.
TABLE 2
Figure DEST_PATH_IMAGE041
Therefore, the overall flow of robust model prediction control based on the sliding-mode observer, which is proposed herein, is as follows:
step 1: initializing, defining minimum value of cost functionJ minIs plus infinity;
step 2: look up table 2 on-line to find and switch stateS i Corresponding toM d(k) AndM q(k)；
and step 3: evaluating a cost function according to equation (27)J；
And 4, step 4: obtained by judging calculationJWhether or not less thanJ minIf yes, the optimal switch state is numberedi opmNumber equal to current switch statei，J minIs equal toJThen entering step 5; if not, directly entering the step 5;
and 5: judging switch state numberiIf it is increased to 7, i.e. it is determined whether the cost function is calculated and compared for all valid switch states, and if so, the switch state is selected to beS iopm And driving the trigger IGBT; if not, increasing the switch state numberiTo continue to calculate the cost function for other valid switch states.

Claims (5)

1. A robust model prediction control method suitable for a three-phase grid-connected inverter is characterized by comprising the following steps:
(1) establishing a system discrete prediction model based on nominal parameters of a grid-connected reactor, wherein state variablesx=[i d i q]TIncluding active currenti dAnd reactive currenti qAnd the current prediction error caused by the mismatching of the nominal parameter and the actual parameter of the grid-connected reactor is defined as a centralized disturbance variableN=[N d N q]T；
(2) Assuming that disturbance variable remains unchanged in a single control period, and carrying out active disturbance variable Nd and reactive disturbance variableNq is expanded into a system state variable, and a sliding mode disturbance observer is constructed based on the prediction model established in the step (1); sliding mode disturbance observer based on construction and last control period expansion state variable estimation value
Figure 293148DEST_PATH_IMAGE001
Calculating the current control period expansion state variable estimated value
Figure 397239DEST_PATH_IMAGE002
Wherein the extended state variable estimate value
Figure 176977DEST_PATH_IMAGE003
Including an active current estimate
Figure 509869DEST_PATH_IMAGE004
Estimate of reactive current
Figure 324241DEST_PATH_IMAGE005
Estimate of active disturbance
Figure 835119DEST_PATH_IMAGE006
And reactive disturbance estimate
Figure 102152DEST_PATH_IMAGE007
；kIs a variable that increases with the operation of the grid-tied inverter, and in practical engineering, the algorithm is iteratively implemented by a digital signal processor,krefers to the current control period;
(3) defining a cost function J containing active current control and reactive current control;
(4) using the active disturbance estimated value in the prediction model established in the step (1)
Figure 973156DEST_PATH_IMAGE008
Replacing active disturbance variablesN dUsing estimated value of reactive disturbance
Figure 642035DEST_PATH_IMAGE009
Substitution of reactive disturbance variablesN qSequentially calculating the switch state as S1To S7Comparing the values of the lower time cost function to obtain the optimal switch state S which minimizes the cost function JiopmAnd then selecting the optimal switching state to trigger the grid-connected inverter.
2. The robust model prediction control method suitable for the three-phase grid-connected inverter as claimed in claim 1, wherein the establishing of the system discrete prediction model based on the nominal parameters of the grid-connected reactor comprises:
the established system discrete prediction model is as follows:
Figure 822350DEST_PATH_IMAGE010
；
wherein the content of the first and second substances,
Figure 576679DEST_PATH_IMAGE011
，
Figure 251374DEST_PATH_IMAGE012
，
Figure 774759DEST_PATH_IMAGE013
，
Figure 890089DEST_PATH_IMAGE014
，
Figure 131715DEST_PATH_IMAGE015
，
Figure 344522DEST_PATH_IMAGE016
；
Lonindicating the nominal inductance parameter, R, of the grid-connected reactoronIndicating the nominal resistance parameter, V, of the grid-connected reactordcRepresenting the DC bus voltage, T, of a three-phase grid-connected invertersRepresents a control period;
Figure 987992DEST_PATH_IMAGE017
，
Figure 510110DEST_PATH_IMAGE018
，
Figure 239031DEST_PATH_IMAGE019
，
Figure 255529DEST_PATH_IMAGE020
；
i a(k)，i b(k) Andi c(k) A sampled value representing the three-phase output current of the present control cycle,v a(k)，v b(k) Andv c(k) A sampling value representing the three-phase output current of the current control period;M=[M a M b M c]Tthe output level vector of the three-phase grid-connected inverter is represented by the calculation formula
Figure 753506DEST_PATH_IMAGE021
，
WhereinS a，S bAndS cthe switching states of the three phases a, b and c are defined as follows:
Figure 213569DEST_PATH_IMAGE022
，
Figure 164207DEST_PATH_IMAGE023
，
Figure 984396DEST_PATH_IMAGE024
。
3. the robust model predictive control method for the three-phase grid-connected inverter as claimed in claim 1, characterized in that the disturbance variable is the active disturbance variable NdAnd reactive disturbance variable NqExpanding the state variable into a system state variable, and constructing a sliding mode disturbance observer;
the sliding mode disturbance observer is as follows:
Figure 336879DEST_PATH_IMAGE025
Figure 279428DEST_PATH_IMAGE026
Figure 904313DEST_PATH_IMAGE027
wherein the content of the first and second substances,
Figure 324930DEST_PATH_IMAGE028
，
Figure 735183DEST_PATH_IMAGE029
，
Figure 848632DEST_PATH_IMAGE030
，
Figure 459349DEST_PATH_IMAGE031
，
Figure 683657DEST_PATH_IMAGE032
a matrix of the observer gains is represented,sat(x) The saturation function is expressed to reduce buffeting of the sliding mode observer, and the calculation formula is
Figure 682837DEST_PATH_IMAGE033
。
4. The robust model predictive control method for the three-phase grid-connected inverter according to claim 1, wherein a cost function J including active current control and reactive current control is defined, and the cost function J is:
Figure 967188DEST_PATH_IMAGE034
。
5. the robust model predictive control method for three-phase grid-connected inverter as claimed in claim 1, wherein the active disturbance estimation value is used
Figure 566665DEST_PATH_IMAGE035
Replacing the active disturbance variable NdUsing estimated value of reactive disturbance
Figure 594664DEST_PATH_IMAGE036
Substituting a reactive disturbance variable NqFinding the optimal switch state that minimizes the cost function JS iopm；
The optimal switching state that minimizes the cost function J is evaluatedS iopmThe process comprises the following steps:
(5-1) initializing, defining minimum value of cost functionJ minNumbering the switch states as positive infinityiIs 1;
(5-2) on-line consulting the following options to find the on-off stateS i Corresponding control vectorM d(k) AndM q(k)；M dq（k）=[M d M q]T
namely: when S1= [000], Mdq (k) = [00] T;
S2=[0 0 1]when, Mdq (k) =
Figure 713930DEST_PATH_IMAGE037
；
S3=[0 1 0]When, Mdq (k) =
Figure 903602DEST_PATH_IMAGE038
；
S4=[0 1 1]When, Mdq (k) =
Figure 491841DEST_PATH_IMAGE039
；
S5=[1 0 0]When, Mdq (k) =
Figure 57951DEST_PATH_IMAGE040
；
S6=[1 0 1]When, Mdq (k) =
Figure 562882DEST_PATH_IMAGE041
；
S7=[1 1 0]When, Mdq (k) =
Figure 923456DEST_PATH_IMAGE042
；
(3) Solving a value function J under the switching state Si;
(4) judging whether the calculated J is smaller than Jmin or not, if so, judging that the optimal switch state number iopm is equal to the current switch state number i, and Jmin is equal to J, and then entering (5); if not, directly entering into step (5);
(5) judging whether the switch state number i is increased to 7, if so, selecting the switch state to be Siopm, and driving and triggering the IGBT; if not, increasing the switch state number i and returning to the step (2) to continue to calculate the cost functions in other effective switch states.
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