CN109660144A - A method of the three-phase inverter bi-mode control based on minimum variance adaptive structure - Google Patents
A method of the three-phase inverter bi-mode control based on minimum variance adaptive structure Download PDFInfo
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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
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Abstract
The present invention relates to a kind of methods of three-phase inverter bi-mode control based on minimum variance adaptive structure, the traditional PI controller design of gird-connected inverter is only limitted to run under fixed-bandwidth, although PR control can mitigate the bandwidth limitation in traditional PI design, but performance is not so good as people's will under off-network mode, the present invention fully takes into account the deficiency of traditional control method, minimum variance adaptive control frame is proposed, the active and reactive power for controlling GCI exports.Parameter in control law is based on system identification online updating, and adaptive feature can adapt to the uncertain energy and still maintain power quality, is more suitable for grid-connected inverters and off-network both of which.
Description
Technical Field
The invention relates to the field of microgrid inverter control, in particular to a three-phase inverter dual-mode control method based on a minimum variance adaptive structure.
Background
In recent years, direct current devices such as Renewable Energy Sources (RES) (e.g., distributed energy based on photovoltaic and wind energy), energy storage devices, and other direct current power sources that require a grid-connected inverter (GCI) to be connected to an alternating current grid have rapidly increased. Recent changes in interconnection standards allow these devices to transfer active power while providing or absorbing reactive power according to grid requirements. The direct use of GCI helps to avoid the use of DC-DC converters or additional energy storage devices. However, the main challenge is that as the system operating point and conditions change, the power flow within the system changes as the continuous load changes in the grid occur. This requires that the GCI be able to perform satisfactorily under different conditions. Therefore, the GCI controller should be dynamic and capable of ensuring higher power quality and maintaining stability under various operating conditions. Another challenge is that the GCI should also be able to operate as an off-grid inverter to form an isolated microgrid when a fault or abnormal condition occurs. Therefore, research to design intelligent GCI controllers is becoming an increasingly focused topic.
With the help of the intelligent controller, the voltage source inverter can be operated in grid-connected and off-grid modes. In grid-connected mode, the GCI gets its voltage reference from the grid to keep synchronization, while in off-grid mode it should generate its own voltage reference and update the voltage reference using the voltage amplitude and frequency-based droop. Several control strategies have been proposed for GCI, including Proportional Integral (PI) control, hysteretic control, constant switching frequency control, control based on space vector modulation, direct power control, control based on grid impedance identification, and Proportional Resonant (PR) control.
PI controller designs are limited to operating at a fixed bandwidth, hysteretic controllers also do not operate well over a wider operating range, vector control can achieve optimal mode switching with constant switching frequency and lower losses, however, vector control designs are complex because they are designed around stable operating points, and cascaded PI controllers in these designs have limited operating margins. Other forms of controllers besides PR designs (e.g., direct power control, space vector modulation based control, and grid impedance identification control) also use fixed bandwidth based PI designs. PR controllers can enhance inverter tracking performance, however, for off-grid mode, conventional PR designs do not perform satisfactorily. Therefore, there is a need for an intelligent controller that enables the inverter to operate well in both modes, thereby ensuring higher power quality and maintaining stability under various operating conditions.
Disclosure of Invention
In view of this, the present invention provides a method for dual-mode control of a three-phase inverter based on a minimum variance adaptive structure, which is beneficial to ensure higher power quality and ensure the stability of the system operating in two modes.
The invention is realized by adopting the following scheme: a three-phase inverter dual-mode control method based on a minimum variance adaptive structure specifically comprises the following steps:
step S1: judging the operation state of the microgrid, when the microgrid is connected to the power grid and operates, enabling the inverter to be in the grid-connected operation state, and entering the step S2; when the micro-grid inverter operates in an isolated island mode, the inverter is in an off-grid operation state, and the step S3 is carried out;
step S2, the three-phase voltage on the power grid side is subjected to abc- αβ conversion, and the voltage phase theta used for the abc-dq conversion is calculatedePerforming abc-dq conversion on three-phase voltage and current at the power grid side to respectively obtain direct-quadrature axis voltage and current vd，vq，idAnd iqCalculating the actual active power PGCIReactive power QGCIActive respectively with grid referenceReactive powerMaking a difference to obtain an errorProceeding to step S4;
step S3, converting the voltage at the point of common coupling to abc- αβ, and calculating the voltage phase theta for abc-dq conversioneAnd then converting the V into V by αβ -dqdGFI、VqGFIRespectively with reference voltagesMaking a difference to obtain an error value epsilonVd0、εVq0Proceeding to step S4;
step S4: identifying system parameters by using a recursive least square method; based on minimizing the square of the error epsilonObtaining a system parameter theta in the minimization process;
step S5: control action v 'controlled by minimum variance'di、v'qi(ii) a If the inverter is in a grid-connected operation state, the method goes to step S6; if the inverter is in the off-grid running state, the step S7 is carried out;
the identified system parameters are used in the minimum variance control, and an alternating current filter based on inductance is adopted on the output side of the inverter, so that the system identification purpose can be met by selecting the first-order linear representation of the system, and the system described by the CARMA model is obtained: is the error in the model representation; by minimizing errorsCalculating a control action to obtain Closed loop system output of MVC at this time
Step S6: v'di、v'qiBy controlling the current idAnd iqThereby controlling the active and reactive power output of the grid-connected inverter; returning to step S1;
step S7: v'di、v'qiBy controlling the current idAnd iqThereby controlling the common coupling point voltage, and returns to step S1.
Further, step S2 specifically includes the following steps:
step S21: analyzing the grid-connected part of the inverter to obtain voltage balance at two ends of an inductor and a resistor:
in the formula, Vxi，VxAnd IxRespectively, an inverter three-phase output voltage, a network three-phase voltage and an inverter three-phase output current, wherein x ═ a, b, c]；
Step S22: performing conversion from abc to dq axis to ωeDq reference frame for rad/s synchronous rotation:
wherein, R represents line resistance, L represents line inductance;
v is to bediAnd vqiThe method is divided into two parts: respectively controlling the currents idAnd iqV of'diAnd v'qiA component; compensating for coupling between d-and q-axis componentsAndthe components, namely:
vdi＝v′di+v″di
vqi＝v′qi+v″qi
in formula (II), v'di＝(R+pL)id，v′qi＝(R+pL)iq，p is leadA numerical operator;
step S23: the voltage phase used in the abc-to-dq conversion is obtained by:
in the formula, vαAnd vβAre the α and β components of the grid voltage vector;
aligning the d-axis of the reference frame along the voltage position obtained from the above equation, vqIs zero and v is assumed to have a constant voltage amplitude on the griddIs also a constant; the active and reactive power outputs of the GCI are: pGCI＝vdid，QGCI＝vdiqThen respectively connecting the power grid reference active power with the power grid reference active powerReactive powerMaking a difference to obtain an error
It can be seen that the GCI has an active power output of idControl, reactive power output fromqControl id，iqAnd are each independently of v'dAnd v'qAnd (5) controlling.
Further, step S4 specifically includes the following steps:
step S41: the system is represented by an nth z-domain transfer function of the form:
expressed as:
y(k)＝-a1y(k-1)-a2y(k-2)-...-any(k-n)
+b0u(k-1)+b1u(k-2)+...bn-1u(k-n)
for sample point k-1:
y(k-1)＝-a1y(k-2)-a2y(k-3)-...any(k-1-n)
+b0u(k-2)+b1u(k-3)+...+bn-1u(k-1-n)
for sample point k-N + 1:
y(k-N+1)＝-a1y(k-N)-a2y(k-N-1)-...-any(k-N+1-n)
+b0u(k-N)+...+bn-1u(k-N+1-n)
wherein N is the observation length;
the difference equation described above is written in the form of the following matrix:
wherein X is represented by the formula X ═ Y: u ] yields, θ is a system parameter, where:
step S42: let ε be the actual system ΦsystemAnd system model ΦmodelError between the performances of (1), then
ε＝Φsystem-Φmodel
From phimodelReplacing the above formula by x.
ε＝Φsysrem-X.θ
Step S43: the basis of least squares identification is to minimize the square of the error ε, where the criterion J is defined as:
when the criterion J is minimized, solving a system parameter theta representing a parameter vector to obtain the following formal equation of theta:
θ(k)＝θ(k-1)+K(k)[Φ(k)-Xt(k)θ(k-1)]
wherein K (k) is Kalman filter gain, and the parameter a of the n-order transfer function model of the system is obtained by solving the above formula1,a2,...,an,b0,b1,...,bn-1(ii) a The controller is designed using the identified system parameters.
Further, step S5 is specifically: control actions v 'are obtained by the following two formulas'di、v'qi：
Wherein, v'di(k)Represents a d-axis voltage sequence, v ', applied at the kth time in the inverter'qi(k)Represents a q-axis voltage sequence applied at the kth time in the inverter;PGCI(k)is the actual active power at the kth instant,is the grid reference active power at the kth moment; the actual reactive power at the time of the kth instant,is the grid reference reactive power at the kth moment.
The specific step S5 includes the following steps:
step S51: for the MVC design of the active power output of the inverter, the system is assumed to be described by a controlled autoregressive moving average model, i.e.
Wherein, is the reference active power to be transferred to the grid; pGCI(k) Is the actual active power, v ', transferred'di(k) Is a d-axis voltage sequence applied at the kth instant in the inverter, andis the error in the model representation;
using the system time delay information, MVC minimizes the variance of the output at k + d relative to the expected value of the output at k + d based on the information collected at time k, i.e., the goal of the controller is to minimize the following objective function: j. the design is a square(k)＝Ex{εP0(k+d) 2Where d is the assumed system delay, ExRepresenting the expected value of the future output d steps, in this case zero. The design employs an ac filter based on inductance L, and therefore a first order linear representation of the system is chosen. This order of the model is sufficient for system identification purposes.
From the above formula can be seen
If the point in time in the prediction is shifted forward by one, the equation can be written as
Wherein the left output signal is advanced one step in time and the right side contains information about the current output signal, the current input signal and the future model estimation error;
step S52: calculating a control action v'di(k)In order to optimize the variance of the output one step ahead in time. Based on the current time input, the current time output and the future model estimation error form an equation:
since the model estimation error is assumed to be white noise, its future value is independent of the past and current signals (i.e., C ═ 1);
when the sum of the two components is set to zero, the minimum variance will be achieved, i.e.
-a1εP0(k)+b0v′di(k)＝0
Thus, the MVC law for active power control is given by
Similarly, MVC Law for reactive power control is given by
Is a reference reactive power, Q, delivered to the gridGCIIs the reactive power actually delivered.
Step S53: obtaining a closed-loop system model for active power control:
substitution of A (z)-1)，B(z-1)，C(z-1) Closed loop system becomes
Thus, it can be observed that the closed loop system output (error from the reference set point) that achieves MVC is similar to white noise that is considered for model estimation error.
Compared with the prior art, the invention has the following beneficial effects:
1. the controller of the invention is tuned on-line without full knowledge of the inverter and filter parameters.
2. The proposed control technique is simple in design, robust and can be easily implemented in existing inverters without significant changes to the control architecture in use.
3. The method of the invention performs well during changes in grid dynamics and DER power output fluctuations.
4. The method of the invention is scalable and can be implemented in practical systems interconnected with larger power grids.
5. The controller of the present invention is adaptive in nature, any parameter changes that cause changes in inverter power output can be identified by the program and the same architecture is applicable to both modes.
Drawings
FIG. 1 is a schematic flow chart of a method according to an embodiment of the present invention.
Fig. 2 is a schematic block diagram of an embodiment of the present invention.
Fig. 3 is a schematic diagram of adaptive MVC in the grid-connected mode according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of adaptive MVC in off-grid mode according to an embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1 to 4, the present embodiment provides a method for dual-mode control of a three-phase inverter based on a minimum variance adaptive structure, which specifically includes the following steps:
step S1: judging the operation state of the microgrid, when the microgrid is connected to the power grid and operates, enabling the inverter to be in the grid-connected operation state, and entering the step S2; when the micro-grid inverter operates in an isolated island mode, the inverter is in an off-grid operation state, and the step S3 is carried out;
step S2, the three-phase voltage on the power grid side is subjected to abc- αβ conversion, and the voltage phase theta used for the abc-dq conversion is calculatedeTo grid side three-phase powerVoltage and current are converted from abc to dq to obtain orthogonal axis voltage and current vd，vq，idAnd iqCalculating the actual active power PGCIReactive power QGCIActive respectively with grid referenceReactive powerMaking a difference to obtain an errorProceeding to step S4;
step S3, converting the voltage at the point of common coupling to abc- αβ, and calculating the voltage phase theta for abc-dq conversioneAnd then converting the V into V by αβ -dqdGFI、VqGFIRespectively with reference voltagesMaking a difference to obtain an error value epsilonVd0、εVq0Proceeding to step S4;
step S4: identifying system parameters by using a recursive least square method; based on minimizing the square of the error epsilonObtaining a system parameter theta in the minimization process;
step S5: control action v 'controlled by minimum variance'di、v'qi(ii) a If the inverter is in a grid-connected operation state, the method goes to step S6; if the inverter is in the off-grid running state, the step S7 is carried out;
the identified system parameters are used in the minimum variance control, and an alternating current filter based on inductance is adopted on the output side of the inverter, so that the system identification purpose can be met by selecting the first-order linear representation of the system, and the system described by the CARMA model is obtained:is the error in the model representation; by minimizing errorsCalculating a control action to obtain Closed loop system output of MVC at this time
Step S6: v'di、v'qiBy controlling the current idAnd iqThereby controlling the active and reactive power output of the grid-connected inverter; returning to step S1;
step S7: v'di、v'qiBy controlling the current idAnd iqThereby controlling the common coupling point voltage, and returns to step S1.
In this embodiment, step S2 specifically includes the following steps:
step S21: analyzing the grid-connected part of the inverter to obtain voltage balance at two ends of an inductor and a resistor:
in the formula, Vxi，VxAnd IxRespectively, an inverter three-phase output voltage, a network three-phase voltage and an inverter three-phase output current, wherein x ═ a, b, c]；
Step S22: performing conversion from abc to dq axis to ωeDq reference frame for rad/s synchronous rotation:
wherein, R represents line resistance, L represents line inductance;
v is to bediAnd vqiThe method is divided into two parts: respectively controlling the currents idAnd iqV of'diAnd v'qiA component; compensating for coupling between d-and q-axis componentsAndthe components, namely:
vdi＝v′di+v″di
vqi＝v′qi+v″qi
in formula (II), v'di＝(R+pL)id，v′qi＝(R+pL)iq，p is a derivative operator;
step S23: the voltage phase used in the abc-to-dq conversion is obtained by:
in the formula, vαAnd vβAre the α and β components of the grid voltage vector;
the d-axis of the reference frame is along the voltage bit obtained from the above equationAlignment, vqIs zero and v is assumed to have a constant voltage amplitude on the griddIs also a constant; the active and reactive power outputs of the GCI are: pGCI＝vdid，QGCI＝vdiqThen respectively connecting the power grid reference active power with the power grid reference active powerReactive powerMaking a difference to obtain an error
It can be seen that the GCI has an active power output of idControl, reactive power output fromqControl id，iqAnd are each independently of v'dAnd v'qAnd (5) controlling.
In this embodiment, step S4 specifically includes the following steps:
step S41: the system is represented by an nth z-domain transfer function of the form:
expressed as:
y(k)＝-a1y(k-1)-a2y(k-2)-...-any(k-n)
+b0u(k-1)+b1u(k-2)+...+bn-1u(k-n)
for sample point k-1:
y(k-1)＝-a1y(k-2)-a2y(k-3)-...-any(k-1-n)
+b0u(k-2)+b1u(k-3)+...+bn-1u(k-1-n)
for sample point k-N + 1:
y(k-N+1)＝-a1y(k-N)-a2y(k-N-1)-...-any(k-N+1-n)
+b0u(k-N)+...+bn-1u(k-N+1-n)
wherein N is the observation length;
the difference equation described above is written in the form of the following matrix:
wherein X is represented by the formula X ═ Y: u ] yields, θ is a system parameter, where:
step S42: let ε be the actual system ΦsystemAnd system model ΦmodelError between the performances of (1), then
ε＝Φsystem-Φmodel
From phimodelReplacing the above formula by x.
ε＝Φsystem-X.θ
Step S43: the basis of least squares identification is to minimize the square of the error ε, where the criterion J is defined as:
when the criterion J is minimized, solving a system parameter theta representing a parameter vector to obtain the following formal equation of theta:
θ(k)＝θ(k-1)+K(k)[Φ(k)-Xt(k)θ(k-1)]
wherein K (k) is Kalman filter gain, and the parameter a of the n-order transfer function model of the system is obtained by solving the above formula1,a2,...,an,b0,b1,...,bn-1(ii) a The controller is designed using the identified system parameters.
In this embodiment, step S5 specifically includes: control actions v 'are obtained by the following two formulas'di、v'qi：
Wherein, v'di(k)Represents a d-axis voltage sequence, v ', applied at the kth time in the inverter'qi(k)Represents a q-axis voltage sequence applied at the kth time in the inverter;PGCI(k)is the actual active power at the kth instant,is the grid reference active power at the kth moment;QGCIis the actual reactive power at the kth moment,is the grid reference reactive power at the kth moment.
The specific step S5 includes the following steps:
step S51: for the MVC design of the active power output of the inverter, the system is assumed to be described by a controlled autoregressive moving average model, i.e.
Wherein, is the reference active power to be transferred to the grid; pGCI(k)Is the actual active power, v ', transferred'di(k)Is a d-axis voltage sequence applied at the kth instant in the inverter, andis the error in the model representation;
using the system time delay information, MVC minimizes the variance of the output at k + d relative to the expected value of the output at k + d based on the information collected at time k, i.e., the goal of the controller is to minimize the following objective function: j. the design is a square(k)＝Ex{εP0(k+d) 2Where d is the assumed system delay, ExRepresenting the expected value of the future output d steps, in this case zero. The design employs an ac filter based on inductance L, and therefore a first order linear representation of the system is chosen. This order of the model is sufficient for system identification purposes.
From the above formula can be seen
If the point in time in the prediction is shifted forward by one, the equation can be written as
Wherein the left output signal is advanced one step in time and the right side contains information about the current output signal, the current input signal and the future model estimation error;
step S52: calculating a control action v'di(k)In order to optimize the variance of the output one step ahead in time. Based on the current time input, the current time output and the future model estimation error form an equation:
since the model estimation error is assumed to be white noise, its future value is independent of the past and current signals (i.e., C ═ 1);
when the sum of the two components is set to zero, the minimum variance will be achieved, i.e.
-a1εP0(k)+b0v′di(k)＝0
Thus, the MVC law for active power control is given by
Similarly, MVC Law for reactive power control is given by
Is a reference reactive power, Q, delivered to the gridGCIIs the reactive power actually delivered.
Step S53: obtaining a closed-loop system model for active power control:
substitution of A (z)-1)，B(z-1)，C(z-1) Closed loop system becomes
Thus, it can be observed that the closed loop system output (error from the reference set point) that achieves MVC is similar to white noise that is considered for model estimation error.
The design of the traditional PI controller of the grid-connected inverter is limited to be operated under a fixed bandwidth, the PR control can relieve the bandwidth limitation in the traditional PI design, but the PR control is not satisfactory in an off-grid mode, the embodiment fully considers the defects of the traditional control method, and a minimum variance adaptive control framework is provided for controlling the active power output and the reactive power output of the GCI. Parameters in the control law are updated on line based on system identification, and the self-adaptive characteristic enables the control law to adapt to uncertain energy sources and still maintain the quality of electric energy, so that the control law is more suitable for two modes of grid connection and grid disconnection of an inverter.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the protection scope of the technical solution of the present invention.
Claims (4)
1. A three-phase inverter dual-mode control method based on a minimum variance adaptive structure is characterized by comprising the following steps: the method comprises the following steps:
step S1: judging the operation state of the microgrid, when the microgrid is connected to the power grid and operates, enabling the inverter to be in the grid-connected operation state, and entering the step S2; when the micro-grid inverter operates in an isolated island mode, the inverter is in an off-grid operation state, and the step S3 is carried out;
step S2, the three-phase voltage on the power grid side is subjected to abc- αβ conversion, and the voltage phase theta used for the abc-dq conversion is calculatedeTo three phases at the power grid sidePerforming abc-dq conversion on the voltage and the current to respectively obtain a rectangular-axis voltage and a rectangular-axis current vd，vq，idAnd iqCalculating the actual active power PGCIReactive power QGCIActive respectively with grid referenceReactive powerMaking a difference to obtain an errorProceeding to step S4;
step S3, converting the voltage at the point of common coupling to abc- αβ, and calculating the voltage phase theta for abc-dq conversioneAnd then converting the V into V by αβ -dqdGFI、VqGFIRespectively with reference voltagesMaking a difference to obtain an error value epsilonVd0、εVq0Proceeding to step S4;
step S4: identifying system parameters by using a recursive least square method;
step S5: control action v 'controlled by minimum variance'di、v'qi(ii) a If the inverter is in a grid-connected operation state, the method goes to step S6; if the inverter is in the off-grid running state, the step S7 is carried out;
step S6: v'di、v'qiBy controlling the current idAnd iqThereby controlling the active and reactive power output of the grid-connected inverter; returning to step S1;
step S7: v'di、v'qiBy controlling the current idAnd iqThereby controlling the common coupling point voltage, and returns to step S1.
2. The method for the dual-mode control of the three-phase inverter based on the minimum variance adaptive structure as claimed in claim 1, wherein: step S2 specifically includes the following steps:
step S21: analyzing the grid-connected part of the inverter to obtain voltage balance at two ends of an inductor and a resistor:
in the formula, Vxi，VxAnd IxRespectively, an inverter three-phase output voltage, a network three-phase voltage and an inverter three-phase output current, wherein x ═ a, b, c]；
Step S22: performing conversion from abc to dq axis to ωeDq reference frame for rad/s synchronous rotation:
wherein, R represents line resistance, L represents line inductance;
v is to bediAnd vqiThe method is divided into two parts: respectively controlling the currents idAnd iqV of'diAnd v'qiA component; compensating for coupling between d-and q-axis componentsAndthe components, namely:
vdi＝v′di+v″di
vqi＝v′qi+v″qi
in formula (II), v'di＝(R+pL)id，v'qi＝(R+pL)iq，p is a derivative operator;
step S23: the voltage phase used in the abc-to-dq conversion is obtained by:
in the formula, vαAnd vβAre the α and β components of the grid voltage vector;
aligning the d-axis of the reference frame along the voltage position obtained from the above equation, vqIs zero and v is assumed to have a constant voltage amplitude on the griddIs also a constant; the active and reactive power outputs of the GCI are: pGCI＝vdid，QGCI＝vdiqThen respectively connecting the power grid reference active power with the power grid reference active powerReactive powerMaking a difference to obtain an error
3. The method for the dual-mode control of the three-phase inverter based on the minimum variance adaptive structure as claimed in claim 1, wherein: step S4 specifically includes the following steps:
step S41: the system is represented by an nth z-domain transfer function of the form:
expressed as:
y(k)＝-a1y(k-1)-a2y(k-2)-...any(k-n)
+b0u(k-1)+b1u(k-2)+...bn-1u(k-n)
for sample point k-1:
y(k-1)＝-a1y(k-2)-a2y(k-3)-...-any(k-1-n)
+b0u(k-2)+b1u(k-3)+...+bn-1u(k-1-n)
for sample point k-N + 1:
y(k-N+1)＝-a1y(k-N)-a2y(k-N-1)-…-any(k-N+1-n)
+b0u(k-N)+...+bn-1u(k-N+1-n)
wherein N is the observation length;
the difference equation described above is written in the form of the following matrix:
wherein X is represented by the formula X ═ Y: u ] yields, θ is a system parameter, where:
step S42: let ε be the actual system ΦsystemAnd system model ΦmodelError between the performances of (1), then
ε＝Φsystem-Φmodel
From phimodelReplacing the above formula by x.
ε＝Φsystem-X.θ
Step S43: the basis of least squares identification is to minimize the square of the error ε, where the criterion J is defined as:
when the criterion J is minimized, solving a system parameter theta representing a parameter vector to obtain the following formal equation of theta:
θ(k)＝θ(k-1)+K(k)[Φ(K)-Xt(k)θ(k-1)]
wherein K (k) is Kalman filter gain, and the parameter a of the n-order transfer function model of the system is obtained by solving the above formula1,a2,...,an,b0,b1,...,bn-1(ii) a The controller is designed using the identified system parameters.
4. The method of claim 3, wherein the method comprises the following steps: step S5 specifically includes: control actions v 'are obtained by the following two formulas'di、v'qi：
Wherein, v'di(k)Represents a d-axis voltage sequence, v ', applied at the kth time in the inverter'qi(k)Represents a q-axis voltage sequence applied at the kth time in the inverter;PGCI(k)is the actual active power at the kth instant,is the grid reference active power at the kth moment;QGCIis the actual reactive power at the kth moment,is the grid reference reactive power at the kth moment.
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|---|---|---|---|---|
| CN106099977A (en) * | 2016-07-01 | 2016-11-09 | 广州供电局有限公司 | Be suitable to energy storage control method and the system of the switching of single-phase micro-capacitance sensor pattern |
-
2019
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|---|---|---|---|---|
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Non-Patent Citations (1)
| Title |
|---|
| ROJAN BHATTARAI ET AL.: "Dual Mode Control of a Three-Phase Inverter Using Minimum Variance Adaptive Architecture", 《IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS》 * |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110311407A (en) * | 2019-06-12 | 2019-10-08 | 合肥工业大学 | Cascaded inverter double mode seamless switching control method based on voltage close loop |
| CN110311407B (en) * | 2019-06-12 | 2022-09-27 | 合肥工业大学 | Double-mode seamless switching control method for cascade inverter based on voltage closed loop |
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