CN115425642A - Model-free prediction control method and system for grid-connected converter - Google Patents

Model-free prediction control method and system for grid-connected converter Download PDF

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CN115425642A
CN115425642A CN202210961193.1A CN202210961193A CN115425642A CN 115425642 A CN115425642 A CN 115425642A CN 202210961193 A CN202210961193 A CN 202210961193A CN 115425642 A CN115425642 A CN 115425642A
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current
connected converter
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voltage
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张祯滨
欧路利可·巴巴悠米
李�真
胡存刚
尹忠刚
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Shandong University
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract

The invention discloses a model-free predictive control method and a model-free predictive control system for a grid-connected converter, wherein the model-free predictive control method comprises the following steps: acquiring power grid current and power grid voltage data, and converting the data to a dq coordinate system; inputting the current under the dq coordinate system into a hybrid cascade parallel extended state observer to obtain a current predicted value and an estimated total disturbance; calculating a selected voltage vector based on the voltage under the dq coordinate system; performing two-step power grid current prediction based on the current prediction value, the estimated total disturbance and the selected voltage vector; and based on the two-step power grid current prediction result, the optimal voltage vector is obtained by taking the minimized cost function as a control target, and the on-off control of the grid-connected converter is carried out. The model-free predictive control of the grid-connected converter of the present invention is implemented using a newly designed CP-ESO and provides improved noise suppression while maintaining high interference suppression of the power converter.

Description

Model-free prediction control method and system for grid-connected converter
Technical Field
The invention relates to the technical field of grid-connected converter control, in particular to a model-free predictive control method and system for a grid-connected converter.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
Model Predictive Control (MPC) is an optimization-based control technique. The method utilizes a dynamic equation to predict the evolution trend of the state variables and the output variables of the future system. The control target of the grid-connected converter is established by using the grid current optimization variable. In each control cycle, the optimization problem is solved within the prediction horizon. A final inverter control input sequence that can optimize the problem is found. Finally, only the first input of the sequence is applied to the system. One of the main challenges of MPC is that its dynamic performance is degraded when model uncertainty occurs. Therefore, to overcome this challenge, it is necessary to use model-free predictive control.
In model-free predictive control of a grid-connected converter, a conventional extended state observer operates under a specific bandwidth. The high gain calculated at this particular bandwidth frequency is used to estimate the grid current and total disturbance.
Conventional extended state observers for model-free predictive control have high bandwidth gain, which can lead to poor performance when high frequency measurement noise is present.
In the prior art, a low-pass filter and a resonance filter are used for separating noise from a measurement signal, but the noise and the measurement signal slow down the quick dynamic performance of prediction control of a grid-connected converter; in the prior art, a low-power-consumption extended state observer and a cascade extended state observer are used for grid-connected converter control, but the low-power-consumption extended state observer has low noise filtering capacity; the cascaded extended state observer has poor immunity.
Therefore, although the above scheme can suppress noise to some extent, suppression of observer disturbance is significantly reduced.
Disclosure of Invention
In order to solve the problems, the invention provides a model-free predictive control method and system for a grid-connected converter, and introduces a novel hybrid cascade parallel extended state observer (CP-ESO), so that improved noise suppression is provided while high interference suppression of a power converter is maintained.
In some embodiments, the following technical scheme is adopted:
a model-free predictive control method for a grid-connected converter comprises the following steps:
acquiring power grid current and power grid voltage data, and converting the data into a dq coordinate system;
inputting the current under the dq coordinate system into a hybrid cascade parallel extended state observer to obtain a current predicted value and an estimated total disturbance;
calculating a selected voltage vector based on the voltage under the dq coordinate system;
performing two-step power grid current prediction based on the current predicted value, the estimated total disturbance and the selected voltage vector;
and based on the two-step power grid current prediction result, the optimal voltage vector is obtained by taking the minimized cost function as a control target, and the on-off control of the grid-connected converter is carried out.
As a further scheme, the hybrid cascade parallel extended state observer specifically includes:
Figure RE-GDA0003918117300000031
Figure RE-GDA0003918117300000032
Figure RE-GDA0003918117300000033
Figure RE-GDA0003918117300000034
wherein the content of the first and second substances,
Figure RE-GDA0003918117300000035
a pair of parallel extended state observers ESO is shown,
Figure RE-GDA0003918117300000036
γ 1i and gamma 2i Is ESO 0i The gain of the observer of (1) is,
Figure RE-GDA0003918117300000037
is to estimate the present current prediction
Figure RE-GDA0003918117300000038
CP-ESO state variable of (1); j ∈ ξ 1,2, 3.., M }; y (t) represents the output, α is the constant control input gain, and M is the total number of sub-frequency stages;
Figure RE-GDA0003918117300000039
is that
Figure RE-GDA00039181173000000310
The first derivative of (a) is,
Figure RE-GDA00039181173000000311
is to estimate the present current prediction
Figure RE-GDA00039181173000000312
U (t) is the controller input.
As a further scheme, when the hybrid cascade parallel extended state observer is structurally designed, the hybrid cascade parallel extended state observer is based on the following four rules:
(1) when a minimum number of sub-frequency stages receive the measured noise signal y, a better noise suppression is achieved;
(2) when only one sub-frequency stage receives the measured noise signal y, the noise suppression is increased by making the number of sub-frequency stages in series as large as possible;
(3) as the number of parallel sub-frequency stages increases, the rejection of interference also increases;
(4) as the total number of series connected sub-frequency stages increases, the noise rejection increases, but the interference rejection decreases.
As a further scheme, the selection voltage vector is calculated based on the voltage under the dq coordinate system, specifically:
based on the voltage under the dq coordinate system, calculating a grid voltage phase angle by utilizing a phase-locked loop, and selecting a voltage vector u by applying the grid voltage phase angle dq 。
As a further scheme, two-step power grid current prediction is performed based on the current predicted value, the estimated total disturbance and the selected voltage vector, specifically:
Figure RE-GDA0003918117300000041
where k is the sampling time, ts is the sampling time, and γ 12 ＝2ω 02 ，γ 13 ＝2ω 03 ，
Figure RE-GDA0003918117300000042
Figure RE-GDA0003918117300000043
ω 03 ＝ω 0 (ii) a M is the total number of sub-frequency stages, ω 0 Represents the entire ESO system bandwidth;
Figure RE-GDA0003918117300000044
is the predicted estimation value of the grid current at the next sampling moment (k + 1),
Figure RE-GDA0003918117300000045
is the estimated current of the current discrete sample (k), α is the constant control input gain, u (k) is the switch state S abc (k) Induced converter voltage i dq (k) Is the measured current at the present sampling instant,
Figure RE-GDA0003918117300000046
is the estimated disturbance at the current ESO bandwidth.
As a further scheme, a minimized cost function is used as a control target to obtain an optimal voltage vector, wherein the cost function is as follows:
Figure RE-GDA0003918117300000047
wherein the content of the first and second substances,
Figure RE-GDA0003918117300000048
is the predicted estimation value of the grid current at the sampling moment (k + 2),
Figure RE-GDA0003918117300000049
the grid current reference value at the (k + 2) moment is sampled.
As a further scheme, each switch state voltage of the three-phase two-level grid-connected converter is evaluated in a cost function, and the voltage u with the minimum value of the cost function is applied dq Corresponding switching state as switching state S of the grid-connected converter abc 。
In other embodiments, the following technical solutions are adopted:
a model-free predictive control system of a grid-connected converter comprises:
the data acquisition module is used for acquiring power grid current and power grid voltage data and converting the data into a dq coordinate system;
the state observation module is used for inputting the current under the dq coordinate system into the hybrid cascade parallel extended state observer to obtain a current predicted value and an estimated total disturbance;
the voltage selection module is used for calculating a selection voltage vector based on the voltage under the dq coordinate system;
the two-step prediction module is used for predicting the current of the two-step power grid based on the current prediction value, the estimated total disturbance and the selected voltage vector;
and the grid-connected converter control module is used for obtaining an optimal voltage vector by taking a minimized cost function as a control target based on the two-step power grid current prediction result, and performing on-off control on the grid-connected converter.
In other embodiments, the following technical solutions are adopted:
a terminal device comprising a processor and a memory, the processor being arranged to implement instructions; the memory is used for storing a plurality of instructions, and the instructions are suitable for being loaded by the processor and executing the model-free predictive control method of the grid-connected converter.
In other embodiments, the following technical solutions are adopted:
a computer readable storage medium, wherein a plurality of instructions are stored, the instructions are suitable for being loaded by a processor of a terminal device and executing the model-free predictive control method of the grid-connected converter.
Compared with the prior art, the invention has the beneficial effects that:
(1) The invention introduces a new hybrid cascaded parallel extended state observer (CP-ESO) which solves the problem that the traditional extended state observer has high bandwidth gain and causes poor performance when high frequency measurement noise exists by realizing high frequency noise suppression and maintaining high level interference suppression in the observer. Model-free predictive control of a grid-connected converter is achieved using a newly designed CP-ESO and provides improved noise suppression while maintaining high interference suppression of the power converter.
Additional features and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
Fig. 1 is a schematic diagram of a three-phase grid-connected converter in an embodiment of the invention;
fig. 2 is a schematic diagram of a model-free predictive control method of a grid-connected converter in the embodiment of the invention;
FIG. 3 is a generalized hybrid cascaded parallel extended state observer in an embodiment of the present invention;
fig. 4 is a schematic diagram of a hybrid cascaded parallel ESO, M =3 sub-frequency levels, in an embodiment of the invention;
fig. 5 is a diagram illustrating different CP-ESO structures for M =4 sub-frequency classes;
FIGS. 6 (a) - (b) are schematic diagrams showing the comparison of noise suppression performance of CP-ESO observer and existing ESO observer in this embodiment;
FIG. 7 is a diagram illustrating the anti-interference performance of the CP-ESO and the conventional ESO observer in this embodiment.
Detailed Description
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.
Example one
As shown in fig. 1, the schematic diagram of the grid-connected converter has the following dynamic models:
Figure RE-GDA0003918117300000071
wherein i abc Representing the grid current, e gabc Representing the grid voltage; u. of abc ＝f(S abc ) Representing the output voltage of the power converter, is the switching state S abc A function of (a); l represents the filter inductance and R represents the filter resistance.
Formula (1) can be converted into:
Figure RE-GDA0003918117300000072
wherein u is abc Is the control input to the converter and is,
Figure RE-GDA0003918117300000073
is a constant control input gain that is,
Figure RE-GDA0003918117300000074
Figure RE-GDA0003918117300000075
since the conventional extended state observer for model-free predictive control has a high bandwidth gain, poor performance results when high frequency measurement noise is present. Therefore, the present embodiment mixes the cascade-parallel extended state observer (CP-ESO) to estimate the total disturbance component at different frequencies within the bandwidth and adds all these components to get the total disturbance quantity F. The hybrid cascade-parallel extended state observer is formed by arranging a plurality of observers in parallel, each tuned to a different sub-frequency less than the system bandwidth. The immunity of the observer (CP-ESO) is significantly improved, and secondly, the noise suppression is superior to the conventional high-gain extended state observer ESO.
In one or more embodiments, a model-free predictive control method for a grid-connected converter is disclosed, which, with reference to fig. 2, specifically includes the following steps:
(1) Obtaining a grid current i abc And the network voltage e g，abc Data are converted into a dq coordinate system to obtain the current in the dq coordinate system
Figure RE-GDA0003918117300000076
And voltage e dq 。
(2) Inputting the current under the dq coordinate system into a hybrid cascade parallel extended state observer to obtain a current predicted value
Figure RE-GDA0003918117300000081
And estimated total disturbance
Figure RE-GDA0003918117300000082
In this embodiment, with reference to fig. 3, the hybrid cascade-parallel extended state observer (CP-ESO) is modeled in the time domain as:
Figure RE-GDA0003918117300000083
wherein, the first and the second end of the pipe are connected with each other,
Figure RE-GDA0003918117300000084
representing a pair of parallel Extended State Observer (ESO) levels,
Figure RE-GDA0003918117300000085
Figure RE-GDA0003918117300000086
γ 1i and gamma 2i Is ESO 0i Observer gain of sub-frequency ESO 0i ，ω 0 = entire ESO system bandwidth.
The observer gains are chosen by gain parameterization to ensure that the two observer poles are located at s = ω 0 . Thus, γ 1i ＝2ω 0i ，
Figure RE-GDA0003918117300000087
ω 01 ＜ω 02 ＜...＜ω 0M ＝ω 0 。
System state
Figure RE-GDA0003918117300000088
And observer state
Figure RE-GDA0003918117300000089
Correlation
Figure RE-GDA00039181173000000810
It should be noted that the number of parallel ESO stages may be 2 or more. In this patent, 2 parallel ESO levels are shown for clarity of the invention
Figure RE-GDA00039181173000000811
The case (1).
In this embodiment, with reference to fig. 4, it is assumed that the total number of sub-frequency levels is M =3, and the time domain dynamics is:
Figure RE-GDA0003918117300000091
the discrete form of equation (4) implemented on the microcontroller is:
Figure RE-GDA0003918117300000092
where k is the sampling instant, T s Is the sampling time gamma 11 ＝2ω 01 ，
Figure RE-GDA0003918117300000093
γ 12 ＝2ω 02 ，
Figure RE-GDA0003918117300000094
Figure RE-GDA0003918117300000095
γ 13 ＝2ω 03 ，
Figure RE-GDA0003918117300000096
ω 03 ＝ω 0 ；z j : j ∈ {1,2,3} is the current estimate
Figure RE-GDA0003918117300000097
CP-ESO state variable.
In this embodiment, the CP-ESO is a novel ESO structure, and has two main advantages:
1) It has very good measurement noise suppression;
2) It has better anti-interference capability than the traditional ESO. These two qualities make it superior to the prior art.
The CP-ESO can be designed in different configurations depending on the preferred control characteristics.
The characteristics of the selected structure of the observer are guided by the following four rules:
(1) when the least number of sub-frequency stages (preferably one stage) receives the measured noise signal y, a better noise suppression is achieved;
(2) when only one level receives the measured noise signal y, the noise suppression is increased by making the number of series-connected sub-frequency stages as large as possible. The noise rejection increases with increasing total series level.
(3) As the number of layers of parallel sub-frequency stages increases, the suppression of interference also increases.
(4) As the total number of series connected sub-frequency stages increases, noise rejection increases, but interference rejection decreases.
Based on the above rule, the following applies to CP-ESO having 4 sub-frequency levels, as shown in (a) - (b) in fig. 5:
i) Fig. 5 (a) and (d) have the best measurement noise immunity/rejection characteristics because they only receive one level of measurement noise signal y.
ii) of all 4 configurations, (d) in fig. 5 will have the best noise rejection/noise immunity because it has 3 sub-frequency stages in series, unlike all other configurations where only 2 sub-frequency stages are in series. However, (d) in fig. 5 will also have the weakest interference suppression.
iii) Fig. 5 (a) and (b) will yield the best noise immunity because they have the largest number of parallel sub-frequency stages. Nevertheless, (a) in fig. 5 will have better noise suppression than (b) because it has only one level to receive the measured noise signal y.
iv) fig. 5 (a) will have the best combination of noise suppression and interference suppression capabilities.
Based on the above rules, the skilled person can select the CP-ESO structure most suitable for the system control objective as required.
(3) Based on the voltage under the dq coordinate system, calculating a grid voltage phase angle by utilizing a phase-locked loop, and selecting a voltage vector u by applying the grid voltage phase angle dq 。
(4) Performing two-step power grid current prediction based on the current prediction value, the estimated total disturbance and the selected voltage vector;
in this example, the two-step prediction method is as follows:
Figure RE-GDA0003918117300000101
where k is the sampling time, ts is the sampling time, and γ 12 ＝2ω 02 ，γ 13 ＝2ω 03 ，
Figure RE-GDA0003918117300000102
Figure RE-GDA0003918117300000111
ω 03 ＝ω 0 ；，ω 0 Representing the overall ESO system bandwidth,
Figure RE-GDA0003918117300000112
is the predicted estimate of the grid current at the next sampling instant (k + 1),
Figure RE-GDA0003918117300000113
is the estimated current of the current discrete sample (k), α =1/L, u (k) is the switch state S in Table 1 abc (k) Induced converter voltage i dq (k) Is the measured current at the present sampling instant,
Figure RE-GDA0003918117300000114
is the estimated disturbance at the current ESO bandwidth.
(5) And based on the two-step power grid current prediction result, the optimal voltage vector is obtained by taking the minimized cost function as a control target, and the on-off control of the grid-connected converter is carried out.
In this embodiment, the control objective is to track the reference by minimizing the cost function J
Figure RE-GDA0003918117300000115
The minimization cost function is:
Figure RE-GDA0003918117300000116
wherein the content of the first and second substances,
Figure RE-GDA0003918117300000117
and is provided with
Figure RE-GDA0003918117300000118
k p ，k i Is the PI controller gain for adjusting the DC bus voltage Vdc, i.e. the DC bus voltage,
Figure RE-GDA0003918117300000119
is a dc bus voltage reference.
For values of n = {0,1, \8230;, 7} in table 1, each switch state voltage u dq The evaluation is performed in a cost function. In these 8 options, the voltage u of the J-minimum is used dq Switching state S as a grid-connected converter abc 。
Table 1: on-off state of three-phase two-level grid-connected converter
Figure RE-GDA00039181173000001110
Figure RE-GDA0003918117300000121
FIGS. 6 (a) - (b) show the results of adding white noise to the measurement output before being fed back to the ESO, respectively; fig. 6 (a) shows the noise suppression performance of the CP-ESO observer in the present embodiment, and fig. 6 (b) shows the noise suppression performance of the existing ESO observer; in fig. 6 (a) and 6 (b), the upper graph shows the a-phase grid current, and the lower graph shows the frequency spectrum of the a-phase grid current.
It can be seen that the present embodiment method is able to filter noise better than standard ESO. The conventional standard ESO results in a Total Harmonic Distortion (THD) of 4.1110%, while the new invention reduces THD to 2.7628%.
FIG. 7 shows a CP-ESO and existing ESO observer disturbance rejection performance diagram; in fig. 7, the upper graph represents the d-axis grid current and the lower graph represents the a-phase grid current. It can be seen that the dc bus capacitance voltage reference increases from 140V to 190V. It causes a corresponding increase in the grid current. Compared with the cascade ESO, the method has the advantages of smaller generated power grid current overshoot and ripple and stronger robustness.
Example two
In one or more embodiments, a model-free predictive control system for a grid-connected converter is disclosed, comprising:
the data acquisition module is used for acquiring power grid current and power grid voltage data and converting the data into a dq coordinate system;
the state observation module is used for inputting the current under the dq coordinate system into the hybrid cascade parallel extended state observer to obtain a current predicted value and estimated total disturbance;
the voltage selection module is used for calculating a selection voltage vector based on the voltage under the dq coordinate system;
the two-step prediction module is used for predicting the current of the two-step power grid based on the current prediction value, the estimated total disturbance and the selected voltage vector;
and the grid-connected converter control module is used for obtaining an optimal voltage vector by taking a minimized cost function as a control target based on the two-step power grid current prediction result, and performing on-off control on the grid-connected converter.
It should be noted that, the specific implementation of each module described above has been described in the first embodiment, and is not described in detail here.
EXAMPLE III
In one or more embodiments, a terminal device is disclosed, which includes a server, where the server includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the model-free predictive control method for a grid-connected converter in the first embodiment. For brevity, further description is omitted herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software.
Example four
In one or more embodiments, a computer-readable storage medium is disclosed, wherein a plurality of instructions are stored, and the instructions are suitable for being loaded by a processor of a terminal device and executing the model-free predictive control method for the grid-connected converter in the first embodiment.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims (10)

1. A model-free predictive control method of a grid-connected converter is characterized by comprising the following steps:
acquiring power grid current and power grid voltage data, and converting the data into a dq coordinate system;
inputting the current under the dq coordinate system into a hybrid cascade parallel extended state observer to obtain a current predicted value and an estimated total disturbance;
calculating a selected voltage vector based on the voltage under the dq coordinate system;
performing two-step power grid current prediction based on the current predicted value, the estimated total disturbance and the selected voltage vector;
and based on the two-step power grid current prediction result, obtaining an optimal voltage vector by taking a minimized cost function as a control target, and performing on-off control on the grid-connected converter.
2. The model-free predictive control method of the grid-connected converter according to claim 1, wherein the hybrid cascade parallel extended state observer is specifically:
Figure FDA0003793162210000011
Figure FDA0003793162210000012
Figure FDA0003793162210000013
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003793162210000021
representing a pair of parallel extended state observers
Figure FDA0003793162210000022
γ 1i And gamma 2i Is ESO 0i The gain of the observer of (1),
Figure FDA0003793162210000023
is to estimate the present current prediction
Figure FDA0003793162210000024
CP-ESO state variable of (1); j belongs to {1,2,3, \8230;, M }; y (t) represents the output, α is the constant control input gain, and M is the sum of the sub-frequency stagesCounting;
Figure FDA0003793162210000025
is that
Figure FDA0003793162210000026
The first derivative of (a) is,
Figure FDA0003793162210000027
is to estimate the present current prediction
Figure FDA0003793162210000028
U (t) is the controller input.
3. The model-free predictive control method of the grid-connected converter according to claim 2, wherein the hybrid cascade parallel extended state observer is based on the following four rules when performing structural design:
(1) when a minimum number of sub-frequency stages receive the measured noise signal y, a better noise suppression is achieved;
(2) when only one sub-frequency stage receives the measured noise signal y, the noise suppression is increased by making the number of sub-frequency stages in series as large as possible;
(3) as the number of parallel sub-frequency stages increases, the rejection of interference also increases;
(4) as the total number of series connected sub-frequency stages increases, the noise rejection increases, but the interference rejection decreases.
4. The model-free predictive control method of the grid-connected converter according to claim 1, wherein the voltage vector is selected based on voltage calculation under the dq coordinate system, and specifically comprises:
calculating a grid voltage phase angle by using a phase-locked loop based on the current in the dq coordinate system, and selecting a voltage vector u by applying the grid voltage phase angle dq 。
5. The model-free prediction control method of the grid-connected converter according to claim 1, wherein two-step grid current prediction is performed based on a current prediction value, an estimated total disturbance and a selected voltage vector, and specifically comprises:
Figure FDA0003793162210000031
wherein k is the sampling time, ts is the sampling time
Figure FDA0003793162210000032
Figure FDA0003793162210000033
M is the total number of sub-frequency stages, ω 0 Represents the entire ESO system bandwidth;
Figure FDA0003793162210000034
is the predicted estimate of the grid current at the next sampling instant (k + 1),
Figure FDA0003793162210000035
is the estimated current of the present discrete sample (k), and u (k) is the switch state S for constant control input gain abc (k) Induced converter voltage i dq (k) Is the measured current at the present sampling instant,
Figure FDA0003793162210000036
is the estimated perturbation at the current ESO bandwidth.
6. The model-free predictive control method of the grid-connected converter according to claim 1, wherein an optimal voltage vector is obtained by taking a minimized cost function as a control target, wherein the cost function is as follows:
Figure FDA0003793162210000037
wherein the content of the first and second substances,
Figure FDA0003793162210000038
is the predicted estimation value of the power grid current at the sampling moment (k + 2),
Figure FDA0003793162210000039
the grid current reference value at the (k + 2) moment is sampled.
7. The model-free predictive control method of a grid-connected converter according to claim 6, characterized in that each switch-state voltage of a three-phase two-level grid-connected converter is evaluated in a cost function, and a voltage u with a minimum value of the cost function is applied dq Corresponding on-off state as the on-off state S of the grid-connected converter abc 。
8. A model-free predictive control system of a grid-connected converter is characterized by comprising:
the data acquisition module is used for acquiring power grid current and power grid voltage data and converting the data into a dq coordinate system;
the state observation module is used for inputting the current under the dq coordinate system into the hybrid cascade parallel extended state observer to obtain a current predicted value and an estimated total disturbance;
the voltage selection module is used for calculating a selection voltage vector based on the voltage under the dq coordinate system;
the two-step prediction module is used for predicting the current of the two-step power grid based on the current prediction value, the estimated total disturbance and the selected voltage vector;
and the grid-connected converter control module is used for obtaining an optimal voltage vector by taking a minimized cost function as a control target based on the two-step power grid current prediction result, and performing on-off control on the grid-connected converter.
9. A terminal device comprising a processor and a memory, the processor being arranged to implement instructions; the memory is used for storing a plurality of instructions, and the instructions are suitable for being loaded by the processor and executing the model-free predictive control method of the grid-connected converter according to any one of claims 1 to 7.
10. A computer readable storage medium having stored therein a plurality of instructions, characterized in that the instructions are adapted to be loaded by a processor of a terminal device and to execute the model-free predictive control method of a grid-connected converter according to any of claims 1 to 7.
CN202210961193.1A 2022-08-11 2022-08-11 Model-free prediction control method and system for grid-connected converter Pending CN115425642A (en)

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